nils-herrmann/clean_scirepeval huggingface dataset:
INCREASING PREVALENCE OF ADDISON DISEASE: RESULTS FROM A NATIONWIDE STUDY.
Venue: Endocrine practice official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists

Primary adrenal insufficiency is a life threatening endocrine disease unless properly treated. However, few studies on the prevalence, concomitances of the disease, and prescribing of drugs have been published. The goal of the study was to establish the prevalence of primary adrenal insufficiency in Iceland and additionally, to study the most common concomitant diseases in patients with primary adrenal insufficiency, as well as the mode of glucocorticoid replacement therapies.  To achieve this, the medical records of all patients in Iceland who had received the International Classification of Diseases and Related Health Problems, 10th Revision, diagnosis code E27, were evaluated for true primary adrenal insufficiency. Additionally, these records were evaluated for concomitant diseases, as well as the mode of glucocorticoid replacement therapy. The study covered the whole population of Iceland over 18 years of age. It was thus a nationwide study. The records were retrieved from large hospitals and clinics and every practicing specialist in endocrinology.  Primary adrenal insufficiency was found in 53 individuals, 26 women and 27 men, yielding a prevalence of 22.1 per 100,000 population. Hypothyroidism was by far the most common concomitant disease. Most patients had their glucocorticoid deficiency replaced with short acting glucocorticoids.  The prevalence of primary adrenal insufficiency in Iceland is higher than in earlier reports, with comorbidities being in line with recent studies. Treatment is according to the latest protocols.
The Effect of Population Aging on Economic Growth, the Labor Force and Productivity

Population aging is widely assumed to have detrimental effects on economic growth yet there is little empirical evidence about the magnitude of its effects. This paper starts from the observation that many U.S. states have already experienced substantial growth in the size of their older population and much of this growth was predetermined by historical trends in fertility. We use predicted variation in the rate of population aging across U.S. states over the period 1980 2010 to estimate the economic impact of aging on state output per capita. We find that a 10% increase in the fraction of the population ages 60+ decreases the growth rate of GDP per capita by 5.5% Two thirds of the reduction is due to slower growth in the labor productivity of workers across the age distribution, while one third arises from slower labor force growth. Our results imply annual GDP growth will slow by 1.2 percentage points this decade and 0.6 percentage points next decade due to population aging.
The relationships between population factors and China's carbon emissions: Does population aging matter?

Reducing carbon emissions and managing the aging crisis represent two major challenges in China that involve various requirements for continued economic growth. This paper investigated the relationships between population factors and carbon emissions and further explored the impact of population aging on carbon emissions at the national and regional levels based on the STIRPAT model and provincial panel data from China. Our results show that at the national level, population aging and population quality are positively correlated with China's carbon emissions. The impact of the population living standard on carbon emissions exhibits an urban rural difference. At the regional level, the impact of population aging on carbon emissions exhibits regional differences.
Unearthing the Antibacterial Mechanism of Medicinal Clay: A Geochemical Approach to Combating Antibiotic Resistance
Venue: Scientific reports

Natural antibacterial clays, when hydrated and applied topically, kill human pathogens including antibiotic resistant strains proliferating worldwide. Only certain clays are bactericidal; those containing soluble reduced metals and expandable clay minerals that absorb cations, providing a capacity for extended metal release and production of toxic hydroxyl radicals. Here we show the critical antibacterial components are soluble Fe2+ and Al3+ that synergistically attack multiple cellular systems in pathogens normally growth limited by Fe supply. This geochemical process is more effective than metal solutions alone and provides an alternative antibacterial strategy to traditional antibiotics. Advanced bioimaging methods and genetic show that Al3+ misfolds cell membrane proteins, while Fe2+ evokes membrane oxidation and enters the cytoplasm inflicting hydroxyl radical attack on intracellular proteins and DNA. The lethal reaction precipitates Fe3+ oxides as biomolecular damage proceeds. Discovery of this bactericidal mechanism demonstrated by natural clays should guide designs of new mineral based antibacterial agents.
Evaluation of antibacterial activity and synergistic effect between antibiotic and the essential oils of some medicinal plants

To demonstrate the in vitro antibacterial properties of five essential oils against ten bacterial strains and study the synergistic effect of the combination of essential oils with standard antibiotics.  Origanum compactum Chrysanthemum coronarium Thymus willdenowii Boiss, Melissa officinalis and Origanum majorana L. were used alone and combined used with standard antibiotics to evaluate their antimicrobial activities. The disk diffusion method was employed.  The results showed that the combined application of the essential oils of the plants with antibiotics led to a synergistic effect in some cases, but antagonistic effect was also observed in some bacteria.  This study shows that the combination of essential oils of the five plants with antibiotics may be useful in the fight against emerging microbial drug resistance.
Development of a Spatially Resolved ^3$$3He Quasi Particle Detector

Andreev surface bound sates are known to exist on the boundaries of superfluid ^3$$3He B. However, the detailed nature of their interaction with bulk quasi particles is not well known. In a manner similar to angle resolved photo emission spectroscopy, surface states can be probed by measuring the change in momentum of bulk quasi particles scattered from the surface. In order to make such a measurement, we have designed a spatially resolved quasi particle detector. The detector consists of an array of micro machined resonators, which are sensitive to quasi particle flux. The detector is based on previously developed micro machined resonators, which have been successfully used to study superfluid ^3$$3He B and ^4$$4He. Presented here is the design of the detector and the fabrication procedure.
Weyl gauge invariant proof of the Spin Statistics Theorem

The traditional standard theory of quantum mechanics is unable to solve the spin statistics problem, i.e. to justify the utterly important \qo{Pauli Exclusion Principle} but by the adoption of the complex standard relativistic quantum field theory. In a recent paper (Ref.  we presented a proof of the spin statistics problem in the nonrelativistic approximation on the basis of the \qo{Conformal Quantum Geometrodynamics} In the present paper, by the same theory the proof of the Spin Statistics Theorem is extended to the relativistic domain in the general scenario of curved spacetime. The relativistic approach allows to formulate a manifestly step by step Weyl gauge invariant theory and to emphasize some fundamental aspects of group theory in the demonstration. No relativistic quantum field operators are used and the particle exchange properties are drawn from the conservation of the intrinsic helicity of elementary particles. It is therefore this property, not considered in the Standard Quantum Mechanics, which determines the correct spin statistics connection observed in  The present proof of the Spin Statistics Theorem is simpler than the one presented in Ref.  because it is based on symmetry group considerations only, without having recourse to frames attached to the particles.
Social Role Theory of Sex Differences

Social role theory is a social psychological theory that pertains to sex differences and similarities in social behavior. Its key principle is that differences and similarities arise primarily from the distribution of men and women into social roles within their society. Through socialization and the formation of gender roles, the behaviors of men and women generally support and sustain the division of labor. In industrialized economies, for example, social roles are organized so that women are more likely than men to be homemakers and primary caretakers of children and to hold caretaking jobs in the paid economy. In contrast, men are more likely than women to be primary family providers and to assume full time roles in the paid economy, often ones that involve physical strength, assertiveness, or leadership skills.  gender; gender difference; gender analysis; gender division of labor; gender equality
Exploring shapelet transformation for time series classification in decision trees
Venue: Knowl. Based Syst.

In data mining tasks, time series classification has been widely investigated. Recent studies using non symbolic learning algorithms have reported significant results in terms of classification accuracy. However, in applications related to decision making processes it is necessary to understand of reasoning used in the classification process. To take this into account, the shapelet primitive has been proposed in the literature as a descriptor of local morphological characteristics. On the other hand, most of the existing work related to shapelets has been dedicated to the development of more effective approaches in terms of time and accuracy, disregarding the need for the classifiers interpretation. In this work, we propose the construction of symbolic models for time series classification using shapelet transformation. Moreover, we develop strategies to improve the representation quality of the shapelet transformation, using feature selection algorithms. We performed experimental evaluations comparing our proposal with the state of the art algorithms present in the time series classification literature. Based upon the experimental results, we argue that the improvement in shapelet representation can contribute to the construction of more interpretable and competitive classifiers in comparison to non symbolic methods.
The Potential of Bamboo as Building Material in Organic Shaped Buildings

Bamboo has been widely known as a sustainable building material due to some reasons among others are bamboo can be easily cultivated and harvested in a relative short time and can be reused. Bamboo as building materials is easy to bend and lithe. Those characters are very suitable for organic shaped building construction. This paper attempts to discuss how bamboo is being used in organic shaped building. Several case studies are taken to describe the relation between shape, structure, construction and joint system. It will classify how bamboo is formed in curved thus result is an organic form. The paper result will show that bamboo can be a potential building material for organic shaped buildings and become an alternative building material other than steel and concrete.
Ultra high amplified strain on 200 mm optical Germanium On Insulator (GeOI) substrates: towards CMOS compatible Ge lasers
Venue: SPIE OPTO

Currently, one of the main challenges in the field of silicon photonics is the fabrication of efficient laser sources compatible with the microelectronic fabrication technology. An alternative to the complexity of integration of group III V laser compounds is advancing from high tensile strains applied to germanium leading to improved emission properties by transforming the material from an indirect to a direct bandgap semiconductor. Theory predicts this transformation occurs at around 4.7% uniaxial tensile strain or 2.0% bi axial tensile strain. Here, we report on ultrahigh strains obtained by amplifying the residual strain from novel optical Germanium On Insulator (GeOI) substrates fabricated by Smart CutTM technology and patterned with micro bridges and micro crosses. The high crystalline quality of the GeOI layers dramatically declined the mechanical failure limits when liberating the Ge microbridges. Record level Raman shift of 8.1 cm 1 for biaxial (micro crosses) and 8.7 cm 1 for uniaxial stress (micro bridges) were reached by carefully designing the geometry of the micro structures. The photoluminescence (PL) evolution is compared to theoretical calculations based on the tight binding model revealing a detailed understanding of the influence of strain on the germanium optical properties.
A signal interpolation method for Fabry Perot interferometer utilized in mechanical vibration measurement

In this investigation, a self developed signal processing method for Fabry Perot interferometer is proposed which can be utilized for high speed dynamic displacement measurements, e.g. mechanical vibration measurements. The lookup table (LUT) integrated with the interference intensity equation has been employed for the interpolation processing of interference signals. With the aid of this method, the interpolation error has been reduced by 40% in comparison with that resulting from the commercial sinusoidal signal processing module. By operations of Fast Fourier Transform (FFT) the displacement measurement distribution can be converted into the frequency spectrum diagram. The interpolation resolution of the proposed interferometric displacement measurement system is about 0.1 nm. Experimental results demonstrate that this interferometer system is available for measuring frequencies till 2 kHz where its corresponding amplitude is 0.15 mm.
Non Parametric Signal Interpolation

This paper considers the problem of interpolation (smoothing) of a partially observable Markov random sequence. For the dynamic observation models, an equation for the interpolation of the posterior probability density is derived. The main goal of this paper is to consider the smoothing prob lem for the case of unknown distributions of an unobservable component of a random Markov sequence. Successful results were obtained for the strongly stationary Markov processes with mixing and for the conditional density be longing to the exponential family of densities. The resulting method is based on the empirical Bayes approach and kernel nonparametric estimation. The equation for the optimal smoothing estimator is derived in the form indepen dent of unknown distributions of an unobservable process. Such form of the equation allows to use the nonparametric estimates for some conditional func tionals in the equation given a set of dependent observations. To compare the nonparametric estimators with optimal mean square smoothing estimators in Kalman scheme, simulation results are given.
Attribute Based Partial Geo Replication System
Venue: 2016 IEEE International Conference on Cloud Engineering (IC2E)

Existing partial geo replication systems do not always provide optimal cost or latency, because their replication decisions are based on statically established data access popularity metrics, regardless of the application types. We demonstrate that additional reduction in cost and latency can be achieved by (1) using the right object attributes for making replication decisions for each type of application, (2) using multi attribute based replications, and (3) combining the popularity based but reactive approach with the more random but proactive approach to data replication. Toward this end, we propose Acorn, an Attribute based COntinuous partial geo ReplicatioN system, and its prototype implementation based on Apache Cassandra. Experiments with two types of global scale, data sharing applications demonstrate up to 54% and 90% cost overhead reduction over existing systems or 38% and 91% latency overhead reduction.
Achieving Causal Consistency under Partial Replication for Geo distributed Cloud Storage

Causal consistency has emerged as an attractive middle ground to architecting cloud storage systems, as it allows for high availability and low latency, while supporting stronger than eventual consistency semantics. However, causally consistent cloud storage systems have seen limited deployment in practice. A key factor is these systems employ full replication of all the data in all the data centers (DCs) incurring high cost. A simple extension of current causal systems to support partial replication by clustering DCs into rings incurs availability and latency problems. We propose Karma, the first system to enable causal consistency for partitioned data stores while achieving the cost advantages of partial replication without the availability and latency problems of the simple extension. Our evaluation with 64 servers emulating 8 geo distributed DCs shows that Karma (i) incurs much lower cost than a fully replicated causal store (obviously due to the lower replication factor) and (ii) offers higher availability and better performance than the above partial replication extension at similar costs.
wet gangrene and transmetatrsal amputation how and why

Diabetic foot ulcer occurs in up to 25% of diabetic patients. Foot ulcer affects patients' quality of life, results to disability and increases morbidity and mortality. In cases of wet gagrene, amputation is the only real solution. Unfortunately, amputation rates among diabetic patients reach 11% In this case, a 57 year old diabetic male presented with wet gangrene and abscess of right lower limb. Past medical history included a successful angioplasty of right common iliac artery for critical stenosis. We present the typical case of diabetic foot, which was treated with transmetatarsal amputation, antibiotics and daily cleansing and addressing changes for eighteen (18) months.
Guided Flight Discovery Instrument Commercial

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Active fragments guided drug discovery and design of selective tropane alkaloids using ultra high performance liquid chromatography quadrupole time of flight tandem mass spectrometry coupled with virtual calculation and biological evaluation
Venue: Analytical and Bioanalytical Chemistry

AbstractTropane alkaloids (TAs) rich in the plant of Physochlaina infundibularis Kuang, which is named Huashanshen (HSS) in China, showed good effects on types of spasms. However, no data were collected to explore the relationship between the specificity for muscarinic receptor subtypes and the structures of these TAs. To address this issue, an extracted ion chromatogram (EIC) strategy using ultra high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UPLC Q/TOF MS) based on the fragmentation behavior of the TA standards was established to rapidly capture the varied TAs from HSS. Based on the provided structural information of diagnostic ions or neutral loss, 29 TAs were efficiently profiled, especially some trace ingredients. In additional, via virtual validation combined with molecular dynamic simulation, approximately a dozen alkaloids were found with high selectivity for muscarinic receptors. In additional, N acetyl convolicine was chosen for selectivity evaluation of M2 or M3 receptors through the use of a dual luciferase reporter assay system at the cellular level and an ACh induced constricted strip test in vitro. After summarizing the active fragments and the structure activity relationship (SAR) information, a new modified TA that takes advantage of both the high affinity and high selectivity for M3 receptors was proposed and evaluated successfully. This study provided an effective approach for the discovery and design of natural products based on highly selective drugs by UPLC Q/TOF MS coupled with virtual calculation and biological evaluation. Graphical AbstractActive fragments guided strategy for selective inhibitors from HSS
Analysis of the Effectiveness of Training Programs and Commitment of Employee to Organization

Training is a very important aspect of an organization to improve employee performance. This study aimed to examine the relationship between the effectiveness of employee training programs with organization commitment in the education sector in Malaysia which was influenced by the motivation of employees in training. The variables identified in the study to measure the effectiveness of training programs are the course content of training, the role of coach and support from the supervisor as independent variables and motivation to study as mediator variables. This study used a questionnaire, observation and interview. The questionnaire involves 208 employees in the education sector in several states in Malaysia. Results of hypothesis testing uses the route model Smart Partial Least Squares (SmartPLS) which confirms that the role of the coach and support from the supervisor acts as an important predictor of organizational commitment and motivation to study acting effectively as a mediator variables between the relationship of training programs and organizational commitment. Meanwhile, the course content is unable to act as an important predictor of employee commitment to the organization.
Extended Dependency Based Word Embeddings for Aspect Extraction
Venue: ICONIP

Extracting aspects from opinion reviews is an essential task of fine grained sentiment analysis. In this paper, we introduce outer product of dependency based word vectors and specialized features as representation of words. With such extended embeddings composed in recurrent neural networks, we make use of advantages of both word embeddings and traditional features. Evaluated on SemEval 2014 task 4 dataset, the proposed method outperform existing recurrent models based methods, achieving a result comparable with the state of the art method. It shows that it is an effective way to achieve better extraction performance by improving word representations.