Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel effector genes revealed by the genomic analysis of the phytopathogenic fungus Fusarium oxysporum f. sp. physali (Foph) that infects cape gooseberry plants

The vascular wilt disease caused by the fungus Fusarium oxysporum f. sp. physali (Foph) is one of the most limiting factors for the production and export of cape gooseberry (Physalis peruviana) in Colombia. A previous study of the transcriptomic profile of a highly virulent strain of F. oxysporum in cape gooseberry plants, from a collection of 136 fungal isolates obtained from wilted cape gooseberry plants, revealed the presence of secreted in the xylem (SIX) effector genes, known to be involved in the pathogenicity of other F. oxysporum formae speciales (ff. spp.). This pathogenic strain was named Foph, due to its specificity for cape gooseberry hosts. Here, we sequenced the genome of Foph, using the Illumina MiSeq platform. We analyzed the assembled genome, focusing on the confirmation of the presence of homologues of SIX effectors and the identification of novel candidates of effector genes unique of Foph. By comparative and phylogenomic analyses based on single-copy orthologues, we identified that Foph is closely related to F. oxysporum ff. spp. , associated with solanaceous hosts. We confirmed the presence of highly identical homologous genomic regions between Foph and Fol, that contain effector genes and identified seven new effector gene candidates, specific to Foph strains. We also conducted a molecular characterization of a panel of 29 F. oxysporum additional stains associated to cape gooseberry crops isolated from different regions of Colombia. These results suggest the polyphyletic origin of Foph and the putative independent acquisition of new candidate effectors in different clades of related strains. The novel effector candidates identified by sequencing and analyzing the genome of Foph, represent new sources involved in the interaction between Foph and cape gooseberry. These resources could be implemented to develop appropriate management strategies of the wilt disease caused by Foph in the cape gooseberry crop.
Mesodermal FGF and BMP govern the sequential stages of zebrafish thyroid specification

Thyroid tissue is the site for de novo synthesis of thyroid hormones which are essential for vertebrate development and growth. Defects in embryonic thyroid morphogenesis are a predominant cause for congenital thyroid diseases but the molecular pathomechanisms are incompletely understood. The first molecularly recognizable step of thyroid development is the specification of thyroid precursors at a defined position in the anterior foregut endoderm. While recent studies identified FGF and BMP pathways as critical signaling factors for thyroid specification, the interplay between extrinsic signaling cues and thyroid transcription factor expression remained elusive. Here, we used zebrafish embryos to decipher the dynamics of thyroid transcription factor induction in relation to FGF and BMP signaling activities in pharyngeal endoderm. We first identified a previously unrecognized endodermal thyroid progenitor cell population expressing Pax2a but not Nkx2.4b. This cell population is characterized by enhanced FGF signaling but initially lacks detectable BMP signaling. A subpopulation of Pax2a-expressing progenitors differentiates subsequently into thyroid lineage-committed precursor cells co-expressing Pax2a and Nkx2.4b. We next combined pharmacological approaches with genetic models permitting inhibition or ectopic overactivation of signaling pathways to timely manipulate FGF and BMP activities. These experiments support a model where FGF signaling primarily regulates Pax2a expression whereas BMP signaling has dual functions in regulation of both Pax2a and Nkx2.4b expression. Collectively, our data allow us to formulate a refined model of thyroid cell specification from foregut endoderm.
Does Keystroke Dynamics tell us about Emotions? A Systematic Literature Review and Dataset Construction

There is strong evidence that emotional states affect the Human’s performance and decision-making. Therefore, understanding Human emotions has become of great concern in the field of Human-Computer Interaction (HCI). One way to an online emotion recognition is through keystroke dynamics. Keystroke Dynamics focuses on the particular way a person types on a keyboard. To provide insight, assess recent works, and guide future researches in this field, a Systematic Literature Review (SLR) is conducted. An SLR adopts a rigorous procedure with the definition of a formal review protocol.The primary aim of this paper is to highlight the effectiveness of using keystroke dynamics biometrics in recognizing emotions by systematically presenting research efforts in the past decade. We provide insight into such an approach by summarizing and discussing the data acquisition procedures, datasets, extracted features, classification methods, and performance measures used in previous researches.In light of these findings and the noticed scarcity of datasets related to emotion recognition through keystroke dynamics, we develop and host an interactive web application to construct a new dataset. The findings of this work reveal new interesting research directions and may motivate the research community to develop automatic emotion recognition systems based on keystroke dynamics.
Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas

Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images. However, data quantity and quality remain a key determinant of, and a significant limit on, the potential of such applications. In our previous work, we explored synthesis of anatomic and molecular MR image network (SAMR) in patients with post-treatment malignant glioms. Now, we extend it and propose Confidence Guided SAMR (CG-SAMR) that synthesizes data from lesion information to multi-modal anatomic sequences, including T1-weighted (T1w), gadolinium enhanced T1w (Gd-T1w), T2-weighted (T2w), and fluid-attenuated inversion recovery (FLAIR), and the molecular amide proton transfer-weighted (APTw) sequence. We introduce a module which guides the synthesis based on confidence measure about the intermediate results. Furthermore, we extend the proposed architecture for unsupervised synthesis so that unpaired data can be used for training the network. Extensive experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.
Identification and characterization of genes associated with the hormonal progression of prostate cancer

Prostate cancer is the most frequently diagnosed cancer as well as the second leading cause of cancer deaths among American men. Androgen deprivation therapy for patients with advanced prostate cancer inevitably fails as the disease progresses to the terminal, hormone-refractory stage. At the present time no effective therapies can be offered to patients in this stage of the disease. Currently, the molecular alterations that contribute to the development of hormone-refractory disease are poorly understood. Here, suppression subtractive hybridization was performed to identify genes of interest in a model of progression of prostate cancer to androgen independence. Subtractive hybridization identified a number of novel transcripts that had previously not been described. From this work it was hypothesized that (1) the novel transcripts identified may prove informative in the clinical management of prostate cancer, and (2) that the known genes identified may function in the progression of prostate cancer. One of these novel transcripts was shown to encode a truncated splice variant of the transmembrane protein T M E F F 2 . T M E F F 2 is specifically expressed in the prostate and the brain, and other groups have developed antibodies targeting T M E F F 2 for the treatment of metastatic prostate cancer. The isoform described here, T M E F F 2 S , encodes a protein secreted by prostate cancer cells. Being a prostate-specific, secreted protein, T M E F F 2 S may prove informative in predicting patient outcome. Additionally, the secretion of T M E F F 2 S may influence the effectiveness of immunotherapy approaches targeting T M E F F 2 . Subtractive hybridization, together with other high-throughput screening techniques, also led to the examination of the potential functions of the 14-3-3 family of
From Discursive Practice to Logic? Remarks on Logical Expressivism

This paper investigates Robert Brandom's programme of logical expressivism and in the process attempts to clarify his use of the term practice, by means of a comparison with the works of sociologist and anthropologist Pierre Bourdieu. The key claim of logical expressivisim is the idea that logical terms serve to make explicit the inferential relations between statements which already hold implicitly in a discursive practice that lacks such terms in its vocabulary. Along with this, it is claimed that the formal validity of an argument is derivative on so-called material inference, in that an inference is taken to be logically valid only if it is a materially good inference and cannot be made into a bad inference by substituting nonlogical for nonlogical vocabulary in its premises and conclusion. We note that no systematic account of logical validity employing this substitutional method has been offered to date; rather, proposals by e.g. Lance and Kremer, Piwek, Kibble and Brandom himself have followed the more conventional path of developing a formally defined system which is informally associated with natural language examples. We suggest a number of refinements to Brandom’s account of conditionals and of validity, supported by analysis of linguistic examples including material from the SNLI and MultiNLI corpora and a review of relevant literature. The analysis suggests that Brandom’s expressivist programme faces formidable challenges once exposed to a wide range of linguistic data, and may not in fact be realisable owing to the pervasive context-dependence of linguistic expressions, including 'logical' vocabulary. A further claim of this paper is that a purely assertional practice may not provide an adequate basis for conditional reasoning, but that a more promising route is provided by the introduction of imperatives, as in so-called "pseudo-imperatives" such as "Get individuals to invest their time and the funding will follow". We conclude the resulting dialogical analysis of conditional reasoning is faithful to Brandom's Sellarsian intuition of linguistic practice as a game of giving and asking for reasons, and conjecture that language is best analysed not as a system of rules but as a Wittgensteinian repertoire of evolving micro-practices.
SARS-CoV-2 Quasispecies Mediate Rapid Virus Evolution and Adaptation

The pandemic spread of SARS-CoV-2 and the resulting global healthcare emergency warrants a better understanding of its biology. The potential of SARS-CoV-2 evolution to create novel dangerous variants remains underexplored. Thus, we passaged SARS-CoV-2 in defined conditions and determined its genomic adaptation dynamics. We demonstrate the presence of remarkably stable SARS-CoV-2 quasispecies. We further show that the quasispecies nature of the virus population ensured rapid adaptation of the spike PRRARS motif upon passaging in Vero cells. On the other hand, SARS-CoV-2 replication in TMPRSS2 expressing cells led to a reverse mutation at the same site. We observed the emergence of novel mutations in envelope protein upon virus culture in Calu-3 and Caco-2 cells. Finally, we show that the heparan sulfate-binding motif (PRRARS) of the SARS-CoV-2 S protein acted as a determinant of negative growth selection. Overall, our research has far-reaching implications for development of antiviral strategies, suggesting viral quasispecies may facilitate rapid emergence of escape mutants under selection pressure, such as the treatment with antivirals against SARS-CoV-2.
Agromonas: a rapid disease assay for Pseudomonas syringae growth in agroinfiltrated leaves

The lengthy process to generate transformed plants is a limitation in current research on the interactions of the model plant pathogen Pseudomonas syringae with plant hosts. Here we present an easy method called agromonas, where we quantify P. syringae growth in agroinfiltrated leaves of Nicotiana benthamiana using a cocktail of antibiotics to select P. syringae on plates. As a proof of concept, we demonstrate that transient expression of PAMP receptors reduces bacterial growth and that transient depletion of a host immune gene and transient expression of a T3 effector increase P. syringae growth in agromonas assays. We show that we can rapidly achieve structure-function analysis of immune components and test the function of immune hydrolases. The agromonas method is easy, fast and robust for routine disease assays with various Pseudomonas strains without transforming plants or bacteria. The agromonas assay offers reliable opportunity for further comprehensive analysis of plant immunity. One sentence summary Agromonas is a rapid and robust disease assay to monitor Pseudomonas syringae growth in agroinfiltrated leaves expressing immune components and their suppressors.
Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory

Due to telecommunications market saturation, it is very important for telco operators to always have fresh insights into their customer’s dynamics. In that regard, social network analytics and its application with graph theory can be very useful. In this paper we analyze a social network that is represented by a large telco network graph and perform clustering of its nodes by studying a broad set of metrics, e.g., node in/out degree, first and second order influence, eigenvector, authority and hub values. This paper demonstrates that it is possible to identify some important nodes in our social network (graph) that are vital regarding churn prediction. We show that if such a node leaves a monitored telco operator, customers that frequently interact with that specific node will be more prone to leave the monitored telco operator network as well; thus, by analyzing existing churn and previous call patterns, we proactively predict new customers that will probably churn. The churn prediction results are quantified by using top decile lift metrics. The proposed method is general enough to be readily adopted in any field where homophilic or friendship connections can be assumed as a potential churn driver.
A Unified Theory of Human Judgements and Decision-Making under Uncertainty

Growing empirical evidence reveals that traditional set-theoretic structures cannot in general be applied to cognitive phenomena. This has raised several problems, as illustrated, for example, by probability judgement errors and decision-making (DM) errors. We propose here a unified theoretical perspective which applies the mathematical formalism of quantum theory in Hilbert space to cognitive domains. In this perspective, judgements and decisions are described as intrinsically non-deterministic processes which involve a contextual interaction between a conceptual entity and the cognitive context surrounding it. When a given phenomenon is considered, the quantum-theoretic framework identifies entities, states, contexts, properties and outcome statistics, and applies the mathematical formalism of quantum theory to model the considered phenomenon. We explain how the quantum-theoretic framework works in a variety of judgement and decision situations where systematic and significant deviations from classicality occur.
Parallel processing of quickly and slowly mobilized reserve vesicles in hippocampal synapses

Vesicles within presynaptic terminals are thought to be segregated into a variety of readily releasable and reserve pools. The nature of the pools and trafficking between them is not well understood, but pools that are slow to mobilize when synapses are active are often assumed to feed pools that are mobilized more quickly, in a series. However, electrophysiological studies of synaptic transmission have suggested instead a parallel organization where vesicles within slowly and quickly mobilized reserve pools would separately feed independent reluctant- and fast-releasing subdivisions of the readily releasable pool. Here we use FM-dyes to confirm the existence of multiple reserve pools at hippocampal synapses and a parallel organization that prevents intermixing between the pools, even when stimulation is intense enough to drive exocytosis at the maximum rate. The experiments additionally demonstrate extensive heterogeneity among synapses in the relative sizes of the slowly and quickly mobilized reserve pools, which suggests equivalent heterogeneity in the numbers of reluctant and fast-releasing readily releasable vesicles that may be relevant for understanding information processing and storage.
Clinical Evaluation of Duchenne Muscular Dystrophy Severity Using Ultrasound Small-Window Entropy Imaging

Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98–5.00) for the control group, 5.04 (IQR: 5.01–5.05) for stage 1 patients, 5.07 (IQR: 5.06–5.07) for stage 2 patients, and 5.07 (IQR: 5.06–5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD.
Is there a genetic correlation between movement and immobility in field populations of a beetle?

Genetic correlations among behavioural traits are often controlled by pleiotropic genes. Many studies suggest the existence of genetic correlations among behavioural traits based on artificial selection experiments in the laboratory. However, few studies have examined whether behavioural correlations in the laboratory are maintained in the field, where natural selection works. Artificial selection experiments showed a behavioural correlation among death feigning, walking movement, and locomotor activity in the red flour beetle (Tribolium castaneum). This study investigated whether this behavioural correlation is observed in wild T. castaneum populations. We also collected beetles from various regions in Japan and investigated the geographic variation in these traits. There was geographic variation in the three behavioural traits. However, these behavioural traits were not correlated. The results suggest that the genetic correlations among behavioural traits are not maintained in the field. Therefore, the results derived from laboratory experiments may be overestimated. The same correlation between traits was not believed to arise in the field, as the indoor results may have been caused by unrealistic selection pressures. Further laboratory and field investigations are both needed.
A power amplification dyad in seahorses

Ubiquitous constraints derived from the muscle’s structure limit the power capacity of fast contracting muscles. Correspondingly, organisms evolved elastic elements that store energy which, when released, can be used to rapidly accelerate appendages. Such latch-mediated spring actuation (LaMSA) systems comprise of a single elastic element and are used to actuate a single mass. Here we reveal a dual LaMSA system in seahorses, in which two elastic elements actuate two masses: the head as they rapidly swing it towards the prey, and the water mass sucked into the mouth to prevent the prey from escaping. This power-amplified system enhances the speeds of both head rotation and suction flows by x10 compared to similarly-sized fish. Furthermore, the dual system provides temporal coordination between head rotation and suction flows, a novel function for LaMSA. These findings extend the known function, capacity and design of LaMSA systems.
Integrative Spatial Single-cell Analysis with Graph-based Feature Learning

We propose GLISS, a strategy to discover spatially-varying genes by integrating two data sources: (1) spatial gene expression data such as image-based fluorescence in situ hybridization techniques, and (2) dissociated whole-transcriptome single-cell RNA-sequencing (scRNA-seq) data. GLISS utilizes a graph-based association measure to select and link genes that are spatially-dependent in both data sources. GLISS can discover new spatial genes and recover cell locations in scRNA-seq data from landmark genes determined from SGE data. GLISS also offers a new dimension reduction technique to cluster the genes, while accounting for the inferred spatial structure of the cells. We demonstrate the utility of GLISS on simulated and real datasets, including datasets on the mouse olfactory bulb and breast cancer biopsies, and two spatial studies of the mammalian liver and intestine.
Information Theory in Computational Biology: Where We Stand Today

“A Mathematical Theory of Communication” was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon’s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology—gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.