Miracle-dz/UniFAR-FTUs huggingface dataset:
Composite health behaviour classifier as the basis for targeted interventions and global comparisons in men's health.

INTRODUCTION Lifestyle-related diseases are the leading cause of death among North American men. We evaluated health behaviours and their predictors that contribute to morbidity and mortality among Canadian men as a means to making recommendations for targeted interventions. METHODS A cross-sectional analysis of Canadian men drawn from 5362 visitors to our online survey page was conducted. The current study sample of 2000 men (inclusion: male and >18 years; exclusion: incomplete surveys) were stratified to the 2016 Canadian census. The primary outcome was the number of unhealthy men classified using our Canadian Composite Classification of Health Behaviour (CCCHB) score. Secondary outcomes included the number of men with unhealthy exercise, diet, smoking, sleep, and alcohol intake, as well as socioeconomic and demographic factors associated with unhealthy behaviours to be used for targeting future interventions. RESULTS Only 118/2000 (5.9%) men demonstrated 5/5 healthy behaviours, and 829 (41.5%) had 3/5 unhealthy behaviours; 391 (19.6%) men currently smoked, 773 (38.7%) demonstrated alcohol overuse, 1077 (53.9%) did not get optimal sleep (<7 or >9 hours per night), 977 (48.9%) failed to exercise >150 minutes/week, and 1235 (61.8%) had an unhealthy diet. Multivariate analysis indicated that men with high school education were at increased risk of unhealthy behaviours (odds ratio [OR] 1.58; 95% confidence interval [CI] 1.15-2.18; p=0.005), as were men living with relatives (OR 2.10; 95% CI1.04-4.26; p=0.039), or with their partner and children (OR 1.34; 95% CI 1.02-1.76; p=0.034). CONCLUSIONS An overwhelming 41.5% of Canadian men had 3/5 unhealthy behaviours, affirming the need for targeted lifestyle interventions. Significant health inequities within vulnerable subgroups of Canadian men were identified and may guide the content and delivery of future interventions.
Characterization of Distributed Microfabricated Strain Gauges on Stretchable Sensor Networks for Structural Applications

Smart structures mimic biological systems by using thousands of sensors serving as a nervous system analog. One approach to give structures this sensing ability is to develop a multifunctional sensor network. Previous work has demonstrated stretchable sensor networks consisting of temperature sensors and impact detectors for monitoring external environments and interacting with other objects. The objective of this work is to develop distributed, robust and reliable strain gauges for obtaining the strain distribution of a designated region on the target structure. Here, we report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices and 8 piezoelectric transducers symmetrically distributed over an area of 150 × 150 mm to map and quantify multiple physical stimuli with a spatial resolution of 2.5 × 2.5 mm. We performed computational modeling of the network stretching process to improve measurement accuracy and conducted experimental characterizations of the microfabricated strain gauges to verify their gauge factor and temperature coefficient. Collectively, the results represent a robust and reliable sensing system that is able to generate a distributed strain profile of a common structure. The reported strain gauge network may find a wide range of applications in morphing wings, smart buildings, autonomous cars and intelligent robots.
Dynamic Sparse Graph for Efficient Deep Learning

We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of lightweight models for the deployment onto embedded devices. However, most of the previous studies optimize for inference while neglect training or even complicate it. Training is far more intractable, since (i) the neurons dominate the memory cost rather than the weights in inference; (ii) the dynamic activation makes previous sparse acceleration via one-off optimization on fixed weight invalid; (iii) batch normalization (BN) is critical for maintaining accuracy while its activation reorganization damages the sparsity. To address these issues, DSG activates only a small amount of neurons with high selectivity at each iteration via a dimension-reduction search (DRS) and obtains the BN compatibility via a double-mask selection (DMS). Experiments show significant memory saving (1.7-4.5x) and operation reduction (2.3-4.4x) with little accuracy loss on various benchmarks.
May Deficiency of Catecholamine Catabolizer Renalase be the Underlying Primary Reason of Autism

Citation: Aydin S (2018) May Deficiency of Catecholamine Catabolizer Renalase be the Underlying Primary Reason of Autism? J Clin Mol Endocrinol 3:1. Copyright: © 2018 Aydin S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
HADOOP-BASED DISTRIBUTED SYSTEM FOR ONLINE PREDICTION OF AIR POLLUTION BASED ON SUPPORT VECTOR MACHINE

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.
PolyHJ: A Polymorphic Main-Memory Hash Join Paradigm for Multi-Core Machines

Relational join is a central data management operation that influences the performance of almost every database query. In this paper, we show that different input features and hardware settings necessitate different main-memory hash join models. Subsequently, we identify four particular models by which hash-based join algorithms can be executed and propose a novel polymorphic paradigm that dynamically subscribes to the best model given workload and hardware characteristics. We refer to our polymorphic paradigm as PolyHJ and suggest a corresponding implementation, which consists of two mechanisms, namely, in-place, cache-aware partitioning (ICP) and collaborative building and probing (ColBP). ICP and ColBP serve substantially in reducing multi-core cache misses, memory bandwidth usage, and cross-socket traffic. Our experimental results demonstrate that PolyHJ can successfully select the right models for the tested workloads and significantly outperform the current state-of-the-art hash-based join schemes.
A SELF-LOADING , SELF-METERING AND SELF-MIXING MICROFLUIDIC REACTOR ARRAY FOR BIOCHEMICAL SCREENING

A power-free, self-contained microfluidic reactor array is developed for high-throughput analyses. This device needs neither external power nor complex instruments for fluid handling. Thus, it offers an easy-touse and inexpensive way to perform multiple nanoliter-volume distinct reactions in parallel format. We demonstrate the power of this device by testing it for rapid screening of protein crystallization conditions.
Biochemical and hematological indices of normal human subjects, pre-treatment, and combined therapy (semi-vegan diet and metformin)-administered diabetic patients of Imo State University Teaching Hospital Orlu, Nigeria

* ,4 Department of Biochemistry, School of Science, Federal University of Technology Owerri, Nigeria. 1 Department of Chemistry, School of Science, Federal University of Technology Owerri, Nigeria. 2 Physiology and Biomedical Research Unit, Department of Biological Sciences, College of Basic and Applied Sciences, Rhema University Aba, Abia State, Nigeria. 3 Department of Optometry, School of Health Technology, Federal University of Technology Owerri. Imo State. Nigeria.
Extinction thresholds in host-parasite dynamics

In this paper, we review the main thresholds that can infl uence the population dynamics of host–parasite relationships. We start by considering the thresholds that have infl uenced the conceptualisation of theoretical epidemiology. The most common threshold involving parasites is the host population invasion threshold, but persistence and infection thresholds are also important. We recap how the existence of the invasion threshold is linked to the nature of the transmission term in theoretical studies. We examine some of the main thresholds that can affect host population dynamics including the Allee effect and then relate these to parasite thresholds, as a way to assess the dynamic consequences of the interplay between host and parasite thresholds on the fi nal outcome of the system. We propose that overlooking the existence of parasite and host thresholds can have important detrimental consequences in major domains of applied ecology, including in epidemiology, conservation biology and biological control.
Phase-field crystal modeling and classical density functional theory of freezing

In this paper the relationship between the classical density functional theory of freezing and phase-field modeling is examined. More specifically a connection is made between the correlation functions that enter density functional theory and the free energy functionals used in phase-field crystal modeling and standard models of binary alloys i.e., regular solution model. To demonstrate the properties of the phase-field crystal formalism a simple model of binary alloy crystallization is derived and shown to simultaneously model solidification, phase segregation, grain growth, elastic and plastic deformations in anisotropic systems with multiple crystal orientations on diffusive time scales.
User Context : An Explanatory Variable in Phishing Susceptibility

Extensive research has been performed to examine the effectiveness of phishing defenses, but much of this research was performed in laboratory settings. In contrast, this work presents 4.5 years of workplace-situated, embedded phishing email training exercise data, focusing on the last three phishing exercises with participant feedback. The sample was an operating unit consisting of approximately 70 staff members within a U.S. government research institution. A multiple methods assessment approach revealed that the individual’s work context is the lens through which email cues are interpreted. Not only do clickers and non-clickers attend to different cues, they interpret the same cues differently depending on the alignment of the user’s work context and the premise of the phishing email. Clickers were concerned over consequences arising from not clicking, such as failing to be responsive. In contrast, non-clickers were concerned with consequences from clicking, such as downloading malware. This finding firmly identifies the alignment of user context and the phishing attack premise as a significant explanatory factor in phishing susceptibility. We present additional findings that have actionable operational security implications. The long-term, embedded and ecologically valid conditions surrounding these phishing exercises provided the crucial elements necessary for these findings to surface and be confirmed. Keywords—decision-making, embedded phishing awareness training, user-centered approach, survey instrument, long-term assessment, operational data, trial deployment, network security, security defenses
Physicochemical Properties and Control Release of Aloe Vera (Aloe barbadensis Miller) Bioactive Loaded Poly (Lactic Co-Glycolide Acid) Synthesized Nanoparticles

Nano-encapsulation is a platform which offers a promising application for control release and the delivery of drugs in pharmaceuticals and antioxidant/ antimicrobial in food systems. Poly (lactic-co-glycolide acid) (PLGA) is a biodegradable and biocompatible co-polymer of lactic acid and glycolic acid which is used for synthesizing food based polymeric nanoparticles (NP). The aim of this study was to evaluate the morphological and physicochemical properties and the controlled release of bioactive components derived from Aloe vera gel loaded PLGA NP. The results shows the mean hydrodynamic diameter of the unloaded NP is 103 nm which is significantly (p p > 0.05) inhibition of the antioxidant potential was observed with loaded NP. The entrapment efficiency for the FDG synthesized was 87%, and the burst effect was observed after 4 h as a result of the encapsulation effect. The release kinetics of bioactive is govern by the combination of mass diffusion and capillary action.
Distribution and Movements of Brood-Rearing Lesser Snow Geese in Relation to Petroleum Development in Arctic Alaska

Environmental scientists were concerned that construction of the Endicott Project in the outer Sagavanirktok River delta, east of Prudhoe Bay, Alaska, would obstruct brood-rearing movements of Alaska's largest established nesting population of lesser snow geese ( Chen caerulescens caerulescens ). During 1980-84, prior to the Endicott Project, the distribution of snow goose flocks during the brood-rearing period was generally less than 5 km from the coast and 15 km from the nesting colony. Prior to development, the average distance between brood-rearing areas used during consecutive years by individual banded birds was 4.7±2.9 km (mean ±s.d.). After development commenced during winter 1984-85, the distribution of brood-rearing flocks expanded inland as far as 9 km and eastward as far as 32 km from the nesting colony. The average distance between brood-rearing areas used during consecutive years by individual banded birds was 8.0±7.5 km. The proportion of brood-rearing snow geese captured on the east side of the Endicott Road and Causeway (east of the nesting colony) during the post-construction years (1985-93) was not significantly different from the proportion using the same area before construction (1980-84). The hypothesis that the Endicott Road and Causeway obstructed eastward movements of brood-rearing snow geese can be rejected.
Industrial Nanoparticle Technology Development

C ubosomes are nanoparticles but instead of the solid particles usually encountered, cubosomes are self-assembled liquid crystalline particles with a solid-like rheology that provides unique properties of practical interest. The discovery of cubosomes is a unique story and spans the fields of food science, differential geometry, biological membranes, and digestive processes. Despite the early realization of their potential, the manufacture of cubosomes on a large scale embodied difficulty because of their complex phase behaviour and viscous properties. This article reviews the development of several processes for practical manufacture and use of cubosomes in the consumer product industry, the intellectual property that resulted, and the eventual use of the patented technology.
Evaluating Buffering Schemes For Next-Generation Internet Qos Router

This paper presents performance evaluation of buffer management schemes for routers in Differentiated Services domain. Two variants of Random Early Detection (RED) proposed for multiqueue, traffic-penalizing network were considered. These are RED In-profile-Outof-profile coupled (RIO-C) and RED Inprofile-Out-of-profile Decoupled (RIO-D). In order to implement these variants, weighted round robin scheduling discipline was employed. The experiments were done using n2. Two physical queues were maintained at the core router holding UDP traffics generated by constant bit rate traffic agent. Simulations were run using different RED thresholds for virtual queues. Analysis of traced data revealed that RIO-C has the lower loss rate 0% than RIO-D (0.08%) for in-profile traffics. On the other hand, in terms of which scheme really applies penalty to violating traffic sources, RIO-D proves better. The study therefore concludes that if QoS is to be supported in the next generation Internet with strict adherence to traffic profiles, RIO-D could be dependent upon.
Selectable Traits in Sorghum Genotypes for Tolerance to Salinity Stress

Sorghum [Sorghum bicolor (L.) Moench] is moderately tolerant to salinity and it is important as a candidate crop for both fodder and grain in salt-affected areas. This pot experiment was conducted at Research Greenhouse of College of Agriculture, Shiraz University, Iran, to evaluate the relative effectiveness of biochemical traits and stress tolerance indices contributing to genotypic differences in salinity tolerance in 30 lines and 14 cultivars of sorghum. In addition, a new indicator, Storage Factor Index (SFI), was defined and used to quantify the Na partitioning between shoot and root. Among the indices, stress tolerance index was found useful as a selection criterion. Furthermore, the tolerant genotypes had higher K/Na ratio in shoot and root with greater SFI, indicating that most of Na was stored in their roots. Although peroxidase and superoxide dismutase were enhanced under salinity conditions in both sensitive and tolerant genotypes, only Catalase (CAT) activity was found to be promoted in tolerant lines/cultivars. Proline accumulation did not appear to be related to salinity tolerance in sorghum lines/cultivars. Overall, our findings suggested that salinity tolerance in sorghum genotypes was not only associated with Na exclusion from the shoot, but also with the enhancement of CAT
Hampton: Prototypes and Exemplars: Page 1 Categories, Prototypes and Exemplars

ion depending on the specific conceptual domain. Highly common and highly similar entities (like rain drops) may be represented as an abstract prototype, while familiar and distinctive classes, like the concept of dog for a dog lover would be represented in granular fashion as the collection of individual dog breeds, themselves represented perhaps by prototypes. In addition to these similarity-based models, other concepts involve detailed schematic knowledge of the kind found in science and other academic disciplines, where similarity becomes less relevant and explicit definitions more common. Given the variety and flexibility of the mind in how it provides meaning to the world, it should not be a surprise to find that our words have similar multiplicity in how their meanings are constructed in Hampton: Prototypes and Exemplars: Page 17 the mind. Words are used for many functions, and this necessarily gives rise to a wide range of semantic structures. Hampton: Prototypes and Exemplars: Page 18
Emerging Literature on Cognitive Intervention Techniques for Early Stage Dementia

Dementia is a growing world-wide phenomenon, impacting more than six million people in the United States. Despite its high projected prevalence, it is a significantly under-represented phenomena, with (under)estimate ranging from 15-25% of the general population. The effect of the aging of the population and significant increase in life expectancy has combined to catapult dementia into the range of one of most alarming healthcare problems. Diverse and emerging literature in the area of cognitive prevention/intervention for mild cognitive impairment (MCI)/early stage dementia will be reviewed. Additionally, future research and clinical directions will be explored.
Human Papilloma Virus Genotypes and Intraepithelial Neoplasia in HIV Positive and Negative Patients with External Condylomata Acuminata

In order to identify differences in clinical, histopathology and virology data of patients with external condylomata acuminata (ECA) by HIV status, 47 HIV positive patients and 42 HIV negative patients were enrolled. Socio-demographic and clinical data was recorded. Histopathology study and HPV detection (Roche Linear Array genotyping assay) were performed. HIV positive patients were mainly single, MSM, with history of STI, multiple sexual partners, receiving antiretroviral therapy and with recurrent peri-anal ECA. HPV-16 and HPV-61 were associated to this group while HPV-6 was found in HIV negative patients with ECA. Intraepithelial neoplasia (IEN) was found in 5.6% of patients, associated to HIV and HPV-16. We found differences in socio-demographic characteristics and HPV genotypes in patients with ECA by HIV status. The association of high grade IEN with HIV infection makes evident the need of defined parameters as markers for early cancer diagnosis.
ANTI-INFLAMMATORY POTENTIAL OF CHLOROPHYTUM BORIVILIANUM SANT. & FERN. ROOT TUBERS

The methanolic extract of Chlorophytum borivilianum Sant. & Fern. root tubers (MECB) (200 & 400mg / Kg b.w., p.o.) were tested in Carrageenan induced rat paw edema and cotton pellet induced granuloma in rats to assess anti-inflammatory potential of MECB. The paw volumes in rats were reduced significantly (P<0.05) as compared to that of control and standard drug. Inhibition of granuloma formation in rats were significant (P<0.05) as compared to that of control and standard drug. This result indicates that the extract is possessing anti-inflammatory potential.
Spam Detection System Using Hidden Markov Model

Spams are the textual context of the system which can damage our system. E-mail is an essential communication tool that has been greatly abused by spam sender to disseminate unwanted information and spread malicious contents to Internet users. Spam filters provide better protective mechanisms that are able to design a system to recognize the spams. We propose an image spam detection system that uses detect spam words. We rely on filtering methods to detect stemming words of spam images and then use Hidden Markov Model of spam filters to detect all the spam images. In the first section of the paper analysis the Introduction of Spam. In the second section of the paper described the related work. In the third section analyzed the problem formulation. In the fourth section described spam detection techniques, different steps for spam detection. In the fifth section described methodology of spam detection and then the different spam feature extraction. Finally present the Conclusion & future works with the references.