Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Design and Analysis of Partitioned Stator Hybrid Excitation Doubly Salient Machine

A new partitioned stator hybrid excitation (HE) doubly salient machine with permanent magnets (PMs) sandwiched between adjacent inner stator tooth tips and with armature and field windings housed in outer and inner stators, respectively, is proposed in this paper. In comparison of the conventional HE machines with single stator, the space utilization is significantly boosted. So more windings and PMs can be employed to obtain increased torque density and improved flux regulation capability. In order to investigate the effect of inner stator yoke, which contributes significant influence to electromagnetic performances, the equivalent magnetic circuit method is employed. Moreover, the multi-level design optimization method is adopted to optimize the key parameters efficiently. Notably, the rated operating point with excitations of PMs and field current of 1 A is defined according to its efficiency. Then, the electromagnetic performances at different operating points are analyzed and compared based on the 2-D finite-element analysis. The results reveal that the output torque is increased at new rated condition due to the flux-enhancing current, and the flux-weakening capability is also improved, so that a wider constant power speed range can be achieved because of the defined rated operating point.
Resource and Service Management for Fog Infrastructure as a Service

Fog computing paradigm has emerged to be complementary to cloud computing paradigm to facilitate deployment of latency-critical, and bandwidth-intensive applications. Researchers have shown the need of fog computing towards deployment of such applications. Several research problems need to be solved prior to the real-world deployment of large-scale fog environments. These research problems in the realm of Fog Infrastructure as a Service have been identified and discussed in this paper.
Single Dose of a Polyanhydride Particle-Based Vaccine Generates Potent Antigen-Specific Antitumor Immune Responses

Many factors affect vaccine efficacy. One of the most salient is the frequency and intervals of vaccine administration. In this study, we assessed the vaccine administration modality for a recently reported polyanhydride-based vaccine formulation, shown to generate antitumor activity. Polyanhydride particles encapsulating ovalbumin (OVA) were prepared using a double-emulsion technique and subcutaneously delivered to mice either as a single-dose or as prime-boost vaccine regimens in which two different time intervals between prime and boost were assessed (7 or 21 days). This was followed by measurement of cellular and humoral immune responses, and subsequent challenge of the mice with a lethal dose of E.G7-OVA cells to evaluate tumor protection. Interestingly, a single dose of the polyanhydride particle-based formulation induced sustained OVA-specific cellular immune responses just as effectively as the prime-boost regimens. In addition, mice receiving single-dose vaccine had similar levels of protection against tumor challenge compared with mice administered prime-boosts. In contrast, measurements of OVA-specific IgG antibody titers indicated that a booster dose was required to stimulate strong humoral immune responses, since it was observed that mice administered a prime-boost vaccine had significantly higher OVA-specific IgG1 serum titers than mice administered a single dose. These findings indicate that the requirement for a booster dose using these particles appears unnecessary for the generation of effective cellular immunity.
EEG Based Network Connectivity Classification in 7 and 9 Years- Old Children

Investigating the brain neural pathways requires extensive knowledge of childrens' cognitive development. Significant variations in the cognitive process of a child, across ages, were assessed through the success in recognizing various stimuli. Longitudinal EEG data were gathered from 45 healthy children at the ages of seven and nine years. During the EEG data acquisition, children were asked to respond to the Flanker stimuli for investigating the development of the response conflict process. In each age group, the coherence and imaginary component of coherency were used to assess the network connectivity of each child. The congruent and incongruent stimuli were tried within delta, theta, alpha and beta bands. Following that, efficacies of various classification algorithms were tested in discriminating the coherency data of the two age groups. It was observed that brain connectivity was more helpful in distinguishing between two age groups using the incongruent Flanker stimuli. For the incongruent condition, the imaginary part of the coherency provides better features for classification. Using the features derived from the theta, alpha and beta bands, a classification accuracy of more than 94.31% could be achieved using the naïve Bayes classifier.
In Vitro Potentiation of Carbapenems with Tannic Acid Against Carbapenemase Producing Enterobacteriaceae: Exploring Natural Products as Potential Carbapenemase Inhibitors

Resistance to antibiotics is increasing worldwide, necessitating urgent action to sustain the efficacy of existing antibiotics in clinical use. We show that tannic acid (TA) in combination with carbapenems can reduce and/or reverse the minimum inhibitory concentrations (MICs) of carbapenems to susceptible values in Enterobacteriaceae that express class A and B carbapenemases. MICs of carbapenems in the presence and absence of TA and other efflux pump inhibitors, TA-carbapenemases inhibition assays and computational studies were undertaken to determine the effect of TA on carbapenem susceptibility in Enterobacteriaceae. TA had the greatest effect on metallo-β-lactamases (MBLs) followed by class A serine-β-lactamases (SBLs). Antibiotic susceptibility testing showed that TA reversed the MICs of MBLs to susceptible values whilst substantially reducing the MICs of SBLs (class A). Tolerable cytotoxicity effect was observed for the concentrations tested. TA inhibited enzymes with a marked difference between ≈50% inhibition (IC50) for NDM-1 and KPC-2. Computational studies including molecular docking, molecular dynamics simulations and binding free energy calculations showed that TA interact with both MBLs and SBLs hydrophobic sites. Moreover, TA had a stronger binding affinity for MBLs than SBLs as the MBLs, specifically VIM-1 and NDM-1, interact with a larger number of their catalytic active-site residues than that of OXA- 48 and KPC-2. These in vitro evaluations together with computational simulation explain the potentiating effect of TA toward carbapenems against carbapenem-resistance enterobacteriaceae. This study proposes TA as a promising adjuvant for MBLs and SBLs.
Programa de ejercicio físico en fibromialgia

Plan para la Promocion de la Actividad Fisica y la Alimentacion Equilibrada (PAFAE). Publicado en la pagina Web de la Consejeria de Igualdad, Salud y Politicas Sociales : www.juntadeandalucia. es/salud (Ciudadania / Nuestra Salud / Vida sana / Alimentacion equilibrada y actividad fisica / Materiales para la promocion del ejercicio fisico y alimentacion saludables / Mas materiales)
Semi-crowdsourced Clustering with Deep Generative Models

We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample pair is in the same cluster. We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset. The two parts share the latent variables. To make the model automatically trade-off between its complexity and fitting data, we also develop its fully Bayesian variant. The challenge of inference is addressed by fast (natural-gradient) stochastic variational inference algorithms, where we effectively combine variational message passing for the relational part and amortized learning of the DGM under a unified framework. Empirical results on synthetic and real-world datasets show that our model outperforms previous crowdsourced clustering methods.
Heavy Alcohol Use and Age Effects on HIV‐Associated Neurocognitive Function

BACKGROUND There is growing concern about the health impact of heavy alcohol use in people infected with human immunodeficiency virus (HIV+). Mixed findings of past studies regarding the cognitive impact of alcohol use in HIV+ adults have been mixed, with inconsistent evidence that alcohol consumption exacerbates HIV-associated brain dysfunction. This study examined contributions of current heavy drinking, lifetime alcohol use disorder (AUD), and age to cognitive deficits in HIV+ adults, and relative to other HIV-associated clinical factors. METHODS Cognitive performance of HIV+ adults (n = 104) was assessed, and comparisons were made between heavy current to nonheavy drinkers (NIAAA criteria), lifetime AUD versus no-AUD, and older (>50 years) versus younger participants. Hierarchical regression analyses were conducted to examine the association between cognitive performance and current heavy drinking, lifetime AUD, and older age, while also correcting for HIV clinical factors and history of other substance use. RESULTS Individuals reporting current heavy drinking and meeting criteria for lifetime AUD demonstrated the greatest degree of deficits across multiple cognitive domains. Deficits were greatest among HIV+ adults with lifetime AUD, and older age was also associated with weaker cognitive performance. Lifetime AUD and older age independently exhibited stronger associations with cognitive performance than HIV clinical factors (e.g., viral load, current CD4, and nadir CD4) or past opiate and cocaine use. CONCLUSIONS Current heavy drinking and lifetime AUD adversely affect cognitive function in HIV+ adults. Greatest deficits existed when there was a history of AUD and continued current heavy drinking, indicating that past AUD continues to have an adverse impact and should not be ignored. That alcohol use was more strongly associated with cognitive performance than HIV clinical factors underscore clinical importance of targeting reduction in heavy alcohol consumption in HIV+ adults.
Investigating Lstm with k-Max Pooling for Text Classification

For most deep learning practitioners, text classification is synonymous with recurrent networks and is tackled at word level. The presented study is demonstrated that word-level inputs are better than character-level inputs. We describe the proposed model that utilizes Spatial-Dropout1D, LSTM and k-max pool. Because the word of these representations is measured in a word similarity task, so SpatialDropout can reduce information redundancy; LSTM keep track of information over longer periods of time. k-max pooling keep more useful information. The proposed model is better than the strong baseline methods on six benchmark datasets for sentiment classification and topic categorization. The code will be open.
QTc and anti-tuberculosis drugs: a perfect storm or a tempest in a teacup? Review of evidence and a risk assessment.

SUMMARYMultidrug-resistant (MDR) and extensively drug-resistant tuberculosis (XDR-TB) are global concerns, with stagnant treatment success rates of roughly 54% and 30%, respectively. Despite adverse events associated with several DR-TB drugs, newly developed drugs and shorter regimens are bringing hope; recent concern has focused on drugs that prolong the corrected QT interval (QTc). QTc prolongation is a risk factor for torsades de pointe (TdP), a potentially lethal cardiac arrhythmia. While QTc prolongation is used in research as a surrogate marker for drug safety, the correlation between QTc and TdP is not perfect and depends on additional risk factors. The electrocardiogram (ECG) monitoring that has been recommended when new drugs are used has created alarm among clinicians and National Tuberculosis Programmes (NTPs). ECG monitoring is often challenging in high-burden settings where treatment alternatives are limited. According to a review of studies, the prevalence of sudden death directly attributable to TdP by QTc-prolonging DR-TB drugs is likely less than 1%. The risk of death from an ineffective MDR-TB/XDR-TB regimen thus far exceeds the risk of death from arrhythmia. In patients with QTc prolongation who develop cardiac events, other significant risk factors in addition to the drugs themselves are nearly always present. Clinicians and NTPs should be aware of and manage all possible circumstances that may trigger an arrhythmia (hypopotassaemia and human immunodeficiency virus infection are probably the most frequent in DR-TB patients). We present the limited but growing evidence on QTc prolongation and DR-TB management and propose a clinical approach to achieve an optimal balance between access to life-saving drugs and patient safety.
Using the National Early Warning Score (NEWS) outside acute hospital settings: a qualitative study of staff experiences in the West of England

Objectives Early warning scores were developed to improve recognition of clinical deterioration in acute hospital settings. In England, the National Early Warning Score (NEWS) is increasingly being recommended at a national level for use outside such settings. In 2015, the West of England Academic Health Science Network supported the roll-out of NEWS across a range of non-acute-hospital healthcare sectors. Research on the use of NEWS outside acute hospitals is limited. The objective of this study was to explore staff experiences of using NEWS in these new settings. Design Thematic analysis of qualitative semi-structured interviews with purposefully sampled healthcare staff. Setting West of England healthcare settings where NEWS was being used outside acute hospitals—primary care, ambulance, referral management, community and mental health services. Participants Twenty-five healthcare staff interviewed from primary care (9), ambulance (3), referral management/acute interface (5), community (4) and mental health services (3), and service commissioning (1). Results Participants reported that NEWS could support clinical decision-making around escalation of care, and provide a clear means of communicating clinical acuity between clinicians and across different healthcare organisations. Challenges with implementing NEWS varied—in primary care, clinicians had to select patients for NEWS and adopt different methods of clinical assessment, whereas for paramedics it fitted well with usual clinical practice and was used for all patients. In community services and mental health, modifications were ‘needed’ to make the tool relevant to some patient populations. Conclusions This study demonstrated that while NEWS can work for staff outside acute hospital settings, the potential for routine clinical practice to accommodate NEWS in such settings varied. A tailored approach to implementation in different settings, incorporating guidance supported by further research on the use of NEWS with specific patient groups in community settings, may be beneficial, and enhance staff confidence in the tool.
Comparison of Methodologies: Analytical Hierarchy Process and Grey Clustering with Entropy Weight for the Multicriteria Assessment of the Energy Sources of Perú

This article introduces a comparison among decision making methodologies applied to energetic policies. Every country has a challenge that is choice the most suitable energetic source in order to the common welfare, it is the reason for use decision making supported by structured methods that can be Analytical Hierarchic Process, Grey Clustering with Entropy Weight (both used in this research) or another. The result shows the differences and similarities due each methodology and for the same case analyzed also the Peruvian energetic sources were qualified. According both methodologies, the most important sources are: hydraulic, natural gas and fuel (saving order). The important sources are: or solar, geothermic, biomass, eolic or geothermic, solar, biomass, eolic (respectively for each method). The less important are: or liquid hydrocarbons, carbon, bio-fuels, sea-wave, tidal, uranium or liquid hydrocarbons, carbon, bio-fuels, uranium, tidal, sea-wave (respectively for each method). Finally this research could be used for improve the decision making in energetic policy topics trough the new method Grey Clustering with Entropy Weight.
Identifying Niche Singers in Online Music Streaming Services

This paper proposed a framework FINS to identify the niche singers by using the listening history from online music streaming services. First of all, we show that the distribution of frequency of songs listen follows the power law. Then, the approach of selecting candidates for niche singers is designed based on Pareto principle. Secondly, the approach to inferring and ranking niche singers is proposed where the users with strong preference are found. Based on the fact that the niche singers are usually supported by a group of loyal fans, the niche singers with lots of loyal fans could be discovered. Finally, based on the number of loyal fans and their listening behavior, the hotness of niche singers could be defined so that the niche singers with top-k hotness score can be derived as our output. Finally, the experimental results show that FINS could effectively identified the niche singers.
Linearly Combining Sensor Measurements Optimally to Enforce an SPR Transfer Matrix

This paper presents methods to combine measurements of a linear time-invariant system in such a manner that the new system obtained is strictly positive real (SPR). In particular, this paper focuses on how to best combine measurements in a linear fashion by minimizing the difference in an $\mathcal{H}_{2}$ or $\mathcal{H}_{\infty}$ sense between the new system and a given desired system. The methods proposed to linearly combine sensor measurements rely on linear matrix inequalities (LMIs), which lead to tractable synthesis procedures. Numerical examples involving noncolocated elastic mechanical systems are provided that illustrate the effectiveness of the proposed techniques when used for output tracking control.
Unifying and Analysing Activities of Daily Living in Extra Care Homes

This work presents the unification and formal analysis of occurring Activities of Daily Living (ADLs) identified by an intelligent well-being monitoring system used for elderly residents in extra care homes. The ADLs considered in this paper are: i) personal grooming and toilet, ii) preparation of breakfast, iii) preparation of lunch, iv) preparation of evening meal and v) sleep. These ADLs are examined as they exhibit multiple or similar occurrences during a typical day. The novelty of this work lies in the introduction of a unification approach that could help for the detection of normal and abnormal behaviour based on the execution of the ADLs from elders in extra care homes equipped with different types of sensors. To unify and detect these types of behaviour, temporal aspects of the ADLs' execution like their duration and time of day are scrutinised. Moreover, the formal analysis of the identified ADLs is conducted, using Petri Nets for the modelling of these activities and model checking for their verification. Finally, the verification results are used to indicate whether an abnormal behaviour takes places during an activity, which could be used as a measure for spotting potential health issues regarding the elders that reside in the monitored homes.
Excavatolide-B Enhances Contextual Memory Retrieval via Repressing the Delayed Rectifier Potassium Current in the Hippocampus

Memory retrieval dysfunction is a symptom of schizophrenia, autism spectrum disorder (ASD), and absence epilepsy (AE), as well as an early sign of Alzheimer’s disease. To date, few drugs have been reported to enhance memory retrieval. Here, we found that a coral-derived natural product, excavatolide-B (Exc-B), enhances contextual memory retrieval in both wild-type and Cav3.2−/− mice via repressing the delayed rectifier potassium current, thus lowering the threshold for action potential initiation and enhancing induction of long-term potentiation (LTP). The human CACNA1H gene encodes a T-type calcium channel (Cav3.2), and its mutation is associated with schizophrenia, ASD, and AE, which are all characterized by abnormal memory function. Our previous publication demonstrated that Cav3.2−/− mice exhibit impaired contextual-associated memory retrieval, whilst their retrieval of spatial memory and auditory cued memory remain intact. The effect of Exc-B on enhancing the retrieval of context-associated memory provides a hope for novel drug development.
A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease

The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson’s disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.
Neurophysiologic and Chronic Safety Testing of a Miniaturized Active Implanted Device with Integrated Electrodes for Bioelectronic Medicine Applications

New dosing paradigms in bioelectronic medicine applications, such as low duty cycle stimulation of the vagus nerve to treat inflammatory disorders, enable architectural shifts in active implantable devices that benefit patients. Herein, we describe various features of the MicroRegulator (MR), an innovative neurostimulation system that includes a unique electrode-integrated implantable nerve stimulator. To verify efficient activation of neuronal targets within the vagus nerve, a geometric emulator of the MR (identical form and electrical contact properties as the clinical MR device) was tested in situ and neurophysiologic outcomes were compared to a control electrode in wide clinical use. The data demonstrated comparable patterns of compound potentials evoked from the MR emulator and the control electrode, with the MR emulator requiring a lower threshold current to depolarize the nerve. To verify chronic mechanical safety, the MR emulator was implanted for 2 months on the vagus nerves of canines. Blood flow through the major cervical vessels was unaffected, and pathologic and histologic findings included normal foreign body encapsulation and an absence of demyelination and nerve damage. Together these finding support the feasibility of the MR system for clinical translation.