Special issue on software citation, indexing, and discoverability.

Software plays a fundamental role in research as a tool, an output, or even as an object of study. This special issue on software citation, indexing, and discoverability brings together five papers examining different aspects of how the use of software is recorded and made available to others. It describes new work on datasets that enable large-scale analysis of the evolution of software usage and citation, that presents evidence of increased citation rates when software artifacts are released, that provides guidance for registries and repositories to support software citation and findability, and that shows there are still barriers to improving and formalising software citation and publication practice.

Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography.

Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems.

CONSORT-TM: Text classification models for assessing the completeness of randomized controlled trial publications.

Objective: To develop text classification models for determining whether the checklist items in the CONSORT reporting guidelines are reported in randomized controlled trial publications.

Materials And Methods: Using a corpus annotated at the sentence level with 37 fine-grained CONSORT items, we trained several sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. To address the problem of small training dataset, we used several data augmentation methods (EDA, UMLS-EDA, text generation and rephrasing with GPT-4) and assessed their impact on the fine-tuned PubMedBERT model.

Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature.

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature.

Multi-Omics Profiling Reveals Phenotypic and Functional Heterogeneity of Neutrophils in COVID-19.

Accumulating evidence has revealed unexpected phenotypic heterogeneity and diverse functions of neutrophils in several diseases. Coronavirus disease (COVID-19) can alter the leukocyte phenotype based on disease severity, including neutrophil activation in severe cases. However, the plasticity of neutrophil phenotypes and their relative impact on COVID-19 pathogenesis has not been well addressed.

Evaluating the Usability of an mHealth App for Empowering Cancer Survivors With Disabilities: Heuristic Evaluation and Usability Testing.

Background: More than 18 million cancer survivors are living in the United States. The effects of cancer and its treatments can have cognitive, psychological, physical, and social consequences that many survivors find incredibly disabling. Posttreatment support is often unavailable or underused, especially for survivors living with disabilities.

Automatic categorization of self-acknowledged limitations in randomized controlled trial publications.

Objective: Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency.

Neural Radiance Field-Inspired Depth Map Refinement for Accurate Multi-View Stereo.

In this paper, we propose a method to refine the depth maps obtained by Multi-View Stereo (MVS) through iterative optimization of the Neural Radiance Field (NeRF). MVS accurately estimates the depths on object surfaces, and NeRF accurately estimates the depths at object boundaries. The key ideas of the proposed method are to combine MVS and NeRF to utilize the advantages of both in depth map estimation and to use NeRF for depth map refinement.

Photoemission Orbital Tomography Using a Robust Sparse PhaseLift.

Photoemission orbital tomography (POT) from photoelectron momentum maps (PMMs) is a powerful technique that visualizes the shape of the molecular orbitals (MOs) of molecular films. For further utilization of POT, a simple and low-cost method of POT is highly required. Here, we propose a new POT method based on the PhaseLift algorithm (PhaseLift POT).

The potential and limitations of large language models in identification of the states of motivations for facilitating health behavior change.

Importance: The study highlights the potential and limitations of the Large Language Models (LLMs) in recognizing different states of motivation to provide appropriate information for behavior change. Following the Transtheoretical Model (TTM), we identified the major gap of LLMs in responding to certain states of motivation through validated scenario studies, suggesting future directions of LLMs research for health promotion.

Objectives: The LLMs-based generative conversational agents (GAs) have shown success in identifying user intents semantically.

Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement.

Cognitive Exercise for Persons with Alzheimer's Disease and Related Dementia Using a Social Robot.

Reminiscence therapy (RT) can improve the mood and communication of persons living with Alzheimer's Disease and Alzheimer's Disease related dementias (PLWD). Traditional RT requires professionals' facilitation, limiting its accessibility to PLWD. Social robotics has the potential to facilitate RT, enabling accessible, home-based RT.

Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells.

The multipotent stem cells of our body have been largely harnessed in biotherapeutics. However, as they are derived from multiple anatomical sources, from different tissues, human mesenchymal stem cells (hMSCs) are a heterogeneous population showing ambiguity in their in vitro behavior. Intra-clonal population heterogeneity has also been identified and pre-clinical mechanistic studies suggest that these cumulatively depreciate the therapeutic effects of hMSC transplantation.

Patient, family caregiver, and economic outcomes of an integrated screening and novel stepped collaborative care intervention in the oncology setting in the USA (CARES): a randomised, parallel, phase 3 trial.

Background: The current standard of care of screening and referring patients for treatment for symptoms, such as depression, pain, and fatigue, is not effective. This trial aimed to test the efficacy of an integrated screening and novel stepped collaborative care intervention versus standard of care for patients with cancer and at least one of the following symptoms: depression, pain, or fatigue.

Methods: This randomised, parallel, phase 3 trial was conducted in 29 oncology outpatient clinics associated with the UPMC Hillman Cancer Center in the USA.

Group authorship, an excellent opportunity laced with ethical, legal and technical challenges.

Group authorship (also known as corporate authorship, team authorship, consortium authorship) refers to attribution practices that use the name of a collective (be it team, group, project, corporation, or consortium) in the authorship byline. Data shows that group authorships are on the rise but thus far, in scholarly discussions about authorship, they have not gained much specific attention. Group authorship can minimize tensions within the group about authorship order and the criteria used for inclusion/exclusion of individual authors.

Dietary habits and plasma lipid concentrations in a general Japanese population.

Introduction: Accumulating data on the associations between food consumption and lipid composition in the body is essential for understanding the effects of dietary habits on health.

Objectives: As part of omics research in the Tohoku Medical Megabank Community-Based Cohort Study, this study sought to reveal the dietary impact on plasma lipid concentration in a Japanese population.

Methods: We conducted a correlation analysis of food consumption and plasma lipid concentrations measured using mass spectrometry, for 4032 participants in Miyagi Prefecture, Japan.

Structural Perspective of NR4A Nuclear Receptor Family and Their Potential Endogenous Ligands.

There are 48 nuclear receptors in the human genome, and many members of this superfamily have been implicated in human diseases. The NR4A nuclear receptor family consisting of three members, NR4A1, NR4A2, and NR4A3 (formerly annotated as Nur77, Nurr1, and NOR1, respectively), are still orphan receptors but exert pathological effects on immune-related and neurological diseases. We previously reported that prostaglandin A (PGA) and prostaglandin A (PGA) are potent activators of NR4A3, which bind directly to the ligand-binding domain (LBD) of the receptor.

Endorsements of five reporting guidelines for biomedical research by journals of prominent publishers.

Biomedical research reporting guidelines provide a framework by which journal editors and the researchers who conduct studies can ensure that the reported research is both complete and transparent. With more than 16 different guidelines for the 11 major study types of medical and health research, authors need to be familiar with journal reporting standards. To assess the current endorsements of reporting guidelines for biomedical and health research, this study examined the instructions for authors (IFAs) of 559 biomedical journals by 11 prominent publishers that publish original research or systematic reviews/meta-analyses.

What Influence Could the Acceptance of Visitors Cause on the Epidemic Dynamics of a Reinfectious Disease?: A Mathematical Model.

The globalization in business and tourism becomes crucial more and more for the economical sustainability of local communities. In the presence of an epidemic outbreak, there must be such a decision on the policy by the host community as whether to accept visitors or not, the number of acceptable visitors, or the condition for acceptable visitors. Making use of an SIRI type of mathematical model, we consider the influence of visitors on the spread of a reinfectious disease in a community, especially assuming that a certain proportion of accepted visitors are immune.

Collective Relaxation Dynamics in a Three-Dimensional Lattice Glass Model.

We numerically elucidate the microscopic mechanisms controlling the relaxation dynamics of a three-dimensional lattice glass model that has static properties compatible with the approach to a random first-order transition. At low temperatures, the relaxation is triggered by a small population of particles with low-energy barriers forming mobile clusters. These emerging quasiparticles act as facilitating defects responsible for the spatially heterogeneous dynamics of the system, whose characteristic length scales remain strongly coupled to thermodynamic fluctuations.

The Reality of a Head-Mounted Display (HMD) Environment Tested via Lightness Perception.

Head-mounted displays (HMDs) are becoming more and more popular as a device for displaying a virtual reality space, but how real are they? The present study attempted to quantitatively evaluate the degree of reality achieved with HMDs by using a perceptual phenomenon as a measure. Lightness constancy is an ability that is present in human visual perception, in which the perceived reflectance (i.e.