Tree architecture is an important component of forest community dynamics - taller trees with larger crowns often outcompete their neighbors, but they are generally at higher risk of wind-induced damage. Yet, we know little about wind impacts on tree architecture in natural forest settings, especially in complex tropical forests. Here, we use airborne light detection and ranging (LiDAR) and 30 yr of forest inventory data in Puerto Rico to ask whether and how chronic winds alter tree architecture. We randomly sampled 124 canopy individuals of four dominant tree species (n = 22-39). For each individual, we measured slenderness (height/stem diameter) and crown area (m2) and evaluated whether exposure to chronic winds impacted architecture after accounting for topography (curvature, elevation, slope, and soil wetness) and neighborhood variables (crowding and previous hurricane damage). We then estimated the mechanical wind vulnerability of trees. Three of four species grew significantly shorter (2-4 m) and had smaller crown areas in sites exposed to chronic winds. A short-lived pioneer species, by contrast, showed no evidence of wind-induced changes. We found that three species' architectural acclimation to chronic winds resulted in reduced vulnerability. Our findings demonstrate that exposure to chronic, nonstorm winds can lead to architectural changes in tropical trees, reducing height and crown areas. La arquitectura de los & aacute;rboles es un componente importante de la din & aacute;mica de la comunidad forestal: los & aacute;rboles m & aacute;s altos con copas m & aacute;s grandes suelen sobrepasar a sus vecinos, pero por lo general corren m & aacute;s riesgo de sufrir da & ntilde;os inducidos por el viento. Sin embargo, es poco lo que se sabe sobre el impacto del viento en la arquitectura de los & aacute;rboles en entornos forestales naturales, sobre todo en bosques tropicales complejos. En este caso, utilizamos LiDAR y 30 a & ntilde;os de datos de campo en Puerto Rico para preguntarnos si los vientos cr & oacute;nicos alteran la arquitectura de los & aacute;rboles. Se tom & oacute; una muestra aleatoria de 124 individuos del dosel de cuatro especies arb & oacute;reas dominantes (n = 22-39). De cada individuo, medimos la esbeltez (altura/di & aacute;metro) y el & aacute;rea de la copa (m2) y evaluamos si la exposici & oacute;n a vientos cr & oacute;nicos influ & iacute;a en la arquitectura teniendo en cuenta la topograf & iacute;a (curvatura, elevaci & oacute;n, pendiente, humedad del suelo) y las variables del vecindario (aglomeraci & oacute;n y da & ntilde;os previos por huracanes). Luego, estimamos la vulnerabilidad mec & aacute;nica de los & aacute;rboles al viento. En los lugares expuestos a vientos cr & oacute;nicos, tres de las cuatro especies crecieron mucho menos (2-4 m) y tuvieron & aacute;reas de copa m & aacute;s peque & ntilde;as. Cecropia schreberiana, en cambio, no mostr & oacute; indicios de cambios inducidos por el viento. La aclimataci & oacute;n arquitect & oacute;nica de tres especies a los vientos cr & oacute;nicos llevaba a una reducci & oacute;n de la vulnerabilidad. Nuestros hallazgos demuestran que la exposici & oacute;n a vientos cr & oacute;nicos puede provocar cambios arquitect & oacute;nicos en los & aacute;rboles tropicales, reduciendo su altura y la superficie de sus copas.

Self-consolidating earth concrete (SCEC) addresses the long construction process of conventional earthen constructions and their structural limitations, while further efforts are needed to enhance its sustainability. This study explores the development of a kaolinite-based self-consolidating earth paste (SCEP) due to their blended powder system, incorporating raw and treated (calcined and ground-calcined) kaolinite under various activation techniques, such as water hydration, sodium hexametaphosphate (NaHMP), and sodium hydroxide (NaOH) activation. The synergistic effect of calcination and mechanosynthesis on rheological, mechanical, structural, and microstructural properties of SCEP were investigated. Mechanically treated kaolinite increased yield stress, plastic viscosity, storage modulus evolution, and build-up index, while delayed the strength development compared to the calcined kaolinite samples. Among the investigated activators, NaOH resulted in more promising structural build-up, storage modulus, and compressive strength development. These findings were elaborated with X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), calorimetry, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM).

Soil microplastics (MPs) are a substantial threat to soil health, particularly by disrupting soil aggregation. Additionally, MPs undergo aging processes in the soil, which may significantly alter their long-term impacts on soil structure. To investigate these effects, we conducted an eight-month soil incubation experiment, examining the influence of MPs and their aging on soil aggregation. The experiment utilized a factorial design with various combinations of MPs and biochar additions: 1% by weight of 1000-mesh polyethylene and polypropylene MPs, and 5-mm biochar, resulting in six treatment groups: [CK], [PE], [PP], [Biochar], [PE + biochar], and [PP + biochar]. Our findings revealed that both MPs and biochar underwent aging throughout the incubation, evidenced by the formation of oxygen-containing functional groups on their surfaces. Microplastics, particularly polyethylene, primarily affected the 0.5-1 mm and >2 mm aggregate fractions, with average reductions of 21% and 77%, respectively. These adverse effects intensified with the aging of MPs. Contrary to expectations, the addition of biochar was found to exacerbate the negative impacts of MPs on the 0.25-0.5 mm aggregates, with a decrease of 11% associated with PE MPs. The influence of biochar on mitigating the damage caused by MPs to soil aggregation is dependent on aggregate size.

Cadmium (Cd) is an environmental non-biodegradable pollutant that induces toxic effects in humans. Therefore, there is a pressing need to identify new methods to relieve cadmium-induced toxic damage. In this study, Weizmannia coagulans (formerly termed Bacillus coagulans) LBK, which was isolated from silage feed, exhibited robust Cd tolerance and adsorption capabilities. In vitro experiments demonstrated that its scavenging rate for 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH) and hydroxyl radicals was 40% and 39%, respectively. In vivo, LBK significantly reduced the mortality rate of cadmium-exposed mice. Moreover, LBK increased the hepatic levels of superoxide dismutase (SOD) and catalase (CAT), and histopathology examination suggested that LBK could attenuate liver damage. W. coagulans LBK significantly altered the composition of the intestinal microbiota and increased the abundance of beneficial bacteria such as Leptospirillaceae and Lactobacillus. Metabolomics analysis of cecal contents revealed that LBK regulated amino acid metabolic disorders caused by Cd exposure and restored the levels of glutamic acid, leucine, and aspartic acid. Based on the aforementioned advantages, W. coagulans LBK may be considered a promising candidate for alleviating oxidative stress caused by acute Cd exposure.

The loss of nitrogen in soil damages the environment. Clarifying the mechanism of ammonium nitrogen (NH4+-N) transport in soil and increasing the fixation of NH4+-N after N application are effective methods for improving N use efficiency. However, the main factors are not easily identified because of the complicated transport and retardation factors in different soils. This study employed machine learning (ML) to identify the main influencing factors that contribute to the retardation factor (Rf) of NH4+-N in soil. First, NH4+-N transport in the soil was investigated using column experiments and a transport model. The Rf (1.29 - 17.42) was calculated and used as a proxy for the efficacy of NH4+-N transport. Second, the physicochemical parameters of the soil were determined and screened using lasso and ridge regressions as inputs for the ML model. Third, six machine learning models were evaluated: Adaptive Boosting, Extreme Gradient Boosting (XGB), Random Forest, Gradient Boosting Regression, Multilayer Perceptron, and Support Vector Regression. The optimal ML model of the XGB model with a low mean absolute error (0.81), mean squared error (0.50), and high test r(2) (0.97) was obtained by random sampling and five-fold cross-validation. Finally, SHapely Additive exPlanations, entropy-based feature importance, and permutation characteristic importance were used for global interpretation. The cation exchange capacity (CEC), total organic carbon (TOC), and Kaolin had the greatest effects on NH4+-N transport in the soil. The accumulated local effect offered a fundamental insight: When CEC > 6 cmol(+) kg(-1), and TOC > 40 g kg(-1), the maximum resistance to NH4+-N transport within the soil was observed. This study provides a novel approach for predicting the impact of the soil environment on NH4+-N transport and guiding the establishment of an early-warning system of nutrient loss.