Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Bayesian network model to predict effects of pesticides on aquatic community endpoints in a rice field – A southern European case study

In recent years, Bayesian network (BN) models have become more popular as a tool to support probabilistic environmental risk assessments (ERA). They can better account for and communicate uncertainty compared to the deterministic approaches currently used in traditional ERA. In this study, we used the BN as a meta-model to predict the potential effect of various pesticides on different biological levels in the aquatic ecosystem. The meta-model links the inputs and outputs of a process-based exposure model (RICEWQ), that is run with various scenarios combination built on meteorological, hydrological, and agricultural scenarios, and a probabilistic case-based effect model (PERPEST), which bases its prediction on a database of microcosm and mesocosm experiments. The research focused on the pesticide exposure in rice fields surrounding a Spanish Natural Park, considering three selected pesticides for this case study: acetamiprid (insecticide), MCPA (herbicide), and azoxystrobin (fungicide). For each of the pesticide types, the developed BN model enabled the prediction of their effects on biological endpoints, endpoint groups, and community in an aquatic ecosystem. Also, it enables comparison between the different pesticide types, their effects on endpoint groups and community. While directly linking future scenarios of climate and agricultural practice to the exposure concentration and indirectly linking them to the effect on biological endpoints as well as community. In summary, azoxystrobin and MCPA seem to have a higher predicted risk for the community with at least one of the biological endpoint being effected compared to acetamiprid. Generally, the developed approach facilitates the communication of uncertainties associated with the predicted effect on different biological levels of the aquatic ecosystem. This transparency in all model components can aid risk management and decision making.
Bayesian linear models with unknown design over finite alphabets

Our topic is the reconstruction of the unknown matrices S and ω for the multivariate linear model Y = Sω + ε under the assumption that the entries of S are drawn from the finite alphabet 𝔄 = 0, 1 and ω is a weight matrix. While a frequentist method has recently been proposed for this purpose, a Bayesian approach seems also desirable. We therefore provide a new hierarchical Bayesian method for this inferential task. Our approach provides estimates of the posterior that may be used to quantify uncertainty. Since matching permutations in both S and ω lead to the same reconstruction Sω, we introduce an order-preserving shrinkage prior to establish identifiability with respect to permutations.
RANDOM SEQUENTIAL ADSORPTION OF MIXTURES OF DIMERS AND MONOMERS ON A PRE-TREATED BETHE LATTICE

We report studies of random sequential adsorption on the pre-patterned Bethe lattice. We consider a partially covered Bethe lattice, on which monomers and dimers deposit competitively. Analytical solutions are obtained and discussed in the context of recent efforts to use pre-patterning as a tool to improve self-assembly in micro- and nano-scale surface structure engineering.
The IRE1α-XBP1 Signaling Axis Promotes Glycolytic Reprogramming in Response to Inflammatory Stimuli

The immune system must be able to tailor its response to different types of pathogens in order to eliminate them and protect the host. When confronted with bacterial pathogens, macrophages, frontline defenders in the immune system, switch to a glycolysis-driven metabolism to carry out their antibacterial functions. ABSTRACT Immune cells must be able to adjust their metabolic programs to effectively carry out their effector functions. Here, we show that the endoplasmic reticulum (ER) stress sensor Inositol-requiring enzyme 1 alpha (IRE1α) and its downstream transcription factor X box binding protein 1 (XBP1) enhance the upregulation of glycolysis in classically activated macrophages (CAMs). The IRE1α-XBP1 signaling axis supports this glycolytic switch in macrophages when activated by lipopolysaccharide (LPS) stimulation or infection with the intracellular bacterial pathogen Brucella abortus. Importantly, these different inflammatory stimuli have distinct mechanisms of IRE1α activation; while Toll-like receptor 4 (TLR4) supports glycolysis under both conditions, TLR4 is required for activation of IRE1α in response to LPS treatment but not B. abortus infection. Though IRE1α and XBP1 are necessary for maximal induction of glycolysis in CAMs, activation of this pathway is not sufficient to increase the glycolytic rate of macrophages, indicating that the cellular context in which this pathway is activated ultimately dictates the cell’s metabolic response and that IRE1α activation may be a way to fine-tune metabolic reprogramming. IMPORTANCE The immune system must be able to tailor its response to different types of pathogens in order to eliminate them and protect the host. When confronted with bacterial pathogens, macrophages, frontline defenders in the immune system, switch to a glycolysis-driven metabolism to carry out their antibacterial functions. Here, we show that IRE1α, a sensor of ER stress, and its downstream transcription factor XBP1 support glycolysis in macrophages during infection with Brucella abortus or challenge with Salmonella LPS. Interestingly, these stimuli activate IRE1α by independent mechanisms. While the IRE1α-XBP1 signaling axis promotes the glycolytic switch, activation of this pathway is not sufficient to increase glycolysis in macrophages. This study furthers our understanding of the pathways that drive macrophage immunometabolism and highlights a new role for IRE1α and XBP1 in innate immunity.
Structural elements facilitate extreme long-range gene regulation at a human disease locus

Enhancer clusters overlapping disease-associated mutations in Pierre Robin sequence (PRS) patients regulate SOX9 expression at genomic distances over 1.25 megabases. We applied optical reconstruction of chromatin architecture (ORCA) imaging to trace 3D locus topology during PRS-enhancer activation. While we observed pronounced changes in locus topology between cell-types, analysis of single chromatin fiber traces revealed that these ensemble-average differences arise not from the presence of cell-type unique conformations, but through changes in frequency of commonly sampled topologies. We further identified two CTCF-bound elements, internal to the SOX9 topologically associating domain, which are positioned near its 3D geometric center and bridge enhancer-promoter contacts in a series of chromatin loops. Ablation of these elements results in diminished SOX9 expression and altered domain-wide contacts. Polymer models with uniform loading across the domain and frequent cohesin collisions recapitulate this multiloop, centrally clustered geometry, suggesting a mechanism for gene regulation over ultralong ranges. Four short bullet points that convey the key message of the paper SOX9 domain topology dynamically changes during a developmental transition Structural elements promote TAD-wide interactions, stripe formation and transcription Structural elements are CTCF-dependent and situated centrally in the 3D TAD structure Polymer simulations of multi-loop model best recapitulate topological features
Potential role of immediate early genes Egr-1, Hr-38 and Kakusei in the foraging behavior and learning of in honeybees

The foraging behavior of honeybees is one of the most systematically composed behaviors among social insects which are admirable to watch. The main purpose of honeybee foraging is to collect food for their colony and since ancient days honeybee products have been used for various medicinal purposes (Singh and Takhellambam 2021). During foraging, honeybees gather information and transmit to their colony members regarding the location, distance, and profitability of forage sites with the help of unique movements called waggle dance. The capacities of honeybees time memory enable the foragers to return to a good food source in anticipation of the time of day. This highly intellectual, dynamic, and well-coordinated behavior of honeybees makes them to be one of the best choices of behavioral model to study various aspects of dynamic behaviors. As a result, vast knowledge in honeybee behavior has been accumulated and several recent studies immerge towards finding the underpinning regulatory biology of honeybee foraging behaviors. Immediate early genes (IE) genes are well documented neural markers and their promising roles in honeybee foraging have been demonstrated. Two of our recent studies showed three IE genes Egr-1, Hr38 and Kakusei involvement during the daily foraging of honeybees. This finding has provided an avenue to further explore and identify the regulatory genes/proteins and neurons that underlie a specific behavior such as learning, memory, communication, and interaction etc. In this study we further analyze our previous published data to examine interaction of the three genes during the daily foraging of honeybees.
Clusterization and deformation of multi-
Λ
 hypernuclei within a relativistic mean-field model

Deformed multi-$\mathrm{\ensuremath{\Lambda}}$ hypernuclei are studied within a relativistic mean-field model. In this paper, I take some $N=Z$ ``hyper-isotope'' chains, i.e., $_{\phantom{\rule{4pt}{0ex}}\phantom{\rule{4pt}{0ex}}n\mathrm{\ensuremath{\Lambda}}}^{8+n}\mathrm{Be},\phantom{\rule{0.16em}{0ex}}_{\phantom{\rule{4pt}{0ex}}\phantom{\rule{4pt}{0ex}}\phantom{\rule{4pt}{0ex}}n\mathrm{\ensuremath{\Lambda}}}^{20+n}\mathrm{Ne}$, and $_{\phantom{\rule{4pt}{0ex}}\phantom{\rule{4pt}{0ex}}\phantom{\rule{4pt}{0ex}}n\mathrm{\ensuremath{\Lambda}}}^{28+n}\mathrm{Si}$ systems where $n=2,\phantom{\rule{0.16em}{0ex}}4$ for Be and $n=2,\phantom{\rule{0.16em}{0ex}}8$ for Ne and Si. A sign of two-$_{2\mathrm{\ensuremath{\Lambda}}}^{6}\mathrm{He}$ cluster structure is observed in the two-body correlation in $_{4\mathrm{\ensuremath{\Lambda}}}^{12}\mathrm{Be}$. In the Ne hyper-isotopes, the deformation is slightly reduced by addition of $\mathrm{\ensuremath{\Lambda}}$ hyperons, whereas it is significantly reduced or even disappears in the Si hyper-isotopes.
mRNA therapy restores ureagenesis and corrects glutathione metabolism in argininosuccinic aciduria

Argininosuccinate lyase (ASL) is a key enzyme integral to the hepatic urea cycle which is required for ammonia detoxification, and the citrulline-nitric oxide (NO) cycle for NO production. ASL deficient patients present with argininosuccinic aciduria (ASA), an inherited metabolic disease with hyperammonaemia and a chronic systemic phenotype with neurocognitive impairment and chronic liver disease. ASL deficiency as an inherited model of systemic NO deficiency, shows enhanced nitrosative and oxidative stress. Here, we describe the dysregulation of glutathione biosynthesis and upstream cysteine utilization in ASL-deficient patients and mice using targeted metabolomics and in vivo positron emission tomography (PET) imaging using (S)-4-(3-18F-fluoropropyl)-L-glutamate ([18F]FSPG). Upregulation of cysteine metabolism contrasted with glutathione depletion and down-regulated antioxidant pathways. hASL mRNA encapsulated in lipid nanoparticles corrected and rescued the neonatal and adult Asl-deficient mouse phenotypes, respectively, enhancing ureagenesis and glutathione metabolism and ameliorating chronic liver disease. We further present [18F]FSPG PET as a novel non-invasive diagnostic tool to assess liver disease and therapeutic efficacy in ASA. These findings support clinical translation of mRNA therapy for ASA.
Improving classification of correct and incorrect protein–protein docking models by augmenting the training set

Protein-protein interactions drive many relevant biological events, such as infection, replication, and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling, like protein-protein docking, can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling produces a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers. Using weak supervision, we developed a data augmentation method that we named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 MCC on the test set, surpassing the state-of-the-art scoring functions.
Npr3 regulates neural crest and cranial placode progenitors formation through its dual function as clearance and signaling receptor

Natriuretic peptide signaling has been implicated in a broad range of physiological processes, regulating blood volume and pressure, ventricular hypertrophy, fat metabolism, and long bone growth. Here we describe a completely novel role for natriuretic peptide signaling in the control of neural crest (NC) and cranial placode (CP) progenitors formation. Among the components of this signaling pathway, we show that natriuretic peptide receptor 3 (Npr3) plays a pivotal role by differentially regulating two developmental programs through its dual function as clearance and signaling receptor. Using a combination of MO-based knockdowns, pharmacological inhibitors and rescue assays we demonstrate that Npr3 cooperate with guanylate cyclase natriuretic peptide receptor 1 (Npr1) and natriuretic peptides (Nppa/Nppc) to regulate NC and CP formation, pointing at a broad requirement of this signaling pathway in early embryogenesis. We propose that Npr3 acts as a clearance receptor to regulate local concentrations of natriuretic peptides for optimal cGMP production through Npr1 activation, and as a signaling receptor to control cAMP levels through inhibition of adenylyl cyclase. The intracellular modulation of these second messengers therefore participates in the segregation of NC and CP cell populations.
A novel method for measuring the priming effect of rewarding brain stimulation

The priming effect of rewarding brain stimulation persists following blockade of D2-like dopamine receptors. Abstract Receipt of an intense reward boosts motivation to work for more of that reward. This phenomenon is called the priming effect of rewards . Previous research on the priming effect of electrical brain stimulation utilized a runway paradigm in which running speed serves as the measure of motivation. One goal of the present study was to develop a method for measuring the priming effect of electrical brain stimulation in conventional operant-conditioning chambers. The speed with which a two-lever response chain was executed serves as the measure of motivation. Using this new paradigm, we document how the priming effect depends on both the strength and cost of the reward. In a second experiment, we assessed a modification of the new method that entails self-administered priming stimulation and alternating blocks of primed and unprimed trials. Reliable, consistent priming effects of substantial magnitude were obtained in the modified paradigm, which is closely analogous to the runway paradigm. The modified paradigm can be readily implemented in the standard operant-conditioning chambers available in most behavioral neuroscience laboratories.
Segment Based Pattern Analysis Reveals a Persistent Regular Rhythm in the Motion Artifact Record of the MIT-BIH Noise Stress Test Database

Background The MIT-BIH Noise Stress Test Database (NSTDB) is a publicly available resource for testing QRS detection algorithms. Serial QRS detection algorithms applied to the NSTDB have apparently failed to detect the presence of a possible heartbeat like rhythm associated with peaks that the NSTDB classifies as noise. The failure to detect this rhythm may arise from the difficulty associated with interpreting noisy RR interval time series produced by serial QRS detection schemes. Algorithm Summary and Experiment To extract rhythm information from noisy peak time/RR interval time series, a peak space signal is created with triangular pulses centered on peaks located by a serial QRS detection algorithm such as Pan-Tompkins. The peak space signal is autocorrelated over 20 s segments and the primary (non-origin) peak in the autocorrelation signal is located. In the presence of reasonably regular sinus rhythm, this peak corresponds to a fundamental RR interval present throughout the 20 s segment. This peak time processing method was applied to the Pan-Tompkins QRS detections in the motion artifact record of the NSTDB. To compare the results to a different algorithm capable of detecting patterns at the segment level, a previously described pattern-based heartbeat detection scheme (Temporal Pattern Search, or “TEPS”) was applied in both single and multiple channel modes to the NSTDB motion artifact record. Results Both the Pan-Tompkins/autocorrelation method and TEPS detected a persistent rhythm around 1000-1050 ms in both channels throughout the entirety of the motion artifact record. The RR interval correlation between Pan-Tompkins/autocorrelation and single channel TEPS was 0.8 and 0.7 in channels 1 and 2 respectively with p values of 0.
A tale of two paths: The evolution of mitochondrial recombination in bivalves with doubly uniparental inheritance

In most animals, mitochondrial DNA is strictly maternally inherited and non-recombining. One exception to these assumptions is called doubly uniparental inheritance (DUI): a phenomenon involving the independent transmission of female and male mitochondrial genomes. DUI is known only from the molluscan class Bivalvia. The phylogenetic distribution of male mitochondrial DNA in bivalves is consistent with several evolutionary scenarios, including multiple independent gains, losses, and varying degrees of recombination with female mitochondrial DNA. In this study, we use phylogenetic methods to test male mitochondrial DNA origination hypotheses and infer the prevalence of mitochondrial recombination in bivalves with DUI. Phylogenetic modeling using site concordance factors supported a single origin of male mitochondrial DNA in bivalves coupled with recombination acting over long evolutionary timescales. Ongoing mitochondrial recombination is present in Mytilida and Venerida, which results in a pattern of concerted evolution of female and male mitochondrial DNA. Mitochondrial recombination could be favored to offset the deleterious effects of asexual inheritance and maintain mitonuclear compatibility across tissues. Cardiida and Unionida have gone without recent recombination, possibly due to an extension of the COX2 gene in male mitochondrial DNA. The loss of recombination may be neutral but could be connected to the role of M mtDNA in sex determination or sexual development. Our results support recombination events in DUI species may occur throughout their genomes. Future investigations may reveal more complex patterns of inheritance of recombinants, which could explain the retention of signal for a single origination of male mitochondrial DNA in protein coding genes.
Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes

Due to insufficient insulin secretion, patients with type 1 diabetes mellitus (T1DM) are prone to blood glucose fluctuations ranging from hypoglycemia to hyperglycemia. While dangerous hypoglycemia may lead to coma immediately, chronic hyperglycemia increases patients’ risks for cardiorenal and vascular diseases in the long run. In principle, an artificial pancreas – a closed-loop insulin delivery system requiring patients manually input insulin dosage according to the upcoming meals – could supply exogenous insulin to control the glucose levels and hence reduce the risks from hyperglycemia. However, insulin overdosing in some type 1 diabetic patients, who are physically active, can lead to unexpected hypoglycemia beyond the control of common artificial pancreas. Therefore, it is important to take into account the glucose decrease due to physical exercise when designing the next-generation artificial pancreas. In this work, we develop a deep reinforcement learning algorithm using a T1DM dataset, containing data from wearable devices, to automate insulin dosing for patients with T1DM. In particular, we build patient-specific computational models using systems biology informed neural networks (SBINN), to mimic the glucose-insulin dynamics for a few patients from the dataset, by simultaneously considering patient-specific carbohydrate intake and physical exercise intensity.
Structural Basis for Selective Proteolysis of ADAM10 Substrates at Membrane-Proximal Sites

The endopeptidase ADAM10 is a critical catalyst for regulated proteolysis of key drivers of mammalian development and physiology, and for non-amyloidogenic cleavage of the Alzheimer’s precursor protein as the primary α-secretase. ADAM10 function in vivo requires formation of a complex with a C8-tetraspanin protein, with different ADAM10-C8-tetraspanin complexes having distinct substrate selectivity, yet the basis for such selectivity remains elusive. We present here a cryo-EM structure of a vFab-ADAM10-Tspan15 complex, which shows that Tspan15 binding relieves ADAM10 autoinhibition and positions the enzyme active site about 20 Å from the plasma membrane for membrane-proximal substrate cleavage. Cell-based assays of N-cadherin shedding establish that the positioning of the active site by the interface between the ADAM10 catalytic domain and the bound tetraspanin influences selection of the preferred cleavage site. Together, these studies reveal the molecular mechanism underlying selective ADAM10 proteolysis at membrane-proximal sites and offer a roadmap for its modulation in disease.
Differential reduction of neuropathic pain symptoms by mGlu4-mediated neuromodulation of amygdala circuits

Neuropathic pain is a common health problem, resulting in exacerbated response to noxious and non noxious stimuli, as well as impaired emotional and cognitive responses. Unfortunately, neuropathic pain is also one of the most difficult pain syndromes to manage, highlighting the importance of better understanding of the brain regions and neuromodulatory mechanisms involved in its regulation. Among the many interconnected brain areas which process pain, the amygdala is known to play an important role in the integration of sensory and emotional pain signals. Here, we questioned the ability of a recently identified neuromodulatory mechanisms associated to the metabotropic glutamate receptors mGlu4 in the amygdala to modulate neuropathic pain. In a murine model of peripheral mononeuropathy induced by a chronic constriction of the sciatic nerve, we demonstrate that pharmacological activation of amygdala mGlu4 receptors efficiently alleviates sensory and depressive-like symptoms in both male and female mice. Moreover, we reveal a differential modulation of those symptoms, activating mGlu4 receptors in the controlateral amygdala, relatively to the side of the mononeuropathy, is necessary and sufficient to relieve both sensory and depressive-like symptoms while ipsilateral activation solely reduces depressive-like symptoms. Furthermore, using photopharmacology, a recent strategy allowing a precise spatiotemporal photocontrol of deep brain endogenous targets, we further demonstrate the rapid and reversible action of mGlu4-mediated neuromodulation on neuropathic pain symptoms. Finally, coupling photopharmacology and analgesic conditioned place preference, we show an important pain-reducing effect of mGlu4 activation. Taken together, these data highlight the analgesic potential of enhancing amygdala mGlu4 activity to counteract neuropathy in the hope of improving existing treatments.