MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays.

Multiplexed assays of variant effect (MAVEs) are a critical tool for researchers and clinicians to understand genetic variants. Here we describe the 2024 update to MaveDB ( https://www.mavedb.

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

Background: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum.

A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation.

Breast cancer remains a significant global health concern and is a leading cause of mortality among women. The accuracy of breast cancer diagnosis can be greatly improved with the assistance of automatic segmentation of breast ultrasound images. Research has demonstrated the effectiveness of convolutional neural networks (CNNs) and transformers in segmenting these images.

Current applications and challenges in large language models for patient care: a systematic review.

Background: The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care.

A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images.

This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, internal validation, and two external validation cohorts. A representation learning framework was utilized for modeling, and performance was assessed against existing methods.

A hybrid human fall detection method based on modified YOLOv8s and AlphaPose.

To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as object detector. It uses SPD-Conv to preserve small object features and adds a small object detection layer, while using BCIOU as the loss function.

MythicVision: a deep learning powered mobile application for understanding Indian mythological deities using weight centric decision approach.

Indian mythology is a treasure trove of divine tales, yet a gap in understanding still exists between foreign tourists and the rich cultural heritage of Indian deities. To address the problem, this paper presents a deep learning-driven mobile application named "MythicVision" designed to help foreign tourists better understand India's rich cultural heritage by recognizing and interpreting images of Indian mythological deities. At first, four state-of-the-art deep models have been trained and evaluated on a custom in-house dataset consists of 10,970 images of various Indian deities sourced from both natural scene and web images.

Upstreamness and downstreamness in input-output analysis from local and aggregate information.

Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input-output (I-O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I-O tables, but only relying on local and aggregate information, e.

Design of an integrated model with temporal graph attention and transformer-augmented RNNs for enhanced anomaly detection.

It is important in the rising demands to have efficient anomaly detection in camera surveillance systems for improving public safety in a complex environment. Most of the available methods usually fail to capture the long-term temporal dependencies and spatial correlations, especially in dynamic multi-camera settings. Also, many traditional methods rely heavily on large labeled datasets, generalizing poorly when encountering unseen anomalies in the process.

FMCW-based contactless heart rate monitoring.

Heart disease is a significant global health issue. Traditional methods for heart rate monitoring typically require close physical contact, which limits the continuity and convenience of monitoring. To achieve real-time, non-contact heartbeat monitoring, researchers have introduced millimeter-wave radar technology.

Minimal sourced and lightweight federated transfer learning models for skin cancer detection.

One of the most fatal diseases that affect people is skin cancer. Because nevus and melanoma lesions are so similar and there is a high likelihood of false negative diagnoses challenges in hospitals. The aim of this paper is to propose and develop a technique to classify type of skin cancer with high accuracy using minimal resources and lightweight federated transfer learning models.

Liquid crystal torons in Poiseuille-like flows.

Three-dimensional (3D) simulations of the structure of liquid crystal (LC) torons, topologically protected distortions of the LC director field, under material flows are rare but essential in microfluidic applications. Here, we show that torons adopt a steady-state configuration at low flow velocity before disintegrating at higher velocities, in line with experimental results. Furthermore, we show that under partial slip conditions at the boundaries, the flow induces a reversible elongation of the torons, also consistent with the experimental observations.

I2Bot: an open-source tool for multi-modal and embodied simulation of insect navigation.

Achieving a comprehensive understanding of animal intelligence demands an integrative approach that acknowledges the interplay between an organism's brain, body and environment. Insects, despite their limited computational resources, demonstrate remarkable abilities in navigation. Existing computational models often fall short in faithfully replicating the morphology of real insects and their interactions with the environment, hindering validation and practical application in robotics.

Techniques and analytic workflow for spatial transcriptomics and its application to allergy and inflammation.

Spatial profiling, through single-cell gene level expression data paired with cell localization, offers unprecedented biological insights within the intact spatial context of cells in healthy and diseased tissue, adding a novel dimension to data interpretation. This review summarizes recent developments in this field, its application to allergy and inflammation, and recent single-cell resolution platforms designed for spatial transcriptomics with a focus on data processing and analyses for efficient biological interpretation of data. By preserving spatial context, these technologies provide critical insights into tissue architecture and cellular interactions unattainable with traditional transcriptomics methods, such as revealing localized inflammatory cell network in atopic dermatitis, and T-cell interactions in the lung in chronic obstructive pulmonary disease.

Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.

Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers.

Biocomputing at the crossroad between emulating artificial intelligence and cellular supremacy.

Biocomputation aims to create sophisticated biological systems capable of addressing important problems in (bio)medicine with a machine-like precision. At present, computational gene networks engineered by single- or multi-layered assembly of DNA-, RNA- and protein-level gene switches have allowed bacterial or mammalian cells to perform various regulation logics of interest, including Boolean calculation or neural network-like computing. This review highlights the molecular building blocks, design principles, and computational tasks demonstrated by current biocomputers, before briefly discussing possible fields where biological computers may ultimately outcompete their electronic counterparts and achieve cellular supremacy.

Benefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation.

Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance.

A Systematic Approach to Prioritise Diagnostically Useful Findings for Inclusion in Electronic Health Records as Discrete Data to Improve Clinical Artificial Intelligence Tools and Genomic Research.

Aims: The recent widespread use of electronic health records (EHRs) has opened the possibility for innumerable artificial intelligence (AI) tools to aid in genomics, phenomics, and other research, as well as disease prevention, diagnosis, and therapy. Unfortunately, much of the data contained in EHRs are not optimally structured for even the most sophisticated AI approaches. There are very few published efforts investigating methods for recording discrete data in EHRs that would not slow current clinical workflows or ways to prioritise patient characteristics worth recording.

Dietary inflammation and childhood adiposity: Analysis of individual participant data from six birth cohorts.

Background & Aims: Childhood adiposity and inflammation impact long-term health. However, associations between dietary inflammation and childhood adiposity are unclear. We investigated if more pro-inflammatory diets are associated with greater adiposity in early-, mid-, and late-childhood.

Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review.

Background: In recent years, the intersection of natural language processing (NLP) and public health has opened innovative pathways for investigating social determinants of health (SDOH) in textual datasets. Despite the promise of NLP in the SDOH domain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field.

Objective: This research protocol describes a systematic review to identify and highlight NLP techniques, including large language models, used for SDOH-related studies.

PROTCROWN: A Manually Curated Resource of Protein Corona Data for Unlocking the Potential of Protein-Nanoparticle Interactions.

The emergence of the "Protein Corona" is a pivotal concept in bioinformatics and nanotechnology, crucial for understanding nanomedicine delivery and nanoparticle-biological entity interactions. After entering a biological fluid, such as blood, nanoparticles (NPs, such as nanomedical carriers) are quickly coated with proteins, forming a protein interface layer called the protein corona. An in-depth investigation into the protein corona is essential for elucidating the biological ramifications of NPs and their prospective applications within the medical field and beyond.

Utilizing Image Processing Techniques for Wound Management and Evaluation in Clinical Practice: Establishing the Feasibility of Implementing Artificial Intelligence in Routine Wound Care.

Objective: To develop a generalizable and accurate method for automatically analyzing wound images captured in clinical practice and extracting key wound characteristics such as surface area measurement.

Methods: The authors used image processing techniques to create a robust algorithm for segmenting pressure injuries from digital images captured by nurses during clinical practice. The algorithm also measured the real-world wound surface area.

Manipulating Fano Coupling in an Opto-Thermoelectric Field.

Fano resonances in photonics arise from the coupling and interference between two resonant modes in structures with broken symmetry. They feature an uneven and narrow and tunable lineshape and are ideally suited for optical spectroscopy. Many Fano resonance structures have been suggested in nanophotonics over the last ten years, but reconfigurability and tailored design remain challenging.