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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Nasrolahi, Ahmad Gena, Cristina Messina, Vito Ejraei, Samet University of Torino Tech4Culture PhD Program Italy University of Torino Computer Science Department Italy University of Torino Historical Studies Department Italy Bisotun World Heritage Site Iran's Ministry of Cultural Heritage Iran

ISBN: (纸本)9781450383677

Community participation in cultural heritage conservation has been a concern since the Venice Charter (1964) so far. This approach has also been highlighted in the World Heritage documents. In this case, it is necessary to engage local people in all stages of protection, conservation and management. In addition, the Faro Convention (2005) emphasized that the responsibilities regarding the cultural heritage management must be shared between authorities and civil society to make possible a joint action among different stakeholders. At the site of Bisotun, participatory monitoring means the systematic reporting and recording of the issues regarding the cultural heritage properties provided by people living in the landscape zone in order to achieve sustainable monitoring of cultural heritage properties. In fact, the local people can monitor progress of safeguarding and protecting their heritage by daily observing and reporting relevant issues if any. The main purpose of participatory monitoring in this research is to engage the local community in the monitoring of cultural heritage properties in the Bisotun World Heritage Site. © 2021 ACM.

关键词： Monitoring

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Ciwars: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

SSRN

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SSRN 2024年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech BlacksburgVA24060 United States Fralin Life Science Institute Virginia Tech BlacksburgVA24060 United States Department of Civil and Environmental Engineering Virginia Tech BlacksburgVA24060 United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech BlacksburgVA24060 United States

The rise of antibiotic resistance (AR) poses substantial threats to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate continuous AR monitoring through longitudinal metagenomic data are lacking. To address this, we introduce Cyberinfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive ARG and taxonomic profiling, along with detection of anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and demonstrates its potential for informing effective and timely interventions to mitigate the spread and transmission of AR. CIWARS is broadly applicable to longitudinal metagenomic data from any environment and thus could be a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use when it is needed. The web server is freely available at https://***/. © 2024, The Authors. All rights reserved.

关键词： Gene encoding

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Probabilistic genotype-phenotype maps reveal mutational robustness of RNA folding, spin glasses, and quantum circuits

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Physical Review Research 2025年第1期7卷 013118-013118页

作者： Anna Sappington Vaibhav Mohanty Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard-MIT Program in Health Sciences and Technology Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard/MIT MD-PhD Program Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA

Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype—represented as a sequence—maps deterministically to a single phenotype, such as a discrete structure. Here we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in the spin-glass ground state search problem, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.

关键词： Quantum computers

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Using prosody to improve dependency parsing 10

Using prosody to improve dependency parsing

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10th International Conference on Speech Prosody 2020

作者： Ghaly, Hussein Mandel, Michael Linguistics PhD Program Graduate Center City University of New York United States Computer and Information Science Brooklyn College United States

The goal of the present study is to use prosodic information to improve automatic syntactic parsing of conversational speech in the Switchboard Corpus. To achieve this, an ensemble classifier, based on a Recurrent Neural Network, is developed to predict the parse with the highest Unlabelled Attachment Score (UAS) from the outputs of multiple dependency parsers, based on syntactic and prosodic features. The main syntactic features proposed, which we refer to as "dependency configurations," represent the relative dependency location of each of a pair of consecutive words. Empirical analysis indicates that configurations with a direct dependency between consecutive words are less likely to be associated with major prosodic breaks. Using syntactic features alone, the system achieved an improvement of 1.1% of UAS on the test set, above the best parser in the ensemble, while using syntactic features combined with prosodic features (pauses and normalized duration) led to a further improvement of 0.4%. Both empirical analysis of dependency configurations and parsing improvement suggest a relationship between prosody and direct dependency relationships between consecutive words. © 2020 International Speech Communications Association. All rights reserved.

关键词： Recurrent neural networks

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Medical Image Segmentation by Improved Nested Unet 2nd

Medical Image Segmentation by Improved Nested Unet

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2nd International Conference on Intelligent Systems and Data science, ISDS 2024

作者： Tran, Song-Toan Le, Minh-Hai Nguyen, Thai-Son Nghi, Vinh-Khanh Nguyen, Thanh-Nguyen School of Engineering and Technology Tra Vinh University Tra Vinh87000 Viet Nam Resource Development Institute Tra Vinh University Tra Vinh87000 Viet Nam PhD program in Computer Science Tra Vinh University Tra Vinh87000 Viet Nam Facilities Management Department Tien Giang University Tien Giang84000 Viet Nam

ISBN: (纸本)9789819796120

Deep learning is revolutionizing the field of medical image segmentation. The U-shaped (Unet) model, with its encoder-decoder architecture and skip connections, has become the dominant architecture for this task. However, variations like Nested-Unet are necessary to address the challenge of adapting network size for datasets of varying scales. This study proposes a novel model based on a multilayer Unet architecture that leverages all output features within nodes of a Nested Unet network. We evaluate the proposed model on four key medical image datasets: electron microscopy (EM), nuclei, polyps (CVC-ClinicDB), and skin lesions. The model achieves impressive Dice scores: 94.08% for EM, 92.68% for nuclei, 87.20% for skin lesions, and 93.02% for the CVC-ClinicDB dataset. These outstanding results demonstrate the superior performance of our proposed model compared to recent advancements in the field. Remarkably, the model achieves this performance while maintaining a size that is half that of a traditional Unet model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular Biology 2025年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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Interactive Visualization and App Development for Precipitation Data in Sub-Saharan Africa

Interactive Visualization and App Development for Precipitat...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Divydharshini Karthikeyan Sudha Shah Aparna S. Varde Clement Alo Department of Computer Science Environmental Management and Science PhD program Department of Earth and Environmental Science Environmental Management and Science PhD program

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Precipitation plays a highly significant part in the hydrological cycle and is responsible for depositing most of the freshwater on earth. It can also impact the climate ecosystem. Considerable work has been performed to understand, model and predict precipitation. However, to the best of our knowledge, hardly any interactive visualizations exist that allow end-users to obtain a better perception of local impacts; also there are no specific apps for this purpose. The two goals of our work are thus: to design a visualization tool that illustrates changes in precipitation in a given time period with analysis; and to design a mobile application (app) that displays Sub-Saharan precipitation patterns, both using big data. This work would be advantageous to environmental scientists, as well as local residents of the respective areas. It would also be beneficial to students interested in conducting climate-based research. It addresses the theme of IoT (Internet of Things) which involves providing ubiquitous access to Internet-Based information on platforms such as smartphones and other mobile devices.

关键词： Data visualization Tools Big Data Meteorology Africa Mobile applications Human computer interaction

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A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records

arXiv

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arXiv 2025年

作者： Mota, Marco Barbero Still, John M. Gamboa, Jorge L. Strobl, Eric V. Stein, Charles M. Kawai, Vivian K. Lasko, Thomas A. Vanderbilt University Medical Center Department of Biomedical Informatics United States Vanderbilt University Medical Center Department of Medicine United States Vanderbilt University Medical Center Department of Psychiatry and Behavioral Sciences United States Vanderbilt University Medical Center Department of Computer Science United States Vanderbilt DBMI PhD program 2525 West End Ave NashvilleTN United States

Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sources from multimodal imperfect EHR data. These sources represent exogenous variables in the data generation process causal graph that estimate latent root causes of the presence of SLE in the health record. We objectively evaluated the sources against the original variables from which they were discovered by training supervised models to discriminate SLE from negative health records using a reduced set of labelled instances. We found 19 predictive sources with high clinical validity and whose EHR signatures define independent factors of SLE heterogeneity. Using the sources as input patient data representation enables models to provide with rich explanations that better capture the clinical reasons why a particular record is (not) an SLE case. Providers may be willing to trade patient-level interpretability for discrimination especially in challenging cases. © 2025, CC BY-NC-ND.

关键词： Electronic health record

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Robust prediction of personalized in vivo response to unseen drugs from in vitro screens using a novel context-aware deconfounding autoencoder

Research Square

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Research Square 2021年

作者： He, Di Liu, Qiao Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient-specific responses to drug treatments is critical for drug development and personalized medicine. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell line data, few of them can reliably predict individual patient clinical responses to new drugs due to data distribution shift and confounding factors. We have developed a novel Context-aware Deconfounding Autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization, significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific in vivo drug responses purely from in vitro screens. Using CODE-AE, we screened 59 drugs for 9,808 cancer patients. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized anti-cancer therapies and drug-response biomarkers. © 2021, CC BY.

关键词： Cell culture

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CODE-AE: A coherent de-confounding autoencoder for predicting patient-specific drug response from cell line transcriptomics

arXiv

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arXiv 2021年

作者： He, Di Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient’s response to drug treatments is critical for developing precision medicine. However, it is often difficult to obtain a sufficient amount of coherent drug response data from patients directly for training a generalized machine learning model. Although the utilization of rich cell line data provides an alternative solution, it is challenging to transfer the knowledge obtained from cell lines to patients due to various confounding factors. Few existing transfer learning methods can reliably disentangle common intrinsic biological signals from confounding factors in the cell line and patient data. In this paper, we develop a Coherent Deconfounding Autoencoder (CODE-AE) that can extract both common biological signals shared by incoherent samples and private representations unique to each data set, transfer knowledge learned from cell line data to tissue data, and separate confounding factors from them. Extensive studies on multiple data sets demonstrate that CODE-AE significantly improves the accuracy and robustness over state-of-the-art methods in both predicting patient drug response and deconfounding biological signals. Thus, CODE-AE provides a useful framework to take advantage of in vitro omics data for developing generalized patient predictive models. The source code is available at https://***/XieResearchGroup/CODE-AE. © 2021, CC BY.

关键词： Learning systems

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