Spatio-temporal microbial regulation of aggregate-associated priming effects under contrasting tillage practices.

Tillage intensity significantly influences the heterogeneous distribution and dynamic changes of soil microorganisms, consequently shaping spatio-temporal patterns of SOC decomposition. However, little is known about the microbial mechanisms by which tillage intensity regulates the priming effect (PE) dynamics in heterogeneous spatial environments such as aggregates. Herein, a microcosm experiment was established by adding 13C-labeled straw residue to three distinct aggregate-size classes (i.e., mega-, macro-, and micro-aggregates) from two long-term contrasting tillage histories (no-till [NT] and conventional plow tillage [CT]) for 160 days to observe the spatio-temporal variations in PE. Metagenomic sequencing and Fourier transform mid-infrared techniques were used to assess the relative importance of C-degrading functional genes, microbial community succession, and SOC chemical composition in the aggregate-associated PE dynamics during straw decomposition. Spatially, straw addition induced a positive PE for all aggregates, with stronger PE occurring in larger aggregates, especially in CT soil compared to NT soil. Larger aggregates have more unique microbial communities enriched in genes for simple C degradation (e.g., E5.1.3.6, E2.4.1.7, pmm-pgm, and KduD in Nitrosospeera and Burkholderia), contributing to the higher short-term PE; however, CT soils harbored more genes for complex C degradation (e.g., TSTA3, fcl, pmm-pgm, and K06871 in Gammaproteobacteria and Phycicoccus), supporting a stronger long-term PE. Temporally, soil aggregates played a significant role in the early-stage PEs (i.e., < 59 days after residue addition) through co-metabolism and nitrogen (N) mining, as evidenced by the increased microbial biomass C and dissolved organic C (DOC) and reduced inorganic N with increasing aggregate-size class. At a later stage, however, the legacy effect of tillage histories controlled the PEs via microbial stoichiometry decomposition, as suggested by the higher DOC-to-inorganic N and DOC-to-available P stoichiometries in CT than NT. Our study underscores the importance of incorporating both spatial and temporal microbial dynamics for a comprehensive understanding of the mechanisms underlying SOC priming, especially in the context of long-term contrasting tillage practices.

Global pattern of soil priming effect intensity and its environmental drivers.

The microbial priming effect-the decomposition of soil organic carbon (SOC) induced by plant inputs-has long been considered an important driver of SOC dynamics, yet we have limited understanding about the direction, intensity, and drivers of priming across ecosystem types and biomes. This gap hinders our ability to predict how shifts in litter inputs under global change can affect climate feedbacks. Here, we synthesized 18,919 observations of CO2 effluxes in 802 soils across the globe to test the relative effects (i.e., log response ratio [RR]) of litter additions on native SOC decomposition and identified the dominant environmental drivers in natural ecosystems and agricultural lands. Globally, litter additions enhanced native SOC decomposition (RR = 0.35, 95% CI: 0.32-0.38), with greater priming effects occurring with decreasing latitude and more in agricultural soils (RR = 0.43) than in uncultivated soils (RR = 0.28). In natural ecosystems, soil pH and microbial community composition (e.g., bacteria: fungi ratio) were the best predictors of priming, with greater effects occurring in acidic, bacteria-dominated sandy soils. In contrast, the substrate properties of plant litter and soils were the most important drivers of priming in agricultural systems since soils with high C:N ratios and those receiving large inputs of low-quality litter had the highest priming effects. Collectively, our results suggest that, though different factors may control priming effects, the ubiquitous nature of priming means that alterations of litter quality and quantity owing to global changes will likely have consequences for global C cycling and climate forcing.

Generation of the induced pluripotent stem cell line (ZSPHARi001-A) from a patient with recessive dystrophic epidermolysis bullosa carrying compound heterozygous mutation in the COL7A1 gene.

The COL7A1 gene mutation causes type VII collagen dysfunction, which subsequently leads to recessive dystrophic epidermolysis bullosa (RDEB). Patients who suffer from RDEB experience severe blisters and chronic trauma, which can eventually result in serious infection and the development of fatal squamous cell carcinoma. In our study, peripheral blood mononuclear cells (PBMCs) from an RDEB patient with the COL7A1 compound heterozygous mutation were collected and then reprogrammed into induced pluripotent stem cells (iPSC). The RDEB iPSC line can provide a cellular resource for the study of pathogenesis and drug screening.

Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy.

Background: In recent years, deep learning has been widely used in a variety of ophthalmic diseases. As a common ophthalmic disease, meibomian gland dysfunction (MGD) has a unique phenotype in in-vivo laser confocal microscope imaging (VLCMI). The purpose of our study was to investigate a deep learning algorithm to differentiate and classify obstructive MGD (OMGD), atrophic MGD (AMGD) and normal groups. Methods: In this study, a multi-layer deep convolution neural network (CNN) was trained using VLCMI from OMGD, AMGD and healthy subjects as verified by medical experts. The automatic differential diagnosis of OMGD, AMGD and healthy people was tested by comparing its image-based identification of each group with the medical expert diagnosis. The CNN was trained and validated with 4,985 and 1,663 VLCMI images, respectively. By using established enhancement techniques, 1,663 untrained VLCMI images were tested. Results: In this study, we included 2,766 healthy control VLCMIs, 2,744 from OMGD and 2,801 from AMGD. Of the three models, differential diagnostic accuracy of the DenseNet169 CNN was highest at over 97%. The sensitivity and specificity of the DenseNet169 model for OMGD were 88.8 and 95.4%, respectively; and for AMGD 89.4 and 98.4%, respectively. Conclusion: This study described a deep learning algorithm to automatically check and classify VLCMI images of MGD. By optimizing the algorithm, the classifier model displayed excellent accuracy. With further development, this model may become an effective tool for the differential diagnosis of MGD.

NIR-Responsive Copolymer Upconversion Nanocomposites for Triggered Drug Release in Vitro and in Vivo.

Light has several advantages as the stimulus for a triggered drug release. Currently, the applications of phototriggered drug-release devices (PDDs) are largely limited by two factors: the limited tissue penetration and detrimental effects caused by excitation light (ultraviolet or visible light). To address this disadvantage, this study developed nanocomposites based on upconversion nanoparticles (UC), which could convert near-infrared light to ultraviolet-visible light and trigger drug release. By loading UC and doxorubicin (DOX) into photoresponsive copolymer PEG-NMAB-PLA (PNP), near-infrared responsive copolymer upconversion nanocomposites (PNP-DOX-UC) were constructed. We proved that PNP-DOX-UC showed the fast release and strong cytotoxicity under near-infrared irradiation in vitro. The therapeutic efficacy study indicated that PNP-DOX-UC+hv had the enhanced antitumor efficiency. In the study, UC becoming an internal ultraviolet-visible light source for near-infrared excitation develops an applicable and efficient approach to meet the requirements for UV/vis excitation, which is a major disadvantage in photosensitive materials developed for pharmaceutical and biomedical applications.

Association of interleukin-10 gene promoter polymorphisms with recurrent miscarriage: a meta-analysis.

PROBLEM: The association between IL-10 gene promoter polymorphisms and the risk of recurrent miscarriages (RM) remains controversial. The present meta-analysis was performed to derive a precise estimate of the relationship. METHOD OF STUDY: We searched the PubMed and EMBASE for studies related to the association between the IL-10 gene polymorphism and the risk of RM. Summary odds ratio (OR) with 95% confidence interval (CI) was estimated to assess the associations. RESULTS: Thirteen studies, including 2047 RM cases and 2055 control samples, were identified. The results showed a significant association between rs1800896, rs1800871 and RM risk (for rs1800896: GA+GG vs AA: OR=1.20, 95% CI=1.04-1.39, P=.02; for rs1800871: CT+TT vs CC: OR=1.64, 95% CI=1.13-2.36, P=.009). No evidence of association was noted between rs1800872 haplotype and RM risk. CONCLUSION: IL-10 gene promoter polymorphisms, rs1800896 and rs1800871, play a potential role in increasing the risk of RM.