Miracle-dz/UniFAR-FTUs huggingface dataset:
Allelic variations of WAK106‐E2Fa‐DPb1‐UGT74E2 module regulate fibre properties in Populus tomentosa

Summary Wood formation, intricately linked to the carbohydrate metabolism pathway, underpins the capacity of trees to produce renewable resources and offer vital ecosystem services. Despite their importance, the genetic regulatory mechanisms governing wood fibre properties in woody plants remain enigmatic. In this study, we identified a pivotal module comprising 158 high‐priority core genes implicated in wood formation, drawing upon tissue‐specific gene expression profiles from 22 Populus samples. Initially, we conducted a module‐based association study in a natural population of 435 Populus tomentosa, pinpointing PtoDPb1 as the key gene contributing to wood formation through the carbohydrate metabolic pathway. Overexpressing PtoDPb1 led to a 52.91% surge in cellulose content, a reduction of 14.34% in fibre length, and an increment of 38.21% in fibre width in transgenic poplar. Moreover, by integrating co‐expression patterns, RNA‐sequencing analysis, and expression quantitative trait nucleotide (eQTN) mapping, we identified a PtoDPb1‐mediated genetic module of PtoWAK106‐PtoDPb1‐PtoE2Fa‐PtoUGT74E2 responsible for fibre properties in Populus. Additionally, we discovered the two PtoDPb1 haplotypes that influenced protein interaction efficiency between PtoE2Fa‐PtoDPb1 and PtoDPb1‐PtoWAK106, respectively. The transcriptional activation activity of the PtoE2Fa‐PtoDPb1 haplotype‐1 complex on the promoter of PtoUGT74E2 surpassed that of the PtoE2Fa‐PtoDPb1 haplotype‐2 complex. Taken together, our findings provide novel insights into the regulatory mechanisms of fibre properties in Populus, orchestrated by PtoDPb1, and offer a practical module for expediting genetic breeding in woody plants via molecular design.
Performance of an EO/IR sensor system in marine search and rescue

This investigation centered on the most challenging search and rescue requirements: finding small targets in high seas. Our course of action was to investigate the capabilities of known hyperspectral and LWIR sensors in realistic conditions of target and environment to drive the design of a sensor system capable of substantially improving search efficiency and efficacy for these conditions. The relevant results from this study include demonstration of significant power in clutter rejection (e.g., whitewater and wave complexity) by the LWIR sensor. In addition, several factors combine to indicate that a modest implementation of HSI and IR sensors would provide significant improvement in search efficiency and efficacy for small targets in high seas. These factors include high PD, low PFA, and the untiring nature of the sensors when combined with the potential of real-time automatic target/background discrimination. This modest implementation would translate directly into faster, more complete coverage, at lower overall costs to the USCG, and a more likely probability of a successful search mission.
Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm

Background Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea. Methods To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary’s Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset. Results The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036). Conclusion GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.
RNA binding protein AUF1/HNRNPD regulates nuclear export, stability and translation of SNCA transcripts

Alpha-synuclein (SNCA) accumulation plays a central role in the pathogenesis of Parkinson's disease. Determining and interfering with the mechanisms that control SNCA expression is one approach to limiting disease progression. Currently, most of our understanding of SNCA regulation is protein-based. Post-transcriptional mechanisms directly regulating SNCA mRNA expression via its 3′ untranslated region (3′UTR) were investigated here. Mass spectrometry of proteins pulled down from murine brain lysates using a biotinylated SNCA 3′UTR revealed multiple RNA-binding proteins, of which HNRNPD/AUF1 was chosen for further analysis. AUF1 bound both proximal and distal regions of the SNCA 3′UTR, but not the 5′UTR or CDS. In the nucleus, AUF1 attenuated SNCA pre-mRNA maturation and was indispensable for the export of SNCA transcripts. AUF1 destabilized SNCA transcripts in the cytosol, primarily those with shorter 3′UTRs, independently of microRNAs by recruiting the CNOT1-CNOT7 deadenylase complex to trim the polyA tail. Furthermore, AUF1 inhibited SNCA mRNA binding to ribosomes. These data identify AUF1 as a multi-tasking protein regulating maturation, nucleocytoplasmic shuttling, stability and translation of SNCA transcripts.
Embryonic exposure to artificial light at night impairs learning abilities and their covariance with behavioural traits in teleost fish

The natural light cycle has profound effects on animals' cognitive systems. Its alteration owing to human activities, such as artificial light at night (ALAN), affects the biodiversity of mammalian and avian species by impairing their cognitive functions. The impact of ALAN on cognition, however, has not been investigated in aquatic species, in spite of the common occurrence of this pollution along water bodies. We exposed eggs of a teleost fish (the zebrafish Danio rerio) to ALAN and, upon hatching, we measured larvae’ cognitive abilities with a habituation learning paradigm. Both control and ALAN-exposed larvae showed habituation learning, but the latter learned significantly slower, suggesting that under ALAN conditions, fish require many more events to acquire ecologically relevant information. We also found that individuals' learning performance significantly covaried with two behavioural traits in the control zebrafish, but ALAN disrupted one of these relationships. Additionally, ALAN resulted in an average increase in larval activity. Our results showed that both fish's cognitive abilities and related individual differences are negatively impacted by light pollution, even after a short exposure in the embryonic stage.
Underweight, overweight and obesity in the children residents in the amazons forest (Peru).

Objectives. To analyze the anthropometric parameters according to age and sex, and to describe the prevalence of overweight and obesity in a young population that because of geographical reasons has very difficult access to the sanitary services. Methods. We studied 255 children, aged 1 to 13 years, from a population of the low forest of the Amazon river (Loreto, Peru), and observed the following variables: weight, height and bicipital, tricipital, subscapular and suprailiac skinfold thickness. Results. The prevalence of underweight was 15.5% (CI 95%: 9.7% 22.9%) in girls and 1.6% (CI 95%: 0.2% 5.6%) in boys. The difference of prevalence between the sexes was statistically significant PD: 14.0% (CI 95%: 7.3% – 21.2%; p=0.00008). Overweight was observed in 8.7% (CI 95%: 4.4% – 15.1%) and 1.6% (CI 95%: 0.2 – 5.5%) of the boys and girls, respectively. The difference of prevalence between the sexes was statistically significant PD: 7.2% (CI 95%: 1.8% – 12.5%; p=0.009). Discussion and Conclusion. We can conclude that differences exist in the prevalence of underweight and overweight between the masculine and feminine genders in the low forest of the river amazons (Peru); being the prevalence of underweight but risen in girls and a prevalence of overweight but risen in boys.
Agro-ecological Zone Emission Factor Model

1 Overview The purpose of the agro-ecological zone emission factor (AEZ-EF) model is to estimate the total CO2equivalent emissions induced by expanded biofuel production. The model combines matrices of carbon fluxes (Mg CO2 ha -1 y) with matrices of changes in land use (ha) by land-use category projected by the GTAP model. The carbon fluxes in AEZ-EF are aggregated to the same 19 regions (Table 1) and 18 AEZs (Figure 1) used by GTAP-BIO-ADV, the version of GTAP currently used by Purdue University researchers for ILUC modeling (e.g., Tyner, Taheripour et al. 2010).
Repeated Levosimendan Infusions in the Management of Advanced Heart Failure: Review of the Evidence and Meta-analysis of the Effect on Mortality

Abstract: In the latest years, several studies described the impact of repetitive/intermittent i.v. levosimendan treatment in the management of advanced heart failure. For this updated review, we systematically searched the literature for clinical trials, registries, and real-world data and identified 31 studies that we commented in a narrative review: 3814 patients were described, of whom 1744 were treated repetitively with levosimendan. On the basis of the nature of the study protocols and of the end points, out of those studies, we further selected 9 that had characteristics, making them suitable for a meta-analysis on mortality. This short list describes data from 680 patients (of whom 399 received repeated doses of levosimendan) and 110 death events (of which 50 occurred in the levosimendan cohort). In the meta-analysis, repetitive/intermittent therapy with i.v. levosimendan was associated with a significant reduction in mortality at the longest time point available: 50 of 399 (12.5%) versus 60 of 281 (21.4%) in the control arms, with a risk ratio of 0.62 (95% confidence interval, 0.42–0.90; P < 0.01). In a sensitivity analysis, removing each trial and reanalyzing the remaining data set did not change the trend, magnitude, or significance of the results. A visual inspection of the funnel plot did not suggest publication bias. The results provide a very strong rationale for continuing to investigate the repetitive use of levosimendan in patients with advanced heart failure by properly powered regulatory clinical trials. Meanwhile, it seems that the use of repetitive/intermittent i.v. levosimendan infusions has become one of the few effective options for preserving the hemodynamic and symptomatic balance in such patients.
Joint Learning of Fully Connected Network Models in Lifting Based Image Coders

The optimization of prediction and update operators plays a prominent role in lifting-based image coding schemes. In this paper, we focus on learning the prediction and update models involved in a recent Fully Connected Neural Network (FCNN)-based lifting structure. While a straightforward approach consists in separately learning the different FCNN models by optimizing appropriate loss functions, jointly learning those models is a more challenging problem. To address this problem, we first consider a statistical model-based entropy loss function that yields a good approximation to the coding rate. Then, we develop a multi-scale optimization technique to learn all the FCNN models simultaneously. For this purpose, two loss functions defined across the different resolution levels of the proposed representation are investigated. While the first function combines standard prediction and update loss functions, the second one aims to obtain a good approximation to the rate-distortion criterion. Experimental results carried out on two standard image datasets, show the benefits of the proposed approaches in the context of lossy and lossless compression.
Prediction of an impaired myocardial work using infarct size in acute myocardial infarction

Background The relationship between myocardial infarct size (MIS) on late gadolinium-enhanced cardiac MRI (LGE MRI) and myocardial work (MW) indices assessed with MW echocardiography (MWE) has not been well characterized. This study aimed to determine an impaired MW using MIS in patients with acute myocardial infarction. Methods Left ventricular (LV) two-dimensional speckle-tracking echocardiography, MWE, and LGE MRI were performed in 33 patients with ST-segment elevation myocardial infarction and in 30 age- and sex-comparable controls. LV global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE) and MIS were acquired, respectively. Results MIS was negatively correlated with GWI (r = −0.60, P = 0.005), GCW (r =- 0.66, P = 0.002) and GWE (r = −0.71, P = 0.0004), but positively correlated with GLS (r = 0.68, P = 0.001). With the receiver operating characteristic curve, the cutoff value of MIS for the prediction of an impaired GLS was 16.5% [area under the curve (AUC) = 0.867)], an impaired GWI was 19.2% (AUC = 0.727), an impaired GCW was 19.2% (AUC = 0.725), an increased GWW was 15.8% (AUC = 0.656), an impaired GWE was 15.8% (AUC = 0.880). Conclusion MIS is a strong predictor of impaired MW. Timely reduction of infarct size is essential to improve myocardial function.
Effectiveness of pneumococcal vaccination on hospitalization and death in the adult and older adult diabetic population: a systematic review

ABSTRACT Introduction Diabetic patients are at a higher risk of getting pneumococcal disease and are therefore recommended to get vaccinated. The aim of our systematic review is the retrieval and analysis of all available evidence on the effect of pneumococcal vaccination on the risk of hospitalization and death in adult patients with diabetes. Research design and methods MEDLINEand EMBASE were searched from inception until January 2023. We included all studies investigating whether pneumococcal vaccination reduces the risk of dying or being hospitalized in diabetic patients. The Newcastle-Ottawa scale was used to assess risk of bias. Results Only two studies, encompassing a total of 68,246 subjects, were considered eligible for inclusion and of high quality. In both studies polysaccharide pneumococcal vaccination was associated with a reduction of the risk of hospitalization or death in adult diabetic patients (aHR: 0.76 in one study, aOR: 0.97 in the other one). However, in neither of the two included studies the lower risk was statistically significant. Conclusions Further research is needed due to the potentially major clinical implications for diabetic patients. The results of this systematic review can serve as a foundation for future studies, indicating the importance of continuing research in this area to improve patient outcomes.
SEVERE WEATHER UNDER A CHANGING CLIMATE: LARGE-SCALE INDICATORS OF EXTREME EVENTS

Global warming has become a generally accepted theory in the past decade or so. A recent wave of severe storms has raised the question of whether global warming has or will result in an increase in both frequency and intensity of such extreme weather events. This paper will examine large-scale indicators that are generally believed to be closely related to severe storms, using extreme value theory. We present numerous methods, such as the Generalized Extreme Value distribution, the Generalized Pareto distribution, and Bayesian approaches incorporating spatial correlation between measurement locations. These approaches are applied to a data set covering the United States and surrounding regions for the years 1958 to 1999, to predict return levels for extreme events. We also attempt to determine if there was a change in the behavior of these events over the time period of interest.
YTHDF1 facilitates PRC1‐mediated H2AK119ub in human ES cells

Polycomb repressive complexes (PRCs) play critical roles in cell fate decisions during normal development as well as disease progression through mediating histone modifications such as H3K27me3 and H2AK119ub. How exactly PRCs recruited to chromatin remains to be fully illuminated. Here, we report that YTHDF1, the N6‐methyladenine (m6A) RNA reader that was previously known to be mainly cytoplasmic, associates with RNF2, a PRC1 protein that mediates H2AK119ub in human embryonic stem cells (hESCs). A portion of YTHDF1 localizes in the nuclei and associates with RNF2/H2AK119ub on a subset of gene loci related to neural development functions. Knock‐down YTHDF1 attenuates H2AK119ub modification on these genes and promotes neural differentiation in hESCs. Our findings provide a noncanonical mechanism that YTHDF1 participates in PRC1 functions in hESCs.
Gasdermin D is the only Gasdermin that provides protection against acute Salmonella gut infection in mice

Significance The host immune response against infection relies on programmed cell death that has recently been shown to involve Gasdermins—a family of membrane-pore-forming proteins. Despite abundant expression of multiple Gasdermins in mammalian gut tissue, we here find using a mouse line lacking all mouse Gasdermins at once that only Gasdermin D provides protection against oral Salmonella infection. To accomplish this protection, both gut epithelial cells and classical immune cells employ Gasdermin D to limit bacterial loads in the mucosa, to control inflammation, to prevent epithelial disruption, and to reduce systemic spread of the pathogen. Hence, this study sheds light on the differential impact of Gasdermins in infectious diseases.
The Relationship Between Low Back Pain and Sagittal Spinal Alignment and Back Muscle Mass in Korean Fishery Workers

Objective To investigate the relationship between low back pain (LBP) and sagittal spino-pelvic parameters along with the relationship between LBP and back muscle mass in Korean male and female fishery workers. Methods This retrospective study included a total of 146 subjects who underwent Fishermen’s health survey conducted between June 2018 and August 2020. LBP was evaluated through visual analogue scale (VAS) and Oswestry Disability Index (ODI). Sagittal spino-pelvic parameters were measured from whole spine standing X-rays. Back muscle cross-sectional areas were identified through lumbar spine magnetic resonance imaging and body composition was analyzed through bioelectrical impedance analysis. Results The study included 75 males and 71 females, with an average age of 58.33 years for males and 56.45 years for females. Female subjects exhibited higher VAS and ODI scores, larger pelvic tilt (PT) and trunk fat mass and smaller trunk muscle mass compared to males. In female, ODI positively correlated with sagittal vertical axis (SVA) and PT. No significant correlations were found between ODI scores and body composition in either sex. The high ODI group showed greater SVA, PT, and pelvic incidence-lumbar lordosis and higher trunk fat/muscle ratio. Psoas muscle mass, total and skeletal muscle mass and trunk muscle mass of that group were smaller than those of low ODI group. Conclusion Spino-pelvic parameters and back muscle mass were associated with ODI and there were differences between Korean male and female fishery workers.
Moderate-Severe TBI as a Progressive Disorder: Patterns and Predictors of Cognitive Declines in the Chronic Stages of Injury

Background Moderate-severe traumatic brain injury (TBI) has been associated with progressive cognitive decline in the chronic injury stages in a small number of studies. Objective This study aimed to (i) replicate our previous findings of decline from 1 to 3+ years post-injury in a larger, non-overlapping sample and (ii) extend these findings by examining the proportion of decliners in 2 earlier time windows, and by investigating novel predictors of decline. Methods N = 48 patients with moderate-severe TBI underwent neuropsychological assessment at 2, 5, 12 months, and 30+ months post-injury. We employed the Reliable Change Index (RCI) to evaluate decline, stability and improvement across time and logistic regression to identify predictors of decline (demographic/cognitive reserve; injury-related). Results The proportions of patients showing decline were: 12.5% (2-5 months post-injury), 17% (5-12 months post-injury), and 27% (12-30+ months post-injury). Measures of verbal retrieval were most sensitive to decline. Of the predictors, only left progressive hippocampal volume loss from 5 to 12 months post-injury significantly predicted cognitive decline from 12 to 30+ months post-injury. Conclusions Identical to our previous study, 27% of patients declined from 12 to 30+ months post-injury. Additionally, we found that the further from injury, the greater the proportion of patients declining. Importantly, earlier progressive hippocampal volume loss predicted later cognitive decline. Taken together, the findings highlight the need for ongoing research and treatment that target these deleterious mechanisms affecting patients in the chronic stages of moderate-severe TBI.
News similarity with natural language processing

News articles are pieces of Natural Language that comply with the model of 5W1H, meaning, they should answer to the following six questions: What, Who, Where, When, Why and How. This project takes advantage of that assumption to create an algorithm capable of building a representation of a news article and a distance between such representations for any pair of politics news. With that knowledge, a global distance between entries based on similarity of content is built. That algorithm is assessed in comparison with the topic modeling algorithm Latent Dirichlet Allocation (LDA). Applications of the system with their corresponding visualisations are presented too.
The role of illness‐related cognition in the relationships between resilience and depression/anxiety in nasopharyngeal cancer patients

Resilience has been reported as an important predictor of better mental health and prognoses in cancer patients, while its mechanisms were not clearly elucidated. In this study, we surveyed a large sample of nasopharyngeal carcinoma patients to investigate the mediating role of illness‐related cognition (illness perception, stigma and meaning in life) on the associations between resilience and symptoms of anxiety and depression.