bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tuning the brakes – Modulatory role of transcranial random noise stimulation on inhibition
Authors: Mandali Alekhya , Torrecillos Flavie , Wiest Christoph , Pogosyan Alek , He Shenghong , Soriano Diogo Coutinho , Tan Huiling , Stagg Charlotte , Cagnan Hayriye 
Published: November 2023

AbstractBackgroundEveryday decision-making requires the ability to flexibly modify and sometimes terminate our actions, such as avoiding a tempting slice of cake to hitting the brakes in an emergency. Neural oscillations, such as beta-band rhythms observed over the medial prefrontal cortex(mPFC), help regulate these context-dependent behaviours. However, how random noise stimulation would modulate neural rhythms and corresponding inhibitory behaviors remain understudied.ObjectivesTo target the mPFC using random noise stimulation and modulate neural activity underlying inhibitory behaviours.MethodsUsing a single-blinded within-subject design, fifteen participants received random noise or sham stimulation in a pseudo-randomized order while performing a Go/Conflict/No-Go task. We measured neural activity and behavior before and after stimulation.ResultsWe show that random noise stimulation significantly improved inhibitory behaviors (4.6±4.42percent) by reducing the number of errors in No-Go trials. This improvement was a function of participants’ impulsivity-levels and baseline performance, i.e., impulsive individuals who made more baseline errors improved more after receiving stimulation. At the neural level, we show that random noise stimulation increases low-beta power at stimulation site, mediated by an increase in the duration of intermittent beta-bursts.ConclusionWe show for the first time that random noise stimulation improves the ability to withhold response to unexpected inhibitory cues as a function of an individual’s impulsivity level. This improvement could be attributed to increased low-beta band power and intermittent-burst duration. These results suggest that random noise stimulation of the mPFC could potentially be used as a neuromodulatory intervention to target maladaptive behaviors in impulse control disorders.
SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes
Authors: Bhogale Shounak , Seward Chris , Stubbs Lisa , Sinha Saurabh 
Published: November 2023

ABSTRACTA common way to investigate gene regulatory mechanisms is to identify differentially expressed genes using transcriptomics, find their candidate enhancers using epigenomics, and search for over-represented transcription factor (TF) motifs in these enhancers using bioinformatics tools. A related follow-up task is to model gene expression as a function of enhancer sequences and rank TF motifs by their contribution to such models, thus prioritizing among regulators.We present a new computational tool called SEAMoD that performs the above tasks of motif finding and sequence-to-expression modeling simultaneously. It trains a convolutional neural network model to relate enhancer sequences to differential expression in one or more biological conditions. The model uses TF motifs to interpret the sequences, learning these motifs and their relative importance to each biological condition from data. It also utilizes epigenomic information in the form of activity scores of putative enhancers and automatically searches for the most promising enhancer for each gene. Compared to existing neural network models of non-coding sequences, SEAMoD uses far fewer parameters, requires far less training data, and emphasizes biological interpretability.We used SEAMoD to understand regulatory mechanisms underlying the differentiation of neural stem cell (NSC) derived from mouse forebrain. We profiled gene expression and histone modifications in NSC and three differentiated cell types and used SEAMoD to model differential expression of nearly 12,000 genes with an accuracy of 81%, in the process identifying the Olig2, E2f family TFs, Foxo3, and Tcf4 as key transcriptional regulators of the differentiation process.
Genetic Insights into Agronomic and Morphological Traits of Drug-Type Cannabis Revealed by Genome-Wide Association Studies
Authors: de Ronne Maxime , Lapierre Éliana , Torkamaneh Davoud 
Published: November 2023

AbstractCannabis sativa L., previously concealed by prohibition, is now a versatile and promising plant, thanks to recent legalization, opening doors for medical research and industry growth. However, years of prohibition have left the cannabis research community underdeveloped and lacking knowledge about cannabis genetics and trait inheritance. To bridge this gap, we conducted a comprehensive genome-wide association study (GWAS), using a panel of 176 drug-type cannabis accessions, curated to represent the Canadian legal market. This pioneering GWAS harnessed the power of high-density genotyping-by-sequencing (HD-GBS), resulting in an exhaustive catalog of 800K genetic variants. These variants served as the bedrock for a GWAS designed to dissect the genetic foundations of nine key traits. To identify the most robust markers associated with these traits, two sophisticated statistical methodologies were used (SUPER and BLINK), ultimately identifying 33 markers significantly associated with agronomic and morphological traits. Several identified markers exert a substantial phenotypic impact, guided us to a rich trove of putative candidate genes that reside in high linkage-disequilibrium (LD) with the markers. These markers show great promise for revolutionizing cannabis breeding to meet diverse needs. In doing so, they lay the solid foundation for an innovative cannabis industry poised to reshape the future.
Mutational scanning reveals oncogenicCTNNB1mutations have diverse effects on signalling and clinical traits
Authors: Krishna Anagha , Meynert Alison , Kelder Martijn , Ewing Ailith , Sheraz Shahida , Ferrer-Vaquer Anna , Grimes Graeme , Becher Hannes , Silk Ryan , Semple Colin A , Kendall Timothy , Hadjantonakis Anna-Katerina , Bird Tom , Marsh Joseph A , Hohenstein Peter , Wood Andrew J , Ozdemir Derya D 
Published: November 2023

AbstractCTNNB1, the gene encoding β-catenin, is a frequent target for oncogenic mutations activating the canonical Wnt signalling pathway, typically via missense mutations within a degron hotspot motif in exon 3. Here, we combine saturation genome editing with a fluorescent reporter assay to quantify signalling phenotypes for all 342 missense mutations in the mutation hotspot, including 74 recurrent mutations reported in over 6000 tumours. Our data define the genetic requirements for β-catenin degron function and reveal diverse levels of signal activation among known driver mutations. Tumorigenesis in different human tissues involves selection forCTNNB1mutations spanning distinct ranges of effect size. In hepatocellular carcinoma, mutations that activate β-catenin relatively weakly are associated with worse prognosis compared to stronger activating mutations, despite greater immune cell infiltration in the tumour microenvironment. Our work therefore provides a resource to understand mutational diversity within a pan-cancer mutation hotspot, with potential implications for targeted therapy.
Mesenchymal Stromal Cells regulate human Hematopoietic Stem Cell survival and regeneration via cAMP/PKA pathway
Authors: Muddineni Siva Sai Naga Anurag , Even Chen Katz , Zipin-Roitman Adi , Beider Katia , Raz Yael , Weizman Eviatar , Nagler Arnon , Milyavsky Michael 
Published: November 2023

AbstractIonizing radiation and chemotherapy suppress normal Hematopoietic Stem and Progenitor Cells (HSPCs) function and present an inherent risk of developing bone marrow failure and secondary leukemias. Mesenchymal Stromal Cells (MSCs) constitute an essential HSPC niche component by regulating HSPC quiescence, self-renewal, survival, and differentiation. Yet, the underlying pro-regenerative signaling pathways activated in HSPCs by MSCs remain poorly understood. Here, using co-culture system of bone marrow-derived MSCs and HSPCs we established that irradiated HSPCs co-cultured with MSCs underwent significantly less apoptosis and maintained theirin vivorepopulating ability. We reveal that MSCs mediate protection by preserving pro-survival MCL1 protein levels in human HSPCs. Moreover, using unbiased HSPC transcriptomic analysis complemented with ex vivo and in vivo validation studies we discovered that MSCs activate cAMP/PKA/CREB signaling pathway in HSPCs to prevent MCL1 decline and apoptosis onset upon irradiation. Pharmacological activation of cAMP signaling pathway in human HSPCs replicated MSC mediated protection of HSPC functionality. Collectively, our results highlight the role of cAMP/PKA/CREB signaling pathway in MSC-HSPC cross-talk and its critical role in the regulation of survival and self-renewal ability of human HSPCs. Furthermore, we also revealed pharmacological agents that can mitigate radiation-induced hematological sequelae.
Vault particles are common contaminants of extracellular vesicle preparations
Authors: Liu Xinming , Nizamudeen Zubair , Hill Christopher J , Parmenter Christopher , Arkill Kenton P , Lambert Daniel W. , Hunt Stuart 
Published: November 2023

AbstractExtracellular vesicles (EVs) may contain a variety of molecular cargo including proteins and nucleic acids. Vault particle components have been repeatedly reported in the literature as EV cargo. Here, we demonstrated by small RNA sequencing that vault RNA (vtRNA) were highly abundant in EV pellets enriched by differential centrifugation. EVs were prepared by commonly used enrichment methods and biochemical assays used to determine whether vault particle components were bona fide EV cargo. EVs were isolated by differential centrifugation, size exclusion chromatography (SEC) and Dynabead immunocapture. RNase and proteinase treatment of EV preparations demonstrated that most vtRNA and major vault protein (MVP) were not enclosed and protected within the EV membrane. Vault-like particles were visualised in differential centrifugation pellets by cryo-transmission electron microscopy. EVs enriched by size exclusion chromatography and those isolated by immunocapture post-ultracentrifugation showed co-purification of MVP, whereas EVs isolated by direct immunocapture from conditioned medium were MVP-negative. Taken together, commonly used isolation techniques, such as differential centrifugation and SEC, can lead to contamination of EVs with vault particles. The current study highlights the importance of determining the topology of putative EV-associated components to determine if they are EV cargo or contaminants that have been co-purified.
External Validation of Machine Learning and EEG for Continuous Pain Intensity Prediction in Healthy Individuals
Authors: Mari Tyler , Henderson Jessica , Ali S. Hasan , Hewitt Danielle , Brown Christopher , Stancak Andrej , Fallon Nicholas 
Published: November 2023

AbstractPrevious research has predicted subjective pain intensity from electroencephalographic (EEG) data using machine learning (ML) models. However, there is a paucity of externally validated ML models for pain assessment, particularly for continuous pain prediction (e.g., decoding pain ratings on a 101-point scale). We aimed to conduct the first external validation paradigm for ML regression models for pain intensity prediction from EEG data. Ninety-one subjects were recruited across three samples. Sample one (n = 40) was used for model development, sample two (n = 51) was used as a cross-subject external validation set, whilst sample three (n = 25) was used as a within-subjects temporal external validation set. Pneumatic pressure stimuli were delivered to the left-hand index fingernail bed at 10 graded intensity levels. Single-trial time-frequency features of peri-stimulus EEG were used to train a Random Forest (RF) model and long short-term memory (LSTM) network to predict pain intensity responses. Results demonstrated that both the RF model and LSTM network predicted pain intensity significantly more accurately than a random prediction model, with the mean absolute error (MAE) of the RF (best performing model) at 19.59, 21.29, and 18.90 for internal validation, cross-subject external validation, and within-subject external validation, respectively. However, neither model was able to predict pain intensity better than a baseline dummy model, which predicted the mean behavioural rating of the training set and did not have access to neural data. Moreover, in a replication of our recent work, we developed a RF model for the classification of low and high-pain trials, which demonstrated internal and external validation accuracies up to 64% and 58%, respectively. Taken together, our results suggest that using ML and EEG to predict continuous pain ratings is not currently feasible. However, classification models demonstrate some potential, consistently outperforming chance across validation samples. Further improvements such as composite measures are required to elevate ML performance to a clinically meaningful level.
SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction
Authors: Wu Zijun , Sinha Saurabh 
Published: November 2023

AbstractReconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene’s expression using transcription factors’ (TFs’) expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named “SPREd” is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g., correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step towards incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.
Titin-based force regulates cardiac myofilament structures mediating length-dependent activation
Authors: Hessel Anthony L. , Kuehn Michel N. , Engels Nichlas M. , Nissen Devin L. , Freundt Johanna K. , Ma Weikang , Irving Thomas C. , Linke Wolfgang A 
Published: November 2023

AbstractThe Frank-Starling law states that the heart’s stroke volume increases with greater preload due to increased venous return, allowing the heart to adapt to varying circulatory demands. Molecularly, increasing preload increases sarcomere length (SL), which alters sarcomere structures that are correlated to increased calcium sensitivity upon activation. The titin protein, spanning the half-sarcomere, acts as a spring in the I-band, applying a SL-dependent force suggested to pull against and alter myofilaments in a way that supports the Frank-Starling effect. To evaluate this, we employed the titin cleavage (TC) model, where a tobacco-etch virus protease recognition site is inserted into distal I-band titin and allows for rapid, specific cleavage of titin in an otherwise-healthy sarcomere. Here, we evaluated the atomic-level structures of amyopathic cardiac myofilaments following 50% titin cleavage under passive stretch conditions using small-angle X-ray diffraction, which measures these structures under near-physiological (functional) conditions. We report that titin-based forces in permeabilized papillary muscle regulate both thick and thin myofilament structures clearly supporting titin’s role in the Frank-Starling mechanism.
Systems analysis of long-term heat stress responses in the C4grassSetaria viridis
Authors: Zhang Peng , Sharwood Robert , Carroll Adam , Estavillo Gonzalo M , von Caemmerer Susanne , Furbank Robert T. 
Published: November 2023

AbstractA substantial number of C4plants are utilized as food and fodder crops and often display improved resource use efficiency compared to C3counterparts. However, their response to future extreme climates such as heatwaves is less understood.Setaria viridis, an emerging C4model grass closely related to important C4 crops, was grown under high temperature for two weeks (42°C as compared to 28°C). High temperature resulted in stunted growth, but surprisingly had little impact on leaf area based photosynthetic rates. Rates of dark respiration significantly increased and there were major alterations in carbon and nitrogen metabolism in the heat-stressed plants, including reduced starch levels, accumulation of soluble sugars and an increase in leaf nitrogen content. Measurements of major phytohormones revealed a dramatic increase in abscisic acid in the heat-stressed plants. Leaf transcriptomics, proteomics and metabolomics analyses were carried out and mapped onto metabolic pathways of photosynthesis, respiration, carbon/nitrogen metabolism and hormone synthesis and signaling. Overall, upregulation of a number of stress-signaling pathways was observed, consistent with multiple potent signals leading to reduced plant growth. A systems model of plant response is presented based on oxidative stress, hormone and sugar signaling pathways.
High-fat diets promote peritoneal inflammation and augment endometriosis-associated abdominal hyperalgesia
Authors: Herup-Wheeler Tristin , Shi Mingxin , Harvey Madeleine E. , Talwar Chandni , Kommagani Ramakrishna , MacLean James A. , Hayashi Kanako 
Published: November 2023

AbstractImmune dysfunction is one of the central components in the development and progression of endometriosis by establishing a chronic inflammatory environment. Western-style high-fat diets (HFD) have been linked to greater systemic inflammation to cause metabolic and chronic inflammatory diseases, and are also considered an environmental risk factor for gynecologic diseases. Here, we aimed to examine how HFD alter an inflammatory environment in endometriosis and discern their contribution to endometriotic-associated hyperalgesia. Our results showed that HFD-induced obesity enhanced abdominal mechanical allodynia that was induced by endometriotic lesions. Peritoneal inflammatory macrophages and cytokine levels increased by lesion induction were elevated by chronic exposure to HFD. Pain-related mediators in the dorsal root ganglia were further stimulated after lesion induction under the HFD condition. Although HFD did not affect inflammatory macrophages in the peritoneal cavity without lesion induction, the diversity and composition of the gut microbiota were clearly altered by HFD as a sign of low-grade systemic inflammation. Thus, HFD alone might not establish a local inflammatory environment in the pelvic cavity, but it can contribute to further enhancing chronic inflammation, leading to the exacerbation of endometriosis-associated abdominal hyperalgesia following the establishment and progression of the disease.
Synergistic Role of NK Cells and Monocytes in Promoting Atherogenesis in Severe COVID-19 Patients
Authors: Gunasena Manuja , Alles Mario , Wijewantha Yasasvi , Mulhern Will , Bowman Emily , Gabriel Janelle , Kettelhut Aaren , Kumar Amrendra , Weragalaarachchi Krishanthi , Kasturiratna Dhanuja , Horowitz Jeffrey C , Scrape Scott , Pannu Sonal R , Liu Shan-Lu , Vilgelm Anna , Wijeratne Saranga , Bednash Joseph S , Demberg Thorsten , Funderburg Nicholas T , Liyanage Namal P.M. 
Published: November 2023

AbstractClinical data demonstrate an increased predisposition to cardiovascular disease (CVD) following severe COVID-19 infection. This may be driven by a dysregulated immune response associated with severe disease. Monocytes and vascular tissue resident macrophages play a critical role in atherosclerosis, the main pathology leading to ischemic CVD. Natural killer (NK) cells are a heterogenous group of cells that are critical during viral pathogenesis and are known to be dysregulated during severe COVID-19 infection. Their role in atherosclerotic cardiovascular disease has recently been described. However, the contribution of their altered phenotypes to atherogenesis following severe COVID-19 infection is unknown. We demonstrate for the first time that during and after severe COVID-19, circulating proinflammatory monocytes and activated NK cells act synergistically to increase uptake of oxidized low-density lipoprotein (Ox-LDL) into vascular tissue with subsequent foam cell generation leading to atherogenesis despite recovery from acute infection. Our data provide new insights, revealing the roles of monocytes/macrophages, and NK cells in COVID-19-related atherogenesis.
Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning
Authors: Qü Albert J. , Tai Lung-Hao , Hall Christopher D. , Tu Emilie M. , Eckstein Maria K. , Mishchanchuk Karyna , Lin Wan Chen , Chase Juliana B. , MacAskill Andrew F. , Collins Anne G. E. , Gershman Samuel J. , Wilbrecht Linda 
Published: November 2023

AbstractDopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison among a wide set of cognitive models based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation contributes to instrumental task performance in mice and is reflected in mesolimbic dopamine signaling.