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Factors associated with mortality in thoracic trauma patients in Cameroon

Heliyon 2025年第1期11卷 e41499页

作者： Kibu, Odette Dzemo Nguefack-Tsague, Georges Maqungo, Sithombo Ngekeng, Serge Dissak Delon, Fanny Nadia Touko, Darwin Oke, Rasheedat Umoh, Cindy Mfondoum, Roland Christie, Sabrinah Ariane Juillard, Catherine Chichom-Mefire, Alain Department of Public Health and Hygiene Faculty of Health Sciences University of Buea Buea Cameroon Department of Public Health Faculty of Medicine and Biomedical Sciences University of Yaoundé I Yaoundé Cameroon Department of Surgery Division of Global Surgery and Division of Orthopaedic Surgery University of Cape Town and Groote Schuur Hospital South Africa Data Science Center for the Study of Surgery Injury and Equity in Africa (D-SINE-Africa) University of Buea Cameroon Department of Surgery Program for the Advancement of Surgical Equity University of California Los Angeles CA United States Faculty of Health Sciences Sustainable Trauma Research Education and Mentorship (STREaM) Project University of Buea Cameroon Department of Surgery Faculty of Health Sciences University of Buea Buea Cameroon

Background: Thoracic trauma is the third most common cause of death after abdominal injury and head trauma in polytrauma patients. The identification of a patient at risk of thoracic trauma mortality is necessary to avoid delays that may lead to morbidity and mortality. Therefore, the objective was to assess the factors associated with mortality among thoracic trauma patients in Cameroon. Materials and methods: This is a retrospective analysis of the Cameroon Trauma Registry currently running in 10 pilot hospital sites across seven of the ten regions. We retrieved data for all patients with thoracic trauma between the period June 2022 to May 2023 and analyzed factors associated to mortality using multivariate logistic regression. Statistical significance was set at p-value <0.05. Results: Out of 4851 trauma patients, 642 (13.2 %) presented with thoracic trauma. Over 80 % were males and the majority [168 (26.2 %)] were within the age group of 25–34 years. The most common injury mechanism was Road Traffic Injury (RTI). Overall thoracic trauma mortality rate was 9 % with road traffic Injury being the commonest mechanism. Factors associated to thoracic trauma mortality were abnormal chest movement and respiration [(AOR = 5.18, 95 % CI = 1.89–14.19, P = 0.001) and (AOR = 4.72, 95 % CI = 1.44–15.42, P = 0.01) respectively]. Tracheal deviation, breath sounds and respiration were other factors associated with mortality. Conclusion: This analysis highlights thoracic trauma in Cameroon and its contribution to the overall injury death toll. Abnormal chest movement and respiration were factors associated with mortality among thoracic trauma patients. To improve the health outcomes, it is important to understand the treatment logic, involve thoracic surgeons and a multidisciplinary team to improve diagnosis and management. © 2024

关键词： Cameroon Mortality Risk factors Thoracic trauma

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Sequences analysis and phylogenetic of leukemia DNA

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Journal of Physics: Conference Series 2021年第1期1763卷

作者： D Lusiyanti N Nacong M I Irawan Mathematics Study Program of Science Faculty Tadulako University Indonesia Dapertemen Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia

DNA is one part of a biological sequence other than RNA and protein. DNA is a nucleic acid that stores all the unique biological information of every living thing and several viruses. Someone who has an illness can be seen in the structure of the DNA which can then be compared with other DNA structures. This process is part of sequence analysis which is the core of bioinformatics. This study aims to analyze the results of sequences alignment of cancer. The sequence was obtained from DNA leukemia taken from GenBank (***). In the sequence analysis, the most basic thing is the sequence alignment. Alignment results can provide information about the level of similarity in the structure of the two sequences. This study will apply the Super Pairwise Alignment method that is implemented in JAVA-based programming languages. Alignment results are carried out by giving different values of gap penalty and gap extend. The results of the study obtained different alignment values, this proves that there is a significant effect of the selection of core parameters used in alignment.

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The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging 23rd

The Constrained Network-Based Statistic: A New Level of Infe...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Noble, Stephanie Scheinost, Dustin Department of Radiology and Biomedical Imaging Yale University New HavenCT United States Department of Biomedical Engineering Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States Interdepartmental Neuroscience Program Yale University New HavenCT United States Child Study Center Yale University New HavenCT United States

ISBN: (纸本)9783030597276

Neuroimaging research aimed at dissecting the network organization of the brain is poised to flourish under major initiatives, but converging evidence suggests more accurate inferential procedures are needed to promote discovery. Inference is typically performed at the cluster level with a network-based statistic (NBS) that boosts power by leveraging known dependence within the local neighborhood. However, existing NBS methods overlook another important form of dependence—shared membership in large-scale brain networks. Here, we propose a new level of inference that pools information within predefined large-scale networks: the Constrained Network-Based Statistic (cNBS). We evaluated sensitivity and specificity of cNBS against existing standard NBS and threshold-free NBS by resampling task data from the largest openly available fMRI database: the Human Connectome Project. cNBS was most sensitive to effect sizes below medium, which accounts for the majority of ground truth effects. In contrast, threshold-free NBS was most sensitive to higher effect sizes. Ground truth maps showed grouping of effects within large-scale networks, supporting the relevance of cNBS. All methods controlled FWER as intended. In summary, cNBS is a promising new level of inference for promoting more valid inference, a critical step towards more reproducible discovery in neuroscience. © 2020, Springer Nature Switzerland AG.

关键词： Statistics

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Characterization and Classification of Purity of Limestone in Madura Island for Industrial Application

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Journal of Physics: Conference Series 2021年第1期1951卷

作者： F Munawaroh L K Muharrami Triwikantoro Z Arifin Natural Science Education Study Program Universitas Trunojoyo Madura Bangkalan Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The characterization and classification of purity of limestone at Madura Island was investigated. Sampling was taken from nine quarries from different areas. The chemical analysis was carried out by X-ray fluorescence (XRF); the crystalline phase was characterized by X-ray diffraction (XRD). Analysis of purity classification based on XRF and XRD results according to British Geological Survey. Limestone has different purity, there are very high, medium, low and impure purity. The sample has a very high purity from Pamekasan (P.1) with CaO content of 99.06 wt% and 100% calcite. The samples are medium purity from Bangkalan (B.1, B.3), Pamekasan (P.2), Sumenep (S.1, and S.3) with CaO content of 91.61- 93.67 wt%. The samples are low purity from S.2 with CaO content 85.79 wt% and 100% dolomite. And the sample has impurities from P.3 and B.2 with CaO content 82.3 wt% and 84.7 wt%. Limestone with very high and medium purity which is processed into PCC can be applied in many industries as filler in rubber, paper making, plastic, paint, food, pharmaceutical, ceramic, adhesives, sealants, agriculture, and animal feed.

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Categorization and analysis of 14 computational methods for estimating cell potency from single-cell RNA-seq data

arXiv

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arXiv 2023年

作者： Wang, Qingyang Zhai, Zhiqian Lian, Qiuyu Song, Dongyuan Li, Jingyi Jessica Department of Statistics and Data Science University of California Los AngelesCA90095-1554 United States Wellcome Trust Cancer Research UK Gurdon Institute University of Cambridge Cambridgeshire CambridgeCB2 1QN United Kingdom Bioinformatics Interdepartmental Ph.D. Program University of California Los AngelesCA90095-7246 United States Department of Human Genetics University of California Los AngelesCA90095-7088 United States Department of Computational Medicine University of California Los AngelesCA90095-1766 United States Department of Biostatistics University of California Los AngelesCA90095-1772 United States Radcliffe Institute for Advanced Study Harvard University CambridgeMA02138 United States

In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden cellular dynamic processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring pseudotime trajectories and RNA velocities, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as the "cell potency." In this review, we provide a comprehensive summary of 14 computational methods that define cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: average-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition for each method. Moreover, we use a unified set of mathematical notations to detail the 14 methods’ methodologies and summarize their usage complexities, including the number of parameters, the number of required inputs, and the changes from the method description in publications to the method implementation in software packages. In conclusion, we regard cell potency estimation as an open question that lacks a consensus on the optimal approach, calling for benchmark datasets and studies. Through categorization and detailed description of existing methods, we aim to provide a solid foundation for future benchmark studies of cell potency estimation methods. A broader open challenge is to understand the comparative advantages of methods that infer cellular dynamics from scRNA-seq data using diverse perspectives, including pseudotime trajectories, RNA velocities, and cell potency. © 2023, CC BY-NC-ND.

关键词： Gene expression

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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The effect of organic acid hydrolysis on the properties of nanocellulose from edamame husk (Glycine max (L.) merill)

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Wahyudi, Muhammad Sapto Holilah Bahruji, Hasliza Prasetyoko, Didik Asranudin Pratama, Agus Wedi Indriani, Dina Wahyu Suryanegara, Lisman Faradilla, RH Fitri Mahardika, Melbi Wardani, Ratih Kusuma Piluharto, Bambang Knight, Victor Feizal Norrrahim, Mohd Nor Faiz Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang Indonesia Study Program of Chemistry Faculty of Science and Technology UIN Sunan Kalijaga Yogyakarta Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center for Applied Microbiology National Research and Innovation Agency (BRIN) Cibinong Indonesia Department of Biosystem Engineering Faculty of Agricultural Technology Universitas Brawijaya Jalan Veteran Malang Indonesia Department of Food Science and Technology Faculty of Agriculture Universitas Halu Oleo Kendari Indonesia Academy Pharmacy of Surabaya Ketintang Gayungan Surabaya 60231 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences University of Jember Jember Indonesia Research Center for Chemical Defence Defence Research Institute Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur 57000 Malaysia

The increasing demand for sustainable materials has highlighted nanocellulose as a promising candidate due to its exceptional mechanical strength, thermal stability, and superior barrier properties. These attributes enable its application in diverse fields, including packaging, biomedical devices, and nanocomposites. This study investigates the valorization of edamame bean husks, a byproduct of the globally consumed legume snack, as a sustainable feedstock for nanocellulose production. Cellulose was extracted from the husks via delignification and bleaching, followed by a green and facile acid hydrolysis method employing organic acids (maleic acid, lactic acid, citric acid, and formic acid) to produce nanocellulose. The resulting nanocellulose exhibited a fibrillar morphology with particle sizes ranging from 7.59 to 17.51 nm, depending on the acid used. X-ray diffraction analysis revealed a crystallinity index ranging from 63.49 % to 67.24 %. Notably, acid hydrolysis significantly enhanced the thermal stability of the nanocellulose compared to both the raw husk and isolated cellulose. These findings demonstrate the potential of edamame agricultural waste as a valuable resource for sustainable and eco-friendly nanocellulose production, contributing to a circular bioeconomy. © 2025 The Authors

关键词： Characterization Edamame husk waste Green synthesis Nanocellulose Organic acids

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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A model for Optimizing the health and economic impacts of Covid-19 under social distancing measures;A study for the number of passengers and their seating arrangements in Aircrafts

arXiv

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arXiv 2020年

作者： Ghorbani, Elaheh Molavian, Hamid Barez, Fred Department of Aviation and Technology Study San Jose State University San JoseCA95192 United States Magic Distance San JoseCA95126 United States Data Science Immersive Program Galvanize Inc. San FranciscoCA94105 United States

Covid-19 has had a disastrous economic impact on countries and industries as countries have gone through the lockdown process to reduce the health impact of Covid-19. As countries have begun the reopening phases, businesses have been allowed to have on-site customers but with reduced number of people on their sites to follow the social distancing measures. This has resulted in a big burden on the economic side of these businesses as their number of customers have decreased substantially. In this study we propose a model to minimize the economic impact of Covid-19 for businesses and organizations that have implemented social distancing measures as well as health impact of Covid-19 for their customers and employees. We introduce quantity Spread in which minimizing Spread gives the optimum number and arrangement of people on a given site with a social distancing measure. We apply our model to a real-world case and optimize the number of passengers and their arrangements under a social distancing measure for two different popular aircraft seat layouts using Annealing Monte Carlo technique. We obtain the optimum numbers and optimal arrangements of passengers considering both family groups and individual passengers for the social distancing measure. The obtained optimal arrangements of passengers show complex patterns with groups and individual passengers are mixed in complex and non-trivial ways. This demonstrates the necessity of using our model or its variants to find these optimal arrangements. Moreover, we show that any other arrangements of passengers with the same number of passengers is a suboptimal arrangement with higher health risks as a result of lower distance between passengers. Our model could be implemented to other social setups such as sports events, theaters, medical centers, etc. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Mahakala: a Python-based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Space-times

arXiv

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arXiv 2023年

作者： Sharma, Aniket Medeiros, Lia Chan, Chi-Kwan Halevi, Goni Mullen, Patrick D. Stone, James M. Wong, George N. Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar PO Manauli Punjab Mohali140306 India School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States CCS-2 Los Alamos National Laboratory Los AlamosNM United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States

We introduce Mahakala, a Python-based, modular, radiative ray-tracing code for curved space-times. We employ Google’s JAX framework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr metrics. JAX also enables Mahakala to run in parallel on both CPUs and GPUs and achieve speeds comparable to C-based codes. Mahakala natively uses the Cartesian Kerr-Schild coordinate system, which avoids numerical issues caused by the "pole" of spherical coordinates. We demonstrate Mahakala’s capabilities by simulating the 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature of Mahakala allows us to easily quantify the relative contribution of different regions of the flow to image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole. We also quantify the relative contribution of the disk, forward jet, and counter jet to 1.3 mm images. Copyright © 2023, The Authors. All rights reserved.

关键词： Python

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