LLukas22/scidocs huggingface dataset:
Advances in facial rejuvenation: botulinum toxin type a, hyaluronic acid dermal fillers, and combination therapies--consensus recommendations.

BACKGROUND
Facial aesthetics and rejuvenation are evolving rapidly due to changes in products, procedures, and patient demographics. Clinicians can benefit from ongoing guidance on products, tailoring treatments to individual patients, treating multiple facial areas, and using combinations of products and ways to optimize outcomes.


METHODS
A multidisciplinary group of aesthetic treatment experts convened to review the properties and uses of botulinum toxin type A (BoNTA) and hyaluronic acid fillers and to update consensus recommendations for facial rejuvenation using these two types of products. The group considered paradigm shifts in facial aesthetics; optimal techniques for using BoNTA and hyaluronic acid fillers alone and in combination; the influence of patient sex, ethnicity, cultural ideals, and skin color on treatment; general techniques; patient education and counseling; and emerging trends and needs in facial rejuvenation.


RESULTS
The group provided specific recommendations by facial area, focusing on relaxing musculature, restoring volume, and recontouring using BoNTA and hyaluronic acid fillers alone and in combination. For the upper face, BoNTA remains the cornerstone of treatment, with hyaluronic acid fillers used to augment results. These fillers are central to the midface because of the need to restore volume. BoNTA and hyaluronic acid in combination can improve outcomes in the lower face.


CONCLUSIONS
Optimal outcomes in facial aesthetics require in-depth knowledge of facial aging and anatomy, an appreciation that rejuvenation is a three-dimensional process involving muscle control, volume restoration, and recontouring, and thorough knowledge of properties and techniques specific to each product in the armamentarium.
Antithrombotic drugs and risk of hemorrhagic stroke in the general population.

OBJECTIVE
To investigate the relationship between hemorrhagic stroke and use of antiplatelets and warfarin using data from The Health Improvement Network.


METHODS
A total of 1,797 incident cases of intracerebral hemorrhage (ICH) and 1,340 of subarachnoid hemorrhage (SAH) were ascertained. Density-based sampling was used to select 10,000 controls free from hemorrhagic stroke. Risk of hemorrhagic stroke was evaluated in current users and nonusers of antiplatelets and warfarin. Unconditional logistic regression models were used to adjust for age, sex, calendar year, alcohol, body mass index, hypertension, and health services utilization.


RESULTS
Aspirin use was not associated with an increased risk of ICH (odds ratio [OR] 1.06, 95% confidence interval [CI] 0.93-1.21), but was associated with a decreased risk of SAH (OR 0.82, 95% CI 0.67-1.00), compared with no therapy. Aspirin use ≥3 years was associated with a decreased risk of SAH (OR 0.63, 95% CI 0.45-0.90) compared with no therapy. Warfarin use was associated with a greatly increased risk of ICH (OR 2.82, 95% CI 2.26-3.53) and a moderately increased risk of SAH (OR 1.67, 95% CI 1.15-2.43) compared with no therapy. International normalized ratio values ≥3 carried a marked risk of ICH (OR 7.01, 95% CI 4.10-11.99).


CONCLUSION
Aspirin is not associated with a risk of ICH compared with no therapy. Chronic low-dose aspirin treatment may have a protective effect on the risk of SAH. Warfarin users in this study cohort were at a much higher risk of ICH than those receiving no therapy, with a marked association with international normalized ratio >3.
Co-training an Improved Recurrent Neural Network with Probability Statistic Models for Named Entity Recognition

Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further. AQ1
Impact of mHealth Chronic Disease Management on Treatment Adherence and Patient Outcomes: A Systematic Review

BACKGROUND
Adherence to chronic disease management is critical to achieving improved health outcomes, quality of life, and cost-effective health care. As the burden of chronic diseases continues to grow globally, so does the impact of non-adherence. Mobile technologies are increasingly being used in health care and public health practice (mHealth) for patient communication, monitoring, and education, and to facilitate adherence to chronic diseases management.


OBJECTIVE
We conducted a systematic review of the literature to evaluate the effectiveness of mHealth in supporting the adherence of patients to chronic diseases management ("mAdherence"), and the usability, feasibility, and acceptability of mAdherence tools and platforms in chronic disease management among patients and health care providers.


METHODS
We searched PubMed, Embase, and EBSCO databases for studies that assessed the role of mAdherence in chronic disease management of diabetes mellitus, cardiovascular disease, and chronic lung diseases from 1980 through May 2014. Outcomes of interest included effect of mHealth on patient adherence to chronic diseases management, disease-specific clinical outcomes after intervention, and the usability, feasibility, and acceptability of mAdherence tools and platforms in chronic disease management among target end-users.


RESULTS
In all, 107 articles met all inclusion criteria. Short message service was the most commonly used mAdherence tool in 40.2% (43/107) of studies. Usability, feasibility, and acceptability or patient preferences for mAdherence interventions were assessed in 57.9% (62/107) of studies and found to be generally high. A total of 27 studies employed randomized controlled trial (RCT) methods to assess impact on adherence behaviors, and significant improvements were observed in 15 of those studies (56%). Of the 41 RCTs that measured effects on disease-specific clinical outcomes, significant improvements between groups were reported in 16 studies (39%).


CONCLUSIONS
There is potential for mHealth tools to better facilitate adherence to chronic disease management, but the evidence supporting its current effectiveness is mixed. Further research should focus on understanding and improving how mHealth tools can overcome specific barriers to adherence.
Impact of sagittal spinopelvic alignment on clinical outcomes after decompression surgery for lumbar spinal canal stenosis without coronal imbalance.

OBJECT
The object of this study was to investigate correlations between sagittal spinopelvic alignment and improvements in clinical and quality-of-life (QOL) outcomes after lumbar decompression surgery for lumbar spinal canal stenosis (LCS) without coronal imbalance.


METHODS
The authors retrospectively reviewed data from consecutive patients treated for LCS with decompression surgery in the period from 2009 through 2011. They examined correlations between preoperative or postoperative sagittal vertical axis (SVA) and radiological parameters, clinical outcomes, and health-related (HR)QOL scores in patients divided according to SVA. Clinical outcomes were assessed according to Japanese Orthopaedic Association (JOA) and visual analog scale (VAS) scores. Health-related QOL was evaluated using the Roland-Morris Disability Questionnaire (RMDQ) and the JOA Back Pain Evaluation Questionnaire (JOABPEQ).


RESULTS
One hundred nine patients were eligible for inclusion in the study. Compared to patients with normal sagittal alignment prior to surgery (Group A: SVA < 50 mm), those with preoperative sagittal imbalance (Group B: SVA ≥ 50 mm) had significantly smaller lumbar lordosis and thoracic kyphosis angles and larger pelvic tilt. In Group B, there was a significant decrease in postoperative SVA compared with the preoperative SVA (76.3 ± 29.7 mm vs. 54.3 ± 39.8 mm, p = 0.004). The patients in Group B with severe preoperative sagittal imbalance (SVA > 80 mm) had residual sagittal imbalance after surgery (82.8 ± 41.6 mm). There were no significant differences in clinical and HRQOL outcomes between Groups A and B. Compared to patients with normal postoperative SVA (Group C: SVA < 50 mm), patients with a postoperative SVA ≥ 50 mm (Group D) had significantly lower JOABPEQ scores, both preoperative and postoperative, for walking ability (preop: 36.6 ± 26.3 vs. 22.7 ± 26.0, p = 0.038, respectively; postop: 71.1 ± 30.4 vs. 42.5 ± 29.6, p < 0.001) and social functioning (preop: 38.7 ± 18.5 vs. 30.2 ± 16.7, p = 0.045; postop: 67.0 ± 25.8 vs. 49.6 ± 20.0, p = 0.001), as well as significantly higher postoperative RMDQ (4.9 ± 5.2 vs. 7.9 ± 5.7, p = 0.015) and VAS scores for low-back pain (2.68 ± 2.69 vs. 3.94 ± 2.59, p = 0.039).


CONCLUSIONS
Preoperative sagittal balance was not significantly correlated with clinical or HRQOL outcomes after decompression surgery in LCS patients without coronal imbalance. Decompression surgery improved the SVA value in patients with preoperative sagittal imbalance; however, the patients with severe preoperative sagittal imbalance (SVA > 80 mm) had residual imbalance after decompression surgery. Both clinical and HRQOL outcomes were negatively affected by postoperative residual sagittal imbalance.
A phase II study of isoflavones, erlotinib, and gemcitabine in advanced pancreatic cancer

Background: The EGFR/Akt/NF-κB signalling pathway is frequently deregulated in pancreatic cancer and contributes to cell growth, metastasis and chemoresistance. An isoflavone, genistein, inactivates Akt and NF-κB and enhances the anti-tumor activity of erlotinib and gemcitabine in experimental systems of pancreas cancer. This phase II study was undertaken to determine the effects of adding isoflavone to a regimen of gemcitabine and erlotinib on survival in patients with advanced pancreatic cancer. Methods: Eligibility included previously untreated patients with advanced pancreatic adenocarcinoma. Patients received gemcitabine 1,000 mg/m2 on days 1, 8, and 15, and erlotinib 150 mg once daily P.O. on day 1 to day 28. Soy isoflavones (Novasoy®) were administered at a dose of 531 mg twice daily P.O. starting day -7 until the end of study participation. Results: Twenty patients with advanced pancreas cancer were enrolled (median age 57.9 years). Sixteen patients had stage IV disease. The median number of cycles was 2 per patient. The median survival time was 5.2 months (95% CI, 4.6—N/A months). The probability of survival at 6 months was 50% (95% CI, 32–78%). Conclusions: The addition of soy isoflavones to gemcitabine and erlotinib did not appear to increase the survival of patients with advanced pancreatic cancer.
How do teachers perceive educational robots in formal education ? A study based on the Thymio robot *

Robots have generated interest in schools since Seymour Papert’s works; however when his Logo turtles were introduced into schools in the 1980s, they proved to be unreliable, expensive, and limited [1]. Since then, we have seen various affordable, reliable, and polyvalent platforms such as Lego Mindstorms [1] or the Bee-Bot [2]. Robotics has become more appealing, and it is an established fact that educational robots can improve children’s motivation [3], [4]. Robotics also embodies a wide range of disciplines, which allows its use in a broad educational area and in interdisciplinary studies. Its use in compulsory schools could bring technology to a larger audience, including both genders. Although there has been an increasing number of extracurricular robotics activities such as robotic contests or festivals [5], which show the widespread adoption of robotics in informal education, several authors are struggling to understand why robots are still underused in schools, for formal education. Some argue, without clear evidence, that this is due to the lack of material available for teachers [6], missing functionalities [3], a paucity of flexibility and dynamism in schools [7], or a dearth of evidence regarding the educational benefits of this approach [4]. Although there is no agreement over the exact reasons for this situation, it seems clear, from these and other studies [8], [9], that teachers play a key role in the introduction of technology in schools. Despite this obvious observation, there is a severe lack of studies analyzing this key factor, and, in particular, the attitude of teachers toward educational robots. Lee et al. [3] examined the perception of such robots in Korea, by teachers, students, and parents. Their results showed that, while the teachers’ opinions of robots were worse than those of the students and parents, none of them wanted robots to replace teachers. Fridin and Belokopytov [8] studied the first-time acceptance by teachers of a socially assistive humanoid robot, showing that teachers’ desire to use robots is mainly linked to their perceived utility as tools. A limiting factor in this study is that the teachers were interacting with a robot for the first time. Kim et al. [9] performed a short survey of 116 Korean teachers who had had an initial experience of using robots in
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.10.027 ⇑ Corresponding author. Tel.: +90 332 2234350; fax E-mail address: melekacar@yahoo.com (M. Acar B Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%). 2010 Elsevier Ltd. All rights reserved.
Drug discovery by dynamic combinatorial libraries

Dynamic combinatorial chemistry is a recently introduced supramolecular approach that uses self-assembly processes to generate libraries of chemical compounds. In contrast to the stepwise methodology of classical combinatorial techniques, dynamic combinatorial chemistry allows for the generation of libraries based on the continuous interconversion between the library constituents. Spontaneous assembly of the building blocks through reversible chemical reactions virtually encompasses all possible combinations, and allows the establishment of adaptive processes owing to the dynamic interchange of the library constituents. Addition of the target ligand or receptor creates a driving force that favours the formation of the best-binding constituent  a self-screening process that is capable, in principle, of accelerating the identification of lead compounds for drug discovery.
Neural systems for guilt from actions affecting self versus others

Guilt is a core emotion governing social behavior by promoting compliance with social norms or self-imposed standards. The goal of this study was to contrast guilty responses to actions that affect self versus others, since actions with social consequences are hypothesized to yield greater guilty feelings due to adopting the perspective and subjective emotional experience of others. Sixteen participants were presented with brief hypothetical scenarios in which the participant's actions resulted in harmful consequences to self (guilt-self) or to others (guilt-other) during functional MRI. Participants felt more intense guilt for guilt-other than guilt-self and guilt-neutral scenarios. Guilt scenarios revealed distinct regions of activity correlated with intensity of guilt, social consequences of actions, and the interaction of guilt by social consequence. Guilt intensity was associated with activation of the dorsomedial PFC, superior frontal gyrus, supramarginal gyrus, and anterior inferior frontal gyrus. Guilt accompanied by social consequences was associated with greater activation than without social consequences in the ventromedial and dorsomedial PFC, precuneus, posterior cingulate, and posterior superior temporal sulcus. Finally, the interaction analysis highlighted select regions that were more strongly correlated with guilt intensity as a function of social consequence, including the left anterior inferior frontal gyrus, left ventromedial PFC, and left anterior inferior parietal cortex. Our results suggest these regions intensify guilt where harm to others may incur a greater social cost.
Effectiveness and cost-effectiveness of an educational intervention for practice teams to deliver problem focused therapy for insomnia: rationale and design of a pilot cluster randomised trial

BACKGROUND
Sleep problems are common, affecting over a third of adults in the United Kingdom and leading to reduced productivity and impaired health-related quality of life. Many of those whose lives are affected seek medical help from primary care. Drug treatment is ineffective long term. Psychological methods for managing sleep problems, including cognitive behavioural therapy for insomnia (CBTi) have been shown to be effective and cost effective but have not been widely implemented or evaluated in a general practice setting where they are most likely to be needed and most appropriately delivered. This paper outlines the protocol for a pilot study designed to evaluate the effectiveness and cost-effectiveness of an educational intervention for general practitioners, primary care nurses and other members of the primary care team to deliver problem focused therapy to adult patients presenting with sleep problems due to lifestyle causes, pain or mild to moderate depression or anxiety.


METHODS AND DESIGN
This will be a pilot cluster randomised controlled trial of a complex intervention. General practices will be randomised to an educational intervention for problem focused therapy which includes a consultation approach comprising careful assessment (using assessment of secondary causes, sleep diaries and severity) and use of modified CBTi for insomnia in the consultation compared with usual care (general advice on sleep hygiene and pharmacotherapy with hypnotic drugs). Clinicians randomised to the intervention will receive an educational intervention (2 x 2 hours) to implement a complex intervention of problem focused therapy. Clinicians randomised to the control group will receive reinforcement of usual care with sleep hygiene advice. Outcomes will be assessed via self-completion questionnaires and telephone interviews of patients and staff as well as clinical records for interventions and prescribing.


DISCUSSION
Previous studies in adults have shown that psychological treatments for insomnia administered by specialist nurses to groups of patients can be effective within a primary care setting. This will be a pilot study to determine whether an educational intervention aimed at primary care teams to deliver problem focused therapy for insomnia can improve sleep management and outcomes for individual adult patients presenting to general practice. The study will also test procedures and collect information in preparation for a larger definitive cluster-randomised trial. The study is funded by The Health Foundation.
Dynamic Properties of the Virtual Synchronous Machine ( VISMA )

The increasing integration of decentralized electrical sources is attended by problems with power quality, safe grid operation and grid stability. The concept of the Virtual Synchronous Machine (VISMA) [1] discribes an inverter to particularly connect renewable electrical sources to the grid that provides a wide variety of static an dynamic properties they are also suitable to achieve typical transient and oscillation phenomena in decentralized as well as weak grids. Furthermore in static operation, power plant controlled VISMA systems are capable to cope with critical surplus production of renewable electrical energy without additional communication systems only conducted by the grid frequency. This paper presents the dynamic properties "damping" and "virtual mass" of the VISMA and their contribution to the stabilization of the grid frequency and the attenuation of grid oscillations examined in an experimental grid set.
Cutting-parameter selection for maximizing production rate or minimizing production cost in multistage turning operations

In this paper, an investigation of optimal cutting parameters for maximizing production rate or minimizing production cost in multistage turning operations is reported. A machining model is constructed based on a polynomial network. The polynomial network can learn the relationships between cutting parameters (cutting speed, feed rate, and depth of cut) and cutting performance (surface roughness, cutting force, and tool life) through a self-organizing adaptive modeling technique. Once the geometric model for machined parts and various time and cost components of the turning operation are given, an optimization algorithm using a sequential quadratic programming method is then applied to the polynomial network for determining optimal cutting parameters. The optimal cutting parameters are subjected to an objective function of maximum production rate or minimum production cost with the constraints of a permissible limit of surface roughness and cutting force and a feasible range of cutting parameters. # 2000 Published by Elsevier Science S.A.
Accessibility Evaluation on Malaysian E-Government Websites

The advent of the WWW has caused a dramatic evolution in getting the information. From the web, most of the people nowadays can get information easily from Internet from anywhere and anytime. These Web sites also become an important tool that the government used to market their institution to prospective customers and, to provide government information and services available on-line. To make these web sites more functional, they must be accessible to all users, including those with disabilities. This study was undertaken with the purpose of identifying the accessibility of e-government websites based on the World Wide Web Consortium (W3C). In addition, the study was also intended to investigate webmaster’s knowledge and practices pertaining to accessibility. The result of the analysis indicated that there were no single Malaysian e-government websites that passed the W3C Priority 1 accessibility checkpoints. A follow-up study using interviews unveiled that most webmasters did not fully adhere to the standard of Web Content Accessibility Guidelines (WCAG).