Anatomical variation of mesophyll conductance due to salt stress in Populus cathayana females and males growing under different inorganic nitrogen sources

Synergistic regulation in leaf architecture and photosynthesis is essential for salt tolerance. However, how plant sex and inorganic nitrogen sources alter salt stress-dependent photosynthesis remains unknown. Leaf anatomical characteristics and photosynthesis of Populus cathayana Rehder females and males were investigated under salt stress conditions combined with nitrate NO3- and ammonium NH4+ supplies to clarify the underlying mechanisms. In salt-stressed females, we observed an increased mesophyll spongy cell density, a reduced chloroplast density, a decreased surface area of chloroplasts adjacent to the intercellular air space (S-c/S) and an increased mesophyll cell area per transverse section width (S/W), consequently causing mesophyll conductance (g(m)) and photosynthesis inhibition, especially under NH4+ supply. Conversely, males with a greater mesophyll palisade tissue thickness and chloroplast density, but a lower spongy cell density had lower S/W and higher S-c/S, and higher g(m) and photosynthesis. NH4+-fed females had a lower CO2 conductance through cell wall and stromal conductance perpendicular to the cell wall, but a higher chloroplast conductance from the cell wall (g(cyt1)) than females supplied with NO3-, whereas males had a higher chloroplast conductance and lower CO2 conductance through cell wall when supplied with NO3- instead of NH4+ under salt stress. These findings indicate sex-specific strategies in coping with salt stress related to leaf anatomy and g(m) under both types of nitrogen supplies, which may contribute to sex-specific CO2 capture and niche segregation.Peer reviewe

Patterns and Distributions of Urban Expansion in Global Watersheds

Abstract Understanding urban expansion at the watershed scale is important because watersheds are important carriers of ecological and environmental impacts. However, current analyses are mainly restricted to administrative units only. Here, we used a long‐term multitemporal data set of urban land to quantify the spatiotemporal trends in the extent and form of urban expansion from 1992 to 2016 in endorheic and exoreic watersheds, globally. Overall, urban expansion in 70% of watersheds (154/220) showed a decelerating trend. The average urban expansion speed of these watersheds in the last 6 years was approximately half of that in the last 24 years. Urban expansion speed in endorheic watersheds lagged behind the counterparts in exoreic watersheds, with the former approximately 1/4 of the latter. More importantly, the pattern of urban expansion in endorheic watersheds was following the low‐density and sprawling trend in exoreic watersheds, which could exert far‐reaching impacts on the sustainability of endorheic watersheds located in arid lands. These findings suggest the need to look beyond administrative city boundaries for land use planning and policies in the context of watershed management

Rodent hole detection in a typical steppe ecosystem using UAS and deep learning

IntroductionRodent outbreak is the main biological disaster in grassland ecosystems. Traditional rodent damage monitoring approaches mainly depend on costly field surveys, e.g., rodent trapping or hole counting. Integrating an unmanned aircraft system (UAS) image acquisition platform and deep learning (DL) provides a great opportunity to realize efficient large-scale rodent damage monitoring and early-stage diagnosis. As the major rodent species in Inner Mongolia, Brandt’s voles (BV) (Lasiopodomys brandtii) have markedly small holes, which are difficult to identify regarding various seasonal noises in this typical steppe ecosystem.MethodsIn this study, we proposed a novel UAS-DL-based framework for BV hole detection in two representative seasons. We also established the first bi-seasonal UAS image datasets for rodent hole detection. Three two-stage (Faster R-CNN, R-FCN, and Cascade R-CNN) and three one-stage (SSD, RetinaNet, and YOLOv4) object detection DL models were investigated from three perspectives: accuracy, running speed, and generalizability.ResultsExperimental results revealed that: 1) Faster R-CNN and YOLOv4 are the most accurate models; 2) SSD and YOLOv4 are the fastest; 3) Faster R-CNN and YOLOv4 have the most consistent performance across two different seasons.DiscussionThe integration of UAS and DL techniques was demonstrated to utilize automatic, accurate, and efficient BV hole detection in a typical steppe ecosystem. The proposed method has a great potential for large-scale multi-seasonal rodent damage monitoring

Surface Adsorption-Mediated Ultrahigh Efficient Peptide Encapsulation with a Precise Ratiometric Control for Type 1 and 2 Diabetic Therapy

A surface adsorption strategy is developed to enable the engineering of microcomposites featured with ultrahigh loading capacity and precise ratiometric control of co-encapsulated peptides. In this strategy, peptide molecules (insulin, exenatide, and bivalirudin) are formulated into nanoparticles and their surface is decorated with carrier polymers. This polymer layer blocks the phase transfer of peptide nanoparticles from oil to water and, consequently, realizes ultrahigh peptide loading degree (up to 78.9%). After surface decoration, all three nanoparticles are expected to exhibit the properties of adsorbed polymer materials, which enables the co-encapsulation of insulin, exenatide, and bivalirudin with a precise ratiometric control. After solidification of this adsorbed polymer layer, the release of peptides is synchronously prolonged. With the help of encapsulation, insulin achieves 8 days of glycemic control in type 1 diabetic rats with one single injection. The co-delivery of insulin and exenatide (1:1) efficiently controls the glycemic level in type 2 diabetic rats for 8 days. Weekly administration of insulin and exenatide co-encapsulated microcomposite effectively reduces the weight gain and glycosylated hemoglobin level in type 2 diabetic rats. The surface adsorption strategy sets a new paradigm to improve the pharmacokinetic and pharmacological performance of peptides, especially for the combination of peptides.Peer reviewe

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

BackgroundPeople usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.MethodsIn this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.ResultsThe final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.ConclusionIn this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction

Recombinant proteins A29L, M1R, A35R, and B6R vaccination protects mice from mpox virus challenge

Since May 2022, mutant strains of mpox (formerly monkeypox) virus (MPXV) have been rapidly spreading among individuals who have not traveled to endemic areas in multiple locations, including Europe and the United States. Both intracellular and extracellular forms of mpox virus have multiple outer membrane proteins that can stimulate immune response. Here, we investigated the immunogenicity of MPXV structural proteins such as A29L, M1R, A35R, and B6R as a combination vaccine, and the protective effect against the 2022 mpox mutant strain was also evaluated in BALB/c mice. After mixed 15 μg QS-21 adjuvant, all four virus structural proteins were administered subcutaneously to mice. Antibody titers in mouse sera rose sharply after the initial boost, along with an increased capacity of immune cells to produce IFN-γ alongside an elevated level of cellular immunity mediated by Th1 cells. The vaccine-induced neutralizing antibodies significantly inhibited the replication of MPXV in mice and reduced the pathological damage of organs. This study demonstrates the feasibility of a multiple recombinant vaccine for MPXV variant strains

In Vivo Delivery of Gremlin siRNA Plasmid Reveals Therapeutic Potential against Diabetic Nephropathy by Recovering Bone Morphogenetic Protein-7

Diabetic nephropathy is a complex and poorly understood disease process, and our current treatment options are limited. It remains critical, then, to identify novel therapeutic targets. Recently, a developmental protein and one of the bone morphogenetic protein antagonists, Gremlin, has emerged as a novel modulator of diabetic nephropathy. The high expression and strong co-localization with transforming growth factor- β1 in diabetic kidneys suggests a role for Gremlin in the pathogenesis of diabetic nephropathy. We have constructed a gremlin siRNA plasmid and have examined the effect of Gremlin inhibition on the progression of diabetic nephropathy in a mouse model. CD-1 mice underwent uninephrectomy and STZ treatment prior to receiving weekly injections of the plasmid. Inhibition of Gremlin alleviated proteinuria and renal collagen IV accumulation 12 weeks after the STZ injection and inhibited renal cell proliferation and apoptosis. In vitro experiments, using mouse mesangial cells, revealed that the transfect ion of gremlin siRNA plasmid reversed high glucose induced abnormalities, such as increased cell proliferation and apoptosis and increased collagen IV production. The decreased matrix metalloprotease level was partially normalized by transfection with gremlin siRNA plasmid. Additionally, we observed recovery of bone morphogenetic protein-7 signaling activity, evidenced by increases in phosphorylated Smad 5 protein levels. We conclude that inhibition of Gremlin exerts beneficial effects on the diabetic kidney mainly through maintenance of BMP-7 activity and that Gremlin may serve as a novel therapeutic target in the management of diabetic nephropathy

Arsenic Trioxide Exerts Antimyeloma Effects by Inhibiting Activity in the Cytoplasmic Substrates of Histone Deacetylase 6

Arsenic trioxide (As2O3) has shown remarkable efficacy for the treatment of multiple myeloma (MM). Histone deacetylases (HDAC) play an important role in the control of gene expression, and their dysregulation has been linked to myeloma. Especially, HDAC6, a unique cytoplasmic member of class II, which mainly functions as α-tubulin deacetylase and Hsp90 deacetylase, has become a target for drug development to treat cancer due to its major contribution in oncogenic cell transformation. However, the mechanisms of action for As2O3 have not yet been defined. In this study, we investigated the effect of As2O3 on proliferation and apoptosis in human myeloma cell line and primary myeloma cells, and then we studied that As2O3 exerts antimyeloma effects by inhibiting activity in the α-tubulin and Hsp90 through western blot analysis and immunoprecipitation. We found that As2O3 acts directly on MM cells at relatively low concentrations of 0.5∼2.5 µM, which effects survival and apoptosis of MM cells. However, As2O3 inhibited HDAC activity at the relatively high concentration and dose-dependent manner (great than 4 µM). Subsequently, we found that As2O3 treatment in a dose- and time-dependent fashion markedly increased the level of acetylated α-tubulin and acetylated Hsp90, and inhibited the chaperone association with IKKα activities and increased degradation of IKKα. Importantly, the loss of IKKα-associated Hsp90 occurred prior to any detectable loss in the levels of IKKα, indicating a novel pathway by which As2O3 down-regulates HDAC6 to destabilize IKKα protein via Hsp90 chaperone function. Furthermore, we observed the effect of As2O3 on TNF-α-induced NF-κB signaling pathway was to significantly reduced phosphorylation of Ser-536 on NF-κB p65. Therefore, our studies provide an important insight into the molecular mechanism of anti-myeloma activity of As2O3 in HDAC6-Hsp90-IKKα-NFκB signaling axis and the rationale for As2O3 can be extended readily using all the HDAC associated diseases