bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antibody-mediated delivery of LIGHT to the tumor boosts Natural Killer cells and delays tumor progression
Authors: Stringhini Marco , Mock Jacqueline , Fontana Vanessa , Murer Patrizia , Neri Dario 
Published: May 2020

AbstractBackgroundLIGHT is a member of the TNF superfamily, which has been claimed to mediate anti-tumor activity on the basis of cancer cures observed in immunocompetent mice bearing transgenic LIGHT-expressing tumors. The preclinical development of a LIGHT-based therapeutic has been hindered by the lack of functional stability exhibited by this protein.MethodsHere, we describe the cloning, expression and characterization of five antibody-LIGHT fusion proteins, directed against the alternatively-spliced EDA domain of fibronectin, a conserved tumor-associated antigen.ResultsAmong the five tested formats, only the sequential fusion of the F8 antibody in single-chain diabody format, followed by the LIGHT homotrimer expressed as a single polypeptide, yielded a protein (termed “F8-LIGHT”) which was not prone to aggregation. A quantitative biodistribution analysis in tumor bearing mice, using radioiodinated protein preparations, confirmed that F8-LIGHT was able to preferentially accumulate at the tumor site, with a tumor-to-blood ratio of ca. five to one twenty-four hours after intravenous administration. Tumor therapy experiments, performed in two murine tumor models (CT26 and WEHI-164), featuring different levels of lymphocyte infiltration into the neoplastic mass, revealed that F8-LiGHT could significantly reduce tumor cell growth and was more potent than a similar fusion protein (KSF-LIGHT), directed against hen egg lysozyme and serving as negative control of irrelevant specificity in the mouse. At a mechanistic level, the activity of F8-LiGHT was mainly due to an intratumoral expansion of Natural Killer cells, whereas there was no evidence of expansion of CD8+ T cells, neither in the tumor, nor in draining lymph nodes.ConclusionWe developed a novel recombinant LIGHT fusion protein, able to accumulate at the site of disease and which displayed anti-tumor activity in two mouse models of cancer.
GS-967 and Eleclazine Block Sodium Channels in Human Induced Pluripotent Stem Cell-derived Cardiomyocytes
Authors: Potet Franck , Egecioglu Defne E. , Burridge Paul W. , George Alfred L. 
Published: May 2020

ABSTRACTGS-967 and eleclazine (GS-6615) are novel sodium channel inhibitors exhibiting antiarrhythmic effects in variousin vitroandin vivomodels. The antiarrhythmic mechanism has been attributed to preferential suppression of late sodium current (INaL). Here, we took advantage of a throughput automated electrophysiology platform (SyncroPatch 768PE) to investigate the molecular pharmacology of GS-967 and eleclazine on peak sodium current (INaP) recorded from human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes. We compared GS-967 and eleclazine to the antiarrhythmic drug lidocaine, the prototypeINaLinhibitor ranolazine, and the slow inactivation enhancing drug lacosamide. In human induced pluripotent stem cell-derived cardiomyocytes, GS-967 and eleclazine caused a reduction ofINaPin a frequency-dependent manner consistent with use-dependent block (UDB). GS-967 and eleclazine had similar efficacy but evoked more potent UDB ofINaP(IC50=0.07 and 0.6 μM, respectively) than ranolazine (7.8 μM), lidocaine (133.5 μM) and lacosamide (158.5 μM). In addition, GS-967 and eleclazine exerted more potent effects on slow inactivation and recovery from inactivation compared to the other sodium channel blocking drugs we tested. The greater UDB potency of GS-967 and eleclazine was attributed to the significantly higher association rates (KON) and moderate unbinding rate (KOFF) of these two compounds with sodium channels. We propose that substantial UDB contributes to the observed antiarrhythmic efficacy of GS-967 and eleclazine.SIGNIFICANCE STATEMENTWe investigated the molecular pharmacology of GS-967 and eleclazine on sodium channels in human induced pluripotent stem cell derived cardiomyocytes using a high throughput automated electrophysiology platform. Sodium channel inhibition by GS-967 and eleclazine has unique features including accelerating the onset of slow inactivation and impairing recovery from inactivation. These effects combined with rapid binding and moderate unbinding kinetics explain potent use-dependent block, which we propose contributes to their observed antiarrhythmic efficacy.
SpaGE: Spatial Gene Enhancement using scRNA-seq
Authors: Abdelaal Tamim , Mourragui Soufiane , Mahfouz Ahmed , Reinders Marcel J.T. 
Published: May 2020

AbstractSingle-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently.
Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits
Authors: Zhang Yiliang , Lu Qiongshi , Ye Yixuan , Huang Kunling , Liu Wei , Wu Yuchang , Zhong Xiaoyuan , Li Boyang , Yu Zhaolong , Travers Brittany G. , Werling Donna M. , Li James J. , Zhao Hongyu 
Published: May 2020

AbstractLocal genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies. We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with significant local genetic correlations. In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations. We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.
Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy
Authors: Iravani Behzad , Arshamian Artin , Fransson Peter , Kaboodvand Neda 
Published: May 2020

ABSTRACTRecent advances in non-linear computational and dynamical modelling have opened up the possibility to param etrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully understand disorders of the brain as it may provide a quantitative platform that is capable of binding multiple neurophysiological processes to phenotype profiles. In this study, we apply a newly developed adaptive frequency-based model of whole-brain oscillations to resting-state fMRI data acquired from healthy controls and a cohort of attention deficit hyperactivity disorder (ADHD) subjects. As expected, we found that healthy control subjects differed from ADHD in terms of attractor dynamics. However, we also found a marked dichotomy in neural dynamics within the ADHD cohort. Next, we classified the ADHD group according to the level of distance of each individual’s empirical network from the two model-based simulated networks. Critically, the model was mirrored in the empirical behavior data with the two ADHD subgroups displaying distinct behavioral phenotypes related to emotional instability (i.e., depression and hypomanic personality traits). Finally, we investigated the applicability and feasibility of our whole-brain model in a therapeutic setting by conducting in silico excitatory stimulations to parsimoniously mimic clinical neuro-stimulation paradigms in ADHD. We tested the effect of stimulating any individual brain region on the key network measures derived from the simulated brain network and its contribution in rectifying the brain dynamics to that of the healthy brain, separately for each ADHD subgroup. This showed that this was indeed possible for both subgroups. However, the current effect sizes were small suggesting that the stimulation protocol needs to be tailored at the individual level. These findings demonstrate the potential of this new modelling framework to unveil hidden neurophysiological profiles and establish tailored clinical interventions.
A Comparison of ‘Pruning’ During Multi-Step Planning in Depressed and Healthy Individuals
Authors: Faulkner Paul , Huys Quentin J.M. , Renz Daniel , Eshel Neir , Pilling Stephen , Dayan Peter , Roiser Jonathan P. 
Published: May 2020

AbstractBackgroundReal-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people “prune” (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals.MethodsThirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms.ResultsConsistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety.LimitationsThe relatively small sample size limited the examination of individual differences. The use of other heuristics that are used to render complex decisions feasible, such as memoization and fragmentation, were not examined.ConclusionsWe found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.
Diversity, host ranges, and potential drivers of speciation among the inquiline enemies of oak gall wasps
Authors: Ward Anna KG , Sheikh Sofia I , Forbes Andrew A 
Published: May 2020

ABSTRACTAnimals that exploit an extended phenotype (e.g., residences, resources, etc.) of other animals are called inquilines. Not strictly parasites, inquilines may nevertheless possess specialized traits that adapt them to particular dimensions of the extended phenotype of their “host”. These adaptations to host traits can in turn lead to fitness tradeoffs that restrict the host range of an inquiline such that shifts to novel hosts might trigger inquiline diversification. Speciationviahost shifting has been studied in many animal parasites, but we know far less about the role of host shifts in inquiline speciation.Synergus(Hymenoptera: Cynipidae: Synergini) is a speciose but taxonomically-challenging group of inquilines that feed on the tissue of galls induced by oak gall wasps (Hymenoptera: Cynipidae: Cynipini). Currently too little is known about NearcticSynergusdiversity or host associations to evaluate whether and how host use affects their diversification. Here, we report on a large collection ofSynergusreared from galls of 33 oak gall wasp species in the upper Midwestern United States. We integrated DNA barcodes, morphology, ecology, and phenology to delimit putative species ofSynergusand describe their host ranges. We find evidence of at least 23Synergusspecies associated with the 33 gall wasp hosts. At least five previously describedSynergusspecies are each a complex of two to five species, while three species fit no prior description. We also find strong evidence that oak tree section and host gall morphology define axes of specialization forSynergus. Without over-interpreting our singlegene tree, it is clear that the North AmericanSynergushave experienced several transitions among gall hosts and tree habitats and that host-use is correlated with reproductive isolation, though it remains too early to tell whether shifts to new hosts are the initiators of speciation events inSynergusinquilines of oak gall wasps, or if host shifts occur after reproductive isolation has already evolved.
Generating synthetic signaling networks for in silico modeling studies
Authors: Xu Jin , Wiley H Steven , Sauro Herbert M 
Published: May 2020

ABSTRACTPredictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools.HighlightsWe provided a Julia script to generate synthetic signaling networks.We defined reaction degree and distance to measure the reaction network topology.We provided the Python scripts to calculate the reaction network topology.The synthetic signaling networks had topological similarities with the BioModels.The synthetic signaling networks had dynamic similarities with the BioModels.
Brain Modeling ToolKit: an Open Source Software Suite for Multiscale Modeling of Brain Circuits
Authors: Dai Kael , Gratiy Sergey L. , Billeh Yazan N. , Xu Richard , Cai Binghuang , Cain Nicholas , Rimehaug Atle E. , Stasik Alexander J. , Einevoll Gaute T. , Mihalas Stefan , Koch Christof , Arkhipov Anton 
Published: May 2020

AbstractExperimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.
Bumblebees learn a relational rule but switch to a win-stay/lose-switch heuristic after extensive training
Authors: MaBouDi HaDi , Solvi Cwyn , Chittka Lars 
Published: May 2020

AbstractMapping animal performance in a behavioural task to underlying cognitive mechanisms and strategies is rarely straightforward, since a task may be solvable in more than one manner. Here, we show that bumblebees perform well on a concept-based visual discrimination task, but spontaneously switch from a concept-based solution to a simpler heuristic with extended training, all while continually increasing performance. Bumblebees were trained in an arena to find reward on displays with shapes of different sizes where they could not use low-level visual cues. One group of bees was rewarded at displays with bigger shapes and another group at displays with smaller shapes. Analysis of total choices shows bees increased their performance over 30 bouts to above chance. However, analyses of first and sequential choices suggest that after approximately 20 bouts, bumblebees changed to a win-stay/lose-switch strategy. Comparing bees’ behaviour to a probabilistic model based on a win-stay/lose-switch strategy further supports the idea that bees changed strategies with extensive training. Analyses of unrewarded tests indicate bumblebees learned and retained the concept of relative size even after they had already switched to a win-stay, lost-shift strategy. We propose that the reason for this strategy switching may be due to cognitive flexibility and efficiency.
Systematic auditing is essential to debiasing machine learning in biology
Authors: Eid Fatma-Elzahraa , Elmarakeby Haitham , Chan Yujia Alina , Martins Nadine Fornelos , ElHefnawi Mahmoud , Van Allen Eli , Heath Lenwood S. , Lage Kasper 
Published: May 2020

AbstractRepresentational biases that are common in biological data can inflate prediction performance and confound our understanding of how and what machine learning (ML) models learn from large complicated datasets. However, auditing for these biases is not a common practice in ML in the life sciences. Here, we devise a systematic auditing framework and harness it to audit three different ML applications of significant therapeutic interest: prediction frameworks of protein-protein interactions, drug-target bioactivity, and MHC-peptide binding. Through this, we identify unrecognized biases that hinder the ML process and result in low model generalizability. Ultimately, we show that, when there is insufficient signal in the training data, ML models are likely to learn primarily from representational biases.
Clinical performance of SARS-CoV-2 IgG antibody tests and potential protective immunity
Authors: Kohmer Niko , Westhaus Sandra , Rühl Cornelia , Ciesek Sandra , Rabenau Holger F. 
Published: May 2020

AbstractAs the current SARS-CoV-2 pandemic continues, serological assays are urgently needed for rapid diagnosis, contact tracing and for epidemiological studies. So far, there is little data on how commercially available tests perform with real patient samples and if detected IgG antibodies provide protective immunity. Focusing on IgG antibodies, we demonstrate the performance of two ELISA assays (Euroimmun SARS-CoV-2 IgG & Vircell COVID-19 ELISA IgG) in comparison to one lateral flow assay ((LFA) FaStep COVID-19 IgG/IgM Rapid Test Device) and two in-house developed assays (immunofluorescence assay (IFA) and plaque reduction neutralization test (PRNT)). We tested follow up serum/plasma samples of individuals PCR-diagnosed with COVID-19. Most of the SARS-CoV-2 samples were from individuals with moderate to severe clinical course, who required an in-patient hospital stay.For all examined assays, the sensitivity ranged from 58.8 to 76.5% for the early phase of infection (days 5-9) and from 93.8 to 100% for the later period (days 10-18) after PCR-diagnosed with COVID-19. With exception of one sample, all positive tested samples in the analysed cohort, using the commercially available assays examined (including the in-house developed IFA), demonstrated neutralizing (protective) properties in the PRNT, indicating a potential protective immunity to SARS-CoV-2. Regarding specificity, there was evidence that samples of endemic coronavirus (HCoV-OC43, HCoV-229E) and Epstein Barr virus (EBV) infected individuals cross-reacted in the ELISA assays and IFA, in one case generating a false positive result (may giving a false sense of security). This need to be further investigated.
A Mathematical Model Approach for Prevention and Intervention Measures of the COVID–19 Pandemic in Uganda
Authors: Mbabazi Fulgensia Kamugisha , Gavamukulya Yahaya , Awichi Richard , Olupot–Olupot Peter , Rwahwire Samson , Biira Saphina , Luboobi Livingstone S. 
Published: May 2020

AbstractThe human–infecting corona virus disease (COVID–19) caused by the novel severe acute respiratory syndrome corona virus 2 (SARS–CoV–2) was declared a global pandemic on March 11th, 2020. Current human deaths due to the infection have raised the threat globally with only 1 African country free of Virus (Lesotho) as of May 6th, 2020. Different countries have adopted different interventions at different stages of the outbreak, with social distancing being the first option while lock down the preferred option for flattening the curve at the peak of the pandemic. Lock down is aimed at adherence to social distancing, preserve the health system and improve survival. We propose a Susceptible–Exposed–Infected–Expected recoveries (SEIR) mathematical model to study the impact of a variety of prevention and control strategies Uganda has applied since the eruption of the pandemic in the country. We analyze the model using available data to find the infection–free, endemic/infection steady states and the basic reproduction number. In addition, a sensitivity analysis done shows that the transmission rate and the rate at which persons acquire the virus, have a positive influence on the basic reproduction number. On other hand the rate of evacuation by rescue ambulance greatly reduces the reproduction number. The results have potential to inform the impact and effect of early strict interventions including lock down in resource limited settings and social distancing.