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Factors Impacting Participatory Post‑Disaster Relocation and Housing Reconstruction:The Case of Tsholotsho District,Zimbabwe

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International Journal of Disaster Risk Science 2024年第1期15卷 58-72页

作者： Nombulelo Kitsepile Ngulube Hirokazu Tatano Subhajyoti Samaddar Department of Social informatics Graduate School of InformaticsKyoto UniversityKyoto 606-8507Japan Disaster Prevention Research institute(DPRI) Kyoto UniversityKyoto 611-0011Japan

Relocation is not typically considered the best planning option for post-disaster reconstruction and rehabilitation,but it may be necessary if the site has suffered severe damage or is at imminent *** is a growing recognition that strong community participation is necessary in the post-disaster relocation decision-making process since relocation can have detrimental effects on a community’s livelihood,cultural system,and way of life,among ***,the realization of this still needs to be *** of yet,few studies have examined a comprehensive account of meaningful community engagement in post-disaster relocation and reconstruction,particularly in developing *** study investigated what factors influenced local communities’ participation in post-disaster relocation and reconstruction works after the 2017Cyclone Dineo flood disaster in the Tsholotsho District of *** research methods such as face-to-face interviews,observations,and focus groups were used to collect qualitative data from a purposive sample of 25 community members and 6 *** empirical investigation showed that despite the fact that the relocation project was conceived as a community-centered project,there was no meaningful community engagement,due to the absence of a participatory framework or planning guidelines for stakeholder engagement,as well as the lack of political willingness among government *** study concluded that the lack of community involvement led to local communities abandoning the reconstruction sites because relocation projects failed to accommodate the cultural beliefs,place attachments,and livelihood concerns of local *** study suggested that it is imperative to enhance the awareness of government officials and other stakeholders about the importance of community participation for the effective implementation of post-disaster relocation *** community participation can also provide avenues fo

关键词： Community rehabilitation Housing reconstruction-Participatory relocation Zimbabwe

Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer:a comprehensive comparative study

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Intelligent Medicine 2024年第2期4卷 114-127页

作者： Ao Chen Chen Li Md Mamunur Rahaman Yudong Yao Haoyuan Chen Hechen Yang Peng Zhao Weiming Hu Wanli Liu Shuojia Zou Ning Xu Marcin Grzegorzek Microscopic Image and medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110169China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Arts and Design Liaoning Petrochemical UniversityFushunLiaoning 113001China institute of medical informatics University of LübeckLüebeckGermany

Background With the gradual increase of infertility in the world,among which male sperm problems are the main factor for infertility,more and more couples are using computer-assisted sperm analysis(CASA)to assist in the analysis and treatment of ***,the rapid development of deep learning(DL)has led to strong results in image classification ***,the classification of sperm images has not been well studied in current deep learning methods,and the sperm images are often affected by noise in practical CASA *** purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm *** The SVIA dataset is a publicly available large-scale sperm dataset containing three *** this work,we used subset-C,which provides more than 125,000 independent images of sperms and impurities,including 121,401 sperm images and 4,479 impurity *** investigate the anti-noise robustness of deep learning classification methods applied on sperm images,we conducted a comprehensive comparative study of sperm images using many convolutional neural network(CNN)and visual transformer(VT)deep learning methods to find the deep learning model with the most stable anti-noise *** This study proved that VT had strong robustness for the classification of tiny object(sperm and impurity)image datasets under some types of conventional noise and some adversarial *** particular,under the influence of Poisson noise,accuracy changed from 91.45%to 91.08%,impurity precison changed from 92.7%to 91.3%,impurity recall changed from 88.8%to 89.5%,and impurity F1-score changed 90.7%to 90.4%.Meanwhile,sperm precision changed from 90.9%to 90.5%,sperm recall changed from 92.5%to 93.8%,and sperm F1-score changed from 92.1%to 90.4%.Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods;the robustness with regard to noise of VT methods based

关键词： Computer-assisted sperm analysis Anti-noise Robustness Deep learning .Image classification Sperm image Conventional noise Adversarial attacks Convolutional neural network Visual transformer

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

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Military medical Research 2024年第1期11卷 115-147页

作者： Yuan-Peng Zhang Xin-Yun Zhang Yu-Ting Cheng Bing Li Xin-Zhi Teng Jiang Zhang Saikit Lam Ta Zhou Zong-Rui Ma Jia-Bao Sheng Victor CWTam Shara WYLee Hong Ge Jing Cai Department of medical informatics Nantong UniversityNantong 226001JiangsuChina Department of Health Technology and informatics the Hong Kong Polytechnic UniversityHong Kong 999077China The Hong Kong Polytechnic University Shenzhen Research institute Shenzhen 518000GuangdongChina Department of Radiation Oncology the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer HospitalZhengzhou 450008China

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’***,the interpretation of medical images can be highly subjective and dependent on the expertise of ***,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical *** contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical *** have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized ***,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical *** this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with *** focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical ***,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of ***,we offer possible solutions to current challenges in radiomics research.

关键词： Artificial intelligence Radiomics Feature extraction Feature selection Modeling Interpretability Multimodalities Head and neck cancer

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

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Chinese Journal of Chemical Engineering 2024年第6期70卷 251-260页

作者： Xiaoxiao Dong Zhuohong He Xu Yan Dong Gao Jingyu Jiao Yan Sun Haibin Wang Haibin Qu Pharmaceutical informatics institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Hisun Biopharmaceutical Co.Ltd. Hangzhou 311404China

Raman spectroscopy has found extensive use in monitoring and controlling cell culture *** this context,the prediction accuracy of Raman-based models is of paramount ***,models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled ***,there is a need for effective methods to rectify these *** objective of this paper is to investigate the efficacy of Kalman filter(KF)algorithm in correcting Raman-based models during cell ***,partial least squares(PLS)models for different components were constructed using data from manually fed-batch cultures,and the predictive performance of these models was ***,various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS ***,a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction *** results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman-controlled *** glucose,the root mean square error of prediction(RMSEP)of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L^(-1).With the implementation of model correction methods,there was a significant improvement in model performance within Raman-controlled *** RMSEP for glucose from updating-PLS,KF-PLS,and PLS-KF-KF was 0.38,0.36 and 0.17 g·L^(-1),***,the proposed PLS-KF-KF model correction method was found to be more effective and stable,playing a vital role in the automated nutrient feeding of cell cultures.

关键词： Raman spectroscopy Model correction Algorithm Model-predictive control Bioprocess

Ferroptosis mediators vary in metabolic syndrome,type-2 diabetes,and hypercholesterolemia:A meta-analysis report

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Genes & Diseases 2024年第3期11卷 109-112页

作者： Indra Kundu Ashlesha Pande Tannishtha Das Susan Idicula-Thomas Biomedical informatics Centre Indian Council of Medical Research-National Institute for Research in Reproductive and Child HealthMumbaiMaharashtra 400012India Department of Biotechnology St.Xavier's CollegeKolkataWest Bengal 700016India

Metabolic syndrome(MetS)is a complex disorder characterized by the coexistence of phenotypes such as obesity,hypertension,hyperglycemia,high triglyceride level,and low level of high-density lipoprotein *** majorly dri... 详细信息

关键词： obesity cholesterol diabetes

Design and optimization of a greener sinomenine hydrochloride preparation process considering variations among different batches of the medicinal herb

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Chinese Journal of Chemical Engineering 2024年第7期71卷 77-90页

作者： Dandan Ren Jiale Xie Tianle Chen Haibin Qu Xingchu Gong Pharmaceutical informatics institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Zhejiang UniversityHangzhou 310058China Jinhua institute of Zhejiang University Jinhua 321016China

The current methods used to industrially produce sinomenine hydrochloride involve several issues,including high solvent toxicity,long process flow,and low atomic utilization efficiency,and the greenness scores of the processes are below 65 *** solve these problems,a new process using anisole as the extractant was *** exhibits high selectivity for sinomenine and can be connected to the subsequent water-washing *** alkalization of the medicinal material,heating extraction,water washing,and acidification crystallization were carried *** process was modeled and *** design space was *** recommended operating ranges for the critical process parameters were 3.0–4.0 h for alkalization time,60.0–80.0℃ for extraction temperature,2.0–3.0(volume ratio)for washing solution amount,and 2.0–2.4 mol·L^(-1) for hydrochloric acid *** new process shows good robustness because different batches of medicinal materials did not greatly impact crystal purity or sinomenine transfer *** sinomenine transfer rate was about 20%higher than that of industrial *** greenness score increased to 90 points since the novel process proposed in this research solves the problems of long process flow,high solvent toxicity,and poor atomic economy,better aligning with the concept of green chemistry.

关键词： Sinomenine hydrochloride Process optimization Anisole

A novel neuroprotective method against ischemic stroke by accelerating the drainage of brain interstitial fluid

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Science China Life Sciences 2024年第10期67卷 2213-2223页

作者： Jingge Lian Liu Yang Hanbo Tan Shaoyi Su Leonor Serrano Lopes Fangxiao Cheng Junhao Yan Yu Fu Wanyi Fu Zhaoheng Xie Jianfei Sun Jicong Zhang Zhiqian Tong Yajuan Gao Hongbin Han Department of Radiology Peking University Third Hospital Beijing Key Laboratory of Magnetic Resonance Imaging Technology institute of medical Technology Peking University Health Science Center Department of informatics Technical University of Munich Department of Anatomy and Histology School of Basic Medical SciencesPeking University Department of Neurology Peking University Third Hospital Department of Electronic Engineering Tsinghua University State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast University School of Biological Science and medical Engineering Beihang University Key Laboratory of Alzheimer's Disease of Zhejiang Province Institute of AgingSchool of Mental HealthWenzhou Medical University NMPA Key Laboratory for Evaluation of medical Imaging Equipment and Technique

Ischemic stroke is a leading cause of death and disability worldwide. Inflammatory response after stroke determines the outcome of ischemic injury. A recent study has reported an efficient method, epidural arterial implantation(EAI), for accelerating interstitial fluid(ISF)drainage, which provides a promising strategy to clear pro-inflammatory cytokines in the brain extracellular space(ECS). In this study, the method of EAI was modified(m-EAI) to control its function of accelerating the ISF drainage at different time points following ischemic attack. The neuroprotective effect of m-EAI on ischemic stroke was evaluated with the transient middle cerebral artery occlusion(tMCAO)rat model. The results demonstrated the accumulation of IL-1β, IL-6, and TNF-α was significantly decreased by activating m-EAI at 7 d before and immediately after ischemic attack in tMCAO rats, accompanied with decreased infarct volume and improved neurological function. This study consolidates the hypothesis of exacerbated ischemic damage by inflammatory response and provides a new perspective to treat encephalopathy via brain ECS. Further research is essential to investigate whether m-EAI combined with neuroprotective drugs could enhance the therapeutic effect on ischemic stroke.

关键词： ischemic stroke brain extracellular space interstitial fluid modified epidural arterial implantation

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A multi-stream network for retrosynthesis prediction

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Frontiers of Computer Science 2024年第2期18卷 231-233页

作者： Qiang ZHANG Juan LIU Wen ZHANG Feng YANG Zhihui YANG Xiaolei ZHANG institute of Artificial Intelligence School of Computer ScienceWuhan UniversityWuhan 430072China College of informatics Huazhong Agricultural UniversityWuhan 430072China

1 Introduction Retrosynthesis aims to predict a set of reactants for producing given molecules[1],which plays a significant part in the biochemistry field,such as molecular pathway design and drug *** existing methods perform better in solving this problem,most of these methods[2,3]only describe the molecules from one kind of perspective,such as they use Simplified Molecular Input Line Entry System(SMILES)strings[4]to represent the atomic components and the relationship between atoms of molecules or leverage Extended Connectivity Fingerprints(ECFPs)represent the information of the molecular sub-structure composition via binary vectors[5].

关键词： synthesis reactant hough

Graph based recurrent network for context specific synthetic lethality prediction

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Science China Life Sciences 2024年

作者： Yuyang Jiang Jing Wang Yixin Zhang ZhiWei Cao Qinglong Zhang Jinsong Su Song He Xiaochen Bo Academy of medical Engineering and Translational Medicine Tianjin University Department of Bioinformatics Institute of Health Service and Transfusion MedicineBeijing School of Medicine Tsinghua University School of informatics Xiamen University

The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more SL interactions with therapeutic potential are essential. Recently, graph neural network-based deep learning methods have been proposed for SL prediction, which reduce the SL search space of wet-lab based methods. However, these methods ignore that most SL interactions depend strongly on genetic context, which limits the application of the predicted results. In this study, we proposed a graph recurrent network-based model for specific context-dependent SL prediction (SLGRN). In particular, we introduced a Graph Recurrent Network-based encoder to acquire a context-specific, low-dimensional feature representation for each node, facilitating the prediction of novel SL. SLGRN leveraged gate recurrent unit (GRU) and it incorporated a context-dependent-level state to effectively integrate information from all nodes. As a result, SLGRN outperforms the state-of-the-arts models for SL prediction. We subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival analysis. Through in vitro experiments and retrospective clinical analysis, we emphasize the potential clinical significance of this context-specific SL prediction model.

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