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arXiv 2024年

作者： Singh, Avinash Teja, Rishabh Singh Moriya, Takashi J. Maeda, Keiichi Kawabata, Koji S. Tanaka, Masaomi Imazawa, Ryo Nakaoka, Tatsuya Gangopadhyay, Anjasha Yamanaka, Masayuki Swain, Vishwajeet Sahu, D.K. Anupama, G.C. Kumar, Brajesh Anche, Ramya M. Sano, Yasuo Raj, A. Agnihotri, V.K. Bhalerao, Varun Bisht, D. Bisht, M.S. Belwal, K. Chakrabarti, S.K. Fujii, Mitsugu Nagayama, Takahiro Matsumoto, Katsura Hamada, Taisei Kawabata, Miho Kumar, Amit Kumar, Ravi Malkan, Brian K. Smith, Paul Sakagami, Yuta Taguchi, Kenta Tominaga, Nozomu Watanabe, Arata Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Department of Astronomy The Oskar Klein Center Stockholm University AlbaNova University Center StockholmSE 106 91 Sweden Indian Institute of Astrophysics II Block Koramangala Karnataka Bengaluru560034 India Pondicherry University UT of Puducherry R.V. Nagar Kalapet Pondicherry605014 India National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Department of Astronomy Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astronomical Institute Tohoku University Aoba Sendai980-8578 Japan Department of Physics Graduate School of Advanced Science and Engineering Hiroshima Astrophysical Science Centre Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Graduate School of Science and Engineering Kagoshima University 1-21-35 Korimoto Kagoshima Kagoshima890-0065 Japan Department of Physics Indian Institute of Technology Bombay Powai Mumbai400076 India South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650504 China Steward Observatory University of Arizona 933N Cherry Avenue TucsonAZ85721 United States Observation and Data Center for Cosmosciences Faculty of Science Hokkaido University Kita-ku Sapporo-shi Hokkaido060-0810 Japan Nayoro Observatory 157-1 Nisshin Nayoro-shi Hokkaido096-0066 Japan Indian Centre for Space Physics 466 Barakhola Netai Nagar West Bengal Kolkata700099 India Cepheid Observatory India Fujii Kurosaki Observatory 4500 Kurosaki

We present a detailed investigation of photometric, spectroscopic, and polarimetric observations of the Type II SN 2023ixf. Earlier studies have provided compelling evidence for a delayed shock breakout from a confined dense circumstellar matter (CSM) enveloping the progenitor star. The temporal evolution of polarization in SN 2023ixf revealed three distinct peaks in polarization evolution at 1.4 d, 6.4 d, and 79.2 d, indicating an asymmetric dense CSM, an aspherical shock front and clumpiness in the low-density extended CSM, and an aspherical inner ejecta/He-core. SN 2023ixf displayed two dominant axes, one along the CSM-outer ejecta and the other along the inner ejecta/He-core, showcasing the independent origin of asymmetry in the early and late evolution. The argument for an aspherical shock front is further strengthened by the presence of a high-velocity broad absorption feature in the blue wing of the Balmer features in addition to the P-Cygni absorption post 16 d. Hydrodynamical light curve modeling indicated a progenitor mass of 10 M☉ with a radius of 470 R☉ and explosion energy of 2 × 1051 erg, along with 0.06 M☉ of 56 Ni, though these properties are not unique due to modeling degeneracies. The modeling also indicated a two-zone CSM: a confined dense CSM extending up to 5×1014 cm, with a mass-loss rate of 10−2 M☉ yr−1 and an extended CSM spanning from 5×1014 cm to at least 1016 cm with a mass-loss rate of 10−4 M☉ yr−1, both assuming a wind-velocity of 10 km s−1. The early nebular phase observations display an axisymmetric line profile of [O I], red-ward attenuation of the emission of Hα post 125 days and flattening in the Ks-band marking the onset of dust formation. Copyright © 2024, The Authors. All rights reserved.

关键词： Supernovae

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RMeDPower for Biology: Guiding Design, Experimental Structure and Analyses of Repeated Measures data for Biological Studies

SSRN

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SSRN 2022年

作者： Shin, Min-Gyoung Kaye, Julia A. Amirani, Naufa Lam, Stephanie Vertudes, Edward Raja, Krishna Thomas, Reuben Finkbeiner, Steven Gladstone Institute of Data Science and Biotechnology Gladstone Institutes San FranciscoCA United States Center for Systems and Therapeutics Gladstone Institutes San FranciscoCA94158 United States BridgeBio Pharma 421 Kipling Street Palo AltoCA94301 United States Taube/Koret Center for Neurodegenerative Disease Research Gladstone Institutes San FranciscoCA94158 United States Gladstone Institutes San FranciscoCA94158 United States Department of Neurology University of California San FranciscoCA94158 United States Department of Physiology University of California San FranciscoCA94158 United States Neuroscience Graduate Program and Biomedical Sciences Program University of California San FranciscoCA94158 United States

Reproducibility is science has plagued efforts to understand biology at both basic and biomedical and preclinical research levels. Poor experimental design and execution can result in datasets that are improperly powered to produce rigorous and reproducible results. In order to help biologists better model their data, here we present a statistical package called RMeDPower in R, which is a complete package of statistical tools that allow a scientist to understand the effect size and variance contribution of a set of variables one has within a dataset to a given response. RMeDPower can estimate the effect size of variables within an experiment based on an initial pilot dataset. In this way, RMeDPower can inform the user how to predict the scope, dimension and size of biological data needed for a particular experimental design. RMeDPower employs a generalized linear mixed model (LMM) -based power analysis, specifically targeting cell culture-based biological experimental designs. © 2022, The Authors. All rights reserved.

关键词： Cell culture

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Anomaly Detection in Climate data Using Stacked and Densely Connected Long Short-Term Memory Model

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Journal of Computers (Taiwan) 2020年第4期31卷 42-53页

作者： Saleh Abbas, Bahtiar Wahyono, Teguh Heryadi, Yaya Soeparno, Haryono Doctor of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering Bina Nusantara University Jakarta Indonesia

Climate anomalies are considered as an important factor closely related to many disasters causing many human losses, such as airline crash, wildfires, drought and flooding in many areas. Many researchers have projected that the rising global temperature will increase the draught, especially in the mid-latitude areas. Taking those problems into account, studies on anomaly detection in climate are crucial. While climate prediction aims to analyze and model regular pattern of climate, climate anomaly studies aim to model climate deviation from its previous general patterns. Long Short Term Memory (LSTM) is a method employed in this research because it has been proven to work effectively in several anomaly detection studies, especially for data with similar characteristics. This paper presents empirical results of using Basic LSTM, Densely Connected (DC) LSTM, and Stacked-DC LSTM models to detect temperature anomaly as a manifestation of climate change in Semarang City. The results show that Stacked DC LSTM produced higher accuracy in detecting anomaly than the other two methods. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Long short-term memory

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Homogenisation for the monodomain model in the presence of microscopic fibrotic structures

arXiv

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arXiv 2020年

作者： Lawson, Brodie A.J. dos Santos, Rodrigo Weber Turner, Ian W. Bueno-Orovio, Alfonso Burrage, Pamela Burrage, Kevin Centre for Data Science Queensland University of Technology QLD Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers Queensland University of Technology QLD Australia Graduate Program on Computational Modeling Universidade de Juiz de Fora Minas Gerais Brazil Department of Computer Science University of Oxford Oxfordshire United Kingdom

MSC Codes 35B27Computational models in cardiac electrophysiology are notorious for long runtimes, restricting the numbers of nodes and mesh elements in the numerical discretisations used for their solution. This makes it particularly challenging to incorporate structural heterogeneities on small spatial scales, preventing a full understanding of the critical arrhythmogenic effects of conditions such as cardiac fibrosis. In this work, we explore the technique of homogenisation by volume averaging for the inclusion of non-conductive micro-structures into larger-scale cardiac meshes with minor computational overhead. Importantly, our approach is not restricted to periodic patterns, enabling homogenised models to represent, for example, the intricate patterns of collagen deposition present in different types of fibrosis. We first highlight the importance of appropriate boundary condition choice for the closure problems that define the parameters of homogenised models. Then, we demonstrate the technique’s ability to correctly upscale the effects of fibrotic patterns with a spatial resolution of 10 µm into much larger numerical mesh sizes of 100-250 µm. The homogenised models using these coarser meshes correctly predict critical pro-arrhythmic effects of fibrosis, including slowed conduction, source/sink mismatch, and stabilisation of re-entrant activation patterns. As such, this approach to homogenisation represents a significant step towards whole organ simulations that unravel the effects of microscopic cardiac tissue heterogeneities. © 2020, CC BY.

关键词： Heart

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Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity

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Translational Psychiatry 2023年第1期13卷 1-11页

作者： Smith, Sydney E. Kosik, Eena L. van Engen, Quirine Kohn, Jordan Hill, Aron T. Zomorrodi, Reza Blumberger, Daniel M. Daskalakis, Zafiris J. Hadas, Itay Voytek, Bradley Neurosciences Graduate Program University of California San Diego La Jolla CA United States Department of Cognitive Science University of California San Diego La Jolla CA United States Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego La Jolla CA United States Department of Psychiatry School of Medicine University of California San Diego La Jolla CA United States Cognitive Neuroscience Unit School of Psychology Deakin University Geelong VIC Australia Department of Psychiatry Central Clinical School Monash University Melbourne VIC Australia Temerty Centre for Therapeutic Brain Intervention Centre for Addiction and Mental Health University of Toronto Toronto ON Canada Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States Kavli Institute for Brain and Mind University of California San Diego La Jolla CA United States

Major depressive disorder (MDD) is a leading cause of disability worldwide. One of the most efficacious treatments for treatment-resistant MDD is electroconvulsive therapy (ECT). Recently, magnetic seizure therapy (MST) was developed as an alternative to ECT due to its more favorable side effect profile. While these approaches have been very successful clinically, the neural mechanisms underlying their therapeutic effects are unknown. For example, clinical “slowing” of the electroencephalogram beginning in the postictal state and extending days to weeks post-treatment has been observed in both treatment modalities. However, a recent longitudinal study of a small cohort of ECT patients revealed that, rather than delta oscillations, clinical slowing was better explained by increases in aperiodic activity, an emerging EEG signal linked to neural inhibition. Here we investigate the role of aperiodic activity in a cohort of patients who received ECT and a cohort of patients who received MST treatment. We find that aperiodic neural activity increases significantly in patients receiving either ECT or MST. Although not directly related to clinical efficacy in this dataset, increased aperiodic activity is linked to greater amounts of neural inhibition, which is suggestive of a potential shared neural mechanism of action across ECT and MST. © 2023, The Author(s).

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Author Correction: Predicting the efficiency of prime editing guide RNAs in human cells

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Nature biotechnology 2024年第3期42卷 529页

作者： Hui Kwon Kim Goosang Yu Jinman Park Seonwoo Min Sungtae Lee Sungroh Yoon Hyongbum Henry Kim Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Graduate School of Data Science Seoul National University Seoul Republic of Korea. Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac. Severance Biomedical Science Institute Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of NanoScience and Technology Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac.

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2019年第4期46卷

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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Adjacent segment disease following Dynesys stabilization for lumbar disorders:A case series of mid-and long-term follow-ups

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World Journal of Clinical Cases 2021年第35期9卷 10850-10860页

作者： Kuan-Ju Chen Chien-Ying Lai Lu-Ting Chiu Wei-Sheng Huang Pang-Hsuan Hsiao Chien-Chun Chang Cheng-Jyh Lin Yuan-Shun Lo Yen-Jen Chen Hsien-Te Chen Department of Orthopedic Surgery China Medical University HospitalTaichung City 404Taiwan Spine Center China Medical University HospitalTaichung City 404Taiwan Management Office for Health Data China Medical University HospitalTaichung City 404Taiwan College of Medicine China Medical UniversityTaichung City 404Taiwan School of Chinese Medicine China Medical UniversityTaichung City 404Taiwan Department of Orthopedic Surgery China Medical University HospitalChina Medical UniversityTaichung City 404Taiwan Spine Center China Medical University HospitalChina Medical UniversityTaichung City 404Taiwan Degree Program of Biomedical Science and Engineering National Chiao Tung UniversityHsinchu City 300Taiwan Department of Biological Science and Technology National Chiao Tung UniversityHsinchu City 300Taiwan Department of Orthopedic Surgery China Medical University Beigang HospitalYunlin County 651Taiwan Graduate Institute of Precision Engineering National Chung Hsing UniversityTaichung City 404Taiwan School of Medicine China Medical UniversityTaichung City 404Taiwan Department of Sport Medicine College of Health CareChina Medical UniversityTaichung City 404Taiwan

BACKGROUND Radiologic adjacent segment degeneration(ASDeg)can occur after spinal *** segment disease(ASDis)is defined as the development of new clinical symptoms corresponding to radiographic changes adjacent to the level of previous spinal *** pre-existing ASDeg is generally considered to result in more severe ASDis;nonetheless,whether the ASDeg status before index surgery influences the postoperative risk of revision surgery due to ASDis warrants *** To identify possible risk factors for ASDis and verify the concept that greater preexisting ASDeg leads to more severe *** data from 212 patients who underwent posterior decompression with Dynesys stabilization from January 2006 to June 2016 were retrospectively *** who underwent surgery for ASDis were categorized as group A(n=13),whereas those who did not were classified as group B(n=199).Survival analysis and Cox proportional hazards models were used to compare the modified Pfirrmann grade,University of California-Los Angeles grade,body mass index,number of Dynesys-instrumented levels,and *** The mean time of reoperation was 7.22(1.65–11.84)years in group A,and the mean follow-up period was 6.09(0.10–12.76)years in group *** significant difference in reoperation risk was observed:Modified Pfirrmann grade 3 vs 4(P=0.53)or 4 vs 5(P=0.46)for the upper adjacent disc,University of California-Los Angeles grade 2 vs 3 for the upper adjacent segment(P=0.66),age of<60 vs>60 years(P=0.9),body mass index<25 vs>25 kg/m2(P=0.3),and sex(P=0.8).CONCLUSION Greater preexisting upper ASDeg was not associated with a higher rate of reoperation for ASDis after Dynesys *** overweight tended to increase reoperation risk after Dynesys surgery for ASDis.

关键词： Adjacent segment degeneration Adjacent segment disease Degenerative lumbar spondylolisthesis Dynamic stabilization Dynesys Spinal stenosis

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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Features Importance in Classification Models for Colorectal Cancer Cases Phenotype in Indonesia

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Procedia Computer science 2019年 157卷 313-320页

作者： Tjeng Wawan Cenggoro Bharuno Mahesworo Arif Budiarto James Baurley Teddy Suparyanto Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 BioRealm LLC Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Recently there has been an interest in predicting complex disease risk using models that combine the effects of many genetic factors together. These are known as polygenic models and are useful in evaluating risk of disease in patients. These models, however, often do not include important non-genetic factors that are important to the prediction of the disease. In this paper, we explore the prediction of colorectal cancer in Indonesia from non-genetic factors using common machine learning algorithms: XGBoost and Elastic Net. The result of this study identified 8 features with strong importance from both XGBoost and Elastic Net. We recommend including these features as covariates in future genetic association studies of colorectal cancer in Indonesia. Ultimately these models may be implemented as tools to screen patients for colorectal cancer risk.

关键词： Feature Importance Feature Selection XGBoost Generalized Linear Model Elastic Net Colorectal Cancer

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