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arXiv 2023年

作者： Goldstein, Ariel Ham, Eric Schain, Mariano Nastase, Samuel A. Zada, Zaid Grinstein-Dabus, Avigail Aubrey, Bobbi Gazula, Harshvardhan Feder, Amir Doyle, Werner Devore, Sasha Dugan, Patricia Friedman, Daniel Reichart, Roi Brenner, Michael Hassidim, Avinatan Devinsky, Orrin Flinker, Adeen Levy, Omer Hasson, Uri Department of Psychology The Neuroscience Institute Princeton University PrincetonNJ United States Google Research United States New York University Grossman School of Medicine New YorkNY United States School of Engineering and Applied Science Harvard University CambridgeMA United States New York University Tandon School of Engineering BrooklynNY United States Blavatnik School of Computer Science Tel-Aviv University Israel Department of Cognitive and Brain Sciences Business School Hebrew University of Jerusalem Israel Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Technion - Israel Institute of Technology Haifa Israel

Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholinguistic models, DLMs use layered sequences of continuous numerical vectors to represent words and context, allowing a plethora of emerging applications such as human-like text generation. In this paper we show evidence that the layered hierarchy of DLMs may be used to model the temporal dynamics of language comprehension in the brain by demonstrating a strong correlation between DLM layer depth and the time at which layers are most predictive of the human brain. Our ability to temporally resolve individual layers benefits from our use of electrocorticography (ECoG) data, which has a much higher temporal resolution than noninvasive methods like fMRI. Using ECoG, we record neural activity from participants listening to a 30-minute narrative while also feeding the same narrative to a high-performing DLM (GPT2-XL). We then extract contextual embeddings from the different layers of the DLM and use linear encoding models to predict neural activity. We first focus on the Inferior Frontal Gyrus (IFG, or Broca’s area) and then extend our model to track the increasing temporal receptive window along the linguistic processing hierarchy from auditory to syntactic and semantic areas. Our results reveal a connection between human language processing and DLMs, with the DLM’s layer-by-layer accumulation of contextual information mirroring the timing of neural activity in high-order language areas. © 2023, CC BY-NC-ND.

关键词： Brain

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54 Real-World Evaluation of Healthcare Utilization in Patients with a Positive Molecular Diagnosis for Inborn Errors of Immunity

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Clinical Immunology 2024年 262卷 109996-109996页

作者： Rider, Nicholas Platt, Craig Ting, Yi-Lee Khayat, Michael Moretz, Chad Facio, Flavia Abraham, Roshini Johnson, Britt Professor/Virginia Tech Carilion School of Medicine Attending Physician Division of Immunology/Boston Children’s Hospital Clinical Program Manager/Invitae Corporation Clinical Bioinformatics Analyst/Invitae Corporation HEOR Lead/Invitae Corporation Professor Clinical Pathology and Director Diagnostic Immunology Laboratory/Nationwide Children’s Hospital Senior Medical Affairs Director/Invitae Corporation

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Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

SSRN

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SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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Latent Regression and Ordination Risk of Infectious Disease and Climate

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Procedia Computer Science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

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Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography

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Artificial Intelligence in Gastroenterology 2021年第2期2卷 27-41页

作者： Kai-Po Chang Shih-Huan Lin Yen-Wei Chu PhD Program in Medical Biotechnology National Chung Hsing UniversityTaichung 40227Taiwan Department of Pathology China Medical University HospitalTaichung 40447Taiwan Institute of Genomics and Bioinformatics National Chung Hsing UniversityTaichung 40227Taiwan Institute of Molecular Biology National Chung Hsing UniversityTaichung 40227Taiwan Agricultural Biotechnology Center National Chung Hsing UniversityTaichung 40227Taiwan Biotechnology Center National Chung Hsing UniversityTaichung 40227Taiwan PhD Program in Translational Medicine National Chung Hsing UniversityTaichung 40227Taiwan Rong Hsing Research Center for Translational Medicine Taichung 40227Taiwan

Artificial intelligence(AI),particularly the deep learning technology,have been proven influential to radiology in the recent *** ability in image classification,segmentation,detection and reconstruction tasks have substantially assisted diagnostic radiology,and has even been viewed as having the potential to perform better than radiologists in some *** radiology,an important subspecialty dealing with complex anatomy and various modalities including endoscopy,have especially attracted the attention of AI researchers and engineers ***,recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology,particularly in the fields for which public databases are available,such as diagnostic abdominal magnetic resonance imaging(MRI)and computed tomography(CT).This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology,with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and *** fields where AI is less developed,this review will also explain the difficulty in AI model training and possible strategies to overcome the technical *** authors’insights of possible future development will be addressed in the last section.

关键词： Artificial intelligence Deep learning Image diagnosis Radiology Magnetic resonance imaging Computed tomography

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Neurobiological mechanisms of dialectical behavior therapy and Morita therapy, two psychotherapies inspired by Zen

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Journal of neural transmission (Vienna, Austria : 1996) 2023年第8期130卷 1077-1088页

作者： Ying Lin Chen-Chi Hsu Chen-Ju Lin Reiko Kuroda Dai-Lun Chiang Feipei Lai Shu-I Wu Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Psychiatry MacKay Memorial Hospital No. 92 Section 2 Chung-Shan North Rd Taipei 10449 Taiwan. Department of Medicine Mackay Medical College New Taipei City Taiwan. Institute of Health and Welfare Policy National Yang-Ming Chiao-Tung University Taipei Taiwan. Division for Environment Health and Safety The University of Tokyo Tokyo Japan. Financial Technology Applications Program Ming-Chuan University Taoyuan Taiwan. Department of Psychiatry MacKay Memorial Hospital No. 92 Section 2 Chung-Shan North Rd Taipei 10449 Taiwan. shuiwu624@***. Department of Medicine Mackay Medical College New Taipei City Taiwan. shuiwu624@***.

Psychotherapy is a learning process. Updating the prediction models of the brain may be the mechanism underlying psychotherapeutic changes. Although developed in different eras and cultures, dialectical behavior therapy (DBT) and Morita therapy are influenced by Zen principles, and both emphasize the acceptance of reality and suffering. This article reviews these two treatments, their common and distinct therapeutic factors, and their neuroscientific implications. Additionally, it proposes a framework that includes the predictive function of the mind, constructed emotions, mindfulness, therapeutic relationship, and changes enabled via reward predictions. Brain networks, including the Default Mode Network (DMN), amygdala, fear circuitry, and reward pathways, contribute to the constructive process of brain predictions. Both treatments target the assimilation of prediction errors, gradual reorganization of predictive models, and creation of a life with step-by-step constructive rewards. By elucidating the possible neurobiological mechanisms of these psychotherapeutic techniques, this article is expected to serve as the first step towards filling the cultural gap and creating more teaching methods based on these concepts.

关键词： Dialectical behavior therapy Morita therapy Predictive coding Psychotherapy Transcultural psychiatry

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Application of expert system for oil palm smallholder-owned plantations

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Umami H. G. Mawandha D. D. Puruhito Purwadi T. Suparyanto D. Sudigyo B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Most of the oil palm smallholders operate independently. As a result of this lack of support and assistance, oil palm owned by independent smallholders has the lowest productivity compared to large private (corporation) plantations and large state plantations. In addition, smallholder-owned plantations often operate without paying attention to sustainability aspects. There is a need for an effective, efficient, and user-friendly mentoring tool for oil palm farmers that are accessed independently. The Android-based platform was developed by applying an expert system to support increased production of oil palm cultivation for smallholders. Several stages of the expert system implementation, including the identification of planters and land profiles, were carried out using the direct interview method. The expert system includes land preparation management, planting material selection, seeding, weeds control, pests and diseases, fertilization, harvesting and transport, plantation administration, chat platforms, and data scrapping from data providers. The type of expert system is a data-driven Decision Support System (DSS).

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Fitness and Host Use Remain Stable in Biological Control Agent after Many Years of Hybridization

SSRN

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SSRN 2022年

作者： Clark, Eliza I. Stahlke, Amanda R. Gaskin, John F. Bean, Dan W. Hohenlohe, Paul A. Hufbauer, Ruth A. Bitume, Ellyn V. Graduate Degree Program in Ecology Department of Agricultural Biology Colorado State University 1177 Campus Delivery Fort CollinsCO80523 United States Initiative for Bioinformatics and Evolutionary Studies Department of Biological Sciences University of Idaho MoscowID United States Colorado Department of Agriculture PalisadeCO United States Institute of Pacific Islands Forestry Pacific Southwest Research Station USDA Forest Service Hilo HI United States U.S. Department of Agriculture Agricultural Research Service SidneyMT United States

Releasing several biotypes or species of biological control agent is common in many biological control programs. However, the consequences of hybridization for fitness and host use of the resulting hybrids are difficult to predict, especially for hybrids between more than two species and with varying levels of introgression. Hybridization of biocontrol agents may increase or decrease fitness, with consequences for control efficacy. Additionally, hybridization may lead to changes in host use patterns that may put non-target host species at increased risk. We collected genomic data to determine ancestry of individuals from ten locations across a hybrid zone of Diorhabda spp., the biological control agent of Tamarix spp. in North America. We paired genomic data with phenotypic data on fitness proxies of body size and early fecundity and host use for individuals from three of those sites. We found two originally released pure species and a wide range of introgression in hybrids between three Diorhabda species. Body size and early fecundity were similar in pure species and hybrids, indicating hybridization is not detrimental to insect fitness or the biocontrol program and may provide variation that allows populations to become locally adapted. Host use of hybrids was very similar to that of pure species, although some hybrid individuals had increased preference for Frankenia salina , a native non-target species. We find that hybridization has likely not been detrimental to the efficacy and safety of the Diorhabda biocontrol program, but possible impacts on F. salina should be monitored, considering ongoing hybridization and evolution in the field. © 2022, The Authors. All rights reserved.

关键词： Anthropometry

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Incorporating Participants’ Welfare into Sequential Multiple Assignment Randomized Trials

arXiv

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arXiv 2022年

作者： Wang, Xinru Deliu, Nina Narita, Yusuke Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore MRC - Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Methods and Models for Economics Territory and Finance Sapienza University of Rome Rome Italy Department of Economics and Cowles Foundation Yale University New HavenCT United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients’ time-varying clinical conditions. The sequential multiple assignment randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants’ welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder (ADHD). © 2022, CC BY.

关键词： Design of experiments

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Ten-Year Compilation of #SaveKPK Twitter Dataset

Ten-Year Compilation of #SaveKPK Twitter Dataset

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Arif Budiarto Kartika Purwandari Anzaludin Samsinga Perbangsa Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Politic is one of the most favorite topics to discuss in social media for people in Indonesia. It was proven when a people movement to support the Commission of Corruption Eradication (KPK) named #SaveKPK has been enlived for ten years and become a trending topic on Twitter for several times. In this research, all tweets contain `#SaveKPK' are crawled and compiled using an alternative algorithm to get twitter historical data instead of using Twitter API. The result described the characteristic of the dataset statistically, from the most frequently used words to the most active users. A clustering algorithm named Latent Dirichlet Allocation (LDA) was run over the gathered text dataset to discover most relevant keywords using unsupervised learning approach.

关键词： Twitter Computer science Resource management Natural language processing Information management Databases

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