LLukas22/scidocs huggingface dataset:
Antihypertensive drugs and the risk of incident rosacea.

BACKGROUND
Despite scarce evidence, use of calcium channel blockers is discouraged in patients with rosacea, whereas beta-blockers are recommended as an off-label treatment for erythematotelangiectatic rosacea.


OBJECTIVES
To study the association of the use of calcium channel blockers, beta-blockers and other antihypertensive drugs with incident rosacea.


METHODS
We conducted a matched case-control study of antihypertensive drugs and incident rosacea, using the U.K.-based General Practice Research Database. Cases had an incident diagnosis of rosacea recorded between 1995 and 2009. Each case was matched to one control on age, sex, general practice and years of history on the database before the index date. Drug use was stratified by timing (≤ or > 180 days before the index date) and duration (number of prescriptions) of drug exposure, in a multivariate conditional logistic regression model.


RESULTS
Among 53 927 cases and 53 927 controls, we observed odds ratios (ORs) around unity for calcium channel blockers across all strata, with a slightly decreased OR of 0·77 (95% CI 0·69-0·86) for current users of dihydropyridine calcium channel blockers with ≥ 40 prescriptions. Among beta-blockers, atenolol and bisoprolol yielded slightly decreased ORs across all exposure strata, whereas propranolol revealed ORs around 1·0, irrespective of timing and duration of exposure. Neither angiotensin-converting-enzyme inhibitors nor angiotensin receptor blockers altered the relative rosacea risk.


CONCLUSIONS
Our data contradict the prevailing notion that calcium channel blockers increase the risk of rosacea. Beta-blocker use was associated with a slightly decreased risk of rosacea, but the effect may be somewhat stronger in patients with erythematotelangiectatic rosacea.
Adjuvant radiation therapy in metastatic lymph nodes from melanoma

PURPOSE
To analyze the outcome after adjuvant radiation therapy with standard fractionation regimen in metastatic lymph nodes (LN) from cutaneous melanoma.


PATIENTS AND METHODS
86 successive patients (57 men) were treated for locally advanced melanoma in our institution. 60 patients (69%) underwent LN dissection followed by radiation therapy (RT), while 26 patients (31%) had no radiotherapy.


RESULTS
The median number of resected LN was 12 (1 to 36) with 2 metastases (1 to 28). Median survival after the first relapse was 31.8 months. Extracapsular extension was a significant prognostic factor for regional control (p = 0.019). Median total dose was 50 Gy (30 to 70 Gy). A standard fractionation regimen was used (2 Gy/fraction). Median number of fractions was 25 (10 to 44 fractions). Patients were treated with five fractions/week. Patients with extracapsular extension treated with surgery followed by RT (total dose ≥50 Gy) had a better regional control than patients treated by surgery followed by RT with a total dose <50 Gy (80% vs. 35% at 5-year follow-up; p = 0.004).


CONCLUSION
Adjuvant radiotherapy was able to increase regional control in targeted sub-population (LN with extracapsular extension).
Open practices and identity: Evidence from researchers and educators' social media participation

George Veletsianos is Canada Research Chair in Innovative Learning and Technology and associate professor with the School of Education and Technology at Royal Roads University in Victoria B.C. His research examines learners’ and scholars’ practices and experiences in emerging online settings such as online social networks and open courses. Address for correspondence: Dr George Veletsianos, School of Education and Technology, Royal Roads University, 2005 Sooke Road, Victoria, BC V9B 5Y2, Canada. Email: veletsianos@gmail.com
Physical activity intervention (Movi-Kids) on improving academic achievement and adiposity in preschoolers with or without attention deficit hyperactivity disorder: study protocol for a randomized controlled trial

BACKGROUND
The prevention of obesity and improvement of academic achievement in children are concerns of industrialized societies. Obesity has been associated with psychological disorders, including attention deficit hyperactivity disorder, whose prevalence has been estimated at 6.8 % in Spanish children and adolescents. It is known that physical activity is positively related to academic achievement and negatively related to the risk of obesity in children. However, studies to test the effectiveness of physical activity interventions in improving academic achievement in preschool children are scarce and have some weaknesses that threaten their validity. Moreover, very few studies have examined their effectiveness in improving symptoms of attention deficit hyperactivity disorder. This paper outlines a two-year multidimensional preschool intervention (Movi-Kids) aimed at preventing obesity and improving academic achievement in children with or without attention deficit hyperactivity disorder.


METHODS/DESIGN
Twenty-one schools from Ciudad Real and Cuenca, Spain, were randomized to intervention and control groups. In the first academic year, children in the third grade of preschool and the first grade of primary school in the intervention group received the Movi-Kids intervention. In the second academic year, schools were crossed over to the other group. The intervention included children, parents and teachers, and the school environment, and consisted of: (i) three hour-long sessions of recreational non-competitive physical activity after school, weekly, (ii) educational materials for parents and teachers addressing sedentary lifestyle risks and (iii) playground modifications to promote physical activity during breaks. Primary outcome measures of this study were academic achievement (intelligence, cognition, memory, attention and perception), assessed by the Battery of General and Differential Aptitudes, and adiposity measures (body mass index, waist circumference, triceps skinfold thickness and body fat percentage). Secondary outcome measures were: attention deficit hyperactivity disorder risk, motor skills, health-related quality of life and sleep quality. These variables will all be measured in both groups at baseline and at the end of the first and second academic years.


DISCUSSION
It seems reasonable that an intervention to promote physical activity based on playground games will be useful for simultaneously improving academic achievement and controlling obesity.


TRIAL REGISTRATION
ClinicalTrials.gov NCT01971827 .
Is there life in Second Life?

Social virtual worlds such as Second Life are digital representations of the real world where human-controlled avatars evolve and interact through social activities. Understanding the characteristics of existing virtual worlds can be extremely valuable to optimize their design. In this work we perform the first extensive analysis of Second Life. We have crawled around 13000 Regions over one month, and gathered information about objects, avatars, and server state. The analysis of our traces shows several surprising results. We find that 30% of the Regions are never visited during a six day period, whereas only few Regions have large peak populations. Moreover, the vast majority of Regions are static, i.e., objects are seldom created or destroyed. Interestingly, avatars interact similarly to humans in real life, gathering in small groups, visiting the same places and meeting the same avatars again, showing a highly predictable behavior. Based on these observations, we discuss several techniques to enhance Second Life or other similar social virtual worlds.
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models

In this paper, we address the problem of speaker classification in multi-party conversation, and collect massive data to facilitate research in this direction. We further investigate temporal-based and content-based models of speakers, and propose several hybrids of them. Experiments show that speaker classification is feasible, and that hybrid models outperform each single component.
A feasibility study of mobile phone text messaging to support education and management of type 2 diabetes in Iraq.

BACKGROUND
We undertook a feasibility study to evaluate feasibility and utility of short message services (SMSs) to support Iraqi adults with newly diagnosed type 2 diabetes.


SUBJECTS AND METHODS
Fifty patients from a teaching hospital clinic in Basrah in the first year after diagnosis were recruited to receive weekly SMSs relating to diabetes self-management over 29 weeks. Numbers of messages received, acceptability, cost, effect on glycated hemoglobin (HbA1c), and diabetes knowledge were documented.


RESULTS
Forty-two patients completed the study, receiving an average 22 of 28 messages. Mean knowledge score rose from 8.6 (SD 1.5) at baseline to 9.9 (SD 1.4) 6 months after receipt of SMSs (P=0.002). Baseline and 6-month knowledge scores correlated (r=0.297, P=0.049). Mean baseline HbA1c was 79 mmol/mol (SD 14 mmol/mol) (9.3% [SD 1.3%]) and decreased to 70 mmol/mol (SD 13 mmol/mol) (8.6% [SD 1.2%]) (P=0.001) 6 months after the SMS intervention. Baseline and 6-month values were correlated (r=0.898, P=0.001). Age, gender, and educational level showed no association with changes in HbA1c or knowledge score. Changes in knowledge score were correlated with postintervention HbA1c (r=-0.341, P=0.027). All patients were satisfied with text messages and wished the service to be continued after the study. The cost of SMSs was €0.065 per message.


CONCLUSIONS
This study demonstrates SMSs are acceptable, cost-effective, and feasible in supporting diabetes care in the challenging, resource-poor environment of modern-day Iraq. This study is the first in Iraq to demonstrate similar benefits of this technology on diabetes education and management to those seen from its use in better-resourced parts of the world. A randomized controlled trial is needed to assess precise benefits on self-care and knowledge.
User fees, health staff incentives, and service utilization in Kabarole District, Uganda.

OBJECTIVE
To determine the impact of user fees on the utilization of health services in a community-based cost-sharing scheme in Kabarole District, western Uganda.


METHODS
Of the 38 government health units that had introduced user-fee financing schemes, 11 were included in the study. Outpatient utilization was assessed as the median number of visits per month before and after cost sharing began.


FINDINGS
After the introduction of cost sharing, overall utilization of general outpatient services, assessed by combining the data from all the participating units, dropped by 21.3%. Utilization increased, however, in facilities located in remote areas, while it decreased in those located in urban or semi-urban areas. The increased utilization in remote facilities was considered to be largely attributable to health workers' incentive payments derived from cost-sharing revenues.


CONCLUSIONS
Incentive payments led the health workers to offer improved services. Other factors may also have been influential, such as an improved drug supply to health facilities and increased public identification with community projects in remote areas.
Anti-cancer agents from medicinal plants

Cancer is a major public health burden in both developed and developing countries. Plant derived agents are being used for the treatment of cancer. Several anticancer agents including taxol, vinblastine, vincristine, the camptothecin derivatives, topotecan and irinotecan, and etoposide derived from epipodophyllotoxin are in clinical use all over the world. A number of promising agents such as flavopiridol, roscovitine, combretastatin A-4, betulinic acid and silvestrol are in clinical or preclinical development.
Exercise treatment for depression: efficacy and dose response.

BACKGROUND
This study, conducted between 1998 and 2001 and analyzed in 2002 and 2003, was designed to test (1) whether exercise is an efficacious treatment for mild to moderate major depressive disorder (MDD), and (2) the dose-response relation of exercise and reduction in depressive symptoms.


DESIGN
The study was a randomized 2x2 factorial design, plus placebo control.


SETTING/PARTICIPANTS
All exercise was performed in a supervised laboratory setting with adults (n =80) aged 20 to 45 years diagnosed with mild to moderate MDD.


INTERVENTION
Participants were randomized to one of four aerobic exercise treatment groups that varied total energy expenditure (7.0 kcal/kg/week or 17.5 kcal/kg/week) and frequency (3 days/week or 5 days/week) or to exercise placebo control (3 days/week flexibility exercise). The 17.5-kcal/kg/week dose is consistent with public health recommendations for physical activity and was termed "public health dose" (PHD). The 7.0-kcal/kg/week dose was termed "low dose" (LD).


MAIN OUTCOME MEASURES
The primary outcome was the score on the 17-item Hamilton Rating Scale for Depression (HRSD(17)).


RESULTS
The main effect of energy expenditure in reducing HRSD(17) scores at 12 weeks was significant. Adjusted mean HRSD(17) scores at 12 weeks were reduced 47% from baseline for PHD, compared with 30% for LD and 29% for control. There was no main effect of exercise frequency at 12 weeks.


CONCLUSIONS
Aerobic exercise at a dose consistent with public health recommendations is an effective treatment for MDD of mild to moderate severity. A lower dose is comparable to placebo effect.
CMOS Current Reference without Resistance

A micropower current reference can be built with CMOS transistors only. It shows low sensitivity with respect to technology, supply voltage and temperature, and it works on very low supply voltages. The circuit is very simple and occupies a small surface area.
Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization.

BACKGROUND
Neuroimaging measures and chemical biomarkers may be important indices of clinical progression in normal aging and mild cognitive impairment (MCI) and need to be evaluated longitudinally.


OBJECTIVE
To characterize cross-sectionally and longitudinally clinical measures in normal controls, subjects with MCI, and subjects with mild Alzheimer disease (AD) to enable the assessment of the utility of neuroimaging and chemical biomarker measures.


METHODS
A total of 819 subjects (229 cognitively normal, 398 with MCI, and 192 with AD) were enrolled at baseline and followed for 12 months using standard cognitive and functional measures typical of clinical trials.


RESULTS
The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD. Nonmemory cognitive measures were only minimally impaired in the subjects with MCI. The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year. Approximately 50% of the subjects with MCI were on antidementia therapies. There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3. Baseline CSF measures of Abeta-42 separated the 3 groups as expected and successfully predicted the 12-month change in cognitive measures.


CONCLUSION
The Alzheimer's Disease Neuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics. The 12-month progression rate of MCI was as predicted, and the CSF measures heralded progression of clinical measures over 12 months.
Deep artifact learning for compressed sensing and parallel MRI

Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is one of the powerful ways to reduce the scan time of MR imaging with performance guarantee. However, the computational costs are usually expensive. This paper aims to propose a computationally fast and accurate deep learning algorithm for the reconstruction of MR images from highly down-sampled k-space data. Theory: Based on the topological analysis, we show that the data manifold of the aliasing artifact is easier to learn from a uniform subsampling pattern with additional low-frequency k-space data. Thus, we develop deep aliasing artifact learning networks for the magnitude and phase images to estimate and remove the aliasing artifacts from highly accelerated MR acquisition. Methods: The aliasing artifacts are directly estimated from the distorted magnitude and phase images reconstructed from subsampled k-space data so that we can get an aliasing-free images by subtracting the estimated aliasing artifact from corrupted inputs. Moreover, to deal with the globally distributed aliasing artifact, we develop a multi-scale deep neural network with a large receptive field. Results: The experimental results confirm that the proposed deep artifact learning network effectively estimates and removes the aliasing artifacts. Compared to existing CS methods from single and multi-coli data, the proposed network shows minimal errors by removing the coherent aliasing artifacts. Furthermore, the computational time is by order of magnitude faster. Conclusion: As the proposed deep artifact learning network immediately generates accurate reconstruction, it has great potential for clinical applications.
A sanitization approach for hiding sensitive itemsets based on particle swarm optimization

Privacy-preserving data mining (PPDM) has become an important research field in recent years, as approaches for PPDM can discover important information in databases, while ensuring that sensitive information is not revealed. Several algorithms have been proposed to hide sensitive information in databases. They apply addition and deletion operations to perturb an original database and hide the sensitive information. Finding an appropriate set of transactions/itemsets to be perturbed for hiding sensitive information while preserving other important information is a NP-hard problem. In the past, genetic algorithm (GA)-based approaches were developed to hide sensitive itemsets in an original database through transaction deletion. In this paper, a particle swarm optimization (PSO)-based algorithm called PSO2DT is developed to hide sensitive itemsets while minimizing the side effects of the sanitization process. Each particle in the designed PSO2DT algorithm represents a set of transactions to be deleted. Particles are evaluated using a fitness function that is designed to minimize the side effects of sanitization. The proposed algorithm can also determine the maximum number of transactions to be deleted for efficiently hiding sensitive itemsets, unlike the state-of-the-art GA-based approaches. Besides, an important strength of the proposed approach is that few parameters need to be set, and it can still find better solutions to the sanitization problem than GA-based approaches. Furthermore, the pre-large concept is also adopted in the designed algorithm to speed up the evolution process. Substantial experiments on both real-world and synthetic datasets show that the proposed PSO2DT algorithm performs better than the Greedy algorithm and GA-based algorithms in terms of runtime, fail to be hidden (F-T-H), not to be hidden (N-T-H), and database similarity (DS). & 2016 Elsevier Ltd. All rights reserved.
Bilinear CNNs for Fine-grained Visual Recognition

We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.
Identifying gaps, barriers, and solutions in implementing pressure ulcer prevention programs.

BACKGROUND
Patients continue to suffer from pressure ulcers (PUs), despite implementation of evidence-based pressure ulcer (PU) prevention protocols. In 2009, Joint Commission Resources (JCR) and Hill-Rom created the Nurse Safety Scholar-in-Residence (nurse scholar) program to foster the professional development of expert nurse clinicians to become translators of evidence into practice. The first nurse scholar activity has focused on PU prevention. Four hospitals with established PU programs participated in the PU prevention implementation project.


PU PREVENTION IMPLEMENTATION PROJECT
Each hospital's team completed an inventory of PU prevention program components and provided copies of accompanying documentation, along with prevalence and incidence data. Site visits to the four participating hospitals were arranged to provide opportunities for more in-depth analysis and support. Following the initial site visit, the project team at each hospital developed action plans for the top three barriers to PU program implementation. A series of conference calls was held between the site visits.


YEAR 1 PROJECT RESULTS
Pressure Ulcer Program Gaps and Recommendations. The four hospitals shared common gaps in terms of limitations in staff education and training; lack of physician involvement; limited involvement of unlicensed nursing staff; lack of plan for communicating at-risk status; and limited quality improvement evaluations of bedside practices. Detailed recommendations were identified for addressing each of these gaps. these


CONCLUSIONS
Recommendations for eliminating gaps have been implemented by the participating teams to drive improvement and to reduce hospital-acquired PU rates. The nurse scholars will continue to study implementation of best practices for PU prevention.
Big data technologies and Management: What conceptual modeling can do

The era of big data has resulted in the development and applications of technologies and methods aimed at effectively using massive amounts of data to support decision-making and knowledge discovery activities. In this paper, the five Vs of big data, volume, velocity, variety, veracity, and value, are reviewed, as well as new technologies, including NoSQL databases that have emerged to accommodate the needs of big data initiatives. The role of conceptual modeling for big data is then analyzed and suggestions made for effective conceptual modeling efforts with respect to big data. & 2017 Elsevier B.V. All rights reserved.