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Public Health Public Health

COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19.

In Frontiers in public health

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

Basit Syed Abdullah, Qureshi Rizwan, Musleh Saleh, Guler Reto, Rahman M Sohel, Biswas Kabir H, Alam Tanvir

2023

CORD-19, COVID-19, SARS-CoV-2, deep learning, machine learning

General General

Smart Yoga Instructor for Guiding and Correcting Yoga Postures in Real Time.

In International journal of yoga

In recent days, Yoga is gaining more prominence and people all over the world have started to practice it. Performing Yoga with proper postures is beneficial. Hence, an instructor is required to monitor the correctness of Yoga postures. However, at times, it is difficult to have an instructor. This study aims to provide a system that will act as a personal Yoga instructor and practitioners can practice Yoga in their comfort zone. The device is interactive and provides audio guidance to perform different Yoga asanas. It makes the use of a camera to capture the picture of the person performing Yoga in a particular position. This captured pose is compared with the benchmark postures. A pretrained deep learning model is used for the classification of different Yoga postures using a standard dataset. Based on the comparison, the practitioner's posture will be corrected using a voice message to move the body parts in a certain direction. As the device performs all the operations in real-time, it has a quick response time of a few seconds. Currently, this work aids the practitioners in performing five Asanas, namely, Ardha Chandrasana/Half-moon pose, Tadasana/Mountain pose, Trikonasana/Triangular pose, Veerabhadrasana/Warrior pose, and Vrikshasana/Tree pose.

Kishore D Mohan, Bindu S, Manjunath Nandi Krishnamurthy

2022

Human posture recognition, Mediapipe, Yoga, pose detection and pose correction, real-time

General General

scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data.

In Bioinformatics advances

MOTIVATION : Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell-type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, no current cell annotation method explicitly utilizes dropout information. Fully utilizing dropout information motivated this work.

RESULTS : We present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene's marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using 14 real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotate's misclassified cells differ greatly from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy.

AVAILABILITY AND IMPLEMENTATION : We implemented scAnnotate as an R package and made it publicly available from CRAN: https://cran.r-project.org/package=scAnnotate.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics Advances online.

Ji Xiangling, Tsao Danielle, Bai Kailun, Tsao Min, Xing Li, Zhang Xuekui

2023

General General

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions.

In Microsystems & nanoengineering

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Hwang Yun Ji, Yu Heejin, Lee Gilho, Shackery Iman, Seong Jin, Jung Youngmo, Sung Seung-Hyun, Choi Jongeun, Jun Seong Chan

2023

Computational nanotechnology, Electronic properties and materials, Nanosensors

General General

Astrocyte structural heterogeneity in the mouse hippocampus.

In Glia

Astrocytes are integral components of brain circuits, where they sense, process, and respond to surrounding activity, maintaining homeostasis and regulating synaptic transmission, the sum of which results in behavior modulation. These interactions are possible due to their complex morphology, composed of a tree-like structure of processes to cover defined territories ramifying in a mesh-like system of fine leaflets unresolved by conventional optic microscopy. While recent reports devoted more attention to leaflets and their dynamic interactions with synapses, our knowledge about the tree-like "backbone" structure in physiological conditions is incomplete. Recent transcriptomic studies described astrocyte molecular diversity, suggesting structural heterogeneity in regions such as the hippocampus, which is crucial for cognitive and emotional behaviors. In this study, we carried out the structural analysis of astrocytes across the hippocampal subfields of Cornu Ammonis area 1 (CA1) and dentate gyrus in the dorsoventral axis. We found that astrocytes display heterogeneity across the hippocampal subfields, which is conserved along the dorsoventral axis. We further found that astrocytes appear to contribute in an exocytosis-dependent manner to a signaling loop that maintains the backbone structure. These findings reveal astrocyte heterogeneity in the hippocampus, which appears to follow layer-specific cues and depend on the neuro-glial environment.

Viana João Filipe, Machado João Luís, Abreu Daniela Sofia, Veiga Alexandra, Barsanti Sara, Tavares Gabriela, Martins Manuella, Sardinha Vanessa Morais, Guerra-Gomes Sónia, Domingos Cátia, Pauletti Alberto, Wahis Jérôme, Liu Chen, Calì Corrado, Henneberger Christian, Holt Matthew G, Oliveira João Filipe

2023-Mar-22

astrocyte, dorsal, hippocampus, morphology, skeleton, ventral

General General

Artificial intelligence's interpretation of the neuroanatomical aspect of Peter Paul Rubens's copy of "The Battle of Anghiari" by Leonardo da Vinci.

In Perception

We tested to see how Ruben's copy of "The Battle of Anghiari" by Leonardo da Vinci would be interpreted by AI in a neuroanatomical aspect. We used WOMBO Dream, an artificial intelligence (AI)-based algorithm that creates images based on words and figures. The keyword we provided for the algorithm was "brain" and the reference image was Ruben's drawing. AI interpreted the whole drawing as a representation of the brain. The image generated by the algorithm was similar to our interpretation of the same painting.

Keshelava Grigol

2023-Mar-22

Leonardo da Vinci, Renaissance, The Battle of Anghiari, artificial intelligence, brain anatomy