The importance of biomedical physical data is underscored by its crucial role in advancing our comprehension of human health, unraveling the mechanisms underlying diseases, and facilitating the development of innovative medical treatments and interventions. This data serves as a fundamental resource, empowering researchers, healthcare professionals, and scientists to make informed decisions, pioneer research, and ultimately enhance global healthcare quality and individual well-being. It forms a cornerstone in the ongoing pursuit of medical progress and improved healthcare outcomes. This article aims to tackle challenges in estimating unknown parameters and reliability measures related to the modified Weibull distribution when applied to censored progressive biomedical data from the initial failure occurrence. In this context, the article proposes both classical and Bayesian techniques to derive estimates for unknown parameters, survival, and failure rate functions. Bayesian estimates are computed considering both asymmetric and symmetric loss functions. The Markov chain Monte Carlo method is employed to obtain these Bayesian estimates and their corresponding highest posterior density credible intervals. Due to the inherent complexity of these estimators, which cannot be theoretically compared, a simulation study is conducted to evaluate the performance of various estimation procedures. Additionally, a range of optimization criteria is utilized to identify the most effective progressive control strategies. Lastly, the article presents a medical application to illustrate the effectiveness of the proposed estimators. Numerical findings indicate that Bayesian estimates outperform other estimation methods by achieving minimal root mean square errors and narrower interval lengths.

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In survival and stochastic lifespan modeling, numerous families of distributions are sometimes considered unnatural, unjustifiable theoretically, and occasionally superfluous. Here, a novel parsimonious survival model is developed using the Bilal distribution (BD) and the Kavya-Manoharan (KM) parsimonious transformation family. In addition to other analytical properties, the forms of probability density function (PDF) and behavior of the distributions\' hazard rates are analyzed. The insights are theoretical as well as practical. Theoretically, we offer explicit equations for the single and product moments of order statistics from Kavya-Manoharan Bilal Distribution. Practically, maximum likelihood (ML) technique, which is based on simple random sampling (SRS) and ranked set sampling (RSS) sample schemes, is employed to estimate the parameters. Numerical simulations are used as the primary methodology to compare the various sampling techniques.

The antimicrobial quantitative structure-activity relationship of plant flavonoids against Gram-positive bacteria was established in our previous works, and the cell membrane was confirmed as a major site of action. To investigate whether plant flavonoids have similar antibacterial effects and mechanisms against both Gram-negative and Gram-positive bacteria, here, the minimum inhibitory concentrations (MICs) of 37 plant flavonoids against Escherichia coli were determined using the microdilution broth method, and then the correlation between their lipophilic parameter ACD/LogP or LogD7.40 value and their MIC was analyzed. Simultaneously, the correlation between the ACD/LogP or LogD7.40 value and the MIC of 46 plant flavonoids reported in the literature against E. coli was also analyzed. Both sets of results showed that there is a significant correlation between the LogP value and the MIC of plant flavonoids against Gram-negative bacteria. However, it is difficult to effectively predict the MIC of plant flavonoids against Gram-negative bacteria from their lipophilic parameters. By comparing two regression curves derived from plant flavonoids against Gram-negative and Gram-positive bacteria, it was further discovered that the antibacterial activities of most plant flavonoids against Gram-negative bacteria are stronger than those against Gram-positive bacteria when their LogP values are less than approximately 3.0, but the opposite is true when their LogP values are more than approximately 3.6. Moreover, this comparison also suggests that unlike mainly acting on the cell membrane of Gram-positive bacteria, plant flavonoids have multiple mechanisms against Gram-negative species, while the cell membrane is also an important action site among them. Combined with the correlation analyses between the enzyme inhibitory activity and the LogP value of the reported flavonoids, it was further suggested that DNA gyrase is another important target of plant flavonoids against Gram-negative bacteria.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.

A digital coding metasurface is a platform connecting the digital space and electromagnetic wave space, and has therefore gained much attention due to its intriguing value in reshaping wireless channels and realizing new communication architectures. Correspondingly, there is an urgent need for electromagnetic information theory that reveals the upper limit of communication capacity and supports the accurate design of metasurface-based communication systems. To this end, we propose a macroscopic model and a statistical model of the digital coding metasurface. The macroscopic model uniformly accommodates both digital and electromagnetic aspects of the meta-atoms and predicts all possible scattered fields of the digital coding metasurface based on a small number of simulations or measurements. Full-wave simulations and experimental results show that the macroscopic model is feasible and accurate. A statistical model is further proposed to correlate the mutual coupling between meta-atoms with covariance and to calculate the entropy of the equivalent currents of digital coding metasurface. These two models can help reconfigurable intelligent surfaces achieve more accurate beamforming and channel estimation, and thus improve signal power and coverage. Moreover, the models will encourage the creation of a precoding codebook in metasurface-based direct digital modulation systems, with the aim of approaching the upper limit of channel capacity. With these two models, the concepts of current space and current entropy, as well as the analysis of information loss from the coding space to wave space, is established for the first time, helping to bridge the gap between the digital world and the physical world, and advancing developments of electromagnetic information theory and new-architecture wireless systems.

Age estimation based on tissues or body fluids is an important task in forensic science. The changes of DNA methylation status with age have certain rules, which can be used to estimate the age of the individuals. Therefore, it is of great significance to discover specific DNA methylation sites and develop new age estimation models. At present, statistical models for age estimation have been developed based on the rule that DNA methylation status changes with age. The commonly used models include multiple linear regression model, multiple quantile regression model, support vector machine model, artificial neural network model, random forest model, etc. In addition, there are many factors that affect the level of DNA methylation, such as the tissue specificity of methylation. This paper reviews these modeling methods and influencing factors for age estimation based on DNA methylation, with a view to provide reference for the establishment of age estimation models.
基于人体组织或体液进行年龄推断是法庭科学的一项重要任务。DNA甲基化状态随年龄变化具有一定的规律，可用于推断个体年龄，因此，发现特定的DNA甲基化位点并开发新的年龄推断模型具有重要意义。目前已有研究利用DNA甲基化状态随年龄发生变化的规律开发出用于推断年龄的统计模型，较常使用的有多元线性回归模型、多元分位数回归模型、支持向量机模型、人工神经网络模型和随机森林模型等。此外，影响DNA甲基化水平的因素较多，如甲基化的组织特异性。本文拟对这些基于DNA甲基化推断年龄的建模方法及影响因素进行综述，以期为建立年龄推断模型提供参考。.

BACKGROUND: Gallbladder adenoma represents a precancerous lesion of gallbladder cancer. However, distinguishing it from cholesteryl polyps of the gallbladder before surgery is challenging. Thus, we aimed to comprehensively explore various risk factors contributing to the formation of gallbladder adenoma to facilitate an informed diagnosis and treatment by clinicians.
METHODS: We conducted a retrospective analysis of patients who had undergone cholecystectomy at the Affiliated Hospital of Qingdao University between January 2015 and December 2022. Following postoperative pathological examination, patients were categorized into cholesterol polyp and adenoma groups. We analyzed their baseline characteristics, ultrasound imaging variables, and biochemical data using logistic, lasso, and stepwise regression. Subsequently, we constructed a preoperative prediction model based on the independent risk factors.
RESULTS: Regression analysis of 520 gallbladder polyps and 288 gallbladder adenomas in the model group revealed that age, gallbladder wall thickness, polyp size, echogenicity, pedunculation, and adenosine deaminase (ADA) levels were independent predictors of gallbladder adenoma, all with P < 0.05. Using these indicators, we established a regression equation: Logistic (P) = -5.615 + 0.018 ∗ age - 4.64 ∗ gallbladder wall thickness + 1.811 ∗ polyp size + 2.855 ∗ polyp echo + 0.97∗ pedunculation + 0.092 ∗ ADA. The resulting area under the curve (AUC) value was 0.894 (95 % CI: 0.872-0.917, P < 0.01), with a sensitivity of 89.20 %, specificity of 79.40 %, and overall accuracy of 84.41 % for adenoma detection.
CONCLUSIONS: Age, polyp size, gallbladder wall thickness, polyp echogenicity, pedunculation, and ADA levels emerge as independent risk factors for gallbladder adenoma.

Understanding how changes in channel form affect the relationships between riparian vegetation and river flow is critical to scientific river regulation in arid environments, but relevant quantitative research is lacking. Aiming to quantify the effect of channel planar complexity, one of the most dynamic aspects of channel form, on riparian vegetation-flow relationships at annual and basin scales, a comparative study was conducted on two rivers in the lower Heihe River, China. Information on riparian vegetation and channel form was derived from multi-source remote sensing imagery and the Copernicus digital elevation model, and river flow and groundwater table data were obtained from field observations. Channel planar complexity was calculated as the ratio of the total length of all channels of a river to the straight-line distance between the two ends of the river, and the riparian vegetation-flow relationship was quantified by a multiple linear model that couples riparian vegetation degradation under zero-flow condition and riparian vegetation improvement by river flow. During 2002-2020, (1) one river was 1.5 and 1.04 times the other in terms of planar complexity and profile slope, respectively; (2) the water-year runoff of one river was 3.2 × 108 to 7.6 × 108 m3 and that of the other river was 0.8 × 108 to 4.8 × 108 m3; (3) the riparian vegetation condition indicator of the two rivers was 2202-4113 and 1242-3362, respectively; (4) the more complex river was 2.3 times the other in the efficiency of flow in improving riparian vegetation. The results indicate that planar complexity is positively correlated with flow\'s efficiency, with the underlying cause being changes in groundwater recharge rates, and that the change in efficiency can be greater than the change in planar complexity itself. The spatio-temporal variability in the effect of channel form on riparian vegetation-flow relationships merits further investigation and serious attention in river regulation.