Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient of estimation stages for segmentation skin lesions based optimization algorithm

Modern dermatology distinguishes premature diagnosis for example an important part in reducing the death percentage and promising less aggressive treatment for patients. The classifications comprise various stages that must be selected suitably using the characteristics of the filter pointing to get a dependable analysis. The dermoscopic images hold challenges to be faced and overcome to enhance the automatic diagnosis of hazardous lesions. It is calculated to survey a different metaheuristic and evolutionary computing working for filter design systems. Approximately general computing techniques are observed to improve features of infect design method. Nevertheless, the median filter (MF) is normally multimodal with respect to the filter factors and so, reliable approaches that can provide optimal solutions are required. The design of MF depends on modern artificial swarm intelligence technique (MASIT) optimization algorithm which has proven to be more effective than other population-based algorithms to improve of estimation stages for segmentation skin lesions. A controlled artificial bee colony (ABC) algorithm is advanced for solving factors optimization problems and, also the physical-programming-depend on ABC way is applied to proposal median filter, and the outcomes are compared to another approaches.
Studies on Phylogeny of Chaetomium Species of India

Chaetomium is a fungus can be exploited economically and commercially. This fungus is extensively used in degradation of cellulolytic material (Umikalsom et al., 1997 & 1998). In the field of Agriculture this organism has been employed as a biocontrol for reducing the disease incidence against several plant pathogens (Soytong et al., 2001, Aggarwal et al., 2004, Dhingra, et al., 2003). This fungus has wide distribution having more than 160 recognized species (Wang et al., 2014). In India alone reports have been suggested that more than 60 species (http:// www.indiabiodiversity.org/species/) were occurring. The contemporary species concept for this fungus includes a broadly defined morphological diversity as well as a large number of synonymies with limited phylogenetic evidence (Wang et al., 2016). Thus it is necessary to find an alternative method for accurate identification of the species and grouping of this genus. The advent of molecular tools for investigations in fungal identification has paved better way for easier and more accurate identification. Furthermore very limited knowledge is known for molecular identification for this fungus (Asgari and Zare, 2011, Sharma et al., 2013). Thus an attempt has been made to identify species and accurate grouping for different species of Chaetomium based on molecular sequencing data of Chaetomium. International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 08 (2018) Journal homepage: http://www.ijcmas. com
Shape memory and osteogenesis capabilities of the electrospun PHBV modified PLLA fibrous mats.

Poly(L-lactide) (PLLA) as one of the most well-known biodegradable polyesters, has been studied extensively for bone tissue engineering. If being properly programmed, scaffolds from PLLA can also be endowed with the capability of shape memory. However, several noted issues, e.g., mechanically brittleness, high glass transition temperature Tg, and relatively poor shape retention and recovery properties, necessitate modification of the PLLA to improve its application efficacy in physiological conditions. This study is proposed to modify PLLA by having the biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) incorporated to form ultrafine composite fibers (i.e., PLLA-PHBV) via electrospinning. Different pairs of PLLA-PHBV at the varying mass ratios of 10:0, 9:1, 8:2, 7:3, 6:4 and 0:10 can be successfully electrospun into fibrous form with the fineness of 2~3 μm. Incorporation of PHBV enables to give rise to desired Tg decreases and also interestingly increases in the Young's modulus of the PLLA-PHBV blends, while gradually increasing the PHBV mass ratios up to 30%. The PLLA-PHBV (7:3) formulation is identified to present excellent shape memory properties with high shape fixing ratio (>98%) and shape recovery ratio (>96%) compared to the unmodified PLLA fiber counterpart. Moreover, the PLLA-PHBV (7:3) fibers also show enhanced osteogenesis-inducing ability in the mouse bone mesenchymal stem cells, even under non-osteoinductive conditions. Collectively, for the first time this study demonstrates the enhanced shape memory and osteogenesis capabilities of the electrospun PLLA-PHBV composite fibers, and the researched PLLA-PHBV (7:3) fiber system could be potentially applied as a multifunctional scaffolding material for applications in bone tissue repair and regeneration.
Computational Prediction of Protein Arginine Methylation Based on Composition–Transition–Distribution Features

Arginine methylation is one of the most essential protein post-translational modifications. Identifying the site of arginine methylation is a critical problem in biology research. Unfortunately, biological experiments such as mass spectrometry are expensive and time-consuming. Hence, predicting arginine methylation by machine learning is an alternative fast and efficient way. In this paper, we focus on the systematic characterization of arginine methylation with composition–transition–distribution (CTD) features. The presented framework consists of three stages. In the first stage, we extract CTD features from 1750 samples and exploit decision tree to generate accurate prediction. The accuracy of prediction can reach 96%. In the second stage, the support vector machine can predict the number of arginine methylation sites with 0.36 R-squared. In the third stage, experiments carried out with the updated arginine methylation site data set show that utilizing CTD features and adopting random forest as the classifier outperform previous methods. The accuracy of identification can reach 82.1 and 82.5% in single methylarginine and double methylarginine data sets, respectively. The discovery presented in this paper can be helpful for future research on arginine methylation.
Motivating Employees with Goal-Based Prosocial Rewards

A recent trend in organizations is to motivate employees with goal-based prosocial rewards, whereby employees must donate their reward to charities upon goal attainment. We conduct an experiment to examine the motivational effects of goal-based prosocial rewards versus goal-based cash rewards, and whether these effects depend on the goal difficulty. We find that when employees are assigned a difficult goal such that the probability of goal attainment is low, goal-based prosocial rewards motivate greater effort than cash rewards. Further, we find that decreasing goal difficulty attenuates the motivational advantage of goal-based prosocial rewards over cash rewards. Our study contributes to the understanding of why and when affect-rich rewards such as prosocial rewards can be more motivating than traditional cash rewards. Our results also suggest that compensation system designers can benefit from using goal-based prosocial rewards when employees face difficult performance goals.
The Effect of Financial Deepening on Economic Growth in the East African Community

Purpose: The study’s objective is to determine the effect of financial deepening on the economic growth of the East Africa Community bloc. Specifically, it aims to establish the effect of the rate of broad money, credit to the private sector, and the rate of value of the traded stock on economic growth. Methodology: The study used descriptive research design and employed the fixed effect model in regression analysis. Broad money was used to proxy the rate of money supply, credit to the private sector was used to represent credit financing while the volume of the traded stock was used as a measure for financial market investment. Results: The findings revealed that all three indicators of financial deepening namely, broad money, credit to the private sector, and volume of traded stock had a positive and significant effect on economic growth in East Africa Community. The coefficient for broad money was 0.4410, the coefficient for credit to the private sector was 0.4022, while the coefficient for the volume of the traded stock was 0.1367. The model had an F statistic of 103.50, confirming its suitability. Implications: The study recommends that the East Africa Community governments should place more emphasis on the efficiency and of money supply, investment, and distribution by commercial banks. The study also recommends that the governments of East Africa Community countries should continue pursuing policies that promote access to credit such as ensuring that interest rates are low. Additionally, the capital market authorities of the East Africa Community countries should conduct sensitization campaigns to promote high participation in the stock market and other capital market products.
Estimation of Tsunami Characteristics from Deposits: Inverse Modeling Using a Deep‐Learning Neural Network

Tsunami deposits provide information for estimating the magnitude and flow conditions of paleotsunamis, and inverse models have potential for reconstructing hydraulic conditions of tsunamis from their deposits. The majority of the previously proposed models are based on oversimplified assumptions and possess some limitations. We present a new inverse model based on the FITTNUSS model, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses a deep neural network (DNN) for the inversion method. In this method, forward model calculations are repeated for random initial flow conditions (e.g., maximum inundation length, flow velocity, maximum flow depth, and sediment concentration) to produce artificial training data sets of depositional characteristics such as thickness and grain‐size distribution. The DNN was then trained to establish a general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that this trained DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku‐oki tsunami deposits. The predicted results of flow conditions were verified by the observational records at Sendai plain. Jackknife resampling was applied to estimate the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.4 ± 0.1 m/s and 4.1 ± 0.2 m, respectively, after the uncertainty analysis. The DNN shows promise for reconstruction of tsunami characteristics from its deposits, which would help in estimating the hydraulic conditions of paleotsunamis.
Sensitivity analysis for publication bias in meta‐analyses

We propose sensitivity analyses for publication bias in meta‐analyses. We consider a publication process such that ‘statistically significant’ results are more likely to be published than negative or “non‐significant” results by an unknown ratio, η. Our proposed methods also accommodate some plausible forms of selection based on a study's standard error. Using inverse probability weighting and robust estimation that accommodates non‐normal population effects, small meta‐analyses, and clustering, we develop sensitivity analyses that enable statements such as ‘For publication bias to shift the observed point estimate to the null, “significant” results would need to be at least 30 fold more likely to be published than negative or “non‐significant” results’. Comparable statements can be made regarding shifting to a chosen non‐null value or shifting the confidence interval. To aid interpretation, we describe empirical benchmarks for plausible values of η across disciplines. We show that a worst‐case meta‐analytic point estimate for maximal publication bias under the selection model can be obtained simply by conducting a standard meta‐analysis of only the negative and ‘non‐significant’ studies; this method sometimes indicates that no amount of such publication bias could ‘explain away’ the results. We illustrate the proposed methods by using real meta‐analyses and provide an R package: PublicationBias.
Modern biomarkers in prostate cancer diagnosis

Introduction The most common malignant neoplasm of the urinary tract is prostate cancer (PCa), which is a heterogeneous disease, ranging from very slowly developing and slightly benign to progressing, aggressive, metastatic and fatal, even when properly treated. Existing, imperfect diagnostic methods often lead to over-diagnosis and over-treatment of PCa. That is why new, better PCa biomarkers are being developed. Material and methods This review summarizes the current results of the most promising and clinically used PCa biomarkers, as well as having the potential to create new diagnostic and prognostic tools, based on the Web of Science (www.apps.webofknowledge.com) and Scopus (www.scopus) databases. com). Results Limited specificity of the prostate-specific antigen (PSA) test brings a need to develop new and better diagnostic tools. In the last few years, new approaches for providing significantly better biomarkers, an alternative to PSA, have been introduced. Modern biomarkers show improvement in being used as not only a diagnostic procedure, but also for staging, evaluating aggressiveness and managing the therapeutic process. We describe the methods recommended in the diagnosis of PCa and new PCa molecular diagnostics technologies. Individual biomarkers are used in various stages of the PCa diagnostic process, which was presented on the developed diagnostic flowchart describing the role of biomarkers in prostate cancer management. Conclusions Given the diverse nature of PCa, one diagnostic test will not answer all questions, so the use of several diagnostic methods will allow physicians to provide patients with better, personalized clinical advice.
Polar substorm on 7 December 2015: preonset phenomena and features of auroral breakup

Abstract. Comprehensive analysis of a moderate 600 nT substorm was performed using simultaneous optical observations inside the auroral oval and in the polar cap, combined with data from satellites, radars, and ground magnetometers. The onset took place near the poleward boundary of the auroral oval that is not typical for classical substorms. The substorm onset was preceded by two negative excursions of the interplanetary magnetic field (IMF) Bz component, with a 1 min interval between them, two enhancements of the antisunward convection in the polar cap with the same time interval, and 15 min oscillations in the geomagnetic H component in the auroral zone. The distribution of the pulsation intensity along meridian has two local maxima, namely at the equatorial and poleward boundaries of the auroral oval, where pulsations occurred in the out-of-phase mode resembling the field line resonance. At the initial stage, the auroral breakup developed as the auroral torch stretched and expanded poleward along the meridian. Later it took the form of the large-scale coiling structure that also distinguishes the considered substorm from the classical one. Magnetic, radar, and the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) satellite data show that, before the collapse, the coiling structure was located between two field-aligned currents, namely downward at the poleward boundary of structure and upward at the equatorial boundary. The set of GEOTAIL satellites and ground data fit to the near-tail current disruption scenario of the substorm onset. We suggest that the 15 min oscillations might play a role in the substorm initiation.
Study on the extending for Richards model

Firstly,this paper put forward new Richards model of multiple populations based on growth model of single population,then gave the biological meaning of related parameters and thought that this model included all relationships effectively among populations (competition,partial-advantage,partial-deleteriousness,mutualism,community,etc). Also,stability of two populations was analyzed.Analysis showed that interaction equation was a special case of this model,which explained multiple population Richards model having extended single population Richards model on certain level.Finally,this paper made some qualitative analysis to new Richards model.
Guiqi Baizhu Decoction Alleviates Radiation Inflammation in Rats by Modulating the Composition of the Gut Microbiota

The gut microbiota is important in metabolism and immune modulation, and compositional disruption of the gut microbiota population is closely associated with inflammation caused by ionizing radiation (IR). Guiqi Baizhu decoction (GQBZD) is a medicinal compound used in traditional Chinese medicine with anti-inflammatory and antioxidation effects, especially in the process of radiotherapy. However, the effect of GQBZD on reducing the damage to the normal immune system in radiotherapy remains unclear. Here, we show that GQBZD reduces body weights, water intake, food intake, diarrhea level and quality of life score, and inflammation and enhances immunity function in rats treated with X-ray radiation. Meanwhile, our data indicate that GQBZD not only reverses IR-induced gut dysbiosis as indicated change of α-diversity and β-diversity of microbiota, the composition of Desulfovibrio, Bacteroides, and Parabacteroides, except for Roseburia and Lachnoclostridium, but also maintains intestinal barrier integrity and promoting the formation of short-chain fatty acids (SCFAs). GQBZD can also reduce the level of phosphorylation P65 (p-P65). Our results demonstrate that GQBZD can significantly alleviate the inflammatory responses and improve the immune damage against IR, and may be used as prebiotic agents to prevent gut dysbiosis and radiation-related metabolic disorders in radiotherapy.
Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.
Automated smart hydroponics system using internet of things

This paper presents a design and implementation of an automated smart hydroponics system using internet of things. The challenges to be solved with this system are the increasing food demand in the world, the need of market of new sustainable method of farming using the Internet of Things. The design was implemented using NodeMcu, Node Red, MQTT and sensors that were chosen during component selection based on required parameters and sending it to the cloud to monitor and be processed. Investigation on previous works done and a review of Internet of Things and Hydroponic systems were done. First the prototype was constructed, programmed and tested, as well as sensors data between two different environments were taken and monitored on cloud-based web page with mobile application. Moreover, a bot has been introduced to control the supply chain and for notification purposes. The system improved its performance and allows it to successfully achieve the aim of the entire system implemented. There are some limitations which can be improved as future work such as including data science with the usage of the artificial intelligence to further improve the crops and get better outcome. Lastly to design end user platform to ease user interaction by using attractive design with no technical configuration involved.
Nascer no Brasil: continuity of care during pregnancy and postpartum period for women and newborns

ABSTRACT OBJECTIVE To estimate the adequacy of health care during pregnancy and the postpartum period in puerperal women and newborn users of the Unified Health System and verify the factors associated with greater adequacy. METHODS We used data obtained in the hospital interview, the prenatal card and the first telephone interview of 12,646 women participating in the study Nascer no Brasil (Birth in Brazil), conducted in 2011 and 2012. In the first stage of the analysis, the sociodemographic and obstetric characteristics of women and the estimation of adequacy of prenatal and postpartum care indicators are described. In the second stage, the cascade of care for actions related to puerperal women and their newborns is presented. Finally, maternal factors associated with the adequacy of the line of care are verified by means of multiple logistic regression. RESULTS Only two of the four prenatal indicators were considered satisfactory: initiation up to the 16th week of pregnancy and adequate number of appointments. The guidance on which maternity to go for delivery, as well as the guidance to perform the puerperal appointment and the performance of the heel prick test have reached partial level of adequacy. The puerperal appointment, the first routine appointment of the newborn and the obtaining of the heel prick test results presented unsatisfactory adequacy. In the joint analysis of indicators regarding the effective use of services, only 1.5% of mothers and their babies received all recommended health care. Multiparous women living in the North, Northeast and Midwest, with lower schooling, presented the lowest chances of continuity of care. CONCLUSIONS The indicators evaluated indicate that almost all women and their children presented partial and disjointed care, showing that the coordination of care is still a challenge in the health care of women and children in the puerperal pregnancy period.
Two Variants in the NOTCH4 and HLA-C Genes Contribute to Familial Clustering of Psoriasis

Psoriasis is a multifactorial immune-mediated skin disease with a strong genetic background. Previous studies reported that psoriasis with a family history (PFH) and sporadic psoriasis (SP) have a distinct manifestation and genetic predisposition. However, the genetic heterogeneity of PFH and SP in the major histocompatibility complex (MHC) region has not been fully elucidated. To explore genetic variants in the MHC region that drive family aggregation of psoriasis, we included a total of 8,127 psoriasis cases and 9,906 healthy controls from Han Chinese and divided psoriasis into two subtypes, PFH (n = 1,538) and SP (n = 5,262). Then, we calculated the heritability of PFH and SP and performed a large-scale stratified association analysis. We confirmed that variants in the MHC region collectively explained a higher heritability of PFH (16.8%) than SP (13.3%). Further stratified association analysis illustrated that HLA-C∗06:02 and NOTCH4:G511S contribute to the family aggregation of psoriasis, and BTNL2:R281K specifically confers risk for SP. HLA-C∗06:02 and NOTCH4:G511S could partially explain why patients with PFH have a stronger genetic predisposition, more complex phenotypes, and more frequent other autoimmune diseases. The identification of the SP-specific variant BTNL2:R281K revealed that the genetic architecture of SP is not just a subset of PFH.