Miracle-dz/UniFAR-FTUs huggingface dataset:
An Exposure Assessment of Arsenic and Other Trace Elements in Ha Nam Province, Northern Vietnam

Concentrations of As and other trace elements were measured in groundwater, rice, hair, urine, and blood samples of people consuming As-contaminated groundwater in a village of Ha Nam province, northern Vietnam to understand the recent status of contamination and assess the possible risks of human exposure. Elevated concentrations of As in groundwater were still observed, exceeding the WHO guideline value in most of the tube wells investigated. Significant positive correlations between As concentrations in groundwater and human samples (hair and urine) were observed. Arsenic concentrations in human and hair appeared to be related to the groundwater usage habit, with higher levels found in drinking group than those in the washing group. Significant good correlations were also encountered between cumulative intakes of As, Mn, and Ba through groundwater consumption and hair concentrations. All these results indicate the chronic exposure to As and some other elements such as Mn and Ba. The total intakes of As, Mn, and Ba through rice and groundwater consumption were estimated to be ranged from 80–836, 49.3–1850, and 311–97100 µg/day, respectively. The daily intakes of As of the study area ranged from 1.6–16.7 µg/kg body wt. /day, mean: 7.15 µg/kg body wt. /day, in which about 85% of the subjects were above the provisional tolerable daily intake proposed by WHO.
Predictive Impact of Modified-Prognostic Nutritional Index for Acute Kidney Injury within 1-week after Living Donor Liver Transplantation

Background. Acute kidney injury (AKI) is one of the common complications after living donor liver transplantation (LDLT) and is associated with increased mortality and morbidity. The prognostic nutritional index (PNI) has been used as a predictive model for postoperative complications. Here, we create a new predictive model based on the PNI and compared its predictive accuracy to other models in patients who underwent LDLT. Material and Methods: The data from 423 patients were collected retrospectively. The patients were dichotomized into the non-AKI and the AKI groups. Multivariate adjustment for significant postoperative variables based on univariate analysis was performed. A new predictive model was created using the results from logistic regression analysis, dubbed the modified-PNI model (mPNI). The area under the receiver operating characteristic curve (AUC) was generated to determine the diagnostic accuracy and cutoff value of individual models. The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to investigate diagnostic improvement by the mPNI. Results: Fifty-four patients (12.7 %) were diagnosed with AKI within 1-week after LDLT. The mPNI had the highest predictive accuracy (AUC = 0.823). The model of end-stage liver disease (MELD) scores and PNI were 0.793 and 0.749, respectively, and the INR and serum bilirubin were 0.705 and 0.637, respectively. The differences in the AUCs were statistically significant among the mPNI, PNI, INR, and serum bilirubin. The cutoff value for mPNI was 8.7. The NRI was 10.4% and the IDI was 3.3%. Conclusions: The mPNI predicted AKI within 1-week better than other scoring systems in patients who underwent LDLT. The recommended cutoff value of mPNI is 8.7.
Stromal Cell-derived Factor-1 and CXC Chemokine Receptor Type-4 are Associated with Cardiovascular Disease in Patients under Hemodialysis

Background: Cardiovascular disease (CVD) is the most common cause of death among patients with end-stage renal disease especially whom under hemodialysis (HD). Stromal cell-derived factor-1 (SDF-1) and its receptor CXC chemokine receptor type-4 (CXCR4) could contribute to CVD. The main aim of this study was to evaluate the association between SDF-1 and CXCR4 with CVD and its related risk factors in patients under HD. Methods: Sixty patients under HD and 29 healthy subjects were recruited in the study. The serum levels and relative messenger RNA (mRNA) expressions of SDF-1 and CXCR4 were measured using enzyme-linked immunosorbent assay and real-time polymerase chain reaction in patients and controls, respectively. CVD history of the patients was obtained. Results: Twenty patients (33.3%) had a history of CVD. The mean levels of serum and relative mRNA expressions of SDF-1 and CXCR4 were higher in patients than controls and also in patients with a history of CVD than patients without it. The serum levels and relative expressions of SDF-1 and CXCR4 were positively correlated with blood urea nitrogen, parathyroid hormone, and high-sensitivity C-reactive protein and inversely correlated with hemoglobin. The history of CVD was the independent predictor of serum levels of SDF-1 and CXCR4 and also relative mRNA expression of CXCR4. Conclusions: The higher levels of serum and relative mRNA expressions of SDF-1 and CXCR4 were associated with CVD in patients under HD. Furthermore, SDF-1 and CXCR4 were associated with several traditional and uremia-related CVD risk factors in such patients.
Adaptive Multiscale Symbolic-Dynamics Entropy for Condition Monitoring of Rotating Machinery

Vibration data from rotating machinery working in different conditions display different properties in spatial and temporal scales. As a result, insights into spatial- and temporal-scale structures of vibration data of rotating machinery are fundamental for describing running conditions of rotating machinery. However, common temporal statistics and typical nonlinear measures have difficulties in describing spatial and temporal scales of data. Recently, statistical linguistic analysis (SLA) has been pioneered in analyzing complex vibration data from rotating machinery. Nonetheless, SLA can examine data in spatial scales but not in temporal scales. To improve SLA, this paper develops symbolic-dynamics entropy for quantifying word-frequency series obtained by SLA. By introducing multiscale analysis to SLA, this paper proposes adaptive multiscale symbolic-dynamics entropy (AMSDE). By AMSDE, spatial and temporal properties of data can be characterized by a set of symbolic-dynamics entropy, each of which corresponds to a specific temporal scale. Afterward, AMSDE is employed to deal with vibration data from defective gears and rolling bearings. Moreover, the performance of AMSDE is benchmarked against five common temporal statistics (mean, standard deviation, root mean square, skewness and kurtosis) and three typical nonlinear measures (approximate entropy, sample entropy and permutation entropy). The results suggest that AMSDE performs better than these benchmark methods in characterizing running conditions of rotating machinery.
A Mobile Cloudlet Assisted Architecture for Handling Multimedia Applications

Multimedia comprises of different types of data formats from various sources. Handling the task of processing this complex multimedia data is a huge challenge and requires offloading to the cloud. The result is subsequently passed back to the Smart Mobile Device (SMD). This processing activity requires substantial amount of processing power and large network bandwidth of the cloud. As the output display on the SMD needs to be of superior quality, there is a need for having a robust architecture which can run these resource intensive multimedia applications in an efficient manner. This work proposes an architecture which is assisted by the cloud, and helps in processing the multimedia applications in a fast and efficient manner. Also, the cloudlet assisted architecture has been proposed, wherein a mobile device will select a cloudlet based on the Borda Scores Method (BSM) and based on the request, the selected cloudlet then acknowledges the multimedia sensor nodes and the results are sent back to the respective mobile device.
Predicting Student Performance and Deficiency in Mastering Knowledge Points in MOOCs Using Multi-Task Learning

Massive open online courses (MOOCs), which have been deemed a revolutionary teaching mode, are increasingly being used in higher education. However, there remain deficiencies in understanding the relationship between online behavior of students and their performance, and in verifying how well a student comprehends learning material. Therefore, we propose a method for predicting student performance and mastery of knowledge points in MOOCs based on assignment-related online behavior; this allows for those providing academic support to intervene and improve learning outcomes of students facing difficulties. The proposed method was developed while using data from 1528 participants in a C Programming course, from which we extracted assignment-related features. We first applied a multi-task multi-layer long short-term memory-based student performance predicting method with cross-entropy as the loss function to predict students’ overall performance and mastery of each knowledge point. Our method incorporates the attention mechanism, which might better reflect students’ learning behavior and performance. Our method achieves an accuracy of 92.52% for predicting students’ performance and a recall rate of 94.68%. Students’ actions, such as submission times and plagiarism, were related to their performance in the MOOC, and the results demonstrate that our method predicts the overall performance and knowledge points that students cannot master well.
An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

Because one of the key issues in improving the performance of Speech Emotion Recognition (SER) systems is the choice of an effective feature representation, most of the research has focused on developing a feature level fusion using a large set of features. In our study, we propose a relatively low-dimensional feature set that combines three features: baseline Mel Frequency Cepstral Coefficients (MFCCs), MFCCs derived from Discrete Wavelet Transform (DWT) sub-band coefficients that are denoted as DMFCC, and pitch based features. Moreover, the performance of the proposed feature extraction method is evaluated in clean conditions and in the presence of several real-world noises. Furthermore, conventional Machine Learning (ML) and Deep Learning (DL) classifiers are employed for comparison. The proposal is tested using speech utterances of both of the Berlin German Emotional Database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) speech databases through speaker independent experiments. Experimental results show improvement in speech emotion detection over baselines.
Conceptual Representation for Crisis-Related Tweet Classification

The importance of social media such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly recognized. During crisis situations, rapid and effective response actions by emergency services are critical to assure the safety of the public. In this paper, we propose a conceptual representation for crisis-related tweet classification. In order to classify a stream of tweets related to the incident, the crisis-related terms in each tweet are represented as conceptual entities such as event entities, category indicator entities, information type entities, URL entities, and user entities. For tweet classification, we have compared support vector machines and deep learning model which combines class activation mapping with one-shot learning in convolutional neural networks. Experimental results on TREC 2018 Incident Streams test collection show significant improvement over the baseline system.
Construction of Conditional Probability Tables of Bayesian Networks using Ontologies and Wikipedia

Building Bayesian Networks automatically serves to reduce time and effort by defining variables and the quantitative relation between variables. However, the quantitative part is the most complicated to solve because of it is statistical information.This research proposes a method to construct the quantitative part of a Bayesian Network based on text mining and ontologies for Intelligent Tutoring Systems. The network structure is built based on the variables and relations of an ontology. Conditional Probability Tables (CPT) are created from Wikipedia information.The constructed CPT reach a correlation of 0.895 against the experts' opinion. This correlation is good due to the subjectivity in the evaluations. We conclude that using the text mining in Wikipedia and ontologies, it is possible to construct CPT that adequately represents knowledge in an educative environment.
Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information

The development of new measures and algorithms to quantify the entropy or related concepts of a data series is a continuous effort that has brought many innovations in this regard in recent years. The ultimate goal is usually to find new methods with a higher discriminating power, more efficient, more robust to noise and artifacts, less dependent on parameters or configurations, or any other possibly desirable feature. Among all these methods, Permutation Entropy (PE) is a complexity estimator for a time series that stands out due to its many strengths, with very few weaknesses. One of these weaknesses is the PE’s disregarding of time series amplitude information. Some PE algorithm modifications have been proposed in order to introduce such information into the calculations. We propose in this paper a new method, Slope Entropy (SlopEn), that also addresses this flaw but in a different way, keeping the symbolic representation of subsequences using a novel encoding method based on the slope generated by two consecutive data samples. By means of a thorough and extensive set of comparative experiments with PE and Sample Entropy (SampEn), we demonstrate that SlopEn is a very promising method with clearly a better time series classification performance than those previous methods.
Effect of tolerance on the evolution of antibiotic resistance under drug combinations

Challenges of drug combinations Combinations of antibiotics are used to treat intractable infections such as methicillin-resistant Staphylococcus aureus. Clinically, however, drugs tend to be used empirically, and results can be contradictory. Liu et al. translated observations made in vitro to patient samples to understand the role of antibiotic tolerance in promoting or suppressing resistance when drug combinations are used (see the Perspective by Berti and Hirsch). Although bacterial populations exposed to multiple antibiotics can develop tolerance to multiple drugs, one drug in a combination may be able counter resistance to a partner drug and provide effective therapy. However, if tolerance has already emerged to one drug, the combination may end up promoting the transmission of resistance to a partner drug. Science, this issue p. 200; see also p. 141 When used in the clinic, antibiotic combination therapy needs to be applied cautiously if tolerance to one of the drugs has emerged. Drug combinations are widely used in clinical practice to prevent the evolution of resistance. However, little is known about the effect of tolerance, a different mode of survival, on the efficacy of drug combinations for preventing the evolution of resistance. In this work, we monitored Staphylococcus aureus strains evolving in patients under treatment. We detected the rapid emergence of tolerance mutations, followed by the emergence of resistance, despite the combination treatment. Evolution experiments on the clinical strains in vitro revealed a new way by which tolerance promotes the evolution of resistance under combination treatments. Further experiments under different antibiotic classes reveal the generality of the effect. We conclude that tolerance is an important factor to consider in designing combination treatments that prevent the evolution of resistance.
Participation in Domains of Physical Activity Among Australian Youth During the Transition From Childhood to Adolescence: A Longitudinal Study.

BACKGROUND Information about the domains of physical activity (PA) that are most prone to decline between late childhood (11 y), early adolescence (13 y), and mid-adolescence (15 y) may support more targeted health promotion strategies. This study explored longitudinal trends in nonorganized PA, organized PA, active transport and active chores/work between childhood and adolescence, and potential sociodemographic moderators of changes. METHODS Data were sourced from the Longitudinal Study of Australian Children (n = 4108). Participation in PA domains was extracted from youth time-use diaries. Potential moderators were sex, Indigenous status, language spoken at home, socioeconomic position, and geographical remoteness. RESULTS A large quadratic decline in nonorganized PA (-48 min/d, P < .001) was moderated by sex (β = 5.55, P = .047) and home language (β = 8.55, P = .047), with girls (-39 min/d) and those from a non-English speaking background (-46 min/d) declining more between 11 and 13 years. Active chores/work increased between 11 and 13 years (+4 min/d, P < .001) and then stabilized. Active transport increased among boys between 11 and 13 years (+6 min/d, P < .001) and then declined between 13 and 15 years (-4 min/d, P < .001). Organized PA remained stable. CONCLUSIONS The longitudinal decline in PA participation may be lessened by targeting nonorganized PA between childhood and adolescence. Future interventions may target girls or those from non-English speaking backgrounds during this transition.
Human zoonotic tuberculosis and livestock exposure in low‐ and middle‐income countries: A systematic review identifying challenges in laboratory diagnosis

Zoonotic tuberculosis (zTB) accounts for 1.4% of the global tuberculosis burden, with the largest disease burden in low‐ and middle‐income countries (LMICs). These populations have increased exposure to zTB due to livestock rearing practices and raw dairy consumption. This qualitative systematic literature review evaluates the quality of the literature that examines the association between human zTB in LMICs and frequent exposure to livestock and livestock products and summarizes current gaps in laboratory detection methods.
Device stability and quality of ventilation of classic laryngeal mask airway versus air-Q and I-gel at different head and neck positions in anesthetized spontaneously breathing children.

BACKGROUND Since its introduction into clinical practice, the use of laryngeal mask airway (LMA) has been dramatically increasing. We aimed to investigate the clinical performance of single use LMA classic, AIR-Q and I-gel at different head and neck positions and during the operative procedure in pediatric elective day case surgery. METHODS One hundred sixty-eight generally anesthetized spontaneously breathing children (2-9yr) were randomized to receive either LMA classic (n=56), I-gel (n=58) or AIR-Q (n=54). The oropharyngeal leak pressure (OLP), exhaled tidal volume (TV), peak inspiratory pressure (PIP), ventilation score and fiberoptic glottis view score were assessed at neutral position then at maximum flexion, extension and left rotation. Afterwards, the ventilation and fiberoptic view scores were assessed in neutral position at fixed time-points until end of surgery. RESULTS Compared to neutral position, maximum neck flexion increased OLP (P=0.000) and compromised the ventilation leading to increased PIP, decreased TV, worsening of ventilation score and fiberoptic glottis view. OLP mildly decreased with extension and left lateral rotation with mild effect on ventilation parameters (P<0.05). At all neck positions, the OLP was higher (P=0.000) and ventilation parameters were better with I-gel (P=0.000). Gradual worsening of ventilation score and fiberoptic view grade was recorded intraoperatively with the three devices, with the least deterioration observed in I-gel group (P=0.000). CONCLUSIONS Having the highest increase in OLP at neck flexion, the I-gel LMA exhibited the best ventilation parameters and fiberoptic view grade at different head and neck positions and throughout the intraoperative period.
Inherently decoupled 1H antennas and 31P loops for metabolic imaging of liver metastasis at 7 T

High field 31P spectroscopy has thus far been limited to diffuse liver disease. Unlike lower field‐strength scanners, there is no body coil in the bore of the 7 T and despite inadequate penetration depth (<10 cm), surface coils are the current state‐of‐the‐art for acquiring anatomical images to support multinuclear studies. We present a system of proton antennas and phosphorus loops for 31P spectroscopy and provide the first ultrahigh‐field phosphorus metabolic imaging of a tumor in the abdomen. Herein we characterize the degree to which antennas are isolated from underlying loops. Next, we evaluate the penetration depth of the two antennas available during multinuclear examinations. Finally, we combine phosphorus spectroscopy (two loops) with parallel transmit imaging (eight antennas) in a patient. The loops and antennas are inherently decoupled (no added circuitry, <0.1% power coupling). The penetration depth of two antennas gives twice that of conventional loops. The liver and full axial slice of the abdomen were imaged with eight transmit/receive antennas using parallel transmit B1‐shimming to overcome image voids. Phosphorus spectroscopy from a liver metastasis resolved individual peaks for phosphocholine and phosphoethenalomine. Proton antennas are inherently decoupled from phosphorus loops. By using two proton antennas it is possible to perform region‐of‐interest image‐based shimming in over 80% of the liver volume, thereby enabling phosphorus spectroscopy of localized disease. Shimming of the full extent of the abdominal cross‐section is feasible using a parallel transmit array of eight antennas. A system architecture capable of supporting eight‐channel parallel transmit and multinuclear spectroscopy is optimal for supporting multiparametric body imaging, including metabolic imaging, for monitoring the response of patients with liver metastases to cancer treatments and for patient risk stratification. In the meantime, the existing infrastructure using two antennas is sufficient for preliminary studies in metabolic imaging of tumors in the liver.
Blood pressure reaction to negative stimuli: Insights from continuous recording and analysis

Abstract Individuals with a tendency toward abnormally enhanced cardiovascular responses to stress are at greater risk of developing essential hypertension later in life. Accurate profiling of continuous blood pressure (BP) reactions in healthy populations is crucial for understanding normal and abnormal emotional reaction patterns. To this end, we examined the continuous time course of BP reactions to aversive pictures among healthy participants. In two experiments, we showed participants negative and neutral pictures while simultaneously measuring their continuous BP and heart rate (HR) reactions. In this study, BP reactions were analyzed continuously, in contrast to previous studies, in which BP responses were averaged across blocks. To compare time points along a temporal continuum, we applied a multi‐level B‐spline model, which is innovative in the context of BP analysis. Additionally, HR was similarly analyzed in order to examine its correlation with BP. Both experiments revealed a similar pattern of BP reactivity and association with HR. In line with previous studies, a decline in BP and HR levels was found in response to negative pictures compared to neutral pictures. In addition, in both conditions, we found an unexpected elevation of BP toward the end of the stimuli exposure period. These findings may be explained by the recruitment of attention resources in the presence of negative stimuli, which is alleviated toward the end of the stimulation. This study highlights the importance of continuous measurement and analysis for characterizing the time course of BP reactivity to emotional stimuli.