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This research presents an improved calculation method for grid power losses, particularly focusing on the challenges posed by new energy access models. With the integration of electric vehicles and the rise of data centers, the demand for electrical energy has surged, leading to increased strain on grid stations and subsequent power losses. The proposed model aimed at reducing these power losses, while also examining existing systems to mitigate and analyze such issues. A significant contribution of this work is the application of the Random Forest machine learning algorithm, which enables efficient and accurate power flow calculations essential for optimizing grid performance. The proposed method is expected to enhance the grid’s ability to handle future energy demands and contribute to the sustainable development of electrical energy systems.

BackgroundMetabolically Associated Fatty Liver Disease (MAFLD) marks a progression from the previous paradigm of Non-Alcoholic Fatty Liver Disease (NAFLD), presenting a redefined diagnostic framework that accentuates metabolic factors while recognizing non-alcoholic contributors. In our investigation, our principal aim was to scrutinize the conceivable correlation between diverse serum folate levels and the prevalence of MAFLD and liver fibrosis.MethodsIn our investigation, we conducted an extensive analysis utilizing data derived from the National Health and Nutrition Examination Survey (NHANES) across the years 2017–2020. We aimed to investigate the association between different serum folate concentrations and the prevalence of MAFLD and liver fibrosis by comprehensive multivariate analysis. This analytical approach considered various variables, encompassing sociodemographic characteristics, lifestyle factors, hypertension, and diabetes. By including these potential confounders in our analysis, we aimed to ensure the stability of the findings regarding the association between different serum folate concentrations and the development of MAFLD and liver fibrosis.ResultsIn our investigation, we utilized multiple linear regression models to thoroughly analyze the data, revealing noteworthy insights. Evidently, elevated levels of both total folate and 5-MTHF exhibited a distinct negative correlation with CAP, while 5-MTHF demonstrated a notable negative correlation with LSM. Furthermore, multiple logistic regression models were employed for an in-depth examination of the data. As the concentrations of total folate and 5-MTHF in the serum increased, a substantial decrease in the likelihood of MAFLD and liver fibrosis occurrence was observed.ConclusionThe findings of this investigation robustly suggest the prevalence of MAFLD and liver fibrosis decreased significantly with the increase of serum concentrations of total folate and 5-MTHF.

In order to solve the problem of inaccurate modeling of fault subspace, traditional fault subspace modeling method did not consider the fact that fault data contain both normal and fault condition information, or did not consider the dynamic factors in the fault data, these flaws may lead to the case that the fault subspace cannot be extracted accurately, a dynamic generalized principal component analysis (DGPCA) method was proposed.By reorganizing the lagged input data, the dynamic characteristics between normal and fault data were extracted by the proposed DGPCA method, and then the fault subspaces could be modeled for further fault diagnosis.Finally, simulation results confirm the availability of the proposed method for fault subspace modeling and fault diagnosis.

COBRA-Like (COBL) genes encode a glycosylphosphatidylinositol (GPI) anchoring protein unique to plants. In current study, 87 COBRA genes were identified in 6 Rosaceae species, including Pyrus bretschneideri (16 genes), Malus domestica (22 genes), Fragaria vesca (13 genes), Prunus mume (11 genes), Rubus occidentalis (13 genes) and Prunus avium (12 genes). We revealed the evolution of the COBRA gene in six Rosaceae species by phylogeny, gene structure, conservative sequence, hydrophobicity analysis, gene replication events and sliding window analysis. In addition, based on the analysis of expression patterns in pear fruit combined with bioinformatics, we identified PbCOBL12 and PbCOBL13 as potential genes regulating secondary cell wall (SCW) formation during pear stone cell development. This study aimed to understand the evolutionary relationship of the COBRA gene in Rosaceae species, clarify the potential function of COBRA in pear fruit development, and provide essential theoretical basis and gene resources for improving pear fruit quality through genetical modification mechanism.

GRAS is a transcription regulator factor, which plays an important role in plant growth and development. Previous analyses found that several GRAS functions have been identified, such as axillary bud meristem formation, radial root elongation, gibberellin signaling, light signaling, and abiotic stress. The GRAS family has been comprehensively evaluated in several species. However, little finding is on the GRAS transcription factors (TFs) in Chinese white pear. In this study, 99 PbGRAS were systemically characterized and renamed PbGRAS1 to PbGRAS99 according to their chromosomal localizations. Phylogenetic analysis and structural features revealed that could be classified into eight subfamilies (LISCL, Ls, SHR, HAM, SCL, PAT, SCR, and DELLA). Further analysis of introns/exons and conserved motifs revealed that they are diverse and functionally differentiated in number and structure. Synteny analysis among Pyrus bretschenedri, Prunus mume, Prunus avium, Fragaria vesca, and Prunus persica showed that GRAS duplicated regions were more conserved. Dispersed duplication events are the most common mechanism and may play a crucial role in the expansion of the GRAS gene family. In addition, cis-acting elements of the PbGRAS gene were found in promoter regions associated with hormone and environmental stress responses. Notably, the expression pattern detected by qRT-PCR indicated that PbGRAS genes were differentially expressed under gibberellin (GA), abscisic acid (ABA), and auxin (IAA) conditions, which are responsive to abiotic stress. PbGRAS89 and PbGRAS99 were highly expressed at different stages of hormone treatment and may play important role in leaf development. Therefore, we selected PbGRAS89 and PbGRAS99 to clone and construct pCAMBIA1301-PbGRAS89, 99 and transferred them into Arabidopsis thaliana. Finally, we observed and compared the changes of overexpressed plants and wild-type plants during regeneration. This method was used to analyze their roles in leaf regeneration of Chinese white pear. In addition, we also constructed pCAMBIA1305-PbGRAS89, 99, and transferred them into onion cells to determine the subcellular localization. Subcellular localization experiments showed that PbGRAS89 and PbGRAS99 were localized in the nucleus. In summary, the results of this study indicate that PbGRAS89 and PbGRAS99 are mainly responsible for leaf regeneration of Chinese white pear, which plays a positive role in callus formation and provides rich resources for studying GRAS gene functions.

Electricity theft has been a major concern to the secure operation of power systems and the interests of power companies. Due to the different methods and types of electricity theft behaviors, it is difficult to determine the suspicion levels of consumers in the research of electricity theft detection. An electricity theft detection method based on stacked autoencoder (SAE) and the undersampling and re-sampling based random forest (UaRe-RF) algorithm is proposed in this work to formulate appropriate strategies for the practical electricity theft detection requirements of the power company. In the proposed method, the supervised SAE is first trained to extract electricity consumption features that are more adaptable to the classification algorithm for electricity theft detection. Then, the UaRe-RF algorithm is used to establish the class-balanced subsets and determine the suspicion level of each electricity theft user. Finally, two cases of different datasets of electricity consumers are studied for demonstrating the effectiveness of the proposed method, and the results show that higher classification accuracy and more targeted detection strategies can be achieved through the proposed method.

The local environment for carbon dioxide electrochemical reduction is a key element to improve the efficiency of catalytic sites. Here, the authors demonstrate substantial gain of the catalyst electrochemical activity through the adoption of a hydrophobic catalyst microenvironment.

BGLU β-glucosidases in glycoside hydrolase family 1 (GH1) are involved in many processes of plant secondary metabolism. In particular, its de-glycosylation function plays an important role in the transport of lignin monolignols. No comprehensive study of the BGLU family in Chinese pear (Pyrus bretschneideri Rehd.) has been reported yet. In this study, the 50 BGLU family members from Chinese white pear were identified. Three candidate genes, PbBGLU1, PbBGLU15, and PbBGLU16, that may be involved in lignin synthesis were screened by bioinformatics analysis and qRT-PCR. Subcellular localization showed that all three of these candidate genes were expressed in the extracellular region. Then, we analyzed the functions of PbBGLU1 and PbBGLU16. In situ hybridization analysis showed that PbBGLU1 transcripts were not only localized to some pulp cell walls, lignin deposition, and stone cell areas of a pear fruit, but that was also a small amount of enrichment in normal pear flesh cells. PbBGLU16 transcripts were only enriched in lignin deposition and stone cell areas of pear fruit. Enzyme activity analysis revealed that GST-PbBGLU1 and GST-PbBGLU16 had a stronger activity and higher catalytic efficiency for coniferin than syringin. In addition, GST-PbBGLU16 exhibited the higher activity and catalytic efficiency for the two substrates compared with GST-PbBGLU1. The transformation of PbBGLU1 and PbBGLU16 into Arabidopsis identified that the lignin contents of Arabidopsis BGLU-45 mutant, PbBGLU1-RE, and PbBGLU16-RE were not changed than that of wild-type. However, compared with wild-type Arabidopsis, the overexpression of the plant’s lignin increased in varying degrees. The effect of PbBGLU16 on the lignin increment was greater than that of PbBGLU1 in Arabidopsis. In pear fruits, with transient overexpression of PbBGLU1, the contents of lignin and stone cells were significantly higher (0.01 < P < 0.05) than those with empty vector injection pear fruits. After transient expression of PbBGLU16, lignin in pear fruit increased significantly (0.01 < P < 0.05) and stone cells showed a very significant difference (P < 0.01) compared with the control group. However, RNA interference silenced these two genes in pear fruit, which seemed to have no impression on lignin and stone cells. This study provides a molecular biological basis for improving pear fruit quality at the molecular level.

Cystathionine γ-synthase (CGS), S-adenosyl-L-homocysteine hydrolase (SAHH), and S-adenosy-L-methionine synthetase (SAMS) play an important role in the regulation of plant growth, development, and secondary metabolism. In this study, a total of 6 CGS, 6 SAHH, and 28 SAMS genes were identified from five Rosaceae species (Pyrus bretschneideri, Prunus persica, Prunus mume, Fragaria vesca, and Malus domestica). The evolutionary relationship and microsynteny analysis in five Rosaceae species revealed that duplicated regions were conserved between three gene families (CGS, SAHH, SAMS). Moreover, the chromosomal locations, gene structures, conserved motifs, cis-elements, physicochemical properties, and Ka/Ks analysis were performed by using numerous bioinformatics tools. The expression of different organs showed that the CGS, SAHH and SAMS genes of pear have relatively high expression patterns in flowers and stems, except for PbCGS1. RNA-seq and qRT-PCR combined analysis showed that PbSAMS1 may be involved in the regulation of pear stone cell development. In summary, this study provides the basic information of CGS, SAHH and SAMS genes in five Rosaceae species, further revealing the expression patterns in the pear fruit, which provides the theoretical basis for the regulation of pear stone cells.

Electricity theft behavior has serious influence on the normal operation of power grid and the economic benefits of power enterprises. Intelligent anti-power-theft algorithm is required for monitoring the power consumption data to recognize electricity power theft. In this paper, an adaptive time-series recurrent neural network (TSRNN) architecture was built up to detect the abnormal users (i.e., the electricity theft users) in time-series data of the power consumption. In fusion with the synthetic minority oversampling technique (SMOTE) algorithm, a batch of virtual abnormal observations were generated as the implementation for training the TSRNN model. The power consumption record was characterized with the sharp data (ARP), the peak data (PEA), and the shoulder data (SHO). In the TSRNN architectural framework, a basic network unit was formed with three input nodes linked to one hidden neuron for extracting data features from the three characteristic variables. For time-series analysis, the TSRNN structure was re-formed by circulating the basic unit. Each hidden node was designed receiving data from both the current input neurons and the time-former neuron, thus to form a combination of network linking weights for adaptive tuning. The optimization of the TSRNN model is to automatically search for the most suitable values of these linking weights driven by the collected and simulated data. The TSRNN model was trained and optimized with a high discriminant accuracy of 95.1%, and evaluated to have 89.3% accuracy. Finally, the optimized TSRNN model was used to predict the 47 real abnormal samples, resulting in having only three samples false predicted. These experimental results indicated that the proposed adaptive TSRNN architecture combined with SMOTE is feasible to identify the abnormal electricity theft behavior. It is prospective to be applied to online monitoring of distributed analysis of large-scale electricity power consumption data.

Electric vehicles have the advantages of zero emissions and high energy efficiency. They have a broad potential in today’s social life, especially in China where they have been widely used. In the current situation, whereby the storage capacity of electric vehicles is continually increasing and the requirements for grid stability are getting higher and higher, V2G technology emerges to keep up with the times. Since the electric vehicle charging station is a large-scale electric vehicle cluster charging terminal, it is necessary to pay attention to the status and controllability of each charging pile. In view of the lack of attention to the actual operation of the electric vehicle charging station in the existing vehicle–network interaction mode, the charging state of the current electric vehicle charging station is fixed. In this paper, deep learning is used to establish a load perception model for electric vehicle charging stations, and K-means clustering is used to optimize the load perception model to realize random load perception and non-intrusive load monitoring stations for electric vehicle charging. The calculation example results show that the proposed method has good performance in the load perception and controllability evaluation of electric vehicle charging stations, and it provides a feasible solution for the practical realization of electric vehicle auxiliary response.

Glycosylation is necessary for many processes of plant secondary metabolism. It can maintain plant homeostasis and is of great significance to normal plant growth and development. At present, the significance of glycosylation for lignin biosynthesis has been proven in some plants, but it has not yet been reported in pears. We used in situ hybridization, in vitro expression, substrate catalysis, transgenic Arabidopsisthaliana, and transient transformation of pear fruit in our investigation, which was predicated on the identification of a gene PbUGT72AJ2 that may be involved in lignin monolignol glycosylation according to our previous work. These results revealed that PbUGT72AJ2 transcripts were localized to some pulp cell walls, lignin deposition, and stone cell areas of pear fruit. The recombinant PbUGT72AJ2-pGEX4T-1 protein had activity against coniferyl alcohol and sinapyl alcohol, and its catalytic efficiency against coniferyl alcohol was higher than that against sinapyl alcohol. When PbUGT72AJ2 was transferred into Arabidopsisthaliana mutants, it was found that some characteristics of Arabidopsisthalianaugt72e3 mutants were restored. In Arabidopsisthaliana, overexpression of PbUGT72AJ2 enhanced the contents of coniferin and syringin, whereas lignification did not change significantly. Transient transformation of pear fruit showed that when PbUGT72AJ2 in pear fruit was silenced by RNA interference, the content of lignin and stone cells in pear fruit increased, whereas the gene PbUGT72AJ2 was overexpressed in pear fruit, and there was almost no change in the pear fruit compared with the control. Lignin deposition in pear fruit was closely related to stone cell development. In this study, we proved that PbUGT72AJ2 plays an important role in lignin deposition and stone cell development in pear fruit, which provides a molecular biological basis for improving pear fruit quality at the molecular level.

The SAUR (small auxin-up RNA) gene family is the biggest family of early auxin response genes in higher plants and has been associated with the control of a variety of biological processes. Although SAUR genes had been identified in several genomes, no systematic analysis of the SAUR gene family has been reported in Chinese white pear. In this study, comparative and systematic genomic analysis has been performed in the SAUR gene family and identified a total of 116 genes from the Chinese white pear. A phylogeny analysis revealed that the SAUR family could be classified into four groups. Further analysis of gene structure (introns/exons) and conserved motifs showed that they are diverse functions and SAUR-specific domains. The most frequent mechanisms are whole-genome duplication (WGD) and dispersed duplication (DSD), both of which may be important in the growth of the SAUR gene family in Chinese white pear. Moreover, cis-acting elements of the PbrSAUR genes were found in promoter regions associated with the auxin-responsive elements that existed in most of the upstream sequences. Remarkably, the qRT-PCR and transcriptomic data indicated that PbrSAUR13 and PbrSAUR52 were significantly expressed in fruit ripening. Subsequently, subcellular localization experiments revealed that PbrSAUR13 and PbrSAUR52 were localized in the nucleus. Moreover, PbrSAUR13 and PbrSAUR52 were screened for functional verification, and Dangshan pear and frandi strawberry were transiently transformed. Finally, the effects of these two genes on stone cells and lignin were analyzed by phloroglucinol staining, Fourier infrared spectroscopy, and qRT-PCR. It was found that PbrSAUR13 promoted the synthesis and accumulation of stone cells and lignin, PbrSAUR52 inhibited the synthesis and accumulation of stone cells and lignin. In conclusion, these results indicate that PbrSAUR13 and PbrSAUR52 are predominantly responsible for lignin inhibit synthesis, which provides a basic mechanism for further study of PbrSAUR gene functions.

Aim. To identify lesional and nonlesional tissues from early gastric cancer (EGC) patients by Raman spectroscopy to build a diagnostic model and effectively diagnose EGC. Method. Specimens were collected by endoscopic submucosal dissection from 13 patients with EGC, and 55 sets of standard Raman spectral data (each integrated 10 times) were obtained using the fiber optic Raman system; there were 33 sets of lesional tissue data, including 18 sets of high-grade intraepithelial neoplasia (HGIN) data and 15 sets of adenocarcinoma data, and 22 sets of nonlesional tissue data. After the preprocessing steps, the average Raman spectrum was obtained. Results. The nonlesional tissues showed peaks at 891 cm-1, 1103 cm-1, 1417 cm-1, 1206 cm-1, 1234 cm-1, 1479 cm-1, 1560 cm-1, and 1678 cm-1. Compared with the peaks corresponding to nonlesional tissues, the peaks of the lesional tissues shifted by different magnitudes, and a new characteristic peak at 1324 cm-1 was observed. Comparing the peak intensity ratio and the integral energy ratio of the lesional tissues with those of the nonlesional tissues revealed a significant difference between the two groups (independent-samplest-test, P

The Haber-Bosch process, producing NH3 from N2, is a crucial yet energetically demanding reaction, inspiring interest in the exploration of ambient-condition alternatives. Here, authors develop a palladium electrocatalyst that shows a high selectivity and activity for N2 reduction to NH3.

Manipulating cell cycle is one of the common strategies used by viruses to generate favorable cellular environment to facilitate viral replication. Coxsackievirus B (CVB) is one of the major viral pathogens of human myocarditis and cardiomyopathy. Because of its small genome, CVB depends on cellular machineries for productive replication. However, how the structural and non-structural components of CVB would manipulate cell cycle is not clearly understood. In this study, we demonstrated that the capsid protein VP1 of CVB type 3 (CVB3) induced cell cycle arrest at G1 phase. G1 arrest was the result of the decrease level of cyclin E and the accumulation of p27Kip1. Study on the gene expression profile of the cells expressing VP1 showed that the expression of both heat shock protein 70-1 (Hsp70-1) and Hsp70-2 was significantly up-regulated. Knockdown of Hsp70 resulted in the increased level of cyclin E and the reduction of p27Kip1. We further demonstrated that the phosphorylation of the heat shock factor 1, which directly promotes the expression of Hsp70, was also increased in the cell expressing VP1. Moreover, we show that CVB3 infection also induced G1 arrest, likely due to dysregulating Hsp70, cyclin E, and p27, while knockdown of Hsp70 dramatically inhibited viral replication. Cell cycle arrest at G1 phase facilitated CVB3 infection, since viral replication in the cells synchronized at G1 phase dramatically increased. Taken together, this study demonstrates that the VP1 of CVB3 induces cell cycle arrest at G1 phase through up-regulating Hsp70. Our findings suggest that the capsid protein VP1 of CVB is capable of manipulating cellular activities during viral infection.

Electricity theft decreases electricity revenues and brings risks to power usage’s safety, which has been increasingly challenging nowadays. As the mainstream in the relevant studies, the state-of-the-art data-driven approaches mainly detect electricity theft events from the perspective of the correlations between different daily or weekly loads, which is relatively inadequate to extract features from hours or more of fine-grained temporal data. In view of the above deficiencies, we propose a novel electricity theft detection scheme based on text convolutional neural networks (TextCNN). Specifically, we convert electricity consumption measurements over a horizon of interest into a two-dimensional time-series containing the intraday electricity features. Based on the data structure, the proposed method can accurately capture various periodical features of electricity consumption. Moreover, a data augmentation method is proposed to cope with the imbalance of electricity theft data. Extensive experimental results based on realistic Chinese and Irish datasets indicate that the proposed model achieves a better performance compared with other existing methods.

Human hepatitis B virus (HBV) infection and HBV-related diseases remain a major public health problem. Individuals coinfected with its satellite hepatitis D virus (HDV) have more severe disease. Cellular entry of both viruses is mediated by HBV envelope proteins. The pre-S1 domain of the large envelope protein is a key determinant for receptor(s) binding. However, the identity of the receptor(s) is unknown. Here, by using near zero distance photo-cross-linking and tandem affinity purification, we revealed that the receptor-binding region of pre-S1 specifically interacts with sodium taurocholate cotransporting polypeptide (NTCP), a multiple transmembrane transporter predominantly expressed in the liver. Silencing NTCP inhibited HBV and HDV infection, while exogenous NTCP expression rendered nonsusceptible hepatocarcinoma cells susceptible to these viral infections. Moreover, replacing amino acids 157–165 of nonfunctional monkey NTCP with the human counterpart conferred its ability in supporting both viral infections. Our results demonstrate that NTCP is a functional receptor for HBV and HDV.