Search

Head smut is a soil-borne fungal disease caused by Sporisorium reilianum that infects maize tassels and ears. This disease poses a tremendous threat to global maize production. A previous study found markedly different and stably heritable tassel symptoms in some maize inbred lines with Sipingtou blood after infection with S. reilianum. In the present study, 55 maize inbred lines with Sipingtou blood were inoculated with S. reilianum and classified into three tassel symptom types (A, B, and C). Three maize inbred lines representing these classes (Huangzao4, Jing7, and Chang7-2, respectively) were used as test materials to investigate the physiological mechanisms of tassel formation in infected plants. Changes in enzyme activity, hormone content, and protein expression were analyzed in all three lines after infection and in control plants. The activities of peroxidase (POD), superoxide dismutase (SOD), and phenylalanine-ammonia-lyase (PAL) were increased in the three typical inbred lines after inoculation. POD and SOD activities showed similar trends between lines, with the increase percentage peaking at the V12 stage (POD: 57.06%, 63.19%, and 70.28% increases in Huangzao4, Jing7, and Chang7-2, respectively; SOD: 27.01%, 29.62%, and 47.07% in Huangzao4, Jing7, and Chang7-2, respectively. These were all higher than in the disease-resistant inbred line Mo17 at the same growth stage); this stage was found to be key in tassel symptom formation. Levels of gibberellic acid (GA3), indole-3-acetic acid (IAA), and abscisic acid (ABA) were also altered in the three typical maize inbred lines after inoculation, with changes in GA3 and IAA contents tightly correlated with tassel symptoms after S. reilianum infection. The differentially expressed proteins A5H8G4, P09233, and Q8VXG7 were associated with changes in enzyme activity, whereas P49353, P13689, and P10979 were associated with changes in hormone contents. Fungal infection caused reactive oxygen species (ROS) and nitric oxide (NO) bursts in the three typical inbred lines. This ROS accumulation caused biofilm disruption and altered host signaling pathways, whereas NO signaling triggered strong secondary metabolic responses in the host and altered the activities of defense-related enzymes. These factors together resulted in the formation of varying tassel symptoms. Thus, interactions between S. reilianum and susceptible maize materials were influenced by a variety of signals, enzymes, hormones, and metabolic cycles, encompassing a very complex regulatory network. This study preliminarily identified the physiological mechanisms leading to differences in tassel symptoms, deepening our understanding of S. reilianum-maize interactions. [ABSTRACT FROM AUTHOR]

Multiple studies have indicated that distinct metabolites are involved in the occurrence and development of osteopenia (ON) and osteoporosis (OP); however, these metabolites in OP and ON have not yet been classified and standardized. This systematic review and meta-analysis included 21 articles aiming to investigate the distinct metabolites in patients with ON and OP. The quality of the included articles was generally high; seventeen studies had >7 stars, and the remaining four received 6 stars. This systematic review showed that three metabolites (phosphatidylcholine (PC) (lipid metabolites), galactose (carbohydrate metabolites), and succinic acid (other metabolites)) increased, four (glycylglycine (gly-gly), cystine (amino acids), sphingomyelin (SM) (lipid metabolites) and glucose (carbohydrate metabolites)) decreased, and five (glutamine, hydroxyproline, taurine (amino acids), lysophosphatidylcholine (LPC) (lipid metabolites), and lactate (other metabolites)) had conflicting directions in OP/ON. The results of the meta-analysis show that gly-gly (MD = −0.77, 95%CI −1.43 to −0.11, p = 0.02) and cystine (MD = −5.52, 95%CI −7.35 to −3.68, p < 0.00001) decreased in the OP group compared with the healthy control group. Moreover, LPC (MD = 1.48, 95%CI 0.11 to 2.86, p = 0.03) increased in the OP group compared with the healthy control group. These results indicate that distinct metabolites were associated with ON and OP, which could be considered a predictor for OP. [ABSTRACT FROM AUTHOR]

The traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the joint use of feature selection algorithms or model optimization methods to achieve maximum classification accuracy. Addressing the issues in both models, this paper introduces a novel network framework, CTSF, specifically designed for Industrial Internet intrusion detection. CTSF effectively addresses the limitations of traditional Transformers in extracting local features while compensating for the weaknesses of SVM. The framework comprises a pre-training component and a decision-making component. The pre-training section consists of both CNN and an enhanced Transformer, designed to capture both local and global features from input data while reducing data feature dimensions. The improved Transformer simultaneously decreases certain training parameters within CTSF, making it more suitable for the Industrial Internet environment. The classification section is composed of SVM, which receives initial classification data from the pre-training phase and determines the optimal decision boundary. The proposed framework is evaluated on an imbalanced subset of the X-IIOTID dataset, which represent Industrial Internet data. Experimental results demonstrate that with SVM using both "linear" and "rbf" kernel functions, CTSF achieves an overall accuracy of 0.98875 and effectively discriminates minor classes, showcasing the superiority of this framework. [ABSTRACT FROM AUTHOR]

(1) Many studies have attempted to utilize metabolomic approaches to explore potential biomarkers for the early detection of osteoarthritis (OA), but consistent and high-level evidence is still lacking. In this study, we performed a systematic review and meta-analysis of differential small molecule metabolites between OA patients and healthy individuals to screen promising candidates from a large number of samples with the aim of informing future prospective studies. (2) Methods: We searched the EMBASE, the Cochrane Library, PubMed, Web of Science, Wan Fang Data, VIP Date, and CNKI up to 11 August 2022, and selected relevant records based on inclusion criteria. The risk of bias was assessed using the Newcastle–Ottawa quality assessment scale. We performed qualitative synthesis by counting the frequencies of changing directions and conducted meta-analyses using the random effects model and the fixed-effects model to calculate the mean difference and 95% confidence interval. (3) Results: A total of 3798 records were identified and 13 studies with 495 participants were included. In the 13 studies, 132 kinds of small molecule differential metabolites were extracted, 58 increased, 57 decreased and 17 had direction conflicts. Among them, 37 metabolites appeared more than twice. The results of meta-analyses among four studies showed that three metabolites increased, and eight metabolites decreased compared to healthy controls (HC). (4) Conclusions: The main differential metabolites between OA and healthy subjects were amino acids (AAs) and their derivatives, including tryptophan, lysine, leucine, proline, phenylalanine, glutamine, dimethylglycine, citrulline, asparagine, acetylcarnitine and creatinine (muscle metabolic products), which could be potential biomarkers for predicting OA. [ABSTRACT FROM AUTHOR]

In order to reduce the production cost of the methane carbon dioxide reforming reaction, and improve its actual production efficiency, in this paper, the optimal working parameters of the methane carbon dioxide reforming reaction are studied. The influence of different factors on methane conversion is studied via a single-factor numerical simulation analysis and the response surface methodology. Firstly, a numerical model of the methane carbon dioxide reforming reaction is established using Ansys Chemkin Pro software to analyze the influence of single factors (reactor temperature, reaction pressure, gas velocity) on methane conversion rate; secondly, the response surface model with the methane conversion rate as the response value is established using the BBD (Box–Behnken design) method; and finally, the order of influence of each variable on methane conversion and the optimal reaction conditions are determined using the response surface method. The factors are listed in order of their influence on methane conversion as follows: reactor temperature > pressure > speed. The results show that when the temperature is 1135.114 K, the pressure is 0.103 MPa and the speed is 10slpm, the methane conversion rate is 93.7018%. In this paper, a method is adopted in which chemical reaction process simulation and numerical results prediction are combined, significantly reducing the simulation time and improving the calculation efficiency and accuracy, thus being of considerable scientific significance and theoretical value. [ABSTRACT FROM AUTHOR]

(1) Background: Many studies have attempted to explore potential biomarkers for the early detection of gout, but consistent and high levels of evidence are lacking. In this study, metabolomics was used to summarize the changes of metabolites in the literature and explore the potential value of metabolites in predicting the occurrence and development of gout. (2) Methods: We searched the databases including the EMBASE, the Cochrane Library, PubMed, Web of Science, VIP Date, Wanfang Data, and CNKI, and the screening was fulfilled on 30 July 2022. The records were screened according to the inclusion criteria and the risk of bias was assessed. Qualitative analysis was performed for all metabolites, and meta-analysis was performed for metabolite concentrations using random effects to calculate the Std mean difference and 95% confidence interval. (3) Results: A total of 2738 records were identified, 33 studies with 3422 participants were included, and 701 metabolites were identified. The qualitative analysis results showed that compared with the healthy control group, the concentration of 56 metabolites increased, and 22 metabolites decreased. The results of the meta-analysis indicated that 17 metabolites were statistically significant. (4) Conclusions: Metabolites are associated with gout. Some specific metabolites such as uric acid, hypoxanthine, xanthine, KYNA, guanosine, adenosine, creatinine, LB4, and DL-2-Aminoadipic acid have been highlighted in the development of gout. [ABSTRACT FROM AUTHOR]

Earthquake forecasting is the process of forecasting the time, location, and magnitude of an earthquake, hoping to gain some time to prepare to reduce the disasters caused by earthquakes. In this paper, the possible relationship between the maximum electron density, the corresponding critical frequency, and the occurrence of earthquakes is explored by means of radio occultation data based on mechanism analysis and actual earthquake-nearby data. A new disturbance frequency index is proposed in this paper as a novel method to help forecast earthquakes. Forecasting of the location and timing of earthquakes is based on the connection between proven new frequency distributions and earthquakes. The effectiveness of this index is verified by backtracking observation around the 2022 Ya'an earthquake. Using this index, occultation data can forecast the occurrence of earthquakes five days ahead of detection, which can help break the bottleneck in earthquake forecasting. [ABSTRACT FROM AUTHOR]

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach. [ABSTRACT FROM AUTHOR]

A scheme for controlling the frequency difference of output pulse pair with double frequency shift loops is proposed. The frequency shift system includes two loop elements of 20 and 200 MHz. The first one carries out a single selective positive frequency shift of 1–20 MHz, and the second one can satisfy a single fixed positive frequency shift of 200 MHz. The reverse cascade technology of two acousto-optic crystals is introduced to solve the limitation of the small frequency shift of crystal size. A multichannel synchronization signal completes the time domain control of each acousto-optic modulator. Finally, the frequency shift difference of the output pulse pair ranges of 0–2 GHz, and the frequency shift accuracy is 5 MHz. [ABSTRACT FROM AUTHOR]

Nucleic acid extraction is crucial for PCR detection of pathogenic bacteria to ensure food safety. In this study, a new magnetic extraction method was developed using 3D printing and magnetic silica beads (MSBs) to extract the target DNA from a large volume of bacterial sample and combined with microfluidic PCR to determine the bacteria. After proteinase K was added into a bacterial sample to lyse the bacteria and release the DNA, it was continuous-flow injected into the serpentine channel of the extraction chip, where magnetic silica bead chains had been formed in advance using a homogeneous magnetic field generated by two concentric semicircle magnets to capture the MSBs. Then, the flowing DNA was captured by the MSB chains, washed with alcohol, dried with gas, and eluted with deionized water to obtain the purified and concentrated DNA. Finally, the extracted DNA templates were injected into a microfluidic PCR chip with lyophilized amplification reagents and determined using a commercial qPCR device. The experimental results showed that the DNA extraction efficiency was more than 90%, and the lower detection limit of Salmonella was 102 CFU/mL. This new Salmonella detection method is promising to provide the rapid, sensitive, and simultaneous detection of multiple foodborne pathogens. [ABSTRACT FROM AUTHOR]

Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir. [ABSTRACT FROM AUTHOR]

Total factor productivity (TFP) is of critical importance to the sustainable development of construction industry. This paper presents an analysis on the impact of migrant workers on TFP in Chinese construction sector. Interestingly, Solow Residual Approach is applied to conduct the analysis through comparing two scenarios, namely the scenario without considering migrant workers (Scenario A) and the scenario with including migrant workers (Scenario B). The data are collected from the China Statistical Yearbook on Construction and Chinese Annual Report on Migrant Workers for the period of 2008–2015. The results indicate that migrant workers have a significant impact on TFP, during the surveyed period they improved TFP by 10.42% in total and promoted the annual average TFP growth by 0.96%. Hence, it can be seen that the impact of migrant workers on TFP is very significant, whilst the main reason for such impact is believed to be the improvement of migrant workers' quality obtained mainly throughout learning by doing. [ABSTRACT FROM AUTHOR]