bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inter-brain coupling analysis reveals learning-related attention of primary school students
Authors: Chen Jingjing , Xu Bing , Zhang Dan 
Published: June 2022

AbstractLearning-related attention is one of the most important factors influencing learning. While technologies have enabled the automatic detection of students’ attention levels, previous studies mainly focused on colleges or high schools, lacking further validations in primary school students. More importantly, the detected attention might fail to be learning-related if students did not attend learning tasks (e.g., the attention level of a student who reads comics secretly during classroom learning). This phenomenon poses challenges to the practical application of automatic attention detection, especially in the primary school stage, which is crucial for students to set up learning attitudes/strategies. Inspired by the emerging inter-person perspective in neuroscience, we proposed an inter-brain attention coupling method to detect learning-related attention as an extension to the existing single-person-based method. To test this method, wearable electroencephalogram (EEG) devices were used to monitor students’ attention levels in a class of primary school students during classroom learning. We found that one’s inter-brain attention coupling, defined as the degree to which an individual student’s attention dynamics match the attention dynamics averaged across classmates, was positively correlated with academic performance: higher performances are associated with higher coupling to the class-average attention dynamics. Moreover, the attention detection framework based on the inter-person perspective outperforms as an indicator of academic performance compared with the widely-used attention level within an individual. The results provide practical insights by extending the applications of detected attention levels from an inter-person perspective and demonstrating its feasibility in monitoring learning-related attention among primary school students.
Modelling the metabolic consequences of antimicrobial exposure
Authors: Alonso-Vásquez Tania , Riccardi Christopher , Passeri Iacopo , Fondi Marco 
Published: June 2022

ABSTRACTBesides genetic mutations, the metabolic state of bacterial cells represents another driving factor in the emergence of antimicrobial resistance and in the actual efficacy of treatments. In this direction, studying how bacteria reprogram their metabolism when facing antimicrobial exposure is crucial to enhance our ability to limit the development and spread of antibiotic resistance. Here we have studied the metabolic consequences of antimicrobial exposure in bacteria using an integrated approach that exploits transcriptomics and computational modelling. Specifically, we asked whether common metabolic strategies emerge during the exposure to antimicrobials, regardless of the kind of antimicrobial used or, on the contrary, antimicrobial-specific pathways exist. To this purpose, we have used an heterogeneous dataset from six published studies on Escherichia coli exposed to different concentrations/types of compounds. We show that experimental condition, not antimicrobial exposure, is the factor that influences the most the resulting metabolic networks. However, despite condition-dependent metabolic signatures being evident, specific changes in flux distributions by antimicrobial exposed cells could be identified. In particular, purine and pyrimidine biosynthesis, and cofactor and prosthetic group biosynthesis were commonly affected by all considered antimicrobials. This suggests the presence of general metabolic strategies to face the stress posed by antimicrobial exposure and that, in turn, may represent an untapped resource for the fight against microbial infections. Finally, our analysis predicted an overall metabolic rewiring following bacteriostatic vs. bactericidal drug exposure that is in line with the current knowledge about the effects of these two classes of compounds on microbial metabolic phenotypes.IMPORTANCEA mechanistic understanding of microbial metabolic reprogramming during antimicrobial exposure is key to facilitate the discovery of new resistance mechanisms and to identify novel areas of intervention to face microbial infections. This study shows how the integration of transcriptomic data and genome-scale metabolic modelling can be used to address this critical issue, and to trace general metabolic strategies exploited by bacteria to face the stress posed by antimicrobial drugs.
Detecting sources of immune activation and viral rebound in HIV infection
Authors: Wietgrefe Stephen W. , Duan Lijie , Anderson Jodi , Marqués Guillermo , Sanders Mark , Cummins Nathan W. , Badley Andrew D. , Dobrowolski Curtis , Karn Jonathan , Pagliuzza Amélie , Chomont Nicolas , Sannier Gérémy , Dubé Mathieu , Kaufmann Daniel E. , Zuck Paul , Wu Guoxin , Howell Bonnie J , Reilly Cavan , Herschhorn Alon , Schacker Timothy W. , Haase Ashley T. 
Published: June 2022

AbstractAntiretroviral therapy (ART) generally suppresses HIV replication to undetectable levels in peripheral blood, but immune activation associated with increased morbidity and mortality is sustained during ART, and infection rebounds when treatment is interrupted. To identify drivers of immune activation and potential sources of viral rebound, we modified RNAscope in situ hybridization to visualize HIV-virus producing cells as a standard to compare the following assays of potential sources of immune activation and virus rebound following treatment interruption: 1) EDITS (envelope detection by induced transcription-based sequencing) assay; 2) HIV-Flow; and 3) Flow-FISH assays that can scan tissues and cell suspensions to detect rare cells expressing env mRNA, gag mRNA/Gag protein and p24 respectively; and 4) an ultrasensitive immunoassay that detects p24 in cell/tissue lysates at subfemtomolar levels. We show that the sensitivity of these assays is sufficient to detect a rare HIV-producing/env mRNA+/p24+ cell in a million uninfected cells. These high-throughput technologies thus provide contemporary tools to detect and characterize rare cells producing virus and viral antigens as potential sources of immune activation and viral rebound.ImportanceAnti-retroviral therapy (ART) has greatly improved the quality and length of life for people living with HIV, but immune activation does not normalize during ART, and persistent immune activation has been linked to increased morbidity and mortality. We report a comparison of assays of two potential sources of immune activation during ART: rare cells producing HIV virus or the virus’ major viral protein, p24, benchmarked on a cell model of active and latent infections and a method to visualize HIV-producing cells. We show that assays of HIV Envelope mRNA (EDITS assay) and gag mRNA and p24 (Flow-FISH, HIV-Flow and ultrasensitive p24 immunoassay) detect HIV-producing cells and p24 at sensitivities of one infected cell in a million uninfected cells, thus providing validated tools to explore sources of immune activation during ART in the lymphoid and other tissue reservoirs.
TMS-Evoked Responses Are Driven by Recurrent Large-Scale Network Dynamics
Authors: Momi Davide , Wang Zheng , Griffiths John David 
Published: June 2022

AbstractA major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. This question finds sharp relief in the case of TMS-EEG evoked potentials (TEPs). TEPs are spatiotemporal waveform patterns with characteristic inflections at ∼50ms, ∼100ms, and ∼150-200ms following a single TMS pulse that disperse from, and later reconverge to, the primary stimulated regions. What parts of the TEP are due to recurrent activity? And what light might this shed on more general principles of brain organization? We studied this using source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from ∼100ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics.
Modeling and Inference Methods for Switching Regime-Dependent Dynamical Systems with Multiscale Neural Observations
Authors: Song Christian Y , Hsieh Han-Lin , Pesaran Bijan , Shanechi Maryam M 
Published: June 2022

AbstractRealizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases. Here, we address both challenges by developing a Switching Multiscale Dynamical System model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity. We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show that these methods can successfully combine the spiking and field potential observations to simultaneously track the regime and brain states accurately. Doing so, these methods lead to better state estimation compared with single-scale switching methods or stationary multiscale methods. These modeling and inference methods effectively incorporate both regime-detection and multiscale observations. As such, they could facilitate investigation of latent switching neural population dynamics and improve future brain-machine interfaces by enabling inference in naturalistic scenarios where regime-dependent multiscale activity and behavior arise.
Threshold-crossing time statistics for gene expression in growing cells
Authors: Nieto César , Ghusinga Khem Raj , Vargas-García César , Singh Abhyudai 
Published: June 2022

AbstractMany intracellular events are triggered by attaining critical concentrations of their corresponding regulatory proteins. How cells ensure precision in the timing of the protein accumulation is a fundamental problem, and contrasting predictions of different models can help us understand the mechanisms involved in such processes. Here, we formulate the timing of protein threshold-crossing as a first passage time (FPT) problem focusing on how the mean FPT and its fluctuations depend on the threshold protein concentration. First, we model the protein-crossing dynamics from the perspective of three classical models of gene expression that do not explicitly accounts for cell growth but consider the dilution as equivalent to degradation: (birth-death process, discrete birth with continuous deterministic degradation, andFokker-Planck approximation). We compare the resulting FPT statistics with a fourth model proposed by us (growing cell) that comprises size-dependent expression in an exponentially growing cell. When proteins accumulate in growing cells, their concentration reaches a steady value. We observe that if dilution by cell growth is modeled as degradation, cells can reach concentrations higher than this steady-state level at a finite time. In the growing cell model, on the other hand, the FPT moments diverge if the threshold is higher than the steady-state level. This effect can be interpreted as a transition between noisy dynamics when cells are small to an almost deterministic behavior when cells grow enough. We finally study the mean FPT that optimizes the timing precision. The growing cell model predicts a higher optimal FPT and less variability than the classical models.
Alcohol-specific transcriptional dynamics of memory reconsolidation
Authors: Goltseker Koral , Garay Patricia , Iwase Shigeki , Barak Segev 
Published: June 2022

AbstractRelapse, a critical issue in alcohol addiction, can be attenuated by disruption of alcohol-associated memories. Memories are thought to temporarily destabilize upon retrieval during the reconsolidation process. Here, we characterized the alcohol-specific transcriptional dynamics that regulate these memories. Using a mouse place-conditioning procedure, we found that alcohol memory retrieval increased the expression ofArcandZif268in the dorsal hippocampus (DH) and medial prefrontal cortex (mPFC). Alcohol seeking was abolished by post-retrieval non-specific inhibition of gene transcription in the DH, as well as by downregulating ARC expression in the DH using antisense-oligodeoxynucleotides. Since sucrose memory retrieval also increasedArcandZif268expression, we performed an RNA-sequencing assay, and revealed alterations in the expression ofAdcy8, Neto1, Slc8a3in the DH andFkbp5in the mPFC, caused by the retrieval of alcohol but not sucrose memories. This offers a first insight into the unique transcriptional dynamics underpinning alcohol memory reconsolidation.
Medulloblastoma Group 3 and 4 Tumors Comprise a Clinically and Biologically Significant Expression Continuum Reflecting Human Cerebellar Development
Authors: Williamson Daniel , Schwalbe Edward C , Hicks Debbie , Aldinger Kimberly A , Lindsey Janet C , Crosier Stephen , Richardson Stacey , Goddard Jack , Hill Rebecca M , Castle Jemma , Grabovska Yura , Hacking James , Pizer Barry , Wharton Stephen B , Jacques Thomas S , Joshi Abhijit , Bailey Simon , Clifford Steven C 
Published: June 2022

SummaryMedulloblastoma is currently sub-classified into distinct DNA methylation subgroups/subtypes with particular clinico-molecular features. Using RNA-seq in large well annotated cohorts of medulloblastoma we show that transcriptionally Group3 and Group4 medulloblastomas exist not as discrete types but as intermediates on a bipolar continuum between archetypal Group3 and Group4 entities. Continuum position is prognostic, reflects propensity for specific DNA copy-number changes, key switches in isoform/enhancer usage and RNA-editing. Examining scRNA-seq profiles we show intra-tumoral transcriptional heterogeneity along the continuum is limited in a subtype-dependent manner. By integrating with a human scRNA-seq reference atlas we show this continuum is mirrored by an equivalent continuum of transcriptional cell types in early fetal cerebellar development. We identify unique developmental niches for all four major subgroups and link each to a common developmental antecedent. Our findings show a transcriptional continuum arising from oncogenic disruption of highly specific fetal cerebellar cell types, linked to almost every aspect of Group3/Group4 molecular biology and clinico-pathology.
Collective Evolution Learning Model for Vision-Based Collective Motion with Collision Avoidance
Authors: Krongauz David L. , Lazebnik Teddy 
Published: June 2022

AbstractCollective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based CM with CA model (i.e., VCMCA) simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local CM with CA which emerges into a global CM with CA dynamics. The proposed algorithm was evaluated in the case of locusts’ swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agent multi-objective dynamics.Author summaryMulti-agent multi-objective tasks are common in nature with examples as collective movement in birds and economic management in humans. These problems are famous for being convoluted to efficiently solve. Nonetheless, nature has been successfully solving it for millennials using an evolution strategy. A prominent example is a task of flocking performed by multiple species, which involves both collective motion and collision avoidance. In our work, we simulate agents that are able to learn behaviors on the individual level, that in turn translate into the desired group (multi-agent) behavior. Using nature-inspired genetic algorithms and reinforcement-learning methods, the agents are successfully implementing a collective behavior similar to the one encountered in nature.
A low-cost device for cryoanesthesia of neonatal rodents
Authors: Jamieson Bradley B. , Cano-Ferrer Xavier , Konstantinou George , de Launoit Elisa , Renier Nicolas , Imbert Albane , Kohl Johannes 
Published: June 2022

AbstractStudying the development of neural circuits in rodent models requires surgical access to the neonatal brain. Since commercially available stereotaxic and anesthetic equipment is designed for use in adults, reliable targeting of brain structures in such young animals can be challenging. Hypothermic cooling (cryoanesthesia) has been used as a preferred anesthesia approach in neonates. This commonly involves submerging neonates in ice, an approach that is poorly controllable. We have developed an affordable, simple to construct device – CryoPup – that allows for fast and robust cryoanesthesia of rodent pups. CryoPup consists of a microcontroller controlling a Peltier element and a heat exchanger. It is capable of both cooling and heating, thereby also functioning as a heating pad during recovery. Importantly, it has been designed for size compatibility with common stereotaxic frames. We validate CryoPup in neonatal mice, demonstrating that it allows for rapid, reliable and safe cryoanesthesia and subsequent recovery. This open-source device will facilitate future studies into the development of neural circuits in the postnatal brain.Specifications table
A dual gene-specific mutator system installs all transition mutations at similar rates in vivo
Authors: Seo Daeje , Eom Ga-eul , Kim Hye Won , Kim Seokhee 
Published: June 2022

ABSTRACTTargeted in vivo hypermutation accelerates directed evolution of proteins through concurrent DNA diversification and selection. Among recently developed methods, the systems employing a fusion protein of a nucleobase deaminase and T7 RNA polymerase present gene-specific targeting. However, their mutational spectra have been largely limited to exclusive or dominant C:G→T:A mutations. Here we describe eMutaT7transition, a new gene-specific mutator system, that installs all the transition mutations (C:G→T:A and A:T→G:C) at comparable rates. By using two mutator proteins in which two efficient deaminases, PmCDA1 and TadA-8e, are separately fused to T7 RNA polymerase, we obtained similar numbers of C:G→T:A and A:T→G:C mutations at a sufficiently high rate (∼3.4 × 10-5 mutations per base per generation or ∼1.3 mutations per 1 kb per day). Through eMutaT7transition-mediated TEM-1 evolution for antibiotic resistance, we generated many mutations also found in clinical isolates. Overall, with a fast mutation rate and wider mutational spectrum, eMutaT7transition is a potential first-line method for gene-specific in vivo hypermutation.
Nanobody-mediated Complement Activation to Kill HIV-infected Cells
Authors: Pedersen Maria Lange , Pedersen Dennis Vestergaard , Lykkegaard Winkler Mikael Becher , Olesen Heidi Gytz , Søgaard Ole Schmeltz , Østergaard Lars , Laursen Nick Stub , Andersen Anna Halling Folkmar , Tolstrup Martin 
Published: June 2022

AbstractThe complement system which is part of the innate immune response against invading pathogens, represents a powerful mechanism for killing of infected cells. Utilizing direct complement recruitment for complement-mediated elimination of HIV-1-infected cells is underexplored. We developed a novel therapeutic modality to direct complement activity to the surface of HIV-1-infected cells. This bispecific complement engager (BiCE) is comprised of a nanobody recruiting the complement-initiating protein C1q, and single-chain variable fragments of broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope (Env) protein. Here, we show that two anti-HIV BiCEs targeting the V3 loop and the CD4 binding site, respectively, increase C3 deposition and mediate complement-dependent cytotoxicity (CDC) of HIV-1 Env expressing Raji cells. Furthermore, anti-HIV BiCEs trigger complement activation on primary CD4 T cells infected with laboratory-adapted HIV-1 strain and facilitates elimination of HIV-1-infected cells over time. In summary, we present a novel approach to direct complement deposition to the surface of HIV-1-infected cells leading to complement-mediated killing of these cells.
New functional vessels form after spinal cord injury in zebrafish
Authors: Ribeiro Ana , da Costa Mariana Rebocho , de Sena-Tomás Carmen , Rodrigues Elsa Charas , Quitéria Raquel , Maçarico Tiago , Rosa Santos Susana Constantino , Saúde Leonor 
Published: June 2022

AbstractThe vascular system is inefficiently repaired after spinal cord injury in mammals, resulting in secondary tissue damage and immune deregulation that contribute to the limited functional recovery. Unlike mammals, zebrafish can repair the spinal cord and restore motility, but the vascular response to injury has not been investigated. Here we describe the zebrafish spinal cord vasculature, from the body size-dependent vessel ingression during development to the stereotypic vessel organization and barrier specialisation in adulthood. After injury, vessels rapidly regrow into the lesion, preceding the glial bridge and regenerating axons. The initial vascularisation of the injured tissue is done by dysmorphic and leaky vessels. Dysfunctional vessels are later removed, as pericytes are recruited and the blood-spinal cord barrier is re-established. Vascular repair involves an early burst of angiogenesis, likely in response to pro-angiogenic factors detected in the injured spinal cord, including the Vegf pathway. However, the inhibition of the Vegfr2 using genetic and pharmacological methods was not able to efficiently block the formation of new blood vessels, suggesting that other signalling pathways are also involved in this process. This study demonstrates that zebrafish can successfully re-vascularise the spinal tissue, reinforcing the value of this organism as a regenerative model for spinal cord injury.