Miracle-dz/UniFAR-FTUs huggingface dataset:
Programming an offline-analyzer of motor imagery signals via python language

Brain Computer Interface (BCI) systems control the user's environment via his/her brain signals. Brain signals related to motor imagery (MI) have become a widespread method employed by the BCI community. Despite the large number of references describing the MI signal treatment, there is not enough information related to the available programming languages that could be suitable to develop a specific-purpose MI-based BCI. The present paper describes the development of an offline-analysis system based on MI-EEG signals via open-source programming languages, and the assessment of the system using electrical activity recorded from three subjects. The analyzer recognized at least 63% of the MI signals corresponding to three classes. The results of the offline analysis showed a promising performance considering that the subjects have never undergone MI trainings.
An identification and prediction methods for feature-subsets of CpG islands methylation based on human peripheral blood leukocytes of chromosome 21q

The pace of technology has allowed classification of feature-subset of methylated and unmethylated of CpG islands of DNA sequence properties. As methylation of CpG islands is involved in various biological phenomena and function of the DNA methylation is correlated to various human diseases such as cancer, analysis of the CpG islands has become important and useful in characterizing and modelling biological phenomena and understanding mechanism of such diseases. However, analysis of the data associated with the CpG islands is a quite new and challenging subject in bioinformatics, systems biology and epigenetics. In this paper, the problems associated with prediction of methylated and unmethylated CpG islands on human chromosome 21q are addressed. In order to carry out the prediction, a data set of 132 samples of the CpG islands from human peripheral blood leukocytes of chromosomes 21q and 4 different feature sub-sets totalling 44 attributes that characterise the methylated and unmethylated groups is extracted for each sample. Due to the nature of this unbalanced data set, in order to avoid disadvantages of traditional leave-one-out (LOO) and m-fold cross validation methods, the LOO method is modified by incorporating the m-fold cross validation approach. In addition, K-nearest neighbour classifier is then adapted for the prediction. The results gained through 440 different comprehensive analyses shows that the methylated CpG islands can be distinguished from the unmethylated CpG islands by a predictive accuracy of between 75% and 80%. More importantly, the modified LOO identifies more clearly and reliably when the feature sub-sets are combined. Another interesting observation is that the modified-LOO-based analysis reveals that the CpGI-specific feature-set achieve the highest predictive accuracy when combined with the other feature sets, which is not the case in the traditional LOO. This also further supports the robustness of the modified-LOO cross validation approach as CpGI-specific feature-set is one of the most important and effective attributes shown in other studies.
n-Best Parsing Revisited

We derive and implement an algorithm similar to (Huang and Chiang, 2005) for finding the n best derivations in a weighted hypergraph. We prove the correctness and termination of the algorithm and we show experimental results concerning its runtime. Our work is different from the aforementioned one in the following respects: we consider labeled hypergraphs, allowing for tree-based language models (Maletti and Satta, 2009); we specifically handle the case of cyclic hypergraphs; we admit structured weight domains, allowing for multiple features to be processed; we use the paradigm of functional programming together with lazy evaluation, achieving concise algorithmic descriptions.
Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example

In this paper, several advanced data-driven nonlinear identification techniques are compared on a specific problem: a simplified glucoregulatory system modeling example. This problem represents a challenge in the development of an artificial pancreas for T1DM treatment, since for this application good nonlinear models are needed to design accurate closed-loop controllers to regulate the glucose level in the blood. Block-oriented as well as state-space models are used to describe both the dynamics and the nonlinear behavior of the insulin-glucose system, and the advantages and drawbacks of each method are pointed out. The obtained nonlinear models are accurate in simulating the patient's behavior, and some of them are also sufficiently simple to be considered in the implementation of a model-based controller to develop the artificial pancreas.
Elevated peripheral blood glutamate levels in major depressive disorder

Purpose There is growing evidence that glutamatergic signaling may be involved in major depressive disorder (MDD). In regard to peripheral blood glutamate changes in MDD, inconsistent findings have been reported. The purpose of the present study was to evaluate whether blood glutamate levels differed between MDD patients and control participants. Materials and methods We conducted a systematic review and meta-analysis of 12 association studies between blood glutamate levels and MDD in a total of 529 MDD patients and 590 controls. Subsequently, we conducted subgroup analyses and a meta-regression analysis to examine the sources of potential heterogeneity. Results A random effects model showed that blood glutamate levels were significantly higher in MDD patients than in controls (standardized mean difference=0.54, 95% CI=0.27–0.82, p=8.5×10−5) with high heterogeneity (I2=75.0%, p<0.05). Subgroup analyses showed elevated glutamate levels in MDD patients compared with controls in plasma, but not serum studies, and in studies using high-performance liquid chromatography but not with mass spectrometry for glutamate assay. A meta-regression analysis showed no effects of age, gender, medication use, sample size, and published year on blood glutamate levels. Conclusion Our findings suggest that altered glutamate levels may be implicated in MDD, which provides further evidence of glutamatergic dysfunction in MDD.
HRV strongly depends on breathing. Are we questioning the right suspect?

The fact that the heart rate variability (HRV) depends on breathing is well known. The HRV is an important phenomenon which reflects the functional state of the autonomous nervous system (ANS), although there are some doubts concerning the actual interpretation of spectral components of HRV and their postulated balance. The assessment of the functional state of the ANS is the task of paramount importance in risk stratification of cardiological patients. HRV is considered to depend mainly on the properties of the sinus node (SN), which achieves neurohumoral input from the ANS. Interestingly, there is growing evidence that the relation between the heart rate (HR) and breathing rate (BR) is really strong. The variety of breathing-related effects that are present in HRV is very rich, including respiratory sinus arrhythmia (RSA), cardiorespiratory synchronization and vivid heart rate response to breathing disorders. If the mean frequency of any of rhythms is changed, the other rhythm adjusts itself. This provokes the question on the actual source of the dynamics observed in the HRV. Is it possible that we observe mainly the dynamics of the respiratory rhythm which is just transduced by the heart effector? What might be the role of the intrinsic dynamics of this effector? Is the RSA a product of neural regulation or rather a by-product: what is its teleological role? In consequence: if we concentrate on the sinus node and its properties in order to understand the nature of the HRV — are we questioning the right suspect? The reasoning is supplied by suitable choice of literature and by the analysis of the computational model. Various consequences are discussed.
A mixed signal ECG processing platform with an adaptive sampling ADC for portable monitoring applications

This paper describes a mixed-signal ECG processing platform with an 12-bit ADC architecture that can adapt its sampling rate according to the input signals rate of change. This enables the sampling of ECG signals with significantly reduced data rate without loss of information. The presented adaptive sampling scheme reduces the ADC power consumption, enables the processing of ECG signals with lower power consumption, and reduces the power consumption of the radio while streaming the ECG signals. The test results show that running a CWT-based R peak detection algorithm using the adaptively sampled ECG signals consumes only 45. 6μW and it leads to 36% less overall system power consumption.
Fetal programming of cardiovascular function through exposure to maternal undernutrition

A substantial and robust body of epidemiological evidence indicates that prenatal dietary experience may be a factor determining cardiovascular disease risk. Retrospective cohort studies indicate that low birth weight and disproportion at birth are powerful predictors of later disease risk. This prenatal influence on non-communicable disease in later life has been termed programming. Maternal nutritional status has been proposed to be the major programming influence on the developing fetus. The evidence from epidemiological studies of nutrition, fetal development and birth outcome is, however, often weak and inconclusive. The validity of the nutritional programming concept is highly dependent on experimental studies in animals. The feeding of low-protein diets in rat pregnancy results in perturbations in fetal growth and dimensions at birth. The offspring of rats fed low-protein diets exhibit a number of metabolic and physiological disturbances, and are consistently found to have high blood pressure from early postnatal life. This experimental model has been used to explore potential mechanisms of programming through which maternal diet may programme the cardiovascular function of the fetus. Indications from this work are that fetal exposure to maternally-derived glucocorticoids plays a key role in the programming mechanism. Secondary to this activity, the fetal hypothalamic–pituitary–adrenal axis may stimulate renin–angiotensin system activity, resulting in increased vascular resistance and hypertension.
Asymptotic Achievable Rate of Two-Dimensional Constraint Codes based on Column by Column Encoding

In this paper, we propose a column by column encoding scheme suitable for two-dimensional (2D) constraint codes and derive a lower bound of its maximum achievable rate. It is shown that the maximum achievable rate is equal to the largest minimum degree of a subgraph of the maximal valid pair graph. A graph theoretical analysis to provide a lower bound of the maximum achievable rate is presented. For several 2D-constraints such as the asymmetric and symmetric non-isolated bit constraints, the values of the lower bound are evaluated.
Apathy and depressive symptoms in older people and incident myocardial infarction, stroke, and mortality: a systematic review and meta-analysis of individual participant data

Background Previous findings suggest that apathy symptoms independently of depressive symptoms measured using the Geriatric Depression Scale (GDS) are associated with cardiovascular disease (CVD) in older individuals. Aims To study whether apathy and depressive symptoms in older people are associated with future CVD, stroke, and mortality using individual patient-data meta-analysis. Methods Medline, Embase, and PsycInfo databases up to September 3, 2013, were systematically searched without language restrictions. We sought prospective studies with older (mean age ≥65 years) community-dwelling populations in which the GDS was employed and subsequent stroke and/or CVD were recorded to provide individual participant data. Apathy symptoms were defined as the three apathy-related subitems of the GDS, with depressive symptoms the remaining items. We used myocardial infarction (MI), stroke, and all-cause mortality as main outcomes. Analyses were adjusted for age, sex, and MI/stroke history. An adaptation of the Newcastle–Ottawa scale was used to evaluate bias. Hazard ratios were calculated using one-stage random-effect Cox regression models. Results Of the 52 eligible studies, 21 (40.4%) were included, comprising 47,625 older people (mean age [standard deviation] 74 [7.4] years), over a median follow-up of 8.8 years. Participants with apathy symptoms had a 21% higher risk of MI (95% confidence interval [CI] 1.08–1.36), a 37% higher risk of stroke (95% CI 1.18–1.59), and a 47% higher risk of all-cause mortality (95% CI 1.38–1.56). Participants with depressive symptoms had a comparably higher risk of stroke (HR 1.36, 95% CI 1.18–1.56) and all-cause mortality (HR 1.44, 95% CI 1.35–1.53), but not of MI (HR 1.08, 95% CI 0.91–1.29). Associations for isolated apathy and isolated depressive symptoms were comparable. Sensitivity analyses according to risk of bias yielded similar results. Conclusion Our findings stress the clinical importance of recognizing apathy independently of depressive symptoms, and could help physicians identify persons at increased risk of vascular disease.
VO2max Testing in Trail Runners: Is There a Specific Exercise Test Protocol?

Abstract Trail running places specific physiological demands on the human body due to its uphill and downhill running sections. We developed and investigated a more sport-specific trail exercise test protocol (inclination and speed incremental protocol), and compared it to two standard exercise test protocols (horizontal step and ramp protocol) in thirteen highly trained trail runners (age 31±6 years, height 179±6.4 cm, weight 69.2±7.9 kg, BMI 21.6±2.1 kg/m2). The maximum oxygen uptake (VO2max) measured during the trail test (62.5±5.9 ml·kg−1·min−1, [95% CI: 59.0-66.1]) was significantly higher compared to both the step test (60.1±5.3 ml·kg−1·min−1, [95% CI: 56.8-63.3], p=0.024) and the ramp test (59.7±5.5 ml·kg−1·min−1, [95% CI: 56.4-63.0], p=0.028). Time to task failure was significantly shorter in the trail test (557±73 s, [95% CI: 512-601]) compared to both the step test (1378±152 s, [95% CI: 1286-1470], p<0.001) and the ramp test (605±95, [95% CI: 547-662], p<0.001). Other physiological measurements obtained were similar. The trail test was the preferred choice in our group of trail runners. This study supports the implementation of the trail test in practice, and recommends that its validity be evaluated further.
Learning a Deep Listwise Context Model for Ranking Refinement

Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can achieve good performance on average but may be suboptimal for individual queries by ignoring the fact that relevant documents for different queries may have different distributions in the feature space. Inspired by the idea of pseudo relevance feedback where top ranked documents, which we refer as the local ranking context, can provide important information about the query's characteristics, we propose to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps us fine tune the initial ranked list. Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results. There are three merits with our model: (1) Our model can capture the local ranking context based on the complex interactions between top results using a deep neural network; (2) Our model can be built upon existing learning-to-rank methods by directly using their extracted feature vectors; (3) Our model is trained with an attention-based loss function, which is more effective and efficient than many existing listwise methods. Experimental results show that the proposed model can significantly improve the state-of-the-art learning to rank methods on benchmark retrieval corpora.
Effects of Pulsed Versus Conventional Versus Combined Radiofrequency for the Treatment of Trigeminal Neuralgia: A Prospective Study.

BACKGROUND During radiofrequency bursts of energy are applied to nervous tissue. The clinical advantages of this treatment remain unclear. OBJECTIVES We compared the effectiveness and pain relief for idiopathic trigeminal neuralgia (TN) after continuous radiofrequency (CRF), pulsed radiofrequency (PRF), and combined continuous and pulsed radiofrequency (CCPRF) treatment of the Gasserian ganglion (GG). STUDY DESIGN We conducted a randomized prospective study. Forty-three patients were included. Eleven patients were treated with PRF at 42°C for 10 minutes (PRF group), 12 patients received CRF for 270 seconds at 75 °C (CRF group), and 20 patients received PRF for 10 minutes at 42°C followed by CRF for at 60°C for 270 seconds (CCPRF group). SETTING Assuit University Hospital, Pain and Neurology outpatient clinics. METHODS Patients were assessed for pain, satisfaction, and consumption of analgesics at baseline and 7 days, one month, 6 months, 12 months, and 24 months after the procedure. The incidence of complications, anesthesia dolorosa, weakness of muscles of mastication, numbness, and technical complications, was evaluated after the procedure. RESULTS Excellent pain relief was achieved after 6, 12, and 24 months, respectively in 95%, 85%, and 70% of patients with CCPRF; 75%, 75%, and reduced to 50% among patients with CRF; and 82%, reduced to 9.1%, and 0% of patients with PRF. No complications were recorded in 75% of patients in the CCPRF and PRF groups. There was one case of anesthesia dolorosa, 4 cases of masseter muscle weakness, and 5 cases of severe numbness recorded in the CRF group. LIMITATION There was a small number of patients in each group. CONCLUSION The best results were observed in the CCPRF group, followed by the CRF group, and then the PRF group.Key words: Pulsed, continuous, radiofrequency, trigeminal neuralgia, Gasserian ganglion.
Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based Environmental Health Vulnerabilities in California

The California Community Environmental Health Screening Tool (CalEnviroScreen) advances research and policy pertaining to environmental health vulnerability. However, CalEnviroScreen departs from its historical foundations and comparable screening tools by no longer considering racial status as an indicator of environmental health vulnerability and predictor of cumulative pollution burden. This study used conceptual frameworks and analytical techniques from environmental health and inequality literature to address the limitations of CalEnviroScreen, especially its inattention to race-based environmental health vulnerabilities. It developed an adjusted measure of cumulative pollution burden from the CalEnviroScreen 2.0 data that facilitates multivariate analyses of the effect of neighborhood racial composition on cumulative pollution burden, net of other indicators of population vulnerability, traffic density, industrial zoning, and local and regional clustering of pollution burden. Principal component analyses produced three new measures of population vulnerability, including Latina/o cumulative disadvantage that represents the spatial concentration of Latinas/os, economic disadvantage, limited English-speaking ability, and health vulnerability. Spatial error regression analyses demonstrated that concentrations of Latinas/os, followed by Latina/o cumulative disadvantage, are the strongest demographic determinants of adjusted cumulative pollution burden. Findings have implications for research and policy pertaining to cumulative impacts and race-based environmental health vulnerabilities within and beyond California.
Mathematical model revealing the evolution of particle breakage and particle-size distribution for rockfill during triaxial shearing

Abstract A mathematical model reflecting the evolution of both particle breakage and particle-size distribution (PSD) for rockfill subjected to triaxial shear has been proposed in this article. First, the PSD of a soil is expressed by a gradation equation which has been shown to be widely applicable by comparison with a large amount of test data from soils encountered in practice. Then two particle breakage factors B g and B r are suggested and proved to be two suitable breakage factors in the model. Mathematical relationships between B g, B r and the gradation equation’s parameters b and m are derived. Then, correlations between the two breakage factors, effective mean normal stress and generalised shear strain during shearing are expressed by empirical functions, with several drained triaxial compression test data verifying the applicability. The inter-relationships of particle breakage, PSD and stress state of rockfill are thereby established, providing what is termed the PSD evolution model. Additionally, the proposed PSD evolution model is verified by successfully predicting the particle breakage factors and PSDs of rockfill triaxial specimens.
Increased Isoprenoid Quinone Concentration Modulates Membrane Fluidity in Listeria monocytogenes at Low Growth Temperatures

ABSTRACT Listeria monocytogenes is a food pathogen capable of growing at a broad temperature range from 50°C to refrigerator temperatures. A key requirement for bacterial activity and growth at low temperatures is the ability to adjust the membrane lipid composition to maintain cytoplasmic membrane fluidity. In this study, we confirmed earlier findings that the extents of fatty acid profile adaptation differed between L. monocytogenes strains. We were able to demonstrate for isolates from food that growth rates at low temperatures and resistance to freeze-thaw stress were not impaired by a lower adaptive response of the fatty acid composition. This indicated the presence of a second adaptation mechanism besides temperature-regulated fatty acid synthesis. For strains that showed weaker adaptive responses in their fatty acid profiles to low growth temperature, we could demonstrate a significantly higher concentration of isoprenoid quinones. Three strains even showed a higher quinone concentration after growth at 6°C than at 37°C, which is contradictory to the reduced respiratory activity at lower growth temperatures. Analyses of the membrane fluidity in vivo by measuring generalized polarization and anisotropy revealed modulation of the transition phase. Strains with increased quinone concentrations showed an expanded membrane transition phase in contrast to strains with pronounced adaptations of fatty acid profiles. The correlation between quinone concentration and membrane transition phase expansion was confirmed by suppression of quinone synthesis. A reduced quinone concentration resulted in a narrower transition phase. Expansion of the phase transition zone by increasing the concentration of non-fatty acid membrane lipids is discussed as an additional mechanism improving adaptation to temperature shifts for L. monocytogenes strains. IMPORTANCE Listeria monocytogenes is a foodborne pathogen with an outstanding temperature range for growth. The ability for growth at temperatures close to the freezing point constitutes a serious contamination potential for cold stored food. The only known mechanism of the species for adaptation of membrane fluidity is modification of the membrane fatty acid composition. We were able to demonstrate that, at least for some strains, this adaptation mechanism is supported by regulation of the menaquinone concentration. The increase of this neutral membrane lipid is correlated with fluidization of the membrane under low-temperature conditions and therefore represents a fatty acid-independent mechanism for adaptation to low temperatures.