bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 relevant genetic variants confirmed in an admixed population
Authors: Texis Tomas , Luis Cruz-Jaramilllo José , García-Muñoz Willebaldo , Anzures-Cortés Lourdes , Haddad-Talancón Lorenza , Sánchez-García Sergio , del Carmen Jiménez Martínez María , Pérez Barragán Edgar , Nieto-Patlán Alejandro , Martínez-Ezquerro José D. , Rubio-Carrasco Kenneth , Rodríguez-Dorantes Mauricio , Cortés-Ramírez Sergio , Mellado-Sánchez Gabriela , Pérez-Tapia Sonia Mayra , Gonzalez-Covarrubias Vanessa 
Published: April 2022

SummaryThe dissection of factors that contribute to COVID-19 infection and severity has overwhelmed the scientific community for almost 2 years. Current reports highlight the role of in disease incidence, progression, and severity. Here, we aimed to confirm the presence of previously reported genetic variants in an admixed population. Allele frequencies were assessed and compared between the general population (N=3079) for which at least 30% have not been infected with SARS-CoV2 as per July 2021 versus COVID-19 patients (N=106).Genotyping data from the Illumina GSA array was used to impute genetic variation for 14 COVID-relevant genes, using the 1000G phase 3 as reference based on the human genome assembly hg19, following current standard protocols and recommendations for genetic imputation. Bioinformatic and statistical analyses were performed using MACH v1.0, R, and PLINK.A total of 7953 variants were imputed on, ABO, CCR2, CCR9, CXCR6, DPP9, FYCO1, IL10RB/IFNAR2, LZTFL1, OAS1, OAS2, OAS3, SLC6A20, TYK2, and XCR1. Statistically significant allele differences were reported for 10 and 7 previously identified and confirmed variants, ABO rs657152, DPP9 rs2109069, LZTFL1 rs11385942, OAS1 rs10774671, OAS1 rs2660, OAS2 rs1293767, and OAS3 rs1859330 p<0.03. In addition, we identified 842 variants in these COVID-related genes with significant allele frequency differences between COVID patients and the general population (p-value <E-2 – E-179).Our observations confirm the presence of genetic differences in COVID-19 patients in an admixed population and prompts for the investigation of the statistical relevance of additional variants on these and other genes that could identify local and geographical patterns of COVID-19.
Estimation of cochlear frequency selectivity using a convolution model of forward-masked compound action potentials
Authors: Deloche François , Parida Satyabrata , Sivaprakasam Andrew , Heinz Michael G. 
Published: April 2022

AbstractFrequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward-masking have been suggested as an alternative means to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAPs. In this work, we seek to provide a direct estimate of the quality factor characterizing AN frequency tuning using many forward-masked CAP responses. The method is based on a convolution model of the CAP that takes into account the masking of AN populations induced by notched-noise maskers with various notch widths and attenuations. The model produces masking patterns that, once convolved by a unitary response, predict forward-masked CAP waveforms. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across the different masking conditions, yielding robust estimates. The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN-fiber tuning curves, without the need for an empirical correction factor. Beyond the estimation of frequency selectivity, the proposed model proves to be accurate in predicting forward-masked CAP responses, and therefore could be extended to study more complex aspects of cochlear signal processing using a similar experimental approach.
Odorant representations indicate nonlinear processing across the olfactory system
Authors: Olivares Jesús , Orio Patricio , Sadílek Viktor , Schmachtenberg Oliver , Canales-Johnson Andrés 
Published: April 2022

ABSTRACTThe brain’s recurrent connectivity supports both oscillatory and non-oscillatory neural activities that coordinate temporally across regions. Understanding how these dynamics contribute to the processing and transmission of olfactory information remains unclear, especially across distributed networks in the olfactory system. In this study, we investigate whether odorant identity can be distinguished through nonlinear interactions in the local field potentials (LFPs) of the olfactory bulb and telencephalic regions (Vv and Dp) in rainbow trout. Our results show that odorant identity modulates complex information-theoretic metrics, specifically information sharing and redundancy, across these brain areas, indicating nonlinear processing. In contrast, traditional linear connectivity measures, such as coherence and phase synchrony, showed little or no significant modulation by odorants. These findings suggest that nonlinear interactions encoded by olfactory oscillations carry crucial odor information across the teleost olfactory system, offering insights into the broader role of nonlinear dynamics in sensory processing.
Immediate neural network impact after the loss of a semantic hub
Authors: Kocsis Zsuzsanna , Jenison Rick L. , Cope Thomas E. , Taylor Peter N. , Calmus Ryan M. , McMurray Bob , Rhone Ariane E. , Sarrett McCall E. , Kikuchi Yukiko , Gander Phillip E. , Berger Joel I. , Kovach Christopher K. , Choi Inyong , Greenlee Jeremy D. , Kawasaki Hiroto , Griffiths Timothy D. , Howard Matthew A. , Petkov Christopher I. 
Published: April 2022

SUMMARYThe human brain extracts meaning from the world using an extensive neural system for semantic knowledge. Whether such broadly distributed systems1–3crucially depend on or can compensate for the loss of one of their highly interconnected hubs4–6is controversial4. The strongest level of causal evidence for the role of a brain hub is to evaluate its acute network-level impact following disconnection and any rapid functional compensation that ensues. We report rare neurophysiological data from two patients who underwent awake intracranial recordings during a speech prediction task immediately before and after neurosurgical treatment that required disconnection of the left anterior temporal lobe (ATL), a crucial hub for semantic knowledge4–6. Informed by a predictive coding framework, we tested three sets of hypotheses includingdiaschisiscausing disruption in interconnected sites7andincompleteorcomplete compensationby other language-critical and speech processing sites8–10. Immediately after ATL disconnection, we observed highly specific neurophysiological alterations in the recorded fronto-temporal network, including abnormally magnified high gamma responses to the speech sounds in auditory cortex. We also observed evidence for rapid compensation, seen as focal increases in effective connectivity involving language-critical sites in the inferior frontal gyrus and speech processing sites in auditory cortex. However, compensation was incomplete, in part because after ATL disconnection speech prediction signals were depleted in auditory cortex. This study provides direct causal evidence for a semantic hub in the human brain and shows striking neural impact and a rapid attempt at compensation in a neural network after the loss of one of its hubs.
Structure of the Dengue Virus RNA Promoter
Authors: Sun Yi-Ting , Varani Gabriele 
Published: April 2022

AbstractDengue virus, a single-stranded positive sense RNA virus, is the most prevalent mosquito-borne pathogen in the world. Like all RNA viruses, it uses conserved structural elements within its genome to control essential replicative steps. A 70 nucleotides stem-loop RNA structure (called SLA) found at the 5’-end of the genome of all flaviviruses, functions as the promoter for viral replication. This highly conserved structure interacts with the viral polymerase NS5 to initiate RNA synthesis. Here we report the NMR structure of a monomeric SLA from Dengue virus serotype 1, assembled to high-resolution from independently folded structural elements. The DENV1 SLA has an L-shape structure, where the top and side helices are coaxially-stacked and the bottom helix is roughly perpendicular to them. Because the sequence is highly conserved among different flavivirus genomes, it is likely that the three-dimensional fold and local structure of SLA are also conserved among flaviviruses and required for efficient replication. This work provides structural insight into the Dengue promoter and provides the foundation for the discovery of new antiviral drugs that target this essential replicative step.
The rhizodynamics robot: Automated imaging system for studying long-term dynamic root growth
Authors: McCaskey Erin , Taylor Isaiah , Pierce Christopher , Ligon Jason , Aydin Enes , Hunner Carrie , Carmichael Amanda , Eserman Lauren , Coffey Emily E.D. , Benfey Philip N , Goldman Daniel I 
Published: April 2022

AbstractThe study of plant root growth in real time has been difficult to achieve in an automated, high-throughput, and systematic fashion. Dynamic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their environments. We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months. The system uses low-cost, widely available materials. As a proof of concept, we employed GROOT to collect images of orchid root growth of multiple species over six months. Beyond imaging plant roots, our system is highly customizable and can be used to collect time-lapse image data of different container sizes and configurations regardless of what is being imaged, making it applicable to many fields that require longitudinal time-lapse recording.
Triglyceride lipolysis driven by glucose restriction triggers liquid-crystalline phase transitions and proteome remodeling of lipid droplets
Authors: Rogers Sean , Gui Long , Kovalenko Anastasiia , Zoni Valeria , Carpentier Maxime , Ramji Kamran , Ben Mbarek Kalthoum , Bacle Amelie , Fuchs Patrick , Campomanes Pablo , Reetz Evan , Speer Natalie Ortiz , Reynolds Emma , Thiam Abdou Rachid , Vanni Stefano , Nicastro Daniela , Henne W. Mike 
Published: April 2022

SummaryLipid droplets (LDs) are reservoirs for triglycerides (TGs) and sterol-esters (SEs), but how these lipids are organized within LDs and influence its proteome remains unclear. Using in situ cryoelectron tomography, we show that glucose restriction triggers lipid phase transitions within LDs generating liquid-crystalline lattices inside them. Mechanistically this requires TG lipolysis, which decreases the LD TG:SE ratio, promoting SE transition to a liquid-crystalline phase. Molecular dynamics simulations reveal TG depletion promotes spontaneous TG and SE de-mixing in LDs, additionally altering the lipid packing of the phospholipid monolayer surface. Fluorescence imaging and proteomics further reveal that liquid-crystalline phases are associated with selective remodeling of the LD proteome. Some canonical LD proteins including Erg6 re-localize to the ER network, whereas others remain LD-associated. Model peptide LiveDrop also redistributes from LDs to the ER, suggesting liquid-crystalline-phases influence ER-LD inter organelle transport. Our data suggests glucose restriction drives TG mobilization, which alters the phase properties of LD lipids and selectively remodels the LD proteome.
Humoral signaling-mediated effects of unilateral brain injury: differences in the left-right sided afferent responses
Authors: Watanabe Hiroyuki , Kobikov Yaromir , Sarkisyan Daniil , Lavrov Igor , Schouenborg Jens , Zhang Mengliang , Bakalkin Georgy 
Published: April 2022

AbstractDisruption of neural tracts descending from the brain to the spinal cord after brain trauma and stroke causes postural and sensorimotor deficits. We previously showed that unilateral lesion to the sensorimotor cortex in rats with completely transected thoracic spinal cord produced asymmetry in hindlimb posture and withdrawal reflexes. Supraspinal signals to hindlimb muscles may be transmitted through the paravertebral chain of sympathetic ganglia that remain intact after the transection. We here demonstrated that prior transection of the spinal cord at the cervical level that was rostrally to segments with preganglionic sympathetic neurons, did not abolish formation of asymmetry in hindlimb posture and musculo-articular resistance to stretch after unilateral brain injury. Thus not the sympathetic system but humoral signals may mediate the effects of brain injury on the lumbar spinal circuits. The asymmetric responses in rats with transected spinal cords were eliminated by bilateral lumbar dorsal rhizotomy after the left-side brain injury, but resistant to deafferentation after the right-side brain lesion. Two mechanisms, one dependent on and one independent of afferent input may account for asymmetric hindlimb motor responses. Resistance to deafferentation may be due to sustained stretch- and effort-unrelated muscle contractions that is often observed in patients with central lesions. Left-right asymmetry is unusual feature of these mechanisms that both are activated by humoral signals.Graphical Abstract
A python-based package for long-lasting video acquisition and semi-automated detection of convulsive seizures in rodents
Authors: Diaz-Arce Daniel , Ghouma Anis , Scalmani Paolo , Mantegazza Massimo , Duprat Fabrice 
Published: April 2022

AbstractBackgroundEpilepsy is a very invalidating pathology characterized by the unpredictable appearance of abnormal cerebral activity leading to seizures and co-morbidities. The ability to detect and even predict seizures is a major challenge and many research laboratories are using rodents’ models of epilepsy to unravel possible mechanisms. The gold standard to record and detect seizures is electroencephalography, but it is very invasive. For rodents used in research, video analysis is a very interesting approach but the major disadvantages are that it is time consuming, prone to human error, and not very reproducible. Commercial solutions for detailed phenotyping analysis on humans or rodents exist but they are costly. Some open source software programs are also available, they provide very interesting and precise behavior data, but none of them are made for high throughput analysis of a large number of video files generated by long lasting recordings.New methodWe developed an open-source python-based package of two software programs that enable automated video acquisition and simple motion analysis associated with a spectral power analysis, which enable a semi-automated identification of convulsive seizures. The method needs cheap webcams and a computer or a server.ResultsUsing two murine epilepsy models (Nav1.1 mutations), we have compared our motion analysis software to human visual inspection and found an 88.8% accuracy in convulsive seizures detection. We then compare our method to the gold standard electrocorticogram analysis and found a 93.2% accuracy. The motion analysis is also interesting to get a readout of the animal activity without the invasiveness of electromyogram recordings.ConclusionsThis new method is easy to use, cost-effective and allows: 1) detection of convulsive seizures in a noninvasive way, 2) high speed analysis of a large number of video files with a good accuracy, and 3) automated acquisition and semi-automated analysis of a very large number of files.HighlightsNoninvasive semi-automated detection of convulsive seizures from videosHigh speed of analysis with a good accuracyAbility to acquire and analyze a very large number of filesEasy to use graphical interfacesCost-effective setup
Resource sharing leads to the emergence of division of labour
Authors: Kreider Jan J. , Janzen Thijs , Bernadou Abel , Elsner Daniel , Kramer Boris H. , Weissing Franz J. 
Published: April 2022

AbstractDivision of labour occurs in a broad range of organisms. Yet, how division of labour can emerge in the absence of pre-existing interindividual differences is poorly understood. Using a simple but realistic model, we show that in a group of initially identical individuals, division of labour emerges spontaneously if returning foragers share part of their resources with other group members. In the absence of resource sharing, individuals follow an activity schedule of alternating between foraging and other tasks. If non-foraging individuals are fed by other individuals, their alternating activity schedule becomes interrupted, leading to task specialisation and the emergence of division of labour. Furthermore, nutritional differences between individuals reinforce division of labour. Such differences can be caused by increased metabolic rates during foraging or by dominance interactions during resource sharing. Our model proposes a plausible mechanism for the self-organised emergence of division of labour in animal groups of initially identical individuals. This mechanism could also play a role for the emergence of division of labour during the major evolutionary transitions to eusociality and multicellularity.
Loss of PRMT2 in myeloid cells in normoglycemic mice phenocopies impaired regression of atherosclerosis in diabetic mice
Authors: Vurusaner Beyza , Thevkar-Nages Prashanth , Kaur Ravneet , Giannarelli Chiara , Garabedian Michael J. , Fisher Edward A. 
Published: April 2022

ABSTRACTRegression of atherosclerosis is impaired in diabetes. However, the factors mediating this effect remain incomplete. We identified Protein Arginine Methyltransferase 2 (PRMT2) as a protein whose expression in macrophages is reduced in hyperglycemia and diabetes. PRMT2 catalyzes arginine methylation to target proteins to modulate gene expression. Because PRMT2 expression is reduced in cells in hyperglycemia, we wanted to determine whether PRMT2 plays a causal role in the impairment of atherosclerosis regression in diabetes. We therefore examined the consequence of deleting PRMT2 in myeloid cells during regression of atherosclerosis in normal and diabetic mice. Remarkably, we found a significant impairment of atherosclerosis regression under normoglycemic conditions in mice lacking PRMT2 (Prmt2-/-) in myeloid cells that mimics the decrease in regression of atherosclerosis in WT mice under diabetic conditions. This was associated with increased plaque macrophage retention, as well as increased apoptosis and necrosis. PRMT2-deficient plaque CD68+ cells under normoglycemic conditions showed increased expression of genes involved in cytokine signaling and inflammation compared to WT. Consistently Prmt2-/- bone marrow derived macrophages (BMDMs) showed an increased response of pro-inflammatory genes to LPS, and a decreased response of inflammation resolving genes to IL-4. This increased response to LPS in Prmt2-/- BMDMs is via enhanced NF-kappa B activity. Thus, the loss of PRMT2 is causally linked to impaired atherosclerosis regression via a heightened inflammatory response in macrophages. That PRMT2 expression was lower in myeloid cells in plaques from human subjects with diabetes supports the relevance of our findings to human atherosclerosis.
Bayesian interpolation for power laws in neural data analysis
Authors: Davidovich Iván A. , Roudi Yasser 
Published: April 2022

AbstractPower laws arise in a variety of phenomena ranging from matter undergoing phase transition to the distribution of word frequencies in the English language. Usually, their presence is only apparent when data is abundant, and accurately determining their exponents often requires even larger amounts of data. As the scale of recordings in neuroscience becomes larger, an increasing number of studies attempt to characterise potential power-law relationships in neural data. In this paper, we aim to discuss the potential pitfalls that one faces in such efforts and to promote a Bayesian interpolation framework for this purpose. We apply this framework to synthetic data and to data from a recent study of large-scale recordings in mouse primary visual cortex (V1), where the exponent of a powerlaw scaling in the data played an important role: its value was argued to determine whether the population’s stimulus-response relationship is smooth, and experimental data was provided to confirm that this is indeed so. Our analysis shows that with such data types and sizes as we consider here, the best-fit values found for the parameters of the power law and the uncertainty for these estimates are heavily dependent on the noise model assumed for the estimation, the range of the data chosen, and (with all other things being equal) the particular recordings. It is thus challenging to offer a reliable statement about the exponents of the power law. Our analysis, however, shows that this does not affect the conclusions regarding the smoothness of the population response to low-dimensional stimuli but casts doubt on those to natural images. We discuss the implications of this result for the neural code in the V1 and offer the approach discussed here as a framework that future studies, perhaps exploring larger ranges of data, can employ as their starting point to examine power-law scalings in neural data.
Prescribed fire selects for a pyrophilous soil subcommunity in a northern California mixed conifer forest
Authors: Fischer Monika S. , Patel Neem J. , de Lorimier Phillip J. , Traxler Matthew F. 
Published: April 2022

ABSTRACTLow intensity prescribed fire is a critical strategy for mitigating the effects of catastrophic wildfires. The above-ground response to fire has been well-documented, including many ecosystem benefits associated with prescribed burning, but fewer studies have directly addressed the effect of prescribed fire on soil organisms. We aimed to understand how soil microbial communities respond to prescribed fire and to determine the ecological processes driving their dynamics. We extensively sampled four plots for 17 months in a mixed conifer forest in northern California, USA; immediately following a low-intensity prescribed fire, a higher-intensity prescribed fire, and two no-burn control plots. We found that prescribed fire significantly altered the community structure for both fungi (ITS) and bacteria (16S), which was sustained throughout the time-series. By comparing our community profiling results with a model of neutral community assembly, we found that the presence of most taxa across all experimental conditions could be explained by neutral processes. However, combining threshold indicator taxa analysis and correlation network analysis with the neutral model identified a cohort of taxa that responded deterministically to prescribed fire. The subcommunity identified through this series of analyses includes both known and new pyrophilous taxa. Beyond this, our analyses revealed network modules within postfire communities which were responsive to fire-intensity. Taken together, these results lay the foundation for building a process-driven understanding of microbial community assembly in the context of the classical disturbance regime of fire.