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Abstract Background Antibiotic resistance is a serious global public health issue. However, there are few reports on trends in antimicrobial susceptibility in Chinese neonates, and most of the existing evidence has been derived from adult studies. We aimed to assess the trends in antimicrobial susceptibility of common pathogens in full-term neonates with invasive bacterial infections (IBIs) in China. Methods This cross-sectional survey study analyzed the antimicrobial susceptibility in Chinese neonates with IBIs from 17 hospitals, spanning from January 2012 to December 2021. Joinpoint regression model was applied to illustrate the trends and calculate the average annual percentage change (AAPC). Using Mantel-Haenszel linear-by-linear association chi-square test, we further compared the antibiotic minimum inhibitory concentrations (MICs) by pathogens between 2019 and 2021 to provide precise estimates of changes. Results The proportion of Escherichia coli with extended-spectrum-beta-lactamase-negative strains increased from 0.0 to 88.5% (AAPC = 62.4%, 95% confidence interval (CI): 44.3%, 82.9%), with two breakpoints in 2014 and 2018 (p-trend

On December 18, 2023, an Ms 6.2earthquake occurred in Jishishan County, Linxia Prefecture, Gansu Province, causing a large number of geological disasters and threatening people's lives and the safety of infrastructure. After the earthquake, Gansu and Qinghai provincial governments quickly deployed hundreds of geology professionals to carried out the investigation and verification geological disasters in the earthquake area. Based on the results of the earthquake geohazard investigation and verification, this paper analyses the characteristics, control factors and development trend of the earthquake-induced geohazards, and puts forward suggestions on disaster prevention and risk mitigation measures. Till December 23, a total of 2044 geohazards have been checked, including 78 new geohazards, 88 existing geohazards with intensified deformation due to earthquake, and 1 878 existing geohazards without obvious different from before. Most of new and intensified deforming geohazards are collapse, accounting for 67.5%, followed by landslide, accounting for 31.9%. Most of new and intensified deforming geohazards are small in scale, accounting for 84.9%, followed by medium, accounting for 10.8%. Disaster mode of new and intensified deforming geohazards are mostly small collapse threatening houses and roads. The co-seismic geohazards are densely distributed along the seismic fault, and the spatial density increases with the enhancement of earthquake intensity. Data from 206 groups of accelerate-meters within 50 km from the epicenter of the National Geological Safety Monitoring Network showed that the peak acceleration of the earthquake area was 30.4～1969.7 mg. and decayed logarithm with the increase of the distance from the epicenter. In addition, the surface deformation monitoring equipment also recorded the co-seismic displacement curve of a typical landslide. Analysis shows, earthquake put a deteriorate effect to rock and soil, which decreased their integration and strength. There is a magnificent geohazard after-effect of earthquake; collapse, landslide and debris flow will be much often than before. So the authors suggest: (1) update earthquake geohazard database as soon as possible, (2) work out targeted prevention and control measures for the geohazards with large potential danger, (3) conduct comprehensive remote sensing monitoring and research of earthquake-induced geohazard mechanism, (4) improve the meteorological early warning model and threshold. By all this efforts the risk of geohazards after earthquake will be reduced and controlled.

Blasting operations, which are crucial to open-pit mine production due to their simplicity and efficiency, require precise control through accurate vibration velocity calculations. The conventional Sadowski formula mainly focuses on blast center distance but neglects the amplification effect of blasting vibration waves by terraced terrain, from which the calculated blasting vibration velocities are smaller than the actual values, affecting the safety of the project. To address this issue, our model introduces the influences of slope and time into Sadowski formula to measure safety through blast vibration displacement. In the northern section of the open-pit quartz mine in Jinchang City, Gansu Province, China, the data of a continuous blasting slope project are referred to. Our findings reveal a noticeable vibration amplification effect during blasting when a multi-stage slope platform undergoes a sudden cross-sectional change near the upper overhanging surface. The amplification vibration coefficient increases with height, while vibration waves within rocks decrease from bottom to top. Conversely, platforms without distinct cross-sectional changes exhibit no pronounced amplification during blasting. In addition, the vibration intensity decreases with distance as the rock height difference change propagates. The results obtained by the proposed blast vibration displacement equation incorporating slope shape influence closely agree with real-world scenarios. According to Pearson correlation coefficient (PPMCC) analysis, the average accuracy rate of our model is 88.84%, which exceeds the conventional Sadowski formula (46.92%).

The research results regarding the threshold values for slope disasters caused by rainfall are crucial for geological disaster prevention. In this study，landslides induced by long-term heavy rainfall occurred in five counties within the Gansu section of the Bailongjiang Basin between 2004 and 2019. Using the frequency method, the rainfall thresholds for early warning of rainfall type landslides with different rock characteristics were investigated. A relationship model between event rainfall and the duration of rainfall leading to landslides was constructed under different probability levels, and a lower threshold for critical cumulative rainfall was provided. Through verification of landslide characteristics and rainfall data resulting from a flash flood disaster in Wudu District in 2020，the accumulated rainfall measured by the rainfall gauge before the landslide is found to be consistent with the threshold given by the model. This finding is of guiding significance for early warning of landslide disasters caused by sustained heavy rainfall.

Abstract Background Response to oxidative stress is universal in almost all organisms and the mitochondrial membrane protein, BbOhmm, negatively affects oxidative stress responses and virulence in the insect fungal pathogen, Beauveria bassiana. Nothing further, however, is known concerning how BbOhmm and this phenomenon is regulated. Results Three oxidative stress response regulating Zn2Cys6 transcription factors (BbOsrR1, 2, and 3) were identified and verified via chromatin immunoprecipitation (ChIP)-qPCR analysis as binding to the BbOhmm promoter region, with BbOsrR2 showing the strongest binding. Targeted gene knockout of BbOsrR1 or BbOsrR3 led to decreased BbOhmm expression and consequently increased tolerances to free radical generating compounds (H2O2 and menadione), whereas the ΔBbOsrR2 strain showed increased BbOhmm expression with concomitant decreased tolerances to these compounds. RNA and ChIP sequencing analysis revealed that BbOsrR1 directly regulated a wide range of antioxidation and transcription-associated genes, negatively affecting the expression of the BbClp1 cyclin and BbOsrR2. BbClp1 was shown to localize to the cell nucleus and negatively mediate oxidative stress responses. BbOsrR2 and BbOsrR3 were shown to feed into the Fus3-MAPK pathway in addition to regulating antioxidation and detoxification genes. Binding motifs for the three transcription factors were found to partially overlap in the promoter region of BbOhmm and other target genes. Whereas BbOsrR1 appeared to function independently, co-immunoprecipitation revealed complex formation between BbClp1, BbOsrR2, and BbOsrR3, with BbClp1 partially regulating BbOsrR2 phosphorylation. Conclusions These findings reveal a regulatory network mediated by BbOsrR1 and the formation of a BbClp1-BbOsrR2-BbOsrR3 complex that orchestrates fungal oxidative stress responses.

Abstract Compared with the traditional solution, the microgrid (MG) employed capacitive coupling inverters (CCIs) with higher reactive power capacity can effectively reduce the probability of low voltage. However, because of the special coupling structure, different power flow control methods will greatly affect the cost and operation efficiency of the CCI. A multi‐layer and timescale optimal power flow control considering inverter‐layer is therefore proposed in this paper. Firstly, the power output characteristics of CCI are analyzed, and the power flow control model of CCI with an energy storage unit is established. Secondly, an optimal control method on different timescales was proposed. By coordinating the active power output and the reserved charging state margin of the energy storage unit, the CCI‐MG reactive power regulation with the most economical and minimum loss was realized, so as to achieve long‐term voltage stability and dynamic satisfaction of load demands. Furthermore, the optimal power flow control of CCI is proposed. The simulation results show that the proposed method can effectively realize the optimal control of active and reactive power of MG, and also improve the voltage stability of AC bus under the economical operation of CCI.

ABSTRACT Background Qi deficiency and phlegm dampness (QPD) is one of the most common traditional Chinese medicine (TCM) syndromes in lung adenocarcinoma (LUAD). This study aimed to identify syndrome‐specific biomarkers for LUAD with QPD syndrome. Methods Peripheral blood mononuclear cells (PBMCs) from LUAD patients with QPD, LUAD patients with non‐QPD (N‐QPD), and healthy control (H) were collected and analyzed with RNA‐seq to identify differentially expressed genes (DEGs). The area under the receiver operator characteristic curve (AUC) of each DEG was calculated, and the top 10 highest AUC DEGs were validated by qRT‐PCR. Logistic regression analysis was used to develop a diagnostic model evaluated with AUC. Results A total of 135 individuals were enrolled in this study (training set: 15 QPD, 15 N‐QPD, 15 H; validation set: 30 QPD, 30 N‐QPD, 30 H). A total of 1480 DEGs were identified between QPD and N‐QPD. The qRT‐PCR results showed that the expression of DDR2 was downregulated, and PPARG was upregulated, which was in line with the finding of the training set. We developed a diagnostic model with these two genes. The AUC of the diagnostic model in the training cohort and validation cohort was 0.891 and 0.777, respectively. Conclusions We identified the two genes (DDR2 and PPARG) as syndrome‐specific biomarkers for LUAD with QPD syndrome and developed a novel diagnostic model, which may help to improve the accuracy and sensibility of clinical diagnosis and provide a new target for natural drug treatment of LUAD.

Abstract It remains a great challenge to achieve strong and reversible hydrogel adhesion. Hydrogel adhesives also suffer from poor environmental stability due to dehydration. To overcome these problems, here reversible adhesive gels are designed using a new switching mechanism and new solvent. For the first time, the study observes UCST (upper critical solution temperature)‐type thermosensitive behaviors of poly(benzyl acrylate) (PBnA) polymer and gel in menthol:thymol deep eutectic solvents (DESs). The temperature‐induced phase transition allows adjusting cohesive force, and hence adhesion strength of PBnA gels by temperature. To further improve the mechanical and adhesion properties, a peptide crosslinker is used to allow energy dissipation when deforming. The resulting eutectogel exhibits thermal reversible adhesion with a high switching ratio of 14.0. The adhesion strength at attachment state reaches 0.627 MPa, which is much higher than most reversible adhesive hydrogels reported before. The low vapor pressure of DES endows the gel excellent environmental stability. More importantly, the gel can be repeatedly switched between attachment and detachment states. The strong and reversible gel adhesive is successfully used to design soft gripper for the transport of heavy cargos and climbing robot capable of moving on vertical and inverted surface in a manner similar to gecko.

In contemporary times, tumors have emerged as the primary cause of mortality in the global population. Ongoing research has shed light on the significance of neurotransmitters in the regulation of tumors. It has been established that neurotransmitters play a pivotal role in tumor cell angiogenesis by triggering the transformation of stromal cells into tumor cells, modulating receptors on tumor stem cells, and even inducing immunosuppression. These actions ultimately foster the proliferation and metastasis of tumor cells. Several major neurotransmitters have been found to exert modulatory effects on tumor cells, including the ability to restrict emergency hematopoiesis and bind to receptors on the postsynaptic membrane, thereby inhibiting malignant progression. The abnormal secretion of neurotransmitters is closely associated with tumor progression, suggesting that focusing on neurotransmitters may yield unexpected breakthroughs in tumor therapy. This article presents an analysis and outlook on the potential of targeting neurotransmitters in tumor therapy.

Abstract Artificial intelligence has made substantial progress in many medical application scenarios. The quantity and complexity of pathology images are enormous, but conventional visual screening techniques are labor-intensive, time-consuming, and subject to some degree of subjectivity. Complex pathological data can be converted into mineable image features using artificial intelligence image analysis technology, enabling medical professionals to quickly and quantitatively identify regions of interest and extract information about cellular tissue. In this study, we designed a medical information assistance system for segmenting pathology images and quantifying statistical results, including data enhancement, cell nucleus segmentation, model tumor, and quantitative analysis. In cell nucleus segmentation, to address the problem of uneven healthcare resources, we designed a high-precision teacher model (HRMED_T) and a lightweight student model (HRMED_S). The HRMED_T model is based on visual Transformer and high-resolution representation learning. It achieves accurate segmentation by parallel low-resolution convolution and high-scaled image iterative fusion, while also maintaining the high-resolution representation. The HRMED_S model is based on the Channel-wise Knowledge Distillation approach to simplify the structure, achieve faster convergence, and refine the segmentation results by using conditional random fields instead of fully connected structures. The experimental results show that our system has better performance than other methods. The Intersection over the Union (IoU) of HRMED_T model reaches 0.756. The IoU of HRMED_S model also reaches 0.710 and params is only 3.99 M.

Abstract Background Sedentary behavior may influence the respiratory health, but the joint effects of sedentary behavior and physical activity on pulmonary function remains poorly elucidated. We aimed to estimate the association between sedentary behavior and physical activity with pulmonary function. Methods A total of 12,343 participants aged 12–79 years were analyzed from the U.S. NHANES 2007–2012. Participants were categorized into 16 groups according to the cross-tabulation of sedentary behavior time (0–4.0, 4.1–8.0, 8.1–12.0, and > 12.0 h/day) and moderate or vigorous physical activity (MVPA) (0, 1–149, 150–299, and ≥ 300 min/week). Generalized linear models were used to test the association of sedentary behavior and MVPA with pulmonary function. Results Participants with sedentary behavior > 4.0 h/day were negatively related to FEV1 (forced expiratory volume in 1 s) (β ranging from -0.015 to -0.009, p 12.0 h/day of sedentary behavior), the negative association of sedentary behavior ≤ 8.0 h/day with FEV1 may be reduced through appropriate MVPA (β ranging from 0.019 to 0.030, p 8.0 h/day, even MVPA ≥ 300 min/week may not decrease the negative relationships. Similar results were also observed in FVC (forced vital capacity) (β ranging from 0.018 to 0.030, p

Abstract Recognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors.

Abstract Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample. It has been widely used in various image classification tasks. Sparseness in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class‐specific information of the test sample, which is very important for classification. For deformable images such as human faces, pixels at the same location of different images of the same subject usually have different intensities. Therefore, extracting features and correctly classifying such deformable objects is very hard. Moreover, the lighting, attitude and occlusion cause more difficulty. Considering the problems and challenges listed above, a novel image representation and classification algorithm is proposed. First, the authors’ algorithm generates virtual samples by a non‐linear variation method. This method can effectively extract the low‐frequency information of space‐domain features of the original image, which is very useful for representing deformable objects. The combination of the original and virtual samples is more beneficial to improve the classification performance and robustness of the algorithm. Thereby, the authors’ algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion scheme. The weighting coefficients in the score fusion scheme are set entirely automatically. Finally, the algorithm classifies the samples based on the final scores. The experimental results show that our method performs better classification than conventional sparse representation algorithms.

Abstract The absorption of photons by atoms encompasses fundamental quantum mechanical aspects, particularly the emergence of randomness to account for the inherent unpredictability in absorption outcomes. We demonstrate that vacuum fluctuations can be the origin of this randomness. An illustrative example of this is the absorption of a single photon by two symmetrically arranged atoms. In the absence of a mechanism to introduce randomness, the Schrödinger equation alone can govern the time evolution of the process initially. Then, it becomes stuck, and an entangled state of the two atoms emerges. This entangled state consists of two components: in one, the first atom is excited by the photon while the second is in the ground state, and in the other, the second atom is excited while the first remains in the ground state. These components form a superposition state characterized by an unbreakable symmetry in the absence of external influences. Consequently, the absorption process remains incomplete. When vacuum fluctuations come into play, they can induce fluctuations in the weights of these components, akin to Brownian motion. Over time, one component diminishes, thereby breaking the entanglement between the two atoms and allowing the photon absorption process to conclude. The remaining component shows which atom completes the photon absorption. Vacuum fluctuations not only introduce randomness but also have the potential to give rise to the Born rule in this context. Furthermore, the Casimir effect, which is closely tied to vacuum fluctuations, presents a promising experimental avenue for validating this mechanism. Similar studies can also be conducted with varying numbers of atoms.

In the field of time series classification, deep learning techniques have shown remarkable performance; however, their effectiveness is often compromised when confronted with challenges of insufficient data and class imbalance. To address this challenge, we propose an interpretable time series data augmentation algorithm integrating variational autoencoders (VAE) and metric learning. The core contribution of this algorithm is manifested in three aspects: First, it eliminates the heteroscedasticity and non-stationarity of the data, ensuring that the data satisfies the hypothesis of normal distribution in the potential space of the encoder, and effectively avoids the approximation error of the real data distribution; Secondly, the algorithm constructs a discriminant VAE potential space, suitable for data augmentation, with metric learning, ensuring that the hidden variable distribution accurately reflects the characteristics of the original data. Finally, this paper explores the multi-seasonal decomposition algorithm of time series to seamlessly integrate the structural features of the original time series in the generated data, thereby enhancing the interpretability of data generation. Through experimental verification on four multivariate time series data sets, including the electrical energy data set, the results demonstrate that the proposed algorithm outperforms existing methods in fidelity and prediction performance, exhibiting high stability and generalization ability, particularly in cases of limited data volume. The introduction of this algorithm not only contributes to enhancing the overall performance of time series classification models but also substantially reduces the cost of data collection and labeling, thereby demonstrating its significant value in practical applications.

Botanical pesticides are one of the most promising alternatives to synthetic insecticides for green pest management. However, their efficacies must be further improved to meet real needs. Here we designed a nanoscale camptothecin (CPT) encapsulated in a star polycation (SPc) and determined its bioactivity against a devastating agricultural pest, Spodoptera frugiperda. The self-assembly of CPT/SPc complex was mainly driven by hydrogen bonding and Van der Waals forces to decrease the particle size from 789 to 298 nm. With the help of SPc, the contact angle of CPT decreased from 116° to 92° on maize leaves, and its retention was increased from 5.53 to 11.97 mg/cm2. The stability of SPc-loaded CPT was also improved in an alkaline environment, which is beneficial for its acting in lepidopteran insect guts. The CPT/SPc complex had stronger larvicidal activity and ovicidal activity against S. frugiperda than CPT alone, led to more complex transcriptomic changes in larvae, and had obvious adverse impacts on the activities of two digestive enzymes. Our findings demonstrated that the encapsulation of CPT by SPc-based nanodelivery system enabled better insecticidal activities against S. frugiperda, which holds great promise for the development of more efficient and sustainable pest control strategies to meet the demands of modern crop protection.

Background The association of sleep onset time and duration with cardiometabolic health is not well characterized. Methods and Results This study included 6696 adults aged 20 to 80 years from the NHANES (National Health and Nutrition Examination Study) 2015 to 2018. Participants were categorized into 9 groups according to the cross‐tabulation of sleep onset time (

ABSTRACT Successful host tissue colonization is crucial for fungal pathogens to cause mycosis and complete the infection cycle, in which fungal cells undergo a series of morphological transition-included cellular events to combat with hosts. However, many transcription factors (TFs) and their mediated networks regulating fungal pathogen colonization of host tissue are not well characterized. Here, a TF (BbHCR1)-mediated regulatory network was identified in an insect pathogenic fungus, Beauveria bassiana, that controlled insect hemocoel colonization. BbHCR1 was highly expressed in fungal cells after reaching insect hemocoel and controlled the yeast (in vivo blastospores)-to-hyphal morphological switch, evasion of immune defense response, and fungal virulence. Comparative analysis of RNA sequencing and chromatin immunoprecipitation sequencing identified a core set of BbHCR1 target genes during hemocoel colonization, in which abaA and brlA were targeted to limit the rapid switch from blastospores to hyphae and fungal virulence. Two targets encoding hypothetical proteins, HP1 and HP2, were activated and repressed by BbHCR1, respectively, which acted as a virulence factor and repressor, respectively, suggesting that BbHCR1 activated virulence factors but repressed virulence repressors during the colonization of insect hemocoel. BbHCR1 tuned the expression of two dominant hemocoel colonization-involved metabolite biosynthetic gene clusters, which linked its regulatory role in evasion of immune response. Those functions of BbHCR1 were found to be collaboratively regulated by Fus3- and Hog1-MAP kinases via phosphorylation. These findings have drawn a regulatory network in which Fus3- and Hog1-MAP kinases phosphorylate BbHCR1, which in turn controls the colonization of insect body cavities by regulating fungal morphological transition and virulence-implicated genes.IMPORTANCEFungal pathogens adopt a series of tactics for successful colonization in host tissues, which include morphological transition and the generation of toxic and immunosuppressive molecules. However, many transcription factors (TFs) and their linked pathways that regulate tissue colonization are not well characterized. Here, we identified a TF (BbHCR1)-mediated regulatory network that controls the insect fungal pathogen, Beauveria bassiana, colonization of insect hemocoel. During these processes, BbHCR1 targeted the fungal central development pathway for the control of yeast (blastospores)-to-hyphae morphological transition, activated virulence factors, repressed virulence repressors, and tuned the expression of two dominant hemocoel colonization-involved immunosuppressive and immunostimulatory metabolite biosynthetic gene clusters. The BbHCR1 regulatory function was governed by Fus3- and Hog1-MAP kinases. These findings led to a new regulatory network composed of Fus3- and Hog1-MAP kinases and BbHCR1 that control insect body cavity colonization by regulating fungal morphological transition and virulence-implicated genes.

Cotton, a crucial economic crop, is also the preferred host plant of the mirid bug Apolygus lucorum. In our previous field experiments, we found that cotton cultivars Kelin 08–15 and BR-S-10 (healthy and herbivore-damaged plants) exhibit distinct attraction and repellence to A. lucorum, respectively. However, the key plant volatiles determining attraction or repulsion effects remain unknown. Here, we investigated the volatiles emitted by these two cotton cultivars before and after herbivore infestation. We found that susceptible Kelin 08–15 emitted a greater diversity and quantity of volatiles than those of BR-S-10, with herbivore-damaged cottons releasing more volatile substances. Electroantennogram (EAG) recordings further revealed that 15 representative volatiles identified above could elicited electrophysiological responses in female and male A. lucorum antennae. Among them, behavioral assays showed that two compounds, 1,3-Diethylbenzene and 4-Ethylbenzaldehyde, exhibited attractive properties, whereas six volatiles including Hexyl Acrylate, Cumene, 2,4-Dimethylstyrene, Eucalyptol, Linalool and Butyl Acrylate demonstrated repellent effects on A. lucorum. Taken together, our findings suggest the critical role of volatile compounds in mediating bug-plant interactions and provide a foundation for the development of strategies to prevent and control of A. lucorum in cotton fields.

To fully utilize the aromatic ring structure of lignin and realize high value addition, organosolv lignin (OL) extracted from papermaking waste liquid was subjected to catalytic pyrolysis for the production of phenolic compounds. Using self-made carbon nanospheres as a template, cellular La0.8Pr0.2NiO3 perovskite was prepared by the sol-gel method and used as catalyst in the pyrolysis of OL at 600 °C for 3 h. The yield of liquid-phase product was 21.34 %, and the selectivity of phenolic compounds reached 70.20 %, constituting increases by 43.41 % and 25.87 % compared with the yield and selectivity of the catalyst-free process, respectively. After four cycles of regeneration, La0.8Pr0.2NiO3 perovskite still demonstrated good structural and catalytic stability. This work provides a theoretical basis for the development of green and sustainable chemical products.

Natural manganese oxides (n-MnOx) widely exist in the nature and may contribute to the elimination of organic contaminants. The present study investigated the degradation of frequently detected fluoroquinolone antibiotic enrofloxacin (EFX) and the degradation kinetics were analyzed. During the reaction, the valence of Mn in n-MnOx changed with the release of Mn ions. It was also found that combing manganese oxidizing bacteria with n-MnOx can enhance the degradation of EFX and alter the degradation pathway as well. The transformation products of EFX were analyzed with UPLC-MS/MS, which revealed seven products. Based on them, it is proposed that the degradation may start with the dehydrogenation from the piperazine moiety that was further broken down. Respirometry tests demonstrated that the degradation with n-MnOx significantly reduced the toxicity of EFX. This study proved the oxidation with n-MnOx as a simple and effective technology to remediate the contamination of enrofloxacin and other fluoroquinolones and the potential of combing the special capacity of microorganisms.

Background: Premature infants often require oxygen supplementation, which can elicit bronchopulmonary dysplasia (BPD) and lead to mitochondrial dysfunction. Mitochondria play important roles in lung development, in both normal metabolism and apoptosis. Enhancing our comprehension of the underlying mechanisms in BPD development can facilitate the effective treatments. Methods: Plasma samples from BPD and non-BPD infants were collected at 36 weeks post-menstrual age and used for metabolomic analysis. Based on hyperoxia-induced animal and cell models, changes in mitophagy and apoptosis were evaluated following treatment with itaconic acid (ITA). Finally, the mechanism of action of ITA in lung development was comprehensively demonstrated through rescue strategies and administration of corresponding inhibitors. Results: An imbalance in the tricarboxylic acid (TCA) cycle significantly affected lung development, with ITA serving as a significant metabolic marker for the outcomes of lung development. ITA improved the morphological changes in BPD rats, promoted SP-C expression, and inhibited the degree of alveolar type II epithelial cells (AEC II) apoptosis. Mechanistically, ITA mainly promotes the nuclear translocation of transcription factor EB (TFEB) to facilitate dysfunctional mitochondrial clearance and reduces apoptosis in AEC II cells by regulating autophagic flux. Conclusion: The metabolic imbalance in the TCA cycle is closely related to lung development. ITA can improve lung development by regulating autophagic flux and promote the nuclear translocation of TFEB, implying its potential therapeutic utility in the treatment of BPD.

BackgroundTo date, the correlation between CD4 on the monocytes (mCD4) expression and the prognosis of patients with septic shock remains unclear. The purpose of this study was to analyze the expression of mCD4 in these patients and further evaluate whether mCD4 expression correlates with either the recovery from multiple organ dysfunction syndrome (MODS) or mortality.MethodsThe study participants were recruited from a tertiary general hospital in China (Affiliated Dongyang Hospital of Wenzhou Medical University). Sepsis and septic shock were diagnosed based on the diagnostic criteria of Sepsis-3. MODS was defined as a Sequential Organ Failure Assessment score of at least two organ systems ≥2. Persistent MODS was defined as the continual meeting of the MODS criteria when re-evaluated one week after admission (day 7). A logistic regression model was used to test whether mCD4 was an independent prognostic factor for mortality in patients with septic shock. A paired sample rank sum test was used to examine the correlation between mCD4 expression and MODS recovery.ResultThe study recruited 79 patients with septic shock as the study group, 74 patients with sepsis as the disease control group, and 56 volunteers as healthy controls. In the first 24 h after admission (day 1), mCD4 expression was significantly reduced in patients with septic shock compared to healthy controls and patients with sepsis. Moreover, mCD4 expression was an independent prognostic factor for in-hospital and 28 day mortality in patients with septic shock. mCD4 expression did not show significant differences in patients with persistent MODS on day 7 compared to day 1. However, mCD4 expression was significantly higher in patients without persistent MODS on day 7 than on day 1.ConclusionmCD4 expression is significantly reduced in patients with septic shock, which is an independent prognostic factor for mortality and closely related to recovery from MODS.

Soil pressure in clay formation tunnels is closely related to soil arch effect and the development of slip surfaces. Firstly, numerical simulation software is used to simulate the actual situation of tunnel excavation, and the change rule of the slip-cracking surface of cohesive soil is analyzed. Secondly, based on the numerical simulation results and the ellipsoid theory, the pressure formula of Terzaghi loose Earth is modified considering that the principal stress trace is catenary. Finally, the calculation results are compared with the finite element calculation results to verify the rationality of the formula in this paper. The relationship between the internal friction angle, cohesion force c, eccentricity ε, looseness coefficient β, and the pressure of loose Earth is further studied. The results show that there is a gap between the sliding crack angles with or without dilatancy angle and it will affect the development form of soil arch. The slip angle decreases gradually with the increase of the buried depth ratio H/D and becomes stable when the buried depth ratio H/D≥3. Compared with deep-buried tunnels, the increase of internal friction angle in the shallow-buried tunnel is more conducive to reducing the overlying soil pressure. The loose soil pressure decreases with the increase of eccentricity ε and loose coefficient β, and the influence of eccentricity ε on loose soil pressure is significantly greater than that of loose coefficient β. Therefore, the change of eccentricity ε should be paid close attention in the project.

Abstract Sparse representation plays an important role in the research of face recognition. As a deformable sample classification task, face recognition is often used to test the performance of classification algorithms. In face recognition, differences in expression, angle, posture, and lighting conditions have become key factors that affect recognition accuracy. Essentially, there may be significant differences between different image samples of the same face, which makes image classification very difficult. Therefore, how to build a robust virtual image representation becomes a vital issue. To solve the above problems, this paper proposes a novel image classification algorithm. First, to better retain the global features and contour information of the original sample, the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample, thus generating a virtual sample. Second, by the principle of sparse representation, the linear expression coefficients of the original sample and the virtual sample can be calculated, respectively. After obtaining these two types of coefficients, calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test sample. These two distances are converted into distance scores. Finally, a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual image. The fused score will determine the final classification result. The experimental results show that the proposed method outperforms other typical sparse representation classification methods.

ABSTRACTRemote sensing-based vegetation index (VI) data are significantly impacted by cloud contamination. Spatiotemporal reconstruction methods demonstrate higher accuracy than temporal reconstruction methods. However, the computing time and random access memory (RAM) consumption of these spatiotemporal reconstruction methods for large-scale reconstruction remains unclear. In this study, a method called spatial-interannual reconstruction (SIR) was proposed to reconstruct cloud-contaminated pixels in MODIS normalized difference VI (NDVI) and enhanced VI (EVI) data. SIR has four major advantages: (1) High accuracy. The average mean absolute error of SIR was 0.0338, which was 20.2% and 23.4% lower than that of two state-of-the-art spatiotemporal reconstruction methods (i.e. interpolation of the mean anomalies (IMA) and Gapfill). (2) High computing speed. The average computing time of SIR was 99.7% and 98.8% lower than IMA and Gapfill, respectively. (3) Low RAM consumption. (4) Simultaneous reconstruction of all invalid values. Reconstructed 250 m spatial resolution and 16-day composite NDVI and EVI datasets in China from 2000 to 2022 (written as ReVIChina) were developed based on the SIR method and MODIS MOD13Q1 data. Spatiotemporal analyses revealed that the reconstructed datasets were more reliable than the original product and a similar dataset.

Abstract This study examines the fracture mechanism of cracks and the final blasting effects on defective rock masses under blasting loads. The failure processes of jointed rock with two prefabricated joints are investigated through numerical simulations using a two-dimensional finite element method. Subsequently, simulations are performed to analyze the blasting of granite specimens with various joint arrangements, focusing on the influence of front joint length, inclination angle, and blast hole distance on failure patterns, displacement, velocity, and stress at the joint ends. The numerical results provide a comprehensive summary of various typical failure modes near blast holes and joints for the first time. Specifically, the simulation successfully captures the characteristics of the ring crack zone, wing cracks, and main crack deflection affected by the front joint. Moreover, the results highlight the shielding effect of the front joint, which enhances damage in the medium between the borehole and the joint while exhibiting the opposite effect behind the joint in terms of stress wave propagation. Overall, this study offers objective insights into the mechanics and failure characteristics of jointed rock masses under blasting loads and serves as a valuable reference for the design and optimization of blasting operations.

Abstract With the advantages of the low cost of transmission lines and high efficiency, the DC microgrid has become a rising star in the low‐voltage network. However, multi‐source and multi‐transformation characteristics in DC microgrids will lead to the existence of various forms of ripple generation sources in the system. The existence of a large amount of ripple not only directly cause the reduction of power quality of DC microgrids, but also decreases the accuracy of electricity billing. It is vital to achieving accurate ripple detection for the reliable power supply of the DC microgrid. A DC‐side ripple detection method, which combines the Whale Optimization Algorithm (WOA), Variational Modal Decomposition (VMD), and Hilbert Transform (HT), is therefore investigated and proposed here. Firstly, the sample entropy (SampEn) is used as the fitness function, applied the whale optimization algorithm, is to determine the optimal decomposition scale and penalty factor, of the variational modal decomposition; then, the ripple‐containing DC signal is decomposed into a series of eigenmodal components (imf) by the optimized variational modal decomposition algorithm. Finally, the Hilbert Transform is performed to obtain the amplitude and frequency of the ripple components. To prove the effectiveness, the proposed method is compared with the conventional Empirical Mode Decomposition (EMD) and the VMD algorithm, by testing simulated DC signals without and with noise. Results show that the proposed method has higher accuracy and noise robustness than the EMD and VMD algorithms.

Genetically modified maize (Zea mays L.) MON810 was approved for importation into China for feed use in 2004; however, the localization data concerning exogenous insertion sequences, which confer insect resistance, have been questionable. MON810 maize plants discovered in northeastern China were used to analyze the molecular characteristics of the exogenous insertion. Using PacBio-HiFi sequencing and PCR assays, we found the insertion was located in chromosome 8, and there was a CaMV35S promoter, hsp70 intron, and insecticide gene cry1Ab, except for genome sequence insertion in the MON810 event. Importantly, the 5′ and 3′ flanking sequences were located in the region of 55869747–55879326 and 68416240–68419152 on chromosome 5, respectively. The results of this study correct previous results on the genomic localization of the insertion structure for the MON810 event. We also found a single-nucleotide polymorphism (SNP) in the hsp70 intron, which is most likely the first SNP found in a transgenic insertion sequence. PCR amplification in conjunction with Sanger sequencing, allele-specific PCR (AS-PCR), and blocker displacement amplification (BDA) assays were all effective at detecting the base variance. The integrated strategy used in this study can serve as a model for other cases when facing similar challenges involving partially characterized genetic modification events or SNPs.

Registration of multi-modal remote sensing images (MRSI) is crucial for unlocking the full potential of heterogeneous remote sensing imagery. However, achieving accurate registration among MRSI is challenging due to the trade-off between geometric invariance and matching accuracy, caused by differences in signal-to-noise ratio and nonlinear radiometric distortion (NRD) arising from varying imaging mechanisms. To tackle the challenge, this paper proposes a lightweight and hybrid feature-guided registration algorithm for MRSI called the hybrid registration algorithm based on multi-dimensional oriented self-similarity features (MOSS). MOSS leverages the advantages of multi-dimensional oriented self-similarity features to progressively enhance registration performance. In the hybrid feature coarse matching stage, oriented self-similarity features are extracted from MRSI, and their directional information is utilized for feature description to estimate the initial affine transformation. The fine matching under multi-dimensional oriented self-similarity features stage takes the outputs of the coarse matching stage to perform a template-like matching process. To evaluate the performance of MOSS, comprehensive experiments are conducted using six different combinations of MRSI, and seven state-of-the-art registration algorithms are selected for comparison. The experimental results demonstrate that MOSS outperforms the compared methods, with the number of correct matches being at least about 1.6 times higher than the comparison methods. Moreover, MOSS exhibits the lowest root mean square error across all experiments, with an average RMSE of 1.86 pixels, achieving an RMSE within 2 pixels. This highlights its effectiveness in achieving precise alignment and robust registration of MRSI.

Background and objective: Hemorheology and blood glucose are commonly used to estimate the risks of thrombosis and stress hyperglycemia after anaesthesia. The sequence of acupoint stimulation might influence the therapeutic effects of acupuncture. In the current study, we aimed at investigating the effect of different acupuncture sequences of “Huiyangjiuzhen” acupoints on the blood glucose and hemorheology in anesthetized rabbits. Methods: Twenty-five rabbits were randomly divided into five groups, including the control group (CG), the positive-sequence group (PSG), the reverse-sequence group (RSG), the disorder-sequence group (DSG), and the random group (RG). Except for the CG and RG, the rabbits in other groups were acupunctured with different sequences of “Huiyangjiuzhen''acupoints when the rabbits were anesthetized. The acupoints in rabbits of the RG were chosen randomly. The levels of blood glucose and hemorheology indexes before and after anaesthesia was detected. Results: In the PSG, Hηb 200/s, Mηb 30/s, Hηr 200/s, ERI, hematocrit and plasma viscosity levels were decreased, and the blood glucose level was not changed. In the DSG, the levels of Mηb 30/s and hematocrit were decreased, and the blood glucose was increased. In the CG, RSG and RG, no hemorheology indexes were changed and the blood glucose was increased. Conclusion: “Huiyangjiuzhen” acupuncture could decrease the risks of post-operative thrombosis and stress hyperglycemia in anesthetized rabbits. This effectiveness depends on both acupuncture and acupuncture sequence at the “Huiyangjiuzhen” acupoints.

[Introduction] As an important thrust of China's "new infrastructure", internet data centers have ushered in opportunities for vigorous development and become new areas of energy use, putting forward higher requirements for the power supply level and capacity of the local distribution network. The innovative application of flexible transmission and distribution technology and key equipment makes the power supply and distribution system more intelligent, more flexible and more reliable, and more able to cope with the challenges brought by the large proportion of DC loads and concentrated high-load energy loads such as Internet data centers, and realize the construction and operation of Internet data centers more low-carbon, more efficient, more reliable and more economical. [Method] Firstly, the basic load requirements of Internet data center were discussed, the overall classification and performance requirements of internet data centers were analyzed. The application of flexible technology in distribution network was studied, with a focus on analyzing and comparing three types of technical routes: "rectification distribution, DC distribution, and AC-DC hybrid power supply and distribution". [Result] The paper provides provides tailored solutions for issues related to system reliability, stability, power quality, power efficiency and acceptance of new energy. [Conclusion] By summarizing the existing research results, flexible transmission and distribution technology is regarded as the core technology of building internet data center. Targeted data center control scheme should be studied from different aspects such as device and algorithms. After summarizing the research results of different aspects, the paper also looks forward to the practice and popularization of head-to-head transmission and distribution technology in internet data center.

Abstract Driven by the demand for carbon emission reduction and environmental protection, battery swapping stations (BSS) with battery energy storage stations (BESS) and distributed generation (DG) have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BSS with photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BSS and uses the spare capacity of building special transformers and the roof PV to supply power to BSS to avoid the investment of transformers. Secondly, this paper establishes the load model of BSS and proposes the charging rules of battery swapping. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BSS operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BSS, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period; on this basis, the demand response model is further proposed. Simulation results show that the proposed strategy can improve the daily profit of BSS through shifting load. And the configuration of BESS can improve the battery swapping capacity and peak‐shaving ability. Moreover, the exponential segmented pricing mechanism can greatly reduce the number of high load periods and reduce the burden on the power supply.

Consumer-feeder relationship is an important basis for the fault location, outage management, load adjustment and power quality management in low-voltage distribution station area (LVDSA). The current identification method of the consumer-feeder relationship based on electrical characteristics analysis is single and lack of reliability. Besides, the consumers of different feeders which locate near the bus at the low voltage side of distribution transformer have high voltage variation similarity, which makes it difficult to distinguish reliably. The spatial distribution of consumers can directly reflects the feeder trend. Therefore, a consumer-feeder relationship identification method for LVDSA considering feature fusion of voltage variation and spatial distribution is proposed in this paper. Firstly, the electrical characteristics based on voltage variation and spatial distribution characteristics based on geographical location are analyzed, and the correlation between the characteristics above and consumer-feeder relationship is explained. Secondly, a semi-supervised spectral clustering (SSC) algorithm based on the feature of voltage variation and spatial distribution is proposed to identify the consumer-feeder relationship. Finally, the proposed method is tested with the real data of LVDSAs in China. The results indicate that the feature fusion of voltage variation and spatial distribution can effectively improve the identification accuracy of consumer-feeder relationship.

The insulated gate bipolar transistor (IGBT) is widely used in the power electronic system, but its aging state is difficult to predict in advance due to the complicated failure mechanism, which will affect the performance of the equipment, and even cause serious disaster. Therefore, we propose a combined prediction model based on adaptive VMD decomposition (AVMD) and attention-based gated recurrent unit (GRU-AT) to achieve accurate prediction of the aging state of IGBT. For the nonlinear characteristics of the IGBT failure characteristic parameters, AVMD with the key parameters optimized by improved sparrow search algorithm (ISSA) was used to disaggregate the character sequence to a series of finite wide subcomponents, which overcome the interference of the irregularity of aging time sequence data to the prediction accuracy. Secondly, the attention mechanism is introduced to capture the temporal feature relationship between the current moment output and the historical degraded data, further improving the generalization ability of the model. Finally, the GRU-AT network is used to model each wide modal sub-component independently, and final prediction results of IGBT aging parameters are obtained by superimposing the prediction results of each mode. The experimental results indicate that the AVMD-GRU-AT model designed in this article has superior prediction performance in both over-the-top single-step prediction and multi-step prediction of aging state of IGBT.

Mismatch repair is a critical step in DNA replication that occurs after base selection and proofreading, significantly increasing fidelity. However, the mechanism of mismatch recognition has not been established for any repair enzyme. Speculations in this area mainly focus on exploiting thermodynamic equilibrium and free energy. Nevertheless, non-equilibrium processes may play a more significant role in enhancing mismatch recognition accuracy by utilizing adenosine triphosphate (ATP). This study aimed to investigate this possibility. Considering our limited knowledge of actual mismatch repair enzymes, we proposed a hypothetical enzyme that operates as a quantum system with three discrete energy levels. When the enzyme is raised to its highest energy level, a quantum transition occurs, leading to one of two low-energy levels representing potential recognition outcomes: a correct match or a mismatch. The probabilities of the two outcomes are exponentially different, determined by the energy gap between the two low energy levels. By flipping the energy gap, discrimination between mismatches and correct matches can be achieved. Within a framework that combines quantum mechanics with thermodynamics, we established a relationship between energy cost and the recognition error.

Abstract The COVID-19 pandemic has led to a global surge in Sinophobia. We examine how Chinese language users responded to COVID-19 on Western social media by compiling a unique database (CNTweets) with over 25 million Chinese tweets mentioning any Chinese characters related to China, the Chinese Communist Party (CCP), Chinese, and Asians from December 2019 to April 2021. Our analysis of Twitter users’ self-reported geographic information shows that most Chinese language users on Twitter originated from Mainland China, Hong Kong, Taiwan, and the United States. We then adopt the Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) and structural topic modeling to further analyze the sentiments, content, and topics of Chinese tweets during the COVID-19 pandemic. Our results suggest that 61.8% of tweets in our database were contributed by only 1% of Twitter users and 62.2% of tweets were negative toward China. Despite the prevalence of anti-China sentiments, the target entity analysis shows that these negative sentiments were more likely to target the Chinese government and CCP than the Chinese people. Our findings also show that the most popular topics were politics (e.g., Hong Kong protests and Taiwan issues), COVID-19, and the United States (e.g., the US-China relations and domestic issues). Anti-China users focused relatively more on political issues such as democracy and freedom, while pro-China users mentioned cultural and economic topics more. Our social network analysis reveals that these pro-China and anti-China Twitter users lacked in-depth engagement in China-related conversations and were highly segregated from each other. We conclude by discussing our contributions to China and social media studies and possible policy implications.

Abstract As renewable power penetration gradually increases in hierarchical distribution networks, certain regions have started to lack the ability to consume. How to improve the consumption capacity of a hierarchical distribution network through optimal dispatching has become a hot topic in the current research on distribution system operation. Firstly, the article makes an accurate and rapid assessment of the consumption capacity of the station area through the limit scenario analysis method; secondly, based on the assessment results, the main network renewable power sources are prioritized for consumption, while the main network and the station area; thirdly, the main network renewable power sources are prioritized for consumption, considering the assessment results while the main network and the station area reach the optimal power flow simultaneously through a multi‐layer dispatching model. Then, through the flexible interconnection system, load balancing and energy optimization are performed for unconsumed distributed generation (DG) and incomplete energy storage system (ESS) in the station area to further improve the efficiency of renewable energy. Finally, the effectiveness of the proposed model is verified by simulation tests.

The structure and expression of exogenous genes in transgenic crops are critical for the target traits. R7569 has the same exogenous insertion structure as the transgenic insect-resistant cotton MON531 but with a deletion in the 3′ end of the cry1Ac gene and the terminator region. Thus, in the present study, transcription, expression, and insecticidal activity assays were conducted to determine the function of the truncated cry1Ac gene. R7569 has a truncated cry1Ac gene with a length of 2554 bp encoding 881 amino acids, and the transcription termination site was mainly concentrated downstream of the truncated position and extended 160–270 bp from the truncated position using rapid amplification of cDNA ends (RACE). The transcript levels of the cry1Ac gene in R7569 were significantly higher than those of MON531 implants, except for during the boll stage. The content of the Cry1Ac protein in R7569 was higher than that of MON531 in the cotton leaf in all three periods. The corrected mortality rates of R7569 and MON531 against bollworms were 93.09% and 88.83%, respectively. The LC50 value of R7569 was 0.732 ng/g (dw), indicating a high level of resistance to bollworm. In this study, for the first time, we found a partial deletion of the target gene in commercially applied transgenic crops, and the partial deletion of the 3′ end of the cry1Ac gene retained a better transcription, expression level, and insecticidal activity, which can provide a specific case for the safety evaluation of transgenic crops.

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is one of the most important insect pests affecting corn crops worldwide. Although planting transgenic corn expressing Bacillus thuringiensis (Bt) toxins has been approved as being effective against FAW, its populations’ resistance to Bt crops has emerged in different locations around the world. Therefore, it is important to understand the interaction between different Bt proteins, thereby delaying the development of resistance. In this study, we performed diet-overlay bioassays to evaluate the toxicity of Cry1Ab, Cry1Ac, Cry1B, Cry1Ca, Cry1F, Cry2Aa, Cry2Ab, Vip3Aa11, Vip3Aa19, and Vip3Aa20, as well as the interaction between Cry1Ab-, Cry1F-, Cry2Ab-, and Vip3Aa-class proteins against FAW. According to our results, the LC50 values of Bt proteins varied from 12.62 ng/cm2 to >9000 ng/cm2 (protein/diet), among which the Vip3Aa class had the best insecticidal effect. The combination of Cry1Ab and Vip3Aa11 exhibited additive effects at a 5:1 ratio. Cry1F and Vip3Aa11 combinations exhibited additive effects at 1:1, 1:2, and 5:1 ratios. The combination of Cry1F and Vip3Aa19 showed an antagonistic effect when the ratio was 1:1 and an additive effect when the ratio was 1:2, 2:1, 1:5, and 5:1. Additionally, the combinations of Cry1F and Vip3Aa20 showed antagonistic effects at 1:2 and 5:1 ratios and additive effects at 1:1 and 2:1 ratios. In addition to the above combinations, which had additive or antagonistic effects, other combinations exhibited synergistic effects, with variations in synergistic factors (SFs). These results can be applied to the establishment of new pyramided transgenic crops with suitable candidates, providing a basis for FAW control and resistance management strategies.

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas.

Abstract Partisan sorting in residential environments is an enduring feature of contemporary American politics, but little research has examined partisan segregation individuals experience in activity spaces through their daily activities. Relying on advances in spatial computation and global positioning system data on everyday mobility flows collected from smartphones, we measure experienced partisan segregation in two ways: place-level partisan segregation based on the partisan composition of its daily visitors and community-level experienced partisan segregation based on the segregation level of places visited by its residents. We find that partisan segregation experienced in places varies across different geographic areas, location types, and time periods. Moreover, partisan segregation is distinct from experienced segregation by race and income. We also find that partisan segregation individuals experience is relatively lower when they visit places beyond their residential areas, but partisan segregation in residential space and activity space is strongly correlated. Residents living in predominantly black, liberal, low-income, non-immigrant, more public transit-dependent, and central city communities tend to experience a higher level of partisan segregation.

Pumpkin polysaccharides (PPe) have a variety of bioactive effects and our previous research showed the acid hydrolysate (PPe-S, a mixture) from PPe had an antioxidative capacity both in vitro and in viro. The aim of this study was to purify PPe-S and investigate the antioxidant stress effects of 2 purified components (PPe-S-1 and PPe-S-2) using Caenorhabditis elegans as model organism. The results showed that PPe-S-2 had a notable antioxidant effect, and could significantly enhance the activities of antioxidant enzymes including superoxide dismutase (SOD) (P < 0.01), catalase (CAT) (P < 0.01) and glutathione reductase (GR) (P < 0.05), and increase the level of glutathione (GSH) (P < 0.01), and decreased the content of malondialdehyde (MDA) (P < 0.05). PPe-S-2 could significantly extend the survival time of C. elegans (P < 0.01), which were stress-induced by hydrogen peroxide and methyl viologen. PPe-S-2 was a heteropolys accharide composed of glucose, arabinose, rhamnose and galactose with the molar ratio of 1.00:0.03:0.02:0.14. The molecular weight of PPe-S-2 was 0.73 kDa detected by high performance liquid chromatography.These studies demonstrated that PPe-S-2 obtained by the acid hydrolysis of PPe had a prominent protective effect to the damage induced by the intracellular free radical generating agents.

The Aphis gossypii is a major threat of cotton worldwide due to its short life cycle and rapid reproduction. Chemical control is the primary method used to manage the cotton aphid, which has significant environmental impacts. Therefore, prioritizing eco-friendly alternatives is essential for managing the cotton aphid. The ladybird, Hippodamia variegata, is a predominant predator of the cotton aphid. Its performance in cotton plantation is directly linked to chemical communication, where volatile compounds emitted from aphid-infested plants play important roles in successful predation. Here, we comprehensively studied the chemical interaction between the pest, natural enemy and host plants by analyzing the volatile profiles of aphid-infested cotton plants using gas chromatography-mass spectrometry (GC-MS). We then utilized the identified volatile compounds in electrophysiological recording (EAG) and behavioral assays. Through behavioral tests, we initially demonstrated the clear preference of both larvae and adults of H. variegata for aphid-infested plants. Subsequently, 13 compounds, namely α-pinene, cis-3-hexenyl acetate, 4-ethyl-1-octyn-3-ol, β-ocimene, dodecane, E-β-farnesene, decanal, methyl salicylate, β-caryophyllene, α-humulene, farnesol, DMNT, and TMTT were identified from aphid-infested plants. All these compounds were electrophysiologically active and induced detectable EAG responses in larvae and adults. Y-tube olfactometer assays indicated that, with few exceptions for larvae, all identified chemicals were attractive to H. variegata, particularly at the highest tested concentration (100 mg/ml). The outcomes of this study establish a practical foundation for developing attractants for H. variegata and open avenues for potential advancements in aphid management strategies by understanding the details of chemical communication at a tritrophic level.

Hepatic ischemia-reperfusion injury (HIRI) adversely affects liver transplant and resection outcomes. Recently, ferroptosis has been associated with HIRI. Dexmedetomidine (Dex), a potent sedative with anti-inflammatory, antioxidant, and anti-apoptotic properties, protects organs from hypoxic or ischemia-reperfusion (I/R) injuries. However, the mechanisms underlying this protective effect against I/R-induced liver injury remain unclear. This study evaluated the effect of Dex on HIRI in mouse models and the oxygen-glucose deprivation/reperfusion (OGD/R) AML12 cell model. We examined ferroptosis-related markers, including Fe2+ levels, reactive oxygen species (ROS) content, mitochondrial morphology, GPX4 protein expression, 4-hydroxynonenal (4-HNE), and Nrf2. The Nrf2 inhibitor ML385 was used in combination with Dex to treat HIRI mice and OGD/R-induced cellular models to explore the pathways by which Dex counteracts ferroptosis. Our results showed that Dex treatment significantly ameliorated OGD/R-induced ferroptosis in AML12 cells, including reduced Fe2+, ROS, malondialdehyde (MDA), and 4-HNE levels. Dex also ameliorated liver tissue damage and reduced serum AST, ALT, and inflammatory factor levels in HIRI mice. Additionally, Dex increased the levels of GSH, an antioxidative stress marker, and GPX4 expression in HIRI mice. Mechanistically, Nrf2 expression and nuclear translocation were significantly inhibited in both HIRI mice and OGD/R-treated AML12 cells. Dex treatment also restored the I/R-induced inhibition of Nrf2 expression and nuclear translocation. ML385 significantly inhibited Dex-promoted Nrf2 nuclear aggregation with Gpx4 protein expression, hindering the efficacy of Dex. In conclusion, Dex ameliorates ferroptosis in HIRI by positively regulating the Nrf2/GPx4 axis, potentially presenting a therapeutic avenue for addressing HIRI.

Crack detection plays a pivotal role in civil engineering applications, where vision-based methods find extensive use. In practice, crack images are sourced from Unmanned Aerial Vehicles (UAV) and handheld photography, and the balance between the utilization of global and local information is the key to detecting cracks from images of different sources: the former tends to eliminate interferences with a global perspective, whereas the latter pays more attention to the description of local details of cracks. However, many existing methods primarily target crack detection in handheld photographs and may not perform optimally on UAV-generated images or those with variable backgrounds or from different sources. In response to this challenge, we propose a robust and innovative method called Crack Detection with Structure Line (CDSL). The primary steps of this method can be summarized as follows: first, based on local information, an indicator called the “crack measure” is derived to directly generate a continuous crack map for effective image binarization; then, based on global information, the crack map is simplified in a unified and analyzable form using structure lines to perform a robust optimization for high-precision crack detection. The experiments we conducted on two publicly available datasets showed that CDSL provided competitive crack detection performance and outperformed four classical or current state-of-the-art methods by at least 13.0 % in the UAV dataset we collected.

ABSTRACT The broad host range fungal insect pathogen, Beauveria bassiana, has been commercialized as an alternative to chemical insecticides for pest control worldwide. B. bassiana represents a unique model system with which to examine host-pathogen interactions, and a wide range of genes and processes have been studied. However, significant aspects of virulence, particularly on the genomic scale, remain poorly studied. Here, we have combined available transcriptomes with three newly generated data sets for a combined total analysis of 76 deep-sequenced samples covering growth, development, stress responses, and infection during the life cycle of B. bassiana. Co-expression network analyses resulted in the identification of gene modules enriched during two critical stages of the infection process, namely (i) cuticle penetration and (ii) in vivo hyphal body (dimorphic transition) growth capable of avoiding innate and humoral immune defenses. These analyses identify unique signatures of metabolism, signaling, secondary metabolite production, host defense suppression, membrane reorganization, effector production, and secretion for each stage, including genetic regulators and epigenetic patterns. These data provide a comprehensive framework for understanding and probing fungal adaptations to its pathogenic life cycle and expand the candidate repertoire for continued dissection of the host-pathogen interaction. IMPORTANCE Insect fungal pathogens have evolved unique strategies for overcoming host structural and immunological defenses that span from the sclerotized cuticle to innate and humoral cellular responses. Two critical stages of the infection process involve (i) cuticle penetration and (ii) immune evasion within the insect hemocoel. A set of 76 global transcriptomic data for B. bassiana that include the cuticle penetration and hemocoel growth stages were analyzed for patterns (gene modules) of expression, yielding unique insights into these different life stages. These analyses integrate gene networks involved in fungal development, stress response and pathogenesis to further the systematic understanding of the global processes integral to the unique adaptation employed by fungal pathogens of insects.

Abstract In view of the limited capacity for public transformers to accept electric vehicles (EVs) and the large cost of putting in new transformers to supply power in the parking lot of urban buildings, a special transformer sharing mode (STSM) is proposed, where charging station operators utilize the special transformers of buildings to supply power for charging stations. To enhance the applicability of STSM, single regulation and joint regulation methods for the charging price strategy of charging stations are put up, and the mathematical model of joint regulation is focused on. First of all, a joint regulation mechanism is established, in which EVs are guided to charge off‐peak and off‐station through the joint regulation. Secondly, integrating charging fees, queuing costs, and transfer costs, the equivalent charging prices for EV users are put forward. Based on the difference of the equivalent charging prices, transfer probability model for EVs’ charging periods and charging stations is proposed. Furthermore, the overall objective of joint regulation is built based on the net income of all parties. And an optimization method for the joint regulation of charging prices is proposed. Then, regulation lifting ratio is put forward to reflect the superiority of joint regulation over single regulation. Based on this, an evaluation model for joint station diameter threshold is established. The practical result proves that through single regulation and joint regulation, approximately 200–500 additional EVs can be powered, which will result in significant savings in infrastructure construction and investment costs.

Accurate information for consumer phase connectivity in a low-voltage distribution network (LVDN) is critical for the management of line losses and the quality of customer service. The wide application of smart meters provides the data basis for the phase identification of LVDN. However, the measurement errors, poor communication, and data distortion have significant impacts on the accuracy of phase identification. In order to solve this problem, this paper proposes a phase identification method of LVDN based on stepwise regression (SR) method. First, a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN. Second, the SR algorithm is used to identify the consumer phase connectivity. Third, by defining a significance correction factor, the results from the SR algorithm are updated to improve the accuracy of phase identification. Finally, an LVDN test system with 63 consumers is constructed based on the real load. The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.