PARAMETER ESTIMATION FOR LATENT MIXTURE MODELS WITHAPPLICATIONS TO PSYCHIATRY

Longitudinal and repeated measurement data commonly arise in many scientific researchareas. Traditional methods have focused on estimating single mean response as a function ofa time related variable and other covariates in a homogeneous population. However, in manysituations the homogeneity assumption may not be appropriate. Latent mixture modelscombine latent class modeling and conventional mixture modeling. They accommodate thepopulation heterogeneity by modeling each subpopulation with a mixing component. Inthis paper, we developed a hybrid Markov Chain Monte Carlo algorithm to estimate theparameters of the latent mixture model. We show through simulation studies that MCMCalgorithm is superior than the EM algorithm when missing value percentage is large.As an extension of latent mixture models, we also propose the use of cubic splines asa curve fitting technique instead of classic polynomial fitting. We show that this methodgives better fits to the data, and our MCMC algorithm estimates the model efficiently. Weapply the cubic spline technique to a data set which was collected in a study of alcoholism.Our MCMC algorithm shows several different P300 amplitude trajectory patterns amongchildren and adolescents.Other topics that are covered in this thesis include the identifiability of the latent mixturemodel and the use of such model to predict a binary outcome. We propose a bivariate versionof the latent mixture model, where two courses of longitudinal responses can be modeled atthe same time. Computational aspects of such models remain to be completed in the future

Crosslinked fluoropolymers exhibiting superior high-temperature energy density and charge-discharge efficiency

Superior high-temperature discharged energy densities in comparison to those of the current dielectric polymers have been demonstrated in the crosslinked fluoropolymers

FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching

The tracking of various fish species plays a profoundly significant role in understanding the behavior of individual fish and their groups. Present tracking methods suffer from issues of low accuracy or poor robustness. In order to address these concerns, this paper proposes a novel tracking approach, named FishMOT (Fish Multiple Object Tracking). This method combines object detection techniques with the IoU matching algorithm, thereby achieving efficient, precise, and robust fish detection and tracking. Diverging from other approaches, this method eliminates the need for multiple feature extractions and identity assignments for each individual, instead directly utilizing the output results of the detector for tracking, thereby significantly reducing computational time and storage space. Furthermore, this method imposes minimal requirements on factors such as video quality and variations in individual appearance. As long as the detector can accurately locate and identify fish, effective tracking can be achieved. This approach enhances robustness and generalizability. Moreover, the algorithm employed in this method addresses the issue of missed detections without relying on complex feature matching or graph optimization algorithms. This contributes to improved accuracy and reliability. Experimental trials were conducted in the open-source video dataset provided by idtracker.ai, and comparisons were made with state-of-the-art detector-based multi-object tracking methods. Additionally, comparisons were made with idtracker.ai and TRex, two tools that demonstrate exceptional performance in the field of animal tracking. The experimental results demonstrate that the proposed method outperforms other approaches in various evaluation metrics, exhibiting faster speed and lower memory requirements. The source codes and pre-trained models are available at: https://github.com/gakkistar/FishMO

Knowledge, attitudes, and practices associated with bioterrorism preparedness in healthcare workers: a systematic review

IntroductionBioterrorism is an important issue in the field of biosecurity, and effectively dealing with bioterrorism has become an urgent task worldwide. Healthcare workers are considered bioterrorism first responders, who shoulder essential responsibilities and must be equipped to deal with bioterrorism. This study aims to extract and summarize the main research components of the bioterrorism knowledge, attitude, and practice dimensions among healthcare workers.MethodThis study utilized a systematic review research design based on the PRISMA 2020 guidelines. A literature search was conducted in the PubMed, Web of Science, and Scopus databases for peer-reviewed literature, and the Mixed Methods Appraisal Tool (MMAT) version 2018 was used to assess the quality of the literature.ResultA total of 16 studies were included in the final selection. Through the analysis and summary of the included studies, three main aspects and 14 subaspects of the knowledge dimension, three main aspects and 10 subaspects of the attitude dimension, and two main aspects and six subaspects of the practice dimension were extracted.ConclusionThis study conducted a literature review on bioterrorism knowledge, attitudes, and practices for healthcare workers based on the PRISMA 2020 guidelines. The findings can guide improvements in health literacy and provide beneficial information to professional organizations that need to respond effectively to bioterrorism

MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data

Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNA-seq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility. Consequently, analyzing scRNA-seq data remains a computational challenge. With the increasing availability of well-annotated scRNA-seq datasets, advanced methods are emerging to aid in cell type identification by leveraging this information. Deep learning neural networks have great potential for analyzing single-cell data. This paper proposes MulCNN, a multi-level convolutional neural network that uses a unique cell type-specific gene expression feature extraction method. This method extracts critical features through multi-scale convolution while filtering noise. Extensive testing using datasets from various species and comparisons with popular classification methods show that MulCNN has outstanding performance and offers a new and scalable direction for scRNA-seq analysis

JAG1 is correlated to suppressive immune microenvironment and predicts immunotherapy resistance in lung adenocarcinoma

BackgroundThe current exploration of the tumor immune microenvironment is enthusiastic, but few studies explored the impact of angiogenesis on the immune microenvironment. Immunotherapy combined with anti-angiogenesis therapy has become one of the first-line treatment for lung adenocarcinoma. Our study aimed to explore the reasons for resistance of immunotherapy, and explore markers for immunotherapy combined with anti-angiogenesis therapy.MethodsFirst, by unsupervised clustering of 36 angiogenesis-related genes in lung adenocarcinoma patients from TCGA database, AGS1 and AGS2 groups were distinguished with significantly different clinical outcomes. Secondly, the immune microenvironment and metabolic characteristics were analyzed. Next, we used the GDSC and GEO database to analyze therapeutic responses. Then, through multivariate Cox regression, the hub gene: JAG1, significantly related to prognosis was selected, and further verified by multi-omics data. Finally, we validated that patient with high JAG1 expression had a low immune-infiltrating tumor microenvironment through single-cell transcriptomic data.ResultsCompared with the AGS1 group, AGS2 showed an immune “cold” phenotype with lower lymphocyte infiltration, and was associated with worse prognoses. At the same time, the immunosuppressive TGF-β response was significantly higher in AGS2. Furthermore, the glycolysis ability of the AGS2 was stronger than AGS1. The expression of JAG1 was significantly higher in the AGS2, and was significantly negatively correlated with the degree of immune infiltration, accompanying with higher glycolytic capacity. The above results indicate that patients with high expression of JAG1 may lead to immunosuppressive phenotype due to its strong glycolytic capacity, thus making immunotherapy resistance.ConclusionPatients with high expression of JAG1 enhanced glycolytic capacity was likely to cause suppressed immune microenvironment. JAG1 may be a marker for resistance of immunotherapy. Combining anti-angiogenesis therapy could be considered to improve the prognosis of those patients

Induction of RIPK3/MLKL-mediated necroptosis by Erigeron breviscapus injection exhibits potent antitumor effect

Colorectal cancer (CRC) is the second leading cause of tumor-related deaths worldwide. Resistance of tumor cells to drug-induced apoptosis highlights the need for safe and effective antitumor alternatives. Erigeron breviscapus (Dengzhanxixin in China) injection (EBI), extracted from the natural herb Erigeron breviscapus (Vant.) Hand.-Mazz (EHM), has been widely used in clinical practice for cardiovascular diseases. Recent studies have suggested that EBI’s main active ingredients exhibit potential antitumor effects. This study aims to explore the anti-CRC effect of EBI and elucidate the underlying mechanism. The anti-CRC effect of EBI was evaluated in vitro using CCK-8, flow cytometry, and transwell analysis, and in vivo through a xenograft mice model. RNA sequencing was utilized to compare the differentially expressed genes, and the proposed mechanism was verified through in vitro and in vivo experiments. Our study demonstrates that EBI significantly inhibits the proliferation of three human CRC cell lines and effectively suppresses the migration and invasion of SW620 cells. Moreover, in the SW620 xenograft mice model, EBI markedly retards tumor growth and lung metastasis. RNA-seq analysis revealed that EBI might exert antitumor effects by inducing necroptosis of tumor cells. Additionally, EBI activates the RIPK3/MLKL signaling pathway, a classical pathway of necroptosis and greatly promotes the generation of intracellular ROS. Furthermore, the antitumor effect of EBI on SW620 is significantly alleviated after the pretreatment of GW806742X, the MLKL inhibitor. Our findings suggest that EBI is a safe and effective inducer of necroptosis for CRC treatment. Notably, necroptosis is a non-apoptotic programmed cell death pathway that can effectively circumvent resistance to apoptosis, which provides a novel approach for overcoming tumor drug resistance