Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Brain Stimulation Programming for Movement Disorders: Current Concepts and Evidence-Based Strategies

Deep brain stimulation (DBS) has become the treatment of choice for advanced stages of Parkinson's disease, medically intractable essential tremor, and complicated segmental and generalized dystonia. In addition to accurate electrode placement in the target area, effective programming of DBS devices is considered the most important factor for the individual outcome after DBS. Programming of the implanted pulse generator (IPG) is the only modifiable factor once DBS leads have been implanted and it becomes even more relevant in cases in which the electrodes are located at the border of the intended target structure and when side effects become challenging. At present, adjusting stimulation parameters depends to a large extent on personal experience. Based on a comprehensive literature search, we here summarize previous studies that examined the significance of distinct stimulation strategies for ameliorating disease signs and symptoms. We assess the effect of adjusting the stimulus amplitude (A), frequency (f), and pulse width (pw) on clinical symptoms and examine more recent techniques for modulating neuronal elements by electrical stimulation, such as interleaving (Medtronic®) or directional current steering (Boston Scientific®, Abbott®). We thus provide an evidence-based strategy for achieving the best clinical effect with different disorders and avoiding adverse effects in DBS of the subthalamic nucleus (STN), the ventro-intermedius nucleus (VIM), and the globus pallidus internus (GPi).
Central Mechanism Controlling Pubertal Onset in Mammals: A Triggering Role of Kisspeptin

Pubertal onset is thought to be timed by an increase in pulsatile gonadotropin-releasing hormone (GnRH)/gonadotropin secretion in mammals. The underlying mechanism of pubertal onset in mammals is still an open question. Evidence accumulated in the last 15 years suggests that kisspeptin/neurokinin B/dynorphin A (KNDy) neurons in the hypothalamic arcuate nucleus play a key role in pubertal onset by triggering pulsatile GnRH/gonadotropin secretin in mammals. Specifically, KNDy neurons are now considered a part of GnRH pulse generator, in which neurokinin B facilitates and dynorphin A inhibits, the synchronized discharge of KNDy neurons in autocrine and/or paracrine manners. Kisspeptin serves as a potent secretagogue of GnRH secretion and thus its release is fundamental to pubertal increase in GnRH/gonadotropin secretion in mammals. Proposed mechanisms inhibiting Kiss1 (kisspeptin gene) expression during childhood to juvenile varies from species to species: we envisage that negative feedback action of estrogen plays a key role in the inhibition of Kiss1 expression in KNDy neurons in rodents and sheep, whereas estrogen-independent inhibition of kisspeptin secretion by γ-amino butyric acid or neuropeptide Y are suggested to be responsible for the pre-pubertal suppression of GnRH/gonadotropin secretion in primates. Taken together, the timing of pubertal onset is postulated to be controlled by upstream regulators for kisspeptin biosynthesis and secretion in mammals.
Emerging Health Concepts in the Probiotics Field: Streamlining the Definitions

The concept of traditional probiotics was initially based on the observations of Elie Metchnikoff in 1907. He thus suggested that the regular consumption of fermented dairy products with lactic acid bacteria (LAB), such as yogurt, was associated with enhanced health and longevity in elderly Bulgarian people. Since then, the term probiotic has been always linked to beneficial bacteria for the host health, although its precise definition has evolved over the time (Figure 1A). Nowadays, thanks to the huge increase in the knowledge of the human gut microbiota and the importance of microbiota imbalances (dysbiosis) in several diseases and syndromes, probiotics can be used to restore the normal balance of the intestinal ecosystem as novel health promoting strategy. This new trend highlights the use of commensal bacteria as probiotics is the natural way to restore a healthy situation within different human ecosystems (intestinal, vaginal, skin…) opening the door to a new type of probiotics commonly called either Next-Generation Probiotics (NGPs) or Live Bio-Therapeutic Products (LBPs). Furthermore, the possibility of using non-viable bacteria, bacterial compounds, growth promoting substrates has opened the door to many new preventive therapy and options related to the field. Open in a separate window Figure 1 (A) Timeline of selected items in the history of probiotic related terms; (B) Scheme of the main terms related to probiotic field.
Toward Molecular Dissection of PrPC-PrPSc Interactions*

Direct interaction between endogenous cellular prion protein (PrPC) and misfolded, disease-associated (PrPSc) conformers is a key event in prion propagation, which precedes templated conversion of PrPC into nascent PrPSc and prion infectivity. Although almost none of the molecular details of this pivotal process are understood, the persistence of individual prion strains suggests that assembly of the prion replicative complex is mechanistically precise. To systematically map defined regions of PrPC sequence that bind tightly to PrPSc, we have generated a comprehensive panel of over 45 motif-grafted antibodies containing overlapping peptide grafts collectively spanning PrP residues 19–231. Grafted antibody binding experiments, performed under stringent conditions, clearly identified only three distinct and independent high affinity PrPSc recognition motifs. The first of these binding motifs lies at the very N-terminal region of the mature PrP molecule within PrP-(23–33); the second motif lies within PrP-(98–110); and the third is contained within PrP-(136–158). Mutational analyses of these PrPSc-binding regions revealed that reactivity of the 23–33 and 98–110 segments are largely dependent upon the presence of multiple positively charged amino acid residues. These studies yield new insight into critical peptidic components composing one side of the prion replicative interface.
Involvement of Relatives in Care of the Dying in Different Care Cultures: Development of a Theoretical Understanding

A prerequisite for the staff to be able to guide relatives through the dying trajectory is that the staff members know what the relative wants and can do. The aim of this study was threefold: (a) to identify and categorize family members’involvement in the care of a dying relative, (b) to develop a theoretical understanding of their involvement, and (c) to discuss the congruence or incongruence between the empirical results and key concepts in Swanson’s and Watson’s theories of caring. The results show that the relatives’involvement could be classified into three categories: to know, to be, and to do. Good correspondence was found between these three categories and key concepts in Swanson’s and Watson’s theories of caring.
EEG-based brain network analysis in stroke patients during a motor execution task

Post-stroke survivors often suffer motor function disorders, which are usually associated with anatomical and functional alterations of brain network. Previous EEG-based brain network analyses mainly focused on stroke-linked brain network in resting state and single aspect (globally or regionally), leaving the pattern of functional connectivity (FC) in stroke patients during specific motion task uncovered yet. In this study, we investigated stroke specific FC patterns in patients who suffered unilateral hemispheric stroke during a motor execution task. Partial correlation coefficients between multiple electroencephalography (EEG) channels were computed to construct the functional networks for healthy controls and stroke patients. The graph-based analysis was then performed to characterize specific FC patterns in stroke patients. Results suggested that brain networks were characterized in stroke patients by lower global efficiency and clustering coefficient in alpha and beta band, compared to healthy controls. Regionally, stroke patients exhibited weaker local connection in motor area of affected hemisphere during motor execution, which may explain their motor deficits. The findings of our study may offer new insight to study the neural plasticity and brain reorganization after stroke.
Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies.
EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles*

Electroencephalogram (EEG)-based prediction of driver emergency braking intention can help develop an assistance system to improve driving safety for brain-controlled vehicles. However, existing studies are focused on how to build an individual detection model for each participant. In this paper, to build a universal model, a convolutional neural network (CNN) is used to extract the features of brain signals and build the universal model. Experimental results from 13 subjects show that the proposed CNN-based method outperforms the linear discriminant analysis (LDA)-based method and has a comparable performance with individual models. This work lays a foundation for future developments of an EEG-based universal model of driver emergency braking intention detection.
Delayed administration of dapsone protects from tissue damage and improves recovery after spinal cord injury

After spinal cord injury (SCI), a complex cascade of pathophysiological processes increases the primary damage. The inflammatory response plays a key role in this pathology. Recent evidence suggests that myeloperoxidase (MPO), an enzyme produced and released by neutrophils, is of special importance in spreading tissue damage. Dapsone (4,4′‐diaminodiphenylsulfone) is an irreversible inhibitor of MPO. Recently, we demonstrated, in a model of brain ischemia/reperfusion, that dapsone has antioxidant, antiinflammatory, and antiapoptotic effects. The effects of dapsone on MPO activity, lipid peroxidation (LP) processes, motor function recovery, and the amount of spared tissue were evaluated in a rat model of SCI. MPO activity had increased 24.5‐fold 24 hr after SCI vs. the sham group, and it had diminished by 38% and 19% in the groups treated with dapsone at 3 and 5 hr after SCI, respectively. SCI increased LP by 45%, and this increase was blocked by dapsone. In rats treated with dapsone, a significant motor function recovery (Basso‐Beattie‐Bresnahan score, BBB) was observed beginning during the first week of evaluation and continuing until the end of the study. Spontaneous recovery 8 weeks after SCI was 9.2 ± 1.12, whereas, in the dapsone‐treated groups, it reached 13.6 ± 1.04 and 12.9 ± 1.17. Spared tissue increased by 42% and 33% in the dapsone‐treated groups (3 and 5 hr after SCI, respectively) vs. SCI without treatment. Dapsone significantly prevented mortality. The results show that inhibition of MPO by dapsone significantly protected the spinal cord from tissue damage and enhanced motor recovery after SCI. © 2011 Wiley‐Liss, Inc.
A Review on Big Data Maturity Models

Big Data is the current currency in the competitive market of the digital economy. The ability to be successful in Big Data is related to the maturity level of the organization. A Maturity Model (MM) is a tool that can be applied to assess the “As-is” situation regarding specific key dimensions, where the maturity levels indicate an organization’s current capabilities and the desirable state. Most of the available literature on the Maturity Models for Big Data is still in white papers, reports, and internet materials from developers’ websites. The existing literature might not have covered all the available Maturity Models for Big Data. This paper reviewed the existing literature to achieve three objectives: first, to provide a comprehensive overview about the Maturity, Maturity Model, Capability Maturity Model, and the Maturity Model for Big Data; second, to identify the existing Big Data Maturity Models and to summarize the most used models; third, to highlight the limitations of the existing Big Data Maturity Models.
Exploring the sensory function reconstruction by the combined surgery*

Targeted muscle reinnervation and end-to-side coaptation are surgical techniques to reconstruct neural-function of residual peripheral nerves. These techniques have made some clinical achievements. The present study investigate the effect of sensory function reconstruction of median nerve and medial cutaneous nerve of the forelimb by combining targeted muscle reinnervation and the end-to-side coaptation techniques. The results showed that the signals presented different level fluctuation correlated with the tactile stimulation. The hematoxylin-eosin staining showed that the tactile corpuscles were remaining following the combined surgery. These results demonstrate the feasibility of combining the techniques and the rat model.
Network Functions Virtualization: The Long Road to Commercial Deployments

Network operators are under pressure to offer efficient network-based services while keeping service deployment costs to a minimum. Network functions virtualization (NFV) can potentially revolutionize network-based services bringing low-deployment costs for network operators. The NFV has been introduced to ultimately extend the non-proprietary and open-standard-based model to network and service deployments, significant improvements to today’s proprietary locked implementations. Notwithstanding the continuous efforts of both academia and industry to support the NFV paradigm, the current NFV solutions offered are still in its infancy. In this survey, we provide a detailed background of NFV to establish a comprehensive understanding of the subject, ranging from the basics to more advanced topics. Moreover, we offer a comprehensive overview of the NFV main concepts, standardization efforts, the benefits of NFV, and discussions of the NFV architecture as defined by the European telecommunications standardization institute (ETSI). Furthermore, we discuss the NFV applicability and current open source projects. We then highlight NFV requirements, design considerations, and developmental architectural impairments and barriers to commercial NFV deployments. Finally, we conclude enumerating future directions for NFV development.
Risk Analysis in Fieldbus Networks Using the Example of KNX

Building automation is increasingly used to minimize energy consumption and increasing comfort and flexibility in buildings. When the KNX Association was founded in 1999 the need for security was quite low. Systems were usually not connected to the internet and so the standard lacks of nowadays common security requirements. To get an idea where the most serious problems are located in installations we present in this paper a method to calculate a risk for the different network segments. Without limiting the generality KNX is used as one example of fieldbuses. The basic concepts in the communication of fieldbus devices are present in KNX and the results can easily be transferred to other fieldbus protocols.
Power Distribution Networks Load Forecasting Using Deep Belief Networks: The South African Case

Load forecasting is considered a time series problem, whose accuracy is important in operations and planning of micro and large power systems. South Africa is a developing country that is in its 25th year of democracy. The study of load forecasting using artificial intelligence techniques in South Africa is limited. The application of deep learning techniques in South African load forecasting is non-existent. This paper overcomes these shortfalls by introducing deep learning techniques in South African load forecasting. This was conducted using a real South African distribution sub-stations loading data and a sophisticated deep learning technique, deep belief networks. The substation is an 80 MVA, 88/11 kV distribution substation. Weather parameters, and more specifically temperature, have been seen to improve the accuracy of load forecasts. It has, however, been shown that this is not always the case. This paper investigated the impact of temperature on the load forecasting error when using a sophisticated deep learning technique, deep belief networks. The impact of using cleaned data and uncleaned data was also investigated. The lowest error was obtained with non-cleaned data with temperature as an input parameter. The obtained errors were around 4%. Hence, deep learning techniques can be applied in South African distribution networks for load forecasting, and therefore cost reduction through improved planning.
NAV-VIR: an audio-tactile virtual environment to assist visually impaired people*

This paper introduces the NAV-VIR system, a multimodal virtual environment to assist visually impaired people in virtually discovering and exploring unknown areas from the safety of their home. The originality of NAV-VIR resides in (1) an optimized representation of the surrounding topography, the spatial gist, based on human spatial cognition models and the sensorimotor supplementation framework, and (2) a multimodal orientation-aware immersive virtual environment relying on two synergetic interfaces: an interactive force feedback tablet, the F2T, and an immersive HRTF-based 3D audio simulation relying on binaural recordings of real environments. This paper presents NAV-VIR functionalities and its preliminary evaluation through a simple shape and movement perception task.
Implementation of High-Availability Server Cluster by Using Fencing Concept

Contemporary business-technical challenges require the implementation of advanced security techniques and the reliability of applications and services. Distributed computer systems allow access to powerful computing resources and are often based on server clusters and advanced virtualization techniques. This paper presents one solution for the implementation of high-availability server ceph cluster using the Proxmox open source virtualization platform. The Intelligent Platform Management Interface (IPMI)-based fencing technique has achieved an enviable level of reliability of virtual machines running on the cluster. It has been shown that the implementation of open source virtualization solutions, a properly designed server cluster, and advanced fencing techniques can significantly improve the reliability and availability of services and applications.
A Dual Convolution Network Using Dark Channel Prior for Image Smoke Classification

Through a comparative analysis, we confirm that the value of the dark channel pixels of the smoke image is higher than the non-smoke image. It means that the dark channel of the smoke image has more elaborate information of the smoke, which is of great benefit to our detailed feature extraction of smoke. On this background, we propose a dual convolution network using dark channel prior for image smoke classification (DarkC-DCN) for the image smoke classification. In DarkC-DCN, basing on the AlexNet, and through continuous structural improvement and optimization, we improve a detailed CNN to extract the detailed features of dark channel images. Similarly, to extract the general features in the image, we further design another residual network based on the AlexNet, which is the main framework of the entire network. To ascertain the robustness of the network, the two channels are trained separately for various inputs. In addition, we perform feature fusion before the common fully connected layer. In the experiment, we also add some non-smoke data similar to smoke in the public smoke data set for data expansion. The experimental results indicate that the model has a good performance in general. The accuracy value reaches 98.56%.
Multiclass Detection and Tracking of Transient Motor Activation based on Decomposed Myoelectric Signals

Performance and efficacy of neuroprosthetic devices depend critically on the ability to detect the users motor intent with high temporal resolution. Delayed and incorrect responses significantly reduce usability, controllability and intuitiveness of prosthetic systems. Substantial efforts have been conducted to detect the steady-state phase of motor intention. However, detection, classification, and tracking of transient phases for one complete muscle contraction is still not possible. Clinically-established control systems rely mainly on surface electromyography (sEMG) signals in stationary, steady-state contractions, that have limited temporal resolution. Characterization of neural activities during different stages of a dynamic, transient contraction would allow for the development of a clinically-viable myoelectric system with a high temporal resolution that can significantly enhance the level of intuitiveness and usability of prosthetic devices. This could increase the response bandwidth and realize natural and dexterous control while avoiding exaggerated compensatory movements. For this purpose, in this paper, we explore the use of motor unit action potential trains (MUAPTs) for designing a movement intention detection technique. The goal is to classify and track the transient phases of muscle activation. Data collected from three subjects, during flexion tasks with four individual digits, is used to evaluate the algorithm. The performance is compared with that of the standard sEMG-based approach. Results showed a substantial advantage of the MUAPT-based phase detection algorithm over the conventional sEMG-based technique. It is confirmed that decoding, classification, and tracking of all stages of a dynamic, transient contraction is feasible using the proposed MUAPT-based approach, as a robust and efficient alternative for conventional sEMG-based algorithms.
Ultrasonic Characterization of Corneal Thickness and Axial Length of Pig Eyeball Caracterización Ultrasónica del Grosor Corneal y la Longitud Axial del Globo Ocular Porcino

Ultrasound is a tool frequently used to diagnose ocular pathologies due to non-invasive and nonionizing features. Pachymetry and echography are standard techniques used to determine ocular anomalies related to different diseases. However, research in this field is even so extensive, mainly focused on high resolution and low-cost diagnosis. In this work, the initial stage of our research project, which is oriented to detection of parameters related to ocular pathologies, is described. In such stage, the ultrasonic characterization of two measures related to ocular size of healthy pig: corneal thickness and axial length, that will be used in detection of ocular diseases, was performed. Two transducers, with different central frequency, were used for this characterization. Obtained results (1016±43μm and 995±121μm for the corneal thickness; and 19.77±0.49 mm and 20.40±0.60 mm for the axial length) agree with range of values reported in the literature (833μm – 1013μm for the corneal thickness and 23mm – 24mm for the axial length).