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In North Dakota, USA, harsh winter conditions cause considerable winter injury to many floricane fruiting raspberries. Short growing seasons in North Dakota inevitably result in an incomplete fruit harvest of primocane fruiting raspberries. In this study, the winter hardiness of six floricane fruiting raspberry and five primocane fruiting raspberry cultivars had been evaluated for multiple years since 2016. Results showed that five floricane cultivars (Prelude, Boyne, Killarney, Nova, and Encore) showed less than 20% buds damaged, whereas cultivar Amethyst had 20% to 50% buds damaged in the winter. Severe bud damage was observed in five primocane fruiting cultivars (Polana, Autumn Bliss, BP-1, Heritage, and Joan J) in which Polana had the least winter bud damage with significant variations among years, followed by Heritage, Joan J, and Autumn Bliss, and BP-1. No winter damage was observed of the root system. In addition, the fruit quality, cane height, and yield of the six floricane fruiting cultivars were evaluated. Results showed that ‘Killarney’ fruit was the largest (2.50 g per berry) followed by ‘Nova’ (2.35), ‘Encore’ (2.24), ‘Amethyst’ (2.12), ‘Prelude’ (1.94), and ‘Boyne’ (1.84). No significant differences in total soluble solid (TSS) among cultivars were observed except that the TSS of Prelude was significantly lower than the other five cultivars. The cane height of ‘Killarney’ was the shortest (75 cm), whereas ‘Amethyst’ grew the longest canes (129 cm). Prelude is the earliest ripening floricane cultivar (late June) with a short harvest duration (20 to 25 days). The fruit harvest of ‘Killarney’, ‘Boyne’, and ‘Nova’ started 3 to 5 days after ‘Prelude’ with a longer harvest duration (25 to 30 days). ‘Encore’ and ‘Amethyst’ were harvested from mid-late July to early-mid August. Yield estimation indicated that ‘Killarney’ and ‘Nova’ produced more berries than other cultivars.

Abstract Respiratory disease caused by coronavirus infection remains a global health crisis. Although several SARS-CoV-2-specific vaccines and direct-acting antivirals are available, their efficacy on emerging coronaviruses in the future, including SARS-CoV-2 variants, might be compromised. Host-targeting antivirals provide preventive and therapeutic strategies to overcome resistance and manage future outbreak of emerging coronaviruses. Cathepsin L (CTSL) and calpain-1 (CAPN1) are host cysteine proteases which play crucial roles in coronaviral entrance into cells and infection-related immune response. Here, two peptidomimetic α-ketoamide compounds, 14a and 14b, were identified as potent dual target inhibitors against CTSL and CAPN1. The X-ray crystal structures of human CTSL and CAPN1 in complex with 14a and 14b revealed the covalent binding of α-ketoamide groups of 14a and 14b to C25 of CTSL and C115 of CAPN1. Both showed potent and broad-spectrum anticoronaviral activities in vitro, and it is worth noting that they exhibited low nanomolar potency against SARS-CoV-2 and its variants of concern (VOCs) with EC50 values ranging from 0.80 to 161.7 nM in various cells. Preliminary mechanistic exploration indicated that they exhibited anticoronaviral activity through blocking viral entrance. Moreover, 14a and 14b exhibited good oral pharmacokinetic properties in mice, rats and dogs, and favorable safety in mice. In addition, both 14a and 14b treatments demonstrated potent antiviral potency against SARS-CoV-2 XBB 1.16 variant infection in a K18-hACE2 transgenic mouse model. And 14b also showed effective antiviral activity against HCoV-OC43 infection in a mouse model with a final survival rate of 60%. Further evaluation showed that 14a and 14b exhibited excellent anti-inflammatory effects in Raw 264.7 mouse macrophages and in mice with acute pneumonia. Taken together, these results suggested that 14a and 14b are promising drug candidates, providing novel insight into developing pan-coronavirus inhibitors with antiviral and anti-inflammatory properties.

Abstract Among C‐glycosides, C‐alkyl glycosides are significant building blocks for natural products and glycopeptides. However, research on efficient construction methods for C‐alkyl glycosides remains relatively limited. Compared with Michael acceptors, non‐activated olefins are more challenging substrates and have rarely been employed in the construction of C‐glycosides. Here, a highly efficient and convenient approach for the synthesis of C‐alkyl glycosides through a nickel‐catalyzed C(sp3)‐C(sp3) coupling reaction is presented. A distinctive feature of this method is its utilization of non‐activated olefins as the anomeric radical acceptors for hydroalkylation, allowing for the direct formation of C‐glycoside bonds in a single step. Furthermore, this method demonstrates excellent compatibility with a broad scope of highly reactive functional groups. Mechanistic investigations suggest that the reaction proceeds via a free radical pathway, leading predominantly to the formation of products with α‐configuration. Overall, this innovative methodology offers a versatile and practical approach for the synthesis of C‐alkyl glycosides, offering new avenues for the production of intricate glycosides with potential applications in drug discovery and chemical biology.

Polycrystalline diamond compact (PDC) cutters, the most widely used cutting tool in oil and gas drilling, often fail very quickly because of excessive wear and poor impact toughness in hard and complex formations. In this study, dense PDC samples doped with SiC whiskers (SiCw) were successfully synthesized under high pressure and high temperature (HPHT) conditions. The microstructures, elemental distributions, and phase compositions of the PDC samples were systematically investigated. The mechanical properties including the Vickers hardness, wear resistance, impact toughness and thermal stability, were tested. The introduction of SiCw reduced the Co content in the polycrystalline diamond layer. The testing results showed that the hardness of PDC doped with 0.5 wt% SiCw was 75.25 GPa, wear ratio was 145.6 × 104 and impact toughness reached 4020 J, which were 12.2%, 30.9%, and 142.2% higher than those of the control group (CG), respectively. The PDC-0.5 wt% SiCw had the lowest thermal expansion value, with a 57.5% reduction compared with the CG. In addition, the strengthening mechanism of SiCw was explored. SiCw partially replaced the weakening cobalt phase in the binder phase, thus improving the hardness, wear resistance, and thermal stability of the PDC. The introduction of SiCw inhibited the abnormal growth of WC at the interface of the cemented carbide substrate and changed the fracture mode from intergranular to transgranular, which were the main mechanisms responsible for the significant increase in the impact toughness of PDC.

Two genes (TaHRC and Tsn1) conferring susceptibility to Fusarium head blight and tan spot, Septoria nodorum blotch, and spot blotch in wheat were targeted through wide hybridization with maize expressing Cas9 and guide RNA (gRNA). For each gene, two target sites were selected and corresponding gRNA expression cassettes were synthesized and cloned into a binary vector carrying the CRISPR/Cas9-mediated genome editing machinery. The constructed binary vectors were used to transform the hybrid maize Hi-II through an Agrobacterium-mediated approach to generate T0 and T1 plants, which were used to cross with wheat variety Dayn for targeting Tsn1 or the susceptible allele (TaHRC-S) of TaHRC as well as with the near-isogenic line (Day-Fhb1) of Dayn for targeting the resistant allele (TaHRC-R) of TaHRC. Haploid embryos were rescued in vitro from the wide crosses to generate haploid plants. PCR amplification and sequencing indicated that 15 to 33% of the haploid plants contained the target gene with mutations at the target sites. This wheat × maize hybridization combined with genome editing approach provides a useful alternative tool, not only for targeting susceptibility genes to improve disease resistance without regulatory issues, but also for understanding gene function in wheat. [Graphic: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau. In this study, combined with satellite images and bathymetric data, we comprehensively evaluate the accuracy of a multi-factor combined linear regression model (MLR) and machine learning models, create a depth distribution map and compare it with the spatial interpolation, and estimate the change of water level and water storage based on the inverted depth. The results indicated that the precision of the random forest (RF) was the highest with a coefficient of determination (R2) value (0.9311) and mean absolute error (MAE) values (1.13 m) in the test dataset and had high reliability in the overall depth distribution. The water level increased by 9.36 m at a rate of 0.47 m/y, and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth. The water level change was consistent with that of the Shuttle Radar Topography Mission (SRTM) method. Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.

Nano-NbC and nano-WC were added to the WC-bronze based matrix to improve the performance of the matrix of the impregnated diamond bit. The uniform design method, regression analysis and solver were used to obtain the optimal addition. Laboratory drilling experiments of the strengthened bits were carried out. The results show that after adding nanoparticles, the hardness and the bending strength of the matrix are improved by up to 25.23% and 5.73%. The wear resistance of the diamond composites is significantly increased, with wear ratio increased by up to 57.4%, indicating that the diamond and the matrix are more closely bonded. In conclusion, adding nanoparticles has positive significance on the performance of diamond composites. The mechanical drilling speed of the strengthened bits is 19.63% higher than that of the blank diamond bit, and that the matrix wear is reduced by 32.84%, indicating that the addition of nanoparticles to the matrix can strengthen the drill bit and improve drilling efficiency.

Graphene oxide (GO) is widely used in various fields such as improving the performance of cement-based materials and making composite materials due to its large specific surface area and abundant oxygen-containing functional groups. However, it has also caused water pollution. To remove GO in aqueous solution, sepiolite (SEP) was used to adsorb it. The effects of pH, adsorbent quality, GO concentration, temperature and adsorption time on the ability of SEP to adsorb GO were investigated. The materials were characterized by SAP and laser particle size analyzer, and the adsorption performance and mechanism of SEP for GO were further analyzed by XRD, FTIR, SEM, TEM, XPS, AFM, and Zeta potential microscopic tests. The results showed that: 1) Under the conditions of temperature 303 K, pH = 3, adsorbent mass 30 mg, and initial concentration of GO 100 mg/L, the adsorption effect was the best, and the adsorption rate reached 94.8 %. 2) The adsorption reached equilibrium at 2160 min, and the adsorption process was more in line with the pseudo-second-order adsorption kinetic equation, and the adsorption behavior was controlled by chemical effects. 3) The adsorption of SEP to GO is more consistent with the Langmuir and Temkin adsorption isotherm model, and the reaction is a spontaneous, endothermic, and entropy-increasing process. Experiments showed that SEP had a strong adsorption capacity for GO, which provides a reference for the treatment of toxic GO in aqueous solution and the realization of water ecological protection.

Salt lakes on the Tibetan Plateau (TP) are rich in lithium (Li), boron (B) and other mineral resources, and accurate assessment of the mineral content and spatial distribution of the brine in those salt lakes is important to guide the development and utilization of their mineral resources. There are few studies estimating the mineral content of salt lakes on the TP due to the lack of in situ investigation data. This study introduced an intelligent prediction model combining a feature selection algorithm with a machine learning algorithm using Sentinel-2 satellite data to estimate the Li, B, and TDS contents of Bieruoze Co and Guopu Co lakes on the TP. First, to enrich the spectral information, four mathematical transformations (reciprocal, logarithmic, reciprocal of logarithm, and first-order derivative) were applied to the original bands. Then, feature selection was performed using the genetic algorithm (GA) to select the optimal input variables for the model. Finally, prediction models were constructed by partial least squares regression (PLSR), multiple linear regression (MLR), and random forest (RF). The results showed that: 1) The spectral mathematical transformation provided rich spectral information for the mineral content estimation. 2) The performance of the estimation model constructed by the feature optimization method using GA was better than that of the estimation model constructed based on all spectral bands. Based on GA for feature optimization, the MAPE of GA-RF for estimating Li, B and TDS contents on the testing set was reduced by 77.52%, 28.54% and 36.79%, respectively. 3) Compared with the GA-MLR and GA-PLSR models, GA-RF estimated Li (R2=0.99, RMSE=1.15 mg L-1, MAPE=3.00%), B (R2=0.97, RMSE=10.65 mg L-1, MAPE=2.73%), and TDS (R2=0.93, RMSE=0.60 g L-1, MAPE=1.82%) all obtained the optimal performance. This study showed that the combination of the GA-based feature selection method and the RF model has excellent performance and applicability for monitoring the content of multiple minerals using Sentinel-2 imagery in salt lakes on the TP.

Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content.

Transient creep during an earthquake cycle is very important to understand the rheology of fault and deformation mechanisms in the brittle–plastic transition zone. In this paper, we analyzed the microstructures of samples for mylonites, pseudotachylyte, and cataclasite under optical microscope, SEM, and EBSD, which were collected from the Red River fault in southwest of China, where we uncovered a series of ductile to brittle deformed rocks which recorded transient creep related to earthquakes. The results show that mylonites formed at the inter-seismic creep were overprinted by pseudotachylyte and cataclasite which were produced during co-seismic rupture, and cracks in cataclasite were healed during the post-seismic relaxation. Based on the analysis of the microstructures and deformation mechanism of fault rocks, we propose the oscillation deformation model to explain transient creep of the brittle–plastic transition zone during the seismic cycle in the Red River fault.

Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information.

In order to deal with the increasingly serious pollution of graphene oxide (GO) to the environment. In this paper, the use of red sandstone to treat GO-contaminated aqueous solution is proposed for the first time, and the adsorption capacity and adsorption mechanism of red sandstone to GO are discussed. The controlled variable method was used to explore the optimal pH, concentration, and quality of red sandstone for GO aqueous solution. The adsorption isotherm, thermodynamics, and adsorption kinetics were fitted. Adsorption characterization tests were performed using XRD, AFM, XPS, FT-IR, SAP, TEM, SAP, laser particle size analyzer, and SEM. The results show that when T=303 K, the optimum adsorption condition of red sandstone for GO is pH=4, the mass of the adsorbent is 40 mg, and when the concentration of GO is 80 mg/L, the adsorption capacity is 90 mg/g. The adsorption isotherm model fits the Langmuir model. The adsorption thermodynamic experiments and fitting results show that the reaction is endothermic. XRD and FT-IR tests showed that CaCO3 in red sandstone was involved in the adsorption of GO. SEM, TEM, and AFM microscopic results showed that GO was adsorbed on the surface of red sandstone particles. The XPS test showed that Ca2+ in red sandstone and C=O bond in GO undergo ionic or coordination reaction. The adsorption kinetics fit a pseudo-second-order kinetic model. This study will provide some references for the removal of GO in the environment and the interaction mechanism with natural minerals.

There is a large amount of lakes on the Tibetan Plateau (TP), which are very sensitive to climate change. Understanding the characteristics and driving mechanisms of lake change are crucial for understanding climate change and the effective use of water resources. Previous studies have mainly focused on inter-annual lake variation, but the continuous and long-term intra-annual variation of lakes on the TP remains unclear. To address this gap, we used the global surface water (GSW) dataset and the Shuttle Radar Topography Mission (SRTM) DEM to estimate the water level and storage changes on the TP. The results indicated that the average annual minimum lake water level (LWLmin) and the average annual maximum lake water level (LWLmax) increased by 3.09 ± 0.18 m (0.16 ± 0.01 m/yr) and 3.69 ± 0.12 m (0.19 ± 0.01 m/yr) from 2000 to 2018, respectively, and the largest change of LWLmin and LWLmax occurred in 2002–2003 (0.45 m) and 2001–2002 (0.39 m), respectively. Meanwhile, the annual minimum lake water storage change (LWSCmin) and annual maximum lake water storage change (LWSCmax) were 125.34 ± 6.79 Gt (6.60 ± 0.36 Gt/yr) and 158.07 ± 4.52 Gt (8.32 ± 0.24 Gt/yr) from 2000 to 2018, and the largest changes of LWSCmin and LWSCmax occurred in the periods of 2002–2003 (17.67 Gt) and 2015–2016 (17.51 Gt), respectively. The average intra-year changes of lake water level (LWLCintra-year) and the average intra-year changes of lake water storage (LWSCintra-year) were 0.98 ± 0.23 m and 40.19 ± 10.67 Gt, respectively, and the largest change in both LWLCintra-year (1.44 m) and LWSCintra-year (62.46 Gt) occurred in 2018. The overall trend of lakes on the TP was that of expansion, where the LWLC and LWSC in the central and northern parts of the TP was much faster than that in other regions, while the lakes in the southern part of the TP were shrinking, with decreasing LWLC and LWSC. Increased precipitation was found to be the primary meteorological factor affecting lake expansion, and while increasing glacial meltwater also had an important influence on the LWSC, the variation of evaporation only had a little influence on lake change.

Atmospheric ammonia (NH _3 ) plays a significant role in the nitrogen cycle, and can have impacts on air quality, ecological balance and climate change. While NH _3 associated with natural and agricultural processes has long been considered the primary source, the contribution of combustion-related NH _3 , particularly from vehicular emissions, keeps on the rise. We found that high on-road NH _3 concentrations occurred in a metropolitan city based on mobile measurement, and inferred that urban vehicular NH _3 emission was likely underestimated in the past. NH _3 emission factors (EFs) were derived from ring roads and tunnels, showing levels 74% and 20% higher than the latest standard proposed by Euro VII, respectively. To quantify the underestimation, two methods based on car ownership and traffic flow were used to estimate the annual vehicular NH _3 emission in Shanghai as 2.59 and 1.76 Gg, respectively, substantially surpassing the predicted results by the Dynamic Projection model for Emissions in China. Given these discoveries, we recommend that it is urgent and imperative to establish relevant national standards and limits aiming at regulation on vehicular NH _3 emissions. And more representative EFs measurements should be adopted to improve the accuracy of inventory estimation.

The rare earth extraction process has significant time delay characteristics, making it challenging to identify the time delay and establish an accurate mathematical model. This paper proposes a multi-delay identification method based on improved time-correlation analysis. Firstly, the data are preprocessed by grey relational analysis, and the time delay sequence and time-correlation data matrix are constructed. The time-correlation analysis matrix is defined, and the H∞ norm quantifies the correlation degree of the data sequence. Thus the multi-delay identification problem is transformed into an integer optimization problem. Secondly, an improved discrete state transition algorithm is used for optimization to obtain multi-delay. Finally, based on an Neodymium (Nd) component content model constructed by a wavelet neural network, the performance of the proposed method is compared with the unimproved time delay identification method and the model without an identification method. The results show that the proposed algorithm improves optimization accuracy, convergence speed, and stability. The performance of the component content model after time delay identification is significantly improved using the proposed method, which verifies its effectiveness in the time delay identification of the rare earth extraction process.

Diatomite is a non-metallic mineral resource rich in SiO2, which can be used to modify coastal cement soil. In order to explore the mechanical modification effect of diatomite on coastal cement soil at the age of 7 days, based on coastal cement soil with cement content of 5% (mass fraction), diatomite of 0%, 5%, 10%, 15% and 20% (mass fraction) was mixed for modification. Through the unconfined compressive strength test, the triaxial unconsolidated undrained test, backscattered electron imaging (BSE), and energy-dispersive spectroscopy (EDS) technology, the influence of diatomite content and confining pressure on the peak strength of modified coastal cement soil was explored. The empirical formula between the peak strength of the DE specimen and the content of diatomite and confining pressure was established by curve fitting, and the fitting effect was ideal. When diatomite was mixed with coastal cement soil, the optimal dosage of diatomite was 5% from the perspective of mechanical properties and economic benefits of the maximum growth rate of compression and shear. The unconfined compressive strength test showed that the peak strength and elastic modulus of the modified coastal cement soil with 5% diatomite content were 37% and 57% higher than those of cement soil, respectively. The triaxial unconsolidated undrained test showed that the internal friction angle of the modified coastal cement soil was stable at about 30°, and cohesion of DE-5, DE-10, DE-15, and DE-20 increased by 28%, 48%, 78%, and 97%, respectively, compared to cement soil. The microscopic test found that the pore distribution of modified coastal cement soil is closely related to the strength change. The results show that the addition of diatomite can effectively improve the mechanical properties of soil-cement.

Soil organic matter (SOM) is an essential nutrient for crop growth and development. Hyperspectral satellite images with comprehensive spectral band coverage and high spectral resolution can be used to estimate and draw a spatial distribution map of SOM content in the region, which can provide a scientific management basis for precision agriculture. This study takes Xinzheng City, Henan Province’s agricultural area, as the research object. Based on ZY1-02D hyperspectral satellite image data, the first derivative of reflectance (FDR) was processed on the original reflectance (OR). The SOM characteristic spectral bands were extracted using the correlation coefficient (CC) and least absolute shrinkage and selection operator (Lasso) methods. The prediction model of SOM content was established by multiple linear regression (MLR), partial least squares regression (PLSR), and random forest (RF) algorithms. The results showed that: (1) FDR processing can enhance SOM spectral features and reduce noise; (2) the Lasso feature band extraction method can reduce the model’s input variables and raise the estimation precision; (3) the SOM content prediction ability of the RF model was significantly better than that of the MLR and PLSR models. The FDR-Lasso-RF model was the best SOM content prediction model, and the validation set R2 = 0.921, MAEV = 0.512 g/kg, RMSEV = 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region.