Search Results (26)

Dermacentor is the most widely distributed tick genus in China. Dermacentor nuttalli, a predominant tick species in Inner Mongolia, can carry and transmit pathogenic microorganisms. Here, D. nuttalli were collected from Ordos (O-D) and Hinggan League (H-D) in the Inner Mongolia. D. nuttalli specimens at different developmental stages were subsequently reared under identical laboratory conditions. Sample processing, nucleic acid extraction, high-throughput sequencing, and microbial community analyses were conducted. Bacterial communities in O-D and H-D were annotated to 8 phyla, 145 genera and 16 phyla, 141 genera, respectively, with Proteobacteria showing the highest relative abundance. Differences in dominant bacterial genera were observed across developmental stages between the two regions. The most abundant bacterial species were Arsenophonus_uncultured_bacterium in O-D and Rickettsia japonica in H-D. Viral communities were annotated to 4 orders, 25 families, 61 genera, and 126 species in O-D and 6 orders, 28 families, 49 genera, 135 species in H-D. Notable difference in the viral genera with >1% abundance were identified at different developmental stages in the two regions. To our knowledge, this is the first study to compare microbial community compositions of D. nuttalli across developmental stages in two Inner Mongolian regions under under identical rearing conditions and to report the presence of R. japonica, Tacheng Tick Virus-2, and bovine viral diarrhea virus in D. nuttalli. Full article

Acid phosphatases (ACPase, EC 3.1.3.2) are hydrolytic enzymes widely distributed in both plant and animal tissues. Despite their ubiquitous presence, the production and specific activity of ACPase in these sources remain suboptimal. Consequently, the development of microbial cell factories for large-scale ACPase production has emerged as a significant research focus. In this study, we successfully expressed the phosphatase PAP2 family protein (acid phosphatase) from Acinetobacter nosocomialis 1905 in Bacillus subtilis 168. The specific activity of the crude enzyme solution was 59.60 U/mg. After purification, the enzyme activity increased to 86.62 U/mL, with a specific activity of 129.60 U/mg. Characterization of the enzyme revealed optimal activity at 45 °C and a pH of 6.0. The Km value was determined to be 0.25 mmol/L using p-nitrophenylphosphoric acid disodium salt as the substrate. Additionally, the enzyme activity was found to be enhanced by the presence of Ni²⁺. Dissolved oxygen and medium were subsequently optimized during fermentation on the basis of a commercially available 5 L bioreactor. The recombinant strain B. subtilis 168/pMA5-Acp achieved maximal volumetric enzyme activity of 136.9 U/mL after 12 h of fermentation under optimized conditions: an aeration rate of 1.142 VVM (4 lpm), an agitation speed of 350 rpm, and an optimal ratio of lactose to fish powder (7.5 g/L:12.5 g/L). These optimizations resulted in a 5.9-fold increase in volumetric enzyme activity, a 4.9-fold increase in enzyme synthesis per unit cell volume, and a 48.6% increase in biomass concentration. This study establishes a comprehensive technological framework for prokaryotic fermentation-based ACPase production, potentially addressing the bottleneck in industrial-scale applications. Full article

Catalytic performance decline is a general issue when shaping fine powder into macroscale catalysts (e.g., beads, fiber, pellets). To address this challenge, a phenolic resin-assisted strategy was proposed to prepare porous Co/N carbon beads (ZACBs) at millimeter scale via the phase inversion method followed by confined pyrolysis. Specially, p-aminophenol–formaldehyde (AF) resin-coated zeolitic imidazolate framework (ZIF-67) nanoparticles were introduced to polyacrylonitrile (PAN) solution before pyrolysis. The thermosetting of the coated AF improved the interface compatibility between the ZIF-67 and PAN matrix, inhibiting the shrinkage of ZIF-67 particles, thus significantly improving the void structure of ZIF-67 and the dispersion of active species. The obtained ZACBs exhibited a 99.9% removal rate of tetracycline (TC) within 120 min, with a rate constant of 0.069 min⁻¹ (2.3 times of ZIF-67/PAN carbon beads). The quenching experiments and electron paramagnetic resonance (EPR) tests showed that radicals dominated the reaction. This work provides new insight into the fabrication of high-performance MOF catalysts with outstanding recycling properties, which may promote the use of MOF powder in more practical applications. Full article

Insects constitute the largest proportion of animals on Earth and act as significant reservoirs and vectors in disease transmission. Rice thrips (Haplothrips aculeatus, family Phlaeothripidae) are one of the most common pests in agriculture. In this study, the full genome sequence of a novel Ollusvirus, provisionally named “Rice thrips ollusvirus 1” (RTOV1), was elucidated using transcriptome sequencing and the rapid amplification of cDNA ends (RACE). A homology search and phylogenetic tree analysis revealed that the newly identified virus is a member of the family Aliusviridae (order Jingchuvirales). The genome of RTOV1 contains four predicted open reading frames (ORFs), including a polymerase protein (L, 7590 nt), a glycoprotein (G, 4206 nt), a nucleocapsid protein (N, 2415 nt) and a small protein of unknown function (291 nt). All of the ORFs are encoded by the complementary genome, suggesting that the virus is a negative-stranded RNA virus. Phylogenetic analysis using polymerase sequences suggested that RTOV1 was closely related to ollusvirus 1. Deep small RNA sequencing analysis reveals a significant accumulation of small RNAs derived from RTOV1, indicating that the virus replicated in the insect. According to our understanding, this is the first report of an Ollusvirus identified in a member of the insect family Phlaeothripidae. The characterisation and discovery of RTOV1 is a significant contribution to the understanding of Ollusvirus diversity in insects. Full article

Skeletons play an important role in the human body, and can form gaps of varying sizes once damaged. Bone defect healing involves a series of complex physiological processes and requires ideal bone defect implants to accelerate bone defect healing. Traditional grafts are often accompanied by issues such as insufficient donors and disease transmission, while some bone defect implants are made of natural and synthetic polymers, which have characteristics such as good porosity, mechanical properties, high drug loading efficiency, biocompatibility and biodegradability. However, their antibacterial, antioxidant, anti-inflammatory and bone repair promoting abilities are limited. Flavonoids are natural compounds with various biological activities, such as antitumor, anti-inflammatory and analgesic. Their good anti-inflammatory, antibacterial and antioxidant activities make them beneficial for the treatment of bone defects. Several researchers have designed different types of flavonoid-loaded polymer implants for bone defects. These implants have good biocompatibility, and they can effectively promote the expression of angiogenesis factors such as VEGF and CD31, promote angiogenesis, regulate signaling pathways such as Wnt, p38, AKT, Erk and increase the levels of osteogenesis-related factors such as Runx-2, OCN, OPN significantly to accelerate the process of bone defect healing. This article reviews the effectiveness and mechanism of biomaterials loaded with flavonoids in the treatment of bone defects. Flavonoid-loaded biomaterials can effectively promote bone defect repair, but we still need to improve the overall performance of flavonoid-loaded bone repair biomaterials to improve the bioavailability of flavonoids and provide more possibilities for bone defect repair. Full article

Various plant species contain terpene secondary metabolites, which disrupt insect growth and development by affecting the activity of juvenile hormone-degrading enzymes, and the juvenile hormone (JH) titers maintained in insects. Nerolidol, a natural sesquiterpenol belonging to the terpenoid group, exhibits structural similarities to insect JHs. However, the impact of nerolidol on insect growth and development, as well as its underlying molecular mechanism, remains unclear. Here, the effects of nerolidol on Spodoptera exigua were investigated under treatment at various sub-lethal doses (4.0 mg/mL, 1.0 mg/mL, 0.25 mg/mL). We found that a higher dose (4.0 mg/mL) of nerolidol significantly impaired the normal growth, development, and population reproduction of S. exigua, although a relatively lower dose (0.25 mg/mL) of nerolidol had no significant effect on this growth and development. Combined transcriptome sequencing and gene family analysis further revealed that four juvenile hormone esterase (JHE)-family genes that are involved in juvenile hormone degradation were significantly altered in S. exigua larvae after nerolidol treatment (4.0 mg/mL). Interestingly, the juvenile hormone esterase-like (JHEL) gene Sexi006721, a critical element responsive to nerolidol stress, was closely linked with the significant augmentation of JHE activity and JH titer in S. exigua (R² = 0.94, p < 0.01). Taken together, we speculate that nerolidol can function as an analog of JH by modulating the expression of the enzyme genes responsible for degrading JH, resulting in JH disorders and ultimately disrupting the development of insect larvae. This study ultimately provides a theoretical basis for the sustainable control of S. exigua in the field whilst proposing a new perspective for the development of novel biological pesticides. Full article

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions. Full article

Spodoptera frugiperda, an invasive pest, has a huge impact on food production in Asia and Africa. The potential and advantages of sterile insect techniques for the permanent control of S. frugiperda have been demonstrated, but the methods for their field application are still unavailable. For the purposes of this study, male pupae of S. frugiperda were irradiated with an X-ray dose of 250 Gy to examine the effects of both the release ratio and the age of the irradiated males on the sterility of their offspring. The control effect of the irradiated male release ratio on S. frugiperda was evaluated using field-cage experiments in a cornfield. The results showed that when the ratio of irradiated males to non-irradiated males reached 12:1, the egg-hatching rate of the offspring of S. frugiperda decreased to less than 26%, and there was also no significant difference in mating competitiveness among the different ages. Field-cage testing showed that when irradiated males were released at ratios of 12:1–20:1 to normal males, the leaf protection effect for the corn reached 48–69% and the reduction in the insect population reached 58–83%. In this study, an appropriate release ratio is suggested, and the mating competitiveness of irradiated and non-irradiated males of S. frugiperda is investigated, thus providing a theoretical basis for the use of sterile insect techniques to control S. frugiperda. Full article

The weak classifier ensemble algorithms based on the decision tree model, mainly include bagging (e.g., fandom forest-RF) and boosting (e.g., gradient boosting decision tree, eXtreme gradient boosting), the former reduces the variance for the overall generalization error reduction while the latter focuses on reducing the overall bias to that end. Because of its straightforward idea, it is prevalent in MPM (mineral prospectivity mapping). However, an inevitable problem in the application of such methods is the hyperparameters tuning which is a laborious and time-consuming task. The selection of hyperparameters suitable for a specific task is worth investigating. In this paper, a tree Parzen estimator-based GBDT (gradient boosting decision tree) model (TPE-GBDT) was introduced for hyperparameters tuning (e.g., loss criterion, n_estimators, learning_rate, max_features, subsample, max_depth, min_impurity_decrease). Then, the geological data of the gold deposit in the Xiong ‘ershan area was used to create training data for MPM and to compare the TPE-GBDT and random search-GBDT training results. Results showed that the TPE-GBDT model can obtain higher accuracy than random search-GBDT in a shorter time for the same parameter space, which proves that this algorithm is superior to random search in principle and more suitable for complex hyperparametric tuning. Subsequently, the validation measures, five-fold cross-validation, confusion matrix and success rate curves were employed to evaluate the overall performance of the hyperparameter optimization models. The results showed good scores for the predictive models. Finally, according to the maximum Youden index as the threshold to divide metallogenic potential areas and non-prospective areas, the high metallogenic prospect area (accounts for 10.22% of the total study area) derived by the TPE-GBDT model contained > 90% of the known deposits and provided a preferred range for future exploration work. Full article

This study aimed to investigate the effects of lactic acid bacteria (LAB) inoculants on the fermentation quality, microbial compositions, and predicted functional profiles of forage oat. The forage oat was inoculated with distilled water, Lentilactobacillus buchneri (LB), and Lactiplantibacillus plantarum (LP) as the control (CON), LB and LP treatments, respectively, and the addition of Lentilactobacillus buchneri (LB) or Lactiplantibacillus plantarum (LP) resulted in 1 × 10⁶ colony-forming units/g of fresh weight. After 30 days of fermentation, the lowest pH (4.23) and the lowest content of ammoniacal nitrogen (NH₃-N) in dry matter (DM, 4.39%) were observed in the LP treatment. Interestingly, there was a significant (p < 0.05) difference in lactic acid (LA) concentration among the three treatments. The LP treatment had the highest lactate concentration (7.49% DM). At the same time, a markedly (p < 0.05) elevated acetic acid (AA) concentration (2.48% DM) was detected in the LB treatment. The Shannon and Chao1 indexes of bacterial and fungal communities in all the silage samples decreased compared to those in the fresh materials (FM). Proteobacteria was the dominant phylum in the FM group and shifted from Proteobacteria to Firmicutes after ensiling. Lactobacillus (64.87%) and Weissella (18.93%) were the predominant genera in the CON, whereas Lactobacillus dominated the fermentation process in the LB (94.65%) and LP (99.60%) treatments. For the fungal community structure, the major genus was Apiotrichum (21.65% and 60.66%) in the FM and CON groups after 30 days of fermentation. Apiotrichum was the most predominant in the LB and LP treatments, accounting for 52.54% and 34.47%, respectively. The genera Lactococcus, Pediococcus, and Weissella were negatively associated with the LA content. The genus Ustilago and Bulleromyces were positively associated with the LA content. These results suggest that the addition of LAB regulated the microbial community in oat silage, which influenced the ensiling products, and LP was more beneficial for decreasing the pH and NH₃-N and increasing the LA concentration than LB in forage oat silage. Full article

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection. Full article

As a typical p-type semiconductor gas-sensing material, LaFeO₃ has good response stability to H₂S, but its responsiveness is low, and the detection limit is not low enough for large-scale use in the field of gas sensors. To obtain better performance, we synthesized Pd modified LaFeO₃ using the sol–gel method. A total of 3 wt% of Pd–LaFeO₃ with a high specific surface area had the highest response to H₂S (36.29–1 ppm) at 120 °C, with relatively fast response–recovery times (19.62/15.22 s), and it had higher selectivity to H₂S with other gases. Finally, we detected the H₂S concentrations in the air around the shrimps, and the H₂S concentrations that we obtained by the 3 wt% Pd–LaFeO₃ in this study were within 10% of those obtained by GC–MS. According to the experimental results, noble-metal surface modification improves the performance of gas-sensing materials, and Pd–LaFeO₃ has considerable potential in H₂S detection. Full article

The “Multi-Threat Medical Countermeasure (MTMC)” strategy was proposed to develop a single drug with therapeutic efficacy against multiple pathologies or broad-spectrum protection against various toxins with common biochemical signals, molecular mediators, or cellular processes. This study demonstrated that cytotoxicity, expression of transient receptor potential cation channel subfamily A member 1 (TRPA1) mRNA, and intracellular calcium influx were increased in A549 cells exposed to amide herbicides (AHs), in which the order of cytotoxicity was metolachlor > acetochlor > propisochlor > alachlor > butachlor > propanil > pretilachlor, based on IC₅₀ values of 430, 524, 564, 565, 619, 831, and 2333 μM, respectively. Inhibition/knockout of TRPA1 efficiently protected against cytotoxicity, decreased TRPA1 mRNA expression, and reduced calcium influx. The results suggested that the TRPA1 channel could be a key common target for AHs poisoning. The order of TRPA1 affinity for AHs was propanil > pretilachlor > metolachlor > (propiso/ala/aceto/butachlor), based on K_D values of 16.2, 309, and 364 μM, respectively. The common molecular mechanisms of TRPA1-AHs interactions were clarified, including toxicity-effector groups (benzene ring, nitrogen/oxygen-containing functional groups, halogen) and residues involved in interactions (Lys787, Leu982). This work provides valuable information for the development of TRPA1 as a promising therapeutic target for broad-spectrum antitoxins. Full article

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R² > 0.6) than the single spectral index (R² > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R² = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture. Full article

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (V_cmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R² ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for V_cmax) performed better than the trait-based method (R² ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for V_cmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R² ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis. Full article