bluuebunny/crossref_metadata_2025_split huggingface dataset:
Shared Genetic Susceptibility between Abdominal Aortic Aneurysm and Cardiometabolic Traits
Authors: Zheng Shufen , Tsao Philip S. , Pan Cuiping 
Published: December 2023

AbstractAbdominal aortic aneurysm (AAA) presents abnormal metabolism and co-occurs with cardiometabolic disorders, suggesting a shared genetic susceptibility. We investigated this commonality leveraging recent GWAS studies of AAA and 32 cardiometabolic traits (CMTs). Significant genetic correlations are found between AAA and 21 CMTs, among which are causal relationship with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identified shared causal variants, genes, and pathways, which revealed cholesterol metabolism and immune responses were the shared most prominently. Additionally, we uncovered the tissue and cell type specificity in the shared signals, with strong enrichment across traits in liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leveraged drug-gene databases and identified several lipid-lowering drugs and antioxidants with high potential to treat AAA with comorbidities. Our study provides insight into the shared genetic mechanism for AAA and cardiometabolic traits and potential targets for pharmacological intervention.
Advancing bee conservation in the US: gaps and opportunities in data collection and reporting
Authors: Rousseau Josée S. , Woodard S. Hollis , Jepsen Sarina , Du Clos Brianne , Johnston Alison , Danforth Bryan N. , Rodewald Amanda D. 
Published: December 2023

ABSTRACTBee conservation in the U.S. is currently hindered by challenges associated with assessing the status and trends of a diverse group of >3000 species, many of which are rare, endemic to small areas, and/or exhibit high inter-annual variation in population size. Fundamental information about the distribution of most species across space and time, thus, is lacking yet urgently needed to assess population status, guide conservation plans, and prioritize actions among species and geographies. Using wild bee data from two public data repositories representing the contiguous U.S., we evaluated the availability and sufficiency of data for use in species assessments of wild bees. We also examined the number of bee species recorded in each U.S. state and the proportion of species with recent records (2012-2021). Although efforts to monitor bees continue to grow, there remains a massive paucity of data. Exceedingly few records (0.04%) reported both sampling protocol and effort, greatly limiting the usefulness of the data. Few species or locations have adequate publicly available data to support analyses of population status or trends, and fewer than half of species have sufficient data to delineate geographic range. Despite an exponential increase in data submissions since the 2000s, only 47% of species were reported within the last decade, which may be driven by how data are collected, reported, and shared, or may reflect troubling patterns of local or large-scale declines and extirpations. Based on our analysis, we provide recommendations to improve the quality and quantity of data that can be used to detect, understand, and respond to changes in wild bee populations.
The temporal recovery of contralateral and ipsilateral knee extensor torque following a bout of unilateral knee extensor resistance exercise in young, healthy resistance-trained men
Authors: Davies Robert W. , Barnes Harley L. , Carson Brian P. , Jakeman Philip M. 
Published: December 2023

AbstractThe present study aimed to characterise the temporal recovery pattern of contralateral-homologous torque following a bout of unilateral resistance exercise (RE). Ten young, healthy, recreationally active, resistance-trained men performed 10 sets of 10 repetitions of knee extensor (KE) contractions at 50 % 1RM with 1 min rest between sets. Isometric maximal voluntary contraction (MVC) peak torque (PT), surface electromyography (sEMG), muscle soreness and serum creatine kinase (CK) levels were assessed immediately before and 5 min after RE cessation, and then +4 h, +24 h, +48 h and +72 h later. Data are presented as mean [95 % CI] % change from pre-exercise values. RE evoked a minor increase in CK and pain in the late recovery period (+24 h to +72 h) (P < 0.034) and decreases in ipsilateral KE PT were observed immediately post-exercise (-26 [-33, -18] %, P < 0.001) and up to +48 h (-12 [-19, -4] %, P = 0.006). Measurable decreases in PT were also observed in the non-exercised contralateral KE immediately post-exercise (-8 [-13, -3] %, P = 0.006) up to +24 h (-8 [-15, 0] %, P = 0.020), but were significantly lower than the ipsilateral KE PT (P < 0.05). These findings suggest the presence of crossover fatigue following RE in young, healthy, active, resistance-trained men, however, the magnitude and temporal recovery are substantially less severe and protracted in the contralateral homologous KE.
Sex-specific role for the long noncoding RNAPnkyin mouse behavior
Authors: Saha Parna , Andersen Rebecca E. , Hong Sung Jun , Gil Eugene , Simms Jeffrey , Lim Daniel A. 
Published: December 2023

SUMMARYThe human brain expresses thousands of different long noncoding RNAs (lncRNAs), and aberrant expression of specific lncRNAs has been associated with cognitive and psychiatric disorders. While a growing number of lncRNAs are now known to regulate neural cell development and function, relatively few have been shown to underlie animal behavior, particularly with genetic strategies that establish lncRNA function intrans.Pnkyis an evolutionarily conserved, neural lncRNA that regulates brain development. Using mouse genetic strategies, we show thatPnkyhas sex-specific roles in mouse behavior and that this lncRNA underlies specific behavior by functioning intrans. MalePnky-knockout (KO) mice have deficits in cued fear recall, a type of Pavlovian associative memory. In femalePnky-KO mice, the acoustic startle response (ASR) is increased and accompanied by a decrease in prepulse inhibition (PPI), both of which are behaviors altered in affective disorders. Remarkably, expression ofPnkyfrom a bacterial artificial chromosome (BAC) transgene reverses the ASR phenotype of femalePnky-KO mice, demonstrating thatPnkyunderlies specific animal behavior by functioning intrans. More broadly, these data provide genetic evidence that a lncRNA gene and its function intranscan play a key role in the behavior of adult mammals, contributing fundamental knowledge to our growing understanding of the association between specific lncRNAs and disorders of cognition and mood.
Key phosphorylation sites for robust β-arrestin2 binding at the MOR revisited
Authors: Underwood Owen , Fritzwanker Sebastian , Glenn Jaqueline , Batista-Gondin Arisbel , Drube Julia , Hoffman Carsten , Briddon Stephen J , Schulz Stefan , Canals Meritxell 
Published: December 2023

AbstractAbstract FigureDesensitisation of the mu-opioid receptor (MOR) is proposed to underlie the initiation of opioid analgesic tolerance and previous work has shown that agonist-induced phosphorylation of the MOR C-tail contributes to this desensitisation. Moreover, we and others have shown that phosphorylation is important for β-arrestin recruitment to the receptor, and that ligands of different efficacies induce distinct patterns, or barcodes, of receptor phosphorylation. Within the MOR C-tail, the370TREHPSTANT379motif harbours Ser/Thr residues important for these regulatory functions.375Ser acts as a primary phosphorylation site of a ligand-dependent, hierarchical, and sequential process, whereby flanking370Thr,376Thr and379Thr residues can get subsequently phosphorylated. Here we used HEK293 GRK KO cells, in combination with phosphosite specific antibodies and site-directed mutagenesis of the MOR, to evaluate the contribution of the different GRK subfamilies to ligand-induced phosphorylation barcodes and β-arrestin2 recruitment. We show that both GRK subfamilies (GRK2/3 and GRK5/6) promote phosphorylation of Thr370and Ser375. However, only GRK2/3 induce phosphorylation of Thr376and Thr379, which is required to promote robust β-arrestin recruitment to the receptor. Moreover, while DAMGO and fentanyl can engage all kinases to promote Thr370and Ser375phosphorylation, under endogenous GRK expression conditions, morphine-induced phosphorylation of these residues is specifically mediated by GRK5/6. These data provide insight into the mechanisms of MOR regulation and suggest that the cellular complement of the different GRK subfamilies plays an important role in determining the tissue responses of distinct opioid agonists.
Searchlight-based trial-wise fMRI decoding in the presence of trial-by-trial correlations
Authors: Soch Joram 
Published: December 2023

AbstractIn multivariate pattern analysis (MVPA) for functional magnetic resonance imaging (fMRI) signals, trial-wise response amplitudes are sometimes estimated using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates can be highly correlated due to the temporally smoothed shape of the hemodynamic response function. In previous work (Soch, J., Allefeld, C., & Haynes, J.-D. (2020). Inverse transformed encoding models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding.NeuroImage, 209, 116449, 1-19.https://doi.org/10.1016/j.neuroimage.2019.116449), we have proposed inverse transformed encoding modelling (ITEM), a principled approach for trial-wise decoding from fMRI signals in the presence of trial-by-trial correlations. Here, we (i) perform simulation studies addressing its performance for multivariate signals and (ii) present searchlight-based ITEM analysis – which allows to predict a variable of interest from the vicinity of each voxel in the brain. We empirically validate the approach by confirminga prioriplausible hypotheses about the well-understood visual system.
Deciphering abdominal aortic diseases through T-cell clonal repertoire of perivascular adipose tissue
Authors: Piacentini Luca , Vavassori Chiara , Werba Pablo , Saccu Claudio , Spirito Rita , Colombo Gualtiero I. 
Published: December 2023

AbstractRecent studies suggested that immune-mediated inflammation of perivascular adipose tissue (PVAT) of abdominal aortic aneurysms (AAA) contributes to disease development and progression. Whether the PVAT of AAA is characterized by a specific adaptive immune signature remains unknown. To investigate this hypothesis, we sequenced the T-cell receptor β-chain (TCRβ) in the PVAT of AAA patients and compared with patients with aortic occlusive disease (AOD), who share with the former anatomical site of the lesion, risk factors but differ in pathogenic mechanisms. Our results demonstrate that AAA patients have a lower repertoire diversity than those with AOD and significant differences in V/J gene segment usage. Furthermore, we identified a set of 7 public TCRβ clonotypes that distinguished AAA and AOD with very high accuracy. We also found that the TCRβ repertoire differentially characterizes small and large AAA (aortic diameter <55 mm and ≥55 mm, respectively). This work supports the hypothesis that T-cell-mediated immunity is fundamental in AAA pathogenesis and opens up new clinical perspectives.SummaryDifferent immune mechanisms may play a key role in the pathogenesis of distal aortic aneurysm and aortoiliac occlusive disease. The TCRβ repertoire of perivascular adipose tissue differs between the two pathologic conditions, suggesting the involvement of specific antigen-specific immune responses.
Statistical diversity distinguishes global states of consciousness
Authors: Starkey Joseph , Carhart-Harris Robin L. , Pigorini Andrea , Nobili Lino , Barrett Adam B. 
Published: December 2023

AbstractApplication of complexity measures to neurophysiological time series has seen increased use in recent years to identify neural correlates of global states of consciousness. Lempel-Ziv complexity is currently the de-facto complexity measure used in these investigations. However, by simply counting the number of patterns, this measure theoretically takes its maximum value for data that are completely random. Recently, a measure of ‘statistical complexity’ - which calculates the diversity of statistical interactions - has been devised which aims to account for and remove randomness seen in data. It was recently found that this measure decreases during anaesthesia in fruit flies. This paper investigates this statistical complexity measure on human neurophysiology data from different stages of sleep, and from individuals under the effects of three psychedelic substances: ketamine, lysergic acid diethylamide (LSD), and psilocybin. Results indicate that statistical complexity: (i) differentiates the different stages of sleep analogously to Lempel-Ziv complexity; (ii) increases relative to placebo for all three psychedelic substances. Thus, statistical complexity is a useful alternative measure for investigating the complexity of neural activity associated with different states of consciousness.
Predicting Phenotypic Traits Using a Massive RNA-seq Dataset
Authors: Hadish John Anthony , Honaas Loren A. , Ficklin Stephen Patrick 
Published: December 2023

AbstractTranscriptomic data can be used to predict environmentally impacted phenotypic traits. This type of prediction is particularly useful for monitoring difficult-to-measure phenotypic traits and has become increasingly popular for monitoring high-value agricultural crops and in precision medicine. Despite this increase in popularity, little research has been done on how many samples are required for these models to be accurate, and which normalization should be used. Here we create a massive RNA-seq dataset from publicly availableArabidopsis thalianadata with corresponding measurements for age and tissue type. We use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required. We find that Median Ratios Normalization significantly increases performance when predicting age. We also find that in the case of our dataset, only a few hundred samples are required to predict tissue types, and only a few thousand samples are necessary to accurately predict age. Researchers should consider these results when choosing the number of samples in a transcriptomic experiment and during data-processing.Author SummaryLarge datasets have become ubiquitous in both research and industry, with thousands and sometimes millions of samples being collected for a single project. In biology a prominent new technology is RNA-seq, which can be used to measure the expression level of thousands of genes for a single sample. These measurements are used for a variety of downstream applications, including predicting phenotypic traits (i.e. height, disease, etc.). A number of experiments have attempted to use RNA-seq data to make phenotype predictions with varying success. This is partially due to the small sample size of their experiments. RNA-seq datasets are currently relatively small--only a dozen to a few hundred samples--due to the cost per sample. This is expected to change as the cost of sequencing decreases. In this paper we create a massive conglomerate RNA-seq dataset from publicly availableArabidopsis thalianaRNA-seq data. We use this dataset to determine how many samples are required to accurately predict plant age and tissue type using machine learning models. We also explore the best way to normalize large datasets. Our results show the potential of massive RNA-seq datasets, and can be used to inform experimental design for phenotype prediction.
Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data
Authors: Zielinski Daniel C. , Matos Marta R.A. , de Bree James E. , Glass Kevin , Sonnenschein Nikolaus , Palsson Bernhard O. 
Published: December 2023

AbstractKinetic models of enzymes have a long history of use for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of ‘bottom up’ parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, andin vitro-in vivodifferences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard ‘macroscopic’ kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and ‘microscopic’ rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either withinin vitroexperiments or betweenin vitroandin vivoenzyme function. The code and case studies are provided in the MASSef package built on top of the MASS Toolbox in Mathematica. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.Author SummaryDetailed kinetic models of metabolism offer the promise of enabling new predictions of metabolic behavior and prospective design of metabolic function. However, parameterizing such models remains a substantial challenge. Historically, the kinetics of many enzymes have been measured usingin vitroassays, but integrating this data into consistent large-scale models and filling gaps in available data has been a primary difficulty. Here, we provide an algorithmic approach to parameterize enzyme kinetic models using diverse enzyme kinetic data. The approach reconciles inconsistent data and addresses the issue of gaps in available data implicitly through sampling alternative parameter sets. We provide a number of case studies demonstrating the approach on different enzymes. This work empowers the use of the large amount of historical and machine learning-estimated enzyme data and will aid in the construction of biochemically-accurate models of metabolism.
Development of resident and migratory three-spined stickleback,Gasterosteus aculeatus
Authors: Barnes Megan , Chakrabarti Lisa , MacColl Andrew 
Published: December 2023

AbstractThe three-spined stickleback (Gasterosteus aculeatus)is a teleost fish and a model organism in evolutionary ecology, useful for both laboratory and natural experiments. It is especially valued for the substantial intraspecific variation in morphology, behaviour and genetics. Classic work of Swarup (1958) [1] has described the development in the laboratory of embryos from a single freshwater population, but this was carried out at higher temperature than many stickleback would encounter in the wild and variation between populations was not addressed. Here we describe the development of embryos from two sympatric, saltwater ecotypes of stickleback from North Uist, Scotland raised at 14°C, the approximate temperature of North Uist lochs in the breeding season. The two ecotypes were (a) a large, migratory form in which the adults are completely plated with bony armour and (b) a smaller, low-plated form that is resident year-round in saltwater lagoons. By monitoring embryos every 24-hours post fertilisation, important characteristics of development were observed and photographed to provide a reference for North Uist ecotypes at this temperature. Hatching success was greater than 85% and did not differ between resident and migratory stickleback, but migratory eggs hatched significantly earlier than the resident ecotype. Our work provides a framework that can now be used to compare stickleback populations that may also grow in distinct environmental conditions, to help understand the breadth of normal developmental features and to characterise abnormal development.
Silver nanoparticle biosynthesis utilizingOcimum kilimandscharicumleaf extract and assessment of its antibacterial activity against certain chosen bacteria
Authors: Ouandaogo Horyomba Siaka , Diallo Souleymane , Odari Eddy , Kinyu Johnson 
Published: December 2023

AbstractUsing plants in the biological production of silver nanoparticles (AgNPs) is becoming increasingly important as a practical and environmentally benign method. In the current work, silver nanoparticles were made utilizing the significantOcimum kilimandscharicum, and their potential to combat certain bacteria was discussed. Both aqueous and methanol plant extracts were used for reducing silver nitrate at different time intervals (30 to 150 minutes) and pH (2 to 11). The UV-visible absorption spectrum recorded for both methanol and aqueous extracts revealed successful synthesis of AgNPs. The antimicrobial activity of the AgNPs was evaluated againstEscherichia coliATCC 25922,Salmonella choleraesuiusATCC 10708, andStaphylococcus aureusATCC 25923. 50mg/ml was the concentration of the extracts tested. The best zone of inhibition was recorded for the methanol and aqueous-mediated AgNPs, ranging from 12±1 to 16±1mm. The methanol and aqueous extract silver nanoparticles had the same Minimum Inhibitory Concentration (6.25±0.00 mg/ml), whereas the Minimum Bactericidal Concentrations were 12.5±0.00 and 25±0.00 mg/ml, respectively. The highest inhibition zone of 16±1 mm was observed againstSalmonella choleraesuiuswith 50±0.00 mg/ml aqueous silver nanoparticles. The results show that the silver nanoparticles made withOcimum kilimandscharicumhave potent antibacterial action against those microorganisms.