Boosting adaptive linear weak classifiers for online learning and tracking

Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak classifiers/features to adapt with the change over time. We are proposing a new online boosting algorithm where the form of the weak classifiers themselves are modified to cope with scene changes. Instead of replacement, the parameters of the weak classifiers are altered in accordance with the new data subset presented to the online boosting process at each time step. Thus we may avoid altogether the issue of how many weak classifiers to be replaced to capture the change in the data or which efficient search algorithm to use for a fast retrieval of weak classifiers. A computationally efficient method has been used in this paper for the adaptation of linear weak classifiers. The proposed algorithm has been implemented to be used both as an online learning and a tracking method. We show quantitative and qualitative results on both UCI datasets and several video sequences to demonstrate improved performance of our algorithm. 1

Mental Health Issues in the COVID-19 Pandemic and Responses in Bangladesh : View Point of Media Reporting

Background: The negative impact of COVID-19 on mental health has been reported by media throughout the world, although this role is not well-understood in low-and middle income countries (LMIC). We examined the reporting of mental health issues during the COVID-19 pandemic in Bangladesh and initiatives undertaken to support mental health reported from the viewpoint of media. Methods: We reviewed articles published in 10 local newspapers, including seven Bangla and three English newspapers, during the first year of the COVID-19 pandemic. News topics were identified through discussions among the team members, with searches across online newspapers and portals. Data extrapolated from newspapers were documented in an Excel spreadsheet. A mixed-method approach was used following a framework analysis for analyzing data. Recurring issues and commonly emerging topics were generated from the data. Descriptive statistics were applied for analyzing quantitative data. Results: Between March 2020 and March 2021, we have identified 201 reports on mental health issues including 45 reports (22.4%) focused on stress due to the associated financial crisis, unemployment and loneliness, 50 reports (24.9%) of 80 apparent suicides linked to family issues, disharmony in conjugal relationships, harassment, sexual violence, emotional breakdown, financial crisis, and stigma due to COVID-19.There were 77 reports (38.3%) concerning domestic violence during the pandemic. Twenty-nine reports (14.4%) referenced actions taken by different organizations to address mental health issues in response to the pandemic in Bangladesh. Conclusion: News coverage has the scope to highlight important issues that can emerge as a consequence of the COVID-pandemic, such as mental health, in a low resource setting. Capacity building of the media on the way to report mental health issues during emergency situations could be a useful strategy for more credible reporting on mental health issues during the COVID-19 pandemic for raising awareness of the public and policymakers about the negative consequences on mental health of the COVID-19 pandemic in Bangladesh. Adopting policies to support essential mental health care and promoting the local organizations to take timely public health measures will be imperative for averting the negative consequences of mental health due to the COVID-19 pandemic in Bangladesh

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition in gene function pathways from nuclear to cytoplasmic to extracellular, corresponding with Pediatric Logistic Organ Dysfunction score (PELOD) readings at 0, 24, and 48 h. ANN was the most accurate of the six ML models applied for survival prediction. This study successfully correlated PELOD with transcriptomic data, mapping enriched GE modules in acute sepsis. By integrating network analysis methods to identify key gene modules and using machine learning for sepsis prognosis, this study offers valuable insights for precision-based treatment strategies in future research. The observed temporal-spatial pattern of cellular recovery in sepsis could prove useful in guiding clinical management and therapeutic interventions

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)–VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis

資源限定状況下における医療サービスアクセス向上のための変革的医療サービスシステムモデルの研究

Supervisor:白肌　邦生知識科学研究科博

Supervised Hypergraph Labeling

We address the problem of labeling individual datapoints given some knowledge about (small) subsets or groups of them. The knowledge we have for a group is the likelihood value for each group member to satisfy a certain model. This problem is equivalent to hypergraph labeling problem where each datapoint corresponds to a node and the each subset correspond to a hyperedge with likelihood value as its weight. We propose a novel method to model the label dependence using an Undirected Graphical Model and reduce the problem of hypergraph labeling into an inference problem. This paper describes the structure and necessary components of such model and proposes useful cost functions. We discuss the behavior of proposed algorithm with different forms of the cost functions, identify suitable algorithms for inference and analyze required properties when it is theoretically guaranteed to have exact solution. Examples of several real world problems are shown as applications of the proposed method. 1