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Abstract Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm ( $$P_f$$ P f ) of 10% while still ensuring the probability of detection ( $$P_d$$ P d ) of over 90%.

With the acceleration of urbanization, while the amount of fertilizers used in agricultural production is increasing rapidly in industrial development, heavy metal pollution is also becoming more severe. Excessive use of fertilizers can lead to soil acidification, compaction, and degradation, while the sharp increase in heavy metal pollution observed also has adverse effects on soil quality and crop quality. Therefore, to prevent the loss of phosphorus from soil, improve the utilization rate of nitrogen in soil, and control cadmium pollution, the effects of biochar on the adsorption of nitrogen, phosphorus, and cadmium in soil were explored in this study. The following four experimental treatments were conducted: no biochar application, straw charcoal application, fruit shell charcoal application, and coconut shell charcoal application. Samples were collected from different soil depths (0 cm and 10 cm) after both 14 days and 28 days. The contents of total nitrogen, phosphorus, and cadmium were compared. The research findings indicate that these three types of biochar exhibit significant adsorption effects on nitrogen and phosphorus elements. Nevertheless, the adsorption effect on cadmium is not pronounced, potentially due to the stability of the biochar, the activity of microorganisms in the soil, and the alteration of cadmium speciation, which consequently results in an increase in cadmium content.

IntroductionSkin greasiness occurred on stored apples (Malus domestica Borkh.) is generally believed to result from changes in surface wax components. Previous reports have typically correlated wax changes with greasiness scores to reveal the contributing wax components. A notable limitation of this approach is that greasiness scores are highly subjective and influenced by individual perception.MethodsThis study aimed to assess skin quality by quantitatively analyzing wax morphology changes in greasy ‘Jonagold’ apples using Atomic Force Microscopy (AFM) roughness parameters Ra, Rq, Rmax, and Rz, and to correlate these changes with wax composition.ResultsAFM results revealed that wax crystals disappeared as skin greasiness increased, accompanied by significant declines in roughness parameters Ra, Rq, Rmax, and Rz, which decreased by 70% to 85%. Chemical analysis showed a significant increase in liquid esters, including linoleate and oleate esters, in the surface waxes, which negatively correlated with the decline in roughness parameters. Key genes related to ester production, such as MdFAD2, MdWSD1, and MdWBC11, exhibited increased expression and were also negatively correlated with decreases in Rq, Ra and Rz. Additionally, 1-Methylcyclopropene (1-MCP) treatment suppressed both the development of greasiness and the associated changes.DiscussionOur findings suggest that the increased liquid esters contribute to alternations in wax morphology in greasy apples, and that MdFAD2, MdWSD1, and MdWBC11, play crucial roles in ester biosynthesis. These results highlight the effectiveness of AFM roughness parameters Ra, Rq, Rmax, and Rz in quantifying wax morphology changes in apples during skin greasiness development.

Abstract Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (

Abstract Background In recent years, natural bone extracellular matrix (ECM)-inspired materials have found widespread application as scaffolds for bone tissue engineering. However, the challenge of creating scaffolds that mimic natural bone ECM’s mechanical strength and hierarchical nano-micro-macro structures remains. The purposes of this study were to introduce an innovative bone ECM-inspired scaffold that integrates a 3D-printed framework with hydroxyapatite (HAp) mineralized graphene oxide-collagen (GO-Col) microscaffolds and find its application in the repair of mandibular bone defects. Methods Initially, a 3D-printed polycaprolactone (PCL) scaffold was designed with cubic disks and square pores to mimic the macrostructure of bone ECM. Subsequently, we developed multi-layer mineralized GO-Col-HAp microscaffolds (MLM GCH) to simulate natural bone ECM's nano- and microstructural features. Systematic in vitro and in vivo experiments were introduced to evaluate the ECM-inspired structure of the scaffold and to explore its effect on cell proliferation and its ability to repair rat bone defects. Results The resultant MLM GCH/PCL composite scaffolds exhibited robust mechanical strength and ample assembly space. Moreover, the ECM-inspired MLM GCH microscaffolds displayed favorable attributes such as water absorption and retention and demonstrated promising cell adsorption, proliferation, and osteogenic differentiation in vitro. The MLM GCH/PCL composite scaffolds exhibited successful bone regeneration within mandibular bone defects in vivo. Conclusions This study presents a well-conceived strategy for fabricating ECM-inspired scaffolds by integrating 3D-printed PCL frameworks with multilayer mineralized porous microscaffolds, enhancing cell proliferation, osteogenic differentiation, and bone regeneration. This construction approach holds the potential for extension to various other biomaterial types. Graphical Abstract

To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This model extends the Variational Autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). These enhancements significantly improve both the quality of generated samples and the stability of the training process. By utilizing the VAE-WACGAN model to oversample anomalous data, more realistic synthetic anomalies that closely mirror the actual network traffic distribution can be generated. This approach effectively balances the network traffic dataset and enhances the overall performance of the intrusion detection model. Experimental validation was conducted using two widely utilized intrusion detection datasets, UNSW-NB15 and CIC-IDS2017. The results demonstrate that the VAE-WACGAN method effectively enhances the performance metrics of the intrusion detection model. Furthermore, the VAE-WACGAN-based intrusion detection approach surpasses several other advanced methods, underscoring its effectiveness in tackling network security challenges.

BackgroundObservational studies have indicated a possible connection between Helicobacter pylori (H. pylori) infection and eosinophilic esophagitis (EoE), but their causal relationship has yet to be established. To investigate the causal associations between H. pylori infection and EoE, we performed a Mendelian randomization (MR) analysis.MethodsFirstly, we conducted both univariable and multivariable Mendelian randomization (MR) analyses. Furthermore, a two-step MR was carried out to ascertain the potential underlying pathways of these associations, particularly the involvement of inflammatory cytokines. We employed the inverse-variance weighted (IVW) method as the main analysis in our MR study. To enhance the credibility of the results, we also conducted several sensitivity analyses.ResultsOur study demonstrated a noteworthy correlation between genetically predicted anti-H. pylori IgG antibody levels and a reduced risk of EoE (OR=0.325, 95% CI=0.165–0.643, P value=0.004, adj p value=0.009). No significant causal associations were detected between other H. pylori antibodies and EoE in our study. When it comes to multivariable MR analysis controlling for education attainment, household income, and deprivation individually, the independent causal impact of anti-H. pylori IgG on EoE persisted. Surprisingly, the two-step MR analysis indicated that inflammatory factors (IL-4, IL-5, IL-13, IL-17, and IFN-γ) did not appear to mediate the protective effect of H. pylori infection against EoE.ConclusionFindings suggested that among the range of H. pylori-related antibodies, anti-H. pylori IgG antibody is the sole causal factor associated with protection against EoE. Certain inflammatory factors may not be involved in mediating this association. These findings make a significant contribution to advancing our understanding of the pathogenesis of EoE and its evolving etiology.

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin (YHHRB). The values of the Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and percent bias (PBIAS) indicated the acceptable performance of the soil and water assessment tool (SWAT) model in the YHHRB. Nine descriptors belonging to the categories of climate, soil, vegetation, and topography were used to express the catchment characteristics related to the hydrological processes. The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models, including linear regression (LR) equations, support vector regression (SVR), random forest (RF), k-nearest neighbor (kNN), decision tree (DT), and radial basis function (RBF). Each of the 38 catchments was assumed to be an ungauged catchment in turn. Then, the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments. Furthermore, the similarity-based regionalization scheme was used for comparison with the regression-based approach. The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments. Compared with the traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships. The performances of different approaches were similar in humid regions, while the advantages of the machine learning techniques were more evident in arid regions. When the study area contained nested catchments, the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance. The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

Federated learning is an effective approach for preserving data privacy and security, enabling machine learning to occur in a distributed environment and promoting its development. However, an urgent problem that needs to be addressed is how to encourage active client participation in federated learning. The Shapley value, a classical concept in cooperative game theory, has been utilized for data valuation in machine learning services. Nevertheless, existing numerical evaluation schemes based on the Shapley value are impractical, as they necessitate additional model training, leading to increased communication overhead. Moreover, participants’ data may exhibit Non-IID characteristics, posing a significant challenge to evaluating participant contributions. Non-IID data have greatly affected the accuracy of the global model, weakened the marginal effect of the participants, and led to the underestimated contribution measurement results of the participants. Current work often overlooks the impact of heterogeneity on model aggregation. This paper presents a fair federated learning contribution measurement scheme that addresses the need for additional model computations. By introducing a novel aggregation weight, it enhances the accuracy of the contribution measurement. Experiments on the MNIST and Fashion MNIST dataset show that the proposed method can accurately compute the contributions of participants. Compared to existing baseline algorithms, the model accuracy is significantly improved, with a similar time cost.

ObjectiveThis study aimed to study the molecular epidemiology and clinical characteristics of respiratory syncytial virus (RSV) infection from hospitalized children with ARTI in Bengbu.MethodsOne hundred twenty-four nasopharyngeal swab specimens and clinical data from children with ARTI cases were collected in Bengbu, China, during winter 2021–2022. The samples were detected by qPCR of 13 respiratory viruses. Phylogenetic analysis was constructed using MEGA 7.0. All analyses were performed using SAS software, version 9.4.ResultsIn winter 2021–2022, URTI, NSCAP, SCAP, and bronchiolitis accounted for 41.03%, 27.35%, 17.09%, and 14.53% of hospitalized children in Bengbu, China. The detection rates of the top three were RSV (41.94%), ADV (5.65%), and FluB (5.65%) in hospitalized children through 13 virus detection. RSV is the main pathogen of hospitalized children under 2 years old. Forty-eight sequences of G protein of RSV were obtained through PCR amplification, including RSV-A 37 strains and RSV-B 11 strains. Phylogenetic analysis showed that all RSV-A and RSV-B were ON1 and BA9 genotypes, respectively. ON1 genotypes were further divided into two clades. The majority of ON1 strains formed a unique genetic clade with T113I, V131D, N178 G, and H258Q mutations. Furthermore, RSV infection was an independent risk factor for ventilator use (OR = 9.55, 95% CI 1.87–48.64).ConclusionThere was a high incidence of RSV among hospitalized children during winter 2021–2022 in Bengbu with ON1 and BA9 being the dominant strains. This study demonstrated the molecular epidemiological characteristics of RSV in children with respiratory infections in Bengbu, China.

Abstract Wound healing is a dynamic and highly sequential process involving a series of overlapping spatial and temporal phases, including hemostasis, inflammation, proliferation, and tissue remodeling. Mesenchymal stem cells (MSCs) are multipotent stem cells with self-renewal, multidirectional differentiation potential, and paracrine regulation. Exosomes are subcellular vesicular components 30–150 nm in size and are novel carriers of intercellular communication in regulating the biological behaviors of skin cells. Compared to MSCs, MSC-derived exosomes (MSC-exos) possess lower immunogenicity, easy storage, and highly effective biological activity. MSC-exos, mainly derived from adipose-derived stem cells (ADSCs), bone marrow-derived MSCs (BMSCs), human umbilical cord MSCs (hUC-MSCs), and other stem cell types, play a role in shaping the activity of fibroblasts, keratinocytes, immune cells, and endothelial cells in diabetic wounds, inflammatory wound repair, and even wound-related keloid formation. Therefore, this study focuses on the specific roles and mechanisms of different MSC-exos in wound healing, as well as the current limitations and various perspectives. Deciphering the biological properties of MSC-exos is crucial to providing a promising cell-free therapeutic tool for wound healing and cutaneous regeneration. Graphical Abstract

Abstract Background Oxidative stress is strongly associated with cellular senescence. Numerous studies have indicated that microRNAs (miRNAs) play a critical part in cellular senescence. MiR-181a was reported to induce cellular senescence, however, the potential mechanism of miR-181a in hydrogen peroxide (H2O2)-induced cellular senescence remains obscure. Objective The aim of this study is to investigate the role and regulatory mechanism of miR-181a in H2O2-induced cellular senescence. Methods Human foreskin fibroblasts (HFF) transfected with miR-181a inhibitor/miR-NC with or without H2O2 treatment were divided into four groups: control + miR-NC/miR-181a inhibitor, H2O2 + miR-NC/miR-181a inhibitor. CCK-8 assay was utilized to evaluate the viability of HFF. RT-qPCR was used to measure the expression of miR-181a and its target genes. Protein levels of protein disulfide isomerase family A member 6 (PDIA6) and senescence markers were assessed by western blotting. Senescence-associated β-galactosidase (SA-β-gal) staining was applied for detecting SA-β-gal activity. The activities of SOD, GPx, and CAT were detected by corresponding assay kits. The binding relation between PDIA6 and miR-181a was identified by luciferase reporter assay. Results MiR-181a inhibition suppressed H2O2-induced oxidative stress and cellular senescence in HFF. PDIA6 was targeted by miR-181a and lowly expressed in H2O2-treated HFF. Knocking down PDIA6 reversed miR-181a inhibition-mediated suppressive impact on H2O2-induced oxidative stress and cellular senescence in HFF. Study limitations Signaling pathways that might be mediated by miR-181a/PDIA6 axis were not investigated. Conclusion Downregulated miR-181a attenuates H2O2-induced oxidative stress and cellular senescence in HFF by targeting PDIA6.

Baculovirus infection can prevent the pupation of insects. Juvenile hormone (JH) plays a vital role in regulating insect molting and metamorphosis. However, the molecular mechanism of baculovirus preventing the pupation of larvae by regulating the Juvenile hormone (JH) pathway is still unclear. In this study, we found that the Mamestra brassicae multiple nucleopolyhedroviruses (MbMNPV) infection prolonged the larval stage of fourth instar Helicoverpa armigera (H. armigera) by 0.52 d and caused an increase in JH titer. To identify the genes that contribute to the JH increase in H. armigera-MbMNPV interaction, we analyzed mRNA expression profiles of the fat bodies of H. armigera infected by MbMNPV. A total of 3637 differentially expressed mRNAs (DE-mRNAs) were filtered out through RNA-seq analysis. These DE-mRNAs were mainly enriched in Spliceosome, Ribosome biogenesis in eukaryotes, Aminoacyl-tRNA biosynthesis, Mismatch repair, and RNA degradation signaling pathway, which are related to the virus infection. Real-time PCR was used to verify the RNA sequencing results. To find out which genes caused the increase in JH titer, we analyzed all the DE-mRNAs in the transcriptome and found that the JHE and JHEH genes, which were related to JH degradation pathway, were down-regulated. JHE and JHEH genes in the larvae of MbMNPV-infected group were significantly down-regulated compared with the control group by RT-qPCR. We further proved that the JH is degraded by JHE in H. armigera larvae by RNAi, ELISA, RT-qPCR and bioassay, while the hydrolysis of JH by JHEH in H. armigera larvae can almost be ignored. Knocking down of HaJHE promoted the expression of the JH receptor gene Met and the downstream gene Kr-h1, and the replication of MbMNPV. This study clarified that JH is mainly degraded by JHE in H. armigera larvae. The MbMNPV infection of H. armigera larvae leads to the increase of JH titer by inhibiting the expression of JHE. The increase in JH titer promotes the expression of the JH receptor gene Met and the downstream gene Kr-h1, which prevents the pupation of H. armigera, and promotes MbMNPV replication. This study provides new insights into H. armigera and MbMNPV interaction mechanisms.

Oral submucous fibrosis is a chronic, inflammatory and potentially malignant oral disease. Local delivery of triamcinolone to lesion site is a commonly used therapy. The existing methods for local drug delivery include topical administration and submucosal injection. However, in the wet and dynamic oral microenvironment, these methods have drawbacks such as limited drug delivery efficiency and injection pain. Therefore, it is urgently needed to develop an alternative local drug delivery system with high efficiency and painlessness. Inspired by the structure of band-aid, this study proposed a novel double-layered mucoadhesive microneedle patch for transmucosal drug delivery. The patch consisted of a mucoadhesive silk fibroin/tannic acid top-layer and a silk fibroin microneedle under-layer. When applying the annealing condition for the medium content of β-sheets of silk fibroin, the microneedles in under-layer displayed both superior morphology and mechanical property. The mechanical strength of per needle (0.071N) was sufficient to penetrate the oral mucosa. Sequentially, the gelation efficiency of silk fibroin and tannic acid in top-layer was maximized as the weight ratio of tannic acid to silk fibroin reached 5:1. Moreover, in vitro results demonstrated the double-layered patch possessed undetectable cytotoxicity. The sustained release of triamcinolone was observed from the double-layered patch for at least 7 days. Furthermore, compared with other commercial buccal patches, the double-layered patch exhibited an enhanced wet adhesion strength of 37.74 kPa. In addition, ex vivo mucosal tissue penetration experiment confirmed that the double-layered patch could reach the lamina propria, ensuring effective drug delivery to the lesion site of oral submucous fibrosis. These results illustrate the promising potential of the drug-loaded mucoadhesive microneedle patch for the treatment of oral submucous fibrosis.

In this study, four kinds of Longjing tea, the famous flat green tea and the protected geographical indication product in China, were used to explore the quality difference of the same green tea due to the cultivar, geographic origin, and storage time under the premise of consistent picking conditions and processing technology using the widely targeted metabolomics. Results showed that 483 flavonoid metabolites with 10 subgroups of flavonoids were screened and 118 differential flavonoid metabolites were identified. The number and subgroups of differential flavonoid metabolites produced by different cultivars of Longjing tea were the largest, followed by storage time, and third by the geographic origin. Glycosidification and methylation or methoxylation were the main structural modifications of differential flavonoid metabolites. This study has enriched the understanding of the effects of the cultivar, the geographic origin, and the storage time on the flavonoid metabolic profiles of Longjing tea, and provided worthy information for the traceability of green tea.

Abstract Background Diabetes mellitus was a chronic low‐grade inflammatory disease and had increased circulating inflammatory cytokines and acute phase proteins. We aimed to identify the changes of inflammatory cytokines in newly diagnosed type 2 diabetic patients after short‐term intensive insulin therapy using continuous subcutaneous insulin infusion (CSII). Methods Thirty‐three newly diagnosed type 2 diabetic patients were enrolled between September 2020 to December 2020. Expression of 40 inflammatory cytokines of the patients were tested with RayBiotech antibody array before and after 1 week of intensive insulin therapy of CSII. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was carried out to explore the signaling pathway involved in the therapy. Results Five inflammatory cytokines were downregulated significantly after 1 week of CSII therapy. They were interleukin‐6 receptor (IL‐6R), regulated upon activation normal T‐cell expressed and secreted (RANTES), intercellular adhesion molecule‐1 (ICAM‐1), tissue inhibitor of metalloproteinase‐1 (TIMP‐1), and platelet‐derived growth factor type BB (PDGF‐BB) (p

In recent years, social networks have developed rapidly and have become the main platform for the release and dissemination of fake news. The research on fake news detection has attracted extensive attention in the field of computer science. Fake news detection technology has made many breakthroughs recently, but many challenges remain. Although there are some review papers on fake news detection, a more detailed picture for carrying out a comprehensive review is presented in this paper. The concepts related to fake news detection, including fundamental theory, feature type, detection technique and detection approach, are introduced. Specifically, through extensive investigation and complex organization, a classification method for fake news detection is proposed. The datasets of fake news detection in different fields are also compared and analyzed. In addition, the tables and pictures summarized here help researchers easily grasp the full picture of fake news detection.

Abstract Background Knowledge of protein motions is significant to understand its functions. While currently available databases for protein motions are mostly focused on overall domain motions, little attention is paid on local residue motions. Albeit with relatively small scale, the local residue motions, especially those residues in binding pockets, may play crucial roles in protein functioning and ligands binding. Results A comprehensive protein motion database, namely D3PM, was constructed in this study to facilitate the analysis of protein motions. The protein motions in the D3PM range from overall structural changes of macromolecule to local flip motions of binding pocket residues. Currently, the D3PM has collected 7679 proteins with overall motions and 3513 proteins with pocket residue motions. The motion patterns are classified into 4 types of overall structural changes and 5 types of pocket residue motions. Impressively, we found that less than 15% of protein pairs have obvious overall conformational adaptations induced by ligand binding, while more than 50% of protein pairs have significant structural changes in ligand binding sites, indicating that ligand-induced conformational changes are drastic and mainly confined around ligand binding sites. Based on the residue preference in binding pocket, we classified amino acids into “pocketphilic” and “pocketphobic” residues, which should be helpful for pocket prediction and drug design. Conclusion D3PM is a comprehensive database about protein motions ranging from residue to domain, which should be useful for exploring diverse protein motions and for understanding protein function and drug design. The D3PM is available on www.d3pharma.com/D3PM/index.php .

In present work, a novel γ′′-strengthened Nb-alloyed medium-entropy alloy (MEA) was successfully fabricated and systematically investigated. Our results evidence that the γ′′ phase brings out significant precipitation hardening effect (∼10.2 %volume fraction contributes ∼ 685 MPa strength enhancement). The metastable γ′′ phase could abnormally transform to ε phase, and the morphology and distribution of ε phase shows great impacts on mechanical properties. A hetero-lamellae grain coupling with dual-precipitation (HLG + dual-P) structure was successfully constructed in this Nb-alloyed MEA, which could effectively combine precipitation hardening and hetero-deformation induced (HDI) hardening effects. Deformation characters investigations reveals that the absence of mechanical twinning in γ′′-strengthened MEA originates from the improved critical twinning stress related to the fine dislocation source size and the difficulty of high-energy complex SFs reordering. The combination of massive stacking-faults, Lomer-Cottrell locks and dynamically reinforced HDI hardening contribute to the excellent ductility and work-hardening capacity at high-level strength in the HLG + dual-P structural alloy. Our work provides a useful insight and helpful guidance for developing high-performance precipitation-hardened multi-principal element alloys.

An equiatomic CrCoNi medium-entropy alloy was subjected to high-energy shot peening (HESP) to fabricate a gradient nanostructure (GNS) in this work. The microstructures of the GNS samples at different depths within the deformed layer were thoroughly investigated. The microstructure exhibited a transformation from unstressed coarse grains to deformed coarse grains, followed by the formation of ultrafine grains, and ultimately reaching a final nanocrystalline structure with a uniform size of approximately 50 nm. Detailed structural analysis indicated that the deformation process was primarily influenced by the interaction between dislocations and deformation twins, which was attributed to the low stacking fault energy (SFE) of the alloy. The nanocrystalline mechanism was divided into three stages. In the coarse-grained deformation stage, the dislocation band divided twin/matrix lamellae into sub-segments, and the cross twin divided coarse grains into ultrafine grains simultaneously. In the ultrafine grain deformation stage, dislocations were arranged around the deformation twins in order to break the twins to form incoherent boundaries, destroying the coherent relationship between the twin and matrix. Finally, in the nanocrystalline deformation stage, the nanocrystalline structure was further divided into smaller segments to accommodate additional strains through the interaction between dislocations and twins.

Optimization of the pile driver’s large-body structure is important to achieve the driver’s overall light weight. This paper studies the large-body structure of a hydraulic static pile driver. We used the APDL parametric design language provided by ANSYS to construct a geometric model of the large-body structure and performed a static analysis of the finite element model. Under the assumption that the strength and stiffness meet the design requirements, the optimization model was constructed with the thickness of each plate of the large-body structure as the design variable, the structural strength and stiffness as the constraints, and the minimum mass as the objective function. Finally, two optimization algorithms were used to solve the model, and the comparison of the two sets of solutions shows that the improved BP neural network algorithm based on the particle swarm optimization algorithm performs better. The optimized mass of the large-body structure was reduced from 82,556.1 kg to 65,046.15 kg, a mass reduction of 21%. The lightweight design of the pile driver was achieved.

Abstract Wallerian degeneration (WD) involves the recruitment of macrophages for debris clearance and nerve regeneration, and the cause of the foamy macrophages that are frequently observed in peripheral transection injuries is unknown. Recent studies indicated that these foamy cells are generated by gasdermin D (GSDMD) via membrane perforation. However, whether these foamy cells are pyroptotic macrophages and whether their cell death elicits immunogenicity in peripheral nerve regeneration (PNR) remain unknown. Therefore, we used GSDMD-deficient mice and mice with deficiencies in other canonical inflammasomes to establish a C57BL/6 J mouse model of sciatic nerve transection and microanastomosis (SNTM) and evaluate the role of GSDMD-executed pyroptosis in PNR. In our study, the GSDMD −/− mice with SNTM showed a significantly diminished number of foamy cells, better axon regeneration, and a favorable functional recovery, whereas irregular axons or gaps in the fibers were found in the wild-type (WT) mice with SNTM. Furthermore, GSDMD activation in the SNTM model was dependent on the NLRP3 inflammasome and caspase-1 activation, and GSDMD-executed pyroptosis resulted in a proinflammatory environment that polarized monocytes/macrophages toward the M1 (detrimental) but not the M2 (beneficial) phenotype. In contrast, depletion of GSDMD reversed the proinflammatory microenvironment and facilitated M2 polarization. Our results suggested that inhibition of GSDMD may be a potential treatment option to promote PNR.

The Yellow-Huai-Hai River Basin (YHHRB) is an important ecological barrier in China. There are obvious spatiotemporal differences and intrinsic drivers of vegetation coverage in this region. Using the Normalized Difference Vegetation Index (NDVI) and meteorological data, the spatiotemporal characteristics of vegetation change in the YHHRB from 1982 to 2015 and its related driving factors were analyzed. Combined with CAM5.1-1 degree model data, the impacts of natural and anthropogenic forcings on climate change were separated, and the relative roles of other driving factors, natural and anthropogenic forcings, in vegetation change were further distinguished by using a residual trend method. Results showed that the vegetation coverage increased during the study period, the NDVI increased with a slope of 0.014/10a, and the areas with NDVI significant increased accounted for 75.78%. NDVI was positively correlated with precipitation and temperature in the YHHRB, and the correlation between NDVI and temperature was higher than that of precipitation, indicating that vegetation growth was more sensitive to temperature. Residual analysis indicated that other driving factors, natural and anthropogenic forcings, were responsible for 48.52%, 26.36%, and 25.12% of NDVI variation, respectively. Natural forcing contributed most to vegetation change in the Huai River Basin, whereas anthropogenic forcing and other driving factors had large effects on vegetation coverage in the Hai River Basin. In addition, obvious spatial differences were observed in the relative roles of different driving forces on vegetation conditions. The areas where natural forcing contributed most were mainly distributed in the upper Yellow River Basin, while the areas where other driving factors played a significant role in vegetation restoration were mainly concentrated in Inner Mongolia, Shanxi Province, and northern Hebei Province. Climate change and active human activities both made positive impacts on vegetation restoration, and the change in land use was the main factor causing vegetation degradation. The results are meaningful for the ongoing ecological civilization construction project in the YHHRB.

Quality control and counterfeit product detection have become exceedingly important due to the vertical market of beers in the global economy. China is the largest producer of beer globally and has a massive problem with counterfeit alcoholic beverages. In this research, a modular electronic nose system with 4 MOS gas sensors was designed for collecting the models from four different brands of Chinese beers. A sample delivery subsystem was fabricated to inject and clean the samples. A software-based data acquisition subsystem was programmed to record the time-dependent chemical responses in 28 different models. A back-propagation neural network based on a memristor was proposed to classify the quality of the beers. Data collected from the electronic nose system were then used to train, validate, and test the created memristor back-propagation neural network model. Over 70 tests with changes in the setup parameters, feature extraction methods, and neural network parameters were performed to analyze the classification performance of the electronic nose hardware and neural network. Samples collected from 28 experiments showed a deviation of 9% from the mean value. The memristor back-propagation network was able to classify four brands of Chinese beers, with 88.3% of classification accuracy. Because the memristor neural network algorithm is easy to fabricate in hardware, it is reasonable to design an instrument with low cost and high accuracy in the near future.

The optimization of the pile driver frame is very important to the overall performance of the pile driver. This paper examines the large body structure of a model of a hydraulic static pile driver as the research object. ANSYS APDL finite element software is used to perform static analysis and sensitivity analysis on the large body structure of the pile driver. On the premise of meeting the design requirements, according to the most dangerous working conditions of the pile driver, the size optimization of the frame structure of the pile driver is carried out based on sensitivity analysis. The mathematical model is established with the plate thickness as the design variable, the strength and stiffness of the body structure as the constraints, and the minimum mass of the body structure as the objective function. The optimization results show that the optimization design model based on sensitivity analysis not only meets the strength and stiffness conditions of the large structure, but also reduces the structural mass by 22.1% and greatly reduces the production cost.

Abstract: A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.

Abstract Background Aroma is an important organoleptic quality for fruit and has a large influence on consumer preference. Kiwifruit esters undergo rapid and substantial changes contributing to the flavor during fruit ripening. Part of enzymes and their coding genes have been indicated potential candidates for flavor-related esters synthesis. However, there still exist obvious gaps in the biosynthetic pathways of esters and the mechanisms regulating ester biosynthesis in kiwifruit remain unknown. Results Using gas chromatography-mass spectrometry (GC-MS), volatile compounds of kiwifruit were quantified in response to ethylene (ETH, 100 μl/l, 24 h, 20 °C) and 1-methylcyclopropene (1-MCP, 1 μl/l, 24 h, 20 °C). The results indicated that esters showed the most substantial changes enhanced by ethylene and were inhibited by 1-MCP. Correlations between RNA-seq results and concentrations of esters, constructed using Weighted Gene Co-Expression Network Analysis (WGCNA) indicated that three structural genes (fatty acid desaturase, AdFAD1; aldehyde dehydrogenase, AdALDH2; alcohol acyltransferase, AdAT17) had similar expression patterns that paralled the changes in total ester content, and AdFAD1 transcripts exhibited the highest correlation. In order to search for potential regulators for ester biosynthesis, 14 previously reported ethylene-responsive transcription factors (TFs) were included in the correlation analysis with esters and their biosynthetic genes. Using dual-luciferase assay, the in vivo regulatory activities of TFs on ester biosynthetic gene promoters were investigated and the results indicated that AdNAC5 and AdDof4 (DNA binding with one finger) trans-activated and trans-suppressed the AdFAD1 promoter. Conclusions The present study advanced the molecular basis of ripening-related ester biosynthesis in kiwifruit by identifying three biosynthetic related genes AdFAD1, AdALDH2 and AdAT17 by transcriptome analysis, and highlighted the function of two TFs by transactivation studies.

In recent years, machine learning-based intrusion detection systems (IDSs) have proven to be effective; especially, deep neural networks improve the detection rates of intrusion detection models. However, as models become more and more complex, people can hardly get the explanations behind their decisions. At the same time, most of the works about model interpretation focuses on other fields like computer vision, natural language processing, and biology. This leads to the fact that in practical use, cybersecurity experts can hardly optimize their decisions according to the judgments of the model. To solve these issues, a framework is proposed in this paper to give an explanation for IDSs. This framework uses SHapley Additive exPlanations (SHAP), and combines local and global explanations to improve the interpretation of IDSs. The local explanations give the reasons why the model makes certain decisions on the specific input. The global explanations give the important features extracted from IDSs, present the relationships between the feature values and different types of attacks. At the same time, the interpretations between two different classifiers, one-vs-all classifier and multiclass classifier, are compared. NSL-KDD dataset is used to test the feasibility of the framework. The framework proposed in this paper leads to improve the transparency of any IDS, and helps the cybersecurity staff have a better understanding of IDSs' judgments. Furthermore, the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs. More importantly, this work is unique in the intrusion detection field, presenting the first use of the SHAP method to give explanations for IDSs.

The detection and removal of malicious social bots in social networks has become an area of interest in industry and academia. The widely used bot detection method based on machine learning leads to an imbalance in the number of samples in different categories. Classifier bias leads to a low detection rate of minority samples. Therefore, we propose an improved conditional generative adversarial network (improved CGAN) to extend imbalanced data sets before applying training classifiers to improve the detection accuracy of social bots. To generate an auxiliary condition, we propose a modified clustering algorithm, namely, the Gaussian kernel density peak clustering algorithm (GKDPCA), which avoids the generation of data-augmentation noise and eliminates imbalances between and within social bot class distributions. Furthermore, we improve the CGAN convergence judgment condition by introducing the Wasserstein distance with a gradient penalty, which addresses the model collapse and gradient disappearance in the traditional CGAN. Three common oversampling algorithms are compared in experiments. The effects of the imbalance degree and the expansion ratio of the original data on oversampling are studied, and the improved CGAN performs better than the others. Experimental results comparing with three common oversampling algorithms show that the improved CGAN achieves the higher evaluation scores in terms of F1-score, G-mean and AUC.

To explore the advantages of adversarial learning and deep learning, we propose a novel network intrusion detection model called SAVAER-DNN, which can not only detect known and unknown attacks but also improve the detection rate of low-frequent attacks. SAVAER is a supervised variational auto-encoder with regularization, which uses WGAN-GP instead of the vanilla GAN to learn the latent distribution of the original data. SAVAER's decoder is used to synthesize samples of low-frequent and unknown attacks, thereby increasing the diversity of training samples and balancing the training data set. SAVAER's encoder is used to initialize the weights of the hidden layers of the DNN and explore high-level feature representations of the original samples. The benchmark NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21) and UNSW-NB15 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed SAVAER-DNN is more suitable for data augmentation than the other three well-known data oversampling methods. Moreover, the proposed SAVAER-DNN outperforms eight well-known classification models in detection performance and is more effective in detecting low-frequent and unknown attacks. Furthermore, compared with other state-of-the-art intrusion detection models reported in the IDS literature, the proposed SAVAER-DNN offers better performance in terms of overall accuracy, detection rate, F1 score, and false positive rate.

Steroid hormone molecules may exhibit very different functionalities based on the associated functional groups and their 3D arrangements in space, i.e., absolute configurations and conformations. Infrared (IR) and vibrational circular dichroism (VCD) spectra of four different steroid hormones, namely dehydroepiandrosterone (DHEA), 17α-methyltestosterone (MTTT), (16α,17)-epoxyprogesterone (Epoxy-P4), and dehydroepiandrosterone acetate (AcO-DHEA), were measured in deuterated dimethyl sulfoxide and some also in carbon tetrachloride. Extensive conformational searches were carried out using the recent developed conformer-rotamer ensemble sampling tool (CREST) which also accounts for solvent effects using an implicit solvation model. All the CREST conformational candidates were then reoptimized at the B3LYP-D3BJ/def2-TZVPD with the PCM of solvent. The good agreements between the experimental IR and VCD spectra and the theoretical simulations provide a conclusive information about their conformational distribution and absolute configurations. The experimental and theoretical IR and VCD spectra of AcO-DHEA in the carbonyl and alkene stretching region showed some discrepancies, and the possible causes related to solvent effects, large amplitude motions and levels of theory used in the modelling were explored in detail. As part of the investigation, additional calculations at the B3LYP-D3BJ/6-31++G (2d,p) and B3LYP-D3BJ/cc-pVTZ levels, as well as some ‘mixed’ calculations with the double-hybrid functional B2PLYP-D3 were also carried out. The results indicate that the double-hybrid functional is important for predicting the correct IR band pattern in the carbonyl and alkene stretching region.

MicroRNAs (miRNAs), are a novel class of gene expression regulators, that have been found to participate in regulating host–virus interactions. However, the function of insect-derived miRNAs in response to virus infection is poorly understood. We analyzed miRNA expression profiles in the fat bodies of Helicoverpa armigera (H. armigera) infected with Mamestra brassicae multiple nucleopolyhedroviruses (MbMNPV). A total of 52 differentially expressed miRNAs (DEmiRNAs) were filtered out through RNA-seq analysis. The targets of 52 DEmiRNAs were predicted and 100 miRNA–mRNA interaction pairs were obtained. The predicted targets of DEmiRNAs were mainly enriched in the Wnt signaling pathway, phagosome, and mTOR signaling pathway, which are related to the virus infection. Real-time PCR was used to verify the RNA sequencing results. ame-miR-317-3p, mse-miR-34, novel1-star, and sfr-miR-6094-5p were shown to be involved in the host response to MbMNPV infection. Results suggest that sfr-miR-6094-5p can negatively regulate the expression of four host genes eIF3-S7, CG7583, CG16901, and btf314, and inhibited MbMNPV infection significantly. Further studies showed that RNAi-mediated knockdown of eIF3-S7 inhibited the MbMNPV infection. These findings suggest that sfr-miR-6094-5p inhibits MbMNPV infection by negatively regulating the expression of eIF3-S7. This study provides new insights into MbMNPV and H. armigera interaction mechanisms.

A new approach to improving the interfacial compatibility of SiCf/Ti-6Al-4V composites by using fiber coating coupled with subsequent heat treatment was presented. The SiCf/Ti-6Al-4V composites with C-coated and C/Mo-coated fibers were prepared using the foil-fiber-foil method and subsequently heat treated in vacuum at 750 °C to obtain several kinds of the SiCf/Ti-6Al-4V composites with different interfacial characteristics. Interfacial microstructures of these composites were systematically characterized by means of scanning electron microscopy and X-ray energy dispersive spectroscopy to investigate the interfacial microstructure evolution and reaction dynamics. The effects of interfacial microstructures on interfacial mechanical properties were also investigated using thin-slice fiber push-out tests. The results show that the brittle interfacial reaction layer of TiC in the C-coated SiCf/Ti-6Al-4V composites was obviously thickened and some microvoids even formed near the matrix with increasing heat treatment durations, whereas the matrix adjacent to Mo coating in the C/Mo-coated SiCf/Ti-6Al-4V composites gradually transformed into a ductile β-Ti layer. Interfacial shear strengths of the C/Mo-coated composites were slightly increased with increasing the heat treatment time, while those of the C-coated composites were remarkably improved. Critical issues on modifying interfacial compatibility of the SiCf/Ti composites for further improving the mechanical behavior of the composites were discussed.

Various machine-learning methods have been applied to anomaly intrusion detection. However, the Intrusion Detection System still faces challenges in improving Detection Rate and reducing False Positive Rate. In this paper, a Class-Level Soft-Voting Ensemble (CLSVE) scheme based on the Chaos Bat Algorithm (CBA), called CBA-CLSVE, is proposed for intrusion detection. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) are selected as the base learners of the ensemble. The Chaos Bat Algorithm is used to generate class-level weights to create the weighted voting ensemble. A weighted fitness function considering the tradeoff between maximizing Detection Rate and minimizing False Positive Rate is proposed. In the experiments, the NSL-KDD, UNSW-NB15 and CICIDS2017 datasets are used to verify the scheme. The experimental results show that the class-level weights generated by CBA can be used to improve the combinative performance. They also show that the same ensemble performance can be achieved using about half the total number of features or fewer.

To systematically explore potential biomarkers which can predict disease severity in COVID-19 patients and prevent the occurrence or development of severe COVID-19, the levels of 440 factors were analyzed in patients categorized according to COVID-19 disease severity; including asymptomatic, mild, moderate, severe, convalescent and healthy control groups. Factor candidates were validated by ELISA and functional relevance was uncovered by bioinformatics analysis. To identify potential biomarkers of occurrence or development of COVID-19, patient sera from three different severity groups (moderate, severe, and critical) at three time points (admission, remission, and discharge) and the expression levels of candidate biomarkers were measured. Eleven differential factors associated with disease severity were pinpointed from 440 factors across 111 patients of differing disease severity. The dynamic changes of GDF15 reflect the progression of the disease, while the other differential factors include TRAIL R1, IGFBP-1, IGFBP-4, VCAM-1, sFRP-3, FABP2, Transferrin, GDF15, IL-1F7, IL-5Rα, and CD200. Elevation of white blood cell count, neutrophil count, neutrophil-lymphocyte ratio (NLR), Alanine aminotransferase and Aspartate aminotransferase, low lymphocyte and eosinophil counts in the severe group were associated with the severity of COVID-19. GDF15 levels were observed to be associated with the severity of COVID-19 and the dynamic change of GDF15 levels was closely associated with the COVID-19 disease progression. Therefore, GDF15 might serve as an indicator of disease severity in COVID-19 patients.

Water diversion project (WDP) plays an increasingly important role in improving the water quality of urban rivers along the Yangtze Delta megalopolis of China. However, their effects on the water ecological environment has generated extensive debates. Water quality variables and phytoplankton community dynamics were tracked and monitored during the summer–winter water diversions to explore the comprehensive effects of WDP on the urban aquatic ecological environment. The multi-index comprehensive evaluation model (MCEM) was adopted for further quantitative assessment, together with a newly proposed progressive degree calculation model (PDCM), which broke the limitations of the traditional ways using Markov chains. The grade evaluation was based on the Environmental Quality Standards for Surface Water (GB 3838–2002) in China. The comprehensive water quality evaluated by MCEM was improved with the largest improvement of two grades after WDP in summer, which on the contrary was worsened because of water transfers in winter by a maximum decrement of one grade. The PDCM evaluation on water quality index indicated that WDP has improved the water quality to some extent but without grade changes. Also, the proportion of Cyanobacteria and Chlorophyta decreased in the phytoplankton community of the three rivers after WDP, whereas the proportion of Bacillariophyta increased, indicating that the community structure was more inclined to the Yangtze River. The comprehensive water pollution situation evaluated by MCEM was improved with the largest improvement from middle pollution to lower pollution (one grade) because of water transfers. The changes in aquatic biological indexes evaluated by PDCM were more obvious than those of water quality indexes, which were improved by one grade.

The source regions of the Yangtze River (SRYZ) and the Yellow River (SRYR) are sensitive areas of global climate change. Hence, determining the variation characteristics of the runoff and the main influencing factors in this region would be of great significance. In this study, different methods were used to quantify the contributions of climate change and other environmental factors to the runoff variation in the two regions, and the similarities and differences in the driving mechanisms of runoff change in the two regions were explored further. First, the change characteristics of precipitation, potential evapotranspiration, and runoff were analyzed through the observational data of the basin. Then, considering the non-linearity and non-stationarity of the runoff series, a heuristic segmentation algorithm method was used to divide the entire study period into natural and impacted periods. Finally, the effects of climate change and other environmental factors on runoff variation in two regions were evaluated comprehensively using three methods, including the improved double mass curve (IDMC), the slope change ratio of cumulative quantity (SCRCQ), and the Budyko-based elasticity (BBE). Results indicated that the annual precipitation and potential evapotranspiration increased during the study period in the two regions. However, the runoff increased in the SRYZ and decreased in the SRYR. The intra-annual distribution of the runoff in the SRYZ was unimodal during the natural period and bimodal in the SRYR. The mutation test indicated that the change points of annual runoff series in the SRYZ and SRYR occurred in 2004 and 1989, respectively. The attribution analysis methods yielded similar results that climate change had the greatest effect on the runoff variation in the SRYZ, with a contribution of 59.6%~104.6%, and precipitation contributed 65.3%~109.6% of the increase in runoff. In contrast, the runoff variation in the SRYR was mainly controlled by other environmental factors such as permafrost degradation, land desertification, and human water consumption, which contributed 83.7%~96.5% of the decrease in the runoff. The results are meaningful for improving the efficiency of water resources utilization in the SRYZ and SRYR.

There is an important practical significance to understanding and evaluating the nexus of water, energy, and food (WEF) for ensuring regional security. The Yellow River Basin is a typical area displaying the contradiction between water, energy, and food development caused by the difference in resource distribution and production attributes. The nexus of the WEF system in the Yellow River Basin is systematically and innovatively studied from different perspectives. The aim of the research is to analyze the distribution and transfer direction of single resource elements, the matching and evolution of two kinds of resources linked by water resources, and finally the nexus based on Copula function. The results show that water resources, farmland, and energy production are concentrated in different areas, while the center of gravity shifted to the northwest, northeast, and west of the basin, respectively. For the resource balance of energy production and available industrial water resources, the matching degree has become worse in recent years. For the resource balance of farmland areas and available agricultural water resources, the matching degree in the most source area and middle reaches has improved, but the gap among different sub-regions has widened slightly. It is proven that the three-dimensional t Copula is well characterized by the nexus of the WEF system in the Yellow River Basin. The joint not exceeding the probability of the WEF (W ≤ 7.08 × 1010 m3, E ≤ 6.24 × 108 TCE, F ≤ 4.23 × 107 t) is about 0.3–0.4. The WEF system in the Yellow River Basin (Gn = 0.728 and 0.688) may still have certain security risks in the future compared with other regions in the world, which needs to be regulated by more reasonable policies. This study can provide a theoretical basis for ensuring regional water, energy, and food security.

Sweet cherry (Prunus avium L.), one of the most appreciated and most important commercial temperate fruits, has high sensory quality and nutritional value. Investigating its metabolic variations provides valuable information on the formation of fruit quality. In this study, widely targeted LC-MS/MS based metabolomics was used to identify and quantify metabolic changes during ‘Black Pearl’ sweet cherry development and ripening. A total of 263 significant differentially expressed metabolites (DEMs) were detected during the four fruit-development stages. Significant differences were observed in the composition and content of compounds in the four stages of cherry development, especially sugars, organic acids, and flavonoids. Moreover, transcriptome analysis provided a molecular basis for metabolic variations during fruit development. A total of 6724 significant differentially expressed genes (DEGs) were identified. Further correlation analysis of major DEMs and DEGs showed that 19 key DEGs were involved in sugar metabolism, 23 key DEGs in organic acid metabolism, and 13 key DEGs in flavonoid metabolism. The upregulated genes involved in the flavonoid pathway probably play an important role in regulating the rapid increase of anthocyanin content during fruit development. These comprehensive analysis data provide a better understanding to improve fruit quality traits based on molecular and metabolic levels.

Background. At present, with the continuous development of nanotechnology, great changes have taken place in people’s lives in medical treatment, production, daily leisure, and so on. Nanooptical technology is entirely based on nanotechnology that laser and visible light are limited to submicron structures (nanopores, nanoslits, and nanoneedles). Due to the great development potential of nanooptical technology in nanoscale sensors, TOF camera applications, THz imaging technology, and other imaging equipment materials and applications, people have been interested in it, recently. Scope and Approach. In this review, the importance of good practices for nanooptical technology used in equipment as both nanometer scale sensors and optical auxiliary equipment is described. Based on recent reports, this work discussed the development of nanooptical technology in daily photography and medical imaging from both the positive and the negative sides and compared the engineering techniques. Key Findings and Conclusions. As a kind of new optical technology, nanooptical technology can produce the plasmonic effect under the intense collision of atoms and electrons in nanostructures. It has significant effects in superresolution nanolithography, high-density data storage, near-field optics, and other fields. Although the current nanooptic technology is not extremely mature, the results obtained from current works are pointing out that nanooptical technology is the future of daily imaging and medical imaging, and it also will play a positive role in the improvement of people’s health and ecological environment quality. As a trend, nanooptical technology is developing in the direction of energy-saving, portability, high efficiency, and low pollution, and in the upsurge of environmental protection in the world, nanooptical technology will surely achieve amazing development in the field of daily photography and medical imaging. Under the huge market demand and innovation power, nanophotonics technology will cover all emerging technologies that share the same research field with it and take advantage of each technology (terahertz, cell and molecular microscopy, and nanoscale probes) to develop an unprecedented new century in nanoscience. The future trends of research contain finding new imaging equipment with nanostructure, designing nanooptical products, and improving engineering techniques.

As an important reinforcement for metal matrix composites, the microstructure and mechanical properties of W-core SiC filament have drawn increasing attentions among researchers. In this work, the growth behavior of polycrystalline 3C-SiC on W-wire substrate in the chemical vapor deposition (CVD) process and the evolution of mechanical properties in preparation of W-core SiC filament, were investigated as a function of gas-phase supersaturation. Kinetic studies revealed that the growth of 3C-SiC grains was limited by surface reactions at both 850 °C and 1050 °C, and the deposit experienced similar morphological changes from a porous structure to large clusters, with the increase in supersaturation. Structural analyses and mechanical tests show that the production of pores and the amorphous phase with a low supersaturation, of 9.6 × 107 at 850 °C, resulted in a reduction in the modulus and hardness of the polycrystalline deposits, to 270.3 GPa and 33.9 GPa, while the reduced structural defects (e.g., stacking faults and twins) in highly (111) orientated 3C-SiC grains, as well as the improved surface quality obtained with the medium supersaturation of 1.6 × 107 at 1050 °C, enhanced the tensile strength and the Weibull modulus of W-core SiC filament to 2.88 GPa and 11.2, respectively. During the growth of 3C-SiC grains, the variation in structural defects density is controlled by the critical nucleation energy of the two-dimensional (2D) nucleus.

The long-term overexploitation of groundwater has caused sharp decreases in groundwater table depth and water storage in the agricultural areas of the North China Plain, which has led to obvious changes in the runoff process of the hydrological cycle, affecting the mechanism of runoff generation. Evaluating the impact of groundwater overdraft on runoff generation using hydrological models is the focus of the current work. Herein, a hydrological modeling framework is proposed based on the Variable Infiltration Capacity (VIC) model. The optimal parameters of the VIC model were determined by the synergetic calibration method, combining runoff, evaporation, and water storage levels. Meanwhile, a sliding calibration scheme was employed to explore the implied relationships among runoff coefficient, groundwater exploitation, and model parameters, particularly for the thickness of the second soil layer (i.e., parameter d2), both for the whole period and the sliding window periods. Overall, the VIC model showed good applicability in the southern Haihe river plain, as demonstrated by the low absolute value of the relative error (RE) between the simulated and observed data for runoff and evaporation, with all REs < 8%, as well as large correlation coefficients (CC, all > 0.8). In addition, the CCs between the simulated and the observed data for water storage were all above 0.7. The calibrated optimal parameter d2 increased as the sliding window period increased, and the average d2 gradually increased from 0.372 m to 0.415 m, for which we also found high correlations with both the groundwater table and water storage levels. Additionally, increases in the parameter d2 led to decreases in the runoff coefficient. From 2003 to 2016, the parameter d2 increased from 0.36 m to 0.42 m, and the runoff coefficient decreased by about 0.02.

Modern smart agriculture initiative presents more requests for soil moisture (SM) monitoring over large agricultural areas. Remote sensing techniques facilitate high-resolution surface SM (SSM) estimation at a large scale but lack root zone SM (RZSM) information. Establishing the deduction method of RZSM from the SSM has long been the focus of most attention. Data assimilation methods are promising techniques for RZSM estimation, developing numerous assimilated reanalysis datasets, e.g., ERA5 and the latest Soil Moisture Active and Passive (SMAP) L4 SM product. However, data latency and large computation during data collecting and processing often inhibits further applications. This work proposes a rapid estimation scheme for estimating RZSM with short latency and small computations, based on the Exponential Filter (EF) method. The EF model with single parameter T was firstly calibrated and validated using the SSM and RZSM of ERA5 reanalysis dataset, obtaining the optimum parameter T map for each grid. Then, the fast-updating SMAP L3 SSM product together with the scale-matched optimum T were adopted as inputs into the EF model to retrieve RZSM estimation of each grid. Specifically, such estimation scheme was tested over the central and eastern agricultural areas of China, using a dense monitoring network of 796 SM observation sites, which contains various land uses, as well as meteorological and hydrological conditions. The calibrated optimum parameter T presented an increasing trend with good physical explanations. Furthermore, all the estimated RZSMs were found to have good performances on capturing the temporal-spatial variations of RZSM and well reflecting seasonal RZSM changes. Overall, such an estimation scheme was proven to be a desirable alternative for estimating RZSM over large agricultural areas.

In-depth understanding and accurate simulation of hydrological processes are of great significance for sustainable development and management of water resources. The study focused on a semi-arid catchment, the upper Tang River catchment in northern China, and investigated the performance of the RCCC-WBM model based on the detection results of trend, mutation, and periodicity. Results show that (1) as a result of climate change and intensive human activities, the observed runoff series after TFPW (trend-free pre-whiting) pretreatment presented a significant downward trend with the mutation point in 1996; (2) the abrupt change of air temperature series was also in 1996 with a significant rising trend, while the annual precipitation series exhibited an insignificant declining trend with no obvious mutation during 1973–2014; (3) the precipitation and runoff series had periodic variations roughly 7a multiples with the periodic oscillation strongest around 14a, while the air temperature series showed only one dominant period of 28a; (4) the RCCC-WBM model performed well in discharge simulation before the mutation year but gradually lost its stability after 1996, which was mainly affected by anthropogenic activities. It is essential to accurately identify the characteristics of hydrological elements and improve the applicability of hydrological models in the changing environment in future studies.

Early identification of the causes of cholestasis is important for appropriate management of patients with hyperthyroidism. We report a patient who had hyperthyroidism and severe cholestasis after methimazole (MMI) treatment. The patient was diagnosed as having MMI-induced cholestatic hepatitis. Treatment with MMI was stopped at admission to hospital. However, his serum total bilirubin (TBil) level rose from 410.5 µmol/L to 519.9 µmol/L and prothrombin time activity (PTA) dropped from 81.0% to 52.2% in 10 days. To prevent further deterioration of his liver function, plasma exchange was performed three times, and dexamethasone (10 mg, intravenously) was used each time. His PTA rose to 101% and his TBil continued to increase to 669.8 µmol/L after plasma exchange. He was subsequently diagnosed as having thyrotoxicosis-induced cholestasis and treated with radioactive iodine (380 MBq) 2 weeks after admission. His hyperthyroidism was significantly relieved, but the TBil level further increased to 776.8 µmol/L. Three weeks after admission, oral prednisone (30 mg/day) was used in this patient. Subsequently, his TBil levels gradually decreased and his liver function almost normalized within 3 months. We discuss the literature on cholestasis in the context of hyperthyroidism.

In the past decade, numerous studies have evaluated the roles of noninvasive methods for diagnosing or excluding varices and high-risk varices in patients with liver cirrhosis. The Baveno VI criteria recommend the use of a simple algorithm based on a liver stiffness measurement150×109/L for ruling out high-risk varices in patients with compensated advanced chronic liver disease. A large number of studies have validated the clinical usefulness of Baveno VI criteria for excluding high-risk varices. Several strategies have been proposed to refine the Baveno VI criteria; however, currently there is no review to summarize the diagnostic accuracy and limitations of the Baveno VI criteria after extensive validation. In this review, we summarize the diagnostic accuracy and limitations of the Baveno VI criteria after extensive validation. We also discuss methods to refine these criteria.

Abstract Background Radical surgery for colorectal cancer, associated with moderate to severe postoperative pain, needs multimodal analgesia with opioid for analgesia. Despite considerable advancements, the psychological implications and other side effects with opioid remain substantially unresolved. This study aimed to investigate the impact on mood, side effects relative to opioid, and recovery of the patients with hydromorphone or sufentanil intravenous patient-controlled analgesia (IV-PCA) in a multimodal perioperative analgesia regimen undergoing radical surgery for colorectal cancer. Methods Eighty patients undergoing elective laparoscopic or open radical surgery for colorectal cancer under general anesthesia were randomized to receive postoperative IV-PCA with either sufentanil (group S) or hydromorphone (group H). All patients received additionally flurbiprofen axetil 50 mg 30 min before the end of surgery and wound infiltration with 10 ml of 0.75% ropivacaine at the end of surgery. The primary endpoint was mood changes at 48 and 96 h after surgery. The secondary endpoints were the incidence of opioid-related adverse effects, recovery results and patient satisfaction after surgery. Results Seventy-two patients completed the study finally. There were no significant differences between the two groups with respect to preoperative parameters, surgical and anesthetic characteristics (P > 0.05). No obvious significant differences were observed in VAS score (at rest and during mobilization) and rescue analgesics use (P > 0.05). Compared with group S, the anger scores in the group H at 48 h and 96 h after surgery were significantly lower (P = 0.012 and 0.005; respectively), but the incidences of pruritus and nausea were higher (P = 0.028 and 0.008; respectively). There were no significant differences in the incidences of vomiting, respiratory depression, dizziness, Ramsay score, and hemodynamic changes between the two groups (P > 0.05). Moreover, there were no significant differences in the time to gastrointestinal recovery, time to drainage tube removal, time to walk, hospital stay after surgery and patient satisfaction between the two groups (P > 0.05). Conclusions Under the similar analgesia effect with different opoiods postoperatively, hydromorphone IV-PCA resulted in an improved mood, however, a higher occurrence of pruritus and nausea while compared to sufentanil IV-PCA in a multimodal perioperative analgesia regimen. Both regimens of opioid with IV-PCA may serve as promising candidates for good postoperative pain management, and provide with similar postoperative recovery for the patients undergoing radical surgery for colorectal cancer. Trial registration This study was registered with the Chinese Clinical Trial Registry on September 20, 2015 (URL: http://www.chictr.org.cn. Registry number: ChiCTR-IPR-15007112).

The requirement for 7085 Al alloy as large airframe parts has been increasing due to its low quenching sensitivity and high strength. However, the relationship between high temperature mechanical properties and the evolution of precipitates in hot environments is still unclear. In this work, thermal exposure followed by tensile tests were conducted on the 7085 Al alloy at various temperatures (100 °C, 125 °C, 150 °C and 175 °C). Variations of hardness, electrical conductivity and tensile properties were investigated. The evolution of the nano scale precipitates was also quantitatively characterized by transmission electron microscopy (TEM). The results show that the hardness and electrical conductivity of the alloy are more sensitive to the temperature than to the time. The strength decreases continuously with the increase of temperature due to the transformation from η′ to η phase during the process. Furthermore, the main η phase in the alloy transformed from V3 and V4 to V1 and V2 variants when the temperature was 125 °C. Additionally, with increasing the temperature, the average precipitate radius increased, meanwhile the volume fraction and number density of the precipitates decreased. The strengthening effect of nano scale precipitates on tensile properties of the alloy was calculated and analyzed.

The NLRP3 inflammasome is key to the regulation of innate immunity against pathogens or stress, but the underlying signaling regulation is still unclear. Here the authors show that chloride intracellular channels (CLIC) interface between mitochondria stress and inflammasome activation to modulate inflammatory responses.

The structural, electronic bonding characteristics, mechanical and thermal properties of Ti3AC2 (ASi, Ge and Sn) compounds under pressure from 0 GPa to 60 GPa are investigated by first-principles calculations. Whether under pressure or not, each compound reflects anisotropic elastic properties and their calculated bulk, shear and Young’s moduli are consistent with those determined theoretically and experimentally records. The quasi-harmonic Debye model is introduced in this work to investigate thermal properties of all compounds. Thermal expansion coefficient α of each compound under pressure is firstly discussed in the present paper. It is obviously that α decreases with the pressure increase and Ti3SnC2 shows the highest thermal expansion under pressure. The Debye temperature, CV and Grüneisen parameters are both obtained and the agreement between our calculations and other references are pretty good. Keywords: Ternary carbides, First-principles calculations, Electronic properties, Thermal properties