检索结果-内蒙古大学图书馆

Multi- vs. Unimodal Forest-Bathing in VR to Enhance Affective and Cognitive Recovery after Acute Stress

学校读者我要写书评

暂无评论

Journal of Environmental Psychology 2025年

作者： Leonie Ascone Fariba Mostajeran Anna Mascherek Nour Tawil Thiemo Knaust Larissa Samaan Simone Kühn Neuronal Plasticity Research Group Center for Psychosocial Medicine Clinic and Poli-Clinic for Psychiatry and Psychotherapy University Medical Center Hamburg-Eppendorf (UKE) Martinistraße 52 20246 Hamburg Germany Department of Informatics Human Computer Interaction University of Hamburg Vogt-Kölln-Straße 30 22527 Hamburg Germany Center for Environmental Neurosciences Max-Planck-Institute for Human Development Lentzeallee 94 14195 Berlin Germany Bundeswehr Hospital Hamburg Center for Mental Health Lesserstraße 180 22049 Hamburg Germany

Background Forest-bathing has demonstrated beneficial effects on mental health. In virtual settings, more research is needed to determine whether such effects can be enhanced through multimodal stimulation. This pilot study used visual [V] (360° stereoscopic VR of a Douglas fir forest), auditory [A] (mostly birdsong) recordings, and olfactory [O] stimulation (Douglas fir), presented either unimodally ([V], [O], [A]) or in combination ([VOA]). The potential superiority of the multimodal [VOA] condition in promoting affective and cognitive recovery after stress was tested. Methods N = 136 healthy adults were randomly assigned to one of the four conditions. After a calm-breathing baseline and pre-assessment of affect, participants underwent passive stress induction (image-viewing task), followed by a reassessment of affect and a first cognitive test block (Trail Making Test [executive function], color Stroop [inhibition], Digit Span [working memory]). Subsequently, they were exposed to the forest-stimulation phase. Affect and cognitive performance were reassessed post-intervention. Superiority of the VOA condition in recovery was analyzed using structural equation modeling (SEM), suited to model and test supra-additive effects. Results All groups showed significant stress recovery. The multimodal condition [VOA] demonstrated partial superiority, with selective benefits for positive affect and nature connectedness, but not for negative affect. In the cognitive domain, no broad multimodal advantage emerged, although working memory (Digit Span backwards) was significantly better in VOA compared to the auditory condition. Conclusion Multimodal virtual forest bathing may enhance positive affect and nature connectedness, but its benefits appear domain-specific rather than general. There was limited evidence for cognitive enhancement, restricted to working memory. Future research should focus on enhancing sensory immersion, refining outcomes, and ensuring sufficient statistic

关键词：

Advancing Treatment in Bullous Pemphigoid: A Comprehensive Review of Novel Therapeutic Targets and Approaches

学校读者我要写书评

暂无评论

Clinical Reviews in Allergy and Immunology 2023年第3期65卷 331-353页

作者： Chen, Hsuan-Chi Wang, Chuang-Wei Toh, Wu Han Lee, Hua-En Chung, Wen-Hung Chen, Chun-Bing Department of Medical Education Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Department of Dermatology Drug Hypersensitivity Clinical and Research Center Chang Gung Memorial Hospital Keelung Linkou Taipei Taiwan Cancer Vaccine and Immune Cell Therapy Core Laboratory Department of Medical Research Chang Gung Memorial Hospital Linkou Taiwan Chang Gung Immunology Consortium Chang Gung Memorial Hospital and Chang Gung University Kaohsiung Taiwan Department of Dermatology Xiamen Chang Gung Hospital Xiamen China Xiamen Chang Gung Allergology Consortium Xiamen Chang Gung Hospital Xiamen China Institute for NanoBioTechnology Johns Hopkins University Baltimore MD United States Translational Tissue Engineering Center Johns Hopkins University School of Medicine Baltimore MD United States Department of Computer Science Johns Hopkins University Baltimore MD United States Department of Biology Johns Hopkins University Baltimore MD United States School of Medicine National Tsing Hua University Hsinchu Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Graduate Institute of Clinical Medical Sciences College of Medicine Chang Gung University Taoyuan Taiwan Immune-Oncology Center of Excellence Chang Gung Memorial Hospital Linkou Taiwan Whole-Genome Research Core Laboratory of Human Diseases Chang Gung Memorial Hospital Keelung Taiwan Department of Dermatology Beijing Tsinghua Chang Gung Hospital School of Clinical Medicine Tsinghua University Beijing China Department of Dermatology Ruijin Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Genomic Medicine Core Laboratory Chang Gung Memorial Hospital Linkou Taiwan

Bullous pemphigoid is one of the most common autoimmune bullous diseases occurring primarily in the elderly. Pathogenic autoantibodies against BP180 and BP230 at the dermal–epidermal junction cause subepidermal blisters, erosions, and intense pruritus, all of which adversely affect the patients’ quality of life and may increase their morbidity and mortality. Current systemic treatment options for bullous pemphigoid are limited to corticosteroids and immunosuppressants, which can have substantial side effects on these vulnerable patients that even exceed their therapeutic benefits. Therefore, more precisely, targeting therapies to the pathogenic cells and molecules in bullous pemphigoid is an urgent issue. In this review, we describe the pathophysiology of bullous pemphigoid, focusing on autoantibodies, complements, eosinophils, neutrophils, proteases, and the T helper 2 and 17 axes since they are crucial in promoting proinflammatory environments. We also highlight the emerging therapeutic targets for bullous pemphigoid and their latest discoveries in clinical trials or experimental studies. Further well-designed studies are required to establish the efficacy and safety of these prospective therapeutic options. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Autoimmune bullous dermatosis Biomarker Bullous pemphigoid Pathogenesis Therapeutic target

DVP: Data visualization platform

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Yousefa, Waleed A. Abouelkahire, Ahmed A. Marzouk, Omar S. Mohame, Sameh K Alaggan, Mohammad Human Computer Interaction Laboratory HCI Lab Computer Science Department Faculty of Computers and Information Helwan University

We identify two major steps in data analysis, data exploration for understanding and observing patterns/relationships in data;and construction, design and assessment of various models to formalize these relationships. For each step, there exists a large set of tools and software. For the first step, many visualization tools exist, such as, GGobi, Parallax, and Crystal Vision, and most recently tableau and plottly. For the second step, many Scientific Computing Environments (SCEs) exist, such as, Matlab, Mathematica, R and Python. However, there does not exist a tool which allows for seamless two-way interaction between visualization tools and SCEs. We have designed and implemented a data visualization platform (DVP) with an architecture and design that attempts to bridge this gap. DVP connects seamlessly to SCEs to bring the computational capabilities to the visualization methods in a single coherent platform. DVP is designed with two interfaces, the desktop stand alone version and the online interface. To illustrate the power of DVP design, a free demo for the online interface of DVP is available [1] and very low-level design details are explained in this article. Since DVP was launched, circa 2012, the present manuscript was not published since today for commercialization and patent considerations. Copyright © 2019, The Authors. All rights reserved.

关键词： Data visualization

Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Chalcroft, Liam F. Qu, Jiongqi Martin, Sophie A. Gayo, Iani J.M.B. Minore, Giulio V. Singh, Imraj R.D. Saeed, Shaheer U. Yang, Qianye Baum, Zachary M.C. Altmann, Andre Hu, Yipeng Department of Medical Physics and Biomedical Engineering Wellcome Center for Human Neuroimaging Centre for Medical Image Computing Dementia Research Centre UCL Institute of Neurology Wellcome/EPSRC Centre for Interventional and Surgical Sciences Department of Physics and Astronomy Department of Computer Science University College London London United Kingdom

When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels. In this study, we evaluate the utility of a pre-screening classification network prior to the segmentation network. Experimental results demonstrate that such a classifier, minimising frame classification errors, was able to directly impact the number of false positive and false negative frames. Importantly, the segmentation accuracy on the classifier-selected frames, that would be segmented, remains comparable to or better than those from standalone segmentation networks. Interestingly, the efficacy of the pre-screening classifier was affected by the sampling methods for training labels from multiple observers, a seemingly independent problem. We show experimentally that a previously proposed approach, combining random sampling and consensus labels, may need to be adapted to perform well in our application. Furthermore, this work aims to share practical experience in developing a machine learning application that assists highly variable interventional imaging for prostate cancer patients, to present robust and reproducible open-source implementations, and to report a set of comprehensive results and analysis comparing these practical, yet important, options in a real-world clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Finding high posterior density phylogenies by systematically extending a directed acyclic graph

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Jennings-Shaffer, Chris Rich, David H. Macaulay, Matthew Karcher, Michael D. Ganapathy, Tanvi Kiami, Shosuke Kooperberg, Anna Zhang, Cheng Suchard, Marc A. Matsen, Frederick A. Public Health Sciences Division Fred Hutchinson Cancer Research Center SeattleWA United States University of Technology Sydney Australian Institute for Microbiology & Infection Sydney Australia Department of Math & Computer Science Muhlenberg College AllentownPA United States School of Mathematical Sciences Peking University Beijing China Department of Human Genetics University of California Los Angeles United States Department of Computational Medicine University of California Los Angeles United States Department of Biostatistics University of California Los Angeles United States Department of Genome Sciences University of Washington Seattle United States Howard Hughes Medical Institute Fred Hutchinson Cancer Research Center SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. Mail stop: S2-140 SeattleWA98109-1024 United States

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quickly overwhelmed by the large number of probable trees in the posterior distribution. In this paper we develop methods to sidestep this problem using a recently introduced structure called the subsplit directed acyclic graph (sDAG). This structure can represent many trees at once, and local rearrangements of trees translate to methods of enlarging the sDAG. Here we propose two methods of introducing, ranking, and selecting local rearrangements on sDAGs to produce a collection of trees with high posterior density. One of these methods successfully recovers the set of high posterior density trees across a range of data sets. However, we find that a simpler strategy of aggregating trees into an sDAG in fact is computationally faster and returns a higher fraction of probable trees. © 2024, CC BY.

关键词： Markov chains

Surveillance of Pathogenic Bacteria on a Food Matrix Using Machine-Learning-Enabled Paper Chromogenic Arrays

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Jia, Zhen Luo, Yaguang Wang, Dayang Holliday, Emma Sharma, Arnav Green, Madison M. Roche, Michelle R. Thompson-Witrick, Katherine Flock, Genevieve Pearlstein, Arne J. Yu, Hengyong Zhang, Boce Food Science and Human Nutrition Department University of Florida GainesvilleFL32611 United States Environmental Microbial and Food Safety Lab and Food Quality Lab U.S. Department of Agriculture Agricultural Research Service BeltsvilleMD20705 United States Department of Electrical and Computer Engineering University of Massachusetts LowellMA01854 United States School of Medicine Duke University DurhamNC27710 United States Department of Biomedical and Nutritional Sciences University of Massachusetts LowellMA01854 United States US Army Natick Soldier Research Development and Engineering Center NatickMA01760 United States Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States

Global food systems can benefit significantly from real-time monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens, by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification. © 2023, The Authors. All rights reserved.

关键词： Supply chains

Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation 9th

学校读者我要写书评

暂无评论

Attention Based Hierarchical Aggregation Network for 3D Left...

9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and computer-Assisted Intervention, MICCAI 2018

作者： Li, Caizi Tong, Qianqian Liao, Xiangyun Si, Weixin Sun, Yinzi Wang, Qiong Heng, Pheng-Ann School of Computer Science Wuhan University Wuhan430072 China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9783030120283

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. The atrial segmentation is essential for the understanding of the human atria structure which is vital to the AF treatment. In this paper, we propose a novel three-dimensional (3D) segmentation network combining hierarchical aggregation and attention mechanism for 3D left atrial segmentation, named attention based hierarchical aggregation network (HAANet). In our network, the shallow and deep feature fusion capability of encoder-decoder convolutional neural networks is enhanced through hierarchical aggregation. Besides, attention mechanism is adopted to promote the ability of extracting efficient features. Experimental results demonstrate the HAANet can produce good results for 3D left atrial segmentation and the dice score of our HAANet reaches 92.30. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

Future Vector Enhanced LSTM Language Model for LVCSR

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Qi Qian, Yanmin Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Language models (LM) play an important role in large vocabulary continuous speech recognition (LVCSR). However, traditional language models only predict next single word with given history, while the consecutive predictions on a sequence of words are usually demanded and useful in LVCSR. The mismatch between the single word prediction modeling in trained and the long term sequence prediction in read demands may lead to the performance degradation. In this paper, a novel enhanced long short-term memory (LSTM) LM using the future vector is proposed. In addition to the given history, the rest of the sequence will be also embedded by future vectors. This future vector can be incorporated with the LSTM LM, so it has the ability to model much longer term sequence level information. Experiments show that, the proposed new LSTM LM gets a better result on BLEU scores for long term sequence prediction. For the speech recognition rescoring, although the proposed LSTM LM obtains very slight gains, the new model seems obtain the great complementary with the conventional LSTM LM. Rescoring using both the new and conventional LSTM LMs can achieve a very large improvement on the word error rate. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

The impact of the Turkish population variome on the genomic architecture of rare disease traits

学校读者我要写书评

暂无评论

Genetics in Medicine Open

Genetics in Medicine Open 2024年 2卷 101830-101830页

作者： Coban-Akdemir, Zeynep Song, Xiaofei Ceballos, Francisco C. Pehlivan, Davut Karaca, Ender Bayram, Yavuz Mitani, Tadahiro Gambin, Tomasz Bozkurt-Yozgatli, Tugce Jhangiani, Shalini N. Muzny, Donna M. Lewis, Richard A. Liu, Pengfei Boerwinkle, Eric Hamosh, Ada Gibbs, Richard A. Sutton, V. Reid Sobreira, Nara Carvalho, Claudia M.B. Shaw, Chad A. Posey, Jennifer E. Valle, David Lupski, James R. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX United States Human Genetics Center Department of Epidemiology Human Genetics Environmental Sciences School of Public Health The University of Texas Health Science Center at Houston Houston TX United States Department of Biostatistics and Bioinformatics H. Lee Moffitt Cancer Center & Research Institute Tampa FL United States Instituto de Salud Carlos III National Center of Microbiology Madrid Spain Section of Neurology Department of Pediatrics Baylor College of Medicine Houston TX United States Department of Pathology Baylor University Medical Center Dallas TX United States Texas A&M School of Medicine Texas A&M University Dallas TX United States Division of Genomic Diagnostics Department of Pathology and Laboratory Medicine Children's Hospital of Philadelphia Philadelphia PA United States Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Biostatistics and Bioinformatics Institute of Health Sciences Acibadem Mehmet Ali Aydinlar University Istanbul Turkey Human Genome Sequencing Center Baylor College of Medicine Houston TX United States Department of Pediatrics Baylor College of Medicine Houston TX United States Department of Ophthalmology Cullen Eye Institute Baylor College of Medicine Houston TX United States McKusick-Nathans Department of Genetic Medicine Johns Hopkins University School of Medicine Baltimore MD United States Texas Children's Hospital Houston TX United States Pacific Northwest Research Institute Seattle WA United States Baylor Genetics Houston TX United States

Purpose: The variome of the Turkish (TK) population, a population with a considerable history of admixture and consanguinity, has not been deeply investigated for insights on the genomic architecture of disease. Methods: We generated and analyzed a database of variants derived from exome sequencing data of 773 TK unrelated, clinically affected individuals with various suspected Mendelian disease traits and 643 unaffected relatives. Results: Using uniform manifold approximation and projection, we showed that the TK genomes are more similar to those of Europeans and consist of 2 main subpopulations: clusters 1 and 2 (N = 235 and 1181, respectively), which differ in admixture proportion and variome (https://***/tvdb/). Furthermore, the higher inbreeding coefficient values observed in the TK affected compared with unaffected individuals correlated with a larger median span of long-sized (>2.64 Mb) runs of homozygosity (ROH) regions (P value = 2.09e-18). We show that long-sized ROHs are more likely to be formed on recently configured haplotypes enriched for rare homozygous deleterious variants in the TK affected compared with TK unaffected individuals (P value = 3.35e-11). Analysis of genotype-phenotype correlations reveals that genes with rare homozygous deleterious variants in long-sized ROHs provide the most comprehensive set of molecular diagnoses for the observed disease traits with a systematic quantitative analysis of human Phenotype Ontology terms. Conclusion: Our findings support the notion that novel rare variants on newly configured haplotypes arising within the recent past generations of a family or clan contribute significantly to recessive disease traits in the TK population. © 2024 The Authors

关键词： Admixture Consanguinity Genomic architecture of rare disease traits Runs of homozygosity Turkish population