bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate measures of changes in regional lung air volumes from chest x-rays of small animals
Authors: O’Connell D W , Morgan K S , Ruben G , Croton L C P , Pollock J A , Croughan M K , McGillick E V , Wallace M J , Crossley K J , Pryor E J , Lewis R A , Hooper S B , Kitchen M J 
Published: September 2022

Abstract

Objective. To develop a robust technique for calculating regional volume changes within the lung from x-ray radiograph sequences captured during ventilation, without the use of computed tomography (CT). Approach. This technique is based on the change in transmitted x-ray intensity that occurs for each lung region as air displaces the attenuating lung tissue. Main results. Lung air volumes calculated from x-ray intensity changes showed a strong correlation (R
2 = 0.98) against the true volumes, measured from high-resolution CT. This correlation enables us to accurately convert projected intensity data into relative changes in lung air volume. We have applied this technique to measure changes in regional lung volumes from x-ray image sequences of mechanically ventilated, recently-deceased newborn rabbits, without the use of CT. Significance. This method is suitable for biomedical research studies,enabling quantitative regional measurement of relative lung air volumes at high temporal resolution, and shows great potential for future clinical application.
Characterization of large LGAD sensors for proton counting in particle therapy
Authors: Villarreal O.A.M. , Peroglio G. , Vignati A. , Giordanengo S. , Milian F.M. , Ferrero M. , Menzio L. , Abujami M. , Galeone C. , Ali O.H. , Vignali M.C. , Borghi G. , Ficorella F. , Cirio R. , Monaco V. , Sacchi R. 
Published: September 2022

Abstract
A proton counter prototype based on Low Gain Avalanche Detector (LGAD) technology is being developed for the online monitoring of the fluence rate of therapeutic proton beams. The laboratory characterization of thin (45 μm and 60 μm) LGAD sensors segmented in 146 strips with an unprecedented large area of 2.6 × 2.6 cm2, covering the entire beam cross-section, is presented and discussed. The production includes 14 wafers with different characteristics, designed and produced at Fondazione Bruno Kessler (FBK) of Trento in 2020. The laboratory characterization was carried out at FBK, right after production, and at the University of Torino, after cutting the sensors, using a probe station with a power analyzer for the static DC electrical tests. The tests proved that the production was of very high quality. From 16 sensors randomly selected from different wafers, we observed consistency between the measurements performed at FBK and at the University of Torino, indicating that the cut did not degrade the performance. The sensors were also exposed to the clinical proton beam of the National Center for Oncological Hadrontherapy (CNAO, Pavia, Italy). The results show that LGADs allow achieving, in a very thin active thickness, a good separation between the proton signal, a peak of a very short duration, and the noise. This, combined with the large active area, will allow counting protons delivered with high efficiency at the high rates of a clinical beam.
Monte Carlo simulation of SiPMs with GATE
Authors: Mehadji Brahim , Dupont Mathieu , Fougeron Denis , Morel Christian 
Published: September 2022

Abstract
Silicon photomultipliers (SiPMs) replace photomultiplier tubes (PMTs) for the detection of light in many applications, particularly in high energy physics and medical imaging. We describe a flexible implementation of a SiPM model for the GATE Monte Carlo simulation platform, which is based on the SiPM noise description proposed by Rosado and Hidalgo, and describe an easy and effective method to determine and instantiate the SiPM noise model with simple measurements. We also simulate the micro-cell Single Photon Time Resolution (SPTR) and describe its measurement.
Novel prototype tower structure for the dual-readout fiber calorimeter
Authors: Karadzhinova-Ferrer A. , Agarwala J. , Ampilogov N. , Chmill V. , Ferrari R. , Freddi A. , Gaudio G. , Gigli S.G. , Samec Ž. , Santoro R. , Scagliotti C. , Vercellati F. 
Published: September 2022

Abstract
In this paper, we report on a novel cost-effective module structure for future Dual-Readout fiber Calorimetry. We have designed and constructed the system required for the fabrication of the first proof-of-concept prototype module. The instruments, the procedures and the quality control tools used in the assembly of the prototype, and its equipment, are described exhaustively. A mechanical precision of 50 μm required for the interlocking of the tower structures into a compact module has been achieved. A similar system will be used for the assembly of a full-scale prototype based on the Dual-Readout technique.
Oral mucositis on a chip: modeling induction by chemo- and radiation treatments and recovery
Authors: Ly Khanh L , Luo Xiaolong , Raub Christopher B 
Published: September 2022

Abstract
Oral mucositis (OM) is a debilitating complication affecting roughly 70% of head and neck cancer patients receiving chemotherapy and/or radiation treatment. No broadly effective preventative treatment for OM exists. Therefore, an in vitro model of cancer treatment-induced OM would aid studies into possible origins of the pathology and future drug targets to ameliorate it. In this study, we present a microfluidic oral mucosa triculture tissue construct consisting of a keratinocyte layer attached to a subepithelial fibroblast and endothelial cell-embedded collagen gel. To address the typically low stability of mucosal constructs in microfluidics, ruthenium-catalyzed photocrosslinking was implemented to strengthen the collagen gel and prevent the invasion of keratinocytes, thus maintaining tissue construct geometry and oral mucosa barrier function for over 18 d of culture. Next, the OM chip was exposed to cisplatin (day 10) and damaging radiation (day 11, ± cisplatin at day 10), mimicking damage from cancer therapy. Damage to and then recovery of the tissue layers and function were observed over days 11–18. Therefore, several important features of OM induction and resolution were modeled in microfluidic culture. The OM model on a chip allows for more sophisticated studies into mechanisms of OM and potential treatments.
Development of a high-throughput micropatterned agarose scaffold for consistent and reproducible hPSC-derived liver organoids
Authors: Jiang Shanqing , Xu Fang , Jin Menglong , Wang Zhen , Xu Xiaodong , Zhou Ying , Wang Jibo , Gu Longjun , Fan Han , Fan Yuhang , Zhou Zhangxian , Li Changyong , Chen Pu 
Published: September 2022

Abstract
Liver organoids represent emerging human-relevant in vitro liver models that have a wide range of biomedical applications in basic medical studies and preclinical drug discovery. However, the generation of liver organoids currently relies on the conventional Matrigel dome method, which lacks precise microenvironmental control over organoid growth and results in significant heterogeneity of the formed liver organoids. Here, we demonstrate a novel high-throughput culture method to generate uniform liver organoids from human pluripotent stem cell-derived foregut stem cells in micropatterned agarose scaffold. By using this approach, more than 8000 uniformly-sized liver organoids containing liver parenchyma cells, non-parenchymal cells, and a unique stem cell niche could be efficiently and reproducibly generated in a 48-well plate with a size coefficient of variation significance smaller than that in the Matrigel dome. Additionally, the liver organoids highly expressed liver-specific markers, including albumin (ALB), hepatocyte nuclear factor 4 alpha (HNF4α), and alpha-fetoprotein (AFP), and displayed liver functions, such as lipid accumulation, glycogen synthesis, ALB secretion, and urea synthesis. As a proof of concept, we evaluated the acute hepatotoxicity of acetaminophen (APAP) in these organoids and observed APAP-induced liver fibrosis. Overall, we expect that the liver organoids will facilitate wide biomedical applications in hepatotoxicity analysis and liver disease modeling.
A Simulation Study Comparing the Performance of Time-Varying Inverse Probability Weighting and G-Computation in Survival Analysis
Authors: Rudolph Jacqueline E , Schisterman Enrique F , Naimi Ashley I 
Published: September 2022

Abstract
Inverse probability weighting (IPW) and g-computation are commonly used in time-varying analyses. To inform decisions on which to use, we compared these methods using a plasmode simulation based on data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial (June 15, 2007–July 15, 2011). In our main analysis, we simulated a cohort study of 1,226 individuals followed for up to 10 weeks. The exposure was weekly exercise, and the outcome was time to pregnancy. We controlled for 6 confounding factors: 4 baseline confounders (race, ever smoking, age, and body mass index) and 2 time-varying confounders (compliance with assigned treatment and nausea). We sought to estimate the average causal risk difference by 10 weeks, using IPW and g-computation implemented using a Monte Carlo estimator and iterated conditional expectations (ICE). Across 500 simulations, we compared the bias, empirical standard error (ESE), average standard error, standard error ratio, and 95% confidence interval coverage of each approach. IPW (bias = 0.02; ESE = 0.04; coverage = 92.6%) and Monte Carlo g-computation (bias = −0.01; ESE = 0.03; coverage = 94.2%) performed similarly. ICE g-computation was the least biased but least precise estimator (bias = 0.01; ESE = 0.06; coverage = 93.4%). When choosing an estimator, one should consider factors like the research question, the prevalences of the exposure and outcome, and the number of time points being analyzed.
Compassion Fatigue, Compassion Satisfaction, Burnout and Alcohol Use Among Dental Hygienists
Authors: Brooks Jaime , Giblin-Scanlon Lori , Boyd Linda D , Vineyard Jared 
Published: September 2022

Abstract

Aims
The purpose of this study was to investigate the relationship between demographics, compassion fatigue (CF), compassion satisfaction (CS), burnout (BO) and alcohol use among dental hygienists (DHs).

Methods
The web-based survey consisting of two validated instruments [Alcohol Use Disorders Identification Test (AUDIT) and Professional Quality of Life] to measure alcohol use, CF, CS and BO was conducted with a convenience sample of DHs (n = 963).

Results
The completion rate was 81.6% (n = 786). Nearly one in five DHs (19.1%) reported having their alcohol consumption influenced by the Coronavirus disease 2019 pandemic at least moderately. The number of hours worked per week (x) and AUDIT scores (y) were fully mediated by BO (m) (P &lt; 0.001); the average number of hours worked per week (x) and AUDIT scores (y) were fully mediated by CF (m) (P &lt; 0.001); the average number of patients seen per day (x) was a predictor of AUDIT scores (y) when partially mediated by BO (m) (P &lt; 0.001); and age (x) was a predictor of AUDIT scores (y), mediated by CS (m). Results showed that one in four DHs could qualify for binge drinking (25.6%, n = 177) and 15.1% experienced blackout drinking episodes within the past year (n = 118).

Conclusion
Mediating relationships exist between demographics, CF, CS, BO and alcohol use among DHs. More research needs to be conducted on alcohol use and CF among DHs and protective factors that may reduce the risk of BO, CF or alcohol use.
Defining Internal Tissue Closure: High-Resolution Ultrasound Evaluation of Interi—A Novel Internal Tissue Closure System
Authors: Alfonso David , Bengtson Bradley , McGuire Patricia 
Published: September 2022

Abstract

Background
Seroma remains a leading postsurgical complication in plastic surgery. Conventional drains are ineffective in clearing blood and fluid and closing down surgical spaces. The Interi (Internal Closure System, IC Surgical, Grand Rapids, MI) is comprised of a novel branching internal manifold attached to a self-contained portable pump with a higher, consistent, continuous negative pressure, may reduce this long-standing issue. In addition, high-resolution ultrasound (HRUS) has emerged as an ideal tool to visualize structures, fluid collections, and seromas internally.

Objectives
This study evaluates Interi in full abdominoplasty patients utilizing HRUS to evaluate Interi's ability to evacuate blood and fluid, hold internal tissues together and document, for the first time, what internal tissue healing actually looks like radiographically.

Methods
An IRB approved, Contract Research Organization reviewed retrospective study evaluated consecutive patients undergoing full abdominoplasty utilizing Interi from July 2020 through March 2021 by three plastic surgeons. HRUS visualized and confirmed the presence or absence of fluid collections and healing tissue planes during the postoperative process. Study data and all adverse events were recorded, with HRUS images reviewed by investigators and confirmed by an independent radiologist.

Results
Seventy-one Interi patients were enrolled. Mean age was 43 (range: 21-74) and BMI was 28. Seroma was confirmed clinically and through HRUS in 3/71 patients, and was associated with either clot (2) or failure to activate system (1). Interi's ability to eliminate fluid and approximate/hold surgical tissue planes together was confirmed with HRUS. No other major complications, including abscess, hematomas, or flap necrosis were observed.

Conclusions
This novel Internal Tissue Closure System effectively evacuated blood and fluid, approximated and maintained closure of internal tissue planes in abdominoplasty patients, allowing for primary tissue healing and internal wound closure to occur. Healing tissue planes and any fluid present are easily identified on HRUS visualizing actual internal tissue healing with a simple, widely available radiographic scan.

Level of Evidence: 3
NSF4SL: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers
Authors: Wang Shike , Feng Yimiao , Liu Xin , Liu Yong , Wu Min , Zheng Jie 
Published: September 2022

AbstractMotivationDetecting synthetic lethality (SL) is a promising strategy for identifying anti-cancer drug targets. Targeting SL partners of a primary gene mutated in cancer is selectively lethal to cancer cells. Due to high cost of wet-lab experiments and availability of gold standard SL data, supervised machine learning for SL prediction has been popular. However, most of the methods are based on binary classification and thus limited by the lack of reliable negative data. Contrastive learning can train models without any negative sample and is thus promising for finding novel SLs.ResultsWe propose NSF4SL, a negative-sample-free SL prediction model based on a contrastive learning framework. It captures the characteristics of positive SL samples by using two branches of neural networks that interact with each other to learn SL-related gene representations. Moreover, a feature-wise data augmentation strategy is used to mitigate the sparsity of SL data. NSF4SL significantly outperforms all baselines which require negative samples, even in challenging experimental settings. To the best of our knowledge, this is the first time that SL prediction is formulated as a gene ranking problem, which is more practical than the current formulation as binary classification. NSF4SL is the first contrastive learning method for SL prediction and its success points to a new direction of machine-learning methods for identifying novel SLs.Availability and implementationOur source code is available at https://github.com/JieZheng-ShanghaiTech/NSF4SL.Supplementary informationSupplementary data are available at Bioinformatics online.
SALAI-Net: species-agnostic local ancestry inference network
Authors: Oriol Sabat Benet , Mas Montserrat Daniel , Giro-i-Nieto Xavier , Ioannidis Alexander G 
Published: September 2022

AbstractMotivationLocal ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.ResultsWe present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models’ ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.Availability and implementationWe provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).Supplementary informationSupplementary data are available from Bioinformatics online.
Discovering significant evolutionary trajectories in cancer phylogenies
Authors: Pellegrina Leonardo , Vandin Fabio 
Published: September 2022

AbstractMotivationTumors are the result of a somatic evolutionary process leading to substantial intra-tumor heterogeneity. Single-cell and multi-region sequencing enable the detailed characterization of the clonal architecture of tumors and have highlighted its extensive diversity across tumors. While several computational methods have been developed to characterize the clonal composition and the evolutionary history of tumors, the identification of significantly conserved evolutionary trajectories across tumors is still a major challenge.ResultsWe present a new algorithm, MAximal tumor treeS TRajectOries (MASTRO), to discover significantly conserved evolutionary trajectories in cancer. MASTRO discovers all conserved trajectories in a collection of phylogenetic trees describing the evolution of a cohort of tumors, allowing the discovery of conserved complex relations between alterations. MASTRO assesses the significance of the trajectories using a conditional statistical test that captures the coherence in the order in which alterations are observed in different tumors. We apply MASTRO to data from nonsmall-cell lung cancer bulk sequencing and to acute myeloid leukemia data from single-cell panel sequencing, and find significant evolutionary trajectories recapitulating and extending the results reported in the original studies.Availability and implementationMASTRO is available at https://github.com/VandinLab/MASTRO.Supplementary informationSupplementary data are available at Bioinformatics online.
3CAC: improving the classification of phages and plasmids in metagenomic assemblies using assembly graphs
Authors: Pu Lianrong , Shamir Ron 
Published: September 2022

AbstractMotivationBacteriophages and plasmids usually coexist with their host bacteria in microbial communities and play important roles in microbial evolution. Accurately identifying sequence contigs as phages, plasmids and bacterial chromosomes in mixed metagenomic assemblies is critical for further unraveling their functions. Many classification tools have been developed for identifying either phages or plasmids in metagenomic assemblies. However, only two classifiers, PPR-Meta and viralVerify, were proposed to simultaneously identify phages and plasmids in mixed metagenomic assemblies. Due to the very high fraction of chromosome contigs in the assemblies, both tools achieve high precision in the classification of chromosomes but perform poorly in classifying phages and plasmids. Short contigs in these assemblies are often wrongly classified or classified as uncertain.ResultsHere we present 3CAC, a new three-class classifier that improves the precision of phage and plasmid classification. 3CAC starts with an initial three-class classification generated by existing classifiers and improves the classification of short contigs and contigs with low confidence classification by using proximity in the assembly graph. Evaluation on simulated metagenomes and on real human gut microbiome samples showed that 3CAC outperformed PPR-Meta and viralVerify in both precision and recall, and increased F1-score by 10–60 percentage points.Availability and implementationThe 3CAC software is available on https://github.com/Shamir-Lab/3CAC.Supplementary informationSupplementary data are available at Bioinformatics online.
MPVNN: Mutated Pathway Visible Neural Network architecture for interpretable prediction of cancer-specific survival risk
Authors: Ghosh Roy Gourab , Geard Nicholas , Verspoor Karin , He Shan 
Published: September 2022

AbstractMotivationSurvival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with a lack of interpretability. More interpretable visible neural network architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types.ResultsWe propose a novel Mutated Pathway Visible Neural Network (MPVNN) architecture, designed using prior signaling pathway knowledge and random replacement of known pathway edges using gene mutation data simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction of MPVNN over other similar-sized NN and standard survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that is important in risk prediction for particular cancer types, is reliable.Availability and implementationThe data and code are available at https://github.com/gourabghoshroy/MPVNN.Supplementary informationSupplementary data are available at Bioinformatics online.
BioBulkFoundary: a customized webserver for exploring biosynthetic potentials of bulk chemicals
Authors: Sun Dandan , Ding Shaozhen , Cai Pengli , Zhang Dachuan , Han Mengying , Hu Qian-Nan 
Published: September 2022

Abstract

Summary
Advances in metabolic engineering have boosted the production of bulk chemicals, resulting in tons of production volumes of some bulk chemicals with very low prices. A decrease in the production cost and overproduction of bulk chemicals makes it necessary and desirable to explore the potential to synthesize higher-value products from them. It is also useful and important for society to explore the use of design methods involving synthetic biology to increase the economic value of these bulk chemicals. Therefore, we developed ‘BioBulkFoundary’, which provides an elaborate analysis of the biosynthetic potential of bulk chemicals based on the state-of-art exploration of pathways to synthesize value-added chemicals, along with associated comprehensive technology and economic database into a user-friendly framework.

Availability and implementation
Freely available on the web at http://design.rxnfinder.org/biobulkfoundary/.

Supplementary information
Supplementary data are available at Bioinformatics online.