Miracle-dz/UniFAR-FTUs huggingface dataset:
First complete genome sequences of Streptococcus pyogenes NCTC 8198T and CCUG 4207T, the type strain of the type species of the genus Streptococcus: 100% match in length and sequence identity between PacBio solo and Illumina plus Oxford Nanopore hybrid assemblies

We present the first complete, closed genome sequences of Streptococcus pyogenes strains NCTC 8198T and CCUG 4207T, the type strain of the type species of the genus Streptococcus and an important human pathogen that causes a wide range of infectious diseases. S. pyogenes NCTC 8198T and CCUG 4207T are derived from deposit of the same strain at two different culture collections. NCTC 8198T was sequenced, using a PacBio platform; the genome sequence was assembled de novo, using HGAP. CCUG 4207T was sequenced and a de novo hybrid assembly was generated, using SPAdes, combining Illumina and Oxford Nanopore sequence reads. Both strategies, yielded closed genome sequences of 1,914,862 bp, identical in length and sequence identity. Combining short-read Illumina and long-read Oxford Nanopore sequence data circumvented the expected error rate of the nanopore sequencing technology, producing a genome sequence indistinguishable to the one determined with PacBio. Sequence analyses revealed five prophage regions, a CRISPR-Cas system, numerous virulence factors and no relevant antibiotic resistance genes. These two complete genome sequences of the type strain of S. pyogenes will effectively serve as valuable taxonomic and genomic references for infectious disease diagnostics, as well as references for future studies and applications within the genus Streptococcus.
Balance between resource supply and demand determines nutrient limitation of primary productivity in the ocean

What is the ultimate limiting nutrient in the ocean? The dominant theory, which was first proposed by Redfield and later formalized by Tyrrell[1, 2], states that despite the scarcity of fixed nitrogen (N) in the surface ocean, phosphorus (P) availability ultimately determines primary productivity. Two recent findings directly challenge the assumptions of the Redfield-Tyrrell paradigm: the discovery of systematic variations of phytoplankton cellular N:P:Fe and widespread iron-limitation of phytoplankton. Here we use a simple model of nitrogen, phosphorus, and iron (Fe) cycling to show how the resource demand ratios and biogeography of phytoplankton interact with external resource supply ratios to govern nutrient cycling and primary productivity. We find that all three nutrients can limit global primary productivity, and that the ratio of geochemical supply to biological demand of each nutrient in each ocean region determines the limiting nutrients, with nitrogen N fixation providing a mechanism for the cycles to interact. These results have important consequences for our understanding of biogeochemical cycles, ocean-atmosphere interactions, marine ecology, and the response of ocean ecosystems to climate change. Our work demonstrates the importance of resource ratios and suggests that future studies of the physiological and geochemical regulation of these ratios are indispensable to building accurate theories and future predictions of nutrient cycling and primary productivity.
Aberrant Sensory Encoding in Patients with Autism

Perceptual anomalies in patients with Autism Spectrum Disorder (ASD) have been attributed to irregularities in the Bayesian interpretation (i.e., decoding) of sensory information. Here we show that how sensory information is encoded and adapts to changing stimulus statistics also characteristically differs between healthy and ASD groups. In a visual estimation task, we extracted the accuracy of sensory encoding directly from psychophysical data, bypassing the decoding stage by using information theoretic measures. Initially, sensory representations in both groups reflected the statistics of visual orientations in natural scenes, but encoding capacity was overall lower in the ASD group. Exposure to an artificial statistical distribution of visual orientations altered the sensory representations of the control group toward the novel experimental statistics, while also increasing their total encoding resources. Neither total encoding resources nor their allocation changed significantly in the ASD group. Most interestingly, across both groups the adaptive re-allocation of encoding resources was correlated with subjects’ initial encoding capacity. These findings suggest that neural encoding resources are limited in ASD, and this limitation may explain their reduced perceptual flexibility.
Bipartite Functional Fractionation within the Default Network Supports Disparate Forms of Internally Oriented Cognition

The ‘default network’ (DN) becomes active when the mind is steered internally towards self-generated thoughts but turns dormant when the mind is directed externally towards the outside world. While hypotheses have been proposed to characterise the association and dissociation between different component areas of the DN, it remains unclear how they coalesce into a unitary network and fractionate into different sub-networks. Here we identified two distinct subsystems within the DN – while both subsystems show common disinterest in externally-oriented visuospatial tasks, their functional profiles differ strikingly according to the preferred contents of thoughts, preferred modes of task requirement, and causative neural dynamics among network nodes. Specifically, one subsystem comprises key nodes of the frontotemporal semantic regions. This network shows moderate dislike to visuospatial tasks, shows proclivity for task-contexts with restraints on thoughts and responses, and prefers thoughts that are focused on other people. By contrast, the other subsystem comprises the cortical midline structure and angular gyri. This network shows strong aversion to visuospatial tasks, favours task-contexts allowing free self-generated thoughts without constraints, and prefers thoughts that are focused on self. Furthermore, causative connectivity reveals that task-contexts systematically alter the dynamics within and between subsystems, suggesting flexible adaption to situational demands. This ‘self/inward vs. others/outward’ separation within the broad DN resembles recent discoveries regarding a dyadic structure within the frontoparietal network that comprises regions controlling memories/thoughts vs. regions controlling sensory-motoric processes, and echoes burgeoning views that the brain is organised with a spectrum-like architecture along gradational changes of ‘inward vs. outward’ preferences. Significance Rather than construing the default network (DN) as ‘task-negative’ regions that passively react to off-task mind-wandering, researchers have begun to acknowledge the active role of the DN in supporting internally-directed cognition. Here we found a striking dichotomy within the DN in terms of the subsystems’ task-driven functional and connectivity profiles, extending beyond previous inferences using meta-analysis and resting-state fMRI. This dichotomy reflects a local manifestation of a macro-scale gradient representation spanning across the broad cerebral cortex. This cortical gradient increases its representational complexity, from primitive sensory and motoric processing, through lexical-semantic codes for language tasks, to abstract self-generated thoughts in task-free contexts. These findings enable a framework where the separate yet related literatures of semantic cognition and default-mode processes converge.
Perception of frequency modulation is mediated by cochlear place coding

Natural sounds convey information via frequency and amplitude modulations (FM and AM). Humans are acutely sensitive to the slow rates of FM that are crucial for speech and music. Two coding mechanisms are believed to underlie FM sensitivity, one based on precise stimulus-driven spike timing (time code) for slow FM rates, and another coarser code based on cochlear place of stimulation (place code) for fast FM rates. We tested this long-standing explanation by studying individual differences in listeners with varying degrees of hearing loss that resulted in widely varying fidelity of place-based or tonotopic coding. Our findings reveal that FM detection at both slow and fast rates is closely related to the fidelity of place coding in the cochlea, suggesting a unitary neural code for all FM rates. These insights into the initial coding of important sound features provide a new impetus for improving place coding in auditory prostheses.
Rigidity, normal modes and flexible motion of a SARS-CoV-2 (COVID-19) protease structure

The rigidity and flexibility of two recently reported crystal structures (PDB entries 6Y2E and 6LU7) of a protease from the SARS-CoV-2 virus, the infectious agent of the COVID-19 respiratory disease, has been investigated using pebble-game rigidity analysis, elastic network model normal mode analysis, and all-atom geometric simulations. This computational investigation of the viral protease follows protocols that have been effective in studying other homodimeric enzymes. The protease is predicted to display flexible motions in vivo which directly affect the geometry of a known inhibitor binding site and which open new potential binding sites elsewhere in the structure. A database of generated PDB files representing natural flexible variations on the crystal structures has been produced and made available for download from an institutional data archive. This information may inform structure-based drug design and fragment screening efforts aimed at identifying specific antiviral therapies for the treatment of COVID-19.
An alternative replication niche for Listeria monocytogenes revealed by fluorescent tagging of bacterial secreted effectors

Tracking the dynamics of secreted virulence factors in real time is impaired by the paucity of appropriate fluorescent tools. Here, we took advantage of the fluorogenic reporter FAST to tag secreted proteins and monitor infection dynamics among a population of epithelial cells infected by Listeria monocytogenes (Lm). Thereby, we unveiled the heterogeneity of Lm residence time in internalisation vacuoles. Although half of the bacterial population escaped rapidly, a significant fraction remained entrapped several hours in Long Residence Vacuoles (LRVs), independently of the secretion of the pore-forming toxin listeriolysin O (LLO). Unexpectedly, LLO enabled Lm to proliferate inside these compartments as fast as in the cytosol. LRVs display similarities with the spacious Listeria-containing phagosomes described in macrophages, and could constitute an alternative replication niche for Lm in epithelial cells.
Genomic properties of structural variants and short tandem repeats that impact gene expression and complex traits in humans

Structural variants (SVs) and short tandem repeats (STRs) comprise a broad group of diverse DNA variants which vastly differ in their sizes and distributions across the genome. Here, we show that different SV classes and STRs differentially impact gene expression and complex traits. Functional differences between SV classes and STRs include their genomic locations relative to eGenes, likelihood of being associated with multiple eGenes, associated eGene types (e.g., coding, noncoding, level of evolutionary constraint), effect sizes, linkage disequilibrium with tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS traits. We also identified a set of high-impact SVs/STRs associated with the expression of three or more eGenes via chromatin loops and showed they are highly enriched for being associated with GWAS traits. Our study provides insights into the genomic properties of structural variant classes and short tandem repeats that impact gene expression and human traits.
Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier

Single-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.
Computer-assisted beat-pattern analysis and the flagellar waveforms of bovine spermatozoa

Plagued by hurdles in information extraction, handling, and processing, computer-assisted sperm analysis (CASA) systems have typically neglected the complex flagellar waveforms of spermatozoa, despite their defining role in cell motility. Recent developments in imaging techniques and data processing have produced significantly-improved methods of waveform digitisation. Here, we utilise these improvements to demonstrate that near-complete flagellar capture is realisable on the scale of hundreds of cells, and, further, that meaningful statistical comparisons of flagellar waveforms may be readily performed with widely-available tools. Representing the advent of high-fidelity computer-assisted beat-pattern analysis (CABA), we show how such a statistical approach can distinguish between samples using complex flagellar beating patterns rather than crude summary statistics. Dimensionality-reduction techniques applied to entire samples also reveal qualitatively-distinct components of the beat, and a novel data-driven methodology for the generation of representative synthetic waveform data is proposed.
Endosymbiotic Rickettsiella causes cytoplasmic incompatibility in a spider host

Many arthropod hosts are infected with bacterial endosymbionts that manipulate host reproduction, but few bacterial taxa have been shown to cause such manipulations. Here we show that a bacterial strain in the genus Rickettsiella causes cytoplasmic incompatibility (CI) between infected and uninfected hosts. We first surveyed the bacterial community of the agricultural spider Mermessus fradeorum (Linyphiidae) using high throughput sequencing and found that individual spiders can be infected with up to five different strains of maternally-inherited symbiont from the genera Wolbachia, Rickettsia, and Rickettsiella. The Rickettsiella strain was pervasive, found in all 23 tested spider matrilines. We used antibiotic curing to generate uninfected matrilines that we reciprocally crossed with individuals infected only with Rickettsiella. We found that only 13% of eggs hatched when uninfected females were mated with Rickettsiella-infected males; in contrast, at least 83% of eggs hatched in the other cross types. This is the first documentation of Rickettsiella, or any Gammaproteobacteria, causing CI. We speculate that induction of CI may be much more widespread among maternally-inherited bacteria than previously appreciated. Further, our results reinforce the importance of thoroughly characterizing and assessing the inherited microbiome before attributing observed host phenotypes to well-characterized symbionts such as Wolbachia.
Prospects for precision therapy of bladder urothelial carcinoma

ABSTRACT Introduction: In cancer, precision medicine, frequently referred to as precision oncology, implies the use of specific information about a person’s tumor to assist diagnosis, guide treatment, find out how well treatment is working, or if it is relevant to patient’s prognosis. Area covered: Molecular classification of bladder cancer as the bases for cancer therapy, immunotherapy and algorithms on how to use the different drugs in these patients, and other emerging target related therapies from relevant clinical trials. Expert commentary: Standard therapy relies on chemotherapy, but results associated with chemotherapy are poor. Recently introduced checkpoint inhibitors, anti-PD1 and anti-PDL1 antibodies, represent a major advance for treating advanced/metastatic bladder urothelial carcinoma. Nevertheless, results are far from perfect with many patients gaining no advantage with the use of these agents. In previously treated and in cisplatin ineligible patients, these agents outperformed standard chemotherapy in terms of overall survival
Identification and characterization of retro-DNAs, a new type of retrotransposons originated from DNA transposons, in primate genomes

Mobile elements (MEs) can be divided into two major classes based on their transposition mechanisms as retrotransposons and DNA transposons. DNA transposons move in the genomes directly in the form of DNA in a cut-and-paste style, while retrotransposons utilize an RNA-intermediate to transpose in a “copy-and-paste” fashion. In addition to the target site duplications (TSDs), a hallmark of transposition shared by both classes, the DNA transposons also carry terminal inverted repeats (TIRs). DNA transposons constitute ~3% of primate genomes and they are thought to be inactive in the recent primate genomes since ~37My ago despite their success during early primate evolution. Retrotransposons can be further divided into Long Terminal Repeat retrotransposons (LTRs), which are characterized by the presence of LTRs at the two ends, and non-LTRs, which lack LTRs. In the primate genomes, LTRs constitute ~9% of genomes and have a low level of ongoing activity, while non-LTR retrotransposons represent the major types of MEs, contributing to ~37% of the genomes with some members being very young and currently active in retrotransposition. The four known types of non-LTR retrotransposons include LINEs, SINEs, SVAs, and processed pseudogenes, all characterized by the presence of a polyA tail and TSDs, which mostly range from 8 to 15 bp in length. All non-LTR retrotransposons are known to utilize the L1-based target-primed reverse transcription (TPRT) machineries for retrotransposition. In this study, we report a new type of non-LTR retrotransposon, which we named as retro-DNAs, to represent DNA transposons by sequence but non-LTR retrotransposons by the transposition mechanism in the recent primate genomes. By using a bioinformatics comparative genomics approach, we identified a total of 1,750 retro-DNAs, which represent 748 unique insertion events in the human genome and nine non-human primate genomes from the ape and monkey groups. These retro-DNAs, mostly as fragments of full-length DNA transposons, carry no TIRs but longer TSDs with ~23.5% also carrying a polyA tail and with their insertion site motifs and TSD length pattern characteristic of non-LTR retrotransposons. These features suggest that these retro-DNAs are DNA transposon sequences likely mobilized by the TPRT mechanism. Further, at least 40% of these retro-DNAs locate to genic regions, presenting significant potentials for impacting gene function. More interestingly, some retro-DNAs, as well as their parent sites, show certain levels of current transcriptional expression, suggesting that they have the potential to create more retro-DNAs in the current primate genomes. The identification of retro-DNAs, despite small in number, reveals a new mechanism in propagating the DNA transposons sequences in the primate genomes with the absence of canonical DNA transposon activity. It also suggests that the L1 TPRT machinery may have the ability to retrotranspose a wider variety of DNA sequences than what we currently know.
Assessing the Knowledge of Cardiovascular Disease Among Young People in South Dublin

Introduction: Cardiovascular diseases cause approximately 46% of total deaths in Ireland. These conditions can be preventable with lifestyle changes. By 2020 the number of deaths is expected to rise to over 195,000 people. With the aim, to develop teaching programs about CVD, it is important to assess young peoples' understanding of CVD. Methods: This was a cross-sectional questionnaire-based research study conducted in seven pharmacies in south Dublin. Customers aged between 18-30 were requested to take part in the study. Descriptive and variable provision multivariate analysis was used in information analysis. Results: The least recognized symptom of a myocardial infarction was dyspnea (69%). The least recognized symptom of stroke was headache (65%). In terms of where young people obtain health information, 66% learned from doctors, while family members and the internet were the main source in 18% and 17%. 35% of young people believed weight was the most factor that increased your risk of cardiovascular disease while 22% believed smoking was most important. Discussion: There was deficiencies in cardiovascular disease knowledge among young people, especially related to MI/Stroke.
Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures as well as knowledge of the temporal and spatial properties of the data. Thus, biomedical sensor data need to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. We further show how this framework corrects for covariates to provide accurate assessments of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool around a specific Google implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the resulting software implementation to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity. In particular, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.