Sampling Distribution Research Articles

An Effective Method for Estimating the Population Mean That Utilizes Dual Auxiliary Information

To improve the effectiveness of population estimators, researchers have recently implemented dual supplementary information. They employed traditional rankings, the empirical cumulative distribution function, and indicator functions as supplementary sources of information in their analysis. An improved family of population mean estimators is introduced in this article, which utilizes the relative ranks of the auxiliary information’s configurations to incorporate the relevant information. A first-order approximation is employed to derive the mathematical expressions for the bias and the mean-squared error (MSE) of the proposed family of estimators. The empirical analysis is investigated to demonstrate the practicality of the proposed estimators in real-world scenarios. Additionally, the theoretical conclusions are effectively validated by the Monte Carlo simulation integration. Our results unequivocally indicate that the proposed family of estimators surpasses their current counterparts.

Evaluating Meaningful Changes in Patient-Reported Outcome Measurement Information System-Fatigue Scores from Three Phase 3 Clinical Trials of Sarilumab for Patients With Rheumatoid Arthritis.

The aim of this study was to evaluate changes in fatigue measured by Patient-Reported Outcome Measurement Information System (PROMIS)-Fatigue scores in patients with rheumatoid arthritis (RA) who received sarilumab and to assess the proportion of patients achieving clinically meaningful change. Data from three phase 3 randomized controlled trials of patients with RA who received sarilumab-MOBILITY, TARGET, and MONARCH-were evaluated. The 10 RA-relevant items from the 13-item Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue were scored in the PROMIS T score metric. Mean changes in 10-item PROMIS-Fatigue T score (PROMIS-Fatigue 10a) were compared, proportions of patients reporting meaningful within-person change thresholds were assessed, and results were visualized using empirical cumulative distribution function (eCDF) curves. Patients with RA reported high baseline fatigue. Using PROMIS-Fatigue 10a, at week 24, patients receiving 150 or 200 mg sarilumab every other week in MOBILITY and TARGET had rapidly and significantly improved mean levels of fatigue compared to those who received placebo. When compared to patients who received adalimumab in MONARCH, patients who received sarilumab had a trend toward increased improvement. eCDF curves showed separation between active treatment versus placebo with both sarilumab doses and across the score range for improvement. Higher proportions of patients reported three-, five-, and seven-point improvements in PROMIS-Fatigue scores in groups who received active treatment. Substantial proportions of patients with RA who received sarilumab reported meaningful change in fatigue measured by PROMIS-Fatigue 10a over time. This study demonstrates the ability to convert FACIT-Fatigue score onto the PROMIS T score metric, the rapid reduction in fatigue with biologic therapies, and the use of novel eCDF curves to show individual patient-level change.

Volumetric Imaging From Raman Perspective: Review and Prospect

AbstractVolumetric imaging, which supports quantitative and comprehensive assessment of a 3D sample from an entire volume, has attracted tremendous attention in biomedical research. Fluorescence imaging techniques, such as optical sectioning and light sheet microscopy, enable to reconstruct the 3D distribution of chemicals within a sample. However, current methods rely on exogenous labels, from which considerable perturbation may be introduced in living systems. Raman imaging offers a feasible solution to visualize components in biological samples in a label‐free manner. Besides, the integration of Raman microscopy with 3D approaches will benefit the research of biomedical samples on novel devices, which is dominated by the strongly enhanced spatial resolution, imaging speed, and overall field of view as well as complemented more details of samples. In this overview, recent achievements in 3D visualization of biological samples from the Raman perspective, are explored including scanning mechanism, light sheet, tomography strategy, compressive sensing, holography, and tissue clearing. Importantly, these platforms are compatible with biomedical research, thus allowing the imaging of chemical constituents and the distribution of samples in a whole volume. As a unique volumetric imaging tool for biological discovery, these methods may provide a strategy to accelerate new discoveries across diverse fields of research.

Identifying thresholds for meaningful improvements in NTDT-PRO scores to support conclusions about treatment benefit in clinical studies of patients with non-transfusion-dependent beta-thalassaemia: analysis of pooled data from a phase 2, double-blind, placebo-controlled, randomised trial

ObjectivesTo estimate thresholds for defining meaningful within-patient improvement from baseline to weeks 13–24 and interpreting meaningfulness of between-group difference for the non-transfusion-dependent beta-thalassaemia patient-reported outcome (NTDT-PRO) tiredness/weakness (T/W) and shortness...

TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses.

Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.

Relationship Between Salt Accumulation and Soil Structure Fractals in Cotton Fields in an Arid Inland Basin

The relationship between soil structure and salt accumulation is unclear; thus, experiments on salt accumulation under different soil structures were conducted in cotton fields in arid areas of northwest China. Thirty-nine sets of soil samples were collected from the 0 to 180 cm profile of three experimental areas. The total salt content of the soil extracts and the particle size distribution of the soil samples were determined using a JENCO TDS and a laser particle size analyzer, respectively, and the fractal dimension of the soil structure was obtained using fractal theory. Pearson’s correlation analysis and Tukey’s test (p < 0.01) were used to analyze the correlation between soil salinity, soil particle size distribution, and fractal dimensions in the three profiles. The results showed soil salinity accumulation was affected mutually by soil texture and soil structure, and soil salinity tended to accumulate in fine-grained soil. The soil fractal dimension (D) could indicate soil texture and quantify soil salinity content. When the sand content was more than 50%, there was a significant positive correlation between the soil fractal dimension and soil salinity (correlation coefficient R = 0.943). The results provide valuable insights into cotton production in arid areas.

Adaptive Forecasting of Nuclear Power Plant Operational Status Under Sensor Concept Drift Using a Bridging Distribution Adaptive Network

A large number of sensors are required to collect information during the operation of nuclear power plants to ensure their absolutely safe operation. However, because of the unique nature of nuclear reactions, the physical environment of nuclear power production is prone to changes, leading to concept drift in the data collected by the sensors. Concept drift describes the phenomenon of sample distribution changing over time, which typically negatively impacts the model’s training and inference processes. We found that nongradual distribution changes could be guided by generating transitional intermediary distributions within the distribution, thereby achieving a gradual change process. Based on this, we designed a bridging distribution adaptive network (BDAN), which consisted of identical-depth TDoA (time difference of arrival) homomorphic backbone neural networks on both sides with a latent adaptive bridging module in the middle. By calculating the distribution differences over multiple timesteps, a series of bridge distributions were generated to guide the gradients in the latent space, updating the parameters of the latent adaptive guiding module in a directional manner and enabling the model to perceive nongradual distribution changes in the time domain. Experimental results showed that the BDAN outperformed the previous state-of-the-art benchmark methods by 5.6% in terms of mean squared error in the nuclear power data prediction task under concept drift, achieving the best fault prediction performance.

Simulation-based, Finite-sample Inference for Privatized Data

Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions. In this paper, we propose a simulation-based “repro sample” approach to produce statistically valid confidence intervals and hypothesis tests, which builds on the work of Xie and Wang (2022). We show that this methodology is applicable to a wide variety of private inference problems, appropriately accounts for biases introduced by privacy mechanisms (such as by clamping), and improves over other state-of-the-art inference methods such as the parametric bootstrap in terms of the coverage and type I error of the private inference. We also develop significant improvements and extensions for the repro sample methodology for general models (not necessarily related to privacy), including 1) modifying the procedure to ensure guaranteed coverage and type I errors, even accounting for Monte Carlo error, and 2) proposing efficient numerical algorithms to implement the confidence intervals and p-values.

A Bayesian-Based Credible and Reliable Control Method for Active Vibration Suppression of Structures with Uncertain Parameters

As a classical design method for addressing uncertainty in control systems, robust control is designed with the goal of complete reliability of the control system. Nevertheless, the pursuit of robustness can inadvertently give rise to considerable performance degradation, rendering the resultant closed-loop control system excessively conservative in its design. Reliability-based design methods serve as an effective approach for managing uncertainties, which will achieve a balance between the robustness and performance of the active control system by taking advantage of the characteristic of reliability that allows for a certain level of permissible failure probability. The accurate quantification of uncertainty is the key to achieving reliable control, but common uncertainty quantification methods cannot achieve credible quantification of uncertainty in small samples and update the distribution of new samples. In this paper, a nonprobabilistic Bayesian inference based interval uncertainty quantification method is proposed. This method updates the distribution of interval boundaries and radii by introducing a small number of samples, and obtains interval parameters under a given confidence level. On the basis of uncertainty quantification, the dynamic reliability of the vibration system is derived, and nonprobabilistic reliability is introduced into the design of active controllers through optimization strategies. The reliable control strategy aims to select the optimal controller parameters on a reliable path with a given credibility, in order to solve the problem of overly conservative robust control theory. Two numerical examples were used to verify the feasibility and applicability of this method.

NIMG-55. DEVELOPING A COMPUTATIONAL PIPELINE TO FORECAST AND SPATIALLY MAP HIGH-GRADE GLIOMA RESPONSE TO RADIOTHERAPY USING BIOPHYSICAL MODELS AND DATA-DRIVEN STATISTICAL ANALYSIS

Abstract High-grade glioma (HGG) presents significant treatment challenges, particularly in identifying less responsive tumor areas to target with adaptive radiotherapy (RT). This study introduces a patient-specific computational pipeline designed to predict and spatially map HGG response to RT, focusing on regions demonstrating resilience to RT. Our computational pipeline is centered around a family of 3D biophysical models describing tumor cell invasion, proliferation, and response to RT which can be personalized for individual patients using longitudinal multiparametric MRI data. Utilizing longitudinal multiparametric MRI data reporting on cell density (diffusion weighted MRI) and the extent of disease (T2-FLAIR and contrast-enhanced T1-weighted MRIs) from 21 patients, ranging from baseline to three weeks into RT, we calibrated a family of biophysical models to forecast tumor cell density up to the final week of RT and one-month follow-up. By employing pareto tail analysis on empirical cumulative distribution function fits of voxel-wise changes in apparent diffusion coefficient maps (ΔADC) between the future images (end of RT, one-month follow-up) and pre-treatment images, we identified areas of increased tumor cellularity on both the ground truth and model predicted cellularity maps. The analysis differentiated tumor voxels into “non-responding” and “responding” categories, based on cutoffs determined through ROC analysis across various conditions—including assessments of both enhancing and non-enhancing tumor regions at different time points. Optimal ΔADC cutoffs for predicted and measured data were established, leading to high area under curve (AUC; ranging from 0.803 to 0.934) values and significant Youden’s index scores (ranging from 0.526 to 0.765) across all conditions. Our findings demonstrate the feasibility of this novel pipeline in delineating biologically relevant RT targets, offering a potential tool for enhancing the efficacy of adaptive RT strategies in treating HGG.

The cosmological analysis of X-ray cluster surveys. VI. Inference based on analytically simulated observable diagrams

The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe, yielding important information on the matter components of the Universe. Cosmological analyses with galaxy clusters traditionally employ scaling relations, which are empirical relationships between cluster masses and their observable properties. However, many challenges arise from this approach as the scaling relations are highly scattered, maybe ill-calibrated, depend on the cosmology, and contain many nuisance parameters with low physical significance. For this paper, we used a simulation-based inference method utilizing artificial neural networks to optimally extract cosmological information from a shallow X-ray survey, solely using count rates, hardness ratios, and redshifts. This procedure enabled us to conduct likelihood-free inference of cosmological parameters $ m $ and $ To achieve this, we analytically generated several datasets of 70\,000 cluster samples with totally random combinations of cosmological and scaling relation parameters. Each sample in our simulation is represented by its galaxy cluster distribution in a count rate (CR) and hardness ratio (HR) space in multiple redshift bins. We trained convolutional neural networks (CNNs) to retrieve the cosmological parameters from these distributions. We then used neural density estimation (NDE) neural networks to predict the posterior probability distribution of $ m $ and $ given an input galaxy cluster sample. Using the survey area as a proxy for the number of clusters detected for fixed cosmological and astrophysical parameters, and hence of the Poissonian noise, we analyze various survey sizes. The 1 sigma errors of our density estimator on one of the target testing simulations are 1\,000\,deg$^2$, 15.2<!PCT!> for $ m $ and 10.0<!PCT!> for $ and 10\,000\,deg$^2$, 9.6<!PCT!> for $ m $ and 5.6<!PCT!> for $ We also compare our results with a traditional Fisher analysis and explore the effect of an additional constraint on the redshift distribution of the simulated samples. We demonstrate, as a proof of concept, that it is possible to calculate cosmological predictions of $ m $ and $ from a galaxy cluster population without explicitly computing cluster masses and even the scaling relation coefficients, thus avoiding potential biases resulting from such a procedure.

Open-world disaster information identification from multimodal social media

The application of multimodal deep learning for emergency response and recovery, specifically in disaster social media analysis, is of utmost importance. It is worth noting that in real-world scenarios, sudden disaster events may differ from the training data, which may require the multimodal network to predict them as unknown classes instead of misclassifying them to known ones. Previous studies have primarily focused on model accuracy in a closed environment and may not be able to directly detect unknown classes. Thus, we propose a novel multimodal model for categorizing social media related to disasters in an open-world environment. Our methodology entails utilizing pre-trained unimodal models as encoders for each modality and performing information fusion with a cross-attention module to obtain the joint representation. For open-world detection, we use a multitask classifier that encompasses both a closed-world and an open-world classifier. The closed-world classifier is trained on the original data to classify known classes, whereas the open-world classifier is used to determine whether the input belongs to a known class. Furthermore, we propose a sample generation strategy that models the distribution of unknown samples using known data, which allows the open-world classifier to identify unknown samples. Our experiments were conducted on two public datasets, namely CrisisMMD and MHII. According to the experimental results, the proposed method outperforms other baselines and approaches in crisis information classification.

Beyond risk preferences in sequential decision-making: How probability representation, sequential structure and choice perseverance bias optimal search

Sequential decision-making, where choices are made one after the other, is an important aspect of our daily lives. For example, when searching for a job, an apartment, or deciding when to buy or sell a stock, people often have to make decisions without knowing what future opportunities might arise. These situations, which are known as optimal stopping problems, involve a risk associated with the decision to either stop or continue searching. However, previous research has not consistently found a clear connection between individuals’ search behavior in these tasks and their risk preferences as measured in controlled experimental settings. In this paper, we explore how particular characteristics of optimal stopping tasks affect people’s choices, extending beyond their stable risk preferences. We find that (1) the way the underlying sampling distribution is presented (whether it is based on experience or description), (2) the sequential presentation of options, and (3) the unequal frequencies of choices to reject versus to accept significantly bias people choices. These results shed light on the complex nature of decisions that unfold sequentially and emphasize the importance of incorporating context factors when studying human decision behavior.

Determinants of out-of-pocket expenditure on medicines among adults in Saudi Arabia: a cross-sectional study

IntroductionTo achieve universal health coverage consistent with World Health Organization recommendations, monitoring financial protection is vital, even in the context of free medical care. Toward this end, this study investigated out-of-pocket (OOP) expenditure on medicines and their determinants among adults in Saudi Arabia.MethodsThis analysis was based on cross-sectional data derived from the Family Health Survey conducted by the General Authority for Statistics in 2018. Data analyses for this study were based on the total sample of 10,785 respondents. Descriptive statistics were used to identify the sample distribution for all variables included in the study. Tobit regression analysis was used to examine the determinants of OOP expenditure on medicines.ResultsThe average OOP expenditure on medicines was estimated to be 279.69 Saudi Riyal in the sampled population. Tobit regression analysis showed that age, average household monthly income, education level, and suffering a chronic condition were the main determinants of OOP expenditure on medicines. Conversely, being married and employed were associated with a lower probability of OOP expenditure on medicines.ConclusionThis study could assist policy makers to provide additional insurance funding and benefits to reduce the possibility of catastrophic OOP expenditure on medicines, especially for the most vulnerable demographic.

Pharmacometric and statistical considerations for dose optimization.

The probability of target attainment (PTA) is a common metric in drug dose optimization, but it requires a specific known target concentration threshold. Such target thresholds are not always available for some treatments, and patient and disease groups, particularly when treating children. This study performed pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) simulations to explore different statistical approaches for determining the optimal dose for unknown PK and PKPD targets. To determine an optimal dose, PK and PKPD outcomes in typical patients with a standard adult dosing regimen were simulated and set as the reference profile, and compared to simulated outcomes for different dosing regimens in the population of interest. Statistical distances between the empirical cumulative distribution functions of the outcomes from all possible dosing regimens were calculated and compared to the reference profile. An optimal dose for known PK and PKPD target outcomes was selected to maintain the outcome above the assigned target, while optimal dosing in a population of interest with an unknown target was selected to generate equivalent PK and PKPD outcomes as the typical population. All of the dose optimization methods with commonly used PK and PKPD models and covariates were implemented as an open source freely available Shiny web-application. The developed pharmacometric method for dose optimization in populations with known and unknown target levels were robust and reproducible, and the implementation of a freely accessible Shiny web-application ensures widespread use and could be a useful tool for dose optimization in populations of interest.

Optimal Timing of Organs-at-Risk-Sparing Adaptive Radiation Therapy for Head-and-Neck Cancer under Re-planning Resource Constraints.

Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing for re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC). A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities. The MDP parameters were derived from a dataset comprising 52 HNC patients treated at the University of Texas MD Anderson Cancer Center between 2007 and 2013. Kernel density estimation was used to smooth the sample distributions. Optimal re-planning strategies were obtained when the permissible number of re-plans throughout the treatment was limited to 1, 2, and 3, respectively. The MDP (optimal) solution recommended re-planning when the difference between planned and actual NTCPs (ΔNTCP) was greater than or equal to 1%, 2%, 2%, and 4% at treatment fractions 10, 15, 20, and 25, respectively, exhibiting a temporally increasing pattern. The ΔNTCP thresholds remained constant across the number of re-planning allowances (1, 2, and 3). In limited-resource settings that impeded high-frequency adaptations, ΔNTCP thresholds obtained from an MDP model could derive optimal timing of re-planning to minimize the likelihood of treatment toxicities.

The analysis of gene screening results for common hereditary hearing loss in 2 102 pregnant women in Dali area

Objective:By conducting genetic testing of hereditary hearing loss in pregnant women within 17 weeks of gestation in Dali areas, the importance of genetic testing and genetic counseling during pregnancy was emphasized. Methods:Twenty-one mutation sites of 4 hearing loss genes, including GJB2, GJB3, SLC26A4 and mtDNA, were detected by PCR amplification technology. The positive ratio, mutation ratio and ethnic distribution of positive samples were statistically described. Results:The positive ratios of GJB2 and SLC26A4 genes were 1.24% and 1.43%, respectively, with mutation rates of 40.62% and 46.88% in the positive samples, respectively. The positive ratio of GJB3gene was 0.19%, and mtDNA mutation genes accounted for 0.14%, and all of them were mtDNA（Heterozygous）. There was only one case of GJB2/SLC26A4 double positive multi-gene mutation, with a positive ratio of 0.05%. The frequency of GJB2 c. 235delC site was the highest, accounting for 65.38% of GJB2 mutation genes and 26.56% of mutation gene samples. Conclusion:GJB2 and SLC26A4 are the most common genes of hearing loss, and GJB2 c. 235delC site is the most common mutation site. Identifying the hearing loss mutation site is of great importance to prevent the birth of hereditary hearing loss children, and genetic diagnosis, genetic counseling, and appropriate intervention are crucial to alleviate congenital problems.

Just-in-time framework for robust soft sensing based on robust variational autoencoder

Modeling with high-dimensional data subject to abnormal observations have always been a practical interest. In this paper, under the just-in-time learning (JITL) framework, a robust soft sensor modeling approach is developed based on robust Variational Autoencoder (VAE). Unlike the vanilla VAE that extracts features from the given dataset under the Gaussian prior assumption, robust VAE employs Student’s t-distribution as prior distribution to handle abnormal data. Under assumption of the Student’s t-prior, the proposed robust VAE model is capable of describing collected data contaminated with outliers. Once the robust VAE model is trained, each robust feature variable in the latent space can be determined. Subsequently, similarity measure is calculated using robust Kullback-Leibler divergence between two Student’s t-distributions, that is, the distribution of a new data sample and that of each historical data sample. After completing similarity measurement for a query sample, the weights for input-output historical data can be determined. Based on these weighted historical data samples, a robust probabilistic principal component regression (PPCR) is utilized to perform local modeling for prediction. Numerical simulations, including the Tennessee Eastman and Penicillin fermentation benchmark processes, are utilized to validate the proposed JITL-based robust soft sensor modeling method.

Elucidating genetic variability between randomly bred domestic cats and Persian domestic cats from different geographical locations using microsatellite markers.

The domestic cat (Felis catus) is a newly evolved species in the family Felidae that has developed some great features among mammals. It is critical to conserve these species and prevent inbreeding from reducing their genetic diversity by understanding their genetic relationships and applying the information to breeding management. The diverse population was an excellent choice for studying genetic diversity and inbreeding phenomena. To conduct this research, 128 individuals from 8 populations, including Azerbaijan, Persian, Ahar, Uermia, Tehran, Karaj, Turkish and Shop cat (both genders), were randomly selected from different geographical regions. We selected eight STR markers with different chromosomal locations based on polymorphism and observed allele numbers in the next step. DNA extraction was performed using tail hair root, PCR and electrophoresis, and gel staining was performed according to routine laboratory protocol. For statistical analysis, CONVERT versions POPGENE, ARLEQUIN GenAlEx and R script analysis. Remarkably, our results showed that 23 alleles were identified in 128 samples. The highest number of alleles belonged to the FCa096 locus (eight alleles) in the Persian population, followed by FCa045 (seven alleles) in the Persian and Ahar populations. Another new finding is that the lowest number of alleles belonged to the 35 and FCa77 locus (two alleles). In addition, pairwise differentiation between and within populations was examined using the genetic distance index. Overall, the results showed that the degree of differentiation within the population is high in the Turkish population compared to other population groups and lower in the Azerbaijan population. In addition, principal component discriminant analysis-based analysis based on the ADAGENET package shows the distribution of samples by geographical location. The results show that genetic mixing between populations is high. On this basis, we conclude that randomly bred domestic cats have a higher level of diversity than Persian domestic cats. This is an interesting topic for future work.

Consistent positive correlation sample distribution: Alleviating the negative sample noise issue in contrastive adaptation

Consistent positive correlation sample distribution: Alleviating the negative sample noise issue in contrastive adaptation