Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptome Analysis Reveals the Stress Tolerance to and Accumulation Mechanisms of Cadmium in Paspalum vaginatum Swartz

Cadmium (Cd) is a non-essential heavy metal and high concentrations in plants causes toxicity of their edible parts and acts as a carcinogen to humans and animals. Paspalum vaginatum is widely cultivating as turfgrass due to its higher abiotic stress tolerance ability. However, there is no clear evidence to elucidate the mechanism for heavy metal tolerance, including Cd. In this study, an RNA sequencing technique was employed to investigate the key genes associated with Cd stress tolerance and accumulation in P. vaginatum. The results revealed that antioxidant enzyme activities catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and glutathione S-transferase GST) were significantly higher at 24 h than in other treatments. A total of 6820 (4457/2363, up-/down-regulated), 14,038 (9894/4144, up-/down-regulated) and 17,327 (7956/9371, up-/down-regulated) differentially expressed genes (DEGs) between the Cd1 vs. Cd0, Cd4 vs. Cd0, and Cd24 vs. Cd0, respectively, were identified. The GO analysis and the KEGG pathway enrichment analysis showed that DEGs participated in many significant pathways in response to Cd stress. The response to abiotic stimulus, the metal transport mechanism, glutathione metabolism, and the consistency of transcription factor activity were among the most enriched pathways. The validation of gene expression by qRT-PCR results showed that heavy metal transporters and signaling response genes were significantly enriched with increasing sampling intervals, presenting consistency to the transcriptome data. Furthermore, over-expression of PvSnRK2.7 can positively regulate Cd-tolerance in Arabidopsis. In conclusion, our results provided a novel molecular mechanism of the Cd stress tolerance of P. vaginatum and will lay the foundation for target breeding of Cd tolerance in turfgrass.
Modeling Determinants of Challenge in Learning Statistics in Time of the COVID-19 Pandemic

During the COVID-19 pandemic, there were a lot of challenges encountered by students in learning statistics online that affected their cognitive attitudes. This study aims to evaluate the different determinants that significantly influenced the engineering students' level of challenge in learning statistics in the new normal with the aid of a structured questionnaire by means of a Google form survey. Descriptive statistics and multiple linear regression analysis were employed to extract meaningful information from the gathered data. Results showed that the students' perception score for the level of challenge in learning statistics is 7.37 (1.99), which can be interpreted as "challenging. " This implies that students face challenges as they learn statistics lessons amid the pandemic setup. The regression models constructed have revealed that "age", "sex", "learning environment", "money spent on internet load", "physical health", and "creativity of statistics lessons" are the significant causal factors of the level of challenge in learning statistics. Conclusively, statistics teachers must adjust and be considerate to their students in regard to their learning needs in line with the pandemic setup. The government also must provide a budget for seminars and training to college teachers concerning distance education. Furthermore, it is recommended that statistics teachers must create an interesting learning environment to fully catch students' attention despite challenges.
Mitochondrial Dysfunction in Individuals with Diabetic Kidney Disease: A Systematic Review

Mitochondrial dysfunction is implicated in the pathogenesis of diabetic kidney disease (DKD). Compared to the vast body of evidence from preclinical in vitro and in vivo studies, evidence from human studies is limited. In a comprehensive search of the published literature, findings from studies that reported evidence of mitochondrial dysfunction in individuals with DKD were examined. Three electronic databases (PubMed, Embase, and Scopus) were searched in March 2022. A total of 1339 articles were identified, and 22 articles met the inclusion criteria. Compared to non-diabetic controls (NDC) and/or individuals with diabetes but without kidney disease (DC), individuals with DKD (age ~55 years; diabetes duration ~15 years) had evidence of mitochondrial dysfunction. Individuals with DKD had evidence of disrupted mitochondrial dynamics (11 of 11 articles), uncoupling (2 of 2 articles), oxidative damage (8 of 8 articles), decreased mitochondrial respiratory capacity (1 of 1 article), decreased mtDNA content (5 of 6 articles), and decreased antioxidant capacity (3 of 4 articles) compared to ND and/or DC. Neither diabetes nor glycemic control explained these findings, but rather presence and severity of DKD may better reflect degree of mitochondrial dysfunction in this population. Future clinical studies should include individuals closer to diagnosis of diabetes to ascertain whether mitochondrial dysfunction is implicated in the development of, or is a consequence of, DKD.
Nonlinear Phase-Quantized Constant-Envelope Precoding for Massive MU-MIMO-OFDM

We propose a novel algorithm for phase-quantized constant-envelope precoding in the massive multi-user (MU) multiple-input multiple-output (MIMO) downlink. Specifically, we extend the nonlinear squared-infinity norm Douglas-Rachford splitting (SQUID) precoder to systems that use oversampling digital-to-analog converters (DACs) at the base station (BS) and orthogonal frequency-division multiplexing (OFDM) to communicate over frequency-selective channels. We demonstrate that SQUID is able to generate constant-envelope signals, which enables the use of power-efficient analog radio-frequency circuitry at the BS. By quantizing the phase of the resulting constant-envelope signal, we obtain a finite-cardinality signal that can be synthesized by low-resolution (e.g., 1-bit) DACs. We use error-rate simulations to demonstrate the superiority of SQUID over linear precoders for massive MU-MIMO-OFDM.
Computational Methods for Physiological Signal Processing and Data Analysis

Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.
The Role of Polygenic Susceptibility on Air Pollution-Associated Asthma between German and Japanese Elderly Women

Polygenic susceptibility likely influences individual responses to air pollutants and the risk of asthma. We compared the role of polygenic susceptibility on air pollution-associated asthma between German and Japanese women. We investigated women that were enrolled in the German SALIA cohort (n = 771, mean age = 73 years) and the Japanese Shika cohort (n = 847, mean age = 67 years) with known asthma status. Adjusted logistic regression models were used to assess the associations between (1) particulate matter with a median aerodynamic diameter ≤ 2. 5μm (PM2.5) and nitrogen dioxide (NO2), (2) polygenic risk scores (PRS), and (3) gene-environment interactions (G × E) with asthma. We found an increased risk of asthma in Japanese women after exposure to low pollutant levels (PM2.5: median = 12.7µg/m3, p-value < 0.001, NO2: median = 8.5µg/m3, p-value < 0.001) and in German women protective polygenic effects (p-value = 0.008). While we found no significant G × E effects, the direction in both groups was that the PRS increased the effect of PM2.5 and decreased the effect of NO2 on asthma. Our study confirms that exposure to low air pollution levels increases the risk of asthma in Japanese women and indicates polygenic effects in German women; however, there was no evidence of G × E effects. Future genome-wide G × E studies should further explore the role of ethnic-specific polygenic susceptibility to asthma.
FRACTIONAL INTEGRATION OF CERTAIN SPECIAL FUNCTIONS

We derive an Eulerian integral and a main theorem based upon the fractional integral operator associated with generalized polynomials given by Srivastava [8. 185, Eq.~(7)] and $H$-function of several complex variables given by Srivastava and Panda [11, p.271, Eq.~(4.1)] which provide unification and extension of numerous results in the theory of fractional calculus of special functions in one and more variables. Certain interesting sepcial cases (known and new) have also been discussed.
Menopausal Symptoms and Sleep Quality in Women Aged 40–65 Years

The study examined the relationship between menopausal symptoms and sleep disturbances and the related influencing factors. Methods. We recruited women aged 40-65 years who attended the menopause clinic at Shanghai Jiao Tong University's Sixth People's Hospital from February 2011 to November 2019. The Menopause Rating Scale (MRS) was used to collect women's menopausal symptoms, and the Pittsburgh Sleep Quality Index (PSQI) was used to evaluate the subjects' sleep condition. We used logistic regression models to identify the relationship between menopausal symptoms and sleep quality. Results. A total of 1341 participants were recruited in this study. The most frequent three symptoms assessed by MRS were fatigue (72.9%), sleep disturbance (67%), and hot flashes with night sweats (65%). Participants' age was significantly associated with the severity of menopausal syndrome (P < 0.01). According to the PSQI sleep evaluation, 66.9 percent of participants had sleep disturbances (PSQI > 5). Logistic regression analysis revealed that women with mild, moderate, or severe menopausal syndrome had a 3-, 7-, and 17-fold increased chance of having sleep disturbances compared to women without menopausal syndrome. Conclusion. Women aged 40–65 years were found to have a significantly higher risk of menopausal syndrome and sleep disturbances.
Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection

Image-based rail defect detection could be conceptually defined as an object detection task in computer vision. However, unlike academic object detection tasks, this practical industrial application suffers from two unique challenges, including object ambiguity and insufficient annotations. To overcome these challenges, we introduce the pixel-wise attention mechanism to fully exploit features of annotated defects, and develop a feature augmentation framework to tackle the defect detection problem. The pixel-wise attention is conducted through a learnable pixel-level similarity between input and support features to obtain augmented features. These augmented features contain co-existing information from input images and multi-class support defects. The final output features are augmented and refined by support features, thus endowing the model to distinguish between ambiguous defect patterns based on insufficient annotated samples. Experiments on the rail defect dataset demonstrate that feature augmentation can help balance the sensitivity and robustness of the model. On our collected dataset with eight defected classes, our algorithm achieves 11.32% higher mAP@. 5 compared with original YOLOv5 and 4.27% higher mAP@. 5 compared with Faster R-CNN.
Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State‐Dependent Predictability in CESM2

Predictable internal climate variability on decadal timescales (2–10 years) is associated with large‐scale oceanic processes, however these predictable signals may be masked by the noisy climate system. One approach to overcoming this problem is investigating state‐dependent predictability—how differences in prediction skill depend on the initial state of the system. We present a machine learning approach to identify state‐dependent predictability on decadal timescales in the Community Earth System Model version 2 pre‐industrial control simulation by incorporating uncertainty estimates into a regression neural network. We leverage the network's prediction of uncertainty to examine state dependent predictability in sea surface temperatures by focusing on predictions with the lowest uncertainty outputs. In particular, we study two regions of the global ocean—the North Atlantic and North Pacific—and find that skillful initial states identified by the neural network correspond to particular phases of Atlantic multi‐decadal variability and the interdecadal Pacific oscillation.
Towards efforts to promote renewable energy development in Africa: Does governance quality matter?

In order to avert global climate change, the promotion of renewable energy is getting more attention from government and policymakers. There are many factors that can account for the determinant of renewable energy development. While the drivers of renewable energy development have been extensively examined, the influence of governance quality on renewable energy development is very scanty. To fill the gap in the literature, this study examines the influence of governance quality on renewable energy development in 42 African countries over the period 1996–2020. The empirical evidence is based on the panel corrected standard errors (PCSE) estimation technique to account for cross-sectional dependence. The results show that the impact of governance quality on renewable energy development is negative and statistically significant. This means that as governance quality increases, renewable energy development decreases. This is not surprising given the fact that governance quality in Africa is substantially low. In other words, the results imply that governance quality in most African countries still promote non-renewable and energy intensive production system.
Do people intend to use AI Voice Assistants? An empirical study in Vietnam

ABSTRACT Today, the gradual popularization of artificial intelligence (AI) application technology such as Voice Assistants is making our daily life more convenient and time-saving. However, why do people choose to use Voice assistants to help them with daily tasks? Not many studies have focused on such an exciting issue. Therefore, the current study aimed to investigate the factors influencing users’ intentions to use Voice Assistant. Specifically, this study explored the impact of smartphone voice assistants with anthropomorphic design on the use intention among users. This study collected a sample of 645 smartphone users and attempted to examine the relationships among the constructs. We used path analysis to test all the hypotheses. Besides, an independent sample t-test was used to determine whether or not there were significant differences among the groups (types of Voice Assistants, Frequent and infrequent use, and gender) of the users. The results showed that both voice cues and message interactivity positively influence the social presence and perceived expertise, which affect the use intention of voice assistants. This study confirmed that highly anthropomorphic voice assistants increase voice assistant use intention among users.
Factors Relating to a Safety Culture in the University Perinatal Center: The Nurses’ and Midwives’ Perspective

Background: According to The Joint Commission, a culture of safety is a key component for achieving sustainable and safe health care services, and hospitals must measure and monitor this achievement. Promoting a patient safety culture in health services optimally includes midwifery and nursing. The first aim of this study is to assess the University Perinatal Center’s staff members’ perceptions of safety culture. A second aim is to identify how the perceptions of safety culture actors are related to the socio-demographic characteristic of the respondents. Methods: A descriptive, cross-sectional, correlational design was applied in this study. Registered nurses and midwives were recruited from the University Perinatal Center in Lithuania (N = 233). Safety culture was measured by the Safety Attitudes Questionnaire (SAQ). Results: The mean scores of the responses on the 6 factors of the SAQ ranged from 3.18 (0.46) (teamwork climate) to 3.79 (0.55) (job satisfaction) points. The percentage of positive responses to the SAQ (4 or 5 points on the Likert scale) ranged from 43.2% to 69.0%. The lowest percentage of the respondents provided positive responses to the questions on perception of management and teamwork climate, while the highest percentage of the respondents provided positive responses to the questions on job satisfaction. Perception of management positively correlated with safety climate (r = 0.45, p < 0.01) and working conditions (r = 0.307, p < 0.01). Safety climate positively correlated with job satisfaction (r = 0.397, p < 0.01) and working conditions (r = 0.307, p < 0.01). Job satisfaction positively correlated with working conditions (r = 0.439, p < 0.01). Conclusion: Evaluating the opinions of the safety climate among nurses and midwives who work at the University Perinatal Center showed that teamwork climate and perception of management are weak factors. Therefore, stakeholders should organize more training about patient safety and factors that affect patient safety.
Do time preferences explain low health insurance take‐up?

Low insurance take-up in low-income populations is not easily explained by the standard single-period expected utility model of insurance that overlooks the relevance of time preference when liquidity is constrained. We design field survey instruments to elicit quasi-hyperbolic time preferences, as well as prospect theory risk preferences, and use them to examine whether time preferences explain health insurance behavior of low-income Filipinos. Consistent with theory, those who exhibit stronger time preference are less likely to insure and the partial association is most pronounced at low wealth where liquidity is most likely to be constrained. Among those with better understanding of insurance, lower take-up is also associated with present bias. We do not find that insurance is significantly associated with risk preferences. JEL Classification: D03, D81, D90, I13
Flat rank 2 vector bundles on genus 2 curves

We study the moduli space of trace-free irreducible rank 2 connections over a curve of genus 2 and the forgetful map towards the moduli space of under- lying vector bundles (including unstable bundles), for which we compute a natural Lagrangian rational section. As a particularity of the genus 2 case, connections as above are invariant under the hyperelliptic involution : they descend as rank 2 logarithmic connections over the Riemann sphere. We establish explicit links between the well-known moduli space of the underlying parabolic bundles with the classical approaches by Narasimhan-Ramanan, Tyurin and Bertram. This allow us to explain a certain number of geometric phenomena in the considered moduli spaces such as the classical (16, 6)-configuration of the Kummer surface. We also recover a Poincare family due to Bolognesi on a degree 2 cover of the Narasimhan-Ramanan moduli space. We explicitly compute the Hitchin integrable system on the moduli space of Higgs bundles and compare the Hitchin Hamiltonians with those found by vanGeemen-Previato. We explicitly describe the isomonodromic foliation in the moduli space of vector bundles with sl(2,C)-connection over curves of genus 2 and prove the transversality of the induced flow with the locus of unstable bundles.
Fused Cervical Vertebrae: A Case Series

Vertebral fusion is the fusion of bodies and the various elements of the vertebral arch of adjacent vertebrae. Such vertebral fusion usually is seen in cervical and lumbar region, more so between the second and third cervical vertebrae, usually asymptomatic; but may also result in features suggestive of neurological compression and might also present difficulty in procedures like endotracheal intubation. This present case series involving three cases discusses the presentations and possible consequences of such vertebral fusions. The three Fused Cervical Vertebrae (FCV) studied were all different with respect to the degree and site of fusion. With respect to fusion of vertebral bodies, complete fusion was seen in two specimens and the other had only partial fusion. This series aimed at documenting the varied spectrum of vertebral fusions and the need for earlier detection so as to avoid the anticipated age related complications like degeneration of adjacent segments and neurological deficits. The observations made in this series and the comparative analysis with other articles show that the mode of vertebral fusions are very different in each specimen and thus their clinical presentations might also vary across numerous signs and symptoms for each individual and hence cannot be generalised.
Short- and Long-Term Effects of Suboptimal Selenium Intake and Developmental Lead Exposure on Behavior and Hippocampal Glutamate Receptors in a Rat Model

Selenium (Se) is an essential trace element required for normal development as well as to counteract the adverse effects of environmental stressors. Conditions of low Se intake are present in some European countries. Our aim was to investigate the short- and long-term effects of early-life low Se supply on behavior and synaptic plasticity with a focus on the hippocampus, considering both suboptimal Se intake per se and its interaction with developmental exposure to lead (Pb). We established an animal model of Se restriction and low Pb exposure; female rats fed with an optimal (0.15 mg/kg) or suboptimal (0.04 mg/kg) Se diet were exposed from one month pre-mating until the end of lactation to 12.5 µg/mL Pb via drinking water. In rat offspring, the assessment of motor, emotional, and cognitive endpoints at different life stages were complemented by the evaluation of the expression and synaptic distribution of NMDA and AMPA receptor subunits at post-natal day (PND) 23 and 70 in the hippocampus. Suboptimal Se intake delayed the achievement of developmental milestones and induced early and long-term alterations in motor and emotional abilities. Behavioral alterations were mirrored by a drop in the expression of the majority of NMDA and AMPA receptor subunits analyzed at PND 23. The suboptimal Se status co-occurring with Pb exposure induced a transient body weight increase and persistent anxiety-like behavior. From the molecular point of view, we observed hippocampal alterations in NMDA (Glun2B and GluN1) and AMPA receptor subunit trafficking to the post-synapse in male rats only. Our study provides evidence of potential Se interactions with Pb in the developing brain.