bluuebunny/crossref_metadata_2025_split huggingface dataset:
Photon-Counting Detector CT for Femoral Stent Imaging in an Extracorporeally Perfused Human Cadaveric Model
Authors: Hartung Viktor , Gruschwitz Philipp , Huflage Henner , Augustin Anne Marie , Kleefeldt Florian , Peter Dominik , Lichthardt Sven , Ergün Süleyman , Bley Thorsten Alexander , Grunz Jan-Peter , Petritsch Bernhard 
Published: October 2023

Background and Aims
This study aims to compare the performance of first-generation dual-source photon-counting detector computed tomography (PCD-CT) to third-generation dual-source energy-integrating detector (EID-CT) regarding stent imaging in the femoral arterial runoff.

Methods
Continuous extracorporeal perfusion was established in 1 human cadaver using an inguinal and infragenicular access and peristaltic pump. Seven peripheral stents were implanted into both superior femoral arteries by means of percutaneous angioplasty. Radiation dose-equivalent CT angiographies (high-/medium-/low-dose: 10/5/3 mGy) with constant tube voltage of 120 kVp, matching iterative reconstruction algorithm levels, and convolution kernels were used both with PCD-CT and EID-CT. In-stent lumen visibility, luminal and in-stent attenuation as well as contrast-to-noise ratio (CNR) were assessed via region of interest and diameter measurements. Results were compared using analyses of variance and regression analyses.

Results
Maximum in-stent lumen visibility achieved with PCD-CT was 94.48% ± 2.62%. The PCD-CT protocol with the lowest lumen visibility (BV40: 78.93% ± 4.67%) performed equal to the EID-CT protocol with the best lumen visibility (BV59: 79.49% ± 2.64%, P > 0.999). Photon-counting detector CT yielded superior CNR compared with EID-CT regardless of kernel and dose level (P < 0.001). Maximum CNR was 48.8 ± 17.4 in PCD-CT versus 31.28 ± 5.7 in EID-CT (both BV40, high-dose). The theoretical dose reduction potential of PCD-CT over EID-CT was established at 88% (BV40), 83% (BV48/49), and 73% (BV59/60), respectively. In-stent attenuation was not significantly different from luminal attenuation outside stents in any protocol.

Conclusions
With superior lumen visibility and CNR, PCD-CT allowed for noticeable dose reduction over EID-CT while maintaining image quality in a continuously perfused human cadaveric model.
Intraprocedural Versus Initial Follow-up Minimal Ablative Margin Assessment After Colorectal Liver Metastasis Thermal Ablation
Authors: Lin Yuan-Mao , Paolucci Iwan , Albuquerque Marques Silva Jessica , O'Connor Caleb S. , Fellman Bryan M. , Jones Aaron K. , Kuban Joshua D. , Huang Steven Y. , Metwalli Zeyad A. , Brock Kristy K. , Odisio Bruno C. 
Published: October 2023

Objectives
The aim of this study was to investigate the prognostic value of 3-dimensional minimal ablative margin (MAM) quantified by intraprocedural versus initial follow-up computed tomography (CT) in predicting local tumor progression (LTP) after colorectal liver metastasis (CLM) thermal ablation.

Materials and Methods
This single-institution, patient-clustered, tumor-based retrospective study included patients undergoing microwave and radiofrequency ablation between 2016 and 2021. Patients without intraprocedural and initial follow-up contrast-enhanced CT, residual tumors, or with follow-up less than 1 year without LTP were excluded. Minimal ablative margin was quantified by a biomechanical deformable image registration method with segmentations of CLMs on intraprocedural preablation CT and ablation zones on intraprocedural postablation and initial follow-up CT. Prognostic value of MAM to predict LTP was tested using area under the curve and competing-risk regression model.

Results
A total of 68 patients (mean age ± standard deviation, 57 ± 12 years; 43 men) with 133 CLMs were included. During a median follow-up of 30.3 months, LTP rate was 17% (22/133). The median volume of ablation zone was 27 mL and 16 mL segmented on intraprocedural and initial follow-up CT, respectively (P < 0.001), with corresponding median MAM of 4.7 mm and 0 mm, respectively (P < 0.001). The area under the curve was higher for MAM quantified on intraprocedural CT (0.89; 95% confidence interval [CI], 0.83–0.94) compared with initial follow-up CT (0.66; 95% CI, 0.54–0.76) in predicting 1-year LTP (P < 0.001). An MAM of 0 mm on intraprocedural CT was an independent predictor of LTP with a subdistribution hazards ratio of 11.9 (95% CI, 4.9–28.9; P < 0.001), compared with 2.4 (95% CI, 0.9–6.0; P = 0.07) on initial follow-up CT.

Conclusions
Ablative margin quantified on intraprocedural CT significantly outperformed initial follow-up CT in predicting LTP and should be used for ablation endpoint assessment.
Adolescent environmental enrichment induces social resilience and alters neural gene expression in a selectively bred rodent model with anxious phenotype
Authors: O’Connor Angela M. , Hagenauer Megan H. , Thew Forrester Liam Cannon , Maras Pamela M. , Arakawa Keiko , Hebda-Bauer Elaine K. , Khalil Huzefa , Richardson Evelyn R. , Rob Farizah I. , Sannah Yusra , Watson Stanley J. , Akil Huda 
Published: October 2023

AbstractStress is a major influence on mental health status; the ways that individuals respond to or copes with stressors determine whether they are negatively affected in the future. Stress responses are established by an interplay between genetics, environment, and life experiences. Psychosocial stress is particularly impactful during adolescence, a critical period for the development of mood disorders. In this study we compared two established, selectively-bred Sprague Dawley rat lines, the “internalizing” bred Low Responder (bLR) line versus the “externalizing” bred High Responder (bHR) line, to investigate how genetic temperament and adolescent environment impact future responses to social interactions and psychosocial stress, and how these determinants of stress response interact. Male bLR and bHR rats were exposed to social and environmental enrichment in adolescence prior to experiencing social defeat and were then assessed for social interaction and anxiety-like behavior. Adolescent enrichment caused rats to display more social interaction, as well as nominally less social avoidance, less submission during defeat, and resilience to the effects of social stress on corticosterone, in a manner that seemed more notable in bLRs. For bHRs, enrichment also caused greater aggression during a neutral social encounter and nominally during defeat, and decreased anxiety-like behavior. To explore the neurobiology underlying the development of social resilience in the anxious phenotype bLRs, RNA-seq was conducted on the hippocampus and nucleus accumbens, two brain regions that mediate stress regulation and social behavior. Gene sets previously associated with stress, social behavior, aggression and exploratory activity were enriched with differential expression in both regions, with a particularly large effect on gene sets that regulate social behaviors. Our findings provide further evidence that adolescent enrichment can serve as an inoculating experience against future stressors. The ability to induce social resilience in a usually anxious line of animals by manipulating their environment has translational implications, as it underscores the feasibility of intervention strategies targeted at genetically vulnerable adolescent populations.
Four-dimensional quantitative analysis of cell plate development using lattice light sheet microscopy identifies robust transition points between growth phases
Authors: Sinclair Rosalie , Wang Minmin , Jawaid Zaki , Aaron Jesse , Rossetti Blair , Wait Eric , McDonald Kent , Cox Daniel , Heddleston John , Wilkop Thomas , Drakakaki Georgia 
Published: October 2023

AbstractCell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodeling, and timely polysaccharide deposition, such as callose. Such a dynamic process requires dissection in time and space; hence this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM) we studied cell plate development in four dimensions, monitored by the behavior of the cytokinesis specific GTPase RABA2a.We monitored the entire length of cell plate development, from its first emergence, with the aid of RABA2a, both in the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis, we identified distinct behavioral patterns allowing for the identification of three easily trackable, cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material.We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited phase transition, establishing quantitatively the critical role and timing of the polysaccharide in cell plate expansion and maturation.This study exemplifies the power of LLSM, combined with quantitative analysis to decode and untangle such a complex process.HighlightWe employed lattice light sheet 4D microscopy in plants to dissect cytokinesis, a multistage process involving orchestrating delivery of membranes and timely polysaccharide deposition. Robust quantitative analysis revealed distinct phase shifts, while inhibition of callose deposition abolished the phase transition.
A lineage-specificSTAT5BN642Hmouse model to study NK-cell leukemia
Authors: Klein Klara , Kollmann Sebastian , List Julia , Hiesinger Angela , Kendler Jonatan , Rhandawa Mehak , Trifinopoulos Jana , Maurer Barbara , Grausenburger Reinhard , Moriggl Richard , Rülicke Thomas , Witalisz-Siepracka Agnieszka , Walter Wencke , Hoermann Gregor , Sexl Veronika , Gotthardt Dagmar 
Published: October 2023

AbstractPatients with T- and NK-cell neoplasms frequently have somaticSTAT5Bgain-of-function mutations. The most frequentSTAT5Bmutation isSTAT5BN642H, which is known to drive murine T-cell leukemia although its role in NK-cell malignancies is unclear.Introduction of theSTAT5BN642Hmutation into human NK-cell lines enhances their potential to induce leukemia in mice. We have generated a mouse model that enables tissue-specific expression ofSTAT5BN642Hand have selectively expressed the mutatedSTAT5Bin hematopoietic cells (N642Hvav/+) or exclusively in NK cells (N642HNK/NK). All N642Hvav/+mice rapidly develop an aggressive T-/NK T-cell leukemia, whereas N642HNK/NKmice display an indolent chronic lymphoproliferative disorder of NK cells (CLPD-NK) that progresses to an aggressive leukemia with age. Samples from NK-cell leukemia patients have a distinctive transcriptional signature driven by mutant STAT5B, which overlaps with that of murineSTAT5BN642H-expressing NK cells.We have generated the first reliableSTAT5BN642H-driven pre-clinical mouse model that displays an indolent CLPD-NK progressing to aggressive NK-cell leukemia. This novelin vivotool will enable us to explore the transition from an indolent to an aggressive disease and will thus permit the study of prevention and treatment options for NK-cell malignancies.Key pointsGeneration of a lineage-specificSTAT5BN642Htransgenic mouse model which develops NK-cell leukemiaLeukemic NK cells with a STAT5B gain of function mutation have a unique transcriptional profile in mice and human patients
Federated Learning for multi-omics: a performance evaluation in Parkinson’s disease
Authors: Danek Benjamin , Makarious Mary B. , Dadu Anant , Vitale Dan , Lee Paul Suhwan , Nalls Mike A , Sun Jimeng , Faghri Faraz 
Published: October 2023

SummaryWhile machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson’s Disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.The Bigger PictureThe wide-scale application of artificial intelligence and computationally intensive analytical approaches in the biomedical and clinical domain is largely restricted by access to sufficient training data. This data scarcity exists due to the isolated nature of biomedical and clinical institutions, mandated by patient privacy policies in the health system or government legislation. Federated Learning (FL), a machine learning approach that facilitates collaborative model training is a promising strategy to address these restrictions. Therefore, understanding the limitations of cooperatively trained FL models, and their performance differences to similar, centrally trained models, is crucial to valuing their implementation in the broader biomedical research community.
VIBE: An R-package for advanced RNA-seq data exploration, disease stratification and therapeutic targeting
Authors: Khatri Indu , van Asten Saskia D , Moreno Leandro F. , Higgs Brandon W , Klijn Christiaan , Blokzijl Francis , Kolder Iris CRM 
Published: October 2023

AbstractBackgroundDevelopment of therapies e.g. antibody-based treatments, rely on several factors, including the specificity of target expression and characterization of downstream signaling pathways. While existing tools for analyzing and visualizing RNA-seq data offer evaluation of individual gene-level expression, they lack a comprehensive assessment of pathway-guided analysis, relevant for single- and dual-targeting therapeutics. Here, we introduce VIBE (VIsualization ofBulk RNAExpression data), an R package which provides a thorough exploration of both individual and combined gene expression, supplemented by pathway-guided analyses. VIBE’s versatility proves pivotal for disease stratification and therapeutic targeting in cancer, immune, metabolic, and other disorders.ResultsVIBE offers a wide array of functions that streamline the visualization and analysis of transcriptomics data for single- and dual-targeting therapies such as antibodies. Its intuitive interface allows users to evaluate the expression of target genes and their associated pathways across various indications, aiding in target and disease prioritization. Metadata, such as specific treatment or number of prior lines of therapy, can be easily incorporated to refine the identification of patient cohorts hypothesized to derive benefit from a given drug. Through real-world scenario representations using simulated data, we demonstrate how VIBE can be used to assist in indication selection for several user cases. VIBE integrates statistics in all graphics, enabling data-informed decision-making. Its enhanced user experience features include boxplot sorting and group genes either individually or averaged based on pathways, ensuring custom visuals for insightful decisions. For a deeper dive into its extensive functionalities, please review the vignettes on the GitHub repository (https://github.com/genmab/VIBE).ConclusionsVIBE facilitates detailed visualization of individual and cohort-level summaries such as concordant or discordant expression of two genes or pathways. Such analyses can help to prioritize disease indications that are amenable to treatment strategies like bispecific antibody therapies or pathway-guided monoclonal antibody therapies. By using this tool, researchers can enhance the indication selection and potentially accelerate the development of novel targeted therapies with the end goal of precision, personalization, and ensuring treatments align perfectly with individual patient needs across a spectrum of medical domains.
Optimising analysis choices for multivariate decoding: creating pseudotrials using trial averaging and resampling
Authors: Scrivener C. L. , Grootswagers T. , Woolgar A. 
Published: October 2023

AbstractMultivariate pattern analysis (MVPA) is a popular technique that can distinguish between condition-specific patterns of activation. Applied to neuroimaging data, MVPA decoding for inference uses above chance decoding to identify statistically reliable condition-specific information in neuroimaging data which may be missed by univariate methods. However, several analysis choices influence decoding success, and the combined effects of these choices have not been fully evaluated. We systematically assessed the influence of trial averaging and resampling on decoding accuracy and subsequent statistical outcome on simulated data. Although the optimal parameters varied with the classifier and cross-validation approach used, we found that modest trial averaging using roughly 5-10% of the total number of trials per condition improved accuracy and associated t-statistics. In addition, a resampling value of 2 could improve t-statistics and classification performance, but was not always necessary. We provide code to allow researchers to optimise analyses for the parameters of their data.
Sentinels for future coral reef conditions: assessment of environmental variability and water quality in semi-enclosed inland bays in the southern Caribbean
Authors: de Jong Chiara , van Os Iris , Sepúlveda-Rodríguez Guadalupe , de Baat Milo L. , Schoepf Verena 
Published: October 2023

AbstractThe mangrove-seagrass-coral reef continuum is of immense ecological and socio-economic importance, supporting biodiversity, carbon storage, coastal protection, fisheries, and tourism. The presence of extreme environmental conditions along this continuum could support adaptive refugia for climate-sensitive taxa such as reef-building corals but physicochemical conditions are rarely assessed at sufficient spatiotemporal resolution. Furthermore, coastal development and low water quality increasingly threaten these interconnected coastal ecosystems. Yet, time-integrated pollution monitoring is absent at most locations. Here, we used a multi-disciplinary approach to assess benthic cover, coral diversity, and >20 abiotic parameters characterizing two mangrove- and seagrass-dominated inland bays and two nearby coral reefs in Curaçao (southern Caribbean) during the cool, dry season and warm, wet season. This was combined with time-integrated pollution monitoring using bioindicators to assess nutrients and trace metal pollution (inland bays only), and passive samplers and bioassays to assess organic chemical pollution (all four sites) during the wet season. This approach revealed a previously undocumented extent of strong diel and seasonal environmental variability in the two inland bays, with temperature, pH and dissolved oxygen frequently reaching values predicted under moderate-to-severe future climate scenarios. In addition, the inland bays had greater nutrient concentrations (especially ammonium) and ecotoxicological risks than the nearby reefs during the wet season due to run-off, industrial- and wastewater effluents, ports and boating. Overall, our findings show that Curaçao’s inland bays have significant potential to serve as natural laboratories to study the effects of future ocean conditions on resident taxain situ. This however applies within the context of strong diel fluctuations and with the caveat of co-occurring stressors. Our work confirms the important role of mangrove and seagrass habitats as resilience hotspots for climate-sensitive taxa but also highlights the urgent need to improve monitoring, water quality and protection of these valuable habitats along the mangrove-seagrass-coral reef continuum.
Classification of bioactive peptides: a comparative analysis of models and encodings
Authors: Bizzotto Edoardo , Zampieri Guido , Treu Laura , Filannino Pasquale , Di Cagno Raffaella , Campanaro Stefano 
Published: October 2023

AbstractBioactive peptides are short amino acid chains possessing biological activity and exerting specific physiological effects relevant to human health, which are increasingly produced through fermentation due to their therapeutic roles. One of the main open problems related to biopeptides remains the determination of their functional potential, which still mainly relies on time-consuming in vivo tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were collected from a variety of databases to generate a comprehensive collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some newly defined such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their native sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. By testing a total set of 171 models, it was found that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all the classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.
A highly-contiguous genome assembly of the inbred Babraham pig (Sus scrofa) quantifies breed homozygosity and illuminates porcine immunogenetic variation
Authors: Schwartz John C. , Farrell Colin P. , Freimanis Graham , Sewell Andrew K. , Hammond John A. , Phillips John D. 
Published: October 2023

AbstractThe inbred Babraham pig serves as a valuable biomedical model for research due to its high level of homozygosity, including in the major histocompatibility complex (MHC) loci and likely other important immune-related gene complexes, which are generally highly diverse in outbred populations. As the ability to control for this diversity using inbred organisms is of great utility, we sought to improve this resource by generating a long-read whole genome assembly of a Babraham pig. The Babraham genome wasde novoassembled using PacBio long-reads and error-corrected using Illumina short-reads. The assembled contigs were then mapped to the current porcine reference assembly, Sscrofa11.1, to generate chromosome-level scaffolds. The resulting Babraham pig assembly is nearly as contiguous as Sscrofa11.1 with a contig N50 of 34.95 Mb and contig L50 of 23. The remaining sequence gaps are generally the result of poor assembly across large and highly repetitive regions such as the centromeres and tandemly duplicated gene families, including immune-related gene complexes, that often vary in gene content between haplotypes. We also further confirm homozygosity across the Babraham pig MHC and characterize the allele content across several immune-related gene complexes, including the contiguous assemblies of the antibody heavy chain locus and leukocyte receptor complex. The Babraham pig genome assembly provides an alternate highly contiguous porcine genome assembly as a resource for the livestock genomics community. The assembly will also aid biomedical and veterinary research that utilizes this animal model such as when controlling for genetic variation is critical.