检索结果-内蒙古大学图书馆

Evaluation of meningitis surveillance system in rural area, Rwanda

Scientific Reports 2025年第1期15卷 1-9页

作者： Nyinawabeza, Solange Niyoyita, Jean Claude Nshimiyimana, Emmanuel Ndayisenga, Jerome Umutoni, Angela Stamatakis, Caroline Kibogora Level Two Teaching Hospital Nyamasheke Rwanda Field Epidemiology Training Program Public Health Surveillance & Emergency Preparedness and Response Division Rwanda Biomedical Center (RBC) Kigali Rwanda African Field Epidemiology Network Kigali Rwanda Research Innovation and Data Science Division Rwanda Biomedical Centre (RBC) Kigali Rwanda Division of Global HIV & TB Centers for Disease Control and Prevention Kigali Rwanda

Bacterial meningitis is a significant public health concern, with over 1.2 million cases reported globally each year. Rwanda is at increased risk of meningitis outbreaks due to its proximity to countries that lie in the meningitis belt. Rwanda has been conducting surveillance and recording meningitis outbreak cases across the country since 2012. We evaluated the meningitis surveillance system at Kibogora Level Two Teaching hospital, Nyamasheke district of Rwanda to assess whether the surveillance objectives were being met. The study was cross-sectional, using purposive sampling to select healthcare providers participating in the meningitis surveillance. Rwanda’s bacterial meningitis data from 2017 to 2021 was collected from clinical registers and Rwanda’s electronic integrated disease surveillance system (eIDSR) from Kibogora Level Two Teaching Hospital catchment area, Nyamasheke district, Rwanda. The study area was chosen because a meningitis outbreak was recorded in the area and its bordering country namely Democratic of Republic of Congo (DRC) prior to the current study period. Information on the participant’s demographics, occupation, training, professional experience, and their perception on the surveillance system were gathered using a structured questionnaire. Meningitis surveillance systems attributes including usefulness, acceptability, and flexibility were assessed and categorized as poor (< 50% score), reasonable (50–69%), good (70–90%), or excellent (> 90%) in reference to the study conducted on the evaluation of the meningitis surveillance system in Luanda Province, Angola in March 2017. data collected from clinical registers and eIDSR were used to assess core functions of the meningitis surveillance system including accuracy in detection of cases, laboratory confirmation of cases, and availability of evaluation reports. Descriptive statistics were analyzed using Microsoft Office Excel. Thirty-one healthcare providers working on meningitis surveillance

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Ensemble-Based Weight-Learning Framework and Algorithm for Imputing Missing Values on High-Dimension Low-Sample Size datasets 23

An Ensemble-Based Weight-Learning Framework and Algorithm fo...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ngan, Chun-Kit Yang, Boyuan Worcester Polytechnic Institute Data Science Program WorcesterMA United States

ISBN: (纸本)9781665494571

High-Dimension, Low-Sample Size (HDLSS) datasets are the datasets that their sample sizes are much smaller than the number of feature dimensions. In spite of that, HDLSS datasets appear in many domains and organizations, including social work, medical fields, international travels, and chemical industry. As many HDLSS datasets are very difficult to be collected, their missing values raise many issues and reduce reliability on the datasets to solve a domain and an organizational problem. To address the issues of the missing values in HDLSS datasets, we propose an Ensemble-Based Weight-Learning (EBWF) framework. Given HDLSS datasets and available Machine Learning (ML) classifiers, the EBWF framework is an automated pipeline that can generate the best imputed dataset. To support the EBWF framework, we develop a novel Record Probability Weight Learning (RPWL) imputation algorithm that combines the data record probability calculation from each ML classifier and weight learning computations among all the ML classifiers to impute the missing values in the dataset. To show the effectiveness of our approach, we conduct an experimental case study and evaluation on a survey dataset, i.e., 212 international travelers in Houston for assessing sexual risk-taking behaviors, as a pilot study, where Houston is a critical transit point frequented by international visitors. Based upon the travelers' survey data, where the survey records' missing rate is in the range of 10% to 70%, and the imputed dataset generated by our approach, we show that the classification performance delivered by our framework and algorithm outperform the other baseline approaches'. © 2021 IEEE.

关键词： Chemical industry Machine learning algorithms Pipelines Organizations Machine learning Probability Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Utilization of K-Means Clustering to Examine Library User Segmentation's Impact on Student Graduation Rates 2

Utilization of K-Means Clustering to Examine Library User Se...

引用

2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Ritonga, Ihdi Syahputra Candra, Ade Budiman, Mohammad Andri Universitas Sumatera Utara Data Science and Artificial Intelligence Program Medan Indonesia

ISBN: (纸本)9798350351613

This research aims to optimize library services through user segmentation analysis and to determine the correlation between user activities and student graduation levels using the K-Means clustering algorithm. By analyzing this relationship, the study seeks to enhance both library service effectiveness and student academic success. The methodology involves several stages: data collection, preprocessing (including data merging and cleaning), and data modeling. The K-Means clustering method is specifically used to segment library users based on various factors and to identify patterns that correlate with student graduation levels. The dataset comprises circulation data and student graduation data from the UIN Padangsidimpuan Academic Information System (SIAKAD), including variables such as study duration, Grade Point Average (GPA), and loan levels. The analysis resulted in the identification of three distinct clusters: Cluster 0, Cluster 1, and Cluster 2. These clusters are characterized by different lengths of study, GPA, and library loan levels, providing insights into the preferences and needs of library users. The findings suggest that tailored library services, informed by these clusters, can potentially enhance student engagement and academic performance. For example, libraries could develop specialized discussion services related to student research, thereby improving thesis quality and research outcomes. Additionally, optimizing digital-based library services can further support student success. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation

引用

Molecular Cell 2025年第10期85卷 1968-1981.e7页

作者： Kim, GyeungYun Carroll, Christine L. Wakefield, Zachary Peters Tuncay, Mustafa Fiszbein, Ana Biology Department Boston University Boston 02215 United States Graduate Program in Molecular Biology Cell Biology & Biochemistry Boston University Boston 02215 United States Graduate Program in Cell and Molecular Biology Boston University Boston 02215 United States Bioinformatics Program Faculty of Computing and Data Science Boston University Boston 02215 United States Center for Computing & Data Sciences Boston University Boston 02215 United States

Emerging evidence indicates that splicing factors mediate the close link between transcription and splicing. However, the mechanisms underlying this coupling remain unclear. U1 small nuclear ribonucleoprotein particle (U1 snRNP) not only initiates splicing but also plays a crucial role in preventing premature cleavage and polyadenylation, facilitating long-distance transcriptional elongation. Here, we show that U1 snRNP regulates alternative promoter activity in human cells by inhibiting premature polyadenylation. In genes carrying premature polyadenylation sites between two promoters, U1 snRNP inhibition with antisense oligonucleotides leads to a significant decrease in downstream promoter activity. Conversely, restoring U1 snRNP activity or inhibiting premature polyadenylation rescues downstream promoter activity. Mechanistically, U1 snRNP inhibition correlates with reduced chromatin accessibility, decreased RNA polymerase II serine 5 phosphorylation, and increased promoter-proximal pause at downstream promoters. Our findings support a model in which U1 snRNP favors productive elongation from upstream promoters, triggering downstream promoter activation by destabilizing nucleosomes and promoting promoter escape. © 2025 The Author(s)

关键词： alternative promoters dowsntream promoters promoter-proximal pause splicing factors splicing transcription transcription cooperativity transcription initiation transcription interfierence transcription start sites U1 snRNP

来源：评论

学校读者我要写书评

暂无评论

Herb compounds screening as meningitis inhibitor candidates using neural network and random forest methods

引用

AIP Conference Proceedings 2025年第1期3250卷

作者： Riska Aprilia Mohammad Hamim Zajuli Al Faroby Muhammad Adib Kamali Muhammad Dzulfikar Fauzi Information Technology Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Data Science Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Informatics Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia

Meningitis is an inflammation of the meninges that occurs in the protective lining of the brain and spinal cord caused by bacterial, viral, or fungal infections. This disease is difficult to recognize because it has initial symptoms like the flu where the patient has a fever and headache. Current efforts to prevent the disease by strengthening antibodies. Meanwhile, drug candidates for the treatment of this disease still have not found optimal results in reducing mortality due to meningitis. This study aims to find and analyses herbal compound candidates that might be inhibitors of meningitis. Compound data was acquired from a validated open database. The data acquired are smiles of the chemical bond structure of the compounds. In the data processing process, compound feature extraction is required by applying the concept of molecular fingerprint. The results of feature extraction are used as datasets to build classification models by applying the Multilayer Perceptron (MLP) and Random Forest algorithms. The two models are compared, and a more robust model is selected to be used as a prediction model for herbal compound search. The MLP model has a better accuracy of 0.97 compared to the Random Forest model. The results of screening using the MLP learning model obtained Symphytine, cis-Linalool oxide, and 3-O-Methylcalopocarpin compounds have the highest probability compared to thousands of other herbal compounds. This candidate compound can be used as a recommendation for drug discovery to treat patients who contract Meningitis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep features based convolutional neural network to detect and automatic classification of white blood cells

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9191-9205页

作者： Yentrapragada, Divyateja Student of Postgraduate Program in Data Science and Machine Learning Graham School University of Chicago and Jigsaw Academy ChicagoIL60637 United States

White blood cells (WBCs) play an important role in the human body's immune system and it widely presented in the human body. Recently, the incidence of blood diseases related to WBC increases in the human body. In the research application, optimal WBC is a popular field for the diagnosis of blood disease, the optimal WBC count gives useful information. The Deep Features Based Convolutional Neural Network (DFCNN) is created in this research to detect the count of WBC and automated categorization using CNN from the picture database. With three phases, the proposed method working are feature extraction, feature selection, and classification phases. In the extraction phase, the Combined CNN structure is designed which is a combination of AlexNet, GoogLeNet, and ResNet-50 respectively. The combined CNN architecture is utilized to extract 3000 essential features from the image database. In the feature selection phase, the hybrid Mayfly Algorithm with Particle Swarm Optimization (HMA-PSO) is designed for selecting the essential feature from the feature sets. In the HMA-PSO algorithm, with the help of PSO algorithm, the velocity updating of the mayfly is achieved. The proposed classifier named Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), collects the selected features. This classifier is utilized to classify the WBC types such as Neutrophils, Eosinophils, Monocytes, and Lymphocytes respectively. The proposed method is implemented in the platform of MATLAB and performances are evaluated by statistical measurements such as precision, recall, specificity, accuracy, and F_Measure. The results of the proposed method are compared with the other existing methods such as MA-RNN and PSO-RNN respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Automated Adversarial-Attack Removal with SafetyNet Using ADGIT 10

Automated Adversarial-Attack Removal with SafetyNet Using AD...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Halim, Steven Anandi, Arvio Devin, Vyos Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Cosmetics Recommendation System Based on Ingredients Using Cosine Similarity 7

Cosmetics Recommendation System Based on Ingredients Using C...

引用

7th International Conference on data science and Its Applications, ICoDSA 2024

作者： Alisha Firdaus, Sekar Dwi Nurmuthia, Laksmi Imtiyaz, Raina Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365351

In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare. © 2024 IEEE.

关键词： Skin care products

来源：评论

学校读者我要写书评

暂无评论

Low rates of chronic graft-versus-host disease with ruxolitinib maintenance following allogeneic HCT

引用

Blood 2025年第20期145卷 2312-2316页

作者： DeFilipp, Zachariah Kim, Haesook T. Knight, Laura W. Dhaver, Shilton E. White, Meghan Dholaria, Bhagirathbhai Schroeder, Mark A. Vasu, Sumithira Abedin, Sameem Chung, Jooho El-Jawahri, Areej Frigault, Matthew J. McAfee, Steven Newcomb, Richard A. Spitzer, Thomas R. Chen, Yi-Bin Hobbs, Gabriela S. Hematopoietic Cell Transplant and Cellular Therapy Program Massachusetts General Hospital Boston MA United States Department of Data Science Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health Boston MA United States Cancer Center Protocol Office Massachusetts General Hospital Boston MA United States Department of Hematology-Oncology and Stem Cell Transplantation Vanderbilt University Medical Center Nashville TN United States Bone Marrow Transplantation and Leukemia Section Division of Oncology Washington University School of Medicine in Saint Louis Saint Louis MO United States Division of Hematology Department of Internal Medicine The Ohio State University Columbus OH United States Division of Hematology/Oncology Department of Medicine Medical College of Wisconsin Milwaukee WI United States Center for Leukemia Massachusetts General Hospital Boston MA United States

Despite recent advances in graft-versus-host disease (GVHD) prophylaxis, novel approaches to effective prevention of chronic GVHD (cGVHD) remain of high importance. In this prospective, multicenter, phase 2 trial, ruxolitinib, an oral inhibitor of Janus kinase (JAK) 1 and 2, was administered as maintenance therapy after reduced-intensity allogeneic hematopoietic cell transplantation (HCT). GVHD prophylaxis consisted of tacrolimus and methotrexate. Ruxolitinib began between day +30 to 100 and was administered continuously in 28-day cycles for up to 24 cycles. Seventy-eight participants were enrolled before HCT;63 participants received the intervention. The median start date of ruxolitinib after HCT was day +45. The most common grade ≥3 adverse events were neutropenia, thrombocytopenia, and anemia. Seven participants experienced grade ≥3 infectious events. GVHD-free, relapse-free survival at 1 year after HCT, the primary end point, was 70%. Grade 3 to 4 acute GVHD at 6 months was 4.8%, and moderate-severe cGVHD at 2 years was 16%. cGVHD requiring systemic therapy was 9.5% at 1 year and 13% at 2 years. Overall survival and progression-free survival at 2 years were 76% and 68%, respectively. Prolonged administration of ruxolitinib following HCT is associated with low rates of clinically significant cGVHD. The incorporation of JAK inhibition into GVHD prevention approaches warrants further investigation. This trial was registered at *** as #NCT03286530. © 2025 American Society of Hematology

关键词：

来源：评论

学校读者我要写书评

暂无评论

Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

引用

arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：