Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

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.

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

BACKGROUND: Budd-Chiari syndrome (BCS) is primarily a disease of hepatic vein blockage, which involves a backflow of blood to the liver. Although there have been many causes linked to this disease, most commonly, it occurs due to hypercoagulable states and blood disorders. In recent times, there has been a fast spread of knowledge regarding early diagnosis and various treatment modalities, which has enabled the prevention of mortality in most cases. This has primarily spread through research articles published in various journals. Thus, the article aims to compare the gender trend ratios to identify the associated discrepancies in terms of male and female author contributions who have been the primary authors for articles pertaining to this disease.  Methodology: A PubMed database between the years 2013 and 2022 was used for the bibliometric analysis. The gender of the primary author was analyzed by NamSor, an application programming interface (API). The statistical analysis was conducted using R software, the ARIMA model, and graphs were prepared using Datawrapper.
RESULTS: Out of 667 articles extracted, the analysis showed that there were 455 (68.2%) first male authors and 212 (31.8%) first female authors. We also formulated various other results, which depicted a higher female-to-male author ratio including various journals and different countries. Although there has been an increasing trend of male authors as compared to female authors, this study found that male authorship for research on this disease is still higher.
CONCLUSIONS: This study depicts that there is a necessity to draw attention to the inequitable systems favoring men over women for publications. The predictive analysis conducted also helps to foresee the trend in the next few years and explains the necessity of addressing the disparities among both genders in healthcare systems.

Although promoting healthy eating is a policy objective, the manageability of dietary habits remains uncertain. Personal dietary patterns reflect many factors, some of which are relatively manageable for individuals whilst others are not. In this article, assuming that some sort of information about the manageability of dietary habits is contained in the observed patterns of food consumption, we focused on dietary patterns on their own. We introduced a statistical descriptive model for data from a food frequency questionnaire, estimated the strength of pairwise linkage between foodstuffs, and grouped foodstuffs by applying community detection to the networks of the estimated inter-food linkages. Those linkages represent the co-movement of pairs of food in consumption. Furthermore, we demonstrated an analysis of the relationship between mental health and dietary habits, considering the aspect of the manageability of dietary habits. Using an observational study in Japan, we obtained the following results: 115 foodstuffs were divided into three groups for both genders, but the compositions were different by gender; in the analysis of mental and physical health, some stress response items were associated with a dependence on some of those food groupings (e.g., \"extremely tired\" was negatively associated with a group containing tomatoes, cucumber, mandarin, etc., for female subjects). As the grouping of foodstuffs based on our estimation depicted an internal structure of dietary habit that a healthy eating policy could regard as a constraint, it follows that we should design such a policy along the same lines as that grouping.

UNASSIGNED: Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling.
UNASSIGNED: MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation.
UNASSIGNED: In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups.
UNASSIGNED: The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.

OBJECTIVE: This study aimed to compare and evaluate the efficiency and accuracy of computerized adaptive testing (CAT) under two stopping rules (SEM 0.3 and 0.25) using both real and simulated data in medical examinations in Korea.
METHODS: This study employed post-hoc simulation and real data analysis to explore the optimal stopping rule for CAT in medical examinations. The real data were obtained from the responses of 3rd-year medical students during examinations in 2020 at Hallym University College of Medicine. Simulated data were generated using estimated parameters from a real item bank in R. Outcome variables included the number of examinees\' passing or failing with SEM values of 0.25 and 0.30, the number of items administered, and the correlation. The consistency of real CAT result was evaluated by examining consistency of pass or fail based on a cut score of 0.0. The efficiency of all CAT designs was assessed by comparing the average number of items administered under both stopping rules.
RESULTS: Both SEM 0.25 and SEM 0.30 provided a good balance between accuracy and efficiency in CAT. The real data showed minimal differences in pass/fail outcomes between the 2 SEM conditions, with a high correlation (r = 0.99) between ability estimates. The simulation results confirmed these findings, indicating similar average item numbers between real and simulated data.
CONCLUSIONS: The findings suggest that both SEM 0.25 and 0.30 are effective termination criteria in the context of the Rasch model, balancing accuracy and efficiency in CAT.

OBJECTIVE: Borderline personality disorder (BPD) is known to share characteristics with a variety of personality disorders (PDs) and exhibits diverse patterns of defense mechanisms. To enhance our understanding of BPD, it\'s crucial to shift our focus from traditional categorical diagnostics to the dimensional traits shared with other PDs, as the borderline personality organization (BPO) model suggests. This approach illuminates the nuanced spectrum of BPD characteristics, offering deeper insights into its complexity. While studies have investigated the comorbidity of BPD with other PDs, research exploring the relationship between various personality factors and defense mechanisms within BPD itself has been scarce. The present study was undertaken to investigate the complex interrelationships between various personality factors and defense styles in individuals diagnosed with BPD.
METHODS: Using a network analysis approach, data from 227 patients diagnosed with BPD were examined using the Defense Style Questionnaire and Personality Disorder Questionnaire-4+ for assessment.
RESULTS: Intricate connections were observed between personality factors and defense styles. Significant associations were identified between various personality factors and defense styles, with immature defense styles, such as maladaptive and image-distorting being particularly prominent in BPD in the centrality analysis. The maladaptive defense style had the highest expected influence centrality. Furthermore, the schizotypal, dependent, and narcissistic personality factors demonstrated relatively high centrality within the network.
CONCLUSIONS: Network analysis can effectively delineate the complexity of various PDs and defense styles. These findings are expected to facilitate a deeper understanding of why BPD exhibits various levels of organization and presents with heterogeneous characteristics, consistent with the perspectives proposed by the BPO.

A prediction model based on XGBoost is proposed for ultrasonic degradation of micropollutants\' kinetic constants. After parameter optimization through iteration, the model achieves Evaluation metrics with R2 and SMAPE reaching 0.99 and 2.06%, respectively. The impact of design parameters on predicting kinetic constants for ultrasound degradation of trace pollutants was assessed using Shapley additive explanations (SHAP). Results indicate that power density and frequency significantly impact the predictive performance. The database was sorted based on power density and frequency values. Subsequently, 800 raw data were split into small databases of 200 each. After confirming that reducing the database size doesn\'t affect prediction accuracy, ultrasound degradation experiments were conducted for five pollutants, yielding experimental data. A small database with experimental conditions within the numerical range was selected. Data meeting both feature conditions were filtered, resulting in an optimized 60-data group. After incorporating experimental data, a model was trained for prediction. Degradation kinetic constants for experiments (kE) were compared with predicted constants (for 800 data-based model: kP-800 and for 60 data-based model: kP-60). Results showed ibuprofen, bisphenol A, carbamazepine, and 17β-Estradiol performed better on the 60-data group (kP-60/kE: 1.00, 0.99, 1.00, 1.00), while caffeine suited the model trained on the 800-data group (kP-800/kE: 1.02).

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