Miracle-dz/UniFAR-FTUs huggingface dataset:
The Quality of Life of Turner Women in Comparison Grown-Up GH-Deficient Women wi th

Various aspects of the way of life in 20 adult patients (mean age: 25.7±6.0) with Turner syndrome were studied for their quality of life (QOL). The study found that many more Turner women went on to university (P<0.01) than the general population, whereas GH-deficient women did not. The employment status of both Turner and GH-deficient women does not differ from that of the general population. Only 7 of the 12 Turner and 5 of the 15 GH-deficient employees are satisfied with their present jobs, but 10 of the former and 7 of the latter women think that their jobs are worthwhile. The total annual income of 3 of 19 Turner women exceeded 3 million yen, but not that of any of 10 GHdeficient women who answered the question on their income. Half of the Turner and GH-deficient women complained of shoulder stiffness and one fourth of the Turner and one fifth of the GH-deficient women have a sense of despair and irritability. Seven Turner and 9 GH-deficient women have anxiety about their body, and 6 of the former and 8 of the latter have anxiety about marriage. Only 4 Turner and 3 GH-deficient women are married. Three of the former marriages were arranged by their parents, but all 3 GH-deficient women got married after falling in love. These marriage rates are significantly lower (P<0.005) than that of the age-matched general population. Most of the unmarried women in both groups live with their parents, and half of the Turner and one third of the GH-deficient women have difficulty in getting clothes and shoes to fit them. Otherwise, they have no particular problem in their daily life. In conclusion, Turner women are well educated and work as normal women, but they, as well as GH-deficient women, appear to be anxious about their body and marriage.
Auto-Scoring of Personalised News in the Real-Time Web: Challenges, Overview and Evaluation of the State-of-the-Art Solutions

The problem of automated personalised news recommendation, often referred as auto-scoring has attracted substantial research throughout the last decade in multiple domains such as data mining and machine learning, computer systems, e commerce and sociology. A typical "recommender systems" approach to solving this problem usually adopts content-based scoring, collaborative filtering or more often a hybrid approach. Due to their special nature, news articles introduce further challenges and constraints to conventional item recommendation problems, characterised by short lifetime and rapid popularity trends. In this survey, we provide an overview of the challenges and current solutions in news personalisation and ranking from both an algorithmic and system design perspective, and present our evaluation of the most representative scoring algorithms while also exploring the benefits of using a hybrid approach. Our evaluation is based on a real-life case study in news recommendations.
7.2 243.3pJ/pixel bio-inspired time-stamp-based 2D optic flow sensor for artificial compound eyes

Miniaturized low-power artificial compound eyes in a small form factor and a low payload can be a promising approach to provide wide-field information for micro-air-vehicle (MAV) applications. Recently, research efforts have been made to realize bio-inspired artificial compound eyes to mimic the wide field of view (FoV) of insect visual organs by implementing photoreceptors to independently face different angles [1-2]. However, these approaches have drawbacks. They use complicated fabrication processes to form a hemispherical lens configuration and secure an independent optical path to each photoreceptor. We take a simple and practical approach to realize wide-field optic flow sensing in a pseudo-hemispherical configuration by mounting a number of 2D array optic flow sensors on a flexible PCB module as shown in Figure 7.2. 1. In this scheme, the 2D optic flow sensor should meet the requirements of MAV applications: extremely low power consumption while maintaining robust optic flow generation. Conventional optic flow algorithms, such as Lucas-and-Kanade, require huge amounts of numerical calculations; therefore, they require substantial digital hardware (CPU and/or FPGA), resulting in large power consumption [3-4]. As an alternative approach for low-power implementation, bio-inspired elementary motion detector (EMD) based algorithms (or neuromorphic algorithms) have been studied and implemented in analog VLSI circuits for autonomous navigation [5-6]. However, pure analog signal processing is easily susceptible to temperature and process variations and it is difficult to scale the pixel size or apply low-power design techniques because extensive analog processing is implemented in pixel-level circuits. In this work, we have devised and implemented a time-stamp-based optic flow algorithm, which is modified from the conventional EMD algorithm to give an optimum partitioning of hardware blocks in analog and digital domains as well as assign adequate allocation of pixel-level, column-parallel, and chip-level processing. Temporal filtering, which may require huge hardware resources if implemented in the digital domain, remains in a pixel-level analog processing unit. Feature detection is implemented using digital circuits that are column parallel. The embedded digital core decodes the 2D time-stamp information into velocity using chip-level processing. Finally, the estimated 16b optic flow data are compressed and transmitted to the host through a 4-wired Serial Peripheral Interface (SPI) bus.
In Vivo Dynamics of the Musculoskeletal System Cannot Be Adequately Described Using a Stiffness-Damping-Inertia Model

Background Visco-elastic properties of the (neuro-)musculoskeletal system play a fundamental role in the control of posture and movement. Often, these properties are described and identified using stiffness-damping-inertia (KBI) models. In such an approach, perturbations are applied to the (neuro-)musculoskeletal system and subsequently KBI-model parameters are optimized to obtain a best fit between simulated and experimentally observed responses. Problems with this approach may arise because a KBI-model neglects critical aspects of the real musculoskeletal system. Methodology/Principal Findings The purpose of this study was to analyze the relation between the musculoskeletal properties and the stiffness and damping estimated using a KBI-model, to analyze how this relation is affected by the nature of the perturbation and to assess the sensitivity of the estimated stiffness and damping to measurement errors. Our analyses show that the estimated stiffness and damping using KBI-models do not resemble any of the dynamical parameters of the underlying system, not even when the responses are very accurately fitted by the KBI-model. Furthermore, the stiffness and damping depend non-linearly on all the dynamical parameters of the underlying system, influenced by the nature of the perturbation and the time interval over which the KBI-model is optimized. Moreover, our analyses predict a very high sensitivity of estimated parameters to measurement errors. Conclusions/Significance The results of this study suggest that the usage of stiffness-damping-inertia models to investigate the dynamical properties of the musculoskeletal system under control by the CNS should be reconsidered.
Arabinofuranosyl-5-azacytosine: Antitumor and Cytotoxic Properties1

Arabinofuranosyl-5-azacytosine (ara-AC), a nucleoside com bining the structural elements of 5-azacytidine and arabinofuranosylcytosine, exhibited unusually wide therapeutic activity against several murine leukemias and all three human xenografts of the National Cancer Institute tumor panel. Activity was ob served following either a daily or an intermittent regimen of treatment in the i.p. L1210 model. However, when multiple doses were administered on each treatment day, a greater therapeutic effect was produced and the total dose was reduced. Extensive necrosis was observed by light and electron microscopy in P388 tumors treated with ara-AC. Following s.c. administration, araAC caused regression of the mammary and lung xenografts (MX1 and LX-1) and a 93% inhibition of the human colon tumor (CX1); other analogues of this drug failed to demonstrate a compar ably broad spectrum of activity. Morphological assessment of treated xenografts revealed a general loss of cell-to-cell contact and abundant necrosis 24 h after the administration of ara-AC. In culture, the 50% inhibitory concentrations of ara-AC for P388 and L1210 cells at 24 h were 1.9 and 4.5 ^M, respectively, and the decline in replication rates was dependent on drug concen tration. The cytocidal nature of the drug was demonstrated by cloning experiments in which it was observed that ara-AC abol ished the clonogenicity of lymphoblasts but was only minimally cytotoxic to normal murine bone marrow progenitor cells. As adjudged by flow cytometry, the drug induced a distinct slowing of cell cycle traverse through S phase. Induction of the differen tiation of HL-60 cells in culture was another cytotropic effect of this drug. At 44% differentiation (10 UM ara-AC), 50% of the cultured cells were viable. Its broad spectrum antitumor activity, its selective toxicity to tumor cells, and its ability to produce cytodifferentiation render ara-AC of interest as a potential antineoplastic agent in humans.
Nonlinear Computational Analysis of Postural Stability and its Relationship to Estrogen Deficiency in Postmenopausal Women

This study aims to determine if multi-scale entropy analysis will more accurately differentiate the postural stability of postmenopausal women as a function of their estrogen status than traditional measures of postural assessment. The study participants included 7 non-Hormone Replacement Therapy (HRT) postmenopausal women (mean age = 57) and 4 HRT postmenopausal women (mean age = 55). Postural stability was measured with the NeuroCom Equitest and the use of conditions 1-4 of the Sensory Organization Test (SOT). The subjects received an equilibrium score for each condition with a score near 100 representing good postural stability. A modification of the “Multi-Scale Entropy Analysis” Matlab program created by Costa et al. was used to analyze the raw center of pressure (COP) trajectory data measured for each condition in both the medial-lateral (x-direction) and anterior-posterior (y-direction) directions. The Matlab program created complexity index (CI) scores where a high CI is indicative of low postural stability and a low CI is representative of good postural stability. A basic t-test of unequal variances was performed on the CI scores between non-HRT and HRT subjects, in addition to the equilibrium scores between non-HRT and HRT subjects. The t-test revealed both the NeuroCom Equitest and MSE analysis identified a significant difference in postural stability between non-HRT subjects and HRT subjects an equal number of times (n=2), where HRT subjects exhibited better postural stability than non-HRT. The MSE analysis method was able to significantly differentiate postural stability between non-HRT and HRT subjects in SOT 1 (p=0.0432), whereas the NeuroCom Equitest did not. This is of significance because the subject is standing still on the force plate without having their balance disrupted. Previous studies have had to manipulate the surroundings and perturb the subject’s balance to significantly differentiate postural stability between non-HRT and HRT subjects. The findings from this study indicate that MSE analysis may provide a higher level of sensitivity necessary to assess postural stability as a function of estrogen status. The effect of estrogen on the preservation of postural stability in postmenopausal women may provide further evidence for the consideration of estrogen as a neuroprotectant.
Sensor Space Segmentation for Mobile Robot Learning

Robot learning such as reinforcement learning generally needs a well-de ned state space in order to converge. However, to build such a state space is one of the main issues of the robot learning because of the inter-dependence between state and action spaces, which resembles to the well known \chicken and egg" problem. This paper proposes two methods of actionbased state space construction for vision-based mobile robots. Basic ideas common to the two methods to cope with the inter-dependence are that we de ne a state as a cluster of of input vectors from which the robot can reach the goal state or the state already obtained by a sequence of one kind action primitive regardless of its length, and that this sequence is de ned as one action. The rst method clusters the input vectors as hyper ellipsoids so that the whole state space is segmented into a state transition map in terms of action from which the optimal action sequence is obtained. In order to obtain the such a map, we need a su cient number of data, which means longer learning time. To cope with this, we proposed the second method by which a robot learns purposive behavior within less learning time by incrementally segmenting the sensor space based on the experiences of the robot. The incremental segmentation is performed by constructing local models in the state space, which is based on the function approximation of the sensor outputs to reduce the learning time and on the reinforcement signal to emerge a purposive behavior. To show the validity of the methods, we apply them to a soccer robot which tries to shoot a ball into a goal. The simulation and real experiments are shown.
Cross-Domain Collaborative Learning in Social Multimedia

Cross-domain data analysis is one of the most important tasks in social multimedia. It has a wide range of real-world applications, including cross-platform event analysis, cross-domain multi-event tracking, cross-domain video recommendation, etc. It is also very challenging because the data have multi-modal and multi-domain properties, and there are no explicit correlations to link different domains. To deal with these issues, we propose a generic Cross-Domain Collaborative Learning (CDCL) framework based on non-parametric Bayesian dictionary learning model for cross-domain data analysis. In the proposed CDCL model, it can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property. As a result, our CDCL model can effectively explore the virtues of different information sources to complement and enhance each other for cross-domain data analysis. To evaluate the proposed model, we apply it for two different applications: cross-platform event recognition and cross-network video recommendation. The extensive experimental evaluations well demonstrate the effectiveness of the proposed algorithm for cross-domain data analysis.
Computational model for artificial learning using fonnal concept analysis

The field of artificial intelligence embraces two approaches to artificial learning. The first is motivated by the study of mental processes and states that artificial learning is the study of mechanisms embodied in the human mind. It aims to understand how these mechanisms can be translated into computer programs. The second approach initiated from a practical computing standpoint and has less grandiose aims. It involves developing programs that learn from past data, and may be considered as a branch of data processing. In this paper, we are concerned with the first approach. Artificial learning is interested in the classification learning that is a learning algorithm for categorizing unseen examples into predefined classes based on a set of training examples. We formulated a computational model for binary classification process using formal concept analysis. The classification rules are derived and applied successfully for different study cases.
Rewriting-based Dynamic Information Flow for JavaScript ∗

JavaScript web applications often dynamically load third-party code, which in some cases can steal or corrupt important client information. In this paper, we present a rewriting-based approach for enforcing confidentiality and integrity policies that respectively specify what information can flow into and from untrusted thirdparty code. We have implemented our approach in the Chrome browser, and we present experiments that evaluate the efficiency and precision of our technique on real-world websites.
Non-Scanning Fiber-Optic Near-Infrared Beam Led to Two-Photon Optogenetic Stimulation In-Vivo

Stimulation of specific neurons expressing opsins in a targeted region to manipulate brain function has proved to be a powerful tool in neuroscience. However, the use of visible light for optogenetic stimulation is invasive due to low penetration depth and tissue damage owing to larger absorption and scattering. Here, we report, for the first time, in-depth non-scanning fiber-optic two-photon optogenetic stimulation (FO-TPOS) of neurons in-vivo in transgenic mouse models. In order to optimize the deep-brain stimulation strategy, we characterized two-photon activation efficacy at different near-infrared laser parameters. The significantly-enhanced in-depth stimulation efficiency of FO-TPOS as compared to conventional single-photon beam was demonstrated both by experiments and Monte Carlo simulation. The non-scanning FO-TPOS technology will lead to better understanding of the in-vivo neural circuitry because this technology permits more precise and less invasive anatomical delivery of stimulation.
Genetic Algorithm Based Backpropagation Neural Network Performs better than Backpropagation Neural Network in Stock Rates Prediction

The prevailing notion in society is that wealth brings comfort and luxury, so it is a challenging and daunting task to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. This paper presents a Back Propagation Neural Network and Genetic Based Backpropagation Neural Network to predict the stock price of the day. Stock rate prediction accuracy of backpropagation neural network and genetic algorithm based backpropagation neural network has been compared. The results showed that the genetic algorithm based backpropagation neural network predict stock price more accurately as compared to backpropagation neural network.
Automated segmentation of colon gland using histology images

This paper represents an automated methodology for segmentation of colon glands using histology images. The manifestations of colorectal cancer under microscope has always been challenging as staining and sectioning leads to variation in tissue specimen, which causes conflict in gland appearance. Gland segmentation and classification is very important for the automation of the system. The presented methodology automatically segments the colon gland tissues by using intensity based thresholding which makes this methodology efficient. Unlike other segmentation methods, this methodology is entirely automated and quantifies lumen and epithelial cells only in the region of interest, which makes this method computationally efficient. This methodology is efficient for calculation of number of glands as well as for segmentation of gland area and achieves overall 93.76% accuracy for both.
Effects of a mindfulness-based stress reduction course on the psychological well-being of individuals with an intellectual disability

Aim To evaluate the effect of a 12-week mindfulness-based stress reduction (MBSR) course adapted for people with an intellectual disability (ID). Methods Ten participants with ID completed measures of well-being and understanding of the concepts of mindfulness before and after the course, provided information about additional practise and gave feedback on the perceived benefits of the course. Findings No significant difference was found between mean well-being or understanding scores before and after the course. A significant relationship was found between additional practise and post-course well-being scores. Perceived benefits of mindfulness included reduced stress, increased self-control and confidence. Conclusion MBSR may be effective for people with ID but the benefits may not always be reflected in changes in questionnaire scores. The amount of practise of mindfulness exercises may be an important component of increasing well-being.
Making Sense of Research with Rexplore

While there are many tools and services which support the exploration of research data, by and large these tend to provide a limited set of functionalities, which cover primarily ranking measures and simple mechanisms for relating authors. To try and improve over the current state of affairs, we are developing a novel tool for exploring research data, which is called Rexplore. Rexplore builds on an intelligent algorithm for automatically identifying hierarchical and equivalence relations between research areas, to provide a variety of functionalities and visualizations to help users to make sense of research data. These include visualizations to detect trends in research; ways to cluster authors according to several dynamic similarity measures; and fine-grained mechanisms for ranking authors, taking into account parameters such as ranking criterion, career stage, calendar years, publication venues, etc.
Superconvergence analysis for Maxwell's equations in dispersive media

In this paper, we consider the time dependent Maxwell's equations in dispersive media on a bounded three-dimensional domain. Global superconvergence is obtained for semi-discrete mixed finite element methods for three most popular dispersive media models: the isotropic cold plasma, the one-pole Debye medium, and the two-pole Lorentz medium. Global superconvergence for a standard finite element method is also presented. To our best knowledge, this is the first superconvergence analysis obtained for Maxwell's equations when dispersive media are involved.
Interpersonal communication competence and digital citizenship among pre-service teachers in China’s teacher preparation programs

Abstract The Internet, new media and social change have created issues requiring digital citizenship education, particularly within the socio-cultural context of Chinese education. Recent digital citizenship standards for education developed by organizations such as the International Society for Technology in Education (ISTE) highlight the importance of this issue. Researchers have explored digital citizenship with some mediated behavioral determinants; however, the critical influence of interpersonal communication competence (ICC) has not yet been explored. To expand understanding of the components needed for improving the appropriateness of digital citizenship among future role models of society, this study investigates the relationship between ICC skills and digital citizenship with a sample of 905 pre-service teachers. The results showed that 10 ICC skills were positively predictive of digital citizenship. Environmental control and immediacy were found to be the strongest predictors of digital citizenship among the skills examined. These results theoretically emphasize the importance of ICC skills in mediated behavior and provide practical direction for improving pre-service teachers’ digital citizenship, which can positively enable the behavior instructors display and convey to their future students.