Miracle-dz/UniFAR-FTUs huggingface dataset:
Sixth Annual DC Public Health Case Challenge.indd

Sophie Yang, Amy Geller, Alina Baciu, Alex Akman, Melissa Aune, Rachel Bailey, Jennifer Breau, Eric Cal, Mc Millan Ching, Emanuel Demissie, Allison Doyle, Dominique Earland, Calie Edmonds, Ngozi Elobuike, Garrett Forrester, Hallie Fox, Ikwo Frank, George Gilliam, LCDR Shawna Grover, An Harmanli, Christine Hill, LT Breda Jenkins, Guzal Khayrullina, Caroline King, 2LT Vidya Lala, ENS Michelle Mandeville, Noah Martin, Paige Miles, Alexander Murray, Chigoziem Oguh, Elizabeth Pham, Tim Putnam, Maya Rashad, Emily Shaff er, Maj Tonya Spencer, Ben Szulanczyk, Elizabeth Taormina, Erin Teigen, Tisa Thomas, Adrienne Thomas, and Kimberly Vilmenay
Learning to Switch Optimizers for Quadratic Programming

Quadratic programming (QP) seeks to solve optimization problems involving quadratic functions that can include complex boundary constraints. QP in the unrestricted form is NP -hard; but when restricted to the convex case, it becomes tractable. Active set and interior point methods are used to solve convex problems, and in the nonconvex case various heuristics or relaxations are used to produce high-quality solutions in ﬁnite time. Learning to optimize (L2O) is an emerging approach to design solvers for optimization problems. We develop an L2O approach that uses reinforcement learning to learn a stochastic policy to switch between pre-existing optimization algorithms to solve QP problem instances. In particular, our agent switches between three simple optimizers: Adam, gradient descent, and random search. Our experiments show that the learned optimizer minimizes quadratic functions faster and ﬁnds better-quality solutions in the long term than do any of the possible optimizers switched between. We also compare our solver with the standard QP algorithms in MATLAB and ﬁnd better performance in fewer function evaluations.
Context Free Enciphering Technique for Protein Classification

: In bioinformatics, there is a profound scale of DNA and protein sequences available, but far from being fully utilized. Computational models can facilitate the analyses of large-scale data but require a numeric representation as input. Feature engineering aims at representing non-numeric data with numeric features and can help design features to cast the raw symbolic data effectively. Automated feature engineering, i.e., an encoding scheme preprogrammed the establishment of features, saves the redesigning process and allows the researchers to try different representations with minimal effort. Here an encoding scheme for protein sequences, which encodes the representative sequence dataset into a numeric matrix that can be fed into a downstream learning model. The method, Context-Free Enciphering Technique practice was preferred for a dataset with group of protein sequences. Compared with the traditional methods using task-specific designed features, this method improves the predicting accuracy and serve as an automated feature engineering method for protein sequences.
Deep Learning-based Deaf & Mute Gesture Translation System

: Translation System (TS) is a method used by Deaf individuals to interact with other ordinary persons in the world. Since computers are an important component of our community, the progress of human-computer interaction (HCI) has supported disabled people. And the main purpose of our proposed system is to progress an intelligent system which can turn as a translator between normal and deaf or dumb individuals and can be the communication path between people with speaking deficiency and normal people with both effective and efficient ways. The proposed system consists of a Convolutional Neural Network (CNN) based on the deep learning algorithm for effective extraction of handy features to understand the American Sign Language (ASL), for classifying the hand sign. This paper constructs to interpret ASL and also provides a complete overview of deep learning-based methodologies for sign language recognition. The proposed solution was tested on data samples from ASL data sets and achieved an overall accuracy of 96.68%. The proposed system was appropriate and reliable for Deaf individuals. Furthermore, an efficient and low-cost Hand Gesture Recognition (HGR) system for the real-time video stream from a mobile device camera. A separate individual hand gesture is utilized for validation in this article. The proposed system has to be designed with the front of the camera and the output is given in the form of text or audio.
Simulation and verification of Thomson actuator systems

The inherent characteristics of Thomson coil's (TC) are appropriate to meet the needs of high speed actuators for mechanical switching devices in so-called smart grids. This is due to the large forces that it can exert in the time scale of milliseconds. A coupled COMSOL Multiphysics model is developed in 2D involving PSpice circuits, Magneto-statics, and Moving Mesh Mode for predicting the motion of a conductive armature in the proximity of a coil upon an impulsive discharge of a capacitor bank. In the present work, the forces and the acceleration of an aluminum ring are computed. Subsequently, different case studies are explored to better understand the variables involved and their degree of influence they have on the TC. The developed model was validated experimentally with a built prototype and the results show that it can be used to accurately predict the performance of high speed actuators.
Indonesian Journal of Electrical Engineering and Computer Science

Received Sep 14, 2021 Revised Jan 5, 2022 Accepted Jan 12, 2022 The Indonesian tafseer and translation of Holy Quran is an important source of information and knowledge for Indonesian muslims, since not many Indonesian muslims understand Arabic language in the Quran. However, the tafseer is full of the commentaries and explanation of each surah (chapter) and/or ayah (verse), which form a large document and not so easy to be accessed. Thus, the challenge is how to refer to both tafseer and translation in faster and accurate ways as one needs to always refer to them back and forth. Hence, this study proposes several text mining approaches, i.e. most frequent words, K-means clustering, and association rules, to analyze an Indonesian tafseer and translation of Quran and provide insights of hidden knowledge and relationships based on statistical information derived from it. These insights could be useful for muslims in general and for people that doing research in related areas. This study shows interesting results from combined analysis of the approaches used which can help people accessing information in tafseer more efficiently. As well, interesting relationships have been drawn from terms in the tafseer which could provide further and deeper knowledge on messages in the Quran.
Upcoming oberseminars

Organ segmentation has become an essential step in data analysis for medical research and clinical applications, and several deep learning methods have been explored to enable this automatically. So far most architectures and benchmarks have focused on specific anatomical regions, e.g. thoracic, abdominal, head and neck, etc. Furthermore, the segmentation of other important structures such as the vertebrae is left out as a separate task (spine segmentation). However, the vertebrae actually serve as crucial anatomical landmarks in medical images: the spine runs from the base of the skull to the pelvis and vertebrae are classified based on their regions, e.g. cervical, thoracic, lumbar. Similarly, spine segmentation ignores which organs are at different levels, thus not exploiting the potential of that information. The goal of this thesis is to include labels dependency between the vertebrae and other organs of interest (lungs, liver, kidneys and bladder) in a CT multi-organ segmentation architecture. We hypothesise that a priori knowledge of the spinal region in which an organ is located will improve the results of the organ segmentation and vice-versa.
D ec 2 02 0 Alternating Tree Automata with alitative Semantics

RAPHAËL BERTHON, Université libre de Bruxelles, Belgium NATHANAËL FIJALKOW, CNRS & LaBRI, France EMMANUEL FILIOT, Université libre de Bruxelles, Belgium SHIBASHIS GUHA, Université libre de Bruxelles, Belgium BASTIEN MAUBERT, Università degli Studi di Napoli “Federico II”, Italy ANIELLO MURANO, Università degli Studi di Napoli “Federico II”, Italy LAURELINE PINAULT, Univ Lyon, CNRS, ENS de Lyon, UCB Lyon 1, LIP, France SOPHIE PINCHINAT, Univ Rennes, CNRS, IRISA, France SASHA RUBIN, University of Sydney, Australia OLIVIER SERRE, Université de Paris, IRIF, CNRS, France
Simulated Electroconvulsive Therapy

Objectives Agitation is the most common behavioral symptom of Alzheimer disease (AD) affecting approximately 40% to 60% of the AD population, yet there are no Food and Drug Administration–approved therapies for the myriad of behavioral or psychological symptoms of dementia. There is growing evidence from naturalistic studies that electroconvulsive therapy (ECT) is a safe and effective treatment for agitation in AD patients who are refractory to pharmacotherapy and behavioral interventions. Despite the existing evidence, ECT remains underused because of stigma, lack of education, and concerns regarding adverse cognitive effects. Randomized controlled clinical trials of ECT are an opportunity to provide high-quality evidence of ECT as a safe and efficacious treatment for agitation in the AD population. We describe the methods for the Electroconvulsive Therapy in Alzheimer's Dementia study, which uses a novel, simulated ECT (S-ECT) control group to conduct a single-blind efficacy study of ECT for the treatment of agitation and aggression in individuals with moderate to severe AD. Methods We discuss the rationale, study design, methodology, ethical and practical challenges, and management strategies in using an S-ECT group as the comparator arm in this randomized controlled trial of ECT in AD-related treatment refractory agitation and aggression. Conclusions Validation of the safety and efficacy of ECT in patients with advanced AD with refractory agitation and aggression is necessary. This can be accomplished through creative formulation of S-ECT groups that effectively maintain the blind while providing scientific integrity.
Multiobjective State-Feedback Control for Stochastic Large-Scale System via LMI Approach

This paper investigates a state-feedback control such that multiobjective performance requirements for stochastic large-scale systems can be achieved. The required objectives consist of disturbance attenuation, upper bound on state covariance, and regional constraint on pole placement. Based on the feasibility of a matching condition, three controller design approaches, the decoupled, centralized, and decentralized control, are addressed accordingly. By assuming suitable form of the quadratic Lyapunov function, the constructive conditions to deliver the desired multiobjective performance for the considered stochastic large-scale systems are derived in terms of linear matrix inequalities (LMIs). The effectiveness of the proposed methods is illustrated by a numerical example.
Secondary metabolites from a deep-sea derived fungal strain of Phomopsis lithocarpus FS508

Abstract One new isoindolinone lactam lithocarlactam A (1) and one new triterpene lithocarin D (2), along with nine known natural products (3–11) were isolated from the broth extract of marine fungus Phomopsis lithocarpus FS508, which was isolated from a deep-sea sediment sample. Their structures were elucidated by interpretation of spectroscopic analysis and comparison with previously published literatures for the known compounds. Moreover, compounds 1 and 2 were evaluated for cytotoxic activity against SF-268, MCF-7, HepG-2, and A549 cell lines. However, both of them showed no activity against the tumor cell lines. Graphical Abstract
Moving toward AI-assisted decision-making: Observation on clinicians’ management of multimedia patient information in synchronous and asynchronous telehealth contexts

Background. Artificial intelligence (AI) intends to support clinicians’ patient diagnosis decisions by processing and identifying insights from multimedia patient information. Objective. We explored clinicians’ current decision-making patterns using multimedia patient information (MPI) provided by AI algorithms and identified areas where AI can support clinicians in diagnostic decision-making. Design. We recruited 87 advanced practice nursing (APN) students who had experience making diagnostic decisions using AI algorithms under various care contexts, including telehealth and other healthcare modalities. The participants described their diagnostic decision-making experiences using videos, images, and audio-based MPI. Results. Clinicians processed multimedia patient information differentially such that their focus, selection, and utilization of MPI influence diagnosis and satisfaction levels. Conclusions and implications. To streamline collaboration between AI and clinicians across healthcare contexts, AI should understand clinicians’ patterns of MPI processing under various care environments and provide them with interpretable analytic results for them. Furthermore, clinicians must be trained with the interface and contents of AI technology and analytic assistance.
Contrast-Enhanced Ultrasound of Brain Perfusion in Cardiopulmonary Resuscitation

Abstract To evaluate the feasibility and potential utility of contrast-enhanced ultrasound for real-time imaging of whole-brain perfusion during cardiopulmonary resuscitation (CPR), cardiac arrest was induced in 8- to 7-week-old 10-kg piglets (Sus scrofa domesticus). Contrast-enhanced ultrasound was performed through a parietal cranial window in the coronal plane visualizing the thalami during hemodynamic-directed CPR. Whole-brain mean and maximum pixel intensities in each slice during resuscitation were calculated. Piglets were monitored for 24 hours postarrest. Seven piglets achieved return of spontaneous circulation and 6 survived to 24 hours. Of the 6 surviving piglets, 2 piglets demonstrated greater intra-CPR brain enhancement at maximum 73.2% and 42.1% and mean 36.7% and 31.9% enhancement above background, respectively, compared with maximum 5.8%, 22.9%, 6.0%, and 26.6% and mean 5.1%, 8.9%, 2.9%, and 6.6% above background, respectively, in the other 4. Intra-CPR average mean arterial pressures were similar between all 6 surviving piglets. One piglet achieved return of spontaneous circulation but expired 10 minutes later with enhancement maximum 45.2% and mean 18.9% enhancement above background. The final piglet did not achieve return of spontaneous circulation and exhibited minimal enhancement at maximum 2.8% and mean 0.9% enhancement above background. Contrast-enhanced ultrasound can detect brain perfusion during CPR, identifying a spectrum of cerebral blood flow responses in the brain despite similar systemic hemodynamics. This novel application can form the basis for future large animal model studies and eventually human clinical studies to further explore the neurologic implications of cerebral blood flow responses during resuscitation and stimulate novel strategies for optimizing brain perfusion restoration.
Hsa‐miR‐1290 is associated with stemness and invasiveness in prostate cancer cell lines by targeting RORA

This work examined microRNA‐1290 (miR‐1290)'s effect on regulating the malignant phenotype of prostate cancer (PC) cells. We detected miR‐1290 expression within PC based on open‐sourced datasets as well as in cancer cells and tissues. Loss‐of‐function experiments by miR‐1290 knockdown in PC cell lines were performed. We performed CCK‐8, clone forming, Transwell, and sphere formation assays for examining PC cells' malignant phenotypes following miR‐1290 knockdown. We estimated miR‐1290's target genes using online resources including miRDB, miRbase, miRTarBase and TargetScan. We also performed in vivo studies for validating how miR‐1290 affected tumour formation within the mouse model. According to findings in this work, miR‑1290 showed overexpression within PC cells and tissues. miR‑1290 was indispensable for PC cell growth, stemness and invasion as well as mesenchymal status. Further, we identified RORA (retinoic acid receptor–related orphan receptor A) as miR‑1290's target gene for mediating miR‑1290 within PC cells. To sum up, this work suggests that miR‑1290 up‐regulation enhances PC cell growth and invasion by regulating RORA expression.
New appreciation for an old pathway: the Lands Cycle moves into new arenas in health and disease

The Lands Pathway is a fundamental biochemical process named for its discovery by William EM Lands and revealed in a series of seminal papers published in the Journal of Biological Chemistry between 1958–65. It describes the selective placement in phospholipids of acyl chains, by phospholipid acyltransferases. This pathway has formed a core component of our knowledge of phospholipid and also diglyceride metabolism in mammalian tissues for over 60 years now. Our understanding of how the Lands pathways are enzymatically mediated via large families of related gene products that display both substrate and tissue specificity has grown exponentially since. Recent studies building on this are starting to reveal key roles for the Lands pathway in specific scenarios, in particular inflammation, immunity and inflammation. This review will cover the Lands cycle from historical perspectives first, then present new information on how this important cycle forms a central regulatory node connecting fatty acyl and phospholipid metabolism and how its altered regulation may present new opportunities for therapeutic intervention in human disease.
Initial experiences of transapical beating‐heart mitral valve repair with a novel artificial chordal implantation device

Severe mitral regurgitation (MR) is associated with progressive heart failure and impairment of survival. Degenerative MR accounts for most MV repair surgeries. Conventional mitral valve repair surgery requires cardiopulmonary bypass and is associated with significant morbidity and risks. Transapical beating‐heart mitral valve repair by artificial chordae implantation with transesophageal echocardiography (TEE) guidance has the potential to significantly reduce surgical morbidity. We report the first‐in‐human experience of degenerative MR repair using a novel artificial chordae implantation device (MitralstitchTM system).
Loss of EMP2 Inhibits Melanogenesis of MNT1 Melanoma Cells via Regulation of TRP-2

Melanogenesis is the production of melanin from tyrosine by a series of enzyme-catalyzed reactions, in which tyrosinase and DOPA oxidase play key roles. The melanin content in the skin determines skin pigmentation. Abnormalities in skin pigmentation lead to various skin pigmentation disorders. Recent research has shown that the expression of EMP2 is much lower in melanoma than in normal melanocytes, but its role in melanogenesis has not yet been elucidated. Therefore, we investigated the role of EMP2 in the melanogenesis of MNT1 human melanoma cells. We examined TRP-1, TRP-2, and TYR expression levels during melanogenesis in MNT1 melanoma cells by gene silencing of EMP2. Western blot and RT-PCR results confirmed that the expression levels of TYR and TRP-2 were decreased when EMP2 expression was knocked down by EMP2 siRNA in MNT1 cells, and these changes were reversed when EMP2 was overexpressed. We verified the EMP2 gene was knocked out of the cell line (EMP2 CRISPR/Cas9) by using a CRISPR/Cas9 system and found that the expression levels of TRP-2 and TYR were significantly lower in the EMP2 CRISPR/Cas9 cell lines. Loss of EMP2 also reduced migration and invasion of MNT1 melanoma cells. In addition, the melanosome transfer from the melanocytes to keratinocytes in the EMP2 KO cells cocultured with keratinocytes was reduced compared to the cells in the control coculture group. In conclusion, these results suggest that EMP2 is involved in melanogenesis via the regulation of TRP-2 expression.
Epidermal growth factor-like domain protein 6 recombinant protein facilitates osteogenic differentiation in adipose stem cells via bone morphogenetic protein 2/recombinant mothers against decapentaplegic homolog 4 signaling pathway

ABSTRACT Adipose-derived mesenchymal stem cells (ADSCs) are a class of pluripotent stem cells isolated from the adipose tissue; they can differentiate into osteoblasts after induction and play an important role in bone repair. EGFL6 protein is secreted by adipocytes and osteoblasts and can promote endothelial cell migration and angiogenesis. This study aimed to explore the effect of recombinant EGFL6 protein on the osteogenic differentiation of ADSCs. The cells were incubated with fluorescein isothiocyanate-conjugated antibodies and analyzed by flow cytometry. Alizarin red staining and alkaline phosphatase staining were used to detect the osteogenic differentiation ability. mRNA expression was analyzed by real-time quantitative polymerase chain reaction (RT-qPCR). Protein expression was determined using Western blotting. The osteogenic differentiation ability of ADSCs isolated from the adipose tissue was significantly weakened after EGFL6 knockdown; this ability was restored upon the addition of EGFL6 recombinant protein. BMP2 knockdown inhibited the effect of EGFL6 recombinant protein on osteogenic differentiation. EGFL6 recombinant protein promoted osteogenic differentiation of ADSCs through the BMP2/SMAD4 signaling pathway. This may provide a potential target for the osteogenic differentiation of ADSCs. Graphiical abstract
Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review

Background With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media–based disaster mental health monitoring, such as combining surveillance with social media–based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters.