bluuebunny/crossref_metadata_2025_split huggingface dataset:
“Facebook’s about to know, Karen”
Authors: Walz Linda , Joyce Jack B. , Flint Natalie 
Published: December 2023

Abstract
This paper explores the social action of sanctioning an interlocutor’s conduct in public spaces through social
media. Using membership categorisation analysis (Hester and Eglin 1997), we examine
how, in offline face-to-face disputes filmed by one party, interactants deploy the name ‘Karen’ to sanction someone and threaten
the transposition of the recording onto social media to impose accountability to the public at large. Our findings show how
sanctioning through categorising an individual as a ‘Karen’ is interactionally achieved through framing conduct as entitled or
otherwise problematic, distinguishing in-situ production of ‘Karen’ from a delivery that is perceptually
unavailable to an interlocutor. We explore how social media functions as a resource to shape the ongoing encounter by orienting to
the camera, and thus the online audience, as an external authority.
Code accommodation as a measure of inclusion for bilingual people living with dementia of the Alzheimer’s
type
Authors: Schneider Carolin , Bös Birte 
Published: December 2023

Abstract
This case study explores the dynamics of code choices in interactions involving bilingual people living with
dementia of the Alzheimer’s Type (DAT) and their primary care partners, focusing on two narrative interviews held in private
settings. Drawing on a combination of Communication Accommodation Theory and Conversation Analysis, it takes account of the
patterns, communicative functions and effects of code choices and code switching as practices of interactional adjustment. The
qualitative analysis sheds light on inclusive and non-inclusive interactional adjustments expressed through code choices by
individual speakers, especially focusing on code accommodation at turn boundaries. Results indicate a high language awareness in
the two speakers living with DAT and positive communicational outcomes when code accommodation is performed by the conversational
partner.
Bearing fault feature extraction method: stochastic resonance-based negative entropy of square envelope spectrum
Authors: Zhao Haixin , Jiang Xiaomo , Wang Bo , Cheng Xueyu 
Published: December 2023

Abstract
The early identification of bearing defects has recently attracted increasing attention in the fields of condition monitoring and predictive maintenance because of the critical role of bearings on the reliability and safety of turbomachines. The weak features representing early faults in the vibration signals are often submerged in the environmental noise, which poses a major challenge for the early fault diagnosis of rolling bearings. This study proposes a negative entropy of the square envelope spectrum approach integrated with optimized stochastic resonance (SR)-based signal enhancement for accurate early defect detection of rolling element bearings. The proposed method considers the cyclostationarity and impulsivity of the raw signal, as well as its similarity with the enhanced signal, thus reinforcing the characteristic frequency while integrating the regularity of the raw signal to evaluate the SR performance. A comparison study with different existing methods using both numerical and experimental data was conducted to illustrate the effectiveness and accuracy of the proposed methodology for early defect detection of rolling element bearings in different locations. The results show that the proposed method improves the fault detection by 3.5 d earlier than other SR methods, and produces the best enhancement results for fault detection in the outer race, inner race, and rolling element of bearings, with the increase of characteristic frequency intensity coefficient by 126.3%, 118.1%, and 100.5% compared to traditional envelope signals, respectively.
Effect of hydrogen charging intensities and times on hydrogen embrittlement of Q&P980 steel
Authors: Zhao Linlin , Chen Hao , Zhang Caidong , Wang Guangyao , Lu Shenghai , Chen Zejun , Zhao Aimin 
Published: December 2023

Abstract
Q&P steel has good development prospects because of its excellent mechanical properties, but with the improvement in strength grade, hydrogen-induced delayed fracture (HIDF) is almost inevitable. In this paper, slow strain rate tensile tests and deep-drawn cup tests of Q&P980 steel under different hydrogen charging strengths and times were carried out, and the microstructure and fracture morphology were analysed by SEM. The results show that the plastic loss of Q&P980 steel was more obvious with increasing hydrogen charging intensity and hydrogen charging time, and a good elongation of 6.63% is still retained under the hydrogen content of 2.134 ppm. The deep-drawn cup samples were placed in acidic distilled water and alkaline and acidic solutions, and only a deep-drawn ratio of 1.9 showed HIDF in the three solutions. Specifically, 12 cracks were observed after soaking in HCl solution for two days. The main reason is that the martensite, austenite island and ferrite phase interface of Q&P980 steel increase stress during deformation and with the transformation-induced plasticity (TRIP) effect, resulting in hydrogen segregation at the phase interface and crack initiation leading to HIDF.
Chlorophyll bloom triggered by tropical storm chedza at the southern tip of madagascar island
Authors: Feng Qi , Shen Hao , Zhu Guohao , Zhang Liqiong , Zhou Yaoyao , Haibin LÜ 
Published: December 2023

Abstract
A phytoplankton bloom during the passage of Tropical Storm (TS) Chedza was observed at the southern tip of Madagascar on January 28, 2015. The mechanisms of the chlorophyll bloom were researched with satellite remote sensing data, reanalysis data and Argo buoy data. The results show that there was horizontal transport of chlorophyll-a (Chl-a) with the western coastal current of Madagascar and the South East Madagascar Current (SEMC). At the southern tip of Madagascar, there was a tilted anticyclonic eddy moving westward, which promoted the flow of nitrate at depths from 60 m to 100 m northward into Box A for a month. Simultaneously, the SEMC formed another anticyclonic eddy entering Box C. Following the passage of TS Chedza, cyclone vorticity in Box A increased continuously, and the upwelling became stronger with Ekman pumping. At the same time, the barrier layer weakened, the mixed layer deepened, and the thermocline decreased, which can uplift the nitrate to the upper ocean. After the passage of TS Chedza, sufficient photosynthetically active radiation (PAR) facilitated the Chl-a bloom in Box A. This study contributes to the assessment of the ecological impact of ocean eddies at the southern tip of Madagascar.
Seq2Phase: language model-based accurate prediction of client proteins in liquid–liquid phase separation
Authors: Miyata Kazuki , Iwasaki Wataru 
Published: December 2023

AbstractMotivationLiquid–liquid phase separation (LLPS) enables compartmentalization in cells without biological membranes. LLPS plays essential roles in membraneless organelles such as nucleoli and p-bodies, helps regulate cellular physiology, and is linked to amyloid formation. Two types of proteins, scaffolds and clients, are involved in LLPS. However, computational methods for predicting LLPS client proteins from amino-acid sequences remain underdeveloped.ResultsHere, we present Seq2Phase, an accurate predictor of LLPS client proteins. Information-rich features are extracted from amino-acid sequences by a deep-learning technique, Transformer, and fed into supervised machine learning. Predicted client proteins contained known LLPS regulators and showed localization enrichment into membraneless organelles, confirming the validity of the prediction. Feature analysis revealed that scaffolds and clients have different sequence properties and that textbook knowledge of LLPS-related proteins is biased and incomplete. Seq2Phase achieved high accuracies across human, mouse, yeast, and plant, showing that the method is not overfitted to specific species and has broad applicability. We predict that more than hundreds or thousands of LLPS client proteins remain undiscovered in each species and that Seq2Phase will advance our understanding of still enigmatic molecular and physiological bases of LLPS as well as its roles in disease.Availability and implementationThe software codes in Python underlying this article are available at https://github.com/IwasakiLab/Seq2Phase.
Comparison of multiple modalities for drug response prediction with learning curves using neural networks and XGBoost
Authors: Branson Nikhil , Cutillas Pedro R , Bessant Conrad 
Published: December 2023

Abstract

Motivation
Anti-cancer drug response prediction is a central problem within stratified medicine. Transcriptomic profiles of cancer cell lines are typically used for drug response prediction, but we hypothesize that proteomics or phosphoproteomics might be more suitable as they give a more direct insight into cellular processes. However, there has not yet been a systematic comparison between all three of these datatypes using consistent evaluation criteria.

Results
Due to the limited number of cell lines with phosphoproteomics profiles we use learning curves, a plot of predictive performance as a function of dataset size, to compare the current performance and predict the future performance of the three omics datasets with more data. We use neural networks and XGBoost and compare them against a simple rule-based benchmark. We show that phosphoproteomics slightly outperforms RNA-seq and proteomics using the 38 cell lines with profiles of all three omics data types. Furthermore, using the 877 cell lines with proteomics and RNA-seq profiles, we show that RNA-seq slightly outperforms proteomics. With the learning curves we predict that the mean squared error using the phosphoproteomics dataset would decrease by ∼15% if a dataset of the same size as the proteomics/transcriptomics was collected. For the cell lines with proteomics and RNA-seq profiles the learning curves reveal that for smaller dataset sizes neural networks outperform XGBoost and vice versa for larger datasets. Furthermore, the trajectory of the XGBoost curve suggests that it will improve faster than the neural networks as more data are collected.

Availability and implementation
See https://github.com/Nik-BB/Learning-curves-for-DRP for the code used.
SOHPIE: statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data
Authors: Ahn Seungjun , Datta Somnath 
Published: December 2023

AbstractSummaryThe SOHPIE R package implements a novel functionality for “multivariable” differential co-abundance network (DN, hereafter) analyses of microbiome data. It incorporates a regression approach that adjusts for additional covariates for DN analyses. This distinguishes from previous prominent approaches in DN analyses such as MDiNE and NetCoMi which do not feature a covariate adjustment of finding taxa that are differentially connected (DC, hereafter) between individuals with different clinical and phenotypic characteristics.Availability and implementationSOHPIE with a vignette is available on CRAN repository https://CRAN.R-project.org/package=SOHPIE and published under General Public License (GPL) version 3 license.
CellularPotts.jl: simulating multiscale cellular models in Julia
Authors: Gregg Robert W , Benos Panayiotis V 
Published: December 2023

Abstract

Summary
CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia.

Availability and implementation
The CellularPotts.jl package is released under the MIT license and is available at https://github.com/RobertGregg/CellularPotts.jl. An archived version of the code (v0.3.2) at time of submission can also be found at https://doi.org/10.5281/zenodo.10407783.
GeNNius: an ultrafast drug–target interaction inference method based on graph neural networks
Authors: Veleiro Uxía , de la Fuente Jesús , Serrano Guillermo , Pizurica Marija , Casals Mikel , Pineda-Lucena Antonio , Vicent Silve , Ochoa Idoia , Gevaert Olivier , Hernaez Mikel 
Published: December 2023

Abstract

Motivation
Drug–target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness.

Results
In this work, we introduce GeNNius (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GeNNius by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GeNNius, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space.

Availability and implementation
GeNNius code is available at https://github.com/ubioinformat/GeNNius.
Efficacy and safety of adjuvant immunoadsorption in pemphigus vulgaris and pemphigus foliaceus (IA-Pem Study): a multicentre randomized controlled trial
Authors: van Beek Nina , Eming Rüdiger , Reuss Alexander , Zillikens Detlef , Sárdy Miklós , Günther Claudia , Kiritsi Dimitra , Benoit Sandrine , Beissert Stefan , Gläser Regine , Gollnick Harald , Horváth Orsolya N , Pfeiffer Christiane , Röcken Martin , Schauer Franziska , Schreml Stephan , Steinbrink Kerstin , Zink Alexander , Schade-Brittinger Carmen , Hertl Michael , Schmidt Enno 
Published: December 2023

Abstract

Background
Pemphigus vulgaris (PV) and pemphigus foliaceus (PF) are potentially life-threatening autoimmune blistering diseases. Treatment is based on long-term immunosuppression with high doses of glucocorticosteroids in combination with potentially corticosteroid-sparing agents and/or rituximab. Immunoadsorption (IA) has emerged as a fast-acting adjuvant treatment option.

Objectives
To assess the clinical efficacy of IA in addition to best medical treatment (BMT).

Methods
We conducted a multicentre (26 centres from Germany and Austria) randomized controlled trial in 72 patients with newly diagnosed, relapsed or chronic active PV or PF (34 female patients and 38 male patients, aged 42–72 years) comparing BMT (prednisolone 1.0 mg kg−1 per day plus azathioprine or mycophenolate) with adjuvant IA (BMT + IA). Central 1 : 1 randomization was done at the coordinating centre for clinical trials (KKS Marburg). The primary endpoint was analysed using Kaplan–Meier and Cox regression methods.

Results
The study was ended prematurely owing to safety concerns after random allocation of 72 patients to BMT + IA (n = 34) or BMT (n = 38). The primary endpoint, time to complete remission on therapy, was not significantly different for the two groups [hazard ratio (HR) 1.35, 95% confidence interval (CI) 0.68–2.69; P = 0.39]. The cumulative dose of prednisolone was significantly lower in the BMT + IA group compared with BMT alone (difference −1214, 95% CI −2225 to −70; P = 0.03). In a post hoc analysis, patients with more extensive PV/PF showed a tendency towards a shorter time to remission in the BMT + IA group compared with the BMT group (HR 1.87, P = 0.17 in patients with baseline Pemphigus Disease Area Index ≥ 15). While more adverse events were observed in patients in the BMT group (29 vs. 25), severe adverse events were more frequent in patients in the BMT + IA group (17 events in 10 patients vs. 11 events in 8 patients).

Conclusions
In this study, adjuvant IA did not demonstrate a shorter time to clinical remission, but a corticosteroid-sparing effect was observed. In patients with extensive PV/PF, post hoc analysis suggests that adjuvant IA may lead to earlier remission, but potential adverse events must be carefully weighed against the expected benefits.
Exploring individual fixel-based white matter abnormalities in epilepsy
Authors: Mito Remika , Pedersen Mangor , Pardoe Heath , Parker Donna , Smith Robert E , Cameron Jillian , Scheffer Ingrid E , Berkovic Samuel F , Vaughan David N , Jackson Graeme D 
Published: December 2023

Abstract
Diffusion MRI has provided insight into the widespread structural connectivity changes that characterize epilepsies. Although syndrome-specific white matter abnormalities have been demonstrated, studies to date have predominantly relied on statistical comparisons between patient and control groups. For diffusion MRI techniques to be of clinical value, they should be able to detect white matter microstructural changes in individual patients. In this study, we apply an individualized approach to a technique known as fixel-based analysis, to examine fibre-tract-specific abnormalities in individuals with epilepsy. We explore the potential clinical value of this individualized fixel-based approach in epilepsy patients with differing syndromic diagnoses. Diffusion MRI data from 90 neurologically healthy control participants and 10 patients with epilepsy (temporal lobe epilepsy, progressive myoclonus epilepsy, and Dravet Syndrome, malformations of cortical development) were included in this study. Measures of fibre density and cross-section were extracted for all participants across brain white matter fixels, and mean values were computed within select tracts-of-interest. Scanner harmonized and normalized data were then used to compute Z-scores for individual patients with epilepsy. White matter abnormalities were observed in distinct patterns in individual patients with epilepsy, both at the tract and fixel level. For patients with specific epilepsy syndromes, the detected white matter abnormalities were in line with expected syndrome-specific clinical phenotypes. In patients with lesional epilepsies (e.g. hippocampal sclerosis, periventricular nodular heterotopia, and bottom-of-sulcus dysplasia), white matter abnormalities were spatially concordant with lesion location. This proof-of-principle study demonstrates the clinical potential of translating advanced diffusion MRI methodology to individual-patient-level use in epilepsy. This technique could be useful both in aiding diagnosis of specific epilepsy syndromes, and in localizing structural abnormalities, and is readily amenable to other neurological disorders. We have included code and data for this study so that individualized white matter changes can be explored robustly in larger cohorts in future work.
Shift in the B cell subsets between children with type 1 diabetes and/or celiac disease
Authors: Tompa Andrea , Faresjö Maria 
Published: December 2023

Abstract
Our purpose was to characterize the pattern of B cell subsets in children with a combined diagnosis of type 1 diabetes (T1D) and celiac disease (C) since children with single or double diagnosis of these autoimmune diseases may differ in peripheral B cell subset phenotype patterns. B cells were analyzed with flow cytometry for the expression of differentiation/maturation markers to identify transitional, naive, and memory B cells. Transitional (CD24hiCD38hiCD19+) and memory Bregs (mBregs; CD24hiCD27+CD19+, CD1d+CD27+CD19+, and CD5+CD1d+CD19+) were classified as B cells with regulatory capacity. Children with a combined diagnosis of T1D and C showed a pattern of diminished peripheral B cell subsets. The B cells compartment in children with combined diagnosis had higher percentages of memory B subsets and Bregs, including activated subsets, compared to children with either T1D or C. Children with combined diagnosis had a lower percentage of naive B cells (CD27−CD19+; IgD+CD19+) and an increased percentage of memory B cells (CD27+CD19+; IgD−CD19+). A similar alteration was seen among the CD39+ expressing naive and memory B cells. Memory Bregs (CD1d+CD27+CD19+) were more frequent, contrary to the lower percentage of CD5+ transitional Bregs in children with a combined diagnosis. In children with either T1D or C, the peripheral B cell compartment was dominated by naive cells. Differences in the pattern of heterogeneous peripheral B cell repertoire subsets reflect a shifting in the B cell compartment between children with T1D and/or C. This is an immunological challenge of impact on the pathophysiology of these autoimmune diseases.
Rab46: a novel player in mast cell function
Authors: Pedicini Lucia , Smith Jessica , Savic Sinisa , McKeown Lynn 
Published: December 2023

Abstract
Mast cells are infamous for mediating allergic and inflammatory diseases due to their capacity of rapidly releasing a wide range of inflammatory mediators stored in cytoplasmic granules. However, mast cells also have several important physiological roles that involve selective and agonist-specific release of these active mediators. While a filtering mechanism at the plasma membrane could regulate the selective release of some cargo, the plethora of stored cargo and the diversity of mast cell functions suggests the existence of granule subtypes with distinct trafficking pathways. The molecular mechanisms underlying differential trafficking and exocytosis of these granules are not known, neither is it clear how granule trafficking is coupled to the stimulus. In endothelial cells, a Rab GTPase, Rab46, responds to histamine but not thrombin signals, and this regulates the trafficking of a subpopulation of endothelial-specific granules. Here, we sought to explore, for the first time, if Rab46 plays a role in mast cell function. We demonstrate that Rab46 is highly expressed in human and murine mast cells, and Rab46 genetic deletion has an effect on mast cell degranulation that depends on both stimuli and mast cell subtype. This initial insight into the contribution of Rab46 to mast cell function and the understanding of the role of Rab46 in stimuli-dependent trafficking in other cell types necessitates further investigations of Rab46 in mast cell granular trafficking so that novel and specific therapeutic targets for treatment of the diverse pathologies mediated by mast cells can be developed.