Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Models to Predict Hypertension: A Systematic Review

Background As well as being a risk factor for cardiovascular disease, hypertension is also a health condition in its own right. Risk prediction models may be of value in identifying those individuals at risk of developing hypertension who are likely to benefit most from interventions. Methods and Findings To synthesize existing evidence on the performance of these models, we searched MEDLINE and EMBASE; examined bibliographies of retrieved articles; contacted experts in the field; and searched our own files. Dual review of identified studies was conducted. Included studies had to report on the development, validation, or impact analysis of a hypertension risk prediction model. For each publication, information was extracted on study design and characteristics, predictors, model discrimination, calibration and reclassification ability, validation and impact analysis. Eleven studies reporting on 15 different hypertension prediction risk models were identified. Age, sex, body mass index, diabetes status, and blood pressure variables were the most common predictor variables included in models. Most risk models had acceptable-to-good discriminatory ability (C-statistic>0.70) in the derivation sample. Calibration was less commonly assessed, but overall acceptable. Two hypertension risk models, the Framingham and Hopkins, have been externally validated, displaying acceptable-to-good discrimination, and C-statistic ranging from 0.71 to 0.81. Lack of individual-level data precluded analyses of the risk models in subgroups. Conclusions The discrimination ability of existing hypertension risk prediction tools is acceptable, but the impact of using these tools on prescriptions and outcomes of hypertension prevention is unclear.
Learning Deep Representations By Distributed Random Samplings

In this paper, we propose an extremely simple deep model for the unsupervised nonlinear dimensionality reduction -- deep distributed random samplings, which performs like a stack of unsupervised bootstrap aggregating. First, its network structure is novel: each layer of the network is a group of mutually independent $k$-centers clusterings. Second, its learning method is extremely simple: the $k$ centers of each clustering are only $k$ randomly selected examples from the training data; for small-scale data sets, the $k$ centers are further randomly reconstructed by a simple cyclic-shift operation. Experimental results on nonlinear dimensionality reduction show that the proposed method can learn abstract representations on both large-scale and small-scale problems, and meanwhile is much faster than deep neural networks on large-scale problems.
Einleitung zum Thementeil „Schule und psychosoziale Entwicklung“

[. ] Mit den folgenden drei Beitragen soll gezeigt werden, dass es durchaus empirische Bildungsforschung gibt, die sich um die psychosoziale Entwicklung im eigentlichen Sinne kummert und sich aus dieser Perspektive die Akzente in der Forschung verschieben. Ohne grosen programmatischen Anspruch geht es in diesem Themenschwerpunkt darum, ein wenig Gegenruder zu legen in einer Zeit, in der die Bildungsforschung in der Gefahr steht, von einem politischenWind mitgerissen zu werden, der auf die Sandbank eines auf Fachleistungen reduzierten Bildungsbegriffs zusteuert. [. ] Wie die drei Beispiele exemplarisch belegen, gibt es eine Reihe sehr unterschiedlicher Zugange, um die Zusammenhange zwischen schulstrukturellen Aspekten, Lehrerverhalten und allgemeinem Schulklima einerseits und Elementen der psychosozialen Entwicklung zu untersuchen. Aus entwicklungspsychologischer Sicht ist die Schule als Sozialisationsinstanz kaum zu uberschatzen – und zwar fur die kognitive wie die psychosoziale Entwicklung gleichermasen. (DIPF/Orig.)
A Correlation Analysis between Motivation Types and Learning Achievements of TFC English Major Students

The tuition-free policy has been implemented in China for more than eight years. AS a special group of college students, they have become the target of research and have been studied in many perspectives. This study attempted to reveal the relationship between students’ motivation type and their learning outcome by means of questionnaire and interview. Students’ TEM 4 test results were also adopted to learn the correlation of motivation and achievements. The results showed that compared with the non-tuition-free counterparts, the tuition-free students did outperform in TEM 4 and there was a positive correlation between their motivation type and learning outcome. Most of the tuition-free students have intrinsically-driven motivation while the non-tuition-free students’ motivations are mostly extrinsically-driven, which could help explain the differences in learning outcome. The findings suggest that the language teachers should try their best to create a learning atmosphere which may stimulate students’ learning interest and intrinsic motivation.
Introduction: Chinese-language ecocinema

ABSTRACT This article is a short introduction to the genealogy, theory and practice of Chinese ecocinema. It situates Chinese ecocinema in the broad context of a general ecological and environmental turn in contemporary humanistic and cultural studies. The article pays close attention to contemporary Chinese-language documentaries that embrace an ecological consciousness. Furthermore, the article zeroes in on a specific Taiwanese documentary: My Fancy High Heels 我爱高跟鞋 (2010) in order to tease out the contestation and tension between the environment, human labor and animals on the one hand and the nexus of global manufacture, circulation and consumption of commodities on the other. The conclusion of the article calls for a flexible transnational approach in ecocinema studies.
Statistical mechanics of canonical-dissipative systems and applications to swarm dynamics.

We develop the theory of canonical-dissipative systems, based on the assumption that both the conservative and the dissipative elements of the dynamics are determined by invariants of motion. In this case, known solutions for conservative systems can be used for an extension of the dynamics, which also includes elements such as the takeup/dissipation of energy. This way, a rather complex dynamics can be mapped to an analytically tractable model, while still covering important features of nonequilibrium systems. In our paper, this approach is used to derive a rather general swarm model that considers (a) the energetic conditions of swarming, i.e., for active motion, and (b) interactions between the particles based on global couplings. We derive analytical expressions for the nonequilibrium velocity distribution and the mean squared displacement of the swarm. Further, we investigate the influence of different global couplings on the overall behavior of the swarm by means of particle-based computer simulations and compare them with the analytical estimations.
Data aggregation using temporal and spatial correlations in Advanced Metering Infrastructure

In this paper, we propose a data aggregation mechanism using temporal and spatial correlations in Advanced Metering Infrastructure of smart grid environment. Data aggregation is a process to collect the same type of data and transmit the aggregation data using predetermined function - sum, avg, min, etc. It decreases the transmission number and thus enhances energy efficiency. Each smart meter determines the transfer of new power usage data for reduction of transmission amount of data. When the difference of new data and previous data is less than the specified threshold, it is suppressed (temporal correlation). After clustering of geographically adjacent smart meters, each smart meter calculates the sum with the transmitted value of neighboring nodes within clustering and a node transmits own difference value including the sum (spatial correlation). We use the real power usage data during the most power-intensive week for the performance evaluation of our mechanism. The simulation results show that our approach reduces the transmission amount and energy consumption. We verify the accuracy of transmitted data using temporal and spatial correlation mechanism. Moreover, our approach provides minimal exposure of real power usage data without additional security method.
Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer

Background Ovarian cancer has a poor prognosis due to advanced stage at presentation and either intrinsic or acquired resistance to classic cytotoxic drugs such as platinum and taxoids. Recent large clinical trials with different combinations and sequences of classic cytotoxic drugs indicate that further significant improvement in prognosis by this type of drugs is not to be expected. Currently a large number of drugs, targeting dysregulated molecular pathways in cancer cells have been developed and are introduced in the clinic. A major challenge is to identify those patients who will benefit from drugs targeting these specific dysregulated pathways.The aims of our study were (1) to develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, (2) to assess the association of pathways and transcription factors with overall survival, and (3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers. Methods and Findings According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using ∼35,000 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available dataset of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent dataset to assess the similarities with results from our own dataset. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 versus 41 mo, respectively; permutation p-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that composed the overall survival profile were also able to discriminate between the two risk groups in the independent dataset. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival, of which 16 and 12, respectively, were confirmed in the independent dataset. Conclusions Our study provides new clues to genes, pathways, and transcription factors that contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies.
Caption localisation in video sequences by fusion of multiple detectors

In this article, we focus on the problem of caption detection in video sequences. Contrary to most of existing approaches based on a single detector followed by an ad hoc and costly post-processing, we have decided to consider several detectors and to merge their results in order to combine advantages of each one. First we made a study of captions in video sequences to determine how they are represented in images and to identify their main features (color constancy and background contrast, edge density and regularity, temporal persistence). Based on these features, we then select or define the appropriate detectors and we compare several fusion strategies which can be involved. The logical process we have followed and the satisfying results we have obtained let us validate our contribution.
Epidermal growth factor receptor tyrosine kinase inhibitor versus placebo as maintenance therapy for advanced non- small-cell lung cancer: a meta-analysis of randomized controlled trials.

BACKGROUND Use of epidermal growth factor receptor inhibitors (EGFR-TKIs ) is now standard for non- small-cell lung cancer (NSCLC). However, the effects of EGFR-TKIs in maintenance therapy for advanced NSCLC patients are still unclear. The preent meta-analysis was performed to examine pooled data of randomized control trials (RCT) where EGFR-TKIs were compared against placebo in maintenance regimens for patients with advanced NCSLC to quantify potential benefits and determine safety. METHODS Several data bases were searched, including PubMed, EMBASE and CENTRAL, and we performed an internet search of conference literature. The endpoints were objective response rates (ORR), progression-free survival (PFS) and overall survival (OS). We performed a meta-analysis of the published data, using Comprehensive Meta Analysis software (Version 2.0). with a fixed effects model and an additional random effects model, when applicable. The results of the meta-analysis are expressed as hazard ratios (HRs) or risk ratios (RRs), with their corresponding 95% confidence intervals (95%CIs). RESULTS The final analysis included six trials, covering 3,758 patients. Compared with placebo, EGFR-TKIs maintenance therapy improved ORR and PFS for patients with advanced NSCLC, the difference being statistically significant (P<0.05), but proved unable to prolong patients' OS. The main adverse reactions were diarrhea and rashes. CONCLUSION EGFR-TKIs demonstrated encouraging efficacy, safety and survival when delivered as maintenance therapy for patients with advanced NSCLC after first-line chemotherapy, especially for the patients who had adenocarcinomas, were female, non-smokers and patients with EGFR gene mutations.
LEMP: Fast Retrieval of Large Entries in a Matrix Product

We study the problem of efficiently retrieving large entries in the product of two given matrices, which arises in a number of data mining and information retrieval tasks. We focus on the setting where the two input matrices are tall and skinny, i.e., with millions of rows and tens to hundreds of columns. In such settings, the product matrix is large and its complete computation is generally infeasible in practice. To address this problem, we propose the LEMP algorithm, which efficiently retrieves only the large entries in the product matrix without actually computing it. LEMP maps the large-entry retrieval problem to a set of smaller cosine similarity search problems, for which existing methods can be used. We also propose novel algorithms for cosine similarity search, which are tailored to our setting. Our experimental study on large real-world datasets indicates that LEMP is up to an order of magnitude faster than state-of-the-art approaches.
Explaining ontogenetic shifts in root-shoot scaling with transient dynamics.

BACKGROUND AND AIMS Simple models of herbaceous plant growth based on optimal partitioning theory predict, at steady state, an isometric relationship between shoot and root biomass during plant ontogeny, i.e. a constant root-shoot ratio. This prediction has received mixed empirical support, suggesting either that optimal partitioning is too coarse an assumption to model plant biomass allocation, or that additional processes need to be modelled to account for empirical findings within the optimal partitioning framework. In this study, simulations are used to compare quantitatively two potential explanations for observed non-isometric relationships, namely nutrient limitation during the experiments and initial developmental constraints. METHODS A simple plant growth model was built to simulate the growth of herbaceous species, based on optimal partitioning theory combined with empirically measured plant functional traits. Its ability to reproduce plant relative growth rate and final root weight ratio was assessed against previously published data. Predicted root-shoot ratios during plant ontogeny were compared with experimental observations. The effects of nutrient limitation and initial developmental constraints on root-shoot ratios were then tested. KEY RESULTS The model was found to reproduce overall plant growth patterns accurately, but failed, in its simplest form, at explaining non-isometric growth trajectories. Both nutrient limitation and ontogenetic developmental constraints were further shown to cause transient dynamics resulting in a deviation from isometry. Nitrogen limitation alone was not sufficient to explain the observed trajectories of most plant species. The inclusion of initial developmental constraints (fixed non-optimal initial root-shoot ratios) enabled the reproduction of the observed trajectories and were consistent with observed initial root-shoot ratios. CONCLUSIONS This study highlights the fact that considering transient dynamics enables theoretical predictions based on optimal partitioning to be reconciled with empirically measured ontogenetic root-shoot allometries. The transient dynamics cannot be solely explained by nutrient limitation during the experiments, pointing to a likely role for initial developmental constraints in the observed non-isometric growth trajectories.
Using a vector autoregression framework to measure the quality of English NHS hospitals

In order to address the problem of poor quality information available to health care providers today, McClellan and Staiger (1999) developed a new method to measure quality, which addresses some key limitations of other approaches. Their method produces quality estimates that reflect different dimensions of quality and are able to eliminate systematic bias and noise inherent in these types of measures. While these measures are promising indicators, they have not been applied to other conditions or health systems since their publication. This paper attempts to replicated their 1999 method by calculating these quality measures for English Hospitals using Hospital Episode Statistics for the years 1996-2008 for Acute Myocardial Infarction (AMI) and Hip Replacement. Using the latent outcome measures calculated previously, Vector Autoregressions (VARs) are used to combine the information from different time periods and across measures within each condition. These measures are then used to compare current and past quality of care within and across NHS Acute Trusts. Our results support that this method is well suited to measure and predict provider quality of care in the English setting using the individual patient level data collected.
In Situ Remediation Activities of Rock Phosphate In Heavy-Metal Contaminated Cocoa Plantation Soil In Owena, South Western, Nigeria

2 Abstract: The continuous use of copper-based fungicide over the years in the control of black pod disease has led to heavy-metal accumulation in cocoa soils due to its non biodegradable nature. Considering the limited, available forest, it is therefore, necessary for the existing heavy-metal contaminated cocoa soils to undergo soil remediation in order to reduce the bioavailability of metals for plant uptake and ultimately minimize the risk of heavy metal toxicological effects in consumers of cocoa products. Polypropylene pots were filled with 2.5kg heavy-metal contaminated soil collected from a cocoa plantation in Idanre, Ondo State, Nigeria. The soil samples were thoroughly mixed with Sokoto rock phosphate at the rate of 20, 40 and 60g phosphate per kg soil before sowing cocoa beans in the soils. The experiment was in a completely randomized block design (RCBD) replicated thrice. At six months after planting, the seedlings were removed and processed according to standard procedure. Seedlings' leaves were analyzed for Cu and Pb using Atomic absorption spectrophotometer. Results showed that, bioavailable Cu in soil was reduced by 19, 35 and 42% due to application of 20, 40 and 60g phosphate per kg soil respectively while, Pb was reduced by 12, 23 and 25% respectively. The application of 20g, 40g and 60g rock phosphate reduced foliar Cu by 80, 69 and 85% while foliar Pb was reduced by 88, 89 and 77% respectively. The findings showed that, Sokoto rock phosphate which is readily available is a potential candidate for the remediation of heavy metal contaminated cocoa soils.
Myosin Va mediates BDNF-induced postendocytic recycling of full-length TrkB and its translocation into dendritic spines

ABSTRACT Brain-derived neurotrophic factor (BDNF) plays an important role in neuronal survival, neurite outgrowth and synaptic plasticity by activating the receptor tropomyosin receptor kinase B (TrkB, also known as NTRK2). TrkB has been shown to undergo recycling after BDNF stimulation. We have previously reported that full-length TrkB (TrkB-FL) are recycled through a Rab11-dependent pathway upon BDNF stimuli, which is important for the translocation of TrkB-FL into dendritic spines and for the maintenance of prolonged BDNF downstream signaling during long-term potentiation (LTP). However, the identity of the motor protein that mediates the local transfer of recycled TrkB-FL back to the plasma membrane remains unclear. Here, we report that the F-actin-based motor protein myosin Va (Myo5a) mediates the postendocytic recycling of TrkB-FL. Blocking the interaction between Rab11 and Myo5a by use of a TAT-tagged peptide consisting of amino acids 55–66 of the Myo5a ExonE domain weakened the association between TrkB-FL and Myo5a and thus impaired TrkB-FL recycling and BDNF-induced TrkB-FL translocation into dendritic spines. Finally, inhibiting Myo5a-mediated TrkB-FL recycling led to a significant reduction in prolonged BDNF downstream signaling. Taken together, these results show that Myo5a mediates BDNF-dependent TrkB-FL recycling and contributes to BDNF-induced TrkB spine translocation and prolonged downstream signaling.
An Novel Dynamic Clustering Algorithm Based on Geographical Location for Wireless Sensor Networks

One of the key problems for wireless sensor networks (WSNs) is how to make the best of limited energy. The conventional clustering method has the unique potential to be the framework for energy-conserving wireless sensor networks . In this paper, a novel dynamic clustering algorithm based on geographical location information(GL-DC)is proposed for WSNs. Comparing with other algorithms, GL-DC has two obvious features: Firstly, it improves energy utility by changing the activity of wireless communication module of sensor nodes, energy model and state transition of sensor nodes. Secondly, GL-DC adopts the maximum ratio of residual energy and the square of distance between node and Sink to select cluster head in order to prolong network lifetime. Simulation results show that the performance of GL-DC is better than LEACH , EADEEG and PEGASIS algorithm for wireless sensor network with high-density deployment and low traffic.
3D image reconstruction of transparent gas flow by parallel phase-shifting digital holography

The authors descrive a technique for three-dimensional (3D) imaging of transparent and dynamic object from the phase image obtained by parallel phase-shifting digital holography. The phase image of the transparent and dynamic object is recorded and reconstructed by parallel phase-shifting digital holography. Inverse Abel transform is applied to the phase image to calculate 3D image of transparent and dynamic object. The calculation result is radial distribution of the phase difference of object wave. Radial distribution of the 3D image of the object is obtained by using the radial distribution of the phase difference. Finally, by assuming that the shape of the object is axially symmetric with respect to the direction in which the object moves, the 3D image of the object is reconstructed by using the radial distribution of 3D image of the object. The technique is experimentally demonstrated to obtain the 3D image of transparent gas flow. Holograms of the transparent gas are recorded by the camera at 180,000 frames per second. 3D motion picture of the transparent gas flow, which provides the dynamic information of the inside of the object, was obtained from the holograms.