Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the effect of trans-spinal magnetic stimulation on spinal excitability

During the last decades, spinal cord stimulation (SCS) has attracted much attention due to its capability to modulate the motor and sensory networks. The potential of this technique has been proved, and several investigations have focused on applying it for restoring lower limb function. The majority of SCS approaches are based on electrical stimulation, and few studies have explored magnetic fields for non-invasive SCS. This paper presents a trans-spinal magnetic stimulation (ts-MS) protocol and studies its effects on spinal circuits with seven healthy subjects, considering central and peripheral nervous systems. Motor evoked potentials (MEP) and trans-spinal motor evoked potentials (ts-MEP) were assessed before and after the ts-MS intervention to characterize excitatory responses. After the intervention, we found an increase of almost 30% (not statistically significant) in MEP amplitude, but no changes in ts-MEP amplitude. Further research is required to confirm, in a larger population of subjects, the potential of this technology, which could be used to improve rehabilitation therapies for patients with motor disabilities.
Solving Regression Models with First Order Stochastic Based Optimizers

Most of the regression-based Deep Learning (DL) algorithms which were recently proposed are based on Convolutional Neural Networks (CNN) trained by using l2 loss function. In order to avoid the vulnerability of it to outliers, some authors propose to combine regression methods with a classification function like combination of l2 loss function and softmax or a strategy based on bounding boxes what improves the performance of the DL neural network.In this research we propose using a combination of first order Stochastic Gradient Descent (SGD) optimizer for Artificial Neural Networks (ANNs) with Deep Learning (DL) and Deep Learning Multilayered Perceptron DLMLP with Back Propagation (BP). We focus on optimization of stochastic objectives, parameters and implementation of different activation functions in solving medical regression problems in high-dimensional space.
Improving the Performance of SSVEP BCI with Short Response Time by Temporal Alignments Enhanced CCA

Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) provides high throughput in communication. In SSVEP-BCI, typically, higher accuracy can be achieved with a relatively longer response time. It is therefore a research topic to reduce the response time while keeping high accuracy. We propose a new method, temporal alignments enhanced Canonical Correlation Analysis (TACCA), followed by a decision fusion to improve classification accuracy with short response time. TACCA exploits linear correlation with non-linear similarity between steady-state responses and stimulus frequencies. We compare TACCA and three state-of-the-art methods using data from 54-subjects with response time ranging from 0.5 to 4 seconds. The evaluation results show that TACCA yields mean significant accuracy increase of 10-30% in all segment lengths, especially for the shorter time segment. One-way ANOVA tests show high significant differences between single and multiple phases in TACCA performance.
Deep Learning Models for Subject-Independent ERP-based Brain-Computer Interfaces

Inter-subject variabilities in brain signals can significantly deteriorate the accuracy of brain-computer interface (BCI) systems. Every subject has different brain signals; also, the performance of a subject varies widely between sessions, within a session, between on-line and off-line settings, and even from epoch to epoch. Such variabilities arise in measurements obtained during different BCI sessions require subject and session specific decoder models. This work proposes three different deep learning models for subject-independent decoding of event-related potentials in electroencephalographic signals: 1) shallow convolutional neural network, 2) gated recurrent neural network, and 3) CNN-RNN-Net: a hybrid one-dimensional convolution and a gated recurrent unit model. Experimental results demonstrate that the proposed models outperform the conventional baseline models in decoding subject-independent data. Moreover, among the three models, the CNN-RNN-Net has shown improved classification results.
Emotions and the Structure of the Language

Emotions have an impact on almost every facet of human life - from daily behavior going to work efficiency, everyday decisions, human interactions, as well as human health. Humans express their emotions through the use of language and the things that people say do affect our emotions, but can these emotions affect the structure of the language? And if so, to what extent? Do people speak more or less when they experience certain emotions? Do emotions affect the selection of the grammar? For example, are people more likely to use verbs rather than nouns when experiencing a certain emotion? Do emotions influence the transition between grammars? The aim of this paper is to contribute to the answers of the aforementioned questions by computationally analyzing emotionally rich text. The paper proposes the use of Natural Language Processing techniques to investigate the relation between emotions and language. Results show that emotions affect the length of text and to a lesser degree the tendency toward choosing some parts of speech (i.e. nouns vs verbs). For the structure of the sentence and the transition between grammatical categories, the influence of emotions is negligible.
Fault-Tolerant NCS for Pharmaceutical Process

The use of Ethernet as a communication protocol for Networked Control Systems (NCS) is gaining interest in the automation community. The applications of such protocol range from industrial control to automotive and aerospace. The demand on using new architectures to guarantee system tolerance to faults and failures of single or multiple nodes, is increasing. Such system design decreases production downtime and increases profitability. An NCS Fault-Tolerant system model is proposed in this study for pharmaceutical tablet production. It will be shown that the introduced architecture can accommodate for the failure of more than one controller node in the production scheme.
NVLab, a Networking Virtual Web-Based Laboratory that Implements Virtualization and Virtual Network Computing Technologies

In various ICT courses, it is necessary to establish a proper space where each learner has access to a set of network devices with which he/she can build and test networks with different structures and components. This will enable the learners to practice working on multiple devices as in the case of real world context, and freely apply modifications to the network structure to solve any issue that may arise with the current scenario. While on-campus facilities are designed to meet this criterion, current Web-based laboratories either have fixed network designs, or do not offer a number of real devices to work on. Taking a step forward toward building a Web-based laboratory, we used open source Virtualization and Virtual Network Computing technologies in NVLab, a system that offers the learners tools to draw, configure, test, and troubleshoot network designs using real Operating System instances running in the virtual mode on a host machine. The functionality of the system has been experimented by introducing a case study exercise to a group of remote learners as part of Computer Networks course. Evaluating the participants' advancement was done by comparing their answers to a level test prior and after completing the exercise. The evaluation showed that the learners achieved better results in the level test after completing the exercise and were able to use the system efficiently.
The Impact of Artificial Intelligence on Software Testing

Artificial Intelligence (AI) plays an important role in our life and touch base most of our surrounding applications and systems. A huge amounts of data are created every day from many different sources that need to be monitored and analyzed properly and report results and take actions. A more complex software applications have been built, time is becoming a critical factor to release applications that must be fully tested and comply with Business Requirements. AI plays a key role in Software Testing and can get more accurate results and saves time. This paper discuss the Artificial Intelligence key pillars that can be used in Software Testing. It also open a window on how the future will look like in terms of Artificial Intelligence and the Software Testing. The results show that AI can achieve better results in Software Testing and AI-driven testing will lead the new era of the quality assurance (QA) work in the near future. AI Software Testing will reduce time to market and will increase the efficiency of the organization to produce more sophisticated software and will create smarter automated testing.
Epileptogenic Foci Time Varying Source Loca1ization Using Source Affine Image Reconstruction (SAFFIRE) Algorithm

This paper presents the performance of the Source Affine Image reconstruction algorithm (SAFFIRE) on an epileptogemc foci time-varying source localization. This algorithm is based on the recursive implementation of the mimmum mean squared error (MMSE) estimator. The SAFFIRE algorithm is applied on a synthetic EEG signal originated from different sources' scenarios. The simulation results show how the algorithm is able to localize and track the sources as they vary with time with a high resolution even if the sources are located close to each other.
Spray-congealed microparticles for drug delivery – an overview of factors influencing their production and characteristics

Introduction: In any manufacturing process, the success of producing an end product with the desired properties and yield depends on a range of factors that include the equipment, process and formulation variables. It is the interest of manufacturers and researchers to understand each manufacturing process better and ascertain the effects of various manufacturing-associated factors on the properties of the end product. Unless the manufacturing process is well understood, it would be difficult to set realistic limits for the process variables and raw material specifications to ensure consistently high-quality and reproducible end products. Over the years, spray congealing has been used to produce particulates by the food and pharmaceutical industries. The latter have used this technology to develop specialized drug delivery systems. Areas covered: In this review, basic principles as well as advantages and disadvantages of the spray congealing process will be covered. Recent developments in spray congealing equipment, process variables and formulation variables such as the matrix material, encapsulated material and additives will also be discussed. Expert opinion: Innovative equipment designs and formulations for spray congealing have emerged. Judicious choice of atomizers, polymers and additives is the key to achieve the desired properties of the microparticles for drug delivery.
An Assessment Procedure of Distribution Network Reliability Considering Photovoltaic Power Integration

As photovoltaic (PV) generation has been one of the major renewable energy sources around the world, its PV capacity has also increased. When the large-scale PV systems are integrated into the distribution network, the complexity of the assessment process of the distribution network reliability will increase hazardously. In order to accurately assess this reliability in the distribution network combined with the PV generation, a reliability assessment procedure is proposed. In order to accurately evaluate the impact of the failure of conventional power equipment on reliability, the time-varying failure rate of conventional power equipment is modeled, taking into account the aging period. Then, in order to accurately evaluate the reliability improvement with PV systems integration, the new procedure is proposed highlighting the following contributions: 1) five new indices are added. 2) PV output is modeled so that not only the radiation intensity but also the failure and degradation of PV modules are represented. 3) time-varying islanding operation is considered and integrated. A case study using real-life distribution network topology and data in China is applied to verify that the newly proposed reliability indices display more sensitivity, and the proposed procedure significantly improves the accuracy of the reliability assessment.
A Closed-Loop System Processing High-Density Electrical Recordings and Visual Stimuli to Study Retinal Circuits Properties

Active high-density electrode arrays can record the spiking activity of a large number of single-neurons in brain circuits. This offers the opportunity to develop closed-loop neural interfaces for studying complex brain circuits contingent on their cellular activity. However, this requires adapted solutions to process large-volumes of data acquired by thousands of electrodes and, in turn, generate feedbacks.Here, we present a closed-loop system that exploits System on Chip resources of a Xilinx ZedBoard Zynq-7000 to processes the instantaneous mice retina activity recorded by 4096 closely spaced microelectrodes. This is used to infer in real-time the functional properties of retinal ganglion cells (RGCs) all over the retina. Results show the performances of two interactive algorithms designed for (i) data reduction by clustering single-unit sub-millisecond correlated spike-trains from closely spaced neighboring electrodes (closed-loop latency of 22.9 ± 1.5 ms per second of recording, n = 5 retina); (ii) the classification of major types of RGCs consisting in ON and OFF types of functional responses (closed-loop latency of 117.3 ± 30.9 ms, n = 5 retina).
A Spiking Neural Network Model Mimicking the Olfactory Cortex for Handwritten Digit Recognition

The mammalian olfactory system uses odor-specified temporal codes to represent the quality and the quantity of different odors. In order to better understand this coding strategy, a biologically plausible spiking neural network (SNN) was built and evaluated. In this study, MNIST images of handwritten digits were used to mimic the two-dimensional representation of odor information by the glomeruli in the olfactory bulb. The images were used to train the SNN based on the spike-timing-dependent plasticity (STDP) unsupervised learning rule. The SNN model was implemented by Izhikevich neurons to represent both the pyramidal neurons and the GABAergic interneurons in the piriform cortex. The recognition accuracy of the SNN model was evaluated to gain insights for the temporal coding scheme in the olfactory system. The results suggested that the SNN model can effectively encode 2D neural representations with temporal codes and achieved discrimination accuracies close to animal behavioral performances in odor discrimination tasks.
Protective effects of aqueous and ethanol extracts of rosemary on H2O2-induced oxidative DNA damage in human lymphocytes by comet assay

Abstract Background: Rosemary (Rosmarinus officinalis) possesses various pharmacological properties such as antioxidant, anti-tumorigenesis and anti-mutagenesis activities. In this study, we investigated the possible protective effects of ethanol and aqueous extracts of rosemary on human lymphocyte DNA damage induced by H2O2. The extent of DNA lesions was measured using comet assay. Methods: Blood samples were taken from healthy volunteers and lymphocytes were isolated. The lymphocytes were then incubated in aqueous and ethanol extract of rosemary (0.05, 0.1, 0.5, 1 and 2.5 mg/mL) and H2O2 (50, 100 and 200 mM). Lymphocytes were also incubated with a combination of H2O2 (100 mM) with either 1 or 2.5 mg/mL of both extracts for 30 min at 4°C. Results: Our findings showed that H2O2 treatment led to a significant concentrate-dependent DNA damage in human lymphocyte when compared to respective controls (p<0.001). The DNA damage which was initially occurred as the result of 100 µM H2O2 (Percentage tail DNA 55.1%) was inhibited due to the ethanol extract of rosemary at the doses tested (percentage tail DNA 4.7% and 4.03%). However, the aqueous extract has no effects on H2O2 genotoxicity. Conclusions: We suggest that antioxidant constituents in ethanol extract of rosemary can prevent human lymphocytes oxidative DNA damage which is due to its free radical scavenging activity.
Electrospun nanofiber scaffolds and plasma polymerization: a promising combination towards complete, stable endothelial lining for vascular grafts.

In the quest to reduce risk of thrombosis in vascular grafts, it is essential to provide a surface with morphological and mechanical properties close to those of the extracellular matrix beneath the luminal endothelium, and to favor the growth of a confluent, stable monolayer of endothelial cells. This is accomplished here by combining electrospun poly(ethylene terephthalate) (PET) mats with an amine-rich thin plasma-polymerized coating, designated "L-PPE:N. " Its deposition does not modify the open, highly porous mats and leads only to small changes in mechanical properties. L-PPE:N significantly improves the adhesion and growth of human umbilical vein endothelial cells (HUVEC) and their resistance to flow-induced shear stress. These properties favor the formation of desired confluent HUVEC monolayers on the topmost surface, unlike conventional vascular grafts (ePTFE or woven PET), where cells migrate inside the material. This combination is therefore highly advantageous for the pre-endothelialization of the luminal side of small-diameter vascular prostheses.
Muscle synergy changes with cutaneous stimulation during resting tremor and reaching task in Parkinson's disease

Resting tremor affects voluntary reaching movements in patients with Parkinson’s disease (PD). Previously, we found that cutaneous afferents evoked by electrically simulating the superficial radial nerve could inhibit tremor activity [3]. Yet, the mechanism that cutaneous stimulation affects tremor as well as voluntary movements remains unclear. In this paper, we used the method of muscle synergy analysis to further investigate the muscle activation patterns of tremor and reaching movements. Three PD patients with tremor dominant symptom participated in this preliminary study. The patients performed targeted reaching movements with no visual feedback of their hands, while transcutaneous electrical stimulation was delivered to inhibit tremor activity. Kinematics and EMGs of six muscles were recorded during resting and reaching tasks. Muscle synergy analysis was performed with data during resting and task stages with and without cutaneous stimulation. Results showed that during resting state the time profiles of muscle synergy in the tremor cycle displayed two main alternating components that were distributed to antagonistic muscle pairs in upper arm, forearm and wrist. Cutaneous stimulation affected the vector patterns and time profiles of muscle synergies for tremor and reaching movements differently. Our results indicate that muscle synergies are sensitive to central descending drives of muscles, as well as evoked peripheral afferents.
DtCraft: A High-Performance Distributed Execution Engine at Scale

Recent years have seen rapid growth in <italic>data-driven</italic> distributed systems, such as Hadoop MapReduce, Spark, and Dryad. However, the counterparts for high-performance or <italic>compute-intensive</italic> applications including large-scale optimizations, modeling, and simulations are still nascent. In this paper, we introduce DtCraft, a modern C++ based distributed execution engine to streamline the development of high-performance parallel applications. Users need no understanding of distributed computing and can focus on high-level developments, leaving difficult details, such as concurrency controls, workload distribution, and fault tolerance handled by our system transparently. We have evaluated DtCraft on both micro-benchmarks and large-scale optimization problems, and shown the promising performance from single multicore machines to clusters of computers. In a particular semiconductor design problem, we achieved <inline-formula> <tex-math notation="LaTeX">${30\times }$ </tex-math></inline-formula> speedup with 40 nodes and <inline-formula> <tex-math notation="LaTeX">${15\times }$ </tex-math></inline-formula> less development efforts over hand-crafted implementation.
The Mobile Edge Computing (MEC)-Based VANET Data Offloading Using the Staying-Time-Oriented k-Hop Away Offloading Agent

This paper proposed a Mobile Edge Computing (MEC)-based k-hop away IEEE 802.11p Road Side Unit (RSU) Vehicular-to-Infrastructure (V2I) offloading method. Let a vehicle X regularly use LTE cellular network to communicate with the peer entity in the infrastructure side, i.e., in Internet. When there is a k-hop V2V path that can connect X with the ahead RSU, the V2I offloading can be enabled; additionally, when X has left the signal coverage of the corresponding RSU, if there is a k-hop V2V path that can connect X with the rear RSU, the V2I offloading can still be continued. In the proposed method, a MEC server is used to compute whether the k-hop V2I offloading can be enabled and maintained or not. This work also shows the performance analysis for different offloading scenarios in situations of different vehicular density.
Influence of parent vessel - side branch distal angle on restenosis rates after main vessel stenting in coronary bifurcation lesion.

BACKGROUND AND AIM This study explores predictors of side branch (SB) compromise in the main vessel only stenting group and its influence on long-term follow-up of the patients. METHODS We hypothesised that the geometric factors determining plaque distribution in branching regions influence SB compromise. Angiographic analysis of bifurcation lesions (all Medina types) was performed before, immediately after, and 9-12 months after the procedure. Control angiography was performed when clinically indicated. Specific attention was given to the influence of angle alpha - the angle between main vessel and SB axes. RESULTS Fifty-five patients (62 lesions) formed the study group. The LAD lesions were dominant (73%). Drug-eluting stents were used in 48% and kissing balloon inflation in 31%. The value of angle alpha was associated with significant SB stenosis. There was significant worsening of ostial SB stenosis (from 48% to 69%) after main vessel stenting, with the only independent predictor angle alpha. For SB ostial MLD independent predictors were angle alpha, SB vessel diameter and MB reference diameter. Predictors of SB occlusion (6.5%) were angle alpha <30 degrees and age >82 years. At follow-up (mean 11 months) SB restenosis rate was 52%, but was associated with symptoms only if the main vessel was affected (8/55, 15%). Angle alpha and main vessel reference diameter, main branch minimal diameter after stenting and stent type were predictors of target vessel revascularisation rate (25%). CONCLUSION Angle alpha predicts SB compromise after main vessel stenting and is the main predictor of restenosis in the main vessel.