Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical uncertainty can critically affect simulations of mechanistic models in neuroscience

Understanding neural computation on the mechanistic level requires biophysically realistic neuron models. To analyze such models one typically has to solve systems of coupled ordinary differential equations (ODEs), which describe the dynamics of the underlying neural system. These ODEs are solved numerically with deterministic ODE solvers that yield single solutions with either no or only a global scalar bound on precision. To overcome this problem, we propose to use recently developed probabilistic solvers instead, which are able to reveal and quantify numerical uncertainties, for example as posterior sample paths. Importantly, these solvers neither require detailed insights into the kinetics of the models nor are they difficult to implement. Using these probabilistic solvers, we show that numerical uncertainty strongly affects the outcome of typical neuroscience simulations, in particular due to the non-linearity associated with the generation of action potentials. We quantify this uncertainty in individual single Izhikevich neurons with different dynamics, a large population of coupled Izhikevich neurons, single Hodgkin-Huxley neuron and a small network of Hodgkin-Huxley-like neurons. For commonly used ODE solvers, we find that the numerical uncertainty in these models can be substantial, possibly jittering spikes by milliseconds or even adding or removing individual spikes from the simulation altogether. Author summary Computational neuroscience is built around computational models of neurons that allow the simulation and analysis of signal processing in the central nervous system. These models come typically in the form of ordinary differential equations (ODEs). The solution of these ODEs is computed using solvers with finite accuracy and, therefore, the computed solutions deviate from the true solution. If this deviation is too large but goes unnoticed, this can potentially lead to wrong scientific conclusions. A field in machine learning called probabilistic numerics has recently developed a set of probabilistic solvers for ODEs, which not only produce a single solution of unknown accuracy, but instead yield a distribution over simulations. Therefore, these tools allow one to address the problem state above and quantitatively analyze the numerical uncertainty inherent in the simulation process. In this study, we demonstrate how such solvers can be used to quantify numerical uncertainty in common neuroscience models. We study both Hodgkin-Huxley and Izhikevich neuron models and show that the numerical uncertainty in these models can be substantial, possibly jittering spikes by milliseconds or even adding or removing individual spikes from the simulation altogether. We discuss the implications of this finding and discuss how our methods can be used to select simulation parameters to trade off accuracy and speed.
A model of naturalistic decision making in preference tests

Decisions as to whether to continue with an ongoing activity or to switch to an alternative are a constant in an animal’s natural world, and in particular underlie foraging behavior and performance in food preference tests. Stimuli experienced by the animal both impact the choice and are themselves impacted by the choice, in a dynamic back and forth. Here, we present model neural circuits, based on spiking neurons, in which the choice to switch away from ongoing behavior instantiates this back and forth, arising as a state transition in neural activity. We analyze two classes of circuit, which differ in whether state transitions result from a loss of hedonic input from the stimulus (an “entice to stay” model) or from aversive stimulus input (a “repel to leave” model). In both classes of model, we find that the mean time spent sampling a stimulus decreases with increasing value of the alternative stimulus, a fact that we linked to the inclusion of depressing synapses in our model. The competitive interaction is much greater in “entice to stay” model networks, which has qualitative features of the marginal value theorem, and thereby provides a framework for optimal foraging behavior. We offer suggestions as to how our models could be discriminatively tested through the analysis of electrophysiological and behavioral data. Author summary Many decisions are of the ilk of whether to continue sampling a stimulus or to switch to an alternative, a key feature of foraging behavior. We produce two classes of model for such stay-switch decisions, which differ in how decisions to switch stimuli can arise. In an “entice-to-stay” model, a reduction in the necessary positive stimulus input causes switching decisions. In a “repel-to-leave” model, a rise in aversive stimulus input produces a switch decision. We find that in tasks where the sampling of one stimulus follows another, adaptive biological processes arising from a highly hedonic stimulus can reduce the time spent at the following stimulus, by up to ten-fold in the “entice-to-stay” models. Along with potentially observable behavioral differences that could distinguish the classes of networks, we also found signatures in neural activity, such as oscillation of neural firing rates and a rapid change in rates preceding the time of choice to leave a stimulus. In summary, our model findings lead to testable predictions and suggest a neural circuit-based framework for explaining foraging choices.
Tissue-specific metabolic reprogramming during wound induced de novo organ formation in tomato hypocotyl explants

Plants have remarkable regenerative capacity, which allows them to survive tissue damaging after biotic and abiotic stress. Some of the key transcription factors and the hormone crosstalk involved in wound-induced organ regeneration have been extensively studied in the model plant Arabidopsis thaliana. However, little is known about the role of metabolism in wound-induced organ regeneration. Here, we performed detailed transcriptome analysis and targeted metabolomics approach during de novo organ formation in tomato hypocotyl explants and found tissue-specific metabolic differences and divergent developmental pathways after wounding. Our results indicate that callus growth in the apical region of the hypocotyl depends on a specific metabolic switch involving the upregulation of the photorespiratory pathway and the differential regulation of photosynthesis-related genes and of the gluconeogenesis pathway. The endogenous pattern of ROS accumulation in the apical and basal region of the hypocotyl during the time-course were dynamically regulated, and contributed to tissue-specific wound-induced regeneration. Our findings provide a useful resource for further investigation on the molecular mechanisms involved in wound-induced organ formation in a crop species such as tomato. One-sentence Summary Metabolic switch during wound-induced regeneration
Selection of sites for field trials of genetically engineered mosquitoes with gene drive

Novel malaria control strategies using genetically engineered mosquitoes (GEMs) are on the horizon. Population modification is one approach wherein mosquitoes are engineered with genes rendering them refractory to the malaria parasite coupled with a low-threshold, Cas9-based gene drive. When released into a wild vector population, GEMs preferentially transmit these beneficial genes to their offspring, ultimately modifying a vector population into a non-vector one. Deploying this technology awaits evaluation including ecologically contained field trials. Here, we consider a process for site selection, the first critical step in designing a trial. Our goal is to identify a site that maximizes prospects for success, minimizes risk, and serves as a fair, valid, and convincing test of efficacy and impacts of a GEM product intended for large-scale deployment in Africa. We base site selection on geographical, geological, and biological, rather than social or legal, criteria. We recognize the latter as critically important but not preeminent. We propose physical islands as being the best candidates for a GEM field trial and present an evaluation of 22 African islands. We consider geographic and genetic isolation, biological complexity, island size, topography, and identify two island groups that satisfy key criteria for ideal GEM field trial sites.
Cdc48 influence on separase levels is independent of mitosis and suggests translational sensitivity of separase

Cdc48 (p97/VCP) is a AAA-ATPase that can extract ubiquitinated proteins from their binding partners and channel substrates to the proteasome. A fission yeast cdc48 mutant (cdc48-353) shows low levels of the cohesin protease, separase, and pronounced chromosome segregation defects in mitosis. Separase initiates chromosome segregation when its binding partner securin is ubiquitinated and degraded. The low separase levels in the cdc48-353 mutant have been attributed to a failure to extract ubiquitinated securin from separase resulting in co-degradation of separase along with securin. If true, this establishes Cdc48 as a key regulator of mitosis. In contrast, we show here that low separase levels in the cdc48-353 mutant are independent of mitosis. Moreover, we find no evidence of enhanced separase degradation in the mutant. Instead, we suggest that the cdc48-353 mutant uncovers specific requirements for separase translation. Our results highlight a need to better understand how this key mitotic enzyme is synthesized.
Quantitative Systems Pharmacology Modeling of Avadomide-Induced Neutropenia Enables Virtual Clinical Dose and Schedule Finding Studies

Avadomide is a cereblon E3 ligase modulator and a potent antitumor and immunomodulatory agent. Avadomide trials are challenged by neutropenia as a major adverse event and a dose-limiting toxicity. Intermittent dosing schedules supported by preclinical data provide a strategy to reduce frequency and severity of neutropenia; however, the identification of optimal dosing schedules remains a clinical challenge. Quantitative systems pharmacology (QSP) modeling offers opportunities for virtual screening of efficacy and toxicity levels produced by alternative dose and schedule regimens, thereby supporting decision-making in translational drug development. We formulated a QSP model to capture the mechanism of avadomide-induced neutropenia, which involves cereblon-mediated degradation of transcription factor Ikaros, resulting in a maturation block of the neutrophil lineage. The neutropenia model was integrated with avadomide-specific pharmacokinetic and pharmacodynamic models to capture dose-dependent effects. Additionally, we generated a disease-specific virtual patient population to represent the variability in patient characteristics and response to treatment observed for a diffuse large B-cell lymphoma trial cohort. Model utility was demonstrated by simulating the avadomide effect in the virtual population for various dosing schedules and determining the incidence of high-grade neutropenia, its duration, and the probability of recovery to low-grade neutropenia.
Nasal BCG Exposure Accelerates Dural Lymphatics Development via Macrophages’ Role in Newborn Mice

The dural lymphatics develop mainly during the first postnatal month. Lymphatics may be shaped by immune activation when bacterial infection happens. BCG, a strong immune activator, is widely injected to newborns. Moreover, human beings are nasally exposed in daily life to bacterial stimuli. These background prompted us to investigate whether neonatal BCG injection combined with nasally exposure exerts an influence on dural lymphatics develop. Here, mice received a single dose intracutaneous (i.c.) BCG injection immediately after birth followed by repeated nasal BCG challenge once a day (i.c./nasal group). These mice had an accelerated dural lymphatics growth and increased levels of several cytokines and VEGFR-3. Furthermore, macrophages were identified as a key mediator of these alterations. Mice that received mere one dose i.c. BCG injection showed no significant alterations in these indexes. Additionally, mere repeated nasal BCG challenge induced similar effects to i.c. /nasal challenge but with a slighter extent. Taken together, these findings show that repeated nasal BCG vaccination accelerates dural lymphatics development in neonatal mice, especially in the presence of neonatal i.c. BCG injection.
Metabolic adaptation to progressive mitochondrial dysfunction in aging POLGD257A mice

A decline in mitochondrial function is associated with neurodegeneration and aging. Progressive mitochondrial defects have diverse metabolic consequences that could drive some of the pathophysiological changes that occur with aging. Here, we comprehensively characterized metabolic alterations in PolgD257A mitochondrial DNA mutator mice. Plasma alanine increased dramatically with time, with lactate and other organic acids accumulating to a lesser extent. These changes were reflective of increased glycolysis, rapid gluconeogenesis, and hypoglycemia. Tracing with [15N]ammonium revealed impairment of the urea cycle and diversion to purine catabolism. We also measured alterations in the lipidome, observing a general reduction in canonical lipids and the accumulation of 1-deoxysphingolipids, which are synthesized from alanine via promiscuous serine palmitoyltransferase activity. Consistent with 1-deoxysphingolipid’s association with peripheral neuropathy, PolgD257A mice exhibited thermal hypoalgesia. These results highlight the distinct changes that occur in carbon and nitrogen metabolism upon mitochondrial impairment and key metabolic mechanisms which can drive aging-associated neuropathy.
Inter-Subject Correlation during New Music Listening: A Study of Electrophysiological and Behavioral Responses to Steve Reich’s Piano Phase

Musical minimalism utilizes the temporal manipulation of restricted collections of rhythmic, melodic, and/or harmonic materials. One example, Steve Reich’s Piano Phase, offers listeners readily audible formal structures containing unpredictable events at local levels. Pattern recurrences may generate strong expectations which are violated by small temporal and pitch deviations. A hyper-detailed listening strategy prompted by these minute deviations stands in contrast to the type of listening engagement typically cultivated around functional tonal Western music. Recent research has suggested that the inter-subject correlation (ISC) of electroencephalographic (EEG) responses to natural audio-visual stimuli objectively indexes a state of “engagement”, demonstrating the potential of this approach for analyzing music listening. But can ISCs capture engagement with minimal music, which features less obvious expectation formation and has historically received a wide range of reactions? To approach this question, we collected EEG and continuous behavioral (CB) data while 30 adults listened to an excerpt from Steve Reich’s Piano Phase, as well as three controlled manipulations and a popular-music remix of the work. Our analyses reveal that EEG and CB ISC are highest for the remix stimulus and lowest for our most repetitive manipulation. In addition, we found no statistical differences in overall EEG ISC between our most musically meaningful manipulations and Reich’s original piece. We also found that aesthetic evaluations corresponded well with overall EEG ISC. Finally we highlight co-occurrences between stimulus events and time-resolved EEG and CB ISC. We offer the CB paradigm as a useful analysis measure and note the value of minimalist compositions as a limit case for studying music listening using EEG ISC. We show that ISC is less effective at measuring engagement with this minimalist stimulus than with popular music genres and argue that this may be due to a difference between the type of engagement measured by ISC and the particular engagement patterns associated with minimalism.
Influenca: a gamified assessment of value-based decision-making for longitudinal studies

Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. Here, we developed an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) for repeated assessment over weeks. In this task, players have to identify the most effective medication by selecting the best option after integrating offered points with changing probabilities (according to random Gaussian walks). Participants can complete up to 31 levels with 150 trials each. To encourage replay on their preferred device, in-game screens provide feedback on the progress. Using an initial validation sample of 127 players (2904 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show low to medium intra-class correlations (ICC: 0.22-0.52), indicating substantial within- and between-subject variance. Notably, state items showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.
Design of the recombinant influenza neuraminidase antigen is crucial for protective efficacy

Supplementing influenza vaccines with recombinant neuraminidase (rNA) remains a promising approach for improving the suboptimal efficacy. However, correlations among rNA designs, properties, and protection have not been systematically investigated. Here, we performed a comparative analysis of several rNAs produced from different construct designs using the baculovirus/insect cell system. The rNAs were designed with different tetramerization motifs and NA domains from a recent H1N1 vaccine strain (A/Brisbane/02/2018) and were analyzed for enzymatic properties, antigenicity, thermal and size stability, and protection in mice. We found that rNAs containing the NA head-domain versus the full-ectodomain possess distinct enzymatic properties and that the molecular size stability is tetramerization domain-dependent, whereas protection is more contingent on the combination of the tetramerization and NA domains. Following single-dose immunizations, a rNA possessing the full-ectodomain, non-native enzymatic activity, and the tetramerization motif from the human vasodilator-stimulated phosphoprotein provided substantially higher protection than a rNA possessing the head-domain, native activity and the same tetramerization motif. In contrast, these two rNAs provided comparable protection when the tetramerization motif was exchanged with the one from the tetrabrachion protein. These findings demonstrate that the rNA design is crucial for the protective efficacy and should be thoroughly evaluated for vaccine development, as the unpredictable nature of the heterologous domain combination can result in rNAs with similar key attributes but vastly differ in protection. IMPORTANCE For several decades it has been proposed that influenza vaccines could be supplemented with recombinant neuraminidase (rNA) to improve the efficacy. However, some key questions for manufacturing stable and immunogenic rNA remain to be answered. We show here that the tetramerization motifs and NA domains included in the rNA construct design can have a profound impact on the biochemical, immunological and protective properties. We also show that the single-dose immunization regimen is more informative for assessing the rNA immune response and protective efficacy, which is surprisingly more dependent on the specific combination of NA and tetramerization domains than common attributes for evaluating NA. Our findings may help to optimize the design of rNAs that can be used to improve or develop influenza vaccines.
ATXN10 Is Required for Embryonic Heart Development and Maintenance of Epithelial Cell Phenotypes in the Adult Kidney and Pancreas

Atxn10 is a gene known for its role in cytokinesis during the cell cycle and is associated with Spinocerebellar Ataxia (SCA10), a slowly progressing cerebellar syndrome caused by an intragenic pentanucleotide repeat expansion. Atxn10 is also implicated in the ciliopathy syndromes Nephronophthisis (NPHP) and Joubert Syndrome (JBTS), which are caused by disruption of cilia function leading to nephron loss, impaired renal function, and cerebellar hypoplasia. How Atxn10 disruption contributes to these disorders remains unknown. Here we generated Atxn10 congenital and conditional mutant mouse models. Our data indicate that while ATXN10 protein can be detected around the base of the cilium as well as in the cytosol, its loss does not cause overt changes in cilia formation or morphology. Congenital loss of Atxn10 results in embryonic lethality around E10.5 associated with pericardial effusion and loss of trabeculation. Similarly, tissue specific loss of ATXN10 in the developing endothelium (Tie2-Cre) and myocardium (cTnT-Cre) also results in embryonic lethality with severe cardiac malformations occurring in the latter. Using an inducible Cagg-CreER to disrupt Atxn10 systemically, we show that ATXN10 is also required for survival in adult mice. Loss of ATXN10 results in severe pancreatic and renal abnormalities leading to lethality within a few weeks post ATXN10 deletion in adult mice. Evaluation of these phenotypes further identified rapid epithelial to mesenchymal transition (EMT) in these tissues. In the pancreas, the phenotype includes signs of both acinar to ductal metaplasia and EMT with aberrant cilia formation and severe defects in glucose homeostasis related to pancreatic insufficiency or defects in feeding or nutrient intake. Collectively this study identifies ATXN10 as an essential protein for survival.
Validation of predicted anonymous proteins simply using Fisher’s exact test

Motivation Genomes sequencing has become the primary (and often the sole) experimental method to characterize newly discovered organisms, in particular from the microbial world (bacteria, archaea, viruses). This generates an ever increasing number of predicted proteins the existence of which is unwarranted, in particular among those without homolog in model organisms. As a last resort, the computation of the selection pressure from pairwise alignments of the corresponding “Open Reading Frames” (ORFs) can be used to validate their existences. However, this approach is error-prone, as not usually associated with a significance test. Results We introduce the use of the straightforward Fisher’s exact test as a post processing of the results provided by the popular CODEML sequence comparison software. The respective rates of nucleotide changes at the non-synonymous vs. synonymous position (as determined by CODEML), are turned into entries into a 2×2 contingency table, the probability of which is computed under the Null hypothesis that they should not behave differently if the ORFs do not encode actual proteins. Using the genome sequences of two recently isolated giant viruses, we show that strong negative selection pressures do not always provide a solid argument in favor of the existence of proteins. Contact Jean-Michel.Claverie@univ-amu.fr