The regeneration niche concept states that plant species only occur in habitats where the environmental conditions allow their recruitment. This study focuses on this concept and proposes a novel approach for modelling and experimentally validating the distribution of suitable habitats for the recruitment of invasive plants under the current and future climate. The biological invasion of the Peruvian peppertree (Schinus molle) in Mexico is used as practical example. The values of eight bioclimatic variables associated to sites in which young, naturally established seedlings and saplings were detected were used to model the current distribution of recruitment habitats. A machine-learning algorithm of maximum entropy (MaxEnt) was used to calibrate the model and its output indicated the distribution of occurrence probabilities of young peppertrees in Mexico under the current climate. This model was projected on climate change scenarios predicted for the middle of this century, which indicated that the cover of suitable recruitment habitats for this invasive species will shrink. To validate these predictions, field experiments were performed at three sites where the model predicted reduced occurrence probabilities of young peppertrees. In these experiments, emergence and survival rates of peppertree seedlings were assessed under the current climate and under simulated climate change conditions. As seedling emergence and survival rates were lower under simulated climate change conditions, the experiments validated the model predictions. These results supported our proposal, which combines modelling and experimental approaches to make accurate and valid predictions about the distribution of suitable recruitment habitats for invasive plants in a warmer and drier world.The incidence of childhood atopic dermatitis (AD) and allergic rhinitis (AR) is increasing. This warrants development of measures to predict and prevent these conditions. We aimed to investigate the predictive ability of a spectrum of data mining methods to predict childhood AD and AR using longitudinal birth cohort data. We conducted a 14-year follow-up of infants born to pregnant women who had undergone maternal examinations at nine selected maternity hospitals across Taiwan during 2000-2005. The subjects were interviewed using structured questionnaires to record data on basic demographics, socioeconomic status, lifestyle, medical history, and 24-h dietary recall. Hourly concentrations of air pollutants within 1 year before childbirth were obtained from 76 national air quality monitoring stations in Taiwan. We utilized weighted K-nearest neighbour method (k = 3) to infer the personalized air pollution exposure. Machine learning methods were performed on the heterogeneous attributes set to predict allergic diseases in children. A total of 1439 mother-infant pairs were recruited in machine learning analysis. The prevalence of AD and AR in children up to 14 years of age were 6.8% and 15.9%, respectively.  https://www.selleckchem.com/products/peg400.html  Overall, tree-based models achieved higher sensitivity and specificity than other methods, with areas under receiver operating characteristic curve of 83% for AD and 84% for AR, respectively. Our findings confirmed that prenatal air quality is an important factor affecting the predictive ability. Moreover, different air quality indices were better predicted, in combination than separately. Combining heterogeneous attributes including environmental exposures, demographic information, and allergens is the key to a better prediction of children allergies in the general population. Prenatal exposure to nitrogen dioxide (NO2) and its concatenation changes with time were significant predictors for AD and AR till adolescent.Lignin modifying enzymes from fungi and bacteria are potential biocatalysts for sustainable mitigation of different potentially toxic pollutants in wastewater. Notably, the paper and pulp industry generates enormous amounts of wastewater containing high amounts of complex lignin-derived chlorinated phenolics and sulfonated pollutants. The presence of these compounds in wastewater is a critical issue from environmental and toxicological perspectives. Some chloro-phenols are harmful to the environment and human health, as they exert carcinogenic, mutagenic, cytotoxic, and endocrine-disrupting effects. In order to address these most urgent concerns, the use of oxidative lignin modifying enzymes for bioremediation has come into focus. These enzymes catalyze modification of phenolic and non-phenolic lignin-derived substances, and include laccase and a range of peroxidases, specifically lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), and dye-decolorizing peroxidase (DyP). In this review, we explore the key pollutant-generating steps in paper and pulp processing, summarize the most recently reported toxicological effects of industrial lignin-derived phenolic compounds, especially chlorinated phenolic pollutants, and outline bioremediation approaches for pollutant mitigation in wastewater from this industry, emphasizing the oxidative catalytic potential of oxidative lignin modifying enzymes in this regard. We highlight other emerging biotechnical approaches, including phytobioremediation, bioaugmentation, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based technology, protein engineering, and degradation pathways prediction, that are currently gathering momentum for the mitigation of wastewater pollutants. Finally, we address current research needs and options for maximizing sustainable biobased and biocatalytic degradation of toxic industrial wastewater pollutants.Prenatal exposure to di(2-ethylhexyl) phthalate (DEHP) may cause adverse health outcomes. However, trimester-specific impacts of DEHP exposure on offspring growth from fetal to early childhood stage have not been thoroughly evaluated. In this study, participants who provided a full series of urine specimens at three trimesters were selected from a birth cohort conducted at Wuhan, China from 2014 to 2015. 814 mother-offspring pairs were included in the study. Urinary concentrations of DEHP metabolites were determined using liquid chromatography-tandem mass spectrometry. Z-scores for ultrasound-measured fetal growth parameters at 14.0-18.9, 22.6-27.0, and 29.0-33.9 weeks of gestation, were calculated. Weight, height, and body mass index (BMI) at 6, 12, and 24 months were standardized to z-scores using sex-specific and age-specific WHO child growth standards. Linear regressions with generalized estimating equations were used to assess the relationships of DEHP levels per trimester to fetal growth, birth size, and growth at 6, 12, and 24 months to explore the trimester-specific impacts of DEHP exposure on offspring development.