Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Fine mapping of a major QTL qHYF_B06 for peanut yield

High yield is a major objective for peanut(Arachis hypogaea L.) breeding worldwide. However, fewer yield-related quantitative trait loci(QTL) have been reported in peanut than in other staple food crops such as rice(Oryza sativa), wheat(Triticum aestivum), and maize(Zea mays). This study aimed to identify stable major-effect QTL associated with pod yield per plant, hundred-pod weight for double-seeded pods,hundred-seed weight, shelling percentage, and pod number per plant, allowing us to predict candidate genes by means of transcriptome and genome sequencing. To this end, we used a population of recombinant inbred lines comprising 192 F9:11families derived from a JH6 × KX01-6 cross to construct a highresolution genetic map(1705.7 c M) consisting of 2273 polymorphic SNPs, with 0.75 c M(on average)between adjacent SNPs. We identified two high-confidence, yield-related QTL, qHYF_A08 and qHYF_B06, explaining 5.78%–31.40% of phenotypic variation and with LOD values of 5.10–24.48, in six environments. qHYF_A08 mainly explained the variation in shelling percentage, whereas qHYF_B06explained variation in hundred-pod weight and hundred-seed weight and accounted for 8.77%–31.40%of the variation in effective pod number per plant, pod number per plant, and shelling percentage. We narrowed down qHYF_B06 to an 890-kb interval using an advanced mapping population.Transcriptome and genome analyses revealed that only Arahy.129FS0 and Arahy.3R9A5K in the candidate mapping interval were differentially expressed between JH6 and KX01-6, with substantial structural variations in their promoter and coding regions. Genotypes of 208 peanut accessions determined using a diagnostic CAPS marker suggested that the two haplotypes of Arahy.3R9A5K were highly associated with hundred-seed weight and hundred-pod weight;this diagnostic CAPs marker could therefore be useful for selecting high-yielding lines during peanut breeding. Overall, our results provide valuable information for cloning alleles with favorable effects on peanut yield.

An Auto-Grading Oriented Approach for Off-Line Handwritten Organic Cyclic Compound Structure Formulas Recognition

Auto-grading,as an instruction tool,could reduce teachers’workload,provide students with instant feedback and support highly personalized learning.Therefore,this topic attracts considerable attentions from researchers recently.To realize the automatic grading of handwritten chemistry assignments,the problem of chemical notations recognition should be solved first.The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered fromthe problem of lacking the accurate alignment information between the input and output.They serve the aim of reading notations into electrical devices to better prepare relevant edocuments instead of auto-grading handwritten assignments.To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level.In this work,we propose a component-detectionbased approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas(OCCSFs).Specifically,we define different components of OCCSFs as objects(including graphical objects and text objects),and adopt the deep learning detector to detect them.Then,regarding the detected text objects,we introduce an improved attention-based encoder-decoder model for text recognition.Finally,with these detection results and the geometric relationships of detected objects,this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs.The proposedmethod is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.

Super-enhancer receives signals from the extracellular matrix to induce PD-L1-mediated immune evasion via integrin/ BRAF/TAK1/ERK/ETV4 signaling

Objective:PD-L1 and PD-L2 expression levels determine immune evasion and the therapeutic efficacy of immune checkpoint blockade.The factors that drive inducible PD-L1 expression have been extensively studied,but mechanisms that result in constitutive PD-L1 expression in cancer cells are largely unknown.Methods:DNA elements were deleted in cells by CRISPR/Cas9-mediated knockout.Protein function was inhibited by chemical inhibitors.Protein levels were examined by Western blot,mRNA levels were examined by real-time RT-PCR,and surface protein expression was determined by cellular immunofluorescence and flow cytometry.Immune evasion was examined by in vitro T cell-mediated killing.Results:We determined the core regions(chr9:5,496,378–5,499,663)of a previously identified PD-L1L2-super-enhancer(SE).Through systematic analysis,we found that the E26 transformation-specific(ETS)variant transcription factor(ETV4)bound to this core DNA region but not to DNA surrounding PD-L1L2SE.Genetic knockout of ETV4 dramatically reduced the expressions of both PD-L1 and PD-L2.ETV4 transcription was dependent on ERK activation,and BRAF/TAK1-induced ERK activation was dependent on extracellular signaling fromαvβ3 integrin,which profoundly affected ETV4 transcription and PD-L1/L2 expression.Genetic silencing or pharmacological inhibition of components of the PD-L1L2-SE-associated pathway rendered cancer cells susceptible to T cell-mediated killing.Conclusions:We identified a pathway originating from the extracellular matrix that signaled via integrin/BRAF/TAK1/ERK/ETV4 to PD-L1L2-SE to induce PD-L1-mediated immune evasion.These results provided new insights into PD-L1L2-SE activation and pathways associated with immune checkpoint regulation in cancer.

Osteoimmunology research in rheumatoid arthritis:From single-cell omics approach

Cellular immune responses as well as generalized and periarticular bone loss are the key pathogenic features of rheumatoid arthritis(RA).Under the pathological conditions of RA,dysregulated inflammation and immune processes tightly interact with skeletal system,resulting in pathological bone damage via inhibition of bone formation or induction of bone resorption.Singlecell omics technologies are revolutionary tools in the field of modern biological research.They enable the display of the state and function of cells in various environments from a single-cell resolution,thus making it conducive to identify the dysregulated molecular mechanisms of bone destruction in RA as well as the discovery of potential therapeutic targets and biomarkers.Here,we summarize the latest findings of single-cell omics technologies in osteoimmunology research in RA.These results suggest that single-cell omics have made significant contributions to transcriptomics and dynamics of specific cells involved in bone remodeling,providing a new direction for our understanding of cellular heterogeneity in the study of osteoimmunology in RA.

过渡金属催化N-芳基酞嗪的C—H键活化反应研究进展

N-芳基酞嗪是存在于天然产物及药物分子中的一种重要含氮芳香杂环化合物,同时,也是非常重要的有机合成原料.近年来,随着过渡金属催化C—H键活化研究的发展,通过切断酞嗪衍生物的C—H键来实现制备含氮杂环化合物,吸引了越来越多化学家们的关注.通过对不同偶联组分的分类,综述了近年来酞嗪参与的过渡金属催化C—H键活化反应最新研究进展,并对反应机理进行了论述.

Additive manufacturing of hydroxyapatite bioceramic scaffolds: Dispersion, digital light processing, sintering, mechanical properties, and biocompatibility

Hydroxyapatite(HA)bioceramic scaffolds were fabricated by using digital light processing(DLP)based additive manufacturing.Key issues on the HA bioceramic scaffolds,including dispersion,DLP fabrication,sintering,mechanical properties,and biocompatibility were discussed in detail.Firstly,the ffects of dispersant dosage,solid loading,and sintering temperature were studied.The optimal dispersant dosage,solid loading,and sintering temperature were 2wt%,50vol%,and 1250℃,respectively.Then,the mechanical properties and biocompatibility of the HA bioceramic scaffolds were investigated.The DLP-prepared porous HA bioceramic scaffold was found to exhibit excellent mechanical properties and degradation behavior.From this study,DLP technique shows good potential for manufacturing HA bioceramic scaffolds.

Four-wave mixing in graphdiyne-microfiber based on synchronized dual-wavelength pulses

We demonstrate four-wave mixing(FWM) in the graphdiyne(GDY) microfiber based on the synchronized dualwavelength pump pulses that are transformed from a mode-locked fiber laser. Benefiting from the large nonlinear refractive index of GDY and the synchronized pump pulses, a maximum conversion efficiency of -39.05 dB can be achieved in GDY with only an average pump power of 6.9 mW, greatly alleviating the possible damage compared to previous investigations employing the continuous-wave pump. In addition, our proposal can be applied to measure the effective nonlinear coefficient γ of the GDY-microfiber, which could be extended as a practical measurement tool for γ of nanomaterials-based devices.