bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pervasive patterns in the songs of passerine birds resemble human music universals and are linked with production and cognitive mechanisms
Authors: James Logan S , Oudyk Kendra , Wall Erin M , Chen Yining , Pearse William D , Sakata Jon T 
Published: July 2024

AbstractMusic is a complex learned behavior that is ubiquitous among humans, and many musical patterns are shared across geography and cultures (“music universals”). Knowing whether these universals are specific to humans or shared with other animals is important to understand how production-related factors (motor biases and constraints) or cognitive factors (learning) contribute to the emergence of these acoustic patterns. Bird song is often described as an animal analogue of human music, and some studies of individual avian species highlight acoustic similarities between bird song and music. However, expansive and comparative approaches are necessary to identify universal patterns within bird song, reveal mechanisms associated with these patterns, and draw parallels to music universals. Here, we adopt such an approach and analyze the prevalence of acoustic patterns (sequences) across ∼300 species of passerines, spanning both oscines (songbirds; vocal learners) and their sister clade, suboscines (passerines that produce songs that are not learned), as well as within a global corpus of human vocal music. This approach allowed us to directly test hypotheses that phonation mechanisms or vocal learning shape the emergence of universal patterns. We first document acoustic patterns that were widely shared across passerines and similar to music universals (e.g., small pitch intervals), highlighting the role of shared vocal production mechanisms in these patterns. Consistent with a contribution of vocal learning, we observed patterns (e.g., alternation in durations) there were more similar between oscines and humans than between suboscines and humans. Interestingly, we also discovered patterns (e.g., pitch alternation) that were inconsistent with a contribution of vocal learning and were more similar between suboscines and humans than between oscines and humans. This research provides the broadest evidence of shared universals in vocal performance across birds and humans and highlights convergent mechanisms shaping communication patterns.
Excitability modulations of somatosensory perception do not depend on feedforward neuronal population spikes
Authors: Stephani T. , Villringer A. , Nikulin V. V. 
Published: July 2024

AbstractNeural states shape perception at earliest cortical processing levels. Previous work in humans showed a relationship between initial cortical excitation, as indicated by the N20 component of the somatosensory evoked potential (SEP), pre-stimulus alpha oscillations, and the perceived intensity in a somatosensory discrimination paradigm. Here we address the follow-up question whether these excitability dynamics reflect changes in feedforward or feedback signals. We leveraged high-frequency oscillations (HFO) as a metric for neuronal population spiking activity of the first excitatory feedforward volley in the somatosensory cortex, non-invasively extracted from electroencephalography (EEG) data of 32 male human participants. Using Bayesian statistics, we found evidence against the involvement of HFO in moment-to-moment variability of perceived stimulus intensity, in contrast to previously observed pre-stimulus alpha and N20 effects. Given that the N20 component presumably reflects backpropagating membrane potentials towards the apical dendrites, we argue that top-down feedback processes (e.g., related to alpha oscillations) may thus rely on modulations at distal dendritic sites of involved pyramidal cells rather than on output firing changes at their basal compartments.
CyNET- a network analysis framework for high dimensionality, system level analyses of the functional Immunome
Authors: Kumar Pavanish , Yeo Joo Guan , Poh Su Li , Hazirah Sharifah Nur , Sutamam Nursyuhadah Binte , Ang WX Gladys , Chellamuthu Vasuki Ranjani , Wasser Martin , Thai Yeo Kee , Leong Jing Yao , Ramakrishna Lakshmi , Arkachaisri Thaschawee , Albani Salvatore 
Published: July 2024

AbstractThe immune system is a complex “Network of Networks”, in which various immune cell subsets interact and influence each other’s functions, ultimately determining immune competence and the control or onset of disease. The coordinated interactions between these cell subsets determine whether the outcome is a normal physiological state or a pathological condition. Established statistical procedures largely ignore the interactions between subsets and rely on statistically significant changes in the frequency of cell subsets. We developed CyNET (CytometryNetwork)— an analysis platform based on a network science approach to understand the immune system holistically. CyNET enables us to quantify the systems level and subsets level properties. We show that changes in the centrality of the nodes (immune subsets) reflect better biological functions than changes in frequency. We used CyNET to analyze the immune development along the chronological age gradient. Peripheral blood cells from healthy newborns (cord blood), adults (20 to 55 years), and elderly (70 years and above) human subjects were further used for validation using single-cell transcriptomics. We found that network edge density, degree centralization, and assortativity score reflect the maturation and development of the immune system along the age axis, thus enabling the characterisation of the functional architecture of the Immunome and the identification of key functional hubs within the immune networks.
Unaware Processing of Words Activates Experience-Derived Information in Conceptual-Semantic Brain Networks
Authors: Ghio Marta , Cassone Barbara , Tettamanti Marco 
Published: July 2024

AbstractThe recognition of manipulable objects results from the encoding of sensory input in combination with predictive decoding of experience-derived visuomotor information stored in conceptual-semantic representations. This grounded interpretive processing was previously found to subsist even under unaware perception of manipulable object pictures. In this fMRI study, we first aimed to extend this finding by testing whether experientially grounded visuomotor representations are unawarely recruited when manipulable objects are not visually depicted, but only referred to by words presented subliminally through continuous flash suppression. Second, we assessed the generalizability of decoding experience-derived conceptual information to other semantic categories, by extending our investigation to subliminally presented emotion words and testing for unaware recruitment of grounded emotion representations in the limbic system. Univariate analysis of data sampled from 21 human participants (14 females) showed that manipulable object words selectively activated a left-lateralized visuomotor network, both when words were presented below perceptual threshold and when participants subjectively reported lack of stimulus awareness. Emotion words selectively engaged the bilateral limbic network, although univariate analysis did not provide evidence for its recruitment under subliminal perceptual conditions. In turn, multivariate pattern analysis showed that neural codes associated with both manipulable object and emotion words could be decoded even in the absence of perceptual awareness. These findings suggest that the brain automatically engages in conceptual-semantic decoding of experience-derived information not only when circumstances require to interact with manipulable objects and emotions, but also when these referents are dislocated in time and space and only referred to by words.
Deciphering binding site conformational variability of substrate promiscuous and specialist enzymes
Authors: Thakur Deeksha , Verma Paras , Pandit Shashi Bhushan 
Published: July 2024

AbstractSubstrate promiscuity is the ability of enzymes to catalyze reactions with alternate substrate(s) beyond their physiologically relevant ones. Such promiscuous activities expand enzymes functional landscape, enabling evolution or designing novel biochemical reactions. The molecular basis of substrate promiscuity remains elusive, though previous studies have suggested roles of active site structural features, such as flexibility, hydrophobicity, sub-sites, and electrostatics. Moreover, a recent hypothesis proposes the role of active site conformational variability in promiscuity. Accordingly, promiscuous enzymes could accommodate alternate substrate(s) through their pre-existing conformations, whereas specialists have a dominant conformation for their native substrates. To explore the role of active site conformational flexibility in substrate promiscuity, we compared the conformational states of two substrate promiscuous and specialist enzymes by analyzing their binding site structural dynamics from long-time explicit solvent molecular dynamics. Using tICA, we generated conformational states from simulations of the holo-conformation of enzymes. In addition to visual analysis of the variability of binding site conformation states, we performed quantitative estimation of the same using the native functionality score, which measures the contact similarity of conformations to the native structure. We observed that both specialist and generalist enzymes exhibited varied numbers of substrate binding site competent states, indicating that conformational flexibility to accept alternate substrates could exist in both groups of enzymes. Further, it caters to the view of substrate promiscuity as a continuum feature of enzymes.
Modeling causal signal propagation in multi-omic factor space with COSMOS
Authors: Dugourd Aurelien , Lafrenz Pascal , Mañanes Diego , Paton Victor , Fallegger Robin , Kroger Anne-Claire , Turei Denes , Shtylla Blerta , Saez-Rodriguez Julio 
Published: July 2024

AbstractUnderstanding complex diseases requires approaches that jointly analyze omic data across multiple biological layers, including signaling, gene regulation, and metabolism. Existing data-driven multi-omic analysis methods, such as multi-omic factor analysis (MOFA), can identify associations between molecular features and phenotypes, but they are not designed to integrate existing mechanistic molecular knowledge, which can provide further actionable insights. We introduce an approach that connects data-driven analysis of multi-omic data with systematic integration of mechanistic prior knowledge using COSMOS+ (Causal Oriented Search of Multi-Omics Space). We show how factor analysis’ output can be used to estimate activities of transcription factors and kinases as well as ligand-receptor interactions, which in turn are integrated with network-level prior-knowledge to generate mechanistic hypotheses about paths connecting deregulated molecular features. Our approach offers an interpretable framework to generate actionable insights from multi-omic data particularly suited for high dimensional datasets such as patient cohorts.Abstract Figure
Oxytocin facilitates social behavior of female rats via selective modulation of interneurons in the medial prefrontal cortex
Authors: Schimmer Stephanie , Lefevre Arthur , Lebedeva Julia , Rozov Andrey , Kania Alan , Raftogianni Androniki , Afordakos Konstantinos , Tiwari Rishika , Netser Shai , Patwell Ryan , Hefter Dimitri , Boender Arjen , Podpecan Yuval , Schubert Tim , Sanetra Anna , Trenk Aleksandra , Gugula Anna , Hurlemann Rene , Althammer Ferdinand , Blasiak Anna , Melzer Sarah , Monyer Hannah , Wagner Shlomo , Eliava Marina , Grinevich Valery 
Published: July 2024

ABSTRACTThe hypothalamic neuropeptide oxytocin is best known for its prosocial behavioral effects. However, the precise anatomical and cellular targets for oxytocin in the cortex during social behavior remain elusive. Here we show that oxytocin neurons project directly to the medial prefrontal cortex where evoked axonal oxytocin release facilitates social behaviors in adult female rats. In conjunction, we report that local oxytocin receptor (OTR+) expressing cells are predominantly interneurons whose activation promotes social interaction. In particular, in a state of food deprivation, this inhibitory neuronal subpopulation shifts the preference from appetitive food stimuli towards a social stimulus. We further demonstrate that activation of these OTR+interneurons inhibits principal cells specifically projecting to the basolateral amygdala, thus providing a putative mechanism of selective oxytocin action in this sociability promoting cortical network.
SlNAP2promoted fruit ripening by directly binding theACS2promoter and interacting withEIL3
Authors: Wu Xuetong , Fang Hua , Huang Dengjing , Hou Xuemei , Qiao Yali , Li Changxia , Li Ailing , Huang Yi , Zhang Zhongxing , Liu Zhiya , Liu Yayu , Liao Weibiao 
Published: July 2024

AbstractThe ripening process of tomato fruit is affected by a variety of environmental factors and genetic regulators. NAC transcription factors (TFs) function in a multitude of biological processes, while the current knowledge on the participation of NAC TFs in the regulatory network of fruit ripening is relatively limited. In this study, we isolated a NAC TFs, NAP2, which acts as a positive transcription activator in tomato fruit ripening. We also observed a notable delay in the ripening process ofSlNAP2silenced and knockout mutant fruit. In particular, ethylene production was obviously inhibited inSlNAP2mutant fruit. Y1H and DLR assays showed that SlNAP2 directly binds to the promoter ofSlACS2and activates its transcriptional activity. Furthermore, SlNAP2 and SlEIL3 physically interaction was demonstrated by yeast two-hybrid (Y2H), luciferase complementation (LUC), bimolecular fluorescence complementation (BiFC) and coimmunoprecipitation analysis (CoIP) assays. Meanwhile, the pigment content, firmness and the transcript levels of genes associated with carotenoid and chlorophyll metabolism and cell wall metabolism were also potentially affected by theSlNAP2deletion; however, it has remained unclear whether these genes are also directly regulated bySlNAP2. Therefore, our findings indicate that SlNAP2 directly binds toSlACS2promoter to activate its expression and promote ethylene generation, which in turn interacts with EIL3 to enhance the function of ethylene in tomato fruit ripening. Collectively, our data contribute to understanding the interaction of NAC TFs and ethylene in tomato fruit ripening, thereby enhancing our knowledge of the ripening regulatory network that governs tomato fruit maturation.
Evaluating AI Proficiency in Nuclear Cardiology: Large Language Models take on the Board Preparation Exam
Authors: Builoff Valerie , Shanbhag Aakash , Miller Robert JH , Dey Damini , Liang Joanna X. , Flood Kathleen , Bourque Jamieson M. , Chareonthaitawee Panithaya , Phillips Lawrence M. , Slomka Piotr J 
Published: July 2024

ABSTRACTBackgroundPrevious studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology.ObjectivesThis study assesses four LLMs - GPT-4, GPT-4 Turbo, GPT-4omni (GPT-4o) (Open AI), and Gemini (Google Inc.) - in responding to questions from the 2023 American Society of Nuclear Cardiology Board Preparation Exam, reflecting the scope of the Certification Board of Nuclear Cardiology (CBNC) examination.MethodsWe used 168 questions: 141 text-only and 27 image-based, categorized into four sections mirroring the CBNC exam. Each LLM was presented with the same standardized prompt and applied to each section 30 times to account for stochasticity. Performance over six weeks was assessed for all models except GPT-4o. McNemar’s test compared correct response proportions.ResultsGPT-4, Gemini, GPT4-Turbo, and GPT-4o correctly answered median percentiles of 56.8% (95% confidence interval 55.4% - 58.0%), 40.5% (39.9% - 42.9%), 60.7% (59.9% - 61.3%) and 63.1% (62.5 – 64.3%) of questions, respectively. GPT4o significantly outperformed other models (p=0.007 vs. GPT-4Turbo, p<0.001 vs. GPT-4 and Gemini). GPT-4o excelled on text-only questions compared to GPT-4, Gemini, and GPT-4 Turbo (p<0.001, p<0.001, and p=0.001), while Gemini performed worse on image-based questions (p<0.001 for all).ConclusionGPT-4o demonstrated superior performance among the four LLMs, achieving scores likely within or just outside the range required to pass a test akin to the CBNC examination. Although improvements in medical image interpretation are needed, GPT-4o shows potential to support physicians in answering text-based clinical questions.
Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer
Authors: Brattoli Biagio , Mostafavi Mohammad , Lee Taebum , Jung Wonkyung , Ryu Jeongun , Park Seonwook , Park Jongchan , Pereira Sergio , Shin Seunghwan , Choi Sangjoon , Kim Hyojin , Yoo Donggeun , Ali Siraj M. , Paeng Kyunghyun , Ock Chan-Young , Cho Soo Ick , Kim Seokhwi 
Published: July 2024

ABSTRACTDespite advancements in methodologies, immunohistochemistry (IHC) remains the most utilized ancillary test for histopathologic and companion diagnostics in targeted therapies. However, objective IHC assessment poses challenges. Artificial intelligence (AI) has emerged as a potential solution, yet its development requires extensive training for each cancer and IHC type, limiting versatility. We developed a Universal IHC (UIHC) analyzer, an AI model for interpreting IHC images regardless of tumor or IHC types, using training datasets from various cancers stained for PD-L1 and/or HER2. This multi-cohort trained model outperforms conventional single-cohort models in interpreting unseen IHCs (Kappa score 0.578 vs. up to 0.509) and consistently shows superior performance across different positive staining cutoff values. Qualitative analysis reveals that UIHC effectively clusters patches based on expression levels. The UIHC model also quantitatively assesses c-MET expression with MET mutations, representing a significant advancement in AI application in the era of personalized medicine and accumulating novel biomarkers.
Long-read sequencing identifies copy-specific markers ofSMNgene conversion in spinal muscular atrophy
Authors: Zwartkruis M.M. , Elferink M.G. , Gommers D. , Signoria I. , Blasco-Pérez L. , Costa-Roger M. , van der Sel J. , Renkens I.J. , Green J.W. , Kortooms J.V. , Vermeulen C. , Straver R. , van Deutekom H.W.M. , Veldink J.H. , Asselman F. , Tizzano E.F. , Wadman R.I. , van der Pol W.L. , van Haaften G.W. , Groen E.J.N. 
Published: July 2024

AbstractThe complex 2 Mbsurvival motor neuron (SMN)locus on chromosome 5q13, including the spinal muscular atrophy (SMA)-causing geneSMN1and modifierSMN2, remains incompletely resolved due to numerous segmental duplications. Variation inSMN2copy number, presumably influenced bySMN1toSMN2gene conversion, affects disease severity, thoughSMN2copy number alone has insufficient prognostic value due to limited genotype-phenotype correlations. With advancements in newborn screening andSMN-targeted therapies, identifying genetic markers to predict disease progression and treatment response is crucial. Progress has thus far been limited by methodological constraints. To address this, we used targeted nanopore long-read sequencing to analyze copy-specific variation inSMNand neighboring genes. In 25 healthy controls, we identified single nucleotide variants (SNVs) specific toSMN1andSMN2haplotypes that could serve as gene conversion markers. In 31 SMA patients, 45% of haplotypes showed varyingSMN1toSMN2gene conversion breakpoints, serving as direct evidence of gene conversion as a common genetic characteristic in SMA and prompting further investigation into gene conversion markers as disease modifiers. Our findings illustrate that both methodological advances and the analysis of patient samples are required to advance our understanding of complex genetic loci and address critical clinical challenges.
Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density
Authors: Chettuthara Moncy Jijomon , Fan Yong , Woodham Rachel D. , Ghazi-Noori Ali-Reza , Rezaei Hakimeh , Xiao Wenyi , Bramon Elvira , Ritter Philipp , Bauer Michael , Young Allan H. , Fu Cynthia H.Y. 
Published: July 2024

AbstractBipolar Depression (BD) characterised by changes in mood and activity levels is a leading cause of disability worldwide. Current treatments show limited efficacy. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that is a potential treatment for bipolar depression. We sought to investigate the application of deep learning methods to electroencephalogram (EEG) signals to predict clinical remission. Resting-state EEG data were acquired in 21 BD participants (mean age 51.38 + 10.59 years) at baseline, using a portable 4 electrode EEG device (AF7, AF8, TP9, TP10). Treatment was 6 weeks of home-based tDCS sessions, consisting of 5 sessions per week for 3 weeks and 3 sessions per week for 3 weeks. tDCS was provided in bifrontal montage, anode over left dorsolateral prefrontal cortex (DLPFC) and cathode over right DLPFC, 2 mA, 30 minutes per session. Remission was defined as Montgomery-Åsberg Depression Rating Scale score of less than 8. Power spectral density was derived from EEG signals. Deep learning methods: 1D convolutional neural networks (1DCNNs), long short-term memory (LSTM), gated recurrent units (GRU) and their hybrid models, were investigated for prediction of remission and non-remission status following treatment. Hybrid 1DCNN and GRU model using a combination of delta, theta, and gamma band PSD from AF7 and TP10 electrodes achieved a treatment remission prediction accuracy of 79.55% (sensitivity 76.95%, specificity 83.02%). Compelling prediction accuracy for prediction of treatment remission to a course of tDCS in bipolar depression is achieved from deep learning analysis of resting-state EEG at baseline.