bluuebunny/crossref_metadata_2025_split huggingface dataset:
A deep learning approach to neurite prediction in high throughput fluorescence imaging
Authors: Barch Mariya , Cobb Melanie M. , Tokuno Zachary , Leddy Jen , Prenton Keili , Manubens-Gil Linus , Bellini Nicole , Lam Stephanie , Kaye Julia , Dierssen Mara , Finkbeiner Steven 
Published: April 2021

AbstractChanges to neuronal morphology and loss of neurites and synaptic connections can be an important early indicator of neurological diseases, and a pathognomonic sign of neurodevelopmental disorders. These changes are typically detectable by microscopy in cell culture or histological samples, but quantification can be challenging. The neurites extending from cell soma can be quite thin, dim, overlapping and complex, making them laborious to trace manually and difficult to annotate and quantify computationally or automatically. Moreover, the tools available to aid this aim are limited in their capacity to generalize to high throughput image acquisition such as time-lapse or longitudinal imaging, where imaging conditions can change dramatically over the course of the experiment. In order to facilitate neurite quantification, we developed a deep learning (DL) neurite annotation prediction algorithm (NAPA) to predict the structure and length of neurites. NAPA overcomes experimental variation inherent to fluorescence imaging by learning more broader features that are important for neurite recognition. Based on a dataset with partial annotation, NAPA generated predictions on several unannotated datasets, and was able to capture differences between disease and control conditions. We also defined a sequence of steps to generate custom models with a small number of annotation inputs, and extended the predictions to a 3D tissue sample and longitudinal imaging. With this algorithm we developed an approach to quantify neurites with an accuracy that nears and sometimes exceeds human curation, in 1/100thof the time. This approach makes accurate analysis of large or longitudinal datasets feasible across a broad range of datasets.
Transient amplification enhances the persistence of tropicalising coral populations in marginal high latitude environments
Authors: Cant James , Cook Katie , Reimer James D. , Mezaki Takuma , Nakamura Masako , O’Flaherty Cliodhna , Salguero-Gómez Roberto , Beger Maria 
Published: April 2021

AbstractPredicting the viability of species exposed to increasing climatic stress requires an appreciation for the mechanisms underpinning the success or failure of marginal populations. Rather than traditional metrics of long-term population performance, here we illustrate that short-term (i.e. transient) demographic characteristics, including measures of resistance, recovery, and compensation, are fundamental in the poleward range expansion of hard corals, facilitating the establishment of coral populations at higher-latitudes. Through the annual census of tropical and subtropicalAcroporaspp. colonies in Japan, between 2017-2019, we show how the transient amplification potential of a subtropical coral population supports its enhanced growth within unstable environmental conditions. The transient dynamics of both the tropical and subtropical populations were strongly influenced by their corresponding recruitment patterns. However, we demonstrate that variation in colony survival and fragmentation patterns between the two populations determines their relative capacities for transient amplification. This latitudinal variation in the transient dynamics ofAcroporaspp. populations emphasizes that coral populations can possess the demographic plasticity necessary for exploiting more variable, marginal conditions.
Cannabidiol as an add-on therapy to overcome the slow-onset and, possibly, resistance to antidepressant treatment: involvement of NAPE-PLD in the medial prefrontal cortex
Authors: Scarante Franciele F. , Lopes Vinícius D. , Fusse Eduardo J. , Vicente Maria A. , Araújo Melissa R. , Scomparin Davi S. , Aguiar Rafael P. , Guimarães Francisco S. , Nardini Viviani , Sorgi Carlos Arterio , Faccioli Lucia H. , Hallak Jaime E. C. , Joca Samia , Mackie Kenneth , Zuardi Antonio Waldo , Crippa José Alexandre S. , Campos Alline C. 
Published: April 2021

AbstractAntidepressants such as serotonin uptake inhibitors are the first-line pharmacological treatment for chronic stress-related psychiatric disorders. However, their late-onset therapeutic action and frequent side effects, however, are important challenges for clinicians and patients. Besides, around 30% of major depression patients are considered treatment-resistant. Cannabidiol (CBD) is a non-psychotomimetic phytocannabinoid with a wide range of psychopharmacological effects, but its mechanism of action remains unclear. Here, we found that in male mice submitted to two different repeated stress protocols (chronic unpredictable and social defeat stress), low doses of CBD (7.5mg/Kg) caused an early-onset behavioral effect when combined to the antidepressant escitalopram (ESC-10mg/Kg). The behavioral effects of the ESC+CBD combination depended on the expression/activity of the N-acyl phosphatidylethanolamine phospholipase D (NAPE-PLD, responsible for synthesizing the endocannabinoid anandamide), but not the DAGLα, enzyme in the ventromedial prefrontal cortex. In addition, we described a case series with three treatment-resistant depression that were successfully treated with CBD as adjuvant therapy, as evaluated by standardized clinical rating scales. After 12 weeks of treatment, two patients were considered depression remitted (MADRS score lower than 10) while one patient successfully responded to CBD as add-on treatment (more than 50% decrease from the baseline MADRS). Our results suggest that CBD might be useful as an add-on therapy for optimizing the action of antidepressants. They also suggest that CBD’s beneficial actions depends on the facilitation of N-acylethanolamines actions in the medial prefrontal cortex.HighlightsIn mice, cannabidiol (CBD), but not escitalopram, induced a fast-onset anti-stress action.Combinations of sub-effective doses of CBD and escitalopram produce anti-stress effects after only 7 days.The Escitalopram + CBD treatment modulated synaptic protein markers in the medial prefrontal cortex.CRISPR-Cas9-mediated knockdown of NAPE-PLD in the medial PFC prevents the anti-stress effect of the Escitalopram + CBD.Adding CBD to an antidepressants regimen successfully treated three patients with treatment resistant depression.Graphical abstract
Jetlag Expectations, not Circadian Parameters, Predict Jetlag Symptom Severity in Travelers
Authors: Ullrich Maximilian , Fischer Dorothee , Deutsch Sebastian , Meissner Karin , Winnebeck Eva C 
Published: April 2021

AbstractAfter a flight across multiple time zones, most people show a transient state of circadian misalignment causing temporary malaise known as jetlag disorder. The severity of the elicited symptoms is postulated to depend mostly on circadian factors such as the number of time zones crossed and the direction of travel. Here, we examined the influence of prior expectation on symptom severity, compared to said “classic” determinants, in order to gauge potential psychosocial effects in jetlag disorder.To this end, we monitored jetlag symptoms in travel-inexperienced individuals (n=90, 18-37y) via detailed questionnaires twice daily for one week before and after flights crossing >3 time zones. We found pronounced differences in individual symptom load that could be grouped into 4 basic symptom trajectories. Both traditional and newly devised metrics of jetlag symptom intensity and duration (accounting for individual symptom trajectories) recapitulated previous results of jetlag prevalence at about 50-60% as well as general symptom dynamics.Surprisingly, however, regression models showed very low predictive power for any of the jetlag outcomes. The classic circadian determinants, including number of time zones crossed and direction of travel, exhibited little to no link with jetlag symptom intensity and duration. Only expectation emerged as a parameter with systematic, albeit small, predictive value.These results suggest expectation as a relevant factor in jetlag experience - hinting at potential placebo effects and new treatment options. Our findings also caution against jetlag recommendations based on circadian principles but insufficient evidence linking circadian re-synchronization dynamics with ensuing symptom intensity and duration.Significance StatementJetlag disorder afflicts millions of travelers each year - a nuisance on holiday trips but also a danger in safety and performance-critical operations. For effective prevention and treatment, it is critical to understand what influences jetlag severity, i.e. jetlag symptom intensity and duration. In contrast to what guidelines state, in our study, we did not find that symptom severity could be explained by the number of time zones crossed or travel direction. Rather, travelers’ expectations about how long and strongly they will suffer from jetlag symptoms was the only factor systematically predicting jetlag severity. If this holds true not only for subjective but also objective symptoms, we need to revisit assumptions about how circadian desynchronization relates to experienced jetlag symptoms.
Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
Authors: Eastman Brydon , Przedborski Michelle , Kohandel Mohammad 
Published: April 2021

AbstractThe in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. In practice, it is often prohibitively difficult to ensure the validity of patient-specific parameterizations of these models for any particular patient. As a result, sensitivities to these particular parameters can result in therapeutic dosing schedules that are optimal in principle not performing well on particular patients. In this study, we demonstrate that chemotherapeutic dosing strategies learned via reinforcement learning methods are more robust to perturbations in patient-specific parameter values than those learned via classical optimal control methods. By training a reinforcement learning agent on mean-value parameters and allowing the agent periodic access to a more easily measurable metric, relative bone marrow density, for the purpose of optimizing dose schedule while reducing drug toxicity, we are able to develop drug dosing schedules that outperform schedules learned via classical optimal control methods, even when such methods are allowed to leverage the same bone marrow measurements.
Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses
Authors: Beaudoin Christopher A , Jamasb Arian R , Alsulami Ali F , Copoiu Liviu , van Tonder Andries J , Hala Sharif , Bannerman Bridget P , Thomas Sherine E , Vedithi Sundeep Chaitanya , Torres Pedro H M , Blundell Tom L 
Published: April 2021

SummaryViruses often encode proteins that mimic host proteins in order to facilitate infection. Little work has been done to understand the potential mimicry of the SARS-CoV-2, SARS-CoV, and MERS-CoV spike proteins, particularly the receptor-binding motifs, which could be important in determining tropism of the virus. Here, we use structural bioinformatics software to characterize potential mimicry of the three coronavirus spike protein receptor-binding motifs. We utilize sequence-independent alignment tools to compare structurally known or predicted three-dimensional protein models with the receptor-binding motifs and verify potential mimicry with protein docking simulations. Both human and non-human proteins were found to be similar to all three receptor-binding motifs. Similarity to human proteins may reveal which pathways the spike protein is co-opting, while analogous non-human proteins may indicate shared host interaction partners and overlapping antibody cross-reactivity. These findings can help guide experimental efforts to further understand potential interactions between human and coronavirus proteins.HighlightsPotential coronavirus spike protein mimicry revealed by structural comparisonHuman and non-human protein potential interactions with virus identifiedPredicted structural mimicry corroborated by protein-protein dockingEpitope-based alignments may help guide vaccine effortsGraphical abstract
Orientation and color tuning of the human visual gamma rhythm
Authors: Li Ye , Bosking William , Beauchamp Michael S. , Sheth Sameer A. , Yoshor Daniel , Bartoli Eleonora , Foster Brett L. 
Published: April 2021

AbstractNarrowband gamma oscillations (NBG: ∼20-60Hz) in visual cortex reflect rhythmic fluctuations in population activity generated by underlying circuits tuned for stimulus location, orientation, and color. Consequently, the amplitude and frequency of induced NBG activity is highly sensitive to these stimulus features. For example, in the non-human primate, NBG displays biases in orientation and color tuning at the population level. Such biases may relate to recent reports describing the large-scale organization of single-cell orientation and color tuning in visual cortex, thus providing a potential bridge between measurements made at different scales. Similar biases in NBG population tuning have been predicted to exist in the human visual cortex, but this has yet to be fully examined. Using intracranial recordings from human visual cortex, we investigated the tuning of NBG to orientation and color, both independently and in conjunction. NBG was shown to display a cardinal orientation bias (horizontal) and also an end- and mid-spectral color bias (red/blue and green). When jointly probed, the cardinal bias for orientation was attenuated and an end-spectral preference for red and blue predominated. These data both elaborate on the close, yet complex, link between the population dynamics driving NBG oscillations and known feature selectivity biases in visual cortex, adding to a growing set of stimulus dependencies associated with the genesis of NBG. Together, these two factors may provide a fruitful testing ground for examining multi-scale models of brain activity, and impose new constraints on the functional significance of the visual gamma rhythm.Significance StatementOscillations in electrophysiological activity occur in visual cortex in response to stimuli that strongly drive the orientation or color selectivity of visual neurons. The significance of this induced ‘gamma rhythm’ to brain function remains unclear. Answering this question requires understanding how and why some stimuli can reliably generate gamma activity while others do not. We examined how different orientations and colors independently and jointly modulate gamma oscillations in the human brain. Our data show gamma oscillations are greatest for certain orientations and colors that reflect known biases in visual cortex. Such findings complicate the functional significance of gamma activity, but open new avenues for linking circuits to population dynamics in visual cortex.ClassificationNeuroscience
Aerobic glycolysis is important for zebrafish larval wound closure and tail regeneration
Authors: Scott Claire A. , Carney Tom J. , Amaya Enrique 
Published: April 2021

ABSTRACTThe underlying mechanisms of appendage regeneration remain largely unknown and uncovering these mechanisms in capable organisms has far-reaching implications for potential treatments in humans. Recent studies implicate a requirement for metabolic reprogramming reminiscent of the Warburg effect during successful appendage and organ regeneration. As changes are thus predicted to be highly dynamic, methods permitting direct, real-time visualization of metabolites at the tissue and organismal level, would offer a significant advance in defining the influence of metabolism on regeneration and healing. We sought to examine whether glycolytic activity was altered during larval fin regeneration, utilising the genetically encoded biosensor, Laconic, enabling the spatiotemporal assessment of lactate levels in living zebrafish. We present evidence for a rapid increase in lactate levels within minutes following injury, with a role of aerobic glycolysis in actomyosin contraction and wound closure. We also find a second wave of lactate production, associated with overall larval tail regeneration. Chemical inhibition of glycolysis attenuates both contraction of the wound and regrowth of tissue following tail amputation, suggesting aerobic glycolysis is necessary at two distinct stages of regeneration.
In vitroeffects of S-Licarbazepine as a potential precision therapy onSCN8Avariants causing neuropsychiatric disorders
Authors: Bayraktar Erva , Liu Yuanyuan , Hedrich Ulrike B.S. , Sara Yildirim , Lerche Holger , Wuttke Thomas V , Lauxmann Stephan 
Published: April 2021

AbstractBackground and PurposeAmong genetic epilepsies, variants in sodium channel coding genes constitute a major subgroup. Variants inSCN8A, the coding gene for NaV1.6 channels, are characterized by a variety of symptoms including intractable epileptic seizures, psychomotor delay, progressive cognitive decline, and others such as autistic features, ataxia or dystonia. Standard anticonvulsant treatment has only limited impact on the course of disease.Experimental ApproachPersonalized therapeutic regimens tailored to disease-causing pathophysiological mechanisms may offer the specificity required to overcome intractability. Toward this aim, we investigatedin vitroin neuroblastoma cells the effects of S-Licarbazepine, a third-generation dibenzazepine and enhancer of slow inactivation of voltage gated sodium channels, on three gain-of-function NaV1.6 variants linked to representative phenotypes of mild epilepsy (G1475R), developmental and epileptic encephalopathy (M1760I) and intellectual disability without epilepsy (A1622D).Key ResultsS-Licarbazepine strongly enhances the slow and - less pronounced – the fast inactivation of NaV1.6 wildtype channels. It acts similarly on all tested variants and irrespective of their particular biophysical dysfunction mechanism. Beyond that S-Licarbazepine has variant-specific effects including a partial reversal of pathologically slowed fast inactivation dynamics (A1622D, M1760I) and a trend to reduce the enhanced persistent Na+current by A1622D variant channels.Conclusion and ImplicationsThese data bring out that S-Licarbazepine not only owns substance-specific effects, but also holds variant-specific effects, which can variably contribute to functional compensation of distinct channel-specific biophysical properties and thereby highlighting the role of personalized approaches, which likely will be key to improved and successful treatment not only ofSCN8A-related disease.Bullet pointsWhat is already known?S-Lic strongly modulates slow and - to a less extend - fast inactivation of wild-type NaV1.6 channels.What this study adds?Differential modulatory effects of S-Lic extend to NaV1.6 A1622D, M1760I and G1475R variant channels irrespective of their leading biophysical mode of gain-of-function and variably contribute to variant-specific functional compensation of their altered biophysical properties.Clinical significance:These data suggest therapeutic potential of S-Lic forSCN8Aneuropsychiatric disorders and highlight the role of personalized approaches aimed at increasingly precise correction of underlying pathophysiological mechanisms.
Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease
Authors: Merk Timon , Peterson Victoria , Lipski Witold , Blankertz Benjamin , Turner Robert S. , Li Ningfei , Horn Andreas , Richardson R. Mark , Neumann Wolf-Julian 
Published: April 2021

AbstractBrain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson’s disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement kinematics. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.Significance StatementNeurotechnology will revolutionize the treatment of neurological and psychiatric patients, promising novel treatment avenues for previously intractable brain disorders. However, optimal surgical and computational approaches and their interactions with neurological disorders are unknown. How can recent advances in machine learning and connectomics aid the precision and performance of invasive brain signal decoding strategies? Do the brain disorders treated with such approaches have impact on decoding performance? We propose a real time compatible advanced machine learning pipeline for invasively recorded brain signals in Parkinson’s disease (PD) patients. We report optimal movement decoding strategies with respect to signal source, model architecture and connectomic fingerprint and demonstrate that PD pathophysiology significantly and negatively impacts movement decoding. Our study has broad impacts for the development of smart brain implants for the treatment of PD and other brain disorders.