Miracle-dz/UniFAR-FTUs huggingface dataset:
LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model

This paper presents a novel approach in autonomous robot control, named LLM-BRAIn, that makes possible robot behavior generation, based on operator's commands. LLM-BRAIn is a transformer-based Large Language Model (LLM) fine-tuned from Stanford Alpaca 7B model to generate robot behavior tree (BT) from the text description. We train the LLM-BRAIn on 8,5k instruction-following demonstrations, generated in the style of self-instruct using text-davinchi-003. The developed model accurately builds complex robot behavior while remaining small enough to be run on the robot's onboard microcomputer. The model gives structural and logical correct BTs and can successfully manage instructions that were not presented in training set. The experiment did not reveal any significant subjective differences between BTs generated by LLM-BRAIn and those created by humans (on average, participants were able to correctly distinguish between LLM-BRAIn generated BTs and human-created BTs in only 4.53 out of 10 cases, indicating that their performance was close to random chance). The proposed approach potentially can be applied to mobile robotics, drone operation, robot manipulator systems and Industry 4.0.
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients. This is typically done using local SGD, which helps to improve communication efficiency. However, such a scheme is currently constrained by slow and unstable convergence due to the variety of data on different clients' devices. In this work, we identify three under-explored phenomena of biased local learning that may explain these challenges caused by local updates in supervised FL. As a remedy, we propose FedBR, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges. FedBR has two components. The first component helps to reduce bias in local classifiers by balancing the output of the models. The second component helps to learn local features that are similar to global features, but different from those learned from other data sources. We conducted several experiments to test \algopt and found that it consistently outperforms other SOTA FL methods. Both of its components also individually show performance gains. Our code is available at https://github.com/lins-lab/fedbr.
New Physics in Neutrino Oscillation: Nonunitarity or Nonorthogonality?

Neutrino oscillation phenomenon is a definite evidence of physics beyond the Standard Model (SM) and high precision measurement of neutrino properties will certainly give us clue about what lies beyond the SM. In particular, precise measurements of the mixing matrix elements $U_{\alpha i}$ which relate the neutrino flavor $\alpha$ and mass $i$ eigenstates are crucial since new physics at scale beyond experimental reach can lead to a nonunitary $U$. This in turns results in nonorthogonal neutrino flavor states. How to calculate the oscillation probability in this scenario is an important theoretical issue that will be treated here. We show that probability constructed using theory of projection probability will ensure that the theory remains unitary in time evolution and the probabilities of neutrino of certain flavor being detected as all possible flavor states always sum up to unity. This result is crucial for discovery of new physics through neutrino oscillation phenomena.
Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal

Inter-subject or subject-independent emotion recognition has been a challenging task in affective computing. This work is about an easy-to-implement emotion recognition model that classifies emotions from EEG signals subject independently. It is based on the famous EEGNet architecture, which is used in EEG-related BCIs. We used the ‘Dataset on Emotion using Naturalistic Stimuli’ (DENS) dataset. The dataset contains the 'Emotional Events'- the precise information of the emotion timings that participants felt. The model is a combination of regular, depthwise and separable convolution layers of CNN to classify the emotions. The model has the capacity to learn the spatial features of the EEG channels and the temporal features of the EEG signals variability with time. The model is evaluated for the valence space ratings. The model achieved an accuracy of 73.04%.
Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D Diffusion-based Editor

Recent years have witnessed considerable achievements in editing images with text instructions. When applying these editors to dynamic scene editing, the new-style scene tends to be temporally inconsistent due to the frame-by-frame nature of these 2D editors. To tackle this issue, we propose Control4D, a novel approach for high-fidelity and temporally consistent 4D portrait editing. Control4D is built upon an efficient 4D representation with a 2D diffusion-based editor. Instead of using direct supervisions from the editor, our method learns a 4D GAN from it and avoids the inconsistent supervision signals. Specifically, we employ a discriminator to learn the generation distribution based on the edited images and then update the generator with the discrimination signals. For more stable training, multi-level information is extracted from the edited images and used to facilitate the learning of the generator. Experimental results show that Control4D surpasses previous approaches and achieves more photo-realistic and consistent 4D editing performances. The link to our project website is: https://control4darxiv.github. io/ .
Efficient mapping of RNA‐binding residues in RNA‐binding proteins using local sequence features of binding site residues in protein‐RNA complexes

Protein‐RNA interactions play vital roles in plethora of biological processes such as regulation of gene expression, protein synthesis, mRNA processing and biogenesis. Identification of RNA‐binding residues (RBRs) in proteins is essential to understand RNA‐mediated protein functioning, to perform site‐directed mutagenesis and to develop novel targeted drug therapies. Moreover, the extensive gap between sequence and structural data restricts the identification of binding sites in unsolved structures. However, efficient use of computational methods demanding only sequence to identify binding residues can bridge this huge sequence‐structure gap. In this study, we have extensively studied protein‐RNA interface in known RNA‐binding proteins (RBPs). We find that the interface is highly enriched in basic and polar residues with Gly being the most common interface neighbor. We investigated several amino acid features and developed a method to predict putative RBRs from amino acid sequence. We have implemented balanced random forest (BRF) classifier with local residue features of protein sequences for prediction. With 5‐fold cross‐validations, the sequence pattern derived dipeptide composition based BRF model (DCP‐BRF) resulted in an accuracy of 87.9%, specificity of 88.8%, sensitivity of 82.2%, Mathew's correlation coefficient of 0.60 and AUC of 0.93, performing better than few existing methods. We further validated our prediction model on known human RBPs through RBR prediction and could map ~54% of them. Further, knowledge of binding site preferences obtained from computational predictions combined with experimental validations of potential RNA binding sites can enhance our understanding of protein‐RNA interactions. This may serve to accelerate investigations on functional roles of many novel RBPs.
Comparative genomics reveals the emergence of copper resistance in a non‐pigmented Xanthomonas pathogen of grapevine

Abstract Xanthomonas citri pv. viticola (Xcv) is the causal agent of bacterial canker in grapevine. The pathogen is restricted to India, where it was first reported in the 1970s, and Brazil. In the present study, we report the first complete genome sequence of Xcv LMG965, which is a reference pathotype strain. We also report genome sequences of additional isolates from India and comparative genome‐based studies of isolates from Brazil. Apart from revealing the monophyletic origin of the pathovar, we could also confirm a common frameshift mutation in a gene that is part of the Xanthomonadin pigment biosynthetic gene cluster in all the isolates. The comparative study also revealed multiple intrinsic copper resistance‐related genes in Brazilian isolates, suggesting intense selection, possibly because of heavy and indiscriminate usage of copper as an antimicrobial agent in the orchards. There is also the association of a Tn3‐like transposase in the vicinity of the copper resistance genes, indicating a potential for rapid diversification through horizontal gene transfer events. The findings, along with genomic resources, will allow for systematic genetic and functional studies of Xcv.
Relationship Between Manual Wheelchair Using Skills and Upper Limb Musculoskeletal Disorders in Patients With Spinal Cord Injury

Objectives The aims of this study are to investigate the relationship between wheelchair skills and upper limb pathologies detected by ultrasound and to identify independent predictors of joint pathologies. Design Forty-five patients who were paraplegic because of spinal cord injury were included. Demographic and clinical data were evaluated. The Spinal Cord Independence Measure III and Wheelchair Skills Test Questionnaire was administered. Shoulder, elbow, and wrist joints of both upper limbs were evaluated with ultrasound. Results Nineteen patients (42.2%) had musculoskeletal pain in their upper limbs. The most painful area was shoulder (14, 31.1%), followed by elbow (8, 17.7%), and wrist (3, 6.6%), respectively. The Wheelchair Skills Test Questionnaire scores were worse in patients with elbow effusion (P = 0.041) and lateral epicondyle spur formation (P = 0.006). In the multivariate logistic regression analysis, independent predictors for joint pathologies in ultrasound were Spinal Cord Independence Measure total score, higher body mass index, older age, working with physical strength, female sex, duration of wheelchair usage, higher level of injury, and wheelchair skills (all P < 0.05). Conclusions Among the risk factors for joint pathologies identified in this study, the ability to use wheelchairs, which is a modifiable risk factor, should be considered in the rehabilitation process and improved.
Energetics of Poisson–Kac Stochastic Processes Possessing Finite Propagation Velocity

Abstract A local fluctuation–dissipation theorem for the power delivered by a stochastic forcing is derived for Ornstein–Uhlenbeck processes driven by smooth, i. e. almost everywhere (a. e.)-differentiable stochastic perturbations (Poisson–Kac processes). An analytic expression for the probability density function of the fluctuational power is obtained in the large time limit. As these processes converge, in the Kac limit, toward classical Langevin equations driven by Wiener processes, a coarse-grained analysis of the statistical properties of the fluctuational work is developed.
Whole‐brain analyses indicate the impairment of posterior integration and thalamo‐frontotemporal broadcasting in disorders of consciousness

The study of the brain's dynamical activity is opening a window to help the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and the dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in‐silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting‐state fMRI data, fitted to a linear model of activity propagation. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to severe reduction of glucose metabolism as measured with [18F]FDG‐PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results shed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior.
Structure of the priming arabinosyltransferase AftA required for AG biosynthesis of Mycobacterium tuberculosis

Significance Tuberculosis (TB) has been a mortal enemy to humanity for thousands of years. Despite the introduction of chemotherapeutic drug treatments dating back to the 1950s, TB today remains the leading cause of death by infectious diseases. Mycobacteria tuberculosis (Mtb) is the causative agent for TB. The highly complex and unique cell wall is one of the crucial factors to Mtb pathogenicity. In order to explore unique anti-TB drug target candidates, we sought to determine the structure of the arabinosyltransferase AftA from Mtb, an essential membrane-bound glycosyltransferase-C (GT-C) enzyme of cell wall arabinogalactan biosynthesis which primes the first arabinofuranosyl residue to the matured galactan chain. The structural and functional data will open avenues for anti-TB drug development.
Pancreatic tumors exhibit myeloid-driven amino acid stress and upregulate arginine biosynthesis

Nutrient stress in the tumor microenvironment requires cancer cells to adopt adaptive metabolic programs for survival and proliferation. Therefore, knowledge of microenvironmental nutrient levels and how cancer cells cope with such nutrition is critical to understand the metabolism underpinning cancer cell biology. Previously, we performed quantitative metabolomics of the interstitial fluid (the local perfusate) of murine pancreatic ductal adenocarcinoma (PDAC) tumors to comprehensively characterize nutrient availability in the microenvironment of these tumors. Here, we develop Tumor Interstitial Fluid Medium (TIFM), a cell culture medium that contains nutrient levels representative of the PDAC microenvironment, enabling us to study PDAC metabolism ex vivo under physiological nutrient conditions. We show that PDAC cells cultured in TIFM adopt a cellular state closer to that of PDAC cells present in tumors compared to standard culture models. Further, using the TIFM model, we found arginine biosynthesis is active in PDAC and allows PDAC cells to maintain levels of this amino acid despite microenvironmental arginine depletion. We also show that myeloid derived arginase activity is largely responsible for the low levels of arginine in PDAC tumors. Altogether, these data indicate that nutrient availability in tumors is an important determinant of cancer cell metabolism and behavior, and cell culture models that incorporate physiological nutrient availability have improved fidelity to in vivo systems and enable the discovery of novel cancer metabolic phenotypes.
Bedquiline Resistance Mutations: Correlations with Drug Exposures and Impact on the Proteome in M. tuberculosis

Bedaquiline (BDQ) is an effective drug for the treatment of drug-resistant tuberculosis. Mutations in atpE, which encodes the target of BDQ, are associated with large increases in MICs. Mutations in Rv0678 that derepress the transcription of the MmpL5-MmpS5 efflux transporter are associated with smaller increases in MICs. However, Rv0678 mutations are the most common mutations that are associated with BDQ resistance in clinical isolates, and they also confer cross-resistance to clofazimine (CFZ). ABSTRACT Bedaquiline (BDQ) is an effective drug for the treatment of drug-resistant tuberculosis. Mutations in atpE, which encodes the target of BDQ, are associated with large increases in MICs. Mutations in Rv0678 that derepress the transcription of the MmpL5-MmpS5 efflux transporter are associated with smaller increases in MICs. However, Rv0678 mutations are the most common mutations that are associated with BDQ resistance in clinical isolates, and they also confer cross-resistance to clofazimine (CFZ). To investigate the mechanism of BDQ resistance and the correlation between Rv0678 mutations and target-based atpE mutations, M. tuberculosis strains were exposed to different concentrations of BDQ or CFZ to select Rv0678 mutations and atpE mutations. Gene overexpression strains were constructed to illustrate the roles of MmpL5 and MmpS5. A quantitative proteome analysis was performed to compare the BDQ-resistant mutants to the isogenic strain H37Rv. Here, we report that the Rv0678 mutations were more readily selected than were the atpE mutations at low concentrations of BDQ or CFZ. The atpE mutations were selected by high concentrations of BDQ exposure. The overexpression of both mmpL5 and mmpS5 reduced the susceptibility of Mycobacterium tuberculosis to BDQ and CFZ. Secreted immunogenic proteins and proteins involved in the biosynthesis and transport of phthiocerol dimycocerosates were associated with Rv0678 mutations conferring BDQ resistance in the proteome analysis. In conclusion, exposure to different bedaquiline concentrations resulted in the selection of different mutations. The coexpression of MmpL5 and MmpS5 contributed to drug resistance and upregulated pathogenic proteins in M. tuberculosis, suggesting MmpL5-MmpS5 as a new potential target for antituberculosis drug development. These results warrant further surveillance for the evolution of BDQ resistance during clinical usage.
Non-human primate models of focal cortical ischemia for neuronal replacement therapy

Despite the high prevalence, stroke remains incurable due to the limited regeneration capacity in the central nervous system. Neuronal replacement strategies are highly diverse biomedical fields that attempt to replace lost neurons by utilizing exogenous stem cell transplants, biomaterials, and direct neuronal reprogramming. Although these approaches have achieved encouraging outcomes mostly in the rodent stroke model, further preclinical validation in non-human primates (NHP) is still needed prior to clinical trials. In this paper, we briefly review the recent progress of promising neuronal replacement therapy in NHP stroke studies. Moreover, we summarize the key characteristics of the NHP as highly valuable translational tools and discuss (1) NHP species and their advantages in terms of genetics, physiology, neuroanatomy, immunology, and behavior; (2) various methods for establishing NHP focal ischemic models to study the regenerative and plastic changes associated with motor functional recovery; and (3) a comprehensive analysis of experimentally and clinically accessible outcomes and a potential adaptive mechanism. Our review specifically aims to facilitate the selection of the appropriate NHP cortical ischemic models and efficient prognostic evaluation methods in preclinical stroke research design of neuronal replacement strategies.
Fluctuation in cognitive engagement during listening and reading of erotica and horror stories

ABSTRACT The present study examined whether emotional text content influences cognitive engagement and transportation during listening (Experiment 1) and reading (Experiment 2) of neutral, horror and erotic stories. In Experiment 1, fluctuation in arousal and cognitive engagement were measured by continuous arousal judgments and head movement recordings during story listening. Participants rated experienced transportation and emotional valence after each story. The results showed that emotional texts were more arousing and induced more transportation than neutral stories. There was less head motion overall and a steeper decrease in head motion across time for erotic than neutral or horror stories. In Experiment 2, participants’ head movements and reading times were recorded during reading, and participants rated experienced transportation, arousal, and valence after each text. The results showed that emotional texts were more arousing and induced higher transportation than neutral stories. There was less head motion during reading of erotic than neutral or horror texts. Horror texts were read slower and recalled better than neutral or erotic texts. The present results show that emotional text content impacts cognitive engagement during listening and reading of literary texts and demonstrates the importance of methodological triangulation when examining cognitive engagement.
Diurnal fecal glucocorticoid metabolite rhythms in a cathemeral primate, the red‐bellied lemur (Eulemur rubriventer), and across mammalian species

Measuring glucocorticoids is one of the most reliable and widely used techniques to monitor stress responses, however invasive techniques to collect plasma samples may not be applicable for wild populations. Monitoring excreted glucocorticoids is an effective noninvasive technique that researchers have used increasingly over the past two decades, and it has allowed the investigation of glucocorticoids in a variety of species with a range of activity patterns. Many species exhibit predictable circadian patterns of glucocorticoid secretion in accordance with their daily activity pattern. There remains a gap in our understanding of how excreted glucocorticoid metabolites vary throughout the day and across species, despite the utility of this information when developing sampling protocols and analyzing data. We investigated circadian patterns of glucocorticoid excretion in a cathemeral primate species, Eulemur rubriventer (red‐bellied lemur), in Ranomafana National Park, Madagascar. We collected fecal samples from 10 individuals throughout the day and analyzed fecal glucocorticoid levels across three time points (Early, Midday, and Late), and again across two time points (Morning and Afternoon). We also investigated whether activity pattern, sex (as a control variable), and other traits associated with gut passage rate (diet, body mass) could help predict the presence and timing of circadian patterns of fecal glucocorticoid metabolites across mammal species. We found that fecal glucocorticoid metabolite levels in E. rubriventer fluctuate throughout the day, with lowest levels in the morning and peak levels in the afternoon. None of the variables that we tested predicted whether daily fecal glucocorticoid metabolites changed significantly throughout the day, nor when levels were likely to peak, across species. We stress the importance of controlling for sampling time and reporting these results as standard practice in studies of fecal glucocorticoid metabolites.
High-throughput experimentation for discovery of biodegradable polyesters

Significance Biodegradable polymers are one part of the solution to the global plastics sustainability crisis. Specifically, biodegradable polymers mitigate the challenges of released waste and microplastic accumulation by breaking down in environmental conditions. However, the relationship between the chemical structure of these materials and their biodegradability is poorly understood. Current biodegradation testing methods are time and resource intensive, limiting experimental data from which structure–property relationships can be elucidated. This work introduces high-throughput polymer biodegradation methods applied to a large polymer library from which structure–property relationships are developed and predictive models are built.