Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasminogen-binding proteins as an evasion mechanism of the host’s innate immunity in infectious diseases

Pathogens have developed particular strategies to infect and invade their hosts. Amongst these strategies’ figures the modulation of several components of the innate immune system participating in early host defenses, such as the coagulation and complement cascades, as well as the fibrinolytic system. The components of the coagulation cascade and the fibrinolytic system have been proposed to be interfered during host invasion and tissue migration of bacteria, fungi, protozoa, and more recently, helminths. One of the components that has been proposed to facilitate pathogen migration is plasminogen (Plg), a protein found in the host’s plasma, which is activated into plasmin (Plm), a serine protease that degrades fibrin networks and promotes degradation of extracellular matrix (ECM), aiding maintenance of homeostasis. However, pathogens possess Plg-binding proteins that can activate it, therefore taking advantage of the fibrin degradation to facilitate establishment in their hosts. Emergence of Plg-binding proteins appears to have occurred in diverse infectious agents along evolutionary history of host–pathogen relationships. The goal of the present review is to list, summarize, and analyze different examples of Plg-binding proteins used by infectious agents to invade and establish in their hosts. Emphasis was placed on mechanisms used by helminth parasites, particularly taeniid cestodes, where enolase has been identified as a major Plg-binding and activating protein. A new picture is starting to arise about how this glycolytic enzyme could acquire an entirely new role as modulator of the innate immune system in the context of the host–parasite relationship.
Pronoun Translation in English-French Machine Translation: An Analysis of Error Types

Pronouns are a long-standing challenge in machine translation. We present a study of the performance of a range of rule-based, statistical and neural MT systems on pronoun translation based on an extensive manual evaluation using the PROTEST test suite, which enables a fine-grained analysis of different pronoun types and sheds light on the difficulties of the task. We find that the rule-based approaches in our corpus perform poorly as a result of oversimplification, whereas SMT and early NMT systems exhibit significant shortcomings due to a lack of awareness of the functional and referential properties of pronouns. A recent Transformer-based NMT system with cross-sentence context shows very promising results on non-anaphoric pronouns and intra-sentential anaphora, but there is still considerable room for improvement in examples with cross-sentence dependencies.
A Personalized Physical Activity Program With Activity Trackers and a Mobile Phone App for Patients With Metastatic Breast Cancer: Protocol for a Single-Arm Feasibility Trial

Background About 5% of breast cancer cases are metastatic at diagnosis, and 20%-30% of localized breast cancer cases become secondarily metastatic. Patients frequently report many detrimental symptoms related to metastasis and treatments. The physical, biological, psychological, and clinical benefits of physical activity during treatment in patients with localized breast cancer have been demonstrated; however, limited literature exists regarding physical activity and physical activity behavior change in patients with metastatic breast cancer. Objective The primary objective of this study is to assess the feasibility of a 6-month physical activity intervention with activity trackers in patients with metastatic breast cancer (the Advanced stage Breast cancer and Lifestyle Exercise, ABLE Trial). Secondary objectives are to examine the effects of physical activity on physical, psychological, anthropometrics, clinical, and biological parameters. Methods We plan to conduct a single-center, single-arm trial with 60 patients who are newly diagnosed with metastatic breast cancer. Patients will receive an unsupervised and personalized 6-month physical activity program that includes an activity tracker Nokia Go and is based on the physical activity recommendation. Patients will be encouraged to accumulate at least 150 minutes per week of moderate-to-vigorous intensity physical activity. Baseline and 6-month assessments will include anthropometric measures, functional tests (eg, 6-minute walk test and upper and lower limb strength), blood draws, patient-reported surveys (eg, quality of life and fatigue), and clinical markers of tumor progression (eg, Response Evaluation Criteria In Solid Tumors criteria). Results Data collection occurred between October 2016 and January 2018, and the results are expected in August 2018. Conclusions The ABLE Trial will be the first study to assess the feasibility and effectiveness of an unsupervised and personalized physical activity intervention performed under real-life conditions with activity trackers in patients with metastatic breast cancer. Trial Registration ClinicalTrials.gov NCT03148886; https://clinicaltrials.gov/ct2/show/NCT03148886 (Accessed by WebCite at http://www.webcitation.org/71yabi0la) Registered Report Identifier RR1-10.2196/10487
Banking on tissues.

Correct interactions between transcription factors (TFs) and their binding sites (TFBSs) are of central importance to gene regulation. Recently developed chromatin-immunoprecipitation DNA chip (ChIPchip) techniques and the phylogenetic footprinting method provide ways to identify TFBSs with high precision. In this study, we constructed a userfriendly interactive platform for dynamic binding site mapping using ChIP-chip data and phylogenetic footprinting as two filters. MYBS (Mining Yeast Binding Sites) is a comprehensive web server that integrates an array of both experimentally verified and predicted position weight matrixes (PWMs) from eleven databases, including 481 binding motif consensus sequences and 71 PWMs that correspond to 183 TFs. MYBS users can search within this platform for motif occurrences (possible binding sites) in the promoters of genes of interest via simple motif or gene queries in conjunction with the above two filters. In addition, MYBS enables users to visualize in parallel the potential regulators for a given set of genes, a feature useful for finding potential regulatory associations between TFs. MYBS also allows users to identify target gene sets of each TF pair, which could be used as a starting point for further explorations of TF combinatorial regulation. MYBS is available at http://cg1.iis. sinica.edu. tw/ mybs/.
Physicochemical predictors of Multi‐Walled Carbon Nanotube–induced pulmonary histopathology and toxicity one year after pulmonary deposition of 11 different Multi‐Walled Carbon Nanotubes in mice

Multi‐walled carbon nanotubes (MWCNT) are widely used nanomaterials that cause pulmonary toxicity upon inhalation. The physicochemical properties of MWCNT vary greatly, which makes general safety evaluation challenging to conduct. Identification of the toxicity‐inducing physicochemical properties of MWCNT is therefore of great importance. We have evaluated histological changes in lung tissue 1 year after a single intratracheal instillation of 11 well‐characterized MWCNT in female C57BL/6N BomTac mice. Genotoxicity in liver and spleen was evaluated by the comet assay. The dose of 54 μg MWCNT corresponds to three times the estimated dose accumulated during a work life at a NIOSH recommended exposure limit (0.001 mg/m3). Short and thin MWCNT were observed as agglomerates in lung tissue 1 year after exposure, whereas thicker and longer MWCNT were detected as single fibres, suggesting biopersistence of both types of MWCNT. The thin and entangled MWCNT induced varying degree of pulmonary inflammation, in terms of lymphocytic aggregates, granulomas and macrophage infiltration, whereas two thick and straight MWCNT did not. By multiple regression analysis, larger diameter and higher content of iron predicted less histopathological changes, whereas higher cobalt content significantly predicted more histopathological changes. No MWCNT‐related fibrosis or tumours in the lungs or pleura was found. One thin and entangled MWCNT induced increased levels of DNA strand breaks in liver; however, no physicochemical properties could be related to genotoxicity. This study reveals physicochemical‐dependent difference in MWCNT‐induced long‐term, pulmonary histopathological changes. Identification of diameter size and cobalt content as important for MWCNT toxicity provides clues for designing MWCNT, which cause reduced human health effects following pulmonary exposure.
Analisi di espressione di microRNA in Tumore a Cellule Giganti: identificazione dei target con ruolo di potenziali biomarcatori

Il Tumore a Cellule Giganti dell’osso (TCG) e una rara neoplasia che rappresenta il 5% dei tumori di natura ossea; sebbene venga considerato un tumore a decorso benigno puo manifestare caratteri di aggressivita locale dando origine a recidive locali nel 10-25% dei casi, e nel 2-4% dei casi metastatizza a livello polmonare. In questo studio e stata valutata l’espressione dei miRNA mediante miRNA microarray in 10 pazienti affetti da TCG, 5 con metastasi e 5 liberi da malattia; sono stati riscontrati miRNA differenzialmente espressi tra i 2 gruppi di pazienti e la successiva validazione mediante Real Time PCR ha confermato una differenza significativa per il miR-136 (p=0.04). Mediante analisi bioinformatica con il software TargetScan abbiamo identificato RANK e NF1B come target del miR-136 e ne abbiamo studiato l’espressione mediante Real Time PCR su una piu ampia casistica di pazienti affetti da TCG, metastatico e non, evidenziando una maggior espressione di NF1B nel gruppo di pazienti metastatici, mentre RANK non ha dimostrato una differenza significativa. L’analisi di Western Blot ha rilevato una maggiore espressione di entrambe le proteine nei pazienti metastatici rispetto ai non metastatici. Successivamente e stato condotto uno studio di immunoistochimica su TMA di 163 campioni di pazienti affetti da TCG a diverso decorso clinico che ha dimostrato una maggiore e significativa espressione di entrambe i target nei pazienti con metastasi rispetto ai non metastatici; le analisi di popolazione mediante Kaplan-Meier hanno confermato la correlazione tra over-espressione di RANK, NF1B e ricaduta con metastasi (p=0.001 e p<0.0005 rispettivamente). Lo studio di immunoistochimica e stato ampliato alle proteine maggiormente coinvolte nell’osteolisi che risultano avere un significato prognostico; tuttavia mediante analisi di ROC, la co-over-espressione di RANK, RANKL e NF1B rappresenta il migliore modello per predire la comparsa di metastasi (AUC=0.782, p<0.0005).
New Mining Regs, New Discontent

The Web sites Information for Public and Media page provides links to information such as secretariat press releases educational information kits for classroom use books posters images and the Down to Earth newsletter. Many of the materials are available in French Spanish. Russian Arabic Chinese and/or German. Multilingual fact sheets have been developed on topics including the causes and consequences of desertification partnership agreements between aid donors and affected states and how desertificatjon is fought in certain regions. (excerpt)
A Prognostic Model of Persistent Bacteremia and Mortality in Complicated Staphylococcus aureus Bloodstream Infection.

BACKGROUND Staphylococcus aureus is a leading cause of bacteremia, yet there remains a significant knowledge gap in the identification of relevant biomarkers that predict clinical outcomes. Heterogeneity in the host response to invasive S. aureus infection suggests that specific biomarker signatures could be utilized to differentiate patients prone to severe disease, thereby facilitating earlier implementation of more aggressive therapies. METHODS To further elucidate the inflammatory correlates of poor clinical outcomes in patients with S. aureus bacteremia, we evaluated the association between a panel of blood proteins at initial presentation of bacteremia and disease severity outcomes using 2 cohorts of patients with S. aureus bacteremia (n = 32 and n = 124). RESULTS We identified 13 candidate proteins that were correlated with mortality and persistent bacteremia. Prognostic modeling identified interleukin (IL)-8 and CCL2 as the strongest individual predictors of mortality, with the combination of these biomarkers classifying fatal outcome with 89% sensitivity and 77% specificity (P < .0001). Baseline IL-17A levels were elevated in patients with persistent bacteremia (P < .0001), endovascular (P = .026) and metastatic tissue infections (P = .012). CONCLUSIONS These results demonstrate the potential utility of selected biomarkers to distinguish patients with the highest risk for treatment failure and bacteremia-related complications, providing a valuable tool for clinicians in the management of S. aureus bacteremia. Additionally, these biomarkers could identify patients with the greatest potential to benefit from novel therapies in clinical trials.
Application of dynamic modeling for survival estimation in advanced renal cell carcinoma

Objective In oncology, extrapolation of clinical outcomes beyond trial duration is traditionally achieved by parametric survival analysis using population-level outcomes. This approach may not fully capture the benefit/risk profile of immunotherapies due to their unique mechanisms of action. We evaluated an alternative approach—dynamic modeling—to predict outcomes in patients with advanced renal cell carcinoma. We compared standard parametric fitting and dynamic modeling for survival estimation of nivolumab and everolimus using data from the phase III CheckMate 025 study. Methods We developed two statistical approaches to predict longer-term outcomes (progression, treatment discontinuation, and survival) for nivolumab and everolimus, then compared these predictions against follow-up clinical trial data to assess their proximity to observed outcomes. For the parametric survival analyses, we selected a probability distribution based on its fit to observed population-level outcomes at 14-month minimum follow-up and used it to predict longer-term outcomes. For dynamic modeling, we used a multivariate Cox regression based on patient-level data, which included risk scores, and probability and duration of response as predictors of longer-term outcomes. Both sets of predictions were compared against trial data with 26- and 38-month minimum follow-up. Results Both statistical approaches led to comparable fits to observed trial data for median progression, discontinuation, and survival. However, beyond the trial duration, mean survival predictions differed substantially between methods for nivolumab (30.8 and 51.5 months), but not everolimus (27.2 and 29.8 months). Longer-term follow-up data from CheckMate 025 and phase I/II studies resembled dynamic model predictions for nivolumab. Conclusions Dynamic modeling can be a good alternative to parametric survival fitting for immunotherapies because it may help better capture the longer-term benefit/risk profile and support health-economic evaluations of immunotherapies.
Rare Genetic Blood Disease Modeling in Zebrafish

Hematopoiesis results in the correct formation of all the different blood cell types. In mammals, it starts from specific hematopoietic stem and precursor cells residing in the bone marrow. Mature blood cells are responsible for supplying oxygen to every cell of the organism and for the protection against pathogens. Therefore, inherited or de novo genetic mutations affecting blood cell formation or the regulation of their activity are responsible for numerous diseases including anemia, immunodeficiency, autoimmunity, hyper- or hypo-inflammation, and cancer. By definition, an animal disease model is an analogous version of a specific clinical condition developed by researchers to gain information about its pathophysiology. Among all the model species used in comparative medicine, mice continue to be the most common and accepted model for biomedical research. However, because of the complexity of human diseases and the intrinsic differences between humans and other species, the use of several models (possibly in distinct species) can often be more helpful and informative than the use of a single model. In recent decades, the zebrafish (Danio rerio) has become increasingly popular among researchers, because it represents an inexpensive alternative compared to mammalian models, such as mice. Numerous advantages make it an excellent animal model to be used in genetic studies and in particular in modeling human blood diseases. Comparing zebrafish hematopoiesis to mammals, it is highly conserved with few, significant differences. In addition, the zebrafish model has a high-quality, complete genomic sequence available that shows a high level of evolutionary conservation with the human genome, empowering genetic and genomic approaches. Moreover, the external fertilization, the high fecundity and the transparency of their embryos facilitate rapid, in vivo analysis of phenotypes. In addition, the ability to manipulate its genome using the last genome editing technologies, provides powerful tools for developing new disease models and understanding the pathophysiology of human disorders. This review provides an overview of the different approaches and techniques that can be used to model genetic diseases in zebrafish, discussing how this animal model has contributed to the understanding of genetic diseases, with a specific focus on the blood disorders.
Antihyperglycemic drugs use and new-onset atrial fibrillation: A population-based nested case control study

Currently, the potential risk of atrial fibrillation associated with antihyperglycemic drug use has been a topic of considerable interest. However, it remains uncertain whether different classes of antihyperglycemic drug therapy are associated with the risk of atrial fibrillation risk. Here, we investigated the association between different classes of antihyperglycemic drugs and new-onset atrial fibrillation (NAF). A case-matched study was performed based on the National Health Insurance Program in Taiwan. Patients who had NAF were considered the NAF group and were matched in a 1:4 ratio with patients without NAF, who were assigned to the non-NAF group. Patients were matched according to sex, age, diabetes mellitus duration, index date, and Charlson Comorbidity Index score. We used multivariate logistic regression controlling for potential confounders to examine the association between different classes of antihyperglycemic drug use and the risk of NAF. Overall, we identified 2,882 cases and 11,528 matched controls for the study. After adjusting for sex, age, comorbidities, and concurrent medications, users of biguanides or thiazolidinediones were at a lower risk of developing NAF when compared with non-users (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.71–0.95 and OR 0.72, 95% CI 0.63–0.83, respectively). In contrast, users of insulin were at a higher risk of developing NAF than were non-users (OR 1.19, 95% CI 1.06–1.35). Sulfonylureas, glinides, α-glucosidase inhibitors, and dipeptidyl peptidase-4 inhibitors were not associated with developing the risk of NAF. In conclusion, the use of biguanides or thiazolidinediones may be associated with a low risk of NAF, whereas insulin may be associated with a significant increase in the risk of NAF in patients with type 2 diabetes mellitus during long-term follow-up. Further prospective randomized studies should investigate which specific class of antihyperglycemic drug treatment for diabetes mellitus can prevent or postpone NAF.
A database of simulated tumor genomes towards accurate detection of somatic small variants in cancer

Background Somatic mutations promote the transformation of normal cells to cancer. Accurate identification of such mutations facilitates cancer diagnosis and treatment. A number of callers have been developed to predict them from paired tumor/normal or unpaired tumor sequencing data. However, the small size of currently available experimentally validated somatic sites limits evaluation and then improvement of callers. Fortunately, NIST reference material NA12878 genome has been well-characterized with publicly available high-confidence genotype calls. Results We used BAMSurgeon to create simulated tumors by introducing somatic small variants (SNVs and small indels) into homozygous reference or wildtype sites of NA12878. We generated 135 simulated tumors from 5 pre-tumors/normals. These simulated tumors vary in sequencing and subsequent mapping error profiles, read length, the number of sub-clones, the VAF, the mutation frequency across the genome and the genomic context. Furthermore, these pure tumor/normal pairs can be mixed at desired ratios within each pair to simulate sample contamination. Conclusions This database (a total size of 15 terabytes) will be of great use to benchmark somatic small variant callers and guide their improvement. Contact information jing.mengrabbit@gmail.com
Defining a high-level programming model for emerging NVRAM technologies

Byte-addressable non-volatile memory is poised to become prevalent in the near future. Thanks to device-level technological advances, hybrid systems of traditional dynamic random-access memory (DRAM) coupled with non-volatile random-access memory (NVRAM) are already present and are expected to be commonplace soon. NVRAM offers orders of magnitude performance improvements over existing storage devices. Due to NVRAM's low overheads, many future applications are expected to leverage the fine-grain durable storage provided by NVRAM. Many frameworks for programming NVRAM have been proposed. Unfortunately, these existing frameworks closely mirror the underlying hardware. This lack of abstraction hurts programmer productivity, makes it easy to write buggy code, and limits the compiler's effectiveness. Furthermore, this low level of abstraction does not match the expectations of managed language users. To rectify this situation, in this paper we describe a new high-level NVRAM programming model amenable to managed languages. Because our model is defined at a high level, it is intuitive, not prone to user bugs, and is flexible enough to allow language implementers to perform many optimizations while still adhering to the model. In addition to proposing this model, we also briefly describe how Java can be extended to support our new model. Finally, we present some initial results on the performance overheads of creating durable applications in NVRAM and describe what future work we intend to complete.