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General General

Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification.

In Scientific reports ; h5-index 158.0

Glutamic acid is an alpha-amino acid used by all living beings in protein biosynthesis. One of the important glutamic acid modifications is post-translationally modified 4-carboxyglutamate. It has a significant role in blood coagulation. 4-carboxyglumates are required for the binding of calcium ions. On the contrary, this modification can also cause different diseases such as bone resorption, osteoporosis, papilloma, and plaque atherosclerosis. Considering its importance, it is necessary to predict the occurrence of glutamic acid carboxylation in amino acid stretches. As there is no computational based prediction model available to identify 4-carboxyglutamate modification, this study is, therefore, designed to predict 4-carboxyglutamate sites with a less computational cost. A machine learning model is devised with a Multilayered Perceptron (MLP) classifier using Chou's 5-step rule. It may help in learning statistical moments and based on this learning, the prediction is to be made accurately either it is 4-carboxyglutamate residue site or detected residue site having no 4-carboxyglutamate. Prediction accuracy of the proposed model is 94% using an independent set test, while obtained prediction accuracy is 99% by self-consistency tests.

Shah Asghar Ali, Khan Yaser Daanial

2020-Oct-09

Radiology Radiology

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

In Nature communications ; h5-index 260.0

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .

Jin Cheng, Chen Weixiang, Cao Yukun, Xu Zhanwei, Tan Zimeng, Zhang Xin, Deng Lei, Zheng Chuansheng, Zhou Jie, Shi Heshui, Feng Jianjiang

2020-Oct-09

General General

Quantum transport evidence of Weyl fermions in an epitaxial ferromagnetic oxide.

In Nature communications ; h5-index 260.0

Magnetic Weyl semimetals have novel transport phenomena related to pairs of Weyl nodes in the band structure. Although the existence of Weyl fermions is expected in various oxides, the evidence of Weyl fermions in oxide materials remains elusive. Here we show direct quantum transport evidence of Weyl fermions in an epitaxial 4d ferromagnetic oxide SrRuO3. We employ machine-learning-assisted molecular beam epitaxy to synthesize SrRuO3 films whose quality is sufficiently high to probe their intrinsic transport properties. Experimental observation of the five transport signatures of Weyl fermions-the linear positive magnetoresistance, chiral-anomaly-induced negative magnetoresistance, π phase shift in a quantum oscillation, light cyclotron mass, and high quantum mobility of about 10,000 cm2V-1s-1-combined with first-principles electronic structure calculations establishes SrRuO3 as a magnetic Weyl semimetal. We also clarify the disorder dependence of the transport of the Weyl fermions, which gives a clear guideline for accessing the topologically nontrivial transport phenomena.

Takiguchi Kosuke, Wakabayashi Yuki K, Irie Hiroshi, Krockenberger Yoshiharu, Otsuka Takuma, Sawada Hiroshi, Nikolaev Sergey A, Das Hena, Tanaka Masaaki, Taniyasu Yoshitaka, Yamamoto Hideki

2020-Oct-09

General General

Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors.

In Nature communications ; h5-index 260.0

The transcription regulatory network inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex network. In this study, we use large-scale ChIP-seq to reconstruct it in the maize leaf, and train machine-learning models to predict TF binding and co-localization. The resulting network covers 77% of the expressed genes, and shows a scale-free topology and functional modularity like a real-world network. TF binding sequence preferences are conserved within family, while co-binding could be key for their binding specificity. Cross-species comparison shows that core network nodes at the top of the transmission of information being more conserved than those at the bottom. This study reveals the complex and redundant nature of the plant transcription regulatory network, and sheds light on its architecture, organizing principle and evolutionary trajectory.

Tu Xiaoyu, Mejía-Guerra María Katherine, Valdes Franco Jose A, Tzeng David, Chu Po-Yu, Shen Wei, Wei Yingying, Dai Xiuru, Li Pinghua, Buckler Edward S, Zhong Silin

2020-Oct-09

General General

AI enabled suicide prediction tools: a qualitative narrative review.

In BMJ health & care informatics

Background: Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services. Advances in artificial intelligence (AI) present opportunities for the development of novel tools for predicting suicide.Method: We searched Google Scholar and PubMed for articles relating to suicide prediction using artificial intelligence from 2017 onwards.Conclusions: This paper presents a qualitative narrative review of research focusing on two categories of suicide prediction tools: medical suicide prediction and social suicide prediction. Initial evidence is promising: AI-driven suicide prediction could improve our capacity to identify those at risk of suicide, and, potentially, save lives. Medical suicide prediction may be relatively uncontroversial when it pays respect to ethical and legal principles; however, further research is required to determine the validity of these tools in different contexts. Social suicide prediction offers an exciting opportunity to help identify suicide risk among those who do not engage with traditional health services. Yet, efforts by private companies such as Facebook to use online data for suicide prediction should be the subject of independent review and oversight to confirm safety, effectiveness and ethical permissibility.

D’Hotman Daniel, Loh Erwin

2020-Oct

health care, information science, medical informatics, patient care

General General

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters.

In Translational psychiatry ; h5-index 60.0

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

Bruin Willem B, Taylor Luke, Thomas Rajat M, Shock Jonathan P, Zhutovsky Paul, Abe Yoshinari, Alonso Pino, Ameis Stephanie H, Anticevic Alan, Arnold Paul D, Assogna Francesca, Benedetti Francesco, Beucke Jan C, Boedhoe Premika S W, Bollettini Irene, Bose Anushree, Brem Silvia, Brennan Brian P, Buitelaar Jan K, Calvo Rosa, Cheng Yuqi, Cho Kang Ik K, Dallaspezia Sara, Denys Damiaan, Ely Benjamin A, Feusner Jamie D, Fitzgerald Kate D, Fouche Jean-Paul, Fridgeirsson Egill A, Gruner Patricia, Gürsel Deniz A, Hauser Tobias U, Hirano Yoshiyuki, Hoexter Marcelo Q, Hu Hao, Huyser Chaim, Ivanov Iliyan, James Anthony, Jaspers-Fayer Fern, Kathmann Norbert, Kaufmann Christian, Koch Kathrin, Kuno Masaru, Kvale Gerd, Kwon Jun Soo, Liu Yanni, Lochner Christine, Lázaro Luisa, Marques Paulo, Marsh Rachel, Martínez-Zalacaín Ignacio, Mataix-Cols David, Menchón José M, Minuzzi Luciano, Moreira Pedro S, Morer Astrid, Morgado Pedro, Nakagawa Akiko, Nakamae Takashi, Nakao Tomohiro, Narayanaswamy Janardhanan C, Nurmi Erika L, O’Neill Joseph, Pariente Jose C, Perriello Chris, Piacentini John, Piras Fabrizio, Piras Federica, Reddy Y C Janardhan, Rus-Oswald Oana G, Sakai Yuki, Sato João R, Schmaal Lianne, Shimizu Eiji, Simpson H Blair, Soreni Noam, Soriano-Mas Carles, Spalletta Gianfranco, Stern Emily R, Stevens Michael C, Stewart S Evelyn, Szeszko Philip R, Tolin David F, Venkatasubramanian Ganesan, Wang Zhen, Yun Je-Yeon, van Rooij Daan, Thompson Paul M, van den Heuvel Odile A, Stein Dan J, van Wingen Guido A

2020-Oct-08