bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-model Order ICA: A Data-driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales
Authors: Meng Xing , Iraji Armin , Fu Zening , Kochunov Peter , Belger Aysenil , Ford Judy M. , McEwen Sara , Mathalon Daniel H. , Mueller Bryon A. , Pearlson Godfrey , Potkin Steven G. , Preda Adrian , Turner Jessica , van Erp Theo G.M. , Sui Jing , Calhoun Vince D. 
Published: October 2021

AbstractBackgroundWhile functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown.MethodsWe proposed an independent component analysis (ICA) - based approach to capture information at multiple model orders (component numbers) and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting fMRI (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine (SVM)-based classification.ResultsIn addition to consistent predictive patterns at both multiple-model orders and single model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model order 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared to other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ.ConclusionsIn sum, multi-model order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches.Impact StatementMulti-model order ICA provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single model order analysis. This work expands upon and adds to the relatively new literature on resting fMRI-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.
Early visual processing as a marker of disease, not vulnerability: Event-related potential (ERP) evidence from 22q11.2 deletion syndrome, a population at high risk for schizophrenia
Authors: Francisco Ana A. , Foxe John J. , Horsthuis Douwe J. , Molholm Sophie 
Published: October 2021

AbstractWe investigated visual processing in 22q11.2 deletion syndrome (22q11.2DS), a condition characterized by an increased risk for schizophrenia. Visual processing differences have been described in schizophrenia but remain understudied early in the disease course. Electrophysiology was recorded during a visual adaptation task with different interstimulus intervals to investigate visual processing and adaptation in 22q11.2DS (with (22q+) and without (22q-) psychotic symptoms), compared to control and idiopathic schizophrenia groups. Analyses focused on early windows of visual processing. While increased amplitudes were observed in 22q11.2DS in an earlier time window (90-140 ms), decreased responses were seen later (165-205 ms) in schizophrenia and 22q+. 22q11.2DS, and particularly 22q-, presented increased adaptation effects. We argue that while amplitude and adaptation in the earlier time window may reflect specific neurogenetic aspects associated with a deletion in chromosome 22, amplitude in the later window may be a marker of the presence of psychosis and/or of its chronicity/severity.
Sample Size Analysis for Machine Learning Clinical Validation Studies
Authors: Goldenholz Daniel M. , Sun Haoqi , Ganglberger Wolfgang , Westover M. Brandon 
Published: October 2021

ABSTRACTOBJECTIVEBefore integrating new machine learning (ML) into clinical practice, algorithms must undergo validation. Validation studies require sample size estimates. Unlike hypothesis testing studies seeking a p-value, the goal of validating predictive models is obtaining estimates of model performance. Our aim was to provide a standardized, data distribution- and model-agnostic approach to sample size calculations for validation studies of predictive ML models.MATERIALS AND METHODSSample Size Analysis for Machine Learning (SSAML) was tested in three previously published models: brain age to predict mortality (Cox Proportional Hazard), COVID hospitalization risk prediction (ordinal regression), and seizure risk forecasting (deep learning). The SSAML steps are: 1) Specify performance metrics for model discrimination and calibration. For discrimination, we use area under the receiver operating curve (AUC) for classification and Harrell’s C-statistic for survival models. For calibration, we employ calibration slope and calibration-in-the-large. 2) Specify the required precision and accuracy (≤0.5 normalized confidence interval width and ±5% accuracy). 3) Specify the required coverage probability (95%). 4) For increasing sample sizes, calculate the expected precision and bias that is achievable. 5) Choose the minimum sample size that meets all requirements.RESULTSMinimum sample sizes were obtained in each dataset using standardized criteria.DISCUSSIONSSAML provides a formal expectation of precision and accuracy at a desired confidence level.CONCLUSIONSSAML is open-source and agnostics to data type and ML model. It can be used for clinical validation studies of ML models.
Fruit colour and range size interact to influence diversification
Authors: Hill Adrian P. , Torres Jiménez Maria Fernanda , Chazot Nicolas , Cássia-Silva Cibele , Faurby Søren , Bacon Christine D. 
Published: October 2021

AbstractAimDifferent fruit colours are associated with dispersal by different frugivores, largely based on colour vision type. Frugivore mobility affects overall range size for the plant being dispersed. Here we determine the interaction between different fruit colours, range sizes, and diversification rates by testing two hypotheses: That (1) fruit colours attractive to birds have larger range sizes due to their higher dispersal ability, and that (2) different frugivore disperser groups, bird or mammal, leads to different diversification rate at different range size, where intermediate range size leads to the highest diversification rate.LocationGlobal.Time periodContemporary (or present)Major taxa studiedPalms (Arecaceae)MethodsUsing model selection, we identified three groups of colours with similar diversification rate and likely disperser. Range sizes were estimated and categorized species as small, intermediate, or large-ranged. For model selection and to determine the relationship beween fruit color, range size and diversification rate we used Multi-State Speciation and Extinction (MuSSE) models.ResultsSpecies with intermediate range size had the highest net diversification for all three fruit colour groups. Bird-dispersed palms more likely diversified at small than at large range size while mammal-dispersed palms more likely diversified at larger range size than small. Fruit colours associated with mammal dispersal had more large-ranged species than colours associated with bird dispersal.Main conclusionsThe associated between intermediate range size and higher diversification rate indicates that spatial factors that affect diversification at small and large range sizes result in higher diversification at intermediate ranges. We find striking differences in diversification rate within each range size category between fruit color groups. This suggests that the relationship between diversification rate and range size depends on the specific frugivorous dispersers and their dispersal patterns. This study reveals how fruit traits alter dispersal patterns and how that, in turn, influences diversification.
Effect of channel assembly (KCNQ1 or KCNQ1 + KCNE1) on the response of zebrafish IKs to IKs inhibitors and activators
Authors: Haverinen Jaakko , Hassinen Minna , Vornanen Matti 
Published: October 2021

ABSTRACTIn cardiac myocytes, the slow component of the delayed rectifier K+ current (IKs) ensures repolarization of action potential during beta-adrenergic activation or when other repolarizing K+ currents fail. As a key factor of cardiac repolarization IKs should be present in model species used for cardiovascular drug screening, preferably with pharmacological characteristics similar to those of the human IKs. To this end, we investigated the effects of inhibitors and activators of the IKs on KCNQ1 and KCNQ1+KCNE1 channels of the zebrafish, an important model species, in Chinese hamster ovary cells. Inhibitors of IKs, chromanol 293B and HMR-1556, inhibited zebrafish IKs channels with approximately similar potency as that of mammalian IKs. Chromanol 293B concentration for half-maximal inhibition (IC50) of zebrafish IKs was at 13.1±5.8 and 13.4±2.8 μM for KCNQ1 and KCNQ1+KCNE1 channels, respectively. HMR-1556 was a more potent inhibitor of zebrafish IKs with IC50=0.1±0.1 μM and 1.5±0.8 μM for KCNQ1 and KCNQ1+KCNE1 channels, respectively. R-L3 and mefenamic acid, generally identified as IKs activators, both inhibited zebrafish IKs. R-L3 almost completely inhibited zebrafish IKs generated by KCNQ1 and KCNQ1+KCNE1 channels with similar affinity (IC50 1.1±0.4 and 1.0±0.4 μM, respectively). Mefenamic acid partially blocked zebrafish KCNQ1 (IC50=9.5±4.8 μM) and completely blocked KCNQ1+KCNE1 channels (IC50=3.3±1.8 μM). Although zebrafish IKs responds to IKs inhibitors in the same way as mammalian IKs, its response to activators is atypical, probably due to the differences in the binding domain of KCNE1 to KCNQ1. Therefore, care must be taken when translating the results from zebrafish to humans.
Antithetical contribution of primary and non-primary auditory cortex while listening to speech in noisy scenes
Authors: Hausfeld Lars , Hamers Iris M. H. , Formisano Elia 
Published: October 2021

SummaryInvasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. Due to the measurements’ limited coverage and/or spatial resolution, however, the distinct contribution of primary and non-primary auditory areas remains unclear. Using 7-Tesla fMRI, here we measured brain responses of participants attending to one speaker, without and with another concurrent speaker. Using voxel-wise modeling, we observed significant speech envelope tracking in bilateral Heschl’s gyrus (HG) and right middle superior temporal sulcus (mSTS), despite the sluggish fMRI responses and slow temporal sampling. Neural activity was either positively (HG) or negatively (mSTS) correlated to the speech envelope. Further analyses comparing the similarity between spatial response patterns in theconcurrent speakersandsingle speakerconditions indicated that whereas tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, right mSTS selectively represented the relevant speech signal. Additionally, in right mSTS, the similarity strength correlated with the participant’s comprehension of relevant speech. These results indicate that primary and non-primary AC antithetically process ongoing speech suggesting a push-pull of acoustic and linguistic information.
DLA-Ranker: Evaluating protein docking conformations with many locally oriented cubes
Authors: Behbahani Yasser Mohseni , Laine Élodie , Carbone Alessandra 
Published: October 2021

AbstractProteins ensure their biological functions by interacting with each other, and with other molecules. Determining the relative position and orientation of protein partners in a complex remains challenging. Here, we address the problem of ranking candidate complex conformations toward identifying near-native conformations. We propose a deep learning approach relying on a local representation of the protein interface with an explicit account of its geometry. We show that the method is able to recognise certain pattern distributions in specific locations of the interface. We compare and combine it with a physics-based scoring function and a statistical pair potential.
Hematopoietic stem and progenitor cells improve survival from sepsis by boosting immunomodulatory cells
Authors: Morales-Mantilla Daniel E. , Kain Bailee , Le Duy , Flores Anthony R. , Paust Silke , King Katherine Y. 
Published: October 2021

AbstractNew therapeutic strategies to reduce sepsis-related mortality are urgently needed, as sepsis accounts for 1 in 5 deaths worldwide. Since hematopoietic stem and progenitor cells (HSPCs) are responsible for producing blood and immune cells, including in response to immunological stress, we explored their potential for treating sepsis. In a mouse model of Group A Streptococcus (GAS)-induced sepsis, severe immunological stress was associated with significant depletion of bone marrow HSPCs and mortality within approximately 5-7 days. We hypothesized that the inflammatory environment of GAS infection drives rapid HSPC differentiation and depletion that can be rescued by infusion of donor HSPCs. Indeed, infusion of 10,000 naïve HSPCs into GAS-infected mice resulted in rapid myelopoiesis and a 50-60% increase in overall survival. Surprisingly, mice receiving donor HSPCs displayed a similar pathogen load compared to untreated mice. Flow cytometric analysis revealed a significantly increased number of myeloid-derived suppressor cells in HSPC-infused mice, which correlated with reduced inflammatory cytokine levels and restored HSPC levels. These findings suggest that HSPCs play an essential immunomodulatory role that may translate into new therapeutic strategies for sepsis.
DyScore: A Boosting Scoring Method with Dynamic Properties for Identifying True Binders and Non-binders in Structure-based Drug Discovery
Authors: Li Yanjun , Zhou Daohong , Zheng Guangrong , Li Xiaolin , Wu Dapeng , Yuan Yaxia 
Published: October 2021

AbstractThe accurate prediction of protein-ligand binding affinity is critical for the success of computer-aided drug discovery. However, the accuracy of current scoring functions is usually unsatisfactory due to their rough approximation or sometimes even omittance of many factors involved in protein-ligand binding. For instance, the intrinsic dynamic of the protein-ligand binding state is usually disregarded in scoring function because these rapid binding affinity prediction approaches are only based on a representative complex structure of the protein and ligand in the binding state. That is, the dynamic protein-ligand binding complex ensembles are simplified as a static snapshot in calculation. In this study, two novel features were proposed for characterizing the dynamic properties of protein-ligand binding based on the static structure of the complex, which is expected to be a valuable complement to the current scoring functions. The two features demonstrate the geometry-shape matching between a protein and a ligand as well as the dynamic stability of protein-ligand binding. We further combined these two novel features with several classical scoring functions to develop a binary classification model called DyScore that uses the Extreme Gradient Boosting algorithm to classify compound poses as binders or non-binders. We have found that DyScore achieves state-of-the-art performance in distinguishing active and decoy ligands on both enhanced DUD dataset and external test sets with both proposed novel features showing significant contributions to the improved performance. Especially, DyScore exhibits superior performance on early recognition, a crucial requirement for success in virtual screening andde novodrug design. The standalone version of DyScore and Dyscore-MF are freely available to all at:https://github.com/YanjunLi-CS/dyscoreKey PointsTwo novel binding features were proposed for characterizing the dynamic properties of protein-ligand binding only based on a static snapshot of complex.Based on the XGBoost machine learning method, the DyScore recognition model was proposed to accurately classify compound binding poses as binders or non-binders. DyScore consistently outperforms all the state-of-the-art published models on three different metrics by a large margin.DyScore showed superior performance in early recognition with an average of 73.3% success rate for the top three ranked compounds for each protein target.The standalone version of DyScore and DyScore-MF are freely available to all at:https://github.com/YanjunLi-CS/dyscoreTOC
PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization
Authors: Baylon Javier L. , Ursu Oleg , Muzdalo Anja , Wassermann Anne Mai , Adams Gregory L , Spale Martin , Mejzlik Petr , Gromek Anna , Pisarenko Viktor , Hancharyk Dzianis , Jenkins Esteban , Bednar David , Chang Charlie , Clarova Kamila , Glick Meir , Bitton Danny A. 
Published: October 2021

ABSTRACTTherapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target “undruggable” proteins that are associated with a wide range of pathologies. Despite their importance, there are currently no adequate molecular design capabilities that inform medicinal chemistry decisions on peptide programs. More specifically, SAR (Structure-Activity Relationship) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multi-sequence alignment of non-natural amino acids and enhanced HELM (Hierarchical Editing Language for Macromolecules) visualization. Via stepwise SAR analysis of a ChEMBL peptide dataset, we demonstrate PepSeA’s power to accelerate decision making in lead optimization campaigns in pharmaceutical settings. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design–make–test cycles.
In vivo MR spectroscopy reflects synapse density in a Huntington’s disease mouse model
Authors: Zarate Nicole , Gundry Katherine , Yu Dahyun , Casby Jordan , Eberly Lynn E , Öz Gülin , Gomez-Pastor Rocio 
Published: October 2021

AbstractStriatal medium spiny neurons are highly susceptible in Huntington’s disease (HD), resulting in progressive synaptic perturbations that lead to neuronal dysfunction and death. Non-invasive imaging techniques, such as proton magnetic resonance spectroscopy (1H-MRS), are used in HD mouse models and patients with HD to monitor neurochemical changes associated with neuronal health. However, the association between brain neurochemical alterations and synaptic dysregulation is unknown, limiting our ability to monitor potential treatments that may affect synapse function. We conducted in vivo longitudinal 1H-MRS in the striatum followed by ex-vivo analyses of excitatory synapse density of two synaptic circuits disrupted in HD, thalamo-striatal (T-S) and cortico-striatal (C-S) pathways, to assess the relationship between neurochemical alterations and changes in synapse density. We used the zQ175(Tg/0) HD mouse model as well as zQ175 mice lacking one allele of CK2α’(zQ175(Tg/0):CK2α’(+/−)), a kinase previously shown to regulate synapse function in HD. Longitudinal analyses of excitatory synapse density showed early and sustained reduction in T-S synapses in zQ175 mice, preceding C-S synapse depletion, which was rescued in zQ175:CK2α’(+/−). Changes in T-S and C-S synapses were accompanied by progressive alterations in numerous neurochemicals between WT and HD mice. Linear regression analyses showed C-S synapse number positively correlated with 1H-MRS-measured levels of GABA while T-S synapse number positively correlated with levels of alanine, phosphoethanolamine and lactate, and negatively correlated with total creatine levels.These associations suggest that these neurochemical concentrations measured by 1H-MRS may facilitate monitoring circuit-specific synaptic dysfunction in the zQ175 mouse model and in other HD pre-clinical studies.Significance StatementThe pathogenic events of many neurodegenerative diseases including HD are triggered by reductions in number of synapses. Therefore, in vivo measures that reflect synapse number represent a powerful tool to monitor synaptic changes in numerous brain disorders. In this study, we showed that non-invasive in vivo1H-MRS reflects excitatory synapse number in the striatum of the zQ175 mouse model of HD. The combination of longitudinal 1H-MRS and immunofluorescence synapse detection revealed that distinct neurochemical levels significantly correlated with different striatal glutamatergic synaptic input pathways, suggesting that 1H-MRS could distinguish circuit-dependent synapse changes in HD. These results provide potential neurochemical biomarkers to monitor synaptic changes in future pre-clinical trials with HD models.
Inferring Deep Brain Stimulation Induced Short-term Synaptic Plasticity Using Novel Dual Optimization Algorithm
Authors: Ghadimi Alireza , Steiner Leon Amadeus , Popovic Milos R. , Milosevic Luka , Lankarany Milad 
Published: October 2021

AbstractExperimental evidence in both human and animal studies demonstrated that deep brain stimulation (DBS) can induce short-term synaptic plasticity (STP) in the stimulated nucleus. Given that DBS-induced STP may be connected to the therapeutic effects of DBS, we sought to develop an appropriate computational predictive model that infers the dynamics of STP in response to DBS at different frequencies. Existing methods for estimating STP – either model-based or model-free approaches – require access to pre-synaptic spiking activity. However, in the context of DBS, extracellular stimulation (e.g. DBS) can be used to elicit presynaptic activations directly. We present a model-based approach that integrates multiple individual frequencies of DBS-like electrical stimulation as pre-synaptic spikes and infers parameters of the Tsodyks-Markram (TM) model from post-synaptic currents of the stimulated nucleus. By distinguishing between the steady-state and transient responses of the TM model, we develop a novel dual optimization algorithm that infers the model parameters in two steps. First, the TM model parameters are calculated by integrating multiple frequencies of stimulation to estimate the steady state response of post-synaptic current through a closed-form analytical solution. The results of this step are utilized as the initial values for the second step in which a non-derivative optimization algorithm is used to track the transient response of the post-synaptic potential across different individual frequencies of stimulation. Moreover, we apply our algorithm to empirical data recorded from acute rodent brain slices of the subthalamic nucleus (STN) during DBS-like stimulation to infer dynamics of STP for inhibitory synaptic inputs.