bluuebunny/crossref_metadata_2025_split huggingface dataset:
CD39+PD-1+CD8+T cells mediate metastatic dormancy in breast cancer
Authors: de Lara Paulino Tallón , Castañón Héctor , Vermeer Marijne , Núñez Nicolás , Silina Karina , Sobottka Bettina , Urdinez Joaquín , Cecconi Virginia , Yagita Hideo , Attar Farkhondeh Movahedian , Hiltbrunner Stefanie , Glarner Isabelle , Moch Holger , Tugues Sònia , Becher Burkhard , van den Broek Maries 
Published: June 2020

SummarySome breast tumors metastasize aggressively whereas others remain in a state of metastatic dormancy for months or even years. The mechanism governing such metastatic dormancy remains largely unknown. Through high-parametric single-cell mapping in mice, we identified a discrete population of CD39+PD-1+CD8+T cells present both in primary tumors and in dormant metastasis, which was hardly found in aggressively metastasizing tumors. Using blocking antibodies, we found that dormancy depended on TNFαand IFNγ. Of note, immunotherapy reduced the number of dormant cancer cells in the lungs. Adoptive transfer of purified CD39+PD-1+CD8+T cells prevented metastatic outgrowth. In human breast cancer, the frequency of CD39+PD-1+CD8+but not of total CD8+T cells correlated with delayed metastatic relapse after resection (disease-free survival), thus underlining the biological relevance of CD39+PD-1+CD8+T cells for controlling experimental and human breast cancer. Furthermore, density of CD39+PD-1+CD8+T cells may serve as a novel biomarker and may serve as a potential immunotherapy target. Thus, we discovered that a primary breast tumor primes a systemic, CD39+PD-1+CD8+T cell response that is essential for metastatic dormancy in the lungs.
Getting stuck in a rut as an emergent feature of a dynamic decision-making system
Authors: Warburton Matthew , Brookes Jack , Hasan Mohamed , Leonetti Matteo , Dogar Mehmet , Wang He , Cohn Anthony G. , Mushtaq Faisal , Mon-Williams Mark A. 
Published: June 2020

AbstractHuman sensorimotor decision-making has a tendency to get ‘stuck in a rut’, being biased towards selecting a previously implemented action structure (‘hysteresis’). Existing explanations cannot provide a principled account of when hysteresis will occur. We propose that hysteresis is an emergent property of a dynamical system learning from the consequences of its actions. To examine this, 152 participants moved a cursor to a target on a tablet device whilst avoiding an obstacle. Hysteresis was observed when the obstacle moved sequentially across the screen between trials, but not with random obstacle placement. Two further experiments (n = 20) showed an attenuation when time and resource constraints were eased. We created a simple computational model capturing dynamic probabilistic estimate updating that showed the same patterns of results. This provides the first computational demonstration of how sensorimotor decision-making can get ‘stuck in a rut’ through the dynamic updating of its probability estimates.Significance StatementHumans show a bias to select the organisational structure of a recently carried out action, even when an alternative option is available with lower costs. This ‘hysteresis’ is said to be more efficient than creating a new plan and it has been interpreted as a ‘design feature’ within decision-making systems. We suggest such teleological arguments are redundant, with hysteresis being a naturally emergent property of a dynamic control system that evolved to operate effectively in an uncertain and partially observable world. Empirical experimentation and simulations from a ‘first principle’ computational model of decision-making were consistent with our hypothesis. The identification of such a mechanism can inform robotics research, suggesting how robotic agents can show human-like flexibility in complex dynamic environments.
Tracking Adoptive T Cell Therapy Using Magnetic Particle Imaging
Authors: Rivera-Rodriguez Angelie , Hoang-Minh Lan B. , Chiu-Lam Andreina , Sarna Nicole , Marrero-Morales Leyda , Mitchell Duane A. , Rinaldi Carlos 
Published: June 2020

ABSTRACTAdoptive cellular therapy (ACT) is a potent strategy to boost the immune response against cancer. ACT is an effective treatment for blood cancers, such as leukemias and lymphomas, but faces challenges treating solid tumors and cancers in locations like the brain. A critical step for success of ACT immunotherapy is achieving efficient trafficking of T cells to solid tumors, and the non-invasive and quantitative tracking of adoptively transferred T cell biodistribution would accelerate its development. Here, we demonstrate the use of Magnetic Particle Imaging (MPI) to non-invasively track ACT T cellsin vivo. Labeling T cells with the superparamagnetic iron oxide nanoparticle tracer ferucarbotran did not affect T cell viability, phenotype, or cytotoxic functionin vitro. Following ACT, ferucarbotran-labeled T cells were detected and quantified using MPIex vivoandin vivo, in a mouse model of invasive brain cancer. Proof-of-principlein vivoMPI demonstrated its capacity to detect labeled T cells in lungs and liver after intravenous administration and to monitor T cell localization in the brain after intraventricular administration.Ex vivoimaging using MPI and optical imaging suggests accumulation of systemically administered ferucarbotran-labeled T cells in the brain, where MPI signal from ferucarbotran tracers and fluorescently tagged T cells were observed.Ex vivoimaging also suggest differential accumulation of nanoparticles and viable T cells in other organs like the spleen and liver. These results support the use of MPI to track adoptively transferred T cells and accelerate the development of ACT treatments for brain tumors and other cancers.
Neuronal activity in sensory cortex predicts the specificity of learning
Authors: Wood Katherine C. , Angeloni Christopher F. , Oxman Karmi , Clopath Claudia , Geffen Maria N. 
Published: June 2020

AbstractLearning to avoid dangerous signals while preserving normal responses to safe stimuli is essential for everyday behavior and survival. Fear learning has a high level of inter-subject variability. Following identical experiences, subjects exhibit fear specificities ranging from high (specializing fear to only the dangerous stimulus) to low (generalizing fear to safe stimuli). Pathological fear generalization underlies emotional disorders, such as post-traumatic stress disorder. The neuronal basis of fear specificity remains unknown. Here, we identified the neuronal code that underlies inter-subject variability in fear specificity using longitudinal imaging of neuronal activity before and after differential fear conditioning in the auditory cortex of mice. Neuronal activity prior to, but not after learning predicted the level of specificity following fear conditioning across subjects. Stimulus representation in auditory cortex was reorganized following conditioning. However, the reorganized neuronal activity did not relate to the specificity of learning. These results present a novel neuronal code that determines individual patterns in learning.
Presynaptic precursor vesicles originate from thetrans-Golgi network, promoted by the small GTPase RAB2
Authors: Götz Torsten W. B. , Puchkov Dmytro , Lützkendorf Janine , Nikonenko Alexander G. , Quentin Christine , Lehmann Martin , Sigrist Stephan J. , Petzoldt Astrid G. 
Published: June 2020

SummaryReliable delivery of presynaptic material, including active zone and synaptic vesicle proteins from neuronal somata to synaptic terminals is prerequisite for faithful synaptogenesis and neurotransmission. However, molecular mechanisms controlling the somatic assembly of presynaptic precursors remain insufficiently understood. Here we show that in mutants of the small GTPase RAB2 active zone and synaptic vesicle proteins accumulated in the neuronal somata at thetrans-Golgi network and were consequently depleted at synaptic terminals, provoking neurotransmission deficits. The ectopic presynaptic material accumulations consisted of heterogeneous vesicles and short tubules of 40×60 nm and segregated in subfractions either positive for active zone proteins or co-positive for synaptic vesicle proteins and LAMP1, a lysosomal membrane protein. Genetically,rab2behaved epistatic overarl8, a lysosomal adaptor controlling axonal export of precursors. Collectively, we here identified a Golgi-associated assembly sequence in presynaptic precursor vesicle biogenesis controlled by RAB2 dependent membrane remodelling and protein sorting at thetrans-Golgi.
FIRM: Flexible Integration of single-cell RNA-sequencing data for large-scale Multi-tissue cell atlas datasets
Authors: Ming Jingsi , Lin Zhixiang , Zhao Jia , Wan Xiang , Yang Can , Wu Angela Ruohao 
Published: June 2020

AbstractSingle-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs, and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. These massive datasets are usually collected from many samples using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms. Inherent heterogeneities between platforms, tissues, and other batch effects makes scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet, accurate integration is essential for gaining deeper insights into cell biology. Through comprehensive data exploration, we found that accurate integration is often hampered by differences in cell-type compositions. Herein we describe FIRM, an algorithm that addresses this problem and achieves efficient and accurate integration of heterogeneous scRNA-seq datasets across multiple tissue types, platforms, and experimental batches. We applied FIRM to numerous large-scale scRNA-seq datasets from mouse, mouse lemur, and human, comparing its performance in dataset integration with other state-of-the-art methods. FIRM-integrated datasets show accurate mixing of shared cell type identities and superior preservation of original structure without overcorrection, generating robust integrated datasets for downstream exploration and analysis. It is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a reliable and versatile tool for scRNA-seq analysis, especially for cell atlas data integration.
Decoding neural signals and discovering their representations with a compact and interpretable convolutional neural network
Authors: Petrosuan Arthur , Lebedev Mikhail , Ossadtchi Alexei 
Published: June 2020

AbstractBrain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. In recent years, we have seen an emergence of new algorithms for BCI decoding including those based on the deep-learning principles. Here we describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time, such as the one described here. In these architectures the weights are optimized not only to align with the target sources but also to tune away from the interfering ones, in both the spatial and the frequency domains. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models.We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI IV competition dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Moreover, using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from another ECoG dataset with known sensor positions.As such, the proposed solution offers a good decoder and a tool for investigating neural mechanisms of motor control.
Xrn1 influence on gene transcription results from the combination of general effects on elongating RNA pol II and gene-specific chromatin configuration
Authors: Begley Victoria , Jordán-Pla Antonio , Peñate Xenia , Garrido-Godino Ana I. , Challal Drice , Cuevas-Bermúdez Abel , Mitjavila Adrià , Barucco Mara , Gutiérrez Gabriel , Singh Abhyudai , Alepuz Paula , Navarro Francisco , Libri Domenico , Pérez-Ortín José E. , Chávez Sebastián 
Published: June 2020

AbstractmRNA homeostasis is favored by crosstalk between transcription and degradation machineries. Both the Ccr4-Not and the Xrn1-decaysome complexes have been described to influence transcription. While Ccr4-Not has been shown to directly stimulate transcription elongation, the information available on how Xrn1 influences transcription is scarce and contradictory. In this study we have addressed this issue by mapping RNA polymerase II (RNA pol II) at high resolution, using CRAC and BioGRO-seq techniques inSaccharomyces cerevisiae. We found significant effects of Xrn1 perturbation on RNA pol II profiles across the genome. RNA pol II profiles at 5’ exhibited significant alterations that were compatible with decreased elongation rates in the absence of Xrn1. Nucleosome mapping detected altered chromatin configuration in the gene bodies. We also detected accumulation of RNA pol II shortly upstream of polyadenylation sites by CRAC, although not by BioGRO-seq, suggesting higher frequency of backtracking before pre-mRNA cleavage. This phenomenon was particularly linked to genes with poorly positioned nucleosomes at this position. Accumulation of RNA pol II at 3’ was also detected in other mRNA decay mutants. According to these and other pieces of evidence, Xrn1 seems to influence transcription elongation at least in two ways: by directly favoring elongation rates and by a more general mechanism that connects mRNA decay to late elongation.
Template-based prediction of protein structure with deep learning
Authors: Zhang Haicang , Shen Yufeng 
Published: June 2020

AbstractAccurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residueresidue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56%, 13%, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16%, 9% and 8% in terms of TM-score, respectively. These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.Availabilityhttps://github.com/ShenLab/ThreaderAI
How citizen science could improve Species Distribution Models and their independent assessment
Authors: Florence Matutini , Jacques Baudry , Guillaume Pain , Morgane Sineau , Joséphine Pithon 
Published: June 2020

AbstractSpecies distribution models (SDM) have been increasingly developed in recent years but their validity is questioned. Their assessment can be improved by the use of independent data but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability. We used opportunistic presence-only data along with presence-absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross-validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent data sets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork. Cross-validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data was strongly filtered. Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer’s participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.
Nonlinearities between inhibition and T-type calcium channel activity bidirectionally regulate thalamic network oscillations
Authors: Lu Adam C , Lee Christine K , Kleiman-Weiner Max , Truong Brian , Wang Megan , Huguenard John R , Beenhakker Mark P 
Published: June 2020

AbstractAbsence seizures result from 3-5 Hz generalized thalamocortical oscillations that depend on highly regulated inhibitory neurotransmission in the thalamus. Efficient reuptake of the inhibitory neurotransmitter GABA is essential, and reuptake failure worsens seizures. Here, we show that blocking GABA transporters (GATs) in acute brain slices containing key parts of the thalamocortical seizure network modulates epileptiform activity. As expected, we found that blocking either GAT1 or GAT3 prolonged oscillations. However, blocking both GATs unexpectedly suppressed oscillations. Integrating experimental observations into single-neuron and network-level computational models shows how a non-linear dependence of T-type calcium channel opening on GABABreceptor activity regulates network oscillations. Receptor activity that is either too brief or too protracted fails to sufficiently open T-type channels necessary for sustaining oscillations. Only within a narrow range does prolonging GABABreceptor activity promote channel opening and intensify oscillations. These results have implications for therapeutics that modulate GABA transporters.
RAVE: comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data
Authors: Magnotti John F. , Wang Zhengjia , Beauchamp Michael S. 
Published: June 2020

AbstractDirect recording of neural activity from the human brain using implanted electrodes (iEEG, intracranial electroencephalography) is a fast-growing technique in human neuroscience. While the ability to record from the human brain with high spatial and temporal resolution has advanced our understanding, it generates staggering amounts of data: a single patient can be implanted with hundreds of electrodes, each sampled thousands of times a second for hours or days. The difficulty of exploring these vast datasets is the rate-limiting step in discovery. To overcome this obstacle, we created RAVE (“R Analysis and Visualization of iEEG”). All components of RAVE, including the underlying “R” language, are free and open source. User interactions occur through a web browser, making it transparent to the user whether the back-end data storage and computation is occurring on a local machine, a lab server, or in the cloud. Without writing a single line of computer code, users can create custom analyses, apply them to data from hundreds of iEEG electrodes, and instantly visualize the results on cortical surface models. Multiple types of plots are used to display analysis results, each of which can be downloaded as publication-ready graphics with a single click. RAVE consists of nearly 50,000 lines of code designed to prioritize an interactive user experience, reliability and reproducibility.
Patient Similarity Network of Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic and Clinical Features
Authors: Bhalla Sherry , Melnekoff David T. , Keats Jonathan , Onel Kenan , Madduri Deepu , Richter Joshua , Richard Shambavi , Chari Ajai , Cho Hearn Jay , Dudley Joel T. , Jagannath Sundar , Laganà Alessandro , Parekh Samir 
Published: June 2020

ABSTRACTThe remarkable genetic heterogeneity of Multiple Myeloma (MM) poses a significant challenge for proper prognostication and clinical management of patients. Accurate dissection of the genetic and molecular landscape of the disease and the robust identification of homogeneous classes of patients are essential steps to reliable risk stratification and the development of novel precision medicine strategies. Here we introduce MM-PSN, the first multi-omics Patient Similarity Network of newly diagnosed MM. MM-PSN has enabled the identification of three broad patient groups and twelve distinct sub-groups defined by five data types generated from genomic and transcriptomic patient profiling of 655 patients. The MM-PSN classification uncovered novel associations between distinct MM hallmarks with significant prognostic implications and allowed further refinement of risk stratification. Our analysis revealed that gain of 1q is the most important single lesion conferring high risk of relapse, and its association with an MMSET translocation is the most significant determinant of poor outcome. We developed a classifier and validated these results in an independent dataset of 559 pts. Our findings suggest that gain of 1q should be incorporated in routine staging systems and risk assessment tools. The MM-PSN classifier is available as a free resource to allow for an easy implementation in most clinical settings.