bluuebunny/crossref_metadata_2025_split huggingface dataset:
How the Anatomy of the Epidermal Cells Is Correlated to the Transient Response of Stomata
Authors: Zekri Maryam Alsadat , Tholen Daniel , Koller Lukas , Lang Ingeborg , Theroux-Rancourt Guillaume 
Published: February 2024

AbstractHere, we show the possible correlation between the anatomical characteristics of epidermal cells ofArabidopsis thalianawith the stomata transient opening, which is commonly called the Wrong-Way Response (WWR). The WWR was induced by either reduced air humidity or leaf excision. Five genotypes ofA. thalianaCol8,epf1epf2, lcd1-1, SALK069, and UBP, respectively, with anatomical differences in epidermal cells such as stomatal density, stomata size, size, and shape of the pavement cells were selected. These genotypes allowed us to investigate the mutual effects of stomata density and size on WWR. Scanning Electron Microscopy (SEM) was applied for image acquisition of the abaxial and adaxial surface of the leaves and the main features of the epidermal cells were extracted by one of the additions to the MiToBo plugin of ImageJ/Fiji called PaCeQuant. The stomatal conductance to water vapor (gs) was measured using the portable photosynthesis measurement system LICor-6800. Our linear models showed that the size of the stomata explained the rate of WWR induced by reduced air humidity, so genotypes with smaller stomata showed a smaller rate of the WWR. After leaf excision, however, there was no correlation between the size of the stomata and the rate of the WWR. Moreover, we found that after both, reduced air humidity and leaf excision, the size of the pavement cells on the abaxial surface is correlated to the rate of the WWR; genotypes with smaller pavement cells on the abaxial surface had a smaller rate of WWR.
DiffGAN: a conditional generative adversarial network for phasing single molecule diffraction data to atomic resolution
Authors: Matinyan S. , Filipcik P. , Genderen E. van , Abrahams J.P. 
Published: February 2024

AbstractIntroductionProteins that adopt multiple conformations pose significant challenges in structural biology research and pharmaceutical development, as structure determination via single particle cryo-electron microscopy (cryo-EM) is often impeded by data heterogeneity. In this context, the enhanced signal-to-noise ratio of single molecule cryo-electron diffraction (simED) offers a promising alternative. However, a significant challenge in diffraction methods is the loss of phase information, which is crucial for accurate structure determination.MethodsHere, we present DiffGAN, a conditional generative adversarial network (cGAN) that estimates the missing phases at high resolution from a combination of high-resolution single particle diffraction data and low-resolution image data.ResultsFor simulated datasets, DiffGAN allows effectively determine protein structures at atomic resolution from diffraction patterns and noisy low-resolution images.DiscussionOur findings suggest that combining single particle cryo-electron diffraction with advanced generative modeling, as in DiffGAN, could revolutionize the way protein structures are determined, offering a more accurate and efficient alternative to existing methods.
Unveiling Frailty: Comprehensive and Sex-Specific Characterization in Prematurely Aging PolgA Mice
Authors: Yılmaz Dilara , Singh Amit , Wehrle Esther , Kuhn Gisela A. , Mathavan Neashan , Müller Ralph 
Published: February 2024

AbstractFrailty, a geriatric syndrome, is assessed using the frailty phenotype (FP) and frailty index (FI). While these approaches have been applied to aging mice, their effectiveness in prematurely aging mouse models such as PolgAD257A/D257A(PolgA) has not been completely explored. We demonstrated that frailty became evident in PolgA mice around 40 weeks, validated through body weight loss, reduced walking speed, decreased physical activity, and weaker grip strength. Moreover, we also identified sex differences in these mice with females exhibiting higher frailty compared to males. Frailty prevalence in PolgA mice at 40 weeks parallels that observed in naturally aging mice at 27 months and aging humans at 65-70 years. These findings contribute to understanding frailty onset and sex-specific patterns, emphasizing the significance of the PolgA mouse model in investigating aging and related disorders.
Interpretable Atomistic Prediction and Functional Analysis of Conformational Ensembles and Allosteric States in Protein Kinases Using AlphaFold2 Adaptation with Randomized Sequence Scanning and Local Frustration Profiling
Authors: Raisinghani Nishank , Alshahrani Mohammed , Gupta Grace , Tian Hao , Xiao Sian , Tao Peng , Verkhivker Gennady 
Published: February 2024

AbstractThe groundbreaking achievements of AlphaFold2 (AF2) approaches in protein structure modeling marked a transformative era in structural biology. Despite the success of AF2 tools in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and fold-switching systems. The recent NMR-based structural determination of the unbound ABL kinase in the active state and two inactive low-populated functional conformations that are unique for ABL kinase presents an ideal challenge for AF2 approaches. In the current study we employ several implementations of AF2 methods to predict protein conformational ensembles and allosteric states of the ABL kinase including (a) multiple sequence alignments (MSA) subsampling approach; (b) SPEACH_AF approach in which alanine scanning is performed on generated MSAs; and (c) introduced in this study randomized full sequence mutational scanning for manipulation of sequence variations combined with the MSA subsampling. We show that the proposed AF2 adaptation combined with local frustration mapping of conformational states enable accurate prediction of the ABL active and intermediate structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AF2 predictions and limitations in detecting hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AF2 methods. This study uncovered previously unappreciated, fundamental connections between distinct patterns of local frustration in functional kinase states and AF2 successes/limitations in detecting low-populated frustrated conformations, providing a better understanding of benefits and limitations of current AF2-based adaptations in modeling of conformational ensembles.
A low-abundance class of Dicer-dependent siRNAs produced from a variety of features inC. elegans
Authors: Knittel Thiago L. , Montgomery Brooke E. , Tate Alex J. , Deihl Ennis W. , Nawrocki Anastasia S. , Hoerndli Frederic J. , Montgomery Taiowa A. 
Published: February 2024

ABSTRACTCanonical small interfering RNAs (siRNAs) are processed from double-stranded RNA (dsRNA) by the endoribonuclease Dicer. siRNAs are found in plants, animals, and some fungi where they associate with Argonautes to direct RNA silencing. InCaenorhabditis elegans, some endogenous small RNAs, such as 22G-RNAs and 26G-RNAs, share certain attributes with canonical siRNAs but exhibit unique characteristics known only to occur in nematodes. For instance, 22G-RNAs do not originate from dsRNA and are not processed by Dicer, whereas 26G-RNAs require Dicer but lack the typical duplex intermediate with symmetrical 3’-overhangs and are produced only antisense to their mRNA templates. To identify canonical siRNAs inC. elegans, we first characterized the siRNAs produced from exogenous dsRNA. As predicted based on earlier studies, exogenous dsRNA is processed into ∼23-nt duplexes with 2-4-nt 3’-overhangs, ultimately yielding siRNAs devoid of 5’ G-containing sequences that bind with high affinity to the Argonaute RDE-1. Leveraging these characteristics, we searched for their endogenous counterparts and identified thousands of endogenous loci representing dozens of unique elements that give rise to mostly low to moderate levels of siRNAs, called 23H-RNAs. These loci include repetitive elements, alleged coding genes, pseudogenes, non-coding RNAs, and unannotated features, many of which adopt hairpin structures reminiscent of the hpRNA/RNA interference (RNAi) pathway in flies and mice. Our results expand the known repertoire ofC. eleganssmall RNAs and demonstrate that key features of the endogenous siRNA pathway are relatively unchanged in animals.
Salbutamol, a short acting beta-2 agonist, reduces risk and improves prognosis of prostate cancer
Authors: Lehrer Steven , Rheinstein Peter H. 
Published: February 2024

AbstractBackgroundBeta-blockers, a class of drugs commonly used to manage blood pressure, have been the subject of research regarding their relationship to prostate cancer risk, prognosis, and treatment. Beta blockers reduce risk and improve prognosis of prostate cancer. Perioperative use of a non-selective beta blocker improves outcome after radical prostatectomy. But a related class of drugs, beta 2 adrenergic agonists, has received little attention in prostate cancer.MethodsWe studied the relationship of the beta 2 adrenergic agonist salbutamol to prostate cancer risk and survival. We analyzed FDA MedWatch data to determine whether salbutamol could influence the risk of prostate cancer. We used UK Biobank (UKBB) data to assess the effect of salbutamol on prostate cancer (PC) survival.ResultsSalbutamol significantly reduces prostate cancer risk, Proportional Reporting Ratio (PRR) and 95% confidence interval (lower bound; upper bound): 0.131 (0.11; 0.155) and improves prognosis. Mean survival was 7.35 years for subjects not taking salbutamol, 10.5 years for subjects taking salbutamol (p = 0.041, log rank test. To adjust for the effect of age we performed proportional hazards regression, survival time dependent variable, age and salbutamol use independent variables. Salbutamol use was significantly related to survival time (p = 0.016) and independent of the significant effect of age (p < 0.001).ConclusionSalbutamol and other beta-adrenergic agonists could represent a new class of drugs for treatment of prostate cancer.
An interim exploratory biomarker analysis of a Phase 2 clinical trial to assess the impact of CT1812 in Alzheimer’s disease
Authors: Lizama BN , North HA , Pandey K , Williams C , Duong D , Cho E , Di Caro V , Ping L , Blennow K , Zetterberg H , Lah J , Levey AI , Grundman M , Caggiano AO , Seyfried NT , Hamby ME 
Published: February 2024

ABSTRACTCT1812 is a novel, brain penetrant small molecule modulator of the sigma-2 receptor (S2R) that is currently in clinical development for the treatment of Alzheimer’s disease (AD). Preclinical and early clinical data show that, through S2R, CT1812 selectively prevents and displaces binding of amyloid beta (Aβ) oligomers from neuronal synapses and improves cognitive function in animal models of AD. SHINE is an ongoing Phase 2 randomized, double-blind, placebo-controlled clinical trial (COG0201) in patients with mild to moderate AD, designed to assess the safety and efficacy of 6 months of CT1812 treatment. To elucidate the mechanism of action in AD patients and pharmacodynamic biomarkers of CT1812, the present study reports exploratory cerebrospinal fluid (CSF) biomarker data from an interim analysis of the first set of patients in SHINE (part A). Untargeted mass spectrometry-based discovery proteomics can detect more than 2,000 proteins in patient CSF and has documented utility in accelerating the identification of novel AD biomarkers reflective of diverse pathophysiologies beyond amyloid and tau and enabling identification of pharmacodynamic biomarkers in longitudinal interventional trials. We leveraged this technique to analyze CSF samples taken at baseline and after 6 months of CT1812 treatment. Proteome-wide protein levels were detected using tandem mass tag-mass spectrometry (TMT-MS), change from baseline was calculated for each participant, and differential abundance analysis by treatment group was performed. This analysis revealed a set of proteins significantly impacted by CT1812, including pathway engagement biomarkers (i.e., biomarkers tied to S2R biology) and disease modification biomarkers (i.e., biomarkers with altered levels in AD vs. healthy control CSF but normalized by CT1812, and biomarkers correlated with favorable trends in ADAS-Cog11 scores). Brain network mapping, Gene Ontology, and pathway analyses revealed an impact of CT1812 on synapses, lipoprotein and amyloid beta biology, and neuroinflammation. Collectively, the findings highlight the utility of this method in pharmacodynamic biomarker identification and providing mechanistic insights for CT1812, which may facilitate the clinical development of CT1812 and enable appropriate pre-specification of biomarkers in upcoming clinical trials of CT1812.HIGHLIGHTSEffects of CT1812 on AD patients were investigated in a randomized Phase 2 clinical trialPharmacodynamic biomarkers of CT1812 were identified through unbiased analysis of proteomics quantitation data acquired using TMT Mass Spectrometry (TMT-MS)CT1812 normalized a set of biomarkers altered in ADFindings provide proof of mechanism that CT1812 impacts synapse, inflammation, amyloid-related processes
The Topology of Representational Geometry
Authors: Brown Shael , Farivar Reza 
Published: February 2024

Representational similarity analysis (RSA) is a powerful tool for abstracting and then comparing neural representations across brains, regions, models and modalities. However, typical RSA analyses compares pairs of representational dissimilarities to judge similarity of two neural systems, and we argue that such methods can not capture the shape of representational spaces. By leveraging tools from computational topology, which can probe the shape of high-dimensional data, we augment RSA to be able to detect more subtle yet real differences and similarities of representational geometries. This new method could be used in conjunction with regular RSA in order to make new inferences about neural function.Significance StatementBig data in high-dimensional spaces, like neuroimaging datasets, contain important shape structures. These shape structures can be analyzed to identify the underlying features and dynamics which drive the system. We showed that such analyses, applied to neural activity patterns elicited by viewing various objects, can identify real but subtle and complex features of those objects which are encoded in the brain.
Computer vision analysis of mother-infant interaction identified efficient pup retrieval in V1b vasopressin receptor knockout mice
Authors: Sajjaviriya Chortip , Fujianti  , Azuma Morio , Tsuchiya Hiroyoshi , Koshimizu Taka-aki 
Published: February 2024

AbstractClose contact between lactating rodent mothers and their infants is essential for effective nursing. Whether the mother’s effort to retrieve the infants to their nest requires the vasopressin-V1b vasopressin receptor axis has not been fully defined. To address this question, V1b receptor knockout (V1bKO) and control mice were analyzed in pup retrieval test. Because an exploring mother in a new test cage randomly accessed to multiple infants in changing backgrounds over time, a computer vision-based deep learning analysis was applied to continuously calculate the distances between the mother and the infants as a parameter of their relationship. In an open-field, a virgin female V1bKO mice entered fewer times into the center area and moved shorter distances than wild-type (WT). While this behavioral pattern persisted in V1bKO mother, the pup retrieval test demonstrated that total distances between a V1bKO mother and infants came closer in a shorter time than with a WT mother. Moreover, in the medial preoptic area, parts of the V1b receptor transcripts were detected in galanin- andc-fos-positive neurons following maternal stimulation by infants. This research highlights the effectiveness of deep learning analysis in evaluating the mother-infant relationship and the critical role of V1b receptor in pup retrieval during the early lactation phase.Highlights(1)Mother-infant relationship was calculated as distances by computer vision and object detection analysis.(2)V1b knockout female mice tended to avoid entering centerfield in a new cage compared to control female.(3)Lack of V1b vasopressin receptor in mice facilitated the pup retrieval in early infancy, even with shorter distances of total movements.
PyF2F: A robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments
Authors: Hernandez Altair C. , Ortiz Sebastian , Betancur Laura I. , Dojčilović Radovan , Picco Andrea , Kaksonen Marko , Oliva Baldo , Gallego Oriol 
Published: February 2024

AbstractSummaryStructural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software that challenge reproducibility and limit their implementation by highly specialised researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F performance.
Apnoea suppresses brain activity in infants
Authors: Zandvoort Coen S. , Dereymaeker Anneleen , Baxter Luke , Jansen Katrien , Naulaers Gunnar , de Vos Maarten , Hartley Caroline 
Published: February 2024

AbstractApnoea – the cessation of breathing – is commonly observed in premature infants. These events can reduce cerebral oxygenation and are associated with poorer neurodevelopmental outcomes. However, relatively little is known about how apnoea and shorter pauses in breathing impact brain function in infants, which will provide greater mechanistic understanding of how apnoea affects brain development. We analysed simultaneous recordings of respiration, electroencephalography (EEG), heart rate, and peripheral oxygen saturation in 124 recordings from 118 infants (post-menstrual age: 38.6 ± 2.7 weeks [mean ± standard deviation]) during apnoeas (pauses in breathing greater than 15 seconds) and shorter pauses in breathing between 5 and 15 seconds. EEG amplitude significantly decreased during both apnoeas and shorter pauses in breathing compared with normal breathing periods. Change in EEG amplitude was significantly associated with change in heart rate during apnoea and breathing pauses and, during apnoeas only, with oxygen saturation change. No associations were found between EEG amplitude and pause duration or post-menstrual age. The decrease in EEG amplitude may be a result of the changing metabolism and/or homeostasis following changes in oxygen and carbon dioxide concentrations, which alters the release of neurotransmitters. As apnoeas often occur in premature infants, frequent disruption to brain activity may impact neural development and result in long-term neurodevelopmental consequences.
Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets
Authors: Hong Matthew M. , Froelicher David , Magner Ricky , Popic Victoria , Berger Bonnie , Cho Hyunghoon 
Published: February 2024

AbstractFinding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging due to the significant burden of estimating kinship between all pairs of individuals across datasets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals, and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us datasets. On a dataset of 200K individuals split between two parties, SF-Relate detects 94.9% of third-degree relatives, and 99.9% of second-degree or closer relatives, within 15 hours of runtime. Our work enables secure identification of relatives across large-scale genomic datasets.