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Abstract Acute kidney injury (AKI) is a serious disorder associated with significant morbidity and mortality. AKI and ischemia/reperfusion (hypoxia/reoxygenation, H/R) injury can be induced due to several reasons. Paeoniflorin (PF) is a traditional herbal medicine derived from Paeonia lactiflora Pall. It exerts diverse therapeutic effects, including anti-inflammatory, antioxidative, antiapoptotic, and immunomodulatory properties; thus, it is considered valuable for treating several diseases. However, the effects of PF on H/R injury-induced AKI remain unknown. In this study, we established an in vitro H/R model using COCL2 and investigated the functions and underlying mechanisms of PF on H/R injury in HK-2 cells. The cell vitality was evaluated using the cell count kit-8 assay. The DCFH-DA fluorescence probe was used to measure the levels of reactive oxygen species (ROS). Oxidative damage was detected using superoxide dismutase (SOD) and malondialdehyde (MDA) assay kits. Apoptotic relative protein and Keap1/Nrf2/HO-1 signaling were evaluated by Western blotting. Our results indicated that PF increased cell viability and SOD activity and decreased the ROS and MDA levels in HK-2 cells with H/R injury. PF inhibits apoptosis by increasing Bcl-2 and decreasing Bax. Furthermore, PF significantly upregulated the expression of HO-1 and Nrf2, but downregulated the expression of HIF-1α and Keap1. PF considerably increased Nrf2 nuclear translocation and unregulated the HO-1 expression. The Nrf2 inhibitor (ML385) could reverse the abovementioned protective effects of PF, suggesting that Nrf2 can be a critical target of PF. To conclude, we found that PF attenuates H/R injury-induced AKI by decreasing the oxidative damage via the Nrf2/HO-1 pathway and inhibiting apoptosis.

Insulator faults are an important factor in causing outages and accidents in power transmission lines. In response to problems related to inefficient insulator positioning, limited robustness of insulator defect feature extraction methods, and the scarcity of defective insulator samples leading to poor classifier generalization, a method for insulator defect detection and recognition based on vision big-model transfer learning and a stochastic configuration network (SCN) is proposed. First, data augmentation methods, such as Mosaic and Mixup, are employed to mitigate overfitting in the YOLOv7 network. Second, StyleGanv3 adversarial generative networks are used to augment the dataset of defective insulators, which enhances dataset diversity. Third, a vision big-model transfer learning method based on DINOv2 is introduced to extract features from insulator images. Finally, an SCN classifier is used to determine the status of insulators. Experimental results demonstrate that the applied data augmentation methods effectively mitigate overfitting. YOLOv7 accurately detects insulator positions, and the use of the DINOv2 feature extraction method increases the accuracy of insulator defect recognition by 28.6%. Compared with machine learning classification methods, the SCN classifier achieves the highest accuracy improvement of 17.4%. The proposed method effectively detects insulator positions and recognizes insulator defects.

Abstract Background Aggregation of α-synuclein (α-syn) is a prominent feature of Parkinson’s disease (PD) and other synucleinopathies. Currently, α-syn seed amplification assays (SAAs) using cerebrospinal fluid (CSF) represent the most promising diagnostic tools for synucleinopathies. However, CSF itself contains several compounds that can modulate the aggregation of α-syn in a patient-dependent manner, potentially undermining unoptimized α-syn SAAs and preventing seed quantification. Methods In this study, we characterized the inhibitory effect of CSF milieu on detection of α-syn aggregates by means of CSF fractionation, mass spectrometry, immunoassays, transmission electron microscopy, solution nuclear magnetic resonance spectroscopy, a highly accurate and standardized diagnostic SAA, and different in vitro aggregation conditions to evaluate spontaneous aggregation of α-syn. Results We found the high-molecular weight fraction of CSF (> 100,000 Da) to be highly inhibitory on α-syn aggregation and identified lipoproteins to be the main drivers of this effect. Direct interaction between lipoproteins and monomeric α-syn was not detected by solution nuclear magnetic resonance spectroscopy, on the other hand we observed lipoprotein-α-syn complexes by transmission electron microscopy. These observations are compatible with hypothesizing an interaction between lipoproteins and oligomeric/proto-fibrillary α-syn intermediates. We observed significantly slower amplification of α-syn seeds in PD CSF when lipoproteins were added to the reaction mix of diagnostic SAA. Additionally, we observed a decreased inhibition capacity of CSF on α-syn aggregation after immunodepleting ApoA1 and ApoE. Finally, we observed that CSF ApoA1 and ApoE levels significantly correlated with SAA kinetic parameters in n = 31 SAA-negative control CSF samples spiked with preformed α-syn aggregates. Conclusions Our results describe a novel interaction between lipoproteins and α-syn aggregates that inhibits the formation of α-syn fibrils and could have relevant implications. Indeed, the donor-specific inhibition of CSF on α-syn aggregation explains the lack of quantitative results from analysis of SAA-derived kinetic parameters to date. Furthermore, our data show that lipoproteins are the main inhibitory components of CSF, suggesting that lipoprotein concentration measurements could be incorporated into data analysis models to eliminate the confounding effects of CSF milieu on α-syn quantification efforts.

Ultra-short-term power load forecasting (USTPLF) can provide strong support and guarantee the decisions on unit start-up, shutdown, and power adjustment. The ultra-short-term power load (USTPL) has strong non-smoothness and nonlinearity, and the time-series characteristics of the load data themselves are difficult to explore. Therefore, to fully exploit the intrinsic features of the USTPL, a stochastic configuration networks (SCNs) USTPLF method based on K-means clustering (K-means) and empirical mode decomposition (EMD) is proposed. First, the load data are decomposed into several intrinsic mode functions (i.e., IMFs) and residuals (i.e., Res) by EMD. Second, the IMFs are classified by K-means, and the IMF components of the same class are summed. Third, the SCNs is used to forecast the electric load on the basis of the classified data. Lastly, on the basis of the real load of Shenzhen City, the proposed method is applied for emulation authentication. The result verifies the efficiency of the proposed measure.

Abstract The recently introduced 5G New Radio is the first wireless standard natively designed to support critical and massive machine type communications (MTC). However, it is already becoming evident that some of the more demanding requirements for MTC cannot be fully supported by 5G networks. Alongside, emerging use cases and applications towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general and MTC in particular. Next generation wireless networks, namely 6G, should therefore be an agile and efficient convergent network designed to meet the diverse and challenging requirements anticipated by 2030. This paper explores the main drivers and requirements of MTC towards 6G, and discusses a wide variety of enabling technologies. More specifically, we first explore the emerging key performance indicators for MTC in 6G. Thereafter, we present a vision for an MTC-optimized holistic end-to-end network architecture. Finally, key enablers towards (1) ultra-low power MTC, (2) massively scalable global connectivity, (3) critical and dependable MTC, and (4) security and privacy preserving schemes for MTC are detailed. Our main objective is to present a set of research directions considering different aspects for an MTC-optimized 6G network in the 2030-era.

The internal parameters and topology of the power converter are unknown in some practical cases. Existing modeling methods based on impedance frequency scanning method can only guarantee that the dynamic modeling is effective at a single working point. To make the established dynamic model effective in a wide range, an equivalent modeling method for power converter based LSTM Neural Network is presented. At first, the equivalence of black-box modeling problem and deep loop neural network is studied. Then, dynamic modeling method black-box power converter on wide operating range by using LSTM neural network is proposed. Finally, the simulation results under large disturbance and multi-operating points show that the proposed method is effective under wide operation range.

An electro-hydraulic profiling mechanism has been gradually applied to provide suitable downforce for a no-till row unit and to ensure the consistency of the seed sowing depth. In order to improve the control effect of the sowing depth system and solve the problems of a complex structure, scattered valve sets and equipment suitability of an existing electro-hydraulic row unit, this paper takes the 2BJ-470B no-till row unit as the carrier and innovatively designs an integrated pressure-reducing cylinder (IPRC) based on electro-hydraulic technology by analyzing the sowing depth control process of an electro-hydraulic seeder. In addition, we develop the integrated electro-hydraulic-driven sowing depth measurement and control system. Combined with the feedforward compensation PID control algorithm, the dynamic regulation of the downforce is realized with the IPRC. The field test shows that under the setting of a sowing depth of 50 mm and a vehicle speed of 9~10 km·h−1, the qualified rate of the sowing depth under the three adjustment methods of self-weight adjustment, spring adjustment and electro-hydraulic adjustment is 89.2%, 96.7% and 98.6%, respectively, and the corresponding maximum coefficient of variation of the sowing depth is 16.7%, 12.9% and 6.4%, respectively. The seed groove environmental qualification rate (SFEQ) is further analyzed in combination with the soil compaction, and the mean values of the qualification rates of the three control methods at different vehicle speeds are 88.3%, 91.6% and 98.6%, respectively. The integrated electro-hydraulic-driven row unit has significant advantages over mechanical and self-weight regulation, and its whole machine integration degree and high adaptability realize the comprehensive control of the sowing depth and soil compaction strength.

The dynamic behaviors of microgrid become more complicated due to the increasing implementation of distributed energy generation, energy storage. It is significant to study the dynamic response for power planning, analysis, and control of microgrid. Hence, an accurate dynamic equivalent model is essential, since it can help to evaluate the performance by simulation to avoid the loss and danger in practical test. However, the dynamic model based on differential equation cannot be established because of the lack of information in most of time. To build dynamic model when microgrid is a black-box system, a gated recurrent unit based neural network is proposed in this paper. The proposed neural network can be treated as a black-box differential–algebraic equations. The structure design and model training procedure are presented in detail. Study cases are implemented to evaluate the performance of modeling method. The comparison results show that the proposed dynamic modeling method can precisely estimate the dynamic response of microgrid.

Terpenoids are the most diverse class of plant primary and specialized metabolites, and trans-prenyltransferases (trans-PTs) are the first branch point to synthesize precursors of various chain lengths for further metabolism. Whereas the catalytic mechanism of the enzyme is known, there is no reliable method for precisely predicting the functions of trans-PTs. With the exponentially increasing number of available trans-PTs genes in public databases, an in silico functional prediction method for this gene family is urgently needed. Here, we present PTS-Pre, a web tool developed on the basis of the “three floors” model, which shows an overall 86% prediction accuracy for 141 experimentally determined trans-PTs. The method was further validated by in vitro enzyme assays for randomly selected trans-PTs. In addition, using this method, we identified nine new GFPPSs from different plants which are beyond the previously reported Brassicaceae clade, suggesting these genes may have occurred via convergent evolution and are more likely lineage-specific. The high accuracy of our blind prediction validated by enzymatic assays suggests that PTS-Pre provides a convenient and reliable method for genome-wide functional prediction of trans-PTs enzymes and will surely benefit the elucidation and metabolic engineering of terpenoid biosynthetic pathways.

To realize the real-time monitoring of the cotton precision seeding operation process and improve the intelligence level of cotton precision planters, based on automatic color matching detection technology and visualization technology, this study designs a monitoring system for the sowing quality of cotton precision planters. The monitoring system is based on the double-silo turntable type cotton vertical disc hole seed metering device as the research carrier, and is composed of a missed seeding monitoring module and a visualization module. Among them, the missed seeding monitoring module includes an incremental rotary encoder, color code electric eye color fiber optic sensor, color code sensor amplifier, etc.; the visualization module includes data acquisition module, industrial computer, and so on. The missing seeding monitoring module is installed on the seed spacer of the cotton precision seed metering device. It uses Labview software for graphical programming and is equipped with a multi-functional industrial computer. It realizes the monitoring of parameters such as the number of sowings, the number of missed sowings, the speed of the hole seeder, the forward speed of the machine, and the sowing area. The results of the bench test and field test of the sowing monitoring system showed that the accuracy rate of the system’s broadcast monitoring was over 93%, and the accuracy rate of missed broadcast monitoring was over 91%. The system solved the technical problem that cotton film-laying and sowing were not easy to detect. It could accurately detect the quality of cotton sowing in real time and meet the actual requirements of sowing monitoring.

To improve the accuracy of the parameters used in the discrete element simulation test, this study calibrated the simulation parameters of cotton seeds by combining a physical test and simulation test. Based on the intrinsic parameters used for the physical test of cotton seed, according to the freefall collision method, inclined plane sliding method, and inclined plane rolling method, the contact parameters of cotton seeds and cotton seeds, stainless steel, and nylon were measured, respectively. The physical test of the accumulation angle and angle of repose of the cotton seeds was conducted. It was obtained to process the image of the seed pile with Matrix Laboratory software. The Plackett–Burman test was used to screen the significance of the simulation parameters. The optimal value range of the significant parameters was determined according to the steepest climbing test. The second-order regression model of the significant parameters, the stacking-angle error, and the angle-of-repose error were obtained according to the Box–Behnken design test. Taking the minimum stacking-angle error and angle-of-repose error as the optimization target values, the following optimal parameter combination was obtained: the interspecies collision recovery coefficient was 0.413, the interspecies static friction coefficient was 0.695, and the interspecies rolling friction coefficient was 0.214. Three repetitive simulation experiments were conducted to prove the reliability of the calibration results. The research results can be used for discrete element simulation experiments for cotton precision seed metering.

Methane-producing archaea and methylotrophic bacteria use tetrahydromethanopterin (H4MPT) and/or tetrahydrofolate (H4F) as coenzymes in one-carbon (C1) transfer pathways. The α-proteobacterium Methylobacterium extorquens AM1 contains a dihydromethanopterin reductase (DmrA) and two annotated dihydrofolate reductases (DfrA and DfrB). DmrA has been shown to catalyze the final step of H4MPT biosynthesis; however, the functions of DfrA and DfrB have not been examined biochemically. Moreover, sequence alignment (BLAST) searches have recognized scores of proteins that share up to 99% identity with DmrA but are annotated as diacylglycerol kinases (DAGK). In this work, we used bioinformatics and enzyme assays to provide insight into the phylogeny and substrate specificity of selected Dfr and DmrA homologs. In a phylogenetic tree, DmrA and homologs annotated as DAGKs grouped together in one clade. Purified histidine-tagged versions of the annotated DAGKs from Hyphomicrobium nitrativorans and M. nodulans (respectively, sharing 69 and 84% identity with DmrA) showed only low activity in phosphorylating 1,2-dihexanoyl-sn-glycerol when compared with a commercial DAGK from Escherichia coli. However, the annotated DAGKs successfully reduced a dihydromethanopterin analog (dihydrosarcinapterin, H2SPT) with kinetic values similar to those determined for M. extorquens AM1 DmrA. DfrA and DfrB showed little or no ability to reduce H2SPT under the conditions studied; however, both catalyzed the NADPH-dependent reduction of dihydrofolate. These results provide the first evidence that DfrA and DfrB function as authentic dihydrofolate reductases, while DAGKs with greater than 69% identity to DmrA may be misannotated and are likely to function in H4MPT biosynthesis.

Although many Chinese cities have made remarkable progress in promoting age-friendly housings, few empirical studies related to the premium price of housings with additional elevators installed have been reported. In this study, we constructed propensity score matching–hedonic price models as well as quantile–hedonic models to assess the impact of adding elevators on housing prices. This study concludes: (1) The impact of additional elevators on housing prices varies depending on the floor level. (2) Single-facing and older housings are more likely to be negatively affected by the addition of elevators, and this interaction increases with floor level. (3) Adding an elevator reduces the price of low-priced housings on low floors and raises the price of high-priced housings on low floors, while for middle-floor and high-floor housings, the premium increases with the price of the housing. (4) Compared with the commercial community housings, the elevator premium of the affordable community housings is higher. Our findings provide an important reference for local governments to formulate appropriate charging and compensation mechanisms for adding elevators to multistory housings to promote retrofitting of aging communities.