Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mediating Role of Spirituality on Depression, Alcohol Use Disorder, and Problem Gambling Among Korean College Students

Supplemental digital content is available in the text. Abstract Excessive alcohol use and gambling problems among Korean college students in Korea have been recognized as significant public health issues. The purpose of this study was to identify the direct, indirect, and total influence of depression on alcohol use disorder and problem gambling to determine relative magnitudes of the specific mediating effects of spirituality. A cross-sectional correlational survey was conducted in South Korea with 239 college students. Study variables included the Center for Epidemiological Studies Depression Scale, a Spirituality Scale for Koreans, the Alcohol Consumption Questions, and the Korean version of the Canadian Problem Gambling Severity Index. Multiple-mediation analyses were performed. The results found that depression has a negative effect on each of the six subgroups of spirituality. In terms of the effects of spirituality on alcohol use disorder and problem gambling, spirituality was found to have a positive effect only on alcohol use disorder. The relationship between depression and those two addictive behaviors was significantly mediated by spirituality. Bootstrapping revealed that transcendence and connectedness were significantly mediating the relationship between depression and alcohol use disorder. For problem gambling, compassion and connectedness were found to be mediators affecting the relationship between depression and problem gambling. The finding suggests that spirituality's subscales affect two specific addiction problems in different ways. College students' excessive drinking and problem gambling can be prevented by developing and applying an intervention program that strengthens the connectedness of spirituality's subscales.
Tau mis-splicing correlates with motor impairments and striatal dysfunction in a model of tauopathy.

Tauopathies are neurodegenerative diseases caused by the abnormal metabolism of the microtubule associated protein Tau, which is highly expressed in neurons and critically involved in microtubule dynamics. In the adult human brain, the alternative splicing of exon 10 in tau pre-mRNA produces equal amounts of protein isoforms with either three (3 R) or four (4 R) microtubule binding domains. Imbalance in the 3 R : 4 R tau ratio is associated with primary tauopathies that develop atypical parkinsonism, such as Progressive Supranuclear Palsy and Corticobasal Degeneration. Yet, the development of effective therapies for those pathologies is an unmet goal. Here we report motor coordination impairments in the htau mouse model of tauopathy which bear abnormal 3 R : 4 R tau isoforms contents, and contrariwise to TauKO mice, are unresponsive to L-DOPA. Preclinical-PET imaging, array tomography and electrophysiological analyses pointed the dorsal striatum as the candidate structure mediating such phenotypes. Indeed, local modulation of tau isoforms by RNA trans-splicing in the striata of adult htau mice, prevented motor coordination deficits and restored basal neuronal firing. Together, these results constitute readout that abnormal striatal tau-isoforms contents might lead to parkinsonian-like phenotypes and provide proof of concept that modulation of tau mis-splicing could be a plausible disease-modifying therapy for some primary tauopathies.
A novel dendritic mesoporous silica based sustained hydrogen sulfide donor for the alleviation of adjuvant-induced inflammation in rats

Abstract Purpose S-propargyl-cysteine (SPRC), an excellent endogenous hydrogen sulfide (H2S) donor, could elevate H2S levels via the cystathionine γ-lyase (CSE)/H2S pathway both in vitro and in vivo. However, the immediate release of H2S in vivo and daily administration of SPRC potentially limited its clinical use. Methods To solve the fore-mentioned problem, in this study, the dendritic mesoporous silica nanoparticles (DMSN) was firstly prepared, and a sustained H2S delivery system consisted of SPRC and DMSN (SPRC@DMSN) was then constructed. Their release profiles, both in vitro and in vivo, were investigated, and their therapeutical effect toward adjuvant-induced arthritis (AIA) rats was also studied. Results The spherical morphology of DMSN could be observed under scanning Electron Microscope (SEM), and the transmission electron microscope (TEM) images showed a central-radiational pore channel structure of DMSN. DMSN showed excellent SPRC loading capacity and attaining a sustained releasing ability than SPRC both in vitro and in vivo, and the prolonged SPRC releasing could further promote the release of H2S in a sustained manner through CSE/H2S pathway both in vitro and in vivo. Importantly, the SPRC@DMSN showed promising anti-inflammation effect against AIA in rats was also observed. Conclusions A sustained H2S releasing donor consisting of SPRC and DMSN was constructed in this study, and this sustained H2S releasing donor might be of good use for the treatment of AIA.
Relative abundance of nitrogen cycling microbes in coral holobionts reflects environmental nitrate availability

Recent research suggests that nitrogen (N) cycling microbes are important for coral holobiont functioning. In particular, coral holobionts may acquire bioavailable N via prokaryotic dinitrogen (N2) fixation or remove excess N via denitrification activity. However, our understanding of environmental drivers on these processes in hospite remains limited. Employing the strong seasonality of the central Red Sea, this study assessed the effects of environmental parameters on the proportional abundances of N cycling microbes associated with the hard corals Acropora hemprichii and Stylophora pistillata. Specifically, we quantified changes in the relative ratio between nirS and nifH gene copy numbers, as a proxy for seasonal shifts in denitrification and N2 fixation potential in corals, respectively. In addition, we assessed coral tissue-associated Symbiodiniaceae cell densities and monitored environmental parameters to provide a holobiont and environmental context, respectively. While ratios of nirS to nifH gene copy numbers varied between seasons, they revealed similar seasonal patterns in both coral species, with ratios closely following patterns in environmental nitrate availability. Symbiodiniaceae cell densities aligned with environmental nitrate availability, suggesting that the seasonal shifts in nirS to nifH gene abundance ratios were probably driven by nitrate availability in the coral holobiont. Thereby, our results suggest that N cycling in coral holobionts probably adjusts to environmental conditions by increasing and/or decreasing denitrification and N2 fixation potential according to environmental nitrate availability. Microbial N cycling may, thus, extenuate the effects of changes in environmental nitrate availability on coral holobionts to support the maintenance of the coral–Symbiodiniaceae symbiosis.
Learning how to behave: cognitive learning processes account for asymmetries in adaptation to social norms

Changes to social settings caused by migration, cultural change or pandemics force us to adapt to new social norms. Social norms provide groups of individuals with behavioural prescriptions and therefore can be inferred by observing their behaviour. This work aims to examine how cognitive learning processes affect adaptation and learning of new social norms. Using a multiplayer game, I found that participants initially complied with various social norms exhibited by the behaviour of bot-players. After gaining experience with one norm, adaptation to a new norm was observed in all cases but one, where an active-harm norm was resistant to adaptation. Using computational learning models, I found that active behaviours were learned faster than omissions, and harmful behaviours were more readily attributed to all group members than beneficial behaviours. These results provide a cognitive foundation for learning and adaptation to descriptive norms and can inform future investigations of group-level learning and cross-cultural adaptation.
Hedgehog signalling controls sinoatrial node development and atrioventricular cushion formation

Smoothened is a key receptor of the hedgehog pathway, but the roles of Smoothened in cardiac development remain incompletely understood. In this study, we found that the conditional knockout of Smoothened from the mesoderm impaired the development of the venous pole of the heart and resulted in hypoplasia of the atrium/inflow tract (IFT) and a low heart rate. The blockage of Smoothened led to reduced expression of genes critical for sinoatrial node (SAN) development in the IFT. In a cardiac cell culture model, we identified a Gli2–Tbx5–Hcn4 pathway that controls SAN development. In the mutant embryos, the endocardial-to-mesenchymal transition (EndMT) in the atrioventricular cushion failed, and Bmp signalling was downregulated. The addition of Bmp2 rescued the EndMT in mutant explant cultures. Furthermore, we analysed Gli2+ scRNAseq and Tbx5−/− RNAseq data and explored the potential genes downstream of hedgehog signalling in posterior second heart field derivatives. In conclusion, our study reveals that Smoothened-mediated hedgehog signalling controls posterior cardiac progenitor commitment, which suggests that the mutation of Smoothened might be involved in the aetiology of congenital heart diseases related to the cardiac conduction system and heart valves.
Decomposition of LiDAR waveforms with negative tails by Gaussian mixture model

Abstract. Waveform decomposition techniques are commonly used to extract attributes of targets from light detection and ranging (LiDAR) waveforms. The conventional Gaussian decomposition (GD) cannot deal with system waveforms (SWs) with non-Gaussian shapes, whereas the recently proposed B-spline-based decomposition method holds an assumption of similarity transformation. We present a multi-Gaussian decomposition (MGD) algorithm that employs a Gaussian mixture model (GMM) to represent the SW. Compared with the GD algorithm, the MGD algorithm exploits the characteristic of the SW using the GMM and hence can fit the received waveforms better than the GD algorithm. In contrast to the B-spline-based method, the proposed algorithm holds a more reasonable assumption that a received waveform is a convolution result of the SW with the target response, which accords with the ranging principle better. The MGD algorithm was validated using the experimental waveforms with negative tails collected by our self-designed LiDAR system. The GD algorithm and the B-spline-based decomposition were also introduced and studied for comparison. The experimental results show that the GMM with six components fit the SW with an acceptable residual. The experimental results also show that the MGD algorithm produced better decomposition results than the other two algorithms in terms of range retrieval, whereas the B-spline-based decomposition showed the best performance with regards to the root-mean-square-error for waveform fitting.
Instrumentation of the light refraction for measuring the refractive index of transparent liquids

Abstract. We propose new instrumentation to measure the refractive index (RI) of transparent liquids as a function of temperature. The physical phenomenon used to carry out the measurement is the light refraction that occurs in transparent liquids. A width change in a reference object is produced by light refraction, which is related to the RI. Image processing was used to measure the object width using an image from a digital microscope. The instrument measures the RI from a range of 15°C to 100°C with an accuracy of 10 − 4 % . The measurement procedure was calibrated with distilled water and acetonitrile samples, and the Sellmeier and Cauchy equation was used to compare our results. Finally, as an appropriate application, the RI of lophine in a solution was measured.
Multi-Agent Systems and Digital Twins for Smarter Cities

An intelligent combination of the Internet of Things (IoT) and approaches to modeling and simulation is one of the most challenging endeavors for future cities, manufacturing industries, and predictive maintenance. Digital Twins take on a unique role here. However, the question of what a Digital Twin is and what differentiates it from a regular model is still open. We present an experimental setup for integrating an existing simulation model of Hamburg's traffic system with the city's real-time sensor network. The Digital Twin is implemented using the large-scale multi-agent framework MARS. The entire process from the model description to retrieving real-time data from the IoT sensors and incorporating it in the simulation is presented. As a first prototypical example, a multi-modal mobility model was connected to real-world bike-sharing locations in Hamburg. We find that the combination of multi-agent systems and IoT sensors as a Digital Twin shows enormous potential for city planners, policy stakeholders, and other decision-makers. By correcting the course of a simulation via real-time data, the corridor-of-uncertainty that is intrinsic to some simulation models' use can be reduced significantly. Furthermore, any divergence of simulated and sampled data can lead to a deeper understanding of complex adaptive systems like big cities.
Influencing the Intention to Adopt Anti-Littering Behavior: An Approach With Modified TPB Model

Background: Littering has been identified as a major issue in India that has negative impacts on the environment as well as public health. Focus of the article: Guided by the Theory of Planned Behavior, this empirical study determines the influence of three major concepts namely attitude, subjective norms, and self-efficacy on the intention to adopt anti-littering behavior. Research Hypotheses: The hypotheses stipulate that attitude, subjective norms, and self-efficacy have a significant influence on the intention to perform anti-littering behavior. Methods: In total, 750 individuals were interviewed at 34 locations across the states of New Delhi and Punjab (India) in a field survey. The data have been analyzed through confirmatory factor analysis in AMOS 21.0 to classify the considerations of the antecedents of the intention according to their relative significance using the natural gaps in standardized regression weight values. Subsequently, path analysis has been used to test a series of hypotheses concerning the direct effects of attitude, subjective norms, and self-efficacy on the intention to adopt anti-littering behavior. Results: The findings of the study revealed that all three constructs; attitude, subjective norms, and self-efficacy have a significant influence on the intention to adopt anti-littering behavior. Subjective norms influence intention to the highest extent closely followed by attitude. Recommendations for Research/Practice: Social marketers are recommended to target subjective norms and attitude while designing social marketing interventions to promote anti-littering behavior. Limitations: One limitation of this study is the existing gap between self-reported behavioral intention and actual behavior.
Predicting the lifetime of pull requests in open‐source projects

A recent survey using industrial projects has shown that providing an estimate of the lifetime of pull requests to developers helps to speed up their conclusion. Previous work has explored pull request lifetime prediction in open‐source projects using regression techniques but with a broad margin of error. The first objective of our work was to reduce the average error rate of the prediction obtained by the regression techniques so far. We performed experiments with different regression techniques and achieved a significant decrease in the mean error rate. The second objective of our work was to obtain a more effective and useful predictive model that can classify pull requests according to five discrete time intervals. We proposed new predictive attributes for the estimation of the time intervals and employed attribute selection strategies to identify subsets of attributes that could improve the predictive behavior of the classifiers. Our classification approach achieved the best accuracy in all the 20 projects evaluated in comparison with the literature. The average accuracy was of 45.28% to predict pull request lifetime, with an average normalized improvement of 14.68% in relation to the majority class and 6.49% in relation to the state‐of‐the‐art.
The 2004 Difficult Airway Society guidelines for the management of difficult tracheal intubation: revolutionary and enduring

1Consultant, Royal UnitedHospitals BathNHS Foundation Trust, CombePark, Bath, UK 2Consultant, St. JamesHospital, James Street, Dublin, Ireland . Correspondence to: T. Cook Email: timcook007@gmail.com Accepted: 21April 2021
GlyNet: a multi-task neural network for predicting protein–glycan interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet’s output of continuous values provides more detailed results than classification models. Such continuous outputs are easily converted by a following classifier, and in this form GlyNet outperforms reported classifiers. GlyNet is the first multi-output regression model for protein-glycan interactions and will serve as an important benchmark, facilitating development of quantitative computational glycobiology.
Partitioning the temporal changes in abundance-based beta diversity into loss and gain components

Ecologists have long recognized that the losses and gains in local species abundances can either decrease or increase spatial beta diversity, phenomena often referred to as biotic homogenization and differentiation, respectively. However, quantifying such dynamic impacts of species abundances on beta diversity has remained a methodological challenge. Here, we develop a numerical method to additively partition the temporal changes in beta diversity into distinct components that reflect the losses and gains in local species abundances. Our method is based on Ružička and Bray–Curtis indices and the normalized abundance-based Whittaker’s beta diversity. The temporal changes in these measures are partitioned into components that represent biotic homogenization and differentiation driven by abundance losses and gains at both species and community levels. Application of the method to a Swedish fish community dataset revealed decreases in beta diversity between 1990 and 2018. The homogenization of fish communities was explained by gains, but not losses, in species abundances across sites. Species-level partitioning further showed that the homogenization was largely caused by the increased population sizes of a particular species in sites where it was already present. The results highlight that our partitioning method effectively identifies local population and community processes embedded in regional biodiversity patterns. We believe that explicit analyses of the losses and gains in species abundances should bring deeper insights into the dynamics of beta diversity.
Niche-Relationships Within and Among Intertidal Reef Fish Species

Niche-related processes (e.g., density or niche-breadth compensation and competition) are fundamental to a broad understanding of community ecology and ecosystem functioning. Most evidences of competition are from controlled indoor trials with few species, and it remains a challenge to estimate competition among multiple species in the field. Here, we analyze stable isotopes and distributional data from 51 fish taxa in six locations in the southwestern Atlantic to predict intraspecific trophic pressure (ITP) and the potential competitive strength among species in a trophic-based framework. We used two proxies built upon 2-dimensional isotopic space (δ13C vs. δ15N), its predicted overlap, and fish density to calculate winner and loser taxa in potential paired interspecific competitive interactions. The intraspecific proxy indicated that cryptobenthic fishes are under high among-individual trophic pressure (high densities and small niche sizes). Also, cryptobenthic behavior together with feeding specialization and extremely small-sizes were the most important traits related to low success in interspecific simulations. Although cryptobenthic fishes face strong competitive pressures, there are some known inherent trade-offs to cryptobenthic life such as trophic and habitat use specializations. These seem to compensate and ensure coexistence among cryptobenthic fishes and non-cryptobenthic species. Habitat loss/degradation via urbanization, invasive species and climate-change-driven sea-level rise can reduce the suitability of habitat and increase competition on cryptobenthic species, especially in shallow reefs and intertidal shores.
Maternal Dietary Protein Patterns During Pregnancy and the Risk of Infant Eczema: A Cohort Study

Background: Previous studies have suggested that maternal dietary protein was associated with allergic diseases in offspring, but few studies have evaluated the influence of dietary protein patterns. This study aimed to explore the prospective association between maternal dietary protein patterns during pregnancy and the risk of infant eczema. Methods: A total of 713 mother-child pairs from a prospective cohort in Guangzhou, China were recruited. Maternal dietary protein was estimated using a validated face-to-face food frequency questionnaire at 20–28 weeks' gestation from 2017 to 2018. Dietary protein patterns were calculated based on the sources of protein. The data of infant eczema was assessed at 6 months of age using the symptom questionnaire of eczema. Logistic regression was carried out to examine the associations between maternal dietary protein patterns and infant eczema. Results: The cumulative incidence of infant eczema at 6 months of age was 51.19%. Mothers of infants with eczema consumed more protein from poultry source during pregnancy than mothers of infants without eczema, while no statistical differences were observed in maternal intakes of protein from cereals and tubers, vegetables, fruits, red meat, fish and seafood, eggs, dairy, soybean, and nuts and seeds. Four dietary protein patterns were identified and termed poultry, plant, dairy and eggs, and red meat and fish. The cumulative incidence of eczema was 61.2, 45.8, 48.0, 51.4% for these four patterns, respectively. Compared to the poultry dietary pattern, the plant pattern and the dairy and eggs pattern were associated with a reduced risk of infant eczema, and the adjusted odds ratios (95% confidence interval) were 0.572 (0.330–0.992), 0.478 (0.274–0.837), respectively. No such association was observed for the red meat and fish dietary protein pattern. Conclusion: This is the first study that focused on the association between maternal dietary protein during pregnancy from a whole-diet perspective and infant eczema. Compared with the poultry dietary protein pattern, the maternal plant pattern and the dairy and eggs pattern during pregnancy were associated with a reduced risk of infant eczema.