A point mutation in the gene encoding magnesium chelatase I subunit influences strawberry leaf color and metabolism.

Magnesium chelatase (MgCh) catalyzes the insertion of magnesium into protoporphyrin IX, a vital step in chlorophyll (Chl) biogenesis. The enzyme consists of 3 subunits, MgCh I subunit (CHLI), MgCh D subunit (CHLD), and MgCh H subunit (CHLH). The CHLI subunit is an ATPase that mediates catalysis. Previous studies on CHLI have mainly focused on model plant species, and its functions in other species have not been well described, especially with regard to leaf coloration and metabolism. In this study, we identified and characterized a CHLI mutant in strawberry species Fragaria pentaphylla. The mutant, noted as p240, exhibits yellow-green leaves and a low Chl level. RNA-Seq identified a mutation in the 186th amino acid of the CHLI subunit, a base conserved in most photosynthetic organisms. Transient transformation of wild-type CHLI into p240 leaves complemented the mutant phenotype. Further mutants generated from RNA-interference (RNAi) and CRISPR/Cas9 gene editing recapitulated the mutant phenotype. Notably, heterozygous chli mutants accumulated more Chl under low light conditions compared with high light conditions. Metabolite analysis of null mutants under high light conditions revealed substantial changes in both nitrogen and carbon metabolism. Further analysis indicated that mutation in Glu186 of CHLI does not affect its subcellular localization nor the interaction between CHLI and CHLD. However, intramolecular interactions were impaired, leading to reduced ATPase and MgCh activity. These findings demonstrate that Glu186 plays a key role in enzyme function, affecting leaf coloration via the formation of the hexameric ring itself, and that manipulation of CHLI may be a means to improve strawberry plant fitness and photosynthetic efficiency under low light conditions.

Pan-Genomic Analysis Identifies the Chinese Strain as a New Subspecies of Xanthomonas fragariae.

Xanthomonas fragariae is classified as a quarantine pathogen by the European and Mediterranean Plant Protection Organization. It commonly induces typical angular leaf spot (ALS) symptoms in strawberry leaves. X. fragariae strains from China (YL19, SHAQP01, and YLX21) exhibit ALS symptoms in leaves and more severe symptoms of dry cavity rot in strawberry crowns. Conversely, strains from other countries do not cause severe dry cavity rot symptoms in strawberries. After employing multilocus sequence analysis (MLSA), average nucleotide identity (ANI), and amino acid identity (AAI), we determined that Chinese strains of X. fragariae are genetically distinct from other strains and can be considered a new subspecies. Subsequent analysis of 63 X. fragariae genomes published at NCBI using IPGA and EDGAR3.0 revealed the pan-genomic profile, with 1,680 shared genes present in all 63 strains, including 71 virulence-related genes. Additionally, we identified 123 genes exclusive to all the Chinese strains, encompassing 12 virulence-related genes. The qRT-PCR analysis demonstrated that the expression of XopD, XopG1, CE8, GT2, and GH121 out of 12 virulence-related genes of Chinese strains (YL19) exhibited a constant increase in the early stages (6, 24, 54, and 96 hours postinoculation [hpi]) of strawberry leaf infected by YL19. So, the presence of XopD, XopG1, CE8, GT2, and GH121 in Chinese strains may play important roles in the early infection process of Chinese strains. These findings offer novel insights into comprehending the population structure and variation in the pathogenic capacity of X. fragariae.

Flagella-Driven Motility Is Critical to the Virulence of Xanthomonas fragariae in Strawberry.

Xanthomonas fragariae (X. fragariae) is the causal agent of angular leaf spots (ALS) in strawberry plants. Recently, a study in China isolated X. fragariae strain YL19, which was observed to cause both typical ALS symptoms and dry cavity rot in strawberry crown tissue; this was the first X. fragariae strain to have both these effects in strawberry. In this study, from 2020 to 2022, we isolated 39 X. fragariae strains from diseased strawberries in different production areas in China. Multilocus sequence typing (MLST) and phylogenetic analysis showed that X. fragariae strain YLX21 was genetically different from YL19 and other strains. Tests indicated that YLX21 and YL19 had different pathogenicities toward strawberry leaves and stem crowns. YLX21 did not cause ALS symptoms, rarely caused dry cavity rot in strawberry crown after wound inoculation, and never caused dry cavity rot after spray inoculation, but it did cause severe ALS symptoms after spray inoculation. However, YL19 caused more severe symptoms in strawberry crowns under both conditions. Moreover, YL19 had a single polar flagellum, while YLX21 had no flagellum. Motility and chemotaxis assays showed that YLX21 had weaker motility than YL19, which may explain why YLX21 tended to multiply in situ within the strawberry leaf rather than migrate to other tissues, causing more severe ALS symptoms and mild crown rot symptoms. Taken together, the new strain YLX21 helped us reveal critical factors underlying the pathogenicity of X. fragariae and the mechanism by which dry cavity rot in strawberry crowns forms.

Systemic Colonization of Xanthomonas fragariae Strain YL19 Causing Dry Cavity Rot of Strawberry Crown Tissue in China.

Xanthomonas fragariae usually causes angular leaf spot (ALS) of strawberry, a serious bacterial disease in many strawberry-producing regions worldwide. Recently, a new strain of X. fragariae (YL19) was isolated from strawberry in China and has been shown to cause dry cavity rot in strawberry crown. In this study, we constructed a green fluorescent protein (GFP)-labeled Xf YL19 (YL19-GFP) to visualize the infection process and pathogen colonization in strawberries. Foliar inoculation of YL19-GFP resulted in the pathogen migrating from the leaves to the crown, whereas dip inoculation of wounded crowns or roots resulted in the migration of bacteria from the crowns or roots to the leaves. These two invasion types both resulted in the systematic spread of YL19-GFP, but inoculation of a wounded crown was more harmful to the strawberry plant than foliar inoculation. Results increased our understanding of the systemic invasion of X. fragariae, and the resultant crown cavity caused by Xf YL19.

Complete Genome Sequence Resource for Xanthomonas fragariae Causing Crown Infection Pockets in Strawberry.

Xanthomonas fragariae is a global quarantine pathogen, which typically inflicts angular leaf spots. In the present study, we report a new 4.11-Mb high-quality genome sequence of X. fragariae YL19. YL19 can cause the typical angular leaf spot symptoms on strawberry plants in China as well as crown infection pocket symptoms. This new symptom has not been reported in other X. fragariae. Compared with typical X. fragariae strains, including PD885, NBC2815, PD5205, Fap21, and Fap29, the genome and plasmid in YL19 were smaller in size, lacking 109 coding genes, and have more carbohydrate-active enzyme and secondary metabolism genes. The YL19 genome ought to clarify the molecular mechanisms of genome evolution, host adaptation, and pathological process of X. fragariae and help improve strawberry management strategies.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study.

The characteristics of bone fragments are the main influencing factors for the choice of treatment in intertrochanteric fractures. This study aimed to develop a deep learning algorithm for recognizing and segmenting individual fragments in CT images of complex intertrochanteric fractures for orthopedic surgeons. This study was based on 160 hip CT scans (43,510 images) of complex fractures of three types based on the Evans-Jensen classification (40 cases of type 3 (IIA) fractures, 80 cases of type 4 (IIB)fractures, and 40 cases of type 5 (III)fractures) retrospectively. The images were randomly split into two groups to construct a training set of 120 CT scans (32,045 images) and a testing set of 40 CT scans (11,465 images). A deep learning model was built into a cascaded architecture composed by a convolutional neural network (CNN) for location of the fracture ROI and another CNN for recognition and segmentation of individual fragments within the ROI. The accuracy of object detection and dice coefficient of segmentation of individual fragments were used to evaluate model performance. The model yielded an average accuracy of 89.4% for individual fragment recognition and an average dice coefficient of 90.5% for segmentation in CT images. The results demonstrated the feasibility of recognition and segmentation of individual fragments in complex intertrochanteric fractures with a deep learning approach. Altogether, these promising results suggest the potential of our model to be applied to many clinical scenarios.

Optimized LightGBM Power Fingerprint Identification Based on Entropy Features.

The huge amount of power fingerprint data often has the problem of unbalanced categories and is difficult to upload by the limited data transmission rate for IoT communications. An optimized LightGBM power fingerprint extraction and identification method based on entropy features is proposed. First, the voltage and current signals were extracted on the basis of the time-domain features and V-I trajectory features, and a 56-dimensional original feature set containing six entropy features was constructed. Then, the Boruta algorithm with a light gradient boosting machine (LightGBM) as the base learner was used for feature selection of the original feature set, and a 23-dimensional optimal feature subset containing five entropy features was determined. Finally, the Optuna algorithm was used to optimize the hyperparameters of the LightGBM classifier. The classification performance of the power fingerprint identification model on imbalanced datasets was further improved by improving the loss function of the LightGBM model. The experimental results prove that the method can effectively reduce the computational complexity of feature extraction and reduce the amount of power fingerprint data transmission. It meets the recognition accuracy and efficiency requirements of a massive power fingerprint identification system.

Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China.

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

External Noise and External Signal Induced Transition of Gene Switch and Coherence Resonance in the Genetic Regulatory System.

The transition of gene switch induced by external noises (multiplicative external noise and additive external noise) and external signals is investigated in the genetic regulatory system. Results show that the state-to-state transition of gene switch as well as resonant behaviors, such as the explicit coherence resonance (ECR), implicit coherence resonance (ICR) and control parameter coherence biresonance (CPCBR), can appear when noises are injected into the genetic regulatory system. The ECR is increased with the increase of the control parameter value when starting from the supercritical Hopf bifurcation parameter point, and there exists a critical control parameter value for the occurrence of ECR. However, the ICR is decreased as the control parameter value is increased when starting from the subcritical Hopf bifurcation point. In particular, the coherence of ECR is higher and more sensitive to noise than that of ICR. When an external signal is introduced into the system, the enhancement or suppression of the CPCBR and the number of peaks strongly depend on the frequency and amplitude of the external signal. Furthermore, the gene regulation system can selectively enhance or decrease the noise-induced oscillation signals at preferred frequency and amplitude of an external signal.

[Atmospheric Influences Analysis on the Satellite Passive Microwave Remote Sensing].

Passive microwave remote sensing offers its all-weather work capabilities, but atmospheric influences on satellite microwave brightness temperature were different under different atmospheric conditions and environments. In order to clarify atmospheric influences on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), atmospheric radiation were simulated based on AMSR-E configuration under clear sky and cloudy conditions, by using radiative transfer model and atmospheric conditions data. Results showed that atmospheric water vapor was the major factor for atmospheric radiation under clear sky condition. Atmospheric transmittances were almost above 0.98 at AMSR-E's low frequencies (< 18.7 GHz) and the microwave brightness temperature changes caused by atmosphere can be ignored in clear sky condition. Atmospheric transmittances at 36.5 and 89 GHz were 0.896 and 0.756 respectively. The effects of atmospheric water vapor needed to be corrected when using microwave high-frequency channels to inverse land surface parameters in clear sky condition. But under cloud cover or cloudy conditions, cloud liquid water was the key factor to cause atmospheric radiation. When sky was covered by typical stratus cloud, atmospheric transmittances at 10.7, 18.7 and 36.5 GHz were 0.942, 0.828 and 0.605 respectively. Comparing with the clear sky condition, the down-welling atmospheric radiation caused by cloud liquid water increased up to 75.365 K at 36.5 GHz. It showed that the atmospheric correction under different clouds covered condition was the primary work to improve the accuracy of land surface parameters inversion of passive microwave remote sensing. The results also provided the basis for microwave atmospheric correction algorithm development. Finally, the atmospheric sounding data was utilized to calculate the atmospheric transmittance of Hailaer Region, Inner Mongolia province, in July 2013. The results indicated that atmospheric transmittances were close to 1 at C-band and X-band. 89 GHz was greatly influenced by water vapor and its atmospheric transmittance was not more than 0.7. Atmospheric transmittances in Hailaer Region had a relatively stable value in summer, but had about 0.1 fluctuations with the local water vapor changes.

High resolution Tibetan Plateau regional reanalysis 1961-present.

With the rapid global warming in recent decades, the Tibetan Plateau (TP) has suffered severe impacts, such as glacier retreat, glacial lake expansion, and permafrost degradation, which threaten the lives and properties of the local and downstream populations. Regional Reanalysis (RR) is vital for TP due to the limitations of observations. In this work, a 62-year (1961-2022) long atmospheric regional reanalysis with spatial resolution of 9 km (convective gray-zone scale) and temporal resolution of 1 hour over the TP (TPRR) was developed using the Weather Research and Forecasting (WRF) model, combined with re-initialization method, spectral nudging (SN), and several optimizations. TPRR is forced by ERA5 at hourly intervals. TPRR outperforms ERA5, realistically capturing climatological characteristics and seasonal variations of precipitation and T2m (air temperature at 2m above ground level). Moreover, TPRR better reproduces the frequency and intensity of precipitation, as well as the diurnal cycle of precipitation. This study also quantifies the wetting trend of 0.0071 mm/year over the TP amid global warming using TPRR.

Overall quality changes and deterioration mechanism of fragrant rapeseed oils during 6-Month storage.

The strong-fragrant rapeseed oil (SFRO) is a popular rapeseed oil in China with a low refining degree only degumming with hot water, which remarkably affects its storage stability. The present study compared the overall changes of physical/chemical/nutrient quality of FROs at various temperatures, light wavelengths and headspace volumes. Results showed that red light (680 nm) had a most significant adverse effect on the overall quality of SFRO with the higher correlation coefficients to PV and TOTOX of 0.71 and 0.70, and lower correlation coefficients to chlorophyll and tocopherol of -0.95 and -0.53, respectively. Further studies revealed that red light accelerated the oxidation of fragrant rapeseed oils by degrading chlorophyll to initiate the photo-oxidation process and synthesize high amount of secondary oxidation products including aliphatic and aromatic oxidized compounds from linolenic acid. These findings provided a reference to control the deterioration of FROs by preventing the transmittance of red light.

Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests.

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400-600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

Soil bioengineering application and practices in Nepal.

The small mountainous country Nepal is situated in the central part of the Himalayas. Its climate varies from tropical in the south to arctic in the north; and natural vegetation follows the pattern of climate and altitude. Water-induced disaster problems including soil erosion, debris flow, landslides and flooding are common due to the unstable landscape. Soil erosion is the most important driving force for the degradation of upland and mountain ecosystems. Soil bioengineering has been used in Nepal for nearly 30 years to deal with erosion problems on slopes, in high way construction and riverbank stabilization. The main soil bioengineering techniques used in Nepal are brush layering, palisades, live check dams, fascines and vegetative stone pitching. This study is based on the geology, climate and vegetation of Nepal and briefly summarizes the application of soil bioengineering on slopes and stream banks, with especial attention to the role of vegetation on slope and stream bank stabilization. Furthermore, this paper addresses the role of community participation and responsibility for successful application of vegetation-based techniques in management, maintenance and utility aspects for the future. In recent years, soil bioengineering techniques are extensively used due to their cost-effectiveness, using locally available materials and low-cost labour in comparison to more elaborate civil engineering works. However, scientific implementation and record-keeping and evaluation of the work are indeed essential.

[Study of the microwave emissivity characteristics of vegetation over the Northern Hemisphere].

The microwave emissivity is a function of structure, water content, and surface roughness, and all these factors have obvious seasonal variations. In the present study, the half-month averaged emissivities in summer and winter of 2003 over the vegetation of Northern Hemisphere were estimated using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) combined with IGBP (International Geosphere-Biosphere Project labels) land classification data. Then the emissivities of vegetation land covers at different frequencies, the polarization and their seasonal variations were analyzed respectively. The results show that the emissivities of vegetation increase with the increase in frequencies, and decline with the frequency increasing over snow region. In summer, the vegetation emissivity at V-polarization of 89 GHz is larger than 0.944, and all emissivities are relatively stable and the RMSE of time series emissivity variation is less than 0.007 2. In winter, emissivities decrease over snow covered area, especially for higher frequencies. Furthermore, with the increase in vegetation density, the emissivities increase and emissivity polarization difference decreases.

[The relationship between the variation rate of MODIS land surface temperature and AMSR-E soil moisture and its application to downscaling].

Using AMSR-E soil moisture, MODIS land surface temperature (Ts) and vegetation index product, the authors discuss the relationship between the variation rate of land surface temperature and surface soil moisture. Selecting the plains region of central United States as the study area, the authors propose the distribution triangle of the variation rate of land surface temperature and soil moisture. In the present paper, temperature variation and vegetation index (TVVI), a new index containing the information of temperature variation and vegetation, is introduced. The authors prove that TVVI and soil moisture show a steady relationship of exponential function; and build a quantitative model of soil moisture(SM) and instantaneous surface temperature variation (VTs). The authors later achieve downscaling of AMSR-E soil moisture data, through the above stated functional relationships and high-resolution MODIS data. Comparison with measured data on ground surface indicates that this method of downscaling is of high precision

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet.

In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-trained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function to solve the problem of data imbalance in V-I trajectory identification. All parameters are retrained to extract features from the color V-I trajectory images for device classification. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets. The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 minutes compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification.

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations.

This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012-2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 µg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

A global daily soil moisture dataset derived from Chinese FengYun Microwave Radiation Imager (MWRI)(2010-2019).

Surface soil moisture (SSM) is an important variable in drought monitoring, floods predicting, weather forecasting, etc. and plays a critical role in water and heat exchanges between land and atmosphere. SSM products from L-band observations, such as the Soil Moisture Active Passive (SMAP) Mission, have proven to be optimal global estimations. Although X-band has a lower sensitivity to soil moisture than that of L-band, Chinese FengYun-3 series satellites (FY-3A/B/C/D) have provided sustainable and daily multiple SSM products from X-band since 2008. This research developed a new global SSM product (NNsm-FY) from FY-3B MWRI from 2010 to 2019, transferred high accuracy of SMAP L-band to FY-3B X-band. The NNsm-FY shows good agreement with in-situ observations and SMAP product and has a higher accuracy than that of official FY-3B product. With this new dataset, Chinese FY-3 satellites may play a larger role and provide opportunities of sustainable and longer-term soil moisture data record for hydrological study.

An RFI-suppressed SMOS L-band multi-angular brightness temperature dataset spanning over a decade (since 2010).

The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with in-situ measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.