Miracle-dz/UniFAR-FTUs huggingface dataset:
How Do Interventions That Exemplify the Joint Principles of the Patient Centered Medical Home Affect Hemoglobin A1C in Patients With Diabetes

Objective: To review the impact of the Joint Principle of the Patient Centered Medical Home (PCMH) on hemoglobin A1C (HbA1C) in primary care patients with diabetes. Methods: Systematic review of English articles using approximate terms for (1) the 7 principles of the PCMH, (2) primary care, and (3) HbA1C. We included experimental and observational studies. Three authors independently extracted data and obtained summary estimates for concepts with more than 2 high-quality studies. Results: Forty-three studies published between 1998 and 2012 met inclusion criteria, 33 randomized and 10 controlled before–after studies. A physician-directed medical practice (principle 2) lowered HbA1C values when utilizing nursing (mean difference [MD] −0.36, 95% confidence interval [CI] −0.43 to −0.28) or pharmacy care management (MD −0.76; 95% CI −0.93 to −0.59). Whole-person orientation (principle 3) also lowered HbA1C (MD −0.72, 95% CI −0.98 to −0.45). Studies of coordinated and integrated care (principle 4) and quality and safety interventions (principle 5) did not consistently lower HbA1C when reviewed in aggregate. We did not identify high-quality studies to make conclusions for personal physician (principle 1), enhanced access (principle 6), and payment (principle 7). Conclusion: Our review found individual interventions that reduced the HbA1C by up to 2.0% when they met the definitions set by of the Joint Principles of the PCMH. Two of the principles—physician-led team and whole-person orientation—consistently lowered the HbA1C. Other principles had limited data or made little to no impact. Based on current evidence, PCMH principles differentially influence the HbA1C, and there are opportunities for additional research.
Complexes containing cationic and anionic pH-sensitive liposomes: comparative study of factors influencing plasmid DNA gene delivery to tumors

pH-sensitive liposomes represent an effective gene vector in cancer therapy. However, their use is greatly hampered by their relatively low transfection efficiency. To improve the transfection efficiency of pH-sensitive liposomes, we prepared complexes containing 3β-[N-(N′,N′-dimethylaminoethane) carbamoyl] cholesterol (DC-Chol) and dioleoylphosphatidyl ethanolamine (DOPE) liposomes and pH-sensitive liposomes composed of cholesteryl hemisuccinate (CHEMS) and DOPE, and evaluated the influence of various factors on plasmid DNA (pDNA) transfection efficiency. All DC-Chol/DOPE liposome/pDNA and pH-sensitive liposome complexes showed similarly potent pH sensitivity. In the presence of serum-containing medium, two optimized complexes of DC-Chol/DOPE liposomes/pDNA and pH-sensitive PEGylated liposomes showed high transfection efficiency of 22.94% and 20.07%, respectively. Notably, DC-Chol/DOPE (2:3) liposomes/pH-sensitive PEGylated (1%) liposome complexes with a charge ratio of 1:1 (m/m [+/−]) showed enhanced accumulation in tumors in vivo. Our results show the influence of various factors on pDNA transfection efficiency in complexes of DC-Chol/DOPE liposomes and pH-sensitive PEGylated liposomes. Understanding of such mechanisms will lead to better design of complexes of DC-Chol/DOPE liposomes and pH-sensitive liposomes for gene therapy.
A probabilistic relational algebra for the integration of information retrieval and database systems

We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. In PRA, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Based on intensional semantics, the tuple weights of the result of a PRA expression always conform to the underlying probabilistic model. We also show for which expressions extensional semantics yields the same results. Furthermore, we discuss complexity issues and indicate possibilities for optimization. With regard to databases, the approach allows for representing imprecise attribute values, whereas for information retrieval, probabilistic document indexing and probabilistic search term weighting can be modeled. We introduce the concept of vague predicates which yield probabilistic weights instead of Boolean values, thus allowing for queries with vague selection conditions. With these features, PRA implements uncertainty and vagueness in combination with the relational model.
Multi-modal Brain Tumor Segmentation via Latent Atlases

In this work, a generative approach for patient-speciﬁc segmentation of brain tumors across diﬀerent MR modalities is presented. It is based on the latent atlas approach presented in [7, 8]. The individual segmentation of each scan supports the segmentation of the ensemble by sharing common information. This common information, in the form of a spatial probability map of the tumor location is inferred concurrently with the evolution of the segmentations. The joint segmentation problem is solved via a statistically driven level-set framework. We illustrate the method on an example application of multimodal and longitudinal brain tumor segmentation, reporting promising segmentation results.
Presence of Bacterial Phage-Like DNA Sequences in Commercial Taq DNA Polymerase Reagents

ABSTRACT Many studies have reported the presence of bacterial DNA contamination in commercial Taq DNA polymerase reagents. This is the first report of the presence of phage-like DNA sequences in certain commercial Taq DNA polymerase reagents. Precautions are needed when using amplification reagents with exogenous DNAs.
An improved design algorithm based on interval analysis for spatial parallel manipulator with specified workspace

We propose an algorithm that enable one to determine almost all the geometries of a simplified Gough platform whose workspace must include an arbitrary set of poses. Five design parameters have been identified and we assume that the stroke of the linear actuator is known. This algorithm is based on an interval analysis approach and its result is a set of ranges for the design parameters such that the workspace of any robot whose geometry is defined by values within the ranges will include the specified poses.
A Three-Layered Graph-Based Learning Approach for Remote Sensing Image Retrieval

With the emergence of huge volumes of high-resolution remote sensing images produced by all sorts of satellites and airborne sensors, processing and analysis of these images require effective retrieval techniques. To alleviate the dramatic variation of the retrieval accuracy among queries caused by the single image feature algorithms, we developed a novel graph-based learning method for effectively retrieving remote sensing images. The method utilizes a three-layer framework that integrates the strengths of query expansion and fusion of holistic and local features. In the first layer, two retrieval image sets are obtained by, respectively, using the retrieval methods based on holistic and local features, and the top-ranked and common images from both of the top candidate lists subsequently form graph anchors. In the second layer, the graph anchors as an expansion query retrieve six image sets from the image database using each individual feature. In the third layer, the images in the six image sets are evaluated for generating positive and negative data, and SimpleMKL is applied to learn suitable query-dependent fusion weights for achieving the final image retrieval result. Extensive experiments were performed on the UC Merced Land Use-Land Cover data set. The source code has been available at our website. Compared with other related methods, the retrieval precision is significantly enhanced without sacrificing the scalability of our approach.
A multiobjective genetic programming approach for pricing and hedging derivative securities

Genetic programming has become increasingly important in the broad field of finance. Due to the fact that in the context of pricing derivatives a lot of models are not amenable to analytical exact solutions, this is especially true for the research area of contingent claim pricing. Previous work in this field almost certainly always focuses on pricing such derivatives. In doing so, the implied hedging performance is totally ignored due to the lack of an analysis of price sensitivities which are fundamental building-blocks in hedging-strategies. In this contribution we apply a multiobjective genetic programming approach to the American put pricing problem and evaluate our individuals based on the pricing as well as on the hedging performance by means of symbolic differentiation.
Optimization of Lime Pretreatment of Sweet Sorghum Bagasse for Enzymatic Saccharification

With recent emphasis on development of alternatives to fossil fuels, sincere attempts are being made on finding suitable lignocellulosic feedstocks for biological conversion to fuels and chemicals. Sweet Sorghum is among the most widely adaptable cereal grasses, with high drought resistance, and ability to grow on low quality soils with low inputs. It is a C4 crop with high photosynthetic efficiency and biomass yield. Our research objective is to optimize lime pretreatment of sweet sorghum bagasse for its enzymatic conversion into fermentable sugars for biofuels. Sweet sorghum biomass was ground and passed through a 35 mesh screen. Moisture content of the biomass was approximately 12%. It had 32.3% cellulose, 21.2% hemicelluloses, and 8.3% acid detergent lignin on dry matter basis. Lime pretreatment was provided in atmospheric pressure at 100oC. The lime concentration was varied from 0.05 g/g biomass to 0.2 g/g biomass; reaction time was 1 hour to 3 hours, and liquid loading was varied from 7 to 20 ml lime solution /g biomass sample. Pretreated biomass was hydrolyzed with mixture of two enzymes, Accellerase® 1500 (Cellulase) at 0.24 mL/g of SSB and Accellerase® XC (xylanase) at 0.25 ml/g of SSB, and measured for total reducing sugars by DNS (1,3-dinitrosalicylic acid) reagent.
Changes in readiness to quit and self-efficacy among adolescents receiving a brief office intervention for smoking cessation

Abstract Purpose: To examine changes in readiness to quit and self-efficacy among adolescents who received a clinic-based, brief office intervention (BOI) for smoking cessation. Methods: This study utilized a prospective, pre–post- treatment design. Participants were adolescent smokers (34 females, 35 males) with a mean ± SD age of 15.8 ± 1.4 years; 86% were Caucasian, who were randomly assigned to receive the BOI as part of a larger clinical trial. They were recruited from three cities in the Midwest and Northeastern part of the United States. After the baseline assessment, the BOI was designed for adolescents to receive four weekly individual sessions with a research counselor lasting between 10 and 40 min each. The BOI includes motivational interviewing and cognitive–behavioral techniques. Readiness to quit was assessed at each treatment session using the stages of change algorithm. The validated Adolescent Smoking Self-Efficacy Scale (SES) was used to assess self-efficacy at baseline (week 0) prior to the intervention and at post-treatment (week 4). The SES items comprise three factors or subscales: opportunities to smoke, emotional stress, and friends' influence. Results: The percentage of adolescents who made improvement on readiness to quit from the baseline treatment session was statistically significant (p < .001) for each of the three subsequent treatment sessions. Self-efficacy scores increased significantly (p < .004) from baseline to post-treatment for all three subscales. Conclusions: Adolescents receiving a BOI progressed in their readiness and self-efficacy to quit. Understanding the change process among adolescent smokers during treatment could influence the design of future stop smoking interventions.
Formant-based synthesis of singing

Rule-driven formant synthesis is a legacy technique that still has certain advantages over currently prevailing methods. The memory footprint is small and the flexibility is high. Using a modular, interactive synthesis engine, it is easy to test the perceptual effect of different source waveform and formant filter configurations. The rule system allows the investigation of how different styles and singer voices are represented in the low-level acoustic features, without changing the score. It remains difficult to achieve natural-sounding consonants and to integrate the higher abstraction levels of musical expression.
NUTRITIONAL INTAKE EVOLUTION IN ADOLESCENT SPORTING BOYS OVER THE LAST TWO DECADES

Abstract The AIM of the study was to evaluate the nutritional trends in young elite male soccer players, attending national soccer league at RFC Bruges over the last two decades. At the start of each season, players and parents are instructed about normal healthy nutrition and fluid intake by dieticians. METHODS: Since1983, dieticians perform dietary habit surveys in the adolescent player groups. They instruct players and parents how to record all food and fluid intake during 3 days, a training-day, a match-day and an off-day. It is asked to do the recordings when players and parents are together and parents are asked to supervise the recording. Intakes are calculated using the Becel institute nutrition software (BINS), Becel®, 2003. RESULTS: A significant decrease of energy intake/m2 is observed over the last 20 years. Body composition, measured as age-matched body mass index remained at median levels for the population during this period. An important modification of dietary content towards the recommended daily intakes is observed. Fat, saturated fat and cholesterol intake decreased dramatically. Carbohydrate intake increased. CONCLUSIONS: A positive evolution towards the recommended dietary composition is observed over the years. However, the decrease in caloric intake without influence on the body mass index could suggest that these elite male footballers have a decreased physical activity as compared to 20 years ago.
Nerve sprouting in innervated adult skeletal muscle induced by exposure to elevated levels of insulin-like growth factors

Partial denervation or paralysis of adult skeletal muscle is followed by nerve sprouting, probably due to release of diffusible sprout- inducing activity by inactive muscle. Insulin-like growth factors (IGF1 and IFG2) are candidates for muscle-derived sprouting activity, because (a) they induce neurite growth from peripheral neurons in vitro; and (b) their mRNA levels in adult skeletal muscle increase severalfold after denervation or paralysis. We sought to determine whether the presence of elevated levels of IGFs in innervated adult skeletal muscle was sufficient to produce intramuscular nerve growth. Low concentrations of IGFs induced massive neurite growth from enriched embryonic chick motoneurons in vitro. Half-maximal responses required 0.2 nM IGF2 or IGF1, or 20 nM insulin. Similar hormone binding properties of motoneuron processes in vitro were observed. Exposure of adult rat or mouse gluteus muscle in vivo to low quantities of exogenous IGF2 or IGF1 led to intramuscular nerve sprouting. Numbers of sprouts in IGF-exposed muscles were 10-fold higher than in vehicle- exposed or untreated muscles, and 12.2% of the end plates in IGF- exposed muscle (control: 2.7%) had sprouts growing from them. The nerve growth reaction was accompanied by elevated levels of intramuscular nerve-specific growth-associated protein GAP43. Additional properties of IGF-exposed muscle included modest proliferation of interstitial cells and elevated interstitial J1 immunoreactivity. These results suggest that elevated levels of IGFs in denervated or paralyzed muscle might trigger coordinate regenerative reactions, including nerve sprouting and expression of nerve growth-supporting substrate molecules by activated interstitial cells.
Making a chocolate chip: development and evaluation of a 6K SNP array for Theobroma cacao

Theobroma cacao, the key ingredient in chocolate production, is one of the world's most important tree fruit crops, with ∼4,000,000 metric tons produced across 50 countries. To move towards gene discovery and marker-assisted breeding in cacao, a single-nucleotide polymorphism (SNP) identification project was undertaken using RNAseq data from 16 diverse cacao cultivars. RNA sequences were aligned to the assembled transcriptome of the cultivar Matina 1-6, and 330,000 SNPs within coding regions were identified. From these SNPs, a subset of 6,000 high-quality SNPs were selected for inclusion on an Illumina Infinium SNP array: the Cacao6kSNP array. Using Cacao6KSNP array data from over 1,000 cacao samples, we demonstrate that our custom array produces a saturated genetic map and can be used to distinguish among even closely related genotypes. Our study enhances and expands the genetic resources available to the cacao research community, and provides the genome-scale set of tools that are critical for advancing breeding with molecular markers in an agricultural species with high genetic diversity.
TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations

We describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on temporal information processing. TempEval-3 follows on from previous TempEval events, incorporating: a three-part task structure covering event, temporal expression and temporal relation extraction; a larger dataset; and single overall task quality scores.
From welfare states to welfare sectors: Explaining sectoral differences in occupational pensions with economic and political power of employees

Studies analysing welfare have previously focused on countries as units. In the course of pension cuts and the increasing importance of occupational welfare, our traditional understanding of a homogeneous welfare state is being challenged. In this article, I distinguish between both economic individual power (employee skills) and political collective power (trade unions), and their relation with different occupational pensions. A combined analysis by both factors is not common, where employee skills and power resources are traditionally treated as separate, rival explanations of public welfare. Combining the ‘method of difference’ with the ‘method of agreement’, the article first presents the within-country variety of occupational pensions in Germany, Italy, the United Kingdom and Denmark. Occupational pensions in the same economic sectors across countries are then used as the units of analysis in order to illustrate the plausible determinants of economic individual power and political collective power.
Accepted for Publication in Ieee Transactions on Signal Processing 1 Sorted Order-k Voronoi Diagrams for Model-independent Source Localization in Wireless Sensor Networks

—Localization of an isotropic source using energy measurements from randomly deployed sensors is considered. In particular, an optimization problem that does not require knowledge of the underlaying energy decay model is proposed, and a condition under which the optimal solution can be computed is given. This condition employs a new geometric construct introduced here, called the sorted order-K Voronoi diagram. We give centralized and distributed algorithms for source localization in this setting. Finally, analytical results and simulations are used to verify the performance of the developed algorithms.
Enabling Cross-Domain Collaboration in Molecular Dynamics Workflows

Molecular dynamics (MD) simulation is used increasingly often for materials design to reduce the costs associated with the pure experimental approach. Complex MD simulations are, however, notoriously hard to set up. It requires expertise in several distinct areas, including the peculiarities of a particular simulator tool, the chemical properties of the family of materials being studied, as well as in general C/C++ or Python programming. In this paper, we describe how MetaMDS, a web-based collaborative environment, allows experts of different domains to work together to create building blocks of MD simulations. These building blocks, capturing domain-specific knowledge at various levels of abstraction, are stored in a repository, and are shared with other users, who can reuse them to build complex simulation workflows. This approach has the potential to boost productivity in chemical and materials science research through separating concerns and promoting reuse in MD workflows. Keywords–Simulation; Metaprogramming; Online collaboration; Programming abstractions.
Online Algorithms for Sum-Product Networks with Continuous Variables

Sum-product networks (SPNs) have recently emerged as an attractive representation due to their dual interpretation as a special type of deep neural network with clear semantics and a tractable probabilistic graphical model. We explore online algorithms for parameter learning in SPNs with continuous variables. More specifically, we consider SPNs with Gaussian leaf distributions and show how to derive an online Bayesian moment matching algorithm to learn from streaming data. We compare the resulting generative models to stacked restricted Boltzmann machines and generative moment matching networks on real-world datasets.
Chromosome 12p13 variants predict recurrence of ischaemic stroke in a Chinese population

A recent genome‐wide association study identified two genetic variants (rs12425791 and rs11833579) on chromosome 12p13 that confer risk of ischaemic stroke. The purpose of this study was to examine whether these two polymorphisms are associated with stroke onset and prognosis in a Chinese population.
The Power of Music : Searching for Power-Laws in Symbolic Musical Data

Recently, many research contributions focus on showing that specific real-life phenomena or measured quantities of our everyday life obey a power-law, a scale free distribution. Inspired by the numerous studies in power-laws, in this paper, we explore the existence of power-law relationships in musical data. We provide the context where such power-laws do exist, and we give experimental results that validate our assumptions. The results are based on several musical collections related to classic, European and Asian music.
Vectorization of Hybrid Breadth First Search on the Intel Xeon Phi

The Breadth-First Search (BFS) algorithm is an important building block for graph analysis of large datasets. The BFS parallelisation has been shown to be challenging because of its inherent characteristics, including irregular memory access patterns, data dependencies and workload imbalance, that limit its scalability. We investigate the optimisation and vectorisation of the hybrid BFS (a combination of top-down and bottom-up approaches for BFS) on the Xeon Phi, which has advanced vector processing capabilities. The results show that our new implementation improves by 33%, for a one million vertices graph, compared to the state-of-the-art.
A NEW STRAIGHT LINE RECONSTRUCTION METHODOLOGY FROM MULTI-SPECTRAL STEREO AERIAL IMAGES

In this study, a new methodology for the reconstruction of line features from multispectral stereo aerial images is presented. We take full advantage of the existing multispectral information in aerial images all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The line to line correspondences between the stereo images are established using a new pair-wise stereo matching approach. The approach involves new constraints, and the redundancy inherent in pair relations gives us a possibility to reduce the number of false matches in a probabilistic manner. The methodology is tested over three different urban test sites and provided good results for line matching and reconstruction.