Multifactorial Effects Research Articles

Pharmacognostic Evaluation and In-vitro Evaluation of Antioxidant activity of Mesua ferrea Linn

India is the great heritage of medicinal herb and aromatic plants. Mesua ferrea is a well known plant for its medicinal property in Ayurveda and other traditional system of medicine. It possess multifactorial pharmacological effect because of its rich source of secondary metabolites. According to phytochemical studies, M. ferrea is a good source of polyphenolic, flavonoids contents that gives significant antioxidant property. The aim of this research work was to perform detailed pharmacognostic study like micsoscopy, physicochemical parameters, preliminary phytochemical screening, and determination total phenolic content as well as antioxidant activity of alcoholic, hydro alcoholic and water extracts of M. ferrea. Preliminary phytochemical study showed the presence of sterols only in alcoholic extract, and flavonoids, phenols, coumarins, and saponins, glycosieds in both alcoholic and water extract. Hydroalcoholic extract contain highest total phenolic content, and showed antioxidant activity to the greater extent than the alcoholic and water extract. In vitro study revealed the remarkable antioxidant effect and highest phenolic content in hydroalcoholic extract.

Herbal remedies for male infertility and spermatogenic activity in animals: A review

Male infertility is becoming an important untouched area that needs immediate attention due to the increasing demand for breeding strategies, keeping in view the production and increasing per animal productivity. Many additives and antioxidants have been tried for enhancing the seminal quality, but still there is no evidence of full- proof effect on the conception rates in female animals. However, herbal preparations which promise multi-factorial effect in the breeding male animals can be explored, and in turn could prove to be a better tool to encounter the problem of male infertility holistically. The herbal preparations and its effect at the cellular, molecular and metabolic level still needs to be understood. However, the advantage of using the herbal ingredients could be, use of available local herbal ingredients which are more economical, affordable, can reduce the use of hormonal therapy, have less side effects on long term usage, and have greater acceptability by the farmers. These herbal ingredients will be useful in breeding programmes for improvement of germplasm in terms of productivity. The current review covers how the herbs can be utilized in improving the semen quality and quantity, enhancing function of sertoli and leydig cells, mating behaviour, fecundity, seminal antioxidant status, hypophyseal adrenal gonadal axis cum endocrine regulation, microcirculation of testes, as well as in semen cryopreservation and post thaw quality of different species.

Pleiotropic effects of extracellular vesicles from induced pluripotent stem cell–derived cardiomyocytes on ischemic cardiomyopathy: A preclinical study

Stem cell-secreted extracellular vesicles (EVs) play essential roles in intercellular communication and restore cardiac function in animal models of ischemic heart disease. However, few studies have used EVs derived from clinical-grade stem cells and their derivatives with stable quality. Moreover, there is little information on the mechanism and time course of the multifactorial effect of EV therapy from the acute to the chronic phase, the affected cells, and whether the effects are direct or indirect. Induced pluripotent stem cell-derived cardiomyocytes (iPSCM) were produced using a clinical-grade differentiation induction system. EVs were isolated from the conditioned medium by ultracentrifugation and characterized in silico, in vitro, and in vivo. A rat model of myocardial infarction was established by left anterior descending artery ligation and treated with iPSCM-derived EVs. iPSCM-derived EVs contained microRNAs and proteins associated with angiogenesis, antifibrosis, promotion of M2 macrophage polarization, cell proliferation, and antiapoptosis. iPSCM-derived EV treatment improved left ventricular function and reduced mortality in the rat model by improving vascularization and suppressing fibrosis and chronic inflammation in the heart. EVs were uptaken by cardiomyocytes, endothelial cells, fibroblasts, and macrophages in the cardiac tissues. The pleiotropic effects occurred due to the direct effects of microRNAs and proteins encapsulated in EVs and indirect paracrine effects on M2 macrophages. Clinical-grade iPSCM-derived EVs improve cardiac function by regulating various genes and pathways in various cell types and may have clinical potential for treating ischemic heart disease.

Plant diversity on islands in the Anthropocene: Integrating the effects of the theory of island biogeography and human activities

Understanding how the complexities of biodiversity are maintained on islands has long been one of the central issues studied in conservation biology. With the global onset of anthropogenically driven biodiversity changes, the influence of anthropogenic activities challenges the applicability of the widely accepted Equilibrium Theory of Island Biogeography (ETIB). This study aimed to test how island area, isolation (MD: distance to the mainland, and ND: distance to the nearest island), and human activities (habitat heterogeneity, and proportion of road area relative to island area to refer to the human impact on island) separately and jointly affect the species diversity of vascular plants. The number of plant species with different growth forms (woody plants, herbs, and lianas) was calculated on 35 islands in Sanyang Wetland, which is a human-dominated wetland near the highly populated city of Wenzhou, China. We also evaluated the relative importance of these variables on the number of species using a random forest analysis. The number of species increased with increasing island area and habitat heterogeneity and decreased with increasing isolation for different growth forms. A significant, positive relationship was observed between the number of species and the proportion of road area for herbs, but not for woody plants and lianas. Habitat heterogeneity was the most important predictor for all plants, the proportion of road area was the most important predictor for herbs, and MD was the most important predictor for woody plants and lianas. These results indicate that ETIB remains relevant in the context of human disturbance, but the relative importance of ecological processes on species diversity differs significantly among plant growth forms. Therefore, we should consider plant functional groups when testing multifactorial effects on species diversity on human-dominated islands.

Obesity and Miscarriage.

Obesity affects nearly 40% of reproductive-aged women and has serious implications for women's overall and reproductive health. Women with an elevated body mass index (BMI) have higher rates of anovulation and irregular menses, lower success with fertility treatment, and significantly higher rates of pregnancy complications, such as hypertension/preeclampsia, gestational diabetes, and preterm delivery. Many studies have also shown an association between obesity and early pregnancy loss. However, the causal association between BMI and miscarriage has not been elucidated, likely due to the multifactorial effects that BMI may have on early pregnancy development. In addition, BMI as an isolated variable fails to capture other relevant confounding health risk factors, such as nutrition, physical activity, and insulin resistance. In this review, we will summarize the current literature demonstrating the association between BMI and miscarriage, highlight the research that attempts to explain the association, and finally provide data on therapeutic interventions to improve reproductive outcomes in women suffering from obesity and early pregnancy loss.

CEST2022 - mapping multi-pool CEST signal changes in an animal model of brain tumor with quasi-steady-state reconstruction-empowered CEST quantification

Chemical exchange saturated transfer (CEST) MRI has biomarker potential to assess tissue microenvironment in brain tumors. Multi-pool Lorentzian or spinlock models provides useful insights into the CEST contrast mechanism. However, T1 contribution to the complex overlapping effects of brain tumors is difficult under the non-equilibrium state. Therefore, this study evaluated T1 contributions on multi-pool parameters with quasi-steady-state (QUASS) algorithm reconstructed equilibrium data. MRI scans were performed in rat brain tumor models, including relaxation, diffusion, and CEST imaging. A pixel-wise seven-pool spinlock-model was employed to fit QUASS reconstructed CEST Z-spectra and evaluated the magnetization transfer (MT), amide, amine, guanidyl, and nuclear-overhauled effect (NOE) signals in tumor and normal tissues. In addition, T1 was estimated from the spinlock-model fitting and compared with measured T1. We observed tumor had a statistically significant increase in the amide signal (p < 0.001) and decreases in the MT and NOE signals (p < 0.001). On the other hand, the differences in amine and guanidyl between the tumor and contralateral normal regions were not statistically significant. The differences between measured and estimated T1 values were 8% in the normal tissue and 4% in the tumor. Furthermore, the isolated MT signal strongly correlated with R1 (r = 0.96, P < 0.001). In summary, we successfully unraveled multi-factorial effects in the CEST signal using spinlock-model fitting and QUASS method and demonstrated the effect of T1 relaxation on MT and NOE.

An approach for simulating and evaluating the effect of sulfate attack on the durability of concrete bridges based on a three-dimensional cellular automata model

An accurate evaluation of sulfate attack (SA) diffusion and damage processes is critical for durability design in sulfate-contaminated environments. This study presents a three-dimensional cellular automata model algorithm (3D-CAm) that effectively simulates sulfate diffusion processes in concrete. The model's efficacy was verified through comparison with analytical solutions of Fick's second law (FSL), experimental data, and numerical methods reported in the literature. The outcomes demonstrate that the model has a high degree of accuracy in simulating the diffusion processes of SA in concrete. Moreover, based on the diffusion characteristics of sulfate, damage evolution, and deterioration of concrete mechanical properties, the 3D-CAm is developed and applied for modeling and evaluating the long-term effects of SA on the durability of a concrete bridge. The time and space-dependent multifactorial diffusivity stochastic effects were considered. The model algorithms are developed in Python to achieve this purpose. The simulation results revealed that the randomness of the multifactorial had a negative effect on the increasing sulfate diffusion and accelerating the degradation of the mechanical properties of concrete. Moreover, the relationship between sulfate concentration, damage degree, and corrosion time is presented. The results indicate that the damage degree plays a critical role in the deterioration of the mechanical properties of concrete, where with the increase in service time, the mechanical properties show a noteworthy decrease due to the increase in corrosion depth and deterioration of the cross-section area. To conclude, the 3D-CAm model provides a suitable and effective method for evaluating the diffusion and damage processes of SA in concrete bridges. It offers a valuable way to evaluate damage risk and design durable projects in corrosive environments.

Landscape heterogeneity drives spatial distribution of palm community in a Neotropical rainforest reserve affected by defaunation

Background: Our study presents the first assessment of the relationships between environmental heterogeneity and the palm community at the landscape scale at the northern boundary of tropical rainforest distribution in America.&#x0D; Question: How does landscape-level heterogeneity (edaphic, topographic as well as the density, diversity, and tree biomass) shapes the palm community in a protected reserve affected by defaunation?&#x0D; Study site and date: The study was carried out in Los Tuxtlas Tropical Biology Station, Veracruz, Mexico (2020).&#x0D; Methods: We delineated landscape units (LUs) based on soil and geomorphology to characterize the biophysical mosaic. We built rank-abundance curves to compare the palm richness and their abundance among LUs. Permutational multivariate analysis of variance (PERMANOVA) and nonmetric multidimensional scaling (NMDS) were applied to detect shifts in the palm community across LUs.&#x0D; Results: Integrating abiotic and biotic components by delimiting LUs allowed us to evaluate the multifactorial effect of environmental heterogeneity on the palm community. The density and composition of the palms changed significantly among the LUs, influenced by soil coarse fragments, elevation, slope inclination, and annual precipitation. Tree richness and density had a significant effect on the palm community only when analyzed in combination with the soil-topography variables.&#x0D; Conclusions: The results reveal that there is high edaphic and topographic heterogeneity that influences palm species distribution at the landscape scale. The environmental heterogeneity approach allows capturing novel abiotic variation to explain the distribution patterns of palm species and their coexistence in highly diverse tropical communities.

Racial/ethnic and socioeconomic differences in breast cancer surgery performed and delayed surgical treatment: Mediating impact on mortality.

585 Background: Breast cancer is the most common malignancy affecting women of all racial and ethnic backgrounds in the United States (US). Although Socioeconomic status (SES), race/ethnicity, and surgical type/delays are associated with breast cancer mortality outcomes, studies on these associations have been contrasting. This study examined the racial/ethnic and SES differences in surgical treatment types and delays. Also, we quantified the extent to which these differences explained the racial/ethnic disparities in breast cancer mortality. Methods: We studied 290,066 women 40+ years old diagnosed with breast cancer between 2010 and 2017 identified from the Surveillance, Epidemiology, and End Results database. We performed logistic regression models to examine the association of SES and race/ethnicity with surgical treatment type and delays. We performed mediation analysis models to quantify the extent to which mortality differences were mediated by treatment, sociodemographic, and clinicopathologic factors. The study subjects were de-identified, and there was no patient contact; thus, the study was exempted from an Institutional Review Board's (IRB) approval. A p-value less than 0.05 indicated significant associations for the descriptive statistics, logistic and Cox proportional hazard regressions, and mediation analysis. We used Stata 17 to perform the analyses. Results: Non-Hispanic (NH) Black [Odds ratio (OR) = 1.16, 95% CI: 1.13-1.19] and Hispanic women [OR = 1.27, 95% CI: 1.24-1.31] were significantly more likely to undergo mastectomy compared to NH White women. Similarly, NH Black and Hispanic women had higher odds of delayed surgical treatment than NH Whites. Patients in the highest SES quintile, compared to those in lowest the lowest, were less likely to experience breast cancer-specific mortality (BCSM). Variations in treatment, SES, and clinicopathological factors significantly explained 70% of the excess BCSM among NH Blacks compared to their NH White counterparts. Conclusions: In conclusion, health disparity continues to be a major societal concern in the US. It affects people of all ages, socioeconomic classes, and ethnic/racial backgrounds. The findings of this study showed that racial and ethnic minorities, the majority of whom have a lower socioeconomic position, are significantly disadvantaged regarding the evaluation, quality, cost, and duration of medical care. Future studies can assess the multifactorial effects of sociodemographic factors like SES, access to health care assessment, and educational background amidst other variables as a multidimensional predictor of treatment delay and overall survival of breast cancer and other health issues among minoritized populations.

Emotion Regulation Strategies in Job Satisfaction of Normal Developing Individuals and Individuals with Social Anxiety Disorder

Job satisfaction which affects personal health, family harmony, company performance, and even social stability has been widely studied as a priority in psychology. Emotional regulation is intimately connected to job satisfaction and appears to be an efficient strategy to boost job satisfaction. This paper focuses on the link between emotion regulation on job satisfaction in individuals with Social Anxiety Disorder (SAD) and the normal developing individuals that may exist in different forms. Through reviewing previous research, extensive theoretical and empirical research has confirmed that emotion regulation methods can positively influence the job satisfaction of a wide range of populations. However, individuals with SAD originally showed disorders of emotion regulation. Whether the approach to the link between emotion regulation and job satisfaction under complicated multifactorial effects like individuals with SAD corresponds to the same causal model or has its own independent relationship model will be an important issue of future experimental and theoretical research. Modeling emotion regulation and job satisfaction under the influence of generalized mental disorders not only help company management but also improve SAD individuals mental health.

Multifactorial Effects of Covid-19: A Review of Published Autopsy Reports

Chawla, Karan; Hong, Stephen BS; Shields, Ryan BS; Ford, Lexi BS; Lurie, Fedor MD, PhD Author Information

The mechanics of frost heave with stratigraphic microstructure evolution

Frost heave is a major cause of engineering geology hazards in cold regions, which is mainly the result of frost-heaving pressure due to the non-linear transport-freezing of water in the pore network. However, few studies have fully quantified the contribution of the strata pore-network structure to that pressure and the hazard to cold-region projects under multifactorial effects. Therefore, to address this long-term challenge in cold regions, we pioneered a generic, interdisciplinary, thermal-hydrological-mechanical fractal model for the quantitative study of the effect of strata microstructure on frost heave and hence the assessment of construction risk for engineering geology projects. First, a strata frost heave model based on fractal theory is proposed, with pore fractal dimension, maximum pore diameter, and pore tortuosity fractal dimension used to characterize strata pore density, pore size, and throat complexity, respectively. Secondly, the numerical model is compared with field observations in cold-region projects, and reasonable agreements can be obtained. The results indicate that various fractal parameters have different effects on engineering geology projects in cold regions. The strata displacement changes by 9.7–20.3% as microscopic parameters change. This study contributes to a better understanding of the micro- and macro-interactions of soil in cold regions, as well as a reference for geoengineering stability in cold regions under frost heave conditions.

RAGE Inhibitors in Neurodegenerative Diseases.

Nonenzymatic reactions of reducing sugars with primary amino groups of amino acids, proteins, and nucleic acids, followed by oxidative degradations would lead to the formation of advanced glycation endproducts (AGEs). The AGEs exert multifactorial effects on cell damage leading to the onset of neurological disorders. The interaction of AGEs with the receptors for advanced glycation endproducts (RAGE) contribute to the activation of intracellular signaling and the expression of the pro-inflammatory transcription factors and various inflammatory cytokines. This inflammatory signaling cascade is associated with various neurological diseases, including Alzheimer's disease (AD), secondary effects of traumatic brain injury (TBI), amyotrophic lateral sclerosis (ALS), and diabetic neuropathy, and other AGE-related diseases, including diabetes and atherosclerosis. Furthermore, the imbalance of gut microbiota and intestinal inflammation are also associated with endothelial dysfunction, disrupted blood-brain barrier (BBB) and thereby the onset and progression of AD and other neurological diseases. AGEs and RAGE play an important role in altering the gut microbiota composition and thereby increase the gut permeability and affect the modulation of the immune-related cytokines. The inhibition of the AGE-RAGE interactions, through small molecule-based therapeutics, prevents the inflammatory cascade of events associated with AGE-RAGE interactions, and thereby attenuates the disease progression. Some of the RAGE antagonists, such as Azeliragon, are currently in clinical development for treating neurological diseases, including AD, although currently there have been no FDA-approved therapeutics based on the RAGE antagonists. This review outlines the AGE-RAGE interactions as a leading cause of the onset of neurological diseases and the current efforts on developing therapeutics for neurological diseases based on the RAGE antagonists.

Thermal state of the body of workers exposed to extreme levels of infrared radiation and prevention overheating

Introduction. In an industrial environment, the thermal effect of a heating microclimate on a person through extreme levels of infrared radiation is a stressful factor that has a pronounced negative effect on the formation of the thermal state of the body, which requires the development of effective measures to prevent overheating. &#x0D; The purpose of the study is to investigate the peculiarities of the formation of the thermal state of the body in the conditions of a production experiment under exposure to extreme levels of infrared radiation against the background of long-term heavy factory work and the justification of a set of effective measures to prevent overheating. &#x0D; Materials and methods. The study of the thermal state of the body is conducted in the conditions of real blacksmithing production in accordance with the methodological instructions of MUK 4.3.1895–04 and the provisions of the standard GOST R ISO 9886–2008.&#x0D; Results. In modern forging and pressing production, one of the most unfavourable factors of the production environment is the intense thermal radiation from the metal heated to technological temperatures, which affects the total for 5–6 hours per shift against the background of high temperature from the heating furnaces themselves, which leads to the development of chronic overheating in workers. The main sources of thermal radiation include incandescent to a temperature of over 1200 °C with metal forgings of various lengths and various profile configurations, which largely determines the variability in the degree of radiation intensity of the radiant energy flow at the workplaces of blacksmiths and heaters. The formed thermal state of the workers of the studied occupational groups, dressed in standard cotton workwear, during the performance of the main work operations during the first half of the working shift in the cold and warm periods of the year, is estimated as the maximum permissible, requiring a limit of working time – no more than one hour per working shift. It is necessary to develop requirements for the working regime for prolonged heavy physical work in conditions of extreme exposure to a heating microclimate and the mandatory use of modern effective personal protective equipment (PPE) from infrared radiation.&#x0D; Limitations. The limitation of the study is the possibility of using the results obtained directly to a specific production, characterized by multifactorial effects against the background of extreme levels of the heating microclimate, rigidly determined by the technological process. The obtained indicators of the thermal condition of workers dressed in standard cotton workwear are estimated as the maximum permissible for a period of no more than one hour per work shift, which justifies the need to search for new effective personal protective equipment for the body of workers.&#x0D; Conclusion. It is necessary to develop requirements for the working regime during prolonged hard physical work in conditions of extreme exposure to a heating microclimate and the mandatory use of modern effective PPE from infrared radiation of extreme levels.

Edaravone in the treatment of concussion and post-concussion syndrome

ABSTRACT. The review describes the pathogenetic mechanisms of the development of traumatic brain injury (TBI), contusion and post-concussion syndrome, and presents the modern possibilities of their pharmacotherapy. The analysis of experimental and clinical studies showed that the multifactorial effect of edaravone on the main pathogenetic links of brain damage in TBI and numerous positive clinical effects make edaravone a promising drug in the treatment of TBI, concussion and post-concussive syndrome.

Quantifying the effects of urban fabric and vegetation combination pattern to mitigate particle pollution in near-road areas using machine learning

There are many intertwined factors such as urban fabric, vegetation combination, and meteorological conditions that influence the spatial distribution of fine particles (PM2.5, aerodynamic diameter less than 2.5 μm) in near-road urban areas, and it is difficult yet important to disentangle them. Identifying the relative importance of those factors could facilitate designing urban fabric and roadside vegetation combination patterns to mitigate the negative impacts of particles. This study first proposes an effective and reliable integrated method of the ENVI-met microclimate model and interpretable machine learning to quantify the multifactorial effects. Data from ENVI-met models with different built environments and meteorological conditions are used to train and test the machine learning (ML) models. All ML models show high accuracy in predicting PM2.5 concentrations with R2 high than 0.87. Results indicate that height level and study area are the two most important features, explaining 22.8% and 20.4% of the variation in PM2.5 concentration, respectively. The feature pairwise interactions account for 45.8% of ambient particle concentrations. The combination of trees and hedges in urban fabrics with longer building length than width could mitigate PM2.5 concentrations in residential areas. The integrated methods in this study could be extrapolated to future research to perform source identification analysis.

Dietary Nitrate and Corresponding Gut Microbiota Prevent Cardiac Dysfunction in Obese Mice.

Identifying therapeutic approaches that prevent cardiometabolic diseases are clearly important, and nitrate represents one such potential compound given its multifactorial metabolic effects. We aimed to determine whether nitrate could prevent high-fat diet (HFD)-induced cardiac abnormalities and whether this was dependent on the gut microbiome. Dietary nitrate attenuated HFD-induced pathological changes in cardiac remodelling, left ventricle reactive oxygen species, adipose inflammation, lipid homeostasis, glucose intolerance, and gut dysbiosis. Fecal microbial transplantation from nitrate-fed mice also prevented serum dyslipidemia, left ventricle reactive oxygen species, glucose intolerance, and cardiac dysfunction. Therefore, the cardioprotective effects of nitrate are related to mitigating gut dysbiosis, highlighting a nitrate-gut-heart axis.

Всхожесть семян хвойных интродуцентов в условиях Центрального Казахстана при различной стратификации и внесении в субстрат хвойного опада

This study observes the issues of the possible usage of wastes from timber industry in the sprouting of seeds of coniferous plants in the arid climate of Central Kazakhstan. Such treatment of coniferous seeds is easily implementable and cost-effective, compared to the other methods. However, the complex chemical composition of needle litter in the soil substrate has the multifactorial and controversial effects on such indicators as germination energy, seed germination capacity and rest energy. In addition, the article discusses the use of long-term natural seed stratification as an alternative method of planting seeds in forest nurseries. The other studies have shown that natural stratification has a more significant effect on the indicators of seed rest energy and germination energy. However, it should be said that these parameters mostly correlate with the generic assignment of the objects. Thus, representatives of the genera Picea and Abies have more significant rates of germination during spring stratification. Whereas, representatives of the genera Pinus and Pseudotsuga – with natural stratification. This may be the result of their ecological and biological adaptation. The conditions of Central Kazakhstan are characterized by a sharply continental climate, which includes not only a sharp climate change, but also a rather low snow cover and low temperatures. Under these conditions, the seeds of the genera Pinus and Pseudotsuga may have higher adaptive abilities, which may become a further subject of research. At the same time, it was determined that despite the rather convincing data obtained during autumn stratification, the highest results of seed germination in most experimental species were noted during spring stratification, including variation with the use of needle litter. Again, it is not entirely advisable to introduce the use of this method based only on the indicators of seed germination in arid conditions of Central Kazakhstan, since the favorable period for seed germination in this area is unstable from year to year. Thus, based on the results of the study, it was concluded that the most suitable method for germinating seeds of conifers, with some exceptions, is natural stratification. The use of needle litter affects insignificantly the rates of seed germination, and in some cases even suppresses this process.

Utility of Environmental Complexity as a Predictor of Alzheimer’s Disease Diagnosis: A Big-Data Machine Learning Approach

BackgroundRural-urban differences and spatial navigation deficits have received much attention in Alzheimer’s Disease research. While individual environmental and neighborhood factors have been independently investigated, their integrative, multifactorial effects on Alzheimer’s diagnosis have not. Here we explore this “environmental complexity” for predictive power in classifying Alzheimer’s from cognitively-normal status.MethodsWe utilized data from the National Alzheimer’s Coordinating Center (NACC) uniform data set containing annual visits since 2005 and selected individuals with multiple visits and who remained in their zipcode (N = 22,553). We georeferenced each subject with 3-digit zipcodes of their residences since entering the program. We calculated environmental complexity measures using geospatial tools from street networks and landmarks for spatial navigation in subjects’ zipcode zones. Zipcode zones were grouped into two cognitive classes (Cognitively-Normal and Alzheimer’s-inclined) based on the ratios of AD and dementia subjects to all subjects in an individual zipcode zone. We randomly selected 80% of the data to train a neural network classifier model on environmental complexity measures to predict the cognitive class for each zone, controlling for salient demographic variables. The remaining 20% served as the test set for performance evaluation.ResultsOur proposed model reached excellent classification ability on the testing data: 83.87% accuracy, 95.23% precision, 83.33% recall, and 0.8889 F1-score (F1-score=1 for perfect prediction). The most salient features of “Alzheimer’s-inclined” zipcode zones included longer street-length average, higher circuity, and slightly fewer points of interest. Most “cognitively-normal” zipcode zones appeared in or near urban areas with high environmental complexity measures.ConclusionEnvironmental complexity, reflected in frequency and density of street networks and landmarks features, predicted with high precision the cognitive status of 3-digit zipcode zones based on the etiologic diagnoses and observed cognitive impairment of NACC subjects residing in these zones. The zipcode zones vary widely in size (1.6 km2 to 35,241 km2), and large zipcode zones suffer high spatial heterogeneity. Other proven AD risk factors, such as PM2.5, disperse across zones, and so do individual’s activities, leading to spatial uncertainty. Nevertheless, the model classifies diagnosis well, establishing the need for prospective experiments to quantify effects of environmental complexity on Alzheimer’s development.

Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

The addition of rubber to concrete improves resistance to chloride ion attacks. Therefore, rapidly determining the chloride permeability coefficient (DCI) of rubber concrete (RC) can contribute to promotion in coastal areas. Most current methods for determining DCI of RC are traditional, which cannot account for multi-factorial effects and suffer from low prediction accuracy. Machine learning (ML) techniques have good non-linear learning capabilities and can consider the effects of multiple factors compared with traditional methods. However, ML models easily fall into the local optimum due to their parameters' influence. Therefore, a mixed whale optimization algorithm (MWOA) was developed in this paper to optimize ML models. The main strategies are to introduce Tent mapping to expand the search range of the algorithm, to use an adaptive t-distribution dimension-by-dimensional variation strategy to perturb the optimal fitness individual to thereby improve the algorithm's ability to jump out of the local optimum, and to introduce adaptive weights and adaptive probability threshold values to enhance the adaptive capacity of the algorithm. For this purpose, data were collected from the published literature. Three machine learning models, Extreme Learning Machine (ELM), Random Forest (RF), and Elman Neural Network (ELMAN), were built to predict the DCI of RC, and the three models were optimized using MWOA. The calculations show that the MWOA is effective with the optimized ELM, RF, and ELMAN models improving the prediction accuracy by 54.4%, 62.9%, and 36.4% compared with the initial model. The MWOA-ELM model was found to be the optimal model after a comparative analysis. The accuracy of the multiple linear regression model (MRL) and the traditional mathematical model is calculated to be 87.15% and 85.03%, which is lower than that of the MWOA-ELM model. This indicates that the ML model that is optimized using the improved whale optimization algorithm has better predictive ability than traditional models, providing a new option for predicting the DCI of RC.