Miracle-dz/UniFAR-FTUs huggingface dataset:
Using primary care prescribing data to improve GP awareness of antidepressant adherence issues.

BACKGROUND Adherence to antidepressant therapy remains a major issue worldwide. Most people with depression are treated in a general practice setting, but many stop taking antidepressants before completing a six-month course as recommended by guidelines. OBJECTIVES To determine antidepressant adherence rates as indicated in primary care prescribing data and pharmacy dispensing data; to demonstrate commonly occurring patterns related to non-adherence, using a prescription visualisation tool we have developed; and to determine whether prescribing data is a good predictor of dispensing based adherence. METHODS We analysed general practice electronic prescribing data for the year ending 31 December 2006 and linked pharmacy dispensing records by National Health Index. We calculated medication adherence for patients starting antidepressants using a six-month evaluation period and a gap-based adherence measure. Patients with a gap of more than 15 days in antidepressant therapy were considered non-adherent. Using a prescription visualisation tool, we described common modes of non-adherence. RESULTS Out of 2713 patients, 153 satisfied our inclusion criteria. Thirty-nine percent of patients showed poor adherence based on prescribing and 68% showed poor adherence on dispensing. Prescribing based non-adherence had a positive predictive value of 98% (95% CI 92%-99%) and negative predictive value of 51% (CI 47%-52%) for dispensing based non-adherence. Three broad categories of non-adherence were identified: 1) failure to return for re-prescription, 2) failure to maintain adherence despite initial attempts and 3) failure to return for re-prescription in a timely manner. CONCLUSIONS Prescribing data identifies substantial adherence issues in antidepressant therapy. Clinicians should consider adherence issues as part of the overall treatment regime and discuss such issues during consultations.
CUDA-level Performance with Python-level Productivity for Gaussian Mixture Model Applications

Typically, scientists with computational needs prefer to use high-level languages such as Python or MATLAB; however, large computationally-intensive problems must eventually be recoded in a low level language such as C or Fortran by expert programmers in order to achieve sufficient performance. In addition, multiple strategies may exist for mapping a problem onto parallel hardware depending on the input data size and the hardware parameters. We show how to preserve the productivity of high-level languages while obtaining the performance of the best low-level language code variant for a given hardware platform and problem size using SEJITS, a set of techniques that leverages just-in-time code generation and compilation. As a case study, we demonstrate our technique for Gaussian Mixture Model training using the EM algorithm. With the addition of one line of code to import our framework, a domain programmer using an existing Python GMM library can run her program unmodified on a GPU-equipped computer and achieve performance that meets or beats GPU code hand-crafted by a human expert. We also show that despite the overhead of allowing the domain expert's program to use Python and the overhead of just-in-time code generation and compilation, our approach still results in performance competitive with hand-crafted GPU code.
Demonstrating the effect of software feedback on a distributed real-time MPEG video audio player

This paper presents the design, implementation and experimental analysis of a distributed, real-time MPEG video and audio player. The player is designed for use across the Internet, a shared environment with variable tra c and with great diversity in network bandwidth and host processing speed. We use a novel toolkit approach to build software feedback mechanisms for client/server synchronization, dynamic Quality-of-Service control, and system adaptiveness. Our experimental results show that the feedback mechanisms are e ective, and that the player performs very well in the Internet environment.
Recommendations for switching frames in mobile video streaming

Real time multimedia application is a challenging issue in the mobile networks, due to high compression requirements and limited and varying channel bandwidth. H.264/AVC addresses these issues with its excellent coding efficiency, error resiliency features and provision of bit rate adaptivity, using switching (SP-) frames. To reduce the quantization errors due to multiple quantization, SP- frames are usually coded with finer quantization parameters (QP) than the rest of the sequence, resulting in an increased bit rate, sometimes exceeding to that of intra (I-) frames. This limits the basic advantages of SP-frames over I-frames and that is better coding efficiency, leading to poor utilization of the resources like bandwidth. Thus the question is "what is the safe range of QP such that the bit rate of the switching frames does not exceed that of the I-frames"? The aim of this paper to suggest a solution to this problem by analyzing characteristics of SP-frames and to suggest a safe range of QP for switching frames ensuring that its bit rate remains lower than that of I-frames.
Analysis of VBR coded VoIP for traffic classification

Classification of Voice over Internet Protocol (VoIP) traffic is important for network management operations. The media traffic, which carries the voice on Real-time Transport Protocol (RTP), is subjected to variation in transmitted packet sizes and content due to the usage of Variable Bit Rate (VBR) codecs. In the absence of session level information, the RTP header does not uniquely identify the VBR voice codecs defined as dynamic payload type. In this paper we present a method to classify VoIP traffic coded with three VBR codecs - iSAC, SILK and Speex. We first formulate features to characterize an RTP flow based on packet size and entropy values of the packet content. The features are used for classification of RTP traffic based on codec using machine learning techniques. The paper reports classification results using the three machine learning algorithms, namely 1-NN, C4.5 and Naive Bayes. The results show an accuracy of over 98% for offline classification with the reduced feature set. The paper also presents the performance of the classifiers with varying size of available traffic.
A Pilot Study to Establish a Clinical Model to Perform Phase II Studies of Breast Cancer Chemopreventive Agents in Women at High Risk with Biomarkers as Surrogate Endpoints for Activity

Purpose: Use of surrogate end point biomarkers in phase II trials may help select agents that appear to have activity and might be evaluated in future phase III definitive trials of breast cancer prevention. We performed a pilot clinical trial to establish the logistics for a clinical model to perform phase II studies of breast cancer chemopreventive agents in women at high risk with novel imaging techniques and candidate surrogate end point biomarkers for activity. We chose tamoxifen to establish proof of principal with a known effective agent. Experimental Design: Women at a high risk of developing a new breast cancer and for whom tamoxifen was recommended were eligible. The women underwent baseline and 3 and 6 months mammogram and magnetic resonance imaging (MRI) of one breast to identify areas of water-like intensity (epithelial) and to determine the changes over time and MRI-directed core breast biopsies of these areas for surrogate end point biomarkers analysis. Results: From August 1999 to March 2001, 26 women underwent baseline imaging and core biopsies. Sixteen women took tamoxifen and 10 chose not to. Overall, 79% of the samples contained glandular tissue evaluable for histology, but only 66% of the samples were evaluable for marker analysis. Only 12 patients had specimens with glandular tissue sufficient for marker analysis both at baseline and in at least one follow-up. Because of the small number of women with matched samples, marker analysis was not informative. Conclusions: This study shows the feasibility of obtaining serial core breast biopsies from women at a high risk of developing a new breast cancer. Patient participation in this model is satisfactory, and such a model may provide indication of drug activity. MRI-directed biopsy did not provide a high yield of evaluable samples, and additional work on adequate collection of epithelial tissue for surrogate end point biomarker analysis is thus necessary.
Identification of RFID Tags in Framed-Slotted ALOHA with Robust Estimation and Binary Selection

In RFID systems, tag collision resolution is significantly important for fast tag identification. In this letter, we propose an accurate and robust tag estimation method and an effective tag anti-collision protocol. Performance analysis and simulation results show that the proposed estimation and anti-collision algorithm consumes 10-15% less time slots than the binary tree protocol and dynamic framed slotted ALOHA (DFSA). The proposed algorithm has also tolerance to the inaccuracy of tag estimation
The High Performance Embedded Computing Software Initiative: C++ and Parallelism Extensions to the Vector, Signal, and Image Processing Library Standard

The High Performance Embedded Computing Software Initiative (HPEC-SI) program is developing a unified computation and communication Application Programming Interface (API) and framework for high performance signal processing tasks on parallel computers. The goal of the program is to address the high cost of software in Department of Defense (DoD) systems by improving the portability and productivity of signal processing application development threefold, while improving performance by one half compared to current practices. This paper describes the motivation for the HPEC-SI program, its goals and approaches, and progress of the HPEC-SI Working Groups in extending the Vector, Signal, and Image Processing Library (VSIPL) standard to C++ and transparent operation in parallel computing systems. The C++ extension to VSIPL is described, and highlights of its advantages are considered. This paper also examines results from the Demonstration Working Group, and describes requirements and plans developed by the Applied Research Working Group for data parallel extensions to VSIPL and describes Development Working Group progress so far in developing parallel VSIPL.
Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds

Experimental programs have been underway for several years to determine the environmental effects of chemical compounds, mixtures, and the like. Among these programs is the National Toxicology Program (NTP) on rodent carcinogenicity. Because these experiments are costly and time-consuming, the rate at which test articles (i.e., chemicals) can be tested is limited. The ability to predict the outcome of the analysis at various points in the process would facilitate informed decisions about the allocation of testing resources. To assist human experts in organizing an empirical testing regime, and to try to shed light on mechanisms of toxicity, we constructed toxicity models using various machine learning and data mining methods, both existing and those of our own devising. These models took the form of decision trees, rule sets, neural networks, rules extracted from trained neural networks, and Bayesian classifiers. As a training set, we used recent results from rodent carcinogenicity bioassays conducted by the NTP on 226 test articles. We performed 10-way cross-validation on each of our models to approximate their expected error rates on unseen data. The data set consists of physical-chemical parameters of test articles, alerting chemical substructures, salmonella mutagenicity assay results, subchronic histopathology data, and information on route, strain, and sex/species for 744 individual experiments. These results contribute to the ongoing process of evaluating and interpreting the data collected from chemical toxicity studies.
Maize transformation technology development for commercial event generation

Maize is an important food and feed crop in many countries. It is also one of the most important target crops for the application of biotechnology. Currently, there are more biotech traits available on the market in maize than in any other crop. Generation of transgenic events is a crucial step in the development of biotech traits. For commercial applications, a high throughput transformation system producing a large number of high quality events in an elite genetic background is highly desirable. There has been tremendous progress in Agrobacterium-mediated maize transformation since the publication of the Ishida et al. (1996) paper and the technology has been widely adopted for transgenic event production by many labs around the world. We will review general efforts in establishing efficient maize transformation technologies useful for transgenic event production in trait research and development. The review will also discuss transformation systems used for generating commercial maize trait events currently on the market. As the number of traits is increasing steadily and two or more modes of action are used to control key pests, new tools are needed to efficiently transform vectors containing multiple trait genes. We will review general guidelines for assembling binary vectors for commercial transformation. Approaches to increase transformation efficiency and gene expression of large gene stack vectors will be discussed. Finally, recent studies of targeted genome modification and transgene insertion using different site-directed nuclease technologies will be reviewed.
Does the Number of Oocytes Retrieved Influence Pregnancy after Fresh Embryo Transfer?

Background The nature of the association between the number of oocytes retrieved and in vitro fertilization (IVF) outcomes after fresh embryo transfer remains unclear because of conflicting results reported in the studies on this subject. In addition, the influence of the quality of the embryos transferred is usually neglected. The objective of this study is to assess the relationships of the number of oocytes retrieved, the number and quality of embryos transferred, and the prospects of pregnancy after fresh embryo transfer. Methods The data on 3131 infertile women undergoing their first IVF treatment cycle between January 2009 and December 2010 were collected retrospectively. Restricted cubic splines and stratified analyses were used to explore the relationships between the number of oocytes retrieved, the number and quality of embryos transferred, and the IVF outcomes. Results When stratified by the number and quality of transferred embryos, no significant differences in the chances for clinical pregnancy and live birth were found in three groups of oocytes yielded (≤6, 7–14, or ≥15). The relationship between the number of oocytes retrieved and pregnancy is nearly a reflection of the pattern of the relationship between the number of oocytes retrieved and the probability of having two good-quality embryos transferred. The patients with the “optimal” number of oocytes were not only younger but also had the highest probability of having two good-quality embryos replaced. Conclusions Similarly aged patients have similar pregnancy prospects after fresh embryo transfer when the same number and quality of embryos are replaced, irrespective of their number of oocytes. Selecting the desired number of good-quality embryos for transfer is the key to IVF success. Thus, aiming at retrieving an optimal number of oocytes to maximize IVF outcomes in a fresh cycle could place undue stress on the patients and may not be the best medical decision.
Social-Aware Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks

Device-to-device (D2D) communication is a vital component for the next generation cellular network to bring hop gains, improve spectral reuse, and enhance system capacity. These benefits depend on efficiently solving several technical problems, among which resource allocation that shares spectrum resources between cellular users and D2D pairs is critically challenging. We propose a social-aware D2D resource sharing scheme that exploits social network properties of community and centrality for the new system design paradigm. Extensive simulations with realistic network settings demonstrate the effectiveness of our proposed scheme, which significantly improves the system performance compared to the existing schemes.
Perceived Risks and Benefits of Emerging Technologies in Professional Psychology

Technology has changed the way many operate within their profession. Psychologists have used technology for decades but often with caution. Due to the private nature of clinical practice, confidentiality and following ethical guidelines are imperative. As technology continues to rapidly alter the way people interact, the field of psychology is faced with many new ways to practice, teach, advertise, and supervise. This study explored the ways professionals view the use of technology in the psychological profession particularly in its relationship to risk and benefit to patient care. Professional and student members of the American Psychological Association were surveyed to explore their beliefs of risks and benefit to best patient care using advanced technologies. Data were collected using a semi-structured interview asking for input on a vignette of a fictitious psychologist who uses advanced technology such as internet therapy, video chat therapy via Skype, or other emerging technologies. Participants were more likely to consider using Smartphone technology than Second Life technology. Across all technologies assessed within this study, qualitative data analysis revealed Technology and Practice iv increased access to psychological services and cost savings as the major benefits to using technology. The major concerns were ethical in nature, especially in regard to confidentiality and boundary concerns. Increased privacy, increased feelings of support, reduction of stigma, efficiency, and improved work quality were identified as possible benefits of technology while loss of intimacy and rapport, inadvertent promotion of pathology, safety concerns, loss of privacy, and technology failures were identified as risks. Implications for future research, clinical work, and professional regulation are offered. Technology and Practice v Table of
Assessing Long-Term Health and Cost Outcomes of Patient-Centered Medical Homes Serving Adults With Poor Diabetes Control

The patient-centered medical home (PCMH) is an integrated primary care delivery model particularly suited for patients with poor diabetes control. Although PCMH models targeting adults with diabetes have shown some early success, little is known about the long-term benefits of medical homes in terms of health and cost outcomes. The performance of a PCMH model in adults with poor diabetes control was assessed using simulated controlled trial data obtained from the Archimedes model of disease progression and health care utilization. Using the Cardio-Metabolic Risk data set, we compared health and cost outcomes over a 20-year period between adults with poor diabetes control (HbA1c >9%) receiving standard care and these same adults receiving care under a PCMH model with a 49% HbA1c intervention improvement rate at a per-beneficiary per-month care management cost of $20 per month. The results suggest that the PCMH model has the potential to not only reduce the proportion of the population with bilateral blindness, foot amputations, and myocardial infarctions—and the mortality rate—but it can also do so in a cost-effective manner ($7898 per quality-adjusted life year). The PCMH model is cost saving for the population 50 to 64 years old and it is particularly cost-effective for men ($883 per quality-adjusted life year). Moreover, these effects are relatively large for adults 30 to 49 years old (lower bilateral blindness and death rates), women (lower foot amputation and death rates), and men (lower bilateral blindness and myocardial infarction rates). The PCMH model has potential long-term benefits to both patients with poor diabetes control as well as health care systems and providers willing to invest in this health care delivery approach.
A comment on 'A rate of convergence result for a universal D-semifaithful code'

In the above paper (see ibid., vol.39, no.3, p.813-20, 1993) Yu and Speed propose a universal pointwise D-semifaithful code whose expected compression ratio, for discrete memoryless sources, approaches the rate-distortion function at a rate O(n/sup -1/ log n). They also conjecture that this is the fastest achievable convergence rate for pointwise D-semifaithful codes. In this correspondence, we use a simple extension of Kraft's inequality and prove that this conjecture is true, at least for the Hamming distortion measure. >
Corticosteroid injections for painful shoulder: a meta-analysis.

BACKGROUND There are no systematic reviews of corticosteroids for shoulder pain that calculate the numbers needed to treat. AIM We wished to determine the effectiveness in terms of improvement of symptoms of intra-articular and subacromial injections of corticosteroid for rotator cuff tendonitis and frozen shoulder. DESIGN OF STUDY Systematic review and meta-analysis of randomised controlled trials. METHOD Data sources included the Cochrane Controlled Trials Register, Medline, EMBASE, hand searches and author contacts. The review methods required any randomised controlled trial in which the effectiveness of subacromial or intra-articular steroid injections versus placebo and versus non-steroidal anti-inflammatory medication, could be ascertained. The outcome was improvement of symptoms. The data abstraction was done independently, as was the validity assessment. The data was pooled using Review Manager 4.1. RESULTS Seven studies were reviewed for corticosteroids versus placebo and three for corticosteroids versus nonsteroidal anti-inflammatory drugs (NSAIDs). The relative risk for improvement for subacromial corticosteroid injection for rotator cuff tendonitis was 3.08 (95% confidence interval [CI] = 1.94 to 4.87). The number needed to treat based on the pooled relative risk was 3.3 (95% CI = 1.8 to 7.7) patients to obtain one improvement. The relative risk for high dose (50 mg of prednisone or more) was 5.9 (95% CI = 2.8 to 12.6). The relative risk for improvement with steroids compared with NSAIDs was 1.43 (95% CI = 0.95 to 2.16). The number needed to treat for corticosteroids versus NSAIDs was 2.5 (95% CI = 1 to 9) for one significant study. The relative risks for intra-articular steroid injection for rotator cuff tendonitis were not statistically significant. CONCLUSION Subacromial injections of corticosteroids are effective for improvement for rotator cuff tendonitis up to a 9-month period. They are also probably more effective than NSAID medication. Higher doses may be better than lower doses for subacromial corticosteroid injection for rotator cuff tendonitis.