Improving flavor of strong fragrant rapeseed oils by supplementing commercial peptides and sugars.

The strong fragrant rapeseed oils (SFROs) attract an increasing consumers' preference due to their strong flavor and attractive appearance. With the accumulated knowledge of flavor formation pathways during producing SFROs, the present study proposed a novel method to enhance SFRO's flavor by directly adding the reducing sugars (glucose and xylose) and commercial peptides during roasting process. Results indicated that supplementation of 3 % rapeseed peptide, 1 % glucose and 0.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability.

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.

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.

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.

Southern Hemisphere dominates recent decline in global water availability.

Global land water underpins livelihoods, socioeconomic development, and ecosystems. It remains unclear how water availability has changed in recent decades. Using an ensemble of observations, we quantified global land water availability over the past two decades.

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

This study presents a novel ensemble of surface ozone (O) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O surface concentrations on a fine spatial resolution of 0.

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.

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

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. strains from China (YL19, SHAQP01, and YLX21) exhibit ALS symptoms in leaves and more severe symptoms of dry cavity rot in strawberry crowns.

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

usually causes angular leaf spot (ALS) of strawberry, a serious bacterial disease in many strawberry-producing regions worldwide. Recently, a new strain of (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 YL19 (YL19-GFP) to visualize the infection process and pathogen colonization in strawberries.

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

() is the causal agent of angular leaf spots (ALS) in strawberry plants. Recently, a study in China isolated strain YL19, which was observed to cause both typical ALS symptoms and dry cavity rot in strawberry crown tissue; this was the first strain to have both these effects in strawberry. In this study, from 2020 to 2022, we isolated 39 strains from diseased strawberries in different production areas in China.

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.

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.

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.

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.

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.

Global spatiotemporally continuous MODIS land surface temperature dataset.

Land surface temperature (LST) plays a critical role in land surface processes. However, as one of the effective means for obtaining global LST observations, remote sensing observations are inherently affected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products. Here, we propose a solution.

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

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 YL19.

A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019).

Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently launched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, while Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36 km resolution (2002-2019).

Driving forces of land surface temperature anomalous changes in North America in 2002-2018.

The land surface temperature (LST) changes in North America are very abnormal recently, but few studies have systematically researched these anomalies from several aspects, especially the influencing forces. After reconstructing higher quality MODIS monthly LST data (0.05° * 0.

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.

Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data.

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements.