Environmental Factors Research Articles

Improvement of polygenic modeling of blood pressure traits using lifestyle information in the UK Biobank.

Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to collinearity between the genetic and environmental components of the phenotype, which violates the assumptions of most statistical methods for polygenic modeling (i.e., environmental factors are non-randomized over the genetic factors). This phenomenon is also known as "reverse causation", and could lead to biased predictions due to the difficulty in disentangling the genetic and environmental effects. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted for genetic and other non-genetic factors. The results show that proper adjustment of the lifestyle variables allows for improved model performance and reduces the bias generated by reverse causation. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables.

Calcium signaling controls early stage biofilm formation and dispersal in Vibrio fischeri.

Biofilm formation and dispersal are critical steps in both symbiotic and pathogenic colonization. Relative to biofilm formation, the process of dispersal in the model symbiont Vibrio fischeri, and other bacteria, is understudied. Here, we adapted an imaging assay to study early biofilm formation and the dispersal process in V. fischeri. We demonstrated that our assay can quantify biofilm formation and dispersal over time, can reveal phenotypic differences in diverse natural wild-type isolates, and is sensitive enough to investigate the impact of environmental factors. Our data confirm that calcium is a potent biofilm formation signal and identify the diguanylate cyclase CasA as a key regulator. This work leads the way for more in-depth research about unknown mechanisms of biofilm dispersal.

Durum wheat nuclear factor Y (NF-Y) a subfamily: structure, phylogeny, and expression analysis in response to hormones and abiotic stresses

Nuclear factor-Y (NF-Y) transcription factors are heterotrimeric complexes that are widely distributed in eukaryotes and play essential roles in many biological processes. Although NF-YA proteins have been characterized in numerous plants, their contribution to the response of durum wheat (Triticum turgidum ssp. durum) to environmental factors has not been reported. Thus, this study was aimed at identification and characterization of Triticum turgidum TtNF-YA family members through genome-wide analysis. Twelve NF-YA genes were discovered in Triticum turgidum. Discovered genes were distributed across eight chromosomes, while their encoded proteins were localized in cell nucleus. Structure and motif pattern analyses revealed that the TtNF-YA genes were relatively conserved. The expression of TtNF-YAs genes was significantly induced by several stressors and their expression profiles differed in various tissues and at various development stages. Notably, TtNF-YA2 A-1 and TtNF-YA2B-1 exhibited the greatest increase in response to Polyethylene glycol, while TtNF-YA4 A and TtNF-YA4B-1 showed the highest increase under salt stress. Additionally, TtNF-YA5B-1 and TtNF-YA6 A-1 displayed pronounced upregulation when exposed to exogenous Abscisic acid, suggesting that TtNF-YA are involved in a series of cellular and developmental events. This finding was corroborated by the recognition of several cis-regulatory elements in the TtNF-YAs promoter region, associated with the applied treatments. Overexpression of TtNF-YA2 A-1, TtNF-YA2B-1, TtNF-YA4 A, TtNF-YA4 A-1, TtNF-YA4B-1, and TtNF-YA5 A-2 genes in Saccharomyces cerevisiae showed that these genes increase cell tolerance to multiple stresses. Our results will facilitate subsequent functional analysis of TtNF-YAs genes, which emerge as promising targets for genetic engineering for increasing wheat tolerance to multiple stresses.

Beyond obesity: lean metabolic dysfunction-associated steatohepatitis from unveiling molecular pathogenesis to therapeutic advancement.

Nonalcoholic fatty liver disease (NAFLD), now known by the name of metabolic dysfunction-associated fatty liver disease (MAFLD), with increased global incidence, has been recognized as a significant metabolic disorder. NAFLD includes a spectrum liver disease from hepatocellular fat accumulation (isolated steatosis) to an advanced form of liver injury known as nonalcoholic steatohepatitis (NASH), which refers to distinct histologic features, including hepatocellular steatosis and injury, necroinflammation, and eventually fibrosis. Nonobese or lean individuals associated with metabolic dysregulation usually demonstrated diverse risk factors compared to obese MAFLD. The presence of normal range body mass index (BMI) and excess visceral adiposity with increased cardiometabolic and renal comorbidities, along with sarcopenia, has been evidenced to be associated with lean MASH. Genetic predispositions accompanying lifestyle and environmental factors contribute to disease initiation and progression. The genetic influence in pathophysiology indicated the significant contributions of the following genes: PNPLA3, TM6SF2, APOB, LIPA, MBOAT7, and HSD17B13, and the impact of their disease-specific variants in the development of obesity-independent MASH. The epigenetic modifications exhibited differential DNA methylation patterns in the genes involved in lipid metabolism, particularly hypomethylation of PEMT. Diet-induced and genetic animal models of lean MASH, including Slc: Wistar/ST rats, PPAR-α, PTEN, and MAT1A knockout mice models, are indicated to be pivotal in the exploration of disease progression and observing the effect of therapeutic interventions. This comprehensive review comprises the molecular and genetic pathophysiology, molecular diagnostics, and therapeutic aspects of lean MASH to enunciate a diagnostic approach that combines detailed clinical phenotyping regarding genomic analysis.

Predicting the Consumption of Iron-Rich Foods During Pregnancy in Senegal: A Path Analysis.

A limited consumption of iron-rich foods (IRF) is associated with a higher risk of anemia throughout the human life cycle, particularly during pregnancy. Using the extended version of the theory of planned behavior (eTPB), this study aims to identify pathways by which individual (attitude, subjective norm, the perceived behavioral control) and environmental-related factors may influence IRF consumption among pregnant women (PW) from all regions of Senegal. To evaluate IRF consumption in the past day and night, a food frequency questionnaire consisting of a list of IRF with yes/no responses was used. Constructs of the eTPB were assessed through a face-to-face interview conducted with each woman using a valid and reliable questionnaire with Likert scales. Our findings reveal that 9 out of 10 PW (n = 429) had the intention to consume IRF, while about 80% did consume them. Path analyses were conducted. There was no association between the intention and the behavior of interest. The consumption of IRF was predicted by control beliefs or the perceived ability of women to perform the behavior (β = 0.23, p < 0.001). In turn, control beliefs were positively associated with environmental barriers (β = 0.40, p < 0.001). In light of the results, we reiterate the importance of implementing a multisectoral approach to improve the consumption of IRF among PW. Yet, further research is required to better understand pathways through which the intention, psychosocial and environmental factors influence IRF consumption as well as the role of other potential causes of anemia among PW.

Therapeutic Potential and Translational Challenges for Bacterial Extracellular Vesicles in Inflammatory Bowel Disease.

Despite the availability of numerous new immune-directed therapeutics, the major constituents of inflammatory bowel disease (IBD)-ulcerative colitis (UC) and Crohn's disease (CD)-continue to afflict millions worldwide, resulting in significant morbidity and long-term health risks. IBD results from a triad of immune, environmental (eg, gut microbiome), and genetic (including epigenetic) mechanisms, and therefore has been subject to a wide variety of therapeutic strategies. Among these, the administration of probiotics, particularly Gram-positive lactic acid bacteria (LAB), targeting both immune and environmental factors, has shown promising potential for efficacy in selected populations in early clinical trials. However, knowledge gaps and inconsistent efficacy currently prevent recommendations for the use of probiotics in larger IBD patient populations. The inconsistent efficacy of probiotics is likely due to variable cell viability and potency after administration, further exacerbated by IBD patient heterogeneity. Thus, an alternative to live probiotics for IBD has emerged in the form of bacterial extracellular vesicles (BEVs)-cell-secreted nanovesicles containing abundant bioactive cargo that, like live probiotics, can regulate immune and environmental factors but with fewer viability limitations and safety concerns. In this review, we summarize the work done to date establishing the potential of BEVs to provide the therapeutic benefits in IBD and discuss the hurdles BEVs must overcome to achieve clinical translation. We also consider future directions for BEV therapeutics, especially treatment potential for necrotizing enterocolitis (NEC), which shares similarities in pathophysiology with IBD.

Unraveling the genetic diversity, seed oil fatty acid constituents and antioxidant activity of different okra (Abelmoschus esculentus (L.) Moench) populations.

Abelmoschus esculentus (L.) Moench is cultivated globally for use as a salad or culinary herb. It has a narrow genetic diversity and is sensitive to heat and drought stress. Therefore, it is very important to detect different chemotypes and genotypes to be applied in its breeding programs. The genetic diversity is equally distributed within and among the populations. Principal coordinates analysis, genetic diversity and polymorphism parameters revealed a high level of within-population diversity for some populations. PhiPT values, genetic differentiation index and total heterozygosity amount explored a high level of genetic differentiation between some populations, with a low average rate of gene flow. Nei's genetic distance and STRUCTURE analysis assigned the populations to two distinct and some intermediate genotype groups. Seed oils were composed of the same unsaturated and saturated fatty acids in a ratio of nearly 2:1. Linoleic and oleic fatty acids were the first and second main unsaturated fatty acids, followed by palmitic acid as a major saturated fatty acid in all populations. Clustering analyses explored the populations classified into two chemotype groups. However, it was not similar to those obtained by the molecular genetic data. 2,2-Diphenyl-1-picrylhydrazyl (DPPH; IC50 μg mL-1) analysis revealed a moderate to weak antioxidant activity for a few populations, which significantly correlated with palmitic acid amount. Environmental factors have a stronger effect on seed oil fatty acids profiles than the genetic structure. The detected genotypes and chemotypes could be used for the development of genetic and chemical diversity, respectively, in this crop. © 2025 Society of Chemical Industry.

Optimization control of central air conditioning based on the improved butterfly optimization algorithm

Abstract This paper proposes an optimization method for central air conditioning water systems based on an improved Butterfly Optimization Algorithm (BSGBOA). First, regression analysis is used to study the relationship between the power consumption of equipment such as cooling water pumps and cooling towers and various environmental factors, leading to the establishment of a mathematical model. To address the issues of local optima and slow convergence speed in the traditional Butterfly Optimization Algorithm (BOA), several improvement strategies are introduced, including Bernoulli mapping-based initialization, adaptive weight coefficients, a sine-cosine strategy, and Gaussian mutation. These improvements significantly enhance the global search capability and convergence speed of the algorithm while avoiding the problem of local optima. Experimental results show that the improved Butterfly Optimization Algorithm (BSGBOA) demonstrates significant advantages in optimizing the energy consumption of central air conditioning systems, achieving a total energy consumption reduction of 16.98%, thereby proving the effectiveness of the optimization method and the superiority of the algorithm. This study provides a new technological pathway for building energy conservation and improving energy utilization efficiency, offering important application value and&amp;#xD;

Planning sustainable futures: critical drivers of success in the global context

The growth of the tourism industry has emphasized the need for sustainable development within the tourism sector (Lang et al. in Sustain Sci. 7(S1):25–43. https://doi.org/10.1007/s11625-011-0149-x, 2012}. Sustainable tourism development balances economic benefits with environmental preservation and minimizes negative impacts. To achieve this, a long-term sustainable strategy for planning, managing, and monitoring tourism activities is necessary (Fahmawee and Jawabreh in Jordan GeoJ Tour Geos. 46(1):27–36. https://doi.org/10.30892/gtg.46103-997, 2023}. This study analyses sustainable tourism strategies in 15 UNWTO recognized tourism villages across Asia–Pacific using a multiple case study approach. Cross case analysis identified success factors in four sustainability dimensions, i.e., social, economic, cultural, and environmental factors. The study results reveal that community empowerment, cultural preservation, and eco-friendly infrastructure are key drivers of sustainable tourism development. These findings provide actionable insights for stakeholders to replicate these practices.

Identification of a Distal Enhancer That Regulates TGF-β-Induced SNAI1 Expression.

Snail is a zinc finger transcription factor encoded by the SNAI1 gene and triggers a cellular process termed epithelial-mesenchymal transition (EMT) upon its increased expression and/or functional activation. Snail expression and activity are regulated by various extracellular stimuli, including cytokines and environmental factors. Transforming growth factor-β (TGF-β) is a Snail inducer that functions via Smad3-mediated transcriptional activation. In the present study, we identified a distal enhancer that modulates TGF-β-induced SNAI1 expression. ChIP-seq and Hi-C analyses showed that the enhancer is located 46 kb downstream of the SNAI1 gene; in TGF-β-stimulated cells, it associates with Smad3 and interacts with the SNAI1 proximal promoter. Inhibiting the activity of the enhancer using CRISPRi attenuated TGF-β-induced SNAI1 expression, stress fiber formation, and cell motility enhancement, suggesting that the enhancer mediates TGF-β-induced EMT. The enhancer contains a Smad-binding CAGA motif and an activator protein-1 (AP-1) binding motif that function in transcriptional activation. Ras-responsive element binding protein 1 (RREB1), a transcription factor required for TGF-β-induced Snail expression, regulated the basal activity of the enhancer but not its inducibility by TGF-β. In contrast to the enhancer, the association of Smad3 with the proximal promoter was not evident. These findings suggest that the proximal promoter and the distal enhancer respond to distinct signaling cues, integrate them, and cooperatively function to drive SNAI1 expression.

Advanced smart assistance with enhancing social interaction and daily activities for visually impaired individuals using deep learning with modified seagull optimization

Visually impaired individuals face daily challenges in social engagement and routine activities due to limited access to real-time environmental information. Damage detection is a common approach in infrastructure that combines steel and concrete reinforcement to achieve optimal durability and structural strength. These bridges, designed to withstand diverse loads such as seismic forces, traffic weight, and environmental factors, are significant for maintaining structural integrity. Damage detection comprises applying advanced structural health monitoring methods to identify and assess potential deterioration or damage in concrete bridge components. Machine learning (ML) models, pattern detection, and statistical analysis are extensively adopted to identify subtle changes and process sensor information in structural response that might indicate corrosion, cracks, or other structural problems. Earlier detection and continuous monitoring of damage enable prompt intervention, ensuring longevity and safety while reducing the need for extensive repairs or the risk of unexpected failures. This study proposes an Automated Damage Detection using a Modified Seagull Optimizer with Ensemble Learning (ADD-MSGOEL) method for visually impaired people. The ADD-MSGOEL method is designed to enhance the social life and daily functioning of visually impaired people by accurately detecting damage and potential hazards in their surroundings. Initially, the ADD-MSGOEL method utilizes contrast enhancement (CLAHE) to enhance the image quality. Next, the features are extracted using the Dilated Convolution Block Attention Module with EfficientNet (DCBAM-EfficientNet) module, which derives the intrinsic and complex features. Moreover, the MSGO model is employed to choose the optimal parameter for the DCBAM-EfficientNet module. At last, an ensemble of three models, namely long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and sparse autoencoder (SAE) models, are implemented for the classification and detection of the damages. To demonstrate the effectiveness of the ADD-MSGOEL technique, a series of experiments were conducted using the CODEBRIM dataset. The experimental validation of the ADD-MSGOEL technique portrayed a superior accuracy value of 97.59% over existing models.

Predicting dementia in people with Parkinson’s disease

Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson’s dementia (PDD) post-diagnosis, further validated by data from the Parkinson’s Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson’s patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention.

Hormonal regulation of root growth under moderately elevated temperatures.

Roots anchor plants in the ground, providing an interface for interactions with the environment and sensing potential stressors. At the same time, they contribute to the acclimatization to stressful conditions through their growth plasticity. Root growth is a combination of cell division and cell elongation, ultimately shaping root system architecture depending on environmental stimuli. Root thermomorphogenesis refers to the altered root growth response under moderately elevated ambient temperatures, characterized, for example, by an increase in primary root growth during early seedling development. While the molecular regulation of shoot thermomorphogenesis is comparatively well understood, the gene- and hormone-regulatory networks underlying root growth responses to warm temperature have only begun to be uncovered in recent years. In this article, we review the latest findings of how root growth, comprised of cell division and elongation, is regulated by the phytohormones auxin, cytokinins and brassinosteroids at optimal temperatures. We then summarize our current understanding of root growth responses to warm temperatures during early seedling development and the key role of auxin in this process. Furthermore, we address the contributions of cell division versus cell elongation to root thermomorphogenesis, discuss whether the root is autonomous in sensing and reacting to increased temperatures, and provide an outlook of how root thermomorphogenesis research can be applied to crops. Root growth is a complex process which is tightly regulated and strongly depends on environmental factors. During early seedling development, elevated ambient temperatures stimulate auxin signalling which leads to an increase in both cell division and elongation, resulting in elongated primary roots. It appears that the root can autonomously sense and react to temperature changes at this stage. Root thermomorphogenesis seems to be conserved among many plants including crops, but its ecophysiological relevance remains open to further research.

Impact of Carrageenan-Based Encapsulation on the Physicochemical, Structural, and Antioxidant Properties of Freshwater Snail (Bellamya bengalensis) Protein Hydrolysates

This study investigated the encapsulation of snail protein hydrolysates (SPHs) using carrageenan as a microencapsulating agent at concentrations of 1%, 2%, and 3%. SPHs were prepared from the soft tissue of freshwater snails (Bellamya bengalensis) through enzymatic hydrolysis using bromelain, resulting in a degree of hydrolysis of 48.05%. The encapsulation process was carried out using the spray-drying technique. Encapsulation with 3% carrageenan enhanced the yield, encapsulation efficiency (up to 84.96%), colloidal stability (up to −33.8 mV), and thermal stability (up to 75 °C). The particle size increased as the carrageenan concentration increased, reaching 206.9 nm at 3%, and the uniform polydispersity index (0.26) indicated stable encapsulation. While encapsulation reduces solubility and antioxidant activity (DPPH, FRAP, ABTS, and HRSA), it effectively protects SPH from environmental factors such as hygroscopicity and storage stability, thus maintaining high scavenging activity. Fourier transform infrared spectroscopy confirmed that carrageenan and SPH strongly interact. Scanning electron microscopy revealed that the particles had better shapes and smooth, cohesive surfaces. This study demonstrates the effectiveness of carrageenan as an encapsulating agent for SPH, enhancing its stability and bioactivity for potential applications in the food and nutraceutical industries as a bioactive additive and offering an alternative to conventional coating materials.

Bridging the United States population diversity gaps in clinical research: roadmap to precision health and reducing health disparities.

Precision medicine promises improved health outcomes by tailoring treatments to individual genetic and environmental factors. However, achieving this potential is hindered by persistent health disparities and the underrepresentation of racially and ethnically diverse populations in clinical trials. Limited diversity in research exacerbates health inequities, reducing the generalizability of findings and widening gaps in access to effective treatments. This review outlines a multi-faceted strategy to bridge diversity gaps in clinical trials, focusing on community engagement, clinical pharmacology, and regulatory science. Key approaches include decentralized trials, targeted recruitment, advanced data modeling, and comprehensive integration of genetic and social determinants of health data. Regulatory frameworks, such as diversity action plans, play a crucial role in ensuring equitable access to precision health innovations. Increasing representation in research enhances the reliability of clinical data and fosters health equity by addressing differences in disease prevalence, treatment responses, and healthcare access. By leveraging technological advancements and inclusive research methodologies, this framework aims to transform clinical trials into a roadmap for equitable healthcare. Ensuring diverse participation in research is essential for the successful implementation of precision medicine and realizing the full potential of precision health, ultimately reducing health disparities and promoting fair access to medical advancements across all populations.

Exploring Dynamics in Post-stroke Motor Rehabilitation: A Systematic Review of Implementation Barriers and Facilitators using the COM-B Model.

PurposeThis systematic review aimed to identify barriers and facilitators in post-stroke motor rehabilitation across patient, caregiver, and healthcare professional levels, employing the capability, opportunity, motivation, behavior (COM-B) model and the theoretical domains framework (TDF).Materials and MethodsA systematic search of PubMed, EMBASE, Cochrane Library, Web of Science, and CINAHL, following PRISMA guidelines, covered literature until January 20, 2025. Studies reporting qualitative, quantitative, or mixed-methods data on post-stroke motor rehabilitation barriers and facilitators were included. Quality assessment utilized MMAT 2018.ResultsAnalyzing 48 studies, we found that barriers to post-stroke motor rehabilitation encompassed motor impairments, cognitive issues, resource scarcity, and environmental factors, while facilitators included good physical abilities, resource accessibility, and social support. The COM-B model elucidated the dynamic interplay between capability, opportunity, and motivation, suggesting educational interventions and telerehabilitation as avenues for improvement.ConclusionThis systematic review, guided by the COM-B model, identifies key barriers and facilitators in post-stroke motor rehabilitation. It emphasizes the importance of targeted education, improved telerehabilitation infrastructure, and robust social support systems to address multifaceted challenges.

Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet; therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. This study introduces a Leveraging Explainability Artificial Intelligence and Optimization Algorithm for Accurate Dementia Prediction and Classification Model (LXAIOA-ADPCM) technique in medical diagnosis. The main intention of the LXAIOA-ADPCM technique is to progress a novel algorithm for dementia prediction using advanced techniques. Initially, data normalization is performed by utilizing min–max normalization to convert input data into a beneficial format. Furthermore, the feature selection process is performed by implementing the naked mole‐rat algorithm (NMRA) model. For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. Finally, the Grad‐CAM is employed as an XAI technique to enhance transparency by providing human-understandable insights into their decision-making processes. A broad array of experiments using the LXAIOA-ADPCM technique is performed under the Dementia Prediction dataset. The simulation validation of the LXAIOA-ADPCM technique portrayed a superior accuracy output of 95.71% over existing models.

Agromorphological characterization and molecular evaluation of a marker associated with Zym-1 allele resistance of local zucchini genotypes

Zucchini (Cucurbita pepo L.) is a widely cultivated vegetable with high economic value and important nutritional value. Zucchini yellow mosaic virus (ZYMV) is the most common virus diseases that causes loss of productivity in Zucchini cultivation. This study was conducted to determine of 250 Zucchini in the S3 stage genotypes, in terms of agro-morphological characteristics such as plant morphology, fruit characteristics and yield and to determine the resistance levels of the genotypes to ZYMV. As a result of molecular analysis, 43 genotype were found to be homozygous dominant (RR) and 10 genotype as heterozygous (Rr).resistant to Zym-1 allele. According to the results of factor analysis, 12 significant factors were identified. As a result, factor analysis, used in plant morphological characterisation studies, allows a systematic examination of a large number of variables that affect the morphological characteristics of plants, and this method is considered a critical tool for understanding the relationships of plants with environmental and genetic factors. At the end of the study, morphological variability was found high among the Zucchini genotypes. This evaluation of plant trait variability can assist geneticists and breeders to identify populations with desirable characteristics for inclusion in zucchini breeding programs. In addition, the levels of resistance to these disease are crucial for breeding programs aimed at developing cultivars resistant to disease.

Reading Adam Smith through a Montesquieuian lens: climate, population, and progress

ABSTRACT The role of the environment in Adam Smith’s thought remains a neglected area of study. By reading Smith through a Montesquieuian lens, new light can be shed on previously overlooked themes. This paper examines how Smith thought climate impacted the development of nations and asks why he thought some nations were more developed than others. I argue that the environmental conditions of a territory principally decided its potential for Smith, while the realisation of this potential depended on cultural norms and policy. Smith is shown to be deeply interested in the interaction of environmental and non-environmental factors in his account of the development of different nations. Re-assessing the important role of climate in Smith’s thinking not only brings him closer to Montesquieu than might be expected, but it also helps us reflect on contemporary debates on unlimited growth.

Exposure to Extreme Heat Increases Preterm Birth Risk: Hypothetical Pathophysiological Mechanisms.

Preterm birth (PTB), delivery before 37 weeks of gestation, is the leading cause of neonatal mortality globally, accounting for nearly half of all neonatal deaths. While numerous established risk factors for PTB have been identified, ongoing research continues to elucidate additional contributing factors. Epidemiological studies increasingly demonstrate that elevated ambient temperature is an environmental risk factor for PTB, with odds increasing 16% during heat waves and 5% per 1°C temperature rise. This is particularly concerning given escalating global warming trends. While maternal heat susceptibility during pregnancy may be linked to compromised thermoregulation from gestational adaptations, the exact pathophysiological mechanisms leading to heat-associated PTB remain unclear, hindering therapeutic development. This review proposes multitudes potential pathophysiologic mechanisms leading to PTB that can be induced by heat. They include but are not limited to metabolic derangement, mitochondria dysfunction, inflammation, endothelial dysfunction, oxidative stress, and change in cell fate. These mechanisms are derived from integrated knowledge of pregnancy physiology, parturition processes, and temperature effects on physiological pathways. We also outline future experimental approaches to test these hypotheses.