Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing

Background and Purpose: This study aims to determine whether machine learning (ML) and natural language processing (NLP) from electronic health records (EHR) improve the prediction of 30-day readmission after stroke. Methods: Among index stroke admissions between 2011 and 2016 at an academic medical center, we abstracted discrete data from the EHR on demographics, risk factors, medications, hospital complications, and discharge destination and unstructured textual data from clinician notes. Readmission was defined as any unplanned hospital admission within 30 days of discharge. We developed models to predict two separate outcomes, as follows: (1) 30-day all-cause readmission and (2) 30-day stroke readmission. We compared the performance of logistic regression with advanced ML algorithms. We used several NLP methods to generate additional features from unstructured textual reports. We evaluated the performance of prediction models using a five-fold validation and tested the best model in a held-out test dataset. Areas under the curve (AUCs) were used to compare discrimination of each model. Results: In a held-out test dataset, advanced ML methods along with NLP features out performed logistic regression for all-cause readmission (AUC, 0.64 vs. 0.58; p < 0.001) and stroke readmission prediction (AUC, 0.62 vs. 0.52; p < 0.001). Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.
Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.
Effect of the Re-Vitrification of Embryos at Different Stages on Embryonic Developmental Potential

Background Using re-vitrified human embryos for frozen-warmed embryo transfer (FET) is a valuable option when there are no other cryopreserved embryos to use, however, except for the PGT cases, no published data are available for FET with human embryos that were re-vitrified at different developmental stages. Objective To evaluate the effect of re-vitrification of embryos at different stages on embryonic developmental potential. Method This study included clinical retrospective and mouse experimental studies. For the retrospective study, a total of 25 FET cycles with re-vitrified day 3 embryos (re-vitrification group 1) and 54 FET cycles with re-vitrified day 5 blastocysts (re-vitrification group 2) between January 2015 and December 2019 were included in this study. The corresponding FET cycles with once-vitrified embryos were identified using propensity score (PS) matching according to the time of embryo transfer. For the mouse experimental study, we divided embryos into 5 groups: fresh (group 1), vitrified at the 8-cell stage (group 2), vitrified at the early blastocyst stage (group 3), vitrified at the 8-cell stage, and re-vitrified at the 8-cell (group 4) or early blastocyst stage (group 5). The fresh embryos was selected as control group. The primary outcome in this study was delivery outcomes. Results No significant difference in delivery rate was detected between re-vitrification group 1 (24.00%) and the corresponding control group (28.00%). However, re-vitrification group 2 (46.3%) showed a significant decrease in delivery rate compared with the two corresponding control groups (63.89% and 64.12%) (P < 0.05). Our experiment using mouse embryos also confirmed the clinical data, and showed that re-vitrification at the blastocyst stage following the first round of vitrification at the 8-cell stage reduced the delivery rate. In addition, both re-vitrified groups showed a significantly higher expression level of BAX. However, only re-vitrification at the blastocyst stage increased the expression level of CASPASE3. Conclusions Re-vitrification at the 8-cell and blastocyst stages has different effects on embryonic developmental potential, as re-vitrification at blastocyst stage following a previous vitrification at 8-cell stage reduced the delivery rate, while vitrification at the 8-cell stage twice achieved comparable pregnancy outcomes to the once-vitrified group.
Brain Frequency-Specific Changes in the Spontaneous Neural Activity Are Associated With Cognitive Impairment in Patients With Presbycusis

Presbycusis (PC) is characterized by preferential hearing loss at high frequencies and difficulty in speech recognition in noisy environments. Previous studies have linked PC to cognitive impairment, accelerated cognitive decline and incident Alzheimer’s disease. However, the neural mechanisms of cognitive impairment in patients with PC remain unclear. Although resting-state functional magnetic resonance imaging (rs-fMRI) studies have explored low-frequency oscillation (LFO) connectivity or amplitude of PC-related neural activity, it remains unclear whether the abnormalities occur within all frequency bands or within specific frequency bands. Fifty-one PC patients and fifty-one well-matched normal hearing controls participated in this study. The LFO amplitudes were investigated using the amplitude of low-frequency fluctuation (ALFF) at different frequency bands (slow-4 and slow-5). PC patients showed abnormal LFO amplitudes in the Heschl’s gyrus, dorsolateral prefrontal cortex (dlPFC), frontal eye field and key nodes of the speech network exclusively in slow-4, which suggested that abnormal spontaneous neural activity in PC was frequency dependent. Our findings also revealed that stronger functional connectivity between the dlPFC and the posterodorsal stream of auditory processing, as well as lower functional coupling between the PCC and key nodes of the DMN, which were associated with cognitive impairments in PC patients. Our study might underlie the cross-modal plasticity and higher-order cognitive participation of the auditory cortex after partial hearing deprivation. Our findings indicate that frequency-specific analysis of ALFF could provide valuable insights into functional alterations in the auditory cortex and non-auditory regions involved in cognitive impairment associated with PC.
Serious Game Top Eleven as an Educational Tool in Sports Economics

The present study aims to explore the use of the serious game Top Eleven as an auxiliary educational tool in a sports economics undergraduate course. This attempt is warranted because serious games can provide real life/work experiences and set the ground for managerial – economics skills development. The selection of the specific serious game is based on the ample and varied sports economics data provided and its free of charge online accessibility. Following the case study methodology, a virtual football club was created and managed within Top Eleven. The stated research questions were based on the analysis and application of the generated data to a number of economic concepts identified in a specific sports economics textbook. Based on the analysis results, twelve out of 17 in total identified economic concepts were successfully analyzed using eleven economic data sources of items embedded in the game. The implementation phase showed that Top Eleven could be used as an educational supporting tool in the form of a virtual internship to gain practical experience by applying sports economic concepts for the financial management of a professional football club. It is argued that the game offers a sensible working environment in sports economics and the associated sport management field.
Estimating the Suitability for the Reintroduced Arabian Oryx (Oryx leucoryx, Pallas 1777) of Two Desert Environments by NIRS-Aided Fecal Chemistry

The re-introduction paradigm is that Arabian Oryx (Oryx leucoryx) herds adjust the size of their home ranges depending on the availability of vegetation, which is directly related to rainfall. In Israel, Arabian oryx were released in two hyper-arid sites: the Arava Valley and in the Paran wilderness, belonging to the Sudanese and the Saharo–Arabian biogeographic zones, respectively. While post-release survival was similar in both, reproductive success in the Paran wilderness reintroduction site was extremely low, resulting in an acute decline of the reintroduced population over time. The hypothesis that impaired nutrition might be associated with this finding was assessed with near-infrared spectroscopy (NIRS)-aided chemistry of monthly sampled fecal pellets, used as remote sensing evidence of ingested diets, throughout a year. Fecal nitrogen (FN), used as an estimate of nutritional status, was consistently higher in the Arava. Grass was never the sole or even a major dietary component. The dietary contribution of tannin-rich browse was high and steady all year-round in the Arava and increased steadily in Paran from winter to summer, corresponding to the period of availability of Acacia raddiana pods in both regions. The oryx in Paran had a home-range that was ten-fold, compared to the Arava, suggesting less feed availability. Acacia browsing may mitigate the effects of temporal variance in primary production. Under such conditions, oryx should be preferably released in areas that support significant acacia stands.
Comparing Two Subjective Rating Scales Assessing Cognitive Load During Technology-Enhanced STEM Laboratory Courses

Cognitive load theory is considered universally applicable to all kinds of learning scenarios. However, instead of a universal method for measuring cognitive load that suits different learning contexts or target groups, there is a great variety of assessment approaches. Particularly common are subjective rating scales, which even allow for measuring the three assumed types of cognitive load in a differentiated way. Although these scales have been proven to be effective for various learning tasks, they might not be an optimal fit for the learning demands of specific complex environments such as technology-enhanced STEM laboratory courses. The aim of this research was therefore to examine and compare the existing rating scales in terms of validity for this learning context and to identify options for adaptation, if necessary. For the present study, the two most common subjective rating scales that are known to differentiate between load types (the cognitive load scale by Leppink et al. and the naïve rating scale by Klepsch et al.) were slightly adapted to the context of learning through structured hands-on experimentation where elements such as measurement data, experimental setups, and experimental tasks affect knowledge acquisition. N = 95 engineering students performed six experiments examining basic electric circuits where they had to explore fundamental relationships between physical quantities based on the observed data. Immediately after the experimentation, the students answered both adapted scales. Various indicators of validity, which considered the scales’ internal structure and their relation to variables such as group allocation as participants were randomly assigned to two conditions with a contrasting spatial arrangement of the measurement data, were analyzed. For the given dataset, the intended three-factorial structure could not be confirmed, and most of the a priori-defined subscales showed insufficient internal consistency. A multitrait–multimethod analysis suggests convergent and discriminant evidence between the scales which could not be confirmed sufficiently. The two contrasted experimental conditions were expected to result in different ratings for the extraneous load, which was solely detected by one adapted scale. As a further step, two new scales were assembled based on the overall item pool and the given dataset. They revealed a three-factorial structure in accordance with the three types of load and seemed to be promising new tools, although their subscales for extraneous load still suffer from low reliability scores.
The Basic Psychological Need Satisfaction and Need Frustration at Work Scale: A Validation Study

The aim of the present study was to adapt and validate the Basic Psychological Need Satisfaction and Frustration Scale within self-determination theory (SDT) within the work domain. Confirmatory factor analyses of three Norwegian samples and one English sample as well as multi-group analyses to examine measurement invariance were performed. The results showed that the adapted work-related scale with its six-factor structure fitted the data well in all four samples, and partial measurement invariance was obtained across samples and languages. Furthermore, internal consistencies for the subscales were acceptable and the subscales predicted work-related correlates as expected, demonstrating the criterion validity of the scale. The current study contributes to a unifying measurement for future research on one of the central underpinnings of SDT within the work domain.
Ocean-Atmosphere Interactions during Hurricanes Marco and Laura (2020)

During August of the 2020 Atlantic Hurricane Season, the Gulf of Mexico (GoM) was affected by two subsequent storms, Hurricanes Marco and Laura. Hurricane Marco entered the GoM first (22 August) and was briefly promoted to a Category 1 storm. Hurricane Laura followed Marco closely (25 August) and attained Category 4 status after a period of rapid intensification. Typically, hurricanes do not form this close together; this study aims to explain the existence of both hurricanes through the analysis of air-sea fluxes, local thermodynamics, and upper-level circulation. The GoM and its quality of warm, high ocean heat content waters proved to be a resilient and powerful reservoir of heat and moisture fuel for both hurricanes; however, an area of lower ocean heat content due to circulation dynamics was crucial in the evolution of both Marco and Laura. An analysis of wind shear further explained the evolution of both hurricanes. Furthermore, a suite of satellite observations and ocean model outputs were used to evaluate the biophysical modulations in the GoM. The cold core eddy (CCE) and Mississippi River surface plume had the greatest biophysical oceanic responses; the oceanic modulations were initialized by Marco and extended temporally and spatially by Laura. Reduced sea surface temperatures (SST), changes in sea surface salinity (SSS), and changes in Chlorophyll-a (Chl-a) concentrations are related to translation speeds, and respective contributions of hurricane winds and precipitation are evaluated in this work.
Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research.
Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session

Recent studies have addressed stress level classification via electroencephalography (EEG) and machine learning. These works typically use EEG-based features, like power spectral density (PSD), to develop stress classifiers. Nonetheless, these classifiers are usually limited to the discrimination of two (stress and no stress) or three (low, medium, and high) stress levels. In this study we propose an alternative for quantitative stress assessment based on EEG and regression algorithms. To this aim, we conducted a group of 23 participants (mean age 22.65 ± 5.48) over a stress-relax experience while monitoring their EEG. First, we stressed the participants via the Montreal imaging stress task (MIST), and then we led them through a 360-degree virtual reality (VR) relaxation experience. Throughout the session, the participants reported their self-perceived stress level (SPSL) via surveys. Subsequently, we extracted spectral features from the EEG of the participants and we developed individual models based on regression algorithms to predict their SPSL. We evaluated stress regression performance in terms of the mean squared percentage error (MSPE) and the correlation coefficient (R2). The results yielded from this evaluation (MSPE = 10.62 ± 2.12, R2 = 0.92 ± 0.02) suggest that our approach predicted the stress level of the participants with remarkable performance. These results may have a positive impact in diverse areas that could benefit from stress level quantitative prediction. These areas include research fields like neuromarketing, and training of professionals such as surgeons, industrial workers, or firefighters, that often face stressful situations.
The Interactions Between Antibiotic Resistance Genes and Heavy Metal Pollution Under Co-Selective Pressure Influenced the Bio-Enzyme Activity

The spread of antibiotic resistance genes (ARGs) has brought potential risks to public health. However, the interactions between heavy metals and ARGs, as well as their potential effect on bio-enzyme activity under the pressure of co-selectivity in soil still remain poorly understood. In this work, the distribution characteristics and the co-selective relationship of 28 ARGs and eight heavy metals in soil in a dairy farm were visualized via the geographic information system (GIS) technique. Eight kinds of heavy metals were detected by an atomic fluorescence spectrometer and atomic absorption spectrophotometer, which were further evaluated via the single factor pollution index value. The GIS analysis showed that arsenic (As) was the key element responsible for soil pollution, which was found to be positively related to soil depths. The top three comprehensive scores of ARGs ranked the orders of sul2 > tetX > blaTEM, indicating the high potential of risk caused by these genes in the soil environment. In addition, the functional predications performed with the 16 SrDNA sequencing data based on the KEGG database indicated that the sulfonamides in soil involved multiple pathways, especially the metabolism, transport and catabolism, and membrane transport processes. This suggested that most bio-enzymes were found to be expressed in low activities in different pathways. Significant correlations were observed between the heavy metals and ARGs (p < 0.05), particularly between the ARGs and As, Cu, Ni, Pb, and Zn (p < 0.01). This study offers deep insights into the potential interactions between heavy metals and ARGs in soil and provides guidance for the fabrication of enzyme-based smart materials for soil remediation in dairy farms.
Cyclic Nucleotide-Gated Ion Channel 6 Mediates Thermotolerance in Arabidopsis Seedlings by Regulating Hydrogen Peroxide Production via Cytosolic Calcium Ions

We previously reported the involvement of cyclic nucleotide-gated ion channel 6 (CNGC6) and hydrogen peroxide (H2O2) in plant responses to heat shock (HS). To demonstrate their relationship with plant thermotolerance, we assessed the effect of HS on several groups of Arabidopsis (Arabidopsis thaliana) seedlings: wild-type, cngc6 mutant, and its complementation line. Under exposure to HS, the level of H2O2 was lower in the cngc6 mutant seedlings than in the wild-type (WT) seedlings but obviously increased in the complementation line. The treatment of Arabidopsis seeds with calcium ions (Ca2+) increased the H2O2 levels in the seedlings under HS treatment, whereas treatment with a Ca2+ chelator (EGTA) inhibited it, indicating that CNGC6 may stimulate the accumulation of H2O2 in a manner dependent on an increase in cytosolic Ca2+ ([Ca2+]cyt). This point was verified by phenotypic observations and thermotolerance testing with transgenic plants overexpressing AtRbohB and AtRbohD (two genes involved in HS-responsive H2O2 production), respectively, in a cngc6 background. Real-time reverse transcription-polymerase chain reactions and Western blotting suggested that CNGC6 enhanced the gene transcription of HS factors (HSFs) and the accumulation of HS proteins (HSPs) via H2O2. These upon results indicate that H2O2 acts downstream of CNGC6 in the HS signaling pathway, increasing our understanding of the initiation of plants responses to high temperatures.