Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified spline regression based on randomly right-censored data: A comparative study

ABSTRACT In this paper, we propose modified spline estimators for nonparametric regression models with right-censored data, especially when the censored response observations are converted to synthetic data. Efficient implementation of these estimators depends on the set of knot points and an appropriate smoothing parameter. We use three algorithms, the default selection method (DSM), myopic algorithm (MA), and full search algorithm (FSA), to select the optimum set of knots in a penalized spline method based on a smoothing parameter, which is chosen based on different criteria, including the improved version of the Akaike information criterion (AICc), generalized cross validation (GCV), restricted maximum likelihood (REML), and Bayesian information criterion (BIC). We also consider the smoothing spline (SS), which uses all the data points as knots. The main goal of this study is to compare the performance of the algorithm and criteria combinations in the suggested penalized spline fits under censored data. A Monte Carlo simulation study is performed and a real data example is presented to illustrate the ideas in the paper. The results confirm that the FSA slightly outperforms the other methods, especially for high censoring levels.
Lagrangian Motion Magnification revisited: Continuous, Magnitude Driven Motion Scaling for Psychophysiological Experiments

Video motion magnification forms a relatively novel family of visualization techniques, that aim to magnify imperceivably small motions in videos. The most prominent techniques are based on Eulerian video processing and local phase shifting, which modify pixel time courses, rather than relying on explicit motion estimation.In this work, we show that under ideal conditions in the context of psychophysiological experiments, a Lagrangian motion magnification approach based on dense optical flow estimation, can be superior to Eulerian motion magnification strategies. We present a novel, continuous and motion magnitude driven forward warping scheme of small motions, which implements motion compensation and magnification into a single motion estimation step. Our approach does not rely on temporal filtering and works in the presence of large motion. It does not require the explicit identification of fast moving objects and more generally no segmentation and or matting in the image domain is necessary. We apply our method to the visualization of blinking related modulations in micro-saccadic eye movements ((i.a. iridodonesis), pupil dilation (hippus) and micro-expression analysis.
Clinical evaluation of Miller class I and II recessions treatment with the use of modified coronally advanced tunnel technique with either collagen matrix or subepithelial connective tissue graft: A randomized clinical study

AIM To compare outcomes of modified coronally advanced tunnel technique (MCAT) combined with either collagen matrix (CM) or subepithelial connective tissue graft (SCTG) in the treatment of Miller class I and II multiple gingival recessions in the mandible. MATERIALS AND METHODS The study encompassed 91 recessions in 29 patients for whom MCAT was combined with CM on one side of the mandible and SCTG on the contralateral one. The following clinical parameters were measured: gingival recession height (GR) and width (RW), probing depth (PD), clinical attachment level (CAL), width of keratinized tissue (KT), gingival thickness (GT), mean (MRC) and complete root coverage (CRC) and Root Coverage Esthetic Score (RES). RESULTS The MRC proportions on the CM- and SCTG-treated sides were 53.20% and 83.10%, respectively. CRC was achieved in 9 out of 45 (20%) gingival defects treated with CM and 31 out of 46 (67%) treated with SCTG. There were statistically significant differences in MRC, CRC, GR, RW, KT, GT and RES between CM- and CTG-treated sides. CONCLUSIONS Modified coronally advanced tunnel technique leads to reduction in gingival recession both when combined CM and SCTG, of which the latter is more efficient as far as root coverage and aesthetic parameters are concerned.
Timing of revascularization in patients with transient ST-segment elevation myocardial infarction: a randomized clinical trial

Aims Patients with acute coronary syndrome who present initially with ST-elevation on the electrocardiogram but, subsequently, show complete normalization of the ST-segment and relief of symptoms before reperfusion therapy are referred to as transient ST-segment elevation myocardial infarction (STEMI) and pose a therapeutic challenge. It is unclear what the optimal timing of revascularization is for these patients and whether they should be treated with a STEMI-like or a non-ST-segment elevation myocardial infarction (NSTEMI)-like invasive approach. The aim of the study is to determine the effect of an immediate vs. a delayed invasive strategy on infarct size measured by cardiac magnetic resonance imaging (CMR). Methods and results In a randomized clinical trial, 142 patients with transient STEMI with symptoms of any duration were randomized to an immediate (STEMI-like) [0.3 h; interquartile range (IQR) 0.2-0.7 h] or a delayed (NSTEMI-like) invasive strategy (22.7 h; IQR 18.2-27.3 h). Infarct size as percentage of the left ventricular myocardial mass measured by CMR at day four was generally small and not different between the immediate and the delayed invasive group (1.3%; IQR 0.0-3.5% vs. 1.5% IQR 0.0-4.1%, P = 0.48). By intention to treat, there was no difference in major adverse cardiac events (MACE), defined as death, reinfarction, or target vessel revascularization at 30 days (2.9% vs. 2.8%, P = 1.00). However, four additional patients (5.6%) in the delayed invasive strategy required urgent intervention due to signs and symptoms of reinfarction while awaiting angiography. Conclusion Overall, infarct size in transient STEMI is small and is not influenced by an immediate or delayed invasive strategy. In addition, short-term MACE was low and not different between the treatment groups.
Respiratory and cardiac monitoring at night using a wrist wearable optical system

Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold standard for such task. However, it is very unwieldy and therefore not suitable for long-term analysis. Here, we present a non-intrusive wearable system that, by using photoplethysmography, it can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably during the night. The performance of the proposed approach was evaluated empirically in the Department of Psychology at the University of Fribourg. Each participant was wearing two smart-bracelets from Ava as well as a complete polysomnographic setup as reference. The resulting mean absolute errors are 17.4ms (MAPE 1.8%) for the beat-to-beat intervals, 0.13beats-per-minute (MAPE 0.20%) for the pulse rate, and 0.9breaths-per-minute (MAPE 6.7%) for the breath rate.
Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two-dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients. We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time- and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.
YHMI: a web tool to identify histone modifications and histone/chromatin regulators from a gene list in yeast

Abstract Post-translational modifications of histones (e.g. acetylation, methylation, phosphorylation and ubiquitination) play crucial roles in regulating gene expression by altering chromatin structures and creating docking sites for histone/chromatin regulators. However, the combination patterns of histone modifications, regulatory proteins and their corresponding target genes remain incompletely understood. Therefore, it is advantageous to have a tool for the enrichment/depletion analysis of histone modifications and histone/chromatin regulators from a gene list. Many ChIP-chip/ChIP-seq datasets of histone modifications and histone/chromatin regulators in yeast can be found in the literature. Knowing the needs and having the data motivate us to develop a web tool, called Yeast Histone Modifications Identifier (YHMI), which can identify the enriched/depleted histone modifications and the enriched histone/chromatin regulators from a list of yeast genes. Both tables and figures are provided to visualize the identification results. Finally, the high-quality and biological insight of the identification results are demonstrated by two case studies. We believe that YHMI is a valuable tool for yeast biologists to do epigenetics research.
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees.

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty in the output variable, using for example a quantile loss in Random Forests (Meinshausen, 2006). To the best of our knowledge, no extension has been provided yet for dealing with uncertainties in the input variables, even though such uncertainties are common in practical situations. We propose here such an extension by showing how standard regression trees optimizing a quadratic loss can be adapted and learned while taking into account the uncertainties in the inputs. By doing so, one no longer assumes that an observation lies into a single region of the regression tree, but rather that it belongs to each region with a certain probability. Experiments conducted on several data sets illustrate the good behavior of the proposed extension.
Learning Sparse Neural Networks via Sensitivity-Driven Regularization

The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications. Regularize-and-prune methods aim at meeting these challenges by sparsifying the network weights. In this context we quantify the output sensitivity to the parameters (i.e. their relevance to the network output) and introduce a regularization term that gradually lowers the absolute value of parameters with low sensitivity. Thus, a very large fraction of the parameters approach zero and are eventually set to zero by simple thresholding. Our method surpasses most of the recent techniques both in terms of sparsity and error rates. In some cases, the method reaches twice the sparsity obtained by other techniques at equal error rates.
Preliminary Evaluation of Fetal Congenital Heart Defects Changes on Fetal-Maternal Heart Rate Coupling Strength

Monitoring fetal heart rate in an important aspect in evaluating fetal well being. Maternal-fetal interaction has shown evolution during fetal maturation. In this work, we studied maternal-fetal heart rate synchronization in early and late gestation fetuses. We also evaluated variations in the synchronization due to congenital heart defect (CHD). Maternal-fetal heart rate synchronization for 22 early gestation (Age < 32 weeks), $late gestation (Age >32 weeks) and 7 CHD fetuses (5 of them with gestational age < 32 weeks). The synchronization ratio between the mother and the fetus was more localized at certain fetus heart rate in the early gestation group while it was spreading over more fetal heart rate for the late group. For example, for maternal primary cycle of 3 beat- to-beat (m=3), the synchronization ratio of 5 fetus beats (n=5) contributed 60±30% of the whole coupling ratios for the early group while it contributed 30°30% for the late group (p< 0.01). On the other hand, the coupling ratio of m:n=3:7 contributed 4±17% of the early group and 13±24% for the late group (p< 0.05). The standard deviation of the phase coherence index $(\lambda_{-\mathrm{S}\mathrm{D}})$ for both the late and the CHD groups were significantly higher than the early group at different values. For example, $\lambda -\mathrm{S}\mathrm{D} was 0.006\pm 0.004$ for the early group while it was 0.009±0.008 for the late group (p< 0.01) and 0.01± 0.002 for the CHD group (p< 0.01) for m=3. The variation between the early and late normal groups might indicate a healthy development of the autonomic nervous system while the higher variation in the CHD group could be a good marker for impairment of the cardiac autonomic activity. Further coupling analysis with more abnormal cases is needed to verify these findings.
Second-Order Equivalent Circuits for the Design of Doubly-Tuned Transformer Matching Networks

The doubly-tuned magnetic transformer, comprising coupled inductors shunted by capacitors, is today widely in use as interstage network and for impedance matching in silicon millimeter waves amplifiers. It provides several advantages, compared with simple LC resonators, but the design is made complex by the high order of the network, featuring multiple resonances, and by the large number of components to be selected. In this paper, a novel approach for the analysis and design of such a network is proposed. It is shown that the response can be very well approximated, in the neighborhood of each resonance frequency, by a second-order parallel or series RLC equivalent circuit. This yields simple equations for the impedance, the bandwidth, and the power loss, giving intuition into the network behavior, and greatly simplifying the design. Based on the results of the analysis, guidelines for the network optimization are proposed, targeting minimum power loss and a flat broadband response. Design examples of practical interest are presented: the component values estimated by hand calculation are in very good agreement with those provided by a numerical circuit optimizer.
Different Feedback Methods For An SSVEP-Based BCI

In this paper we examined different ways to inform the user of the classification progress in our online SSVEPbased BCI speller. Different user feedback was given based on the distance from the classification threshold, separately calculated for each stimulus. We focused on the comparison of the accuracies and spelling times associated with each different feedback type. We tested eight different methods, one without feedback for comparison, and the two paradigms each (an increase and a decrease), for three varying parameters, during an online spelling task. The eighth method was a combination of the best performing feedback modalities. A 28 target speller was used for spelling the same word with different feedback methods. The level of comfort was assessed by the seven healthy participants, using a questionnaire. We found substantial decreases in spelling times; they were reduced to 12–77% of the no-feedback condition spelling times, for each of our subjects, with at least one of the parameters. However, this parameter, as expected, was different for each user. According to the personal fastest feedback methods, a combination of them was also used for spelling. These combined feedback methods usually resulted in a slower spelling than the individual best feedback, but still faster than without any feedback. Overall, the average spelling times with the different feedback methods were: no feedback, 95.09 s, increasing size, 62.94 s, decreasing size, 87.73 s, increasing contrast, 77.80 s, decreasing contrast, 124.37 s, increasing duty-cycle, 134.70 s, and decreasing dutycycle, 103.77 s.
Ameliorative effect of carnosine and N‐acetylcysteine against sodium nitrite induced nephrotoxicity in rats

The widespread use of sodium nitrite (NaNO2) for various industrial purposes has increased human exposure to alarmingly high levels of nitrate/nitrite. Because NaNO 2 is a strong oxidizing agent, induction of oxidative stress is one of the mechanisms by which it can exert toxicity in humans and animals. We have investigated the possible protection offered by carnosine (CAR) and N‐acetylcysteine (NAC) against NaNO 2‐induced nephrotoxicity in rats. Animals orally received CAR at 100 mg/kg body weight/d for seven days or NAC at 100 mg/kg body weight/d for five days followed by a single oral dose of NaNO 2 at 60 mg/kg body weight. The rats were killed after 24 hours, and the kidneys were removed and processed for various analyses. NaNO 2 induced oxidative stress in kidneys, as shown by the decreased activities of antioxidant defense, brush border membrane, and metabolic enzymes. DNA‐protein crosslinking and DNA fragmentation were also observed. CAR/NAC pretreatment significantly protected the kidney against these biochemical alterations. Histological studies supported these findings, showing kidney damage in NaNO 2‐treated animals and reduced tissue impairment in the combination groups. The protection offered by CAR and NAC against NaNO 2‐induced damage, and their nontoxic nature, makes them potential therapeutic agents against nitrite‐induced nephrotoxicity.
Context-Aware Data-Driven Intelligent Framework for Fog Infrastructures in Internet of Vehicles

Internet of Vehicles (IoV) is the evolution of vehicular ad-hoc networks and intelligent transportation systems focused on reaping the benefits of data generated by various sensors within these networks. The IoV is further empowered by a centralized cloud and distributed fog-based infrastructure. The myriad amounts of data generated by the vehicles and the environment have the potential to enable diverse services. These services can benefit from both variety and velocity of the generated data. This paper focuses on the data at the edge nodes to enable fog-based services that can be consumed by various IoV safety and non-safety applications. This paper emphasizes the challenges involved in offering the context-aware services in an IoV environment. In order to overcome these challenges, this paper proposes a data analytics framework for fog infrastructures at the fog layer of traditional IoV architecture that offers context-aware real time, near real-time and batch services at the edge of a network. Finally, the appropriateness of the proposed framework is verified through different use cases in the IoV environment.
MDockPeP: An ab‐initio protein–peptide docking server

Protein–peptide interactions play a crucial role in a variety of cellular processes. The protein–peptide complex structure is a key to understand the mechanisms underlying protein–peptide interactions and is critical for peptide therapeutic development. We present a user‐friendly protein–peptide docking server, MDockPeP. Starting from a peptide sequence and a protein receptor structure, the MDockPeP Server globally docks the all‐atom, flexible peptide to the protein receptor. The produced modes are then evaluated with a statistical potential‐based scoring function, ITScorePeP. This method was systematically validated using the peptiDB benchmarking database. At least one near‐native peptide binding mode was ranked among top 10 (or top 500) in 59% (85%) of the bound cases, and in 40.6% (71.9%) of the challenging unbound cases. The server can be used for both protein–peptide complex structure prediction and initial‐stage sampling of the protein–peptide binding modes for other docking or simulation methods. MDockPeP Server is freely available at http://zougrouptoolkit.missouri. edu/mdockpep. © 2018 Wiley Periodicals, Inc.
On the generalizability of ECG-based obstructive sleep apnea monitoring: merits and limitations of the Apnea-ECG database

Obstructive sleep apnea syndrome (OSAS) is a sleep disorder that affects a large part of the population and the development of algorithms using cardiovascular features for OSAS monitoring has been an extensively researched topic in the last two decades. Several studies regarding automatic apneic event classification using ECG derived features are based on the public Apnea-ECG database available on PhysioNet. Although this database is an excellent starting point for apnea topic investigations, in our study we show that algorithms for apneic-epochs classification that are successfully trained on this database (sensitivity < 85%, false detection rate <20%) perform poorly (sensitivity\textit<55%, false detection rate < 40%) in other databases which include patients with a broader spectrum of apneic events and sleep disorders. The reduced performance can be related to the complexity of breathing events, the increased number of non-breathing related sleep events, and the presence of non-OSAS sleep pathologies.
Autonomic cardiac activity in adults with short and long sleep onset latency

Autonomic cardiac activity during sleep has been widely studied. Research has mostly focused on cardiac activity between different sleep stages and wakefulness as well as between normal and pathological sleep. This work investigates autonomic activity changes during sleep onset in healthy subjects with long and short sleep onset latency (SOL). Polysomnography (PSG) and electrocardiography (ECG) were simultaneously recorded in 186 healthy subjects during a single night. Autonomic activity was assessed based on frequency domain analysis of RR intervals and results show that the analysis of RR intervals differs significantly between the short SOL and the long SOL groups. We found that the spectral power in the low frequency band (LF) was significantly higher in the long SOL group compared to the short SOL group in the first 10 minutes in bed intended to sleep. There was no significant difference for LF and the spectral power in the high frequency band (HF) 10 minutes before and after sleep onset between the two groups. Only in the short SOL group there was a significant increase in HF from the first 10 minutes in bed intended to sleep to 10 minutes before SO, while LF decreased significantly in both groups. The effect of time (5.5-min bin) on the heart rate variability (HRV) features around sleep onset showed that both LF and HF differed significantly during the period surrounding sleep onset only in the short SOL group.
Data Space Adaptation for Multiclass Motor Imagery-based BCI

Various adaptation techniques have been proposed to address the non-stationarity issue faced by electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, most of these adaptation techniques are only suitable for binary-class BCIs. This paper proposes a supervised multiclass data space adaptation technique (MDSA) to transform the test data using a linear transformation such that the distribution difference between the multiclass train and test data is minimized. The results of using the proposed MDSA on BCI Competition IV dataset 2a improved the classification accuracy by an average of 4.3% when 20 trials per class were used from the test session to estimate adaptation transformation. The results also showed that the proposed MDSA algorithm outperformed the multi pooled mean linear discrimination (MPMLDA) technique with as few as 10 trials per class used for calculating the transformation matrix. Hence the results showed the effectiveness of the proposed MDSA algorithm in addressing non-stationarity issue for multiclass EEG-based BCI.