Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling pollinator and nonpollinator selection on flower colour variation

Flower colour variation is ubiquitous within and between populations, which is why it has long been a focal point for studies of natural selection. This body of work has uncovered a wide range of selective agents, including pollinators, herbivores and various abiotic factors. Nevertheless, we lack an integrative framework for predicting the phenotypic outcome in terms of floral pigmentation when these forces act collectively and often in opposition. We here present such a framework through a model that incorporates selection on pigmentation at the vegetative phase (i.e. through survival to reproduction) and at the flowering phase (i.e. on pollinator attraction). We focus on anthocyanins as common class of pigments although the model is equally applicable to any compounds that can be jointly expressed in vegetative tissue and in flowers. We explore the dynamics of our model in a theoretical context and in four scenarios based on classic systems for studying selection on flower colour. Our model predicts that pollinators are the main driver for flower colour evolution, but selection on seedling survival plays a major role in the absence of pollen limitation, that is, if pollinator abundance is sufficiently high, or if pollinator preference is absent or weak (high variance in colour preference). In each of the case studies, our model recovered the predicted patterns of fitness for each floral morph given the strength and nature of selection. This work suggests that selection at the vegetative phase must act alone or be exceptionally strong to negate pollinator preference for particular colours. Nevertheless, the influence of differential survival associated with anthocyanin production leaves a clear signature on the fitness curves, suggesting that nonpollinator agents of selection can often be detected from empirical data. Synthesis: Overall, the application of this model to empirical systems will be key for understanding how flower colour diversity evolves and for predicting how changes in climate and pollinator communities may jointly alter evolutionary trajectories.
Bayesian updating for predictions of delayed strains of large concrete structures: influence of prior distribution

Abstract The aging of large concrete structures such as Nuclear Containment Buildings (NCB) or bridges involves a continuous strain evolution in time, which may affect their durability, safety and the safety of their environment. Then, the evaluation of structural integrity requires an accurate assessment of the long-term strain level. When considering probabilistic analysis of the delayed mechanical behavior of large concrete structures, the prediction results may present large uncertainties, which do not provide clear indicators aiming at supporting decisions related to structures’ maintenance. In this context, Bayesian updating enables to reduce uncertainties, by combining a prior state of knowledge with noisy monitoring data of the structure response. It requires the definition of a prior probability distribution, which summarizes all available information before collecting monitoring data. In former work concerning Bayesian approaches applied to the analysis of delayed strains, the prior distribution is usually defined through expert judgement, which constitutes a quite subjective process which may have a significant influence on Bayesian updating results. Moreover, the previously cited work involved strong hypotheses related to observation noise, which is usually assumed to be perfectly known. The present contribution aims at evaluating the influence of prior distributions defined by expert judgement on Bayesian updating results, through an illustrative case study of a well instrumented 1:3 scale NCB. The present work proposes also a Bayesian framework suitable for cases where the observation noise of data is unknown. The influence of the amount of monitoring data on the uncertainty reduction provided by Bayesian updating is also studied. Results underline that the modeling choices of the analyst are of paramount importance in the framework of long-term strains predictions, regardless the quantity of available data. Furthermore, results also suggest that Bayesian updating is well suitable for providing significant uncertainty reduction, even in the case of structures which dispose of a limited amount of monitoring data.
Discovering motifs and genomic patterns with SMT: a high-performance data structure for counting kmers

Motivation The search for conserved motifs in DNA sequences is an important problem in bioinformatics. The growing availability of large-scale genomic data poses significant challenges for computational biology, particularly in terms of efficiency in analysis, kmer identification, and noise presence. The detection of conserved motifs and patterns in DNA sequences is crucial for understanding gene functions and regulations. Therefore, it is essential to develop a data structure that can handle these large volumes of information and provide accurate and fast results. Results We present SMT, an innovative tool designed to efficiently store and count kmers, optimizing memory usage and computation time. The application of SMT has also proven effective in discovering motifs in noisy datasets, allowing the identification of conserved regions in sequences. Furthermore, SMT enables exact searches in constant time and recovers the most abundant k-mers, as well as performs approximate searches in linear time to find fragments with up to d mutations. This approach facilitates large-scale data analysis and provides important insights into the conserved properties of biological sequences. The application of SMT in motif discovery demonstrates its potential to drive research in bioinformatics and genomics. Supplementary data and results are available to provide additional information and support the conclusions presented in this work. Availability and implementation The source code of the presented method is publicly available at https://github.com/jadermcg/SMT.
Few-shot learning: temporal scaling in behavioral and dopaminergic learning

How do we learn associations in the world (e.g., between cues and rewards)? Cue-reward associative learning is controlled in the brain by mesolimbic dopamine1–4. It is widely believed that dopamine drives such learning by conveying a reward prediction error (RPE) in accordance with temporal difference reinforcement learning (TDRL) algorithms5. TDRL implementations are “trial-based”: learning progresses sequentially across individual cue-outcome experiences. Accordingly, a foundational assumption—often considered a mere truism—is that the more cuereward pairings one experiences, the more one learns this association. Here, we disprove this assumption, thereby falsifying a foundational principle of trial-based learning algorithms. Specifically, when a group of head-fixed mice received ten times fewer experiences over the same total time as another, a single experience produced as much learning as ten experiences in the other group. This quantitative scaling also holds for mesolimbic dopaminergic learning, with the increase in learning rate being so high that the group with fewer experiences exhibits dopaminergic learning in as few as four cue-reward experiences and behavioral learning in nine. An algorithm implementing reward-triggered retrospective learning explains these findings. The temporal scaling and few-shot learning observed here fundamentally changes our understanding of the neural algorithms of associative learning.
An approach for relating social structure to cognitive structure

It is proposed that the decision making process is intrinsically formulative in nature; i.e., for the individual the crux of the process is in the development of a frame, a knowledge base, that can be used to make the decision, rather than the evaluation of the alternatives ’per se’. A two stage model of decision making is forwarded. In the first stage the individual develops his frame. This is the critical, formulative, stage and dependent on the socio‐cultural environment. In the second stage the individual makes a decision by evaluating his frame. This stage is mechanical, evaluative, and determined by the frame formulated during the first stage. A consequence of the basic thesis is that the social and cognitive processes can not be decoupled if we are to understand decision making behavior. However, there are few methods currently available that allow the researcher to look at the relationship between the social and the cognitive process. Herein, a set of methods that permit the researcher to look at .
Machine Learning-aided Discovery of Novel Chemotype Antagonists for G Protein-coupled Receptors: The Case of the Adenosine A2A Receptor

Identifying the correct chemotype of ligands targeting receptors (i.e., agonist or antagonist) is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson’s disease and cancer. The identified antagonists were tested here in a radioligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310±23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.
From southern Africa and beyond: historical biogeography of a monocotyledonous bulbous geophyte

Aim Within sub-Saharan Africa, plants inhabiting more seasonal and arid landscapes showcase unique distributional patterns that hint at fascinating evolutionary histories. The Ledebouriinae (Scilloideae, Asparagaceae) are widespread throughout such climates in sub-Saharan Africa, and Madagascar, the Middle East, India, and Sri Lanka. Long-distance dispersal has been hypothesized as leading to such a widespread distribution; however, low taxon sampling and taxonomic uncertainties have made uncovering the history of the Ledebouriinae difficult. Here, using the most comprehensive sampling of the lineage to date, we hypothesize that both vicariance and dispersal events impacted the biogeographical history of these bulbous monocots within and outside of Africa. Location Sub-Saharan Africa, Madagascar, Asia Taxon Ledebouriinae (Scilloideae, Asparagaceae) Methods We infer age estimates using penalized likelihood as implemented in treePL. Capitalizing on our broad geographic sampling, we use ‘BioGeoBEARS’ to reconstruct ancestral ranges and investigate the role of vicariance and dispersal. Results Our results suggest the Ledebouriinae originated within the past ∼30 myr in southeastern sub-Saharan Africa, with the major subclades arising soon thereafter. Although long-distance dispersal cannot be fully ruled out, our results lead us to hypothesize vicariance was the major process responsible for the current distribution of Ledebouria in Eurasia. We recover two distinct Ledebouria groups that overlap in eastern Africa, but are divided into mostly northern and southern clades with divergent biogeographical histories, and each showing an independent dispersal to Madagascar. A similar north-south split is seen in Drimiopsis. Additionally, we recover a complex biogeographic history in the predominantly sub-Saharan African Ledebouria clade, with a rapid radiation estimated at ∼14 mya. Main conclusions We recover evidence to suggest that the expansion of seasonal rainfall and aridity in sub-Saharan Africa, coupled with orogeny, may have fostered the diversification of the Ledebouriinae and many subclades. Miocene-driven aridification may have caused fragmentation of a once widespread distribution that led to their occurrence in Eurasia.
Mean Square Convergence of Multi-innovation Projection Algorithm for Time-varying Multivariable Systems

Mean Square convergence of the multi-innovation projection identification algorithm for time- varying multivariable systems is proved by using stochastic process theory,and the formulae of computing the upper bounds of the parameter estimation error given by the niulti-innovation projection identification algorithm are derived. The way of choosing the data window length is stated for the minimum upper bound of the mean square parameter estimation error to be obtained.
Background environment modulates motor contagions in humans

Motor contagions refer to implicit effects in one’s actions induced by the observation of actions made by others. A plethora of studies over the last two decades have exhibited that observed, as well as predicted, actions can induce various kinds of motor contagions in a human observer. However, motor contagion has always been investigated in regard to different features of an observed action and it remains unclear whether the environment, in which an observed action takes place, modulates motor contagions as well. Here we investigated the effect of the observed environment on motor contagions using an empirical hand steering task in which the participants were required to move a cursor through visual channels of different shapes. We observed the movement time of observers to be influenced by both the movement of the cursor they observed, as well as the background (channel shape) in which the cursor movement was observed. Observers consistently made faster movements after observing steering movements in a ‘narrowing’ channel compared to a ‘widening’ channel. These results show a distinct effect of the environment, in which an observed action occurs, on one’s own movement.
Uniformity of performance during the collection of maximum voluntary contraction tasks for the muscles of the forearm

Electromyographic (EMG) signals are used to gain insight into muscle activation patterns and thus neuromuscular control. To allow for comparisons between studies and participants, the EMG signal is generally normalised, with the signal obtained during a task that is designed to elicit maximum voluntary contraction (MVC) frequently used as the basis. Recommendations for how to collect MVCs have been made; however, previous studies have not been able to determine if the EMG variation noted within a population was due to different muscle activation patterns, or the tasks being performed differently, or other variables such as skin impedance, or the stochastic nature of electromyography. EMG signals were recorded during hand-wrist tasks selected to elicit MVCs in the muscles of the forearm – pull up, push down, radial pull, ulnar pull, pull, pronation, finger flexion, finger extension, and grip – as well as two activities of daily living – pouring a glass of water from a jug and turning a key in a lock. A load cell mounted to a statically mounted handle was used to record contemporaneously the forces and moments exerted by participants in pull up, push down, radial pull, ulnar pull, pull, pronation, finger flexion, and finger extension tasks. Ninety percent of tasks yielded the expected load cell outputs for the directed tasks and thus were considered as having been performed correctly. The tasks performed incorrectly were not the same for all participants, nor were they all performed by the same participants. Of note was that there were instances when a task was performed incorrectly but still an expected MVC was achieved. The EMG signals showed similar variation to that seen in previous studies. However, the applied forces and moments did not appear to explain the variation seen in the tasks that elicited MVCs. The results of this study indicate that different muscle activation patterns may be used to exert the same force by the hand. Thus, it may not be possible for a given task to elicit MVC in the same muscle in all people. However, by using several activities, MVCs for the forearm muscles may be obtained for most of the population. Beyond designing EMG protocols, the results of this study suggest that people have unique muscle activation patterns and raise questions as to whether this is a result of physiology or conditioning.
An integrated map of genetic variation from 1,062 wheat genomes

The construction of a high-quality wheat genome variation map is important to wheat genetic studies and breeding. In this study, we developed the second-generation whole-genome genetic variation map of wheat (VMap 2.0) by integrating whole-genome sequencing data of 1,062 diverse wheat accessions from 20 species/subspecies. VMap 2.0 contains 195.96 million single nucleotide polymorphisms (SNPs), 2.22 million insertions, and 4.72 million deletions, achieving a high density of variation map in which one variant exists in every 73 base pairs on average. By systematically analyzing the phylogenetic relationships and genetic diversity of tetraploid wheat, hexaploid wheat, and diploid goatgrass (Aegilops tauschii), we found that the genetic diversity of wild emmer wheat was 2.4 times higher than that of common wheat. In contrast, the genetic diversity of diploid goatgrass is 7.8 times higher than the D subgenome of hexaploid wheat. With the high-density genetic variations, VMap 2.0 is anticipated to facilitate high-resolution trait dissection and expedite prediction-based breeding of wheat.
CRD: a De novo Design algorithm for prediction of Cognate Protein Receptors for small molecule ligands

While predicting a new ligand to bind to a protein is possible with current methods, the converse of predicting a receptor for a ligand is highly challenging, except for very closely-related known protein-ligand complexes. Predicting a receptor for any given ligand will be path-breaking in understanding protein function, mapping sequence-structure-function relationships and for several aspects of drug discovery including studying the mechanism of action of phenotypically discovered drugs, off-target effects and drug repurposing. We use a novel approach for predicting receptors for a given ligand through de novo design combined with structural bioinformatics. We have developed a new algorithm CRD, that has multiple modules which combines fragment-based sub-site finding, a machine learning function to estimate the size of the site, a genetic algorithm that encodes knowledge on protein structures and a physics-based fitness scoring scheme. CRD has a pseudo-receptor design component followed by a mapping component to identify possible proteins that house the site. CRD is designed to cater to ligands with known and unknown complexes. CRD accurately recovers sites and receptors for several known natural ligands including ATP, SAM, Glucose and FAD. It designs similar sites for similar ligands, yet to some extent distinguishes between closely related ligands. More importantly CRD correctly predicts receptor classes for several drugs such as penicillins and NSAIDs. We expect CRD to be a valuable tool in fundamental biology research as well as in the drug discovery and biotechnology industry.
Nucleolar structure connects with global nuclear organization

The nucleolus is a multi-functional nuclear body. To tease out the roles of nucleolar structure without resorting to multi-action drugs, we knocked down RNA polymerase I subunit RPA194 in HeLa cells by siRNA. Loss of RPA194 resulted in nucleolar structural segregation and effects on both nucleolus-proximal and distal nuclear components. The perinucleolar compartment was disrupted, centromere-nucleolus interactions were significantly reduced, and the intranuclear locations of specific genomic loci were altered. Moreover, Cajal bodies, distal from nucleoli, underwent morphological and compositional changes. To distinguish whether these global reorganizations are the results of nucleolar structural disruption or inhibition of ribosome synthesis, the pre-ribosomal RNA processing factor, UTP4, was also knocked down, which did not lead to nucleolar segregation, nor the intranuclear effects seen with RPA195A knockdown, demonstrating that they do not arise from a cessation of ribosome synthesis. These findings point to a commutative system that links nucleolar structure to the maintenance and spatial organization of certain nuclear bodies and genomic loci.
Neuronal and Astrocyte Insulin-like Growth Factor-1 Signaling Differentially Modulates Ischemic Stroke Damage

Ischemic stroke is a leading cause of death and disability, as therapeutic options for mitigating the long-term deficits precipitated by the event remain limited. Acute administration of the neuroendocrine modulator insulin-like growth factor-1 (IGF-1) attenuates ischemic stroke damage in preclinical models, and clinical studies suggest IGF-1 can reduce the risk of stroke and improve overall outcomes. The cellular mechanism by which IGF-1 exerts this protection is poorly defined, as all cells within the neurovascular unit express the IGF-1 receptor. We hypothesize that the functional regulation of both neurons and astrocytes by IGF-1 is critical in minimizing damage in ischemic stroke. To test this, we utilized inducible astrocyte-specific or neuron-specific transgenic mouse models to selectively reduce IGF-1R in the adult mouse brain prior to photothrombotic stroke. Acute changes in blood brain barrier permeability, microglial activation, systemic inflammation, and biochemical composition of the brain were assessed 3 hours following photothrombosis, and significant protection was observed in mice deficient in neuronal and astrocytic IGF-1R. When the extent of tissue damage and sensorimotor dysfunction was assessed for 3 days following stroke, only the neurological deficit score continued to show improvements, and the extent of improvement was enhanced with additional IGF-1 supplementation. Overall, results indicate that neuronal and astrocytic IGF-1 signaling influences stroke damage but IGF-1 signaling within these individual cell types is not required for minimizing tissue damage or behavioral outcome.
Alloreactivity and autoreactivity converge to support B cell epitope targeting in transplant rejection

Antibody (Ab) responses against human leukocyte antigen (HLA) proteins mismatched between donor and recipient are leading cause of allograft loss in kidney transplantation. However, therapies targeting alloreactive B cell and Ab-secreting cell (ASC) are lacking, motivating the need to understand how to prevent and abrogate these alloresponses. Using molecular, structural, and proteomic techniques, we profiled the B cell response in a kidney transplant recipient with antibody-mediated rejection and graft loss. We found that this response spanned the rejected organ and peripheral blood, stimulated the differentiation of multiple B cell subsets, and produced a high-affinity, donor-specific, anti-HLA response. We found epitopic immunodominance that relied on highly exposed, solvent-accessible mismatched HLA residues as well as structural and biomolecular evidence of autoreactivity against the recipient’s self-HLA allele. These alloreactive and autoreactive signatures converged in the recipient’s circulating donor-specific Ab repertoire, suggesting that rejection requires both the recognition of non-self and breaches of tolerance to lead to alloinjury and graft loss.