Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards on-chip real-time classification of extra-cellular neural recordings

On-line classification of neural recordings can be extremely useful in brain-machine interface, prosthetic applications or therapeutic intervention. In this work we present a feasibility study for developing compact low-power VLSI systems able to classify neural recordings in real-time, using spike-based neuromorphic circuits. We developed a framework for classifying extra-cellular recordings made in rat auditory cortex in response to different auditory stimuli and porting the classification algorithm onto a spiking multi-neuron VLSI chip with programmable synaptic weights. We present recording methods and software classification algorithms; we demonstrate real-time classification in hardware and quantify the system performance; finally, we identify the potential sources of problems in developing such types of systems and propose strategies for overcoming them.
Sex-Specific Computational Models of Kidney Function in Patients With Diabetes

The kidney plays an essential role in homeostasis, accomplished through the regulation of pH, electrolytes and fluids, by the building blocks of the kidney, the nephrons. One of the important markers of the proper functioning of a kidney is the glomerular filtration rate. Diabetes is characterised by an enlargement of the glomerular and tubular size of the kidney, affecting the afferent and efferent arteriole resistance and hemodynamics, ultimately leading to chronic kidney disease. We postulate that the diabetes-induced changes in kidney may exhibit significant sex differences as the distribution of renal transporters along the nephron may be markedly different between women and men, as recently shown in rodents. The goals of this study are to (i) analyze how kidney function is altered in male and female patients with diabetes, and (ii) assess the renal effects, in women and men, of an anti-hyperglycemic therapy that inhibits the sodium-glucose cotransporter 2 (SGLT2) in the proximal convoluted tubules. To accomplish these goals, we have developed computational models of kidney function, separate for male and female patients with diabetes. The simulation results indicate that diabetes enhances Na+ transport, especially along the proximal tubules and thick ascending limbs, to similar extents in male and female patients, which can be explained by the diabetes-induced increase in glomerular filtration rate. Additionally, we conducted simulations to study the effects of diabetes and SGLT2 inhibition on solute and water transport along the nephrons. Model simulations also suggest that SGLT2 inhibition, which constricts the afferent arteriole to attenuate glomerular hyperfiltration, can then limit Na+-glucose transport, consequently raising luminal [Cl-] at the macula densa and finally restoring the tubuloglomerular feedback signal. By inducing osmotic diuresis in the proximal tubules, SGLT2 inhibition reduces paracellular transport, eventually leading to diuresis and natriuresis. Those effects on urinary excretion are blunted in women, in part due to their higher distal transport capacity.
Causal inference using deep-learning variable selection identifies and incorporates direct and indirect causalities in complex biological systems

In complex diseases, causal structure learning across biological variables is critical to identify modifiable triggers or potential therapeutic agents. A limitation of existing causal learning methods is that they cannot identify indirect causal relations, those that would interact through latent mediating variables. We developed the first computational method that identifies both direct and indirect causalities, causal inference using deep-learning variable-selection (causalDeepVASE). To accurately identify indirect causalities and incorporate them with direct causalities, causalDeepVASE develops a deep neural network approach and extends a flexible causal inference method. In simulated and biological data of various contexts, causalDeepVASE outperforms existing methods in identifying expected or validated causal relations. Further, causalDeepVASE facilitates a systematic understanding of complex diseases. For example, causalDeepVASE uniquely identified a possible causal relation between IFNγ and creatinine suggested in a polymicrobial sepsis model. In future biomedical studies, causalDeepVASE can facilitate the identification of driver genes and therapeutic agents.
Fast and efficient CRISPR-mediated genome editing in Aureobasidium pullulans using Cas9 ribonucleoproteins

Aureobasidium pullulans is a ubiquitous, polyextremotolerant, “yeast-like” ascomycete used for the industrial production of pullulan and other products and as biocontrol agent in the agriculture. Its application potential and its wide-spread occurrence make A. pullulans an interesting study object. The availability of a fast and efficient genome editing method is an obvious advantage for future basic and applied research on A. pullulans. In this study, we describe the development of a CRISPR/Cas9-based genome editing method using ribonucleoproteins (RNPs). We demonstrate that this method can be used for single and multiplex genome editing using only RNPs by targeting ura3 (encoding for orotidine-5′-phosphate decarboxylase), praics (encoding for phosphoribosyl aminoimidazole-succinocarboxamide synthase) and asl (encoding for arginine succinate lyase). We demonstrate the applicability of Trichoderma reesei pyr4 and Aspergillus fumigatus pyrG to complement the ura3 deficiency. Further, we show that the usage of RNPs can boost the homologous recombination rate up to nearly 100%, even when using only 20bp long homologous flanks. Therefore, the repair cassettes can be constructed by a single PCR, abolishing the need for laborious and time-consuming cloning. The here presented method allows fast and efficient genome editing for gene deletions, modifications, and insertions in A. pullulans.
Typical and disrupted brain circuitry for conscious awareness in full-term and preterm infants

One of the great frontiers of consciousness science is understanding how early consciousness arises in the development of the human infant. The reciprocal relationship between the default mode network (DMN) and frontoparietal networks — the dorsal attention network (DAN) and executive control network (ECN) — is thought to facilitate integration of information across the brain and its availability for conscious access to a wide set of mental operations. It remains unknown whether the brain mechanism of conscious awareness is instated in infants from birth. To address this gap, we asked what the impact of prematurity and neonate age is on the development the default mode and fronto-parietal networks, and of their reciprocal relationship. To address these questions, we used the Developing Human Connectome Project (dHCP), a unique Open Science project which provides a large sample of neonatal functional Magnetic Resonance Imaging (fMRI) data with high temporal and spatial resolution. Resting state fMRI data for full-term neonates (N = 282, age 41.2 w ± 12 d), and preterm neonates scanned at term-equivalent age (TEA) (N = 73, 40.9 w ± 14.5 d), or before TEA (N = 73, 34.6 w ± 13.4 d) were obtained from the dHCP, and for a reference adult group (N = 176, 22 – 36 years), from the Human Connectome Project. For the first time, we show that the reciprocal relationship between the DMN and DAN was present at full-term birth or TEA. Although different from the adult networks, the DMN, DAN and ECN were present as distinct networks at full-term birth or TEA, but premature birth disrupted network development. By contrast, neonates before TEA showed dramatic underdevelopment of high-order networks. Only the DAN was present as a distinct network and the reciprocal network relationship was not yet formed. Our results suggest that, at full-term birth or by term-equivalent age, infants possess key features of the neural circuitry that enables integration of information across diverse sensory and high-order functional modules, giving rise to conscious access. Conversely, they suggest that this brain infrastructure is not present before infants reach term-equivalent age. These findings improve understanding of the ontogeny of high-order network dynamics that support conscious awareness, and of their disruption by premature birth.
SprayNPray: user-friendly taxonomic profiling of genome and metagenome contigs

Shotgun sequencing of cultured microbial isolates/individual eukaryotes (whole-genome sequencing) and microbial communities (metagenomics) has become commonplace in biology. Very often, sequenced samples encompass organisms spanning multiple domains of life, necessitating increasingly elaborate software for accurate taxonomic classification of assembled sequences. While many software tools for taxonomic classification exist, SprayNPray offers a quick and user-friendly, semi-automated approach, allowing users to separate contigs by taxonomy (and other metrics) of interest. Easy installation, usage, and intuitive output, which is amenable to visual inspection and/or further computational parsing, will reduce barriers for biologists beginning to analyze genomes and metagenomes. This approach can be used for broad-level overviews, preliminary analyses, or as a supplement to other taxonomic classification or binning software. SprayNPray profiles contigs using multiple metrics, including closest homologs from a user-specified reference database, gene density, read coverage, GC content, tetranucleotide frequency, and codon-usage bias. The output from this software is designed to allow users to spot-check metagenome-assembled genomes, identify, and remove contigs from putative contaminants in isolate assemblies, identify bacteria in eukaryotic assemblies (and vice-versa), and identify possible horizontal gene transfer events.
A regulatory circuit motif dictates whether protein turnover fluxes are more predictive as biomarkers than protein abundances

The identification of prognostic biomarkers fuels personalized medicine. Here we tested two underlying, but often overlooked assumptions: 1) measurements at the steady state are sufficient for predicting the response to drug action, and 2) specifically, measurements of molecule abundances are sufficient. It is not clear that these are justified, as 1) the response results from non-linear molecular relationships, and 2) the steady state is defined by both abundance and orthogonal flux information. An experimentally validated mathematical model of the cellular response to the anti-cancer agent TRAIL was our test case. We developed a mathematical representation in which abundances and fluxes (static and kinetic network features) are largely independent, and simulated heterogeneous drug responses. Machine learning revealed predictive power, but that kinetic, not static network features were most informative. Analytical treatment of the underlying network motif identified kinetic buffering as the relevant circuit design principle. Our work suggests that network topology considerations ought to guide biomarker discovery efforts. Graphic abstract Highlights – Biomarkers are usually molecule abundances but underlying networks are dynamic – Our method allows separate consideration of heterogeneous abundances and fluxes – For the TRAIL cell death network machine learning reveals fluxes as more predictive – Network motif analyses could render biomarker discovery efforts more productive eTOC blurb Precision medicine relies on discovering which measurements of the steady state predict therapeutic outcome. Loriaux et al show – using a new analytical approach – that depending on the underlying molecular network, synthesis and degradation fluxes of regulatory molecules may be more predictive than their abundances. This finding reveals a flaw in an implicit but hitherto untested assumption of biomarker discovery efforts and suggests that dynamical systems modeling is useful for directing future clinical studies in precision medicine.
An ancient polymorphic regulatory region within the BDNF gene associated with obesity modulates anxiety-like behaviour in mice and humans

Obesity and anxiety are morbidities notable for their increased impact on society during the recent COVID-19 pandemic. Understanding the mechanisms governing susceptibility to these conditions will increase quality of life and our resilience to future pandemics. In the current study we explored the function of a highly conserved regulatory region (BE5.1) within the BDNF gene that harbours a polymorphism strongly associated with obesity (rs10767664; p=4.69×10−26). Analysis in primary cells suggested that the major T-allele of BE5.1 was an enhancer whereas the obesity associated A-allele was not. However, CRISPR/CAS9 deletion of BE5.1 from the mouse genome (BE5.1KO) produced no significant effect on the expression of BDNF transcripts in the hypothalamus, no change in weight gain after 28 days and only a marginally significant increase in food intake. Nevertheless, transcripts were significantly increased in the amygdala of female mice and elevated zero maze and marble burying tests demonstrated a significant increase in anxiety-like behaviour that could be reversed by diazepam. Consistent with these observations, human GWAS cohort analysis demonstrated a significant association between rs10767664 and anxiousness in human populations. Intriguingly, interrogation of the human GTEx eQTL database demonstrated no effect on BDNF mRNA levels associated with rs10767664 but a highly significant effect on BDNF-antisense (BDNF-AS) gene expression and splicing suggesting a possible mechanism. We discuss our findings within the context of the known function and regulation of BDNF in obesity and anxiety whilst exploring the validity of interrogating GWAS data using comparative genomics and functional analysis using CRISPR genome editing in mice.
Bayesian optimisation for yield in high-dimensional trait-space identifies crop ideotypes in Oil Seed Rape

The improvement of crop yield has long been a major breeding target and is increasingly becoming a goal in many areas of plant research. Yield has been shown to be a complex trait, depending on multiple genes, plant architecture and plant-environment interactions. This complexity is frequently reduced by focussing on contributing factors to yield (yield traits). However, a quantitative understanding of the interplay between yield traits, and the effect of these relationships on yield is largely unexplored. Consequently, the extent to which crop varieties achieve their optimal morphology in a given environment and how this impacts on seed yield is unknown. Here we use causal inference to model the hierarchically structured effects of 27 macro and micro yield traits on each other over the course of plant development, and on seed yield in Spring and Winter oilseed rape plants. We perform Bayesian optimisation on the modelled yield potential, identifying the morphology of ideotype plants which are expected to be higher yielding than the existing varieties in the studied panels. We find that existing Spring varieties occupy the optimal regions of trait-space, but that potentially high yielding strategies are unexplored in extant Winter varieties. In addition to concrete recommendations for varietal improvement in oilseed rape, this work provides a novel, general methodological framework for the study of crop breeding as an optimisation problem.
Machine-learning-based predictions of caloric restriction associations across ageing-related genes

Caloric restriction (CR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel CR-related genes and CR-related genetic features. This work used a Machine Learning (ML) approach to classify ageing-related genes as CR-related or NotCR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype-Tissue Expression (GTEx) expression features, Gene-Friends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking CR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict CR-relatedness among ageing-related genes currently lacking CR-related annotations in the data, resulting in a set of promising candidate CR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted CR-relatedness remain to be validated in future wet-lab experiments.
ARF4-mediated Retrograde Trafficking Drives Chemoresistance in Glioblastoma

Glioblastoma (GBM) is the most common type of adult malignant brain tumor, with a median survival of only 21 months. This is partly due to the high rate of resistance to conventional therapy, including temozolomide (TMZ), leading to recurrence rates close to 100%. It still remains unknown what drives the development of this resistance. To identify the unknown genes driving the development of this resistance, we performed a genome-wide CRISPR knockout screen comparing a DMSO-treated population with a TMZ-treated population over 14 days. We identified 4 previously unstudied genes – ARF4, PLAA, SPTLC1, and PIGK – that showed significant elevations in expression in recurrent tumors in patient datasets, along with significant survival benefits corresponding to low gene expression. Further investigation of ARF4, known to be involved in retrograde trafficking, allowed us to identify a mechanism of resistance that is mediated by increased retrograde transport of EGFR into the nucleus. Ultimately, our CRISPR-Cas9 screen has identified a promising therapeutic target, ARF4, which may drive GBM’s high resistance to chemotherapy.
The genomic formation of Tanka people, an isolated “Gypsies in water” in the coastal region of Southeast China

Objectives Three different hypotheses proposed via the controversial evidence from cultural, anthropological and uniparental genetic analysis respectively stated that Tanka people probably originated from Han Chinese, ancient Baiyue tribe, or the admixture of them. Therefore, the genetic origin and admixture history of the Tanka people, an isolated “Gypsies in water” in the coastal region of Southeast China, are needed to be genetically clarified. Materials and methods To elucidate the genetic origin of the Southeast Tanka people and explore their genetic relationship with surrounding indigenous Tai-Kadai and Austronesian people and Neolithic-to-historic ancients from the Yellow River Basin (YRB) and Fujian, we conducted a large-scale population genomic study among 1498 modern and ancient Eurasians, in which 73 Tanka and 4 Han people were first reported here. Both allele-shared and haplotype-based statistical methods were used here, including PCA, ADMIXTURE, f-statistics, ALDER, qpGraph-/TreeMix and qpAdm/qpWave, ChromoPainter and FineSTRUCTURE Results We found a specific genetic cline in PCA plots and detected the Tanka-specific homogeneous ancestry in model-based ADMIXTURE results, suggesting differentiated demographic history between Tanka and surrounding Hans. Formal tests based on sharing allele patterns showed a close relationship between Tanka people and Han Chinese, but the Tanka population harbored more southern East Asian ancestry related to Austronesian and Tai-Kadai people compared with southern Hans. Besides, the reconstructed differentiated demographic history revealed that southern Xinshizhou Tanka harbored more ancestry related to the Tai-Kadai or coastal ancient Neolithic to Bronze Age East Asians compared with northern Shacheng Tanka. The qpGraph-/TreeMix-based phylogenetic framework, qpAdm/qpWave-based admixture modeling and FineSTRUCTURE-based dendrogram among ancient northern and southern East Asians further demonstrated that the primary ancestry of modern Tanka derived from ancient millet farmers in the YRB with additional admixture from multiple southern East Asian sources. Discussion Sharing ancestry estimated from the f-statistics and sharing haplotypic landscape inferred from the ChromoPainter and FineSTRUCTURE showed that Southeast Tanka people not only had a close genetic relationship with both Northern Hans and YRB millet farmers but also possessed more southern East Asian ancestry related to Austronesian, Tai-Kadai and Hmong-Mien people. Our genomic data and fitted admixture models supported modern Tanka originated from ancient North China and obtained additional gene flow from ancient southern East Asians in the processes of southward migrations.
Cognition in Recent Suicide Attempts: Altered Executive Function

Background: Neuropsychological alterations can lead to inaccurate perception, interpretation, and response to environmental information, which could be a risk factor for suicide. Methods: Ninety-six subjects were recruited from the Psychiatry Department of the Araba University Hospital—Santiago, including 20 patients with a recent attempt and diagnosis of major depressive disorder (MDD) according to DSM-V, 33 MDD patients with history of attempted suicide, 23 non-attempter MDD patients, and 20 healthy controls. All participants underwent a clinical interview and neuropsychological assessment on the following cognitive domains: working memory, processing speed, decision-making, executive function, and attention. Backward multiple regressions were performed adjusting for significant confounding variables. For group comparisons, ANOVA and Bonferroni post-hoc tests were performed with a p < 0.05 significance level. Results: The patient groups did not differ regarding severity of depression and stressful events in the last 6 months. In comparison to healthy controls, depressed patients with lifetime suicide attempts had more general trauma (p = 0.003), emotional abuse (p = 0.003), emotional negligence (p = 0.006), and physical negligence (p = 0.009), and depressed patients with recent suicide attempts had experienced more child sexual abuse (p = 0.038). Regarding neuropsychological assessment, all patient groups performed significantly worse than did healthy controls in processing speed, decision-making, and attention. Comparisons between patient groups indicated that recent suicide attempters had poorer performance on executive function in comparison to both depressed lifetime attempters and depressed non-attempters (B = 0.296, p = 0.019, and B = 0.301, p = 0.028, respectively). Besides, women with recent attempts had slightly better scores on executive function than males. Regarding the rest of the cognitive domains, there were no significant differences between groups. Conclusion: Executive function performance is altered in recent suicide attempts. As impaired executive function can be risk factor for suicide, preventive interventions on suicide should focus on its assessment and rehabilitation.