Distribution of Therapeutic Efficacy of Ranunculales Plants Used by Ethnic Minorities on the Phylogenetic Tree of Chinese Species

The medicinal properties of plants can be evolutionarily predicted by phylogeny-based methods, which, however, have not been used to explore the regularity of therapeutic effects of Chinese plants utilized by ethnic minorities. This study aims at exploring the distribution law of therapeutic efficacy of Ranunculales plants on the phylogenetic tree of Chinese species. We collected therapeutic efficacy data of 551 ethnomedicinal species belonging to five species-rich families of Ranunculales; these therapeutic data were divided into 15 categories according to the impacted tissues and organs. The phylogenetic tree of angiosperm species was used to analyze the phylogenetic signals of ethnomedicinal plants by calculating the net relatedness index (NRI) and nearest taxon index (NTI) in R language. The NRI results revealed a clustered structure for eight medicinal categories (poisoning/intoxication, circulatory, gastrointestinal, eyesight, oral, pediatric, skin, and urinary disorders) and overdispersion for the remaining seven (neurological, general, hepatobiliary, musculoskeletal, otolaryngologic, reproductive, and respiratory disorders), while the NTI metric identified the clustered structure for all. Statistically, NRI and NTI values were significant in 5 and 11 categories, respectively. It was found that Mahonia eurybracteata has therapeutic effects on all categories. iTOL was used to visualize the distribution of treatment efficacy on species phylogenetic trees. By figuring out the distribution of therapeutic effects of Ranunculales medicinal plants, the importance of phylogenetic methods in finding potential medicinal resources is highlighted; NRI, NTI, and similar indices can be calculated to help find taxonomic groups with medicinal efficacy based on the phylogenetic tree of flora in different geographic regions.

A China Healthcare Security Diagnosis Related Groups Script Development Based On R Language

Background: To develop a set of R scripts that could efficiently and accurately identify the home page information of medical records and perform China Healthcare Security Diagnosis Related Groups (CHS-DRG) simulating grouping.Methods: Based on the CHS-DRG grouping rules, we abstracted the DRG grouping process into a standard algorithm and compiled the R script Z-DRG. The DRG simulating groupings by Z-DRG were compared with the DRG results from the regional CHS-DRG integrated service platform to evaluate the accuracy.Results: The Z-DRG includes one function module (zdrgfun. Rc), one operation module (zdrgpro. R) and one database form (zdrgcodes.RData). The function module set 7 algorithm steps and 8 custom functions. The functions were set for multiple diagnoses, multiple operations, joint diagnosis and operation. Only (17.85±0.11) milliseconds were taken for CHS-DRG simulating grouping of one case. Compared with the regional CHS-DRG results, the accuracy rate was 99.10%. The difference in the number of other diagnoses is the main reason that affected the accuracy.Conclusions: Z-DRG is easy to operate. The CHS-DRG simulating groupings were efficient and accurate. The simulation results could be effectively applied for medical institutions to carry out CHS-DRG grouping prediction and improve the implementation effect of CHS-DRG payment work.

Big Data Analytics and Visualization for Food Health Status Determination Using Bigmart Data

Being premature, the traditional data visualization techniques suffer from several challenges and lack the ability to handle a huge amount of data, particularly in gigabytes and terabytes. In this research, we propose an R-tool and data analytics framework for handling a huge amount of commercial market stored data and discover knowledge patterns from the dataset for conveying the derived conclusion. In this chapter, we elaborate on pre-processing a commercial market dataset using the R tool and its packages for information and visual analytics. We suggest a recommendation system based on the data which identifies if the food entry inserted into the database is hygienic or non-hygienic based on the quality preserved attributes. For a precise recommendation system with strong predictive accuracy, we will put emphasis on Algorithms such as J48 or Naive Bayes and utilize the one who outclasses the comparison based on accuracy. Such a system, when combined with R language, can be potentially used for enhanced decision making.

Analysis of polish community on streaming platform twitch.tv during COVID-19 epidemy

The paper describes the process of analyzing the data of the Polish community on the Twitch.tv streaming platform. The research was conducted on the basis of three issues. Separate graphs have been generated for each of the questions. The code was written in R language. Conclusions were drawn on the basis of the generated graphs.

Research on the Influencing Factors of Russian Foreign Trade Based on R Language Regression Analysis

Based on the systematic analysis of the development of Russian foreign trade and the characteristics of the regional distribution structure of trade, this work further studies the influencing factors of Russia’s foreign trade by using the R language regression analysis method and constructs three econometric models from import, export, and total import and export. The real effective exchange rate and various instruments and equipment and accessories are the main factors affecting Russia’s import trade, energy, minerals, timber, and related products are the main factors affecting its export trade, and Russia’s GDP and international oil prices are the major factors affecting the total import and export volume. A correct understanding of the factors affecting Russia’s foreign trade will help to understand Russia’s economic and trade development and its changing trend and provide a reliable reference value for the further expansion and optimization of economic and trade cooperation between other economies and Russia.

PREDICTION OF CLIMATE CHANGE USING ARIMA MODEL

The climate and weather system prediction has always attracted interest. Climate change risks including physical risks, liability risks and transition risks, it’s directly affecting the insurance industry. Climate change is majorly affecting the insurance sector; they are such as extreme heat during summer and extreme rainfall (Flood). It affects both insurance and reinsurance sector. Constructing the model is a necessary process but choosing the model which suits our data is very necessary. In those days the weather reports telecast in news but now even our smart phone notified the weather. In this paper study the climate prediction algorithms using R and also using Cost-free R language tool to forecast the climate using time ARIMA model for the Indian climate.

A spatial ecological analysis of the reintroduction of the Eurasian beaver

As ‘nature’s ecological engineers’ beavers can intentionally modify their habitat by building structures. This ability can have wider environmental benefits, including benefits for other habitats and species. However, this ability to modify the environment can sometimes be destructive, bringing beavers into conflict with land managers and others. To understand the complex connections between Eurasian beavers and ecosystems, this study was based on R language analysis tool that used land cover types, river network distribution and observational record studies of Eurasian beavers to find their most preferred environmental resources and potential habitats. The results found that reintroduced Eurasian beavers have a high potential for settlement and dispersal in restored areas.

Identification of TNFAIP8 as an Immune-Related Biomarker Associated With Tumorigenesis and Prognosis in Cutaneous Melanoma Patients

Tumor necrosis factor-α–induced protein 8 (TNFAIP8) is a member of the TIPE/TNFAIP8 family which is associated with inflammation and tumorigenesis. The potential role of TNFAIP8 in a tumor immune microenvironment in skin cutaneous melanoma (SKCM) has not yet been investigated. The TNFAIP8 expression was evaluated via gene expression profiling interactive analysis (GEPIA). We also evaluated the influence of TNFAIP8 on overall survival via GEPIA and PrognoScan. After GO and KEGG pathway analyses, the correlation between the TNFAIP8 expression level and immune cells or gene markers of the immune infiltration level was explored by R-language. The result showed the TNFAIP8 expression was significantly reduced in SKCM in comparison with normal control. In SKCM, the TNFAIP8 expression in higher levels was associated with the better overall survival. The high expression of TNFAIP8 was positively correlated with the immune score and promoted immune cell infiltration in SKCM patients. TNFAIP8 can be a positive prognosis marker or new immunotherapy target in SKCM.

Projections of Tropical Fermat-Weber Points

This paper is motivated by the difference between the classical principal component analysis (PCA) in a Euclidean space and the tropical PCA in a tropical projective torus as follows. In Euclidean space, the projection of the mean point of a given data set on the principle component is the mean point of the projection of the data set. However, in tropical projective torus, it is not guaranteed that the projection of a Fermat-Weber point of a given data set on a tropical polytope is a Fermat-Weber point of the projection of the data set. This is caused by the difference between the Euclidean metric and the tropical metric. In this paper, we focus on the projection on the tropical triangle (the three-point tropical convex hull), and we develop one algorithm and its improved version, such that for a given data set in the tropical projective torus, these algorithms output a tropical triangle, on which the projection of a Fermat-Weber point of the data set is a Fermat-Weber point of the projection of the data set. We implement these algorithms in R language and test how they work with random data sets. We also use R language for numerical computation. The experimental results show that these algorithms are stable and efficient, with a high success rate.

Methodology for the analysis of geospatial and vehicle datasets in the R language

The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.