Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Growth Hormone Administration on Muscle Strength in Men over 50 Years Old

Growth hormone (GH) use has been speculated to improve physical capacity in subjects without GH deficiency (GHD) through stimulation of collagen synthesis in the tendon and skeletal muscle, which leads to better exercise training and increased muscle strength. In this context, the use of GH in healthy elderly should be an option for increasing muscle strength. Our aim was to evaluate the effect of GH therapy on muscle strength in healthy men over 50 years old. Fourteen healthy men aged 50–70 years were evaluated at baseline for body composition and muscle strength (evaluated by leg press and bench press exercises, which focus primarily on quadriceps—lower body part and pectoralis major—upper body part—muscles, resp.). Subjects were randomised into 2 groups: GH therapy (7 subjects) and placebo (7 subjects) and reevaluated after 6 months of therapy. Thirteen subjects completed the study (6 subjects in the placebo group and 7 subjects in the GH group). Subjects of both groups were not different at baseline. After 6 months of therapy, muscle strength in the bench press responsive muscles did not increase in both groups and showed a statistically significant increase in the leg press responsive muscles in the GH group. Our study demonstrated an increase in muscle strength in the lower body part after GH therapy in healthy men. This finding must be considered and tested in frail older populations, whose physical incapacity is primarily caused by proximal muscle weakness. The trial was registered with NCT01853566.
Diagnostic and prognostic benefits of computed tomography coronary angiography using the 2016 National Institute for Health and Care Excellence guidance within a randomised trial

Objectives To evaluate the diagnostic and prognostic benefits of CT coronary angiography (CTCA) using the 2016 National Institute for Health and Care Excellence (NICE) guidelines for the assessment of suspected stable angina. Methods Post hoc analysis of the Scottish COmputed Tomography of the HEART (SCOT-HEART) trial of 4146 participants with suspected angina randomised to CTCA. Patients were dichotomised into NICE guideline-defined possible angina and non-anginal presentations. Primary (diagnostic) endpoint was diagnostic certainty of angina at 6 weeks and prognostic endpoint comprised fatal and non-fatal myocardial infarction (MI). Results In 3770 eligible participants, CTCA increased diagnostic certainty more in those with possible angina (relative risk (RR) 2.22 (95% CI 1.91 to 2.60), p<0.001) than those with non-anginal symptoms (RR 1.30 (1.11 to 1.53), p=0.002; pinteraction <0.001). In the possible angina cohort, CTCA did not change rates of invasive angiography (p=0.481) but markedly reduced rates of normal coronary angiography (HR 0.32 (0.19 to 0.52), p<0.001). In the non-anginal cohort, rates of invasive angiography increased (HR 1.82 (1.13 to 2.92), p=0.014) without reducing rates of normal coronary angiography (HR 0.78 (0.30 to 2.05), p=0.622). At 3.2 years of follow-up, fatal or non-fatal MI was reduced in patients with possible angina (3.2% to 1.9%%; HR 0.58 (0.34 to 0.99), p=0.045) but not in those with non-anginal symptoms (HR 0.65 (0.25 to 1.69), p=0.379). Conclusions NICE-guided patient selection maximises the benefits of CTCA on diagnostic certainty, use of invasive coronary angiography and reductions in fatal and non-fatal myocardial infarction. Patients with non-anginal chest pain derive minimal benefit from CTCA and increase the rates of invasive investigation. Trial registration number ClinicalTrials.gov: NCT01149590;post results.
Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation

Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNN-based method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models.
A Method for Automatically Adapting an MT System to Different Domains

In order to achieve high translation quality for existing documents in a special domain using conventional MT systems, a domain adaptive translation method based on bilingual corpora has been proposed. In this method, source sentences in a bilingual corpus are translated by the MT system and the results are compared with the target expressions in the corpus. The identifying parse trees of the machine translations with parse trees of the manual translations are investigated for three levels of mismatches: words, predicates, and sentences. The method proceeds as follows. First, it extracts poorly translated expressions by comparing parse trees and classifies the expressions into three levels of the differences. Second, it modifies the MT system at different levels corresponding to the three kinds of mismatches. The experiments for the word level mismatches showed 84% of incorrectly translated words and their correct translations can be found automatically for subsequent registration in a user dictionary.
Origanum Vulgare and Thymbra Capitata Essential Oils from Spain: Determination of Aromatic Profile and Bioactivities

Oregano (Thymbra capitata and Origanum vulgare) essential oils (EOs), cultivated and extracted in the South-East of Spain, were analysed by GC/MS to determine their composition. (E)-β-Caryophyllene (0.5–4.9%), thymol (0.2–5.8%), p-cymene (3.8–8.2%), γ-terpinene (2.1–10.7%) and carvacrol (58.7–77.4%) were determined as the main molecules. This characterisation was completed with enantioselective gas chromatography, where (-)-(E)-β-caryophyllene, (+)-α-pinene and (+)-β-pinene were determined as the main enantiomers. Antioxidant activity was evaluated positively by several methods, accounting for activity against free radicals and reducing power. Important inhibitory activity on lipoxygenase (LOX) and acetylcholinesterase (AChE) was observed supporting potential anti-inflammatory, anti-Alzheimer and insecticidal activities, mainly due to carvacrol. These properties support the potential use of oregano EOs as natural cosmetic and natural pharmaceutical ingredients.
Assessment of Body Composition Using Dual Energy X-Ray Absorptiometry in Patients with Liver Cirrhosis: Comparison with Anthropometry

Objectives The aim of this study was to evaluate changes of body composition in cirrhotic patients. Dual energy x-ray absorptiometry (DEXA) and anthropometry were used, and the values obtained were compared. Methods Mid-arm fat and muscle areas were calculated by anthropometry in 66 cirrhotic patients and 94 healthy controls. In 37 of the cirrhotic patients and 39 of the controls, fat mass, lean soft tissue mass and bone mineral contents were measured with DEXA. Results The number of cirrhotic patients with measured values below the fifth percentile of normal controls was 21 (31.8%) by mid-arm fat area, six (9.1%) by mid-arm muscle area, 15 (40.5%) by fat mass and 0 (0%) by lean soft tissue mass. The fat mass in cirrhotic patients was less than in controls, whereas lean soft tissue mass and bone mineral content were not different. Fat depletion was severe in Child-class C patients and with severe ascites. Mid-arm fat area and fat mass showed close correlation (r = 0.85, p<0.01), but mid-arm muscle area and lean soft tissue mass showed poor correlation (r = 0.32, p<0.05). Conclusion Cirrhotic patients showed lower fat component, with preserved lean soft tissue mass and bone mineral content. In clinical practice, the measurement of mid-arm fat area was useful for the assessment of fat mass.
Subjective fit with organisational culture :Implications for the buffering role of participative control in the stress-strain relationship

Considerable research has produced mixed results relating to the stress-buffering effects of participative control on employee adjustment. This study proposed that stress-buffering effects of participative control would be more pronounced for those perceiving high, as opposed to low, subjective fit. Three significant interactions were found in a sample of 119 employees. The results revealed a three-way interaction between role conflict, participative control, and subjective fit on intentions to leave. Further analyses found a significant three-way interaction between role overload, participative control, and subjective fit on physiological symptoms and psychological health. In all interactions, participative control buffered the negative effects of the stressors on levels of employee adjustment only when employees’ subjective fit with the organisational culture was high. The theoretical importance of the results is discussed.
Quantum Coherent Saturable Absorption for Mid-infrared Ultra-short Pulses

We theoretically show that quantum coherent saturable absorption can be used to obtain ultra-short pulses from mid-infrared quantum cascade lasers (QCLs). In this proposal, quantum cascade structures are processed as two electrically isolated sections. The two sections will be biased with two different voltages so that one of the sections produces gain as is done in typical QCLs, while the other produces quantum coherent resonant absorption for the propagating waves. The quantum coherent absorbing section is saturable and favors the generation of ultra-short pulses. We find that stable ultra-short pulses on the order of ∼100 ps are created from a two-section QCL when the pumping in the gain and absorbing sections remains within critical limits. The intensity and the duration of the stable pulses can be significantly varied when the pumping in the gain and absorbing sections and the length of the gain and absorbing sections are varied. " Self-mode-locking of quantum cascade lasers with giant ultrafast optical nonlinearites, " Monolithic active mode locking of quantum cascade lasers, " Appl. Stability of pulse emission and enhancement of intracavity second-harmonic generation in self-mode-locked quantum cascade lasers, " IEEE J. Active mode locking of broadband quantum cascade lasers, " IEEE J. Mode-locked pulses from mid-infrared quantum cascade lasers, " Opt. Generation of picosecond pulses and frequency combs in actively mode locked external ring cavity quantum cascade lasers, " Appl. Phys. Coherent sampling of active mode-locked terahertz quantum cascade lasers and frequency syntesis, " Nat. Mid-infrared fequency comb based on a quantum cascade laser, " Nature 492, 229–233 (2012). High-resolution multi-heterodyne spectroscopy based on Fabry-Perot quantum cascade laser, " Appl. Coherent frequency combs produced by self frequency modulation in quantum cascade lasers, " Appl. Dynamics of actively mode-locked quantum cascade lasers, " Opt. Multimode regimes in quantum cascade lasers: From coherent instabilities to spatial hole burning, " Phys. Self-induced transparency modelocking of quantum cascade lasers, " Phys. Analytical and computational study of self-induced transparency modelock-ing in quantum cascade lasers, " Phys. Suppression of spatial hole burning and pulse stabilization for actively modelocked quantum cascade lasers using quantum coherent absorption, " J. A density matrix model of transport and radiation in quantum cascade lasers, " New J. Temperature-dependent coherent carrier transport in quantum cascade lasers, " New J. Calculation of the microscopic parameters of a self-induced transparency modelocked quantum cascade laser, " Opt. Self-induced transparency modelocking of quantum cascade lasers in the presence of …
Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews

Most existing rat able aspect generating methods for aspect mining focus on identifying and rating aspects of reviews with overall ratings, while huge amount of unrated reviews are beyond their ability. This drawback motivates the research problem in this paper: predicting aspect ratings and overall ratings for unrated reviews. To solve this problem, we novelly propose a topic model based on Latent Dirichlet Allocation with indirect supervision. Compared with the previous bag-of-words representation of review documents, we utilize the quad-tuples of (head, modifier, rating, entity) to explicitly model the associations between modifiers and ratings. Specifically, our solution for aspect mining in unrated reviews is decomposed into three steps. Firstly, rat able aspects are generated over sentiments from training reviews with overall ratings. Afterwards, inference of aspect identification and rating for unrated reviews are provided. Finally, overall ratings are predicted for unrated reviews. Under this framework, aspect and sentiment associations are captured in the form of joint probabilities through a generative process. The effectiveness of our approach is testified on a real-world dataset crawled from Trip Advisor http://www.tripadvisor. com/, and extensive experiments show that our method significantly outperforms state-of-the-art methods.
Variable Splitting as a Key to Efficient Image Reconstruction

The problem of reconstruction of digital images from their degraded measurements has always been a problem of central importance in numerous applications of imaging sciences. In real life, acquired imaging data is typically contaminated by various types of degradation phenomena which are usually related to the imperfections of image acquisition devices and/or environmental effects. Accordingly, given the degraded measurements of an image of interest, the fundamental goal of image reconstruction is to recover its close approximation, thereby “reversing” the effect of image degradation. Moreover, the massive production and proliferation of digital data across different fields of applied sciences creates the need for methods of image restoration which would be both accurate and computationally efficient. Developing such methods, however, has never been a trivial task, as improving the accuracy of image reconstruction is generally achieved at the expense of an elevated computational burden. Accordingly, the main goal of this thesis has been to develop an analytical framework which allows one to tackle a wide scope of image reconstruction problems in a computationally efficient manner. To this end, we generalize the concept of variable splitting, as a tool for simplifying complex reconstruction problems through their replacement by a sequence of simpler and therefore easily solvable ones. Moreover, we consider two different types of variable splitting and demonstrate their connection to a number of existing approaches which are currently used to solve various inverse problems. In particular, we refer to the first type of variable splitting as Bregman Type Splitting (BTS) and demonstrate its applicability to the solution of complex reconstruction problems with composite, cross-domain constraints. As specific applications of practical importance, we consider the problem of reconstruction of diffusion MRI signals from sub-critically sampled, incomplete data as well as the problem of blind deconvolution of medical ultrasound images. Further, we refer to the second type of variable splitting as Fuzzy Clustering Splitting (FCS) and show its application to the problem of image denoising. Specifically, we demonstrate how this splitting technique allows us to generalize the concept of neighbourhood operation as well as to derive a unifying approach to denoising of imaging data under a variety of different noise scenarios.
Action Recognition using Temporal Bag-of-Words from Depth Maps

In this paper, we present a methodology for human action recognition from a sequence of depth maps obtained using Microsoft Kinect. Specifically, we use a Temporal Bag-of-Words model as representation scheme to capture the variation of features across the temporal domain. Our methodology builds the Temporal Bag-of-Words model on top of the spatiotemporal features extracted from interest points. The local spatio-temporal features provide some invariance to scale, viewpoint changes by capturing the local information. In order to make the representation insensitive to temporal sequence misalignment, we propose using the Temporal Bag-of-Words model in a hierarchical manner by recursively partitioning the depth maps sequence into sub-sequences in temporal domain. Classification is done using SVM. We test our algorithm on our own dataset consisting of eight different actions.
Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information

The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time warping (LSDTW). LSDTW nds an alignment that maximizes statistical dependency between sequences, measured by a squared-loss variant of mutual information. The benet of this novel information-theoretic formulation is that LSDTW can align sequences with dierent
Archive management: the missing component

The National Oceanic and Atmospheric Administration's (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) views the area of Archive Management, as comprising three components: Information Technology Infrastructure, Customer Service, and Scientific Stewardship. This last component, Scientific Stewardship, can be characterized as the long-term preservation of the scientific integrity, monitoring and improving the quality, and the extraction of further knowledge from the data. As our data volumes increase, this component, while being recognized as being important, has suffered. Without this component, Archive Management is static, sterile, and lacks the true ability to provide meaningful information and knowledge derived from the archived data. Proper Scientific Stewardship is performed by scientists and data managers knowledgeable in the scientific assessment of a particular data type, and the practice will ensure more effective data management.
Traffic grooming for internetworking using optical networks

Traffic grooming on logical topologies is an important problem in internetworking. This paper explores the use of regular topologies as possible models for the logical topology of WDM wavelength routed networks. After defining the problem, a genetic algorithm approach to designing logical topologies has been proposed. The approach has been compared to the optimal design which can be done only for very small networks. The experiments reported here indicate that using the new approach is quite viable and works reasonably well for networks of practical size.
Incremental dialogue act understanding

This paper presents a machine learning-based approach to the incremental understanding of dialogue utterances, with a focus on the recognition of their communicative functions. A token-based approach combining the use of local classifiers, which exploit local utterance features, and global classifiers which use the outputs of local classifiers applied to previous and subsequent tokens, is shown to result in excellent dialogue act recognition scores for unsegmented spoken dialogue. This can be seen as a significant step forward towards the development of fully incremental, on-line methods for computing the meaning of utterances in spoken dialogue.
Aip1p Interacts with Cofilin to Disassemble Actin Filaments

Actin interacting protein 1 (Aip1) is a conserved component of the actin cytoskeleton first identified in a two-hybrid screen against yeast actin. Here, we report that Aip1p also interacts with the ubiquitous actin depolymerizing factor cofilin. A two-hybrid–based approach using cofilin and actin mutants identified residues necessary for the interaction of actin, cofilin, and Aip1p in an apparent ternary complex. Deletion of the AIP1 gene is lethal in combination with cofilin mutants or act1-159, an actin mutation that slows the rate of actin filament disassembly in vivo. Aip1p localizes to cortical actin patches in yeast cells, and this localization is disrupted by specific actin and cofilin mutations. Further, Aip1p is required to restrict cofilin localization to cortical patches. Finally, biochemical analyses show that Aip1p causes net depolymerization of actin filaments only in the presence of cofilin and that cofilin enhances binding of Aip1p to actin filaments. We conclude that Aip1p is a cofilin-associated protein that enhances the filament disassembly activity of cofilin and restricts cofilin localization to cortical actin patches.
Anatomical analysis of brain MRI in Alzheimer’s disease

It is accepted that the onset of disease starts almost decades before appearing clinical symptoms. Developing new pharmacotherapeutic trials need to evaluate the effect of therapy in disease progression and it is ascertained that Neuroimaging based on high quality MR images is a useful way of dealing with this challenge. Various processing methods have been used to diagnose Alzheimer's disease and evaluate strength of it in three major populations (AD, MCI and NC). Each of these methods has suggested that some anatomical structures or anatomically unspecified Regions of Interest (ROI) have been influenced by disease in various grades. This paper aims to survey the recent research in MRI Neuroimaging methods and evaluate the impact of various brain parts in AD diagnosing methods. A number of various regions in all of three main tissues (CSF, GM AND WM) suffer from the effects of disease. It is revealed that temporal lobe, hippocampus and cortex are the most important structures.
Fingerprint-based location positoning using improved KNN

Location estimation has become one of the most popular research areas for the wide application of Location Based Services (LBS). K nearest neighbors (KNN) algorithm is commonly used in fingerprinting approach, and it has been widely used for decades due to its simplicity and effectiveness. However, the main drawback of KNN algorithm is obvious. Theoretical behavior can hardly be obtained because KNN is sensitive to the value of K and it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous irrelevant features. In order to fill this gap, an improved KNN algorithm is introduced. And this algorithm is beneficial to location estimation in a real GSM network.
Psychology is social: Exploring universals in performance capacity and performance style

Our goal in this discussion is to map out a scientifically legitimate and practical path toward internationalizing the social psychology curriculum. Toward developing an appropriate conceptual framework, we distinguish between the study of two different types of performance: performance capacity, how well isolated individuals can perform tasks as determined by their physical characteristics, and performance style, the way things are done through collaborative meaning making. Whereas performance capacity can be explained causally, performance style is best explained through normative accounts. Social psychology has largely approached questions of performance style by applying a reductionist and causal framework. We highlight the alternative approach reflected in the cultural-narrative turn and identify sub-research areas that need to be strengthened in order to internationalize psychology.