Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia

Vegetation changes play a vital role in modifying local temperatures although, until now, the climate feedback effects of vegetation changes are still poorly known and large uncertainties exist, especially over Central Asia. In this study, using remote sensing and re-analysis of existing data, we evaluated the impact of vegetation changes on local temperatures. Our results indicate that vegetation changes have a significant unidirectional causality relationship with regard to local temperature changes. We found that vegetation greening over Central Asia as a whole induced a cooling effect on the local temperatures. We also found that evapotranspiration (ET) exhibits greater sensitivity to the increases of the Normalized Difference Vegetation Index (NDVI) as compared to albedo in arid/semi-arid/semi-humid regions, potentially leading to a cooling effect. However, in humid regions, albedo warming completely surpasses ET cooling, causing a pronounced warming. Our findings suggest that using appropriate strategies to protect vulnerable dryland ecosystems from degradation, should lead to future benefits related to greening ecosystems and mitigation for rising temperatures

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

Transcriptome Sequencing and Comparative Analysis of Saccharina japonica (Laminariales, Phaeophyceae) under Blue Light Induction

BACKGROUND: Light has significant effect on the growth and development of Saccharina japonica, but there are limited reports on blue light mediated physiological responses and molecular mechanism. In this study, high-throughput paired-end RNA-sequencing (RNA-Seq) technology was applied to transcriptomes of S. japonica exposed to blue light and darkness, respectively. Comparative analysis of gene expression was designed to correlate the effect of blue light and physiological mechanisms on the molecular level. PRINCIPAL FINDINGS: RNA-seq analysis yielded 70,497 non-redundant unigenes with an average length of 538 bp. 28,358 (40.2%) functional transcripts encoding regions were identified. Annotation through Swissprot, Nr, GO, KEGG, and COG databases showed 25,924 unigenes compared well (E-value <10(-5)) with known gene sequences, and 43 unigenes were putative BL photoreceptor. 10,440 unigenes were classified into Gene Ontology, and 8,476 unigenes were involved in 114 known pathways. Based on RPKM values, 11,660 (16.5%) differentially expressed unigenes were detected between blue light and dark exposed treatments, including 7,808 upregulated and 3,852 downregulated unigenes, suggesting S. japonica had undergone extensive transcriptome re-orchestration during BL exposure. The BL-specific responsive genes were indentified to function in processes of circadian rhythm, flavonoid biosynthesis, photoreactivation and photomorphogenesis. SIGNIFICANCE: Transcriptome profiling of S. japonica provides clues to potential genes identification and future functional genomics study. The global survey of expression changes under blue light will enhance our understanding of molecular mechanisms underlying blue light induced responses in lower plants as well as facilitate future blue light photoreceptor identification and specific responsive pathways analysis

Synthetical Analysis on Geological Factors Ccontrolling Coalbed Methane

AbstractThe gas-controlling property is the important content for the coalbed methane (CBM) theoretical research, and it has the important role for guiding the CBM exploration and development. The evolution features of the coal-bearing strata and structure and the current CBM preservation condition are the keys determining the CBM enrichment and reservoir formation. In the case that the earth curst is stable in the sedimentary period of the coal-bearing strata and after the coal-bearing strata are deposited, the coal seam deposited by the coal-accumulation has the large and stable thickness, the earth curst is stably subsided after the coal-accumulation period or the strength of the structural movement is low and the uplifted amplitude is little, then it is favorable for the CBM enrichment. In the area there the coal-bearing strata have the simple structure, the enclosing rock of coal seam is stable and compact, the seam buried depth is deep, and in the stagnant area with the simple hydrogeological condition, the CBM-controlling property is well. The research on the CBM-controlling property is restricted by the exploration degree, with respect to the area with the low exploration degree, the research on the CBM-controlling property could be combined with the exploration results of the area with the high exploration degree, on the basis of analysing the CBM distribution features and control factors of the area with the high exploration degree, adopting the analysis method such as the geological analogy and so on, it conducts the research work from the evolution features of the coal-bearing strata andstructure and the current CBM preservation condition

Interface Effects on He Ion Irradiation in Nanostructured Materials

In advanced fission and fusion reactors, structural materials suffer from high dose irradiation by energetic particles and are subject to severe microstructure damage. He atoms, as a byproduct of the (n) transmutation reaction, could accumulate to form deleterious cavities, which accelerate radiation-induced embrittlement, swelling and surface deterioration, ultimately degrade the service lifetime of reactor materials. Extensive studies have been performed to explore the strategies that can mitigate He ion irradiation damage. Recently, nanostructured materials have received broad attention because they contain abundant interfaces that are efficient sinks for radiation-induced defects. In this review, we summarize and analyze the current understandings on interface effects on He ion irradiation in nanostructured materials. Some key challenges and research directions are highlighted for studying the interface effects on radiation damage in nanostructured materials

Silencing of the Wheat Protein Phosphatase 2A Catalytic Subunit TaPP2Ac Enhances Host Resistance to the Necrotrophic Pathogen Rhizoctonia cerealis

Eukaryotic type 2A protein phosphatases (protein phosphatase 2A, PP2A) consist of a scaffold subunit A, a regulatory subunit B, and a catalytic subunit C. Little is known about the roles of PP2Ac proteins that are involved in plant responses to necrotrophic fungal pathogens. Sharp eyespot, caused by the necrotrophic fungus Rhizoctonia cerealis, is a destructive disease of wheat (Triticum aestivum), an important staple food crop. Here, we isolated TaPP2Ac-4D from wheat, which encodes a catalytic subunit of the heterotrimeric PP2A, and characterized its properties and role in plant defense response to R. cerealis. Based on the sequence alignment of TaPP2Ac-4D with the draft sequences of wheat chromosomes from the International Wheat Genome Sequencing Consortium (IWGSC), it was found that TaPP2Ac-4D gene is located on the long arm of the wheat chromosome 4D and has two homologs assigned on wheat chromosomes 4A and 4B. Sequence and phylogenetic tree analyses revealed that the TaPP2Ac protein is a typical member of the PP2Ac family and belongs to the subfamily II. TaPP2Ac-4B and TaPP2Ac-4D displayed higher transcriptional levels in the R. cerealis-susceptible wheat cultivar Wenmai 6 than those seen in the resistant wheat line CI12633. The transcriptional levels of TaPP2Ac-4B and TaPP2Ac-4D were significantly elevated in wheat R. cerealis after infection and upon H2O2 treatment. Virus-induced gene silencing results revealed that the transcriptional knockdown of TaPP2Ac-4D and TaPP2Ac-4B significantly increased wheat resistance to R. cerealis infection. Meanwhile, the transcriptional levels of certain pathogenesis-related (PR) and reactive oxygen species (ROS)-scavenging enzyme encoding genes were increased in TaPP2Ac-silenced wheat plants. These results suggest that TaPP2Ac-4B and TaPP2Ac-4D negatively regulate defense response to R. cerealis infection possibly through modulation of the expression of certain PR and ROS-scavenging enzyme genes in wheat. This study reveals a novel function of the plant PP2Ac genes in plant immune responses

Evaluating how lodging affects maize yield estimation based on UAV observations

Timely and accurate pre-harvest estimates of maize yield are vital for agricultural management. Although many remote sensing approaches have been developed to estimate maize yields, few have been tested under lodging conditions. Thus, the feasibility of existing approaches under lodging conditions and the influence of lodging on maize yield estimates both remain unclear. To address this situation, this study develops a lodging index to quantify the degree of lodging. The index is based on RGB and multispectral images obtained from a low-altitude unmanned aerial vehicle and proves to be an important predictor variable in a random forest regression (RFR) model for accurately estimating maize yield after lodging. The results show that (1) the lodging index accurately describes the degree of lodging of each maize plot, (2) the yield-estimation model that incorporates the lodging index provides slightly more accurate yield estimates than without the lodging index at three important growth stages of maize (tasseling, milking, denting), and (3) the RFR model with lodging index applied at the denting (R5) stage yields the best performance of the three growth stages, with R2 = 0.859, a root mean square error (RMSE) of 1086.412 kg/ha, and a relative RMSE of 13.1%. This study thus provides valuable insight into the precise estimation of crop yield and demonstra\tes that incorporating a lodging stress-related variable into the model leads to accurate and robust estimates of crop grain yield

Robust ferromagnetism of single crystalline CoxZn1−xO (0.3 ≤ x ≤ 0.45) epitaxial films with high Co concentration

In contrast to conventional dilute magnetic semiconductors with concentrations of magnetic ions of just a few percent, here, we report the fabrication of epitaxial Cox Zn 1− xO single crystalline films with Co concentrations from x = 0.3 up to 0.45 by radio-frequency oxygen-plasma-assisted molecular beam epitaxy. The films retain their single crystalline wurtzite structure without any other crystallographic phase from precipitates, based on reflection high energy electron diffraction, X-ray diffraction, transmission electron microscopy, and Raman scattering. The results of X-ray diffraction, optical transmission spectroscopy, and in-situ X-ray photoelectron spectroscopy confirm the incorporation of Co2+ cations into the wurtzite lattice. The films exhibit robust ferromagnetism and the magneto-optical Kerr effect at room temperature. The saturation magnetization reaches 265 emu/cm3 at x = 0.45, which corresponds to the average magnetic moment of 1.5 μB per Co atom

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices

The wheat LLM-domain-containing transcription factor TaGATA1 positively modulates host immune response to Rhizoctonia cerealis

Wheat (Triticumaestivum) is essential for global food security. Rhizoctonia cerealis is the causal pathogen of sharp eyespot, an important disease of wheat. GATA proteins in model plants have been implicated in growth and development; however, little is known about their roles in immunity. Here, we reported a defence role of a wheat LLM-domain-containing B-GATA transcription factor, TaGATA1, against R. cerealis infection and explored the underlying mechanism. Through transcriptomic analysis, TaGATA1 was identified to be more highly expressed in resistant wheat genotypes than in susceptible wheat genotypes. TaGATA1 was located on chromosome 3B and had two homoeologous genes on chromosomes 3A and 3D. TaGATA1 was demonstrated to localize in the nucleus, possess transcriptional-activation activity, and bind to GATA-core cis-elements. TaGATA1 overexpression significantly enhanced resistance of transgenic wheat to R. cerealis, whereas silencing of TaGATA1 suppressed the resistance. RT-qPCR and chromatin immunoprecipitation-qPCR results indicated that TaGATA1 directly bound to and activated certain defence genes in host immune response to R. cerealis. Collectively, TaGATA1 positively regulates immune responses to R. cerealis through activating expression of defence genes in wheat. This study reveals a new function of plant GATAs in immunity and provides a candidate gene for improving crop resistance to R. cerealis