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Five new furofuran lignans and their derivatives, (−)-glaberide I 4- O - β -D-glucopyranoside (1a), (+)-glaberide I 4- O - β -D-glucopyranoside (1b), (+)-glaberide I 7'-ethoxy-4- O - β -D-glucopyranoside (2a), (−)-glaberide I 7'-ethoxy-4- O - β -D-glucopyranoside (2b), and (−)-isoeucommin A (3b), along with fifteen known analogs were isolated from the stems of Dendrobium 'Sonia'. These compounds were classified into ten pairs of enantiomers or diastereoisomers via chiral resolution, and their structures were determined based on extensive spectroscopic data. Their absolute configurations were determined by hydrolysis, comparison of experimental and calculated electronic circular dichroism (ECD) data, and single-crystal X-ray diffraction analysis. The isolates were evaluated for their ability to inhibit nitric oxide (NO) production in RAW264.7 cells. Among them, syringaresinol (5) exhibited prominent inhibition activity, with an IC 50 value of 28.4 ± 3.0 μmol·L−1, and there was a slight difference between 5a , 5b and the racemic mixture 5. [ABSTRACT FROM AUTHOR]

Existing approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management. Plain Language Summary: Traditional methods for predicting short‐term/seasonal variations in land total water storages rely on hydrological models. However, these models have a drawback—they are better at predicting water stored in specific parts of the land system like soil moisture than giving an accurate forecast for the overall integrated land total water storage. In this study, we demonstrate the applicability of machine learning in uncovering relationships between the de‐season and de‐linearized terms of global water storage variability as observed by the Gravity Recovery and Climate Experiment (GRACE) satellites, and the preceding hydrometeorological variables such as sea surface temperature. These relationships can then be utilized to forecast monthly changes in land total water storage up to 1 year ahead, and even to predict exceptional drought events based on near real‐time observational forcing data. The validation by actual GRACE observations, lends further credence to the effectiveness of the method developed here, showcasing its potential to forecast trends in global land total water storage for the upcoming year. The potential applications of these predictions in early warning systems are highly promising, and we anticipate that they can assist decision‐makers in enhancing current drought and water resource management practices. Key Points: We identify a stable lag relationship between hydrometeorological variables and the GRACE derived total water storage change (TWSC)Using this stable lag relationship, we are able to forecast the global TWSC up to 1 year ahead with solely observational data as inputsOur forecasts exhibit high consistency with actual GRACE data in terms of global mean land water storage trends for the following year [ABSTRACT FROM AUTHOR]

Background: Natural medicines present a considerable analytical challenge due to their diverse botanical origins and complex multi‐species composition. This inherent complexity complicates their rapid identification and analysis. Tangerine peel, a product of the Citrus species from the Rutaceae family, is widely used both as a culinary ingredient and in traditional Chinese medicine. It is classified into two primary types in China: Citri Reticulatae Pericarpium (CP) and Citri Reticulatae Pericarpium Viride (QP), differentiated by harvest time. A notable price disparity exists between CP and another variety, Citri reticulatae "Chachi" (GCP), with differences being based on the original variety. Methods: This study introduces an innovative method using portable miniature mass spectrometry for swift on‐site analysis of QP, CP, and GCP, requiring less than a minute per sample. And combined with machine learning to differentiate the three types on site, the method was used to try to distinguish GCP from different storage years. Results: This novel method using portable miniature mass spectrometry for swift on‐site analysis of tangerine peels enabled the characterization of 22 compounds in less than one minute per sample. The method simplifies sample processing and integrates machine learning to distinguish between the CP, QP, and GCP varieties. Moreover, a multiple‐perceptron neural network model is further employed to specifically differentiate between CP and GCP, addressing the significant price gap between them. Conclusions: The entire analytical time of the method is about 1 minute, and samples can be analyzed on site, greatly reducing the cost of testing. Besides, this approach is versatile, operates independently of location and environmental conditions, and offers a valuable tool for assessing the quality of natural medicines. [ABSTRACT FROM AUTHOR]

To achieve the goal that China and Nepal jointly announce the new height of Mount Qomolangma (MQ), the campaign of Qomolangma Height Survey (QHS) was carried out from 2019 to 2020. A high precision geoid model realizing the common height datum for both sides is necessary for determining the unique height of MQ. However, high altitude and rugged topography make it extremely difficult to conduct terrestrial gravity measurements in this region, the accuracy of geoid model is restricted by terrestrial gravity data gaps. In the campaign of 2020 QHS, the airborne gravity survey was carried out over MQ and its surrounding areas, the airborne gravity data covering an area of 12,700 km2 were successfully collected. For the first time, the high precision observations of terrestrial gravity and BeiDou Navigation Satellite System (BDS) at the peak of MQ were collected. These datasets pave the way for the precise determination of the orthometric height of MQ. According to the definition of the International Height Reference System (IHRS), we developed the IHRS-based gravimetric quasigeoid model by combining the airborne and terrestrial gravity data. Validations against highly accurate GNSS leveling data at 61 benchmarks demonstrate that the accuracy of the quasigeoid model is 3.8 cm, and the addition of airborne gravity data improves the accuracy by 51.3%. Based on the IHRS, the final orthometric height of the snow surface of the peak of MQ is determined to be 8848.86 m. [ABSTRACT FROM AUTHOR]

Renowned for their nutritional benefits, citrus fruits are harvested at various stages in China for functional food production. This study introduces an innovative analytical method, DART-MS, enabling direct qualitative analysis of citrus samples without the need for preprocessing. Simultaneously, the combination of chemometrics can be applied to distinguish between three different citrus samples: Citri Reticulatae Pericarpium, Citri Reticulatae Pericarpium Viride, and Citri Reticulatae "Chachi". Notably, given the international regulatory concerns surrounding synephrine, a precise quantitative analysis method for synephrine was developed. The limit of detection (LOD) and the limit of quantification (LOQ) were 39 ng mL−1 and 156 ng mL−1, respectively. The recovery rates obtained varied from 98.46% to 100.71%. Furthermore, the intra-day and inter-day precision demonstrated robust consistency, with values spanning 5.0–6.1% and 5.03–6.08%, respectively, offering quicker results compared to those from HPLC-MS, promising a safer assessment of herbal and food products. [ABSTRACT FROM AUTHOR]

Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater. Key Points: Groundwater potential mapping was carried out at the Central Highlands of VietnamHybrid Machine learning Models based on Naïve Bayes Tree were developed and usedResults showed that the proposal models are potential and accurate tools [ABSTRACT FROM AUTHOR]

The management of colorectal cancer (CRC) poses a significant challenge, necessitating the development of innovative and effective therapeutics. Our research has shown that notoginsenoside Ft1 (Ng-Ft1), a small molecule, markedly inhibits subcutaneous tumor formation in CRC and enhances the proportion of CD8+ T cells in tumor-bearing mice, thus restraining tumor growth. Investigation into the mechanism revealed that Ng-Ft1 selectively targets the deubiquitination enzyme USP9X, undermining its role in shielding β -catenin. This leads to a reduction in the expression of downstream effectors in the Wnt signaling pathway. These findings indicate that Ng-Ft1 could be a promising small-molecule treatment for CRC, working by blocking tumor progression via the Wnt signaling pathway and augmenting CD8+ T cell prevalence within the tumor environment. [ABSTRACT FROM AUTHOR]

Oleanane-type ginsenosides are a class of compounds with remarkable pharmacological activities. However, the lack of effective preparation methods for specific rare ginsenosides has hindered the exploration of their pharmacological properties. In this study, a novel glycoside hydrolase PlGH3 was cloned from Paenibacillus lactis 154 and heterologous expressed in Escherichia coli. Sequence analysis revealed that PlGH3 consists of 749 amino acids with a molecular weight of 89.5 kDa, exhibiting the characteristic features of the glycoside hydrolase 3 family. The enzymatic characterization results of PlGH3 showed that the optimal reaction pH and temperature was 8 and 50 °C by using p-nitrophenyl-β-d-glucopyranoside as a substrate, respectively. The Km and kcat values towards ginsenoside Ro were 79.59 ± 3.42 µM and 18.52 s−1, respectively. PlGH3 exhibits a highly specific activity on hydrolyzing the 28-O-β-d-glucopyranosyl ester bond of oleanane-type saponins. The mechanism of hydrolysis specificity was then presumably elucidated through molecular docking. Eventually, four kinds of rare oleanane-type ginsenosides (calenduloside E, pseudoginsenoside RP1, zingibroside R1, and tarasaponin VI) were successfully prepared by biotransforming total saponins extracted from Panax japonicus. This study contributes to understanding the mechanism of enzymatic hydrolysis of the GH3 family and provides a practical route for the preparation of rare oleanane-type ginsenosides through biotransformation. Key points: • The glucose at C-28 in oleanane-type saponins can be directionally hydrolyzed. • Mechanisms to interpret PlGH3 substrate specificity by molecular docking. • Case of preparation of low-sugar alternative saponins by directed hydrolysis. [ABSTRACT FROM AUTHOR]

Wearable heaters are essential for people living in cold regions, but creating heaters that are low-cost, lightweight, and high air permeability poses challenges. In this study, we developed a wearable heater using carbon nanotube/water polyurethane (CNT/WPU) nanocomposite fibers that achieve high extension rate and conductivity. We produced low-cost and mass-produced fibers using the wet spinning. With heat treatment, we increased the elongation rate of the fibers to 1893.8% and decreased the resistivity to 0.07 Ω*m. then wove the fibers into a heating fabric using warp knitting, that resistance is 493 Ω. Achieved a uniform temperature of 58 °C at voltage of 36 V, with a thermal stability fluctuation of −5.0 °C to +6.3 °C when bent from 0° to 360°. Our results show that wearable heaters have excellent flexibility and stretchability, due to nanocomposite fibers and special braided structure, which offer a novel idea for wearable heaters. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the diagnostic value of musculoskeletal ultrasound measurements of subacromial bursa (SAB) thickness, supraspinatus tendon (SUP) thickness, acromiohumeral distance (AHD), and SUP‐to‐AHD ratio (AHD%) in patients with shoulder impingement syndrome (SIS). Methods: This was a prospective cross‐sectional observational study. Thirty patients with SIS (60 shoulders) admitted between January 2019 and January 2020 were enrolled. The SUP thickness, SAB thickness, AHD, and AHD% (calculated as AHD% = [(SUP / AHD) × 100%]) were measured in 60 shoulders using musculoskeletal ultrasound. Results: The affected shoulder displayed thicker SUP and SAB (t = 7.838), narrower AHD (t = 2.324), and larger AHD% (t = 6.875) than the unaffected shoulder (P <.05). The SUP thickness showed a linear positive correlation with AHD (r =.503) and AHD% (r =.792) in the affected shoulder (P <.05). On receiver operating characteristic analysis, AHD*AHD% showed the best diagnostic performance in both measurements (area under the curve: 0.877). Conclusion: This study revealed that SIS symptoms may be related to a larger AHD% with SUP thickening. As diagnostic criteria, the cut‐off values of AHD% (65.6%) and AHD*AHD% (0.504) have good sensitivity and specificity and can help improve the differential diagnosis of patients with SIS. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) plays a very important role in China's economic and social development and ecological security, so studying the spatiotemporal variation characteristics of net primary productivity (NPP) and its influencing factors is of great significance for protecting the stable development of its ecological environment. This article takes the YRB as the research area, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, climate data, terrain data, land data, social data, and the gravity recovery and climate experiment (GRACE) data. The spatiotemporal evolution characteristics of vegetation NPP in the YRB from 2000 to 2022 were explored using methods such as trend analysis, correlation analysis, and geographic detectors, and the correlation characteristics of NPP with meteorological factors, social factors, and total water storage (TWS) were evaluated. The results indicate that the NPP of vegetation in the YRB showed an increasing trend (4.989 gC·m−2·a−1) from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The correlation coefficient indicates that temperature and accumulated temperature have a significant positive impact on the change of NPP, while TWS has a significant negative impact. In the study of the factors affecting vegetation NPP in the YRB, the most influential factors are soil type (0.48), precipitation (0.46), and temperature (0.32). The strong correlation between TWS and vegetation NPP in the YRB is about 39%, with a contribution rate of about 0.12, which is a factor that cannot be ignored in studying vegetation NPP changes in the YRB. [ABSTRACT FROM AUTHOR]

Triterpenoid saponins are crucial natural products widely distributed in various medicinal plants, with Dipsacus asperoides being particularly rich in these compounds. However, the glycosyltransferases responsible for the biosynthesis of α-hederin, one of the primary bioactive secondary metabolites in D. asperoides, have not been elucidated. In this study, transcriptomic and compound analyses revealed 359 differentially expressed genes associated with secondary metabolism, with 271 involved in triterpenoid saponin glycosylation. Through correlation analysis, 71 candidate glycosyltransferases were identified, and two novel glycosyltransferases were functionally characterized. It was shown that DaUGT121 catalyzes the conversion of hederagenin into cauloside A, while DaUGT103 acts as a cauloside A 1,2-rhamnosyltransferase transforming cauloside A into α-hederin. These findings illuminate the biosynthesis of triterpenoid saponins in D. asperoides, providing insights into the molecular mechanisms and offering novel tools for synthesizing natural products with diverse sugar moieties., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Net primary productivity (NPP) is an important parameter reflecting vegetation growth, and water is one of the necessary factors for vegetation growth. Investigating the mutual influence between NPP and water is significant for ensuring the stable development of the ecological environment. This study focuses on the Yangtze River Basin (YRB) as the research area, and based on medium-resolution imaging spectrometer (MODIS) data, climate data, and gravity recovery and climate experiment (GRACE) data, the spatiotemporal evolution characteristics of vegetation NPP and terrestrial water storage (TWS) in the YRB from 2000 to 2022 are explored and analyzes the mutual influence of NPP with climate factors and TWS. The results show that vegetation NPP (4.10 gC·m -2 ·a -1 ) and TWS (0.55 mm) in the YRB have exhibited an increasing trend from 2000 to 2022, with a strong correlation between the two, which is related to recent environmental policies. Analysis of the impact of climate factors on NPP reveals that temperature and TWS significantly positively impact NPP changes. Furthermore, comparisons between NPP and TWS indicate that changes in TWS substantially promote plant growth. In addition, the comparison between NPP and TWS indicates that changes in TWS have an important promoting effect on plant growth. Surface water (SWS) and soil water (SM) have a significant promoting effect on plant growth, but with a strong lag, while the consumption of groundwater (GWS) has been promoting plant growth without significant lag., (© 2024. The Author(s).)

Background: Isoquercitrin (quercetin-3-O-β-D-glucopyranoside) has exhibited promising therapeutic potentials as cardioprotective, anti-diabetic, anti-cancer, and anti-viral agents. However, its structural complexity and limited natural abundance make both bulk chemical synthesis and extraction from medical plants difficult. Microbial biotransformation through heterologous expression of glycosyltransferases offers a safe and sustainable route for its production. Despite several attempts reported in microbial hosts, the current production levels of isoquercitrin still lag behind industrial standards., Results: Herein, the heterologous expression of glycosyltransferase UGT78D2 gene in Bacillus subtilis 168 and reconstruction of UDP-glucose (UDP-Glc) synthesis pathway led to the synthesis of isoquercitrin from quercetin with titers of 0.37 g/L and 0.42 g/L, respectively. Subsequently, the quercetin catabolism blocked by disruption of a quercetin dioxygenase, three ring-cleavage dioxygenases, and seven oxidoreductases increased the isoquercitrin titer to 1.64 g/L. And the hydrolysis of isoquercitrin was eliminated by three β-glucosidase genes disruption, thereby affording 3.58 g/L isoquercitrin. Furthermore, UDP-Glc pool boosted by pgi (encoding glucose-6-phosphate isomerase) disruption increased the isoquercitrin titer to 10.6 g/L with the yield on quercetin of 72% and to 35.6 g/L with the yield on quercetin of 77.2% in a 1.3-L fermentor., Conclusion: The engineered B. subtilis strain developed here holds great potential for initiating the sustainable and large-scale industrial production of isoquercitrin. The strategies proposed in this study provides a reference to improve the production of other flavonoid glycosides by engineered B. subtilis cell factories., (© 2024. The Author(s).)