Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of chronic neuropathic pain: a protocol for a multiple treatment comparison meta-analysis of randomised controlled trials

Introduction Chronic neuropathic pain is associated with reduced health-related quality of life and substantial socioeconomic costs. Current research addressing management of chronic neuropathic pain is limited. No review has evaluated all interventional studies for chronic neuropathic pain, which limits attempts to make inferences regarding the relative effectiveness of treatments. Methods and analysis We will conduct a systematic review of all randomised controlled trials evaluating therapies for chronic neuropathic pain. We will identify eligible trials, in any language, by a systematic search of CINAHL, EMBASE, MEDLINE, AMED, HealthSTAR, DARE, PsychINFO and the Cochrane Central Registry of Controlled Trials. Eligible trials will be: (1) enrol patients presenting with chronic neuropathic pain, and (2) randomise patients to alternative interventions (pharmacological or non-pharmacological) or an intervention and a control arm. Pairs of reviewers will, independently and in duplicate, screen titles and abstracts of identified citations, review the full texts of potentially eligible trials and extract information from eligible trials. We will use a modified Cochrane instrument to evaluate risk of bias of eligible studies, recommendations from the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) to inform the outcomes we will collect, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to evaluate our confidence in treatment effects. When possible, we will conduct: (1) in direct comparisons, a random-effects meta-analysis to establish the effect of reported therapies on patient-important outcomes; and (2) a multiple treatment comparison meta-analysis within a Bayesian framework to assess the relative effects of treatments. We will define a priori hypotheses to explain heterogeneity between studies, and conduct meta-regression and subgroup analyses consistent with the current best practices. Ethics and Dissemination We do not require ethics approval for our proposed review. We will disseminate our findings through peer-reviewed publications and conference presentations. Trial registration number PROSPERO (CRD42014009212).
Water repellency under different plant species in a calcareous forest soil in a semiarid Mediterranean environment

Water repellency (WR) is a property that has implications on the hydrologic balance in affected soils. In semi‐arid areas where water supply is limited, even slight WR may play an important role in the infiltration and spatial distribution of precipitation into the soil. Acidic and sandy‐textured soils have been demonstrated to be more prone to develop WR, but there are studies reporting water repellent properties in other soil types. In the present study we investigated soil WR under four plant species in a semi‐arid area with a calcareous, medium‐textured forest soil. For this purpose, 160 soil samples were taken at micro‐sites under different species (Pinus halepensis, Quercus coccifera, Juniperus oxycedrus and Rosmarinus officinalis). Soil WR was measured with the water drop penetration time (WDPT) test on air‐dried samples. Samples with WDPT > 5 s were classified as water repellent. WR was present in 20% of the samples, and its persistence ranged mainly between 10 and 30 s. Under P. halepensis and Q. coccifera, WR occurred more frequently (40 and 30%, respectively) than under J. oxycedrus and R. officinalis (only 5% in both cases). In order to know the causes of the difference in occurrence of WR under the selected species and to establish relationships, soil organic matter (SOM) content and pH were measured for a selection of 66 samples, including all 32 water repellent and 34 wettable samples selected from those taken under the four species (n = 66). A negative relationship between WR and pH was found for all species. Moreover, in the case of P. halepensis soil samples, a positive correlation between WR and SOM content was found. A discriminant analysis allowed for the distinction between water repellent and wettable conditions in calcareous soils, based on the information provided by pH, SOM content and vegetation type. Soil pH turned out to be the most important parameter for discrimination. The hydrological and ecological implications from these results are discussed with special focus on the areas in the region extensively afforested with P. halepensis. Copyright © 2007 John Wiley & Sons, Ltd.
Optimization of Energy Consumption in Wireless Sensor Networks based on Nature-Inspired Algorithms

Sensor Networks (WSNs) consists of a huge number of tiny, low-priced, and battery-powered devices with limited on board sensing, processing and communication capabilities. The batteries of sensor nodes of WSNs are usually with limited capacity; hence it is essential to conserve battery energy to prolonging the WSNs lifetime. Therefore, this paper deals with the matter of energy consumption minimization to maximize the overall network lifespan. In this research, a mathematical model for the lifetime of WSN is formulated based on several parameters to find out the optimal solution of the energy problem in the field of wireless sensor networks using the Modified Particle Swarm Optimization (MPSO) and Ant Colony Optimization (ACO) algorithms. The proposed system has been implemented using MATLAB 7.6. 0 (R2008a) software environment. The computer simulation results show that the presented approach for power consumption minimization is faster than the previous works by 10 times, and the network lifetime is increased by at least 8 times. Furthermore, the conducted simulation indicates that the MPSO algorithm offers superior results in terms of accuracy (99.36%) in comparison with ACO algorithm (97.92%). In this regards, MPSO algorithm acts with much better efficiency as computational time minimizes, simple, has stable convergence characteristics, and designed with adaptable inertial weight and acceleration factors than ACO algorithm.
Learning Comprehensible Conceptual User Models for User Adaptive Meta Web Search

In course of the OYSTER project our goal is to induce conceptual user models that allow for a transparent query refinement and information filtering in the domain of Www meta-seareh. User models which describe a user’s interest with respect to an underlying ontology allow for a manual user model editing process and also pose a well defined problem for a conceptual inductive learning task. OYSTER is a research prototype that is currently being developed at the university of Osnabriick. Contents. In the first section we will motivate the use of conceptual user models and according learning techniques. We then describe the user model representation formalism that is used within the OYSTER 1 project (c.f. [11]). In the third section we demonstrate how different learning task can be carried out on these models. Finally, we discuss our approach and provide a short summary which includes a brief overview of related projects. In this paper, we try to give a precise though not too formal overview of the interesting user modeling and ontology refinement processes within OYSTER. Further technicalities had to be truncated with respect to limited space.
The Effect of Economic Downturns on the Career of Immigrants

This paper provides some empirical evidence about the supposed vulnerability of immigrants to the recent economic downturn, by using longitudinal data from the Italian Labour Force Survey. Immigrant workers always show a higher probability of ending an ongoing employment spell when compared to their native colleagues. However, their observable personal, household and job characteristics are more likely associated to higher separation rates, so that, when comparing similar workers, differences between immigrants and natives disappear, both before and after the
Living on the Edge: Can Eurasian Red Squirrels (Sciurus vulgaris) Persist in Extreme High-elevation Habitats?

Abstract Studying intraspecific spatiotemporal variation in vital rates among populations over a range of environmental conditions is essential to reveal intrinsic and extrinsic factors affecting population dynamics. Mammal populations living at higher elevations often have higher adult survival, shorter breeding seasons, and lower reproductive output per season than at lower elevations. We studied dynamics of a Eurasian red squirrel (Sciurus vulgaris) population in high-elevation, mountain pine (Pinus mugo) forest with extreme winters, in the Central Italian Alps, and compared vital rates with populations in more productive habitats at lower elevations. Average density was 0.14 ± 0.07 squirrels ha−1 (range 0.07–0.30 ha−1), and numbers typically increased in summer–autumn as a result of seasonal reproduction and immigration. Mean persistence time was only 12.5 months, and there was a nearly complete population turnover in only two years. Local survival and recruitment rate were correlated with seasonal population growth rate, and partial effect of survival explained 80% of variation in growth rate. While reproductive rate in mountain pine habitat was more similar than in more productive habitats at lower elevations, density and autumn–winter survival were much lower. Thus, red squirrels did not show the adaptations observed in several other mammal species, but might invest heavily in early reproduction to compensate for short life expectancy.
Laboring Online: Are There "New" Labor Processes In Virtual Game Worlds?

As unemployment figures rise in the developed world, questions regarding the meaning of “labor” and the intrinsic ”value” of work re-emerge. This paper examines labor practices in virtual game worlds to extend existing theoretical explorations regarding concepts of labor and work in the information systems field. The cases explored in this study observe the labor processes associated with two virtual game worlds. We explore whether labor processes are being replicated in virtual environments and, if so, whether “conventional” hegemonies identified by Marxist literature regarding labor are also found in these virtual worlds. This paper contributes to critical information systems research by exploring emancipatory claims regarding labor practices in ICT-enabled work. We present the findings from empirical studies of the Puzzle Pirates and Farmville virtual worlds where we examine the forms of labor undertaken online and their significance in the construction of hegemonic power relationships. The research utilizes a structured ethnographic-style methodology to explore daily working life found in these game environments. This paper contributes to critical information systems research by testing the robustness of existing theories of labor process in the problematic and expansive space of virtual worlds.
Biosurveillance Evaluation of SNOMED CT's Terminology (BEST Trial): Coverage of Chief Complaints

The current United States Health Information Technology Standards Panel's interoperability specification for biosurveillance relies heavily on chief complaint data for tracking rates of cases compatible with a case definition for diseases of interest (e.g. Avian Flu). We looked at SNOMED CT to determine how well this large general medical ontology could represent data held in chief complaints. In this experiment we took 50,000 records (Comprehensive Examinations or Limited Examinations from primary care areas at the Mayo Clinic) from December 2003 through February 2005 (Influenza Season). Of these records, 36,097 had non-null Chief Complaints. We randomly selected 1,035 non-null Chief Complaints and two Board-certified internists (one Infectious Diseases specialist and one general internist) reviewed the mappings of the 1,035 chief complaints. Where the reviewers disagreed, a third internist adjudicated. SNOMED CT had a sensitivity of 98.7% for matching clinical terms found in the chief complaint section of the clinical record. The positive predictive value was 97.4%, the negative predictive value was 89.5%, the specificity was 81.0%, the positive likelihood ratio was 5.181 and the negative likelihood ratio was 0.016. We conclude that SNOMED CT and natural language parsing engines can well represent the clinical content of chief complaint fields. Future research should focus on how well the information contained in the chief complaints can be relied upon to provide the basis of a national strategy for biosurveillance. The authors recommend that efforts be made to examine the entire clinical record to determine the level of improvement in the accuracy of biosurveillance that can be achieved if we were to incorporate the entire clinical record into our biosurveillance strategy.
Single-Carrier Modulation for Large-Scale Antenna Systems

Large-scale antenna (LSA) has gained a lot of attention due to its great potential to significantly improve system throughput. In most existing works on LSA systems, orthogonal frequency division multiplexing (OFDM) is presumed to deal with frequency selectivity of wireless channels. Although LSA-OFDM is a natural evolution from multiple-input multiple-output OFDM (MIMO-OFDM), the drawbacks of LSA-OFDM are inevitable, especially when used for the uplink. In this paper, we investigate single-carrier (SC) modulation for the uplink transmission in LSA systems based on a novel waveform recovery theory, where the receiver is designed to recover the transmit waveform while the information-bearing symbols can be recovered by directly sampling the recovered waveform. The waveform recovery adopts the assumption that the antenna number is infinite and the channels at different antennas are independent. In practical environments, however, the antenna number is always finite and the channels at different antennas are also correlated when placing hundreds of antennas in a small area. Therefore, we will also analyze the impacts of such non-ideal environments.
Assessing the Retrieval Effectiveness of a Speech Retrieval System by Simulating Recognition Errors

We show how the recognition performance of a speech recognition component in a speech retrieval system affects the retrieval effectiveness. A speech retrieval system facilitates content-based retrieval of speech documents, i.e. audio recordings containing spoken text. The speech retrieval process receives queries from users and for every query it ranks the speech documents in decreasing order of their probabilities that they are relevant to the query. The speech recognition component is an important part of a speech retrieval system, since it detects the occurrences of indexing features in the documents. Because the recognition of indexing features in continuous speech is error prone, the question arises how much an error prone recognition of indexing features affects the retrieval effectiveness. As an answer to this question and main contribution of this paper we simulated the recognition of indexing features in speech documents on standard information retrieval test collections and show the resulting retrieval accuracies.
Capillary hemangioma as an uncommon benign tumor of the gingiva: A clinical case report

Background: Non-syndromic cleft lip with or without cleft palate (nsCL/P) is one of the most common congenital abnormalities of the orofacial region with a multifactorial etiology. The present study aimed to investigate the association of two common polymorphisms of methylenetetrahydrofolate reductase (MTHFR) gene (c.677C>T and c.1298A>C) with the occurrence of nsCL/P in an Iranian population. Methods: Forty-five nsCL/P patients, 43 mothers of patients, and 101 unrelated controls participated in the present study. Analysis of c.677C>T and c.1298A>C polymorphisms in MTHFR gene was conducted using polymerase chain reaction and restriction enzyme digestions. Results: There was no statistical difference in genotype and allele frequencies for c.677C>T variants between patients or their mothers and the control group. However, differences in the frequencies of alleles and genotypes of c.1298A>C polymorphism were statistically significant between patients and control group (P=0.01 for alleles and P=0.005 for genotypes). The odds ratios (OR) for the CC versus AA homozygotes were 6.1 (95% CI 1.8-20.5) and 4.2 (95% CI 1.1-15.4), in patients and mothers, respectively. Conclusions: We found no association between genetic polymorphism of MTHFR c.677C>T and the risk of nsCL/P in the population studied. Yet the results suggested that c.1298A>C polymorphism of MTHFR gene may be a risk factor for the occurrence of nsCL/P in the Iranian population.
Sentiment Topic Model with Decomposed Prior

This paper deals with the problem of jointly mining topics, sentiments, and the association between them from online reviews in an unsupervised way. Previous methods often treat a sentiment as a special topic and assume a word is generated from a flat mixture of topics, where the discriminative performance of sentiment analysis is not satisfied. A key reason is that providing rich priors on the polarity of a word for the flat mixture is difficult as the polarity often depends on the topic. To solve the problem we propose a novel model. We decompose the generative process of a word’s sentiment polarity to a two-level hierarchy: the first level determines whether a word is used as a sentiment word or just an ordinary topic word, and the second level (if the word is used as a sentiment word) determines the polarity of it. With the decomposition, we provide separate prior for the the first level to encourage the discrimination between sentiment words and ordinary topic words. This prior is relatively easy to obtain compared to the concrete prior of the word polarities. We construct the prior based on part-of-speech tags of words and embed the prior into the model. Experiments on four real online review data sets show that our model consistently outperforms previous methods in the task of sentiment analysis, and simultaneously performs well in the sub-tasks of discovering ordinary topics, sentiment-specific topics, and extracting topic-specific sentiment words.
Centipede Venoms and Their Components: Resources for Potential Therapeutic Applications

Venomous animals have evolved with sophisticated bio-chemical strategies to arrest prey and defend themselves from natural predators. In recent years, peptide toxins from venomous animals have drawn considerable attention from researchers due to their surprising chemical, biochemical, and pharmacological diversity. Similar to other venomous animals, centipedes are one of the crucial venomous arthropods that have been used in traditional medicine for hundreds of years in China. Despite signifying pharmacological importance, very little is known about the active components of centipede venoms. More than 500 peptide sequences have been reported in centipede venomous glands by transcriptome analysis, but only a small number of peptide toxins from centipede has been functionally described. Like other venomous animals such as snakes, scorpions, and spiders, the venom of centipedes could be an excellent source of peptides for developing drugs for treatments as well as bio-insecticides for agrochemical applications. Although centipede venoms are yet to be adequately studied, the venom of centipedes as well as their components described to date, should be compiled to help further research. Therefore, based on previous reports, this review focusses on findings and possible therapeutic applications of centipede venoms as well as their components.
Multi-objective Genetic Algorithm Optimization of a Neural Network for Estimating Wind Speed Prediction Intervals

In this work, the non-dominated sorting genetic algorithm-II (NSGA-II) is applied to determine the weights of a neural network trained for short-term forecasting of wind speed. More precisely, the neural network is trained to produce the lower and upper bounds of the prediction intervals of wind speed. The objectives driving the search for the optimal values of the neural network weights are the coverage of the prediction intervals (to be maximized) and the width (to be minimized). A real application is shown with reference to hourly wind speed, temperature, relative humidity and pressure data in the region of Regina, Saskatchewan, Canada. Correlation analysis shows that the wind speed has weak dependence on the above mentioned meteorological parameters; hence, only hourly historical wind speed is used as input to a neural network model trained to provide in output the one-hour-ahead prediction of wind speed. The originality of the work lies in proposing a multi-objective framework for estimating wind speed prediction intervals (PIs), optimal both in terms of accuracy (coverage probability) and efficacy (width). In the case study analyzed, a comparison with two single-objective methods has been done and the results show that the PIs produced by NSGA-II compare well with those and are satisfactory in both objectives of high coverage and small width.
Word Co-occurrence Augmented Topic Model in Short Text

The large amount of text on the Internet cause people hard to understand the meaning in a short limit time. Topic models (e.g. LDA and PLSA) has been proposed to summarize the long text into several topic terms. In the recent years, the short text media such as tweet is very popular. However, directly applies the transitional topic model on the short text corpus usually gating non-coherent topics. Because there is no enough words to discover the word co-occurrence pattern in a short document. The Bi-term topic model (BTM) has been proposed to improve this problem. However, BTM just consider simple bi-term frequency which cause the generated topics are dominated by common words. In this paper, we solve the problem of the frequent bi-term in BTM. Thus, we proposed an improvement of word co-occurrence method to enhance the topic models. We apply the word co-occurrence information to the BTM. The experimental result that show our PMI-β-BTM gets well result in the both of regular short news title text and the noisy tweet text. Moreover, there are two advantages in our method. We do not need any external data and our proposed methods are based on the original topic model that we did not modify the model itself, thus our methods can easily apply to some other existing BTM based models.
Performance analysis of opportunistic relaying over imperfect non-identical log-normal fading channels

Motivated by the fact that full diversity order is achieved using the “best-relay” selection technique, we consider opportunistic amplify-and-forward and decode-and-forward relaying systems. We focus on the outage probability of such a systems and then derive closed-form expressions for the outage probability of these systems over independent but non-identical imperfect Log-normal fading channels. We consider the error of channel estimation as a Gaussian random variable. As a result the estimated channels distribution are not Log-normal either as would be in the case of the Rayleigh fading channels. This is exactly the reason why our simulation results do not exactly matched with analytical results. However, this difference is negligible for a wide variety of situations.