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Abstract In the potato mechanical planting industry, the high multi-seeding rate and miss-seeding rate are problems that exist in many potato precision seed metering devices. The main potato seeds in China are cut potatoes with different shapes and sizes. However, there is a lack of research on precision seeding technology for cut potatoes, and the adaptability of many existing potato seeding devices on cut potato is poor. The mechanized precision seeding of potato can effectively reduce labor consumption, improve the seeding effect (decrease multi-seeding rate and miss-seeding rate), improve the improve production of potato per acre and improve the quality commodity potatoes. In response to the above issues, a precision seed metering device with novel scoops, which can adapt to various sizes and shapes of cut potatoes, was developed. In this study, the different seed collecting scenarios (SCSs) were constructed. Through the statics and kinematic analysis, the excess seeds removing (ESR) process was divided in to three areas. The kinematic model of seed potato was established to study the influence of different SCSs and sprocket rotational speeds on the ESR performance, and preliminary determined the appropriate sprocket rotational speed range for ESR process. Then, the coupled simulation results of EDEM and RecurDyn revealed that different sprocket speeds and SCSs had a significant influence on the ESR performance. Finally, the bench tests and field tests were conducted to verify the actual performance of the precision seed metering device. The results showed that, with the increase of the sprocket rotational speed, the ESR performance of the device improved, the multi-seeding rate significantly decreased, but the miss-seeding rate increased. High sprocket rotational speed will remove more seed potatoes and cause high miss-seeding rate. The bench tests showed the appropriate sprocket rotational speed for medium and large potato was 35 r/ min. The field tests obtained the best qualified-seeding rate of 91.54%, the miss-seeding rate of 3.08% and the multi-seeding rate of 5.38%. This study can provide theoretical reference for the design of potato precision seed metering device.

Polydiacetylene (PDA) liposomes have been widely applied for detection due to their distinctive optical properties. However, the liquid phase in which PDA liposomes are dispersed generates several drawbacks, for instance, instability, compromise of detection sensitivity induced by dilution, and separation of target sampling and detection, making it inconvenient for application. In this paper, various functionalized PDA liposomes for detecting target were prepared, which were also immobilized into swelling microneedles to construct a solid-phase detection system. The PDA liposomes-complexed microneedles (PDA/MNs) enable the integration of target sampling and detection in one platform. The effects of the dispersing matrix phase on the detection sensitivity of PDA liposomes were systematically investigated from both environmental and chemical perspectives. PDA/MNs exhibited higher sensitivity than their counterparts in liquid phase. PDA/MNs were optimized and validated for lead ion (Pb2+) and sialic acid (SA) detections. For Pb2+ detection, the limit of detection (LOD) of the PDA/MNs was 13.7 μM and 2.5 times lower than the liquid phase. For SA detection, the LOD of the PDA/MNs was 0.83 μM and 1.7 times lower than the liquid phase. The results suggested that such PDA/MNs were validated to provide a label-free, stable, sensitive, and convenient tool in an all-in-one manner for physiologic target detection.

Abstract This study focuses on enhancing the pozzolanic activity of bentonite through heat treatment to improve its compressive strength, while also considering its expansion properties for applications. Sodium bentonite was subjected to various temperatures and analyzed using thermogravimetric-differential scanning calorimetry (TG-DSC), X-ray diffraction (XRD), and scanning electron microscopy-energy dispersive spectroscopy (SEM–EDS). The results indicated that at 100 °C, adsorbed and interlayer water in montmorillonite was lost, and constitution water was eliminated at 700 °C. With further temperature increases, montmorillonite decomposes into an amorphous phase at 900 °C. At 1100 °C, the amorphous phase recrystallized into magnesium–aluminum silicate, which further decomposed into cristobalite. The study concludes that bentonite heat-treated at 800–900 °C can be effectively used as an additive in mining backfill materials to enhance compressive strength while maintaining its expansion properties.

The coordinated development of the resources, environment and social economy in ecologically fragile areas has become one of the urgent issues for all countries in the world. In this study, the ecologically fragile area of Ningxia was used as the typical case area. This study constructs a “four main subjects and eight dimensions” comprehensive evaluation model based on the Sustainable Development Goals (SDGs) to analyze the situation of the resource and environmental carrying capacity (RECC). This study employs the coupled coordination degree model and the obstacle degree model, integrating them with Pearson correlation analysis to quantitatively evaluate the coupled coordination relationship and influencing factors between RECC and socioeconomic development in Ningxia. The study shows that: (a) the RECC in Ningxia is on an upward trend, with obvious spatial differences, and the pressure on resources and environment in the provincial capital city is more obvious; (b) the coordination of RECC and socioeconomic development coupling in Ningxia is on a steady upward trend, but the internal development differences are more obvious, and the degree of coordination in the provincial capital cities is relatively high; (c) the coordinated development of RECC and socioeconomic development is affected by a combination of factors such as climate, water, soil, ecological resources, urbanization, and traffic accessibility in Ningxia. The findings offer scientific support for the coordination of socioeconomic development and resource environmental protection in ecologically fragile regions. Furthermore, they serve as a reference for promoting ecological civilization construction in fragile ecosystems globally.

Refinery filtration processes often face challenges related to rapidly increasing permeate pressure differentials and the consequent need for frequent back-flushing. This study investigates the impact of high-intensity immersed sonotrode ultrasound device on flow patterns to address these issues, both numerically and experimentally. Numerical simulations reveal that ultrasound promotes axial circular mixing of the bulk fluid, increasing average flow velocities around the filter tube from 5.11 × 10-5 m/s to 8.76 × 10-3-6.09 × 10-2 m/s, thereby facilitating cleansing of filter tube surfaces. Additionally, high-frequency pressure fluctuations contribute to enhancing the filtration process during positive pressure phases, while robust online back-flushing effects are generated during negative pressure phases. Although the wire-wrapped filter tube attenuates ultrasound energy as it penetrates the tube gaps, ultrasound still induces turbulent mixing inside and outside the filter tubes, aiding in the removal of impurities from the gaps. The utilization of ultrasound is demonstrated to not inflict harm on upstream and downstream facilities. Experimental results demonstrate that ultrasound-assisted filtration with 600 W and 1000 W power inputs reduces filtration pressure differences by 18 % and 73 %, respectively, affirming ultrasound’s effectiveness in mitigating and preventing blockages, highlighting its significance for industrial applicability.

Abstract Background Chronic lung and heart diseases are more likely to lead an intensive end point after stroke onset. We aimed to investigate characteristics and outcomes of endovascular thrombectomy (EVT) in patients with acute large vessel occlusion stroke (ALVOS) and identify the role of comorbid chronic cardiopulmonary diseases in ALVOS pathogenesis. Methods In this single-center retrospective study, 191 consecutive patients who underwent EVT due to large vessel occlusion stroke in neurological intensive care unit were included. The chronic cardiopulmonary comorbidities and several conventional stroke risk factors were assessed. The primary efficacy outcome was functional independence (defined as a mRS of 0 to 2) at day 90. The primary safety outcomes were death within 90 days and the occurrence of symptomatic intracranial hemorrhage(sICH). Univariate analysis was applied to evaluate the relationship between factors and clinical outcomes, and logistic regression model were developed to predict the prognosis of ALVOS. Results Endovascular therapy in ALVOS patients with chronic cardiopulmonary diseases, as compared with those without comorbidity, was associated with an unfavorable shift in the NHISS 24 h after EVT [8(4,15.25) versus 12(7.5,18.5), P = 0.005] and the lower percentage of patients who were functionally independent at 90 days, defined as a score on the modified Rankin scale of 0 to 2 (51.6% versus 25.4%, P = 0.000). There was no significant between-group difference in the frequency of mortality (12.1% versus 14.9%, P = 0.580) and symptomatic intracranial hemorrhage (13.7% versus 19.4%, P = 0.302) or of serious adverse events. Moreover, a prediction model showed that existence of cardiopulmonary comorbidities (OR = 0.456, 95%CI 0.209 to 0.992, P = 0.048) was independently associated with functional independence at day 90. Conclusions EVT was safe in ALVOS patients with chronic cardiopulmonary diseases, whereas the unfavorable outcomes were achieved in such patients. Moreover, cardiopulmonary comorbidity had certain clinical predictive value for worse stroke prognosis.

Salmonella is a foodborne pathogen that causes disruption of intestinal mucosal immunity, leading to acute gastroenteritis in the host. In this study, we found that Salmonella Typhimurium (STM) infection of the intestinal tract of mice led to a significant increase in the proportion of Lacticaseibacillus, while the secretion of IL-22 from type 3 innate lymphoid cells (ILC3) increased significantly. Feeding Lacticaseibacillus rhamnosus GG (LGG) effectively alleviated the infection of STM in the mouse intestines. TLR2−/− mice experiments found that TLR2-expressing dendritic cells (DCs) are crucial for LGG‘s activation of ILC3. Subsequent in vitro experiments showed that heat-killed LGG (HK-LGG) could promote DCs to secrete IL-23, which in turn further promotes the activation of ILC3 and the secretion of IL-22. Finally, organoid experiments further verified that IL-22 secreted by ILC3 can enhance the intestinal mucosal immune barrier and inhibit STM infection. This study demonstrates that oral administration of LGG is a potential method for inhibiting STM infection.

The Bacillus velezensis strain NT35, which has strong biocontrol ability, was isolated from the rhizosphere soil of Panax ginseng. The antifungal effects of the NT35 strain against the mycelium and spore growth of Ilyonectria robusta, which causes ginseng rusty root rot, were determined. The inhibitory rate of I. robusta mycelial growth was 94.12% when the concentration of the NT35 strain was 107 CFU·mL−1, and the inhibitory rates of I. robusta sporulation and spore germination reached 100 and 90.31%, respectively, when the concentration of the NT35 strain was 104 and 108 CFU·mL−1, respectively. Strain NT35 had good prevention effects against ginseng rust rot indoors and in the field with the control effect 51.99%, which was similar to that of commercial chemical and biocontrol agents. The labeled strain NT35-Rif160-Stre400 was obtained and colonized ginseng roots, leaves, stems and rhizosphere soil after 90 days. Bacillus velezensis NT35 can induce a significant increase in the expression of five defensive enzyme-encoding genes and ginsenoside biosynthesis-related genes in ginseng. In the rhizosphere soil, the four soil enzymes and the microbial community improved during different periods of ginseng growth in response to the biocontrol strain NT35. The NT35 strain can recruit several beneficial bacteria, such as Luteimonas, Nocardioides, Sphingomonas, and Gemmatimonas, from the rhizosphere soil and reduce the relative abundance of Ilyonectria, Fusarium, Neonectria and Dactylonectria, which cause root rot and rusty root rot in ginseng plants. The disease indices were significantly negatively correlated with the abundances of Sphingomonas and Trichoderma. Additionally, Sphingomonadales, Sphingomonadaceae and actinomycetes were significantly enriched under the NT35 treatment according to LEfSe analysis. These results lay the foundation for the development of a biological agent based on strain NT35.

Abstract The generally undesirable bandgap and electron–hole complexation of inorganic sonosensitizers limit the efficiency of reactive oxygen species (ROS) generation, affecting the effectiveness of sonodynamic therapy (SDT). Comparatively, the novel polyvinylpyrrolidone‐modified copper bismuthate (PCBO) sonosensitizers are manufactured for a “three‐step” SDT promotion. In brief, first, the strong hybridization between Bi 6s and O 2p orbitals in PCBO narrows the bandgap (1.83 eV), facilitating the rapid transfer of charge carriers. Additionally, nonequivalent [CuO4]6− layers reduce crystal symmetry, confer PCBO unique piezoelectricity, and improve electron–hole separation under ultrasonic (US) excitation. This allows PCBO to convert US energy into chemical energy to produce ROS, achieving the accumulation of abundant ROS, resulting in apoptosis and tumor suppression. Concurrently, PCBO also acts as a glutathione scavenger to reduce tumor antioxidant capacity and improve efficacy. To the best of authors understanding, this study reveals PCBO as an innovative piezoelectric sonosensitizer and provides a meaningful paradigm for designing energy conversion strategies for tumor suppression.

In order to address the problem of a high miss-seeding rate in mechanized potato planting work, a novel reseeding device is designed and analyzed. Based on dynamic and kinematic principles, the seed potato’s motion analysis model in the seed preparation process was constructed. The analysis results indicate that the seed preparation performance is positively related to the seed preparation opening length l1 and inclination angle of the seed-returning pipe θ. Then, the potato’s motion analysis model in the reseeding process was constructed. The analysis showed that the displacement of seeding potatoes in the horizontal direction ds is influenced by the initial seeding potato’s speed v0t, dropping height hs, and the angle between the seeding pipe and the horizontal ground βs. The horizontal moving distance xr of the reseeding potatoes is influenced by the angle between the bottom of the reseeding pipe and horizontal ground βs2, the distance from its centroid to the reseeding door d, and the dropping height of the potato hr. The analysis results indicated that the reseeding potato can be effectively discharged into the furrow. Then, a prototype of a reseeding control system was constructed based on the STM32 microcontroller, electric pushers, and through-beam laser sensors. The simulation analysis was conducted to verify the theoretical analysis by using EDEM2020 software. The simulation results indicated that with the increase in the seeding chain speed, the seed preparation success rate initially increased slowly and then decreased gradually. The seed preparation performance can be increased by increasing the seed preparation opening length or decreasing the seed-returning pipe inclination angle. The impact on the successful seed preparation rate is ranked by significance as follows: seed preparation opening length > seed-returning pipe inclination angle > chain speed. Then, the prototype reseeding device and the corresponding seed metering device were manufactured and a series of bench tests and field tests were conducted. The bench test results showed an average successful seed preparation rate of 93.6%. The average qualified-seeding rate, miss-seeding rate, and multi-seeding rate in the field test were 89.6%, 2.46%, and 7.94%, respectively. This study can provide a theoretical reference for the design of potato reseeding devices.

Background Falls pose a serious health risk for the elderly, particular for those who are living alone. The utilization of WiFi-based fall detection, employing Channel State Information (CSI), emerges as a promising solution due to its non-intrusive nature and privacy preservation. Despite these advantages, the challenge lies in optimizing cross-individual performance for CSI-based methods. Objective This study aimed to develop a resilient real-time fall detection system across individuals utilizing CSI, named TCS-Fall. This method was designed to offer continuous monitoring of activities over an extended timeframe, ensuring accurate and prompt detection of falls. Methods Extensive CSI data on 1800 falls and 2400 daily activities was collected from 20 volunteers. The grouped coefficient of variation of CSI amplitudes were utilized as input features. These features capture signal fluctuations and are input to a convolutional neural network classifier. Cross-individual performance was extensively evaluated using various train/test participant splits. Additionally, a user-friendly CSI data collection and detection tool was developed using PyQT. To achieve real-time performance, data parsing and pre-processing computations were optimized using Numba's just-in-time compilation. Results The proposed TCS-Fall method achieved excellent performance in cross-individual fall detection. On the test set, AUC reached 0.999, no error warning ratio score reached 0. 955 and correct warning ratio score reached of 0.975 when trained with data from only two volunteers. Performance can be further improved to 1.00 when 10 volunteers were included in training data. The optimized data parsing/pre-processing achieved over 20× speedup compared to previous method. The PyQT tool parsed and detected the fall within 100 ms. Conclusions TCS-Fall method enables excellent real-time cross-individual fall detection utilizing WiFi CSI, promising swift alerts and timely assistance to elderly. Additionally, the optimized data processing led to a significant speedup. These results highlight the potential of our approach in enhancing real-time fall detection systems.

Focusing on submerged combustion technology for the treatment of high salinity and high chemical oxygen demand (COD) wastewater, this study explores the characteristics of gas-liquid two-phase flow and heat transfer during the submerged combustion process, employing both experimental and numerical simulation methods.Consequently, the impact of essential parameters on the efficacy of submerged combustion in treating wastewater was analyzed. The experimental results show that increasing the fuel flow rate and immersion depth is beneficial to improve the temperature rise rate and shorten the evaporation time. With the increase of the organic solution flow rate and the initial COD value of the organic solution, the COD removal rate decreased, and the effect of the organic solution flow rate on the COD removal efficiency was more obvious. The use of a flat orifice plate or tapered orifice plate immersed tube structure can improve the thermal efficiency by over 10%. However, it is not conducive to the stability of the pressure in the combustor, increasing the fluctuation of the liquid level. Compared with that of traditional single-effect evaporation crystallization, the energy consumption of the submerged combustion device is decreased, and the total operation cost is reduced by 22.5%.

Abstract Background Patients with non‐small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) experience worse clinical outcomes but respond better to immunotherapy than patients with NSCLC without COPD. Mucosal‐associated invariant T (MAIT) cells, a versatile population of innate immune T lymphocytes, have a crucial function in the response to infection and tumors. This study investigated the distribution of MAIT cells in COPD‐associated NSCLC and their involvement in the immune response. Methods Flow cytometry, immunohistochemistry, and immunofluorescence were performed on tissue samples of patients with NSCLC, with or without COPD, treated with or without anti‐programmed death 1 (PD1) immunotherapy. MAIT cells were stimulated with 5‐OP‐RU using a mouse subcutaneous tumor model. Results Tumors contained significantly more MAIT cells than paraneoplastic tissues, and CD8+ MAIT cells accounted for more than 90% of these cells. Patients with NSCLC and COPD had higher CD8+ MAIT cell counts than those with NSCLC without COPD. Additionally, patients with NSCLC and COPD displayed reduced expression of the activation marker, CD69, and functional markers, granzyme B (GZMB) and interferon γ (IFNγ), and higher expression of the immune exhaustion marker, PD1. Among patients who received immunotherapy, the proportion with a complete or partial response was higher in those with COPD than in those without COPD. In patients with NSCLC and COPD, the major pathologic response (MPR) group had higher MAIT levels than those in the no major pathologic response (NPR) group. In the mouse subcutaneous tumor model stimulation of MAIT cells using 5‐OP‐RU enhanced the antitumor effects of anti‐PD1. Conclusions In patients with NSCLC and COPD, response to immunotherapy is associated with accumulation of CD8+ MAIT cells showing immune exhaustion. These findings may contribute to innovative approaches for immunotherapy targeting CD8+ MAIT cells.

Abstract Objective The differential diagnosis between adult-onset Still’s disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model. Methods All data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python. Results The results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still’s disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/ . Conclusion We created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.

Abstract Background The most prevalent mesenchymal-derived gastrointestinal cancers are gastric stromal tumors (GSTs), which have the highest incidence (60–70%) of all gastrointestinal stromal tumors (GISTs). However, simple and effective diagnostic and screening methods for GST remain a great challenge at home and abroad. This study aimed to build a GST early warning system based on a combination of machine learning algorithms and routine blood, biochemical and tumour marker indicators. Methods In total, 697 complete samples were collected from four hospitals in Gansu Province, including 42 blood indicators from 318 pretreatment GST patients, 180 samples of gastric polyps and 199 healthy individuals. In this study, three algorithms, gradient boosting machine (GBM), random forest (RF), and logistic regression (LR), were chosen to build GST prediction models for comparison. The performance and stability of the models were evaluated using two different validation techniques: 5-fold cross-validation and external validation. The DeLong test assesses significant differences in AUC values by comparing different ROC curves, the variance and covariance of the AUC value. Results The AUC values of both the GBM and RF models were higher than those of the LR model, and this difference was statistically significant (P

With the construction of new-type power system under the ”double carbon” target and the increasing diversification of the energy demand of the user side, the short-term load forecasting of power system is facing new challenges. In order to fully exploit the massive information contained in big data, this paper proposed a new short-term load forecasting method for new-type power system considering multiple factors, which based on Graph Convolutional Network (GCN) and Long Short-Term Memory network (LSTM). Spearman rank correlation coefficient was used to analyze the correlation between load and meteorological factors, and a quantitative model including meteorological factors, date factors and regional factors was established. Thus, GCN and LSTM were jointly used to extract the spatial and temporal characteristics of massive data respectively, and finally the short-term power load forecasting was achieved. The public data sets were used for performance verification compared with three comparison models, LSTM, CNN-LSTM and TCN-LSTM. The results show that the proposed method can make full use of the influence of multi-dimensional data, meanwhile improve the load prediction accuracy and training efficiency effectively.

The interactive operation of multi-energy Microgrid (MEMG) in energy market is facing more and more incomplete information decision-making requirements due to the enhanced privacy of users, which brings great challenges to the participation flexibility of MEMG in demand response programs. Meanwhile, considering the uncertainty of the external MEMG unit combination, the randomness of the environmental meteorology, the interactive characteristics of the external MEMGs are complex and time-varying. To solve these problems, a novel GRU-TCN network based Interactive Behavior Learning method for MEMG interactive operation is proposed. The proposed Interactive Behavior Learning framework needs merely externally available input information and historical interaction power data to efficiently predict the interaction power, which protects the data privacy of users. Besides, the GRU-TCN network also leverages the strengths of Recurrent Neural Network and Convolutional Neural Networks while incorporating the self-attention mechanism. This combination enables the GRU-TCN network to effectively capture the relationship between interactive power and the available resources data beyond the MEMG. The example analysis and test are carried out on a typical MEMG system, and the case study turns out that the proposed GRU-TCN network has the capability to capture coupling mechanisms even in the presence of incomplete information and present more accurate prediction of multi-energy interactive data.

Microbial community composition, function, and viability are important for biofilm-based sewage treatment technologies. Most studies of microbial communities mainly rely on the total deoxyribonucleic acid (DNA) extracted from the biofilm. However, nucleotide materials released from dead microorganisms may interfere with the analysis of viable microorganisms and their metabolic potential. In this study, we developed a protocol to assess viability as well as viable community composition and function in biofilm in a sewage treatment system using propidium monoazide (PMA) coupled with real-time quantitative polymerase chain reaction (qPCR) and metagenomic technology. The optimal removal of PMA from non-viable cells was achieved by a PMA concentration of 4 μM, incubation in darkness for 5 min, and exposure for 5 min. Simultaneously, the detection limit can reach a viable bacteria proportion of 1%, within the detection concentration range of 102–108 CFU/mL (colony forming unit/mL), showing its effectiveness in removing interference from dead cells. Under the optimal conditions, the result of PMA–metagenomic sequencing revealed that 6.72% to 8.18% of non-viable microorganisms were influenced and the composition and relative abundance of the dominant genera were changed. Overall, this study established a fast, sensitive, and highly specific biofilm viability detection method, which could provide technical support for accurately deciphering the structural composition and function of viable microbial communities in sewage treatment biofilms.

In the aquaculture system of ornamental fish, the interaction between bacterial microbiota and ciliate protozoa can prevent or promote disease outbreaks, and different physicochemical conditions will affect the relationships between them. We investigated the interaction between bacterial microbiota and the parasite Tetrahymena pyriformis when infecting Poecilia reticulata (guppy) under different physicochemical conditions. The abundance of T. pyriformis in water, the relative abundance of bacterial species, and histopathological observation were studied or monitored using environmental DNA (eDNA) extraction technology, the qPCR method, and 16s rRNA sequencing, respectively. The morphological identification and phylogenetic analysis of T. pyriformis were carried out. The infected guppy tissue was also stained by the hematoxylin and eosin methods. The results showed: (1) the bacterial communities of water samples were mainly composed of species assigned to Proteobacteria and Bacteroidetes, and Tabrizicola and Puniceicoccaceae were positively correlated with fish mortality, T. pyriformis abundance, and temperature. (2) Arcicella and Methyloversatilis universalis with different correlations between ciliates appeared in different treatment groups, the result of which proved that environmental factors affected the interaction between bacteria and T. pyriformis. (3) Lower temperatures and a higher pH were more beneficial for preventing disease outbreaks.

Formation obstacle avoidance is a critical aspect of cooperation among unmanned surface vehicles (USVs). In practical scenarios involving multiple USVs, managing obstacle avoidance during formation assembly and navigation is essential to ensure the success of cooperative tasks. This study devised a formation cooperative obstacle-avoidance scheme utilizing dynamical systems (DS). The traditional interfered fluid dynamical system (IFDS) applied in two-dimensional planes was enhanced to address local minima issues. Furthermore, robust virtual structure patterns were implemented to effectively decouple velocity vectors. Streamlines were optimized by adjusting velocity amplitudes within specific distance intervals, facilitating precise formation assembly amidst multiple obstacles. Additionally, a novel inter-vehicle disturbance method, distinct from the IFDS, was developed to enhance inter-vehicle collision avoidance. The effectiveness of the proposed method in enabling USV formations to adeptly navigate obstacles while maintaining formation integrity and collision-avoidance capabilities was analyzed theoretically and confirmed through simulation.

To study the correlation of low-frequency line spectrum characteristics of underwater typical physical fields, an underwater joint measuring device was developed, which integrated the measurement functions of acoustic field, electric field, magnetic field, and pressure field. Secondly, the underwater physical field signals of more than 30 ships with different targets were obtained through tests in a sea area. Finally, based on the measured data, the correlation between the low-frequency (below 100 Hz) line spectrum characteristics of the acoustic field, electric field, magnetic field, and pressure field of typical ships was analyzed. The results show that the line spectrum characteristics of low-frequency pressure signal and low-frequency electric field are strongly correlated and have common sources. The line spectra of the low-frequency acoustic field, pressure field, and low-frequency electric field signal are correlated and have common sources at some frequency points, which provides a new idea for the joint detection and identification of targets based on underwater physical fields.

From the Late Carboniferous to the end of Middle Permian, the most intense glacial event in history of Phanerozoic occurred and it is also the last transition period from icehouse stage to greenhouse stage in geological history. In this paper, the paleotemperature and paleoclimate in the first member of the Middle Permian Maokou Formation (Mao-1) were restored by means of thin section identification, scanning electron microscope, major elements, trace elements, carbon isotopes, oxygen isotopes and strontium isotopes, and the major climate evolution process and its driving mechanism in the Middle Permian were discussed. The results show that the biological assemblage type (mainly composed of foraminifera, brachiopods and molluscs, without reef-building organisms or calcareous green algae) and rock structure characteristics (supported by bioclastic and plaster, without oolitic or other non-skeletal particles) of the limestone-marl alternations in the Mao-1 member are similar to those of the international typical cool-water carbonate. During the limestone sedimentary period, the paleotemperature of seawater was 3.72 to 12.38 ℃ (8.15 ℃ in average, δ18O standard) or 13.79 to 14.28 ℃ (13.90 ℃ in average, ω(Mg)/ ω(Ca) standard), while during the marl sedimentary period, the paleotemperature of seawater was 7.00 to 14.24 ℃ (10.97 ℃ in average, δ18O standard) and 13.82 to 15.41 ℃ (14.27 ℃ in average, ω(Mg)/ω(Ca) standard). The paleotemperature changes in the sedimentary period of Mao-1 Member was mainly driven by the short eccentricity cycle of Milankovitch. The cyclical change of the short eccentricity was the driving mechanism of paleotemperature and paleoclimate cycle changes.

Abstract Fasting is a popular dietary strategy because it grants numerous advantages, and redox regulation is one mechanism involved. However, the precise redox changes with respect to the redox species, organelles and tissues remain unclear, which hinders the understanding of the metabolic mechanism, and exploring the precision redox map under various dietary statuses is of great significance. Twelve redox-sensitive C. elegans strains stably expressing genetically encoded redox fluorescent probes (Hyperion sensing H2O2 and Grx1-roGFP2 sensing GSH/GSSG) in three organelles (cytoplasm, mitochondria and endoplasmic reticulum (ER)) were constructed in two tissues (body wall muscle and neurons) and were confirmed to respond to redox challenge. The H2O2 and GSSG/GSH redox changes in two tissues and three organelles were obtained by confocal microscopy during fasting, refeeding, and satiation. We found that under fasting condition, H2O2 decreased in most compartments, except for an increase in mitochondria, while GSSG/GSH increased in the cytoplasm of body muscle and the ER of neurons. After refeeding, the redox changes in H2O2 and GSSG/GSH caused by fasting were reversed in most organelles of the body wall muscle and neurons. In the satiated state, H2O2 increased markedly in the cytoplasm, mitochondria and ER of muscle and the ER of neurons, while GSSG/GSH exhibited no change in most organelles of the two tissues except for an increase in the ER of muscle. Our study systematically and precisely presents the redox characteristics under different dietary states in living animals and provides a basis for further investigating the redox mechanism in metabolism and optimizing dietary guidance.

ABSTRACTThe implementation of process analytical technologies is positioned to play a critical role in advancing biopharmaceutical manufacturing by simultaneously resolving clinical, regulatory, and cost challenges. Raman spectroscopy is emerging as a key technology enabling in-line product quality monitoring, but laborious calibration and computational modeling efforts limit the widespread application of this promising technology. In this study, we demonstrate new capabilities for measuring product aggregation and fragmentation in real-time during a bioprocess intended for clinical manufacturing by applying hardware automation and machine learning data analysis methods. We reduced the effort needed to calibrate and validate multiple critical quality attribute models by integrating existing workflows into one robotic system. The increased data throughput resulting from this system allowed us to train calibration models that demonstrate accurate product quality measurements every 38 s. In-process analytics enable advanced process understanding in the short-term and will lead ultimately to controlled bioprocesses that can both safeguard and take necessary actions that guarantee consistent product quality.

Abstract Understanding and mastering the structural evolution of water oxidation electrocatalysts lays the foundation to finetune their catalytic activity. Herein, we demonstrate that surface reconstruction of spinel oxides originates from the metal-oxygen covalency polarity in the MT–O–MO backbone. A stronger MO–O covalency relative to MT–O covalency is found beneficial for a more thorough reconstruction towards oxyhydroxides. The structure-reconstruction relationship allows precise prediction of the reconstruction ability of spinel pre-catalysts, based on which the reconstruction degree towards the in situ generated oxyhydroxides can be controlled. The investigations of oxyhydroxides generated from spinel pre-catalysts with the same reconstruction ability provide guidelines to navigate the cation selection in spinel pre-catalysts design. This work reveals the fundamentals for manipulating the surface reconstruction of spinel pre-catalysts for water oxidation.

A landing string is directly exposed to seawater and subjected to significant stresses and complex deformations due to environmental loads such as wind, waves, and ocean currents during the phase in which the drill string carries the casing to the wellhead. Meanwhile, as the water depth increases, the weight of the drill string increases, leading to an increase in the tensile loads borne by the drill string, which can easily cause a risk of failure. Therefore, a quasi-static load calculation model for the deepwater insertion of the pipe column was established. Using the Ansys platform, simulations were conducted for average wind, wave, and ocean current conditions during different months throughout the year. The ultimate loads and stress distributions of the string were derived from theoretical analyses and numerical simulations for different operational sea states, and the suggested safe operating window and desired BOP trolley restraining reaction force for landing strings’ lowering are given according to the existing industry standards. The research findings can help in identifying the potential risks and failure modes of the deepwater landing string under different working conditions.

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strategy.

With the continuous exploration of researchers in the field of sodium-ion batteries, the performance of these batteries has been greatly improved, and they have a wide range of application prospects in large-scale energy storage, traffic power and other fields. Hard carbon is the most important anode material for sodium-ion batteries. Although it has the advantages of low cost, stable structure and performance, it still has the problems of low initial Coulombic efficiency (ICE) and poor rate performance in application. In order to solve the problem of low ICE of hard carbon anode in sodium-ion batteries, in recent years the literature about hard carbon anode in sodium-ion batteries has been comprehensively reviewed. Based on the microstructure of hard carbon material, the causes of low ICE of hard carbon are analyzed. At the same time, from the point of view of material structure design and regulation, the current optimization strategies of hard carbon anode ICE are summarized, including the following aspects: optimization and improvement of the carbonization process, precursor screening and design, surface coating strategy, micro-pore structure control, catalytic carbonization strategy. We hope that this review will provide reference for further optimization of hard carbon properties and its large-scale application in sodium-ion batteries.

Hydrochar is considered to be a good adsorbent for the separation of metal ions from aqueous solutions. However, the yield of hydrochar from raw straw is generally low, because the hydrothermal carbonization occurs via dehydration, polymerization, and carbonization. In this work, various hydrochar samples were prepared from rice straw with nitrogen and phosphorus salt; moreover, toilet sewage was used instead of nitrogen, and phosphorus salt and water were used to promote the polymerization and carbonization process. The modified carbon was characterized using XRD, XPS, SEM, and FTIR, and the adsorption capacity was investigated. A significant increase in hydrochar yield was observed when toilet sewage was used as the solvent in the hydrothermal carbonization process. The adsorption capacity of N/P-doped rice straw hydrochar for Cd2+ and Zn2+ metal ions was 1.1–1.4 times higher than that those using the rice straw hydrochar. The Langmuir models and pseudo-second-order models described the metal adsorption processes in both the single and binary-metal systems well. The characterization results showed the contribution of the surface complexation, the electrostatic interaction, the hydrogen bond, and the ion exchange to the extraction of Cd2+ and Zn2+ using N/P-doped rice straw hydrochar.

Scientifically evaluating the influence of ecological restoration projects on the water conservation function (WCF) of regional ecosystems is the foundation for formulating regional ecological restoration policies and optimizing and adjusting ecological restoration projects. In this paper, we considered fully the runoff generation and confluence process in the Qilian Mountains with the actual situation of the basin and re-rated the parameter Z to improve the simulation accuracy of InVEST model. On this basis, the impact of ecological restoration project on the WCF in the upper reaches of Shiyang River Basin (SRB) in the eastern part of Qilian Mountains was quantified. The results showed that, on the whole, the water conservation depth (WCD) of forest land was the largest (138.5 mm) and that of cultivated land was the smallest (24.78 mm), while the water conservation coefficient of forest land was also the largest (93.36%) and that of unused land was the smallest (16.67%). From 1986 to 2018, the WCD showed an increasing trend in the upper reaches of SRB, among them, the WCD in the western tributaries increased faster than that in the eastern tributaries from 1986 to 2000. The significantly increased areas were mainly distributed in the middle reaches of the western tributaries and the river source areas of the eastern tributaries, while the significantly decreased areas were mainly distributed in the river source areas of the western tributaries and the cultivated land expansion area in the middle reaches of the eastern tributaries. From 2000 to 2018, the WCD of the eastern tributaries increased more than that of the western tributaries. The significantly increased areas were mainly distributed in the four eastern tributaries, and the significantly decreased areas were scattered in the middle and lower reaches of each tributary. From 1986 to 2000, the overall influence of land use change on the increase in WCD was negative, while the influence of climate and land use change on the increase in water conservation were both positive from 2000 to 2018. The influence of land use change on WCD was different in different tributaries. Among them, that of the western tributaries (except the Dongda River) was positive in two different periods, while that of the eastern tributaries (except the Xiying River) was changed from negative to positive. The implementation of ecological restoration project was one of the main reasons for the improvement of WCF in Qilian Mountains from 2000 to 2018, with a contribution of 9.04%. In the future, the protection and restoration of decreased areas of WCF should be strengthened, and the Z value determined in this paper is expected to be applied in the arid inland river basins of northwest China.

Electrochemically producing formate by oxidizing methanol is a promising way to add value to methanol. Noble metal-based electrocatalysts, which have been extensively studied for the methanol oxidation reaction, can catalyze the complete oxidation of methanol to carbon dioxide, but not the mild oxidation to formate. As a result, exploring efficient and earth-abundant electrocatalysts for formate production from methanol is of interest. Herein, we present the electro-oxidation of methanol to formate, catalyzed by iron-substituted lanthanum cobaltite (LaCo1−xFexO3). The Fe/Co ratio in the oxides greatly influences the activity and selectivity. This effect is attributed to the higher affinity of Fe and Co to the two reactants: CH3OH and OH−, respectively. Because a balance between these affinities is favored, LaCo0.5Fe0.5O3 shows the highest formate production rate, at 24.5 mmol ​h−1 goxide−1, and a relatively high Faradaic efficiency of 44.4% in a series of (LaCo1−xFexO3) samples (x ​= ​0.00, 0.25, 0.50, 0.75, 1.00) at 1.6 ​V versus a reversible hydrogen electrode.

This review explored the potential of edible insects to address the challenges of malnutrition and food security. Although grain production in China has met the Food and Agriculture Organization standards, the shortage of protein supply is still a big issue. Moreover, expanding livestock farming is considered unsustainable and environmentally unfriendly. Edible insects have become an alternative with higher sustainable and ecological properties. There are 324 species of insects currently consumed in China, and they have high nutritional value, with a rich source of protein and unsaturated fatty acids. Insect farming provides numerous benefits, including green feeds for livestock, poultry, and aquaculture, sustainable organic waste management, as well as industrial and pharmaceutical raw materials. The food toxicological evaluations conducted in China indicated that edible insects are safe for general consumption by the Chinese, but allergies and other related food safety issues should not be ignored. Consumer acceptance is another barrier to overcome, with different schemas between China and Western countries. More research on the potential functions of edible insects and their product development may enhance their acceptance in China. Overall, incorporating edible insects into our diet is a promising solution to address challenges related to protein supply and food security. To ensure safety and sustainability, appropriate legislation, quality regulations, large-scale insect farms, and acceptable processing techniques are necessary. Moreover, more scientific research and social awareness are required to promote the culture and utilization of edible insects in China.

With the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time.

In social interactions, people frequently encounter gain (i.e., all outcomes are gains from the status-quo) or loss (all outcomes are losses from the status-quo) social dilemmas, where their personal interests conflict with social interests. We ask whether there are any behavioral differences in social interactions when it comes to gains and losses. Using the Prisoner’s Dilemma games, in three studies we observed that participants were less cooperative in the loss domain than in the gain domain. This effect was robust, not moderated by payoff amount (Study 1), cooperation index (Study 1), domain comparison (Studies 1 and 2), and personal loss aversion (Study 3). Social motive and belief explained this effect: compared to the gain domain, participants in the loss domain aroused more pro-self motive and less prosocial motive, and showed stronger beliefs that their partner would defect, which led them to cooperate less. These findings suggest that gain and loss domains affect individual motivation and belief, subsequently affecting strategic choices in social dilemmas.

The impact of the land use landscape pattern on river hydrochemistry has significant scale effects. It is the premise and foundation of landscape optimization and the conservation of water resources to accurately identify the core scale of the landscape pattern impacting on river hydrochemistry. Taking Binggou River Basin in the source region of Shiyang River Basin as the study area, this paper selected the three spatial scales of catchment area, buffer zone, and riparian zone, and the relationships between land use types and landscape indices and river hydrochemical characteristics were analyzed at different spatio-temporal scales using redundancy analysis. The results showed that the hydrochemistry characteristics of Binggou River Basin had strong temporal variability, and the rainy season was the key period when the land use landscape pattern affected the river hydrochemistry. The sub-watershed and riparian scales were the core scales of influence of the landscape pattern on river hydrochemistry in Binggou River Basin. In particular, the landscape pattern index contributed the most to the river hydrochemistry at the sub-watershed scale on the whole, which was the core scale affecting river hydrochemistry. The “source” or “sink” functions of the same land use type also changed as the scale changed. Forest land showed weak “source” effect at the sub-watered scale, but a strong interception effect on the soil erosion process at the riparian scale, which is a typical “sink” landscape.

The integrated energy system (IES) is recognized as a promising energy utilization approach enabled to both improve the energy efficiency and reduce pollutant emissions. Although the economic-environmental dispatch (EED) problem of the IES has been widely studied, the fact that the IES is operated under off-design conditions, having a significant influence on the efficiency of energy devices, is neglected usually, resulting in the scheduled operations, for the IES could not be accurate enough in many actual situations. This study investigates the EED problem of the IES under off-design conditions. Technically, by integrating an efficiency correction process into the traditional energy hub (EH) model, a dynamic energy hub (DEH) model is first formulated for adapting itself to variable energy conversion efficiencies. Then, a deep neural network (DNN)-based efficiency correction method is proposed to predict and correct the time-varying efficiency of energy devices based on three main off-design conditions including the load rate, air temperature, and pressure. A multi-objective EED model is finally formulated for the IES, with the framework of the DEH model, aiming at establishing a trade-off between operational cost and emitted pollutants. Case studies show that the proposed approach helps in enhancing the accuracy of IES dispatch while having a good performance in both the operational cost and pollutant emission reduction.

Compared with optical coherence tomography (OCT) in the near-infrared domain, the visible-light OCT (vis-OCT) system affords a higher axial resolution for discerning subtle pathological changes associated with early diseases. However, the significant material dispersion at the visible-light range leads to a severe problem for dispersion management in vis-OCT systems, which results in a compromised axial resolution. While dispersion compensators (such as prism pairs) are commonly used, a digital method is still highly desirable and has been widely used to compensate for the residual dispersion imbalance between the reference and sample arms in an OCT system. In this paper, we develop a generic approach to effectively compensate for the system dispersion, especially the higher-order dispersion in the vis-OCT system, by using a single arbitrary measurement of the mirror-reflection (SAMMR) method and its resulting phase information. Compared with the previous methods, including the method based on the Taylor series iterative fitting and differential method, the proposed method does not need to extract the dispersion coefficients or use the metric functions and affords a better performance for axial resolution and the signal-to-noise ratio in vis-OCT systems. Its effectiveness is further validated in an OCT system operating in the near-infrared domain.

Riserless mud-recovery (RMR) drilling technology was widely applied in recent years. Compared with traditional deepwater drilling, RMR drilling can improve drilling efficiency, reduce risks, and minimize environmental effects. This paper focuses primarily on the stability of a mud-return circulation system in an RMR system. First, various factors that affect the stability of a mud-return circulation system are analyzed. An analytical model for the skid-and-mud-return line is established. Second, relevant data are derived from theoretical calculations and experiments. ABAQUS software is used to analyze the effects of each factor on the stability of the mud-return circulation system. The influencing patterns of each factor on the stability of the mud-return circulation system are summarized. Furthermore, the stability of the system under different operating conditions is analyzed based on the coupling of multiple factors. The support vector regression with derivative significance weight analysis (SVR-DWSA) algorithm is employed to perform a weight analysis of the effect on the system’s stability. Finally, based on the research findings on the stability of the mud-return circulation system, relevant conclusions and recommendations are drawn. The results of this study provide valuable references for the application of RMR technology.

Researchers working on image processing have had a hard time handling low-light images due to their low contrast, noise, and brightness. This paper presents an improved method that uses the Retinex theory to enhance low-light images, with a network model mainly composed of a Decom-Net and an Enhance-Net. Residual connectivity is fully utilized in both the Decom-Net and Enhance-Net to reduce the possible loss of image details. Additionally, Enhance-Net introduces a positional pixel attention mechanism that directly incorporates the global information of the image. Specifically, Decom-Net serves to decompose the low-light image into illumination and reflection maps, and Enhance-Net serves to increase the brightness of the illumination map. Finally, via adaptive image fusion, the reflectance map and the enhanced illuminance map are fused to obtain the final enhanced image. Experiments show better results in terms of both subjective visual aspects and objective evaluation indicators. Compared to RetinexNet, the proposed method shows improvements in the full-reference evaluation metrics, including a 4.6% improvement in PSNR, a 1.8% improvement in SSIM, and a 10.8% improvement in LPIPS. Additionally, it achieved an average improvement of 17.3% in the no-reference evaluation metric NIQE.

With the rapid development of communication technology and information processing technology, the construction of the Urban Energy Internet (UEI) has become one of the important construction elements of the new power system, and it is necessary to assess and analyse its development status and potential. However, the results of the current assessment of the maturity of UEI development are relatively rare, and the transformation path of urban smart energy construction needs to be studied in depth. On this basis, this study aims to propose an improved and comprehensive evaluation model for the maturity of UEI development. This study first considers the dynamic development process of the UEI and proposes an evaluation index system for the maturity of UEI development that includes three dimensions of development status, development benefits and development prospects. Secondly, a comprehensive evaluation model based on GRA-KL-TOPSIS is constructed by using the AHP-entropy weighting method to calculate the combined weights of indicators and considering the Kulla back-Leibler distance to replace the Euclidean distance in the traditional evaluation method. Finally, the maturity of Energy Internet development is calculated for five typical first-tier cities in China (Beijing, Shanghai, Guangzhou, Tianjin and Shenyang), and the final ranking of the five cities is Shanghai > Beijing > Guangzhou > Tianjin > Shenyang. The results of the study prove the scientific validity of the model. Compared to the unimproved Topsis method, the evaluation results calculated based on the improved Topsis evaluation model are more objective and realistic in reflecting the score and rating of the cities. The analysis of the empirical results shows that cities at different stages of development should make up for their shortcomings and increase their investment in infrastructure development, technological innovation and the introduction of talents in order to accelerate the digital and intelligent development of energy.

Flower color is an important characteristic of ornamental plants and is determined by various chemical components, including anthocyanin. In the present study, combined metabolomics and transcriptomics analysis was used to explore color variations in the chrysanthemums of three cultivars, of which the color of JIN is yellow, FEN is pink, and ZSH is red. A total of 29 different metabolites, including nine anthocyanins, were identified in common in the three cultivars. Compared with the light-colored cultivars, all of the nine anthocyanin contents were found to be up-regulated in the dark-colored ones. The different contents of pelargonidin, cyanidin, and their derivates were found to be the main reason for color variations. Transcriptomic analysis showed that the color difference was closely related to anthocyanin biosynthesis. The expression level of anthocyanin structural genes, including DFR, ANS, 3GT, 3MaT1, and 3MaT2, was in accordance with the flower color depth. This finding suggests that anthocyanins may be a key factor in color variations among the studied cultivars. On this basis, two special metabolites were selected as biomarkers to assist in chrysanthemum breeding for color selection.

Drought is a significant environmental stress factor that adversely affects maize productivity. However, many details regarding the molecular mechanisms of maize against drought are still unclear. In this study, leaf transcriptomics and physiological traits of two maize genotypes with differing drought resistance were analyzed. Transcriptome sequencing identified 8985 and 7305 differentially expressed genes (DEGs) in SD902 and SD609, respectively. Functional analysis suggested that numerous genes are highly involved in oxidative defense, protein modification, photosynthesis, phytohormone response, MAPK signaling, and transcription factors (TFs). Compared to SD902, SD609 had a higher expression of DEGs related to antioxidant enzymes, photosynthetic electron transport, heat shock proteins, and indole-3-acetic acid (IAA) signaling under drought conditions, which might contribute to its tolerance mechanisms to drought. Stress-induced TFs may play a crucial regulatory role in genotypic differences. Moreover, the physiological changes and gene expression abundance determined using quantitative reverse transcription polymerase chain reaction were consistent with the RNA sequencing data. The study results suggest that the higher drought tolerance of SD609 than SD902 can be attributed to stronger stress defense capabilities, IAA signal transduction, and more stable photosynthesis. Our findings provide new insights into the molecular mechanisms of maize against drought stress, and the candidate genes identified may be used in breeding drought-tolerant maize cultivars.

Regarding the composting of rural waste, numerous studies either addressed the composting of a single waste component or were conducted at a laboratory/pilot scale. However, far less is known about the mixed composting effect of multi-component rural waste on a large scale. Here, we examined nutrient transformation, maturity degree of decomposition, and succession of microbial communities in large-scale (1,000 kg mixed waste) compost of multi-component wastes previously optimized by response models. The results showed that multi-component compost can achieve the requirement of maturity and exhibit a higher nutritional value in actual compost. It is worth noting that the mixed compost effectively removed pathogenic fungi, in which almost no pathogenic fungi were detected, and only two pathogenic bacteria regrown in the cooling and maturation stages. Structural equation models revealed that the maturity (germination index and the ratio of ammonium to nitrate) of the product was directly influenced by compost properties (electrical conductivity, pH, total organic carbon, moisture, temperature, and total nitrogen) compared with enzymes (cellulase, urease, and polyphenol oxidase) and microbial communities. Moreover, higher contents of total phosphorus, nitrate-nitrogen, and total potassium were conducive to improving compost maturity, whereas relatively lower values of moisture and pH were more advantageous. In addition, compost properties manifested a remarkable indirect effect on maturity by affecting the fungal community (Penicillium and Mycothermus). Collectively, this evidence implies that mixed compost of multi-component rural waste is feasible, and its efficacy can be applied in practical applications. This study provides a solution for the comprehensive treatment and utilization of rural waste.

Farmed animals produce excrement containing excessive amounts of toxic heavy metals as a result of consuming compound feed as well as receiving medical treatments, and the presence of these heavy metals may aggravate the risk of spreading drug-resistance genes through co-selection during manure treatment and application processes. However, research on the association between heavy metals and antimicrobial resistance is still lacking. In this study, metagenomic sequencing was used to explore the effects of the co-selection of environmentally toxic heavy metals on the resistome in manure. A relevance network analysis showed that metal-resistance genes (MRGs), especially for copper (Cu) and zinc (Zn), were positively correlated with multiple types of antibiotic-resistance genes (ARGs) and formed a complex network. Most bacteria that co-occurred with both MRGs and ARGs simultaneously are members of Proteobacteria and accounted for 54.7% of the total microbial species in the relevance network. The remaining bacteria belonged to Firmicutes, Bacteroidetes and Actinobacteria. Among the four phyla, Cu- and Zn-resistance genes had more complex correlations with ARGs than other MRG types, reflecting the occurrence of ARG co-selection under the selective pressure of high Cu and Zn levels. In addition, approximately 64.8%, 59.1% and 68.4% of MRGs that correlated with the presence of plasmids, viruses and prophages, respectively, are Cu- or Zn-resistant, and they co-occurred with various ARGs, indicating that mobile genetic elements participate in mediating ARG co-selection in response to Cu and Zn pressure. The results indicated that the use of heavy-metal additives in feed induces the increases of drug resistance genes in manure through co-selection, aggravating the risk of antimicrobial resistance diffusion from animal farm to manure land applications.

The scale-up of the coupling of water electroreduction (HER) with organic electrooxidation remains challenging. Here the authors address this challenge by coupling HER with electrooxidation of raw biomass chitin, cogenerating acetate and green hydrogen safely at high current density.

Background In intensive and large-scale farms, abnormal estradiol levels in sows can cause reproductive disorders. The high incidence rate of reproductive disturbance will induce the elimination of productive sows in large quantities, and the poor management will bring great losses to the pig farms. The change in estradiol level has an important effect on follicular development and estrus of sows. To solve this practical problem and improve the productive capacity of sows, it is significant to further clarify the regulatory mechanism of estradiol synthesis in porcine granulosa cells (GCs). The most important function of granulosa cells is to synthesize estradiol. Thus, the studies about the complex transcriptome in porcine GCs are significant. As for precursor-messenger RNAs (pre-mRNAs), their post-transcriptional modification, such as alternative polyadenylation (APA) and alternative splicing (AS), together with long non-coding RNAs (lncRNAs), may regulate the functions of granulosa cells. However, the above modification events and their function are unclear within pig granulosa cells. Methods Combined PacBio long-read isoform sequencing (Iso-Seq) was conducted in this work for generating porcine granulosa cells’ transcriptomic data. We discovered new transcripts and possible gene loci via comparison against reference genome. Later, combined Iso-Seq data were adopted to uncover those post-transcriptional modifications such as APA or AS, together with lncRNA within porcine granulosa cells. For confirming that the Iso-Seq data were reliable, we chose four AS genes and analyzed them through RT-PCR. Results The present article illustrated that pig GCs had a complex transcriptome, which gave rise to 8,793 APA, 3,465 AS events, 703 candidate new gene loci, as well as 92 lncRNAs. The results of this study revealed the complex transcriptome in pig GCs. It provided a basis for the interpretation of the molecular mechanism in GCs.

A bacterial strain JI39 that had plant growth-promoting traits was isolated from the rhizosphere soil of Panax ginseng. It had the ability to produce high indole-3-acetic acid (13.1 μg/ml), phosphate solubilization (164.2 μg/ml), potassium solubilization (16.1 μg/ml), and nitrogen fixation. The strain JI39 was identified to be Arthrobacter nicotinovorans based on morphological, physiological, and biochemical traits and through 16S rDNA sequence analysis. The optimal culture environment for strain growth was 1.0% NaCl, 30°C, pH 6.0, and without UV irradiation. The strain can produce cellulase and protease. The strain JI39 can significantly promote the growth of ginseng. After ginseng seeds were treated with 3 × 108 CFU/ml of JI39 bacterial suspension, the shoot's length was significantly increased by 64.61% after 15 days. Meanwhile, the fresh weight of 2-year-old ginseng roots was significantly increased by 24.70% with a treatment by the 108 CFU/ml bacterial suspension after 150 days in the field. The gene expression of phenylalanine ammonia-lyase (PAL), β-1.3 glucanase (β-1,3-GA), chitinase (CHI), superoxide dismutase (SOD), and peroxidase (POD) of ginseng was upregulated, and it also can improve the soil urease, phosphatase, invertase, and catalase activity. In conclusion, the bacterial strain JI39 could efficiently promote the growth of ginseng and has the potential to be a good microbial fertilizer for ginseng.

In this article, we construct an efficient numerical algorithm with the second-order time accuracy for a two-dimensional nonlinear fourth-order fractional wave equation. We introduce an auxiliary variable to transform the fractional fourth-order wave problem into a low order coupled system, and then integrate the resulting equations by using Riemann–Liouville fractional integral. For the obtained fractional integral system, we use the Crank–Nicolson scheme in time with the weighted and shifted Grünwald integral (WSGI) approximation and the finite element method in space to arrive at the fully discrete coupled system. We show the detailed numerical algorithms and carry out numerical tests by taking two two-dimensional examples on rectangular and circular space domains to verify the effectiveness and convergence efficiency for our algorithm.

It is commonly known that exogenously applied melatonin can alleviate the impact of drought stress, but the mechanism used by melatonin to regulate stomatal behavior and carbon (C) and nitrogen (N) metabolism to increase drought resistance remains elusive. Herein, our aim was to investigate the influence of exogenous melatonin on the regulation of C and N metabolism in maize plants under water deficit. In this study, we analyzed stomatal behavior, the key components of C and N metabolism, and the gene expression and activity of enzymes involved in the C and N metabolism in maize plants. The results showed that the application of melatonin (100 μM) significantly increased maize growth and sustained the opening of stomata, and secondarily increased the photosynthetic capacity in maize. Under drought stress, foliar application of melatonin induced the gene transcription and activities of sucrose phosphate synthetase, ADP-glucose pyrophosphorylase, phosphoenolpyruvate carboxylase, and citrate synthase, resulting in the enhancement of sucrose and starch synthesis and the tricarboxylic acid (TCA) cycle. This enhancement in sugar biosynthesis and the TCA cycle might lead to stronger N assimilation. As anticipated, NO3– reduction and NH4+ assimilation were also strengthened after melatonin treatment under drought stress. An increase was observed in some key enzymatic activities and transcription involved in nitrogen metabolism, such as that of nitrate reductase, nitrite reductase, glutamate synthase, and glutamine synthetase, in melatonin-treated, drought-stressed maize. Moreover, melatonin attenuated the drought-induced damage by reducing protein degradation and increasing the level of proline. Conclusively, our results indicate that exogenous melatonin enhances drought tolerance in maize via promoting stomatal opening and regulating C and N metabolism and related gene expression.

Non-intrusive load monitoring has broad application prospects because of its low implementation cost and little interference to energy users, which has been highly expected in the industrial field recently due to the development of learning algorithms. Targeting at the investigation of practical and reliable load monitoring in field implementations, a non-intrusive load disaggregation approach based on an enhanced neural network learning algorithm is proposed in this article. The presented appliance monitoring approach establishes the neural network model following the supervised learning strategy at first and then utilizes the unsupervised learning based optimization to enhance the flexibility and adaptability for diverse scenarios, leading to the improvement of disaggregation performance. By verifications on the REDD public dataset, the proposed approach is demonstrated to be with good performance in non-intrusive load monitoring. In addition to the accuracy enhancement, the proposed approach is also with good scalability, which is efficient in recognizing the newly added appliance.