The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig\'s method. To compare the influence of various normalization methods in both the EWM and Hellwig\'s method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries\' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max-min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig\'s method. The results are compared with the outcome obtained by Hellwig\'s method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig\'s method results.

UNASSIGNED: This study aimed to develop an effective virtual reality (VR)-based intervention program to improve trauma symptoms of survivors of the 2023 Kahramanmaraş earthquake.
UNASSIGNED: In line with this aim, the sample of the study consisted of 34 earthquake survivors aged 15-72 years (mean: 38.09, standard deviation (SD): 15.09) who were directly affected by the Kahramanmaraş earthquake on February 6, 2023. A five-stage intervention program (normalization, reinterpretation, creating a safe place, developing problem-focused coping strategies, and social support) was applied to 17 participants (mean: 36.88, SD: 13.65), who constituted the intervention group, using VR technology. All participants assigned to the intervention group received the intervention, which included normalization, reinterpreting the earthquake, creating a safe place, problem-focused coping, and increasing social support, one time in a standardized manner. In the stages of reinterpretation, creating a safe place and problem-focused coping VR technology was used and, the stages of normalization and increasing social support were carried out with psychotherapeutic work involving one-to-one interaction between the researcher and the participant. The five-stage intervention program started to be implemented 51 days after the February 6 Kahramanmaraş earthquakes and all stages of the intervention were completed within seven days. Measurements were taken from the participants at two different times: pre-intervention pre-test and post-intervention post-test. The 17 participants in the control group (mean: 39.29, SD: 16.75) were placed on a waiting list. Data were collected using the \"Sociodemographic Information Form\", \"Posttraumatic Growth Inventory\", \"Scale for Determining the Level of Post-Earthquake Trauma\" and \"Ways of Coping Scale\".
UNASSIGNED: Before the intervention, the groups were controlled in terms of posttraumatic growth, post-earthquake trauma level, fatalistic coping, social support-seeking coping, and helplessness style coping levels and no difference was observed between them (p>0.05). After the intervention, it was found that the posttraumatic growth and social support-seeking coping scores of the earthquake survivors who received VR-supported intervention were significantly higher than the scores of the control group, and the post-earthquake trauma level, fatalistic coping and helplessness style coping scores were significantly lower than the control group scores (p<0.05). As a result of the in-group analyses, it is seen that the post-traumatic growth, social support-seeking coping and problem-focused coping scores of the intervention group participants after the VR-supported intervention increased statistically significantly compared to the pre-intervention, while the post-earthquake trauma level, fatalistic coping and helplessness style coping scores decreased statistically significantly compared to the pre-intervention (p<0.05). However, it is seen that the scores of the control group participants from all other scales of the Ways of Coping Scale, except for the fatalistic coping subscale, did not change statistically significantly (p>0.05).
UNASSIGNED: As a result of the analysis, it is seen that the VR-supported intervention program developed is effective in improving the trauma symptoms of earthquake survivors. The rapid and statistically significant reduction in the trauma levels of earthquake survivors as a result of the developed intervention shows that the relevant intervention can be applied in other trauma areas and suggested for further studies.

UNASSIGNED: Evaluation of the variabilities in apparent diffusion coefficient (ADC) measurements of the spleen (ADCspleen) and the paraspinal muscles (ADCmuscle) to identify the reference organ for normalizing the ADC from the abdominal diffusion weighted imaging (DWI).
UNASSIGNED: Two MRI scanners, with 314 abdominal exams on the GE and 929 on the Siemens system, were used for MRI examinations including DWI (b-values, 50 and 800 s/mm2). For a subset of 73 exams on the Siemens system a second exam was conducted. Four regions of interest (ROIs) in each exam were placed to measure the ADCspleen and the bilateral ADCmuscle. ADC variability between patients (on each scanner separately), ADC variability due to ROI placement between the two ROIs in each organ, and variability in the subset between the first and second exams were assessed.
UNASSIGNED: The ADCspleen was more scattered and variable than the ADCmuscle in the comparability (n = 929 and 314 for two MRI scanners, respectively) and repeatability (n = 73) datasets. The Bland-Altmann bias and limits of agreement (LoAs) for the ADCspleen (ICC, 0.47; CV, 0.070) and ADCmuscle (ICC, 0.67; CV, 0.023) in the repeatability datasets (n = 73) were -0.1 (-25.7%-25.6%) and -0.3 (-8.8%-8.1%), respectively. For the Siemens system, the Bland-Altmann bias and LoAs for the ADCspleen (ICC, 0.72; CV, 0.061) and ADCmuscle (ICC, 0.53; CV, 0.030) in the comparability datasets (n = 929) were 2.1 (-20.0%-24.2%) and 0.7 (-10.0%-11.4%), respectively. Similar findings have been found in the GE system (n = 314). The CVs for the ADCmuscle measurements were lower than those of the ADCspleen both in the repeatability and the comparability analyses (all p < 0.001).
UNASSIGNED: Paraspinal muscles demonstrate better reference characteristics than the spleen in estimating ADC variability of abdominal DWI.

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. We develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model\'s utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman\'s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

This study was carried out to determine the relationship between the fear of COVID-19 in the elderly aged 65 years and over and their levels of adaptation to the \"new normal.\" This descriptive cross-sectional study was completed with 623 elderly individuals. It was determined that the individuals who adapted well to the \"new normal\" had high levels of adaptation to old age, while their levels of fear of COVID-19 were slightly above average (p < 0.01). Elderly individuals have tried to adapt to the \"new normal\" while also experiencing fear of COVID-19. In order to minimize the fear experienced by the elderly during COVID-19, adequate support and psychological support should be provided.

In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis.
In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis.
Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data.
We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.