Data transformation in biological assays

The analysis of variance is the statistical test most used for comparison of three or more means simultaneously. Its application requires, however, the compliance to some assumptions, with main emphasis on normality of the data and homoscedasticity of variances. When such requirements are not met, one of the alternatives is the data transformation to enable the continuity of the experimental evaluation. With the proposition of the Tukey’s data transformation system, understood as a power transformation system, i.e. the application of nth root on a data set (X⅟n) this statistical procedure has methodologically evolved to ensure such solutions. In the present research we proposed a complement to this system, denominated here as transformation in four steps, with inclusion of two hypothesis tests to evaluate normality and homoscedasticity. This was applied on experimental data to evaluate the amount of radiation available at soil level within stands of Acacia mearnsii De Wild. We have proposed a model for data transformation to simultaneously obtain homoscedasticity and normality. The methodology was appropriate to ensure these two statistical aspects on the experimental data, allowing comparison of eight treatments by conventional analysis of variance. Index terms: Analysis of variance, homoscedasticity, normality.

Use of Resampling Method to Construct Variance Index and Repeatability Limit of Damage Characteristic Curve

The simplified viscoelastic continuum damage model has been widely accepted as a tool to predict fatigue performance of asphalt concrete. One key component in the model is the damage characteristic curve that results from a cyclic fatigue test. This curve characterizes the relationship between material integrity (stiffness) and the level of damage in the material. As with any experimental measurement, it is important to know and quantify the variability of the damage curve, but traditional statistical methods are ill-suited for experiments that yield functional data as opposed to univariate data. In this study, a variance index of the damage characteristic curve is first proposed and compared with the expert judgment of the variance of a set of nine different asphalt mixtures. Then, an example analysis for establishing the repeatability limit of a specific mixture as the application of the variance index is presented using the resampling method and hypothesis test. The major findings are as follows: 1) the proposed variance index can match the expert judgment of variability; 2) the shape of the damage characteristic curve can affect the performance of the variance index; 3) the resampling method and hypothesis test can be applied to flag inconsistent data in multi-user or multi-laboratory results; and 4) the resampling method can also be used to construct the repeatability limit of the variance index.

Efficacy and Safety of Artemether-Lumefantrine for Treatment of Uncomplicated P. Falciparum malaria in Ethiopia: A Systematic Review and Meta-Analysis

Background: Anti-malarial drug resistance, in particular resistance to Plasmodium falciparum, challenges the treatment and control of malaria. In Ethiopia, the first-line treatment of uncomplicated falciparum malaria has been changed from sulphadoxine-pyrimethamine (SP) to artemether-lumefantrine (AL) in 2004. To maximize efficacy of anti-malarial drugs and ensure adequate treatment outcomes; monitoring drug efficacy regularly is vital to establish rational malaria treatment guidelines. This systematic review and meta-analysis is performed to obtain an overall stronger evidence to guide management of uncomplicated falciparum malaria from the existing literature in Ethiopia after policy changes in 2004.Methods: A systematic literature search was performed using the preferred reporting items for systematic review and meta-analysis (PRISMA) from published therapeutic efficacy studies conducted in Ethiopia from 2004 to 2020. The search was performed from Pubmed, Google Scholar and Clinical trial registry databases to identify literature. Two reviewers independently assessed study eligibility and extracted data. While computing the efficacy of AL, polymerase chain reaction (PCR)-corrected cure rate (adequate clinical and parasitological response, ACPR) at 28th day was considered as the primary endpoint. Meta-analysis was computed using OpenMeta-Analysis software to calculate the pooled ACPR. Statistical heterogeneity was evaluated with the Cochran chi-square test (X2) test and inverse variance index (I2). Publication bias was analyzed using funnel plots and Egger’s test statistics. The review protocol is registered in PROSPERO, number CRD42020201859.Results: Out of studies screened, fifteen studies fulfilled the inclusion criteria, and were included in final analysis with a total number of 1523 participants. Treatment success of AL for uncomplicated falciparum malaria in all combined studies was 98.4% [(95% CI 97.6–99.1), P< 0.001]. Polymerase chain reaction (PCR)-corrected AL treatment success rate of 98.7% [(95% CI 97.7-99.6), P<0.001)]. The efficacy of AL with PCR-corrected cure rates ranging from 95.0 to 99.4% in per-protocol analysis, and 88.8 to 97.4% in intention-to-treat analysis. Based on the analysis, Cochrane chi-square test (X2) test and inverse variance index (I2) indicated that the included studies with heterogeneity (X2=20.48, (df=14), P=0.116 and I2=31.65%). The highest parasite positivity rate at day-3 was 5.7%. Adverse events ranged from mild to serious but were not directly attributed to the drug.Conclusion: The present review has shown that AL is efficacious and safe for treatment of uncomplicated malaria in Ethiopia. However, few therapeutic efficacy studies were conducted in Ethiopia after treatment guideline was revised in 2004. AL has been used more than a decade in the study population without other alternative artemisinin-based combination therapy in Ethiopia and considering that the potential evolution of drug resistance is of a great concern, regular and continuous monitoring of its efficacy is warranted.

Pemetaan Kondisi Ekologi Perkotaan Skala Mikro Menggunakan Citra Landsat 8 di Kota Semarang

Semarang City, as a capital city of a province in Indonesia, has experienced intensive rural or suburban migration to urban robinareas. Consequently, the land surface temperature (LST) is getting warmer in urbanized areas and leading to microscale temperature variation from time to time. The objectives of this study are 1) to investigate the geographical distribution of LST and vegetation covers in Semarang, and 2) to map microscale urban ecological condition with regards to LST based on urban thermal field variance index (UTFVI). Landsat 8 data was utilized to derive LST as well as vegetation covers by means of normalized difference vegetation index (NDVI). Next, UTFVI was classified based LST. This study revealed that relatively low NDVI values which mainly consists of built up areas and grassland are predominantly concentrated in the northern and central parts of Semarang. High NDVI values representing vegetation covers are predominantly located in the southern portion of Semarang. The most ecologically depressed areas are mainly distributed in the central portions of the city toward the northern portions of coastal areas. Prioritized sub-districts to be ecologically restored are Ngaliyan, Semarang Utara and Semarang Barat. Upcoming studies should emphasize on finding suitable measures to restore ecologically stressed areas.

A Novel Framework for NIDS Throuh Fast Knn Classifier on CICIDS 2017 Dataset

This paper investigates the performance of a Fast kNearest Neighbor Classifier (FkNN) for Network Intrusion Detection System (NIDS) on Cloud Environment. For this study Variance Index based Partial Distance Search (VIPDS) kNN [7] is adopted as an FkNN classifier. A benchmark dataset CICIDS2017[16] is considered for the evaluation process because it is a 78 featured dataset with most updated cloud related attacks. To achieve this objective a frame work is proposed for implementing FkNN and compared with kNN classifier by considering two performance measures Accuracy and computational time. This study explores the gain in the computational time without compromising the Accuracy while using FkNN instead of kNN over a large featured dataset. The conclusions are drawn as per the results obtained from the experiments conducted on CICIDS2017 dataset.