Miracle-dz/UniFAR-FTUs huggingface dataset:
Turning high-throughput structural biology into predictive inhibitor design

A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilised advance is the increase in structural biology throughput, which has progressed from an artisanal endeavour to a monthly throughput of up to 100 different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high throughput crystallography data into predictive models for ligand design. Here we designed a simple machine learning approach that predicts protein-ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein-ligand complexes, and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high throughput crystallography campaign against the SARS-CoV-2 Main Protease (MPro), obtaining parallel measurements of over 200 protein-ligand complexes and the binding activity. This allows us to design a one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a non-covalent and non-peptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.
Prefrontal cortex supports speech perception in listeners with cochlear implants

Cochlear implants are neuroprosthetic devices that can restore hearing in people with severe to profound hearing loss by electrically stimulating the auditory nerve. Because of physical limitations on the precision of this stimulation, the acoustic information delivered by a cochlear implant does not convey the same level of acoustic detail as that conveyed by normal hearing. As a result, speech understanding in listeners with cochlear implants is typically poorer and more effortful than in listeners with normal hearing. The brain networks supporting speech understanding in listeners with cochlear implants are not well understood, partly due to difficulties obtaining functional neuroimaging data in this population. In the current study, we assessed the brain regions supporting spoken word understanding in adult listeners with right unilateral cochlear implants (n=20) and matched controls (n=18) using high-density diffuse optical tomography (HD-DOT), a quiet and non-invasive imaging modality with spatial resolution comparable to that of functional MRI. We found that while listening to spoken words in quiet, listeners with cochlear implants showed greater activity in left prefrontal cortex than listeners with normal hearing, specifically in a region engaged in a separate spatial working memory task. These results suggest that listeners with cochlear implants require greater cognitive processing during speech understanding than listeners with normal hearing, supported by compensatory recruitment of left prefrontal cortex.
RecT: A Recursive Transformer Architecture for Generalizable Mathematical Reasoning

There has been increasing interest in recent years on investigating whether neural models can learn mathematical reasoning. Previous approaches have attempted to train models to derive the final answer directly from the question in a single step. However, they have typically failed to generalize to problems more complex than those in the training set. In this paper, we posit that these failures can be circumvented by introducing a strongly supervised recursive framework to the traditional transformer architecture. Rather than having the model output the answer directly in a single shot, we reduce each problem into a sequence of intermediate steps that are teacher forced during training. During inference, the autoregressive model recursively generates each intermediate step until it arrives at the final solution. We validate our method by training models to solve a popular mathematical reasoning task: complex addition and subtraction with parentheses. Our model not only attains a near perfect accuracy on problems of similar difficulty to the train set but also showcases generalization capabilities: while current state-of-the-art neural architectures completely fail to extrapolate to more complex arithmetic problems, we achieve a 66.26% accuracy.
First Record of Bioluminescence in a Sipunculan Worm

During a search for bioluminescent marine annelids around the island of Carrie Bow Cay, Belize, we discovered bioluminescence in the sipunculan worm Nephasoma pellucidum. The identification of N. pellucidum was verified by DNA barcoding of ∼650 bp of the mitochondrial cytochrome c oxidase I gene, using sequences of the same species from previous studies as references. Our study constitutes the first record of a bioluminescent sipunculan species. Green luminescence was detected in four of the five individuals of N. pellucidum collected from coral rubble from a shallow reef flat and one deeper (14 m) site. To test for bioluminescent activity, all collected annelids (including sipunculans) were sorted into morphotypes and identified to the lowest taxonomic level possible. After several hours of dark adaptation, they were mechanically stimulated, and luminescent reactions were noted and recorded by photography and/or video whenever possible. Of a total of nine examined sipunculan species, N. pellucidum was the only one that showed bioluminescent activity. The underlying biochemical processes for the bioluminescence are so far unknown, but transcriptome data indicate the presence of proteins with similarities to Renilla-type luciferases. We did not find similarities to any known photoproteins. Our findings broaden the known diversity of bioluminescent annelid taxa and support the notion that bioluminescence arose multiple times in annelids. The bioluminescent properties of N. pellucidum add a new reason to pursue this species as a model species, as has been previously proposed.
Metabolic Activation of the Toxic Natural Products From Herbal and Dietary Supplements Leading to Toxicities

Currently, herbal and dietary supplements have been widely applied to prevent and treat various diseases. However, the potential toxicities and adverse reactions of herbal and dietary supplements have been increasingly reported, and have gradually attracted widespread attention from clinical pharmacists and physicians. Metabolic activation of specific natural products from herbal and dietary supplements is mediated by hepatic cytochrome P450 or intestinal bacteria, and generates chemical reactive/toxic metabolites that bind to cellular reduced glutathione or macromolecules, and form reactive metabolites-glutathione/protein/DNA adducts, and these protein/DNA adducts can result in toxicities. The present review focuses on the relation between metabolic activation and toxicities of natural products, and provides updated, comprehensive and critical comment on the toxic mechanisms of reactive metabolites. The key inductive role of metabolic activation in toxicity is highlighted, and frequently toxic functional groups of toxic natural products were summarized. The biotransformation of drug cytochrome P450 or intestinal bacteria involved in metabolic activation were clarified, the reactive metabolites-protein adducts were selected as biomarkers for predicting toxicity. And finally, further perspectives between metabolic activation and toxicities of natural products from herbal and dietary supplements are discussed, to provide a reference for the reasonable and safe usage of herbal and dietary supplements.
DXA-Derived Adiposity and Lean Indices for Management of Cardiometabolic and Musculoskeletal Frailty: Data Interpretation Tricks and Reporting Tips

The proper assessment and follow-up of obesity and sarcopenia are relevant for the proper management of the complications of cardiometabolic and musculoskeletal frailty. A total body dual-energy X-ray absorptiometry (DXA) scan should be systematically incorporated in the rehabilitative routine management of patients with obesity and sarcopenia. In the former patients, the total body DXA can be used to assess the fat tissue amount and distribution, while in the latter patients, it can be used to quantify the reduction of appendicular lean mass and to investigate the inter-limb lean mass asymmetry. This tutorial article provides an overview of different DXA-derived fat and lean indices and describes a step-by-step procedure on how to produce a complete DXA report. We suggest that the systematic incorporation of these indices into routine examinations of the patients with obesity and sarcopenia can be useful for identifying the patients at risk for cardiometabolic and neuromuscular impairment-related comorbidities and for evaluating the effectiveness of pharmacological and rehabilitative interventions.
Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli

Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions.
Sensor-Based Categorization of Upper Limb Performance in Daily Life of Persons With and Without Neurological Upper Limb Deficits

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life. Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits. Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters. Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit. Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.
Multi-Omics Characterization of Host-Derived Bacillus spp. Probiotics for Improved Growth Performance in Poultry

Microbial feed ingredients or probiotics have been used widely in the poultry industry to improve production efficiency. Spore-forming Bacillus spp. offer advantages over traditional probiotic strains as Bacillus spores are resilient to high temperature, acidic pH, and desiccation. This results in increased strain viability during manufacturing and feed-pelleting processes, extended product shelf-life, and increased stability within the animal’s gastrointestinal tract. Despite numerous reports on the use of Bacillus spores as feed additives, detailed characterizations of Bacillus probiotic strains are typically not published. Insufficient characterizations can lead to misidentification of probiotic strains in product labels, and the potential application of strains carrying virulence factors, toxins, antibiotic resistance, or toxic metabolites. Hence, it is critical to characterize in detail the genomic and phenotypic properties of these strains to screen out undesirable properties and to tie individual traits to clinical outcomes and possible mechanisms. Here, we report a screening workflow and comprehensive multi-omics characterization of Bacillus spp. for use in broiler chickens. Host-derived Bacillus strains were isolated and screened for desirable probiotic properties. The phenotypic, genomic and metabolomic analyses of three probiotic candidates, two Bacillus amyloliquefaciens (Ba ATCC PTA126784 and ATCC PTA126785), and a Bacillus subtilis (Bs ATCC PTA126786), showed that all three strains had promising probiotic traits and safety profiles. Inclusion of Ba ATCC PTA12684 (Ba-PTA84) in the feed of broiler chickens resulted in improved growth performance, as shown by a significantly improved feed conversion ratio (3.3%), increased of European Broiler Index (6.2%), and increased average daily gain (ADG) (3.5%). Comparison of the cecal microbiomes from Ba PTA84-treated and control animals suggested minimal differences in microbiome structure, indicating that the observed growth promotion presumably was not mediated by modulation of cecal microbiome.
Matrix Profile-Based Interpretable Time Series Classifier

Time series classification (TSC) is a pervasive and transversal problem in various fields ranging from disease diagnosis to anomaly detection in finance. Unfortunately, the most effective models used by Artificial Intelligence (AI) systems for TSC are not interpretable and hide the logic of the decision process, making them unusable in sensitive domains. Recent research is focusing on explanation methods to pair with the obscure classifier to recover this weakness. However, a TSC approach that is transparent by design and is simultaneously efficient and effective is even more preferable. To this aim, we propose an interpretable TSC method based on the patterns, which is possible to extract from the Matrix Profile (MP) of the time series in the training set. A smart design of the classification procedure allows obtaining an efficient and effective transparent classifier modeled as a decision tree that expresses the reasons for the classification as the presence of discriminative subsequences. Quantitative and qualitative experimentation shows that the proposed method overcomes the state-of-the-art interpretable approaches.
Grand Challenge at the Frontiers of Synaptic Neuroscience

The brain does not operate like a typical computer—there is no central processing unit, there are no separate memory banks, and performance degrades gracefully when individual components fail. This is because all brain functioning is distributed across vast networks of neurons, which are connected by synapses that process and store information. To understand the brain, we therefore need to know how synapses work, hence the central importance of the Synaptic Neuroscience research field that this Frontiers Specialty Journal covers. In 2000, Abbott and Nelson overviewed the field of synaptic plasticity and likened it to the taming of a beast (Abbott and Nelson, 2000). Synaptic plasticity is indeed a particularly daunting task in neuroscience: experiments are slow and challenging, outcomes vary with precise experimental conditions, and interpretations of the results are inherently prone to disagreement, due to experimental as well as theoretical complexities. We are now more than two decades after the publication of Abbott and Nelson’s review, we have seen the immense progress since, and we are in a good position to speculate on what is at the Frontiers of Synaptic Neuroscience. Here, in personal view, I have collected a non-exhaustive list of challenges, phrased as questions.
Magnetic Field Intensity Modification to Force Free Model of Magnetic Clouds: Website of Wind Examples From Launch to July of 2015

We describe a new NASA website that shows normalized magnetic field ( B ) magnitude profiles within Wind magnetic clouds (MCs) (i.e., observations versus basic model versus modified model) for 209 MCs observed from launch in late 1994 to July of 2015, where model modification is based on the studies of Lepping et al. (Solar Phys, 2017, 292:27) and Lepping et al. (Solar Phys, 2018, 293:162); the basic force free magnetic cloud parameter fitting model employing Bessel functions (Lepping et al., J. Geophys. Res., 1990, 95:11957) is called the LJB model here. The fundamental principles should be applicable to the B -data from any spacecraft at 1 AU. Earlier (in the LJB study), we justified why the field magnitude can be thought of as decoupled from the field direction within an MC, and further, we justified this idea in terms of actual observations seen over a few decades with examples of MCs from Wind data. The model modification is achieved by adding a correction (“Quad”) value to the LJB model (Bessel function) value in the following manner: B (est)/B 0 ≈ [LJB Model + Quad (CA,u)], where B 0 is the LJB-estimated field magnitude value on the MC’s axis, CA is the relative closest approach (See Supplementary Appendix A), and u is the distance that the spacecraft travels through the MC from its entrance point. In an average sense, the Quad technique is shown to be successful for 82% of the past modeled MCs, when Quality (Q 0) is good or excellent (see Supplementary Appendix A). The Quad technique is successful for 78% of MCs when all cases are considered. So Q 0 of the MC LJB-fit is not a big factor when the success of the Quad scheme is considered. In addition, it is found that the Quad technique does not work better for MC events with higher solar wind speed. Yearly occurrence frequency of all MC events (NYearly) and those MC events with Δσ N/σ N2 ≥ 0.5 (NΔσN/σN2≥0.5) are well correlated, but there is no solar cycle dependence for normalizing NΔσN/σN2≥0.5 with NYearly.
Influence of Sex in the Molecular Characteristics and Outcomes of Malignant Tumors

Background Sex is frequently underestimated as a prognostic biomarker in cancer. In this study, we evaluated a large cohort of patients and public datasets to determine the influence of sex on clinical outcomes, mutational status, and activation of immune pathways in different types of cancer. Methods A cohort of 13,619 Oncosalud-affiliated patients bearing sex-unrelated cancers was followed over a 20-year period. Hazard ratios (HRs) for death were estimated for female vs. male patients for each cancer type and then pooled in a meta-analysis to obtain an overall HR. In addition, the mutational status of the main actionable genes in melanoma (MEL), colorectal cancer (CRC), and lung cancer was compared between sexes. Finally, a gene set enrichment analysis (GSEA) of publicly available data was conducted, to assess differences in immune processes between sexes in MEL, gastric adenocarcinoma (GC), head and neck cancer (HNC), colon cancer (CC), liver cancer (LC), pancreatic cancer (PC), thyroid cancer (TC), and clear renal cell carcinoma (CCRCC). Results Overall, women had a decreased risk of death (HR = 0.73, CI95: 8%–42%), with improved overall survival (OS) in HNC, leukemia, lung cancer, lymphoma, MEL, multiple myeloma (MM), and non-melanoma skin cancer. Regarding the analysis of actionable mutations, only differences in EGFR alterations were observed (27.7% for men vs. 34.4% for women, p = 0.035). The number of differentially activated immune processes was higher in women with HNC, LC, CC, GC, MEL, PC, and TC and included cellular processes, responses to different stimuli, immune system development, immune response activation, multiorganism processes, and localization of immune cells. Only in CCRCC was a higher activation of immune pathways observed in men. Conclusions The study shows an improved survival rate, increased activation of immune system pathways, and an enrichment of EGFR alterations in female patients of our cohort. Enhancement of the immune response in female cancer patients is a phenomenon that should be further explored to improve the efficacy of immunotherapy.
Plasmatic Exosome Number and Size Distinguish Prostate Cancer Patients From Healthy Individuals: A Prospective Clinical Study

There is a urgent need for valuable strategy in early and less invasive diagnosis for cancer. Preliminary data have shown that the plasmatic levels of exosomes increase in cancer condition. This study investigates the relevance of plasmatic levels and size distribution of exosomes in 42 individuals with no signs of urological disease (CTR) as compared to 65 prostate cancer patients (PCa). It was used Nanoparticle Tracking Analysis (NTA), a highly reliable and sensitive method for exosomes characterization and quantification. The relation structure among the NTA-derived parameters was assessed by means of Principal Component Analysis, which allowed detecting the global discriminant power of NTA test in terms of Receiver Operating Characteristic (ROC) curve and the selection of cut-off thresholds. The results showed that PCa had significantly higher plasmatic levels of exosomes and that the exosomes were smaller in size as compared to the CTR; the values reached 89% sensitivity and 71% specificity, in distinguishing PCa from CTR. These results propose a new exosome-based non-invasive clinical approach for the clinical follow-up of prostate cancer undergoing surgical treatment; in addition this method may be developed as a new screening test for prostate cancer’s early diagnosis. While this clinical study was performed in prostate cancer, it may represent a proof of concept extendable to virtually all cancers, as it is suggested by both pre-clinical evidence and clinical data obtained with different technical approaches.