BnaMPK3 Is a Key Regulator of Defense Responses to the Devastating Plant Pathogen Sclerotinia sclerotiorum in Oilseed Rape

The disease caused by Sclerotinia sclerotiorum has traditionally been difficult to control, resulting in tremendous economic losses in oilseed rape (Brassica napus). Identification of important genes in the defense responses is critical for molecular breeding, an important strategy for controlling the disease. Here, we report that a B. napus mitogen-activated protein kinase gene, BnaMPK3, plays an important role in the defense against S. sclerotiorum in oilseed rape. BnaMPK3 is highly expressed in the stems, flowers and leaves, and its product is localized in the nucleus. Furthermore, BnaMPK3 is highly responsive to infection by S. sclerotiorum and treatment with jasmonic acid (JA) or the biosynthesis precursor of ethylene (ET), but not to treatment with salicylic acid (SA) or abscisic acid. Moreover, overexpression (OE) of BnaMPK3 in B. napus and Nicotiana benthamiana results in significantly enhanced resistance to S. sclerotiorum, whereas resistance is diminished in RNAi transgenic plants. After S. sclerotiorum infection, defense responses associated with ET, JA, and SA signaling are intensified in the BnaMPK3-OE plants but weakened in the BnaMPK3-RNAi plants when compared to those in the wild type plants; by contrast the level of both H2O2 accumulation and cell death exhibits a reverse pattern. The candidate gene association analyses show that the BnaMPK3-encoding BnaA06g18440D locus is a cause of variation in the resistance to S. sclerotiorum in natural B. napus population. These results suggest that BnaMPK3 is a key regulator of multiple defense responses to S. sclerotiorum, which may guide the resistance improvement of oilseed rape and related economic crops

A deep learning-based prognostic framework for aeroengine exhaust gas temperature margin

The value of the gas-path parameter, exhaust gas temperature margin (EGTM), is the critical index for predicting aeroengine performance degradation. Accurate predictions help to improve engine maintenance, replacement schedules, and flight safety. The outside air temperature (OAT), altitude of the airport, the number of flight cycles, and water washing information were chosen as the sample input variables for the data-driven prognostic model for predicting the take-off EGTM of the on-wing engine. An attention-based deep learning framework was proposed for the aeroengine performance prediction model. Specifically, the multiscale convolutional neural network (CNN) structure is designed to initially learn sequential features from raw input data. Subsequently, the long short-term memory (LSTM) structure is employed to further extract the features processed by the multiscale CNN structure. Furthermore, the proposed attention mechanism is adopted to learn the influence of features and time steps, assigning different weights according to their importance. The actual operation data of the aeroengine are used to conduct experiments, where the experimental results verify the effectiveness of our proposed method in EGTM prediction

An overview of European efforts in generating climate data records

The Coordinating Earth Observation Data Validation for Reanalysis for Climate Services project (CORE-CLIMAX) aimed to substantiate how Copernicus observations and products can contribute to climate change analyses. CORE-CLIMAX assessed the European capability to provide climate data records (CDRs) of essential climate variables (ECVs), prepared a structured process to derive CDRs, developed a harmonized approach for validating essential climate variable CDRs, identified the integration of CDRs into the reanalysis chain, and formulated a process to compare the results of different reanalysis techniques. With respect to the Copernicus Climate Change Service (C3S), the systematic application and further development of the CORE-CLIMAX system maturity matrix (SMM) and the spinoff application performance metric (APM) were strongly endorsed to be involved in future implementations of C3S. We concluded that many of the current CDRs are not yet sufficiently mature to be used in reanalysis or applied in climate studies. Thus, the production of consistent high-resolution data records remains a challenge that needs more research urgently. Extending ECVs to close climate cycle budgets (e.g., essential water variables) is a next step linking CDRs to sectoral applications