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Background: Variations in the fusion (F) protein of respiratory syncytial virus (RSV) with main antigenic sites I–V and Ø may affect the development of RSV vaccines and therapies. Methods: In the study, 30 respiratory specimens positive for RSV were randomly selected from children with acute lower respiratory infections (ALRI) in Beijing every year from 2012 to 2021 for F gene sequencing. Then, 300 F gene sequences and 508 uploaded to GenBank from China were subjected to phylogenetic analysis. Results: The results indicated the nucleotide identities were 95.4–100% among 446 sequences of RSV A, and 96.3–100% among 362 of RSV B. The most common variant loci were N80K (100.00%) and R213S (97.76%) for site Ø, and V384I/T (98.43%) for site I among sequences of RSV A, and M152I (100.00%), I185V (100.00%), and L172Q/H (94.48%) for site V, and R202Q (99.45%) for site Ø among sequences of RSV B. N276S appears in 95.29% sequences of RSV A, while S276N and N262 I/S appear in 1.38% and 0.55% sequences of RSV B, respectively. No variation was found in all sequences at the binding sites of 14N4 and motavizumab. Conclusions: There were cumulative variations of the RSV F gene, especially at some binding sites of antigenic sites.

In order to solve the problems that existing tomato maturity detection methods struggle to take into account both common tomato and cherry tomato varieties in complex field environments (such as light change, occlusion, and fruit overlap) and the model size being too large, this paper proposes a lightweight tomato maturity detection model based on improved YOLO11, named GFS-YOLO11. In order to achieve a lightweight network, we propose the C3k2_Ghost module to replace the C3K2 module in the original network, which can ensure a feature extraction capability and reduce model computation. In order to compensate for the potential feature loss caused by the light weight, this paper proposes a feature-refining module (FRM). After embedding each feature extraction module in the trunk network, it improves the feature expression ability of common tomato and cherry tomato in complex field environments by means of depth-separable convolution, multi-scale pooling, and channel attention and spatial attention mechanisms. In addition, in order to further improve the detection ability of the model for tomatoes of different sizes, the SPPFELAN module is also proposed in this paper. In combining the advantages of SPPF and ELAN, multiple parallel SPPF branches are used to extract features of different levels and perform splicing and fusion. To verify the validity of the method, this study constructed a dataset of 1061 images of common and cherry tomatoes, covering tomatoes in six ripened categories. The experimental results show that the performance of the GFS-YOLO11 model is significantly improved compared with the original model; the P, R, mAP50, and MAP50-95 increased by 5.8%, 4.9%, 6.2%, and 5.5%, respectively, and the number of parameters and calculation amount were reduced by 35.9% and 22.5%, respectively. The GFS-YOLO11 model is lightweight while maintaining high precision, can effectively cope with complex field environments, and more conveniently meet the needs of real-time maturity detection of common tomatoes and cherry tomatoes.

Northern corn leaf blight (NCLB) is caused by a fungus and can be susceptible to the disease throughout the growing period of corn, posing a significant impact on corn yield. Aiming at the problems of under-segmentation, over-segmentation, and low segmentation accuracy in the traditional segmentation model of northern corn leaf blight, this study proposes a segmentation method based on an improved U-Net network model. By introducing a convolutional layer and maximum pooling layer to a VGG19 network, the channel attention module and spatial attention module (CBAM) are fused, and the squeeze excitation (SE) attention mechanism is combined. This enhances image feature decoding, integrates feature maps of each layer, strengthens the feature extraction process, expands the sensory fields and aggregates context information, and reduces the loss of location and dense semantic information caused by the pooling operation. Findings from the study show that the proposed NCLB-Net has significantly improved the MIoU and PA indexes, reaching 92.43% and 94.71%, respectively. Compared with the traditional methods, U-Net, SETR, DAnet, OCnet, PSPNet, etc., the MIoU is improved by 20.81%, 16.10%, 9.79%, 5.27%, and 11.06%, and the PA is improved by 11.49%, 8.18%, 9.54%, 13.11%, and 6.26%, respectively.

With the continuous development of the shipping market, bow thrusters have become more important for ship maneuvering. Therefore, the performance of bow thrusters is studied in this paper. In order to obtain an unsteady performance of the bow thruster under different ship speed conditions, the SST k-ω turbulence model is adopted to predict the hydrodynamics of the bow thruster. With the ship’s speed increasing gradually, the variation characteristics of hydrodynamic coefficients and the flow field distribution at key positions are analyzed. The results show that with an increase in ship speed to three knots, the thrust coefficient and torque coefficient of the bow thruster decrease by 2.69~4.07% and 2.34~3.08%. In addition, the blade vibration amplitude intensifies. In the departure direction, the propeller load is more susceptible to being influenced and decreases by an additional 2.34~4.16% compared with that in the berthing direction. Meanwhile, it is found that the velocity distribution is asymmetrical. The inlet velocity at the bow side is faster, which results in the maximum peak pressure being about three times the minimum peak pressure. In addition, the pressure’s nonuniformity in the tunnel increases gradually with the increase in ship speed. Compared with the pressure distribution in the berthing direction, the pressure distribution before and after the propeller is more uniform, which is consistent with the results of hydrodynamic change and velocity distribution. The research in this paper has a certain reference significance for understanding the hydrodynamic performance of bow thrust operation.

NiFe (oxy)hydroxide has been widely used as a benchmark anodic catalyst for oxygen evolution reactions (OERs) in alkaline water electrolysis devices; however, the energy saving actually takes contributions from both the anodic OER and cathodic hydrogen evolution reaction (HER). In this work, we observe the catalytic promotion upon the in situ-derived NiFe (oxy)hydroxide from the NiFe alloy monolithic electrode and also point out that the coupled nickel cathode is contaminated, leading to the loss of HER activity and a reduction in overall efficiency. It is found that Ni2+ and Fe3+ cations are inevitably detached from the anode into the electrolyte and electrodeposited on the nickel cathode after the three-month industrial simulation. This research presents the significant enhancement of the oxygen evolution catalysis using an in situ aging process and emphasizes that the catalytic application should not only be isolated on the half reaction, but a reasonable coupled electrode match to get rid of the contamination from the electrolyte is also of great significance to sufficiently present the intrinsic catalytic yielding for the real application.

The Zn-coated high-Si advanced high-strength steel (AHSS) tends to suffer Zn-assisted liquid metal embrittlement (LME) during the resistance spot welding (RSW) process. In this study, the LME behaviors of electrogalvanized (EG) and galvannealed (GA) high-Si steels were comparatively investigated. The maximum lengths of the LME cracks at the shoulder and center of the spot weld were approximately 366.6 μm and 1486.5 μm, respectively, for the EG yet 137.0 μm and 1533.3 μm, respectively, for the GA high-Si steels. Additionally, all EG and GA welded joints were etched to measure the nugget size. It was found that the increased welding current could aggravate the formation tendency of the LME cracks for both the EG and GA high-Si steels. Furthermore, the statistical results revealed that the electrogalvanized high-Si AHSS exhibited a relatively higher LME susceptibility than the galvannealed high-Si AHSS. It was deemed that the internal oxidation produced during the annealing before the Zn coating was the crucial factor that led to the difference in the LME susceptibilities for the EG and GA high-Si steels.

Shandong Province in China is rich in paleontological fossils and has a long history of fossil research. However, research on the distribution characteristics and potential factors of discovered fossil sites in Shandong Province is limited and insufficient, making it difficult to comprehensively plan for the protection and utilization of fossil sites in Shandong Province. The study constructs a basic geographical information system (GIS) database with 133 discovered fossil sites and geological and socio-economic data of Shandong Province and studies fossil sites’ spatial distribution characteristics and the spatial relationship with potential factors at a regional scale. The results are as follows: (1) The fossil sites in Shandong Province are concentrated in the mountainous area of central Shandong and the hilly area of the Shandong Peninsula, with significant uneven distribution characteristics, including two agglomeration areas and seven sub-agglomeration areas. (2) Natural geographical conditions, such as topography, paleogeography, and stratigraphy, play a positive role in the distribution of fossil sites, and there are apparent concentrations in the following areas: at an altitude greater than 100 m; the Lower Paleozoic and Cretaceous sedimentary rocks; and the active areas of paleo-tectonics. (3) A certain degree of negative correlation exists between socio-economic conditions, such as roads and population density, and the number of fossil sites, and a positive correlation exists between disposable personal income and those fossil sites. The operational procedure presented here is a simple, objective, applicable method that can enhance our understanding of the spatial distribution patterns and influencing factors of the discovered fossil sites of Shandong Province and support more effective and appropriate planning for paleontological heritage conservation.

Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R2) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops.

Neuroinflammation is a major feature of type 2 diabetic mellitus (T2DM), adversely affecting hippocampal neurogenesis. However, the precise mechanism is not fully understood, and therapeutic approaches are currently lacking. Therefore, we determined the effects of exercise on neuroinflammation and hippocampal neurogenesis in T2DM mice, with a specific focus on understanding the role of the irisin and related cascade pathways in modulating the beneficial effects of exercise in these processes. Ten-week exercise significantly decreased T2DM-induced inflammation levels and markedly promoted hippocampal neurogenesis and memory function. However, these positive effects were reversed by 10 weeks of treatment with cyclo RGDyk, an inhibitor of irisin receptor signaling. Additionally, exercise helped reduce the M1 phenotype polarization of hippocampal microglia in diabetic mice; this effect could be reversed with cyclo RGDyk treatment. Moreover, exercise markedly increased the levels of fibronectin type III domain-containing protein 5 (FNDC5)/irisin protein while decreasing the expression of Toll-like receptor 4 (TLR4), myeloid differential protein-88 (MyD88), and nuclear factor kappa-B (NF-κB) in the hippocampus of T2DM mice. However, blocking irisin receptor signaling counteracted the down-regulation of TLR4/MyD88/NF-κB in diabetic mice undergoing exercise intervention. Conclusively, exercise appears to be effective in reducing neuroinflammation and enhancing hippocampal neurogenesis and memory in diabetes mice. The positive effects are involved in the participation of the irisin/TLR4/MyD88/NF-κB signaling pathway, highlighting the potential of exercise in the management of diabetic-induced cognitive decline.

Lactic acid (LA) plays a crucial role in the silage process, which occurs through LA fermentation. Consequently, there is a strong correlation between lactic acid production and the efficiency of the silage. However, traditional methods face challenges like long fermentation times, low acid production, and unstable quality, limiting agricultural preservation. This paper aims to explore innovations in lactic acid production technologies and show how these technologies have driven the development of silage fermentation for agricultural conservation. First, the important role of LA in agricultural preservation and the limitations of traditional silage techniques are presented. Next, advancements in LA production methods are thoroughly examined, covering the selection of microbial strains and the substitution of fermentation substrates. Following this, new technologies for silage fermentation are explored, drawing from innovations in LA production. These include the selection of LA strains, optimization of fermentation conditions, and improvements in fermentation techniques. These innovations have proven effective in increasing LA production, improving feed quality, extending shelf life, and providing new solutions to enhance agricultural production and sustainability.

The modern world is witnessing an unprecedented rise in environmental exposures to hazardous substances such as pesticides, heavy metals, and synthetic chemicals [...]

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management.

In this work, we applied commercially available 2-pyridinecarboxylic acid to modify cellulose by simple manipulations, and then anchored low-toxicity metal nickel onto the modified cellulose to prepare the heterogeneous catalyst (CL-AcPy-Ni). The obtained catalyst was characterized by FT-IR, TG-DSC, BET, XRD, SEM-EDS, ICP-OES, XPS, and GPC. The catalytic performance of CL-AcPy-Ni in the Suzuki cross-coupling reaction was investigated using 4-methyl iodobenzene and phenylboronic acid as the model substrates reacting in THF under 120 °C for 24 h. The catalytic ability of CL-AcPy-Ni for various halobenzenes and phenylboronic acid derivatives was also further investigated under optimal conditions and demonstrated good catalytic activity, and a series of diaryls were successfully synthesized. Finally, this green nickel-based catalyst could be reused for five successive cycles by simple centrifugation.

Porous carbon materials have gained increasing attention in catalysis applications due to their tailorable surface properties, large specific surface area, excellent thermal stability, and low cost. Even though porous carbon materials have been employed for thermal-catalytic dry reforming of methane (DRM), the structure–function relationship, especially the critical factor affecting catalytic performance, is still under debate. Herein, various porous carbon-based samples with disparate pore structures and surface properties are prepared by alkali (K2CO3) etching and the following CO2 activation of low-cost petroleum pitch. Detailed characterization clarifies that the quinone/ketone carbonyl functional groups on the carbon surface are the key active sites for DRM. Density functional theory (DFT) calculations also show that the C=O group have the lowest transition state energy barrier for CH4* cleavage to CH3* (2.15 eV). Furthermore, the cooperative interplay between the specific surface area and quinone/ketone carbonyl is essential to boost the cleavage of C-H and C-O bonds, guaranteeing enhanced DRM catalytic performance. The MC-600-800 catalyst exhibited an initial CH4 conversion of 51% and a reaction rate of 12.6 mmolCH4 gcat.−1 h−1 at 800 °C, CH4:CO2:N2= 1:1:8, and GHSV = 6000 mL gcat.−1 h−1. Our work could pave the way for the rational design of metal-free carbon-based DRM catalysts and shed new light on the high value-added utilization of heavy oils.

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region.

Head-to-tail sequences have been reported in human bocavirus (HBoV) 1-4. To reveal their features and functions, HBoV DNA was screened among respiratory specimens from pediatric patients with an acute respiratory infection (ARI) between April 2020 and December 2022, followed by HBoV genotyping. Head-to-tail sequences were detected using nested PCR, TA cloning, and Sanger sequencing, and these findings were confirmed by mNGS and amplicon sequencing. The secondary structure was predicted using the Mfold web server. The results indicated that head-to-tail sequences were detected in 42 specimens through TA cloning from 351 specimens positive for HBoV1 DNA, yielding 92 sequences into 32 types and 2 categories. Additionally, head-to-tail sequences were detected in 16 specimens by amplicon sequencing, yielding 60 sequences categorized into 23 types. The 374nt type, detected in 13 specimens, contains variants 374a and 374b, which differ in the unpaired loop regions of the palindrome or complementary reverse sequences, implying a switch of template chains during the replication process. The mNGS results in three specimens confirmed the presence of circular genome in copies below 1%. In conclusion, head-to-tail sequences of HBoV1 were common in children with ARI and were highly diverse in length and sequences. The variants may be generated by the switch of the template chain in the rolling-circle replication model.

This study evaluates the utility of OpenCap (v0.3), a smartphone-based motion capture system, for performing gait analysis in patients with neurological disorders. We compared kinematic and kinetic gait parameters between 10 healthy controls and 10 patients with neurological conditions, including stroke, Parkinson’s disease, and cerebral palsy. OpenCap captured 3D movement dynamics using two smartphones, with data processed through musculoskeletal modeling. The key findings indicate that the patient group exhibited significantly slower gait speeds (0.67 m/s vs. 1.10 m/s, p = 0.002), shorter stride lengths (0.81 m vs. 1.29 m, p = 0.001), and greater step length asymmetry (107.43% vs. 91.23%, p = 0.023) compared to the controls. Joint kinematic analysis revealed increased variability in pelvic tilt, hip flexion, knee extension, and ankle dorsiflexion throughout the gait cycle in patients, indicating impaired motor control and compensatory strategies. These results indicate that OpenCap can effectively identify significant gait differences, which may serve as valuable biomarkers for neurological disorders, thereby enhancing its utility in clinical settings where traditional motion capture systems are impractical. OpenCap has the potential to improve access to biomechanical assessments, thereby enabling better monitoring of gait abnormalities and informing therapeutic interventions for individuals with neurological disorders.

Objective: This systematic review investigates the long-term effect of cochlear implantation (CI) on clinical outcomes in tinnitus patients with sensorineural hearing loss (SNHL). Database Sources: PubMed, Embase, and the Cochrane Library were searched from inception to 30 April 2024. Manual searches of reference lists supplemented these searches when necessary. Review Methods: Original studies included in the meta-analysis had to contain comparative pre- and postoperative data for SNHL patients who underwent CI. Outcomes measured were the Tinnitus Handicap Inventory (THI), Visual Analog Scale (VAS), and Tinnitus Questionnaire (TQ). Results: A total of 28 studies comprising 853 patients showed significant tinnitus improvement after CI: THI mean difference (MD) −14.02 [95%CI −15.29 to −12.76, p < 0.001], TQ MD −15.85 [95%CI −18.97 to −12.74, p < 0.05], and VAS MD −3.12 [95%CI −3.49 to −2.76, p < 0.05]. Subgroup analysis indicated a significant difference between follow-up periods in THI (p < 0.0001) and VAS loudness (p = 0.02). Conclusions: Cochlear implantation substantially improves tinnitus in patients with hearing loss, though the effect may diminish over time. Further research is needed to confirm these findings.

The aim of this study was to evaluate the effect of conjugated linoleic acid (CLA) on milk fat globule (MFG) size and the ruminal microbiome of goats. Twenty-four mid-lactation Saanen dairy goats weighing 49 ± 4.5 kg (168 ± 27 d in milk, 1.2 ± 0.1 kg milk/d, 2–3 years old) were randomly divided into four groups—a control (CON) group, which was fed a basal diet, and three CLA supplementation groups, in which 30 g CLA (low-dose group, L-CLA), 60 g CLA (medium-dose group, M-CLA), or 90 g CLA (high-dose group, H-CLA) was added to the basal diet daily. The experiment lasted for 21 days, during which time goat milk was collected for composition and MFG size analysis. On day 21 of feeding, ruminal fluid was collected from the CON and H-CLA groups for analysis of the changes in microorganismal abundance. The results showed that CLA supplementation did not affect milk production, milk protein, or lactose content in the dairy goats (p > 0.05), but significantly reduced the milk fat content (p < 0.01) compared with the CON group. The CLA supplementation significantly decreased the D[3,2] and D[4,3] of the MFGs in a dose-dependent manner (p < 0.01). Moreover, dietary CLA inclusion increased the proportion of small-sized MFGs and decreased that of large-sized ones. The results of 16S rRNA gene sequencing showed that CLA-induced milk fat depression in dairy goats was accompanied by significant changes in the relative abundance of ruminal bacterial populations, most of which belonged to the Firmicutes and Bacteroidetes phyla. The relative abundance of Rikenellaceae_RC9_gut_group and Prevolellaceae_UCG-003 in Bacteroidetes and UCG-002, Succiniclasticum, and norank_f__norank_o__Clostridia_vadinBB60_group in Firmicutes was significantly higher in the CON group than in the H-CLA group. In contrast, the relative abundance of norank_f__UCG-011, norank_f_Eubacterium_coprostanoligenes_group, unclassified_f__Lachnospiraceae, and UCG-001 in Firmicutes and norank_f__Muribaculaceae in Bacteroidetes was significantly higher in the H-CLA group than in the CON group. Correlation analysis showed that the milk fat content was negatively correlated with the relative abundance of some bacteria, including members of Firmicutes and Bacteroidetes. Similarly, MFG size (D[3,2] and D[4,3]) was negatively correlated with several members of Firmicutes and Bacteroidetes, including Lachnospiraceae, norank_f__UCG-011, UCG-001, norank_f__Eubacterium_coprostanoligenes_group (Firmicutes), and norank_f__Muribaculaceae (Bacteroidetes), while positively correlated with the relative abundance of some members of Firmicutes and Bacteroidetes, including Mycoplasma, Succiniclasticum, norank_f__norank_o__Clostridia_vadinBB60_group, UCG-002 (Firmicutes), and Rikenellaceae_RC9_gut_group (Bacteroidetes). Overall, our data indicated that CLA treatment affected milk fat content and MFG size in dairy goats, and these effects were correlated with the relative abundance of ruminal bacterial populations. These results provide the first evidence to explain the mechanism underlying diet-induced MFG from the perspective of the ruminal microbiome in dairy goats.

Background: Diabetic peripheral neuropathy (DPN) is considered one of the most common chronic complications of diabetes. Impairment of mitochondrial function is regarded as one of the causes. Iron–sulfur clusters are essential cofactors for numerous iron–sulfur (Fe-S)-containing proteins/enzymes, including mitochondrial electron transport chain complex I, II, and III and aconitase. Methods: To determine the impact of hyperglycemia on peripheral nerves, we used Schwann-like RSC96 cells and classical db/db mice to detect the expression of Fe-S-related proteins, mitochondrially enzymatic activities, and iron metabolism. Subsequently, we treated high-glucose-induced RSC96 cells and db/db mice with pioglitazone (PGZ), respectively, to evaluate the effects on Fe-S cluster biogenesis, mitochondrial function, and animal behavior. Results: We found that the core components of Fe-S biogenesis machinery, such as frataxin (Fxn) and scaffold protein IscU, significantly decreased in high-glucose-induced RSC96 cells and db/db mice, accompanied by compromised mitochondrial Fe-S-containing enzymatic activities, such as complex I and II and aconitase. Consequently, oxidative stress and inflammation increased. PGZ not only has antidiabetic effects but also increases the expression of Fxn and IscU to enhance mitochondrial function in RSC96 cells and db/db mice. Meanwhile, PGZ significantly alleviated sciatic nerve injury and improved peripheral neuronal behavior, accompanied by suppressed oxidative stress and inflammation in the sciatic nerve of the db/db mice. Conclusions: Iron–sulfur cluster deficiency may contribute to hyperglycemia-mediated DPN.

Transcranial sonography (TCS) has been introduced to assess hyper-echogenicity in the substantia nigra of the midbrain for Parkinson’s disease (PD); however, its subjective and resource-demanding nature has impeded its widespread application. An AI-empowered TCS-based PD classification tool is greatly demanding, yet relevant research is severely scarce. Therefore, we proposed a novel dual-channel CNXV2-DANet for TCS-based PD classification using a large cohort. A total of 1176 TCS images from 588 subjects were retrospectively enrolled from Beijing Tiantan Hospital, encompassing both the left and right side of the midbrain for each subject. The entire dataset was divided into a training/validation/testing set at a ratio of 70%/15%/15%. Development of the proposed CNXV2-DANet was performed on the training set with comparisons between the single-channel and dual-channel input settings; model evaluation was conducted on the independent testing set. The proposed dual-channel CNXV2-DANet was compared against three state-of-the-art networks (ConvNeXtV2, ConvNeXt, Swin Transformer). The results demonstrated that both CNXV2-DANet and ConvNeXt V2 performed more superiorly under dual-channel inputs than the single-channel input. The dual-channel CNXV2-DANet outperformed the single-channel, achieving superior average metrics for accuracy (0.839 ± 0.028), precision (0.849 ± 0.014), recall (0.845 ± 0.043), F1-score (0.820 ± 0.038), and AUC (0.906 ± 0.013) compared with the single channel metrics for accuracy (0.784 ± 0.037), precision (0.817 ± 0.090), recall (0.748 ± 0.093), F1-score (0.773 ± 0.037), and AUC (0.861 ± 0.047). Furthermore, the dual-channel CNXV2-DANet outperformed all other networks (all p-values < 0.001). These findings suggest that the proposed dual-channel CNXV2-DANet may provide the community with an AI-empowered TCS-based tool for PD assessment.

While machine learning methods have been successful in predicting air pollution, current deep learning models usually focus only on the time-based connection of air quality monitoring stations or the complex link between PM2.5 levels and explanatory factors. Due to the lack of effective integration of spatial correlation, the prediction model shows poor performance in PM2.5 prediction tasks. Predicting air pollution levels accurately over a long period is difficult because of the changing levels of correlation between past pollution levels and the future. In order to address these challenges, the study introduces a Convolutional Long Short-Term Memory (ConvLSTM) network-based neural network model with multiple feature extraction for forecasting PM2.5 levels in air quality prediction. The technique is composed of three components. The model-building process of this article is as follows: Firstly, we create a complex network layout with multiple branches to capture various temporal features at different levels. Secondly, a convolutional module was introduced to enable the model to focus on identifying neighborhood units, extracting feature scales with high spatial correlation, and helping to improve the learning ability of ConvLSTM. Next, the module for spatiotemporal fusion prediction is utilized to make predictions of PM2.5 over time and space, generating fused prediction outcomes that combine characteristics from various scales. Comparative experiments were conducted. Experimental findings indicate that the proposed approach outperforms ConvLSTM in forecasting PM2.5 concentration for the following day, three days, and seven days, resulting in a lower root mean square error (RMSE). This approach excels in modeling spatiotemporal features and is well-suited for predicting PM2.5 levels in specific regions.

The bit error rate (BER) in relation to the signal-to-noise ratio (SNR) serves as a widely recognized metric for assessing the performance of communication systems. The concept of SNR is so integral that many existing studies presume its definition to be understood, often omitting the specifics of its calculation in their simulations. Notably, the computation of SNR from the perspective of the transmitter yields distinct behaviors and outcomes compared to that from the receiver’s side, particularly when the channel encompasses more than mere noise. Typically, research papers utilize the transmitter-side (or ensemble-average) SNR to benchmark the BER performance across various methodologies. Conversely, the receiver-side (or short-term) SNR becomes pertinent when prioritizing the receiver’s performance. In the context of simulating the long-term evolution (LTE) downlink, applying both SNR calculation approaches reveals that the receiver-side SNR not only produces a significantly lower BER compared to the transmitter-side SNR but also alters the relative BER performance rankings among the channel models tested. It is deduced that while the transmitter-side SNR is apt for broad performance comparisons, it falls short in thoroughly examining the BER behavior of a receiver across varying SNR scenarios. Therefore, the transmitter-side SNR is useful when comparing the performance of the simulated system with other studies. Conversely, if the primary concern is the actual BER performance of the receiver, the receiver-side SNR could provide a more accurate performance assessment.

Cytomegalovirus infection contributes to 10–30% of congenital hearing loss in children. Vertebrate peripheral auditory organs include the outer, middle, and inner ear. Their development is regulated by multiple signaling pathways. However, most ear diseases due to viral infections are due to congenital infections and reactivation and affect healthy adults to a lesser extent. This may be due to the fact that viral infections affect signaling pathways that are important for the development of peripheral hearing organs. Therefore, an in-depth understanding of the relationship between viral infections and the signaling pathways involved in the development of peripheral hearing organs is important for the prevention and treatment of ear diseases. In this review, we summarize the effects of viruses on signaling pathways and signaling molecules in the development of peripheral auditory organs.

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model’s receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value.

This research leverages bibliometric methodologies, enhanced by the visual analytics capabilities of CiteSpace, to meticulously examine the evolution and current trends in energy trading within power markets, analyzing 642 scholarly articles from the Web of Science Core Collection spanning from 1996 to 2023. The study aims to illuminate the prevailing research landscape, growth patterns, and future directions in energy trading dynamics. Key findings include: (1) A noticeable escalation in the volume of publications, especially from 2021 to 2023, indicating a burgeoning interest and rapid evolution in this research area; (2) The author and institutional collaboration networks are in a nascent stage, with a predominantly China-centric international collaboration pattern, including significant partnerships with the United States, Australia, and the United Kingdom; (3) The focal points of research are centered around themes such as “energy management”, “demand-side innovation”, “decentralized energy trading”, and “strategic optimization”, covering areas such as intelligent grid technologies, energy market dynamics, and sustainable energy solutions. The study recommends enhancing collaborative networks, fusing technological and strategic dimensions in research, increasing focus and funding for emerging technologies, and promoting wider international and cross-disciplinary collaborations to enrich the understanding of energy trading dynamics in the context of electricity markets.

There were several factors associated with respiratory syncytial virus (RSV) severe acute lower respiratory infection (RSV-sALRI) in infants and young children. It is vital to develop a convenient scoring system to predict RSV-sALRI in children. Pediatric patients with RSV-ALRI from January 2009 to December 2021 were recruited retrospectively. Two-third of them were randomly grouped into the development set and one-third to the validation set. In the development set, risk factors for RSV-sALRI were transferred into the logistic regression analysis, then their receiver operating characteristic (ROC) curves were built to obtain the area under the ROC curve (AUC), and regression coefficients for each predictor were converted to points. Finally, the value of the scoring system was evaluated in the validation set. A total of 1 066 children with RSV-ALRI were recruited, including 710 in the development set and 356 in the validation set. By logistic regression analysis, six factors (younger than 2 years, gestational age p < 0.001). A simplified scoring system was developed in the study with high prediction value for RSV-sALRI in children.

To improve the wear resistance of the materials used for blades in engineering machinery, this study focused on the microstructural characteristics, mechanical properties, and wear behavior of HB500 grade wear-resistant steel developed using an optimized heat treatment system. To improve the temperature uniformity of the heat treatment furnace, the method of cyclic heating was used to heat the components. Carefully designing the quenching equipment, such as using a cross-shaped press, was employed to enhance the quenching effect and reduce the deformation of the steel plates. The crystal orientation analysis revealed a uniform and fine-grained microstructure, primarily characterized by plate-type tempered martensite, which indicated a good hardenability. The microstructure observations showed that the width of martensite is approximately 200 nm, with a significant presence of dislocations and carbides. Tensile tests and multi-temperature gradient impact tests indicated superior mechanical properties compared to similar grade wear-resistant steels, including a Rockwell hardness of 53, tensile strength of 1610 MPa, yield strength of 1404 MPa, and total elongation around 12.7%. The results of friction and wear experiments indicate that the wear rate decreases as the load increases from 100 N to 300 N, demonstrating an excellent wear resistance under a large load. Observations of the worn surfaces indicated that the wear mainly involved adhesive wear, fatigue wear, and oxidative wear. The properties’ improvements were attributed to microstructure refinement and precipitation strengthening. This study indicates that designing a heat treatment system to control temperature uniformity and stability is feasible.

Hyperspectral image (HSI) classification is a vital part of the HSI application field. Since HSIs contain rich spectral information, it is a major challenge to effectively extract deep representation features. In existing methods, although edge data augmentation is used to strengthen the edge representation, a large amount of high-frequency noise is also introduced at the edges. In addition, the importance of different spectra for classification decisions has not been emphasized. Responding to the above challenges, we propose an edge-aware and spectral–spatial feature learning network (ESSN). ESSN contains an edge feature augment block and a spectral–spatial feature extraction block. Firstly, in the edge feature augment block, the edges of the image are sensed, and the edge features of different spectral bands are adaptively strengthened. Then, in the spectral–spatial feature extraction block, the weights of different spectra are adaptively adjusted, and more comprehensive depth representation features are extracted on this basis. Extensive experiments on three publicly available hyperspectral datasets have been conducted, and the experimental results indicate that the proposed method has higher accuracy and immunity to interference compared to state-of-the-art (SOTA) method.

Terahertz spectroscopy has unique advantages in the study of biological molecules in aqueous solutions. However, water has a strong absorption capability in the terahertz region. Reducing the amount of liquid could decrease interference with the terahertz wave, which may, however, affect the measurement accuracy. Therefore, it is particularly important to balance the amount and water content of liquid samples. In this work, a terahertz metamaterial sensor based on metallic strips is designed, fabricated, and used to detect reverse micelles. An aqueous confinement environment in reverse micelles can improve the signal-to-noise ratio of the terahertz response. Due to “water pool” trapped in reverse micelles, the DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine) solution and DOPC emulsion can successfully be identified in intensity by terahertz spectroscopy. Combined with the metamaterial sensor, an obvious frequency shift of 30 GHz can be achieved to distinguish the DOPC emulsion (5%) from the DOPC solution. This approach may provide a potential way for improving the sensitivity of detecting trace elements in a buffer solution, thus offering a valuable toolkit toward bioanalytical applications.

Gas turbines are widely applied in many fields, and blade cooling is a key way to improve gas turbines’ power and efficiency. In order to explore a high-efficiency cooling method, a new variant configuration with unique coolant supply passage arrangements is proposed and explored in this paper. The numerical simulation method of solving the Navier–Stokes equations is used after mesh independence calculation and turbulence model validation. The results show that the variant structure has better streamlines distribution with double vortex flows in both the inner and outer chambers. Compared to the original configuration, the heat transfer intensity in the outer chamber is improved, and the globally averaged Nusselt number is 17.1% larger. The case with uniformly distributed nozzles has the best flow and heat transfer performance. As the nozzle number increases, the total pressure loss and friction coefficient decrease, but the heat transfer increases first and then decreases. The case of three nozzles has the best comprehensive cooling behavior. The aspect ratio has important influences on the double-vortex cooling configuration. Cases with small aspect ratios have higher local heat transfer intensity, but the flow loss is larger. The case with aspect ratio 4 has the best cooling performance.

Simple nucleophilic aliphatic substitution gives access to mono- and diphosphine ligands with a CpC group in the backbone. The monophosphine ligand coordinates to gold(I) via the phosphine site, to thallium(I) via the cyclopentadienyl site and to ruthenium(II) via a combination of both, resulting in an ansa-type structure. Coordination with the cyclopentadiene site is not possible for the diphosphine ligand. In this case, monodentate coordination to gold(I) and bidentate coordination to the [PdCl(μ2-Cl)]2, the [Rh(CO)(μ2-Cl)]2, and the Rh(CO)Cl fragment is observed, showing the variability in coordination modes possible for the long-chain diphosphine ligand. Ligands and complexes were characterized by means of NMR and IR spectroscopy, elemental analysis and X-ray structure analysis.

A high-precision low-cost mid-infrared photoacoustic sensor for greenhouse composite gases based on aspherical beam shaping is proposed and demonstrated. The assembled optical source module and luminous characteristics of infrared source are innovatively investigated and analyzed with aspherical beam shaping. The proposed aspherical-beam-shaping-technique could effectively reduce optical loss and enhance system sensitivity, achieving an effective power utilization ratio of a radiation source of 91% and sidewall noise ratio of 8.9%. Experiments verify the 1.7 times improvement in responsivity and 50% enhancement in minimum detection limit (MDL) on average. In terms of comprehensive greenhouse gas composites and with short integration time of 1 s, MDLs of CO2, CH4, N2O, NF3, SF6, PFC-14, and HFC-134a are 73 ppb, 267 ppb, 72 ppb, 81 ppb, 14 ppb, 9 ppb and 115 ppb, respectively. Furthermore, a 48 h continuous monitoring of H2O, CO2 and CH4 in the atmosphere is conducted and verifies the performance of the gas sensor. The developed sensor allows for the rapid route of low-cost and high-precision detection of multiple greenhouse gases.

In the current work, a facile ion-solution spraying strategy was employed for one-step fabrication of CsPbBr3 perovskite thin films under atmosphere. The dependences of sample properties on annealing parameters (toleration temperature and duration time) were investigated in detail. As the results suggested, the sample prepared at 200 °C for 15 min featured better properties than others. The sample displayed a cubic phase with good crystallinity, a dense and compact morphology, a bandgap energy of 2.289 eV, and an average decay lifetime of 55.536 ns. Furthermore, the sample presented a Br-rich state, which was favorable for the carrier behavior and structure stability.

Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances with conventional channel estimations in ATSC 3.0 systems for linear and nonlinear channel models. We also evaluate the robustness of the ML-based methods against channel model mismatch and signal-to-noise ratio (SNR) mismatch. The results show that the ML-based channel estimations achieve good mean squared error (MSE) performance for linear and nonlinear channels if the channel statistics used for the training stage match those of the deployment stage. Otherwise, the ML estimation models may overfit the training channel, leading to poor deployment performance. Furthermore, the deep neural network (DNN)-based method does not outperform the linear channel estimation methods in nonlinear channels.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals’ brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks.

Electric pylons are crucial components of power infrastructure, requiring accurate detection and identification for effective monitoring of transmission lines. This paper proposes an innovative model, the EP-YOLOv8 network, which incorporates new modules: the DSLSK-SPPF and EMS-Head. The DSLSK-SPPF module is designed to capture the surrounding features of electric pylons more effectively, enhancing the model’s adaptability to the complex shapes of these structures. The EMS-Head module enhances the model’s ability to capture fine details of electric pylons while maintaining a lightweight design. The EP-YOLOv8 network optimizes traditional YOLOv8n parameters, demonstrating a significant improvement in electric pylon detection accuracy with an average mAP@0.5 value of 95.5%. The effective detection of electric pylons by the EP-YOLOv8 demonstrates its ability to overcome the inefficiencies inherent in existing optical satellite image-based models, particularly those related to the unique characteristics of electric pylons. This improvement will significantly aid in monitoring the operational status and layout of power infrastructure, providing crucial insights for infrastructure management and maintenance.

Recombination events in human adenovirus (HAdV) have led to some new highly pathogenic or infectious types. It is vital to monitor recombinant HAdVs, especially in children with acute respiratory tract infections (ARIs). In the retrospective study, HAdV positive specimens were collected from pediatric patients with ARIs during 2015 to 2021, then typed by sequence analysis of the penton base, hexon and fiber gene sequence. For those with inconsistent typing results, a modified method with species-specific primer sets of a fiber gene sequence was developed to distinguish co-infections of different types from recombinant HAdV infections. Then, plaque assays combined with meta-genomic next-generation sequencing (mNGS) were used to reveal the HAdV genomic characteristics. There were 466 cases positive for HAdV DNA (2.89%, 466/16,097) and 350 (75.11%, 350/466) successfully typed with the most prevalent types HAdV-B3 (56.57%, 198/350) and HAdV-B7 (32.00%, 112/350), followed by HAdV-C1 (6.00%, 21/350). Among 35 cases (7.51%, 35/466) with inconsistent typing results, nine cases were confirmed as co-infections by different types of HAdVs, and 26 cases as recombinant HAdVs in six genetic patterns primarily clustered to species C (25 cases) in pattern 1–5, or species D (1 case) in pattern 6. The novel recombinant HAdV of species D was identified with multiple recombinant events among HAdV-D53, HAdV-D64, and HAdV-D8, and officially named as HAdV-D115. High-frequency recombination of HAdVs in six genetic recombination patterns were identified among children with ARIs in Beijing. Specifically, there is a novel Adenovirus D human/CHN/S8130/2023/115[P22H8F8] designed as HAdV D115.

Aluminosilicates, abundant and crucial in both natural environments and industry, often involve uncontrollable chemical components when derived from minerals, making further chemical purification and reaction more complicated. This study utilizes pure alumina and fumed silica powders as more controllable sources, enhancing aluminosilicate reactivity through room temperature (non-firing) processing and providing a robust framework that resists mechanical stress and high temperature. By embedding iron-based metal–organic frameworks (Fe-MOF/non-firing aluminosilicate membranes) within the above matrix, these ceramic membranes not only preserve their mechanical robustness but also gain significant chemical functionality, enhancing their capacity to removing phytochromes from the vegetables. Sodium hydroxide and sodium silicate were selected as activators to successfully prepare high-strength, non-firing aluminosilicate membranes. These membranes demonstrated a flexural strength of 8.7 MPa under wet-culture conditions with a molar ratio of Al2O3:SiO2:NaOH:Na2SiO3 at 1:1:0.49:0.16. The chlorophyll adsorption of spinach conducted on these membranes showed a removal rate exceeding 90% at room temperature and pH = 9, highlighting its potential for the selective adsorption of chlorophyll. This study underscores the potential of MOF-enhanced aluminosilicate ceramic membranes in environmental applications, particularly for agricultural pollution control.

Combinatorial drug therapy has emerged as a critically important strategy in medical research and patient treatment and involves the use of multiple drugs in concert to achieve a synergistic effect. This approach can enhance therapeutic efficacy while simultaneously mitigating adverse side effects. However, the process of identifying optimal drug combinations, including their compositions and dosages, is often a complex, costly, and time-intensive endeavor. To surmount these hurdles, we propose a novel microfluidic device capable of simultaneously generating multiple drug concentration gradients across an interlinked array of culture chambers. This innovative setup allows for the real-time monitoring of live cell responses. With minimal effort, researchers can now explore the concentration-dependent effects of single-agent and combination drug therapies. Taking neural stem cells (NSCs) as a case study, we examined the impacts of various growth factors—epithelial growth factor (EGF), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF)—on the differentiation of NSCs. Our findings indicate that an overdose of any single growth factor leads to an upsurge in the proportion of differentiated NSCs. Interestingly, the regulatory effects of these growth factors can be modulated by the introduction of additional growth factors, whether singly or in combination. Notably, a reduced concentration of these additional factors resulted in a decreased number of differentiated NSCs. Our results affirm that the successful application of this microfluidic device for the generation of multi-drug concentration gradients has substantial potential to revolutionize drug combination screening. This advancement promises to streamline the process and accelerate the discovery of effective therapeutic drug combinations.

The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system.

The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources. In this study, we investigate the impacts on GRACE monthly gravity field solutions of incorporating the GPS data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission. To that end, we produce GRACE/GOCE combined monthly gravity field solutions through combination on the normal equation level and compare them with the GRACE-only solutions, for which we have considered the state-of-the-art ITSG-Grace2018 solutions. Analysis in the spectral domain reveals that the combined solutions have a notably lower noise level beyond degree 30, with cumulative errors up to degree 96 being reduced by 31%. A comparison of the formal errors reveals that the addition of GOCE GPS data mainly improves (near-) sectorial coefficients and resonant orders, which cannot be well determined by GRACE alone. In the spatial domain, we also observe a significant reduction by at least 30% in the noise of recovered mass changes after incorporating the GOCE GPS data. Furthermore, the signal-to-noise ratios of mass changes over 180 large river basins were improved by 8–20% (dependent on the applied Gaussian filter radius). These results demonstrate that the GOCE GPS data can augment the GRACE monthly gravity field solutions and support a future GOCE-type mission for tracking more accurate time-varying gravity fields.

Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ model based on the aforementioned detection method and the characteristics of straw return in Jilin Province was then used to classify plots into different straw return cover types. The model used Mobilenetv2 as the backbone network to reduce the number of model parameters, and the channel-wise feature pyramid module based on channel attention (CA-CFP) and a low-level feature fusion module (LLFF) were used to enhance the segmentation of the plot details. In addition, a composite loss function was used to solve the problem of class imbalance in the dataset. The results show that the extraction accuracy is optimal when a 2048 × 2048-pixel scale image is used as the model input. The total parameters of the improved model are 3.79 M, and the mean intersection over union (MIoU) is 96.22%, which is better than other comparative models. After conducting a calculation of the form–grade mapping relationship, the error value of the area prediction was found to be less than 8%. The results show that the proposed rapid straw return detection method based on Sh-DeepLabv3+ can provide greater support for straw return detection.

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.

The effects of global warming, population growth, and economic development are increasing the frequency of extreme weather events, such as drought. Among abiotic stresses, drought has the greatest impact on soil biological activity and crop yields. The rhizosphere microbiota, which represents a second gene pool for plants, may help alleviate the effects of drought on crops. In order to investigate the structure and diversity of the bacterial communities on drought stress, this study analyzed the differences in the bacterial communities by high-throughput sequencing and bioinformatical analyses in the rhizosphere of Arabidopsis thaliana under normal and drought conditions. Based on analysis of α and β diversity, the results showed that drought stress had no significant effect on species diversity between groups, but affected species composition. Difference analysis of the treatments showed that the bacteria with positive responses to drought stress were Burkholderia-Caballeronia-Paraburkholderia (BCP) and Streptomyces. Drought stress reduced the complexity of the rhizosphere bacterial co-occurrence network. Streptomyces was at the core of the network in both the control and drought treatments, whereas the enrichment of BCP under drought conditions was likely due to a decrease in competitors. Functional prediction showed that the core bacteria metabolized a wide range of carbohydrates, such as pentose, glycans, and aromatic compounds. Our results provide a scientific and theoretical basis for the use of rhizosphere microbial communities to alleviate plant drought stress and the further exploration of rhizosphere microbial interactions under drought stress.

Due to boron’s metalloid properties, aromatic boron reagents are prevalent synthetic intermediates. The direct borylation of aryl C-H bonds for producing aromatic boron compounds offers an appealing, one-step solution. Despite significant advances in this field, achieving regioselective aryl C-H bond borylation using simple and readily available starting materials still remains a challenge. In this work, we attempted to enhance the reactivity of the electron-donor-acceptor (EDA) complex by selecting different bases to replace the organic base (NEt3) used in our previous research. To our delight, when using NH4HCO3 as the base, we have achieved a mild visible-light-mediated aromatic C-H bond borylation reaction with exceptional regioselectivity (rr > 40:1 to single isomers). Compared with our previous borylation methodologies, this protocol provides a more efficient and broader scope for aryl C-H bond borylation through the use of N-Bromosuccinimide. The protocol’s good functional-group tolerance and excellent regioselectivity enable the functionalization of a variety of biologically relevant compounds and novel cascade transformations. Mechanistic experiments and theoretical calculations conducted in this study have indicated that, for certain arenes, the aryl C-H bond borylation might proceed through a new reaction mechanism, which involves the formation of a novel transient EDA complex.

Improving the thermal storage stability of nanosuspension concentrate (SC) prepared from low-melting-point pesticide is a recognized problem. In this work, using pyraclostrobin as the raw material, 25 wt% of pyraclostrobin nano-SC was prepared through a water-based grinding method, and the optimal grinding conditions were obtained as follows: a grinding time of 23 h, D-3911 as dispersant and a dispersant dosage of 12 wt%. The pyraclostrobin nano-SC D90 size prepared based on this best formula was 216 nm. Adding glycerin could improve the stability of nano-SC at room temperature, but its thermal storage stability was still poor. For this problem, sodium lignosulfonate and cetyltrimethylammonium bromide (NaLS/CTAB) colloidal spheres were prepared through electrostatic and hydrophobic self-assembly and characterized. The delamination and precipitation of nano-SC can be significantly improved by adding an appropriate amount of colloidal spheres, and the nano-SC D90 size decreased from 2726 to 1023 nm after 7 days of thermal storage. Farmland experiments indicated the control efficiency of pyraclostrobin nano-SC against flowering cabbage downy mildew disease was about 30% higher than that of SC. Especially after adding the wetting agent, the effect of nano-SC could be comparable to that of commercial Kairun (currently the best pyraclostrobin formulation in the world).

Swallowing oral solid dosage forms is challenging for those who have medication swallowing difficulties, including patients with dysphagia. One option is to mix the drug (whole or crushed) with a thick vehicle (medication lubricant). Previous in vitro studies consistently suggest that thick vehicles could impact the dissolution of solid dosage forms, potentially influencing their therapeutic effectiveness, but do not account for changes that happen during oral processing and swallowing. This study aims to investigate the potential impact of medication lubricants on drug release and examine the effect of oral processing. In vitro dissolution of whole and crushed paracetamol tablets mixed with five commercially available medication lubricants (two IDDSI level 2, two IDDSI level 3, and one IDDSI level 4) were tested with and without oral processing; a medication lubricant with/without paracetamol was placed in the mouth (five healthy volunteers), prepared for swallowing, but then expectorated and assessed for physical characteristics and drug release. Medication lubricants, both alone and mixed with crushed paracetamol tablets, showed a significant decrease in viscosity after oral processing. Without oral processing, IDDSI level 3 and 4 lubricants significantly delayed the dissolution of paracetamol tablets. After oral processing, particularly with crushed tablets, there was a substantial increase in the dissolution rate. These findings suggest that dissolution testing overestimates the impact of medication lubricants on drug dissolution. Therefore, using in vitro dissolution tests to predict the dissolution rate of medications mixed with thick vehicles is discouraged. It is essential to consider ways to incorporate the effects of the oral environment and oral processing on thick vehicles used for oral medication administration.

The genus Pogonatum stands out as the most diverse within the family Polytrichaceae, encompassing over 50 species. Pogonatum tahitense has been recorded across various Pacific regions, including Hawaii in the United States and Tahiti in French Polynesia, as well as in Asia, such as in Taiwan in China, Java in Indonesia, and Sabah in Malaysia. In the current study, a specimen collected in Tibet, China, is described, confirming its taxonomic classification as P. tahitense through a comprehensive analysis integrating morphological evidence and molecular study based on sequences from the plastid (rbcL, rps4, trnL-F), mitochondrial (nad5), and nuclear (ITS2) regions. This documentation represents the first record of the species within mainland China. A time-calibrated, molecular-based phylogenetic analysis was conducted, employing various approaches for ancestral range inference. The findings suggest that P. tahitense originated during the Pleistocene epoch, approximately 1.8 mya, in Tibet, China.