Miracle-dz/UniFAR-FTUs huggingface dataset:
Muscle biology after spinal cord injury: Recent advances and future challenges

In this issue of Acta Physiologica, Lundell et al. report on changes in levels or phosphorylation of proteins indicative of the relative states of activation of protein synthesis, the ubiquitin-proteasome pathway, and autophagy in skeletal muscle biopsies taken from individuals 1, 3 and 12 months after spinal cord injury (SCI). Unique aspects of this study are the analysis of changes in these pathways over time in a cohort of 7 individuals with cervical SCI, and the examination of markers for each of these pathways in a single cohort of subjects with SCI as compared to controls. SCI results in severing of the axons of upper motor neurons and ascending sensory fibers, resulting in paralysis and loss of sensation. This article is protected by copyright. All rights reserved.
SaFePlay: A Portable Biomechanics Measurement and Analysis System of Lower Limbs

Gait analysis of walking, running and exercising helps greatly to prevent injury and improves performance in sports activity. However, current mainstream gait detection and locomotion measurement techniques relying on motion capture need bulky devices such as pressure plates and motion capture cameras. They are installed in a laboratory environment and must be operated by professional technicians. In other words, the traditional system in the lab cannot measure outdoor activities. SaFePlay, Smart Footwear Platform, is a portable lower limb motion monitoring, biomechanics measurement, and analysis system. This system consists of smart insoles, smart knee braces and an analysis engine installed on a mobile device. Users are able to review data over a specific period of time to have a general understanding of his or her gait-related health conditions. The historical data can also be provided to professional coaches or physical therapists to help them do the further action to the users.
Toward E-Health Applications for Suicide Prevention

E-health applications are methods used by medical practitioners to assess and follow their patients' mental and physical conditions. They may also provide feedback to patients and help them to change their behavior. Our research project aims to propose a mobile application (app in short) for suicide prevention. The principle is to develop a connected tool used by the patient to report about his/her health status. The use of a connected tool at home has the potential to overcome well-known limitations of self-reporting in a clinical context. However, developing an efficient e-health app is challenging, especially for sensible topics such as suicide prevention. The application must be developed carefully (data protection, HMI, ergonomic, question choice) in order to increase its acceptance by both patients and practitioners. Here, we relate ongoing world-wide initiatives and we propose some requirements towards the development of an efficient intelligent-health (i-health) application for suicide prevention.
Targeting glioma stem cells via the Hedgehog signaling pathway

Cancer is one of the leading causes of death worldwide. Gliomas are among the most devastating tumor types, and current clinical therapies are unsatisfactory. Recent reports revealed the importance of glioma-propagating cells in the malignancy of gliomas. These cells, also referred to as glioma stem cells (GSCs), share similarities with neural stem cells (NSCs). The Hedgehog (Hh) signaling pathway controls tissue polarity, patterning maintenance, and maintenance of NSCs during embryonic development. Aberrant activation of the Hh pathway resulting from mutation and deregulation has recently been recognized to cause tumorigenesis in a wide variety of tissues, including gliomas and GSCs. In this review, we explore the role of the Hh signaling pathway in GSCs and its potential as a therapeutic strategy.
Identification of Terrestrial Alkalic Rocks Using Thermal Emission Spectroscopy: Applications to Martian Remote Sensing

We present a detailed study examining the use of laboratory thermal emission spectra (5-25 μm at 2 cm-I spectral sampling) for identification and classification of alkalic volcanic rocks. Modal mineralogies and derived bulk rock chemistries of a suite of terrestrial alkali basalts, trachyandesites, trachytes, and rhyolites were determined using linear spectral deconvolution. Model-derived mineral modes were compared to modes measured using an electron microprobe mapping technique to access the accuracy of linear deconvolution in determining mineral abundances. Standard deviations of 1 <J of absolute differences between measured and modeled mineral abundances range from 0.68 to 15.02 vol %, with an average of 5.67 vol %. Bulk-rock chemistries were derived by combining modeled end-member compositions (wt%) in proportion to their abundances (recalculated to wt%). Derived oxide data compare well with measured oxide data, with la standard deviations ranging from 0.12 to 2.48 wt %. Modeled mineralogies, derived mineral chemistries, and derived bulk chemistries were examined to assess their accuracy in classifying alkalic rocks. Derived bulk chemistry is the most effective classification tool, modal mineralogy is the least, and basalts and rhyolites are typically more accurately classified than trachyandesites and trachytes. However, no single classification scheme can accurately identify all rock samples, indicating that a combination of classification schemes is necessary to distinguish alkalic volcanic rocks using thermal emission spectra. To examine the accuracy of volcanic rock classification at lower spectral resolutions, thermal emission spectra were resampled to the resolution of the Thermal Emission Spectrometer (TES) instrument (10 cm-I), and a second series of linear deconvolutions was performed. Results from modeled mineralogies and derived
Ratio threshold queries over distributed data sources

In this paper we consider triggers over distributed data from various sources such as: “Notify when sale of luxury goods constitute more than 20% of the overall sales”. In such queries client desires to be notified whenever the ratio of two aggregates, over distributed data, crosses the specified threshold. The challenge lies in being able to execute the queries with the minimal amount of communication necessary for update propagation. We address the challenge by proposing schemes for converting the client threshold condition into conditions on individual distributed data sources such that (1) violation of the client threshold occurs only if one or more source conditions are violated (zero false negative), and (2) the number of source violations when client threshold is not violated is small (minimize false positives). Using performance evaluation we show that our algorithms result in up to an order of magnitude less number of false positives compared to the approaches in the literature.
International Journal of Intelligent Computing and Cybernetics Evidence of a mechanism of neural adaptation in the closed loop control of directions

Purpose – Using cortical neurons of animals to control external devices allows experimenters a unique opportunity to study the capability of the brain to utilize a new actuator. The purpose of this paper is to investigate the ability of unrestrained rats to control a directional task using motor cortical signals. Design/methodology/approach – In freely moving rats, signals recorded from the motor cortex of the brain enabled the use of a closed loop brain machine interface (BMI) system to replace paddle pressing for a directional task. In this system, ring rates from several (two to ten) motor cortical neurons at several consecutive time points were used as input to a support vector machine (SVM) classifier. The decision-function value obtained from the SVM was then used to determine which relay should be activated to produce paddle-pressing signals in the task. All five animals tested were able to use this interface immediately and significant changes in neural activity arise in a single, 45-min, experimental session. Neural data from three of the subjects were examined for changes from the calibration phase (data used to build the SVM model) to the late cortically controlled phase. Findings – Detailed analysis shows that neural activity changes significantly from the calibration phase to the cortically controlled phase, furthermore, the decision-function values arising from these neural signals change to support better performance. By examining which neurons and times are selected by the SVM to have significant impact on the decision-function value as well as which of these elements change significantly, a mechanism of adaptation begins to emerge in which the SVM properly assigns high importance to dimensions that easily predict the desired output, however, under closed-loop control, the animal selects a small number of neurons (at most or all times) and chooses to make the firing rates more distinguishable. Video taken of one of the subjects further suggests the nature of the behavioral correlates of these changes. Practical implications – In the design of practical BMI devices for human patients, one effective strategy might involve creating mappings from multi-neuron ensembles using state-of-the-art machine learning techniques, but expect that the patients who use the devices will adapt to the devices using single neuron modulation changes. Originality/value – In the design of practical BMI devices for human patients, one effective strategy might involve creating mappings from multi-neuron ensembles using state-of-the-art machine learning techniques, but expect that the patients who use the devices will adapt to the devices using single neuron modulation changes. The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self-organization and have no central control.
Innumerable studies on single nucleotide polymorphisms: What could be its utility?

After the human genome map was made public and techniques became simpler and universally available to study single nucleotide polymorphisms (SNPs), it became apparent that SNP’s are quite common and for every hundred nucleotides in human genome sequence one SNPs is found. [1] Hence out of 3 billion nucleotides sequence that spans human genome 30 million SNP’s can be expected. They have been identified in codons, introns and promoter regions of the various genes. Biomedical researchers are now able to genotype biological samples for thousands of SNPs. Human hap map studies[2] and other studies have shown that certain SNP’s of a particular gene are present either in a very high or in a very low number in different populations, i.e., SNP polymorphisms are not always random.
Trend-adaptive multi-scale PCA for data fault detection in IoT networks

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. In such applications, the frequent appearance of fault data makes it difficult to extract correct information, thereby making confuses in interpreting and analyzing collected data. To address this problem, it is necessary to have a mechanism to detect fault data. In this paper, we present a Trend-adaptive Multi-Scale Principal Component Analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection. Moreover, the Trend-adaptive MS-PCA shows that it outperforms fault detection techniques using PCA and MS-PCA in term of fault sensitiveness.
Subjective oropharyngeal symptoms for abnormal swallowing in Japanese patients with obstructive sleep apnea syndrome: a descriptive questionnaire study

Objectives: This study aimed to investigate the prevalence of clinical symptoms related to abnormal swallowing in a large sample of obstructive sleep apnea syndrome (OSAS) patients. Methods:Oropharyngeal symptoms for abnormal swallowing were assessed by a self-administered questionnaire in 507 consecutive patients (females: 65, males: 442; mean age: 49.6 ± 12.6 years old) with clinical symptoms of OSAS, enrolled for cardiorespiratory evaluation. Results:Overall, 16.2% of patients (82/507) had at least one symptom for abnormal swallowing and 6.3% (32/507) had two or more symptoms. The most frequent symptom was difficulty with coughing up phlegm during or after a meal (8.3%). Demographic, sleep, and clinical variables did not differ between the patients with and without abnormal symptoms. Conclusions:The results of the current study showed that 16% of middle-aged OSAS patients reported pharyngeal symptoms related to abnormal swallowing, regardless of the severity of OSAS.
A single-coil driven two-axis water-immersible micro scanning mirror

In this paper, we report a new electromagnetically-driven two-axis water-immersible scanning mirror with single inductor coil actuator. By making the resonance frequencies significantly different, the scanning motion of the two axes can be decoupled due to the dynamic structural filtering effect. For verification, a prototype micro scanning mirror was designed, fabricated and tested. The resonance frequencies of the fast and slow axes in air and water were determined to be 720/72 Hz and 690/55 Hz, respectively. The optical tilting angle of both fast and slow axes at the resonance frequencies was characterized, which shows good linearity with the amplitude of the driving current. With both axes driven simultaneously at frequencies close to their resonance, stable and repeatable 2D raster scanning was successfully achieved in both air and water, respectively. 2D B-mode ultrasound microscopy was also demonstrated by using this micro scanning mirror.
Implementation of adversarial scenario to malware analytic

As the worldwide internet has non-stop developments, it comes with enormous amount automatically generated malware. Those malware had become huge threaten to computer users. A comprehensive malware family classifier can help security researchers to quickly identify characteristics of malware which help malware analysts to investigate in more efficient way. However, despite the assistance of the artificial intelligent (AI) classifiers, it has been shown that the AI-based classifiers are vulnerable to so-called adversarial attacks. In this paper, we demonstrate how the adversarial settings can be applied to the classifier of malware families classification. Our experimental results achieved high successful rate through the adversarial attack. We also find the important features which are ignored by malware analysts but useful in the future analysis.
Size-based performance analysis of haar-features for detection of facial images from low resolution surveillance videos

Face detection has been a very prominent research field since past two decades. In most of the works done so far, haar-features have been the key components in the task of object detection and have been very instrumental owing to their simplicity, efficient computation, scalability and ability in the encoding of structural contents of facial images in the form of compact binary representation. The selection of the size of example images for the training of the haar-features decides the base size of the detector which consequently influences the overall performance of the face detection system. In this work, we have done the performance analysis of modified version of face detection algorithm with respect to the size of the haar-features based trained detectors and applied the concept to process facial images from low-resolution surveillance videos. It has been empirically concluded that hybridization of the classifiers from different base size results in better detection rate as compared to using haar-features trained from the single base size in detecting low-resolution faces.
On a rational transfer function-based approach to ℋ∞ filter design for time-delay linear systems

This paper considers the problem of H∞ filter design for time-delay systems. An LTI finite dimensional system, called comparison system, is constructed in order to exploit recent results on stability analysis and H∞ norm calculation, which were proven to be strongly related to those of time-delay systems. Differently of what can be viewed as a common feature of filter design methods available in the literature to date, the one presented here addresses time-delay systems filtering with classical numeric routines based on Riccati equation and H∞ theory of LTI systems. The design algorithm is simple, efficient and easy to implement. An illustrative example is solved and is used to put in evidence the most important characteristic of the design procedure.
Mitigating Cardiovascular Risk in Type 2 Diabetes With Antidiabetes Drugs: A Review of Principal Cardiovascular Outcome Results of EMPA-REG OUTCOME, LEADER, and SUSTAIN-6 Trials

The U.S. Food and Drug Administration (FDA) issued a diabetes guidance in 2008 mandating that all new antidiabetes drugs rule out excess cardiovascular (CV) risk, defined as an upper bound of the two-sided 95% CI for major adverse CV events (MACE) of less than 1.80 preapproval and 1.30 postapproval. Over 25 large, prospective, randomized, controlled clinical trials involving nearly 195,000 subjects thus far have been completed or are ongoing in accordance with this guidance. The results of seven trials have been presented so far—three with dipeptidyl peptidase 4 inhibitors, one with a sodium–glucose cotransporter 2 (SGLT2) inhibitor, and three with glucagon-like peptide 1 receptor agonists (GLP-1 RA). While all seven trials showed noninferiority in the rate of MACE with the use of these agents compared with placebo, three of them revealed CV benefits. Treatment with empagliflozin (an SGLT2 inhibitor) and treatment with liraglutide (a GLP-1 RA) both significantly reduced the risk of MACE, mortality from CV causes, and mortality from any cause when compared with placebo. Treatment with semaglutide, another GLP-1 RA, showed a significantly lower rate of MACE but not mortality from CV or any cause compared with placebo. In all of the trials, the effects of treatment on outcomes were out of proportion to the small differences in glycemic control levels, suggesting that the effects observed were likely unrelated to differences in the glucose-lowering efficacy. Overall, the results of these trials yield a favorable benefit-risk balance for these therapies in mitigating CV risk in patients with type 2 diabetes. More research is needed to elucidate the underlying mechanisms and confirm whether the CV benefits are a class effect or whether the benefits persist in patients without established CV disease or are evident even in patients without diabetes.
Role of nitrosative and oxidative stress in neuropathy in patients with type 2 diabetes mellitus

Objectives: Evidences of oxidative and/or nitrosative stress in type 2 diabetes mellitus were demonstrated in experimental and human studies. This study is aimed to assess the serum peroxynitrite and oxidized lipoproteins in patients with type 2 diabetes mellitus presented with clinical and laboratory evidences of peripheral neuropathy. Materials and Methods: Eighty four patients with type 2 diabetes mellitus (51 of them had neuropathy) and 31 apparent healthy subjects were studied in the unit of neurophysiology at the University Hospital of Medical College, Al-Nahrin University in Baghdad, Iraq. Neuropathy total symptom score (NTSS), neuropathy impairment score in the lower leg (NIS-LL), and nerve conduction velocity of sensory (ulnar and sural) and motor (ulnar and common peroneal) nerves were used to assess the neuropathy. Fasting venous blood was obtained from each participant for the determination of serum glucose and oxidized lipoproteins. Results: The electrophysiology study revealed significant decrease in conduction velocity of ulnar (sensory and motor components), sural, and common peroneal nerves in diabetic neuropathy compared to diabetics without neuropathy and healthy subjects. Significant high level of serum peroxynitrite was found in diabetic patients with or without neuropathy compared with non-diabetics. The changes in serum-oxidized lipoproteins in patients with diabetics with or without neuropathy were non-significantly differed from healthy subjects. Neither nitrosative stress nor oxidative stress indices correlated with the variables that are related to the neuropathy. Conclusion: It concludes that evidence of nitrosative and to less extent the oxidative stress is associated with neuropathy in type 2 diabetes mellitus and their indices not correlated with variables related to neuropathy.
Sensor-based space robotics-ROTEX and its telerobotic features

In early 1993 the space robot technology experiment ROTEX was flown with space-shuttle Columbia. A multisensory robot onboard the spacecraft successfully worked in autonomous modes, teleoperated by astronauts, as well as in different telerobotic ground control modes. These included online teleoperational and telesensor-programming: a task-level oriented programming technique involving learning-by-showing concepts in a virtual environment. The robot's key features were its multisensory gripper and the local sensory feedback schemes that are the basis for shared autonomy. The corresponding man-machine interface concepts, which use a six-degree-of-freedom non-force-reflecting control ball and visual feedback to the human operator, are explained. Stereographic simulation on the ground was used to predict not only the robot's free motion but even the sensor-based path refinement onboard. Prototype tasks performed by this space robot were the assembly of a truss structure, connecting/disconnecting an electrical plug (orbit replaceable unit exchange), and grasping free-floating objects. >
Opaque superlanguages and sublanguages in discrete event systems

Opacity describes the inability for an external observer to know what happened in a system. Recently, opacity has been investigated in the framework of discrete event systems. In our previous paper, we define two types of opacities: strong opacity and weak opacity. Given a general observation mapping, a language is strongly opaque if all strings in the language are confused with some strings in another language and it is weakly opaque if some strings in the language are confused with some strings in another language. In this paper, we investigate properties of opacities. We show that opacities are closed under union, but may not be closed under intersection. Based on these properties, we discuss how to modify languages to satisfy the strong opacity, weak opacity, and no opacity by investigating the sublanguages and superlanguages that are strongly opaque, weakly opaque, and not opaque respectively. We find the largest sublanguages and smallest superlanguages. Examples are given to illustrate results.