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Journal articles on the topic 'Deep CCA'
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Lindenbaum, Ofir, Neta Rabin, Yuri Bregman, and Amir Averbuch. "Seismic Event Discrimination Using Deep CCA." IEEE Geoscience and Remote Sensing Letters 17, no. 11 (November 2020): 1856–60. http://dx.doi.org/10.1109/lgrs.2019.2959554.
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Karami, Mahdi, and Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.
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We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shared latent representation that describes the common underlying sources of variation and a set of viewspecific components. To approximate the posterior distribution of the latent multi-view layer, an efficient variational inference procedure is developed based on the solution of probabilistic CCA. The model is then generalized to an arbitrary number of views. An empirical analysis confirms that the proposed deep multi-view model can discover subtle relationships between multiple views and recover rich representations.
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Gao, Quanxue, Huanhuan Lian, Qianqian Wang, and Gan Sun. "Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3938–45. http://dx.doi.org/10.1609/aaai.v34i04.5808.
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For cross-modal subspace clustering, the key point is how to exploit the correlation information between cross-modal data. However, most hierarchical and structural correlation information among cross-modal data cannot be well exploited due to its high-dimensional non-linear property. To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra-modal data. More specifically, the proposed model consists of three components: 1) deep canonical correlation analysis (Deep CCA) model; 2) self-expressive layer; 3) Deep CCA decoders. The Deep CCA model consists of convolutional encoders and correlation constraint. Convolutional encoders are used to obtain the latent representations of cross-modal data, while adding the correlation constraint for the latent representations can make full use of the information of the inter-modal data. Furthermore, self-expressive layer works on latent representations and constrain it perform self-expression properties, which makes the shared coefficient matrix could capture the hierarchical intra-modal correlations of each modality. Then Deep CCA decoders reconstruct data to ensure that the encoded features can preserve the structure of the original data. Experimental results on several real-world datasets demonstrate the proposed method outperforms the state-of-the-art methods.
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Li, Bin, and Yuqing He. "Computational Logistics for Container Terminal Handling Systems with Deep Learning." Computational Intelligence and Neuroscience 2021 (April 26, 2021): 1–18. http://dx.doi.org/10.1155/2021/5529914.
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Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.
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Jain, Pankaj K., Abhishek Dubey, Luca Saba, Narender N. Khanna, John R. Laird, Andrew Nicolaides, Mostafa M. Fouda, Jasjit S. Suri, and Neeraj Sharma. "Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm." Journal of Cardiovascular Development and Disease 9, no. 10 (September 27, 2022): 326. http://dx.doi.org/10.3390/jcdd9100326.
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Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.
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Jiang, Guoping, Wu Zhang, Ting Wang, Songming Ding, Xiaoliang Shi, Shuirong Zhang, Weiwei Shi, Angen Liu, and Shusen Zheng. "Characteristics of genomic alterations in Chinese cholangiocarcinoma patients." Japanese Journal of Clinical Oncology 50, no. 10 (June 13, 2020): 1117–25. http://dx.doi.org/10.1093/jjco/hyaa088.
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Abstract Objective Cholangiocarcinoma (CCA) is a primary malignancy, which is often diagnosed as advanced and inoperable due to the lack of effective biomarkers and poor sensitivity of clinical diagnosis. Here, we aimed to identify the genomic profile of CCA and provided molecular evidence for further biomarker development. Methods The formalin-fixed paraffin-embedded and matching blood samples were sequenced by deep sequencing targeting 450 cancer genes and genomic alteration analysis was performed. Tumor mutational burden (TMB) was measured by an algorithm developed in-house. Correlation analysis was performed by Fisher’s exact test. Results The most commonly altered genes in this cohort were TP53 (41.27%, 26/63), KRAS (31.75%, 20/63), ARID1A and IDH1 (15.87%, 10/63, for both), SMAD4 (14.29%, 9/63), FGFR2 and BAP1 (12.70%, 8/63, for both), and CDKN2A (11.11%, 7/63). BAP1 mutations were significantly correlated with the CCA subtype. LRP2 mutations were significantly associated with the younger intrahepatic CCA (iCCA) patients, while BAP1 was associated with iCCA patients aged 55–65 years old. BAP1 and LRP2 mutations were associated with TMB. Conclusions Most Chinese CCA patients were 50–70 years old. BAP1 and LRP2 mutations were associated with the age of iCCA patients.
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Yuan, Fei, Xiaoquan Ke, and En Cheng. "Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning." Journal of Marine Science and Engineering 7, no. 11 (October 27, 2019): 380. http://dx.doi.org/10.3390/jmse7110380.
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Ship recognition based on ship-radiated noise is one of the most important and challenging subjects in underwater acoustic signal processing. The recognition methods for ship-radiated noise recognition include traditional methods and deep learning (DL) methods. Developing from the DL methods and inspired by audio–video speech recognition (AVSR), the paper further introduces multimodal deep learning (multimodal-DL) methods for the recognition of ship-radiated noise. In this paper, ship-radiated noise (acoustics modality) and visual observation of the ships (visual modality) are two different modalities that the multimodal-DL methods model on. The paper specially designs a multimodal-DL framework, the multimodal convolutional neural networks (multimodal-CNNs) for the recognition of ship-radiated noise. Then the paper proposes a strategy based on canonical correlation analysis (CCA-based strategy) to build a joint representation and recognition on the two different single-modality (acoustics modality and visual modality). The multimodal-CNNs and the CCA-based strategy are tested on real ship-radiated noise data recorded. Experimental results show that, using the CCA-based strategy, strong-discriminative information can be built from weak-discriminative information provided from a single-modality. Experimental results also show that as long as any one of the single-modalities can provide information for the recognition, the multimodal-DL methods can have a much better multiclass recognition performance than the DL methods. The paper also discusses the advantages and superiorities of the multimodal-Dl methods over the traditional methods for ship-radiated noise recognition.
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Chapman, James, and Hao-Ting Wang. "CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework." Journal of Open Source Software 6, no. 68 (December 18, 2021): 3823. http://dx.doi.org/10.21105/joss.03823.
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Yu, Yi, Suhua Tang, Kiyoharu Aizawa, and Akiko Aizawa. "Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data." IEEE Transactions on Neural Networks and Learning Systems 30, no. 4 (April 2019): 1250–58. http://dx.doi.org/10.1109/tnnls.2018.2856253.
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Peng, Yun, Shenyi Zhao, and Jizhan Liu. "Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine." Agriculture 11, no. 9 (September 10, 2021): 869. http://dx.doi.org/10.3390/agriculture11090869.
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Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse different deep features extracted from Convolutional Neural Network (CNN), plus Support Vector Machine (SVM) is proposed. In this research, based on an open dataset, three types of state-of-the-art CNNs, seven species of deep features, and a multi-class SVM classifier were studied. First, the images were resized to meet the input requirements of a CNN. Then, the deep features of the input images were extracted by a specific deep features layer of the CNN. Next, two kinds of deep features from different networks were fused by CCA to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused features. When applied to an open dataset, the model outcome shows that the fused deep features with any combination can obtain better identification performance than by using a single type of deep feature. The fusion of fc6 (in AlexNet network) and Fc1000 (in ResNet50 network) deep features obtained the best identification performance. The average F1 Score of 96.9% was 8.7% higher compared to the best performance of a single deep feature, i.e., Fc1000 of ResNet101, which was 88.2%. Furthermore, the F1 Score of the proposed method is 2.7% higher than the best performance obtained by using a CNN directly. The experimental results show that the method proposed in this paper can achieve fast and accurate identification of grape varieties. Based on the proposed algorithm, the smart machinery in agriculture can take more targeted measures based on the different characteristics of different grape varieties for further improvement of the yield and quality of grape production.
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Zeng, Donghuo, Yi Yu, and Keizo Oyama. "Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-Modal Retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 16, no. 3 (September 4, 2020): 1–23. http://dx.doi.org/10.1145/3387164.
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Koźlenia, Dawid, Jarosław Domaradzki, and Izabela Trojanowska. "MULTIVARIATE RELATIONSHIPS BETWEEN MORPHOLOGY, MOVEMENT PATTERNS AND SPEED ABILITIES IN ELITE YOUNG, MALE ATHLETES." Kinesiologia Slovenica 26, no. 1 (May 15, 2020): 33–45. http://dx.doi.org/10.52165/kinsi.26.1.33-45.
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Speed and agility are crucial abilities in many team sports such as soccer, basketball or handball. Therefore determing factors affect speed abilities is relevant. To investigate multidimensional correlations of the FMS test results with speed abilities. 35 male team sport players, aged 21.31±0.93. Body weight and body height were measured and BMI was calculated (kg/m2 ). Three modules of the FMS test were used to analyse: Deep Squat, Hurdle Step, In-line Lunge. Linear speed was measured based on the 20m Linear Speed test, agility was evaluated using the Agility T-test. Data were analysed by Canonical Correlation Analysis (CAA). The CCA analysis demonstrated statistically significant correlation between morphological features and agility. Correlation was found between 20m Linear Speed and In-line Lunge. However it was not revealed any significant correlations of neither speed skills nor morphological characteristics with chosen FMS subtests. High canonical loadings and weights suggested the presence of correlations (not significant) between individual measurements. The correlations between morphological measurements and functional movement were very close to statistical significance. The CCA analysis allowed for showing multivariate links between morphological features and functional abilities. This results demonstrated moderate correlations between body morphology and agility and between movement patterns and agility. Better scores in agility were correlated with good performance in In-line Lunge and Hurdle Step test.
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Sun, Shiying, Ning An, Xiaoguang Zhao, and Min Tan. "A PCA–CCA network for RGB-D object recognition." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141775282. http://dx.doi.org/10.1177/1729881417752820.
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Object recognition is one of the essential issues in computer vision and robotics. Recently, deep learning methods have achieved excellent performance in red-green-blue (RGB) object recognition. However, the introduction of depth information presents a new challenge: How can we exploit this RGB-D data to characterize an object more adequately? In this article, we propose a principal component analysis–canonical correlation analysis network for RGB-D object recognition. In this new method, two stages of cascaded filter layers are constructed and followed by binary hashing and block histograms. In the first layer, the network separately learns principal component analysis filters for RGB and depth. Then, in the second layer, canonical correlation analysis filters are learned jointly using the two modalities. In this way, the different characteristics of the RGB and depth modalities are considered by our network as well as the characteristics of the correlation between the two modalities. Experimental results on the most widely used RGB-D object data set show that the proposed method achieves an accuracy which is comparable to state-of-the-art methods. Moreover, our method has a simpler structure and is efficient even without graphics processing unit acceleration.
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Al-Mohannadi, Aisha, Somaya Al-Maadeed, Omar Elharrouss, and Kishor Kumar Sadasivuni. "Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement." Sensors 21, no. 20 (October 14, 2021): 6839. http://dx.doi.org/10.3390/s21206839.
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Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.
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Czech, Andreas. "Deep sequencing of tRNA's 3′-termini sheds light on CCA-tail integrity and maturation." RNA 26, no. 2 (November 12, 2019): 199–208. http://dx.doi.org/10.1261/rna.072330.119.
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Liu, Qianqian, Yong Jiao, Yangyang Miao, Cili Zuo, Xingyu Wang, Andrzej Cichocki, and Jing Jin. "Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA." Neurocomputing 378 (February 2020): 36–44. http://dx.doi.org/10.1016/j.neucom.2019.10.049.
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Lamichhane, Aastha, Muhamad Khoiru Zaki, Emmanuel Okiria, and Keigo Noda. "Decision-making in climate change adaptation through a cross-sectoral approach: review." IOP Conference Series: Earth and Environmental Science 1016, no. 1 (April 1, 2022): 012034. http://dx.doi.org/10.1088/1755-1315/1016/1/012034.
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Abstract Climate change adaptation (CCA) is an emerging topic in recent years since global temperature is continuing to rise and posing threats to natural biodiversity and human life. Its diverse nature requires efforts from a broad range of sectors to cope or adjust. This review paper aims to systematically study the cross-sectoral approaches in CCA decision making and develop probable strengths and shortcomings of those approaches. Literatures concerned with Multi-Criteria Decision-Making Method, or Multi-Criteria Analysis (MCA) are reviewed since it is considered as a holistic approach to integrate multiple sectors and combine monetary and non-monetary terms prevailing in sectoral and regional aspects, mainly in agriculture and water resource management. The literatures were searched through Scopus and PRISMA method was adopted to systematically refine the published articles based on our criteria. Out of 383 articles discovered, 139 were related to CCA out of which, 33 articles which applied MCA as their methodological approach were shortlisted for the core study. The result showed that MCA is extensively used in CCA decision making, prioritizing options, and formulating adaptation strategies at local and regional scale and considered as a flexible, transparent, and effective method because of an active engagement of stakeholders and experts’ judgement. But its inability to address the underlying uncertainties of climate change scenario is one of the major drawbacks seen. Thus, many literatures suggest incorporating Sensitivity analysis, Dynamic Adaptive Pathways, Real Option Analysis, or Robust Decision-Making Analysis with MCA to overcome those deep uncertainties.
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Wang, Dongqing, Carrie A. Karvonen-Gutierrez, Elizabeth A. Jackson, Michael R. Elliott, Bradley M. Appelhans, Emma Barinas-Mitchell, Lawrence F. Bielak, Mei-Hua Huang, and Ana Baylin. "Western Dietary Pattern Derived by Multiple Statistical Methods Is Prospectively Associated with Subclinical Carotid Atherosclerosis in Midlife Women." Journal of Nutrition 150, no. 3 (November 5, 2019): 579–91. http://dx.doi.org/10.1093/jn/nxz270.
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ABSTRACT Background The menopause has adverse effects on cardiometabolic profiles that are linked to an increased risk of atherosclerosis in women. A healthy diet during the menopausal transition may counteract the menopause-induced atherosclerotic risk. Objective This prospective cohort study aimed to examine the associations between empirically derived dietary patterns and subclinical carotid atherosclerosis in midlife women. Methods A total of 1246 midlife women (average age at baseline: 46.3 y) from the Study of Women's Health Across the Nation who completed dietary assessments and had a carotid ultrasound scan were included. Dietary data were collected at 3 time points, during 1996–1997, 2001–2003, and 2005–2007. Measures of carotid atherosclerosis included common carotid artery intima-media thickness (CCA-IMT), adventitial diameter (AD), and carotid plaque index collected during 2009–2013. Three statistical methods, including principal component analysis (PCA), reduced rank regression (RRR), and partial least squares regression (PLS), were used to identify dietary patterns. Results A Western dietary pattern was identified from each method and a Prudent dietary pattern from PCA. High adherence to the Western pattern was associated with higher CCA-IMT. Women in the fourth quartile of the Western pattern identified by PCA, RRR, and PLS had 0.042 mm (95% CI: 0.011, 0.073), 0.033 mm (95% CI: 0.0086, 0.057), and 0.049 mm (95% CI: 0.025, 0.074), respectively, larger CCA-IMT than women in the first quartile; these differences correspond to 30%, 24%, and 35% of the sample SD, respectively. The Prudent pattern was not significantly associated with CCA-IMT. No significant associations were found between the identified dietary patterns and AD or carotid plaque. Conclusions The positive association between the Western diet and CCA-IMT was robust under different dietary pattern derivation methods. The adoption of a diet low in red meat, processed meat, deep-fried products, and sugar-sweetened beverages among midlife women is associated with a lower future risk of atherosclerosis.
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Iqtait, Musab, Fatma Susilawati Mohamad, and Fadi Alsuhimat. "Active Appearance Model for Age Prediction: A Comparison." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 539. http://dx.doi.org/10.14419/ijet.v7i4.15.28364.
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Individual age gives key demographic data. It is viewed as a paramount delicate biometric characteristic for individual identification, contrasted with other pattern recognition issues. Age estimation is a complex issue particularly in relation to facial pictures with different ages, since the aging procedure varies extraordinarily across different age groups. In this work, we investigate deep learning techniques for age prediction based on Active Appearance Models (AAM) and six classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA) and Projection Twin Support Vector Machine (PTSVM) to improve the precision of age prediction based on the present methods. In this algorithm, we extracted the traits of the facial images as traits vectors using AAM model, and the classifiers are utilized to predict the age. We were able to recognize that the accuracy of CCA algorithm is the best, the intermediate is SVR and the KNN algorithm is the lowest.
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Siamandoura, Paraskevi, and Constantina Tzia. "Comparative Study of Novel Methods for Olive Leaf Phenolic Compound Extraction Using NADES as Solvents." Molecules 28, no. 1 (January 1, 2023): 353. http://dx.doi.org/10.3390/molecules28010353.
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Natural deep eutectic solvents (NADES) composed of choline chloride with maltose (CMA), glycerol (CGL), citric (CCA) and lactic acid (CLA) combined with microwave (MAE), ultrasound (UAE), homogenate (HAE) and high hydrostatic pressure (HHPAE)-assisted extraction methods were applied to recover and compare olive leaf phenolic compounds. The resultant extracts were evaluated for their total phenol content (TPC), phenolic profile and antioxidant activity and compared with those of water and ethanol:water 70% v/v extracts. HAE was proven to be the most efficient method for the recovery of olive leaf phenolic compounds. The highest TPC (55.12 ± 1.08 mg GAE/g d.w.) was found in CCA extracts after HAE at 60 °C and 12,000 rpm, and the maximum antioxidant activity (3.32 ± 0.39 g d.w./g DPPH) was found in CGL extracts after UAE at 60 °C for 30 min. The TPCs of ethanol extracts were found to be higher than those of NADES extracts in most cases. The predominant phenolic compounds in the extracts were oleuropein, hydrohytyrosol and rutin.
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Varghese, Anna M., Juber Ahamad A. Patel, Yelena Yuriy Janjigian, Fanli Meng, S. Duygu Selcuklu, Catherine Zimel, David Michael Hyman, et al. "Non-invasive detection of acquired resistance to FGFR inhibition in patients with cholangiocarcinoma harboring FGFR2 alterations." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 4096. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.4096.
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4096 Background: FGFR2 alterations are present in 14% of cholangiocarcinomas (CCA) and are promising targets of investigational FGFR-directed therapies. Cell-free DNA profiling has emerged as a non-invasive approach to monitor disease and longitudinally characterize tumor evolution. We describe the use of circulating tumor DNA (ctDNA) among patients (pts) with FGFR2-altered CCA receiving FGFR-targeted therapy in the identification of acquired FGFR2 mutations (mut) at resistance. Methods: Serial blood samples were collected from 8 pts with FGFR-altered CCA for ctDNA isolation and next generation sequencing. Plasma ctDNA collected at baseline and resistance to FGFR-targeted therapy were sequenced using a custom ultra-deep coverage cfDNA panel, MSK-ACCESS, incorporating dual index primers and unique molecular barcodes to enable background error suppression and high-sensitivity mut detection. The assay was enhanced to include all protein-coding exons and relevant introns of FGFR2. In 5/8 pts, genomic profiling of an initial tumor biopsy was performed. Results: 8 pts with FGFR2-altered CCA (7 gene fusions, 1 amplification) were treated with FGFR-targeted therapies. 7/8 pts exhibited stable disease or partial response. 19 total acquired mut in FGFR2 were detected at resistance in 5/8 pts (between 1-9 unique mut identified in each sample). All mut were located in the kinase domain. Conclusions: Acquired mut in FGFR2 are seen in pts who have developed resistance to targeted therapy. CtDNA can be used to identify these mut at the time of acquired resistance. The multitude of FGFR2 mut observed within individual pts suggest heterogeneity and evolutionary convergence of resistance mechanisms. Our results illustrate the utility of ctDNA as a less invasive way to monitor for signs of resistance and to identify other potential targetable alterations. [Table: see text]
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Webster, Nicole S., Luke D. Smith, Andrew J. Heyward, Joy E. M. Watts, Richard I. Webb, Linda L. Blackall, and Andrew P. Negri. "Metamorphosis of a Scleractinian Coral in Response to Microbial Biofilms." Applied and Environmental Microbiology 70, no. 2 (February 2004): 1213–21. http://dx.doi.org/10.1128/aem.70.2.1213-1221.2004.
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ABSTRACT Microorganisms have been reported to induce settlement and metamorphosis in a wide range of marine invertebrate species. However, the primary cue reported for metamorphosis of coral larvae is calcareous coralline algae (CCA). Herein we report the community structure of developing coral reef biofilms and the potential role they play in triggering the metamorphosis of a scleractinian coral. Two-week-old biofilms induced metamorphosis in less than 10% of larvae, whereas metamorphosis increased significantly on older biofilms, with a maximum of 41% occurring on 8-week-old microbial films. There was a significant influence of depth in 4- and 8-week biofilms, with greater levels of metamorphosis occurring in response to shallow-water communities. Importantly, larvae were found to settle and metamorphose in response to microbial biofilms lacking CCA from both shallow and deep treatments, indicating that microorganisms not associated with CCA may play a significant role in coral metamorphosis. A polyphasic approach consisting of scanning electron microscopy, fluorescence in situ hybridization (FISH), and denaturing gradient gel electrophoresis (DGGE) revealed that coral reef biofilms were comprised of complex bacterial and microalgal communities which were distinct at each depth and time. Principal-component analysis of FISH data showed that the Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Cytophaga-Flavobacterium of Bacteroidetes had the largest influence on overall community composition. A low abundance of Archaea was detected in almost all biofilms, providing the first report of Archaea associated with coral reef biofilms. No differences in the relative densities of each subdivision of Proteobacteria were observed between slides that induced larval metamorphosis and those that did not. Comparative cluster analysis of bacterial DGGE patterns also revealed that there were clear age and depth distinctions in biofilm community structure; however, no difference was detected in banding profiles between biofilms which induced larval metamorphosis and those where no metamorphosis occurred. This investigation demonstrates that complex microbial communities can induce coral metamorphosis in the absence of CCA.
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Rukavina, Iva, Maria João Rodrigues, Catarina G. Pereira, Inês Mansinhos, Anabela Romano, Sylwester Ślusarczyk, Adam Matkowski, and Luísa Custódio. "Greener Is Better: First Approach for the Use of Natural Deep Eutectic Solvents (NADES) to Extract Antioxidants from the Medicinal Halophyte Polygonum maritimum L." Molecules 26, no. 20 (October 11, 2021): 6136. http://dx.doi.org/10.3390/molecules26206136.
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In this study, natural deep eutectic solvents (NADES) formed by choline chloride (ChCl), sucrose, fructose, glucose, and xylose, were used to extract antioxidants from the halophyte Polygonum maritimum L. (sea knotgrass) and compared with conventional solvents (ethanol and acetone). NADES and conventional extracts were made by an ultrasound-assisted procedure and evaluated for in vitro antioxidant properties by the radical scavenging activity (RSA) on the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, oxygen radical absorbance capacity (ORAC), and copper chelating activity (CCA). Samples were profiled by liquid chromatography (LC)-electrospray ionization (ESI)-QTOF-MS analysis. ChCl:fructose was more efficient in the DPPH assay, than the acetone extract. ChCl:sucrose and ChCl:fructose extracts had the highest ORAC when compared with the acetone extract. NADES extracts had higher CCA, than the acetone extract. The phenolic composition of the NADES extracts was less complex than the conventional extracts, but the proportions of major antioxidants, such as flavonols and flavan-3-ols, were similar in all the solvents. Myricitrin was the major flavonoid in all of the samples, while gallic acid was the main phenolic acid in the conventional extracts and present in a greater amount in ChCl:fructose. Results suggest that NADES containing ChCl and sucrose/fructose can replace conventional solvents, especially acetone, in the extraction of antioxidants from sea knotgrass.
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Jia, Hongfei, Yu Wang, Yifan Duan, and Hongbing Xiao. "Alzheimer’s Disease Classification Based on Image Transformation and Features Fusion." Computational and Mathematical Methods in Medicine 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/9624269.
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It has become an inevitable trend for medical personnel to analyze and diagnose Alzheimer’s disease (AD) in different stages by combining functional magnetic resonance imaging (fMRI) and artificial intelligence technologies such as deep learning in the future. In this paper, a classification method was proposed for AD based on two different transformation images of fMRI and improved the 3DPCANet model and canonical correlation analysis (CCA). The main ideas include that, firstly, fMRI images were preprocessed, and subsequently, mean regional homogeneity (mReHo) and mean amplitude of low-frequency amplitude (mALFF) transformation were performed for the preprocessed images. Then, mReHo and mALFF images were extracted features using the improved 3DPCANet, and these two kinds of the extracted features were fused by CCA. Finally, the support vector machine (SVM) was used to classify AD patients with different stages. Experimental results showed that the proposed approach was robust and effective. Classification accuracy for significant memory concern (SMC) vs. mild cognitive impairment (MCI), normal control (NC) vs. AD, and NC vs. SMC, respectively, reached 95.00%, 92.00%, and 91.30%, which adequately proved the feasibility and effectiveness of the proposed method.
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Khalilzad, Zahra, and Chakib Tadj. "Using CCA-Fused Cepstral Features in a Deep Learning-Based Cry Diagnostic System for Detecting an Ensemble of Pathologies in Newborns." Diagnostics 13, no. 5 (February 24, 2023): 879. http://dx.doi.org/10.3390/diagnostics13050879.
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Crying is one of the means of communication for a newborn. Newborn cry signals convey precious information about the newborn’s health condition and their emotions. In this study, cry signals of healthy and pathologic newborns were analyzed for the purpose of developing an automatic, non-invasive, and comprehensive Newborn Cry Diagnostic System (NCDS) that identifies pathologic newborns from healthy infants. For this purpose, Mel-frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) were extracted as features. These feature sets were also combined and fused through Canonical Correlation Analysis (CCA), which provides a novel manipulation of the features that have not yet been explored in the literature on NCDS designs, to the best of our knowledge. All the mentioned feature sets were fed to the Support Vector Machine (SVM) and Long Short-term Memory (LSTM). Furthermore, two Hyperparameter optimization methods, Bayesian and grid search, were examined to enhance the system’s performance. The performance of our proposed NCDS was evaluated with two different datasets of inspiratory and expiratory cries. The CCA fusion feature set using the LSTM classifier accomplished the best F-score in the study, with 99.86% for the inspiratory cry dataset. The best F-score regarding the expiratory cry dataset, 99.44%, belonged to the GFCC feature set employing the LSTM classifier. These experiments suggest the high potential and value of using the newborn cry signals in the detection of pathologies. The framework proposed in this study can be implemented as an early diagnostic tool for clinical studies and help in the identification of pathologic newborns.
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Sun, J., X. Y. Gu, Y. Y. Feng, S. F. Jin, W. S. Jiang, H. Y. Jin, and J. F. Chen. "Summer and winter living coccolithophores in the Yellow Sea and the East China Sea." Biogeosciences 11, no. 3 (February 10, 2014): 779–806. http://dx.doi.org/10.5194/bg-11-779-2014.
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Abstract. This paper describes the distribution of living coccolithophores (LCs) in the Yellow Sea and the East China Sea in summer and winter, and its relationship with environmental factors by canonical correspondence analysis (CCA). We carried out a series of investigations on LCs distribution in the Yellow Sea and the East China Sea in July and December 2011. 210 samples from different depths were collected from 44 stations in summer and 217 samples were collected from 45 stations in winter. Totally 20 taxa belonging to coccolithophyceae were identified using a polarized microscope at the 1000 × magnification. The dominant species of the two seasons were Gephyrocapsa oceanica, Emiliania huxleyi, Helicosphaera carteri, and Algirosphaera robusta. In summer the abundance of coccolithophore cells and coccoliths ranged 0–176.40 cells mL−1, and 0–2144.98 coccoliths mL−1, with the average values of 8.45 cells mL−1, and 265.42 coccoliths mL−1, respectively. And in winter the abundance of cells and coccoliths ranged 0–71.66 cells mL−1, and 0–4698.99 coccoliths mL−1, with the average values of 13.91 cells mL−1 and 872.56 coccoliths mL−1, respectively. In summer, the LCs in surface layer were mainly observed on the coastal belt and southern part of the survey area. In winter, the LCs in surface layer had high value in the continental shelf area of section P. The comparison among section A, section F, section P and section E indicated lower species diversity and less abundance in the Yellow Sea than those in the East China Sea in both seasons. Temperature and the nitrate concentration may be the major environmental factors controlling the distribution and species composition of LCs in the studying area based on CCA. Abbreviations: LCs: Living Coccolithophores; CCA: canonical correspondence analysis; DCM: Deep Chlorophyll Maximum
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Yacob, Yasmin Mohd, Hiam Alquran, Wan Azani Mustafa, Mohammed Alsalatie, Harsa Amylia Mat Sakim, and Muhamad Safiih Lola. "H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner." Diagnostics 13, no. 3 (January 17, 2023): 336. http://dx.doi.org/10.3390/diagnostics13030336.
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Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.
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Schuler, Paul A. "MOBEX Cayenne 2013: Lessons Learned & Response Enhancements Derived from the International Mobilization, Preparedness & Response Exercise in French Guiana." International Oil Spill Conference Proceedings 2014, no. 1 (May 1, 2014): 42–49. http://dx.doi.org/10.7901/2169-3358-2014.1.42.
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ABSTRACT Oil Spill Response Limited (OSRL) and Clean Caribbean & Americas (CCA) conducted the first International Mobilization, Preparedness & Response Exercise (MOBEX) since the 2010 Gulf of Mexico “Macondo” incident and since the merger of CCA and OSRL in January 2013. The exercise was hosted by Shell Exploration & Production France SAS (Shell E&P), the operating partner of the Zaedyus joint venture drilling prospect offshore French Guiana. It was the first MOBEX to support a deep water exploratory drilling scenario. MOBEX Cayenne 2013, was conducted in French Guiana, May 15–17, 2013, and included the following five foundational exercise components found in all MOBEXs:Tabletop Exercise (Simulation)Mobilization of Tier 1 and 2 (Shell), and Tier 3 (OSRL) response equipment and personnelConference and Technical SeminarEquipment Deployment DemonstrationDelegate Exchange/Forum A number of planning activities, and MOBEX itself, contributed to enhancing the overall oil spill preparedness capability in the operating area. Planning activities included coordination visits and meetings with operators and governmental authorities, familiarization with sensitive environmental resources, logistics planning within the international, national and local infrastructure, expansion of indigenous response capability through training of local responders and vessel operators, and familiarization with governmental policies and procedures across a wide range of issues. This paper discusses oil spill preparedness and response lessons learned and enhancements derived from the planning and conduct of MOBEX Cayenne 2013.
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Garibaldi-Márquez, Francisco, Gerardo Flores, Diego A. Mercado-Ravell, Alfonso Ramírez-Pedraza, and Luis M. Valentín-Coronado. "Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning." Sensors 22, no. 8 (April 14, 2022): 3021. http://dx.doi.org/10.3390/s22083021.
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Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they are implemented under controlled conditions. The development of a sound weed control system begins by recognizing the crop and the different weed plants presented in the field. In this work, a classification approach of Zea mays L. (Crop), narrow-leaf weeds (NLW), and broadleaf weeds (BLW) from multi-plant images are presented. Moreover, a large image dataset was generated. Images were captured in natural field conditions, in different locations, and growing stages of the plants. The extraction of regions of interest (ROI) is carried out employing connected component analysis (CCA), whereas the classification of ROIs is based on Convolutional Neural Networks (CNN) and compared with a shallow learning approach. To measure the classification performance of both methods, accuracy, precision, recall, and F1-score metrics were used. The best alternative for the weed classification task at early stages of growth and in natural corn field environments was the CNN-based approach, as indicated by the 97% accuracy value obtained.
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Yu, Tianwei. "AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments." PLOS Computational Biology 18, no. 1 (January 26, 2022): e1009826. http://dx.doi.org/10.1371/journal.pcbi.1009826.
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In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, information contained in one data type pertaining to the other data type may be complex and in nonlinear form. Deep learning provides a convenient alternative to extract low-dimensional nonlinear data embedding. In addition, the deep learning setup can naturally incorporate the effects of clinical confounding factors into the integrative analysis. Here we report a deep learning setup, named Autoencoder-based Integrative Multi-omics data Embedding (AIME), to extract data representation for omics data integrative analysis. The method can adjust for confounder variables, achieve informative data embedding, rank features in terms of their contributions, and find pairs of features from the two data types that are related to each other through the data embedding. In simulation studies, the method was highly effective in the extraction of major contributing features between data types. Using two real microRNA-gene expression datasets, one with confounder variables and one without, we show that AIME excluded the influence of confounders, and extracted biologically plausible novel information. The R package based on Keras and the TensorFlow backend is available at https://github.com/tianwei-yu/AIME.
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Church, Sarah E., Cheryl London, Christine M. Toedebusch, Ben Sutton, Sarah Weigel, and Erin Piazza. "Abstract 46: High-plex spatial transcriptomic characterization of canine tumor tissue." Cancer Research 83, no. 7_Supplement (April 4, 2023): 46. http://dx.doi.org/10.1158/1538-7445.am2023-46.
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Abstract Background: Combination therapy to treat hematological and solid malignancies including chemotherapy, radiation, targeted and immunotherapy all hold huge potential for eliciting clinical responses. Informative pre-clinical testing of these approaches can be greatly facilitated using immune competent animals with spontaneous tumors. Pet dogs are immunologically outbred, immune competent and develop spontaneous tumors such as non Hodgkin’s lymphoma, glioblastoma, osteosarcoma, urothelial carcinoma and melanoma that share remarkable clinical, biological and genetic features with their human counterparts. As such, pre clinical testing of therapeutic approaches in dogs with cancer promises to accurately inform human clinical trial design. For this comparative approach to provide maximum information to accelerate human clinical translation of novel combination therapies and identify correlative biomarkers of therapeutic response, it is necessary to develop research tools for deep interrogation of the canine tumor microenvironment (TME). Here we present spatial transcriptomic analysis of multiple canine tumor and tissue types using GeoMx® digital spatial profiler (DSP) Canine Cancer Atlas (CCA) panel. Methods: FFPE slides or tissue microarrays were used to profile tumor and normal tissue from canines. Each slide was stained with tissue specific immunofluorescent antibodies, including Pan-cytokeratin, CD45, Vimentin, IBA1, CD3, and/or CD68. Regions of interest were selected to assess the TME and normal tissue as possible. Slides were then run on the DSP using the CCA panel that contains 1900-canine specific genes using standard DSP methods. Results: We were able to spatially detect over 1700 genes across multiple tissue types from canines, including osteosarcoma, glioblastoma, melanoma and normal tissues. Genes were detected in spatial compartments including malignant tumor, tumor stroma and normal tissue. Conclusions: Together the GeoMx CCA allow for interrogation of the TME of multiple tumor types and has the potential to inform spatial biomarkers for response to therapy, as well as translate the effectiveness of these therapies to humans. Citation Format: Sarah E. Church, Cheryl London, Christine M. Toedebusch, Ben Sutton, Sarah Weigel, Erin Piazza. High-plex spatial transcriptomic characterization of canine tumor tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 46.
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S. Garea, Alberto S., Dora B. Heras, and Francisco Argüello. "TCANet for Domain Adaptation of Hyperspectral Images." Remote Sensing 11, no. 19 (September 30, 2019): 2289. http://dx.doi.org/10.3390/rs11192289.
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The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques.
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Chanioti, Sofia, Maria Katsouli, and Constantina Tzia. "Novel Processes for the Extraction of Phenolic Compounds from Olive Pomace and Their Protection by Encapsulation." Molecules 26, no. 6 (March 22, 2021): 1781. http://dx.doi.org/10.3390/molecules26061781.
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Olive pomace, the solid by-product derived from olive oil production consists of a high concentration of bioactive compounds with antioxidant activity, such as phenolic compounds, and their recovery by applying innovative techniques is a great opportunity and challenge for the olive oil industry. This study aimed to point out a new approach for the integrated valorization of olive pomace by extracting the phenolic compounds and protecting them by encapsulation or incorporation in nanoemulsions. Innovative assisted extraction methods were evaluated such as microwave (MAE), homogenization (HAE), ultrasound (UAE), and high hydrostatic pressure (HHPAE) using various solvent systems including ethanol, methanol, and natural deep eutectic solvents (NADESs). The best extraction efficiency of phenolic compounds was achieved by using NADES as extraction solvent and in particular the mixture choline chloride-caffeic acid (CCA) and choline chloride-lactic acid (CLA); by HAE at 60 °C/12,000 rpm and UAE at 60 °C, the total phenolic content (TPC) of extracts was 34.08 mg gallic acid (GA)/g dw and 20.14 mg GA/g dw for CCA, and by MAE at 60 °C and HHPAE at 600 MPa/10 min, the TPC was 29.57 mg GA/g dw and 25.96 mg GA/g dw for CLA. HAE proved to be the best method for the extraction of phenolic compounds from olive pomace. Microencapsulation and nanoemulsion formulations were also reviewed for the protection of the phenolic compounds extracted from olive pomace. Both encapsulation techniques exhibited satisfactory results in terms of encapsulation stability. Thus, they can be proposed as an excellent technique to incorporate phenolic compounds into food products in order to enhance both their antioxidative stability and nutritional value.
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Hussen, Dalia F., Alaa K. Kamel, Mona K. Mekkawy, Engy A. Ashaat, and Mona O. El Ruby. "Phenotypic and Molecular Cytogenetic Analysis of a Case of Monosomy 1p36 Syndrome due to Unbalanced Translocation." Molecular Syndromology 11, no. 5-6 (2020): 284–95. http://dx.doi.org/10.1159/000510428.
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Monosomy 1p36 syndrome is one of the most common submicroscopic deletion syndromes, which is characterized by the presence of delayed developmental milestones, intellectual disability, and clinically recognizable dysmorphic craniofacial features. The syndrome comprises 4 cytogenetic groups including pure terminal deletions, interstitial deletions, complex rearrangements, and derivative chromosomes 1 due to unbalanced translocations, where unbalanced translocations represent the least percentage of all cases of monosomy 1p36 (7%). Most patients with monosomy 1p36 due to an unbalanced translocation can be cytogenetically diagnosed using conventional techniques. However, chromosomal microarray analysis is mandatory in these cases to detect copy number variance and size of the deletion and allows for setting a phenotype-genotype correlation. Here, we studied a 1.5-year-old female patient who showed intellectual disability, delayed milestones, hypotonia, seizures, and characteristic dysmorphic features including brachycephaly, straight eyebrows, deep-set eyes, downslanting palpebral fissures, midface hypoplasia, depressed nasal bridge, long philtrum, and pointed chin. Conventional cytogenetic analysis (CCA), microarray study, and fluorescence in situ hybridization (FISH) analysis were performed. CCA showed a translocation involving chromosomes 1 and 21, 45,XX,der(1)t(1;21)(p36.32;q21.1)dn. Microarray analysis revealed copy number losses at both 1p36 and proximal 21q. FISH confirmed the presence of the 1p36 deletion, but was not performed for 21q. We have concluded that phenotype-genotype correlation for monosomy 1p36 syndrome can be performed for the fundamental clinical manifestations; however, the final aspect of the syndrome depends on composite factors. Monosomy 1p36 due to unbalanced translocation may present either classically or with additional altered features of various severity based on the copy number variations involving different chromosomes.
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Maksyukov, S. Yu, Nadezhda D. Pilipenko, and K. D. Pilipenko. "Comparative characteristics of the results of facial morphometry in patients with deep incisal overlap in the treatment of bracket systems and eliners." Russian Journal of Dentistry 24, no. 2 (October 3, 2020): 95–98. http://dx.doi.org/10.17816/1728-2802-2020-24-2-95-98.
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The relevance of studying the problem of deep incisal overlap (HF) among dentofacial anomalies (CCA) is due to the high prevalence of this pathology. Among modern methods of orthodontic treatment of pathology, the use of bracket systems and aligners is highlighted. The effectiveness of these techniques can be compared in determining the morphometric characteristics obtained by tele-radiography (TEG).Material and methods. The study involved 118 people with hydraulic fracturing, the average age was 38.7 8.5 years (64 women; 54 men). The first group consisted of 49 patients who underwent correction of AFA with eliners; second, 69 patients with bracket systems. To assess the effectiveness of treatment, TRG was performed. To present the results, in the case of quantitative characteristics, the arithmetic mean of the sample value (X) and the error of the mean (m) were calculated. For qualitative signs, the frequency of the sign (%) and its standard error (m%) were calculated.Results: The values of the mandibular angle (G, ArGoMe), and the angles AB / ANS (AB / SpP), APg / ANS (MM), as well as the vertical dimensions of the jaws reached values characteristic of an orthognathic bite. Angle increase SNB, NSL / ML4; angle reduction ANB.Output. Elimination of a deep bite is possible both with the use of bracket systems and aligners.
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Cai, He Long, Jun Sheng Mou, and Zi Yong Hou. "Microstructure, Texture and Property of Interstitial-Free (IF) Steel after Ultra-Fast Annealing." Advanced Materials Research 1120-1121 (July 2015): 1003–7. http://dx.doi.org/10.4028/www.scientific.net/amr.1120-1121.1003.
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In this paper, common continous annealing (CCA) and ultra-fast annealing (UFA) were carried out on a cold-rolled interstitial-free (IF) steel, respectively. The microstructure of the annealed IF steel was characterized by means of scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The mechanical property was examined using tensile test. The optimum annealing process parameters were then obtained. The results showed that, the recrystallization occurs at the temperature in the range of 780-830°C. The fraction of equiaxed grain increases with the annealing temperature increasing. The well combination of mechanical properties and formability was obtained when the IF steel annealed at 820°C, which was the result of the fine dispersed second phase particles. {001} texture was absent in the whole thickness of all the annealed IF steels. In addition, the strongest γ texture was found, and this was a potential way to improve the deep drawability of annealed steel sheets.
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Gargot, T. "Sensory and Motor Difficulties in Autism." European Psychiatry 65, S1 (June 2022): S64. http://dx.doi.org/10.1192/j.eurpsy.2022.208.
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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social interaction and communication, and by restricted and repetitive behaviors. A meta-analysis describes motor difficulties in ASD (Downey and Rapport, 2012) in 85% to 90% of cases (Liu et al. 2010). Electronic devices will help to better characterize these movement impairments (Gargot et al., in press). Another meta-analysis shows difficulties in sensorial integration in ASD, with a prevalence ranging from 45 to 95% (Ben Sasson, 2009). Sensorimotor contigencies are learned interactively (Jacquey et al., 2020), in a perception-action loop, an early milestone in development (Piaget, 1937). A cascade model (Bonnet-Brilhault, 2017) hypothesizes that social difficulties stem in sensorimotor difficulties (Cook, 2016; Neal, 2011; Dziuk et al. 2007; Kojovic, 2019), themselves atypically developed due to a peculiar neurobiological background. This model predicts that early sensorimotor reeducation could prevent the development of social difficulties, whereas the rehabilitation of social skills would improve only part of the impairment, that is driven by sensorimotor processes. How to target these issues? Grandin, 1992 and Edelson et al., 1999 validated the efficacy of deep pressure therapy in ASD. It is important to improve attractivity and decrease the stigmatization of this method. Oto is a compressive armchair with inflatable cushions controlled electronically. It records level of pressure and its duration, chosen by the user. A systematic review showed the efficacy of virtual reality in ASD (Mesa-Gresa et al, 2018). A CAVE device could habituate children to ecological audio-visual stimulations. Disclosure Thomas Gargot was paid by the French ministry of research (Doctoral school), French ministry of health (médaille d’argent, CCA-AHU) for a PhD and CCA position. During his PhD, he prepared several scientific presentations with Dr Asselborn. This work led
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Valmir Júnior, Manoel, Raimundo Nonato Távora Costa, and José Vanglésio de Aguiar. "ANÁLISE DE COMPONENTES DO BALANÇO HÍDRICO EM CULTURA DE CAUPI (Vigna unguiculata (L) Walp), SOB CONDIÇÕES DE RECARGA HÍDRICA NATURAL." IRRIGA 6, no. 3 (December 18, 2001): 91–103. http://dx.doi.org/10.15809/irriga.2001v6n3p91-103.
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ANÁLISE DE COMPONENTES DO BALANÇO HÍDRICO EM CULTURA DE CAUPI (Vigna unguiculata (L) Walp), SOB CONDIÇÕES DE RECARGA HÍDRICA NATURAL Manoel Valnir JúniorRaimundo Nonato Távora Costa José Vanglésio de AguiarUniversidade Federal do Ceará – Departamento de Engenharia Agrícola. Campus do Pici. Bloco 804. CEP 60455-760 – Fortaleza-CE 1 RESUMO O estudo foi desenvolvido no período de 15.05 a 10.06.1997 com o objetivo de estimar e analisar os principais componentes do balanço hídrico na cultura do caupi (Vigna unguiculata L. Walp.), em solo de textura franco-arenosa, na área do Laboratório de Hidráulica e Irrigação do Departamento de Engenharia Agrícola, CCA/UFC, município de Fortaleza - CE.Utilizou-se a metodologia do balanço hídrico, a qual baseia-se na lei de conservação das massas, implicando na soma algébrica dos fluxos durante um intervalo de tempo, ser igual as variações da quantidade de água armazenada em uma camada de solo.Dos resultados obtidos, conclui-se que : - as variações dos potenciais totais nas profundidades 0,375m e 0,625m, permitiram a obtenção a campo da curva de retenção de água no solo a 0,50m; - no período do balanço as perdas por drenagem profunda e escoamento superficial representaram respectivamente 13% e 27% da precipitação total; - a estimativa da drenagem profunda utilizando valores unitários de gradientes de potenciais totais de água no solo na equação de Darcy, detectaram-se variações de 120%; - a estimativa diária de evapotranspiração da cultura foi limitada pelo caráter aleatório das recargas e por conseguinte do tempo disponível necessário ao processo de redistribuição de água no solo. UNITERMOS: caupi, percolação profunda, escoamento superficial. VALMIR JUNIOR, M., COSTA, R. N. T., AGUIAR, J. V. ANALYSIS OF COMPONENTS OF THE SOIL WATER BALANCE IN COWPEA CULTIVAR (Vigna unguiculata (L) WALP), UNDER CONDITIONS OF NATURAL HYDRIC RECHARGE 2 ABSTRACT This study was carried out from 05/15 to 06/10/1997 aiming to estimate and analyze the main components of the water balance on the development of cowpea (Vigna unguiculata, L. Walp). The experiment was conducted at the Irrigation and Hydraulic Laboratory of the Agricultural Engineering Department of CCA/UFC, Fortaleza – CE, Brazil, in a sandy-loam soil. The water balance method based on the mass conservation law applied, which implies that the algebric sum of water flowing during a time interval equals the water stored in the soil profile. The results showed that in the variations in total potential at the depths of 0.375m and 0.625m allowed to obtain the soil water retention curve at 0.50m; deep percolation and runoff represent 13% and 27% of the total precipitation, respectively; in estimating deep percolation using unit values of total soil water potential gradients in Darcy equation, variations reached 120%; daily estimative of crop evapotranspiration was limited by the random aspect of recharges and consequently the availabre time required to the soil water redistribution process. KEYWORDS: cowpea, deep percolation, superficial drain.
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Geng, Qiang, Huifeng Yan, and Xingru Lu. "Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images." Computational Intelligence and Neuroscience 2022 (March 8, 2022): 1–17. http://dx.doi.org/10.1155/2022/3394475.
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With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob’s decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve’s classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.
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Hamani, Clement, Helen Mayberg, Brian Snyder, Peter Giacobbe, Sidney Kennedy, and Andres M. Lozano. "Deep brain stimulation of the subcallosal cingulate gyrus for depression: anatomical location of active contacts in clinical responders and a suggested guideline for targeting." Journal of Neurosurgery 111, no. 6 (December 2009): 1209–15. http://dx.doi.org/10.3171/2008.10.jns08763.
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Object Deep brain stimulation (DBS) of the subcallosal cingulate gyrus (SCG), including Brodmann area 25, is currently being investigated for the treatment of major depressive disorder (MDD). As a potential emerging therapy, optimal target selection within the SCG has still to be determined. The authors compared the location of the electrode contacts in responders and nonresponders to DBS of the SCG and correlated the results with clinical outcome to help in identifying the optimal target within the region. Based on the location of the active contacts used for long-term stimulation in responders, the authors suggest a standardized method of targeting the SCG in patients with MDD. Methods Postoperative MR imaging studies of 20 patients with MDD treated with DBS of the SCG were analyzed. The authors assessed the location of the active contacts relative to the midcommissural point and in relation to anatomical landmarks within the medial aspect of the frontal lobe. For this, a grid with 2 main lines was designed, with 1 line in the anterior-posterior and 1 line in the dorsal-ventral axis. Each of these lines was divided into 100 units, and data were converted into percentages. The anterior-posterior line extended from the anterior commissure (AC) to the projection of the anterior aspect of the corpus callosum (CCa). The dorsal-ventral line extended from the inferior portion of the CC (CCi) to the most ventral aspect of the frontal lobe (abbreviated “Fr” for the formula). Results Because the surgical technique did not vary across patients, differences in stereotactic coordinates between responders and nonresponders did not exceed 1.5 mm in any axis (x, y, or z). In patients who responded to the procedure, contacts used for long-term stimulation were in close approximation within the SCG. In the anterior-posterior line, these contacts were located within a 73.2 ± 7.7 percentile distance from the AC (with the AC center being 0% and the line crossing the CCa being 100%). In the dorsal-ventral line, active contacts in responders were located within a 26.2 ± 13.8 percentile distance from the CCi (with the CCi edge being 0% and the Fr inferior limit being 100%). In the medial-lateral plane, most electrode tips were in the transition between the gray and white matter of SCG. Conclusions Active contacts in patients who responded to DBS were relatively clustered within the SCG. Because of the anatomical variability in the size and shape of the SCG, the authors developed a method to standardize the targeting of this region.
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Wang, L. Y., R. Y. Duan, J. F. Liu, S. Z. Yang, J. D. Gu, and B. Z. Mu. "Molecular analysis of the microbial community structures in water-flooding petroleum reservoirs with different temperatures." Biogeosciences Discussions 9, no. 4 (April 27, 2012): 5177–203. http://dx.doi.org/10.5194/bgd-9-5177-2012.
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Abstract. Temperature is one of the most important environmental factors regulating the activity and determining the composition of the microbial community. Analysis of microbial communities from six water-flooding petroleum reservoirs at temperatures from 20 to 63 °C by 16S rRNA gene clone libraries indicates the presence of physiologically diverse and temperature-dependent microorganisms in these subterrestrial ecosystems. In high-temperature petroleum reservoirs, most of the archaeal sequences belong to the thermophilic archaea including the genera Thermococcus, Methanothermobacter and Thermoplasmatales, most of the bacterial sequences belong to the phyla Firmicutes, Thermotogae and Thermodesulfobacteria; in low-temperature petroleum reservoirs, most of the archaeal sequences are affiliated with the genera Methanobacterium, Methanoculleus and Methanocalculus, most of the bacterial sequences to the phyla Proteobacteria, Bacteroidetes and Actinobacteria. Canonical correspondence analysis (CCA) revealed that temperature, mineralization, ionic type as well as volatile fatty acids showed correlation with the microbial community structures. These organisms may be adapted to the environmental conditions of these petroleum reservoirs over geologic time by metabolizing buried organic matter from the original deep subsurface environment and became the common inhabitants in subsurface environments.
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Alquran, Hiam, Mohammed Alsalatie, Wan Azani Mustafa, Rabah Al Abdi, and Ahmad Rasdan Ismail. "Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion." Bioengineering 9, no. 10 (October 19, 2022): 578. http://dx.doi.org/10.3390/bioengineering9100578.
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Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilizing the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimize dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes.
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Maksyukov, S. Yu, N. D. Pilipenko, and K. D. Pilipenko. "Features of clinical manifestations in patients suffering from dental abnormality." Russian Journal of Dentistry 24, no. 1 (August 12, 2020): 19–22. http://dx.doi.org/10.18821/1728-2802-2020-24-1-19-22.
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Anomalies of the dentition represent one of the most common groups of pathologies in dentistry. Many complaints made by patients with dentofacial anomalies (CCA) make this problem quite relevant. In this regard, of particular interest is a more detailed study of the features of clinical manifestations in patients suffering from RF.Material and methods. The study involved 118 people with deep incisal overlap (hydraulic fracturing), the average age was 38.7 8.5 years; women accounted for 54.24%; men-45.76%. Determining the dental status for studying the clinical manifestations of abnormalities included: studying the anamnesis, patient complaints, internal and external oral examinations. When analyzing the results of the study, biostatistical methods were used.Results. An analysis of the data allows clustering of patients' complaints into the following groups: painful, functional and aesthetic. During a dental examination of patients with hydraulic fracturing, increased abrasion of the teeth, increased tone of the chewing muscles proper, and a change in the ratio of elements of the temporomandibular joint (TMJ) were visualized.Conclusions. The variety of patient complaints with this pathology indicates the relevance of this problem. Most complaints of an aesthetic nature both among women and among men are a manifestation of dissatisfaction with the appearance.
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Elsonbaty, Amr, A. A. Elsadany, and Waleed Adel. "On Reservoir Computing Approach for Digital Image Encryption and Forecasting of Hyperchaotic Finance Model." Fractal and Fractional 7, no. 4 (March 24, 2023): 282. http://dx.doi.org/10.3390/fractalfract7040282.
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Forecasting the dynamical behaviors of nonlinear systems over long time intervals represents a great challenge for scientists and has become a very active area of research. The employment of the well-known artificial recurrent neural networks (RNNs)-based models requires a high computational cost, and they usually maintain adequate accuracy for complicated dynamics over short intervals only. In this work, an efficient reservoir-computing (RC) approach is presented to predict the time evolution of the complicated dynamics of a fractional order hyperchaotic finance model. Compared with the well-known deep learning techniques, the suggested RC-based forecasting model is faster, more accurate for long-time prediction, and has a smaller execution time. Numerical schemes for fractional order systems are generally time-consuming. The second goal of the present study is to introduce a faster, more efficient, and simpler simulator to the fractional order chaotic/hyperchaotic systems. The RC model is utilized in a proposed RC-based digital image encryption scheme. Security analysis is carried out to verify the performance of the proposed encryption scheme against different types of statistical, KPA, brute-force, CCA, and differential attacks.
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Baig, Raheel, Abdur Rehman, Abdullah Almuhaimeed, Abdulkareem Alzahrani, and Hafiz Tayyab Rauf. "Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach." Applied Sciences 12, no. 13 (June 21, 2022): 6317. http://dx.doi.org/10.3390/app12136317.
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Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%.
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Ernst, Thomas, Mathias Schmidt, Jenny Rinke, Vivien Schäfer, Anja Waldau, Ellen Obstfelder, Nils Winkelmann, et al. "Molecularly Defined Clonal Evolution in Patients with Chronic Myeloid Leukemia Independent of the BCR-ABL Status." Blood 124, no. 21 (December 6, 2014): 4513. http://dx.doi.org/10.1182/blood.v124.21.4513.4513.
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Abstract Molecularly defined clonal evolution has been identified as a key phenomenon in the biology of acute myeloid leukemia. Molecular aberrations may be markers of individual subclones of the disease, but also responsible for treatment resistance. In most patients with chronic myeloid leukemia (CML), treatment with tyrosine kinase inhibitors (TKI) induces complete cytogenetic remissions (CCyR) characterized by polyclonal hematopoiesis. However, some CML patients show emergence of clonal cytogenetic abnormalities (CCA) in Philadelphia (Ph) negative cells during treatment indicating clonal hematopoiesis. We searched for BCR-ABL independent gene mutations in both Ph-negative and Ph-positive clones in 29 chronic phase CML patients (male, n=16; median age 58 years, range 29-73 years) using targeted deep next-generation sequencing of 25 genes frequently mutated in myeloid disorders: ASXL1, BRAF, CBL, DNMT3A, ETV6, EZH2, FLT3, IDH1, IDH2, JAK2, KIT, KRAS, LNK, MPL, NPM1, NRAS, RUNX1, SF3B1, SRSF2, TP53, TET2, U2AF1, UTX, WT, and ZRSR2. Ph-negative clones were analyzed in 14 CML patients who developed CCA in Ph-negative cells (trisomy 8, n=10; nonrecurrent reciprocal translocations, n=2; monosomy 7, n=2). At time of analysis, patients were in CCyR (n=7), partial CyR (n=6), or minimal CyR (n=1) after a median of 21 months (range 7-114 months) of TKI treatment with imatinib, and/or nilotinib, and/or dasatinib. After exclusion of known polymorphisms, mutations were detected in 6/14 patients (43%) affecting the genes DNMT3A, EZH2, RUNX1, TET2, TP53, U2AF1, and ZRSR2. These patients were in CCyR (n=2), partial CyR (n=3), or minimal CyR (n=1), respectively, indicating BCR-ABL independent mutations in both Ph-negative and Ph-positive subclones. In two patients, the mutations were also found in corresponding diagnostic samples at higher or lower mutation level. To further investigate BCR-ABL independent gene mutations in Ph-positive clones, 15 randomly selected CML patients at diagnosis were analyzed. Mutations additional to BCR-ABL were found in 5/15 CML patients (33%) affecting ASXL1, DNMT3A, RUNX1, and TET2. None of the mutations were recognized in corresponding constitutional DNA specimens indicating that all mutations had been somatically acquired. Deep-sequencing of subsequent samples obtained in early CCyR after three months of TKI treatment revealed one DNMT3A mutation in Ph-negative cells which was also present in Ph-positive cells at diagnosis. Follow-up investigation showed that the mutation persisted in Ph-negative cells throughout CCyR and deep molecular remission (MR4.5) up to month 36 thereby not significantly changing its mutation level of approximately 15% implying a clonal hematopoiesis before the acquisition of the BCR-ABL rearrangement. In summary, BCR-ABL independent gene mutations were frequently found in Ph-negative and Ph-positive clones of CML patients and may be considered as important cofactors in the evolution of CML. Additional mutations acquired in the Ph-positive clone may impact on response to TKI treatment. Mutations preexisting to the occurrence of the BCR-ABL rearrangement may predispose patients to secondary hematological neoplasms. Our findings provide novel genetic information regarding CML biology and warrant further studies and impact on the design and performance of discontinuation trials. Disclosures Schnittger: MLL Munich Leukemia Laboratory: Other. Haferlach:MLL Munich Leukemia Laboratory: Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Equity Ownership. Hochhaus:Novartis: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; ARIAD: Honoraria, Research Funding; Pfizer: Consultancy, Research Funding.
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Dantas, Ênio W., Ariadne N. Moura, and Maria do Carmo Bittencourt-Oliveira. "Cyanobacterial blooms in stratified and destratified eutrophic reservoirs in semi-arid region of Brazil." Anais da Academia Brasileira de Ciências 83, no. 4 (December 2011): 1327–38. http://dx.doi.org/10.1590/s0001-37652011000400019.
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This study investigated the dynamics of cyanobacteria in two deep, eutrophic reservoirs in a semi-arid region of Brazil during periods of stratification and destratification. Four collections were carried out at each reservoir at two depths at three-month intervals. The following abiotic variables were analyzed: water temperature, dissolved oxygen, pH, turbidity, water transparency, total phosphorus, total dissolved phosphorus, orthophosphate and total nitrogen. Phytoplankton density was quantified for the determination of the biomass of cyanobacteria. The data were analyzed using CCA. Higher mean phytoplankton biomass values (29.8 mm³.L-1) occurred in the period of thermal stratification. A greater similarity in the phytoplankton communities also occurred in this period and was related to the development of cyanobacteria, mainly Cylindrospermopsis raciborskii (>3.9 mm³.L-1). During the period of thermal destratification, this species co-dominated the environment with Planktothrix agardhii, Geitlerinema amphibium, Microcystis aeruginosa and Merismopedia tenuissima, as well as with diatoms and phytoflagellates. Environmental instability and competition among algae hindered the establishment of blooms more during the mixture period than during the stratification period. Thermal changes in the water column caused by climatologic events altered other physiochemical conditions of the water, leading to changes in the composition and biomass of the cyanobacterial community in tropical reservoirs.
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Gajigan, Andrian P., Aletta T. Yñiguez, Cesar L. Villanoy, Maria Lourdes San Diego-McGlone, Gil S. Jacinto, and Cecilia Conaco. "Diversity and community structure of marine microbes around the Benham Rise underwater plateau, northeastern Philippines." PeerJ 6 (May 16, 2018): e4781. http://dx.doi.org/10.7717/peerj.4781.
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Microbes are central to the structuring and functioning of marine ecosystems. Given the remarkable diversity of the ocean microbiome, uncovering marine microbial taxa remains a fundamental challenge in microbial ecology. However, there has been little effort, thus far, to describe the diversity of marine microorganisms in the region of high marine biodiversity around the Philippines. Here, we present data on the taxonomic diversity of bacteria and archaea in Benham Rise, Philippines, Western Pacific Ocean, using 16S V4 rRNA gene sequencing. The major bacterial and archaeal phyla identified in the Benham Rise are Proteobacteria, Cyanobacteria, Actinobacteria, Bacteroidetes, Marinimicrobia, Thaumarchaeota and, Euryarchaeota. The upper mesopelagic layer exhibited greater microbial diversity and richness compared to surface waters. Vertical zonation of the microbial community is evident and may be attributed to physical stratification of the water column acting as a dispersal barrier. Canonical Correspondence Analysis (CCA) recapitulated previously known associations of taxa and physicochemical parameters in the environment, such as the association of oligotrophic clades with low nutrient surface water and deep water clades that have the capacity to oxidize ammonia or nitrite at the upper mesopelagic layer. These findings provide foundational information on the diversity of marine microbes in Philippine waters. Further studies are warranted to gain a more comprehensive picture of microbial diversity within the region.
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Moon, Jaeyoung, Il Bae, and Shiho Kim. "Automatic Parking Controller with a Twin Artificial Neural Network Architecture." Mathematical Problems in Engineering 2019 (September 19, 2019): 1–18. http://dx.doi.org/10.1155/2019/4801985.
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We propose an artificial deep neural network- (ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-the-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle’s behavior resemble a real vehicle.
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Sheidai, Masoud, Mohammad Mohebi Anabat, Fahimeh Koohdar, and Zahra Noormohammadi. "Identifying potential adaptive SNPs within combined DNA sequences in Genus Crocus L. (Iridaceae family): A multiple analytical approach." Caryologia 75, no. 3 (February 12, 2023): 159–67. http://dx.doi.org/10.36253/caryologia-1560.
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The genus Crocus L. of Iridaceae family contains about 160 species and is considered as a complex group of plant taxa with regard to evolutionary and phylogenetic events. Inter-specific hybridization and gene flow contribute to species genetic homogeneity in one hand and high within species genetic variability and species genetic content overlaps caused species resolution a problem. In spite of extensive molecular phylogenetic studies in this genus, nothing is known about DNA sequences or Single nucleotide polymorphisms (SNPs) which are of adaptive nature. Moreover, nothing is known about which geographical or environmental factors plays role in species local adaptation and speciation events within Crocus L. genus. Therefore, the present study was conducted to answer the above said questions. We used a combined molecular data set of internal transcribed spacer (ITS) nuclear gene and trnL-F intergenic spacer (trnL-F) sequences of chloroplast genome. A multiple analytical method of Canonical correlation (CCA), Redundency analysis (RDA), and Latent Factor Mixed Model (LFMM)identified a few potential adaptive SNPs. Moreover, population criterions like Tajimas’ D, molecular clock test, as well as skyline-plot revealed a smooth and continuous genetic changes for most of the Crocus species, but the occurrence of a sudden deep nucleotide substitution for Crocus taxa of Iran. The impact of latitude was significantly higher on nucleotide changes compared to that of longitudinal distribution of Crocus species.