Miracle-dz/UniFAR-FTUs huggingface dataset:
Power and area efficient turbo decoder implementation for mobile wireless systems

The authors present a low power and area efficient turbo soft-input soft-output (SISO) decoder based on two-step soft-output Viterbi algorithm (SOVA) targeting wireless mobile communication systems. Our turbo SISO decoder is based on trace back algorithm (TBA) and saves area and power by replacing the FIFO memory with an additional transition metric unit (TMU). The paper presents the implementation of SOVA decoder for constraint lengths K=3, 4, and 5, describing the design methodology and evaluation environment. Simulation results are provided showing up to 20% power saving and 46% area saving compared to a conventional SOVA decoder implementation.
Weaving our way towards a new generation of fibre-optic chemical sensors based on spider silk

From the spider's perspective, silk is not only a building material but also a safety net, a weapon and a sensory organ to detect the presence of preys on its web. Indeed, this primeval material has been shaped over hundreds of millions of years by spiders to create a myriad of silk fibre types with different level of toughness, elasticity, stickiness depending on its attributed function in the web. From a human perspective, scientists are currently working on harnessing all the extraordinary properties of this material for applications spiders would never thought of, from biocompatible tissue engineering (enhancement of skin regeneration and nerve guides) to biodegradable electronics and development of specialised textile and composites. However, the potential of using spider silk fibre for chemical sensing has been overlooked. In this communication, we will explore the potential of using spider silk as a new type of fibre optic chemical sensor in a fully bio-inspired approach.
Exogean : an Expert on Eukaryotic Gene Annotation

Introduction In the field of eukaryotic gene annotation a number of tools exist to identify genes in an automatic way. Such programs that provide primary information towards the identification of genes can be classified in two categories: alignment programs, that compare a genomic DNA sequence to protein or cDNA sequences (of the same or a different species) ; ab initio programs that use the statistical composition (content and signal information) of the genomic DNA sequence to find genes. In most cases however, these programs taken in isolation are not sufficient to annotate a genomic sequence reliably, because the first approach is based on uncomplete data, depends on sequence conservation, and does not provide complete gene models, and the second one often has variable or low performances. This explains why recently many efforts have been made to develop programs that combine the two sources of information. These new automatic gene annotation methods combine results from primary programs belonging to either one of the two categories [1,2], or from the two categories [3,4,5,6,7,8]. The primary as well as the combining programs are based on automatic methods that use a statistical approach to model the gene structure. Consequently, these programs generally do not have parameters to allow a human annotator to modify their behaviour according to his expertise. If they do have parameters, these are not intuitive enough for the user to take advantage of his knowledge when calibrating. Thus, despite advances in automatic gene annotation in eukaryotes, the gold standard remains those gene structures generated by expert biologist/bioinformaticians through a semi-manual analysis of a sequence on which several types of evidence have been accumulated, combining them and taking decisions when conflicting situations arise [9, 10].
Prevalence of Self-Reported Diabetes and Its Associated Factors: A Population-Based Study in Brazil

Aim. The aim of this study was to estimate the prevalence of diabetes and its associated risk factors in adults from Brasilia, Brazil. Methods. The present cross-sectional population-based study consisted of interviews with individuals aged 18–65 years. Participants were selected through two-stage probability sampling by clusters and stratified by sex and age. Demographic and clinical data were collected directly with participants from February to May 2012. Self-reported diabetes prevalence was calculated at a 95% confidence interval (CI). Prevalence ratios (PR) were adjusted by Poisson regression with robust variance. Results. In all, 1,820 individuals were interviewed. Diabetes prevalence in the adult population of Brasilia was 10.1% (95% CI, 8.5%–11.6%). Variables associated with diabetes were an age between 35 and 49 years (PR = 1.83; 95% CI, 1.19–2.82) or 50 and 65 years (PR = 1.95; 95% CI, 1.17–3.23), hypertension (PR = 4.04; 95% CI, 2.66–6.13), respiratory disease (PR = 1.67; 95% CI, 1.11–2.50), cardiovascular disease (PR = 1.74; 95% CI, 1.15–2.63), and pain/discomfort (PR = 1.71; 95% CI, 1.21–2.41). Conclusion. Diabetes is a prevalent condition in adults living in Brasilia, and disease risk increases with age and comorbidities. Future health policies should focus on screening programs and prevention for the more vulnerable groups.
Context Mining based Mental Health Model for Lifecare Platform

With the emergence of the 4th industrial revolution, IT convergence engineering based artificial intelligence and intelligent system has constantly been researched in today's society. In particular, healthcare service based on IT-BT convergence helps to improve quality of people's life and provide user-oriented healthcare contents actively. Currently, the healthcare industry has gradually changed its healthcare paradigm from conventional healthcare to mental diseases care and tries to solve the social problem with depression, one of mental disorders. This study proposes the context mining based mental health model for the lifecare platform. This study makes use of users’ profiles about depression and health weather index provided by Korea Meteorological Administration to classify and define semantic ontology based context information, and to develop the context mining model for depression index service. The proposed context mining based mental health model uses personalized context information so that it is possible to provide personalized depression index service, rather than unified healthcare service. Also, the proposed one uses user-based information for modeling so that it can provide guidelines for developing data model of depression. In addition, it is possible to provide accurate and specified service for users and efficient depression index service through customized service. The result of the proposed method shows that the context mining model not only promotes the theory and practical ability but also consolidates their understanding of web engineering models and concepts.
Off-line hand-written character recognition using integrated 1D HMMs based on feature extraction filters

The purpose of our research is to improve the recognition rate of an off-line handwritten character recognition system using HMM (hidden Markov model), so that we can use the system for practical application. Due to the insufficient recognition rate of ID HMM character recognition systems and the requirement for a huge number of learning samples to construct 2D HMM character recognition systems, HMM-based character recognition systems have not yet achieved sufficient recognition performance for practical use. In this research, we propose the character recognition method that integrates 4 simply structured 1D HMMs all of which are based on feature extraction using linear filters. The results of our evaluation experiment using the Hand-Printed Character Database (ETL6) showed that the first rank recognition rate of the test samples was 98.5% and that the cumulative recognition rate of top 3 candidates was 99.3%. Although our method is relatively easy to implement, it can work even better than 2D HMM method. These results show the proposed method is very effective.
Assertiveness Training and Psycho-Education as Preventive Measures of Drug Abuse among Secondary School Students in Makurdi Local Government Area

This study investigated Assertiveness Training and Psycho-education as preventive measures of drug abuse among secondary school students in Makurdi Local Government Area. The study adopted a pre-test, post-test and control group experimental design with a 3x2 factorial matrix. The sampling techniques adopted were the simple random method where each student was given an opportunity to be selected, and randomization of experimental groups and the control group to the schools through balloting was also done. Drug abuse prevention questionnaire (DAPQ), a standardized instrument developed by Skinner (1982), with a reliability coefficient of . 97 were used for data collection. A total of 40 secondary school students made up of 28 males and 12 females with ages ranging from 15-23 years took part in the study. They also had a mean age of 18.38 Three hypotheses were raised and tested. The results obtained revealed that firstly, assertiveness training and psycho-education have significant treatment effects in the prevention of drug abuse among secondary school students (F(2)= 6.88; P< 0.05 ). Secondly, sex had no significant effect on prevention of drug abuse F(1)= 0.32; P> 0.05 and thirdly, there was also no statistically significant interaction effects on drug abuse among secondary school students F(2)= 0.82; P> 0.05. Therefore, it is recommended that high premium is placed on developing and mainstreaming prevention programs on drug abuse into the school curriculum in secondary schools for students to become more assertive and acquire more knowledge on drugs and its effects on mental health.
Non-rigid Registration Meets Surface Reconstruction

Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. Outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the out performance and robustness of our framework.
Multicast connection capacity of WDM switching networks with limited wavelength conversion

Currently, many bandwidth-intensive applications require multicast services for efficiency purposes. In particular, as wavelength division multiplexing (WDM) technique emerges as a promising solution to meet the rapidly growing demands on bandwidth in present communication networks, supporting multicast at the WDM layer becomes an important yet challenging issue. In this paper, we introduce a systematic approach to analyzing the multicast connection capacity of WDM switching networks with limited wavelength conversion. We focus on the practical all-optical limited wavelength conversion with a small conversion degree d (e.g., d=2 or 3), where an incoming wavelength can be switched to one of the d outgoing wavelengths. We then compare the multicast performance of the network with limited wavelength conversion to that of no wavelength conversion and full wavelength conversion. Our results demonstrate that limited wavelength conversion with small conversion degrees provides a considerable fraction of the performance improvement obtained by full wavelength conversion over no wavelength conversion. We also present an economical multistage switching architecture for limited wavelength conversion. Our results indicate that the multistage switching architecture along with limited wavelength conversion of small degrees is a cost-effective design for WDM multicast switching networks.
Maximum-likelihood estimation of multivariate normal graphical models : large-scale numerical implementation and topology selection

We consider large-scale numerical implementations of maximum-likelihood estimation of multivariate normal graphical models with conditional independence. We show how to formulate this as a convex optimization problem, which gives a wealth of methods that can readily be applied. We give details on large-scale implementation based on both a coordinate steepest descent method, and a conjugate gradient method. With these methods we can routinely solve problems with several 1000 of nodes on a standard PC. As an application of the large-scale implementations we investigate topology selection for normal graphical models, where we wish to learn the node-edge incidence matrix of the underlying graph based only on measurements (i.e., based on the sample covariance estimate).
Sleep Disorders in Parkinsonian Macaques: Effects of l-Dopa Treatment and Pedunculopontine Nucleus Lesion

Patients with Parkinson's disease (PD) display significant sleep disturbances and daytime sleepiness. Dopaminergic treatment dramatically improves PD motor symptoms, but its action on sleep remains controversial, suggesting a causal role of nondopaminergic lesions in these symptoms. Because the pedunculopontine nucleus (PPN) regulates sleep and arousal, and in view of the loss of its cholinergic neurons in PD, the PPN could be involved in these sleep disorders. The aims of this study were as follows: (1) to characterize sleep disorders in a monkey model of PD; (2) to investigate whether l-dopa treatment alleviates sleep disorders; and (3) to determine whether a cholinergic PPN lesion would add specific sleep alterations. To this end, long-term continuous electroencephalographic monitoring of vigilance states was performed in macaques, using an implanted miniaturized telemetry device. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine treatment induced sleep disorders that comprised sleep episodes during daytime and sleep fragmentation and a reduction of sleep efficiency at nighttime. It also induced a reduction in time spent in rapid eye movement (REM) sleep and slow-wave sleep and an increase in muscle tone during REM and non-REM sleep episodes and in the number of awakenings and movements. l-Dopa treatment resulted in a partial but significant improvement of almost all sleep parameters. PPN lesion induced a transient decrease in REM sleep and in slow-wave sleep followed by a slight improvement of sleep quality. Our data demonstrate the efficacy of l-dopa treatment in improving sleep disorders in parkinsonian monkeys, and that adding a cholinergic PPN lesion improves sleep quality after transient sleep impairment.
Immunomodulatory Effects of Probiotic Supplementation in Schizophrenia Patients: A Randomized, Placebo-Controlled Trial

Although peripheral immune system abnormalities have been linked to schizophrenia pathophysiology, standard antipsychotic drugs show limited immunological effects. Thus, more effective treatment approaches are required. Probiotics are microorganisms that modulate the immune response of the host and, therefore, may be beneficial to schizophrenia patients. The aim of this study was to examine the possible immunomodulatory effects of probiotic supplementation in chronic schizophrenia patients. The concentrations of 47 immune–related serum proteins were measured using multiplexed immunoassays in samples collected from patients before and after 14 weeks of adjuvant treatment with probiotics (Lactobacillus rhamnosus strain GG and Bifidobacterium animalis subsp. lactis strain Bb12; n = 31) or placebo (n = 27). Probiotic add–on treatment significantly reduced levels of von Willebrand factor (vWF) and increased levels of monocyte chemotactic protein–1 (MCP–1), brain–derived neurotrophic factor (BDNF), RANTES, and macrophage inflammatory protein–1 beta (MIP–1) beta with borderline significance (P ≤ 0.08). In silico pathway analysis revealed that probiotic–induced alterations are related to regulation of immune and intestinal epithelial cells through the IL–17 family of cytokines. We hypothesize that supplementation of probiotics to schizophrenia patients may improve control of gastrointestinal leakage.
EEG-based quality of teleoperator identification using emotional states model

Fatigue recognition has been a point of interest in many fields including robotic teleoperation. In our previous work we developed a method for evaluating the fatigue state of teleoperator using Quality of teleoperator (QoT). QoT can be estimated based on Radial Basis Function (RBF) network with 5 QoT indicators as input variables namely, long term excitement, short term excitement, boredom, meditation, and frustration. In this paper a new approach is presented for measuring the QoT based on EEG signals which is composed of two stages. In the first stage the EEG signals is projected to the Pleasure, Arousal, and Dominance (PAD) emotional states model. The resulted three emotional states then projected to QoT single dimension scale in the second stage. The experimental results showed improvement in the cross validation error, reduced form 10.7891 to 9.6133, and the complexity of the model is reduced by minimizing the number of variables from five to three. The statistical analysis results show positive correlation between pleasure and dominance states with the QoT value (p<;0.05) and insignificant correlation between arousal state and QoT value.
Auditory brainstem responses to clicks and tone bursts in C57 BL/6J mice

SUMMARY In auditory research, hearing function of mouse mutants is assessed in vivo by evoked potential recording. Evaluation of the response parameters should be performed with reference to the evoked responses recorded from wild-type mice. This study reports normative data calculated on auditory brainstem responses (ABRs) obtained from 20 wild-type C57 BL/6J mice at a postnatal age between 21 and 45 days. Acoustic stimuli consisted tone bursts at 8, 14, 20, 26, 32 kHz, and clicks. Each stimulus was delivered in free field at stimulation intensity starting from a maximum of 100 dB peak equivalent SPL (dB peSPL) at decreasing steps of 10 dB with a repetition rate of 13/sec. Evoked responses were recorded by needle electrodes inserted subcutaneously. At high intensity stimulation, five response waveforms, each consisting of a positive peak and a subsequent negative valley, were identified within 7 msec, and were labelled with sequential capital Roman numerals from I to V. Peak IV was the most robust and stable at low intensities for both tone burst and click stimuli, and was therefore utilized to estimate hearing thresholds. Both latencies and amplitudes of ABR peaks showed good reproducibility with acceptable standard deviations. Mean wave IV thresholds measured across all animals ranged from a maximum of 23 dB peSPL for clicks to a minimum of 7 dB peSPL for 20 kHz-tone burst stimuli. Statistical analysis of the distribution of latencies and amplitudes of peaks from I to V performed for each stimulus type yielded a normative data set which was utilised to obtain the most consistent fitting-curve model. This could serve as a reference for further studies on murine models of hearing loss.
A Power and Delay Optimization Method Using Input Reordering in Cell-Based CMOS Circuits

We present a method for power and delay optimization by input reordering. We observe that the reordering has a significant effect on the power dissipation of the gate which drives the reordered gate. This is because the input capacitance depends on the signal values of other inputs. This property, however, has not been utilized for power reduction. Previous approaches focus on the reduction of the power dissipated by internal capacitances of the reordered gate. We propose a heuristic algorithm considering the total power consumed in the driving gate and the reordered gate. Experimental results using 30 benchmark circuits show that our method reduces the power dissipation in all the circuits by 5.9% on average. There is a possibility that power dissipation is reduced by 22.5% maximum. In the case of delay and power optimization, our method reduces delay by 6.7% and power dissipation by 5.3% on average. key words: input reordering, transistor reordering, power estimation
Treatments for Shoulder Impingement Syndrome

AbstractMany treatments for shoulder impingement syndrome (SIS) are available in clinical practice; some of which have already been compared with other treatments by various investigators. However, a comprehensive treatment comparison is lacking.Several widely used electronic databases were searched for eligible studies. The outcome measurements were the pain score and the Constant–Murley score (CMS). Direct comparisons were performed using the conventional pair-wise meta-analysis method, while a network meta-analysis based on the Bayesian model was used to calculate the results of all potentially possible comparisons and rank probabilities.Included in the meta-analysis procedure were 33 randomized controlled trials involving 2300 patients. Good agreement was demonstrated between the results of the pair-wise meta-analyses and the network meta-analyses. Regarding nonoperative treatments, with respect to the pain score, combined treatments composed of exercise and other therapies tended to yield better effects than single-intervention therapies. Localized drug injections that were combined with exercise showed better treatment effects than any other treatments, whereas worse effects were observed when such injections were used alone. Regarding the CMS, most combined treatments based on exercise also demonstrated better effects than exercise alone. Regarding surgical treatments, according to the pain score and the CMS, arthroscopic subacromial decompression (ASD) together with treatments derived from it, such as ASD combined with radiofrequency and arthroscopic bursectomy, showed better effects than open subacromial decompression (OSD) and OSD combined with the injection of platelet-leukocyte gel. Exercise therapy also demonstrated good performance. Results for inconsistency, sensitivity analysis, and meta-regression all supported the robustness and reliability of these network meta-analyses.Exercise and other exercise-based therapies, such as kinesio taping, specific exercises, and acupuncture, are ideal treatments for patients at an early stage of SIS. However, low-level laser therapy and the localized injection of nonsteroidal anti-inflammatory drugs are not recommended. For patients who have a long-term disease course, operative treatments may be considered, with standard ASD surgery preferred over arthroscopic bursectomy and the open surgical technique for subacromial decompression. Notwithstanding, the choice of surgery should be made cautiously because similar outcomes may also be achieved by the implementation of exercise therapy.