bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain signatures indexing variation in internal processing during perceptual decision-making
Authors: Nakuci Johan , Samaha Jason , Rahnev Dobromir 
Published: January 2023

AbstractBrain activity is highly variable even while performing the same cognitive task with consequences for performance. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimentally inducing changes (e.g., via attention manipulation) to identify neuronal and behavioral consequences or studying spontaneous changes in ongoing brain dynamics. However, changes in internal processing could arise from many factors, such as variation in strategy or arousal, that are independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials and relate this activity to behavioral performance. Subjects (N = 25) performed a motion direction discrimination task with six interleaved levels of motion coherence. Modularity-maximization based clustering identified two discrete spatial-temporal clusters, or subtypes, of trials with different patterns of brain activity. Surprisingly, even though Subtype 1 occurred more frequently with lower motion coherence, it was nonetheless associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight trial-to-trial variability in decision processes usually masked to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.Highlights⍰ Brain activity is highly variable.⍰ We find multiple and distinct stimulus-driven patterns in EEG.⍰ With changes in decision-making and drift-diffusion parameters.⍰ These results suggest a new way to identify brain states relevant to behavior.Graphical Abstract
A Network Pharmacology Approach to Elucidate the Anti-inflammatory Effects of Ellagic Acid
Authors: Hoang Skyler H. , Dao Hue , Lam Emerson My 
Published: January 2023

AbstractEllagic acid (EA) is a naturally occurring polyphenolic compound found in various fruits and vegetables like strawberries, raspberries, pomegranates, and nuts such as pecans and walnuts. With its antioxidant properties, EA has shown potential health benefits, although further research is necessary to fully comprehend its effects, mechanisms, and safe and effective application as a complementary medicine. Notably, there is accumulating evidence of EA’s anti-inflammatory effects; however, the precise underlying mechanism remains unclear. To investigate the anti-inflammatory properties of EA, a network pharmacology approach was employed. The study identified 52 inflammation-related targets of EA and revealed significant signaling pathways and relevant diseases associated with inflammation through GO and KEGG analysis. Furthermore, topological analysis identified 10 important targets, including AKT1, VEGFA, TNF, MAPK3, ALB, SELP, MMP9, MMP2, PTGS2, and ICAM1. Molecular docking and molecular dynamics simulations (integrated with were conducted molecular mechanics Poisson-Boltzmann), indicating that AKT1, PTGS2, VEGFA, and MAPK3 are the most likely targets of EA. In summary, this study not only confirmed the anti-inflammatory effects of EA observed in previous research but also identified the most probable targets of EA.
Anthracyclines inhibit SARS-CoV-2 infection
Authors: Wang Zhen , Pan Qinghua , Ma Ling , Zhao Jianyuan , McIntosh Fiona , Liu Zhenlong , Ding Shilei , Lin Rongtuan , Chen Shan , Finzi Andrés , Liang Chen 
Published: January 2023

ABSTRACTVaccines and drugs are two effective medical interventions to mitigate SARS-CoV-2 infection. Three SARS-CoV-2 inhibitors, remdesivir, paxlovid, and molnupiravir, have been approved for treating COVID-19 patients, but more are needed, because each drug has its limitation of usage and SARS-CoV-2 constantly develops drug resistance mutations. In addition, SARS-CoV-2 drugs have the potential to be repurposed to inhibit new human coronaviruses, thus help to prepare for future coronavirus outbreaks. We have screened a library of microbial metabolites to discover new SARS-CoV-2 inhibitors. To facilitate this screening effort, we generated a recombinant SARS-CoV-2 Delta variant carrying the nano luciferase as a reporter for measuring viral infection. Six compounds were found to inhibit SARS-CoV-2 at the half maximal inhibitory concentration (IC50) below 1 μM, including the anthracycline drug aclarubicin that markedly reduced viral RNA-dependent RNA polymerase (RdRp)-mediated gene expression, whereas other anthracyclines inhibited SARS-CoV-2 by activating the expression of interferon and antiviral genes. As the most commonly prescribed anti-cancer drugs, anthracyclines hold the promise of becoming new SARS-CoV-2 inhibitors.IMPORTANCEMicrobial metabolites are a rich source of bioactive molecules. The best examples are antibiotics and immunosuppressants that have transformed the practice of modern medicine and saved millions of lives. Recently, some microbial metabolites were reported to have antiviral activity, including the inhibition of Zika virus and Ebola virus. In this study, we discovered several microbial metabolites that effectively inhibit SARS-CoV-2 infection, including anthracyclines that have also been shown to inhibit other viruses including Ebola virus through enhancing interferon responses, which indicates potentially broad antiviral properties of these microbial metabolites and can lead to the discovery of pan-antiviral molecules.
Uncovering specific mechanisms across cell types in dynamical models
Authors: Hauber Adrian L. , Rosenblatt Marcus , Timmer Jens 
Published: January 2023

AbstractOrdinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results.To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.Contactadrian.hauber@fdm.uni-freiburg.deAuthor SummaryMathematical models enable insights into biological systems beyond what is possible in the wet lab alone. However, constructing useful models can be challenging, since they both need a certain amount of complexity to adequately describe real-world observations, and simultaneously enough simplicity to enable understanding of these observations and precise predictions. Regularization techniques were suggested to tackle this challenge, especially when building models that describe two different types of cells, such as healthy and cancer cells. Typically, both cell types have a large portion of biological mechanisms in common, and the task is to identify the relevant differences that need to be included into the model.For more than two types of cells, the existing approaches are not readily applicable, because they require defining one of the cell types as reference, which potentially influences the results. In this work, we present a regularization method that is independent from the choice of a reference. We demonstrate its working principle and compare its performance to existing approaches. Since we implemented this method in a freely available software package, it is accessible to a broad range of researchers and will facilitate the construction of useful mathematical models for multiple types of cells.
Galectin-3 and Heart Failure with Preserved Ejection Fraction: Clarifying an Emerging Relationship
Authors: Baccouche Basil M. , Rhodenhiser Emmajane 
Published: January 2023

ABSTRACTIntroductionHFpEF is one of the leading causes of death whose burden is estimated to expand in the coming decades. This paper examines the relationship between circulating levels of galectin-3, an emerging risk factor for cardiovascular disease, and the clinical diagnosis of HFpEF.MethodsThe authors reviewed peer-reviewed literature and 18 studies met inclusion criteria. Study characteristics, study outcome definitions, assay characteristics, main findings, and measures of association were tabulated and summarized.ResultsFive (1–5) studies found significant associations between galectin-3 and HFpEF diagnosis compared to healthy controls, and one (6) did not. Five studies (7–11) found significant associations between galectin-3 concentration in circulation and severity of diastolic dysfunction. Three studies (12–14) found a statistically significant association between circulating galectin-3 and all-cause mortality or rehospitalization. Two studies (15,16) found levels of circulating galectin-3 to be a statistically significant predictor of later HFpEF onset. Finally, two studies examined whether galectin-3 was associated with incident HFpEF, one (17) found a significant association and the other (18) did not.ConclusionGiven the paucity of effective therapeutics for HFpEF, galectin-3 shows promise as a possible HFpEF-linked biomarker that may, with further study, inform and predict treatment course to reduce morbidity and mortality.
Intron retention of an adhesion GPCR generates single transmembrane-helix isoforms to enable 7TM-adhesion GPCR function
Authors: Bormann Anne , Körner Marek B. , Dahse Anne-Kristin , Gläser Marie S. , Irmer Johanna , Lede Vera , Alenfelder Judith , Lehmann Joris , Hall Daniella C.N. , Thane Michael , Schleyer Michael , Kostenis Evi , Schöneberg Torsten , Bigl Marina , Langenhan Tobias , Ljaschenko Dmitrij , Scholz Nicole 
Published: January 2023

SUMMARYAdhesion G protein-coupled receptors (aGPCR) function as metabotropic mechanosensors in the nervous system and other organs. aGPCR are heavily spliced forecasting an extraordinary molecular structural diversity. Many predicted isoforms lack the transmembrane (7TM) signaling subunit, but to what extent these non-GPCR isoforms are produced and what physiological purpose they serve is unknown. Alternative splicing through intron retention ofADGRL/Latrophilin/CirlmRNA inDrosophilagenerates transcripts encoding unconventional proteins with an extracellular domain anchored by a single transmembrane helix (Cirl1TM). Here, we show thatCirl1TMtranscripts are translatedin vivoand that Cirl1TMbinds Cirl7TMN-terminal fragment-dependently. This interaction enables mechanosensory neurons to distinguish input intensities through Gαo-dependent signaling. Similarly, a direct interaction was found for mammalian GPR126/ADGRG6 isoforms. Together, our findings define intron retention and isoform-specific heteromerization as extraordinary molecular strategies to adjustCirl-dependent mechanosensation and demonstrate physiological relevance of versatile aGPCR isoform repertoire to tune cellular responsiveness.
Pupil dilation scales with movement distance of real but not of imagined reaching movements
Authors: Voudouris Dimitris , Schuetz Immo , Schinke Tabea , Fiehler Katja 
Published: January 2023

AbstractPupillary responses have now been reliably identified for cognitive and motor tasks, but less is known about their relation to mentally simulating movements (known as motor imagery). Previous work found pupil dilations during the performance of simple finger movements, where peak pupillary dilation scaled with the complexity of the finger movement and force required. Recently, pupillary dilations were reported during imagery of grasping and piano playing. Here we examined whether pupillary responses are sensitive to the dynamics of the underlying motor task for both executed and imagined reach movements. Participants reached or imagined reaching to one of three targets placed at different distances from a start position. Both executed and imagined movement times scaled with target distance, and they were highly correlated, confirming previous work and suggesting that participants did imagine the respective movement. Increased pupillary dilation was evident during motor execution compared to rest, with stronger dilations for larger movements. Pupil dilations also occurred during motor imagery, however they were generally weaker than during motor execution and they were not influenced by imagined movement distance. Instead, dilations during motor imagery resembled pupil responses obtained during a non-motor imagery task (imagining a previously viewed painting). Our results demonstrate that pupillary responses can reliably capture the dynamics of an executed goal-directed reaching movement, but suggest that pupillary responses during imagined reaching movements reflect general cognitive rather than motor-specific components of the motor imagery process.New and noteworthyPupil size is influenced by the performance of cognitive tasks. Here we demonstrate that pupil size increases during execution and mental simulation of goal-directed reaching movements compared to rest. Pupil dilations scale with movement amplitude only during motor execution, whereas they are similar during motor imagery and a non-motor imagery, cognitive task.
Floral presence and flower identity alter cereal aphid endosymbiont communities on adjacent crops
Authors: Zytynska Sharon E. , Sturm Sarah , Hawes Cathy , Weisser Wolfgang W , Karley Alison 
Published: January 2023

AbstractFloral plantings adjacent to crops fields can recruit populations of natural enemies by providing flower nectar and non-crop prey to increase natural pest regulation. Observed variation in success rates might be due to changes in the unseen community of protective endosymbionts hosted by many herbivorous insects, which can confer resistance to various specialist natural enemies, e.g. parasitoid wasps. Reduced insect control may occur if highly protective symbiont combinations increase in frequency via selection effects, and this is expected to be stronger in lower diversity systems.We used a large-scale field trial to analyse the bacterial endosymbiont communities hosted by cereal aphids (Sitobion avenae) collected along transects into strip plots of barley plants managed by either conventional or integrated (including floral field margins and reduced inputs) methods. In addition, we conducted an outdoor pot experiment to analyse endosymbionts inS. avenaeaphids collected on barley plants that were either grown alone or alongside one of three flowering plants, across three time points.In the field, aphids hosted up to four symbionts. The abundance of aphids and parasitoid wasps was reduced towards the middle of all fields while aphid symbiont species richness and diversity decreased into the field in conventional, but not integrated, field-strips. The proportion of aphids hosting different symbiont combinations varied across cropping systems, with distances into the fields, and were correlated with parasitoid wasp abundances.In the pot experiment, aphids hosted up to six symbionts. Flower presence increased natural enemy abundance and diversity, and decreased aphid abundance. The proportion of aphids hosting different symbiont combinations varied across the flower treatment and time, and were correlated with varying abundances of the different specialist parasitoid wasp species recruited by different flowers.Synthesis and applications. Floral plantings and flower identity can have community-wide impacts on the combinations of bacterial endosymbionts hosted by herbivorous insects. Our work highlights the potential of within-season selection for symbiont-mediated pest resistance to natural enemies with biological control impacts. This could be mitigated through increased recruitment of diverse natural enemies by incorporating functional diversity of floral resources into the environment.
Novel reassortments of variation underlie repeated adaptation to whole genome duplication in two outcrossing Arabidopsis species
Authors: Bohutínská Magdalena , Petříková Eliška , Booker Tom R. , Cobo Cristina Vives , Vlček Jakub , Šrámková Gabriela , Poštulková Alžběta , Hojka Jakub , Marhold Karol , Yant Levi , Kolář Filip , Schmickl Roswitha 
Published: January 2023

AbstractPolyploidy, the result of whole genome duplication (WGD), is widespread across the tree of life and is often associated with speciation or adaptability. It is thought that adaptation in autopolyploids (within-species polyploids) may be facilitated by increased access to genetic variation. This variation may be sourced from gene flow with sister diploids and new access to other tetraploid lineages, as well as from increased mutational targets provided by doubled DNA content. Here we deconstruct the origins of haplotype blocks displaying the strongest selection signals in established, successful autopolyploids,Arabidopsis lyrataandArabidopsis arenosa. We see strong signatures of selection in 17 genes implied in meiosis, cell cycle, and transcription across all four autotetraploid lineages present in our expanded sampling of 983 sequenced genomes. Most prominent in our results is the finding that the tetraploid-characteristic haplotype blocks with the most robust signals of selection were completely absent in diploid sisters. In contrast, the fine-scaled variant mosaics in the tetraploids originated from highly diverse evolutionary sources. These include novel reassortments of trans-specific polymorphism from diploids, new mutations, and tetraploid-specific inter-species hybridization. We speculate that this broad-scale allele acquisition and re-shuffling enabled the autotetraploids to rapidly adapt to the challenges inherent to WGD, and may further promote their adaptation to environmental challenges.Lay summaryPolyploidy, the result of whole genome duplication, is associated with speciation and adaptation. To fuel their often remarkable adaptations, polyploids may access and maintain adaptive alleles more readily than diploids. Here we identify repeated signals of selection on genes that are thought to mediate adaptation to whole genome duplication in twoArabidopsisspecies. We found that the tetraploid-characteristic haplotype blocks, found in genes exhibiting the most robust signals of selection, were never present in their diploid relatives. Instead, these blocks were made of mosaics forged from multiple allelic sources. We hypothesize that this increased variation helped polyploids to adapt to the process that caused this increase – genome duplication – and may also help them adapt to novel environments.
Microtubule decay is a driver of neuronal ageing and a promising target for intervention
Authors: Okenve-Ramos Pilar , Gosling Rory , Chojnowska-Monga Monika , Gupta Kriti , Shields Samuel , Sanchez-Soriano Natalia 
Published: January 2023

AbstractNatural ageing is accompanied by a decline in motor, sensory and cognitive functions, all impacting life quality. Ageing is the predominant risk factor for many neurodegenerative diseases, including Parkinson’s and Alzheimer’s disease. We need therefore to gain a better understanding of the cellular and physiological processes underlying age-related neuronal decay. However, gaining this understanding is a slow process due to the long time required to age mammalian or vertebrate model animals.Here we introduce a new cellular model within theDrosophilabrain where neurons show typical ageing hallmarks known from the primate brain, including axonal swellings, cytoskeletal decay, a reduction in axonal calibre and morphological changes arising at synaptic terminals. In the fly brain, these changes occur within just a few weeks, ideal to study the underlying mechanisms. We observe that decay of the neuronal microtubule cytoskeleton clearly precedes other ageing hallmarks. We show that the microtubule-binding factors Tau, EB1 and Shot, are necessary for microtubule maintenance in axons and synapses. Their functional loss during ageing triggers microtubule bundle decay followed by the decline in axons and synapses. Genetic manipulations that improve microtubule networks, slow down other neuronal ageing hallmarks and confer aged specimens with the ability to outperform age-matched controls. Our work suggests therefore that microtubule networks are a key lesion site in ageing neurons and offer promising opportunities to improve neuronal decay in advanced age.
Inferring membrane properties during clathrin-mediated endocytosis using machine learning
Authors: Lin Zhiwei , Mao Zhiping , Ma Rui 
Published: January 2023

ABSTRACTEndocytosis is a fundamental cellular process for eukaryotic cells to transport molecules into the cell. To understand the molecular mechanisms behind the process, researchers have obtained abundant biochemical information about the protein dynamics involved in endocytosis via fluorescence microscopy and geometric information about membrane shapes via electron tomography. However, measuring the biophysical information, such as the osmotic pressure and the membrane tension, remains a problem due to the small dimension of the endocytic invagination. In this work, we combine Machine Learning and Helfrich model of the membrane, as well as the dataset of membrane shapes extracted from the electron tomography to infer biophysical information about endocytosis. Our results show that Machine Learning is able to find solutions that both match the experimental profile and fulfill the membrane shape equations. Furthermore, we show that at the early stage of endocytosis, the inferred membrane tension is negative, which implies strong compressive forces acting at the boundary of the endocytic invagination. This method provides a generic framework to extract membrane information from the super-resolution imaging.SIGNIFICANCEEndocytosis is a fundamental cellular process that has been extensively studied with the help of fluorescence microscopy and electron microscopy. A large amount of data has been accumulated about the protein dynamics and the membrane shapes. In this work, we combine the widely used Helfrich model and experimental data of membrane shapes to infer the physical information about endocytosis, including the membrane tension and the osmotic pressure. Our work not only proves Machine Learning as a power tool is able to solve the complicated membrane shape equations, but also provides novel biological insights about the initiation of endocytosis in yeast cells.