Miracle-dz/UniFAR-FTUs huggingface dataset:
Importance of Coverage and Endemicity on the Return of Infectious Trachoma after a Single Mass Antibiotic Distribution

Background As part of the SAFE strategy, mass antibiotic treatments are useful in controlling the ocular strains of chlamydia that cause trachoma. The World Health Organization recommends treating at least 80% of individuals per community. However, the role of antibiotic coverage for trachoma control has been poorly characterized. Methodology/Principal Findings In a collection of cluster-randomized clinical trials, mass oral azithromycin was administered to 40 villages in Ethiopia. The village prevalence of ocular chlamydia was determined before treatment, and at two and six months post-treatment. The mean prevalence of ocular chlamydia was 48.9% (95% CI 42.8 to 55.0%) before mass treatments, decreased to 5.4% (95% CI 3.9 to 7.0%) at two months after treatments (p<0.0001), and returned to 7.9% (95% CI 5.4 to 10.4%) by six months after treatment (p = 0.03). Antibiotic coverage ranged from 73.9% to 100%, with a mean of 90.6%. In multivariate regression models, chlamydial prevalence two months after treatment was associated with baseline infection (p<0.0001) and antibiotic coverage (p = 0.007). However, by six months after treatment, chlamydial prevalence was associated only with baseline infection (p<0.0001), but not coverage (p = 0.31). Conclusions/Significance In post-hoc analyses of a large clinical trial, the amount of endemic chlamydial infection was a strong predictor of chlamydial infection after mass antibiotic treatments. Antibiotic coverage was an important short-term predictor of chlamydial infection, but no longer predicted infection by six months after mass antibiotic treatments. A wider range of antibiotic coverage than found in this study might allow an assessment of a more subtle association.
How clinicians use prescription drug monitoring programs: a qualitative inquiry.

OBJECTIVES Prescription drug monitoring programs (PDMPs) are now active in most states to assist clinicians in identifying potential controlled drug misuse, diversion, or excessive prescribing. Little is still known about the ways in which they are incorporated into workflow and clinical decision making, what barriers continue to exist, and how clinicians are sharing PDMP results with their patients. DESIGN Qualitative data were collected through online focus groups and telephone interviews. SETTING Clinicians from pain management, emergency and family medicine, psychiatry/behavioral health, rehabilitation medicine, internal medicine and dentistry participated. PATIENTS Thirty-five clinicians from nine states participated. METHODS We conducted two online focus groups and seven telephone interviews. A multidisciplinary team then used a grounded theory approach coupled with an immersion-crystallization strategy for identifying key themes in the resulting transcripts. RESULTS Some participants, mainly from pain clinics, reported checking the PDMP with every patient, every time. Others checked only for new patients, for new opioid prescriptions, or for patients for whom they suspected abuse. Participants described varied approaches to sharing PDMP information with patients, including openly discussing potential addiction or safety concerns, avoiding discussion altogether, and approaching discussion confrontationally. Participants described patient anger or denial as a common response and noted the role of patient satisfaction surveys as an influence on prescribing. CONCLUSION Routines for accessing PDMP data and how clinicians respond to it vary widely. As PDMP use becomes more widespread, it will be important to understand what approaches are most effective for identifying and addressing unsafe medication use.
Non-publication and delayed publication of randomized trials on vaccines: survey

Objective To evaluate the extent of non-publication or delayed publication of registered randomized trials on vaccines, and to investigate potential determinants of delay to publication. Design Survey. Data sources Trials registry websites, Scopus, PubMed, Google. Study selection Randomized controlled trials evaluating the safety or the efficacy or immunogenicity of human papillomavirus (HPV), pandemic A/H1N1 2009 influenza, and meningococcal, pneumococcal, and rotavirus vaccines that were registered in ClinicalTrials.gov, Current Controlled Trials, WHO International Clinical Trials Registry Platform, Clinical Study Register, or Indian, Australian-New Zealand, and Chinese trial registries in 2006-12. Electronic databases were searched up to February 2014 to identify published manuscripts containing trial results. These were reviewed and classified as positive, mixed, or negative. We also reviewed the results available in ClinicalTrials.gov. Main outcome measures Publication status of trial results and time from completion to publication in peer reviewed journals. Data synthesis Cox proportional hazards regression was used to evaluate potential predictors of publication delay. Results We analysed 384 trials (85% sponsored by industry). Of 355 trials (404 758 participants) that were completed, 176 (n=151 379) had been published in peer reviewed journals. Another 42 trials (total sample 62 765) remained unpublished but reported results in ClinicalTrials.gov. The proportion of trials published 12, 24, 36, and 48 months after completion was 12%, 29%, 53%, and 73%, respectively. Including results posted in ClinicalTrials.gov, 48 months after study completion results were available for 82% of the trials and 90% of the participants. Delay to publication between non-industry and industry sponsored trials did not differ, but non-industry sponsored trials were 4.42-fold (P=0.008) more likely to report negative or mixed findings. Negative results were reported by only 2% of the published trials. Conclusions Most vaccine trials are published eventually or the results posted in ClinicalTrials.gov, but delays to publication of several years are common. Actions should focus on the timely dissemination of data from vaccine trials to the public.
Organizational Risk Assessment Based on Attacks Repetition

Risk assessment is a very critical and important process to protect the organization assets and reputation against security threats and risks. It provides a clear picture of the current threats that the organization is facing and helps the top management to take the right decision to eliminate or mitigate those risks. Usually if the vulnerability is exploited, the same attack may be happen twice or more in a different time periods because the vulnerability has been exploited and not mitigated. In this paper, we are illustrating our observation of the relation between the risk value and the number of attacks targeting this risk.
The Management of Pregnancy in Women with Hepatocellular Adenoma: A Plea for an Individualized Approach

Because of the risk of hormone-induced growth and spontaneous rupture of hepatocellular adenoma (HCA) during pregnancy, special considerations are required. Due to the scarcity of cases, there is no evidence-based algorithm for the evaluation and management of HCA during pregnancy. We think it should be questioned if it is justified to discourage pregnancy in all women with HCA. The biological behavior of this benign lesion might be less threatening than presumed and a negative advice concerning pregnancy has great impact on the lives of these young female patients. The balance between the pros and cons of hepatic adenomas and pregnancy should be reconsidered. In our center, pregnancy in women with an HCA up to 5 cm is no longer discouraged in close consultation with the patient, her partner, and members of the liver expert team.
Improving Phrase-Based Statistical Translation by Modifying Phrase Extraction and Including Several Features

Nowadays, most of the statistical translation systems are based on phrases (i.e. groups of words). In this paper we study different improvements to the standard phrase-based translation system. We describe a modified method for the phrase extraction which deals with larger phrases while keeping a reasonable number of phrases. We also propose additional features which lead to a clear improvement in the performance of the translation. We present results with the EuroParl task in the direction Spanish to English and results from the evaluation of the shared task "Exploiting Parallel Texts for Statistical Machine Translation" (ACL Workshop on Parallel Texts 2005).
Identification and Quantification of Corn, Soybean and Cotton Genetically Modified by Real Time PCR

In order to obtain a cheaper method for quantification of transgenic events in corn, soybeans and cotton, primers for real time PCR have been developed and optimized, with fluorescent BRYT Green system. The DNA was extracted from grains, with and without event, by CTAB method. The following events have been studied for corn: MON810, Bt11, MON89034, GA21, TC1507, NK603, MIR162, PRO3; Soybean: GTS-40-3-2, MON87701; MON89788; for cotton: MON1445, MON531, LLCotton25, 281-24-236; 3006-210-23, GHB614, T304-40; GHB119, MON15985, MON88913, besides the respective primers for the endogenous genes of corn, soybean and cotton. The sensitivity was 0.057%, the coefficient of linearity R2 ranged from 0.98 to 0.99 and the efficiency of PCR 0.9 to 1.1. The quantification of events ranged from 92 to 115, with a relative error (RE) from 2 to 18%, and a variance of 0.33 to 3.0. The precision acceptance criterion was observed for all analyses, as well the repeatability and reproducibility. As it was found that the measurement of accuracy and reproducibility were within the international acceptance criterion, it may infer the robustness of the methodology. Therefore, the results from replicates with two different technicians, and validation of results by comparison with those obtained by Eurofins Brazil, showed the possibility of specific and quantitative analysis of transgenic events with a cheaper method with sensitivity, repeatability and robustness.
Levels of explanation and the workings of science

Abstract I address two questions that underlie most of the articles in this special issue: 1) What do different levels of explanation in psychology reveal? And 2) how do the dynamics of science affect what can be learned? I suggest that understanding hypothesis testing and generation in the abstract can provide a useful framework for understanding how cognitive modelling and neuroscience may interact. I further suggest that the preference for simple explanations and the dynamics of hypothesis testing may play out in different ways within the two fields, and that their overlap may prove most useful in the realm of hypothesis generation.
Mapping ERK2-MKP3 Binding Interfaces by Hydrogen/Deuterium Exchange Mass Spectrometry*

ERK2, a prototypic member of the MAPK family, plays a central role in regulating cell growth and differentiation. MKP3, an ERK2-specific phosphatase, terminates ERK2 signaling. To understand the molecular basis of ERK2 recognition by MKP3, we carried out hydrogen/deuterium exchange mass spectrometry experiments to map the interaction surfaces between the two proteins. The results show that the exquisite specificity of MKP3 for ERK2 is governed by two distinctive protein-protein interactions. To increase the “effective concentration” of the interacting molecules, the kinase interaction motif in MKP3 (64RRLQKGNLPVR74) and an MKP3-specific segment (101NSSDWNE107) bind the common docking site in ERK2 defined by residues in L16, L5, β7-β8, and αd-L8-αe, located opposite the kinase active site. In addition to this “tethering” effect, additional interactions between the 364FTAP367 sequence in MKP3 and the ERK2 substrate-binding site, formed by residues in the activation lip and the P+1 site (β9-αf loop), L13 (αf-αg loop), and the MAPK insert (L14-α1L14-α2L14), are essential for allosteric activation of MKP3 and formation of a productive complex whereby the MKP3 catalytic site is correctly juxtaposed to carry out the dephosphorylation of phospho-Thr183/phospho-Tyr185 in ERK2. This bipartite protein-protein interaction model may be applicable to the recognition of other MAPKs by their cognate regulators and substrates.
Predict Forex Trend via Convolutional Neural Networks

Abstract Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts. This study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The main goal of our approach is combining the time-series modeling and convolutional neural networks (CNNs) to build a trading model. We propose three steps to build the trading model. First, we preprocess the input data from quantitative data to images. Second, we use a CNN, which is a type of deep learning, to train our trading model. Third, we evaluate the model’s performance in terms of the accuracy of classification. The experimental results show that if the strategy is clear enough to make the images obviously distinguishable the CNN model can predict the prices of a financial asset. Hence, our approach can help devise trading strategies and help clients automatically obtain personalized trading strategies.
Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring

Synergistic applications of multi-resolution satellite data have been of a great interest among user communities for the development of an improved and more effective operational monitoring system of natural resources, including vegetation and soil. In this study, we conducted an inter-comparison of two remote sensing products, namely, visible/near-infrared surface reflectances and spectral vegetation indices (VIs), from the high resolution Advanced Thermal Emission and Reflection Radiometer (ASTER) (15 m) and lower resolution Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m – 500 m) sensors onboard the Terra platform. Our analysis was aimed at understanding the degree of radiometric compatibility between the two sensors' products due to sensor spectral bandpasses and product generation algorithms. Multiple pairs of ASTER and MODIS standard surface reflectance products were obtained at randomly-selected, globally-distributed locations, from which two types of VIs were computed: the normalized difference vegetation index and the enhanced vegetation indices with and without a blue band. Our results showed that these surface reflectance products and the derived VIs compared well between the two sensors at a global scale, but subject to systematic differences, of which magnitudes varied among scene pairs. An independent assessment of the accuracy of ASTER and MODIS standard products, in which “in-house” surface reflectances were obtained using in situ Aeronet atmospheric data for comparison, suggested that the performance of the ASTER atmospheric correction algorithm may be variable, reducing overall quality of its standard reflectance product. Atmospheric aerosols, which were not corrected for in the ASTER algorithm, were found not to impact the quality of the derived reflectances. Further investigation is needed to identify the sources of inconsistent atmospheric correction results associated with the ASTER algorithm, including additional quality assessments of the ASTER and MODIS products with other atmospheric radiative transfer codes.
Towards Document Distribution in Private Cloud Storage Systems

Cloud storage technology has becoming a cost-effective solution for organizations to manage their data in an efficient manner. However, the information stored in private clouds are not usually analyzed by organizations. This avoids organizations obtaining knowledge and taking advantages for data management. This paper presents a method for the extraction of semantic knowledge from private cloud storage repositories as well as the visualization of the acquired knowledge. In this approach, the knowledge extraction is based on the topic detection from repositories of text files (documents) stored in a private cloud storage, the extracted semantic knowledge is indexed as structured data, whereas an application, based on a topic index, enables the organization to visualize the knowledge in the form of graphs of topics per cloud storage location. The implementation of the proposed method shows the feasibility of this approach to get and visualize semantic knowledge extracted from documents.
ADMM Penalty Parameter Selection by Residual Balancing

Appropriate selection of the penalty parameter is crucial to obtaining good performance from the Alternating Direction Method of Multipliers (ADMM). While analytic results for optimal selection of this parameter are very limited, there is a heuristic method that appears to be relatively successful in a number of different problems. The contribution of this paper is to demonstrate that their is a potentially serious flaw in this heuristic approach, and to propose a modification that at least partially addresses it.
Use of indicators for assessing resilience of critical infrastructures

The overall resilience of modern societies is largely determined by and dependent on resilience of their critical infrastructures such as energy grids, transportation systems, governmental bodies or water supply. This is clearly recognized by the European Union in its policies and research agenda, such as the DRS actions and projects (DRS: Disaster-Resilience: safeguarding and securing society, including adapting to climate change) [6]. In this context, the issue of “measuring resilience” has an important place and it is tackled primarily by means of indicators, within the DRS-14 line of calls [6] emphasizing the need for “. a better understanding of critical infrastructure (and). for defining measures to achieve a better resilience against threats in an integrated manner including natural and human threats/events (e.g. due to human errors or terrorist/criminal attacks). ”. The overall goal of the current research agenda is, hence, to improve current approaches by providing an innovative “holistic” methodology for assessing resilience of critical infrastructure. The methodology proposed here is based on resilience indicators. The EU does not provide a clear definition or framework for tackling the concept of resilience – single projects and activities currently follow a number of often quite different paths. Thus, one main goal of the recent research agenda is to establish common frameworks, approaches, definitions and guidelines.
Work team efficacy: An assessment of group confidence estimation methods

Methods of assessing group efficacy were examined using a multiparty role play negotiation. Group efficacy is defined as a group’s perceived capability to perform. Three methods of measuring group efficacy were compared: (a) group potency, (b) an aggregation of group members’ estimates, and (c) group discussion. These methods were used to assess group efficacy regarding several group performance characteristics. Analyses were conducted to assess various validity and measurement issues. All methods and types demonstrated construct validity; however, predictive validity was modest and varied across the measures. The group discussion method predicted group outcomes better than the potency scale or aggregate method. As expected, generalized efficacy measures predicted general performance indicators, and task-specific measures predicted task-specific performance.
From XML to Relational Models

For many businesses and organizations, the achievement of interoperability has proven to be a highly desirable goal. However, without efficient schema mapping mechanisms or models that allow for the storage and management of information from several distinct systems, the goal of interoperability is impossible to attain. Due to the role of XML as a standard in information exchange, considerable research has been undertaken to find effective methods or algorithms for the conversion from XML to database models. This paper reviews leading research in the field - focusing particularly on three novel approaches taken - and proposes an original schema mapping mechanism using a conceptual model which, due its higher level of abstraction, maximizes the preservation of semantics.
Antisecretory Factor–Inducing Therapy Improves Patient-Reported Functional Levels in Meniere's Disease

Objectives The aim of this study was to evaluate the effectiveness of specially processed cereal (SPC) as a suitable adjunctive treatment for Meniere's disease. Methods We performed a randomized double-blinded, placebo-controlled, crossover study in a tertiary referral center of patients who had a diagnosis of Meniere's disease based on the guidelines of the Committee on Hearing and Equilibrium of the American Academy of Otolaryngology–Head and Neck Surgery (AAO-HNS). The main outcome measure was the AAO-HNS Functional Level Scale (FLS). Results Thirty-nine patients completed the study without any reported complications. The mean pretreatment FLS score for the entire study cohort was 3.8 (median, 4; range, 1 to 6). The overall FLS score improved significantly (p < 0.001), to 2.8 (median, 3), after SPC treatment. No patients showed worsening on the FLS during SPC or placebo treatment. Of the 39 patients, 23 showed improvement on the FLS, and no change was observed in the remaining 16. The median improvement on the FLS in these 23 patients was 2 points (mean, 1.7; range, 1 to 4). The mean FLS score after placebo cereal treatment was not significantly different from baseline (p = 0.452), but was significantly higher than that after SPC treatment (mean, 3.7; p < 0.001). The marginal difference observed between the baseline FLS score and the placebo FLS score was due to the fact that 5 patients reported 1-point improvements on the FLS after placebo treatment. Nevertheless, significantly fewer patients improved on placebo than on SPC (p < 0.001). Conclusions Treatment with SPC appears to be well tolerated by most patients (91%) without any complications. More than half (59%) of the study cohort reported subjective improvement in functional level.
A PRAGMATIC TECHNIQUE FOR DETECTION AND REMOVAL OF CRACKS IN DIGITIZED PAINTINGS

A Pragmatic technique for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat and bottom-hat transforms. And the outputs of the crack detection stage are compared to know which transform gives better results. Finally, crack filling using order statistics filters is done. These techniques have shown to perform very well on digitized paintings suffering from cracks. KEY WORDSDetection of cracks, order statistics filters, top-hat transform, bottom-hat transform, virtual restoration of paintings.
Pontificia Universidad Católica del Perú Escuela de posgrado

This paper proposes a business case for the implementation of Digital Farming (henceforth DF) in Colombia. It introduces the general concept of DF as a powerful IoT (Internet of Things) tool to help food producers overcome the difficult challenges posed by an ever-growing demand of goods, coupled by increasing concerns over environmental sustainability. At its most general, DF is a network of measuring devices that provides producers real-time information on metrics relevant to their production cycles. The paper then proceeds to characterize the current state at which this technology has been adopted in Colombia, arguing that small and medium producers don’t have the financial capabilities to design and deploy robust DF plans. This fact poses an interesting business opportunity, which is then thoroughly discussed with the aid of the methodological guidelines formulated by Fernando D’Alessio Ipiza.