On comparative analysis of a two dimensional star gold structure via regression models.

In this research, the star gold structure with beta graphene is thoroughly examined. We mainly focus on computing degree-based topological indices, which provide information about the network's connectivity and complexity as well as structural features. In addition, we compute an entropy measure to represent the uncertainty, information richness, and degree of unpredictability in the network. Furthermore, this study explores the relationships between topological descriptors and entropy using regression models that are logarithmic, linear, and quadratic. By merging these regression models, we uncover hidden patterns and understand the underlying ideas governing the network's behaviour. Our findings shed light on the connection between topological indices and entropy. This work improves our understanding of star gold structure dynamics and provides a visual framework for interpreting their behaviour.

Development and external validation of a logistic and a penalized logistic model using machine-learning techniques to predict suicide attempts: A multicenter prospective cohort study in Korea.

Despite previous efforts to build statistical models for predicting the risk of suicidal behavior using machine-learning analysis, a high-accuracy model can lead to overfitting. Furthermore, internal validation cannot completely address this problem. In this study, we created models for predicting the occurrence of suicide attempts among Koreans at high risk of suicide, and we verified these models in an independent cohort. We performed logistic and penalized regression for suicide attempts within 6 months among suicidal ideators and attempters in The Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS). We then validated the models in a test cohort. Our findings indicated that several factors significantly predicted suicide attempts in the models, including young age, suicidal ideation, previous suicidal attempts, anxiety, alcohol abuse, stress, and impulsivity. The area under the curve and positive predictive values were 0.941 and 0.484 after variable selection and 0.751 and 0.084 in the test cohort. The corresponding values for the penalized regression model were 0.943 and 0.524 in the original training cohort and 0.794 and 0.115 in the test cohort. The prediction model constructed through a prospective cohort study of the suicide high-risk group showed satisfactory accuracy even in the test cohort. The accuracy with penalized regression was greater than that with the "classical" logistic model.

Effect of admission time of arrival on quality of in-hospital care in acute ischemic stroke patients in China.

OBJECTIVES: The quality of care for patients may be partly determined by the time they are admitted to the hospital. This study was conducted to explore the effect of admission time and describe the pattern and magnitude of weekly variation in the quality of patient care. STUDY DESIGN: A retrospective observational study. METHODS: Data were collected from the Medical Care Quality Management and Control System for Specific (Single) Diseases in China. A total of 238,122 patients treated for acute ischemic stroke between January 2015 and December 2017 were included. The primary outcomes were completion of the ten process indicators and in-hospital death. RESULTS: The quality of in-hospital care varied according to hospital arrival time. We identified several patterns of variation across the days of the week. In the first pattern, the quality of four indicators, such as stroke physicians within 15 min, was lowest for arrivals between 08:00 and 11:59, increased throughout the day, and peaked for arrivals between 20:00 and 23:59 or 00:00 and 03:59. In the second pattern, the quality of four indicators, such as the application of antiplatelet therapy within 48 h, was not significantly different between days and weeks. There was no difference in in-hospital mortality between the different admission times. CONCLUSIONS: The effect of admission time on the quality of in-hospital care of patients with acute ischemic stroke showed several diurnal patterns. Detecting the times when quality is relatively low may lead to quality improvements in health care. Quality improvement should also focus on reducing diurnal temporal variation.

Updated information concerning trends in suicide rates in elderly in China, 2002-2020.

The aim of this study was to investigate trends in suicide rates (SRs) among the elderly in China. Annual data on SRs among Chinese people ≥ the age of 65 were collected from China's Health Statistics Yearbook from 2002 to 2020. Then, data were stratified by age, region, and sex. Standardized SRs were calculated and analyzed using a conventional joinpoint regression model. Results revealed that overall, SRs among the elderly in China tended to decline from 2002-2020. Fluctuations in SRs, including in 2004-2005 due to the SARS epidemic, in 2009-2010 due to the economic crisis, and in 2019-2020 due to the COVID-19 pandemic, were also observed. Data suggested a relatively greater crude SR among the elderly (vs. young people), in males (vs. females), and in people living in a rural area (vs. those living in an urban area). SRs tended to rise with age. Joinpoint regression analysis identified joinpoints only for males ages 65-69 and over the age of 85 living in a rural area, suggesting that individuals in these groups are more sensitive to negative stimuli and more likely to commit suicide, necessitating closer attention. The findings from this study should help to make policy and devise measures against suicide in the future.

Remediation of phenanthrene contaminated soil by persulphate coupled with Pseudomonas aeruginosa GZ7 based on oxidation prediction model.

Chemical oxidation coupled with microbial remediation has attracted widespread attention for the removal of polycyclic aromatic hydrocarbons (PAHs). Among them, the precise evaluation of the feasible oxidant concentration of PAH-contaminated soil is the key to achieving the goal of soil functional ecological remediation. In this study, phenanthrene (PHE) was used as the target pollutant, and Fe2+-activated persulphate (PS) was used to remediate four types of soils. Linear regression analysis identified the following important factors influencing remediation: PS dosage and soil PHE content for PHE degradation, Fe2+ dosage, hydrolysable nitrogen (HN), and available phosphorus for PS decomposition. A comprehensive model of "soil characteristics-oxidation conditions-remediation effect" with a high predictive accuracy was constructed. Based on model identification, Pseudomonas aeruginosa GZ7, which had high PAHs degrading ability after domestication, was further applied to coupling repair remediation. The results showed that the optimal PS dose was 0.75% (w/w). The response relationship between soil physical, chemical, and biological indicators at the intermediate interface and oxidation conditions was analysed. Coupled remediation effects were clarified using microbial diversity sequencing. The introduction of Pseudomonas aeruginosa GZ7 stimulated the relative abundance of Cohnella, Enterobacter, Paenibacillus, and Bacillus, which can promote material metabolism and energy transformation during remediation.

Establishment and Validation of a Predictive Model for Sarcopenia Based on 2-D Ultrasound and Shear Wave Elastography in the Medial Gastrocnemius Muscle.

OBJECTIVE: To develop and validate a predictive model for sarcopenia. METHODS: A total of 240 subjects who visited our hospital between August 2021 and May 2023 were randomly divided by time of entry into a training set containing 2/3 of patients and a validation set containing 1/3 of patients. The muscle thickness (MT), echo intensity (EI), and shear wave velocity (SWV) of the medial gastrocnemius muscle were measured. Indicators that were meaningful in the univariate analysis in the training set were included in a binary logistic regression to derive a regression model, and the model was evaluated using a consistency index, calibration plot, and clinical validity curve. Diagnostic efficacy and clinical applicability were compared between the model and unifactorial indicators. RESULTS: Four meaningful variables, age, body mass index (BMI), MT, and SWV, were screened into the predictive model. The model was Logit Y = 21.292 + 0.065 × Age - 0.411 × BMI - 0.524 × MT - 3.072 × SWV. The model was well differentiated with an internally validated C-index of 0.924 and an external validation C-index of 0.914. The calibration plot predicted probabilities against actual probabilities showed excellent agreement. The specificity, sensitivity, and Youden's index of the model were 73.80%, 97.40%, and 71.20%, respectively, when using the diagnostic cut-off value of >0.279 for sarcopenia. The logistic model had higher diagnostic efficacy (p < 0.001) and higher net clinical benefit (p < 0.001) over the same threshold range compared to indicators. CONCLUSION: The logistic model of sarcopenia has been justified to have good discriminatory, calibrated, and clinical validity, and has higher diagnostic value than indicators.

Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory.

Introduction: Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. Methods: A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. Results and discussion: The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.

Excess multi-cause mortality linked to influenza virus infection in China, 2012-2021: a population-based study.

Objectives: The aim of this study is to estimate the excess mortality burden of influenza virus infection in China from 2012 to 2021, with a concurrent analysis of its associated disease manifestations. Methods: Laboratory surveillance data on influenza, relevant population demographics, and mortality records, including cause of death data in China, spanning the years 2012 to 2021, were incorporated into a comprehensive analysis. A negative binomial regression model was utilized to calculate the excess mortality rate associated with influenza, taking into consideration factors such as year, subtype, and cause of death. Results: There was no evidence to indicate a correlation between malignant neoplasms and any subtype of influenza, despite the examination of the effect of influenza on the mortality burden of eight diseases. A total of 327,520 samples testing positive for influenza virus were isolated between 2012 and 2021, with a significant decrease in the positivity rate observed during the periods of 2012-2013 and 2019-2020. China experienced an average annual influenza-associated excess deaths of 201721.78 and an average annual excess mortality rate of 14.53 per 100,000 people during the research period. Among the causes of mortality that were examined, respiratory and circulatory diseases (R&C) accounted for the most significant proportion (58.50%). Fatalities attributed to respiratory and circulatory diseases exhibited discernible temporal patterns, whereas deaths attributable to other causes were dispersed over the course of the year. Conclusion: Theoretically, the contribution of these disease types to excess influenza-related fatalities can serve as a foundation for early warning and targeted influenza surveillance. Additionally, it is possible to assess the costs of prevention and control measures and the public health repercussions of epidemics with greater precision.

Predicting Kawasaki disease shock syndrome in children.

Background: Kawasaki disease shock syndrome (KDSS) is a critical manifestation of Kawasaki disease (KD). In recent years, a logistic regression prediction model has been widely used to predict the occurrence probability of various diseases. This study aimed to investigate the clinical characteristics of children with KD and develop and validate an individualized logistic regression model for predicting KDSS among children with KD. Methods: The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68). Results: In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets. Conclusion: Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification.

Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato.

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Assessment of four different dietary amino acid profiles recommended for ISA Brown layer hens: A validation study.

The current feeding study was designed to validate the two dietary essential amino acid profiles (EAAP) established based on linear broken-line (LBL) and quadratic broken-line (QBL) models, in a previous study, against Evonik (AMINOHen) and breeder recommendations for ISA Brown layers for peak production (PP, 20 to 44 weeks of age), and post peak production (post PP, 44 to 75 weeks of age). The EAAP based on LBL models on average had 19.5% and 26.0% lower digestible AA (Lys, Met + Cys, Thr, Trp, Ile and Val), than the EAAP based on QBL models for PP and post PP, respectively. The EAAP based on AMINOHen and breeder recommendation had lower digestible AA than QBL, and higher EAAP than LBL models for both production phases. At 20 weeks of age, 224 ISA Brown layer hens were weighed and randomly allocated to individual battery cages. Each of the four diets was replicated 8 times with 7 birds per replicate. Egg production was recorded daily, and egg weights were measured at the end of each week. Feed consumption was measured at the end of each period. The egg production rate was not significantly affected by the diets and remained at around 98.0% (PP) and 95.0% (post PP) (P > 0.05). Birds fed diets based on LBL recommendation consistently laid smaller eggs, resulting in a lower egg mass (59.8 vs. 62.0 g egg/hen per day during PP, and 60.3 vs. 63.0 g egg/hen per day during post PP; P < 0.05). Diets had no significant effect on feed intake and body weight (P > 0.05). The highest feed conversion ratio (FCR) during PP (P = 0.067) and post PP (P < 0.05) was recorded for the birds offered diets based on LBL recommendation. In conclusion, all four EAAP tested in this study support an above average egg production rate. However, the EAAP based on LBL models may potentially decrease the input feed cost per kilogram of eggs but are not set to optimise FCR and maximise egg mass.

On QSAR modeling with novel degree-based indices and thermodynamics properties of eye infection therapeutics.

Topological descriptors are numerical results generated from the structure of a chemical graph that are useful in identifying the physicochemical characteristics of a wide range of drugs. The introduction of molecular descriptors advances quantitative structure-property relationship research. This article focuses on the nine degree-based topological indices and the linear regression model of the eye infection drugs. We introduced two new indices, namely, the "first revised Randic index" and the "second revised Randic index, for the analysis of eye infection drugs. Topological indices are calculated by using edge partitioning, vertex degree counting, and vertex degree labeling. This analysis is done with a scientific calculator and then authenticated with Matlab, a potent tool for examining data. The experimental data and results of the topological indices serve as inputs for the statistical computations and provide the values of intercepts, slopes, and correlation coefficients. All the correlations for the eye-infection drugs are positive, indicating a direct relationship between the experimental and estimated results of the drugs. There are significant results of the p-test for all of the characteristics of eye infection, such as molecular weight, boiling point, enthalpy, flash point, molar refraction, and molar volume, that validate the accuracy of the computations. A significant link was determined in this study between the defined indices with two properties: molar weight and molar refraction. The molar weight and molar refraction have a correlation coefficient ranging from 0.9. These results demonstrate a strong association between the indices and the properties under investigation. The linear regression approach is a valuable tool for chemists and pharmacists to obtain data about different medicines quickly and cost-effectively.

Point estimation of the 100p percent lethal dose using a novel penalised likelihood approach.

Estimation of the 100p percent lethal dose (LD100p) is of great interest to pharmacologists for assessing the toxicity of certain compounds. However, most existing literature focuses on the interval estimation of LD100p and little attention has been paid to its point estimation. Currently, the most commonly used method for estimating the LD100p is the maximum likelihood estimator (MLE), which can be represented as a ratio estimator, with the denominator being the slope estimated from the logistic regression model. However, the MLE can be seriously biased when the sample size is small, a common nature in such studies, or when the dose-response curve is relatively flat (i.e. the slope approaches zero). In this study, we address these issues by developing a novel penalised maximum likelihood estimator (PMLE) that can prevent the denominator of the ratio from being close to zero. Similar to the MLE, the PMLE is computationally simple and thus can be conveniently used in practice. Moreover, with a suitable penalty parameter, we show that the PMLE can (a) reduce the bias to the second order with respect to the sample size and (b) avoid extreme estimates. Through simulation studies and real data applications, we show that the PMLE generally outperforms the existing methods in terms of bias and root mean square error.

Predictive model to differentiate chronic histaminergic angioedema and chronic spontaneous urticaria with angioedema.

Background: Chronic histaminergic angioedema (CHA) may be classified as a separate acquired angioedema (AE) or as an endotype of chronic spontaneous urticaria (CSU). A recent study suggested them to be independent pathologies. Objective: We carried out an exhaustive analysis between CHA and AE-CSU to explore the possible differentiation between them on the bases of a series of predictors. Methods: An observational, retrospective, cross-sectional, and exploratory study was designed. Fifty-six CHA and 40 AE-CSU patients were included. Data were extracted from the year before and year after time of diagnosis. A predictive model was generated by logistic regression, and its discriminatory power was assessed using the area under the receiver operating characteristic curve. Results: The average frequency of AE attacks per year turned out to be higher in the AE-CSU group than in the CHA group, both before (median [interquartile range] 12 [43] vs 8 [16]) and after (24.3 [51.2] vs 2 [4.25]) diagnosis, respectively. The uvula was more frequently affected in CHA. No other differences were found. However, using 7 clinical characteristics of the patients, a multiple logistic regression model was able to predict, with a specificity of 86.4%, a sensitivity of 92.3%, and an area under the curve of 95.1% (P = .024), that CHA and AE-CSU behaved differently. Conclusion: CHA has similar characteristics to AE-CSU, although they slightly differed in the frequency of attacks and their location. Despite its similarities, a multiple logistic regression model that used clinical and evolutionary characteristics allowed the differentiation of both pathologies and supports the idea that these 2 entities are independent.

Trends in maternal and child health in China and its urban and rural areas from 1991 to 2020: a joinpoint regression model.

The long-term trends in maternal and child health (MCH) in China and the national-level factors that may be associated with these changes have been poorly explored. This study aimed to assess trends in MCH indicators nationally and separately in urban and rural areas and the impact of public policies over a 30‒year period. An ecological study was conducted using data on neonatal mortality rate (NMR), infant mortality rate (IMR), under-five mortality rate (U5MR), and maternal mortality ratio (MMR) nationally and separately in urban and rural areas in China from 1991 to 2020. Joinpoint regression models were used to estimate the annual percentage changes (APC), average annual percentage changes (AAPC) with 95% confidence intervals (CIs), and mortality differences between urban and rural areas. From 1991 to 2020, maternal and child mortalities in China gradually declined (national AAPC [95% CI]: NMRs - 7.7% [- 8.6%, - 6.8%], IMRs - 7.5% [- 8.4%, - 6.6%], U5MRs - 7.5% [- 8.5%, - 6.5%], MMRs - 5.0% [- 5.7%, - 4.4%]). However, the rate of decline nationally in child mortality slowed after 2005, and in maternal mortality after 2013. For all indicators, the decline in mortality was greater in rural areas than in urban areas. The AAPCs in rate differences between rural and urban areas were - 8.5% for NMRs, - 8.6% for IMRs, - 7.7% for U5MRs, and - 9.6% for MMRs. The AAPCs in rate ratios (rural vs. urban) were - 1.2 for NMRs, - 2.1 for IMRs, - 1.7 for U5MRs, and - 1.9 for MMRs. After 2010, urban‒rural disparity in MMR did not diminish and in NMR, IMR, and U5MR, it gradually narrowed but persisted. MCH indicators have declined at the national level as well as separately in urban and rural areas but may have reached a plateau. Urban‒rural disparities in MCH indicators have narrowed but still exist. Regular analyses of temporal trends in MCH are necessary to assess the effectiveness of measures for timely adjustments.

Unrevealing the coupling coordination degree between atmospheric CO2 concentration and human activities from geospatial and temporal perspectives.

Anthropogenic activities exhibit intricate and significant relationships with atmospheric CO2 concentration. Dissecting the spatiotemporal patterns and potential drivers of their coupling coordination relationships from geospatial and temporal perspectives contributes to the benign coordinating development between the two. The coupling coordination degree (D) and types, and their potential influencing factors in China were explored using a coupling coordination model, emerging hotspot analysis, and Multiscale Geographically Weighted Regression model. Results revealed D was dominated by basic coordination in China with notable spatial disparities. Generally, D exhibited higher values in the eastern regions and lower values in the western regions divided by the Hu Line. Furthermore, Central and East China exhibited lower coordination degrees compared to other eastern regions. A total of 15 spatiotemporal dynamic patterns were identified across China. Hot spot patterns were concentrated in the eastern regions of the Hu Line, while cold spots were mainly observed in the western regions. The coupling coordination types exhibited a distinct pattern of "coordination in the east and incoherence in the west, divided by the Hu Line". Over time, there was a shift from lower-level to more benign coordinated types. Additionally, the D and coupling coordination types demonstrated significant spatial agglomeration characteristics, and intercity alliances and enhanced collaborations are essential for sustaining low-carbon improvements. The mechanisms and intensities of various factors on D exhibited spatiotemporal differences. The key drivers influencing coupling coordination types varied depending on the specific type. Additionally, the scales of these drivers affecting D changed over time. It is essential to consider natural and meteorological factors and their scaling effects when developing policies to enhance coupling coordination level. These results have significant implications for assessing the relationship between atmospheric CO2 and human activities and provide guidance for implementing effective low-carbon development policies.

Estimation of the genetic parameters of sheep growth traits based on machine vision acquisition.

In the realm of animal phenotyping, manual measurements are frequently utilised. While machine-generated data show potential for enhancing high-throughput breeding, additional research and validation are imperative before incorporating them into genetic evaluation processes. This research presents a method for managing meat sheep and collecting data, utilising the Sheep Data Recorder system for data input and the Sheep Body Size Collector system for image capture. The study aimed to investigate the genetic parameter changes of growth traits in Ujumqin sheep by comparing machine-generated measurements with manual measurements. The dataset consisted of 552 data points from the offspring of 75 breeding rams and 399 breeding ewes. Six distinct random regression models were assessed to pinpoint the most suitable model for estimating genetic parameters linked to growth traits. These models were distinguished based on the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and covariance between maternal and direct genetic effects. Fixed factors such as individual age, individual sex, and ewe age were taken into account in the analysis. The genetic parameters for the yearling growth traits of Ujumqin sheep were calculated using ASReml software. The Akaike information criterion, the Bayesian information criterion, and fivefold cross-validation were employed to identify the optimal model. Research findings indicate that the most accurate models for manually measured data revealed heritability estimates of 0.12 ± 0.15 for BW, 0.05 ± 0.07 for body slanting length, 0.03 ± 0.07 for withers height, 0.15 ± 0.12 for hip height, 0.11 ± 0.11 for chest depth, 0.13 ± 0.13 for shoulder width, and 0.53 ± 0.15 for chest circumference. The optimal models for machine-predicted data showed heritability estimates of 0.1 ± 0.09 for body slanting length, 0.14 ± 0.12 for withers height, 0.55 ± 0.15 for hip height, 0.34 ± 0.15 for chest depth, 0.26 ± 0.15 for shoulder width, and 0.47 ± 0.16 for chest circumference. In manually measured data, genetic correlations ranged from 0.35 to 0.99, while phenotypic correlations ranged from 0.07 to 0.90. In machine data, genetic correlations ranged from -0.05 to 0.99, while phenotypic correlations ranged from 0.03 to 0.84. The results suggest that machine-based estimations may lead to an overestimation of heritability, but this discrepancy does not impact the selection of breeding models.

The interaction effect of metals exposure and dietary habit on cognitive function in Chinese older adult cohort.

BACKGROUND: As the important factors in cognitive function, dietary habits and metal exposures are interactive with each other. However, fewer studies have investigated the interaction effect of them on cognitive dysfunction in older adults. METHODS: 2,445 registered citizens aged 60-85 years from 51 community health centers in Luohu District, Shenzhen, were recruited in this study based on the Chinese older adult cohort. All subjects underwent physical examination and Mini-cognitive assessment scale. A semi quantitative food frequency questionnaire was used to obtain their food intake frequency, and 21 metal concentrations in their urine were measured. RESULTS: Elastic-net regression model, a machine learning technique, identified six variables that were significantly associated with cognitive dysfunction in older adults. These variables included education level, gender, urinary concentration of arsenic (As) and cadmium (Cd), and the frequency of monthly intake of egg and bean products. After adjusting for multiple factors, As and Cd concentrations were positively associated with increased risk of mild cognitive impairment (MCI) in the older people, with OR values of 1.19 (95% CI: 1.05-1.42) and 1.32 (95% CI: 1.01-1.74), respectively. In addition, older adults with high frequency of egg intake (≥30 times/month) and bean products intake (≥8 times/month) had a reduced risk of MCI than those with low protein egg intake (<30 times/month) and low bean products intake (<8 times/month), respectively. Furthermore, additive interaction were observed between the As exposure and egg products intake, as well as bean products. Cd exposure also showed additive interactions with egg and bean products intake. CONCLUSIONS: The consumption of eggs and bean products, as well as the levels of exposure to the heavy metals Cd and As, have been shown to have a substantial influence on cognitive impairment in the elderly population.

Lunar Lithium-7 Sensing (δ7Li): Spectral Patterns and Artificial Intelligence Techniques.

Lithium, a critical natural resource integral to modern technology, has influenced diverse industries since its discovery in the 1950s. Of particular interest is lithium-7, the most prevalent lithium isotope on Earth, playing a vital role in applications such as batteries, metal alloys, medicine, and nuclear research. However, its extraction presents significant environmental and logistical challenges. This article explores the potential for lithium exploration on the Moon, driven by its value as a resource and the prospect of cost reduction due to the Moon's lower gravity, which holds promise for future space exploration endeavors. Additionally, the presence of lithium in the solar wind and its implications for material transport across celestial bodies are subjects of intrigue. Drawing from a limited dataset collected during the Apollo missions (Apollo 12, 15, 16, and 17) and leveraging artificial intelligence techniques and sample expansion through bootstrapping, this study develops predictive models for lithium-7 concentration based on spectral patterns. The study areas encompass the Aitken crater, Hadley Rima, and the Taurus-Littrow Valley, where higher lithium concentrations are observed in basaltic lunar regions. This research bridges lunar geology and the formation of the solar system, providing valuable insights into celestial resources and enhancing our understanding of space. The data used in this study were obtained from the imaging sensors (infrared, visible, and ultraviolet) of the Clementine satellite, which significantly contributed to the success of our research. Furthermore, the study addresses various aspects related to statistical analysis, sample quality validation, resampling, and bootstrapping. Supervised machine learning model training and validation, as well as data import and export, were explored. The analysis of data generated by the Clementine probe in the near-infrared (NIR) and ultraviolet-visible (UVVIS) spectra revealed evidence of the presence of lithium-7 (Li-7) on the lunar surface. The distribution of Li-7 on the lunar surface is non-uniform, with varying concentrations in different regions of the Moon identified, supporting the initial hypothesis associating surface Li-7 concentration with exposure to solar wind. While a direct numerical relationship between lunar topography and Li-7 concentration has not been established due to morphological diversity and methodological limitations, preliminary results suggest significant economic and technological potential in lunar lithium exploration and extraction.

The generalized odd log-logistic-G regression with interval-censored survival data.

The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets.