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Nitrogen-coordinated single-atom manganese in multi-dimensional nitrogen-doped carbon electrocatalysts (Mn–NC) were successfully constructed by combining two-dimensional nanosheets and one-dimensional nanofibers. The Mn–NC exhibited excellent oxygen reduction reaction catalytic activity with a half-wave potential of 0.88 V, which is higher than the 0.85 V of Pt/C. [ABSTRACT FROM AUTHOR]

The continuous change of load makes it difficult for the power plant to control the emission of pollutants. A reliable NOx concentration prediction model is important to energy conservation and emission reduction. In this study, an evolutionary deep learning framework is proposed to predict the NOx emission of coal fired boiler. In the process of NOx emission, several variables will affect the emission concentration. Therefore, firstly, the variables with high importance to NOx emission are selected through random forest (RF), and the initial input data is filtered to obtain a new input data. Secondly, the convolutional neural network (CNN) is used to further extract the deep characteristics of the new data set. Thirdly, Using Chaos strategy and iterative local search (ILS) method to improve flow direction algorithm (FDA) and enhance the optimization ability of the algorithm. Finally, the improved FDA (IFDA) is used to optimize the hyperparameters of Bi-directional gated recurrent unit (BiGRU), and the optimized BiGRU model is used to forecast the NOx emission concentration. Three different data sets from coal-fired power plants were obtained to verify the proposed hybrid RF-CNN-FDA-BiGRU model. Experimental results show that the proposed model can accurately predict NOx concentration and has better prediction performance. [ABSTRACT FROM AUTHOR]

Queries with threshold are common when dealing with unstructured data such as text corpus. It often requires several exploring attempts for users to achieve final results. In this work, we propose an automatic sampling method for threshold determination without any interaction with users, in which two optimizing algorithms are introduced to reach the lower-bound time complexity in each sampling trial. We evaluate our methods using several experiments and demonstrate the effectiveness of it, which can be an enormously powerful tool for ordinary users. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid growth of text data, and thus the increasing importance of in-depth analysis of text data for various applications. Text data are often organized in a database with documents labeled by attributes like time and location. Different documents manifest different topics. The topics of the documents may change along the attributes of the documents, and such changes have been the subject of research in the past. However, previous analyses techniques, such as topic detection and tracking, topic lifetime, and burstiness, all focus on the topic behavior of the documents in a given attribute range without contrasting to the documents in the overall range. This paper introduces the concept of uniquetopics, referring to those topics that only appear frequently within a small range of documents but not in the whole range. These unique topics may reflect some unique characteristics of documents in this small range not found outside of the range. The paper aims at an efficient pruning-based algorithm that, for a user-given set of keywords and a user-given attribute, finds the maximal ranges along the given attribute and their unique topics that are highly related to the given keyword set. Thorough experiments show that the algorithm is effective in various scenarios. [ABSTRACT FROM AUTHOR]

Accurate prediction of photovoltaic power is of great significance to the storage and utilization of solar power. In this research, a deep learning model for photovoltaic power prediction based on gated recurrent unit network (GRU), improved sine cosine algorithm (ISCA), and complete ensemble empirical mode decomposition (CEEMD) is proposed. Firstly, CEEMD is used to decompose the original photovoltaic data into several intrinsic mode function (IMF) components and one residual. Secondly, each sub-pattern after decomposition is processed by partial least-squares analysis (PLS). Third, the nonlinear strategy is used to improve SCA, and the Hill-climbing strategy is added to the local search part to improve the performance of the algorithm. Fourth, each sub-pattern is predicted by GRU, then the learning rate and the number of hidden layer neurons of GRU are optimized by the ISCA. Finally, the predicted results of each sub-model are combined to generate the final prediction results. In this study, the proposed model is applied to four photovoltaic power data sets, and different experimental comparison models are established. The experimental results show that the CEEMD-PLS-ISCA-GRU model in this study can obtain good prediction results in all data sets. [Display omitted] • A novel PV power forecasting method is proposed based on GRU. • An improved SCA algorithm is proposed. • CEEMD is used to decompose the original data of different months. • The proposed hybrid model accurately predicts the PV power. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is of great significance to the stable operation of wind power equipment. In this study, a hybrid deep learning model based on convolutional neural network (CNN), Bi-directional long short-term memory (BiLSTM), improved sine cosine algorithm (ISCA) and time-varying filter based empirical mode decomposition (TVFEMD) is proposed for wind speed prediction. Firstly, the original wind speed data is decomposed into intrinsic mode functions (IMFs) by TVFEMD to improve the data stability. Then, the importance of each decomposed subcomponent is analyzed using random forest (RF). Thirdly, CNN-BiLSTM is employed to predict the wind speed. And, an improved sine and cosine algorithm (ISCA) is utilized to optimize the model parameters BiLSTM. Finally, the forecasting results of each sub-model are combined to get the final prediction results. In this study, the proposed model is utilized to four monthly wind speed data sets, and different comparison models are established. The experimental results of this study show that TVFEMD and RF can process data more effectively and improve the prediction accuracy. ISCA can optimize the parameters of BiLSTM model and improve the prediction performance. The proposed model in this study can obtain good prediction results on all data sets. • An evolutionary deep learning model is proposed for wind speed prediction. • An improved SCA algorithm is proposed to optimize the parameters of BiLSTM. • TVFEMD is employed to reduce the noise and complexity of the original data. • RF and CNN are used to extract features of wind speed data. • The proposed hybrid model performed better than the comparison models. [ABSTRACT FROM AUTHOR]

An enzyme-linked immunosorbent assay (ELISA) was used for the determination of residues of imidacloprid and thiamethoxam insecticides in honey after simple dilution of the samples without either extraction or cleanup. The ELISA enabled accurate determination of imidacloprid and thiamethoxam down to limits of 20 and 5 ng g-1 in honey, respectively. Average recoveries of imidacloprid and thiamethoxam from the fortified honey samples were 90-120 and 96-122%, and coefficients of variation ranged 5-12 and 3-15%, respectively. The results from the ELISA agreed well with those by liquid chromatography-mass spectrometry (LC-MS) for the insecticide-spiked samples, with a correlation coefficient (r2) of 0.96 and a regression coefficient (slope) of 1.03. The results indicate that ELISA is a suitable tool for the quantification of imidacloprid and thiamethoxam in honey. [ABSTRACT FROM AUTHOR]

Based on numerical weather prediction model Weather Research and Forecasting (WRF) and Hydrologic Modeling System (HEC-HMS), a coupling model is constructed in Taihang Piedmont basin. The WRF model parameter scheme combinations composed of microphysics, planetary boundary layers, and cumulus parameterizations suitable for the study area are optimized. In both time and space, we tested the effects of the WRF model by a multi-index evaluation system composed of relative error, root meantime square error, probability of detection, false alarm ratio, and critical success index and established this system in two stages. A multi-attribute decision-making model based on Technique for Order Preference by Similarity to an Ideal Solution and grey correlation degree is proposed to optimize each parameter scheme. Among 18 parameter scheme combinations, Mellor-Yamada-Janjic, Grell-Devinji, Purdue-Lin, Betts-Miller-Janjić, and Single-Moment6 are ideal choices according to the simulation performance in both time and space. Using the unidirectional coupling method, the rolling rainfall forecast results of the WRF model in the 24 h and 48 h forecast periods are input to HEC-HMS hydrological model to simulate three typical floods. The coupling simulation results are better than the traditional forecast method, and it prolongs the flood forecast period of the Taihang Piedmont basin. [ABSTRACT FROM AUTHOR]

In this study, two types of carbon dioxide‐based amphiphilic block copolymers (CO2‐based copolymers), monomethoxy poly(ethylene glycol)‐block‐poly(propylene carbonate) (mPEG‐b‐PPC) and allyloxy poly(ethylene glycol)‐block‐poly(propylene carbonate) (aPEG‐b‐PPC), were synthesized by one step copolymerization. The structure of the copolymer was determined by FTIR spectrum and 1H NMR characterization. On this base, the development of the preparation and controlling capability of CO2‐based copolymers controlled‐release particles were studied. Drug‐loaded particles were formed by two types of CO2‐based copolymers, which possess sustained‐release function. Given that the surface layer of aPEG‐b‐PPC particles has double bonds, shell cross‐linked particles were prepared by cross‐linking reaction of the allyloxy. Compared with aPEG‐b‐PPC particles, aPEG‐b‐PPC shell cross‐linked particles showed that the release rate was more slowly and suppressed the phenomenon of an initial burst release. [ABSTRACT FROM AUTHOR]

ROS homeostasis, nano-enabled agriculture, Salinity stress, K+ and Na+ homeostasis, Mn3O4 nanoparticles Keywords: ROS homeostasis; nano-enabled agriculture; Salinity stress; Mn3O4 nanoparticles; K+ and Na+ homeostasis EN ROS homeostasis nano-enabled agriculture Salinity stress Mn3O4 nanoparticles K+ and Na+ homeostasis 1935 1937 3 09/20/23 20231001 NES 231001 Cotton is an important fibre and oil crop across the globe. Different lowercase letters indicate the significance level at 0.05. gl To investigate the role of PMO in improving cotton salt tolerance, we treated cotton plants (second true leaf stage) with foliar delivered 200 mg/L PMO and then subjected it to salinity (200 mM NaCl) for 5 days. [Extracted from the article]

Wind energy, as clean energy, has attracted more and more attention. Wind power generation is easily threatened by the irregular fluctuation of wind speed, which interferes with the safety and stability of power system. In this study, a wind speed interval prediction method based on variational mode decomposition (VMD), phase space reconstruction (PSR), whale optimization algorithm (WOA), quantile regression (QR) and gated recurrent unit (GRU) is proposed. Firstly, the wind speed time series is decomposed into a variety of intrinsic mode functions (IMFs) through VMD to reduce the stochasticity. Secondly, all IMFs are then reconstructed using PSR to get the optimal input variables of the model. Then, the QRGRU model is optimized by the improved WOA to get the optimal QRGRU model parameters. Then, the wind speed interval prediction model of PSR-IWOA-QRGRU is established for each intrinsic mode function. Finally, the prediction results of each component are superimposed to realize the wind speed interval prediction. By checking on three different data sets, the effectiveness of the proposed method in wind speed interval prediction is proved. [Display omitted] [ABSTRACT FROM AUTHOR]

The core clinical characteristics of autism, which is a neurodevelopmental disease, involve repetitive behavior and impaired social interactions. Studies have shown that the Notch and Neuregulin1 (NRG1) signaling pathways are abnormally activated in autism, but the mechanism by which these two signaling pathways interact to contribute to the progression of autism has not been determined. Our results suggest that the levels of Notch1, Hes1, NRG1, and phosphorylated ErbB4 in the cerebellum (CB), hippocampus (HC), and prefrontal cortex (PFC) were increased in rats with valproic acid (VPA)-induced autism compared to those in the Con group. However, 3, 5-difluorophenyl-L-alanyl-L-2-phenylglycine tert-butyl (DAPT), which is a Notch pathway inhibitor, ameliorated autism-like behavioral abnormalities and decreased the protein levels of NRG1 and phosphorylated ErbB4 in rats with VPA-induced autism; these results demonstrated that the Notch1/Hes1 pathway could participate in the pathogenesis of autism by regulating the NRG1/ErbB4 signaling pathway. Studies have shown that the Notch pathway regulates microglial differentiation and activation during the onset of neurological disorders and that microglia affect autism-like behavior via synaptic pruning. Therefore, we hypothesized that the Notch1/Hes1 pathway could regulate the NRG1/ErbB4 pathway and thus participate in the development of autism by regulating microglial functions. The present study showed that AG1478, which is an ErbB4 inhibitor, ameliorated the autism-like behaviors in a VPA-induced autism rat model, reduced abnormal microglial activation, and decreased NRG1 and Iba-1 colocalization; however, AG1478 did not alter Notch1/Hes1 activity. These results demonstrated that Notch1/Hes1 may participate in the microglial activation in autism by regulating NRG1/ErbB4, revealing a new mechanism underlying the pathogenesis of autism. • Notch1/Hes1 regulates NRG1/ErbB4 pathway in development of autism. • Notch1/Hes1 participates in microglial activation in VPA-induced autism rats. • NRG1/ErbB4 participates in microglial activation in a VPA-induced autism model. • Notch1/Hes1 participates in microglial activation in autism via NRG1/ErbB4. [ABSTRACT FROM AUTHOR]

An indirect competitive enzyme-linked immunosorbent assay (ELISA) was developed for the detection of danofloxacin (DAN) in beef, chicken and pork muscle meats using polyclonal antisera. The half-maximum inhibition concentration (IC50) and limit of detection (LOD) of the ELISA in assay buffer were 5.4 and 0.10 ng/ml, respectively. The assay showed little cross-reactivities with quinolones structurally related to DAN. No significant changes were found for IC50 values when the pH values of the assay buffer ranged from 5 to 7 and NaCl concentrations ranged from 1 to 4%. The ELISA can tolerate up to 10% methanol, 2.5% acetone or 2.5% acetonitrile in the assay buffer without significant effects on IC50. The average recoveries of DAN from fortified control beef, pork and chicken samples were in the ranges of 85-101%, 87-101% and 92-105%, respectively. The average intra-assay and inter-assay coefficients of variation were in the ranges of 2-7% and 3-12%, respectively. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented crisis, which has been exacerbated because specific drugs and treatments have not yet been developed. In the post-pandemic era, humans and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will remain in equilibrium for a long time. Therefore, we still need to be vigilant against mutated SARS-CoV-2 variants and other emerging human viruses. Plant-derived products are increasingly important in the fight against the pandemic, but a comprehensive review is lacking. This review describes plant-based strategies centered on key biological processes, such as SARS-CoV-2 transmission, entry, replication, and immune interference. We highlight the mechanisms and effects of these plant-derived products and their feasibility and limitations for the treatment and prevention of COVID-19. The development of emerging technologies is driving plants to become production platforms for various antiviral products, improving their medicinal potential. We believe that plant-based strategies will be an important part of the solutions for future pandemics. In the disease triangle, plant-derived products are used as favorable environmental factors that not only inhibit the spread and replication of SARS-CoV-2, but also enhance the body's immunity. [Display omitted] • The review summarizes plant-derived strategies to fight against SARS-CoV-2. • Emerging technologies is driving plants to become production platforms for various antiviral products. • Plant-derived drugs, vaccines, antibodies, adjuvants, and diagnostic reagents are rapidly developing. [ABSTRACT FROM AUTHOR]

With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities. • A dual-scale ensemble framework is proposed for air quality index prediction. • CEEMDAN technology is adopted to obtain multiple intrinsic mode functions. • An improved WOA algorithm based on dimension learning-based hunting is proposed. • Long short-term memory network is used to predict high-frequency components. • Regularized extremum learning machine is used to predict low-frequency components. [ABSTRACT FROM AUTHOR]

Simple Summary: Foxl2 generally presents a sexually dimorphic expression pattern in animal gonads and is highly expressed in the ovary. However, few studies on the transcriptional regulation of Foxl2 have been documented. To understand the transcriptional regulating of Foxl2 high expression in the ovary, we used the Y1H system, a high throughput approach, for the first time to screen the transcription factors binding to the high transcriptional activity region of Foxl2 promoter in Zhikong scallop (Chlamys farreri) gonads. In the present study, the highly transcriptional activity promoter sequence of Cf-Foxl2 was determined at −1000~−616 bp and 11 candidate factors were verified to involve in Cf-Foxl2 transcriptional regulation. Our findings provided valuable data for better understanding the specific transcriptional regulation mechanism of Foxl2 in the ovary and would further assist in the breeding of aquacultural bivalves. Foxl2 is an evolutionarily conserved female sex gene, which is specifically expressed in the ovary and mainly involved in oogenesis and ovarian function maintenance. However, little is known about the mechanism that regulates Foxl2 specific expression during the ovary development. In the present study, we constructed the gonadal yeast one-hybrid (Y1H) library of Chlamysfarreri with ovaries and testes at different developmental stages using the Gateway technology. The library capacity was more than 1.36 × 107 CFU, and the length of the inserted fragment was 0.75 Kb~2 Kb, which fully met the demand of yeast library screening. The highly transcriptional activity promoter sequence of C. farreri Foxl2 (Cf-Foxl2) was determined at −1000~−616 bp by dual-luciferase reporter (DLR) assay and was used as bait to screen possible transcription factors from the Y1H library. Eleven candidate factors, including five unannotated factors, were selected based on Y1H as well as their expressional differences between ovaries and testes and were verified for the first time to be involved in the transcriptional regulation of Cf-Foxl2 by RT-qPCR and DLR. Our findings provided valuable data for further studying the specific regulation mechanism of Foxl2 in the ovary. [ABSTRACT FROM AUTHOR]

As an important transcription factor, SOX2 involves in embryogenesis, maintenance of stem cells and proliferation of primordial germ cell (PGC). However, little was known about its function in mature gonads. Herein, we investigated the SOX2 gene profiles in testis of scallop, Chlamys farreri. The level of C. farreri SOX2 (Cf-SOX2) mRNA increased gradually along with gonadal development and reached the peak at mature stage, and was located in all germ cells, including spermatogonia, spermatocytes, spermatids and spermatozoa. Knockdown of Cf-SOX2 using RNAi leaded to a mass of germ cells lost, and only a few spermatogonia retained in the nearly empty testicular acini after 21 days. TUNEL assay showed that apoptosis occurred in spermatocytes. Furthermore, transcriptome profiles of the testes were compared between Cf-SOX2 knockdown and normal scallops, 131,340 unigenes were obtained and 2,067 differential expression genes (DEGs) were identified. GO and KEGG analysis showed that most DEGs were related to cell apoptosis (casp2, casp3, casp8), cell proliferation (samd9, crebzf, iqsec1) and spermatogenesis (htt, tusc3, zmynd10, nipbl, mfge8), and enriched in p53, TNF and apoptosis pathways. Our study revealed Cf-SOX2 is essential in spermatogenesis and testis development of C. farreri and provided important clues for better understanding of SOX2 regulatory mechanisms in bivalve testis. [ABSTRACT FROM AUTHOR]