LLukas22/scidocs huggingface dataset:
Sunscreen use: controversies, challenges and regulatory aspects.

Mismatches between skin pigmentation and modern lifestyle continue to challenge our naked skin. One of our responses to these challenges is the development and use of sunscreens. The management of sunscreens has to balance their protective effect against erythema, photocarcinogenesis and photoageing owing to the potential toxicity of the ultraviolet (UV) filters for humans and the environment. The protection against UV radiation offered by sunscreens was recently standardized in the European Union (EU) based on international harmonization of measurement techniques. Four different categories of sun protection have been implemented along with recommendations on how to use sunscreen products in order to obtain the labelled protection. The UV filters in sunscreens have long been authorized for use by the EU authority on the basis of data from studies on acute toxicity, subchronic and chronic toxicity, reproductive toxicity, genotoxicity, photogenotoxicity, carcinogenicity, irritation, sensitization, phototoxicity and photosensitization as well as on environmental aspects. New challenges with respect to the safety of UV filters have arisen from the banning of animal experiments for the development of cosmetics. Future debates on sunscreens are likely to focus on nanoparticles and environmental issues, along with motivation campaigns to persuade consumers to protect their skin. However, more efficient sunscreen use will also continue to raise questions on the benefit in preventing vitamin D synthesis in the skin induced by sunlight.
Phase III postoperative adjuvant radiotherapy after radical prostatectomy compared with radical prostatectomy alone in pT3 prostate cancer with postoperative undetectable prostate-specific antigen: ARO 96-02/AUO AP 09/95.

PURPOSE
Local failure after radical prostatectomy (RP) is common in patients with cancer extending beyond the capsule. Two randomized trials demonstrated an advantage for adjuvant radiotherapy (RT) compared with a wait-and-see policy. We conducted a randomized, controlled clinical trial to compare RP followed by immediate RT with RP alone for patients with pT3 prostate cancer and an undetectable prostate-specific antigen (PSA) level after RP.


METHODS
After RP, 192 men were randomly assigned to a wait-and-see policy, and 193 men were assigned to immediate postoperative RT. Eligible patients had pT3 pN0 tumors. Patients who did not achieve an undetectable PSA after RP were excluded from treatment according to random assignment (n = 78; 20%). Of the remaining 307 patients, 34 patients on the RT arm did not receive RT and five patients on the wait-and-see arm received RT. Therefore, 114 patients underwent RT and 154 patients were treated with a wait-and-see policy. The primary end point was biochemical progression-free survival.


RESULTS
Biochemical progression-free survival after 5 years in patients with undetectable PSA after RP was significantly improved in the RT group (72%; 95% CI, 65% to 81%; v 54%, 95% CI, 45% to 63%; hazard ratio = 0.53; 95% CI, 0.37 to 0.79; P = .0015). On univariate analysis, Gleason score more than 6 and less than 7, PSA before RP, tumor stage, and positive surgical margins were predictors of outcome. The rate of grade 3 to 4 late adverse effects was 0.3%.


CONCLUSION
Adjuvant RT for pT3 prostate cancer with postoperatively undetectable PSA significantly reduces the risk of biochemical progression. Further follow-up is needed to assess the effect on metastases-free and overall survival.
Positive predictive value of BI-RADS MR imaging.

PURPOSE
To evaluate the positive predictive values (PPVs) of Breast Imaging and Reporting Data Systems (BI-RADS) assessment categories for breast magnetic resonance (MR) imaging and to identify the BI-RADS MR imaging lesion features most predictive of cancer.


MATERIALS AND METHODS
This institutional review board-approved HIPAA-compliant prospective multicenter study was performed with written informed consent. Breast MR imaging studies of the contralateral breast in women with a recent diagnosis of breast cancer were prospectively evaluated. Contralateral breast MR imaging BI-RADS assessment categories, morphologic descriptors for foci, masses, non-masslike enhancement (NMLE), and kinetic features were assessed for predictive values for malignancy. PPV of each imaging characteristic of interest was estimated, and logistic regression analysis was used to examine the predictive ability of combinations of characteristics.


RESULTS
Of 969 participants, 71.3% had a BI-RADS category 1 or 2 assessment; 10.9%, a BI-RADS category 3 assessment; 10.0%, a BI-RADS category 4 or 5 assessment; and 7.7%, a BI-RADS category 0 assessment on the basis of initial MR images. Thirty-one cancers were detected with MR imaging. Overall PPV for BI-RADS category 4 and 5 lesions was 0.278, with 17 cancers in patients with a BI-RADS category 4 lesion (PPV, 0.205) and 10 cancers in patients with a BI-RADS category 5 lesion (PPV, 0.714). Of the cancers, one was a focus, 17 were masses, and 13 were NMLEs. For masses, irregular shape, irregular margins, spiculated margins, and marked internal enhancement were most predictive of malignancy. For NMLEs, ductal, clumped, and reticular or dendritic enhancement were the features most frequently seen with malignancy. Kinetic enhancement features were less predictive of malignancy than were morphologic features.


CONCLUSION
Standardized terminology of the BI-RADS lexicon enables quantification of the likelihood of malignancy for MR imaging-detected lesions through careful evaluation of lesion features. In particular, BI-RADS assessment categories and morphologic descriptors for masses and NMLE were useful in estimating the probability of cancer.
Effects of exam-room computing on clinician-patient communication

OBJECTIVE: To evaluate the impact of exam-room computers on communication between clinicians and patients. DESIGN AND METHODS: Longitudinal, qualitative study using video-tapes of regularly scheduled visits from 3 points in time: 1 month before, 1 month after, and 7 months after introduction of computers into the exam room. SETTING: Primary care medical clinic in a large integrated delivery system. PARTICIPANTS: Nine clinicians (6 physicians, 2 physician assistants, and 1 nurse practitioner) and 54 patients. RESULTS: The introduction of computers into the exam room affected the visual, verbal, and postural connection between clinicians and patients. There were variations across the visits in the magnitude and direction of the computer’s effect. We identified 4 domains in which exam-room computing affected clinician-patient communication: visit organization, verbal and nonverbal behavior, computer navigation and mastery, and spatial organization of the exam room. We observed a range of facilitating and inhibiting effects on clinician-patient communication in all 4 domains. For 2 domains, visit organization and verbal and nonverbal behavior, facilitating and inhibiting behaviors observed prior to the introduction of the computer appeared to be amplified when exam-room computing occurred. Likewise, exam-room computing involving navigation and mastery skills and spatial organization of the exam-room created communication challenges and opportunities. In all 4 domains, there was little change observed in exam-room computing behaviors from the point of introduction to 7-month follow-up. CONCLUSIONS: Effective use of computers in the outpatient exam room may be dependent upon clinicians’ baseline skills that are carried forward and are amplified, positively or negatively, in their effects on clinician-patient communication. Computer use behaviors do not appear to change much over the first 7 months. Administrators and educators interested in improving exam-room computer use by clinicians need to better understand clinician skills and previous work habits associated with electronic medical records. More study of the effects of new technologies on the clinical relationship is also needed.
Face Recognition in Movie Trailers via Mean Sequence Sparse Representation-Based Classification

This paper presents an end-to-end video face recognition system, addressing the difficult problem of identifying a video face track using a large dictionary of still face images of a few hundred people, while rejecting unknown individuals. A straightforward application of the popular l1-minimization for face recognition on a frame-by-frame basis is prohibitively expensive, so we propose a novel algorithm Mean Sequence SRC (MSSRC) that performs video face recognition using a joint optimization leveraging all of the available video data and the knowledge that the face track frames belong to the same individual. By adding a strict temporal constraint to the l1-minimization that forces individual frames in a face track to all reconstruct a single identity, we show the optimization reduces to a single minimization over the mean of the face track. We also introduce a new Movie Trailer Face Dataset collected from 101 movie trailers on YouTube. Finally, we show that our method matches or outperforms the state-of-the-art on three existing datasets (YouTube Celebrities, YouTube Faces, and Buffy) and our unconstrained Movie Trailer Face Dataset. More importantly, our method excels at rejecting unknown identities by at least 8% in average precision.
Deduction in Concept Languages: From Subsumption to Instance Checking

It is a common opinion that subsumption is the central reasoning task in frame-based knowledge representation languages (or concept languages). Intuitively, a concept C subsumes another concept D if the set of objects represented by C is a superset of the one represented by D. When individual objects are taken into account, the basic deduc-tive task for retrieving information from a knowledge base is instance checking, that amounts to checking whether the knowledge base implies that an individual is an instance of a given concept. In this paper, we address the question of whether instance checking can be solved by means of subsumption algorithms. We do so by considering several languages where subsumption belongs to diierent complexity classes. For such languages we present methods for the instance checking problem, provide a complexity analysis of this problem, and compare it with the subsumption problem. The main result of the paper is that instance checking is not always easily reducible to subsumption. In particular, there are cases where it is strictly harder than subsumption. This impacts on the design of reasoning algorithms for knowledge representation systems based on concept languages.
Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

(Background) We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over Dataset-66, Dataset-160, and Dataset-255, respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.
Predicting the potential habitat of oaks with data mining models and the R system

Oak forests are essential for the ecosystems of many countries, particularly when they are used in vegetal restoration. Therefore, models for predicting the potential habitat of oaks can be a valuable tool for work in the environment. In accordance with this objective, the building and comparison of data mining models are presented for the prediction of potential habitats for the oak forest type in Mediterranean areas (southern Spain), with conclusions applicable to other regions. Thirty-one environmental input variables were measured and six base models for supervised classification problems were selected: linear and quadratic discriminant analysis, logistic regression, classification trees, neural networks and support vector machines. Three ensemble methods, based on the combination of classification tree models fitted from samples and sets of variables generated from the original data set were also evaluated: bagging, random forests and boosting. The available data set was randomly split into three parts: training set (50%), validation set (25%), and test set (25%). The analysis of the accuracy, the sensitivity, the specificity, together with the area under the ROC curve for the test set reveal that the best models for our oak data set are those of bagging and random forests. All of these models can be fitted by free R programs which use the libraries and functions described in this paper. Furthermore, the methodology used in this study will allow researchers to determine the potential distribution of oaks in other kinds of areas. 2010 Elsevier Ltd. All rights reserved.
Telehealth behavior therapy for the management of type 1 diabetes in adolescents.

BACKGROUND
Poor management of type 1 diabetes mellitus (T1DM) may result in serious medical complications. Psychological intervention may improve adherence to medical regimens; however, access to trained professionals is limited, particularly in rural communities. Telehealth interventions may address this by allowing families to access services at home; however, little is known about the efficacy of such services.


METHOD
This study presents results from a pilot trial of a randomized waitlist controlled trial of Telehealth Behavioral Therapy (TBT) for youths with T1DM. Primary outcome measures were adherence to the diabetes regimen, glycemic control, and level of family discord. Thirty-two youths (23 female) with T1DM (aged 9 to 17 years) and one parent or caregiver participated. Telehealth Behavioral Therapy sessions were conducted thrice weekly for 12 weeks by phone and lasted an average of 15 min each.


RESULTS
Results indicated that youths in treatment decreased their hemoglobin A1c by 0.74 compared to 0.09 in the waitlist, though this was not statistically significant. Youths in treatment reported increased unsupportive and decreased caring parental behaviors.


CONCLUSION
Telehealth Behavioral Therapy improves access to knowledgeable providers and results in a clinically significant improvement in glycemic control. Despite some youths experiencing an increase in unsupportive parental behaviors, TBT is a promising method of service delivery that warrants further investigation.
[Clinical observation of sophocarpidine combined with Salviae miltiorrhizae injection in treating chronic hepatitis B].

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The Effectiveness of WhatsApp Mobile Learning Activities Guided by Activity Theory on Students' Knowledge Management.

This research paper explores the effectiveness of using mobile technologies to support a blended learning course titled Scientific Research Methods in Information Science. Specifically, it discusses the effects of WhatsApp mobile learning activities guided by activity theory on students' knowledge Management (KM). During the 2014 academic year, the researcher adopted an experimental approach-based comparison between an experimental group (34 students) and a control group (34 students). The learning process of the experimental group was based on continuity between 2 hours of in-class learning and 1 hour of learning activities that were mediated by WhatsApp instant messaging each week. The control group’s experience was 100% in-class with no app mediation. The researcher used the t-test to compare the means of the control and experimental groups in the test and the students’ attitudes at 0.05 alpha levels. This research paper is useful for exploring the effectiveness of mobile technologies in supporting blended learning courses.
SMS Spam Filtering Using Machine Learning Techniques: A Survey

Objective: To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion. Data sources: Original articles written in English found in Sciencedirect.com, Google-scholar.com, Search.com, IEEE explorer, and the ACM library. Study selection: Those articles dealing with machine learning and hybrid approaches for SMS spam filtering. Data extraction: Many articles extracted by searching a predefined string and the outcome was reviewed by one author and checked by the second. The primary paper was reviewed and edited by the third author. Results: A total of 44 articles were selected which were concerned machine learning and hybrid methods for detecting SMS spam messages. 28 methods and algorithms were extracted from these papers and studied and finally 15 algorithms among them have been compared in one table according to their accuracy, strengths, and weaknesses in detecting spam messages of the Tiago dataset of spam message. Actually, among the proposed methods DCA algorithm, the large cellular network method and graph-based KNN are three most accurate in filtering SMS spams of Tiago data set. Moreover, Hybrid methods are discussed in this paper.
Feedback Control of Melt Pool Temperature During Laser Cladding Process

Laser cladding is a multiple-parameter-dependent process, and a feedback control is critical for the process stabilization. This paper presents a generalized predictive control strategy with input constraints to stabilize the melt pool temperature during a high-power diode laser cladding process. A dual-color pyrometer was used to monitor the melt pool temperature. A state-space dynamic model relating the laser drive signal (laser power) to the melt pool temperature was identified experimentally using the subspace method. A generalized predictive controller with input constraints was implemented in real time using the state-space model. The closed-loop process was able to track the melt pool temperature to a reference temperature profile. Laser cladding of H13 tool steel on a substrate with uneven surface showed that the closed-loop process was able to compensate for an under-fill with 3-mm depth after 40-layer depositions.
Novel Reversible Multiplier Circuit in Nanotechnology

Reversible computation is of the growing interests to power minimization having applications in low power CMOS design, quantum computing, optical information processing, DNA computing, bioinformatics and nanotechnology. This paper proposes a novel 4x4 bit reversible Multiplier circuit. It is faster and has lower hardware complexity compared to the existing designs. In addition, the proposed reversible multiplier is better than the existing counterparts in term of number of gates and number of garbage outputs. Haghparast and Navi recently proposed a 4x4 reversible gate called "MKG". The reversible MKG gate can work singly as a reversible full adder. In this paper we use MKG gates to construct the reversible multiplier circuit. The proposed reversible multiplier circuit can multiply two 4-bits binary numbers. It can be generalized for NxN bit multiplication.
A combined two's complement and floating-point comparator

This paper presents the design of a combined two's complement and IEEE 754-compliant floating-point comparator. Unlike previous designs, this comparator incorporates both operand types into one unit, while still maintaining low area and high speed. The comparator design uses a novel magnitude comparator with logarithmic delay, plus additional logic to handle both two's complement and floating point operands. The comparator fully supports 32-bit and 64-bit floating-point comparisons, as defined in the IEEE 754 standard, as well as 32-bit and 64-bit two's complement comparisons. Area and delay estimates are presented for designs implemented in AMI C5N 0.5 /spl mu/m CMOS technology.
Effects of cannabidiol in animal models predictive of antipsychotic activity

The effects of cannabidiol (CBD) were compared to those produced by haloperidol in rats submitted to experimental models predictive of antipsychotic activity. Several doses of CBD (15–480 mg/kg) and haloperidol (0.062–1.0 mg/kg) were tested in each model. First, CBD increased the effective doses 50% (or) ED50 of apomorphine for induction of the sniffing and biting stereotyped behaviors. In addition, both CBD and haloperidol reduced the occurrence of stereotyped biting induced by apomorphine (6.4 mg/kg), increased plasma prolactin levels and produced palpebral ptosis, as compared to control solutions. However, CBD did not induce catalepsy even at the highest doses, in contrast to haloperidol. Such a pharmacological profile is compatible with that of an “atypical” antipsychotic agent, though the mechanism of action is uncertain and may not be identical to that of the dopamine antagonists.
Benign prostatic hyperplasia: racial differences in treatment patterns and prostate cancer prevalence.

OBJECTIVE
• To compare prostate cancer, prostate-related surgery and acute urinary retention rates, as well as associated healthcare resource use over 11 years in African American and Caucasian men with benign prostatic hyperplasia (BPH).


PATIENTS AND METHODS
• The BPH-related medical and surgical charges and events were determined for 398 African American men and 1656 Caucasian men followed for a mean of 10.2 years within a health maintenance organization. • Racial differences in clinical outcomes were evaluated using time-to-event analysis, stratifying results by baseline prostate-specific antigen (PSA) values.


RESULTS
• Risk of a prostate cancer diagnosis was 2.2 times greater in African American than Caucasian men (95% CI 1.48-3.35, P < 0.001) in analyses adjusting for serum PSA level. • Although African Americans were more likely to receive medical therapy for symptoms of BPH than Caucasians (43.5% vs 37.2%, respectively; P= 0.029), there were no clinically meaningful differences with respect to subsequent acute urinary retention or BPH-related surgery between them, or BPH-related medical charges (US $407 vs US $405 per month).


CONCLUSION
• As evidenced by this analysis of 'real-world' clinical practice, African Americans with BPH have a much greater risk of developing prostate cancer than similar Caucasian men highlighting the need for education and early detection in this population.