Supplemental File Research Articles

Evaluation of safety and efficacy of intracranial self-expanding drug-eluting stents for symptomatic intracranial atherosclerotic stenosis: a prospective, multicentre, randomised controlled, superiority clinical trial protocol

BackgroundIn-stent restenosis (ISR) is a crucial factor that affects the long-term efficacy of intracranial bare metal stent (BMS) implantation for intracranial atherosclerotic stenosis (ICAS). Patients with intracranial ISR are at...

Exploring Latent Transferability of feature components

Feature disentanglement techniques have been widely employed to extract transferable (domain-invariant) features from non-transferable (domain-specific) features in Unsupervised Domain Adaptation (UDA). However, due to the complex interplay among high-dimensional features, the separated “non-transferable” features may still be partially informative. Suppressing or disregarding them, as commonly employed in previous methods, can overlook the inherent transferability. In this work, we introduce two concepts: Partially Transferable Class Features and Partially Transferable Domain Features (PTCF and PTDF), and propose a succinct feature disentanglement technique. Different with prior works, we don’t seek to thoroughly peel off the non-transferable features, as it is challenging practically. Instead, we take the two-stage strategy consisting of rough feature disentanglement and dynamic adjustment. We name our model as ELT because it can systematically Explore Latent Transferability of feature components. ELT can automatically evaluate the transferability of internal feature components, dynamically giving more attention to features with high transferability and less to features with low transferability, effectively solving the problem of negative transfer. Extensive experimental results have proved its efficiency. The code and supplementary file will be available at https://github.com/njtjmc/ELT.

Sparse model-based clustering of three-way data via lasso-type penalties

Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are increasingly common in various fields. Despite their widespread use and successful applications, these models suffer from over-parameterization, making them not suitable for even moderately sized matrix-variate data. To address this issue, we introduce a sparse model-based clustering approach for three-way data. Our approach assumes that the matrix mixture parameters are sparse and have different degrees of sparsity across clusters, enabling the induction of parsimony in a flexible manner. Estimation relies on the maximization of a penalized likelihood, with specifically tailored group and graphical lasso penalties. These penalties facilitate the selection of the most informative features for clustering three-way data where variables are recorded over multiple occasions, as well as allowing the identification of cluster-specific association structures. We conduct extensive testing of the proposed methodology on synthetic data and validate its effectiveness through an application to time-dependent crime patterns across multiple U.S. cities. Supplementary files for this article are available online.

CosinorTest: An R Shiny App for Cosinor-model-based circadian and differential analysis in transcriptomic applications.

'CosinorTest' application is an interactive R Shiny tool designed to facilitate circadian and differential circadian analysis with transcriptomic data using Cosinor-model-based methods. This novel application integrates multiple major statistical algorithms to identify circadian rhythmicity in gene expression data and enables the comparison of differential circadian patterns between two experimental conditions. Key features of the 'CosinorTest' app include circadian rhythmicity detection, differential patterns assessment, circadian and differential analyses with repeated measurement, and interactive data visualization, all contributing to a comprehensive understanding of underlying biological mechanisms. As the application doing all comprehensive circadian analysis using the Cosinor model, the 'CosinorTest' app fills a crucial gap in the field of circadian biology and transcriptomics, providing a powerful and user-friendly platform for researchers, especially those without profound programming skills to explore the circadian gene expression regulation, and further advance circadian research. CosinorTest is freely available at https://circadiananalysis.shinyapps.io/circadianapp/ . hading@augusta.edu. Supplementary files are available at bioRχiv online.

Collectively Working towards Plant-Pollinator Interactions Data Interoperability and Reuse: Lessons Learned from the WorldFAIR Project

The WorldFAIR project worked with eleven case studies to advance the implementation of the FAIR data principles (Findable, Accessible, Interoperable, and Reusable; Wilkinson et al. 2016). The Case Study on Agricultural Biodiversity (Drucker et al. 2022) addressed the challenges of advancing interoperability and mobilizing plant-pollinator interaction data for reuse. In the first phase of the project we performed a landscape analysis and found that a significant amount of data on plant-pollinator interaction was available as supplementary files of research articles, in a range of formats such as PDFs, Excel® spreadsheets, and text files. The diversity of approaches and the lack of appropriate data vocabularies led to confusion, information loss, and the need for complex data interpretation and transformation. In the second phase of the project, we performed six studies to adopt standards recommended in the first phase. Our approach for plant-pollinator data standardization was based on Darwin Core (Darwin Core Task Group 2009) and Ecological Data Language (EML, Jones et al. 2019), in conjunction with a data model and vocabulary proposed by the Brazilian Network of Plant-Pollinator Interactions (REBIPP). The studies underwent a process of “FAIRification” (i.e., transforming data into a format that adheres to the FAIR data principles) using the Global Biotic Interactions (GloBI, Poelen et al. 2014) platform. Two studies also explored the publishing model for Biotic Interactions as part of the GBIF New Data Model. The standardization strategy was successful and increased the interoperability of plant-pollinator interaction data, resulting in a process that allows tracking the provenance of the data, as well as facilitates the reuse of datasets (Drucker et al. 2024). This effort led to the development of "FAIR best practices" and guidelines for sharing plant-pollinator interaction data, as well as a FAIR assessment tool designed to help researchers and institutions evaluate adherence to the FAIR principles (Drucker et al. 2024). A tutorial to standardize plant-pollinator datasets (Gonzalez-Vaquero et al. 2024) and a cookbook with guidelines and recommendations for publishing agriculture-related plant-pollinator data (Salim et al. 2024) were developed to help different actors standardize plant-pollinator interaction data in accordance with the FAIR principles. Moving from diverse approaches and siloed initiatives to widely available FAIR plant-pollination interaction data for scientists and decision-makers will enable the development of integrative studies that enhance our understanding of species biology, behavior, ecology, phenology, and evolution.

Analyzing Data Sharing Policies in Library and Information Science: Journal Metrics, Open Access Status, and Publisher Volume

The objective of this study is to explore the prevalence and characteristics of data sharing policies in library and information science (LIS) journals, focusing on their relationship with journal metrics, publisher volume (number of journals managed by the publisher), and open access (OA) status. The study examined the link between data sharing policies and journal metrics in LIS journals indexed in Scopus. Using secondary data from the SJR portal and qualitative data from author guidelines, the study revealed several key findings: First, a positive relationship existed between publisher volume and the presence of data sharing policies, with high-volume publishers consistently implementing such policies. Second, 50.2% of LIS journals lacked data sharing guidelines, indicating a significant gap. Third, journals that encouraged or required data sharing under certain conditions tended to perform better in metrics such as quartiles, h-index, and citation impact. Fourth, data sharing policies were more common in higher-ranked journals and were linked to better journal metrics. Fifth, higher-ranked journals were more likely to include details on data statements, DOIs, and repositories than lower-ranked journals. Lastly, non-OA journals were more likely to encourage practices such as including data repositories and supplementary files in submissions, compared to OA journals, revealing unexpected disparities. In conclusion, data sharing policies in LIS journals showed associations with journal ranking and publisher volume, with non-OA journals showing higher presence of certain data sharing practices.

The first firefly fossil (Coleoptera: Lampyridae) from Dominican amber

Fireflies (Coleoptera: Lampyridae) comprise ~2,500 extant species worldwide (Ferreira et al., 2020). Considering the extant diversity of firefly species, their fossil record is scant, with only eight taxa across four subfamilies: Luciolinae, Ototretinae, Lamprohizinae, and Lampyrinae (Supplementary file 1; Kazantsev, 2024). Dominican amber may have the best quality of preservation among fossil resins, with a rich diversity of fossil specimens (Grimaldi & Engel, 2005) from the early Miocene (20–15 Ma) (Iturralde-Vinent & MacPhee, 2019). However, no firefly taxon was previously known from this deposit. Here, we describe the first Lampyridae fossil from Dominican amber. It represents a new species of the extant Neotropical genus Lychnacris Motschulsky, 1853, which has 19 extant species known from the island of Hispaniola in the Caribbean (Kazantsev & Perez-Gelabert, 2009, 2013; Perez-Gelabert, 2011).

Systematic review of treatments for the gastrointestinal manifestations of systemic lupus erythematosus

ObjectivesTo comprehensively assess and present the evidence for treatments used in the management of the gastrointestinal manifestations of SLE. MethodsA systematic search of the literature from January 1990 to June 2022 was performed using the following databases: MEDLINE, EMBASE, PubMed and Cochrane. Key words relating to the gastrointestinal system, SLE, and treatment were used. Where there was sufficient evidence for the treatment of a manifestation, we excluded case series with <10 cases and case reports. However, for rarer manifestations with insufficient higher-level evidence, smaller case series and case reports were included. ResultsA total of 29 studies including 767 patients were included in the review; six cohort studies, 11 case-control studies, and 11 case series. Specific gastrointestinal manifestations included enteritis (5 studies), mesenteric vasculitis (3 studies), acute pancreatitis (5 studies), chronic pancreatitis (1 study), intestinal pseudo-obstruction (IPO) (2 studies), hepatitis (4 studies), protein-losing enteropathy (PLE) (6 studies), acute acalculous cholecystitis (2 studies), and Budd-Chiari Syndrome (1 study). Evidence for the treatment of Ascites (13 case reports), peritonitis (3 case reports), and miscellaneous GI manifestations (11 case reports) are included as a supplemental file. Most studies demonstrated a benefit from pulsed intravenous methylprednisolone (IVMP) in severe or life-threatening manifestations, and oral prednisolone for less severe manifestations. However, the quality of evidence was low, with a high risk of bias in all studies. ConclusionThis review highlights the need for standardised disease definitions and terminology, as well as consideration of including gastrointestinal manifestations in disease scoring systems. There is a significant need for high-quality clinical trials in the treatment of the gastrointestinal manifestations of SLE, which will likely need to be multi-centre. We hope that this review will promote awareness of the gastrointestinal manifestations of SLE, and serve as a practical guide for evidence-based treatment.

Dendritic cells pulsed with multifunctional Wilms’ tumor 1 (WT1) peptides combined with multiagent chemotherapy modulate the tumor microenvironment and enable conversion surgery in pancreatic cancer

BackgroundWe aimed to develop a chemoimmunotherapy regimen consisting of a novel Wilms’ tumor 1 (WT1) peptide-pulsed dendritic cell (WT1-DC) vaccine and multiagent chemotherapy and to investigate the safety, clinical outcomes,...

Enhanced Response Envelope via Envelope Regularization

The response envelope model provides substantial efficiency gains over the standard multivariate linear regression by identifying the material part of the response to the model and by excluding the immaterial part. In this article, we propose the enhanced response envelope by incorporating a novel envelope regularization term based on a nonconvex manifold formulation. It is shown that the enhanced response envelope can yield better prediction risk than the original envelope estimator. The enhanced response envelope naturally handles high-dimensional data for which the original response envelope is not serviceable without necessary remedies. In an asymptotic high-dimensional regime where the ratio of the number of predictors over the number of samples converges to a nonzero constant, we characterize the risk function and reveal an interesting double descent phenomenon for the envelope model. A simulation study confirms our main theoretical findings. Simulations and real data applications demonstrate that the enhanced response envelope does have significantly improved prediction performance over the original envelope method, especially when the number of predictors is close to or moderately larger than the number of samples. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

User guide for Social Determinants of Health Survey data in the All of Us Research Program.

Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions. This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey. We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.

Cross-section parameterisation and optimisation of double-hat beams under dynamic three-point bending

Double-hat beam is a lightweight and high-performance bending-resistant structure. In this work, the impact performance of a double-hat beam under dynamic three-point bending load is verified by drop weight tests. Three geometric parameters and splines are applied to obtain many novel cross-section shapes to improve the crashworthiness of double-hat beams. Compared with double-hat beam, the mean crushing force (MCF) of DB42-51-6 is increased by 45.4%, and the specific mean crushing force (SMCF) of DB22-46-41 is increased by 15.3%. It is also found that quadrangular cross-sections outperform rectangular ones, rectangular cross-sections outperform cruciform ones, and elliptical cross-sections perform the worst. The impact performance of butterfly shaped cross-sections is better than that of rectangular and gourd-shaped ones, and convex corner is conducive to improving the bending resistance of double-hat beam compared with the concave corner. Three cross-sections are selected for multi-objective optimisation to improve their crashworthiness, and multiple sets of Pareto solutions are obtained and presented in supplementary file to provide a variety of feasible solutions. These results have important implications for the impact performance design of double-hat structures and vehicle bodies.

A computation of two-loop six-point Feynman integrals in dimensional regularization

We compute three families of two-loop six-point massless Feynman integrals in dimensional regularization, namely the double-box, the pentagon-triangle, and the hegaxon-bubble family. This constitutes the first analytic computation of two-loop master integrals with eight scales. We use the method of canonical differential equations. We describe the corresponding integral basis with uniform transcendentality, the relevant function alphabet, and analytic boundary values at a particular point in the Euclidean region up to the fourth order in the regularization parameter ϵ. The results are expressed as one-fold integrals over classical polylogarithms. We provide a set of supplementary files containing our results in machine-readable form, including a proof-of-concept implementation for numerical evaluations of the one-fold integrals valid within a subset of the Euclidean region.

ShinySperm: navigating the sperm proteome landscape.

Context Integrated omics studies hold a crucial role in improving our understanding of reproductive biology. However, the complex datasets so generated are often only accessible via supplementary data files, which lack the capacity for interactive features to allow users to readily interrogate and visualise data of interest. Aims The intent of this technical note was to develop an interactive web-based application that enables detailed interrogation of a representative sperm proteome, facilitating a deeper understanding of the proteins identified, their relative abundance, classifications, functions, and associated phenotypes. Methods We developed a Shiny web application, ShinySperm (https://reproproteomics.shinyapps.io/ShinySperm/ ), utilising R and several complementary libraries for data manipulation (dplyr), interactive tables (DT), and visualisation (ggplot2, plotly). ShinySperm features a responsive user interface, dynamic filtering options, interactive charts, and data export capabilities. Key results ShinySperm allows users to interactively search, filter, and visualise sperm proteomics data based on key features (e.g. protein classification, sperm cell domain, presence, or absence at different maturation stages). This application responds live to filtering options and enables the generation of interactive plots and tables, thus enhancing user engagement and understanding of the data. Conclusions ShinySperm provides a robust platform for the dynamic exploration of epididymal sperm proteome data. It significantly improves accessibility and interpretability of complex datasets, allowing for effective data-driven insights. Implications This technical note highlights the potential of interactive web applications in reproductive biology and provides a plug and play script for the field to produce applications for meaningful researcher interaction with complex omic datasets.

Intervention at an early threshold for post-hemorrhagic ventricular dilatation in preterm infants: a systematic review and meta-analysis.

Very few adequately powered studies exploring early thresholds for intervention in the management of post-hemorrhagic ventricular dilatation (PHVD) in preterm infants have identified consistent neurodevelopmental advantages at 12-30months. We aimed to conduct a meta-analysis on the efficacy and safety of early versus conservative thresholds for intervention, primarily aimed at normalizing cerebrospinal fluid (CSF) pressure, in the management of PHVD in preterm infants. Multiple databases were searched for eligible papers, and prospective randomized trials involving preterm infants were selected. The results are expressed as relative risks (RRs) with 95% confidence intervals (CIs). The main outcome was survival without moderate-to-severe neurodevelopmental impairment at 12-30months. Ten articles representing seven randomized trials comparing early versus conservative thresholds for interventions were included. Five trials (n = 545 infants) reported no difference in the main outcome between early and conservative groups [RR 0.99 (0.71, 1.37)]. Sensitivity analysis excluding data from a medication trial did not alter the main outcome [RR 1.15 (0.95, 1.39)]. Infants in the early threshold group received significantly more interventions [RR 1.48 (1.05, 2.09)]. Deaths before discharge/during the initial study period [RR 1.04 (0.70, 1.54)] or a composite of death or shunt insertion [RR 1.04 (0.86, 1.27)] were comparable between the two groups. Early intervention for PHVD, before a clinical or ultrasound threshold is met, leads to additional clinical procedures but does not improve survival without moderate-severe neurodevelopmental impairment at 12-30months. Caution should be exercised in interpreting these results due to significant variation between the studies. Supplementary file 3 (MP4 131172kb).

Supplementary file; ICHD 3 diagnostic criteria for Migraine

No abstract available

Toward better understanding and management of chemobrain: the potential utilities of the MemTrax memory test

ObjectiveTo study the effects of chemotherapy on cognitive function in breast cancer patients, and to investigate the relationship of MemTrax test of memory and related functions to the FACT-Cog functional self-assessment for the evaluation and management of chemobrain.MethodsIn this prospective cohort study, clinical information of pathologically confirmed female breast cancer patients who decided to receive chemotherapy were collected in a questionnaire which was developed for this study and provided as a supplementary file. The FACT-Cog self-assessment and MemTrax test were administered before and after the chemotherapy treatments. Patients with chemobrain were identified using published criteria based on FACT-Cog scores, and MemTrax scores from chemobrain patients were analyzed.ResultsFifty-six patients participated in this study, of which 41 participants completed 4 or more cycles of chemotherapy and were included in the final analyses here. Using the reported high end of minimal clinical differences (10.6 points) of FACT-Cog before and after chemotherapy, 18 patients suffered from chemobrain in this study. In these 18 chemobrain patients, no cognitive impairments were detected by MemTrax, which paradoxically demonstrated an improvement in the normal cognitive range.ConclusionThe cognitive impairment induced by chemotherapy in breast cancer patients is detectable by the FACT-Cog in a Chinese cohort but is not detected by the MemTrax memory test. The fact that the more objective MemTrax could not detect the impairment could alleviate patients’ concerns which in turn would be beneficial for patients’ mental health.

The antidepressant effect of Shexiang Baoxin Pills on myocardial infarction rats with depression may be achieved through the inhibition of the NLRP3 inflammasome pathway.

Patients with myocardial infarction (MI) frequently experience a heightened incidence of depression, thereby increasing the risk of adverse cardiovascular events. Consequently, early detection and intervention in depressive symptoms among patients with MI are imperative. Shexiang Baoxin Pills (SBP), a Chinese patent medicine employed for the treatment of MI, exhibits diverse mechanisms targeting this condition. Nevertheless, its therapeutic efficacy on postmyocardial infarction depressive symptoms remains unclear. The aim of this study is to investigate the effectiveness and mechanism of SBP in managing depression during acute myocardial infarction (AMI). A rat model combining MI and depression was established, and the rats were randomly divided into four groups: the model (MOD) group, SBP group, Fluoxetine (FLX) group, and Sham group. After 28 days of drug intervention, cardiac function was assessed using echocardiography while behavior was evaluated through sucrose preference test (SPT), forced swimming test (FST), and open-field test (OFT). Additionally, levels of inflammatory factors in serum and hippocampus were measured along with NLRP3 inflammasome-related protein expression via Western blotting and immunofluorescence. SBP can enhance cardiac function in rats with AMI and depression, while significantly ameliorating depressive-like behavior. Compared to the Sham group, levels of IL-1β, IL-18, TNF-α, and other inflammatory factors were markedly elevated in the MOD group. However, expressions of these inflammatory factors were reduced to varying degrees following treatment with SBP or FLX. Analysis of NLRP3 inflammasome-related proteins in the hippocampus revealed a significant upregulation of IL-1β, IL-18, NLRP3, ASC, caspase-1, and GSDMD in the MOD group; conversely, these measures were significantly attenuated after SBP intervention. We have observed a significant amelioration in depression-like behavior upon SBP administration during the treatment of AMI, suggesting that this effect may be attributed to the inhibition of NLRP3-mediated pyroptosis. (The main findings are summarized in the graphical abstract in the supplementary file.).

Efficient protein structure archiving using ProteStAr.

The introduction of Deep Minds' Alpha Fold 2 enabled the prediction of protein structures at an unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data, which hinders the effective use of predicted structures in large-scale analyses. Here, we present ProteStAr, a compressor dedicated to CIF/PDB, as well as supplementary PAE files. Its main contribution is a novel approach to predicting atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates, the largest component of the protein structure files. The compression is lossless by default, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e. BinaryCIF, Foldcomp, PDC, our approach offers a superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of the multicore architecture of current central processing units and operates with speeds of about 1 GB/s. The presence of Python and C++ API further increases the usability of the presented method. The source code of ProteStAr is available at https://github.com/refresh-bio/protestar.

Anti-inflammatory decurrencyclics A-B, two undescribed nor-dammarane triterpenes from triangular sea bell Turbinaria decurrens

Significant concerns are raised regarding the legitimacy of the spectroscopic data provided in the titled manuscript. Many of the spectra provided in the supplemental material file show signs of being purposefully manipulated. Readers of the manuscript should treat the conclusions drawn, including the structures proposed, with extreme caution.