ROR-γ drives androgen receptor expression and represents a therapeutic target in castration-resistant prostate cancer.

The androgen receptor (AR) is overexpressed and hyperactivated in human castration-resistant prostate cancer (CRPC). However, the determinants of AR overexpression in CRPC are poorly defined. Here we show that retinoic acid receptor-related orphan receptor γ (ROR-γ) is overexpressed and amplified in metastatic CRPC tumors, and that ROR-γ drives AR expression in the tumors. ROR-γ recruits nuclear receptor coactivator 1 and 3 (NCOA1 and NCOA3, also known as SRC-1 and SRC-3) to an AR-ROR response element (RORE) to stimulate AR gene transcription. ROR-γ antagonists suppress the expression of both AR and its variant AR-V7 in prostate cancer (PCa) cell lines and tumors. ROR-γ antagonists also markedly diminish genome-wide AR binding, H3K27ac abundance and expression of the AR target gene network. Finally, ROR-γ antagonists suppressed tumor growth in multiple AR-expressing, but not AR-negative, xenograft PCa models, and they effectively sensitized CRPC tumors to enzalutamide, without overt toxicity, in mice. Taken together, these results establish ROR-γ as a key player in CRPC by acting upstream of AR and as a potential therapeutic target for advanced PCa

Global patterns of woody residence time and its influence on model simulation of aboveground biomass

Woody residence time (τw) is an important parameter that expresses the balance between mature forest recruitment/growth and mortality. Using field data collected from the literature, this study explored the global forest τw and investigated its influence on model simulations of aboveground biomass (AGB) at a global scale. Specifically, τw was found to be related to forest age, annual temperature, and precipitation at a global scale, but its determinants were different among various plant function types. The estimated global forest τw based on the filed data showed large spatial heterogeneity, which plays an important role in model simulation of AGB by a dynamic global vegetation model (DGVM). The τw could change the resulting AGB in tenfold based on a site-level test using the Monte Carlo method. At the global level, different parameterization schemes of the Integrated Biosphere Simulator using the estimated τw resulted in a twofold change in the AGB simulation for 2100. Our results highlight the influences of various biotic and abiotic variables on forest τw. The estimation of τw in our study may help improve the model simulations and reduce the parameter\u27s uncertainty over the projection of future AGB in the current DGVM or Earth System Models. A clearer understanding of the responses of τw to climate change and the corresponding sophisticated description of forest growth/mortality in model structure is also needed for the improvement of carbon stock prediction in future studies

Relationship Between Outdoor Air Pollutant Exposure and Premature Delivery in China- Systematic Review and Meta-Analysis

Objective: Preterm birth (PTB) is considered as a public health problem and one of the main risk factors related to the global disease burden. The purpose of this study aims to explore the influence of exposure to major air pollutants at different pregnancies on PTB.Methods: The relationship between air pollutants and PTB in China was collected from cohort studies and case-control studies published before 30 April 2022. Meta-analysis was carried out with STATA 15.0 software.Results: A total of 2,115 papers were retrieved, of which 18 papers met the inclusion criteria. The comprehensive effect of pollutant exposure and PTB were calculated. PM2.5 during entire pregnancy and O3 exposure during third trimester were positively associated with preterm birth. Every 10 μg/m3 increase in the average concentration of PM2.5 during the whole pregnancy will increase the risk of premature delivery by 4%, and every 10 μg/m3 increase in the average concentration of O3 in the third trimester will increase the risk of premature delivery by 1%.Conclusion: Exposure to PM2.5 entire prenatal pregnancy and O3 in third trimester is associated with an increased risk of preterm birth occurrence

Tumor-Microenvironment-Activatable Nanoreactor Based on a Polyprodrug for Multimodal-Imaging-Medicated Enhanced Cancer Chemo/Phototherapy.

Anticancer nanomedicine-based multimodal imaging and synergistic therapy hold great promise in cancer diagnosis and therapy owing to their abilities to improve therapeutic efficiency and reduce unnecessary side effects, producing promising clinical prospects. Herein, we integrated chemotherapeutic drug camptothecin (CPT) and near-infrared-absorbing new indocyanine green (IR820) into a single system by charge interaction and obtained a tumor-microenvironment-activatable PCPTSS/IR820 nanoreactor to perform thermal/fluorescence/photoacoustic-imaging-guided chemotherapy and photothermal therapy simultaneously. Specifically, the generated PCPTSS/IR820 showed an excellent therapeutic agent loading content and size stability, and the trials in vitro and in vivo suggested that the smart PCPTSS/IR820 could deeply permeate into tumor tissues due to its suitable micellar size. Upon near-infrared laser irradiation, the nanoreactor further produced a terrific synergism of chemo-photo treatment for cancer therapy. Therefore, the PCPTSS/IR820 polyprodrug-based nanoreactor holds outstanding promise for multimodal imaging and combined dual therapy

The tunable wettability in multistimuli-responsive smart graphene surfaces

The tunable wettability of smart graphene films onto stainless steel substrates with a multi-response to different environmental stimuli has been investigated including light irradiation, pH, electric field, and annealing temperature. Conductive graphene film exhibited the controllable transition from water-repellent to water-loving characteristic in response to different environment fields, which primarily resulted from the morpho-chemically synergistic effect as well as the restoration of electronic stucture. Based on the fundamental theories of wettability, mechanisms in switching from hydrophobicity to hydrophilicity for smart graphene surface including thermal chemistry, electrostatic, photo-induced surface chemistry, solvent, and pH methods were presented

Diallyl Trisulfide, the Antifungal Component of Garlic Essential Oil and the Bioactivity of Its Nanoemulsions Formed by Spontaneous Emulsification

The aim of this study was to evaluate the chemical compounds of garlic essential oil (EO), and determine the antifungal efficacy of garlic EO and its major components, diallyl trisulfide and its nanoemulsions against wood-rotting fungi, Trametes hirsuta and Laetiporus sulphureus. GC-MS analysis revealed that the major constituents of garlic EO were diallyl trisulfide (39.79%), diallyl disulfide (32.91%), and diallyl sulfide (7.02%). In antifungal activity, the IC50 value of garlic EO against T. hirsuta and L. sulphureus were 137.3 and 44.6 μg/mL, respectively. Results from the antifungal tests demonstrated that the three major constituents were shown to have good antifungal activity, in which, diallyl trisulfide was the most effective against T. hirsuta and L. sulphureus, with the IC50 values of 56.1 and 31.6 μg/mL, respectively. The diallyl trisulfide nanoemulsions showed high antifungal efficacy against the examined wood-rotting fungi, and as the amount of diallyl trisulfide in the lipid phase increases, the antifungal efficacy of the nanoemulsions increases. These results showed that the nanoemulsions and normal emulsion of diallyl trisulfide have potential to develop into a natural wood preservative

Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas

Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved

Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas

Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved

The sustainable production of succinic anhydride from renewable biomass

Summary: The selective production of C4 bulk chemicals from biomass is significant to replace the traditional method from petroleum resource. In this work, the succinic anhydride (SAN) is directly prepared from bio-based furanic platform compounds utilizing the visible light-induced oxygenation process, in which m-tetraphenyl porphyrin (H2TPP) and molecular oxygen was employed as the photocatalyst and terminal oxidant, respectively. Under optimal conditions, a 99.9% conversion with 97.8% selectivity of SAN was obtained from furoic acid (FAC) at room temperature. Moreover, the transformation of furfural and furfuryl alcohol with this system can also generate SAN, and the product selectivity is controllable by tuning light intensity and time. Furthermore, the EPR detection, isotope labeling, and control experiments exhibited that the generation of singlet oxygen plays a crucial role and 5-hydroxy-2(5H)-furanone is the main intermediate during the reaction. Finally, a possible reaction mechanism for the production of SAN from furanic compound is proposed