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Climate-influenced vector-borne diseases in Africa:a call to empower the next generation of African researchers for sustainable solutions

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Infectious Diseases of Poverty 2024年第2期13卷 83-92页

作者： Judicaël Obame-Nkoghe Adjoavi Esse Agossou Gerald Mboowa Basile Kamgang Cyril Caminade Dawn C.Duke Andrew Karanja Githeko Obed M.Ogega Nestor Engone Elloué Fatou Bintou Sarr DieudonnéNkoghe Pierre Kengne Nicaise T.Ndam Christophe Paupy Moses Bockarie Patricks Voua Otomo Molecular and Cellular Biology Laboratory(LabMC) Biology DepartmentUniversity of Science and Technology of MasukuBP 901FrancevilleGabon Ecology and Health Research Unit Interdisciplinary Center for Medical Research of FrancevilleBP 769FrancevilleGabon Department of Zoology and Entomology Faculty of Natural and Agricultural SciencesUniversity of the Free StatePrivate Bag x13Phuthaditjhaba 9866Republic of South Africa Laboratory of Pharmacology and Improved Traditional Medicines Department of Animal PhysiologyFaculty of Science and TechnologyUniversity of Abomey-CalaviBP 526CotonouBenin The African Center of Excellence in Bioinformatics and Data-Intensive Sciences Infectious Diseases InstituteCollege of Health SciencesMakerere UniversityP.O Box 22418KampalaUganda Africa Centers for Disease Control and Prevention African Union CommissionRoosevelt StreetP.O.Box 3243W21 K19 Addis AbabaEthiopia Centre for Research in Infectious Diseases P.O.Box 13591YaoundéCameroon Earth System Physics Department The Abdus Salam International Centre for Theoretical PhysicsTriesteItaly Africa Research Excellence Fund 99 Charterhouse StreetLondon EC1M 6HRUK Community Health Support Program P.O.Box 872Kisumu 40100Kenya The African Academy of Sciences NairobiKenya Center for Phylosophical Studies and Research(CERP) Omar Bongo University(UOB)BP 13131LibrevilleGabon UMRED Health Training and Research UnitUniversity of Iba Der Thiam of ThièsBP 967ThièsSenegal National Parasitic Diseases Control Program Ministry of HealthLibrevilleGabon MIVEGEC Univ.MontpellierCNRSIRDMontpellierFrance MERIT IRDParis CitéUniversity75006 ParisFrance Department of Parasitology Noguchi Memorial Institute for Medical ResearchUniversity of GhanaLG 54AccraGhana School of Community Health Sciences Njala UniversityBoSierra Leone

We look at the link between climate change and vector-borne diseases in low-and middle-income countries in *** large endemicity and escalating threat of diseases such as malaria and arboviral diseases,intensified by climate change,disproportionately affects vulnerable communities *** highlight the urgency of prioritizing research and development,advocating for robust scientific inquiry to promote adaptation strategies,and the vital role that the next generation of African research leaders will play in addressing these *** significant challenges such as funding shortages within countries,various pan-African-oriented funding bodies such as the African Academy of sciences,the Africa Research Excellence Fund,the Wellcome Trust,the *** Institutes of Health,and the Bill and Melinda Gates Foundation as well as initiatives such as the African Research Initiative for Scientific Excellence and the Pan-African Mosquito Control Association,have empowered(or are empowering)these researchers by supporting capacity building activities,including continental and global networking,skill development,mentoring,and African-led *** article underscores the urgency of increased national investment in research,proposing the establishment of research government agencies to drive evidence-based *** between governments and scientific communities,sustained by pan-African funding bodies,is *** these efforts,African nations are likely to enhance the resilience and adaptive capacity of their systems and communities by navigating these challenges effectively,fostering scientific excellence and implementing transformative solutions against climate-sensitive vector-borne diseases.

关键词： Climate change Vector-borne diseases Africa Environmental justice Research and development Pan-African funding initiatives

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Author Correction: Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG

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Genome biology 2022年第1期23卷 213页

作者： Zhana Duren Fengge Chang Fnu Naqing Jingxue Xin Qiao Liu Wing Hung Wong Center for Human Genetics and Department of Genetics and Biochemistry Clemson University Greenwood SC 29646 USA. zduren@clemson.edu. Center for Human Genetics and Department of Genetics and Biochemistry Clemson University Greenwood SC 29646 USA. Department of Statistics Department of Biomedical Data Science and Bio-X Program Stanford University Stanford CA 94305 USA. Department of Statistics Department of Biomedical Data Science and Bio-X Program Stanford University Stanford CA 94305 USA. whwong@stanford.edu.

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Predicting Unethical Physician Behavior At Scale: A Distributed Computing Framework

Predicting Unethical Physician Behavior At Scale: A Distribu...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Anastasia Keck Miguel Romero Robert Sandor Diane Woodbridge Paul Intrevado University of San Francisco San Francisco CA USA Data Science Program University of San Francisco

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

As the amount of publicly shared data increases, developing a robust pipeline to stream, store and process data is critical, as the casual user often lacks the technology, hardware and/or skills needed to work with such voluminous data. In this research, the authors employ Amazon EC2 and EMR and Apache Spark to explore 28.5 gigabytes of CMS Open Payments data in an attempt to identify physicians who may have a high propensity to act unethically, owing to significant transfers of wealth from medical companies. A Random Forest Classifier is employed to predict the top decile of physicians who have the highest risk of unethical behavior in the following year, resulting in an F-Score of 91%. The authors also employ an anomaly detection algorithm that correctly identified a high-profile case of a physician leaving his prestigious position, failing to disclose anomalously-large transfers of wealth from medical companies.

关键词： Medical services Cloud computing Organizations Forestry Machine learning Distributed databases Anomaly detection

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Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records

arXiv

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arXiv 2021年

作者： Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S.O. Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Centre for Quantitative Medicine and Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Faculty of Medicine Nursing and Health Sciences Monash University VIC Australia Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Division of Pulmonary Allergy and Critical Care Medicine Duke University DurhamNC United States Department of Emergency Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. © 2021, CC BY.

关键词： Learning systems

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Comparing the Accuracy of Multiple Commercial Wearable Devices: A Method

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Procedia Computer science 2019年 157卷 567-572页

作者： Bens Pardamean Haryono Soeparno Bharuno Mahesworo Arif Budiarto James Baurley Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 BioRealm LLC USA

To maintain a good fitness level, performing a minimum daily physical activity is important. Many people think that they have already met the suggested guidelines. However, most of them were wrong when they quantify their activity level using a personal activity tracker device. This problem indicates the need for personal physical activity tracker or wearable device to maintain the fitness level. However, as wide-range wearable devices available today, it is difficult to find the most suitable one regarding the price and quality. This paper proposes a study to explore the association between the quality and the price of the devices. The quality is evaluated by comparing the accuracy of the heart rate sensor and step counter of several wearable devices with the ground truth measurements. The test result is then analyzed using a series of statistical methods including, a paired t-test, Pearson’s correlation test, and Bland-Altman plot. Both t-test and correlation coefficient is used to plot the association between quality and price.

关键词： Wearable Device Fitness Tracker Correlation Heart Rate Step Counting Bland-Altman

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Distributed data Analytics Framework for Cluster Analysis of Parking Violation

Distributed Data Analytics Framework for Cluster Analysis of...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Nan Lin Evan Liu Fiorella Tenorio Xi Yang Diane Woodbridge Data Science Program University of San Francisco University of San Francisco San Francisco CA USA

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

In this research, we employed distributed systems to explore the similarities in parking ticket records using unsupervised machine learning algorithms on a large dataset. Using 37 million ticket records (9 GB) collected by the New York City Department of Finance, we applied an algorithm to cluster existing tickets and dive deeper to find the distribution of precincts within different clusters. Amazon Web Services including S3, EC2 and EMR, and tools like MongoDB and Apache Spark were used in this endeavor. In this study, computational time and cost for different EMR settings were evaluated. We conclude that there are significant computational advantages to using distributed systems when implementing unsupervised learning on a large dataset as well as storing and managing data. We also observed that it is time efficient for a cluster with more workers instead of fewer workers with large memory space for the utilized data set. However we observed a trade-off between the execution time and the total cost for the cluster configuration.

关键词： Clustering algorithms Sparks Servers Urban areas Distributed databases Cloud computing Machine learning algorithms

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A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Taufik Budhi Pramono R Ardharyan Islamy Saprudin Joni Johanda Putra Teddy Suparyanto Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Aquaculture Study Program Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Aquatic Resources Management Brawijaya University Malang East Java Indonesia Graduate Program Aquatic Resources Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.

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Weeds e-Catalog as a Tool for Identification of Weeds in Plantation

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Hangger Gahara Mawandha Teddy Suparyanto Bens Pardamean Agrotechnology Department Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Agricultural Engineering Department Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Universitas Bina Nusantara Jakarta Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta

Weeds are one of the organisms that interfere with plant growth. Information about the identity of weeds becomes very important on plantations. Although weeds data digitization has been done a lot, currently there is still not much weeds data information system can be accessed online. Weeds management is often dealt with weeds herbarium or weeds photographs. In an effort to provide a good information system for farmers, this research aims to create a database of various types of weeds and an information system that can be accessed online. The methodology for developing a weed catalog information system uses the Software Development Life Cycle (SDLC). Weed samples in database were collected through systematic random sampling in the form of images and text. The result of this study is a Weeds Electronic Catalog or Weeds e-Catalog that can facilitate the information of weeds identity such as names, classifications, morphology, life cycles, and habitats of various types of weeds that grow on plantation land. Weeds e-Catalog can be used by plantation practitioners and farmers to make decisions in controlling weeds.

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An integrated system of mobile application and IoT solution for pond monitoring

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Ferdinan Darmalim Alam Ahmad Hidayat Tjeng Wawan Cenggoro Kartika Purwandari Sutristo Darmalim Bens Pardamean NusaNet Jakarta Indonesia 12710 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The current progress of information and communication technology has been critical to the development of the Internet of Things (IoT) in a aquaculture system. IoT solution in this system utilizes interconnectivity between devices to obtain fish pond monitoring data from sensing devices and then transmit the data to a remote server to be analyzed for decision-making in pond management. In this paper, we propose a smart pond IoT solution that provides a continuous and real-time pond monitoring. The system principally embeds five sensors that measure critical environmental parameters to determine water quality: temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), and salinity. The IoT solution also integrates the development of a mobile application on the android platform that allows remote monitoring capabilities for users to maintain efficient pond management. The application is designed with a friendly-user interface that helps the farmer to monitor sensor measurements, manage fish production cycle, record fish feeding schedule, and also monitor the health status of fish from multiple ponds.

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Fast and Effective Clustering Method for Ancestry Estimation

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Procedia Computer science 2019年 157卷 306-312页

作者： Arif Budiarto Bharuno Mahesworo James Baurley Teddy Suparyanto Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 BioRealm LLC USA Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Ancestry estimation which provides family history information is one of the most popular services in direct-to-consumer genomic testing. It is also an important task which aimed to reduce the confounding by ancestry on the relationship of genotypes and disease risk in assocation studies. Several methods have been developed to generate the best ancestry estimated scores even though some of them are still facing inefficient computation time. In this paper, a combination method between KMeans clustering and PCA is proposed estimate ancestry estimation from SNP genotyping data. This method was compared with baseline model, called fastSTRUCTURE, in term of the quality of clustering and computation time. Public data from 1000 Genome project is used to train and evaluate the proposed model and the baseline model. The proposed model can successfully generate clusters with better accuracy than fastSTRUCTURE (91.02% over 90.39%). More importantly, it can boost the computation time until 100 times faster than fastSTRUCTURE (from 490 seconds to 4.86 seconds).

关键词： Ancestry Estimation Population Stratification Clustering Bioinformatics Genomics

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