Journal articles on the topic 'Activity Recognition (AR)'
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Roy, Debaditya, Sarunas Girdzijauskas, and Serghei Socolovschi. "Confidence-Calibrated Human Activity Recognition." Sensors 21, no. 19 (September 30, 2021): 6566. http://dx.doi.org/10.3390/s21196566.
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Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.
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Harkrider, Ashley W., and Steven Brad Smith. "Acceptable Noise Level, Phoneme Recognition in Noise, and Measures of Auditory Efferent Activity." Journal of the American Academy of Audiology 16, no. 08 (September 2005): 530–45. http://dx.doi.org/10.3766/jaaa.16.8.2.
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Acceptable noise level (ANL) is unrelated to sentence recognition in noise but may be related to phoneme recognition in noise (PRN). Individual differences in efferent activity in medial olivocochlear bundle (MOCB) and acoustic reflex (AR) pathways may account for intersubject variability in ANL and PRN. Monotic and dichotic ANL, monotic PRN, contralateral suppression of transient evoked otoacoustic emissions, and ipsilateral and contralateral acoustic reflex thresholds were measured in 31 adults with normal hearing. Results indicate that monotic ANL and PRN are unrelated. Monotic and dichotic ANL are related, suggesting that nonperipheral factors mediate ANL. Intersubject variability in ANL cannot be accounted for by individual differences in MOCB or AR activation. Intersubject variability in PRN cannot be accounted for by individual differences in MOCB or contralateral AR activation. It may be influenced by the ipsilateral AR pathway. Efferent activity in the contralateral AR arc is correlated with efferent activity in the MOCB.
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Baik, Kyong-Up, Sang-Hun Jung, and Byung-Zun Ahn. "Recognition of pharmacophore of ar-turmerone for its anticancer activity." Archives of Pharmacal Research 16, no. 3 (September 1993): 254–56. http://dx.doi.org/10.1007/bf02974492.
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Freedman, Richard, Hee-Tae Jung, and Shlomo Zilberstein. "Plan and Activity Recognition from a Topic Modeling Perspective." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 360–64. http://dx.doi.org/10.1609/icaps.v24i1.13683.
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We examine new ways to perform plan recognition (PR) using natural language processing (NLP) techniques. PR often focuses on the structural relationships between consecutive observations and ordered activities that comprise plans. However, NLP commonly treats text as a bag-of-words, omitting such structural relationships and using topic models to break down the distribution of concepts discussed in documents. In this paper, we examine an analogous treatment of plans as distributions of activities. We explore the application of Latent Dirichlet Allocation topic models to human skeletal data of plan execution traces obtained from a RGB-D sensor. This investigation focuses on representing the data as text and interpreting learned activities as a form of activity recognition (AR). Additionally, we explain how the system may perform PR. The initial empirical results suggest that such NLP methods can be useful in complex PR and AR tasks.
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Lupión, Marcos, Javier Medina-Quero, Juan F. Sanjuan, and Pilar M. Ortigosa. "DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería." Sensors 21, no. 2 (January 8, 2021): 405. http://dx.doi.org/10.3390/s21020405.
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Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.
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Lupión, Marcos, Javier Medina-Quero, Juan F. Sanjuan, and Pilar M. Ortigosa. "DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería." Sensors 21, no. 2 (January 8, 2021): 405. http://dx.doi.org/10.3390/s21020405.
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Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.
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Lauer, Luisa, Kristin Altmeyer, Sarah Malone, Michael Barz, Roland Brünken, Daniel Sonntag, and Markus Peschel. "Investigating the Usability of a Head-Mounted Display Augmented Reality Device in Elementary School Children." Sensors 21, no. 19 (October 5, 2021): 6623. http://dx.doi.org/10.3390/s21196623.
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Augmenting reality via head-mounted displays (HMD-AR) is an emerging technology in education. The interactivity provided by HMD-AR devices is particularly promising for learning, but presents a challenge to human activity recognition, especially with children. Recent technological advances regarding speech and gesture recognition concerning Microsoft’s HoloLens 2 may address this prevailing issue. In a within-subjects study with 47 elementary school children (2nd to 6th grade), we examined the usability of the HoloLens 2 using a standardized tutorial on multimodal interaction in AR. The overall system usability was rated “good”. However, several behavioral metrics indicated that specific interaction modes differed in their efficiency. The results are of major importance for the development of learning applications in HMD-AR as they partially deviate from previous findings. In particular, the well-functioning recognition of children’s voice commands that we observed represents a novelty. Furthermore, we found different interaction preferences in HMD-AR among the children. We also found the use of HMD-AR to have a positive effect on children’s activity-related achievement emotions. Overall, our findings can serve as a basis for determining general requirements, possibilities, and limitations of the implementation of educational HMD-AR environments in elementary school classrooms.
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Sanchez-Comas, Andres, Kåre Synnes, and Josef Hallberg. "Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies." Sensors 20, no. 15 (July 29, 2020): 4227. http://dx.doi.org/10.3390/s20154227.
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Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.
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Kang, James Jin, Tom Luan, and Henry Larkin. "Data Processing of Physiological Sensor Data and Alarm Determination Utilising Activity Recognition." International Journal of Information, Communication Technology and Applications 2, no. 1 (September 24, 2016): 108–31. http://dx.doi.org/10.17972/ijicta20162132.
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Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests and examples of using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.
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Al-Taei, Ali. "A Smartphone -Based Model for Human Activity Recognition." Ibn AL- Haitham Journal For Pure and Applied Science 30, no. 3 (December 29, 2017): 243. http://dx.doi.org/10.30526/30.3.1628.
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Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classifier overperforms other classifiers that were examined in this research paper.
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Razzaq, Muhammad, Ian Cleland, Chris Nugent, and Sungyoung Lee. "Multimodal Sensor Data Fusion for Activity Recognition Using Filtered Classifier." Proceedings 2, no. 19 (October 19, 2018): 1262. http://dx.doi.org/10.3390/proceedings2191262.
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Activity recognition (AR) is a subtask in pervasive computing and context-aware systems, which presents the physical state of human in real-time. These systems offer a new dimension to the widely spread applications by fusing recognized activities obtained from the raw sensory data generated by the obtrusive as well as unobtrusive revolutionary digital technologies. In recent years, an exponential growth has been observed for AR technologies and much literature exists focusing on applying machine learning algorithms on obtrusive single modality sensor devices. However, University of Jaén Ambient Intelligence (UJAmI), a Smart Lab in Spain has initiated a 1st UCAmI Cup challenge by sharing aforementioned varieties of the sensory data in order to recognize the human activities in the smart environment. This paper presents the fusion, both at the feature level and decision level for multimodal sensors by preprocessing and predicting the activities within the context of training and test datasets. Though it achieves 94% accuracy for training data and 47% accuracy for test data. However, this study further evaluates post-confusion matrix also and draws a conclusion for various discrepancies such as imbalanced class distribution within the training and test dataset. Additionally, this study also highlights challenges associated with the datasets for which, could improve further analysis.
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Yin, Xuyue, Xiumin Fan, Wenmin Zhu, and Rui Liu. "Synchronous AR assembly assistance and monitoring system based on ego-centric vision." Assembly Automation 39, no. 1 (February 4, 2019): 1–16. http://dx.doi.org/10.1108/aa-03-2017-032.
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Purpose Aiming at presenting an interaction-free assembly assistance tool, the purpose of this paper is to propose a synchronous augmented reality (AR) assembly assistance and monitoring system. The system monitors operator’s hands activity and process completeness to recognize the assembly state, then display the AR contents contextually. Design/methodology/approach An assembly behavior recognition method is proposed based on gesture recognition. An assembly completeness inspection method is proposed based on SURF feature matching. Assembly state and AR display state are solved by a novel sequential hybrid AR display control strategy. A synchronous multi-channel AR view output strategy is proposed based on QR matrix decomposition. Findings A prototype system has been developed, and case study is performed on an industrial product. Experiments are performed to verify the feasibility, efficiency and recognition accuracy of the proposed methods. Research limitations/implications The proposed system assists users to perform assembly tasks with automatic visual guidance and vision monitoring, avoiding distractions caused by redundant human–computer interactions. Practical implications All methods are integrated to work on only one head-worn device, making the proposed system portable and cheaper. The vision processing pipelines and the view output channels are reconfigurable for customization. Originality/value This paper proposes an interaction-free AR assembly assistance and monitoring system. Assembly behavior recognition and assembly completeness inspection methods are integrated to monitor the assembly state. A sequential hybrid AR display control strategy is proposed to contextually update the AR contents. A synchronous multi-channel AR view output strategy is proposed to fulfill different visualization needs.
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Ali Adel Al-Taei. "A Mobile Based Activity Recog-nition Model." journal of the college of basic education 23, no. 98 (December 26, 2022): 75–92. http://dx.doi.org/10.35950/cbej.v23i98.8846.
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In the last decade, activity recognition (AR) of humans via smart phones became important and attractive subject for scholars and developers in many areas from health care to real-time security systems. In this research, we worked on AR that based on data collected from Android-based smart phone's accelerometers held at waist region while performing different activities (i.e. walking, jogging, climbing stairs, downing stairs, sitting, and standing). To achieve this goal, six classification algorithms were performed: Naïve Bayes (NB), Multi Layer Perceptron (MLP), Bayes Network (BN), Sequential Minimal Optimization (SMO), Kstar, and Decision Tree (DT). Experimental results of the six models were illustrated and analyzed. Comparison results declare that MLP algorithm outperforms other algorithms.
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Xu, Liwen, Guoli Wang, and Xuemei Guo. "A two-layer framework for activity recognition with multi-factor activity pheromone matrix." MATEC Web of Conferences 189 (2018): 10001. http://dx.doi.org/10.1051/matecconf/201818910001.
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With rapid population aging and increasingly indoor sensing technologies, mining effective information in sensor data is in need that we can analyse individual behaviour semantics, or even predict intentions. The model for indoor activity recognition (AR) is usually based on statistic while sensor data can impliedly reflect abundant information in order. Behaviour will trigger environment perception sensors. Inspired by information transmission in nature, persistent action keeps activity pheromone accumulating and inactive action keeps it volatilizing along with time shift. Different from statistic model, our framework proposes a method to construct multi factor features named activity pheromone matrix (APM). It has a double-layer model for recognizing daily activities include the high-overlapping. The experimental results show that our method can effectively promote the accuracy of activities recognition compared with the existing statistical models, even the high-overlapping activities in small areas.
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Chauhan, Gaurav, and Hannelore V. Heemers. "Somatic Alterations Impact AR Transcriptional Activity and Efficacy of AR-Targeting Therapies in Prostate Cancer." Cancers 13, no. 16 (August 5, 2021): 3947. http://dx.doi.org/10.3390/cancers13163947.
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Inhibiting the activity of the ligand-activated transcription factor androgen receptor (AR) is the default first-line treatment for metastatic prostate cancer (CaP). Androgen deprivation therapy (ADT) induces remissions, however, their duration varies widely among patients. The reason for this heterogeneity is not known. A better understanding of its molecular basis may improve treatment plans and patient survival. AR’s transcriptional activity is regulated in a context-dependent manner and relies on an interplay between its associated transcriptional regulators, DNA recognition motifs, and ligands. Alterations in one or more of these factors induce shifts in the AR cistrome and transcriptional output. Significant variability in AR activity is seen in both castration-sensitive (CS) and castration-resistant CaP (CRPC). Several AR transcriptional regulators undergo somatic alterations that impact their function in clinical CaPs. Some alterations occur in a significant fraction of cases, resulting in CaP subtypes, while others affect only a few percent of CaPs. Evidence is emerging that these alterations may impact the response to CaP treatments such as ADT, radiation therapy, and chemotherapy. Here, we review the contribution of recurring somatic alterations on AR cistrome and transcriptional output and the efficacy of CaP treatments and explore strategies to use these insights to improve treatment plans and outcomes for CaP patients.
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Onthoni, Djeane Debora, and Prasan Kumar Sahoo. "Artificial-Intelligence-Assisted Activities of Daily Living Recognition for Elderly in Smart Home." Electronics 11, no. 24 (December 11, 2022): 4129. http://dx.doi.org/10.3390/electronics11244129.
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Activity Recognition (AR) is a method to identify a certain activity from the set of actions. It is commonly used to recognize a set of Activities of Daily Living (ADLs), which are performed by the elderly in a smart home environment. AR can be beneficial for monitoring the elder’s health condition, where the information can be further shared with the family members, caretakers, or doctors. Due to the unpredictable behaviors of an elderly person, performance of ADLs can vary in day-to-day life. Each activity may perform differently, which can affect the sequence of the sensor’s raw data. Due to this issue, recognizing ADLs from the sensor’s raw data remains a challenge. In this paper, we proposed an Activity Recognition for the prediction of the Activities of Daily Living using Artificial Intelligence approach. Data acquisition techniques and modified Naive Bayes supervised learning algorithm are used to design the prediction model for ADL. Our experiment results establish that the proposed method can achieve high accuracy in comparison to other well-established supervised learning algorithms.
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Helsen, Christine, Tien Nguyen, Thomas Vercruysse, Staf Wouters, Dirk Daelemans, Arnout Voet, and Frank Claessens. "The T850D Phosphomimetic Mutation in the Androgen Receptor Ligand Binding Domain Enhances Recruitment at Activation Function 2." International Journal of Molecular Sciences 23, no. 3 (January 29, 2022): 1557. http://dx.doi.org/10.3390/ijms23031557.
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Several key functions of the androgen receptor (AR) such as hormone recognition and co-regulator recruitment converge in the ligand binding domain (LBD). Loss- or gain-of-function of the AR contributes to pathologies such as the androgen insensitivity syndrome and prostate cancer. Here, we describe a gain-of-function mutation of the surface-exposed threonine at position 850, located at the amino-terminus of Helix 10 (H10) in the AR LBD. Since T850 phosphorylation was reported to affect AR function, we created the phosphomimetic mutation T850D. The AR T850D variant has a 1.5- to 2-fold increased transcriptional activity with no effect on ligand affinity. In the androgen responsive LNCaP cell line grown in medium with low androgen levels, we observed a growth advantage for cells in which the endogenous AR was replaced by AR T850D. Despite the distance to the AF2 site, the AR T850D LBD displayed an increased affinity for coactivator peptides as well as the 23FQNLF27 motif of AR itself. Molecular Dynamics simulations confirm allosteric transmission of the T850D mutation towards the AF2 site via extended hydrogen bond formation between coactivator peptide and AF2 site. This mechanistic study thus confirms the gain-of-function character of T850D and T850 phosphorylation for AR activity and reveals details of the allosteric communications within the LBD.
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de Waal, P. P., D. S. Wang, W. A. Nijenhuis, R. W. Schulz, and J. Bogerd. "Functional characterization and expression analysis of the androgen receptor in zebrafish (Danio rerio) testis." REPRODUCTION 136, no. 2 (August 2008): 225–34. http://dx.doi.org/10.1530/rep-08-0055.
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The biological activity of androgens, important for male sexual differentiation and development, is mediated by the androgen receptor (AR) that binds to specific DNA recognition sites regulating the transcription of androgen target genes. We investigated androgen production by adult zebrafish testis tissue, and identified 11β-hydroxyandrostenedione, 11-ketoandrostenedione (OA), and 11-ketotestosterone (11-KT) as main products, and hence potential ligands, for the zebrafish Ar. These androgens were then included in the pharmacological characterization of the zebrafish Ar. The zebrafish Ar responded well in terms of binding and transactivation to synthetic androgens as well as to testosterone and 11-KT, and reasonably well to OA and androstenedione. In situ hybridization analysis of zebrafish testis revealed that ar mRNA expression was detected in the subpopulation of Sertoli cells contacting early spermatogonia.
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İsmail, K., and K. Özacar. "HUMAN ACTIVITY RECOGNITION BASED ON SMARTPHONE SENSOR DATA USING CNN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 263–65. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-263-2020.
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Abstract. Human activity recognitions have been widely used nowadays by end users thanks to extensive usage of smartphones. Smartphones, by self-containing low-cost sensing technology, can track our daily activities for serving healthcare, sport, interactive AR/VR games and so on. However, smartphone technology is evolving and the techniques of using the data that smartphones go through are also improving. In this study, we used built-in sensing technologies (accelerometer and gyroscope) available in nearly every smartphone to detect the most common 5 daily activities of human by taking the data of these sensors and extract the features for a Convolutional Neural Network (CNN) model. We prepare a dataset and use TensorFlow to train the collected data from the sensors then filtered it to be processed. We also discuss the differences in CNN model accuracy with different optimizers. To demonstrate the model, we developed an android application that successfully predict an activity. We believe that after improving this application, it can be used for especially lonely old people to immediately warn authorities in case of any daily incidents.
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Taheri, Mohammad, Tayyebeh Khoshbakht, Elena Jamali, Julia Kallenbach, Soudeh Ghafouri-Fard, and Aria Baniahmad. "Interaction between Non-Coding RNAs and Androgen Receptor with an Especial Focus on Prostate Cancer." Cells 10, no. 11 (November 16, 2021): 3198. http://dx.doi.org/10.3390/cells10113198.
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The androgen receptor (AR) is a member of the nuclear receptor superfamily and has three functional domains, namely the N-terminal, DNA binding, and C-terminal domain. The N-terminal domain harbors potent transactivation functions, whereas the C-terminal domain binds to androgens and antiandrogens used to treat prostate cancer. AR has genomic activity being DNA binding-dependent or through interaction with other DNA-bound transcription factors, as well as a number of non-genomic, non-canonical functions, such as the activation of the ERK, AKT, and MAPK pathways. A bulk of evidence indicates that non-coding RNAs have functional interactions with AR. This type of interaction is implicated in the pathogenesis of human malignancies, particularly prostate cancer. In the current review, we summarize the available data on the role of microRNAs, long non-coding RNAs, and circular RNAs on the expression of AR and modulation of AR signaling, as well as the effects of AR on their expression. Recognition of the complicated interaction between non-coding RNAs and AR has practical importance in the design of novel treatment options, as well as modulation of response to conventional therapeutics.
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Erickson, Kirk I., Destiny L. Miller, Andrea M. Weinstein, Stephanie L. Akl, and Sarah Banducci. "Physical activity and brain plasticity in late adulthood: a conceptual and comprehensive review." Ageing Research 3, no. 1 (March 6, 2012): 6. http://dx.doi.org/10.4081/ar.2012.e6.
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A growing body of evidence from neuroscience, epidemiology, and kinesiology suggests that physical activity is effective as both a prevention and treatment for cognitive problems throughout the lifespan. Given the expected increase in the proportion of older adults in most countries over the next 40 years, physical activity could be a low-cost and relatively accessible method for maintaining cognitive function throughout later life. Despite the emerging recognition of physical activity as a powerful method to enhance brain health, there is continued confusion from both the public and scientific communities about what the extant research has discovered about the potential for physical activity to improve neurocognitive health and which questions remain unanswered. In this review, we outline four overarching themes that provide a conceptual structure for understanding the questions that have been asked and have been addressed, as well as those that have yet to be answered. These themes are descriptive, mechanistic, applied, and moderating questions. We conclude from our review that descriptive questions have been the first and most thoroughly studied, but we have much yet to learn about the underlying mechanisms, application, and moderating factors that explain how and to what extent physical activity improves brain health.
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Nickols, Nicholas George, Fei Yang, Benjamin C. Li, Jerzy O. Szablowski, Chieh-Mei Wang, and Peter Brendan Dervan. "A second-generation DNA-binding pyrrole-imidazole polyamide with antitumor activity." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): e13588-e13588. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e13588.
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e13588 Background: Pyrrole-imidazole polyamides are synthetic minor groove binders with programmable sequence recognition. A polyamide that targets the DNA sequence 5’-WGWWCW-3’ (W=A/T) found in the Androgen Receptor (AR) response element inhibits a subset of AR driven transcripts and is cytotoxic in a number of cancer cell lines including LNCaP (prostate cancer), without evidence of DNA damage. Pharmacokinetic studies show bioavailability after intravenous or subcutaneous injection. This polyamide suppressed growth of LNCaP tumor xenografts in mice. However, escalated dosing resulted in significant weight loss that could deter further development. We now report a second-generation polyamide with decreased systemic toxicity that retains efficacy towards LNCaP xenografts. Methods: We investigated the toxicity of our lead polyamide in normal (C57BL/6)mice by necropsy. We then synthesized a focused library of four polyamides that retain the DNA-recognition sequence of our lead molecule, but with chemical substitutions at non-recognition elements. We treated groups of normal male mice (n=4 each) with escalating doses up to 10 mg/kg and observed them for 10 days or until signs of significant distress, after which mice were necropsied. The least toxic polyamide was then tested for suppression of LNCaP xenograft growth (n=14 per treatment arm: 1 mg/kg polyamide versus saline vehicle alone). Tumors were allowed to grow to 200 mm3 prior to treatment by subcutaneous injection every three days for a cycle of six injections. Tumors were then resected and measured. Results: The primary target organ of toxicity for our lead polyamide was the liver, which demonstrated toxicity at 3 mg/kg and dose-limiting toxicity at less than 10 mg/kg. One polyamide in our library demonstrated no evidence of toxicity by necropsy and serum analysis up to 10 mg/kg. This polyamide also suppressed growth of established LNCaP xenografts (T/C: 51%, p=0.0073) at a dose of 1 mg/kg. The other polyamides demonstrated greater toxicity and were not tested further. Conclusions: We report a second-generation polyamide with minimal systemic toxicity in rodents that retains in vivo antitumor activity in prostate cancer xenografts.
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Kheyrkhah Shali, Roya, and Seyed Kamaledin Setarehdan. "The Effective Brain Areas in Recognition of Dyslexia." International Clinical Neuroscience Journal 7, no. 3 (June 21, 2020): 147–52. http://dx.doi.org/10.34172/icnj.2020.16.
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Background: The brain has four lobes consist of frontal, parietal, occipital, and temporal. Most researchers have reported that the left occipitotemporal region of the brain, which is the combined region of the occipital and temporal lobes, is less active in children with dyslexia like Sklar, Glaburda, Ashkenazi and Leisman. Methods: There are different methods and tools to investigate how the brain works, such as magnetic resonance imaging (MRI), positron emission tomography (PET), magneto-encephalography (MEG) and electroencephalography (EEG). Among these, EEG determines the electrical activity of the brain with the electrodes placed on the special areas on the scalp. In this research, we processed the EEG signals of dyslexic children and healthy ones to determine what the areas of the brain are most likely to cause the disease. Results: For this purpose, we extracted 43 features, including relative spectral power (RSP) features, mean, standard deviation, skewness, kurtosis, Hjorth, and AR parameters. Then an SVM classifier is used to separate two classes. Finally, we show the particular brain activation pattern by calculating the correlation coefficients and co-occurrence matrices, which suggests the activation of the working memory region as an active area. Conclusion: By identifying the brain areas involved in reading activity, it has expected that psychologists and physicians will be able to design the therapeutic exercises to activate this part of the brain.
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van der Ploeg, Phyllis, Laura A. M. van Lieshout, Yvonne J. W. Wesseling-Rozendaal, Anja Van De Stolpe, Steven L. Bosch, Marjolein H. F. M. Lentjes-Beer, Paul J. van Diest, et al. "Identification of signal transduction pathway activity with potential clinical target in high-grade serous ovarian and tubal carcinoma." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e17541-e17541. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e17541.
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e17541 Background: High-grade serous carcinoma (HGSC) is the most common subtype of epithelial ovarian and tubal cancer. It has a high mortality rate, even after successful first-line treatment with debulking surgery and chemotherapy. Although therapeutic options for targeted therapy are rapidly expanding, identification of patients who respond to these therapies remains a challenge. In recognition of the importance of the functional phenotype of cancer cells, Verhaegh et al. (Cancer Res 2014) developed assays to measure functional activity of signal transduction pathways (STPs) based on mRNA expression levels of pathway-specific target genes. In this study, we aimed to identify HGSCs with STP activity with potential clinical target by comparing their activity assessment with STP activity in normal Fallopian tube epithelium (FTE), the tissue of origin of most HGSCs. Methods: We included 67 primary tumor samples taken prior to start of chemotherapy of postmenopausal patients diagnosed with advanced stage HGSC and 8 morphologically normal FTE samples of healthy postmenopausal women. Using OncoSignal pathway assays, we assessed functional pathway activity of the androgen receptor (AR), estrogen receptor (ER), PI3K, Hedgehog, TGF-𝛽 and Wnt pathways. Differences in STP activity between groups were compared with Mann-Whitney U tests. Cut-off value for aberrant STP activity was defined as two standard deviations above the mean value of STP activity measured in FTE samples. Results: In the HGSC group we observed lower median ER (p < 0.001) and Wnt (p = 0.046) pathway activity and higher median PI3K (p = 0.025) and TGF-𝛽 pathway (p = 0.030) activity as compared to the normal FTE group. In individual HGSC samples, aberrant activity was identified for the AR (n = 14), PI3K (n = 16), Hedgehog (n = 4), TGF-𝛽 (n = 36) and Wnt (n = 10) pathways. Frequently observed combinations of aberrant STP activity were AR/TGF-𝛽 (n = 8), TGF-𝛽/Wnt (n = 6) and PI3K/Hedgehog (n = 3). In total, we identified at least one STP with potential clinical target in 52 of the 67 HGSC samples. Conclusions: Our analysis enabled the identification of STP activity with potential clinical target in 78% of the analyzed HGSC samples. Differentiation between normal and aberrant STP activity could have clinical utility in the selection of HGSC patients for targeted therapy.
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Rubiani, H., E. Samsoleh, and S. Fitri. "Activity recognition (AR) to detect dementia using wi-fi based wireless sensor network with receive signal srength indicator (RSSI) method." IOP Conference Series: Materials Science and Engineering 1115, no. 1 (March 1, 2021): 012079. http://dx.doi.org/10.1088/1757-899x/1115/1/012079.
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Alam, Ashraful, Anik Das, Md Shahriar Tasjid, and Ahmed Al Marouf. "Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 17 (September 6, 2021): 141. http://dx.doi.org/10.3991/ijim.v15i17.25197.
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<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>
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Sunardi, Sunardi, Arief Ramadhan, Edi Abdurachman, Agung Trisetyarso, and Muhammad Zarlis. "Acceptance of augmented reality in video conference based learning during COVID-19 pandemic in higher education." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3598–608. http://dx.doi.org/10.11591/eei.v11i6.4035.
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Three years after the COVID-19 pandemic emerged, we have adapted to the new normal, especially in the education field. Learning with video conferences has become our daily activity, and learning tools have gotten more prominent attention to gain student engagement, especially in emergency remote teaching (ERT). Since the trends of metaverse campaigns by meta, augmented reality (AR) has increased recognition in education contexts. However, very little research about the acceptance of augmented reality in video conferences, especially among university students. This paper aims to measure acceptance of AR in video conferences to motivate and inspire students to gain benefits and get impactful technology in the learning process. The research gathered data from a survey of 170 university students (from 5 majors in the study program and 17 different demographic areas) using unified theory of acceptance of technology 2 (UTAUT2). The result reveals that variables significantly impact acceptance: performance expectancy, hedonic motivation, and habit. The least significant but still positive effects are effort expectancy, social influence, and facilitating conditions. The study will provide helpful information on AR technology in video conferences and help top-level management in the university that provides online/distance learning in the early diffusion stage for metaverse in education.
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Rahmani, Mohammad Hasan, Rafael Berkvens, and Maarten Weyn. "Chest-Worn Inertial Sensors: A Survey of Applications and Methods." Sensors 21, no. 8 (April 19, 2021): 2875. http://dx.doi.org/10.3390/s21082875.
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Inertial Measurement Units (IMUs) are frequently implemented in wearable devices. Thanks to advances in signal processing and machine learning, applications of IMUs are not limited to those explicitly addressing body movements such as Activity Recognition (AR). On the other hand, wearing IMUs on the chest offers a few advantages over other body positions. AR and posture analysis, cardiopulmonary parameters estimation, voice and swallowing activity detection and other measurements can be approached through chest-worn inertial sensors. This survey tries to introduce the applications that come with the chest-worn IMUs and summarizes the existing methods, current challenges and future directions associated with them. In this regard, this paper references a total number of 57 relevant studies from the last 10 years and categorizes them into seven application areas. We discuss the inertial sensors used as well as their placement on the body and their associated validation methods based on the application categories. Our investigations show meaningful correlations among the studies within the same application categories. Then, we investigate the data processing architectures of the studies from the hardware point of view, indicating a lack of effort on handling the main processing through on-body units. Finally, we propose combining the discussed applications in a single platform, finding robust ways for artifact cancellation, and planning optimized sensing/processing architectures for them, to be taken more seriously in future research.
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Moy, G. W., L. M. Mendoza, J. R. Schulz, W. J. Swanson, C. G. Glabe, and V. D. Vacquier. "The sea urchin sperm receptor for egg jelly is a modular protein with extensive homology to the human polycystic kidney disease protein, PKD1." Journal of Cell Biology 133, no. 4 (May 15, 1996): 809–17. http://dx.doi.org/10.1083/jcb.133.4.809.
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During fertilization, the sea urchin sperm acrosome reaction (AR), an ion channel-regulated event, is triggered by glycoproteins in egg jelly (EJ). A 210-kD sperm membrane glycoprotein is the receptor for EJ (REJ). This conclusion is based on the following data: purified REJ binds species specifically to EJ dotted onto nitrocellulose, an mAb to REJ induces the sperm AR, antibody induction is blocked by purified REJ, and purified REJ absorbs the AR-inducing activity of EJ. Overlapping fragments of REJ cDNA were cloned (total length, 5,596 bp). The sequence was confirmed by microsequencing six peptides of mature REJ and by Western blotting with antibody to a synthetic peptide designed from the sequence. Complete deglycosylation of REJ followed by Western blotting yielded a size estimate in agreement with that of the mature amino acid sequence. REJ is modular in design; it contains one EGF module and two C-type lectin carbohydrate-recognition modules. Most importantly, it contains a novel module, herein named the REJ module (700 residues), which shares extensive homology with the human polycystic kidney disease protein (PKD1). Mutations in PKD1 cause autosomal dominant polycystic kidney disease, one of the most frequent genetic disease of humans. The lesion in cellular physiology resulting from mutations in the PKD1 protein remains unknown. The homology between REJ modules of the sea urchin REJ and human PKD1 suggests that PKD1 could be involved in ionic regulation.
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Apostolakis, Konstantinos C., Nikolaos Dimitriou, George Margetis, Stavroula Ntoa, Dimitrios Tzovaras, and Constantine Stephanidis. "DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions." Open Research Europe 1 (July 30, 2021): 87. http://dx.doi.org/10.12688/openreseurope.13715.1.
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Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains. Their potential has not gone unnoticed in the security sector with several law enforcement agencies (LEAs) employing AI applications in their daily operations for forensics and surveillance. In this paper, we present the DARLENE ecosystem, which aims to bridge existing gaps in applying AR and AI technologies for rapid tactical decision-making in situ with minimal error margin, thus enhancing LEAs’ efficiency and Situational Awareness (SA). Methods: DARLENE incorporates novel AI techniques for computer vision tasks such as activity recognition and pose estimation, while also building an AR framework for visualization of the inferenced results via dynamic content adaptation according to each individual officer’s stress level and current context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results and will constitute the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.
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Apostolakis, Konstantinos C., Nikolaos Dimitriou, George Margetis, Stavroula Ntoa, Dimitrios Tzovaras, and Constantine Stephanidis. "DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions." Open Research Europe 1 (January 21, 2022): 87. http://dx.doi.org/10.12688/openreseurope.13715.2.
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Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains, including the security sector. Several law enforcement agencies (LEAs) employ AI in their daily operations for forensics and surveillance. AR is also gaining traction in security, particularly with the advent of affordable wearable devices. Equipping police officers with the tools to facilitate an elevated situational awareness (SA) in patrolling and tactical scenarios is expected to improve LEAs’ safety and capacity to deliver crucial blows against terrorist and/or criminal threats. Methods: In this paper we present DARLENE, an ecosystem incorporating novel AI techniques for activity recognition and pose estimation tasks, combined with a wearable AR framework for visualization of the inferenced results via dynamic content adaptation according to the wearer’s stress level and operational context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results, constituting the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.
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Gunawan, Chicha Rizka, Nurdin Nurdin, and Fajriana Fajriana. "Acehnese Traditional Clothing Recognition Prototype System Design Based On Augmented Reality." International Journal of Engineering, Science and Information Technology 2, no. 3 (October 18, 2022): 100–105. http://dx.doi.org/10.52088/ijesty.v2i3.314.
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Acehnese traditional clothing is one of the cultural heritages in Indonesia. In today's modern era, the problem faced is the lack of media to introduce cultural heritage in Aceh. Therefore, a media was formed that could introduce Aceh's traditional clothing, namely Southeast Aceh. The press utilizes Augmented Reality (AR) technology so that users can add virtual objects to the natural environment that are easy to use. In this study, a system design using Unified Modeling Language (UML) diagrams has been carried out, including use case diagrams, activity diagrams, and sequence diagrams. This system is built using the C++ language using the Unity application and the vuforiaSDK platform. Then the test results were obtained on the Southeast Aceh traditional clothing recognition application. Namely, the minimum distance that can display 3d objects is a distance of 5 cm, and the maximum distance that can be detected is 80 cm. Based on the test results in the distance test table, the best distance obtained, which results in the detection of markers that are still clear and bright, is at a distance between 5 cm to 70 cm. Meanwhile, at a distance of more than 80 cm, the marker cannot detect markers to display 3D objects because the distance between the camera and the marker is too far. Likewise, with the angular slope, the minimum angle of inclination detected is an angle of 0°, while the maximum angle of inclination detected is an angle of 75°. Based on the test results on the angle slope table, the best angle is obtained, which results in detecting markers that are still clear and bright at a distance between 0-60°. After that, testing is also carried out based on the lighting, where if the light is too bright or too dark, the camera cannot detect the marker.
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Nikesitch, Nicholas, Eliana Beraldi, Fan Zhang, Hans Adomat, Robert Bell, Ladan Fazli, Christopher Wells, Nicholas Pinette, Yuzhuo Wang, and Martin Gleave. "Abstract 395: Characterizing the role of chaperone-mediated autophagy in prostate cancer treatment resistance." Cancer Research 82, no. 12_Supplement (June 15, 2022): 395. http://dx.doi.org/10.1158/1538-7445.am2022-395.
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Abstract Androgen deprivation therapy (ADT) and androgen receptor pathway inhibition (ARPI) remains the standard of care for advanced prostate cancer (PCa). As part of its canonical activities, the androgen receptor (AR) plays an important role in PCa metabolism. AR inhibition with ARPI subjects PCa cells to acute metabolic stress caused by reduced biosynthesis and energy production. The upregulation of ARPI-induced stress response mechanisms is essential adaptive mechanism for cell proliferation and survival. This process requires PCa cells undergoing rapid phenotypic changes to adapt to their environment and circumvent lethal outcomes. By profiling the proteomic pathway alterations associated with the ARPI stress response in LNCaP cells, we noted a significant upregulation of chaperone mediated autophagy (CMA), a stress response mechanism yet to be defined in PCa. CMA, a selective protein degradation pathway, specifically targets protein substrates via a CMA recognition motif and is an essential survival mechanism in cancer cells during energy depleted metabolic stress. Within in-vitro and in-vivo PCa models, ARPI induced CMA is reflected through the increased activity of CMA lysosomes and specifically by the increased expression of the CMA marker, lysosomal associated membrane protein 2a (L2A). L2A knockdown using shRNA not only elicited a strong anti-proliferative effect in PCa cells, but also compromised PCa metabolism with decreases in ATP levels and mTORC1 signaling. Proteomic analysis by mass spectrometry further identified that CMA promotes chromatin silencing, and the suppression of pathways associated with transcription. Conversely, L2A overexpression not only promoted PCa cell proliferation during Enza treatment, but upregulated of hallmark target genes of AR-indifferent PCa growth. The upregulation of CMA facilitated proteome remodeling during ARPI, leading to increased mTORC1 signaling and biosynthesis. In summary, our data illustrates the importance of CMA in mediating the ARPI stress response in PCa, providing novel insights into the mechanisms of ARPI treatment resistance. Citation Format: Nicholas Nikesitch, Eliana Beraldi, Fan Zhang, Hans Adomat, Robert Bell, Ladan Fazli, Christopher Wells, Nicholas Pinette, Yuzhuo Wang, Martin Gleave. Characterizing the role of chaperone-mediated autophagy in prostate cancer treatment resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 395.
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Qiao, Zhiyuan, Guangcai Wang, Hong Fu, and Xiaojing Hu. "Identification of Groundwater Radon Precursory Anomalies by Critical Slowing down Theory: A Case Study in Yunnan Region, Southwest China." Water 14, no. 4 (February 11, 2022): 541. http://dx.doi.org/10.3390/w14040541.
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In this study, we use the critical slowing down (CSD) theory to identify the precursory anomalies of groundwater radon based on the 1000-day continuous data from 8 monitoring stations in Yunnan Province, China during the seismically active period of 1993–1996. The low-frequency and high-frequency information were extracted from raw groundwater radon data to calculate their one-step lag autocorrelation (AR-1) and variance, respectively, in order to identify the precursory anomalies. The results show that the anomaly characteristics can be divided into three categories: sudden jump anomalies, persistent anomalies, and fluctuation anomalies. The highest average seismic recognition rate is 72.78%, based on the high-frequency information’s autocorrelation, while the lowest is 45.08%, based on the low-frequency information’s variance. The crustal activity and the change in hydrogeological conditions are possibly the main factors influencing groundwater radon anomalies in the selected period in the study area. There is a positive correlation between the anomaly occurrence time and epicentral distance when epicentral distance is less than 300 km, which may be related to the seismogenic modes and hydrogeological conditions. This study provides a reference for identifying groundwater radon anomalies before earthquakes by mathematical methods.
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Czernicka, Lidia, Agnieszka Ludwiczuk, Edward Rój, Zbigniew Marzec, Agata Jarzab, and Wirginia Kukula-Koch. "Acetylcholinesterase Inhibitors among Zingiber officinale Terpenes—Extraction Conditions and Thin Layer Chromatography-Based Bioautography Studies." Molecules 25, no. 7 (April 3, 2020): 1643. http://dx.doi.org/10.3390/molecules25071643.
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Although numerous studies have been conducted on ginger extracts and fractions, the data on the pharmacological activity of single constituents of Zingiber officinale are still insufficient. To assess the antidementia properties of the plant, a thin layer chromatography (TLC)-based bioautography acetylcholinesterase inhibitory assay was performed on the Zingiber officinale diethyl ether extract. It led to the recognition of three active inhibitors among volatile constituents of the plant: ar-curcumene (A), α-sesquiphellandrene (B) and a-zingiberene (C). The identification of the components was possible thanks to the application of a TLC–HPLC-MS interface analysis of active zones and the GC-MS qualitative analysis of the tested samples. Based on the obtained results, the influence of several extraction techniques (hydrodistillation—HD, pressurized liquid extraction or accelerated solvent extraction—ASE, shaking maceration–SM, supercritical fluid extraction–SFE, and ultrasound-assisted extraction—UAE) on the recovery of the active metabolites from plant material was assessed to deliver enriched extracts. As a result, HD and SFE, were found to be the most efficient methods to recover the volatile components and the concentrations of A, B, and C reached 0.51 ± 0.025, 0.77 ± 0.045, and 1.67 ± 0.11 percent, respectively. Only HD and SFE were found to recover monoterpene hydrocarbons from the plant matrix. The remaining techniques provided extracts rich in more complex constituents, like sesquiterpenes.
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Van der Ploeg, Phyllis, Yvonne J. W. Wesseling-Rozendaal, Eveline C. Biezen-Timmermans, and Jurgen M. J. Piek. "Abstract 4002: Identification of signal transduction pathway activity with potential clinical target in high-grade serous ovarian carcinoma." Cancer Research 82, no. 12_Supplement (June 15, 2022): 4002. http://dx.doi.org/10.1158/1538-7445.am2022-4002.
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Abstract Background: High-grade serous carcinoma (HGSC) is the most common subtype of ovarian cancer. It has a high mortality rate, even after successful first-line treatment with debulking surgery and chemotherapy. Although therapeutic options for targeted therapy are rapidly expanding, identification of patients who respond to these therapies remains a challenge. In recognition of the importance of the functional phenotype of cancer cells, Verhaegh et al. (Cancer Res 2014) developed assays to measure functional activity of signal transduction pathways (STPs) based on mRNA expression levels of pathway-specific target genes. In this study, we aimed to identify HGSCs with STP activity with a potential clinical target by comparing their activity with STP activity in normal Fallopian tube epithelium (FTE), the tissue of origin of most HGSCs. Methods: We included 50 primary tumor samples taken prior to start of chemotherapy of postmenopausal patients diagnosed with advanced stage HGSC and 9 morphologically normal FTE samples of healthy postmenopausal women. Using pathway assays, we assessed functional pathway activity of the androgen receptor (AR), estrogen receptor (ER), PI3K, MAPK, Hedgehog, TGFβ and Notch pathways. Differences in STP activity between groups were compared with Mann-Whitney U tests. Cut-off value for aberrant STP activity was defined as two standard deviations above the mean value of STP activity measured in FTE samples. Results: In the HGSC group we observed lower median ER (p < 0.001) pathway activity and higher median PI3K (p < 0.001), Hedgehog (p < 0.001) and TGFβ pathway (p = 0.020) activity as compared to the FTE group. In individual HGSC samples, aberrant activity was identified for the MAPK (n = 10), PI3K (n = 22), Hedgehog (n = 28) and TGFβ (n = 21) pathways. Frequently observed combinations of aberrant STP activity were Hedgehog/TGFβ (n = 12) and Hedgehog/PI3K (n = 9). In total, we identified at least one STP with potential clinical target in 88% (44/50) of HGSC samples. Conclusions: Our analysis enabled the identification of STP activity with a potential clinical target in 88% of the analyzed HGSC samples. Differentiation between normal and aberrant STP activity could have clinical utility in the selection of HGSC patients for targeted therapy. A prospective study (STAPOVER) has been designed to demonstrate clinical utility. Citation Format: Phyllis Van der Ploeg, Yvonne J.W. Wesseling-Rozendaal, Eveline C. Biezen-Timmermans, Jurgen M.J. Piek. Identification of signal transduction pathway activity with potential clinical target in high-grade serous ovarian carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4002.
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Ogino, Mikito, and Yasue Mitsukura. "Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram." Sensors 18, no. 12 (December 18, 2018): 4477. http://dx.doi.org/10.3390/s18124477.
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Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation.
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Farag, Mohammed M. "Matched Filter Interpretation of CNN Classifiers with Application to HAR." Sensors 22, no. 20 (October 21, 2022): 8060. http://dx.doi.org/10.3390/s22208060.
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Time series classification is an active research topic due to its wide range of applications and the proliferation of sensory data. Convolutional neural networks (CNNs) are ubiquitous in modern machine learning (ML) models. In this work, we present a matched filter (MF) interpretation of CNN classifiers accompanied by an experimental proof of concept using a carefully developed synthetic dataset. We exploit this interpretation to develop an MF CNN model for time series classification comprising a stack of a Conv1D layer followed by a GlobalMaxPooling layer acting as a typical MF for automated feature extraction and a fully connected layer with softmax activation for computing class probabilities. The presented interpretation enables developing superlight highly accurate classifier models that meet the tight requirements of edge inference. Edge inference is emerging research that addresses the latency, availability, privacy, and connectivity concerns of the commonly deployed cloud inference. The MF-based CNN model has been applied to the sensor-based human activity recognition (HAR) problem due to its significant importance in a broad range of applications. The UCI-HAR, WISDM-AR, and MotionSense datasets are used for model training and testing. The proposed classifier is tested and benchmarked on an android smartphone with average accuracy and F1 scores of 98% and 97%, respectively, which outperforms state-of-the-art HAR methods in terms of classification accuracy and run-time performance. The proposed model size is less than 150 KB, and the average inference time is less than 1 ms. The presented interpretation helps develop a better understanding of CNN operation and decision mechanisms. The proposed model is distinguished from related work by jointly featuring interpretability, high accuracy, and low computational cost, enabling its ready deployment on a wide set of mobile devices for a broad range of applications.
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Karanam, Balasubramanyam, Clayton Yates, and Ravi Anchoori. "Abstract 1781: Up284, a novel inhibitor to treat quadruple negative breast cancer." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1781. http://dx.doi.org/10.1158/1538-7445.am2022-1781.
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Abstract Quadruple negative breast cancer (QNBC), lacking the expression of ER (estrogen receptor), PR (progesterone receptor), HER2 (human epidermal growth factor receptor 2) and AR (androgen receptor), is the breast cancer subtype with the worst prognosis, and QNBC disproportionately afflicts African Americans. It has no standard-of-care treatment targets and thus efficacious and safe treatments are urgently sought for this unmet medical need, and to address the disparity in breast cancer outcomes. The current proposal is motivated by data showing elevated expression of proteasome subunit RPN13 is associated with both African American race and lower survival in QNBC patients, that RPN13 selectively targeted by a small molecule Up284 developed by Up Therapeutics. Both triple negative breast cancer and QNBC cell lines show evidence of greater vulnerability to proteasome inhibitors. However, licensed 20S proteasome inhibitors, e.g. bortezomib, have proven ineffective against solid tumors, with emergence of resistance, and dose limiting toxicities including thrombocytopenia and neutropenia. Up284 has a target and structure designed to overcome the limitations of the licensed drugs with respect to drug resistance. Up284 blocks substrate recognition and deubiquitination rather than just one of the three 20S catalytic activities), poor activity against solid tumors (Up284 has a novel spiro structure with evidence of improved drug access to tumor as compared to peptide-based 20S inhibitors), key toxicities of thrombocytopenia and neutropenia (unlike 20S inhibitors, Up284 does not target the immunoproteasome expressed by hematopoietic cells and does not show these toxicities). Up284 shows broad anticancer activity in vitro, including against QNBC lines with a robust therapeutic index, a promising safety profile and pharmacodynamics, and the ability to control xenograft tumor. By inhibiting proteasome ubiquitin receptor RPN13 function and its associated deubiquitinase activity, Up284 triggers more rapid accumulation and increased molecular weight polyubiquinated protein aggregates than is induced by 20S inhibitors. These toxic misfolded protein aggregates produce an unresolved ER stress, activate the canonical Unfolded Protein Response (UPR) signaling cascade and more rapidly triggers apoptosis than 20S inhibitor. The safety parameters and promising efficacy of Up284 against breast cancer lines encourages us to validate Up284 efficacy in more QNBC lines and animal models. We discuss Up284 efficacy and pharmacological analysis in our presentation. Citation Format: Balasubramanyam Karanam, Clayton Yates, Ravi Anchoori. Up284, a novel inhibitor to treat quadruple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1781.
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Pruskus, Valdas. "KULTŪRINIO KAPITALO RAIŠKOS YPATUMAI." Problemos 67 (January 1, 2005): 48–62. http://dx.doi.org/10.15388/problemos.2005..4092.
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Straipsnyje aptariamos sociologinės ir ekonominės kultūrinio kapitalo sampratos. Analizuojami kultūrinio kapitalo egzistavimo pavidalai ir jų ypatumai, kultūrinio ir natūraliojo kapitalo panašumai bei skirtumai. Kultūrinis kapitalas suprantamas kaip kultūrinių gėrybių (kultūros produktų ir paslaugų) šaltinis, kuris jas teikia dabar gyvenantiems ir privalės teikti ateityje. Kaip pavienis individas ar visuomenė, mes galime pabloginti arba pagerinti kultūrinio kapitalo būklę. Siekdami savo asmeninių ir visuomeninių tikslų, stengiamės kultūrinį kapitalą valdyti. Parodoma, kad jis gali esmingai veikti ekonomikos raidą, tapdamas instrumentu, leidžiančiu ekonomikos „paraiškas“ (pasiekimus) kultūriškai pripažinti, įsisavinti ir atitinkamai išreikšti, taigi kurti aukštesnio lygio socialinio gyvenimo kokybę. Kaip ekosistemos palaiko biosferą, taip kultūrinis kapitalas ir jo infrastruktūros palaiko socialinį universumą (visuomenės stabilumą), kartu sudarydami sąlygas esmingai plėtoti ekonomikos galias.Prasminiai žodžiai: kultūrinis kapitalas, natūralus kapitalas, žmogiškasis kapitalas, kultūrinė įvairovė, socialinio teisingumo rodikliai.
CULTURE CAPITAL AND PECULARITIES OF ITS EXPRESSIONValdas Pruskus
Summary Despite the recognition of the importance of culture in modern society the main attention is paid to the production of economical goods. In its turn it requires from economical subjects specific things (corresponding abilities and skills) and puts its own requirements, including that of creativity. On the other hand it’s obvious that it is necessary in producing not only economical but also cultural values (products and services). Economy and culture, though different as such are the two most important fields of human activity, so it is possible to assume that expression of creativity distinguishes itself by certain originality. The author investigates creativity as the process of making rational decisions by an individual and as manifestation of irrational powers of an artist (creator), analyses costs of cretion of a cultural value as well as ways of the increase of income for works of art. On the ground of the three elements of creativity (imagination, sound keenness of wit, taste) peculiarities of creativity are revealed in creating economical and cultural values.Keywords: cultural and economical values, creativity, making rational decisions, imagination, taste, consumer behaviour in the market.
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Muravskyi, Volodymyr, Pavlo Denchuk, and Oleh Reveha. "Accounting and audit of electronic transactions in metaverses." Herald of Economics, no. 2 (July 30, 2022): 128. http://dx.doi.org/10.35774/visnyk2022.02.128.
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Introduction. The latest stage in the development of the Internet is the active use of VR and AR technologies, which in the context of virtualization of information processes leads to the spread of meta-environments. The functioning of metaverses, which are integrated information environments of virtual communications, is associated with the emergence and circulation of intangible objects, which requires the improvement of their accounting and auditing. The fundamental basis of financial and economic activities in the metaverses are electronic transactions, so it is important to improve their accounting and control in the management of enterprises. The main aim is to research the prospects for improving the accounting and auditing of electronic transactions, which provide for the formation of contractual relationships, their implementation and payment using cryptocurrencies in the metaverses.Methods.In the process of the article purpose realization the system, innovative, institutional approaches and methods of economic and mathematical modeling, bibliographic and comparative analysis are used.Results. The influence of the peculiarities of financial and economic activity in metaverses on the method of accounting is investigated. Recognition of NFT as an accounting object using variable methods of their estimation in metaverses is determined: initial value according to past identification of cost components, fair value through current valuation, market value according to market value and prospective value.The necessity of using audit control methods to ensure the trust of participants in electronic transactions in meta-environments, as well as to confirm the accuracy of their reporting and other accounting information. A method of identifying electronic transactions has been developed, which has the following features: trade scams; legalization of money obtained illegally; use by attackers. The method of auditing the enterprises activities in the metaverses has been improved: introduction of accounting outsourcing in the field of financial accounting of electronic transactions; separation of information flows, which relate only to virtual financial and economic operations in metaverses; research of the internal control service of enterprises, assessment of current experience and training of staff to work with specific virtual accounting objects; formation of financial statements reflecting intangible assets related to the functioning of meta-environments.Perspectives. It is determined that further research is needed to improve the taxonomy of standard financial reporting to fully and accurately reflect the new accounting objects of the metaverses.
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Turnip, Togu Novriansyah, Lidya Pebrina Manurung, Marthin Halomoan Tampubolon, and Ronaldo Sitanggang. "Rancang Bangun Aplikasi Mobile Pengenalan Gedung dengan Teknologi Augmented Reality Berbasis Marker." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 3 (June 15, 2021): 557. http://dx.doi.org/10.25126/jtiik.2021834498.
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<p>Universitas atau kampus merupakan institusi pendidikan tinggi dan penelitian yang memberikan gelar akademik dalam berbagai bidang. Kampus tentunya memiliki beberapa gedung yang dapat digunakan sebagai ruangan kelas, laboratorium, ruang dosen, dll. Pada waktu tertentu sebuah kampus tidak jarang dikunjungi oleh tamu, yang berkeliling kampus dan mengunjungi gedung-gedung di lingkungan kampus. Tidak hanya tamu, Kampus juga kedatangan mahasiswa baru setiap tahun ajaran baru. Setiap kegiatan tur kampus yang dilakukan tamu maupun mahasiswa baru, harus selalu dituntun oleh dosen maupun mahasiswa. Berdasarkan kasus tersebut, penelitian ini bertujuan untuk mengimplementasikan Teknologi <em>Augmented reality</em> (AR) dengan menggunakan metode <em>Marker</em>-<em>Based</em>. Aplikasi ini menjadi aplikasi yang dapat digunakan sebagai pengenalan gedung kampus. Setiap gedung akan mempunyai <em>marker</em> atau penanda unik khusus yang berbeda pada setiap gedung. Konsep dari pengimplementasian aplikasi ini adalah, dengan mengarahkan kamera yang dibuka melalui aplikasi ini dan mengarahkan kamera tersebut ke <em>marker</em> yang ditemui di gedung yang sedang dikunjungi. Kamera akan mengidentifikasi <em>marker</em>, jika <em>marker</em> dikenali maka objek 3D dari gedung tersebut akan muncul tepat diatas <em>marker</em> untuk memberikan pengguna bagaimana bentuk keseluruhan gedung. Tidak hanya objek 3D, aplikasi juga menyediakan informasi mengenai gedung tersebut dan juga gambar dari posisi <em>user</em> beserta dua gedung terdekat yang dapat dikunjungi pengguna setelahnya. Dengan menggunakan aplikasi ini, pengunjung tidak memerlukan seseorang untuk menuntunnya berkeliling di sekitaran kampus. Aplikasi ini sudah diuji dengan <em>usability testing </em>dan kepuasan pengguna mencapai 83,4 % yang berarti bahwa aplikasi dapat digunakan dan berfungsi bagi pengguna</p><p><strong><em>Abstract</em></strong></p><p><em>The university or campus is a higher education and research institution that provides academic degrees in various fields. The university certainly has several buildings that can be used as classrooms, laboratories, lecturers' rooms, etc. At a certain time, a university is not infrequently visited by guests, who tour the campus and visit buildings in the university environment. Not only guests, but the University also has new students every new school year. Every campus tour activity carried out by guests or new students, must always be guided by both lecturers and students. Based on these cases, this study aims to implement Augmented reality (AR) Technology using the Marker-Based method. This application is a building recognition application that can be used at a University. Each building will have a unique marker on each building. The concept of implementing this application is, by directing the camera opened through this application and directing the camera to the marker found in the building being visited. The camera will identify the marker, if the marker is recognized then the 3D object of the building will appear directly above the marker to give the user what the overall shape of the building looks like. Not only 3D objects, but the application also provides information about the building and also a picture of the user's position along with the two closest buildings that the user can visit afterward. By using this application, visitors do not need someone to guide them around the campus. This application has been tested with usability testing and user satisfaction reaches 83,4% which means that the application can be used and functioning for the user.</em></p>
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Andrews, J. C., and S. Winters-Hilt. "7UTILIZATION OF CELL PROFILING TO EVALUATE BOVINE SPERMATOZOA IN NORMAL AND SIMULATED MICROGRAVITY." Reproduction, Fertility and Development 16, no. 2 (2004): 126. http://dx.doi.org/10.1071/rdv16n1ab7.
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We developed a method to evaluate bovine sperm membranes in normal (1G) and simulated microgravity (Sim-μG). Bovine spermatozoa are used as a model system because they have cellular membranes analogous to those of other cell types, and yet are much simpler because they have no cytoplasm and do not participate in DNA transcription or mRNA translation. They can be cultured as single cells and are easily evaluated for membrane characteristics using flow cytometry. These features make the mammalian spermatozoon a useful model for exploring the principles of membrane structure/function in the presence of a variety of environmental challenges such as simulated microgravity. Cryopreserved, washed beef bull sperm (4–8×106mL−1)were incubated under non-capacitating conditions (modified glucose-free Tyrode’s medium containing low bicarbonate, HEPES buffer, pyruvate and 3mgmL−1 BSA V; 23°C in air), and these spermatozoa remained alive for 24–48h at 1G. To simulate μG, spermatozoa were incubated under the same conditions, in a HARV 10 rotating wall vessel (RWV, Synthecon, Inc, Houston, TX, USA) at 9rpms. Spermatozoa were incubated in 1G and Sim-μG environments for 2.5–4.5h and subsequently exposed to 0, 60 or 80μgmL−1 LC for 0, 4, 8, 12, 16 and 20min. Three fluorochrome combinations were used as probes at each [LC]/time point: (1) propidium Iodide (dead status)+SYBR 14 (live status); (2) PI+FITC-PSA (acrosome reactions [ARs]); (3) PI+MitoTracker Deep Red (mitochondrial activity). Approximately 1million spermatozoa from 3 bulls were evaluated over 4 days. Data were acquired on a FACSVantage SE flow cytometer, and initially analyzed (quality control) using the bundled FACSVantage SE software package (Cell Quest, BD BioSciences, San Jose, CA, USA). This provided graphics of simple cell relations (fluorescence v. LC exposure time). For further statistical analysis, and incorporation of non-parametric statistical tools (including pattern recognition using Support Vector Machines), the data were processed using a collection of Perl scripts and C programs. Results: Live/dead status: When Sim-μG+60μgmL−1 LC sperm were compared to 1G+60μgmL−1LC, and 80μgmL−1 LC sperm, their profiles were more similar to the 1G 80μgmL−1 LC profiles. AR status: the Sim-μG+60μgmL−1 LC profiles were similar to the 1G+60μgmL−1 LC profiles. Mitochondrial Status: the Sim-μG+60μgmL−1LC profiles were more similar to 1G+80μgmL−1 LC profiles. Summary: although Sim-μG sperm lost their motility within 3h, they were alive. Cell profiles indicate that Sim-μG sperm nuclear membranes are less stable and their mitochondria are less functional than the 1G controls, but their acrosomes are intact indicating that fertilizing potential may remain. Additional experiments are needed to determine the time course for Sim-μG, induced changes, and whether Sim-μG sperm can penetrate eggs. Funding: NASA (2002)-Stennis-24 and The University of New Orleans.
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Yang, Chun-Song, Kasey Jividen, Teddy Kamata, Natalia Dworak, Luke Oostdyk, Bartlomiej Remlein, Yasin Pourfarjam, et al. "Androgen signaling uses a writer and a reader of ADP-ribosylation to regulate protein complex assembly." Nature Communications 12, no. 1 (May 11, 2021). http://dx.doi.org/10.1038/s41467-021-23055-6.
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AbstractAndrogen signaling through the androgen receptor (AR) directs gene expression in both normal and prostate cancer cells. Androgen regulates multiple aspects of the AR life cycle, including its localization and post-translational modification, but understanding how modifications are read and integrated with AR activity has been difficult. Here, we show that ADP-ribosylation regulates AR through a nuclear pathway mediated by Parp7. We show that Parp7 mono-ADP-ribosylates agonist-bound AR, and that ADP-ribosyl-cysteines within the N-terminal domain mediate recruitment of the E3 ligase Dtx3L/Parp9. Molecular recognition of ADP-ribosyl-cysteine is provided by tandem macrodomains in Parp9, and Dtx3L/Parp9 modulates expression of a subset of AR-regulated genes. Parp7, ADP-ribosylation of AR, and AR-Dtx3L/Parp9 complex assembly are inhibited by Olaparib, a compound used clinically to inhibit poly-ADP-ribosyltransferases Parp1/2. Our study reveals the components of an androgen signaling axis that uses a writer and reader of ADP-ribosylation to regulate protein-protein interactions and AR activity.
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"Design of LSTM-RNN on a Sensor Based HAR using Android Phones." International Journal of Recent Technology and Engineering 8, no. 5 (January 30, 2020): 4250–57. http://dx.doi.org/10.35940/ijrte.e6821.018520.
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Activity Recognition (AR) is monitoring the liveliness of a person by using smart phone. Smart phones are used in a wider manner and it becomes one of the ways to identify the human’s environmental changes by using the sensors in smart mobiles. Smart phones are equipped in detecting sensors like compass sensor, gyroscope, GPS sensor and accelerometer. Human Activity Recognition (HAR) framework collects the raw data from sensors and observes the human movement using different classification methods. This paper focuses for Activity Recognition (AR) based on smart phone by analyzing the performance of various Deep Learning (DL) approach using in-built gyroscope and accelerometers. In this work, HAR dataset can be utilized from UCI based Machine Learning repository. The sensors such as gyroscope and accelerator are used to record the signals and performs various activities namely walkingdownstairs, walking-upstairs, jogging, standing, walking and sitting while a user wearing the smartphone in a pocket. The performance metrics has analyzed to recognize user’s activities using DL approach namely Recurrent Neural Network with Long-Short Term Memory (RNN- LSTM) were applied. The result provides 96% better accuracy for RNN-LSTM with minimum Mean Absolute Percentage Error (MAPE) when compare to other machine learning classifier.
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Alizadeh-Ghodsi, Mohammadreza, Katie Owen, Scott L. Townley, Damien Zanker, Samuel PG Rollin, Adrienne R. Hanson, Raj Kumar Shrestha, et al. "Potent stimulation of the androgen receptor instigates a viral mimicry response in prostate cancer." Cancer Research Communications, July 6, 2022. http://dx.doi.org/10.1158/2767-9764.crc-21-0139.
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Abstract Inhibiting the androgen receptor (AR), a ligand-activated transcription factor, with androgen deprivation therapy is a standard-of-care treatment for metastatic prostate cancer (PCa). Paradoxically, activation of AR can also inhibit the growth of PCa in some patients and experimental systems, but the mechanisms underlying this phenomenon are poorly understood. This study exploited a potent synthetic androgen, methyltestosterone (MeT), to investigate AR agonist-induced growth inhibition. MeT strongly inhibited growth of PCa cells expressing AR, but not AR-negative models. Genes and pathways regulated by MeT were highly analogous to those regulated by DHT, although MeT induced a quantitatively greater androgenic response in PCa cells. MeT potently down-regulated DNA methyltransferases, leading to global DNA hypomethylation. These epigenomic changes were associated with dysregulation of transposable element expression, including upregulation of endogenous retrovirus (ERV) transcripts after sustained MeT treatment. Increased ERV expression led to accumulation of double-stranded RNA and a “viral mimicry” response characterised by activation of interferon signalling, upregulation of MHC Class I molecules and enhanced recognition of murine PCa cells by CD8+ T cells. Positive associations between AR activity and ERVs/anti-viral pathways were evident in patient transcriptomic data, supporting the clinical relevance of our findings. Collectively, our study reveals that the potent androgen MeT can increase the immunogenicity of PCa cells via a viral mimicry response, a finding that has potential implications for the development of strategies to sensitize this cancer type to immunotherapies.
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Li, Yue, Eugene Ch'ng, Sue Cobb, and Simon See. "Presence and Communication in Hybrid Virtual and Augmented Reality Environments." PRESENCE: Virtual and Augmented Reality, December 22, 2021, 29–52. http://dx.doi.org/10.1162/pres_a_00340.
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Abstract The use of virtual reality (VR) and augmented reality (AR) in connected environments is rarely explored but may become a necessary channel of communication in the future. Such environments would allow multiple users to interact, engage, and share multidimensional data across devices and between the spectrum of realities. However, communication between the two realities within a hybrid environment is barely understood. We carried out an experiment with 52 participants in 26 pairs, within two environments of 3D cultural artifacts: (1) a Hybrid VR and AR environment (HVAR) and (2) a Shared VR environment (SVR). We explored the differences in perceived spatial presence, copresence, and social presence between the environments and between users. We demonstrated that greater presence is perceived in SVR when compared with HVAR, and greater spatial presence is perceived for VR users. Social presence is perceived greater for AR users, possibly because they have line of sight of their partners within HVAR. We found positive correlations between shared activity time and perceived social presence. While acquainted pairs reported significantly greater presence than unacquainted pairs in SVR, there were no significant differences in perceived presence between them in HVAR.
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Almahasneh, Majedaldein, Adeline Paiement, Xianghua Xie, and Jean Aboudarham. "MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images." Machine Vision and Applications 33, no. 1 (November 29, 2021). http://dx.doi.org/10.1007/s00138-021-01261-y.
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AbstractPrecisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-spectral imaging scenarios where all image bands observe the same scene. Thus, we refer to this special multi-spectral scenario as multi-layer. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR localisation (segmentation and detection) where different image bands (and physical locations) have their own set of results. Furthermore, to address the difficulty of producing dense AR annotations for training supervised machine learning (ML) algorithms, we adapt a training strategy based on weak labels (i.e. bounding boxes) in a recursive manner. We compare our detection and segmentation stages against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs) and state-of-the-art deep learning methods (Faster RCNN, U-Net). Additionally, both detection and segmentation stages are quantitatively validated on artificially created data of similar spatial configurations made from annotated multi-modal magnetic resonance images. Our framework achieves an average of 0.72 IoU (segmentation) and 0.90 F1 score (detection) across all modalities, comparing to the best performing baseline methods with scores of 0.53 and 0.58, respectively, on the artificial dataset, and 0.84 F1 score in the AR detection task comparing to baseline of 0.82 F1 score. Our segmentation results are qualitatively validated by an expert on real ARs.
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Guo, Xuanru, Takashi Yoshinaga, Aaron Hilton, Shuji Harumoto, Ema Hilton, Shizuka Ono, and Takeharu Seno. "The Sense of Presence between Volumetric-Video and Avatar-Based Augmented Reality and Physical-Zoom Teaching Activities." PRESENCE: Virtual and Augmented Reality, April 19, 2022, 1–14. http://dx.doi.org/10.1162/pres_a_00351.
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Abstract Various types of Augmented Reality (AR) have been successfully applied to many highly operable and spatial teaching courses in medicine, chemistry, and others. AR has already shown advantages over traditional face-to-face and online teaching, such as Zoom or Teams meetings. However, it is unclear when comparing teaching activity whether new AR technologies can offer similar advantages disciplines that are non-highly operational or non-spatial in nature, such as Introduction to Psychology. In order to present teaching activities in three dimensions and all directions and realize the presence of a teacher's face movement, we developed two new methods: Volumetric video Augmented Reality (VAR) and Avatar-based Augmented Reality (AAR). We compared the effects of teaching psychology using four methods: VAR, AAR, face-to-face, and Zoom. The participants’ data were collected via questionnaires with which we conducted variance analysis and Pearson correlation analysis. Our experiments showed that there were no significant differences in the effects of teaching under the four different teaching methods, but the AAR and VAR groups had significantly higher curriculum novelty and satisfaction, 3D sensation and presence, and teacher's attractiveness than face-to-face and Zoom groups. The research demonstrated that both VAR and AAR can improve the sense of presence and satisfaction in teaching psychology-related courses.
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Raeburn, Gideon, Martin Welton, and Laurissa Tokarchuk. "Developing a play-anywhere handheld AR storytelling app using remote data collection." Frontiers in Computer Science 4 (September 15, 2022). http://dx.doi.org/10.3389/fcomp.2022.927177.
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Immersive story experiences like immersive theater productions and escape rooms have grown in popularity in recent years, offering the audience a more active role in the events portrayed. However, many of these activities were forced to close at the start of the COVID-19 pandemic, arising from restrictions placed on group activities and travel. This created an opportunity for a story experience that users could take part in around their local neighborhoods. Five mobile applications (apps) were developed toward this goal, aiming to make effective use of available local map data, alongside virtual content overlaid on users' surroundings through Augmented Reality (AR), to offer additional story features not present in the real environment. The first two apps investigated the feasibility of such an approach, including the remote field testing of the apps, where participants used their own devices across a variety of locations. Two follow-up apps further aimed to offer an improved user experience, also adopting a more standardized testing procedure, to better ensure each app was completed in an intended manner by those participating remotely. Participants rated their experience through immersion and engagement questionnaire factors that tested for their appropriateness to rate such experiences, in addition to providing their feedback. A final app applied the same AR story implementation to a curated site-specific study, once pandemic restrictions had eased. This combination of remote studies and subsequent curated study offered a reverse methodology to much previous research in this field, but was found to offer advantages in corroborating the results of the remote studies, and also in offering new insights to further improve such an AR story app, that is designed to be used at an outdoor location of the user's choosing. Such an app offers benefits to those who may prefer the opportunity to take part in such an activity solo or close to home, as well as for storytellers to develop an outside story for use at a variety of locations, making it available to a larger audience, without the challenges and costs in migrating it to different locations.