LLukas22/scidocs huggingface dataset:
Drugs for insomnia.

INTRODUCTION
Sleep is a vital neurochemical process involving sleep-promoting and arousal centers in the brain. Insomnia is a pervasive disorder characterized by difficulties in initiating or maintaining or non-refreshing (poor quality) sleep and clinically significant daytime distress. Insomnia is more prevalent in women and old age and puts sufferers at significant physical and mental health risks. This review summarizes published data on the current and emerging insomnia drug classes, rationale for development and associated risks/benefits. (Summary of Product Characteristics and Medline search on "hypnotic" or specific drug names and "Insomnia").


AREAS COVERED
GABA(A) receptor modulators facilitate sleep onset and some improve maintenance but increase risk of dependence, memory, cognitive and psychomotor impairments, falls, accidents and mortality. Melatonin receptor agonists improve quality of sleep and/or sleep onset but response may develop over several days. They have more benign safety profiles and are indicated for milder insomnia, longer usage and (prolonged release melatonin) older patients. Histamine H-1 receptor antagonists improve sleep maintenance but their effects on cognition, memory and falls remain to be demonstrated. Late-stage pipeline orexin OX1/OX2 and serotonin 5HT2A receptor antagonists may hold the potential to address several unmet needs in insomnia pharmacotherapy but safety issues cast some doubts over their future.


EXPERT OPINION
Current and new insomnia drugs in the pipeline target different sleep regulating mechanisms and symptoms and have different tolerability profiles. Drug selection would ideally be based on improvement in the quality of patients' sleep, overall quality of life and functional status weighed against risk to the individual and public health.
A century of research on the amoeboflagellate genus Naegleria

The amoeboflagellate genus Naegleria contains pathogenic and nonpathogenic species. As most species are morphologically indistinguishable, species are defined and identified by molecular methods. For routine identification, isoenzyme analyses are performed. For the description of a new species, sequences of ribosomal DNA are increasingly used and the analyses of these sequences also allow us to define the phylogenetic relationships between species and strains. In the present monograph 27 Naegleria lineages are discussed and identified as separate species. Using molecular methods, Naegleria spp. have been identified which either form dividing flagellates or which do not form flagellates at all, thus contradicting the accepted definition of the genus. Willaertia, which forms dividing flagellates, is the genus that is the closest relative of the genus Naegleria. The genus Naegleria has some particularities in its molecular biology, such as circular ribosomal DNA plasmids, group I introns in the small and large subunit ribosomal DNA, and an unusual pyrophosphate-dependant phosphofructokinase. The phylogeny of the Naegleria spp. is compared to the situation concerning the other genera of the familyVahlkampfiidae. Also discussed is the state of affairs concerning species designation based on phylogeny in the genus Acanthamoeba, another free-living amoeba with species pathogenic to man.
Utility of glycated hemoglobin in diagnosing type 2 diabetes mellitus: a community-based study.

CONTEXT
Although glycated hemoglobin (HbA1c) has recently been incorporated as a diagnostic test by the American Diabetes Association, its validity needs to be established in Asian Indians in a community setting.


OBJECTIVE
The objective of the study was to assess the validity of HbA1c as a screening and diagnostic test in individuals with newly detected diabetes mellitus.


DESIGN AND SETTING
Community based randomized cross sectional study in urban Chandigarh, a city in north India, from April 2008 to August 2009.


SUBJECTS
Subjects included 1972 subjects aged 20 yr or older.


INTERVENTION
Intervention included an oral glucose tolerance test and glycated hemoglobin in all the subjects.


MAIN OUTCOME MEASURES
Utility of HbA1c as a diagnostic method in newly detected diabetes mellitus subjects was evaluated.


RESULTS
Using World Health Organization criteria for diagnosis of diabetes mellitus, 134 (6.7%) had newly detected diabetes mellitus, 192 (9.7%) known diabetes mellitus, 329 (16.6%) prediabetes, and 1317 (69.4%) were normal of 1972 people screened. Using only the ADA criteria, 38% people were underdiagnosed. An HbA1c level of 6.1% had an optimal sensitivity and specificity of 81% for diagnosing diabetes. A HbA1c level of 6.5% (+/-2 SD) and 7% (+/-2.7 SD) had sensitivity and specificity of 65 and 88% and 42 and 92%, respectively, with corresponding positive predictive value and negative predictive value of 75.2 and 96.5% and 90.4 and 94.4%, respectively, for diagnosis of newly detected diabetes mellitus.


CONCLUSION
A HbA1c cut point of 6.1% has an optimal sensitivity and specificity of 81% and can be used as a screening test, and a cut point of 6.5% has optimal specificity of 88% for diagnosis of diabetes.
Balanced truncation of linear time-varying systems

In this paper, balanced truncation of linear time-varying systems is studied in discrete and continuous time. Based on relatively basic calculations with time-varying Lyapunov equations/inequalities we are able to derive both upper and lower error bounds for the truncated models. These results generalize well-known time-invariant formulas. The case of time-varying state dimension is considered. Input-output stability of all truncated balanced realizations is also proven. The method is finally successfully applied to a high-order model.
Trends in calving ages and calving intervals for Iranian Holsteins in Fars province, southern Iran

To determine trends in calving ages and calving interval as measures of reproductive performance in Iranian Holsteins in Fars province, southern Iran during 2000–2005. Data about birth dates and previous calving dates for cows which calved between 21 March 2004 to 20 March 2005 were retrieved from the database of the Agricultural Jihad Organization of the province. Overall, calving records for 1134 cows from 12 herds during 2000–2005 were included in the statistical models. Mean calving interval was 435 days in 2000 and declined significantly to 389 days during the study period (P < 0.001). Age at first calving decreased significantly from 30 months in 2000 to 26 months in 2005 (P < 0.001). The corresponding measures for age at second calving were 44 and 41 months, respectively (P = 0.003). Cows which calved during warm months had significantly (P < 0.001) higher calving interval (420 days) compared with calving in cold months (411 days). No significant effect for parity on calving interval was observed in this study. Reproductive performance revealed relatively positive trend in the Iranian Holsteins in Fars province. This could be attributed to better management and nutrition practices during this period and also to the fact that large genetic trend for milk which has been observed in countries with decreasing reproductive performance has not occurred in these herds yet.
Strategic Orientation of Small to Medium Scale Manufacturing Firms in Developing Country: A Case of Auto Parts Manufacturing Small to Medium Enterprises (SMEs) in Pakistan

In today's industrial world, SMEs contribute significantly in almost all the sectors especially manufacturing. Business and functional strategies of these SMEs are important research areas. In this qualitative research, multiple case study method has been utilized to explore the business strategies of SMEs operating in auto parts manufacturing sector of Pakistan. Business strategies of SMEs are analyzed as prospectors, analyzers, defenders and reactors utilizing Miles and Snow typology (1978). The researchers investigate that how this business strategy is linked to functional strategies of SMEs operating in business environment marked with limited domestic market and low export orientation. Though operating in same business environment, SMEs vary in their competitive priorities and methods to achieve organizational goal in not so thriving auto parts sector. Manufacturing, Human Resource Management (HRM) and marketing being important functional strategies are explored to find the differences and similarities even when SMEs follow the similar strategic posture. The use of multiple methods for finding the business strategies of SMEs has been resorted to understand the implicit and explicit ways of conducting business. (Ahmad Y, Pirzada D.Saeed, Khan MT. Strategic Orientation of Small to Medium Scale Manufacturing Firms in Developing Country: A Case of Auto Parts Manufacturing Small to Medium Enterprises (SMEs) in Pakistan. Life Sci J 2013;10(3):517-527) (ISSN:1097-8135). http://www.lifesciencesite.com. 75
An Energy-Balanced Clustering Routing Algorithm for Wireless Sensor Network

In this paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via clusterheads). This paper focuses on reducing the power consumption of wireless sensor networks. Firstly, we proposed an Energy-balanced Clustering Routing Algorithm called LEACH-L, which is suitable for a large scope wireless sensor network. Secondly, optimum hop-counts are deduced. Lastly, optimum position of transmitting node is estimated. Simulation results show that our modified scheme can extend the network lifetime by up to 80% before first node dies in the network. Through both theoretical analysis and numerical simulations, it is shown that the proposed algorithm achieves higher performance than the existing clustering algorithms such as LEACH, LEACH-M.
Biological Eagle-Eye-Based Visual Platform for Target Detection

In this paper, the systematic design and implementation of a biological eagle-eye-based visual platform is presented for target detection. Inspired by the structure and properties of the eagle eye, the hardware configuration of the visual platform is presented, and software programs including image stitching and target detection are developed. The saliency analysis method is adopted to estimate the position of the potential target. The optical triangulation is utilized to obtain the relative azimuth information of the estimated target. Comparative experimental results show that the overall platform is efficient at target detection.
Gene Interaction in DNA Microarray Data Is Decomposed by Information Geometric Measure

MOTIVATION
Given the vast amount of gene expression data, it is essential to develop a simple and reliable method of investigating the fine structure of gene interaction. We show how an information geometric measure achieves this.


RESULTS
We introduce an information geometric measure of binary random vectors and show how this measure reveals the fine structure of gene interaction. In particular, we propose an iterative procedure by using this measure (called IPIG). The procedure finds higher-order dependencies which may underlie the interaction between two genes of interest. To demonstrate the method, we investigate the interaction between the two genes of interest in the data from human acute lymphoblastic leukemia cells. The method successfully discovered biologically known findings and also selected other genes as hidden causes that constitute the interaction.


AVAILABILITY
Softwares are currently not available but are possibly made available in future at http://www.mns.brain.riken.go.jp/~nakahara/DNA_pub.html where all the related information is also linked.
Genome-Wide Alteration of Histone H3K9 Acetylation Pattern in Mouse Offspring Prenatally Exposed to Arsenic

Chronic exposure to arsenic in drinking water, especially in utero or perinatal exposure, can initiate neurological and cognitive dysfunction, as well as memory impairment. Several epidemiological studies have demonstrated cognitive and learning deficits in children with early exposure to low to moderate levels of arsenic, but pathogenic mechanisms or etiology for these deficits are poorly understood. Since in vivo studies show a role for histone acetylation in cognitive performance and memory formation, we examined if prenatal exposure to arsenic causes changes in the epigenomic landscape. We exposed C57Bl6/J mice to 100 μg/L arsenic in the drinking water starting 1 week before conception till birth and applied chromatin immunoprecipitation followed by high-throughput massive parallel sequencing (ChIP-seq) to evaluate H3K9 acetylation pattern in the offspring of exposed and control mice. Arsenic exposure during embryonic life caused global hypo-acetylation at H3K9 and changes in functional annotation with highly significant representation of Krüppel associated box (KRAB) transcription factors in brain samples from exposed pups. We also found that arsenic exposure of adult mice impaired spatial and episodic memory, as well as fear conditioning performance. This is the first study to demonstrate: a) genome wide changes in H3K9 acetylation pattern in an offspring prenatally exposed to arsenic, and b) a connection between moderate arsenic exposure and cognitive impairment in adult mice. The results also emphasize the applicability of Next Generation Sequencing methodology in studies aiming to reveal the role of environmental factors, other than dietary restriction, in developmental reprogramming through histone modifications during embryonic development.
Innovation of value engineering in low-carbon buildings

It is important to low-carbon buildings development that value engineering is a management technique and scientific method developed, firstly was put forward at twentieth century. Combined with the characteristics of low-carbon buildings and value engineering analysis, this paper put forward the theory of low-carbon value and calculation formula for that provide reference ideas for the development of the value engineering theory.
Event summarization for system management

In system management applications, an overwhelming amount of data are generated and collected in the form of temporal events. While mining temporal event data to discover interesting and frequent patterns has obtained rapidly increasing research efforts, users of the applications are overwhelmed by the mining results. The extracted patterns are generally of large volume and hard to interpret, they may be of no emphasis, intricate and meaningless to non-experts, even to domain experts. While traditional research efforts focus on finding interesting patterns, in this paper, we take a novel approach called event summarization towards the understanding of the seemingly chaotic temporal data. Event summarization aims at providing a concise interpretation of the seemingly chaotic data, so that domain experts may take actions upon the summarized models. Event summarization decomposes the temporal information into many independent subsets and finds well fitted models to describe each subset.
SVM based context awareness using body area sensor network for pervasive healthcare monitoring

In the present growing era advancement of computer processing power, data communication capabilities, low power micro electronics devices and micro sensors increases the popularity of wireless sensor network in real life. Body area sensor network is a group of sensor nodes inside and outside the human body for continuous monitoring of health conditions, behavior and activities. Context awareness in pervasive health care is a proactive approach which is different from a conventional event-driven model (for example: visiting doctor when sick) and here we are continuously monitoring a patient health conditions through the use of Body area sensor network. This paper presents a layered architecture of Wide Area Wireless Sensor Body Area Network (WA-WSBAN) along with data fusion techniques, standards and sensor network hardware requirement for context awareness.
 A BodyMedia sensor dataset collected from 9 different sensor nodes has been used to classify the user activities with reference to different sensor readings. The context information derived from the proposed Wide Area Wireless Sensor Body Area Network (WA-WSBAN) architecture may be used in pervasive healthcare monitoring to detect various events and accurate episodes and unusual patterns and activities obtained from the study can be marked for later review. In this research work patient activity and gender classification has been done by using one to all and multi kernel based support vector data classification. The similar practices may be utilized for the study of various observations in real time health care applications and proactive measures may be initiated based on results obtained from data classification.
Diagnosis of stator winding insulation of large generator based on partial discharge measurement

The Stator winding insulation is aged by exposure to a combination of thermal, electrical, mechanical, and environmental stresses. Diagnosis of stator winding insulation is an important measure of ensuring the safe operation and extending the remaining life of large generator. In the paper, two new parameters are proposed for the diagnosis of stator winding insulation based on the test results of multistress accelerating aging test of stator bars. The test results show that the skewness of partial discharge phase resolved distribution and the high-frequency crest of partial discharge (PD) impulse change much more remarkably with the aging time than do the conventional parameters. Therefore, the two parameters could be considered as characteristic parameters for the diagnosis of stator winding insulation of the large generator. With these characteristic parameters, authors successfully assessed the insulation condition of actual stator bars removed from several large generators.
An Investigation of Gamification Typologies for Enhancing Learner Motivation

In this paper we present a new gamified learning system called Reflex which builds on our previous research, placing greater emphasis on variation in learner motivation and associated behaviour, having a particular focus on gamification typologies. Reflex comprises a browser based 3D virtual world that embeds both learning content and learner feedback. In this way the topography of the virtual world plays an important part in the presentation and access to learning material and learner feedback. Reflex presents information to learners based on their curriculum learning objectives and tracks their movement and interactions within the world. A core aspect of Reflex is its gamification design, with our engagement elements and processes based on Marczewski's eight gamification types [1]. We describe his model and its relationship to Bartle's player types [2] as well as the RAMP intrinsic motivation model [3]. We go on to present an analysis of experiments using Reflex with students on two 2nd year Computing modules. Our data mining and cluster analysis on the results of a gamification typology questionnaire expose variation in learner motivation. The results from a comprehensive tracking of the interactions of learners within Reflex are discussed and the acquired tracking data is discussed in context of gamification typologies and metacognitive tendencies of the learners. We discuss correlations in actual learner behaviour to that predicted by gamified learner profile. Our results illustrate the importance of taking variation in learner motivation into account when designing gamified learning systems.
Patient Risk Prediction Model via Top-k Stability Selection

The patient risk prediction model aims at assessing the risk of a patient in developing a target disease based on his/her health profile. As electronic health records (EHRs) become more prevalent, a large number of features can be constructed in order to characterize patient profiles. This wealth of data provides unprecedented opportunities for data mining researchers to address important biomedical questions. Practical data mining challenges include: How to correctly select and rank those features based on their prediction power? What predictive model performs the best in predicting a target disease using those features? In this paper, we propose top-k stability selection, which generalizes a powerful sparse learning method for feature selection by overcoming its limitation on parameter selection. In particular, our proposed top-k stability selection includes the original stability selection method as a special case given k = 1. Moreover, we show that the top-k stability selection is more robust by utilizing more information from selection probabilities than the original stability selection, and provides stronger theoretical properties. In a large set of real clinical prediction datasets, the top-k stability selection methods outperform many existing feature selection methods including the original stability selection. We also compare three competitive classification methods (SVM, logistic regression and random forest) to demonstrate the effectiveness of selected features by our proposed method in the context of clinical prediction applications. Finally, through several clinical applications on predicting heart failure related symptoms, we show that top-k stability selection can successfully identify important features that are clinically meaningful.
New Single Phase Multilevel reduced count devices to Perform Active Power Filter

In this paper a New Single Phase Multilevel Active Power Filter SPMAPF is proposed. The presented structure is characterized by the use of Asymmetric Neutral Point Clamped Five-Level type of Converter which aimed to enhance the power quality affected by nonlinear load. To mitigate harmonics, reduce the exchanged of reactive power and load unbalance under grid and load several conditions; a nonlinear controller based sliding mode is developed and applied. The proposed control law of the SPMAPF has switching components, which incline the system's trajectory on the sliding surface. The obtained results show the effectiveness of the sliding mode control technique to reduce the total harmonic distortion of source current harmonics produced by nonlinear loads and also to reduce the value of the dc bus voltage close to the network voltage. Simulation results are presented and discussed.
Evaluation of RTKLIB's Positioning Accuracy Using low-cost GNSS Receiver and ASG-EUPOS

The paper focuses on a comparison of different positioning methods provided by  free and open source  software  (FOSS)  package  called  RTKLIB.  The  RTKLIB  supports  real‐time  and  post‐processed positioning. The most  important modes  of  operation  tested  by  the  authors  are Kinematic,  Static,  Fixed  and Precise  Point  Positioning  (PPP).  The  data  for  evaluation  were  obtained  from  low‐cost  Global  Navigation Satellite System  (GNSS)  receiver. The  tested  receiver was based on  the u‐bloxʹs LEA‐6T GNSS module. This receiver  provides  different  types  of  information  including  raw  carrier  phase  measurements.  It  gives  the possibility for centimeter‐level precision of positioning. As the supporting source of data ASG‐EUPOS system was used. ASG‐EUPOS is a Polish network of GNSS reference stations providing the real‐time corrections and post processing services for the entire territory of Poland. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 7