Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends in alternative medicine use in the United States, 1990-1997: results of a follow-up national survey.

CONTEXT A prior national survey documented the high prevalence and costs of alternative medicine use in the United States in 1990. OBJECTIVE To document trends in alternative medicine use in the United States between 1990 and 1997. DESIGN Nationally representative random household telephone surveys using comparable key questions were conducted in 1991 and 1997 measuring utilization in 1990 and 1997, respectively. PARTICIPANTS A total of 1539 adults in 1991 and 2055 in 1997. MAIN OUTCOMES MEASURES Prevalence, estimated costs, and disclosure of alternative therapies to physicians. RESULTS Use of at least 1 of 16 alternative therapies during the previous year increased from 33.8% in 1990 to 42.1% in 1997 (P < or = .001). The therapies increasing the most included herbal medicine, massage, megavitamins, self-help groups, folk remedies, energy healing, and homeopathy. The probability of users visiting an alternative medicine practitioner increased from 36.3% to 46.3% (P = .002). In both surveys alternative therapies were used most frequently for chronic conditions, including back problems, anxiety, depression, and headaches. There was no significant change in disclosure rates between the 2 survey years; 39.8% of alternative therapies were disclosed to physicians in 1990 vs 38.5% in 1997. The percentage of users paying entirely out-of-pocket for services provided by alternative medicine practitioners did not change significantly between 1990 (64.0%) and 1997 (58.3%) (P=.36). Extrapolations to the US population suggest a 47.3% increase in total visits to alternative medicine practitioners, from 427 million in 1990 to 629 million in 1997, thereby exceeding total visits to all US primary care physicians. An estimated 15 million adults in 1997 took prescription medications concurrently with herbal remedies and/or high-dose vitamins (18.4% of all prescription users). Estimated expenditures for alternative medicine professional services increased 45.2% between 1990 and 1997 and were conservatively estimated at $21.2 billion in 1997, with at least $12.2 billion paid out-of-pocket. This exceeds the 1997 out-of-pocket expenditures for all US hospitalizations. Total 1997 out-of-pocket expenditures relating to alternative therapies were conservatively estimated at $27.0 billion, which is comparable with the projected 1997 out-of-pocket expenditures for all US physician services. CONCLUSIONS Alternative medicine use and expenditures increased substantially between 1990 and 1997, attributable primarily to an increase in the proportion of the population seeking alternative therapies, rather than increased visits per patient.
Grb2-Mediated Recruitment of USP9X to LAT Enhances Themis Stability following Thymic Selection

Themis is a new component of the TCR signaling machinery that plays a critical role during T cell development. The positive selection of immature CD4+CD8+ double-positive thymocytes and their commitment to the CD4+CD8− single-positive stage are impaired in Themis−/− mice, suggesting that Themis might be important to sustain TCR signals during these key developmental processes. However, the analysis of Themis mRNA levels revealed that Themis gene expression is rapidly extinguished during positive selection. We show in this article that Themis protein expression is increased in double-positive thymocytes undergoing positive selection and is sustained in immature single-positive thymocytes, despite the strong decrease in Themis mRNA levels in these subsets. We found that Themis stability is controlled by the ubiquitin-specific protease USP9X, which removes ubiquitin K48-linked chains on Themis following TCR engagement. Biochemical analyses indicate that USP9X binds directly to the N-terminal CABIT domain of Themis and indirectly to the adaptor protein Grb2, with the latter interaction enabling recruitment of Themis/USP9X complexes to LAT, thereby sustaining Themis expression following positive selection. Together, these data suggest that TCR-mediated signals enhance Themis stability upon T cell development and identify USP9X as a key regulator of Themis protein turnover.
The LIUM speech transcription system: a CMU Sphinx III-based system for French broadcast news

This paper presents the system used by the LIUM to participate in ESTER, the french broadcast news evaluation campaign. This system is based on the CMU Sphinx 3.3 (fast) decoder. Some tools are presented which have been added on different steps of the Sphinx recognition process: segmentation, acoustic model adaptation, word-lattice rescoring. Several experiments have been conducted on studying the effects of the signal segmentation on the recognition process, on injecting automatically transcribed data into training corpora, or on testing different approaches for acoustic model adaptation. The results are presented in this paper. With very few modifications and a simple MAP acoustic model estimation, Sphinx3.3 decoder reached a word error rate of 28.2%. The entire system developed by LIUM obtained 23.6% as official word error rate for the ESTER evaluation, and 23.4% as result of an unsubmited system.
EXPERIENCE IN METRICS AND MEASUREMENTS FOR N-VERSION PROGRAMMING

The N-Version Programming (NVP) approach applies the idea of design diversity to obtain fault-tolerant software units, called N-Version Software (NVS) units. The effectiveness of this approach is examined by the software diversity achieved in the member versions of an NVS unit. We define and formalize the concept of design diversity and software diversity in this paper. Design diversity is a property naturally applicable to the NVP process to increase its fault-tolerance attributes. The baseline design diversity is characterized by the employment of independent programming teams in the NVP. More design diversity investigations could be enforced in the NVP design process, including different languages, different tools, different algorithms, and different methodologies. Software diversity is the resulting dissimilarities appearing in the NVS member versions. We characterize it from four different points of view that are designated as: structural diversity, fault diversity, tough-spot diversity, and failure diversity. Our goals are to find a way to quantify software diversity and to investigate the measurements which can be applied during the life cycle of NVS to gain confidence that operation will be dependable when NVS is actually employed. The versions from a six-language N-Version Programming project for fault-tolerant flight control software were used in the software diversity measurement.
Machine Learning for High Throughput Genomic Data Analysis

Machine learning methods have been successfully applied to computational biology and bioinformatics for decades with both unsupervised learning and supervised learning. Recent advancement in high throughput genomic data profiling, such as high throughput sequencing and large-scale gene expression profiling, has became a powerful tool for both fundamental biological research and medicine. For example, high throughput sequencing now is possible to sequence billions of bases both fast and cheap, such as Illumina's latest sequencer HiSeq X that can sequence 32 human genomes per week with each costing less than \$1000. With the generation of millions or even billions of signals (e.g. sequencing reads) per experiment and thousands or even millions of experiments per study (e.g. large-scale gene expression profiling), there arises a great need for more advanced machine learning models for analysing high throughput genomic data using both unsupervised and supervised learning methods. In this thesis, we try to solve two main challenges in high throughput genomic data analysis, 1) deconvolving the sequencing data from more than one cell population, e.g. heterogeneous tumor tissues, using unsupervised probabilistic learning methods such as mixture models with latent variables; 2) modelling the nonlinear and hierarchical patterns within high throughput genomic data using supervised deep learning methods such as convolutional neural networks. We present five new models to solve these two challenges, each of them is applied to a specific problem. The first three models focus on deconvolving tumor heterogeneity: Chapter 2 presents a probabilistic model to deconvolve tumor purity and ploidy; Chapter 3 further extends the model to infer tumor subclonal populations; Chapter 4 presents a probabilistic model to deconvolve tumor transcriptome expression. The last two models focus on applying deep learning methods in analysing large scale genomic data: Chapter 5 presents a deep learning method for gene expression inference; Chapter 6 presents a deep learning method to understand sequence conservation.
A Robust System Architecture for Mining Semi-Structured Data

The value of extracting knowledge from semi-structured data is readily apparent with the explosion of the WWW and the advent of digital libraries. This paper proposes a versatile system architecture for text mining that maintains structured data components in a relational database and unstructured concepts in a concept library. After a detailed explanation of our system architecture, we briefly describe IRIS, our prototype rule generation system
Analysis of Dynamic Heuristics for Workflow Scheduling on Grid Systems

Scheduling is an important factor for the efficient execution of computational workflows on grid environments. A large number of static scheduling heuristics has been presented in the literature. These algorithms allocate tasks before job execution starts and assume a precise knowledge of timing information, which may be difficult to obtain in general. To overcome this limitation of static strategies, dynamic scheduling strategies may be needed for a changing environment such as the grid. While they incur runtime overheads, they may better adapt to timing changes during job execution. In this work, we analyse five well-known heuristics (min-min, max-min, sufferage, HEFT and random) when used as static and dynamic scheduling strategies in a grid environment in which computing resources exhibit congruent performance differences. The analysis shows that non-list based heuristics are more sensitive than list-based heuristics to inaccuracies in timing information. Static list-based heuristics perform well in the presence of low or moderate inaccuracies. Dynamic versions of these heuristics may be needed only in environments where high inaccuracies are observed. Our analysis also shows that list-based heuristics significantly outperform non-list based heuristics in all cases and, therefore, constitute the most suitable strategies by which to schedule workflows either statically or dynamically
Phosphenes electrically evoked with DTL electrodes: a study in patients with retinitis pigmentosa, glaucoma, and homonymous visual field loss and normal subjects.

PURPOSE To develop an improved, easy, and safe method for reliably eliciting electrical phosphenes and to compare reported perceptions and values of chronaxie and rheobase in healthy individuals and patients with distinct ophthalmic diseases. METHODS DTL electrodes were used with a four-alternative, forced-choice method to determine psychophysically the strength-duration curves by using the Weiss model of electrical tissue stimulation in 47 subjects: healthy individuals (n = 17; n = 6 under light- and dark-adapted conditions), patients with open-angle glaucoma (n = 9), retinitis pigmentosa (RP; n = 14), amblyopia (n = 3), or homonymous visual field loss (n = 4). RESULTS In all subjects, thresholds were safely determined without side effects. Subjects reported a homogenous, central, white, steady phosphene. The rheobase was higher in glaucoma and patients with RP than in healthy subjects (0.1129 +/- 0.0314 and 0.6868 +/- 0.1054 vs. 0.0383 +/- 0.0057 mA; P = 0.05 and <0.00001, respectively), and it increased with age (r = 0.51; P = 0.038). The rheobase was lower in light- than in dark-adapted conditions (difference 0.015 mA; P = 0.016). In patients with RP, no correlation was found between rheobase or chronaxie and retinal thickness (in OCT) or number of years after reading loss, but there was a correlation between rheobase and visual acuity (P = 0.014). In patients with RP or glaucoma, no inhomogeneity of phosphenes was reported, but all patients with homonymous visual field loss reported lateralization of the phosphene into the area of visual loss. CONCLUSIONS Use of DTL electrodes to elicit electrical phosphenes is safe, fast, and reliable. It bears significant advantages over corneal electrodes and provides a valuable tool to elucidate ophthalmologic disease processes and to screen candidates for retinal prostheses.
Electricity load and price forecasting with influential factors in a deregulated power industry

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.
Attention deficit hyperactivity symptoms and disorder (ADHD) among African children: a review of epidemiology and co-morbidities.

OBJECTIVE To review literature from Africa on the epidemiology of ADHD as well attention deficit hyperactivity symptoms and associated co-morbid conditions among African children. METHOD A literature search was done through Pubmed/MEDLINE and Google Scholar using then following terms, "attention deficit", "hyperactivity disorders", "epidemiology", "co-morbid conditions", "Africa". Nine studies met the inclusion criteria with four studies coming from South Africa, two each from Democratic Republic of Congo and Nigeria respectively and one from Ethiopia. RESULTS The prevalence of ADHD varied with rates of between 5.4% and 8.7%, amongst school children, 1.5% amongst children from the general population between 45.5% to 100.0% amongst special populations of children with possible organic brain pathology. Common associated co-morbid conditions were oppositional defiant disorder, conduct disorder as well as anxiety/depressive symptoms. CONCLUSION Published data from Africa is limited. For effective healthcare policy further studies are needed to define the magnitude and burden of ADHD and other childhood neurodevelopmental disorders in Africa.
Collaborative Decoding of Interleaved Reed–Solomon Codes and Concatenated Code Designs

Interleaved Reed-Solomon codes are applied in numerous data processing, data transmission, and data storage systems. They are generated by interleaving several codewords of ordinary Reed-Solomon codes. Usually, these codewords are decoded independently by classical algebraic decoding methods. However, by collaborative algebraic decoding approaches, such interleaved schemes allow the correction of error patterns beyond half the minimum distance, provided that the errors in the received signal occur in bursts. In this work, collaborative decoding of interleaved Reed-Solomon codes by multisequence shift-register synthesis is considered and analyzed. Based on the framework of interleaved Reed-Solomon codes, concatenated code designs are investigated, which are obtained by interleaving several Reed-Solomon codes, and concatenating them with an inner code.
Art Talk: Developing Visual and Verbal Literacy

The National Association for the Education of Young Children (NAEYC) and the National Art Education Association (NAEA) have endorsed best practices for art experiences with young children. These practices include developmentally appropriate materials, equipment, and activities (Bredekamp & Copple 1997; Colbert & Taunton 1992). Adults are encouraged to facilitate young artists’ development by providing adequate opportunities for them to explore a variety of materials, media, and tools. In addition, adults are encouraged to extend children’s learning with verbal stimulation.
Unified Spectral Clustering with Optimal Graph

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to severe information loss and performance degradation. First, predefined similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a unified framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.
Language, Reading, and Readability Formulas: Implications for Developing and Adapting Tests

Issues pertaining to language and reading while developing and adapting tests are examined. Strengths and limitations associated with the use of readability formulas are discussed. Their use should be confined to paragraphs and longer passages, not items. Readability methods that consider both quantitative and qualitative variables and are performed by seasoned professionals are recommended. Research examining the use of readability formulas applied to test content is needed.
Repository April 2016 Interactive Brain Tumor Segmentation with Inclusion Constraints

This thesis proposes an improved interactive brain tumor segmentation method based on graph cuts, which is an efficient global optimization framework for image segmentation, and star shape, which is a general segmentation shape prior with minimal user assistance. Our improvements lie in volume ballooning, compactness measure and inclusion constraints. Volume ballooning is incorporated to help to ”balloon” segmentation for situations where the foreground and background have similar appearance models and changing relative weight between appearance model and smoothness term cannot help to achieve an accurate segmentation. We search different ballooning parameters for different slices since an appropriate ballooning force may vary between slices. As the evaluation for ”goodness of segmentation” in parameter searching, two new compactness measures are introduced, ellipse fitting and convexity deviation. Ellipse fitting is a measure of compactness based on the deviation from an ellipse of best fit, which prefers segmentation with an ellipse shape. And convexity deviation is a more strict measure for preferring convex segmentation. It uses the number of convexity violation pixels as the measure for compactness. Inclusion constraints is added between slices to avoid side slice segmentation larger than the middle slice problem. The inclusion constraints consist of mask inclusion, which is implemented by an unary term in graph cuts, and pairwise inclusion, which is implemented by a pairwise term. Margin is allowed in inclusion so that the inclusion region is enlarged. With all these improvements, the final result is promising. The best performance for our dataset is 88% compared to the previous system in [25] that achieved 87%.
What can local and geographic population limits tell us about distributions?

PREMISE OF THE STUDY Understanding the evolutionary and ecological factors that determine plant distributions is of primary importance in botanical research. These factors may vary in predictable ways across different spatial scales, and thus, we can leverage scale to reveal the underlying processes limiting plant distributions. METHODS We review various research considerations across local and geographic scales, including the investigation of dispersal and habitat limitation, evolutionary factors, abiotic and biotic factors, and research logistics. We also present two case studies, slender monkeyflower (Mimulus leptaleus) and cut-leaf monkeyflower (Mimulus laciniatus), in the California Sierra Nevada. KEY RESULTS At a local spatial scale (within 50 m), no seeds were produced from plants sown at sites located just beyond known patches of M. leptaleus, but within the species' geographic range. At a much broader spatial scale (kilometers), at the highest and lowest elevations of the species' range, we found greatly reduced abundance and fecundity in plants sown outside of the geographic range limits of M. laciniatus. CONCLUSIONS These cases illustrate two contrasting spatial scales, yet agree in their illustration of strong habitat limitation. We end by discussing future avenues of research and by suggesting ways botanical researchers can frame their studies to maximize information gained on species requirements, distribution limits, and conservation among varying spatial scales.
A new, specular reflection-based, precorneal tear film stability measurement technique in a rabbit model: viscoelastic increases tear film stability.

PURPOSE To develop a safe, noninvasive, noncontact, continuous in vivo method to measure the dehydration rate of the precorneal tear film and to compare the effectiveness of a viscoelastic agent in maintaining the precorneal tear film to that of a balanced salt solution. METHODS Software was designed to analyze the corneal reflection produced by the operating microscope's coaxial illumination. The software characterized the shape of the reflection, which became distorted as the precorneal tear film evaporated; characterization was accomplished by fitting an ellipse to the reflection and measuring its projected surface area. Balanced salt solution Plus (BSS+) and a 2% hydroxypropylmethylcellulose viscoelastic were used as the test agents. The tear film evaporation rate was characterized and compared over a period of 20 minutes in 20 eyes from 10 New Zealand white rabbits. RESULTS The ellipse axes ratio and surface area were found to decrease initially after each application of either viscoelastic or BSS+ and then to increase linearly as the tear film began to evaporate (P < 0.001) for eyes treated with BSS+ only. Eyes treated with BSS+ required 7.5 ± 2.7 applications to maintain sufficient corneal hydration during the 20-minute test period, whereas eyes treated with viscoelastic required 1.4 ± 0.5 applications. The rates of evaporation differed significantly (P < 0.043) between viscoelastic and BSS+. CONCLUSIONS The shape and surface area of the corneal reflection are strongly correlated with the state of the tear film. Rabbits' corneas treated with viscoelastic remained hydrated significantly longer than corneas treated with BSS+.