bluuebunny/crossref_metadata_2025_split huggingface dataset:
Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs
Authors: He Siyu , Jin Yinuo , Nazaret Achille , Shi Lingting , Chen Xueer , Rampersaud Sham , Dhillon Bahawar S. , Valdez Izabella , Friend Lauren E. , Fan Joy Linyue , Park Cameron Y. , Mintz Rachel L. , Lao Yeh-Hsing , Carrera David , Fang Kaylee W. , Mehdi Kaleem , Rohde Madeline , McFaline-Figueroa José L. , Blei David , Leong Kam W. , Rudensky Alexander Y. , Plitas George , Azizi Elham 
Published: March 2024

Abstract
Spatially resolved gene expression profiling provides insight into tissue organization and cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.
Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis
Authors: Mitra Sneha , Malik Rohan , Wong Wilfred , Rahman Afsana , Hartemink Alexander J. , Pritykin Yuri , Dey Kushal K. , Leslie Christina S. 
Published: March 2024

AbstractWe present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC–seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene–peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.
An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors
Authors: Zhang Xuan , Song Baobao , Carlino Maximillian J. , Li Guangyuan , Ferchen Kyle , Chen Mi , Thompson Evrett N. , Kain Bailee N. , Schnell Dan , Thakkar Kairavee , Kouril Michal , Jin Kang , Hay Stuart B. , Sen Sidharth , Bernardicius David , Ma Siyuan , Bennett Sierra N. , Croteau Josh , Salvatori Ornella , Lye Melvin H. , Gillen Austin E. , Jordan Craig T. , Singh Harinder , Krause Diane S. , Salomonis Nathan , Grimes H. Leighton 
Published: March 2024

AbstractAnalysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.
A Dataset of Electrical Components for Mesh Segmentation and Computational Geometry Research
Authors: Scheffler Benedikt , Bründl Patrick , Nguyen Huong Giang , Stoidner Micha , Franke Jörg 
Published: March 2024

AbstractData quality is of crucial importance in the field of automated or digitally assisted assembly. This paper presents a comprehensive data set of triangle meshes representing electrical and electronic components obtained by scraping Computer Aided Design (CAD) models from the Internet. Consisting of a total of 234 triangle meshes with labelled vertices, this data set was specifically created for segmentation tasks. Its versatility for multimodal tasks is underscored by the presence of various labels, including vertex labels, categories, and subcategories. This paper presents the data set and provides a thorough statistical analysis, including measures of shape, size, distribution, and inter-rater reliability. In addition, the paper suggests several approaches for using the data set, considering its multimodal characteristics. The data set and related findings presented in this paper are intended to encourage further research and advancement in the field of manufacturing automation, specifically spatial assembly.
An integrated multicriteria decision making framework for the selection of waste cement dust filled automotive brake friction composites
Authors: Singh Tej 
Published: March 2024

AbstractThis work discusses selecting optimal brake friction composite alternatives based on an integrated MABAC (multi-attributive border approximation area comparison) and AHP (analytic hierarchy process) approach. Therefore, non-asbestos automotive brake friction composites containing varying proportions of cement dust (50 to 0 wt%) and barium sulfate (0 to 50 wt%) were developed and tribo-evaluated on a Krauss machine following European regulations. Composite made up of 30 wt% cement dust and 20 wt% barium sulfate had the highest friction coefficient (0.361), lowest variability coefficient (0.598), and maximum recovery (123.27%). The composite with the least fading (15.36%) included 50 wt% cement dust, whereas the composite with the lowest wear (9.10 g) and the least frictional fluctuations (0.271) contained 50 wt% barium sulfate. By AHP, the friction coefficient (0.1989), fade (0.1696), recovery (0.1551), and wear (0.1412) were selected as the essential criteria in the performance assessment. Based on the MABAC ranking evaluation, the composite comprises 20 wt% barium sulfate and 30 wt% cement dust has the best tribological profile, whereas the composites of solely cement dust or barium sulfate have the poorest tribological profile. The acquired ranking results were confirmed using other decision-making models and subjected to sensitivity analysis to demonstrate their robustness.
A prospective multicenter assessor blinded pilot study using confocal laser endomicroscopy for intraoperative brain tumor diagnosis
Authors: Byun Yoon Hwan , Won Jae-Kyung , Hong Duk Hyun , Kang Ho , Kim Jang Hun , Yu Mi Ok , Kim Min-Sung , Kim Yong Hwy , Park Kyung-Jae , Jeong Min-Jae , Hwang Kyungmin , Kong Doo-Sik , Park Chul-Kee , Kang Shin-Hyuk 
Published: March 2024

AbstractIn this multi-center, assessor-blinded pilot study, the diagnostic efficacy of cCeLL-Ex vivo, a second-generation confocal laser endomicroscopy (CLE), was compared against the gold standard frozen section analysis for intraoperative brain tumor diagnosis. The study was conducted across three tertiary medical institutions in the Republic of Korea. Biopsy samples from newly diagnosed brain tumor patients were categorized based on location and divided for permanent section analysis, frozen section analysis, and cCeLL-Ex vivo imaging. Of the 74 samples from 55 patients, the majority were from the tumor core (74.3%). cCeLL-Ex vivo exhibited a relatively higher diagnostic accuracy (89.2%) than frozen section analysis (86.5%), with both methods showing a sensitivity of 92.2%. cCeLL-Ex vivo also demonstrated higher specificity (70% vs. 50%), positive predictive value (PPV) (95.2% vs. 92.2%), and negative predictive value (NPV) (58.3% vs. 50%). Furthermore, the time from sample preparation to diagnosis was notably shorter with cCeLL-Ex vivo (13 min 17 s) compared to frozen section analysis (28 min 28 s) (p-value < 0.005). These findings underscore cCeLL-Ex vivo's potential as a supplementary tool for intraoperative brain tumor diagnosis, with future studies anticipated to further validate its clinical utility.
Optimized machine learning model for air quality index prediction in major cities in India
Authors: Natarajan Suresh Kumar , Shanmurthy Prakash , Arockiam Daniel , Balusamy Balamurugan , Selvarajan Shitharth 
Published: March 2024

AbstractIndustrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.
Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs
Authors: Carmichael Josie , Costanza Enrico , Blandford Ann , Struyven Robbert , Keane Pearse A. , Balaskas Konstantinos 
Published: March 2024

AbstractArtificial intelligence (AI) has great potential in ophthalmology. We investigated how ambiguous outputs from an AI diagnostic support system (AI-DSS) affected diagnostic responses from optometrists when assessing cases of suspected retinal disease. Thirty optometrists (15 more experienced, 15 less) assessed 30 clinical cases. For ten, participants saw an optical coherence tomography (OCT) scan, basic clinical information and retinal photography (‘no AI’). For another ten, they were also given AI-generated OCT-based probabilistic diagnoses (‘AI diagnosis’); and for ten, both AI-diagnosis and AI-generated OCT segmentations (‘AI diagnosis + segmentation’) were provided. Cases were matched across the three types of presentation and were selected to include 40% ambiguous and 20% incorrect AI outputs. Optometrist diagnostic agreement with the predefined reference standard was lowest for ‘AI diagnosis + segmentation’ (204/300, 68%) compared to ‘AI diagnosis’ (224/300, 75% p = 0.010), and ‘no Al’ (242/300, 81%, p =  < 0.001). Agreement with AI diagnosis consistent with the reference standard decreased (174/210 vs 199/210, p = 0.003), but participants trusted the AI more (p = 0.029) with segmentations. Practitioner experience did not affect diagnostic responses (p = 0.24). More experienced participants were more confident (p = 0.012) and trusted the AI less (p = 0.038). Our findings also highlight issues around reference standard definition.
Epidemiology and field efficacy of anthelmintic drugs associated with gastrointestinal nematodes of sheep in Nejo district, Oromia, Ethiopia
Authors: Solomon Latera , Haile Geremew , Ahmed Nejash A. , Abdeta Debela , Galalcha Workineh , Hailu Yacob 
Published: March 2024

AbstractGastrointestinal nematodes (GINs) are major constraints to health and productivity of small ruminants. Methods of their control relies mainly on anthelmintic drugs; however, the indiscriminate use of these drugs could lead to the development of anthelmintic resistance (AR). This study aimed to investigate the epidemiology of GINs infection, and field evaluation of anthelmintic efficacy in sheep. The epidemiological data were collected using a cross-sectional study design while a farm-based field study design was employed for the evaluation of anthelminthic efficacy. Furthermore, standard parasitological techniques were employed for qualitative and quantitative worm identification. The overall prevalence indicated 50.3%. Six genera of GINs (Haemonchus, Trichostrongylus, Oesophagostomum/Chabertia, Trichuris, Teladosargia/Ostertagia and Nematodirus) were identified. Among the identified genera, Haemonchus (25.4%) and Trichostrongylus (24.8%) were the dominant genera followed by mixed infection (21.8%), Oesophagostomum/Chabertia (10.4%), Trichuris (7.8%), Teladosargia (Ostertagia) (5.7%) and Nematodirus (4.1%). Mixed infections consisted either of double infections with Haemonchus and Trichostrongylus, or triple infections with Haemonchus, Trichostrongylus and Trichuris. The McMaster egg counting results showed that the mean EPG of infected sheep was 845.6. The results also showed 66 (34.2%), 101 (52.3%) and 26 (13.5%) sheep had low, moderate and heavy worm burden, respectively. Albendazole and Ivermectin showed low efficacy (percentage reductions = 90% and 92%; 95% lower confidence limit = 82.1% and 83.6% respectively) whereas Tetramisole was effective (FECR% = 96.8%; 95% LCL = 93.4%). Factors such as age, body condition, management system and past deworming history of sheep were found to have a statistically significant (p < 0.05) influence on the occurrence and burden of the worms. This is further explained as the highest prevalence and worm burden was detected in sheep of young age (p = 0.008; OR = 0.58; 95% CI = 0.39–0.87), poor body condition (p = 0.001; OR = 0.08; 95% CI = 0.04–0.16) and sheep kept under semi-intensive (p = 0.04; OR = 1.53; 95% CI = 1.02–2.29) with no deworming history for the last two months (p = 0.001; OR = 2.97; 95% CI = 1.94–4.56). The study results revealed that nematode infections were among sheep health constraints that could hurt their productivity while low efficacy of Albendazole and Ivermectin were detected. Therefore, the appropriate management techniques of GIN infections should be designed and implemented. Moreover, a further study involving more sensitive techniques (e.g. Mini-FLOTAC, molecular, and serological techniques) should be conducted by considering different host and environmental risk factors such as production level and seasons.
The neural basis of naturalistic semantic and social cognition
Authors: Thye Melissa , Hoffman Paul , Mirman Daniel 
Published: March 2024

Abstract
Decoding social environments and engaging meaningfully with other people are critical aspects of human cognition. Multiple cognitive systems, including social and semantic cognition, work alongside each other to support these processes. This study investigated shared processing between social and semantic systems using neuroimaging data collected during movie-viewing, which captures the multimodal environment in which social knowledge is exchanged. Semantic and social content from movie events (event-level) and movie transcripts (word-level) were used in parametric modulation analyses to test (1) the degree to which semantic and social information is processed within each respective network and (2) engagement of the same cross-network regions or the same domain-general hub located within the semantic network during semantic and social processing. Semantic word and event-level content engaged the same fronto-temporo-parietal network and a portion of the semantic hub in the anterior temporal lobe (ATL). Social word and event-level content engaged the supplementary motor area and right angular gyrus within the social network, but only social words engaged the domain-general semantic hub in left ATL. There was evidence of shared processing between the social and semantic systems in the dorsolateral portion of right ATL which was engaged by word and event-level semantic and social content. Overlap between the semantic and social word and event results was highly variable within and across participants, with the most consistent loci of overlap occurring in left inferior frontal, bilateral precentral and supramarginal gyri for social and semantic words and in bilateral superior temporal gyrus extending from ATL posteriorly into supramarginal gyri for social and semantic events. These results indicate a complex pattern of shared and distinct regions for social and semantic cognition during naturalistic processing.

Protocol registration
The stage 1 protocol for this Registered Report was accepted in principle on October 11, 2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/ACWQY.
Phototoxicity of low doses of light and influence of the spectral composition on human RPE cells
Authors: Françon Anaïs , Delaunay Kimberley , Jaworski Thara , Lebon Cécile , Picard Emilie , Youale Jenny , Behar-Cohen Francine , Torriglia Alicia 
Published: March 2024

AbstractLight is known to induce retinal damage affecting photoreceptors and retinal pigment epithelium. For polychromatic light, the blue part of the spectrum is thought to be the only responsible for photochemical damage, leading to the establishment of a phototoxicity threshold for blue light (445 nm). For humans it corresponds to a retinal dose of 22 J/cm2. Recent studies on rodents and non-human primates suggested that this value is overestimated. In this study, we aim at investigating the relevance of the current phototoxicity threshold and at providing new hints on the role of the different components of the white light spectrum on phototoxicity. We use an in vitro model of human induced pluripotent stem cells (hiPSC)-derived retinal pigment epithelial (iRPE) cells and exposed them to white, blue and red lights from LED devices at doses below 22 J/cm2. We show that exposure to white light at a dose of 3.6 J/cm2 induces an alteration of the global cellular structure, DNA damage and an activation of cellular stress pathways. The exposure to blue light triggers DNA damage and the activation of autophagy, while exposure to red light modulates the inflammatory response and inhibits autophagy.
Experimental and computational evidence that Calpain-10 binds to the carboxy terminus of NaV1.2 and NaV1.6
Authors: Arratia Luis Manuel , Bermudes-Contreras Juan David , Juarez-Monroy Jorge Armando , Romero-Macías Erik Alan , Luna-Rojas Julio Cesar , López-Hidalgo Marisol , Vega Ana Victoria , Zamorano-Carrillo Absalom 
Published: March 2024

AbstractVoltage-gated sodium channels (NaV) are pivotal proteins responsible for initiating and transmitting action potentials. Emerging evidence suggests that proteolytic cleavage of sodium channels by calpains is pivotal in diverse physiological scenarios, including ischemia, brain injury, and neuropathic pain associated with diabetes. Despite this significance, the precise mechanism by which calpains recognize sodium channels, especially given the multiple calpain isoforms expressed in neurons, remains elusive. In this work, we show the interaction of Calpain-10 with NaV's C-terminus through a yeast 2-hybrid assay screening of a mouse brain cDNA library and in vitro by GST-pulldown. Later, we also obtained a structural and dynamic hypothesis of this interaction by modeling, docking, and molecular dynamics simulation. These results indicate that Calpain-10 interacts differentially with the C-terminus of NaV1.2 and NaV1.6. Calpain-10 interacts with NaV1.2 through domains III and T in a stable manner. In contrast, its interaction with NaV1.6 involves domains II and III, which could promote proteolysis through the Cys-catalytic site and C2 motifs.
High-temperature tensile strength of C/SiC composite under laser-induced high heating flux in an aerobic environment
Authors: Wang Jiawei , Li Bin , Wang Shanshan , Zhang Shunlei , Yang Pengling , Wei Chenghua , Shen Yanlong 
Published: March 2024

AbstractBased on the advantages of laser high brightness, a high-temperature mechanical property measuring device has been developed, which can measure the high-temperature strength of C/SiC composites under the condition of short-term high-temperature rise rate and solve the problem of over-oxidation of materials in conventional high-temperature mechanical properties experiments. The experimental results show that the maximum temperature rise rate is 260 ℃/s at the initial heating stage, and the test time is controlled within 35 s. The tensile strength of the prepared C/SiC composites decreased first and then increased at high temperatures and laser-induced high temperatures. The experimental results are similar to those in the literature under the inert atmosphere. Oxidation has less of an effect on the mechanical characteristics of materials under conditions of rapid temperature rise. The system can be used to test the mechanical properties of composite materials at high temperatures and as a simulation platform for the thermal response of specific thermal protection systems subjected to a constant heat flux. This study can provide a new idea for testing ultra-high temperature mechanical properties of C/SiC materials and provide key technical support for the engineering application and high-temperature testing of C/SiC materials in high-temperature environments.
An event-oriented diffusion-refinement method for sparse events completion
Authors: Zhang Bo , Han Yuqi , Suo Jinli , Dai Qionghai 
Published: March 2024

AbstractEvent cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames, and feature ultra-high sensitivity at low bandwidth. The new mechanism demonstrates great advantages in challenging scenarios with fast motion and large dynamic range. However, the recorded events might be highly sparse due to either limited hardware bandwidth or extreme photon starvation in harsh environments. To unlock the full potential of event cameras, we propose an inventive event sequence completion approach conforming to the unique characteristics of event data in both the processing stage and the output form. Specifically, we treat event streams as 3D event clouds in the spatiotemporal domain, develop a diffusion-based generative model to generate dense clouds in a coarse-to-fine manner, and recover exact timestamps to maintain the temporal resolution of raw data successfully. To validate the effectiveness of our method comprehensively, we perform extensive experiments on three widely used public datasets with different spatial resolutions, and additionally collect a novel event dataset covering diverse scenarios with highly dynamic motions and under harsh illumination. Besides generating high-quality dense events, our method can benefit downstream applications such as object classification and intensity frame reconstruction.