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Weyerer, Veronika, Pamela L. Strissel, Reiner Strick, Danijel Sikic, Carol I. Geppert, Simone Bertz, Fabienne Lange et al. "Integration of Spatial PD-L1 Expression with the Tumor Immune Microenvironment Outperforms Standard PD-L1 Scoring in Outcome Prediction of Urothelial Cancer Patients". Cancers 13, n.º 10 (12 de mayo de 2021): 2327. http://dx.doi.org/10.3390/cancers13102327.
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Background: Immune therapy has gained significant importance in managing urothelial cancer. The value of PD-L1 remains a matter of controversy, thus requiring an in-depth analysis of its biological and clinical relevance. Methods: A total of 193 tumors of muscle-invasive bladder cancer patients (MIBC) were assessed with four PD-L1 assays. PD-L1 scoring results were correlated with data from a comprehensive digital-spatial immune-profiling panel using descriptive statistics, hierarchical clustering and uni-/multivariable survival analyses. Results: PD-L1 scoring algorithms are heterogeneous (agreements from 63.1% to 87.7%), and stems from different constellations of immune and tumor cells (IC/TC). While Ventana IC5% algorithm identifies tumors with high inflammation and favorable baseline prognosis, CPS10 and the TCarea25%/ICarea25% algorithm identify tumors with TC and IC expression. Spatially organized immune phenotypes, which correlate either with high PD-L1 IC expression and favorable prognosis or constitutive PD-L1 TC expression and poor baseline prognosis, cannot be resolved properly by PD-L1 algorithms. PD-L1 negative tumors with relevant immune infiltration can be detected by sTILs scoring on HE slides and digital CD8+ scoring. Conclusions: Contemporary PD-L1 scoring algorithms are not sufficient to resolve spatially distributed MIBC immune phenotypes and their clinical implications. A more comprehensive view of immune phenotypes along with the integration of spatial PD-L1 expression on IC and TC is necessary in order to stratify patients for ICI.
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Tinnathi, Sreenivasu y G. Sudhavani. "Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model". Cybernetics and Information Technologies 22, n.º 4 (1 de noviembre de 2022): 91–110. http://dx.doi.org/10.2478/cait-2022-0041.
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Abstract In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameters of AlexNet. Based on the obtained features, the adaptive matching algorithm is used for locating the forged regions in the tampered images. Simulation outcome showed that the proposed model is effective under the conditions: salt & pepper noise, Gaussian noise, rotation, blurring and enhancement. The enhanced GWO based AlexNet model attained maximum detection accuracy of 99.66%, 99.75%, and 98.48% on MICC-F600, MICC-F2000 and GRIP datasets.
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Pourkashani, Ava, Asadollah Shahbahrami y Alireza Akoushideh. "Copy-move forgery detection using convolutional neural network and K-mean clustering". International Journal of Electrical and Computer Engineering (IJECE) 11, n.º 3 (1 de junio de 2021): 2604. http://dx.doi.org/10.11591/ijece.v11i3.pp2604-2612.
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Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.
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Zheng, Qingyuan, Zhengyu Jiang, Xinmiao Ni, Song Yang, Panpan Jiao, Jiejun Wu, Lin Xiong et al. "Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer". International Journal of Molecular Sciences 24, n.º 3 (1 de febrero de 2023): 2746. http://dx.doi.org/10.3390/ijms24032746.
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Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TSR associated with better prognosis. Uni- and multivariate Cox regression analyses showed that TSR was independently associated with overall survival (p < 0.001 for all analyses) after adjusting for clinicopathological factors including age, gender, and pathologic stage. TSR was found to be a strong prognostic factor that was not redundant with the existing staging system in different subgroup analyses (p < 0.05 for all analyses). Finally, the expression of six genes (DACH1, DEEND2A, NOTCH4, DTWD1, TAF6L, and MARCHF5) were significantly associated with TSR, revealing possible potential biological relevance. In conclusion, we developed an ML algorithm based on WSIs of MIBC patients to accurately quantify TSR and demonstrated its prognostic validity for MIBC patients in two independent cohorts. This objective quantitative method allows application in clinical practice while reducing the workload of pathologists. Thus, it might be of significant aid in promoting precise pathology services in MIBC.
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Ye, Qing, Ruochen Wang, Chi Zhang y Yingfeng Cai. "Research on Intelligent Vehicle Path Tracking with Subsystems Based on Multimodel Intelligent Hierarchical Control Theory". Mathematical Problems in Engineering 2021 (9 de junio de 2021): 1–15. http://dx.doi.org/10.1155/2021/7448517.
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In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system.
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Amiri, Ehsan, Ahmad Mosallanejad y Amir Sheikhahmadi. "Copy-Move Forgery Detection Using an Equilibrium Optimization Algorithm (CMFDEOA)". Statistics, Optimization & Information Computing 11, n.º 3 (20 de abril de 2023): 677–84. http://dx.doi.org/10.19139/soic-2310-5070-1511.
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Image forgery detection is a new challenge. One type of image forgery is a copy-move forgery. In this method, part of the image is copied and placed at the most similar point. Given the existing algorithms and processing software, identifying forgery areas is difficult and has created challenges in various applications. The proposed method based on the Equilibrium Optimization Algorithm (EOA) helps image forgery detection by finding forgery areas. The proposed method includes feature detection, image segmentation, and detection of forgery areas using the EOA algorithm. In the first step, the image converts to a grayscale. Then, with the help of a discrete cosine transform (DCT) algorithm, it is taken to the signal domain. With the help of discrete wavelet transform (DWT), its appropriate properties are introduced. In the next step, the image is divided into blocks of equal size. Then the similarity search is performed with the help of an equilibrium optimization algorithm and a suitable proportion function. Copy-move forgery detection using the Equilibrium Optimization Algorithm (CMFDEOA) can find areas of forgery with an accuracy of about 86.21% for the IMD data set and about 83.98% for the MICC-F600 data set.
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Elaskily, Mohamed A., Monagi H. Alkinani, Ahmed Sedik y Mohamed M. Dessouky. "Deep learning based algorithm (ConvLSTM) for Copy Move Forgery Detection". Journal of Intelligent & Fuzzy Systems 40, n.º 3 (2 de marzo de 2021): 4385–405. http://dx.doi.org/10.3233/jifs-201192.
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Protecting information from manipulation is important challenge in current days. Digital images are one of the most popular information representation. Images could be used in several fields such as military, social media, security purposes, intelligence fields, evidences in courts, and newspapers. Digital image forgeries mean adding unusual patterns to the original images that cause a heterogeneity manner in form of image properties. Copy move forgery is one of the hardest types of image forgeries to be detected. It is happened by duplicating part or section of the image then adding again in the image itself but in another location. Forgery detection algorithms are used in image security when the original content is not available. This paper illustrates a new approach for Copy Move Forgery Detection (CMFD) built basically on deep learning. The proposed model is depending on applying (Convolution Neural Network) CNN in addition to Convolutional Long Short-Term Memory (CovLSTM) networks. This method extracts image features by a sequence number of Convolutions (CNVs) layers, ConvLSTM layers, and pooling layers then matching features and detecting copy move forgery. This model had been applied to four aboveboard available databases: MICC-F220, MICC-F2000, MICC-F600, and SATs-130. Moreover, datasets have been combined to build new datasets for all purposes of generalization testing and coping with an over-fitting problem. In addition, the results of applying ConvLSTM model only have been added to show the differences in performance between using hybrid ConvLSTM and CNN compared with using CNN only. The proposed algorithm, when using number of epoch’s equal 100, gives high accuracy reached to 100% for some datasets with lowest Testing Time (TT) time nearly 1 second for some datasets when compared with the different previous algorithms.
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Rajamani, Sripriya, Aaron Bieringer, Stephanie Wallerius, Daniel Jensen, Tamara Winden y Miriam Halstead Muscoplat. "Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System". Biomedical Informatics Insights 8s2 (enero de 2016): BII.S40208. http://dx.doi.org/10.4137/bii.s40208.
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Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.
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Li, Qianmu, Shunmei Meng, Xiaonan Sang, Hanrui Zhang, Shoujin Wang, Ali Kashif Bashir, Keping Yu y Usman Tariq. "Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing". ACM Transactions on Internet Technology 21, n.º 3 (9 de junio de 2021): 1–33. http://dx.doi.org/10.1145/3408291.
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Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.
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Jue Fu, Ying Zhang,. "Multi-Objective Construction of English Web-Based Independent Learning Based on Mobile Intelligent Information System". Journal of Electrical Systems 20, n.º 3s (4 de abril de 2024): 574–86. http://dx.doi.org/10.52783/jes.1332.
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A Mobile Intelligent Information System (MIIS) represents a transformative tool in the digital age, empowering users with access to dynamic and personalized information anytime, anywhere. Through advanced technologies such as artificial intelligence, machine learning, and natural language processing, MIIS harnesses the power of data to deliver tailored insights and services to users via mobile devices. With its intuitive interface and seamless integration with mobile platforms, MIIS offers a user-friendly experience that adapts to the evolving needs and preferences of users. As mobile technology continues to evolve, MIIS remains at the forefront, revolutionizing the way information is accessed, processed, and utilized in our increasingly interconnected world. This paper presents a multi-objective approach to construct an English web-based independent learning platform leveraging a Mobile Intelligent Information System (MIIS), enhanced by Genetic Integrated Web Optimization (GIWO) with the integration of Ant Bee and Whale Optimization algorithms. The proposed framework aims to optimize multiple objectives, including content relevance, user engagement, and learning effectiveness, in the design and deployment of the learning platform. Through simulated experiments and empirical evaluations, the effectiveness of the GIWO-enhanced MIIS in constructing the web-based learning platform is assessed. Results demonstrate significant improvements in content recommendation accuracy, user satisfaction, and learning outcomes compared to traditional approaches. The integration of Ant Bee and Whale Optimization algorithms further enhances the optimization process, enabling the system to adapt dynamically to evolving user needs and preferences. This study highlights the potential of leveraging MIIS and GIWO algorithms for the multi-objective construction of web-based learning platforms, paving the way for more personalized, efficient, and effective independent learning experiences in English education. The GIWO-enhanced MIIS achieved an average increase of 35% in content relevance, as evidenced by precision and recall scores. Additionally, user satisfaction ratings increased by 40%, indicating higher levels of engagement and perceived usefulness of the platform. Furthermore, learning outcomes improved by 25%, as measured by pre- and post-assessment scores. These simulation results underscore the efficacy of the proposed approach in optimizing multiple objectives for the construction of web-based independent learning platforms in English education.
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Vy, Vu Pham Thao, Melissa Min-Szu Yao, Nguyen Quoc Khanh Le y Wing P. Chan. "Machine Learning Algorithm for Distinguishing Ductal Carcinoma In Situ from Invasive Breast Cancer". Cancers 14, n.º 10 (15 de mayo de 2022): 2437. http://dx.doi.org/10.3390/cancers14102437.
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Purpose: Given that early identification of breast cancer type allows for less-invasive therapies, we aimed to develop a machine learning model to discriminate between ductal carcinoma in situ (DCIS) and minimally invasive breast cancer (MIBC). Methods: In this retrospective study, the health records of 420 women who underwent biopsies between 2010 and 2020 to confirm breast cancer were collected. A trained XGBoost algorithm was used to classify cancers as either DCIS or MIBC using clinical characteristics, mammographic findings, ultrasonographic findings, and histopathological features. Its performance was measured against other methods using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. Results: The model was trained using 357 women and tested using 63 women with an overall 420 patients (mean [standard deviation] age, 57.1 [12.0] years). The model performed well when feature importance was determined, reaching an accuracy of 0.84 (95% confidence interval [CI], 0.76–0.91), an AUC of 0.93 (95% CI, 0.87–0.95), a specificity of 0.75 (95% CI, 0.67–0.83), and a sensitivity of 0.91 (95% CI, 0.76–0.94). Conclusion: The XGBoost model, combining clinical, mammographic, ultrasonographic, and histopathologic findings, can be used to discriminate DCIS from MIBC with an accuracy equivalent to that of experienced radiologists, thereby giving patients the widest range of therapeutic options.
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Luo, Yan, Zi-Ting Huang, Hui-wen Xu, zi-shuo Chen, He-Xuan Su, Hui Liu y Beibei Xu. "Development and validation of multimorbidity index predicting mortality among Chinese older adults". Innovation in Aging 5, Supplement_1 (1 de diciembre de 2021): 606. http://dx.doi.org/10.1093/geroni/igab046.2324.
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Abstract This study aimed to construct a multimorbidity index among Chinese older adults. Participants aged 65-84 years (n=11,757) from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Fourteen self-reported chronic conditions were assessed at baseline. Outcome was all-cause mortality within five-year follow-up. We used restrictive association rules mining to identify the patterns of multiple chronic conditions associated with mortality. The weights of conditions and disease combinations were assigned using logistic regression adjusted by age and sex in training set. Multimorbidity index (MI) with individual diseases and multimorbidity index incorporating disease combinations (MIDC) were developed. We compared the performance of MI and MIDC with condition count and XGBoost algorithm in the validation set. There were no significant differences of c-statistics between condition count (0.687) and MI (0.692) or MIDC (0.689). The c-statistic of XGBoost algorithm (0.675) was the lowest among all models. The Integrated Discrimination Improvement (IDI) and categorical Net Reclassification Index (NRI) for MI (IDI: 0.01, P < 0.001; NRI: 0.01, P = 0.127), MIDC (IDI: 0.004, p = 0.002; NRI: 0.02, P = 0.033), and XGBoost model (IDI: 0.02, P < 0.001; NRI: 0.03, P = 0.004) were significantly positive compared with condition count. However, no significant differences for IDI and NRI were observed between MI and MIDC. Among Chinese older adults, weighted multimorbidity index with individual disease can better predict five-year mortality risk over condition count. There was little improvement in the predictive performance of the index after considering the joint effects of disease combinations.
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Setubal, João C., Marcelo Reis, James Matsunaga y David A. Haake. "Lipoprotein computational prediction in spirochaetal genomes". Microbiology 152, n.º 1 (1 de enero de 2006): 113–21. http://dx.doi.org/10.1099/mic.0.28317-0.
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Lipoproteins are of great interest in understanding the molecular pathogenesis of spirochaetes. Because spirochaete lipobox sequences exhibit more plasticity than those of other bacteria, application of existing prediction algorithms to emerging sequence data has been problematic. In this paper a novel lipoprotein prediction algorithm is described, designated SpLip, constructed as a hybrid of a lipobox weight matrix approach supplemented by a set of lipoprotein signal peptide rules allowing for conservative amino acid substitutions. Both the weight matrix and the rules are based on a training set of 28 experimentally verified spirochaetal lipoproteins. The performance of the SpLip algorithm was compared to that of the hidden Markov model-based LipoP program and the rules-based algorithm Psort for all predicted protein-coding genes of Leptospira interrogans sv. Copenhageni, L. interrogans sv. Lai, Borrelia burgdorferi, Borrelia garinii, Treponema pallidum and Treponema denticola. Psort sensitivity (13–35 %) was considerably less than that of SpLip (93–100 %) or LipoP (50–84 %) due in part to the requirement of Psort for Ala or Gly at the −1 position, a rule based on E. coli lipoproteins. The percentage of false-positive lipoprotein predictions by the LipoP algorithm (8–30 %) was greater than that of SpLip (0–1 %) or Psort (4–27 %), due in part to the lack of rules in LipoP excluding unprecedented amino acids such as Lys and Arg in the −1 position. This analysis revealed a higher number of predicted spirochaetal lipoproteins than was previously known. The improved performance of the SpLip algorithm provides a more accurate prediction of the complete lipoprotein repertoire of spirochaetes. The hybrid approach of supplementing weight matrix scoring with rules based on knowledge of protein secretion biochemistry may be a general strategy for development of improved prediction algorithms.
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Речкалов, Т. В. y М. Л. Цымблер. "A parallel data clustering algorithm for Intel MIC accelerators". Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie), n.º 2 (28 de marzo de 2019): 104–15. http://dx.doi.org/10.26089/nummet.v20r211.
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Алгоритм PAM (Partitioning Around Medoids) представляет собой разделительный алгоритм кластеризации, в котором в качестве центров кластеров выбираются только кластеризуемые объекты (медоиды). Кластеризация на основе техники медоидов применяется в широком спектре приложений: сегментирование медицинских и спутниковых изображений, анализ ДНК-микрочипов и текстов и др. На сегодня имеются параллельные реализации PAM для систем GPU и FPGA, но отсутствуют таковые для многоядерных ускорителей архитектуры Intel Many Integrated Core (MIC). В настоящей статье предлагается новый параллельный алгоритм кластеризации PhiPAM для ускорителей Intel MIC. Вычисления распараллеливаются с помощью технологии OpenMP. Алгоритм предполагает использование специализированной компоновки данных в памяти и техники тайлинга, позволяющих эффективно векторизовать вычисления на системах Intel MIC. Эксперименты, проведенные на реальных наборах данных, показали хорошую масштабируемость алгоритма. The PAM (Partitioning Around Medoids) is a partitioning clustering algorithm where each cluster is represented by an object from the input dataset (called a medoid). The medoid-based clustering is used in a wide range of applications: the segmentation of medical and satellite images, the analysis of DNA microarrays and texts, etc. Currently, there are parallel implementations of PAM for GPU and FPGA systems, but not for Intel Many Integrated Core (MIC) accelerators. In this paper, we propose a novel parallel PhiPAM clustering algorithm for Intel MIC systems. Computations are parallelized by the OpenMP technology. The algorithm exploits a sophisticated memory data layout and loop tiling technique, which allows one to efficiently vectorize computations with Intel MIC. Experiments performed on real data sets show a good scalability of the algorithm.
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Gu, Mile. "A classical algorithm to mimic quantum decisions". National Science Review 6, n.º 4 (1 de julio de 2019): 608–9. http://dx.doi.org/10.1093/nsr/nwz081.
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Balochian, Saeed y Hossein Baloochian. "Social mimic optimization algorithm and engineering applications". Expert Systems with Applications 134 (noviembre de 2019): 178–91. http://dx.doi.org/10.1016/j.eswa.2019.05.035.
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Feng, Yueyi, Qingting Feng, Lingkai Xu, Yiqing Jiang, Fang Meng y Xiaochen Shu. "A Novel Biomarker Based on miRNA to Predict the Prognosis of Muscle-Invasive Bladder Urothelial Carcinoma". Journal of Oncology 2019 (6 de diciembre de 2019): 1–11. http://dx.doi.org/10.1155/2019/2654296.
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Muscle-invasive bladder urothelial carcinoma (MIBC) is characteristic of high mortality and high recurrence. Distinguishing the prognostic risk of MIBC at the molecular level of miRNA expression is rarely performed and thus of great significance for the management and treatment of MIBC in clinics. Adaptive lasso Cox’s proportional hazards model was used to explore the relationship between differential expression miRNAs (DEmiRNAs) and MIBC survival. Furthermore, we evaluated the epithelial-mesenchymal transition (EMT) score and immune infiltration abundance by exploring EMT signature genes and TIMER database, respectively. A total of 8 DEmiRNAs were detected to be associated with the survival rate of MIBC by using the lasso Cox algorithm. Through the linear combination of these 8 DEmiRNAs, we constructed a calculated marker, which could be used to distinguish the prognosis risk in both TCGA dataset (HR = 2.03, 95% CI = (1.47, 2.83)) and independent validation dataset (HR = 7.74, 95% CI = (1.05, 56.93)). Meanwhile, the constructed marker had reasonably high predictive values of the AUC (area under the curve) in the TCGA dataset and validation dataset being 0.73 and 0.63, respectively. In addition, we observed that the expression values of let-7c, miR-100, and miR-145 were associated with EMT score and the abundance of macrophage in tumor tissue as well. This newly identified risk score signature based on the combination of 8 miRNAs could significantly predict the prognostic risk of MIBC and might provide insight into immunotherapy and targeted therapy of MIBC.
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Leversen, Nils Anders, Gustavo A. de Souza, Hiwa Målen, Swati Prasad, Inge Jonassen y Harald G. Wiker. "Evaluation of signal peptide prediction algorithms for identification of mycobacterial signal peptides using sequence data from proteomic methods". Microbiology 155, n.º 7 (1 de julio de 2009): 2375–83. http://dx.doi.org/10.1099/mic.0.025270-0.
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Secreted proteins play an important part in the pathogenicity of Mycobacterium tuberculosis, and are the primary source of vaccine and diagnostic candidates. A majority of these proteins are exported via the signal peptidase I-dependent pathway, and have a signal peptide that is cleaved off during the secretion process. Sequence similarities within signal peptides have spurred the development of several algorithms for predicting their presence as well as the respective cleavage sites. For proteins exported via this pathway, algorithms exist for eukaryotes, and for Gram-negative and Gram-positive bacteria. However, the unique structure of the mycobacterial membrane raises the question of whether the existing algorithms are suitable for predicting signal peptides within mycobacterial proteins. In this work, we have evaluated the performance of nine signal peptide prediction algorithms on a positive validation set, consisting of 57 proteins with a verified signal peptide and cleavage site, and a negative set, consisting of 61 proteins that have an N-terminal sequence that confirms the annotated translational start site. We found the hidden Markov model of SignalP v3.0 to be the best-performing algorithm for predicting the presence of a signal peptide in mycobacterial proteins. It predicted no false positives or false negatives, and predicted a correct cleavage site for 45 of the 57 proteins in the positive set. Based on these results, we used the hidden Markov model of SignalP v3.0 to analyse the 10 available annotated proteomes of mycobacterial species, including annotations of M. tuberculosis H37Rv from the Wellcome Trust Sanger Institute and the J. Craig Venter Institute (JCVI). When excluding proteins with transmembrane regions among the proteins predicted to harbour a signal peptide, we found between 7.8 and 10.5 % of the proteins in the proteomes to be putative secreted proteins. Interestingly, we observed a consistent difference in the percentage of predicted proteins between the Sanger Institute and JCVI. We have determined the most valuable algorithm for predicting signal peptidase I-processed proteins of M. tuberculosis, and used this algorithm to estimate the number of mycobacterial proteins with the potential to be exported via this pathway.
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Steiner, Matthias Johann. "Solving Degree Bounds for Iterated Polynomial Systems". IACR Transactions on Symmetric Cryptology 2024, n.º 1 (1 de marzo de 2024): 357–411. http://dx.doi.org/10.46586/tosc.v2024.i1.357-411.
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For Arithmetization-Oriented ciphers and hash functions Gröbner basis attacks are generally considered as the most competitive attack vector. Unfortunately, the complexity of Gröbner basis algorithms is only understood for special cases, and it is needless to say that these cases do not apply to most cryptographic polynomial systems. Therefore, cryptographers have to resort to experiments, extrapolations and hypotheses to assess the security of their designs. One established measure to quantify the complexity of linear algebra-based Gröbner basis algorithms is the so-called solving degree. Caminata & Gorla revealed that under a certain genericity condition on a polynomial system the solving degree is always upper bounded by the Castelnuovo-Mumford regularity and henceforth by the Macaulay bound, which only takes the degrees and number of variables of the input polynomials into account. In this paper we extend their framework to iterated polynomial systems, the standard polynomial model for symmetric ciphers and hash functions. In particular, we prove solving degree bounds for various attacks on MiMC, Feistel-MiMC, Feistel-MiMC-Hash, Hades and GMiMC. Our bounds fall in line with the hypothesized complexity of Gröbner basis attacks on these designs, and to the best of our knowledge this is the first time that a mathematical proof for these complexities is provided. Moreover, by studying polynomials with degree falls we can prove lower bounds on the Castelnuovo-Mumford regularity for attacks on MiMC, Feistel-MiMC and Feistel-MiMCHash provided that only a few solutions of the corresponding iterated polynomial system originate from the base field. Hence, regularity-based solving degree estimations can never surpass a certain threshold, a desirable property for cryptographic polynomial systems.
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Zhang, Wen-Li, Kun Yang, Yi-Tao Xin y Ting-Song Zhao. "Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks". Sensors 20, n.º 23 (25 de noviembre de 2020): 6745. http://dx.doi.org/10.3390/s20236745.
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Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent security systems, has received much attention from many researchers in recent years. However, existing multi-objective tracking algorithms still suffer from trajectory drift and interruption problems in crowded scenes, which cannot provide valuable data for managers. In order to solve the above problems, this paper proposes a Multi-Object Tracking algorithm for RGB-D images based on Asymmetric Dual Siamese networks (ADSiamMOT-RGBD). This algorithm combines appearance information from RGB images and target contour information from depth images. Furthermore, the attention module is applied to repress the redundant information in the combined features to overcome the trajectory drift problem. We also propose a trajectory analysis module, which analyzes whether the head movement trajectory is correct in combination with time-context information. It reduces the number of human error trajectories. The experimental results show that the proposed method in this paper has better tracking quality on the MICC, EPFL, and UMdatasets than the previous work.
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Gui, Zhiming, Xiaoling Ying y Chunxiao Liu. "NXPH4 Used as a New Prognostic and Immunotherapeutic Marker for Muscle-Invasive Bladder Cancer". Journal of Oncology 2022 (4 de octubre de 2022): 1–21. http://dx.doi.org/10.1155/2022/4271409.
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Background. One of the most common malignant tumors of the urinary system is muscle-invasive bladder cancer (MIBC). With the increased use of immunotherapy, its importance in the field of cancer is becoming abundantly evident. This study classifies MIBC according to GSVA score from the perspective of the GSEA immune gene set. Methods. This study integrated the sequencing and clinical data of MIBC patients in TCGA and GEO databases, then scored the data using the GSVA algorithm, the CNMF algorithm was implemented to divide the subtypes of GEO and TCGA datasets, respectively, and finally screened and determined the key pathways in combination with clinical data. Simultaneously, LASSO Cox regression model was constructed based on key pathway genes to assess the model’s predictive ability (ROC) and describe the immune landscape differences between high- and low-risk groups; key genes were further analyzed and verified in patient tissues. Results. 404 TCGA and 297 GEO datasets were divided into C1-3 groups (TCGA-C1:120/C2:152/C3:132; GEO- C1:112/C2:101/C3:84), of which TCGA-C2 ( n = 152 ) subtype and GEO-C1 ( n = 112 ) subtype had the worst prognosis. LASSO Cox regression model with ROC ( train set = 0.718 , test set = 0.667 ) could be constructed. When combined with the Cancer Immunome Atlas database, it was found that patients with high-risk scores were more sensitive to PD-1 inhibitor and PD-1 inhibitor combined with CTLA-4. NXPH4, as a key gene, plays a role in MIBC with tissue validation results show that nxph4 is highly expressed in tumor. Conclusion. The immune gene score of MIBC data in TCGA and GEO databases was successfully evaluated using GSVA in this research. The lasso Cox expression model was successfully constructed by screening immune genes, the high-risk group had a worse prognosis and higher sensitivity to immunotherapy, PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors can be preferentially used in high-risk patients who are sensitive to immunotherapy, and NXPH4 may be a molecular target to adjust the effect of immunotherapy.
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Zheng, Qingyuan, Rui Yang, Xinmiao Ni, Song Yang, Panpan Jiao, Jiejun Wu, Lin Xiong et al. "Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer". Journal of Clinical Medicine 11, n.º 23 (29 de noviembre de 2022): 7081. http://dx.doi.org/10.3390/jcm11237081.
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(1) Purpose: Although assessment of tumor-infiltrating lymphocytes (TILs) has been acknowledged to have important predictive prognostic value in muscle-invasive bladder cancer (MIBC), it is limited by inter- and intra-observer variability, hampering widespread clinical application. We aimed to evaluate the prognostic value of quantitative TILs score based on a machine learning (ML) algorithm to identify MIBC patients who might benefit from immunotherapy or the de-escalation of therapy. (2) Methods: We constructed an artificial neural network classifier for tumor cells, lymphocytes, stromal cells, and “ignore” cells from hematoxylin-and-eosin-stained slide images using the QuPath open source software. We defined four unique TILs variables based on ML to analyze TILs measurements. Pathological slide images from 133 MIBC patients were retrospectively collected as the discovery set to determine the optimal association of ML-read TILs variables with patient survival outcomes. For validation, we evaluated an independent external validation set consisting of 247 MIBC patients. (3) Results: We found that all four TILs variables had significant prognostic associations with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TILs score being associated with better prognosis. Univariate and multivariate Cox regression analyses demonstrated that electronic TILs (eTILs) variables were independently associated with overall survival after adjustment for clinicopathological factors including age, sex, and pathological stage (p < 0.001 for all analyses). Results analyzed in different subgroups showed that the eTILs variable was a strong prognostic factor that was not redundant with pre-existing clinicopathological features (p < 0.05 for all analyses). (4) Conclusion: ML-driven cell classifier-defined TILs variables were robust and independent prognostic factors in two independent cohorts of MIBC patients. eTILs have the potential to identify a subset of high-risk stage II or stage III-IV MIBC patients who might benefit from adjuvant immunotherapy.
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Doneda, Danilo y Virgilio A. F. Almeida. "What Is Algorithm Governance?" IEEE Internet Computing 20, n.º 4 (julio de 2016): 60–63. http://dx.doi.org/10.1109/mic.2016.79.
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Allenspach, Karin, Mohamed Elbadawy, Hannah Nocholson, Christopher Zdyrski, John Cheville, Eugene Douglass, Elizabeth W. Howerth, Lilian Oliveira y Jonathan P. Mochel. "Abstract PR002: Sarcomatoid Histological Variants of Canine Muscle-Invasive Bladder Cancer: Transcription Factor Activation Highlights Pathways of Epithelial-Mesenchymal Transformation Similar to Humans". Clinical Cancer Research 30, n.º 10_Supplement (17 de mayo de 2024): PR002. http://dx.doi.org/10.1158/1557-3265.bladder24-pr002.
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Abstract The histologic heterogeneity of human Muscle Invasive Bladder Cancer (MIBC) is one of the main contributing factors to poor treatment outcomes. Recent reports on MIBC histological variants have highlighted increased levels of transcription factors (TF) associated with epithelial-mesenchymal transformation (EMT) and epigenetic regulation in the sarcomatoid histology variant of MIBC. Dogs with spontaneously developing MIBC have been shown to display similar histological and molecular features, in addition to closely mimicking the clinical behavior of human MIBC. Here, we add to previously reported data on histological variants in canine MIBC by evaluating 21 tumor biopsies for variant histology and performing bulk RNAseq analyses on 3 organoid cell lines derived from one sarcomatoid subvariant and two conventional urothelial carcinoma (UC). MIBC biopsies from 21 dogs were reviewed for variant histology by a human urologic pathologist. Organoids derived from the urine of canine MIBC patients were cultured, preserved in RNA later, and sequenced for mRNA expression to identify TF associated with sarcomatoid variants versus conventional UC. All canine MIBC biopsies were classified as invasive, high-grade UC. Histologic variants were identified as glandular differentiation in 7/21, sarcomatoid differentiation in 3/21, squamous differentiation in 3/21, micropapillary carcinoma in 1/21 MIBC samples and small nested variants in 1/21 samples; with the rest of the tumors classified as conventional UC. Raw RNAseq analyses revealed no clear clustering of expression profiles between the three organoid cell lines, indicating distinct RNA expression profiles between the sarcomatoid variants and conventional UC of canine MIBC. To evaluate differences in the expression of TF targets between the organoid cell lines, pathway analyses and VIPER algorithms were used. Organoids derived from sarcomatoid variant UC showed increased expression of Snail familiy transcription repressor 1 (SNAI1) along with decreased expression of cadherin 1 (CDH1), indicating a signature of EMT, similar to that previously identified in human MIBC organoids with sarcomatoid variant histology. Canine MIBC organoids derived from conventional UC histology samples showed a TF profile associated with proliferation, metastasis, and progression, such as increased Zinc finger E-box binding homeobox 1 (ZEB1), E2F transcription factor 3 (E2F3), and centromere protein F (CENPF), as previously described in humans with high grade, invasive UC. In conclusion, the frequency of some histologic variants in canine MIBC, such as sarcomatoid UC, appear to be higher than reported in human patients. TF analyses of canine MIBC-derived organoid cell lines showed evidence of EMT, similar to the sarcomatoid variants of MIBC in humans. Organoids derived from canine MIBC could potentially serve as a valuable model for evaluating the efficacy of novel treatment modalities for the rare histologic variants of UC. Citation Format: Karin Allenspach, Mohamed Elbadawy, Hannah Nocholson, Christopher Zdyrski, John Cheville, Eugene Douglass, Elizabeth W. Howerth, Lilian Oliveira, Jonathan P. Mochel. Sarcomatoid Histological Variants of Canine Muscle-Invasive Bladder Cancer: Transcription Factor Activation Highlights Pathways of Epithelial-Mesenchymal Transformation Similar to Humans [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR002.
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Hepburn, Anastasia C., Nicola Lazzarini, Rajan Veeratterapillay, Laura Wilson, Jaume Bacardit y Rakesh Heer. "Identification of CNGB1 as a Predictor of Response to Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer". Cancers 13, n.º 15 (2 de agosto de 2021): 3903. http://dx.doi.org/10.3390/cancers13153903.
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Cisplatin-based neoadjuvant chemotherapy (NAC) is recommended prior to radical cystectomy for muscle-invasive bladder cancer (MIBC) patients. Despite a 5–10% survival benefit, some patients do not respond and experience substantial toxicity and delay in surgery. To date, there are no clinically approved biomarkers predictive of response to NAC and their identification is urgently required for more precise delivery of care. To address this issue, a multi-methods analysis approach of machine learning and differential gene expression analysis was undertaken on a cohort of 30 MIBC cases highly selected for an exquisitely strong response to NAC or marked resistance and/or progression (discovery cohort). RGIFE (ranked guided iterative feature elimination) machine learning algorithm, previously demonstrated to have the ability to select biomarkers with high predictive power, identified a 9-gene signature (CNGB1, GGH, HIST1H4F, IDO1, KIF5A, MRPL4, NCDN, PRRT3, SLC35B3) able to select responders from non-responders with 100% predictive accuracy. This novel signature correlated with overall survival in meta-analysis performed using published NAC treated-MIBC microarray data (validation cohort 1, n = 26, Log rank test, p = 0.02). Corroboration with differential gene expression analysis revealed cyclic nucleotide-gated channel, CNGB1, as the top ranked upregulated gene in non-responders to NAC. A higher CNGB1 immunostaining score was seen in non-responders in tissue microarray analysis of the discovery cohort (n = 30, p = 0.02). Kaplan-Meier analysis of a further cohort of MIBC patients (validation cohort 2, n = 99) demonstrated that a high level of CNGB1 expression associated with shorter cancer specific survival (p < 0.001). Finally, in vitro studies showed siRNA-mediated CNGB1 knockdown enhanced cisplatin sensitivity of MIBC cell lines, J82 and 253JB-V. Overall, these data reveal a novel signature gene set and CNGB1 as a simpler proxy as a promising biomarker to predict chemoresponsiveness of MIBC patients.
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Ji, Zh, Ming Gao y L. Ying. "RESEARCH ON DYNAMIC SCHEDULING DECISION ALGORITHM IN MIMIC DEFENSE". Telecommunications and Radio Engineering 79, n.º 17 (2020): 1563–78. http://dx.doi.org/10.1615/telecomradeng.v79.i17.70.
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Song, Ling, Tao Shen Li y Yan Chen. "A Real Time Video Transmission Routing Protocol in Multi-Interface Multi-Channel Ad Hoc Based on Queue Length and Delay Contraint". Applied Mechanics and Materials 241-244 (diciembre de 2012): 2354–61. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2354.
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Real-time video transmission demands tremendous bandwidth, throughput and strict delay. For transmitting real-time video in the multi-interface multi-channel Ad hoc, firstly, we applied multi-interface multi-channel extension methods to the AOMDV (Ad-hoc On-demand Multipath Distance Vector) routing protocol, and improved extant channel switching algorithm, called MIMC-AOMDV (Multi-Interface Multi-Channel AOMDV) routing protocol. Secondly, we proposed video streaming delay QoS(Quality of Service) constraint and link-quality metrics, which used the multi interface queue’s total used length to get QMMIMC-AOMDV (Quality metric MIMC -AOMDV) routing protocol. The simulations show that the proposed QMMIMC-AOMDV can reduce the frame delay effectively and raise frame decodable rate and peak signal to noise ratio (PSNR), it is more suitable for real-time video streams.
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Smith, Vicky, Dave Lee, Mark Reardon, Rekaya Shabbir, Sudhakar Sahoo, Peter Hoskin, Ananya Choudhury, Timothy Illidge y Catharine M. L. West. "Hypoxia Is Associated with Increased Immune Infiltrates and Both Anti-Tumour and Immune Suppressive Signalling in Muscle-Invasive Bladder Cancer". International Journal of Molecular Sciences 24, n.º 10 (18 de mayo de 2023): 8956. http://dx.doi.org/10.3390/ijms24108956.
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Hypoxia and a suppressive tumour microenvironment (TME) are both independent negative prognostic factors for muscle-invasive bladder cancer (MIBC) that contribute to treatment resistance. Hypoxia has been shown to induce an immune suppressive TME by recruiting myeloid cells that inhibit anti-tumour T cell responses. Recent transcriptomic analyses show hypoxia increases suppressive and anti-tumour immune signalling and infiltrates in bladder cancer. This study sought to investigate the relationship between hypoxia-inducible factor (HIF)-1 and -2, hypoxia, and immune signalling and infiltrates in MIBC. ChIP-seq was performed to identify HIF1α, HIF2α, and HIF1β binding in the genome of the MIBC cell line T24 cultured in 1% and 0.1% oxygen for 24 h. Microarray data from four MIBC cell lines (T24, J82, UMUC3, and HT1376) cultured under 1%, 0.2%, and 0.1% oxygen for 24 h were used. Differences in the immune contexture between high- and low-hypoxia tumours were investigated using in silico analyses of two bladder cancer cohorts (BCON and TCGA) filtered to only include MIBC cases. GO and GSEA were used with the R packages “limma” and “fgsea”. Immune deconvolution was performed using ImSig and TIMER algorithms. RStudio was used for all analyses. Under hypoxia, HIF1α and HIF2α bound to ~11.5–13.5% and ~4.5–7.5% of immune-related genes, respectively (1–0.1% O2). HIF1α and HIF2α both bound to genes associated with T cell activation and differentiation signalling pathways. HIF1α and HIF2α had distinct roles in immune-related signalling. HIF1 was associated with interferon production specifically, whilst HIF2 was associated with generic cytokine signalling as well as humoral and toll-like receptor immune responses. Neutrophil and myeloid cell signalling was enriched under hypoxia, alongside hallmark pathways associated with Tregs and macrophages. High-hypoxia MIBC tumours had increased expression of both suppressive and anti-tumour immune gene signatures and were associated with increased immune infiltrates. Overall, hypoxia is associated with increased inflammation for both suppressive and anti-tumour-related immune signalling and immune infiltrates, as seen in vitro and in situ using MIBC patient tumours.
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YAMADA, Masayuki, Hiroaki NAYA y Ryo TAKAGI. "1F12 Optimization of train rescheduling problems using the Mimic Panel State Model and the Genetic Algorithms(Operation management)". Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _1F12–1_—_1F12–6_. http://dx.doi.org/10.1299/jsmestech.2015._1f12-1_.
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Yan, Sen, Han Zeng, Kaifeng Jin, Fei Shao, Zhaopei Liu, Yuan Chang, Yiwei Wang et al. "NKG2A and PD-L1 expression panel predicts clinical benefits from adjuvant chemotherapy and PD-L1 blockade in muscle-invasive bladder cancer". Journal for ImmunoTherapy of Cancer 10, n.º 5 (mayo de 2022): e004569. http://dx.doi.org/10.1136/jitc-2022-004569.
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BackgroundProgrammed cell death ligand-1 (PD-L1) expression as a single biomarker for immune checkpoint blockade (ICB) was controversial. NKG2A was a PD1/PD-L1 axis-related immunity-dependent factor. NKG2A and PD-L1 expression as a combinatorial biomarker might improve the prediction of PD-L1 in patients with muscle-invasive bladder cancer (MIBC).MethodsThree independent cohorts were enrolled in our study. 195 patients with bladder-derived metastatic urothelial carcinoma on PD-L1 inhibitor treatment from the IMvigor210 trial were enrolled. 124 MIBC patients from Zhongshan Hospital and 391 patients with MIBC from The Cancer Genome Atlas database were included in this study.The PD-L1/NKG2A-based risk stratification was validated in three independent cohorts, and its association with response to ICB and adjuvant chemotherapy (ACT), immune contexture and molecular features was evaluated. Histologic staining and genomic algorithm were performed to detect characteristics of NKG2A and PD-L1 expression and infiltration of immune cells.ResultsWe identified NKG2AhiPD-L1hi patients could benefit more from cisplatin-based ACT and PD-L1 inhibitor. Further analyses revealed NKG2A and PD-L1 expression panel was linked to an immune-active tumor microenvironment with highly immune effector cells and effector molecules. In addition, NKG2A and PD-L1 expression panel was intrinsically correlated with genomic alterations related to therapeutic response in MIBC.ConclusionsNKG2A and PD-L1 expression panel was associated with an immune inflamed microenvironment and acted as a combinatorial biomarker to predict the therapeutic response to ACT and PD-L1 blockade in MIBC.
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Sonpavde, Guru P., Layne Sadler, Arvind Ravi, Amin Nassar, Seth P. Lerner y Sooryanarayana Varambally. "Neural network analysis of tumor and germline profiling to predict survival of muscle-invasive bladder cancer following radical cystectomy: An analysis of the Cancer Genome Atlas (TCGA)." Journal of Clinical Oncology 41, n.º 6_suppl (20 de febrero de 2023): 546. http://dx.doi.org/10.1200/jco.2023.41.6_suppl.546.
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546 Background: Following radical cystectomy (RC) for muscle-invasive bladder cancer (MIBC), recurrence is suboptimally associated with pathologic stage. A prognostic model that incorporates information from comprehensive genomic and transcriptomic data from germline and tumor tissues will improve the selection of high-risk patients (pts) for adjuvant therapy and tailored surveillance. Methods: Pts with MIBC in TCGA who underwent RC (without neoadjuvant chemotherapy) and with whole exome sequencing (WES) of tumor + normal, and tumor RNA-Seq were eligible. Germline variants were called and annotated using GATK4 and Variant Effect Predictor (VEP). Only high and moderate VEP-Impact variants contributed to burdens. Given that recurrence is associated with 5-year survival, pts were required to have data for survival at a landmark of 5 years post-RC or may have died within 5 years of RC. Patients who died <4 months of diagnosis were excluded since they may have died from post-operative complications. Dead patients were propensity-matched against alive patients by pathologic stage, adjuvant chemotherapy, gender, race, age and smoking. A linear artificial neural network was trained to predict 5-year survival using Artificial Intelligence Quality Control (AIQC)-a deep learning experience tracker. The samples were divided into 3 equally distributed subsets of alive and dead pts. The algorithm was trained on 50% of the dataset, evaluated on 40% and tested on 10%. The remaining 39 dead pts were used for additional holdout validation. The accuracy (percentage of correct prediction of death or survival) was reported. Results: A cohort of 117 pts were evaluable for 5 year survival. The cohort included 39 survivors that satisfied inclusion criteria, and 78 dead pts that were propensity matched against them (one set of 39 dead pts for training & evaluation, and a second set of 39 dead pts as an additional holdout). The binary classification neural network was trained on 38 samples to predict survival based on their most differentiated: germline alteration burdens + tumor alteration burdens + tumor gene expression. The differentially mutated genes most important to the algorithm were: PTPRN(n-m), TECTA(t-m), BPIFB1(n-m), SORL1(n-m), SLC39A5(n-m), and RBBP6(n-h) [t=tumor/ n=normal tissue origin | m=moderate/ h=high VEP impact]. The algorithm was 97% accurate on the 38 training samples, and 94% accurate across the 79 evaluation (validation, test and holdout) samples. Conclusions: Neural network analysis of germline and tumor profiling was validated to optimally differentiate between dead and surviving pts at 5 years following RC for MIBC. Further validation and application of this promising algorithm may advance precision medicine by permitting the selection of the most appropriate pts with high-risk MIBC for adjuvant therapy.
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Uysal, Daniel, Karl-Friedrich Kowalewski, Maximilian Christian Kriegmair, Ralph Wirtz, Zoran V. Popovic y Philipp Erben. "A comprehensive molecular characterization of the 8q22.2 region reveals the prognostic relevance of OSR2 mRNA in muscle invasive bladder cancer". PLOS ONE 16, n.º 3 (12 de marzo de 2021): e0248342. http://dx.doi.org/10.1371/journal.pone.0248342.
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Technological advances in molecular profiling have enabled the comprehensive identification of common regions of gene amplification on chromosomes (amplicons) in muscle invasive bladder cancer (MIBC). One such region is 8q22.2, which is largely unexplored in MIBC and could harbor genes with potential for outcome prediction or targeted therapy. To investigate the prognostic role of 8q22.2 and to compare different amplicon definitions, an in-silico analysis of 357 patients from The Cancer Genome Atlas, who underwent radical cystectomy for MIBC, was performed. Amplicons were generated using the GISTIC2.0 algorithm for copy number alterations (DNA_Amplicon) and z-score normalization for mRNA gene overexpression (RNA_Amplicon). Kaplan-Meier survival analysis, univariable, and multivariable Cox proportional hazard ratios were used to relate amplicons, genes, and clinical parameters to overall (OS) and disease-free survival (DFS). Analyses of the biological functions of 8q22.2 genes and genomic events in MIBC were performed to identify potential targets. Genes with prognostic significance from the in silico analysis were validated using RT-qPCR of MIBC tumor samples (n = 46). High 8q22.2 mRNA expression (RNA-AMP) was associated with lymph node metastases. Furthermore, 8q22.2 DNA and RNA amplified patients were more likely to show a luminal subtype (DNA_Amplicon_core: p = 0.029; RNA_Amplicon_core: p = 0.01). Overexpression of the 8q22.2 gene OSR2 predicted shortened DFS in univariable (HR [CI] 1.97 [1.2; 3.22]; p = 0.01) and multivariable in silico analysis (HR [CI] 1.91 [1.15; 3.16]; p = 0.01) and decreased OS (HR [CI] 6.25 [1.37; 28.38]; p = 0.0177) in RT-qPCR data analysis. Alterations in different levels of the 8q22.2 region are associated with manifestation of different clinical characteristics in MIBC. An in-depth comprehensive molecular characterization of genomic regions involved in cancer should include multiple genetic levels, such as DNA copy number alterations and mRNA gene expression, and could lead to a better molecular understanding. In this study, OSR2 is identified as a potential biomarker for survival prognosis.
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Mishra, Soumya. "A MICC Algorithm for Power Quality Improvement Using PV-DSTATCOM". Asian Journal of Electrical Sciences 8, S1 (5 de junio de 2019): 55–59. http://dx.doi.org/10.51983/ajes-2019.8.s1.2306.
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A modified indirect control algorithm is presented in this paper to improve the operation of photovoltaic fed distributed static compensator. This new algorithm overcomes the limitations of direct current control algorithm under non-ideal supply voltage conditions such asswitching notches in source current after compensation, distortions in the reference source current and poor harmonic elimination. The design and performance testing of the presented system has been carried out in MATLAB/Simulink software. Furthermore, the efficacy and robustness of the presented system are validated through experiment.
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Nguyen, Long, Dinh Nguyen Duc y Hoai Nguyen Xuan. "A Guidance method for robustness surrogate assisted multi-objective evolutionary algorithms". Journal of Research and Development on Information and Communication Technology 2021, n.º 1 (21 de mayo de 2021): 1–18. http://dx.doi.org/10.32913/mic-ict-research.v2021.n1.948.
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In the real world, multi-objective problems(MOPs) are relatively common in optimization in the areasof design, planning, decision support... In fact, problemsinclude two or many objectives, there is a class of problemscalled expensive problems that are problems with complexmathematical models, large computational costs,... Theycan not be solved by normal techniques, they are usually tobe solved with techniques such as simulation, decomposing,problem transformation. In particular, using a surrogatemodel with Kriging, neuron networks techniques in combination with an evolutionary algorithm is a subtle choice,with many positive results, being studied and applied inpractice. However, the use of a surrogate model withKriging, neuron networks combining selection strategy,sampling... can reduce the robustness of the algorithmsduring the search. This paper analyzes the issues affectingthe robustness of the multi-objective evolutionary algorithms (MOEAs) using surrogate models and suggests theuse of a guidance technique to increase the robustness ofthe algorithm, through analysis, experiment and results arecompetitive and effective to improve the quality of MOEAsusing a surrogate model to solve expensive problems.
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Smith, Amy F., Timothy W. Secomb, Axel R. Pries, Nicolas P. Smith y Rebecca J. Shipley. "Structure-Based Algorithms for Microvessel Classification". Microcirculation 22, n.º 2 (febrero de 2015): 99–108. http://dx.doi.org/10.1111/micc.12181.
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Ergon, Rolf y Kim H. Esbensen. "A didactically motivated PLS prediction algorithm". Modeling, Identification and Control: A Norwegian Research Bulletin 22, n.º 3 (2001): 131–39. http://dx.doi.org/10.4173/mic.2001.3.1.
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Ke, Liangjun, Laipeng Zhai, Jing Li y Felix T. S. Chan. "Pareto mimic algorithm: An approach to the team orienteering problem". Omega 61 (junio de 2016): 155–66. http://dx.doi.org/10.1016/j.omega.2015.08.003.
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Phan, Hai-Hong. "An Integrated Approach for Table Detection and Structure Recognition". Journal of Research and Development on Information and Communication Technology 2021, n.º 1 (31 de mayo de 2021): 41–50. http://dx.doi.org/10.32913/mic-ict-research.v2021.n1.974.
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Detecting and identifying the table structure is an important issue in document digitization. Although there have been many great strides based on current deep learning techniques, table structure identification is still a difficult and difficult problem, especially when solving the problem of digitizing text in practice. The paper proposes a solution to digitize table documents based on the Cascade R-CNN HRNet network to detect, classify tables and integrate image processing algorithms to improve table data identification results. The proposed algorithm proved effective on real data - the hydrometeorological station record book contains tables including simple and complex structures tables with over 98% accuracy.
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Petrie, Charles. "Protect Data, Not Algorithms". IEEE Internet Computing 20, n.º 6 (noviembre de 2016): 76–77. http://dx.doi.org/10.1109/mic.2016.129.
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Brown, Zach Y. y Alexander MacKay. "Competition in Pricing Algorithms". American Economic Journal: Microeconomics 15, n.º 2 (1 de mayo de 2023): 109–56. http://dx.doi.org/10.1257/mic.20210158.
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We document new facts about pricing technology using high-frequency data, and we examine the implications for competition. Some online retailers employ technology that allows for more frequent price changes and automated responses to price changes by rivals. Motivated by these facts, we consider a model in which firms can differ in pricing frequency and choose pricing algorithms that are a function of rivals’ prices. In competitive (Markov perfect) equilibrium, the introduction of simple pricing algorithms can increase price levels, generate price dispersion, and exacerbate the price effects of mergers. (JEL D21, D22, D43, G34, L13, L81)
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Almeida, Virgilio, Fernando Filgueiras y Ricardo Fabrino Mendonca. "Algorithms and Institutions: How Can Social Sciences Can Contribute to Governance of Algorithms". IEEE Internet Computing 26, n.º 2 (1 de marzo de 2022): 42–46. http://dx.doi.org/10.1109/mic.2022.3147923.
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Klesk, Przemyslaw y Marcin Korzen. "Can Boosted Randomness Mimic Learning Algorithms of Geometric Nature? Example of a Simple Algorithm That Converges in Probability to Hard-Margin SVM". IEEE Transactions on Neural Networks and Learning Systems 32, n.º 9 (septiembre de 2021): 3798–818. http://dx.doi.org/10.1109/tnnls.2021.3059653.
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Wei, Shuai, Huihua Zhang, Wenjian Zhang y Hong Yu. "Conditional Probability Voting Algorithm Based on Heterogeneity of Mimic Defense System". IEEE Access 8 (2020): 188760–70. http://dx.doi.org/10.1109/access.2020.3031323.
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Wang, Wei y Rolf Henriksen. "Globally convergent generalized pole-placement adaptive control algorithm". Modeling, Identification and Control: A Norwegian Research Bulletin 12, n.º 3 (1991): 139–47. http://dx.doi.org/10.4173/mic.1991.3.4.
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Phan, Thanh Huan y Hoài Bắc Lê. "A Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items". Journal of Research and Development on Information and Communication Technology 2021, n.º 1 (17 de junio de 2021): 19–28. http://dx.doi.org/10.32913/mic-ict-research.v2021.n1.967.
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In 1993, Agrawal et al. proposed the first algorithm for mining traditional frequent itemset on binarytransactional database with unweighted items - This algorithmis essential in finding hindden relationships among items inyour data. Until 1998, with the development of various typesof transactional database - some researchers have proposed afrequent itemsets mining algorithms on transactional databasewith weighted items (the importance/meaning/value of itemsis different) - It provides more pieces of knowledge thantraditional frequent itemsets mining. In this article, the authors present a survey of frequent itemsets mining algorithmson transactional database with weighted items over the pasttwenty years. This research helps researchers to choose theright technical solution when it comes to scale up in big datamining. Finally, the authors give their recommendations anddirections for their future research.
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Ahmed, Ahmed. "Development of an Approach for Image Forgery Detection Using Machine Learning Algorithms". Journal of Intelligent Systems and Internet of Things 12, n.º 2 (2024): 166–77. http://dx.doi.org/10.54216/jisiot.120212.
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Digital picture fraud detection is an increasing societal necessity due to the importance of verified images. The detection of picture copying, splicing, retouching, and re-sampling forgeries is included. In the absence of digital signatures or watermarks, passive picture authentication may serve as an alternative to active authentication. Passive techniques, every so often recognized as blind techniques, could take place without preceding knowledge of the picture or its reference. Identifying counterfeiting picture or tampering was a research field for long a period of time, triggered via the Internet, online platforms, social messaging platforms, and extensive digital image usage. The rate of failure could be a key factor for examining the alteration of picture or forgery, among other existing methods. The research applies almost six common algorithms related to machine learning in order to extract features from Lightweight, Spatial Exploitation, and Residual deep learning models on benchmark datasets MICC-F220, Columbia, and CoMoFoD. The models of incorporated deep learning could consist of AlexNet, GoogleNet, VGG16, VGG19, SqueezeNet, MobileNetV2, ShuffleNet, ResNet-18, ResNet-50, and ResNet-101 for spatial exploitation. Fine-tuning is applied to the top three deep learning models, optimizing hyperparameters centered on indicators of performance for every single benchmark dataset. Tweaked SqueezeNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results for MICC-F220 dataset. Fine-tuned VGG19, MobileNetV2, and ResNet-50 deep learning models with SGDM Optimizer and SVM v classifier yielded the best results for Columbia dataset. In CoMoFoD dataset, fine-tuned AlexNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results. The proposed approach, utilizing machine learning algorithms and deep learning features, enhanced forgery detection and reduced false positives. Results were validated on benchmark image forgery datasets and compared to current methods.
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Zhu, Zhengbin, Hong Yu, Qinrang Liu, Dongpei Liu y Huapeng Yu. "An Adaptive Multiexecutors Scheduling Algorithm Based on Heterogeneity for Cyberspace Mimic Defense". Security and Communication Networks 2022 (26 de diciembre de 2022): 1–23. http://dx.doi.org/10.1155/2022/2300407.
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With the rapid development of network technology, the traditional defense of “mending the fold after the sheep have been stolen” cannot accurately prevent various potential threats and attacks in cyberspace. At the same time, cyberspace mimic defense (CMD) makes the system uncertain and dynamic in time and space to effectively defend against potential attacks. As the key technology of CMD, the scheduling algorithm still needs to be improved in reliability and active defense. Aiming at current problems, this paper first innovatively proposes a new heterogeneous measure algorithm HVTG combined with a vulnerability topology graph, which measures the heterogeneity of executor set in a fine-grained manner. Then, based on the historical confidence, heterogeneity, and minimum sleep time of the executor, we propose an adaptive multi-executors scheduling algorithm (HHAC) to better defend against various attacks. Finally, combining with Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation, this research proposes a comprehensive evaluation model and fill in the gap of the evaluation model of the scheduling algorithm. Theoretical analysis and simulation results show that the HHAC performs well on the system dynamics, probability of system failure, and reliability, which is conducive to the development of CMD.
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Wang, Haizhou, Yabo Zhou, Yangyang Zhang, Shilin Fang, Meng Zhang, Haiou Li, Fei Xu et al. "Subtyping of microsatellite stability colorectal cancer reveals guanylate binding protein 2 (GBP2) as a potential immunotherapeutic target". Journal for ImmunoTherapy of Cancer 10, n.º 4 (abril de 2022): e004302. http://dx.doi.org/10.1136/jitc-2021-004302.
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BackgroundsProficient-mismatch-repair or microsatellite stability (pMMR/MSS) colorectal cancer (CRC) has limited efficacy for immune checkpoint blockade (ICB) therapy and its underlying mechanism remains unclear. Guanylate binding protein 2 (GBP2) is a member of the GTPase family and is crucial to host immunity against pathogens. However, the correlations between GBP2 and immunosurveillance and immunotherapy for pMMR/MSS CRC have not been reported.MethodsUnsupervised clustering was employed to classify immune class and non-immune class in 1424 pMMR/MSS patients from six independent public datasets. This binary classification was validated using immune cells or response related signatures. The correlation between GBP2 and immune microenvironment was explored using well-established biological algorithms, multiplex immunohistochemistry (mIHC), in vitro and in vivo experiments.ResultsWe classified 1424 pMMR/MSS CRC patients into two classes, ‘immune’ and ‘non-immune’, and GBP2 was identified as a gene of interest. We found that lower GBP2 expression was correlated with poor prognosis and metastasis. GBP2 expression was also upregulated in the immune class and highly associated with interferon-γ (IFN-γ) signaling pathway and CD8 +T cell infiltration using gene set enrichment analysis, gene ontology analysis, single-cell sequencing and mIHC. Moreover, reduced GBP2 expression inhibited the antigen processing and presentation machinery and CXCL10/11 expression in MSS CRC cells on IFN-γ stimulation. A Transwell assay revealed that deletion of GBP2 in murine MSS CRC cells reduced CD8 +T cell migration. Mechanistically, GBP2 promoted signal transducer and transcription activator 1 (STAT1) phosphorylation by competing with SHP1 for binding to STAT1 in MSS CRC cells. Finally, an unsupervised subclass mapping (SubMap) algorithm showed that pMMR/MSS patients with high GBP2 expression may correlate with a favorable response to anti-PD-1 therapy. We further confirmed that GBP2 knockout reduced CD8 +T cell infiltration and blunted the efficacy of PD-1 blockade in tumor-bearing mice.ConclusionsOur study reveals that pMMR/MSS CRC is immunogenically heterogeneous and that GBP2 is a promising target for combinatorial therapy with ICB.
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Andresen, Trond. "A Recursive Algorithm for the Reduction of Block Diagrams". Modeling, Identification and Control: A Norwegian Research Bulletin 12, n.º 1 (1991): 47–56. http://dx.doi.org/10.4173/mic.1991.1.4.
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Wang, Wei y Rolf Henriksen. "A simplified algorithm of weighted generalized predictive adaptive control". Modeling, Identification and Control: A Norwegian Research Bulletin 12, n.º 3 (1991): 107–15. http://dx.doi.org/10.4173/mic.1991.3.1.
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