Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement

Remote camera-based measurement of physiology has great potential for healthcare and affective computing. Recent advances in computer vision and signal processing have enabled photoplethysmography (PPG) measurement using commercially available cameras. However, there remain challenges in recovering accurate noncontact PPG measurements in the presence of rigid head motion. When a subject is moving, their face may be turned away from one camera, be obscured by an object, or move out of the frame resulting in missing observations. As the calculation of pulse rate variability (PRV) requires analysis over a time window of several minutes, the effect of missing observations on such features is deleterious. We present an approach for fusing partial color-channel signals from an array of cameras that enable physiology measurements to be made from moving subjects, even if they leave the frame of one or more cameras, which would not otherwise be possible with only a single camera. We systematically test our method on subjects ( N=25) using a set of six, 5-min tasks (each repeated twice) involving different levels of head motion. This results in validation across 25 h of measurement. We evaluate pulse rate and PRV parameter estimation including statistical, geometric, and frequency-based measures. The median absolute error in pulse rate measurements was 0.57 beats-per-minute (BPM). In all but two tasks with the greatest motion, the median error was within 0.4 BPM of that from a contact PPG device. PRV estimates were significantly improved using our proposed approach compared to an alternative not designed to handle missing values and multiple camera signals; the error was reduced by over 50%. Without our proposed method, errors in pulse rate would be very high, and estimation of PRV parameters would not be feasible due to significant data loss.
Structural Brain Network Mining Based on Core Theory

In recent decades, the rapid development of neuroimaging technology has provided an effective and noninvasive method for exploring the structure and functional connectivity of the human brain. Corresponding neural image data mining plays an increasingly important role in predicting neurological and psychiatric diseases because it may find clinically significant structural patterns that can promote the understanding and diagnosis of neurological and psychiatric diseases. Most of the recent researches focus on sub-graph models of brain networks. However, due to the high complexity of brain network connections, the number of sub-graphs increases exponentially with the increase of graph patterns. Traditional sub-graph mining is both time-consuming and memory-intensive. In this paper, by measuring the difference in brain network connectivity between normal individuals and patients, we propose a coritivity-based brain network mining method to help diagnose neurological or psychiatric diseases. Since solving the network coritivity is an NP-Hard problem, we propose a heuristic algorithm based on the Max-Min Ant System(MMAS), which can quickly give the approximate coritivity of the network. We conducted experiments on three real-world brain network datasets for disease diagnosis. The experiment results show that our method achieves great results and can provide meaningful suggestions.
Nocturnal Heart Rate Variability Spectrum Characterization in Preschool Children With Asthmatic Symptoms

Asthma is a chronic lung disease that usually develops during childhood. Despite that symptoms can almost be controlled with medication, early diagnosis is desirable in order to reduce permanent airway obstruction risk. It has been suggested that abnormal parasympathetic nervous system (PSNS) activity might be closely related with the pathogenesis of asthma, and that this PSNS activity could be reflected in cardiac vagal control. In this work, an index to characterize the spectral distribution of the high frequency (HF) component of heart rate variability (HRV), named peakness (<inline-formula><tex-math notation="LaTeX">$\wp$</tex-math></inline-formula>), is proposed. Three different implementations of <inline-formula><tex-math notation="LaTeX">$\wp$</tex-math></inline-formula>, based on electrocardiogram (ECG) recordings, impedance pneumography (IP) recordings and a combination of both, were employed in the characterization of a group of preschool children classified attending to their risk of developing asthma. Peakier components were observed in the HF band of those children classified as high-risk (<inline-formula> <tex-math notation="LaTeX">$p < 0.005$</tex-math></inline-formula>), who also presented reduced sympathvoagal balance. Results suggest that high-risk of developing asthma might be related with a lack of adaptability of PSNS.
A Theoretical and Practical Approach for Underexcitation Protection and Control Studies of Hydrogenerators in a Real-Time Environment

The installation and settings of protection devices and excitation control systems require experience since a failure can cause damage to the synchronous generator and negative impact on the power grid. A test bed implemented in a real-time digital simulator (RTDS) is presented in this paper as an alternative for the training and learning process that involves the knowledge of an effective coordination between such protection and control systems of synchronous generators. A complete generation system is implemented in a RTDS, including an excitation system model with its limiters. These limiters are coordinated with the protection functions implemented in a physical relay. The contributions of this work are the demonstration of the importance of this type of study (i.e., real-time simulations results through hardware-in-the-loop tests), the consolidation of the impedance (R-X) and admittance (G-B) planes as the more appropriate ones to analyze the coordination between the ANSI 40 function and the underexcitation limiter, and the presentation of a study focused on hydrogenerators (including novel settings for the ANSI 40 protection).
Blood-based DNA Methylation Biomarkers for Early Detection of Colorectal Cancer

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection of CRC can significantly reduce this mortality rate. Unfortunately, recommended screening modalities, including colonoscopy, are hampered by poor patient acceptance, low sensitivity and high cost. Recent studies have demonstrated that colorectal oncogenesis is a multistep event resulting from the accumulation of a variety of genetic and epigenetic changes in colon epithelial cells, which can be reflected by epigenetic alterations in blood. DNA methylation is the most extensively studied dysregulated epigenetic mechanism in CRC. In this review, we focus on current knowledge on DNA methylation as potential blood-based biomarkers for early detection of CRC.
Comparing the efficacy of therapeutic packages in Persian Medicine with Classical Medicine in overweight patients: a randomized clinical trial

Background Obesity is a major risk factor for many diseases. In Persian Medicine, much emphasis has been put on the balanced weight that maintains health. Therefore, the treatment of obesity is a top priority for health care in Persian and Classical Medicine. Objective To compare the efficacy of therapeutic packages in Persian Medicine with Classical Medicine in overweight treatment. Methods This randomized clinical trial was conducted at Ghaem Hospital in Mashhad, Iran, from August 22, 2016 to December 20, 2016. Sixty-nine overweight women with body mass index (BMI) of 27–29.9 and waist circumference more than 88 cm based on inclusion and exclusion criteria, were included in the study. To control bias in sampling, with considering that the age variable is an interventional factor in the process of effective treatment, in addition to using the random allocation method, the population were studied in three groups of 18–30 years old, 31–50 years old and >50 years old, who were classified by blocking method, and three types of treatment interventions were performed in each age group: Group A (Classical diet and orlistat), Group B (Persian diet and MDB) and Group C (classical diet and MDB). Calorie consumption was equivalent in the three groups. Blinding was done only for patients. The treatment period was three months in all three groups. Subsequently, we analyzed the effectiveness of the therapeutic packages on anthropometric index, body composition and laboratory parameters in the three groups. The data were analyzed using SPSS version 16. We used descriptive statistics, Kolmogorov–Smirnov, ANOVA, paired-samples t-test, non-parametric Wilcoxon test and regression methods for the analyses of primary and secondary outcomes. Results Although anthropometric index and fat mass decreased highly significantly in all three intervention groups (p<0.001), the B group led to a further decrease in BMI (Mean ± SD: 1.61±1.03), weight (Mean ± SD: 4.79±2.26), waist circumference (Mean ± SD: 4.25±0.59) and Fat mass (Mean ± SD: 3.24±2.70), compared with the other two groups. Lipid profile and FBS in groups B and C had significant decrease before and after the intervention, but Group A had no significant effect before or after the intervention. Other laboratory parameters did not change significantly before or after the treatment in the three groups. Conclusion In this study, Classical Medicine and Persian Medicine had the same levels of effectiveness but Persian Medicine brought a greater reduction in body weight, fat mass%, lipid profile and fasting blood glucose than Classical Medicine during the study period. So Persian Medicine can be effective in weight loss as an alternative therapy, but more studies are needed. Trial registration The trial was registered at the Iranian Registry of Clinical Trials (http://www.irct.ir) with the Irct ID: (IRCT 2016041927477N1). Funding This paper is supported by a grant from the Research Council, Mashhad University of Medical Sciences, Mashhad, Iran.
Defining the index trauma in post-traumatic stress disorder patients with multiple trauma exposure: impact on severity scores and treatment effects of using worst single incident versus multiple traumatic events

ABSTRACT Background: A diagnosis of post-traumatic stress disorder (PTSD) requires the identification of one or more traumatic events, designated the index trauma, which serves as the basis for assessment of severity of PTSD. In patients who have experienced more than one traumatic event, severity may depend on the exact definition of the index trauma. Defining the index trauma as the worst single incident may result in PTSD severity scores that differ from what would be seen if the index trauma included multiple events. Objective: This study aimed to investigate the impact of the definition of the index trauma on PTSD baseline severity scores and treatment outcome. Method: A planned secondary analysis was performed on data from a subset (N = 58) of patients enrolled in a trial evaluating the efficacy of a 12 week residential dialectical behavioural therapy programme for PTSD related to childhood abuse (DBT-PTSD). Assessments of the severity of PTSD were conducted at admission, at the end of the 12 week treatment period, and at 6 and 12 weeks post-treatment, using the Clinician-Administered PTSD Scale. The index trauma was defined with respect to both the worst single incident and up to three qualitatively distinct traumatic events. Results: When the index trauma included multiple traumas, PTSD severity scores were significantly higher and improvements from pre- to post-treatment were significantly lower than when the index trauma was defined as the worst single incident. Conclusions: In patients with PTSD who have experienced multiple traumas, defining the index trauma as the worst single incident may miss some aspects of clinically relevant symptomatology, thereby leading to a possibly biased interpretation of treatment effects. In DBT-PTSD, treatment effects were lower when the index trauma included multiple traumatic events. More research is needed to determine the impact of the various index trauma definitions on the evaluation of other trauma-focused treatments.
Silencing of ENO1 by shRNA Inhibits the Proliferation of Gastric Cancer Cells

α-Enolase is a significant subunit of enolase and acts as a glycolytic enzyme responsible for catalyzing the conversion of 2-phosphoglycerate to phosphoenolpyruvate in the anaerobic glycolysis pathway. The research about their role is known little in tumor invasion and metastasis. This research analyzed the effect of α-enolase in proliferation and progression of human gastric cancer. The constructed PLKO.1-ENO1 shRNA vector was transfected into 293 T cells and used to infect gastric cancer cells, MKN45, by using lentivirus method. Negative controls were generated by infection with viruses containing empty vector PLKO.1-scramble-shRNA by the same protocol and using wild-type MKN45 cells as blank control. The silencing effect was confirmed by reverse transcription polymerase chain reaction and Western blotting at messenger RNA and protein levels, respectively. Cell proliferation and chemosensitivity were tested by methyl-thiazolyl-tetrazolium assay. Cell apoptosis was tested by flow cytometry. The cell line α-enolase short hairpin RNA stabling silence α-enolase was successfully constructed. In the α-enolase short hairpin RNA cell lines, messenger RNA and protein expression of α-enolase were significantly lower than those in negative control and blank control groups. The proliferation and clone formation ability were significantly inhibited, cell apoptosis was increased significantly, and the inhibition rate of chemotherapy drugs was increased (P < .05). Our data provide strong evidence that α-enolase short hairpin RNA interference vector can effectively suppress the proliferation and increase chemosensitivity of MKN45 cells, which may provide a novel gene therapy for gastric cancer.
Hierarchical Probabilistic Framework for Fetal R-Peak Detection, Using ECG Waveform and Heart Rate Information

The abdominal fetal electrocardiogram (fECG) can provide valuable information about fetal well-being. However, fetal R-peak detection in abdominal fECG recordings is challenging due to the low signal-to-noise ratio (SNR) and the nonstationary nature of the fECG waveform in the abdominal recordings. In this paper, we propose a multichannel hierarchical probabilistic framework for fetal R-peak detection that combines predictive models of the ECG waveform and the heart rate. The performance of our method was evaluated on set-A of the 2013 Physionet/Computing in Cardiology Challenge and compared to the performance of several methods that have been proposed in the literature. The hierarchical probabilistic framework presented in this study outperforms other methods for fetal R-peak detection with a mean overall detection accuracy for set-A of 99.6%. Even for recordings with low SNR our method enables reliable fetal R-peak detection (Ac 99.4%).
Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models

Land Surface Models (LSMs) are essential to reproduce biophysical processes modulated by vegetation and to predict the future evolution of the land‐climate system. To assess the performance of an ensemble of LSMs (JSBACH, JULES, ORCHIDEE, CLM, and LPJ‐GUESS) a consistent set of land surface energy fluxes and leaf area index (LAI) has been generated. Relationships of interannual variations of modeled surface fluxes and LAI changes have been analyzed at global scale across climatological gradients and compared with those obtained from satellite‐based products. Model‐specific strengths and deficiencies were diagnosed for tree and grass biomes. Results show that the responses of grasses are generally well represented in models with respect to the observed interplay between turbulent fluxes and LAI, increasing the confidence on how the LAI‐dependent partition of net radiation into latent and sensible heat are simulated. On the contrary, modeled forest responses are characterized by systematic bias in the relation between the year‐to‐year variability in LAI and net radiation in cold and temperate climates, ultimately affecting the amount of absorbed radiation due to LAI‐related effects on surface albedo. In addition, for tree biomes, the relationships between LAI and turbulent fluxes appear to contradict the experimental evidences. The dominance of the transpiration‐driven over the observed albedo‐driven effects might suggest that LSMs have the incorrect balance of these two processes. Such mismatches shed light on the limitations of our current understanding and process representation of the vegetation control on the surface energy balance and help to identify critical areas for model improvement.
Model Predictive Control for DC–DC Boost Converters With Reduced-Prediction Horizon and Constant Switching Frequency

The implementation of multistep direct model predictive control (MPC) for dc–dc boost converters overcomes the well-known issue of nonminimum phase behavior. However, it can lead to a high computational burden depending on the prediction horizon length. In this paper, a simple and computationally efficient MPC method for dc–dc boost converters is proposed. The key novelty of the presented control strategy lies in the way dynamic references are handled. The control strategy is capable of providing suitable references for the inductor current and the output voltage, without requiring additional control loops. Moreover, this reference design allows the predictive controller to be implemented with a single-step prediction horizon. Thus, a significant reduction in the required real-time calculations executed in the control hardware is achieved. To obtain constant switching frequency, the power switch commutation instants within a sampling period are considered as control inputs. Therefore, the predictive controller is formulated as a continuous control set MPC. Additionally, the proposed formulation is able to deal with different operation modes of the converter without changing the controller structure. Finally, an observer is used to dynamically modify the reference to provide robustness to system parameter uncertainties. Simulation and experimental results show an accurate tracking of dynamic inductor current and output voltage references, while respecting the restrictions on maximum inductor current levels of the converter.
Educational Process Mining: A Systematic Literature Review

Process mining (PM) is an emerging research discipline that is concerned with obtaining fact-based insights on how business processes are executed within an organization. It combines model-based and data-oriented analysis techniques to discover, monitor and improve executed processes by extracting knowledge from event logs generated and stored in information systems. When process mining techniques are applied to educational data, they are often referred to as Educational Process Mining (EPM), and in different case studies EPM results were promising. This paper presents a systematic review of the current status of research in this important area. We examined the literature on research with associated case studies conducted in the domain during the past eight years (2009-2016). Search terms identified 59 papers, but inclusion and exclusion criteria limited the key studies to 37. The research questions, methodologies and findings of these published papers were analysed carefully. This paper aims to highlight the use of process mining to improve educational processes, and to provide researchers with a comprehensive background for understanding the current work being undertaken in this field.
The governance of land use strategies: Institutional and social dimensions of land sparing and land sharing

Agricultural land use is a key interface between the goals of ensuring food security and protecting biodiversity. “Land sparing” supports intensive agriculture to save land for conservation, whereas “land sharing” integrates production and conservation on the same land. The framing around sparing versus sharing has been extensively debated. Here, we focused on a frequently missing yet crucial component, namely the governance dimension. Through a case‐study in Ethiopia, we uncovered stakeholder preferences for sparing versus sharing, the underlying rationale, and implementation capacity challenges. Policy stakeholders preferred sparing whereas implementation stakeholders preferred sharing, which aligned with existing informal institutions. Implementation of both strategies was limited by social, biophysical, and institutional factors. Land use policies need to account for both ecological patterns and social context. The findings from simple analytical frameworks (e.g., sparing vs. sharing) therefore need to be interpreted carefully, and in a social‐ecological context, to generate meaningful recommendations for conservation practice.
Scrotal circumference: A predictor of testosterone concentration and certain attributes of seminal vesicles influencing buffalo male fertility

Aim: The aim of this study was to evaluate the relationship of scrotal circumference (SC) with plasma testosterone, seminal vesicles (SVs) weight, and its secretion as measurable indicators of fertility and also to sequence and establish phylogenetic relatedness of certain SV protein genes with other species as such integrated approach is lacking. Materials and Methods: Altogether, 59 apparently healthy male buffaloes sacrificed at slaughterhouse were selected (irrespective of breed) for measuring SC and collecting blood and paired SVs. The SC was measured at greater curvature using soft thread. In the present study, blood plasma testosterone, cholesterol, protein, and glucose in addition to SV fructose, citric acid and proteins in SV fluid were also estimated. The SV tissue was fixed in RNAlater for RNA extraction. Male buffaloes were categorized as per total SV weight into Group I (<5.0 g), Group II (5.0-7.84 g), and Group III (>8.0 g) and dentitions-I (≤18 months), II (18-24 months), and III (≥24 months) to assess the effect of weight and dentition age on SC, SV weight, and its certain secretions. Data were analyzed using linear model procedure including Tukey HSD test and Pearson’s correlation coefficient. Variance inflation and condition index were also used to assess multicollinearity. Results: Gross and histomorphological evaluation of SVs did not show any abnormality. Macronutrients (plasma protein, glucose, and cholesterol) showed non-significant (p>0.05) variation between groups. The SC and SV weight varied significantly (p<0.05) with a significant positive relationship with plasma testosterone, SV protein, fructose, and citric acid. In addition, testosterone concentration also showed increasing trend from Groups I to III but increased significantly (p<0.05) from Group II to III with positive and significant correlations with SV protein, fructose, and citric acid similar to SV weight and SC. Binders of sperm protein (BSP1, 3, and 5) genes (full length) were sequenced and established an evolutionary relationship which is lacking in buffalo. Conclusion: The present findings established a significant positive correlation of SC with that of other fertility parameters related to SVs weight and its secretions: Fructose, citric acid, and protein (inclusive of BSPs sequenced full length), and testosterone. Therefore, the present integrated approach along with certain semen quality attributes reflecting epididymis function could be used as a predictive fertility marker for grading and selection of breeding bulls and their progenies to develop outstanding bull mother farm.