Nitrate transport velocity data in the global unsaturated zones

Nitrate pollution in groundwater, which is an international problem, threatens human health and the environment. It could take decades for nitrate to transport in the groundwater system. When understanding the impacts of this nitrate legacy on water quality, the nitrate transport velocity (vN) in the unsaturated zone (USZ) is of great significance. Although some local USZ vN data measured or simulated are available, there has been no such a dataset at the global scale. Here, we present a Global-scale unsaturated zone Nitrate transport Velocity dataset (GNV) generated from a Nitrate Time Bomb (NTB) model using global permeability and porosity and global average annual groundwater recharge data. To evaluate GNV, a baseline dataset of USZ vN was created using locally measured data and global lithological data. The results show that 94.50% of GNV match the baseline USZ vN dataset. This dataset will largely contribute to research advancement in the nitrate legacy in the groundwater system, provide evidence for managing nitrate water pollution, and promote international and interdisciplinary collaborations

The role of miRNAs in Behçet’s disease

The symptoms of Behçet’s disease (BD), a multisystemic condition with autoimmune and inflammation as hallmarks, include arthritis, recurring oral and vaginal ulcers, skin rashes and lesions, and involvement of the nervous, gastrointestinal, and vascular systems. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), may be important regulators of inflammation and autoimmune disease. These ncRNAs are essential to the physiological and pathophysiological disease course, and miRNA in particular has received significant attention for its role and function in BD and its potential use as a diagnostic biomarker in recent years. Although promising as therapeutic targets, miRNAs must be studied further to fully comprehend how miRNAs in BD act biologically

Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning

Groundwater nitrate contamination poses a potential threat to human health and environmental safety globally. This study proposes an interpretable stacking ensemble learning (SEL) framework for enhancing and interpreting groundwater nitrate spatial predictions by integrating the two-level heterogeneous SEL model and SHapley Additive exPlanations (SHAP). In the SEL model, five commonly used machine learning models were utilized as base models (gradient boosting decision tree, extreme gradient boosting, random forest, extremely randomized trees, and k-nearest neighbor), whose outputs were taken as input data for the meta-model. When applied to the agricultural intensive area, the Eden Valley in the UK, the SEL model outperformed the individual models in predictive performance and generalization ability. It reveals a mean groundwater nitrate level of 2.22 mg/L-N, with 2.46% of sandstone aquifers exceeding the drinking standard of 11.3 mg/L-N. Alarmingly, 8.74% of areas with high groundwater nitrate remain outside the designated nitrate vulnerable zones. Moreover, SHAP identified that transmissivity, baseflow index, hydraulic conductivity, the percentage of arable land, and the C:N ratio in the soil were the top five key driving factors of groundwater nitrate. With nitrate threatening groundwater globally, this study presents a high-accuracy, interpretable, and flexible modeling framework that enhances our understanding of the mechanisms behind groundwater nitrate contamination. It implies that the interpretable SEL framework has great promise for providing valuable evidence for environmental management, water resource protection, and sustainable development, particularly in the data-scarce area

UK’s experiences of using numerical modelling to help sustainably handle agricultural diffuse water pollution

Nitrate water pollution, which is mainly caused by agricultural activities, remains an international problem. Too much nitrate in water bodies can cause serious long-term environmental issues and threaten both economic and human health. This presentation introduces the experiences of using numerical modelling to help handle nitrate water pollution in the United Kingdom. Since it is critical to prevent diffuse water pollution from agricultural sources, this talk also covers the ongoing efforts of adopting innovative modelling technologies to help improve water and fertiliser efficiencies and hence farmers’ income, while reducing agricultural activities' adverse impacts on the water environment

Evaluating the Utility of <i>Simplicillium lanosoniveum</i>, a Hyperparasitic Fungus of <i>Puccinia graminis</i> f. sp. <i>tritici</i>, as a Biological Control Agent against Wheat Stem Rust

Wheat stem rust is one of the wheat diseases caused by Puccinia graminis Pers. f. sp. tritici (Pgt). This disease has been responsible for major losses to wheat production worldwide. Currently used methods for controlling this disease include fungicides, the breeding of stem rust-resistant cultivars, and preventive agricultural measures. However, the excessive use of fungicides can have various deleterious effects on the environment. A hyperparasitic fungus with white mycelia and oval conidia, Simplicillium lanosoniveum, was isolated from the urediniospores of Pgt. When Pgt-infected wheat leaves were inoculation with isolates of S. lanosoniveum, it was found that S. lanosoniveum inoculation inhibited the production and germination of urediniospores, suggesting that S. lanosoniveum could inhibit the growth and spread of Pgt. Scanning electron microscopy revealed that S. lanosoniveum could inactivate the urediniospores by inducing structural damage. Overall, findings indicate that S. lanosoniveum might provide an effective biological agent for the control of Pgt

The α Adrenoceptor Agonist and Sedative/Anaesthetic Dexmedetomidine Excites Diverse Neuronal Types in the Ventrolateral Preoptic Area of Male Mice

The unique sedative activities with rapid arousal of dexmedetomidine (Dex) are not fully understood. Growing evidence suggests the involvement of the ventrolateral preoptic area (VLPO) in sleep–wake cycle. The major type in the VLPO is sleep-active neurons, inhibited by noradrenaline (NA(−) neurons). The other type of neurons is activated by NA (NA(+) neurons), which are wake-active. Previous research showed that Dex-induced sedation and sleep homeostasis likely share common mechanisms. To explore the underlying mechanisms of Dex in the VLPO, in vivo polysomnography recording and in vitro electrophysiological recording were used in our study. Bath application of Dex (2 μM) increased the firing rate of both VLPO NA(−) and NA(+) neurons. Compared to the control group, there was no difference in the firing rate of both VLPO NA(−) and NA(+) neurons after Dex (2 μM) and RS79948 (1 mM) administration, an α 2 receptor antagonist. No difference was detected regarding resting membrane potential (RMP) amplitude of both VLPO NA (−) and NA(+) neurons after application of Dex (2 μM). Moreover, Dex (2 μM) significantly reduced the frequency of miniature inhibitory postsynaptic currents (mIPSCs) in both VLPO NA(−) and NA(+) neurons. These electrophysiology results were consistent with behavioral sedation, with increased nonrapid eye movement sleep (NREM sleep) and increased expression of c-Fos in the VLPO during the dark phase after intraperitoneal injection with Dex (80 μg/kg). In conclusion, Dex activates NA(−) and NA(+) neurons in the VLPO via presynaptic α 2 receptors. This mechanism may explain the unique sedative properties with rapid arousal. Summary Statement Dexmedetomidine is an important ICU sedative. The mechanism of dexmedetomidine is not fully understood. Activating NA(−) and NA(+) neurons in the VLPO by dexmedetomidine using polysomnography and electrophysiological recording, this may explain the unique sedative properties with rapid arousal

ER-α36 mediates cisplatin resistance in breast cancer cells through EGFR/HER-2/ERK signaling pathway

Abstract Background ER-α36, a novel ER-α66 variant, has been demonstrated to promote tamoxifen resistance in breast cancer cells. However, the role and mechanisms of ER-α36 in cisplatin resistance of breast cancer cells remain unclear. This study investigates the expression and role of ER-α36 in cisplatin resistance of breast cancer cells and elucidates its underlying mechanisms. Methods The expression of ER-α36 and the proteins involved in nongenomic estrogen signaling was evaluated by western blot analysis. Cisplatin sensitivity was explored by CCK-8 assay, monolayer colony formation assay and apoptosis assays, respectively. ER-α36 siRNAs/shRNAs and overexpression vector were transfected into cells to down-regulate or up-regulate ER-α36 expression. Loss-and gain-of function assays were performed to investigate the role of ER-α36 in cisplatin sensitivity. The interaction between ER-α36 and EGFR/HER-2 were detected using CoIP. A mouse xenograft model of breast cancer was established to verify the role of ER-α36 in vivo. Results ER-α36 is expressed at higher levels in cisplatin-resistant breast cancer cells compared to cisplatin sensitive cells. Cisplatin induced up-regulation of ER-α36 in a dose-dependent manner in breast cancer cells. Overexpression of ER-α36 leaded to cell resistant to cisplatin and knockdown of ER-α36 in cisplatin-resistant breast cancer cells restored cisplatin sensitivity. The up-regulation of ER-α36 resulted in increased activation of nongenomic estrogen signaling, which was responsible for cisplatin resistance. Disruption of ER-α36-mediated nongenomic estrogen signaling with kinase inhibitors significantly inhibited cisplatin-induced expression of ER-α36 and increased cisplatin sensitivity. The in vivo experiment also confirmed that up-regulation of ER-α36 attenuated cisplatin sensitivity in a mouse xenograft model of breast cancer. Conclusions The results for the first time demonstrated that ER-α36 mediates cisplatin resistance in breast cancer cells through nongenomic estrogen signaling, suggesting that ER-α36 may serve as a novel target for cisplatin resistance and a potential indicator of cisplatin sensitivity in breast cancer treatment

The potential impacts of future climate and land-use changes on groundwater nitrates in England & Wales

Nitrate water pollution, which is mainly caused by excess nitrogen fertiliser application on agricultural lands, remains an international problem. To tackle this issue, several methods, e.g. the nitrate-time-bomb (NTB) modelling, have been undertaken to analyse the trend of groundwater nitrate concentration to support the UK government’s policymaking in handling nitrate water pollution. However, it is also important to consider the influencing factors such as land-use change and climate change when estimating future trends in nitrate leaching to groundwater of Great Britain (GB). Therefore, it is timely and necessary to develop new methods to answer the crucial question: how would future land-use change and climate change affect the groundwater nitrates of GB? A national-scale model, called LUC-NIF, has been developed in this study to implement the complicated principles of the European four land-use-change scenarios in GB. After the model calibration, validation and sensitivity analysis, the spatio-temporal land-use maps and nitrate leaching maps under the four scenarios have been generated for GB (2018–2080). The UK Climate Projections (UKCP) which provides projections of climate change in the UK were processed and adapted to produce eleven scenarios of Future Flow Climate data (11 RCM runs). The rainfall and potential evapotranspiration (1km by 1km) from 11 RCM runs were fed into a national groundwater recharge model to generate groundwater recharge data under climate change. The NTB model, which simulates the nitrate transport in the unsaturated zones and aquifers, was integrated into the LUC-NIF and the hydrological model to simulate the groundwater nitrate concentrations in 28 major aquifers in England & Wales under the projected 4 land-use change and 11 climate-change scenarios. The results show that the total leached nitrate from the soils will have declining trends from 2018 under all the scenarios but with variant decreasing rates, and will reach the historical levels of 1942–1954 by 2080. The average groundwater nitrate concentrations in 28 aquifer zones reach their own peak values at different years before starting to decrease. The results produced in this research can help people get a glimpse into the likely nitrate-leaching and groundwater nitrate trends in GB, however, the limitations of this research should be considered when using these results

Data on hematological parameters and generalized severe periodontitis in the United States

This article describes an ensemble of datasets used to understand the relationship between generalized severe periodontitis and hematological parameters. This dataset combines public periodontal examination data, hematological parameters data, and demographic data from the National Center for Health Statistics from 2009 to 2014. The stage of periodontitis was identified by attachment loss conducted by dental examiners, who were dentists (D.D.S./ D.M.D.) licensed in at least one U.S. state, while matching current classification criteria from the American Academy of Periodontology and the European Federation of Periodontology. Based on the NHANES database, information on age, gender, education level (< 9th grade, 9–11th grade, high school, college, graduate), race/ethnicity (Mexican American, Hispanic, non-Hispanic White, non-Hispanic Black, and other races), PIR (poverty income ratio) were acquired from the demographic data. Hematological parameters (including HB, HCT, mean cell volume, mean cell hemoglobin, mean cell hemoglobin concentration, red cell distribution width, platelet count, mean platelet volume, and red blood cell count) and glucose data had been obtained from laboratory data. Smoking data had been obtained from questionnaire data