bluuebunny/crossref_metadata_2025_split huggingface dataset:
Academic Tracker: Software for Tracking and Reporting Publications Associated with Authors and Grants
Authors: Thompson Patrick T. , Powell Christian D. , Moseley Hunter N.B. 
Published: March 2022

AbstractIn recent years, United States federal funding agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF), have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. Enforcement is primarily through annual and final reports submitted to these funding agencies, where all peer-reviewed publications must be registered through the appropriate mechanism as required by the specific federal funding agency. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and even funding delays for current and new research grants. So, it’s important to make sure every peer-reviewed publication is reported properly and in a timely manner. For large collaborative research efforts, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle. In order to help with this reporting burden, we have developed the Academic Tracker software package, implemented in the Python 3 programming language and supporting Linux, Windows, and Mac operating systems. Academic Tracker helps with publication tracking and reporting by comprehensively searching major peer-reviewed publication tracking web portals, including PubMed, Crossref, ORCID, and Google Scholar, given a list of authors. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes. The source code and extensive documentation is hosted on GitHub (https://moseleybioinformaticslab.github.io/academic_tracker/) and is also available on the Python Package Index (https://pypi.org/project/academic_tracker) for easy installation.
Hardware-Efficient Compression of Neural Multi-Unit Activity
Authors: Savolainen Oscar W. , Zhang Zheng , Feng Peilong , Constandinou Timothy G. 
Published: March 2022

AbstractBrain-machine interfaces (BMI) are tools for treating neurological disorders and motor-impairments. It is essential that the next generation of intracortical BMIs is wireless so as to remove percutaneous connections, i.e. wires, and the associated mechanical and infection risks. This is required for the effective translation of BMIs into clinical applications and is one of the remaining bottlenecks. However, due to cortical tissue thermal dissipation safety limits, the on-implant power consumption must be strictly limited. Therefore, both the neural signal processing and wireless communication power should be minimal, while the implants should provide signals that offer high behavioural decoding performance (BDP). The Multi-Unit Activity (MUA) signal is the most common signal in modern BMIs. However, with an ever-increasing channel count, the raw data bandwidth is becoming prohibitively high due to the associated communication power exceeding the safety limits. Data compression is therefore required. To meet this need, this work developed hardware-efficient static Huffman compression schemes for MUA data. Our final system reduced the bandwidth to 27 bps/channel, compared to the standard MUA rate of 1 kbps/channel. This compression is over an order of magnitude more than has been achieved before, while using only 0.96 uW/channel processing power and 246 logic cells. Our results were verified on 3 datasets and less than 1% loss in BDP was observed. As such, with the use of effective data compression, an order more of MUA channels can be fitted on-implant, enabling the next generation of high-performance wireless intracortical BMIs.
Titers of antibodies the receptor-binding domain (RBD) of ancestral SARS-CoV-2 are predictive for levels of neutralizing antibodies to multiple variants
Authors: Tran Trung The , Vaage Eline Benno , Mehta Adi , Chopra Adity , Kolderup Anette , Anthi Aina , König Marton , Nygaard Gro , Lind Andreas , Müller Fredrik , Nissen-Meyer Lise Sofie , Magnus Per , Trogstad Lill , Mjaaland Siri , Søraas Arne , Midtvedt Karsten , Åsberg Anders , Barratt-Due Andreas , Medhus Asle W. , Høivk Marte Lie , Lundin Knut , Karlsen Randi Fuglaas , Dahle Reidun , Danielsson Karin , Thomassen Kristine Stien , Kro Grete Birkeland , Cox Rebecca J. , Zhou Fan , Langeland Nina , Aukrust Pål , Melum Espen , Åvitsland Tone Lise , Wiencke Kristine , Holter Jan Cato , Munthe Ludvig A. , Grødeland Gunnveig , Andersen Jan-Terje , Vaage John Torgils , Lund-Johansen Fridtjof 
Published: March 2022

ABSTRACTDiagnostic assays currently used to monitor the efficacy of COVID-19 vaccines measure levels of antibodies to the receptor-binding domain of ancestral SARS-CoV-2 (RBDwt). However, the predictive value for protection against new variants of concern (VOCs) has not been firmly established. Here, we used bead-based arrays and flow cytometry to measure binding of antibodies to spike proteins and receptor-binding domains (RBDs) from VOCs in 12,000 sera. Effects of sera on RBD-ACE2 interactions were measured as a proxy for neutralizing antibodies. The samples were obtained from healthy individuals or patients on immunosuppressive therapy who had received two to four doses of COVID-19 vaccines and from COVID-19 convalescents. The results show that anti-RBDwt titers correlate with the levels of binding- and neutralizing antibodies against the Alpha, Beta, Gamma, Delta, Epsilon and Omicron variants. The benefit of multiplexed analysis lies in the ability to measure a wide range of anti-RBD titers using a single dilution of serum for each assay. The reactivity patterns also yield an internal reference for neutralizing activity and binding antibody units per milliliter (BAU/ml). Results obtained with sera from vaccinated healthy individuals and patients confirmed and extended results from previous studies on time-dependent waning of antibody levels and effects of immunosuppressive agents. We conclude that anti-RBDwt titers correlate with levels of neutralizing antibodies against VOCs and propose that our method may be implemented to enhance the precision and throughput of immunomonitoring.
Dimerization processes for light-regulated transcription factor Photozipper visualized by high-speed atomic force microscopy
Authors: Tsuji Akihiro , Yamashita Hayato , Hisatomi Osamu , Abe Masayuki 
Published: March 2022

AbstractDimerization is critical for transcription factors (TFs) to bind DNA and regulate a wide variety of cellular functions; however, the single-molecular mechanisms remain to be completely elucidated. Here, we used high-speed atomic force microscopy (HS-AFM) to observe the dimerization process for a photoresponsive TF Photozipper (PZ), which consists of light-oxygen-voltage-sensing (LOV) and basic-region-leucine-zipper (bZIP) domain. HS-AFM visualized not only the oligomeric states of PZ molecules forming monomers and dimers under controlled dark–light conditions but also the domain structures within each molecule. Successive AFM movies captured the dimerization process for an individual PZ molecule and the monomer–dimer reversible transition during dark–light cycling. The high-resolution AFM images of the domain structures in PZ molecules demonstrated that the bZIP domain entangled under dark conditions was loosened owing to light illumination and fluctuated around the LOV domain. These observations revealed the role of the bZIP domain in the dimerization processes of a TF.
Glutathione-mediated plant response to high-temperature
Authors: Dard Avilien , Weiss Alizée , Bariat Laetitia , Picault Nathalie , Pontvianne Frédéric , Riondet Christophe , Reichheld Jean-Philippe 
Published: March 2022

ABSTRACTClimate change induce global warming and intense heat waves that affect plant development and productivity. Among the molecular perturbations that high temperature induces in living cells is the accumulation of reactive oxygen species (ROS), which can damage macromolecules of the cell and perturb the cellular redox state. To cope with deleterious effects of ROS, plant, as other organisms, have developed strategies to scavenge ROS and to regulate their redox state. Among those, glutathione plays a major role in maintaining the cellular redox state and the function of key antioxidant enzymes like peroxidases. Here, we investigated the contribution of the redox systems in plant adaptation to high temperature. We studied two different high temperature regimes: a rise of ambient temperature to 27°C inducing a plant developmental adaptation program called thermomorphogenesis, and a 37°C treatment mimicking intense heat wave and affecting plant viability. Using the genetically encoded redox marker roGFP, we show that high temperature regimes lead to cytoplasm and nuclear oxidation and impact profoundly the glutathione pool rather than the glutathione redox state. Moreover, plant can restore the pool within a few hours, which likely contribute to plant adaptation to high temperature. However, conditional glutathione deficient mutants fail to adapt to intense heat waves or to induce thermomorphogenesis, suggesting that glutathione is involved in both heat adaptation mechanisms. We also evaluate by RNAseq analyses, how plant change its genome expression signature upon heat stress and identified a marked genome expression deviation in mutant deficient in glutathione antioxidant which might contribute to its sensitivity to high temperature. Thus, we define glutathione as a major antioxidant molecule acting in the adaptation of plant to rise of temperature.
Mapping the subcortical connectome using in vivo diffusion MRI: feasibility and reliability
Authors: Kai Jason , Khan Ali R , Haast Roy AM , Lau Jonathan C 
Published: March 2022

AbstractTractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed anin vivoinvestigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectomein vivo.
Social Learning of a Spatial Task by Observation Alone
Authors: Doublet Thomas , Nosrati Mona , Kentros Clifford G. 
Published: March 2022

ABSTRACTInteractions between conspecifics are central to the acquisition of useful memories in the real world. Observational learning, i.e., learning a task by observing the success or failure of others, has been reported in many species, including rodents. However, previous work in rats with NMDA-receptor blockade has shown that even extensive observation of an unexplored space through a clear barrier is not sufficient to generate a stable hippocampal representation of that space. This raises the question of whether rats can learn a spatial task in a purely observed space from watching a conspecific, and if so, does this somehow stabilize their hippocampal representation? To address these questions, we designed an observational spatial task in a two-part environment that is nearly identical to that of the aforementioned electrophysiological study, in which an observer rat watches a demonstrator animal to learn the location of a hidden reward. Our results demonstrate that rats do not need to physically explore an environment to learn a reward location, provided a conspecific demonstrates where it is. We also show that the behavioral memory is not affected by NMDA receptor blockade, suggesting that the spatial representation underlying the behavior has been consolidated by observation alone.
DysregulatedH19/Igf2expression disrupts cardiac-placental axis during development of Silver Russell Syndrome-like mouse models
Authors: Chang Suhee , Fulmer Diana , Hur Stella K. , Thorvaldsen Joanne L. , Li Li , Lan Yemin , Rhon-Calderon Eric A. , Leu N Adrian , Chen Xiaowen , Epstein Jonathan A. , Bartolomei Marisa S. 
Published: March 2022

AbstractDysregulation of the imprintedH19/IGF2locus can lead to Silver-Russell Syndrome (SRS) in humans. However, the mechanism of how abnormalH19/IGF2expression contributes to various SRS phenotypes remains unclear, largely due to incomplete understanding of the developmental functions of these two genes. We previously generated a mouse model with humanizedH19/IGF2ICR (hIC1) on the paternal allele that exhibitedH19/Igf2dysregulation together with SRS-like growth restriction and perinatal lethality. Here we dissect the role ofH19andIgf2in cardiac and placental development utilizing multiple mouse models with varying levels ofH19andIgf2. We report severe cardiac defects such as ventricular septal defects (VSDs) and thinned myocardium, placental anomalies including thrombosis and vascular malformations, together with growth restriction in mouse embryos that correlated with the extent ofH19/Igf2dysregulation. Transcriptomic analysis using cardiac endothelial cells of these mouse models shows thatH19/Igf2dysregulation disrupts pathways related to extracellular matrix (ECM) and proliferation of endothelial cells. Our work links the heart and placenta through regulation byH19andIgf2, demonstrating that accurate dosage of bothH19andIgf2is critical for normal embryonic development, especially related to the cardiac-placental axis.
Noise facilitates entrainment of a population of uncoupled limit cycle oscillators
Authors: Kumpost Vojtech , Hilbert Lennart , Mikut Ralf 
Published: March 2022

ABSTRACTMany biological oscillators share two properties: they are subject to stochastic fluctuations (noise) and they must reliably adjust their period to changing environmental conditions (entrainment). While noise seems to distort the ability of single oscillators to entrain, in populations of oscillators noise allows entrainment for a wider range of input amplitudes and periods. Here, we investigate, how this effect depends on the noise intensity and the number of oscillators in the population. We have found that, if a population consists of a sufficient number of oscillators, increasing noise intensity leads to faster entrainment after a phase change of the input signal (jet lag) and increases sensitivity to low-amplitude input signals.SIGNIFICANCELive is characterized by rhythms, such as daily changes in activity or the heartbeat. These rhythms are reflected in molecular oscillations generated at the level of individual cells. These oscillations are inherently noisy, but still cells reliably synchronize to external signals and provide reliable timing for other biological processes. Here, we show how noise can be beneficial to cell populations in terms of synchronization to external signals. Specifically, noise can increase the sensitivity to weak external signals and speed up adjustment to jet-lag-like perturbations.
Growth instabilities shape morphology and genetic diversity of cellular aggregates
Authors: Golden Alexander , Dukovski Ilija , Segrè Daniel , Korolev Kirill S. 
Published: March 2022

AbstractCellular aggregates assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of an aggregate influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.
Classification of Movement-Related Oscillations in sEEG Recordings with Machine Learning
Authors: Rockhill Alexander P. , Mantovani Alessandra , Stedelin Brittany , Raslan Ahmed M. , Swann Nicole C. 
Published: March 2022

AbstractPrevious electrophysiological research has characterized canonical oscillatory patterns associated with movement mostly from recordings of primary sensorimotor cortex. Less work has attempted to decode movement based on electrophysiological recordings from a broader array of brain areas such as those sampled by stereoelectroencephalography (sEEG). Here we decoded movement using a linear support vector machine (SVM). We were able to accurately classify sEEG spectrograms during a keypress movement in a task versus those during the inter-trial interval. Furthermore, the important time-frequency patterns for this classification recapitulated findings from previous studies that used non-invasive electroencephalography (EEG) and electrocorticography (ECoG) and identified brain regions that were not associated with movement in previous studies. Specifically, we found these previously described patterns: beta (13 - 30 Hz) desynchronization, beta synchronization (rebound), pre-movement alpha (8 - 15 Hz) modulation, a post-movement broadband gamma (60 - 90 Hz) increase and an event-related potential. These oscillatory patterns were newly observed in a wide range of brain areas accessible with sEEG that are not accessible with other electrophysiology recording methods. For example, the presence of beta desynchronization in the frontal lobe was more widespread than previously described, extending outside primary and secondary motor cortices. We provide evidence for a system of putative motor networks that exhibit unique oscillatory patterns by describing the anatomical extent of the movement-related oscillations that were observed most frequently across all sEEG contacts.Significance StatementSeveral major motor networks have been previously delineated in humans, however, much less is known about the population-level oscillations that coordinate this neural circuitry, especially in cortex. Therapies that modulate brain circuits to treat movement disorders, such as deep brain stimulation (DBS), or use brain signals to control movement, such as brain-computer interfaces (BCIs), rely on our basic scientific understanding of this movement neural circuitry. In order to bridge this gap, we used stereoelectroencephalography (sEEG) collected in human patients being monitored for epilepsy to assess oscillatory patterns during movement.
Transcriptional control of nucleus accumbens neuronal excitability by Retinoid X Receptor Alpha tunes sensitivity to drug rewards
Authors: Godino Arthur , Salery Marine , Durand-de Cuttoli Romain , Estill Molly S. , Holt Leanne M. , Futamura Rita , Browne Caleb J. , Mews Philipp , Hamilton Peter J. , Neve Rachael L. , Shen Li , Russo Scott J. , Nestler Eric J. 
Published: March 2022

ABSTRACTThe complex nature of the transcriptional networks underlying addictive behaviors suggests intricate cooperation between diverse gene regulation mechanisms that go beyond canonical activity-dependent pathways. Here we implicate in this process a novel nuclear receptor transcription factor, Retinoid X Receptor Alpha (RXRα), which we identified bioinformatically as associated with addiction-like behaviors. In the nucleus accumbens (NAc) of male and female mice, we show that, while its own expression remains unaltered after cocaine exposure, RXRα controls plasticity- and addiction-relevant transcriptional programs in both dopamine receptor D1- and D2-expressing medium spiny neurons, which in turn modulate intrinsic excitability and synaptic activity of these NAc cell types. Behaviorally, bidirectional viral and pharmacological manipulation of RXRα regulates drug reward sensitivity in both non-operant and operant paradigms. Together, this study demonstrates a key role for NAc RXRα in promoting drug addiction, and paves the way for future studies of rexinoid signaling in psychiatric disease states.
Model-free Prediction Test with Application to Genomics Data
Authors: Cai Zhanrui , Lei Jing , Roeder Kathryn 
Published: March 2022

AbstractTesting the significance of prediction in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that, given confounding variables Z, X does not significantly contribute to the prediction of Y under the model-free setting, where X and Z are possibly high dimensional. We propose a general framework that first fits nonparametric regression models on the Y|X and Y|(X, Z), then compares the prediction power of the two models. The proposed method allows us to leverage the strength of the most powerful regression algorithms developed from the modern machine learning community. The p-value for the test can be easily obtained by permutation. In simulations, we find that the proposed method is more powerful compared to existing methods. The proposed method allows us to draw biologically meaningful conclusions from two gene expression data analyses without strong distributional assumptions: (a) testing prediction power of sequencing RNA for the proteins in CITE-seq data, and (b) identification of spatially variable genes in spatially resolved transcriptomics data.