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Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study.
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This study reports a case of multidrug resistant Candida krusei as the cause of early neonatal sepsis in a term small-for-gestational age neonate weighing 1680 g that successfully responded to voriconazole therapy. Both blood culture and urine culture of the neonate sent on day 4 and day 8 respectively showed Gram positive oval budding yeast cells on Gram staining which was confirmed as C. krusei susceptible only to voriconazole by Vitek 2 Compact (Biomérieux, France) automated system. Voriconazole was given for fourteen days leading to good clinical response with microbiological clearance of fungus from blood and no side-effects.
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This article considers the issue of domain mean estimation utilizing bivariate auxiliary information based enhanced direct and synthetic logarithmic type estimators under simple random sampling (SRS). The expressions of mean square error (MSE) of the proposed estimators are provided to the 1 s t order approximation. The efficiency criteria are derived to exhibit the dominance of the proposed estimators. To exemplify the theoretical results, a simulation study is conducted on a hypothetically drawn trivariate normal population from R programming language. Some applications of the suggested methods are also provided by analyzing the actual data from the municipalities of Sweden and acreage of paddy crop in the Mohanlal Ganj tehsil of the Indian state of Uttar Pradesh. The findings of the simulation and real data application exhibit that the proposed direct and synthetic logarithmic estimators dominate the conventional direct and synthetic mean, ratio, and logarithmic estimators in terms of least MSE and highest percent relative efficiency.
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Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease.
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New forms of interaction made possible by developments in special educational technologies can now help students with dyscalculia. Artificial intelligence (AI) has emerged as a promising tool in recent decades, particularly between 2001 and 2010, offering avenues to enhance the quality of education for individuals with dyscalculia. Therefore, the implementation of AI becomes crucial in addressing the needs of students with dyscalculia. Content analysis techniques were used to examine the literature covering the influence of AI on dyscalculia and its potential to assist instructors in promoting education for individuals with dyscalculia. The study sought to create a foundation for a more inclusive dyscalculia education in the future through in-depth studies. AI integration has had a big impact on educational institutions as well as people who struggle with dyscalculia. This paper highlights the importance of AI in improving the educational outcomes of students affected by dyscalculia.
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The present study assessed the diet characteristics, trophic guild structure, and microplastics (MPs) ingestion by a group of fish sourced from the tidal creeks of the Mumbai coast along the Eastern Arabian Sea. The diet patterns were characterized using gastro-somatic index (GaSI), relative length of gut (RGL), diet overlap (α), diet breadth (Bi), and relative abundance of food items (%IOP), with a special emphasis on MPs contamination. The findings revealed that teleost and shrimps are the most preferred food items for these species. Whereas MPs contribute 5.50% of the total diet composition of the selected fishes. Additionally, we tried to find out the influence of fish size and dietary attributes on MP ingestion, identifying trophic level as a significant factor influencing contamination levels. Using the Polymer Hazard Index (PHI), the potential risks posed by MPs in fish samples were assessed, revealing notable discrepancies among polymers. For instance, polyvinyl chloride showed the highest PHI score of 198.75 and a hazard score reaching the highest risk category (IV), indicating substantial environmental risk. In summary, our findings highlight the impact of fish diet characteristics on MPs contamination and the varying levels of MP risks along the Mumbai coast, categorized according to polymer type from low to high risk.
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Dieta , Peces , Microplásticos , Contaminantes Químicos del Agua , Animales , Microplásticos/análisis , Contaminantes Químicos del Agua/análisis , Dieta/veterinaria , Monitoreo del Ambiente , Ingestión de Alimentos , Cadena Alimentaria , Océanos y MaresRESUMEN
BACKGROUD: The Northeast India, being part of two global biodiversity hotspot namely the Indo-Burma and Eastern Himalayan Hotspots supports a wide variety of rich aquatic biodiversity including fishes. The family Danionidae is a widely diverse group inhabiting the upper colder stretches of river although few are abundant in the lower stretches. The persisting similarity in the morphological appearance and body colouration within the members of this family seeks an integrated method to identify the species correctly. METHODS AND RESULTS: In the present study, the mt-DNA barcode was generated for correct identification and confirmation of the species. A total of nine mitochondrial cytochrome c oxidase subunit I gene sequences were generated for each species under the study. The pairwise distance values ranged from 0.09 to 9.11% within species and 9.06-32.71% between species. A neighbour-joining tree was constructed based on the Kimura 2 parameter model. Two major groups were observed where Danioninae formed a sister group to the Chedrinae and Rasborinae. CONCLUSION: The present study is a preliminary work to document and identify the species under the family Danionidae from Brahmaputra basin, Assam, using molecular tools and establish the phylogenetic relationship.
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Código de Barras del ADN Taxonómico , Complejo IV de Transporte de Electrones , Filogenia , Animales , India , Complejo IV de Transporte de Electrones/genética , Código de Barras del ADN Taxonómico/métodos , Peces/genética , Peces/clasificación , ADN Mitocondrial/genética , BiodiversidadRESUMEN
PURPOSE: Stevia rebaudiana Bertoni is a perennial herb, widely used as a natural sweetener around the globe. The key compounds responsible for its sweetness includes stevioside and rebaudioside-A. In order to improve these steviol glycosides, the present study was initiated to study the effect of induced mutagenesis on growth parameters, steviol glycosides and nuclear DNA content in Stevia rebaudiana Bertoni using ten doses of gamma-rays (5-100 kR). MATERIALS AND METHODS: Healthy seeds of 'Madhuguna' variety of Stevia rebaudiana Bertoni developed and maintained at stevia breeding farm, Agrotechnology division, CSIR-Institute of Himalayan Bioresource Technology, Palampur (HP), India were irradiated with ten doses of gamma rays (600 seeds each/dose) ranging from 5 kR to 100 kR (i.e., 5, 10, 15, 20, 30, 40, 50, 60, 80 and 100 kR) using Co60 gamma irradiation chamber at CCS Haryana Agricultural University, Hisar, (Haryana), India. RESULTS: Significant variations were recorded for all the seedling traits studied while major impact was noticed on the seedling after reaching the cotyledonary stage and doses above 40 kR showed absolute mortality of the seedlings. Based on probit analysis, the optimum LD50 dose lies in the range of 20-23 kR. Glycosidic profiling of 296 mutants using high-performance liquid chromatography showed decreased total steviol glycoside content with increased radiation dose. Doses 5 kR and 10 kR, were found to be effective in increasing the overall glycosidic content. A total of 72 promising mutants were also screened for increased rebaudioside-A stevioside ratio. Comparison of nuclear DNA content using flow cytometry revealed a similar decrease in the total nuclear DNA content with increase in dosage of gamma rays. The average genome size at 5, 10, 15, 20 and 30 kR treatments were 2.72, 2.69, 2.68, 2.70 and 2.66 pg as compared to 2.72 pg in control. CONCLUSIONS: Mild dose of gamma rays (5 and 10 kR) in stevia were found to be effective in improving the mean steviol glycoside content and may be used in future stevia mutation programmes.
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Diterpenos de Tipo Kaurano , Rayos gamma , Stevia , Stevia/efectos de la radiación , Tolerancia a Radiación , Glucósidos , Relación Dosis-Respuesta en la RadiaciónRESUMEN
This manuscript develops few efficient difference and ratio kinds of imputations to handle the situation of missing observations given that these observations are polluted by the measurement errors (ME). The mean square errors of the developed imputations are studied to the primary degree approximation by adopting Taylor series expansion. The proposed imputations are equated with the latest existing imputations presented in the literature. The execution of the proposed imputations is assessed by utilizing a broad empirical study utilizing some real and hypothetically created populations. Appropriate remarks are made for sampling respondents regarding practical applications.
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In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study.
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BACKGROUND: Zanthoxylum heitzii is a spice used to prepare several dishes and to treat tumors, syphilis, malaria, cardiac palpitations, urogenital infections in the west region of Cameroon, but the antitumor mechanisms and chemical composition are not yet investigated. This study was aimed to determine the antiproliferative effects of four extracts from the fruits and barks of Zanthoxyllum heitzii (Rutaceae) on apoptosis in human promyelocytic cells, their mechanisms and the chemical composition. The 3-(4, 5-dimethylthiazole-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to determine the fifty percent inhibition (IC50) concentration of the cell lines after treatment. The effect on morphology was observed using a light or fluorescence microscopy. The rate of apoptosis and the cell cycle were measured using flow cytometry (FCM). The phytochemical analysis of the extract was carried with HPLC/MS methods. RESULTS: The phytochemical analysis of the extracts indicated the presence of four known polyphenols (Syringic acid, Juglon, Luteolin and Myricetin) in both fruits and barks of Z. heitzii but in different quantities. Syringic acid and Myricetin concentrations were between 17-21 fold higher in the fruits than the stem bark. Rhamnetin (393.35 µg/mL) and Oleuropein (63.10 µg/mL) were identified only in the stem barks of Z. heitzii. Among the four extracts tested for cytotoxicity properties, only the methanol extract of fruits and barks significantly inhibited cell proliferation of HL-60 cells with IC50 value of 20 µg/mL and 12 µg/mL respectively. HL-60 cells treated with Z. heitzii extracts significantly produced reactive oxygen species (ROS) with concurrent loss of mitochondrial membrane potential (MMP). Modifications in the DNA distribution and enhanced of G1/G0 phase cell cycle arrest were observed in a concentration dependent manner. CONCLUSIONS: Polyphenols from Z. heitzii plant exert inhibitory effect on HL-60 cells through the reactive oxygen species (ROS) generation, loss of mitochondrial membrane potential and cell cycle destabilization.