Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing DOACs with warfarin in AF patients with chronic kidney disease or valvular disease: A systematic review and meta-analysis

Objective: To analyze the safety and efficacy of different direct oral anticoagulant agents (DOACs) compared to warfarin in patients with concomitant atrial fibrillation (AF) and valvular disease or concomitant AF and chronic kidney disease (CKD). Methods: We conducted literature searches in MEDLINE, Embase, and EBM Reviews to examine randomized-controlled trials (RCTs) and non-RCTs that included the aforementioned patient populations treated with warfarin or DOAC (rivaroxaban, dabigatran, apixaban, or edoxaban) and assessed outcomes of bleeding, stroke, or systemic/arterial thromboembolism. Meta-analysis was performed for eligible studies using the Mantel-Haenszel method random-effects model. Results: 3,172 studies were screened and 154 studies were selected after two levels of screening. Meta-analysis showed that, in patients with concomitant AF and CKD, DOAC was associated with reduced bleeding in non-RCTs (OR 0.65, 95% Cl [0.49, 0.86], p=0.003), particularly in more severe CKD (eGFR < 60mL/min/1.73m2). Apixaban in particular was associated with reduced bleeding (OR 0.52, 95% Cl [0.44, 0.63], p<0.00001) and stroke incidence (OR 0.60, 95% Cl [0.41, 0.87], p=0.007). In patients with concomitant AF and valvular disease, DOAC was associated with reduced bleeding (OR 0.75, 95% CI [0.57, 0.97], p=0.03) and stroke incidence (OR 0.66, 95% CI [0.47, 0.93], p=0.02) in non-RCTs. Conclusion: Our study studied populations that are typically excluded from large-scale anticoagulation studies and our findings suggest that DOACs may be superior to warfarin both in the prevention of thromboembolic event and in the reduction of bleeding risks in patients with concomitant CKD or valvular disease.
Acute health care use among children during the first 2.5 years of the COVID-19 pandemic in Ontario, Canada: a population-based repeated cross-sectional study

Background: The effects of the decline in health care use at the start of the COVID-19 pandemic on the health of children are unclear. We sought to estimate changes in rates of severe and potentially preventable health outcomes among children during the pandemic. Methods: We conducted a repeated cross-sectional study of children aged 0–17 years using linked population health administrative and disease registry data from January 2017 through August 2022 in Ontario, Canada. We compared observed rates of emergency department visits and hospital admissions during the pandemic to predicted rates based on the 3 years preceding the pandemic. We evaluated outcomes among children and neonates overall, among children with chronic health conditions and among children with specific diseases sensitive to delays in care. Results: All acute care use for children decreased immediately at the onset of the pandemic, reaching its lowest rate in April 2020 for emergency department visits (adjusted relative rate [RR] 0.28, 95% confidence interval [CI] 0.28–0.29) and hospital admissions (adjusted RR 0.43, 95% CI 0.42–0.44). These decreases were sustained until September 2021 and May 2022, respectively. During the pandemic overall, rates of all-cause mortality, admissions for ambulatory care–sensitive conditions, newborn readmissions or emergency department visits or hospital admissions among children with chronic health conditions did not exceed predicted rates. However, after declining significantly between March and May 2020, new presentations of diabetes mellitus increased significantly during most of 2021 (peak adjusted RR 1.49, 95% CI 1.28–1.74 in July 2021) and much of 2022. Among these children, presentations for diabetic ketoacidosis were significantly higher than expected during the pandemic overall (adjusted RR 1.14, 95% CI 1.00–1.30). We observed similar time trends for new presentations of cancer, but we observed no excess presentations of severe cancer overall (adjusted RR 0.91, 95% CI 0.62–1.34). Interpretation: In the first 30 months of the pandemic, disruptions to care were associated with important delays in new diagnoses of diabetes but not with other acute presentations of select preventable conditions or with mortality. Mitigation strategies in future pandemics or other health system disruptions should include education campaigns around important symptoms in children that require medical attention.
Antecedent Determination of Factors Affecting Online Learning Experience Among Primary Students Using Random Forest Classifier

The COVID-19 pandemic led different institution to shift from traditional learning (face-to-face) to fully online learning as evident among different countries. However, most studies have dealt with different levels of education, mostly in senior high school and college. The generalized findings prompted the under exploration of primary students and their online learning experience. The aim of this study was to evaluate the antecedents of factors affecting online learning experience among primary students. Utilizing the community of inquiry framework, specific factors under social presence, teaching presence, and cognitive presence were evaluated. To consider the most influential factors, random forest classifier was utilized in this study. Garnering a 92% accuracy rate model, it was seen from the results that the most influential factors would be open communication and social presence, followed by design and organization and the level of understanding among lessons. Lastly, teacher facilitation also had a significant positive effect on the positive online learning experience among primary students. It could be deduced from the results that integrated points among learners, on-time feedbacks and evaluation, promoted feedback among concerns, and positive feedback for activities provided a positive learning experience for online classes among primary students. In addition, important factors such as the ones discussed should be considered in the continuous promotion of online learning among primary students. Proper guidelines, lesson implementation, and response from teachers would promote an effective online learning experience which institutions should highly consider for continuous online learning set-up implementation.
Photochemical initiation of polariton-mediated exciton propagation

Abstract Placing a material inside an optical cavity can enhance transport of excitation energy by hybridizing excitons with confined light modes into polaritons, which have a dispersion that provides these light–matter quasi-particles with low effective masses and very high group velocities. While in experiments, polariton propagation is typically initiated with laser pulses, tuned to be resonant either with the polaritonic branches that are delocalized over many molecules, or with an uncoupled higher-energy electronic excited state that is localized on a single molecule, practical implementations of polariton-mediated exciton transport into devices would require operation under low-intensity incoherent light conditions. Here, we propose to initiate polaritonic exciton transport with a photo-acid, which upon absorption of a photon in a spectral range not strongly reflected by the cavity mirrors, undergoes ultra-fast excited-state proton transfer into a red-shifted excited-state photo-product that can couple collectively with a large number of suitable dye molecules to the modes of the cavity. By means of atomistic molecular dynamics simulations we demonstrate that cascading energy from a photo-excited donor into the strongly coupled acceptor-cavity states via a photo-chemical reaction can indeed induce long-range polariton-mediated exciton transport.
Devaluing memories of reward: A case for dopamine

We describe a novel role for dopamine in devaluing sensory memories of reward. Mesencephalic dopamine cells activated during a mediated devaluation phase were later chemogenetically reactivated. This retrieval of the devalued reward memory elicited a reduction in the hedonic evaluation of sucrose reward. Through optogenetic and chemogenetic manipulations, we confirm dopamine cells are both sufficient and necessary for mediated devaluation, and retrieval of these memories reflected dopamine release in the nucleus accumbens. Consistent with our computational modelling data, our findings indicate a critical role for dopamine in encoding predictive representations of the sensory features of reinforcement. Overall, we illuminate the elaborate nature of reinforcement signals encoded by dopamine and suggest novel approaches to treating a host of psychobiological disorders.
Increased chromatin accessibility drives transition to androgen receptor splice variant dependence in castration-resistant prostate cancer

Androgen receptor (AR) splice variants, of which ARv7 is the most common, are increased in prostate cancer (PC) that develops resistance to androgen signaling inhibitor drugs, but the extent to which these variants drive AR activity, and whether they have novel functions or dependencies, remain to be determined. We generated a subline of VCaP PC cells (VCaP16) that is resistant to the AR inhibitor enzalutamide (ENZ) and found that AR activity was independent of the full-length AR (ARfl), despite its continued high-level expression, and was instead driven by ARv7. The ARv7 cistrome and transcriptome in VCaP16 cells mirrored that of the ARfl in VCaP cells, although ARv7 chromatin binding was weaker, and strong ARv7 binding sites correlated with higher affinity ARfl binding sites across multiple models and clinical samples. Notably, although ARv7 expression in VCaP cells increased rapidly in response to ENZ, there was a long lag before it gained chromatin binding and transcriptional activity. This lag was associated with an increase in chromatin accessibility, with the AR and nuclear factor I (NFI) motifs being most enriched at these more accessible sites. Moreover, the transcriptional effects of combined NFIB and NFIX knockdown versus ARv7 knockdown were highly correlated. These findings indicate that ARv7 can drive the AR program, but that its activity is dependent on adaptations that increase chromatin accessibility to enhance its intrinsically weak chromatin binding.
Negative Synergistic Effects of Drought and Heat During Flowering and Seed Setting in Soybean

Rising temperatures and intense heatwaves combined with lower precipitations are the new norms of current global scenarios. These altered climatic conditions negatively impact soybean yield potential and quality. Ten soybean cultivars were subjected to four different growing conditions: control, drought, heat, and combined heat and drought to understand the physiological, yield, and molecular changes. Stomatal conductance was reduced by 62% and 10% under drought and heat, respectively. This reduction was further exacerbated to 93% when exposed to both stresses simultaneously. The highest canopy temperature was recorded at +8 °C with combinatorial treatments, whereas heat and drought exhibited +5.4 °C and +2 °C, respectively. Furthermore, combined stress displayed a more pronounced negative impact on greenness-associated vegetative index; the gene expression analysis further corroborated these findings. Particularly, each °C increase in temperature during flowering-seed filling reduced seed weight by ∼7% and ∼4% with and without drought, respectively. The seed protein increased under drought, whereas the oil showed a converse trend under drought and combined stresses. Most physiology and yield traits showed no significant correlations between control or individual and combined stress. This suggests that selection for combinatorial stress may not be appropriate based on nonstress or individual stress performance. Thus, incorporating stress-resilient traits into elite soybean cultivars could significantly boost soybean production under hot and dry climatic conditions.
Unraveling the oscillatory dynamics of mRNA metabolism and chromatin accessibility during the cell cycle through integration of single-cell multiomic data

The cell cycle is a highly regulated process that ensures the accurate replication and transmission of genetic information from one generation of cells to the next. It is a fundamental biological process and plays a crucial role in development, growth, and maintenance of all living organisms, and its dysregulation can lead to a number of pathologies such as autoimmune diseases, neurodegenerative diseases, and cancer. In this work we present a novel approach to study the gene expression and chromatin accessibility dynamics during the cell cycle in mouse embryonic stem cells. To achieve this, we combined high-depth single-cell multiome sequencing, biophysical modeling, and advanced deep learning techniques. First, we used DeepCycle, a deep learning tool that assigns a cell cycle phase to every cell based on its spliced and unspliced mRNA levels. We then developed a biophysical model that describes the dynamics of gene-specific mRNA transcription, nuclear export, and degradation rates during the cell cycle. Our model obtains genome-wide estimates of mRNA transcription, nuclear retention, and degradation half-lives of genes exhibiting oscillatory dynamics in mESCs. The key feature of single-cell multiome sequencing is that it simultaneously provides readouts for gene expression as well as chromatin accessibility in the same cells. By applying our approach to these data we showcase first-of-its-kind exploration of chromatin accessibility during the cell cycle at high temporal resolution. We believe that our work will be pivotal in formulating a coherent quantitative description of mRNA metabolism. Highlights Integration of single-cell and single-nucleus multiome profiling with deep learning and biophysical modeling uncovers cell cycle regulation of RNA metabolism Genome-wide estimation of mRNA transcription, nuclear export, and degradation rates during the cell cycle in mESCs Identification of a prominent wave of transcription during early G1 in unperturbed asynchronously proliferating mESCs Characterization of temporal waves of post-transcriptional mRNA regulation controlling both mRNA degradation and nuclear export during the cell cycle Chromatin accessibility dynamics within TF footprints reveal key cell cycle regulators
Beyond debuttressing: Mechanics of paraglacial rock slope damage during repeat glacial cycles

Cycles of glaciation impose mechanical stresses on underlying bedrock as glaciers advance, erode, and retreat. Fracture initiation and propagation constitute rock mass damage and act as preparatory factors for slope failures; however, the mechanics of paraglacial rock slope damage remain poorly characterized. Using conceptual numerical models closely based on the Aletsch Glacier region of Switzerland, we explore how in situ stress changes associated with fluctuating ice thickness can drive progressive rock mass failure preparing future slope instabilities. Our simulations reveal that glacial cycles as purely mechanical loading and unloading phenomena produce relatively limited new damage. However, ice fluctuations can increase the criticality of fractures in adjacent slopes, which may in turn increase the efficacy of fatigue processes. Bedrock erosion during glaciation promotes significant new damage during first deglaciation. An already weakened rock slope is more susceptible to damage from glacier loading and unloading and may fail completely. We find that damage kinematics are controlled by discontinuity geometry and the relative position of the glacier; ice advance and retreat both generate damage. We correlate model results with mapped landslides around the Great Aletsch Glacier. Our result that most damage occurs during first deglaciation agrees with the relative age of the majority of identified landslides. The kinematics and dimensions of a slope failure produced in our models are also in good agreement with characteristics of instabilities observed in the field. Our results extend simplified assumptions of glacial debuttressing, demonstrating in detail how cycles of ice loading, erosion, and unloading drive paraglacial rock slope damage.
GadCap: A GADGET multispecies model for the Flemish Cap cod, redfish and shrimp.

Since late 1980s, the demersal community of Flemish Cap (NAFO area 3M) has experienced large variations (including the collapse) in the abundance and population structure of its main fishing resources: cod Gadus morhua, redfish Sebastes sp. and shrimp Pandalus borealis, with alternation in their dominant role in the ecosystem. GadCap is an EU project dealing with the development of a GADGET multispecies model for the Flemish Cap cod, redfish and shrimp, as part of the NAFO roadmap for the EAF. The effect of fishing, trophic interactions (including cannibalism) and water temperature in the dynamic of these three major fishing resources has been modeled. The results highlight the interdependent dynamic of these stocks, and reveals strong interactions between recruitment, fishing and predation (including cannibalism), with marked changes in their relative importance by species-age-length over time. The multispecies model shows that disregarding the species interactions would lead to serious underestimates of natural mortality, overestimations of the exploitable biomass, and highlights the need to move beyond single-species management in this highly coupled ecosystem. Preliminary estimates of total SSB and MSY, under different combinations of fishing mortality for all the three stocks, are also presented.
Comparison of Three Spatial Interpolation Methods for Climate Variables in China

Spatial interpolation of climate data is frequently required to provide input for regional plant growth models. As no single method among so many available ones to spatial interpolation of climate variables is optimal for all regions and all variables, it is very important to compare the results obtained using alternative methods applied to each set of data. For estimating 30-year of 10-day mean air temperature and monthly photosynthetic active radiation(PAR) fluxes at specific sites in China, we examined ordinary Kriging(OK) and other two relatively simple methods, one is inverse distance squared(IDS) technique and the other is gradient plus inverse-distance-squared(GIDS) technique. Based on the mean absolute errors from cross-validation tests, the methods were ranked as GIDSOKIDS for interpolating 10-day mean temperature, and IDSGIDSOK for inter- polating monthly mean PAR fluxes. GLDS gives the lowest errors which averaged 1.3℃ for 10-day mean temperature. IDS gives the lowest errors averaged 0.46 MJ/(m2.d) for monthly PAR fluxes. Although OK errors were more consistent for temperatures of different seasons, yet GIDS had lower errors during crop growth seasons. Compared with OK, GIDS was simple to apply for interpolating 10-day mean temperature. Since PAR was affected by so many factors, the performance of GIDS and OK were not as good as we expected. It also implied that the complex methods could not ensure the best results. The multiple linear regressions carried out for GIDS revealed strong gradients in temperature and PAR fluxes varied by month in a fairly consistent way. For all 10-days, r was above 0.94 for temperature, and temperatures decreased with increasing latitude, longitude and elevation. The elevation coefficient for temperature during the year ranged from -0.0033~--0.0048 ℃/m. For PAR, r was lower than that for temperature. The coefficients of PAR for latitude, longitude and elevation ranged from positive values to negative values during the year. Using a 1 km Digital Elevation Model(DEM) for China, the first 10-day mean temperature and monthly mean PAR flux of July were estimated for each pixel to assess the performance of the three interpolation techniques. From temperature maps, we can see that GIDS gives more reasonable estimates in valleys and mountainous area than the other two techniques. The temperature map generated by GIDS shows the elevation effects of temperature in West China especially in valleys and Tibet Plateau. The strong temperature trends towards the northeast in East China and PAR trends towards the northwest were also shown in the maps.
Moment Tensor Inversion for Nuclear Explosions: What Can We Learn from the 6 January and 9 September 2016 Nuclear Tests, North Korea?

ABSTRACT Two nuclear explosions were carried out by the Democratic People’s Republic of North Korea in January and September 2016. Epicenters were located close to those of the 2006, 2009, and 2013 previous explosions. We perform a seismological analysis of the 2016 events combining the analysis of full waveforms at regional distances and seismic array beams at teleseismic distances. We estimate the most relevant source parameters, such as source depth, moment release, and full moment tensor (MT). The best MT solution can be decomposed into an isotropic source, directly related with the explosion and an additional deviatoric term, likely due to near‐source interactions with topographic and/or underground facilities features. We additionally perform an accurate resolution test to assess source parameters uncertainties and trade‐offs. This analysis sheds light on source parameters inconsistencies among studies on previous shallow explosive sources. The resolution of the true MT is hindered by strong source parameters trade‐offs, so that a broad range of well‐fitting MT solutions can be found, spanning from a dominant positive isotropic term to a dominant negative vertical compensated linear vector dipole. The true mechanism can be discriminated by additionally modeling first‐motion polarities at seismic arrays at teleseismic distances. A comparative assessment of the 2016 explosion with earlier nuclear tests documents similar vertical waveforms but a significant increase of amplitude for the 2016 explosions, which proves that the 9 September 2016 was the largest nuclear explosion ever performed in North Korea with a magnitude M w 4.9 and a shallow depth of less than 2 km, although there are no proofs of a fusion explosion. Modeling transversal component waveforms suggests variable size and orientation of the double‐couple components of the 2009, 2013, and 2016 sources.
Original Research Air Pollutant Levels And Asthma Emergency Room Visits In A Highly Populous US Urban County During 2018-19

Objective : Air pollutants are linked to asthma exacerbation. The study purpose was to demonstrate an association between air pollutants levels and asthma emergency room (ER) visit trends in a highly populated US urban county in Georgia during 2018-2019. Methods : Time series analyses were conducted for the variations in daily numbers of children and adult asthma emergency room visits and changes in daily mean PM 2.5 , daily mean PM10 concentrations, daily max 1-hour SO 2 concentrations, daily max 1-hour NO 2 concentrations, daily max 8-hour ozone concentrations, and airborne pollen loads for 2018 to 2019 and potential trends were estimated by using the autoregressive integrated moving average or ARIMA model. Results : During 2018-2019, 15,418 asthma-related ER visits occurred. The pollutants NO 2 , PM 2.5 , PM 10 , and pollen were strong predictors of children's asthma ER visits between 2018 and 2019. No significant associations were observed between the levels of SO 2 , ozone, and children's asthma emergency ER visits. Conclusions : The findings from this time series study strongly suggest that there is a significant contributing relationship between certain air pollutants (NO 2 , PM 2.5 , PM 10 , and pollen) and asthma ER visits in children.