Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors associated with cessation of smoking in health professionals: a scoping review

ABSTRACT Background Offering cessation support to health professionals who smoke to ensure optimal implementation of cessation support for patients is a key recommendation of the WHO Framework Convention on Tobacco Control Article 14 guidelines. However, direct efforts to support this population to quit are limited. Although numerous articles on the topic of tobacco use among health professionals have been published, the factors associated with their own cessation have not been systematically synthesised. Objective We sought to synthesise existing literature on the predictors and processes informing attitudes and beliefs of smoking health professionals’ own cessation. Methods A five-step methodological framework for scoping reviews was followed. We conducted a systematic search of EMBASE, PubMed, Web of Science, and PsycINFO databases, as well as Google Scholar for relevant articles. Titles, abstracts, and full texts were screened against predefined criteria: research published between 1990 and 2021, in English-language peer-reviewed journals; participants included doctors, nurses, medical, and student nurses who smoke. Results The initial search yielded 120, 883 articles, with 27 selected for synthesis. Prevalence estimates and predictors of smoking behaviour have remained the primary focus of smoking health professional research. Few studies explicitly examined the relevant predictors of quit attempts and quit attempt success. There is evidence that age and work environment factors predict quit attempt success in some health professional groups. There is also some evidence of tobacco smoking stigma experiences among nurses and nursing students who smoke. Conclusion Although cessation support is desperately needed for health professionals who smoke, the evidence for factors predicting quit success remains limited. To better guide future research, first, more theoretical work is required to identify the relevant predictors. Second, these should be tested using prospective research designs that take a multi-focal perspective to clarify the targets for change.
Evaluating the Evidence for the Functional Inhibition Account of Alpha-band Oscillations during Preparatory Attention

Abstract The functional inhibition account states that alpha-band (8–14 Hz) power implements attentional control by selectively inhibiting task-irrelevant neural representations. This account has been well supported by decades of correlational research showing attention-related changes in the topography of alpha power in anticipation of task-relevant stimuli and is a viable theory of how attention impacts sensory processing, namely, via alpha power changes in sensory areas before stimulus onset. In addition, attention is known to modulate neural responses to stimuli themselves. Thus, a critical prediction of the functional inhibition account is that preparatory alpha modulations should explain variance in the degree of attention-related modulation of neural responses to stimuli. The present article sought evidence for or against this prediction by scouring the literature on attention and alpha oscillations to review papers that explicitly correlated attention-related changes in prestimulus alpha with attention-related changes in stimulus-evoked neural activity. Surprisingly, out of over 100 papers that were examined, we found only nine that explicitly computed such relationships. The results of these nine papers were mixed, with some in support and some arguing against the functional inhibition account of alpha. Our synthesis draws out common design features that may help explain when effects are observed or not. Even among studies that do find correlations, there is inconsistency as to whether preparatory alpha modulations are predictive of sensory or postsensory components of stimulus responses, highlighting avenues for future research. A clear outcome of this review is that future studies on the role of alpha in attentional processing should analyze correlations between attention effects on alpha and attention effects on stimulus-evoked activity, as more data pertinent to this hypothesized relationship are needed.
Dual inhibition of the endothelin and angiotensin receptor ameliorates renal and inner ear pathologies in Alport mice

Alport syndrome (AS), a type IV collagen disorder, leads to glomerular disease and, in some patients, hearing loss. AS is treated with inhibitors of the renin–angiotensin system; however, a need exists for novel therapies, especially those addressing both major pathologies. Sparsentan is a single‐molecule dual endothelin type‐A and angiotensin II type 1 receptor antagonist (DEARA) under clinical development for focal segmental glomerulosclerosis and IgA nephropathy. We report the ability of sparsentan to ameliorate both renal and inner ear pathologies in an autosomal‐recessive Alport mouse model. Sparsentan significantly delayed onset of glomerulosclerosis, interstitial fibrosis, proteinuria, and glomerular filtration rate decline. Sparsentan attenuated glomerular basement membrane defects, blunted mesangial filopodial invasion into the glomerular capillaries, increased lifespan more than losartan, and lessened changes in profibrotic/pro‐inflammatory gene pathways in both the glomerular and the renal cortical compartments. Notably, treatment with sparsentan, but not losartan, prevented accumulation of extracellular matrix in the strial capillary basement membranes in the inner ear and reduced susceptibility to hearing loss. Improvements in lifespan and in renal and strial pathology were observed even when sparsentan was initiated after development of renal pathologies. These findings suggest that sparsentan may address both renal and hearing pathologies in Alport syndrome patients. © 2023 Travere Therapeutics, Inc and The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
MCE-Net: polyp segmentation with multiple branch series-parallel attention and channel interaction via edge distribution guidance

Objective. Accurate polyp segmentation is vital for diagnosing colorectal cancer. However, it is still challenging for accurate polyp segmentation and several bottlenecks exist, such as incomplete boundary, localization bias and lack of micro blocks along with large fragmented boundaries in uncertain regions. Approach. To address the above issues, a novel polyp segmentation network with multiple branch series-parallel attention (MBSA) and channel interaction via edge distribution guidance is proposed. Initially, the edge distribution guidance strategy is proposed to generate the edge distribution following Cauchy distribution to capture complementary edges with sufficient details. Subsequently, a MBSA module is put forward to extract features from various receptive fields to pinpoint tiny polyps by a multiple kernel dilated convolution block, while combining semantics of different dimensions to filter out noise and refining the details of micro target. Ultimately, the channel interaction model is proposed to improve the segmentation accuracy of the polyps in uncertain area by splitting channels into groups and conducts group-wise interaction to excavate subtle clues contained in different channels. Main results. Extensive experimental results demonstrate that the proposed method is superior over the state-of-the-art methods with the mean dice of 0.8972, 0.9420, 0.8312, 0.8064 and 0.9214 on five public polyp datasets. Significance. The proposed method improves the integrity of the margins and internal details for polyp segmentation, which will provide a powerful aid for doctors to achieve accurate judgments, reducing the likelihood of colorectal cancer and improving the survival chances of patients.
Lexical Diversity and Other Linguistic Measures in Schizophrenia

Abstract Straightforward linguistic measures may be indicators of reduced language production and lexical diversity among individuals with schizophrenia with negative symptoms and neurocognitive impairments. We compared 98 patients with schizophrenia to 101 unaffected controls on six language variables (e.g., number of relationships between objects, use of complex transitions in the narrative structure), number of words produced, and lexical diversity computed as the moving average type-token ratio from both speaking and writing tasks. Patients differed from controls on nearly all of the linguistic measures; number of words produced had the strongest effect, with an average Cohen's d of 0.68; values pertaining to lexical diversity were 0.50 and 0.32, respectively, for the speaking tasks and the writing tasks. Most measures were correlated with alogia and other domains of negative symptoms (including avolition-apathy and anhedonia-asociality), as well as with diverse neurocognitive domains, especially those pertaining to working memory, verbal learning, and verbal category fluency. Further work is needed to understand longitudinal changes in these linguistic variables, as well as their utility as measures of alogia.
Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

Abstract Background To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. Methods Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). Results In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%–98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%–97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. Conclusions MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.
Dirichlet mixtures in text modeling

Word rates in text vary according to global factors such as genre, topic, author, and expected readership (Church and Gale 1995). Models that summarize such global factors in text or at the document level, are called ‘text models. ’ A finite mixture of Dirichlet distribution (Dirichlet Mixture or DM for short) was investigated as a new text model. When parameters of a multinomial are drawn from a DM, the compound for discrete outcomes is a finite mixture of the Dirichlet-multinomial. A Dirichlet multinomial can be regarded as a multivariate version of the Poisson mixture, a reliable univariate model for global factors (Church and Gale 1995). In the present paper, the DM and its compounds are introduced, with parameter estimation methods derived from Minka’s fixed-point methods (Minka 2003) and the EM algorithm. The method can estimate a considerable number of parameters of a large DM, i.e., a few hundred thousand parameters. After discussion of the relationships within the DM — probabilistic latent semantic analysis (PLSA) (Hofmann 1999), the mixture of unigrams (Nigam et al. 2000), and latent Dirichlet allocation (LDA) (Blei et al. 2001, 2003) — the products of statistical language modeling applications are discussed and their performance in perplexity compared. The DM model achieves the lowest perplexity level despite its unitopic nature.
Formation Recognition Based on Hough Transform

As an important property of operation group,formation reflects the cooperative relationships among group members in space arrangement.It often indicates the intention of near future,identity and threat of enemy groups directly.In order to provide much more significant battlefield information and much better support for commanders' situation assessment and command activity,the problem of recognizing made-up lines of formations is researched.Based on Hough Transform,a template-based modeling method for these formations is proposed at first,then the pattern feature extraction strategy based on clustering of points in Hough Space is put forward.At the end of the paper,a formation recognition approach based on the matching between pattern features of a formation and these templates is suggested.Experiment results show that the method is effective.
Copy Number Variation on ABCC2-DNMBP Loci Affects the Diversity and Composition of the Fecal Microbiota in Pigs

A better understanding of the environmental and host factors modulating gut microbiomes is a topic of greatest interest. Recent evidence suggests that genetic variation in the pig genome partially controls the composition of porcine gut microbiota. ABSTRACT Genetic variation in the pig genome partially modulates the composition of porcine gut microbial communities. Previous studies have been focused on the association between single nucleotide polymorphisms (SNPs) and the gut microbiota, but little is known about the relationship between structural variants and fecal microbial traits. The main goal of this study was to explore the association between porcine genome copy number variants (CNVs) and the diversity and composition of pig fecal microbiota. For this purpose, we used whole-genome sequencing data to undertake a comprehensive identification of CNVs followed by a genome-wide association analysis between the estimated CNV status and the fecal bacterial diversity in a commercial Duroc pig population. A CNV predicted as gain (DUP) partially harboring ABCC2-DNMBP loci was associated with richness (P = 5.41 × 10−5, false discovery rate [FDR] = 0.022) and Shannon α-diversity (P = 1.42 × 10−4, FDR = 0.057). The in silico predicted gain of copies was validated by real-time quantitative PCR (qPCR), and its segregation, and positive association with the richness and Shannon α-diversity of the porcine fecal bacterial ecosystem was confirmed in an unrelated F1 (Duroc × Iberian) cross. Our results advise the relevance of considering the role of host-genome structural variants as potential modulators of microbial ecosystems and suggest the ABCC2-DNMBP CNV as a host-genetic factor for the modulation of the diversity and composition of the fecal microbiota in pigs. IMPORTANCE A better understanding of the environmental and host factors modulating gut microbiomes is a topic of greatest interest. Recent evidence suggests that genetic variation in the pig genome partially controls the composition of porcine gut microbiota. However, since previous studies have been focused on the association between single nucleotide polymorphisms and the fecal microbiota, little is known about the relationship between other sources of genetic variation, like the structural variants and microbial traits. Here, we identified, experimentally validated, and replicated in an independent population a positive link between the gain of copies of ABCC2-DNMBP loci and the diversity and composition of pig fecal microbiota. Our results advise the relevance of considering the role of host-genome structural variants as putative modulators of microbial ecosystems and open the possibility of implementing novel holobiont-based management strategies in breeding programs for the simultaneous improvement of microbial traits and host performance.
Bayesian local bandwidth selector in multivariate associated kernel estimator for joint probability mass functions

ABSTRACT This work treats non-parametric estimation of multivariate probability mass functions, using multivariate discrete associated kernels. We propose a Bayesian local approach to select the matrix of bandwidths considering the multivariate Dirac Discrete Uniform and the product of binomial kernels, and treating the bandwidths as a diagonal matrix of parameters with some prior distribution. The performances of this approach and the cross-validation method are compared using simulations and real count data sets. The obtained results show that the Bayes local method performs better than cross-validation in terms of integrated squared error.
Liver directed adeno‐associated viral vectors to treat metabolic disease

The liver is the metabolic center of the body and an ideal target for gene therapy of inherited metabolic disorders (IMDs). Adeno‐associated viral (AAV) vectors can deliver transgenes to the liver with high efficiency and specificity and a favorable safety profile. Recombinant AAV vectors contain only the transgene cassette, and their payload is converted to non‐integrating circular double‐stranded DNA episomes, which can provide stable expression from months to years. Insights from cellular studies and preclinical animal models have provided valuable information about AAV capsid serotypes with a high liver tropism. These vectors have been applied successfully in the clinic, particularly in trials for hemophilia, resulting in the first approved liver‐directed gene therapy. Lessons from ongoing clinical trials have identified key factors affecting efficacy and safety that were not readily apparent in animal models. Circumventing pre‐existing neutralizing antibodies to the AAV capsid, and mitigating adaptive immune responses to transduced cells are critical to achieving therapeutic benefit. Combining the high efficiency of AAV delivery with genome editing is a promising path to achieve more precise control of gene expression. The primary safety concern for liver gene therapy with AAV continues to be the small risk of tumorigenesis from rare vector integrations. Hepatotoxicity is a key consideration in the safety of neuromuscular gene therapies which are applied at substantially higher doses. The current knowledge base and toolkit for AAV is well developed, and poised to correct some of the most severe IMDs with liver‐directed gene therapy.
Molecular classification and identification of an aggressive signature in low‐grade B‐cell lymphomas

Non‐follicular low‐grade B‐cell lymphomas (LGBCL) are biologically diverse entities that share clinical and histologic features that make definitive pathologic categorization challenging. While most patients with LGBCL have an indolent course, some experience aggressive disease, highlighting additional heterogeneity across these subtypes. To investigate the potential for shared biology across subtypes, we performed RNA sequencing and applied machine learning approaches that identified five clusters of patients that grouped independently of subtype. One cluster was characterized by inferior outcome, upregulation of cell cycle genes, and increased tumor immune cell content. Integration of whole exome sequencing identified novel LGBCL mutations and enrichment of TNFAIP3 and BCL2 alterations in the poor survival cluster. Building on this, we further refined a transcriptomic signature associated with early clinical failure in two independent cohorts. Taken together, this study identifies unique clusters of LGBCL defined by novel gene expression signatures and immune profiles associated with outcome across diagnostic subtypes.
Dying To Find Out: The Cost of Time at the Dawn of the Multicancer Early Detection Era

Abstract Cancer is a significant burden worldwide that adversely impacts life expectancy, quality of life, health care costs, and workforce productivity. Although currently recommended screening tests for individual cancers reduce mortality, they detect only a minority of all cancers and sacrifice specificity for high sensitivity, resulting in a high cumulative rate of false positives. Blood-based multicancer early detection tests (MCED) based on next-generation sequencing (NGS) and other technologies hold promise for broadening the number of cancer types detected in screened populations and hope for reducing cancer mortality. The promise of this new technology to improve cancer detection rates and make screening more efficient at the population level demands the development of novel trial designs that accelerate clinical adoption. Carefully designed clinical trials are needed to address these issues.
Unveiling the complexity of nanodiamond structures

Significance Uncovering the structural complexity of nanodiamonds has been a significant experimental and theoretical challenge. We show that cubic nanodiamonds of small sizes (e.g., <5 nm) display the characteristic (200) forbidden reflections in their electron diffraction patterns, which makes them indistinguishable from new diamond (n-diamond). Besides the size effect, our study demonstrates that various defects, including distortions, dislocations, and boundaries, can also make the (200) forbidden reflections visible. Thus, we supplement the explanation of n-diamond structure and reveal that using electron diffraction alone is insufficient to differentiate various diamond nanostructures. These findings shed light on the impact of order/disorder on nanodiamond structures at the single-atom level.
Numerical simulations of the unsteady flow and radiated noise over a cylindrical cavity

,The purpose of this paper is to investigate the acoustical field generated by the flow over a cylindrical cavity on a flat plane, similar to those located on wings and fuselage of transport aircraft, using a hybrid numerical method. Firstly, a CFD computation was carried out, using a Large-Eddy Simulation method, to compute the turbulent structures and the noise generating mechanisms. Secondly, the near-field results were injected in a Kirchhoff integral method code, in order to compute the far-field noise radiation. The retained case for this study is represented by a cavity with a unitary depth/diameter ratio, in the presence of a subsonic flow with a Reynolds number based on the diameter of the cavity equal to 4.6x10 5 . As inflow condition, a fluctuation-free velocity profile was considered. Computational results were compared to experimental data measured in an anechoic wind tunnel for the same geometry configuration, but in the presence of a turbulent boundary layer. As expected, a symmetrical flow pattern was observed joined with a fluid resonant mechanism. The computation shows that the acoustical field is less influenced by the inflow condition and results are in good agreement with the experimental measurements despite a slight under-estimation of the main tone amplitude.