Miracle-dz/UniFAR-FTUs huggingface dataset:
HAPLN1 is a driver for peritoneal carcinomatosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) frequently metastasizes into the peritoneum, which contributes to poor prognosis. Metastatic spreading is promoted by cancer cell plasticity, yet its regulation by the microenvironment is incompletely understood. Here we show that the presence of hyaluronan and proteoglycan link protein-1 (HAPLN1) in the extracellular matrix enhances tumor cell plasticity and PDAC metastasis. Bioinformatic analysis showed that HAPLN1 expression is enriched in the basal PDAC subtype and associated with worse overall patient survival. In a mouse model for peritoneal carcinomatosis, HAPLN1-induced immunomodulation favored a more permissive microenvironment, which accelerated the peritoneal spread of tumor cells. Mechanistically, HAPLN1, via hyaluronic acid synthesis and signaling, promoted adoption of a highly plastic cancer cell state, facilitating EMT, stemness, invasion and immunomodulation in a paracrine manner. Extracellular HAPLN1 modifies cancer cells as well as fibroblasts, rendering them immunomodulatory. We identify HAPLN1 as a prognostic marker and a driver for peritoneal metastasis in PDAC.
Bacterial capsular polysaccharides with antibiofilm activity share common biophysical and electrokinetic properties

Bacterial biofilms are surface-attached communities that are difficult to eradicate due to a high tolerance to antimicrobial agents. The use of non-biocidal surface-active compounds to prevent the initial adhesion and aggregation of bacterial pathogens is a promising alternative to antibiotic treatments and several antibiofilm compounds have been identified, including some capsular polysaccharides released by various bacteria. However, the lack of chemical and mechanistic understanding of the activity of these polymers limits their use to control biofilm formation. Here, we screen a collection of 31 purified capsular polysaccharides and first identify seven new compounds with non-biocidal activity against Escherichia coli and/or Staphylococcus aureus biofilms. We measure and theoretically interpret the electrophoretic mobility of a subset of 21 capsular polysaccharides under applied electric field conditions, and we show that active and inactive polysaccharide polymers display distinct electrokinetic properties and that all active macromolecules share high intrinsic viscosity features. Despite the lack of specific molecular motif associated with antibiofilm properties, the use of criteria including high density of electrostatic charges and permeability to fluid flow enables us to identify two additional capsular polysaccharides with broad-spectrum antibiofilm activity. Our study therefore provides insights into key biophysical properties discriminating active from inactive polysaccharides. The characterization of a distinct electrokinetic signature associated with antibiofilm activity opens new perspectives to identify or engineer non-biocidal surface-active macromolecules to control biofilm formation in medical and industrial settings. Bacteria produce several high molecular weight polysaccharides with ill-defined anti-adhesion properties. Here, the authors identified key molecular and biophysical determinants of active antibiofilm polysaccharides, opening new perspectives to identify or engineer new compounds able to control bacterial adhesion and biofilm formation.
The impact of negative data sampling on antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared to traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.
Background modeling, Quality Control and Normalization for GeoMx RNA data with GeoDiff

Background NanoString’s GeoMx Digital Spatial Profiler (DSP) RNA assay can measure mRNA from hundreds of regions of customizable shape and size, yet it gives unique challenge in Quality Control(QC) and normalizating due to the omnipresent background noise incurred by the non-specific probe binding, which could not be addressed by conventional methods. Results and discussion Using Poisson Background model, Background Score Test, Negative Binomial threshold model and Poisson threshold model for normalization from the R package GeoDiff, we perform tasks including size factor estimation, QC and normalization on GoeMx RNA assay data. They are shown to outperform conventional methods like Limit of Quantification for QC as to consistency/false positive rate and 75% quantile normalization as to eliminating technical variability and recovering true signal. Conclusions We present a statistical model based workflow for QC and normalizing GeoMx RNA data using GeoDiff, justified by statistical theory and validated by real/simulated data.
Correlations across timing cues in natural vocalizations predict biases in judging synthetic sound burst durations

It is well known that animals rely on multiple sources of information in order to successfully identify sounds in natural environments, to make decisions that are optimal for their survival. For example, rats use duration and pitch cues to respond appropriately to prosocial and distress vocalizations (Saito et al., 2019). Vocalization duration cues are known to co-vary with other temporal cues (Khatami et al., 2018), yet little is known about whether animals rely upon such co-variations to successfully discriminate sounds. In the current study, we find natural alarm vocalizations in rats have onset and offset slopes that are correlated with their duration. Accordingly, vocalizations with faster onset slopes are more likely to have shorter durations. Given that vocalization slopes begin and end within milliseconds, they could provide rapid perceptual cues for predicting and discriminating vocalization duration. To examine this possibility, we train rodents to discriminate duration differences in sequences of synthetic vocalizations and examine how artificially changing the slope impacts duration judgments. We find animals are biased to misjudge a range of synthetic vocalizations as being shorter in duration when the onset and offset slopes are artificially fast. Moreover, this bias is reduced when rats are exposed to multiple synthetic vocalization bursts. The observed perceptual bias is accurately captured by a Bayesian decision-theoretic model that utilizes the empirical joint distribution of duration and onset slopes in natural vocalizations as a prior during duration judgements of synthetic vocalizations. This model also explains why the bias is reduced when more evidence is accumulated across multiple bursts, reducing the prior’s influence. These results support the theory that animals are sensitive to fine-grained statistical co-variations in auditory timing cues and integrate this information optimally with incoming sensory evidence to guide their decisions.
SuperFi-Cas9 exhibits extremely high fidelity but reduced activity in mammalian cells

Several advances have been made to SpCas9, the most widely used CRISPR/Cas genome editing tool, to reduce its unwanted off-target effects. The most promising approach is the development of increased fidelity nuclease (IFN) variants of SpCas9, however, their fidelity has increased at the cost of reduced activity. SuperFi-Cas9 has been developed recently, and it has been described as a next-generation high fidelity SpCas9 variant, free from the drawbacks of the first-generation IFNs. In this study, we characterized the on-target activity and the off-target propensity of SuperFi-Cas9 in mammalian cells comparing it to first-generation IFNs. SuperFi-Cas9 demonstrated strongly reduced activity but exceptionally high fidelity exhibiting features that are in many aspects similar to those of the first-generation variants, such as evo- and HeFSpCas9. When combined with ABE8e, SuperFi-Cas9 produced DNA editing with high activity rate as well as high specificity by reducing both bystander and SpCas9-dependent off-target base editing.
FcRγ− NK cell induction by specific CMV and expansion by subclinical viral infections in rhesus macaques

Long-lived ‘memory-like’ NK cells, characterized by FcRγ-deficiency and enhanced responsiveness to antibody-bound virus-infected cells, have been found in certain human cytomegalovirus (HCMV)-seropositive individuals. Because humans are exposed to numerous microbes and environmental agents, specific relationships between HCMV and FcRγ-deficient NK cells (also known as g-NK cells) have been challenging to define. Here, we show that a subgroup of rhesus cytomegalovirus (RhCMV)-seropositive macaques possesses FcRγ-deficient NK cells that stably persist and display phenotype resembling human FcRγ-deficient NK cells. Moreover, these macaque NK cells resembled human FcRγ-deficient NK cells with respect to functional characteristics, including enhanced responsiveness to RhCMV-infected target in an antibody-dependent manner and hypo-responsiveness to tumor and cytokine stimulation. These cells were not detected in specific-pathogen-free (SPF) macaques free of RhCMV and six other viruses; however, experimental infection of SPF animals with RhCMV strain UCD59, but not RhCMV strain 68-1 or SIV, led to induction of FcRγ-deficient NK cells. In non-SPF macaques, co-infection by RhCMV with other common viruses was associated with higher frequencies of FcRγ-deficient NK cells. These results support a causal role for specific cytomegalovirus strain(s) in the induction of FcRγ-deficient NK cells, and suggest that co-infection by other viruses further expands this memory-like NK cell pool.
Lack of ITS sequence homogenization in congeneric plant species with different ploidy levels

The internal transcribed spacers (ITS) exhibit concerted evolution by the fast homogenization of these sequences at the intragenomic level. However, the rate and extension of this process are unclear and might be conditioned by the number and divergence of the different ITS copies. In some cases, such as hybrid species and polyploids, ITS sequence homogenization appears incomplete, resulting in multiple haplotypes within the same organism. Here, we studied the dynamics of concerted evolution in 85 individuals of seven plant species of the genus Erysimum (Brassicaceae) with multiple ploidy levels. We estimated the rate of concerted evolution and the degree of sequence homogenization separately for ITS1 and ITS2 and whether these varied with ploidy. Our results showed incomplete sequence homogenization, especially for polyploid samples, indicating a lack of concerted evolution in these taxa. Homogenization was usually higher in ITS2 than in ITS1, suggesting that concerted evolution operates more efficiently on the former. Furthermore, the hybrid origin of several species appears to contribute to the maintenance of high haplotype diversity, regardless of the level of ploidy. These findings indicate that sequence homogenization of ITS is a dynamic and complex process that might result in varying intra- and inter-genomic diversity levels.
ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes

Motivation The detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing experiments. However, in perturbation experiments, current methods typically fail to correctly match cell states between conditions or erroneously remove population substructure. Here we present the novel, unsupervised algorithm ICAT that employs self-supervised feature weighting and control-guided clustering to accurately resolve cell states across heterogeneous conditions. Results Using simulated and real datasets, we show ICAT is superior in identifying and resolving cell states compared to current integration workflows. While requiring no a priori knowledge of extant cell states or discriminatory marker genes, ICAT is robust to low signal strength, high perturbation severity, and disparate cell type proportions. We empirically validate ICAT in a developmental model and find that only ICAT identifies a perturbation-unique cellular response. Taken together, our results demonstrate that ICAT offers a significant improvement in defining cellular responses to perturbation in single-cell RNA sequencing data. Availability and implementation https://github.com/BradhamLab/icat Supplemental Methods, Tables and Figures are available online.
A developmental pathway for epithelial-to-motoneuron transformation in C. elegans

Motoneurons and motoneuron-like pancreatic beta cells arise from radial glia and ductal cells, both tube-lining progenitors that share molecular regulators. To uncover programs underlying motoneuron formation, we studied a similar, cell-division-independent transformation of the C. elegans tube-lining Y cell into the PDA motoneuron. We find that lin-12/Notch acts through ngn-1/Ngn and its regulator hlh-16/Olig to control transformation timing. lin-12 loss blocks transformation, while lin-12(gf) promotes precocious PDA formation. Early basal expression of both ngn-1/Ngn and hlh-16/Olig depends on sem-4/Sall and egl-5/Hox. Later, coincident with Y-cell morphological changes, ngn-1/Ngn expression is upregulated in a sem-4/Sall and egl-5/Hox-dependent but hlh-16/Olig-independent manner. Subsequently, Y-cell retrograde extension forms an anchored process priming PDA axon extension. Extension requires ngn-1-dependent expression of the cytoskeleton organizers UNC-119, UNC-44/ANK, and UNC-33/CRMP, which also, unexpectedly, activate PDA terminal-gene expression. Our findings reveal key cell-division-independent regulatory events leading to motoneuron generation, suggesting a conserved pathway for epithelial-to-motoneuron/motoneuron-like differentiation.
The optimal clutch size revisited: separating individual quality from the parental survival costs of reproduction

Life-history theory, central to our understanding of diversity in morphology, behaviour and senescence, describes how traits evolve through the optimisation of trade-offs in investment. Despite considerable study, there is only minimal support for trade-offs within species between the two traits most closely linked to fitness – reproductive effort and survival – questioning the theory’s general validity. We used a meta-analysis to separate the effects of individual quality (positive survival/reproduction correlation) from the costs of reproduction (negative survival/reproduction correlation) using studies of reproductive effort and parental survival in birds. Experimental enlargement of brood size caused reduced parental survival. However, the effect size of brood size manipulation was small and opposite to the effect of phenotypic quality, as we found that individuals that naturally produced larger clutches also survived better. The opposite effects on parental survival in experimental and observational studies of reproductive effort provides the first meta-analytic evidence for theory suggesting that quality differences mask trade-offs. Fitness projections using the overall effect size revealed that reproduction presented negligible costs, except when reproductive effort was forced beyond the level observed within species, to that seen between species. We conclude that there is little support for the most fundamental life-history trade-off, between reproductive effort and survival, operating within a population. We suggest that within species, the fitness landscape of the reproduction–survival trade-off is flat until it reaches the boundaries of the between-species fast–slow life-history continuum. Our results provide a quantitative explanation as to why the costs of reproduction are not apparent and why variation in reproductive effort persists within species.
Polyamines shift expression of macrophage L-arginine metabolism related-genes during Leishmania amazonensis infection

Polyamines are molecules involved in macrophage activation and polarization in response to pathogens. The interaction with Leishmania promotes modulation of macrophage immune response to favor arginine deviation to polyamines production, leading to parasite survival. Polyamine levels are a potential modulator of growth factors, driving cells to proliferation, wound healing, oxidative stress, or regulating translation. Here, we investigate the impact of L-arginine and polyamines in the transcriptional regulation of genes involved in arginine metabolization and pro-inflammatory response to L. amazonensis in murine BALB/c macrophages. We found that supplementation with L-arginine is insufficient to modulate macrophage gene expression and infection. Polyamine supplementation altered nitric oxide synthase (Nos2) and nitric oxide (NO) production, as well as other macrophage enzymes. Putrescine supplementation increased transcript levels of polyamine metabolism-related genes Arginase 2 (Arg2), Spermidine synthase (SpdS), and Spermine synthase (SpmS) in both uninfected and L. amazonensis infected macrophages. Putrescine increased Nos2 expression without leading to NO production, while L-arginine plus spermine led to production of NO in uninfected macrophages. Besides, L-arginine supplementation reduced the levels of Il-1b during infection, and L-arginine or L-arginine plus putrescine increased Mcp1 at 24h of infection. The percentage of infected macrophages was lower after putrescine, spermidine, and spermine supplementation than L-arginine supplementation. Our data showed that the polyamines shift L-arginine-metabolism related-genes on BALB/c macrophages and affect infection by L. amazonensis.
BioTEA: Containerized Methods of Analysis for Microarray-Based Transcriptomics Data

Tens of thousands of gene expression data sets describing a variety of model organisms in a many different pathophysiological conditions are currently stored in publicly available databases such as Gene Expression Omnibus (GEO) and ArrayExpress. As microarray technology is giving way to RNA-Seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring at the same time the reproducibility of the results. To meet this need, here we present bioTEA, a novel software tool that combines the ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options—including gene filtering, batch effect handling, sample pairing, statistical test type—to adapt the algorithm flow to the structure of the particular data set. All these options are saved in single text file which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of analysis. Overall, these features make bioTEA an invaluable tool for both bioiformaticians and wet-lab biologists interested in transcriptomics.
Driver Fatigue

Psychological fatigue is defined as a subjectively experienced disinclination to continue performing the task at hand. It generally impairs human efficiency when individuals continue working after they have become aware of their fatigue. It does not depend on energy expenditure and. cannot be measured simply in terms of performance impairment. The interacting causal contributions to fatigue are the length of continuous work spells and daily duty periods, time available for rest and continuous sleep, and the arrangement of duty, rest, and sleep periods within each 24-h cycle. Empirical evidence for the separate and combined effects of these factors on fatigue, performance decrement, and accident risk are briefly reviewed, and the implications of these findings for driving and road safety are considered, with particular reference to the professional driver. This study shows that fatigue is insufficiently recognized and reported as a cause of road accidents and that its effects stem largely from prolonged and irregular working hours, rather than simply from time spent at the wheel.
Discrete-Time Super-Twisting Fractional-Order Observer With Implicit Euler Method

The work presented in this brief describes the design of a discrete-time super-twisting algorithm based fractional-order observer for a class of non-linear fractional-order systems. The proposed observer is shown to achieve higher performance as compared to the conventional integer-order observers in terms of robustness and convergence time. It generalizes the design of observers for the class of non-linear fractional-order systems. The peaking phenomenon is observed to be less significant in the proposed approach. Chattering is suppressed with the Fractional Adams-Moulton Method, which is an implicit Euler discretization technique. The significance of the proposed observer is illustrated through a simulation example.
Personalized Music Recommendation model based on Machine Learning

A music recommendation system suggests songs to an individual user on his preferences. There are many different sorts of music to choose from. The world of music is so vast that it is impossible to listen to all the songs one desires. As a result, we create a model that supports a user in discovering music that he could enjoy. It collects individuals who share the user's passions and picks knowledge and resemblance associations depending on the user's past. The information gathered from user evaluations is used to make suggestions. The main focus of the study is on the context-aware recommendation process's insufficient integration of context data with the emergence of new attractions. Using libraries like NumPy and Pandas, we used a library of songs to uncover connections across individuals and music so that a hit album might be offered to individuals derived from history. In addition to Count Vectorizer (CV), we'll use Cosine similarity (CS). In addition, when a piece of given music is processed, a front end with a flask will provide us with the suggested tracks.