bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer
Authors: Sharma Abhinav , Weitz Philippe , Wang Yinxi , Liu Bojing , Hartman Johan , Rantalainen Mattias 
Published: February 2023

AbstractHistological Grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-lab variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model. The primary performance evaluation focuses on prognostic performance.This observational study is based on two patient cohorts (SöS-BC-4, N=2421 (training and internal test); SCAN-B-Lund, N=1262 (test)) that include routine histological whole slide images together with patient outcomes. A Deep Convolutional Neural Network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from hematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of Recurrence-free survival (RFS) and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. We observed effect sizes (Hazard Ratio) for grade 3 vs 1, for the conventional NHG method (HR=2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI: 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for NHG 1 vs 2 was estimated to be 2.59 (p-value = 0.004) and NHG 1 vs 3 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for grade 1 vs 2 HR=2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for grade 1 vs 3. In multivariable analysis, HR estimates for neither NHG nor predGrade were found to be significant (p-value >0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set, and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade.Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model provides similar prognostic performance as NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.
Specialty-type and state-level variation in paroxetine use among older adult patients
Authors: Cavanah Luke R. , Goldhirsh Jessica L. , Huey Leighton Y. , Piper Brian J. 
Published: February 2023

AbstractIntroductionParoxetine is an older “selective” serotonin reuptake inhibitor (SSRI) that is notable for its lack of selectivity, resulting in a cholinergic adverse-effect profile, especially among older adults (65+).MethodsParoxetine prescription rates and costs per state were ascertained from the Medicare Specialty Utilization and Payment Data. States’ annual prescription rate, corrected per thousand Part D enrollees, outside 95% confidence interval were considered significantly different from the average.ResultsThere was a steady decrease in paroxetine prescriptions (-34.52%) and spending (-16.69%) from 2015-2020 but a consistent, five-fold state-level difference. From 2015-2020, Kentucky (194.9, 195.3, 182.7, 165.1, 143.3, 132.5) showed significantly higher prescriptions rates relative to the national average, and Hawaii (42.1, 37.9, 34.3, 31.7, 27.7, 26.6) showed significantly lower prescription rates. North Dakota was often a frequent elevated prescriber of paroxetine (2016: 170.7, 2018: 143.3), relative to the average. Neuropsychiatry and geriatric medicine frequently prescribed the largest amount of paroxetine prescriptions, relative to the number of providers in that specialty, from 2015-2020.DiscussionDespite the American Geriatrics Society prohibition against paroxetine use in the older adults and many effective treatment alternatives, paroxetine was still commonly used in this population, especially in Kentucky and North Dakota and by neuropsychiatry and geriatric medicine. These findings provide information on the specialty types and states where education and policy reform would likely have the greatest impact on improving adherence to the paroxetine prescription recommendations.
A comprehensive analysis of rheumatoid arthritis B cells reveals the importance of CD11c+vedouble-negative-2 B cells as the major synovial plasma cell precursor
Authors: Wing Elinor , Sutherland Catherine , Miles Katherine , Gray David , Goodyear Carl , Otto Thomas , Breusch Stefan , Cowan Graeme , Gray Mohini 
Published: February 2023

AbstractB cells are key pathogenic drivers of chronic inflammation in rheumatoid arthritis (RA). There is limited understanding of the relationship between synovial B cell subsets and pathogenic antibody secreting cells (ASCs). This knowledge is crucial for the development of targeted therapies. Here, we combine flow cytometry of circulating B cells with single-cell RNA and paired repertoire sequencing of over 27,000 synovial B cells from patients with established RA. Twelve B cell clusters were identified including previously recognised subsets, and a novel cluster that strongly expressed heat shock proteins. All lineages identified by trajectory analysis contribute to the DN2 B cell population, which is the major precursor to synovial ASCs. This was further supported by B cell receptor (BCR) lineage analysis, which revealed clonal relationships between DN2 cells and ASCs. This study advances our understanding of B cells in RA and reveals the origin of pathogenic ASCs in the RA synovium.
Common roles for serotonin in rats and humans for computations underlying flexible decision-making
Authors: Luo Qiang , Kanen Jonathan W. , Bari Andrea , Skandali Nikolina , Langley Christelle , Knudsen Gitte Moos , Alsiö Johan , Phillips Benjamin U. , Sahakian Barbara J. , Cardinal Rudolf N. , Robbins Trevor W. 
Published: February 2023

AbstractSerotonin is critical for adapting behavior flexibly to meet changing environmental demands. Cognitive flexibility is important both for successful attainment of goals, as well as for social interactions, and is frequently impaired in neuropsychiatric disorders, including obsessive-compulsive disorder (OCD). However, a unifying mechanistic framework accounting for the role of serotonin in behavioral flexibility has remained elusive. Here, we demonstrate common effects of manipulating serotonin function across two species (rats and humans) on latent processes supporting choice behavior during probabilistic reversal learning using computational modelling. The findings support a role of serotonin in behavioral flexibility and plasticity, indicated, respectively, by increases or decreases in choice repetition (‘stickiness’) or reinforcement learning rates depending upon manipulations intended to increase or decrease serotonin function. More specifically, the rate at which expected value increased following reward and decreased following punishment (reward and punishment ‘learning rates’) was greatest after sub-chronic administration of the selective serotonin reuptake (SSRI) citalopram (5 mg/kg for 7 days followed by 10 mg/kg twice a day for 5 days) in rats. Conversely, humans given a single dose of an SSRI (20mg escitalopram), which can decrease post-synaptic serotonin signalling, and rats that received the neurotoxin 5,7-dihydroxytryptamine (5,7-DHT), which destroys forebrain serotonergic neurons, exhibited decreased reward learning rates. A basic perseverative tendency (‘stickiness’), or choice repetition irrespective of the outcome produced, was likewise increased in rats after the 12-day SSRI regimen and decreased after single dose SSRI in humans and 5,7-DHT in rats. These common effects of serotonergic manipulations on rats and humans – identified via computational modelling – suggest an evolutionarily conserved role for serotonin in plasticity and behavioral flexibility and have clinical relevance transdiagnostically for neuropsychiatric disorders.
Fine-tuning spatial-temporal dynamics and surface receptor expression support plasma cell-intrinsic longevity
Authors: Jing Zhixin , Galbo Phillip , Ovando Luis , Demouth Megan , Welte Skylar , Park Rosa , Chandran Kartik , Wu Yinghao , MacCarthy Thomas , Zheng Deyou , Fooksman David 
Published: February 2023

ABSTRACTDurable serological memory following vaccination is critically dependent on the production and survival of long-lived plasma cells (LLPCs). Yet, the factors that control LLPC specification and survival remain poorly resolved. Using intra-vital two-photon imaging, we find that in contrast to most plasma cells in the bone marrow (BM), LLPCs are uniquely sessile and organized into clusters that are dependent on APRIL, an important survival factor. Using deep, bulk RNA sequencing, and surface protein flow-based phenotyping, we find that LLPCs express a unique transcriptome and phenotype compared to bulk PCs, fine tuning expression of key cell surface molecules, CD93, CD81, CXCR4, CD326, CD44 and CD48, important for adhesion and homing. Conditional deletion ofCxcr4in PCs following immunization leads to rapid mobilization from the BM, reduced survival of antigen-specific PCs, and ultimately accelerated decay of antibody titer. In naïve mice, the endogenous LLPCs BCR repertoire exhibits reduced diversity, reduced somatic mutations, and increased public clones and IgM isotypes, particularly in young mice, suggesting LLPC specification is non-random. As mice age, the BM PC compartment becomes enriched in LLPCs, which may outcompete and limit entry of new PCs into the LLPC niche and pool.HIGHLIGHTSLLPCs have reduced motility and increased clustering in the BMLLPCs accumulate in the BM PC pool, with mouse ageLLPCs have unique surfaceome, transcriptome, and BCR clonalityCXCR4 controls maintenance of PCs and antibody titers
Inter-laboratory comparison of plant volatile analyses in the light of intra-specific chemodiversity
Authors: Eckert Silvia , Eilers Elisabeth J. , Jakobs Ruth , Anaia Redouan Adam , Aragam Kruthika Sen , Bloss Tanja , Popp Moritz , Sasidharan Rohit , Schnitzler Jörg-Peter , Stein Florian , Steppuhn Anke , Unsicker Sybille B. , van Dam Nicole M. , Yepes Sol , Ziaja Dominik , Müller Caroline 
Published: February 2023

AbstractIntroductionAssessing intraspecific variation in plant volatile organic compounds (VOCs) involves pitfalls that may bias biological interpretation, particularly when several laboratories collaborate on joint projects. Comparative, inter-laboratory ring trials can inform on the reproducibility of such analyses.ObjectivesIn a ring trial involving five laboratories, we investigated the reproducibility of VOC collections with polydimethylsiloxane (PDMS) and analyses by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). As model plant we usedTanacetum vulgare, which shows a remarkable diversity in terpenoids, forming so-called chemotypes. We performed our ring-trial with two chemotypes to examine the sources of technical variation in plant VOC measurements during pre-analytical, analytical, and post-analytical steps.MethodsMonoclonal root cuttings were generated in one laboratory and distributed to five laboratories, in which plants were grown under laboratory-specific conditions. VOCs were collected on PDMS tubes from all plants before and after a jasmonic acid (JA) treatment. Thereafter, each laboratory (donors) sent a subset of tubes to four of the other laboratories (recipients), which performed TD-GC-MS with their own established procedures.ResultsChemotype-specific differences in VOC profiles were detected but with an overall high variation both across donor and recipient laboratories. JA-induced changes in VOC profiles were not reproducible. Laboratory-specific growth conditions led to phenotypic variation that affected the resulting VOC profiles.ConclusionOur ring trial shows that despite large efforts to standardise each VOC measurement step, the outcomes differed both qualitatively and quantitatively. Our results reveal sources of variation in plant VOC research and may help to avoid systematic errors in similar experiments.
The Development of Synapses in Mouse and Macaque Primary Sensory Cortices
Authors: Wildenberg Gregg , Li Hanyu , Kasthuri Narayanan 
Published: February 2023

AbstractWe report that the rate of synapse development in primary sensory cortices of mice and macaques is unrelated to lifespan, as was previously thought. We analyzed 28,084 synapses over multiple developmental time points in both species and find, instead, that net excitatory synapse development of mouse and macaque neurons primarily increased at similar rates in the first few postnatal months, and then decreased over a span of 1-1.5 years of age. The development of inhibitory synapses differed qualitatively across species. In macaques, net inhibitory synapses first increase and then decrease on excitatory soma at similar ages as excitatory synapses. In mice, however, such synapses are added throughout life. These findings contradict the long-held belief that the cycle of synapse formation and pruning occurs earlier in shorter-lived animals. Instead, our results suggest more nuanced rules, with the development of different types of synapses following different timing rules or different trajectories across species.
Strengthening connectivity between premotor and motor cortex increases inter-areal communication in the human brain
Authors: Trajkovic Jelena , Romei Vincenzo , Rushworth Matthew F. S. , Sel Alejandra 
Published: February 2023

AbstractThe ventral premotor cortex (PMv) is an important component of cortico-cortical pathways mediating prefrontal control over primary motor cortex (M1) function. Paired associative stimulation (ccPAS) is known to change PMv influence over M1 in humans, which manifests differently depending on the behavioural context. Here we show that these changes in influence are functionally linked to PMv-M1 phase synchrony changes induced by repeated paired stimulation of the two areas. PMv-to-M1 ccPAS leads to increased phase synchrony in alpha and beta bands while reversed order M1-to-PMv ccPAS leads to decreased theta phase synchrony. These changes are visible at rest but they are predictive of changes in oscillatory power in the same frequencies during movement execution and inhibition, respectively. The results unveil a link between the physiology of the motor network and the resonant frequencies mediating its interactions, and provide a putative mechanism underpinning the relationship between synaptic efficacy and brain oscillations.
Systematic analysis of disease-linked rare germline variants reveals new classes of cancer predisposing genes
Authors: Song Seulki , Koh Youngil , Kim Seokhyeon , Lee Sang Mi , Kim Hyun Uk , Ko Jung Min , Lee Se-Hoon , Yoon Sung-Soo , Park Solip 
Published: February 2023

AbstractDespite recent insights into cancer predisposition genes (CPGs), the contribution of rare germline variants to carcinogenesis remains unclear. Here, we examine the enrichment of rare germline variants in Mendelian disease-associated genes using PCAWG and the 1,000 Genomes Project. Using an integrative approach that considers disease classes, cellular pathways, and tissue expression profiles, we find that subsets of Online Mendelian Inheritance in Man (OMIM) genes are strong candidate CPGs. These genes were classified into five clusters suggesting diverse mechanisms underlie tumor progression. We further analyzedPAHgene which shows the strongest prevalence of rare germline variants in cancers (2.2%) compared to controls (0.7%). This enrichment was validated in two independent cancer cohorts and present the possibility of tumorigenesis by contributing immune-associated pathways. Our data argue that rare germline variants of Mendelian disease-associated genes contribute to cancer progression and suggest new CPG classifications that may underlie diverse tumorigenesis mechanisms.
A biologically realistic model to predict wildlife-vehicle collision risks
Authors: Bénard Annaëlle , Lengagne Thierry , Bonenfant Christophe 
Published: February 2023

AbstractRoad networks have major ecological impacts on living organisms consequent to habitat loss and fragmentation, chemical and acoustic pollution, and direct mortality when wildlife-vehicle collisions are involved (WVC). The many past empirical studies revealed biological traits shared by species most vulnerable to roadkills (e.g. population density). Similarly, spatial locations of WVC hot-spots are associated to landscape features at large spatial scales, and to road characteristics at small spatial scale. We currently lack a comprehensive theoretical framework for WVC. Animal movement in relation to habitats is an essential driver of encounters with roads, but this remains largely ignored in studies, even when movement ecology provides the necessary tools to investigate the impact of animal movement on WVC. We built a general individual-based model incorporating recent knowledge in movement ecology (movement typology: roaming, migratory route crossing a road, active attraction and active repulsion of roads) to estimate WVC risks. We explored the relative effects of animal and vehicle movement parameters (speed, abundance, road sinuosity and animal movement pattern) on collision probability. We show that animal behaviour toward roads has major impacts on the number and risks of WVC, but also modulate the effects of other factors (animal traveling speed, species local abundance, road traffic volume) on WVC. Sensitivity analyses show that the movement and behaviour of the animal has more influence on WVC risks than any of the characteristics of roads and vehicles we tested. Our results suggest that (1) effective roadkill mitigation should be species-specific and could vary in efficiency depending on the target’s movement pattern (mating and migratory seasons, foraging habits…) and (2) empirical studies of WVC should incorporate knowledge about the behavioural habits of the focal species in relation to roads.
A Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling
Authors: George Amy L. , Sidgwick Frances R. , Watt Jessica E. , Martin Mathew P. , Trost Matthias , Marín-Rubio José Luis , Dueñas Maria Emilia 
Published: February 2023

AbstractThermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMT) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification (LFQ) approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukaemia (AML) cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable or better than TMT-DDA in their ability to reproducibly detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labelled quantitation in the TPP pipeline.
High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy
Authors: Lee Moosung , Jeong Hyuntae , Lee Chaeyeon , Lee Mahn Jae , Delmo Benedict Reve , Heo Won Do , Shin Jennifer H. , Park YongKeun 
Published: February 2023

ABSTRACTA critical requirement for studying cell mechanics is three-dimensional (3D) assessment of cellular shapes and forces with high spatiotemporal resolution. Traction force microscopy (TFM) with fluorescence imaging enables the measurement of cellular forces, but it is limited by photobleaching and a slow 3D acquisition speed. Here, we present refractive-index traction force microscopy (RI-TFM), a high-speed volumetric technique that simultaneously quantifies the 3D morphology and traction force of cells. RI-TFM reconstructs a 3D tomogram of single cells on a planar hydrogel elastomer using a high-speed illumination scheme with 0.5-Hz temporal resolution. This allows for quantitative analyses of 3D dry-mass distributions and shear (in-plane) and normal (out-of-plane) tractions of single cells on the extracellular matrix without labelling. When combined with a constrained total variation-based deconvolution algorithm, the proposed method provides 0.55-Pa shear (in-plane) and 1.59-Pa normal (out-of-plane) traction sensitivity for a 1-kPa hydrogel substrate. We demonstrate the utility of RI-TFM by assessing the effects of compromised intracellular stress due to actin disruption or low temperature and capturing the rapid dynamics of cellular junction formation in the spatiotemporal changes in non-planar traction components.
EEG decoding reveals neural predictions for naturalistic material behaviors
Authors: Kaiser Daniel , Stecher Rico , Doerschner Katja 
Published: February 2023

AbstractMaterial properties like softness or stickiness determine how an object can be used. Based on our real-life experience, we form strong expectations about how objects should behave under force, given their typical material properties. Such expectations have been shown to modulate perceptual processes, but we currently do not know how expectation influences the temporal dynamics of the cortical visual analysis for objects and their materials. Here, we tracked the neural representations of expected and unexpected material behaviors using time-resolved EEG decoding in a violation-of-expectation paradigm, where objects fell to the ground and deformed in expected or unexpected ways. Participants were 25 men and women. Our study yielded three key results: First, both objects and materials were represented rapidly and in a temporally sustained fashion. Second, objects exhibiting unexpected material behaviors were more successfully decoded than objects exhibiting expected behaviors within 190ms after the impact, which might indicate additional processing demands when expectations are unmet. Third, general signals of expectation fulfillment that generalize across specific objects and materials were found within the first 150ms after the impact. Together, our results provide new insights into the temporal neural processing cascade that underlies the analysis of real-world material behaviors. They reveal a sequence of predictions, with cortical signals progressing from a general signature of expectation fulfillment towards increased processing of unexpected material behaviors.Significance StatementIn the real world, we can make accurate predictions about how an object’s material shapes its behavior: For instance, we know that cups are typically made of porcelain and shatter when we accidentally drop them. Here, we use EEG to experimentally test how expectations about material behaviors impact neural processing. We showed our participants videos of objects that exhibited expected material behaviors (such as a glass shattering when falling to the ground) or unexpected material behaviors (such as a glass melting upon impact). Our results reveal a hierarchy of predictions in cortex: The visual system rapidly generates signals that index whether expectations about material behaviors are met. These signals are followed by increased processing of objects displaying unexpected material behaviors.