LLukas22/scidocs huggingface dataset:
Modelling the consequences of a reduction in alcohol consumption among patients with alcohol dependence based on real-life observational data

BACKGROUND
Most available pharmacotherapies for alcohol-dependent patients target abstinence; however, reduced alcohol consumption may be a more realistic goal. Using randomized clinical trial (RCT) data, a previous microsimulation model evaluated the clinical relevance of reduced consumption in terms of avoided alcohol-attributable events. Using real-life observational data, the current analysis aimed to adapt the model and confirm previous findings about the clinical relevance of reduced alcohol consumption.


METHODS
Based on the prospective observational CONTROL study, evaluating daily alcohol consumption among alcohol-dependent patients, the model predicted the probability of drinking any alcohol during a given day. Predicted daily alcohol consumption was simulated in a hypothetical sample of 200,000 patients observed over a year. Individual total alcohol consumption (TAC) and number of heavy drinking days (HDD) were derived. Using published risk equations, probabilities of alcohol-attributable adverse health events (e.g., hospitalizations or death) corresponding to simulated consumptions were computed, and aggregated for categories of patients defined by HDDs and TAC (expressed per 100,000 patient-years). Sensitivity analyses tested model robustness.


RESULTS
Shifting from >220 HDDs per year to 120-140 HDDs and shifting from 36,000-39,000 g TAC per year (120-130 g/day) to 15,000-18,000 g TAC per year (50-60 g/day) impacted substantially on the incidence of events (14,588 and 6148 events avoided per 100,000 patient-years, respectively). Results were robust to sensitivity analyses.


CONCLUSIONS
This study corroborates the previous microsimulation modeling approach and, using real-life data, confirms RCT-based findings that reduced alcohol consumption is a relevant objective for consideration in alcohol dependence management to improve public health.
Ontology-based information retrieval for historical documents

This article presents an ontology-based approach to designing and developing new representation IR system instead of conventional keyword-based approach. Such representation improves the precision and recall of document retrieval. Experiments carried out on the ontology-based approach and keyword-based approach demonstrates the effectiveness of the proposed approach.
Knowledge-based acquisition of tradeoff preferences for negotiating agents

A wide range of algorithms have been developed for various types of automated negotiation. In developing such algorithms the main focus has been on their efficiency and their effectiveness. However, this is only part of the picture. Agents typically negotiate on behalf of their owners and for this to be effective the agent must be able to adequately represent the owners' preferences. However, the process by which such knowledge is acquired is typically left unspecified. To remove this shortcoming, we present a case study indicating how the knowledge for a particular negotiation algorithm can be acquired. More precisely, according to the analysis on the automated negotiation model, we identified that user trade-off preferences play a fundamental role in negotiation in general. This topic has been addressed little in the research area of user preference elicitation for general decision making problems as well. In a previous paper, we proposed an exhaustive method to acquire user trade-off preferences. In this paper, we developed another method to remove the limitation of the high user workload of the exhaustive method. Although we cannot say that it can exactly capture user trade-off preferences, it models the main commonalities of trade-off relations and reflects users' individualities as well.
Detection and Diagnosis of Breast Tumors using Deep Convolutional Neural Networks

We present an application of deep Convolutional Neural Networks (CNN) for the detection and diagnosis of breast tumors. The images used in this study have been extracted from the mini-MIAS database of mammograms. The proposed system has been implemented in three stages: (a) crop, rotation and resize of the original mammogram; (b) feature extraction using a pretrained CNN model (AlexNet and VGG); (c) training of a Support Vector Machine (SVM) at the classification task using the previously extracted features. In this research, the goal of the system is to distinguish between three classes of patients: those with benign, malign or without tumor. Experiments show that feature extraction using pretrained models provides satisfactory results, achieving a 64.52% test accuracy. This outcome could be improved via fine-tuning of the final layers or training the whole network parameters. The results of additional experiments using a sample of the Caltech-101 database, for which a 99.38% test accuracy was obtained, exhibit the relevance of the similarity between the data used to train the model and the particular application intended. Additionally, it is worth noting the impact of the data augmentation process and the balance of the number of examples per class on the performance of the system. Keywords—Breast tumor, classification, mammogram, convolutional neural network, support vector machine
Gaussian-based edge-detection methods - a survey

The Gaussian filter has been used extensively in image processing and computer vision for many years. In this survey paper, we discuss the various features of this operator that make it the filter of choice in the area of edge detection. Despite these desirable features of the Gaussian filter, edge detection algorithms which use it suffer from many problems. We will review several linear and nonlinear Gaussian-based edge detection methods.
Shadow Detection in Single Still Images using TAM based Multi Step Algorithm

Processing of foreground images in surveillance applications usually requires that shadows have been identified and removed from the detected foreground. A novel approach to shadow detection from single still images is presented in this paper. This approach includes a pre processing step to identify the presence of shadows in the image and then A Tricolor Attenuation Model based multi step shadow detection algorithm is used to extract shadows. The tricolor attenuation model (TAM) describes the attenuation relationship between shadow and its non shadow region is derived based on image formation theory. In contrast, the method proposed here is not only designed for specific applications but can automatically detect and extract the shadows from single still images, even those with complex outdoor scenes. In addition, this method does not need any user intervention and prior knowledge. It is entirely a data-driven approach. I. Introduction Shadow detection over the past decades covers many explicit applications such as traffic surveillance, face recognition and image segmentation. Real images with shadows can not be used for image synthesis. Shadows give geometric and semantic information in an image, including cues about the shape and the relative spatial position of objects, characteristics of surfaces and light sources. In outdoor scenes, there are mainly two light sources: direct sunlight, which can be regarded as a point light source; diffuse skylight, which can be regarded as an area light source. Shadow occurs when an object totally or partially occludes directly from the light source. Generally, shadow is divided into two parts: 1-Self shadow is a part of shadow on the main object where it is not illuminated by light. 2-Cast shadow is the dark area projected by an object on the background. (See fig. 1) Cast shadow can be further divided into umbra and penumbra region. The umbra region is the part of a cast shadow where direct light is completely blocked and the penumbra region is the part of a cast shadow where light is partially blocked.
The relationship of Internet addiction severity with Attention Deficit Hyperactivity Disorder symptoms in Turkish University students; impact of personality traits, depression and anxiety.

AIM
The aim of this study was to investigate the relationship of Internet addiction (IA) with Attention Deficit Hyperactivity Disorder (ADHD) symptoms while controlling the effect of personality traits, depression and anxiety symptoms in Turkish university students.


METHODS
A total of 271 university students participated in the present study. The students were assessed through the Internet Addiction Scale (IAS), the Wender Utah Rating Short Scale (WURS-25), the Turkish version of the Adult ADHD Self-Report Scale (ASRS), the Eysenck Personality Questionnaire Revised Abbreviated Form (EPQR-A), the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI).


RESULTS
According to IAS, participants were separated into three groups, namely, moderate/high, mild and without IA groups. The rates of groups were 19.9% (n=54), 38.7% (n=105) and 41.3% (n=112), respectively. Correlation analyses revealed that the severity of IAS is positively correlated with WURS-25, ASRS (total, inattention and hyperactivity/impulsivity subscales), neuroticism personality trait, depression and anxiety scores, whereas it is negatively correlated with extraversion personality trait. Hierarchical regression analysis indicated that depression and anxiety symptoms, introversion and neuroticism personality traits and the severity of ADHD symptoms (particularly hyperactivity/impulsivity symptoms) are the predictors for IAS score, respectively.


CONCLUSIONS
The severity of ADHD symptoms has predicted the severity of IA even after controlling the effect of personality traits, depression and anxiety symptoms among Turkish university students. University students with severe ADHD symptoms, particularly hyperactivity/impulsivity symptoms may be considered as a risk group for IA.
Learning Technical Drawing with Augmented Reality and Holograms

Technical drawing is very important for mechanical engineers. The ability to understand and work with technical drawings is a necessary skill for most of them. In general, first year students of mechanical engineering have difficulties in drawing orthographic views and perspectives, since they find it difficult to understand 3D shapes from 2D views. In this paper, we present several tools that we explored to help students visualize 3D models. We studied the most popular AR systems and show examples of using an AR system for the visualization of 3D models. We also present the creation of a low cost prototype, the EducHolo, that enables the visualization and interaction with holograms. With this prototype students can visualize and interact with the hologram of mechanical parts. Using augmented reality and interactive holograms we aim at providing a better perception of the model 3D shape and improving the ability of making the 2D orthographic views and perspectives that they study in the first year of mechanical engineer. Key–Words: Visualization; Augmented Reality; Holograms; 3D models; e-learning.
Surgery in Castleman's disease: a systematic review of 404 published cases.

OBJECTIVES
We undertook a systematic review of 404 published cases of Castleman's disease to identify the role of the surgeon beyond assistance in tissue-based diagnosis.


BACKGROUND
Castleman's disease is a rare primary disease of the lymph node caused by infection with herpesviridae. Little is known about the role of surgery in this condition.


DATA SOURCES
Medline, Embase, Cochrane Database of Systematic Reviews, ISI Thompson Web of Knowledge, and hand search of articles' bibliography.


STUDY SELECTION
Of the 1791 citations identified through the initial electronic search and screened for possible inclusion, 488 articles were retained after title and abstract reviews. Of these, 239 were accepted for this review.


DATA EXTRACTION
A complete dataset containing age, gender, centricity (unicentric vs multicentric), histopathologic type (hyaline vascular [HV] vs plasma cell [PC]), anatomical location of the only focus in unicentric Castleman's disease (UCD) or the dominant focus in multicentric Castleman's disease (MCD), nature of the surgical approach (resective vs diagnostic), and outcome (disease-free survival [DFS] vs death due to disease) was extracted.


RESULTS
A resective or debulking surgical approach was described in 77.0% of all patients, but was far more common in unicentric (262/278; 94.2%) than multicentric (49/126; 38.9%) disease (χ² 146.8; P < 0.0001). Unicentric disease had a significantly higher overall survival (95.3% vs 61.1%; χ² 55.7; P < 0.0001), 3 year DFS (89.7% vs 55.6%; χ² 27.8; P < 0.0001), and 5 year DFS (81.2% vs 34.4%; χ² 28.6; P < 0.0001) than multicentric disease. Failure to treat unicentric disease by resective surgery resulted in a significant mortality (17.6% vs 3.8% χ²; P < 0.05). In multicentric disease, outcomes are comparable between debulking surgery alone, immunochemotherapy alone, or a combination of both (28.0% vs 28.9% vs 50.0%; P = nonsignificant).


CONCLUSIONS
Surgery is the gold standard for treatment of unicentric Castelman's disease. The role of debulking surgery in human immunodeficiency virus (-) MCD needs to be evaluated in prospective studies.
Vision Based Hand Gesture Recognition for Human Computer Interaction

— This project presents an approach to develop a real-time hand gesture recognition enabling human-computer interaction. It is " Vision Based " that uses only a webcam and Computer Vision (CV) technology, such as image processing that can recognize several hand gestures. The applications of real time hand gesture recognition are numerous, due to the fact that it can be used almost anywhere where we interact with computers ranging from basic usage which involves small applications to domain-specific specialized applications. Currently, at this level our project is useful for the society but it can further be expanded to be readily used at the industrial level as well. Gesture recognition is an area of active current research in computer vision. Existing systems use hand detection primarily with some type of marker. Our system, however, uses a real-time hand image recognition system. Our system, however, uses a real-time hand image recognition without any marker, simply using bare hands. I. INTRODUCTION In today " s computer age, every individual is dependent to perform most of their day-today tasks using computers. The major input devices one uses while operating a computer are keyboard and mouse. But there are a wide range of health problems that affects many people nowadays, caused by the constant and continuous work with the computer. Direct use of hands as an input device is an attractive method for providing natural Human Computer Interaction which has evolved from text-based interfaces through 2D graphical-based interfaces, multimedia-supported interfaces, to fully fledged multi participant Virtual Environment (VE) systems. Since hand gestures are completely natural form for communication it does not adversely affect the health of the operator as in case of excessive usage of keyboard and mouse. Imagine the human-computer interaction of the future: A 3Dapplication where you can move and rotate objects simply by moving and rotating your hand-all without touching any input device. In this paper a review of vision based hand gesture recognition is presented.
An ethical adaptor: Behavioral modification derived from moral emotions

This paper presents the motivation, basis and a prototype implementation of an ethical adaptor capable of using a moral affective function, guilt, as a basis for altering a robot's ongoing behavior. While the research is illustrated in the context of the battlefield, the methods described are believed generalizable to other domains such as eldercare and are potentially extensible to a broader class of moral emotions, including compassion and empathy.
Two newly identified cat allergens: the von Ebner gland protein Fel d 7 and the latherin-like protein Fel d 8.

INTRODUCTION
Characterization of the complete IgE binding spectrum of cat allergens is important for the development of improved diagnosis and effective immunotherapeutics. While Fel d 1 remains unchallenged as the major cat allergen, we now report the isolation of two new allergens capable of binding similar concentrations of IgE in the allergic sera of some individuals.


MATERIALS AND METHODS
Cat tongue and submandibular salivary gland cDNA libraries were screened by DNA hybridisation and IgE immunoassay. The isolated DNA fragments were sub-cloned into an E. coli expression system and the IgE reactivity was examined with human cat-allergic sera using a DELFIA IgE quantitation assay.


RESULTS
Fel d 7, an 18 kDa von Ebner gland protein Can f 1 homologue, was isolated from the tongue library. Fel d 8, a 24-kDa latherin-like protein with homology to Equ c 5, was isolated from the submandibular library. The frequency of IgE binding of cat-allergic sera to recombinant Fel d 1, 7 and 8 was 60.5, 37.6 and 19.3%, respectively. Inhibition studies indicated some IgE binding cross-reactivity between Fel d 7 and dog dander extracts.


DISCUSSION
The study reports the isolation and characterization of two new cat allergens. The isolation of these allergens provides the opportunity to determine the role that IgE binding proteins other than Fel d 1 play in cat-allergic disease. For cat-allergic individuals with moderate to mild rhinoconjunctivitis these allergens may play a more important role in the manifestation of their allergic disease.
Methods to improve rehabilitation of patients following breast cancer surgery: a review of systematic reviews

CONTEXT
Breast cancer is the most prevalent cancer amongst women but it has the highest survival rates amongst all cancer. Rehabilitation therapy of post-treatment effects from cancer and its treatment is needed to improve functioning and quality of life. This review investigated the range of methods for improving physical, psychosocial, occupational, and social wellbeing in women with breast cancer after receiving breast cancer surgery.


METHOD
A search for articles published in English between the years 2009 and 2014 was carried out using The Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects, PubMed, and ScienceDirect. Search terms included: 'breast cancer', 'breast carcinoma', 'surgery', 'mastectomy', 'lumpectomy', 'breast conservation', 'axillary lymph node dissection', 'rehabilitation', 'therapy', 'physiotherapy', 'occupational therapy', 'psychological', 'psychosocial', 'psychotherapy', 'exercise', 'physical activity', 'cognitive', 'occupational', 'alternative', 'complementary', and 'systematic review'.


STUDY SELECTION
Systematic reviews on the effectiveness of rehabilitation methods in improving post-operative physical, and psychological outcomes for breast cancer were selected. Sixteen articles met all the eligibility criteria and were included in the review.


DATA EXTRACTION
Included review year, study aim, total number of participants included, and results.


DATA SYNTHESIS
Evidence for exercise rehabilitation is predominantly in the improvement of shoulder mobility and limb strength. Inconclusive results exist for a range of rehabilitation methods (physical, psycho-education, nutritional, alternative-complementary methods) for addressing the domains of psychosocial, cognitive, and occupational outcomes.


CONCLUSION
There is good evidence for narrowly-focused exercise rehabilitation in improving physical outcome particularly for shoulder mobility and lymphedema. There were inconclusive results for methods to improve psychosocial, cognitive, and occupational outcomes. There were no reviews on broader performance areas and lifestyle factors to enable effective living after treatment. The review suggests that comprehensiveness and effectiveness of post-operative breast cancer rehabilitation should consider patients' self-management approaches towards lifestyle redesign, and incorporate health promotion aspects, in light of the fact that breast cancer is now taking the form of a chronic illness with longer survivorship years.
Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting

It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1) The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2) Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.
Long-term efficacy and safety of etravirine-containing regimens in a real-life cohort of treatment-experienced HIV-1-infected patients.

OBJECTIVES
Etravirine (ETR) was approved in France in September 2008 and is used in combination with a boosted protease inhibitor (bPI) and other anti-retrovirals (ART) in HIV-infected pre-treated patients. This study aimed to report in a real-life setting the efficacy and tolerability of ETR-based regimens and factors associated with virological response.


METHODS
The study population included all treatment-experienced patients who initiated an ETR-based regimen between September 2008 and July 2013 from the French Dat'AIDS cohort. Analyses were performed in ART-experienced patients starting ETR after virological failure (VF) or as a maintenance therapy (MT), with or without bPI.


RESULTS
A total of 2006 patients (VF, n = 1014 (51%); MT, n = 992 (49%)) were included. At M12, the proportion of patients with HIV RNA < 50 copies/ml was 71.7% (72.0% and 71.1% with or without bPI) in the VF group and 90.5% (85.0% and 92.3% with or without bPI) in the MT group, without significant differences regarding the use of bPI. ETR was discontinued in 8.8% of patients for adverse events in 23.9% of cases (21.5% in VF, 29.5% in MT), treatment failure in 15.2% (16.2% in VF, 7.4% in MT) or simplification in 5.4% (4.6% in VF, 7.4% in MT). In the VF group, factors associated with virological response were a longer duration of HIV infection (OR = 2.7; p < 0.001) and baseline HIV RNA < 5 log10 copies/mL (OR = 2.1; p = 0.002).


CONCLUSION
This study shows that in ART-experienced patients ETR is well tolerated with a high efficacy when combined with other active drugs, even when the regimen does not include a bPI.