Analysis Of Datasets Research Articles

A GDPR-compliant solution for analysis of large-scale genomics datasets on HPC cloud infrastructure

A GDPR-compliant solution for analysis of large-scale genomics datasets on HPC cloud infrastructure

Mechanistic insights into PROS1 inhibition of bladder cancer progression and angiogenesis via the AKT/GSK3β/β-catenin pathway

Bladder cancer (BLCA) is one of the ten most common cancers worldwide. However, the deregulation of PROS1 and its specific function in BLCA is not well understood. By combining proteomic and transcriptomic datasets, we discovered PROS1 expression was significantly reduced in BLCA tissues and revealed the clinical relevance of PROS1 with BLCA. Analysis of multiple BLCA datasets consistently showed the group with reduced PROS1 expression was linked to cancer-promoting pathways, more aggressive characteristics, and a greater chance of responding positively to immunotherapy. Next, various functional experiments were performed and the results revealed PROS1 overexpression inhibited the proliferation, cell cycle progression, migration, invasion, and angiogenesis of BLCA. In recovery trials, the AKT activator SC79 offered additional proof that PROS1 may influence BLCA cells via the AKT/GSK3β/β-catenin pathway. In conclusion, as an angiogenesis-related gene, PROS1 may play an inhibitory role in the biological functions of bladder cancer.

AUMEs: AU Detection-Based Dual-Stream Multi-task 3DCNN for Micro-expression Recognition

Micro-expressions are brief, involuntary facial movements that can reveal real emotions. However, their short duration and low intensity pose a challenge for feature extraction and learning of neural networks. To overcome this challenge, we propose AUMEs, a 3DCNN-based multi-task learning framework that utilizes deep learning-based Lagrangian motion magnification and optical flow computation methods to enhance spatio-temporal features of micro-expressions, thus solving the problem of weak micro-expression motion intensity. AUMEs also use AU detection as a parallel task to improve the accuracy of micro-expression recognition by transferring knowledge from the AU detection task, and focal loss is utilized in model training to handle category imbalance in the micro-expression dataset. AUMEs achieve competitive results compared with existing SOTA methods on the CASMEII and SAMM datasets, achieving accuracy (Acc.) of 81.05% and 79.85%, UF1 score reaches 0.8880 and 0.7450 on the five-category task, and on the three-category UAR reached 89.02% and 75.86% and 0.8880 and 0.7450 for UF1. Furthermore, in both dataset analyses, the multi-task approach surpassed the single-task method across both the five-category and three-category classifications.

Association of e-cigarette and cigarette use with self-reported chronic obstructive pulmonary disease (COPD): a multivariable analysis of a large United States data set

BackgroundPrior research has linked e-cigarette use with chronic obstructive pulmonary disease (COPD). We examined the relationship between e-cigarette use and COPD prevalence in older adults with varying cigarette use status.MethodsData from the 2020 National Health Interview Survey were used to estimate the association between each of 9 exposure categories based on cigarette use (never, former, current) and e-cigarette use (never, former, current), with respondent-reported physician-diagnosed COPD prevalence in individuals 40 years and older (N = 22,997). Weighted multivariable analysis accounted for cigarette pack years, age of cigarette smoking onset, race, income-to-poverty ratio, rurality, gender, age, and medical comorbidities. Sensitivity of results was tested in 3 separate models with addition of years since quit cigarettes, smoking intensity and duration.Results39.7% of individuals reported ever smoking cigarettes and 10.2% reported ever using e-cigarettes. Among individuals with ever e-cigarette use, 88.5% also reported current or former cigarette smoking. The weighted prevalence of COPD was 7.2%; Among those who reported former cigarette smoking, the highest risk of COPD prevalence compared to never cigarette/never e-cigarette use was in those currently using e-cigarettes (adjusted risk ratio (ARR) 2.82, 95% confidence interval (CI) [1.5, 5.3]). The ARR for former cigarette/current e-cigarette use was significantly larger than the ARR for former cigarette/never e-cigarette use (p < 0.002) in 3 out of 4 models; however, one model had the ARR attenuated to 1.35 (0.67, 2.76) when years since quitting smoking was added to the model. Other cigarette/e-cigarette combinations were also sensitive to how cigarette smoking history was modeled. For example, ARR for former cigarette/former e-cigarette (1.68 [1.00, 2.80] and current cigarette/former e-cigarette (2.50 [1.56,4.02]) were reduced to 1.05 (0.62, 1.77) and 1.04 (0.62, 1.75) respectively, when cigarette smoking duration was substituted for pack-years.ConclusionsCurrent e-cigarette use among former cigarette smokers was associated with significantly higher COPD prevalence compared to never e-cigarette use. However, COPD risk for most cigarette/e-cigarette combinations could be greatly attenuated by how cigarette smoking history was modeled, raising questions about the robustness of these associations in prior research and the possibility of reverse causality in prior cross-sectional research.

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA.

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

Comparison of algorithms for the recognition of ChatGPT paraphrased texts

The rapid development of artificial intelligence, especially AI assistants, is leading to new forms of plagiarism that are difficult to detect using existing methods. Paraphrasing tools make this problem even more complex and challenging especially in minor languages with inadequate resources and tools. This study explores strategies to help detect plagiarism generated by ChatGPT 4.0 and altered by paraphrasing tools. We propose two new datasets consisting of abstracts of doctoral theses in English and Serbian. Both datasets were subjected to ChatGPT paraphrasing, which allowed us to form two classes of texts: human-written and AI-generated, i.e., AI-paraphrased. We then comprehensively compare 19 widely used classification algorithms based on two feature sets: word unigrams and character multigrams. In addition, we compare these to the results of a commercially available pre-trained ChatGPT content detector, ZeroGPT. The results on the English corpus turn out to be very accurate, achieving an accuracy of 95% or more. In contrast, the results on the Serbian corpus were less accurate, achieving an accuracy of just over 85%. Syntax analysis of the training datasets has shown that in Serbian GPT-paraphrased texts, 33.2% of sentences remain the same, and they are found in 63% of documents. GPT-paraphrased English texts showed that 3.2% of sentences remain the same, and they are found in 16% of documents. Syntax analysis of the test datasets has shown that the change of the model temperature influences syntactic features (average number of words and sentences) in English texts and slightly or not in Serbian texts. We attribute all these differences to GPT’s lower paraphrasing ability in minor languages such as Serbian. Presented findings underscore the necessity for making persistent effort in developing tools made for detecting AI-paraphrased texts in academic and professional settings, particularly for minor languages with limited NLP resources, to preserve content integrity and authenticity.

Endoplasmic reticulum stress-related super enhancer promotes epithelial-mesenchymal transformation in hepatocellular carcinoma through CREB5 mediated activation of TNC

Super-enhancers (SEs) are associated with key genes that control cellular state and cell identity. Endoplasmic reticulum stress (ERS) regulates epithelial-mesenchymal transformation (EMT). However, whether SEs are involved in ERS-related activation of EMT in hepatocellular carcinoma (HCC) is unknown. In this study, we identified 17 ERS-related SEs by comparing ERS-HCC cells with untreated control cells using ChIP-seq and RNA-seq. CRISPR-Cas9 and RT-qPCR identified CAMP responsive element binding protein 5 (CREB5) as a key target of ERS-related SE. Analyses of TCGA datasets and tissue arrays showed that CREB5 mRNA and protein expression levels were higher in liver cancer tissues than in paired normal tissues. In addition, overexpression of CREB5 was associated with poor prognosis and an aggressive phenotype in patients with HCC. We also found that activation of ERS enhanced the expression of CREB5, and upregulation of CREB5 significantly increased cell proliferation, migration, and invasion, and promoted EMT, but inhibited apoptosis. More importantly, ERS activation increased the expression of several EMT markers by modulating the expression of CREB5. Mechanistically, CREB5 upregulates the transcription of tenascin-C (TNC) by directly binding to its promoter region, thereby promoting EMT in liver cancer cells. In summary, our findings suggest that ERS activation promotes EMT in liver cancer cells via SE-mediated upregulation of the CREB5/TNC pathway. This result provides a new direction for uncovering how ERS regulates EMT and a foundation for preventing the progression of EMT in HCC.

μ-FTIR Reflectance Spectroscopy Coupled with Multivariate Analysis: A Rapid and Robust Method for Identifying the Extent of Photodegradation on Microplastics.

Understanding the origins of microplastics (MPs) and evaluating the consequences of plastic pollution require precise chemical information. Moreover, MPs undergo chemical changes due to photoaging, which are worth investigating since they can influence the effects of MPs on living beings and the environment. Micro-Fourier-transform infrared (μ-FTIR) spectroscopy is a key technique for screening MPs, combining optical imaging with chemical information from IR spectra. While reflectance μ-FTIR spectroscopy's sensitivity to particle thickness and photodegradation complicates automated spectral matching, it can provide valuable information if coupled with multivariate analysis of the data. This study developed a robust method for identifying MPs, even when they are modified by photodegradation. Various acquisition methods (ATR-IR and μ-transflectance-IR), data pretreatments, and data set analysis procedures were examined, and critical aspects were addressed. The proposed method, using μ-TR-IR and principal component analysis (PCA), proved effective for classifying MPs and analyzing their degradation, offering increased sensitivity and a faster workflow compared with manual spectral comparison. μ-TR-IR showed earlier changes in relevant bands, indicating higher sensitivity to degradation than ATR-IR spectroscopy. Despite the notorious issue of spectral artifacts, our results suggest that valuable information can be collected without using sophisticated preprocessing techniques. On the contrary, the presence of the artifacts allows extracting some information on the particles' thickness. Finally, PCA results were successfully validated for the polymer classification reliability by a test set and compared with the carboxyl index (CI) method to validate the ability to assess degradation. While CI is the most diffused parameter to assess polymer degradation, PCA, which considers the entire spectrum and does not rely on manual integration of single peaks, is inherently more robust than CI and can take into account multiple degradation mechanisms.

AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs

BackgroundMulti-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments.ResultsWe present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heterogeneous networks integrated with multi-omic data in a highly generalizable framework, in contrast to existing methods, which are mostly appropriate for at most two specific omic types. It is powered by Random Walk with Restart for multiplex-heterogeneous networks, with additional capabilities including degree bias adjustment and biased random walk for multi-objective module identification. AMEND was applied to two real-world multi-omic datasets: renal cell carcinoma data from The cancer genome atlas and an O-GlcNAc Transferase knockout study. Additional analyses investigate the performance of various subroutines of AMEND on tasks of node ranking and degree bias adjustment.ConclusionsWhile the analysis of multi-omic datasets in a network context is poised to provide deeper understanding of health and disease, new methods are required to fully take advantage of this increasingly complex data. The current study combines several network analysis techniques into a single versatile method for analyzing biological networks with multi-omic data that can be applied in many diverse scenarios. Software is freely available in the R programming language at https://github.com/samboyd0/AMEND.

Does prism adaptation treatment reduce spatial neglect and improve function?

The potential of using prism adaptation for treating spatial neglect (SN) was questioned when recent meta-analyses found inconsistent evidence. However, analyses of clinical datasets support the use of prism adaptation treatment (PAT) in reducing SN and improving function. The main objective of this review is to evaluate the current state of the evidence of PAT therapeutic effects, identify knowledge gaps, and make suggestions to guide further research and support clinical decision-making. We used the framework of the National Institutes of Health (NIH) Stage Model for Behavioral Intervention Development which provides guidance on best practices for developing effective behavioral interventions that can be implemented in real-world settings. This model emphasizes the interplay between mechanisms underlying therapeutic effects (“who” should receive the treatment and “how” best does it work?) and considerations of adaptability and feasibility in real-world settings. The present critical review led to the following conclusion: the use of the NIH Stage Model reveals the heterogeneity of PAT studies and challenges in advancing PAT as an effective intervention. The key mechanisms such as prism strength, treatment intensity, arm visibility and activities during treatment, and evaluation methods lack consensus. Therefore, clinical research teams must continue to collect evidence to determine critical mechanisms and the optimal protocol. Further research identifying the optimal PAT protocol is needed before another meta-analysis on PAT's clinical efficacy should be conducted again.

Query Mix-Max Method for FDR Estimation Supported by Entrapment Queries.

Estimating the false discovery rate (FDR) is one of the key steps in ensuring appropriate error control in the analysis of shotgun proteomics data. Traditional estimation methods typically rely on decoy sequence databases or spectral libraries, which may not always provide satisfactory results due to limitations of decoy construction methods. This study introduces the query mix-max (QMM) method, a decoy-free alternative for FDR estimation in proteomics. The QMM framework builds upon the existing mix-max procedure but replaces decoy matches with entrapment queries to estimate the number of false positive discoveries. Through simulations and real data set analyses, the QMM method was demonstrated to provide reasonably accurate FDR estimation across various scenarios, particularly when smaller sample-to-entrapment spectra ratios were achieved. The QMM method tends to be conservatively biased, particularly at higher FDR values, which can ensure stringent FDR control. While flexible, the protocol's effectiveness may vary depending on the evolutionary distance between the sample and entrapment organisms. It also requires a sufficient number of entrapment queries to provide stable FDR estimates, especially for low FDR values. Despite these limitations, the QMM method is a promising alternative as one of the first query-based FDR estimation approaches in shotgun proteomics.

Increasing the Efficiency of Ensemble Molecular Dynamics Simulations with Termination of Unproductive Trajectories Identified at Runtime.

The application of molecular dynamics (MD) simulations to study increasingly larger and more complex systems is challenged by the required amounts of trajectory data needed to sample their conformational space appropriately. The analysis and interpretation phase of such massive data sets that have to be stored and fed to the various algorithms to reveal the dynamic behaviors of the systems and the underlying energetics in structural terms related to functional mechanisms are also a significant challenge. To develop computational means that can address these challenges, we are developing a software framework that can increase the efficiency of this process. We present one component of this framework that can reduce the size of the accumulating data set while maintaining the structural attributes, distribution, and relative probability ranking of the minima in the free energy map for the system. This framework component utilizes early termination of individual trajectories identified as unproductive in the sampling of conformational space. The criteria for termination are derived quantities such as collective variables (CVs) and secondary quantities calculated from the time series of CVs. They are computed and applied during the trajectory generation. The approach is illustrated with simulations of the FS peptide and evaluated from comparisons between the free energy surfaces calculated from ensembles of complete, unabridged simulations with those obtained from ensembles in which ∼5-50% of trajectories were terminated early. Our early termination approach can optimize computational efficiency while achieving a robust representation of conformational space.

Human beauty illustrates the economic impact of heritable physical traits

Intergenerational transmission of inequality is a central question in the social sciences. We use one trait, beauty, to infer how much parents' physical characteristics transmit inequality across generations. Analyses of a large-scale longitudinal dataset in the United States, and a much smaller dataset of Chinese parents and children, show that increases in parents' looks are associated with increases in their child's looks. A large dataset of U.S. siblings shows a positive correlation of their beauty. The appropriate weighted average from the three samples shows that a one SD increase in ratings of both parents' looks is associated with a 0.25 SD increase in their child's. Coupling these estimates with those from large literatures measuring the impact of beauty on earnings and the intergenerational elasticity of income suggests that a one SD difference in parents' looks is correlated with a 0.074 SD difference in their adult child's earnings, about U.S. $2,170 annually.

BioLake: an RNA expression analysis framework for prostate cancer biomarker powered by data lakehouse

Biomedical researchers must often deal with large amounts of raw data, and analysis of this data might provide significant insights. However, if the raw data size is large, it might be difficult to uncover these insights. In this paper, a data framework named BioLake is presented that provides minimalist interactive methods to help researchers conduct bioinformatics data analysis. Unlike some existing analytical tools on the market, BioLake supports a wide range of web-based bioinformatics data analysis for public datasets, while allowing researchers to analyze their private datasets instantly. The tool also significantly enhances result interpretability by providing the source code and detailed instructions. In terms of data storage design, BioLake adopts the data lakehouse architecture to provide storage scalability and analysis flexibility. To further enhance the analysis efficiency, BioLake supports online analysis for custom data, allowing researchers to upload their own data via a designed procedure without waiting for server-side approval. BioLake allows a one-time upload of custom data of up to 500 MB to ensure that researchers avoid issues with data being too large for upload. In terms of the built-in dataset, BioLake applies reactive continuous data integration, helping the analysis pipeline to get rid of most preprocessing steps. The only pre-built-in dataset of BioLake in the first public version is TCGA-PRAD mRNA expression data for prostate cancer research, which is the primary focus of the development team of BioLake. In summary, BioLake offers a lightweight online tool to facilitate bioinformatic mRNA data analysis with the support of custom online data processing.

Childhood wasting and associated factors in Africa: evidence from standard demographic and health surveys from 35 countries

IntroductionChild malnutrition remains a critical public health challenge globally. Childhood wasting, a severe form of malnutrition, indicates acute undernutrition, leading to significant loss of muscle and fat tissue. The World Health Organization’s Global Nutrition Target aims to reduce childhood wasting to less than 5% in over half of low- and middle-income countries by 2025. The enduring hunger crisis in Africa is a complex issue that demands our continuous commitment, innovative solutions, and coordinated efforts. This study aims to assess the prevalence and associated factors of childhood wasting in Africa.MethodThis study conducted a secondary analysis of demographic and health survey datasets from 2010 to 2022 in 35 African countries. A total of 212,715 children under the age of 5 years were included, using a weighted sample. We employed a mixed-effects model to evaluate the factors associated with childhood wasting. The significance of the fixed effects was assessed using the adjusted odds ratio (AOR) and the corresponding 95% confidence interval.ResultThe prevalence of childhood wasting in Africa was estimated to be 7.16% (95% CI: 7.05–7.27). Several factors were significantly associated with childhood wasting, including the child’s age, male gender, birth weight, maternal education level, wealth index, lack of antenatal care (ANC) visits, home delivery, multiple gestational births, and rural residence.ConclusionChildhood wasting in Africa exceeds the global target set for 2025, which aims to reduce it to less than 5%. To address this critical issue, educating mothers without formal education and rural residents about antenatal care visits, institutional delivery, and proper child feeding practices is essential. Moreover, a renewed focus on tackling the multifaceted factors of undernutrition, strengthening health systems, and implementing evidence-based interventions tailored to the local context is crucial for achieving meaningful and sustained reductions in wasting prevalence across the region.

CD73 promotes the maturation of murine NK cells and their survival in the tumor microenvironment.

Natural Killer (NK) cells are crucial in recognizing and eliminating tumor cells, making them pivotal in antitumor responses. Adenosine, the product of ATP hydrolysis mediated by CD39 and CD73 ectonucleotidases, has been reported to reduce the proliferation and maturation of NK cells. In this study, we investigate the expression of CD73 in NK cells and its impact on maturation, phenotype, survival, and function. Our findings reveal that while splenic NK cells express minimal levels of CD73, its expression is induced upon activation and in the tumor microenvironment upon adoptive transfer to tumor-bearing mice. Notably, within the tumor microenvironment, CD73 expression in NK cells correlates with elevated levels of PD-L1 and CD226. Accordingly, analysis of human melanoma datasets uncovers a subset of immature tumor-infiltrating NK cells expressing CD73. To further understand the role of CD73 on NK cells, we used a CD73 knockout (KO) murine model and observed that CD73-deficient NK cells display a more immature phenotype and heightened proliferative activity than wild-type (WT) NK cells. Additionally, CD73-deficient NK cells exhibit elevated levels of P2X7R and reduced CD39 expression, suggesting an increased susceptibility to ATP-induced death. Following adoptive transfer to tumor-bearing mice, CD73KO NK cells are present at a lower frequency but demonstrate similar control over tumor growth compared to WT NK cells. In conclusion, our study demonstrates the upregulation of CD73 in NK cells infiltrating tumors and underscores its role as a checkpoint regulating the functional maturation of NK cells.

Stroma gene signature predicts responsiveness to chemotherapy in pancreatic ductal adenocarcinoma patient-derived xenograft models.

Despite many efforts to understand the molecular mechanisms of pancreatic ductal adenocarcinoma (PDAC) treatment resistance, there is still no reliable method for selecting patients who could benefit from standard pharmacological treatment. Here, four PDAC patient-derived xenografts (PDAC-PDXs) with different responses to gemcitabine plus nab-paclitaxel (nanoparticle albumin-bound paclitaxel) were studied to dissect the contribution of both tumor and host microenvironment to treatment response. PDAC-PDXs transplanted into the pancreas of immunodeficient mice retained the main genetic and histopathological characteristics of the original human tumors, including invasiveness and desmoplastic reaction. Response to chemotherapy was associated with a specific 294 stroma gene signature and was not due to the intrinsic responsiveness of tumor cells or differences in drug delivery. Human dataset analysis validated the expression of the 294 stroma gene signature in PDAC clinical samples, confirming PDAC-PDXs as a useful tool to study the biology of tumor-host interactions and to test drug efficacy. In summary, we identified a stroma gene signature that differentiates PDAC-PDXs that are responsive to gemcitabine plus Nab-paclitaxel treatment from those that are not, confirming the active role of the tumor microenvironment in the drug response.

FAM210B activates STAT1/IRF9/IFIT3 axis by upregulating IFN-α/β expression to impede the progression of lung adenocarcinoma

FAM210B (family with sequence similarity 210 member B) is a novel protein that has been linked to tumor development. However, its role and underlying mechanisms in lung adenocarcinoma (LUAD) progression remain largely unexplored. In this study, FAM210B was observed to be down-regulated in LUAD cells. Analyses of public datasets revealed that decreased expression of FAM210B predicts poor survival. Accordingly, in vitro and in vivo studies have confirmed the inhibitory role of FAM210B on the growth and tumor metastasis of LUAD cells. RNA-seq analysis further indicated that FAM210B plays a role in regulating innate immune-related signaling pathways in LUAD cells, particularly involving the production of type I interferon (IFN-α/β). Specifically, FAM210B activates STAT1/IRF9/IFIT3 axis by upregulating IFN-α/β expression, leading to the inhibition of proliferation and migration of LUAD cells. Furthermore, TOM70 (Translocase of outer mitochondrial membrane 70, also named as TOMM70) has been identified as a functional interacting partner of FAM210B in its modulation on the expression of IFN-α/β, as well as the proliferative and metastatic phenotypes of LUAD cells. In conclusion, our study indicates that FAM210B is an important suppressor of cellular viability and mobility during lung cancer progression.

Estimation and Bayesian Prediction for the Generalized Exponential Distribution Under Type-II Censoring

This research focuses on the prediction and estimation problems for the generalized exponential distribution under Type-II censoring. Firstly, maximum likelihood estimations for the parameters of the generalized exponential distribution are computed using the EM algorithm. Additionally, confidence intervals derived from the Fisher information matrix are developed and analyzed alongside two bootstrap confidence intervals for comparison. Compared to classical maximum likelihood estimation, Bayesian inference proves to be highly effective in handling censored data. This study explores Bayesian inference for estimating the unknown parameters, considering both symmetrical and asymmetrical loss functions. Utilizing Gibbs sampling to produce Markov Chain Monte Carlo samples, we employ an importance sampling approach to obtain Bayesian estimates and compute the corresponding highest posterior density (HPD) intervals. Furthermore, for one-sample prediction and, separately, for the two-sample case, we provide the corresponding posterior distributions, along with methods for computing point predictions and predictive intervals. Through Monte Carlo simulations, we evaluate the performance of Bayesian estimation in contrast to maximum likelihood estimation. Finally, we conduct an analysis of a real dataset derived from deep groove ball bearings, calculating Bayesian point predictions and predictive intervals for future samples.

Qualitative assessment of the validity and standardization of the swallow domain in the 5-Domain Niemann-Pick disease type C (NPC) Clinical Severity Scale (5DNPCCSS) and analysis in an NPC clinical trial data set

Qualitative assessment of the validity and standardization of the swallow domain in the 5-Domain Niemann-Pick disease type C (NPC) Clinical Severity Scale (5DNPCCSS) and analysis in an NPC clinical trial data set