LLukas22/scidocs huggingface dataset:
Poor awareness of stroke--a hospital-based study from South India: an urgent need for awareness programs.

BACKGROUND
Stroke treatment has reached a new benchmark with thrombolytic therapy. However, India has witnessed a tremendous increase in morbidity and mortality of stroke over the past few decades. The prime deciding factor is initiation of treatment within the time window, which requires early recognition of stroke symptoms. We wished to analyze the lacunae in the stroke knowledge in our population. This hospital-based study assessed awareness of the public regarding stroke.


METHODS
Two trained medical students interviewed accompanying relatives using a structured questionnaire. Data were analyzed using Statistical Package for the Social Sciences, version 20. Univariate and multivariate analyses were done. Descriptive results are presented as mean ± SD. P less than .05 was considered as statistically significant.


RESULTS
In total, 350 individuals formed the study group; mean age was 40.23 ± 13.4 years constituting 145 males (47.5%) and 205 females (58.5%). Only 50 (14%) respondents could identify that the patient had developed stroke; however, 35% respondents identified brain as the organ involved for the presenting complaints and 34% could identify the warning symptoms of stroke, limb weakness being the most common (30%). Sources of information were friends and media (8%). Median time of reaching the hospital was 10 hours with patients transported by ambulance reaching early (P < .03); 80% of patients were fed in drowsy state during transport. More than 50% of the respondents were not able to identify the risk factors nor were aware that stroke is a preventable or a life-threatening disease. Multivariate logistic regression analysis confirmed that higher level of education was the only variable, which correlated with the organ identification (P < .001), stroke recognition (P < .002), and identification of the warning symptoms (P < .001).


CONCLUSIONS
This hospital-based study reveals that there is a poor knowledge of recognition of stroke, its warning signs, risk factors, and prehospital care. There is an urgent need to design stroke awareness programs in the country with emphasis on ambulance service to minimize the time to thrombolysis.
Cognitive behavior therapy versus other psychosocial treatments for schizophrenia.

Cognitive behavioral therapy (CBT) is now a recommended treatment for people with schizophrenia. In CBT, links are made between the person’s feelings and patterns of thinking that underpin their distress. At present, a variety of interventions have been labeled as CBT and it is difficult to provide a single, unambiguous definition. In recognition, the review authors have constructed criteria that are felt to be both workable and to capture the elements of good practice in CBT. Cognitive behavioral therapy is becoming increasingly available for people with schizophrenia, with recent recommendations of treatment guidelines in, for example, the United Kingdom suggesting that CBT should be more widely available for people with schizophrenia. It should be noted that many of the trials of CBT for psychosis have incorporated additional active therapeutic elements (eg, psychoeducation and relapse prevention) that would be considered adjunctive to techniques that are specifically targeted at eliciting belief change (eg, guided discovery or behavioral experiments). The comparison arm of the trials reported other psychosocial therapies, such as supportive therapy, psychoeducation, family therapy, and other “talking therapies.” The full review distinguishes between trials that described “active” psychosocial interventions (eg, family therapy) aimed at a meaningful symptom reduction and those trials that have used “nonactive” psychosocial interventions (eg, supportive therapy), which act as merely a control for the nonspecific effects of therapy (eg, time spent with therapist). Outcomes are presented separately for active and nonactive psychosocial interventions and the pooled effect of these trials is also presented. Search Methods
Intermittent ischaemic arrest and cardioplegia in coronary artery surgery: coming full circle?

OBJECTIVE
To compare the cardioprotective efficacy of cold crystalloid cardioplegia and intermittent ischaemic arrest in patients undergoing elective coronary artery surgery.


DESIGN
Prospective randomised trial.


SETTING
London teaching hospital.


SUBJECTS
20 patients with at least moderately good left ventricular function undergoing elective coronary artery surgery by one experienced surgeon and needing at least two bypass grafts.


INTERVENTIONS
Patients were randomised to cold crystalloid cardioplegia or intermittent ischaemic arrest.


MAIN OUTCOME MEASURES
The primary determinant of the efficacy of myocardial protection was serial measurement (before and at 1, 6, 24, and 72 hours after the end of cardiopulmonary bypass) of cardiac troponin T (cTnT), a highly sensitive and specific marker of myocardial damage.


RESULTS
There was no significant difference in age, ejection fraction, number of grafts, bypass times, or cross clamp times between the two groups. One patient in the cardioplegia group had a perioperative infarct and was excluded from further study. In both groups there was a significant increase in cTnT, with peak concentrations being reached 6 hours after the end of cardiopulmonary bypass and remaining significantly high at 72 hours. At 6 hours the median (75% interquartile range) concentrations of cTnT were similar in both groups (1.8 (1.0-3.6) micrograms/l for cardioplegia v 1.9 (1.0-3.5) micrograms/l for intermittent ischaemic arrest).


CONCLUSION
This trial shows that intermittent ischaemic arrest, even without systemic cooling or venting of the left ventricle, provides a similar level of myocardial protection to cardioplegia in patients with moderate left ventricular function and short ischaemic times.
Posterior fixation of thoracolumbar burst fracture: short-segment pedicle fixation versus long-segment instrumentation.

OBJECTIVE
The treatment of thoracolumbar burst fracture is a controversial issue. Short-segment (SS) pedicle fixation has become a popular treatment option. However, there are several studies regarding the high rate of failure. The aim of this prospective study was to compare SS versus long-segment (LS) instrumentation.


METHODS
For this purpose, 18 consecutive patients were assigned to two groups. Group 1 included nine patients treated by SS pedicle fixation, whereas group 2 included nine patients treated by LS instrumentation. SS instrumentation was pedicle fixation one level above and below the fractured vertebra. LS instrumentation was hook fixation (claw hooks attached to second upper vertebra and infralaminar hooks attached to first upper vertebra) above and pedicle fixation (pedicle screws attached to first and second lower vertebrae) below the fractured vertebra.


RESULTS
As a result, measurements of local kyphosis, sagittal index, and anterior vertebral height compression showed that the LS group had a better outcome at final follow-up (P < 0.05). Also, the SS group had a 55% failure rate, whereas the LS group had prolonged operative time and increased blood loss. However, there was no difference between the two groups according to Low Back Outcome Score.


CONCLUSIONS
In conclusion, radiographic parameters demonstrated that LS instrumentation is a more effective management of thoracolumbar burst fractures. Nevertheless, clinical outcome was the same between the two groups. However, our conclusions were based on posterior-only surgery. Anterior column support would negate the need for LS fixation. Also, SS would have been more successful if two above and two below pedicle screws were used.
A meta-analysis of acupuncture use in the treatment of cognitive impairment after stroke.

OBJECTIVE
This meta-analysis was conducted to evaluate the efficacy of acupuncture on cognitive impairment (function) after a stroke.


DESIGN
Randomized controlled trials (RCTs) comparing acupuncture with no acupuncture in addition to medicine or rehabilitation were identified from databases (PubMed, Cochrane Central Register of Controlled Trials, Chinese National Knowledge Infrastructure, VIP Chinese Periodical Database, Wangfang Chinese Periodical Database, Chinese Bio-medicine Database, Cochrane Library, and Chinese medical literature databases) and two relevant journals (Chinese Acupuncture and Moxibustion and the Journal of Shanghai Acupuncture and Moxibustion). Meta-analyses were conducted for the eligible RCTs.


RESULTS
Twenty-one trials with a total of 1421 patients met inclusion criteria. Pooled random-effects estimates of the change in the Mini-Mental State Examination were calculated for the comparison of acupuncture with no acupuncture in addition to medicine or rehabilitation. Following 4 weeks and 8 weeks of intervention with acupuncture, the merged mean difference was 3.14 (95% confidence interval [CI], 2.06-4.21; p<.00001) and 2.03 (95% CI, 0.26-3.80; p=0.02), respectively. For the comparison of 3-4 weeks of acupuncture with no acupuncture in addition to medicine or rehabilitation groups, the merged MD in Neurobehavioral Cognitive State Examination total scores was 5.63 (95% CI, 3.95-7.31; p<.00001). For the comparison of 8-12 weeks of acupuncture with no acupuncture in addition to medicine or rehabilitation groups, the P300 latency merged MD was -12.80 (95% CI, -21.08 to -4.51; p<.00001), while the P300 amplitude merged MD was 1.38 (95% CI, 0.93-1.82; p<.00001). Overall, the study quality was rated as moderate on the basis of the Cochrane Handbook for Systematic Reviews of Interventions (part 2: 8.5).


CONCLUSIONS
This meta-analysis suggests that acupuncture had positive effects on cognitive function after stroke and supports the need for additional research on the potential benefits of this therapeutic approach.
A novel way to quantitatively determine the mechanical properties of thin films from the initial-grown surface by nanoindentation

Abstract A novel way was derived in this work to quantitatively determine the mechanical properties of thin films grown on silicon substrates from the initial-grown surface using nanoindentation. The as-deposited surface of the films was firstly glued to steel and subsequently, the silicon substrate was etched away in a hot potassium hydroxide (KOH) solution. Steel served as supporting material after the removal of the substrate. This procedure enables the exposure of the initial-grown surface of the film with a surface roughness close to the one of the silicon substrate (around 1 nm), which typically is significantly lower than the as-deposited surface (usually 20 to 30 nm). Carrying out indents on the initial-grown side allows us to acquire reliable results by avoiding any artifact due to surface roughness. Measurements were performed on nanocrystalline SiC and diamond films to confirm the validity of this method. Finally, a 13 μm thick diamond/SiC composite film composed of nanocrystalline SiC and nanocrystalline diamond was prepared and characterized by applying this method. The composite film exhibits a hardness of 49.8 ± 2.1 GPa, an elastic modulus of 598 ± 25 GPa, which is directly comparable to the value gained by the surface acoustic wave (SAW) method. Moreover, the hardness and elastic modulus of the composite film both accord well with the values predicted from the mechanical properties of the individual phases. Therefore, mechanical property determination from initial-grown surface is a promising approach for a variety of hard brittle thin film systems with high surface roughness.
Data mining Techniques for Predicting Crop Productivity – A review article

In this paper an attempt has been made to review the research studies on application of data mining techniques in the field of agriculture. Some of the techniques, such asID3 algorithms, the k-means, the k nearest neighbor, artificial neural networks and support vector machines applied in the field of agriculture were presented. Data mining in application in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. This article explores the applications of data mining techniques in the field of agriculture and allied sciences. I. Introduction Data mining is the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems. Agriculture and allied activities constitute the single largest component of India's gross domestic product, contributing nearly 25% of the total and nearly 60% of Indian population depends on this profession. Due to vagaries of climate factors the agricultural productivities in India are continuously decreasing over a decade. The reasons for this were studied mostly using regression analysis. In this paper an attempt has been made to compile the research findings of different researchers who used data.
The Techsat-21 autonomous space science agent

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat-21 constellation of three spacecraft scheduled for launch in 2004. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. We also describe our working software prototype and preparations for flight.
A novel outlier cluster detection algorithm without top-n parameter

Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video surveillance, etc. Outlier detection has been widely focused and studied in recent years. The concept about outlier factor of object is extended to the case of cluster. Although many outlier detection algorithms have been proposed, most of them face the top-n problem, i.e., it is difficult to know how many points in a database are outliers. In this paper we propose a novel outlier cluster detection algorithm called ROCF based on the concept of mutual neighbor graph and on the idea that the size of outlier clusters is usually much smaller than the normal clusters. ROCF can automatically figure out the outlier rate of a database and effectively detect the outliers and outlier clusters without top-n parameter. The formal analysis and experiments show that this method can achieve good performance in outlier detection. © 2017 Elsevier B.V. All rights reserved.
Wireless Power Transfer (WPT) for Electric Vehicles (EVs)—Present and Future Trends

© Springer Science+Business Media Singapore 2015 S. Rajakaruna et al. (eds.), Plug In Electric Vehicles in Smart Grids, Power Systems, DOI 10.1007/978-981-287-299-9_2 Abstract 100 year old gasoline engine technology vehicles have now become one of the major contributors of greenhouse gases. Plug-in Electric Vehicles (PEVs) have been proposed to achieve environmental friendly transportation. Even though the PEV usage is currently increasing, a technology breakthrough would be required to overcome battery related drawbacks. Although battery technology is evolving, drawbacks inherited with batteries such as; cost, size, weight, slower charging characteristic and low energy density would still be dominating constrains for development of EVs. Furthermore, PEVs have not been accepted as preferred choice by many consumers due to charging related issues. To address battery related limitations, the concept of dynamic Wireless Power Transfer (WPT) enabled EVs have been proposed in which EV is being charged while it is in motion. WPT enabled infrastructure has to be employed to achieve dynamic EV charging concept. The weight of the battery pack can be reduced as the required energy storage is lower if the vehicle can be powered wirelessly while driving. Stationary WPT charging where EV is charged wirelessly when it is stopped, is simpler than dynamic WPT in terms of design complexity. However, stationary WPT does not increase vehicle range compared to wired-PEVs. State-of-art WPT technology for future transportation is discussed in this chapter. Analysis of the WPT system and its performance indices are introduced. Modelling the WPT system using different methods such as equivalent circuit theory, two port network theory and coupled mode theory is described illustrating their own merits in Sect. 2.3. Both stationary and dynamic WPT for EV applications are illustrated in Sect. 2.4. Design challenges and optimization directions are analysed in Sect. 2.5. Adaptive tuning techniques such as adaptive impedance matching and
Artificial neural networks improve the accuracy of cancer survival prediction.

BACKGROUND
The TNM staging system originated as a response to the need for an accurate, consistent, universal cancer outcome prediction system. Since the TNM staging system was introduced in the 1950s, new prognostic factors have been identified and new methods for integrating prognostic factors have been developed. This study compares the prediction accuracy of the TNM staging system with that of artificial neural network statistical models.


METHODS
For 5-year survival of patients with breast or colorectal carcinoma, the authors compared the TNM staging system's predictive accuracy with that of artificial neural networks (ANN). The area under the receiver operating characteristic curve, as applied to an independent validation data set, was the measure of accuracy.


RESULTS
For the American College of Surgeons' Patient Care Evaluation (PCE) data set, using only the TNM variables (tumor size, number of positive regional lymph nodes, and distant metastasis), the artificial neural network's predictions of the 5-year survival of patients with breast carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.720; ANN, 0.770; P < 0.001). For the National Cancer Institute's Surveillance, Epidemiology, and End Results breast carcinoma data set, using only the TNM variables, the artificial neural network's predictions of 10-year survival were significantly more accurate than those of the TNM staging system (TNM, 0.692; ANN, 0.730; P < 0.01). For the PCE colorectal data set, using only the TNM variables, the artificial neural network's predictions of the 5-year survival of patients with colorectal carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.737; ANN, 0.815; P < 0.001). Adding commonly collected demographic and anatomic variables to the TNM variables further increased the accuracy of the artificial neural network's predictions of breast carcinoma survival (0.784) and colorectal carcinoma survival (0.869).


CONCLUSIONS
Artificial neural networks are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to artificial neural networks to increase prognostic accuracy further. These results are robust across different data sets and cancer sites.
The use of insulin pumps with meal bolus alarms in children with type 1 diabetes to improve glycemic control.

OBJECTIVE
The aim of this study was to determine whether the use of meal bolus alarms would result in fewer missed meal boluses per week in youth with type 1 diabetes using continuous subcutaneous insulin infusion (CSII) therapy.


RESEARCH DESIGN AND METHODS
This was a randomized trial of 48 youth using CSII, who were in suboptimal glycemic control with HbA(1c) (A1C) values > or =8.0%. Twenty-four subjects were randomized to use a Deltec Cozmo insulin pump with meal bolus alarms (experimental group), while the other 24 subjects continued use of their current insulin pumps (control group) without meal bolus alarms.


RESULTS
After 3 months of study, the number of missed meal boluses per week was significantly lower in the experimental group (from 4.9 +/- 3.7 to 2.5 +/- 2.5; P = 0.0005) but not significantly lower in the control group (from 4.3 +/- 2.7 to 4.2 +/- 3.9; P = 0.7610). Also after 3 months, the mean A1C value of the experimental group declined significantly (from 9.32 +/- 1.12 to 8.86 +/- 1.10; P = 0.0430). No significant decline in A1C was present for the control group (from 8.93 +/- 1.04 to 8.67 +/- 1.17; P = 0.1940). After 6 months of study, the significant decline in A1C from baseline in the experimental group was no longer present. Pooling of all available data from the control and experimental groups showed that at baseline and 3 and 6 months, the number of missed meal boluses per week was significantly correlated with A1C values.


CONCLUSIONS
While meal bolus alarms may have the potential to improve suboptimal glycemic control in youth using CSII, our results demonstrated that these alarms had only a transient, modest effect in doing so.
Progress in gene therapy for primary immunodeficiencies using lentiviral vectors.

PURPOSE OF REVIEW
This review gives an overview over the most recent progress in the field of lentiviral gene therapy for primary immunodeficiencies (PIDs). The history and state-of-the-art of lentiviral vector development are summarized and the recent advancements for a number of selected diseases are reviewed in detail. Past retroviral vector trials for these diseases, the most recent improvements of lentiviral vector platforms and their application in preclinical development as well as ongoing clinical trials are discussed.


RECENT FINDINGS
Main focus is on the preclinical studies and clinical trials for the treatment of Wiskott-Aldrich syndrome, chronic granulomatous disease, adenosine deaminase deficient severe combined immunodeficiency (ADA-SCID) and X-linked severe combined immunodeficiency with lentiviral gene therapy.


SUMMARY
Gene therapy for PIDs is an effective treatment, providing potential long-term clinical benefit for affected patients. Substantial progress has been made to make lentiviral gene therapy platforms available for a number of rare genetic diseases. Although many ongoing gene therapy trials are based on ex-vivo approaches with autologous hematopoietic stem cells, other approaches such as in-vivo gene therapy or gene-repair platforms might provide further advancement for certain PIDs.
Pediatric Oral Motor Feeding Assessments: A Systematic Review.

AIM
To describe the clinical properties and psychometric soundness of pediatric oral motor feeding assessments.


METHODS
A systematic search was conducted using Medline, CINAHL, EMBASE, PsycInfo, and HAPI databases. Assessments were analyzed for their clinical and psychometric characteristics.


RESULTS
12 assessment tools were identified to meet the inclusion/exclusion criteria. Clinical properties varied from assessments evaluating oral-motor deficits, screening to identify feeding problems, and monitoring feeding progress. Most assessments were designed for children with developmental disabilities or cerebral palsy. Eleven assessments had psychometric evidence, of these nine had reliability and validity testing (Ability for Basic Feeding and Swallowing Scale for Children, Behavioral Assessment Scale of Oral Functions in Feeding, Dysphagia Disorder Survey, Functional Feeding Assessment-modified, Gisel Video Assessment, Montreal Children's Hospital Feeding Scale, Oral Motor Assessment Scale, Schedule for Oral Motor Assessment, and Screening Tool of Feeding Problems Applied to Children). The Brief Assessment of Motor Function-Oral Motor Deglutition and the Pediatric Assessment Scale for Severe Feeding Problems had reliability testing only. The Slurp Test was not tested for any psychometric properties. Overall, psychometric evidence was inconsistent and inadequate for the evaluative tools.