Diazotrophic Paenibacillus beijingensis BJ-18 Provides Nitrogen for Plant and Promotes Plant Growth, Nitrogen Uptake and Metabolism

Diazotrophic bacteria can reduce N2 into plant-available ammonium (NH4+), promoting plant growth and reducing nitrogen (N) fertilizer requirements. However, there are few systematic studies on the effects of diazotrophic bacteria on biological N2 fixation (BNF) contribution rate and host plant N uptake and metabolism. In this study, the interactions of the diazotrophic Paenibacillus beijingensis BJ-18 with wheat, maize, and cucumber were investigated when it was inoculated to these plant seedlings grown in both low N and high N soils, with un-inoculated plants as controls. This study showed that GFP-tagged P. beijingensis BJ-18 colonized inside and outside seedlings, forming rhizospheric and endophytic colonies in roots, stems, and leaves. The numbers of this bacterium in the inoculated plants depended on soil N levels. Under low N, inoculation significantly increased shoot dry weight (wheat 86.1%, maize 46.6%, and cucumber 103.6%) and root dry weight (wheat 46.0%, maize 47.5%, and cucumber 20.3%). The 15N-isotope-enrichment experiment indicated that plant seedlings derived 12.9–36.4% N from BNF. The transcript levels of nifH in the inoculated plants were 0.75–1.61 folds higher in low N soil than those in high N soil. Inoculation enhanced NH4+ and nitrate (NO3-) uptake from soil especially under low N. The total N in the inoculated plants were increased by 49.1–92.3% under low N and by 13–15.5% under high N. Inoculation enhanced activities of glutamine synthetase (GS) and nitrate reductase (NR) in plants, especially under low N. The expression levels of N uptake and N metabolism genes: AMT (ammonium transporter), NRT (nitrate transporter), NiR (nitrite reductase), NR, GS and GOGAT (glutamate synthase) in the inoculated plants grown under low N were up-regulated 1.5–91.9 folds, but they were not obviously changed under high N. Taken together, P. beijingensis BJ-18 was an effective, endophytic and diazotrophic bacterium. This bacterium contributed to plants with fixed N2, promoted plant growth and N uptake, and enhanced gene expression and enzyme activities involved in N uptake and assimilation in plants. However, these positive effects on plants were regulated by soil N status. This study might provide insight into the interactions of plants with beneficial associative and endophytic diazotrophic bacteria

Additive manufacturing: directed energy deposition process parameters optimization via machine learning

Additive manufacturing (AM) is a rapidly growing industry that creates intricate industrial parts. One of the methods employed by AM is directed energy deposition (DED), which involves melting metal powder with a laser beam to form various components. The process of 3D printing is known to be complex and involves a multitude of parameters, making the optimization of the process a significant challenge. Therefore, the application of machine learning (ML) is being considered as a potential solution. ML is especially useful in analyzing structured data and is ideal for tasks such as regression. In the case of AM, ML has proven to be valuable in optimizing design and manufacturing efficiency. Despite the growing interest in the DED process, there is a dearth of research on the optimization of DED process parameters for mechanical properties via ML. Adjusting process parameters can enhance the mechanical properties of printed components. However, generalizing the results of a single experiment to other scenarios can be difficult. As a result, many researchers have had to repeat their experiments to optimize the production of parts with mechanical properties beyond the initial experimental range. To address the need for efficient and effective techniques to optimize DED process parameters for predictions outside of the original experiment design range, a potential solution is to employ ML. For this project, Response Surface Methodology (RSM) and 9 different ML models were utilized to predict the ultimate tensile strength, yield strength, and average hardness of stainless steel 316L (SS316L) outside of the RSM design range. The RSM model was fitted on real data points while the machine learning models were trained with a combination of synthetic data points generated from the RSM model and real data points. The best performing model was chosen to predict the mechanical properties, and a Python script was created to help identify the optimal process parameters for achieving the desired mechanical properties. This project revealed that there is a highly complex and nonlinear correlation between the input process parameters and the resulting mechanical properties. The Keras Neural Network model demonstrated superior performance among the tested models, owing to its fine-tuning capability. The Scikit learn library models also performed well, closely trailing the Keras Neural Network model. Conversely, the Minitab RSM model exhibited lower performance on average when compared to the Keras Neural Network model and the Scikit learn library models. The use of a Python script for identifying optimal process parameters in the DED process via ML allowed for increased efficiency and the production of parts with better mechanical properties outside of the RSM design range.Bachelor of Engineering (Mechanical Engineering

A Decomposition Method for Both Additively and Non-additively Separable Problems

Chen M, Du W, Tang Y, Jin Y, Yen GG. A Decomposition Method for Both Additively and Non-additively Separable Problems. IEEE Transactions on Evolutionary Computation. 2022:1-1.Problem decomposition is crucial for coping with large-scale global optimization problems, which relies heavily on highly precise variable grouping methods. The state-of-the-art decomposition methods identify separability based on the finite differences principle, which is valid only for additively separable functions but not applicable to non-additively separable functions. Therefore, we need to investigate separability in more depth in order to propose a more general principle and design more universal decomposition methods. In this paper, we conduct a comprehensive theoretical investigation on separability, the core of which is proposing an innovative separability identification principle: the minimum points shift principle. By utilizing the new principle, we develop a general separability grouping (GSG) method that can handle both additively and non-additively separable functions with high accuracy. In addition, we design a new set of benchmark functions based on non-additive separability, which compensates for the lack of non-additively separable functions in the previous test suites. Extensive experiments demonstrate that the proposed GSG achieves high grouping accuracy on both new and CEC series benchmark problems, especially on non-additively separable problems Finally, we verify that the proposed GSG can effectively improve the optimization performance of non-additively separable problems through optimization experiments

QTL Mapping by SLAF-seq and Expression Analysis of Candidate Genes for Aphid Resistance in Cucumber

Cucumber, a very important vegetable crop worldwide, is easily damaged by pests. Aphid is one of the most serious cucumber pests and frequently cause severe damage to commercially produced crops. Understanding the genetic mechanisms underlying pest resistance is important for aphid-resistant cucumber varieties breeding. In this study, two parental cucumber lines, JY30 (aphid susceptible) and EP6392 (aphid resistant), and pools of resistant and susceptible (n = 50 each) plants from 1000 F2 individuals derived from crossing JY30 with EP6392, were used to detect genomic regions associated with aphid resistance in cucumbers. The analysis was performed using specific length amplified fragment sequencing (SLAF-seq), bulked segregant analysis (BSA) and single nucleotide polymorphism index (SNP-index) methods. A main effect QTL (quantitative trait locus) of 0.31 Mb on Chr5, including 43 genes, was identified by association analysis. Sixteen of the 43 genes were identified as potentially associated with aphid resistance through gene annotation analysis. The effect of aphid infestation on the expression of these candidate genes screened by SLAF-seq was investigated in EP6392 plants by qRT-PCR. The results indicated that 7 genes including encoding transcription factor MYB59-like (Csa5M641610.1), auxin transport protein BIG-like (Csa5M642140.1), F-box/kelch-repeat protein At5g15710-like (Csa5M642160.1), transcription factor HBP-1a-like (Csa5M642710.1), beta-glucan-binding protein (Csa5M643380.1), endo-1,3(4)-beta-glucanase 1-like (Csa5M643880.1), and proline-rich receptor-like protein kinase PERK10-like (Csa5M643900.1), out of the 16 genes were down regulated after aphid infestation, whereas 5 genes including encoding probable leucine-rich repeat receptor-like serine/threonine-protein kinase At5g15730-like (Csa5M642150.1), Stress-induced protein KIN2 (Csa5M643240.1 and Csa5M643260.1), F-box family protein (Csa5M643280.1), F-box/kelch-repeat protein (Csa5M643290.1), were up-regulated after aphid infestation. The gene Csa5M642150.1, encoding probable leucine-rich repeat receptor-like serine/threonine-protein kinase At5g15730-like, was most likely a key candidate gene in cucumber plants in response to infestation. This study provides a certain theoretical basis of molecular biology for genetic improvement of cucumber aphid resistance and aphid resistant variety breeding

Anwendung von B-Erkennung auf die modellunspezifische Suche in CMS

Im Jahr 2009 wurde der Large Hadron Collider (LHC) am CERN bei Genf in Betrieb genommen. Mit seiner Hilfe sollen viele oene Fragen, die das Standardmodell der Ele- mentarteilchenphysik nicht klären kann, gelöst werden. Eines der Experimente am LHC ist der Compact Muon Solonoid (CMS), ein Allzweckdetektor, der bei der Suche nach neuer Physik helfen soll. Dabei sollen auch Theorien überprüft werden, in denen Teilchen vorkommen, die in Bottom-Quarks zerfallen und anschlieÿend als B-Jets detektiert werden. Diese B-Jets spielen bei der Suche nach neuer Physik eine wichtige Rolle. Bei der modellunspezischen Suche in CMS (MUSiC) werden die gesammelten Daten bei einer Schwerpunktsenergie von √s =7 TeV mit der Erwartung des Standardmodells, die mithilfe von Monte-Carlo-Simulationen dargestellt wird, verglichen, ohne einen modellbe- zogenen Ansatz zu verfolgen. So kann man allgemeine Abweichungen erkennen und die modellspezischen Analysen bei ihrer Suche unterstützen. Zu diesem Zweck wurde die Erkennung von B-Jets in MUSiC implementiert. Die Anwendung von B-Erkennung auf Daten aus dem Jahr 2010 in MUSiC soll im Nachfolgenden untersucht warden

Additional file 5: Table S4. of Comparative RNA-seq based transcriptome profiling of waterlogging response in cucumber hypocotyls reveals novel insights into the de novo adventitious root primordia initiation

List of differentially expressed genes annotated as transcription factors. (XLS 43 kb

An outbreak of norovirus-associated acute gastroenteritis associated with contaminated barrelled water in many schools in Zhejiang, China.

OBJECTIVES:More than 900 students and teachers at many schools in Jiaxing city developed acute gastroenteritis in February 2014. An immediate epidemiological investigation was conducted to identify the pathogen, infection sources and route of transmission. METHODS:The probable cases and confirmed cases were defined as students or teachers with diarrhoea or vomiting present since the term began in February 2014. An active search was conducted for undiagnosed cases among students and teachers. Details such as demographic characteristics, gastrointestinal symptoms, and drinking water preference and frequency were collected via a uniform epidemiological questionnaire. A case-control study was implemented, and odds ratios (ORs) and 95% confidence intervals were calculated. Rectal swabs from several patients, food handlers and barrelled water factory workers, as well as water and food samples, were collected to test for potential bacteria and viruses. RESULTS:A total of 924 cases fit the definition of the probable case, including 8 cases of laboratory-confirmed norovirus infection at 13 schools in Jiaxing city between February 12 and February 21, 2014. The case-control study demonstrated that barrelled water was a risk factor (OR: 20.15, 95% CI: 2.59-156.76) and that bottled water and boiled barrelled water were protective factors (OR: 0.31, 95% CI: 0.13-0.70, and OR: 0.36, 95% CI: 0.16-0.77). A total of 11 rectal samples and 8 barrelled water samples were detected as norovirus-positive, and the genotypes of viral strains were the same (GII). The norovirus that contaminated the barrelled water largely came from the asymptomatic workers. CONCLUSIONS:This acute gastroenteritis outbreak was caused by barrelled water contaminated by norovirus. The outbreak was controlled after stopping the supply of barrelled water. The barrelled water supply in China represents a potential source of acute gastroenteritis outbreaks due to the lack of surveillance and supervision. Therefore, more attention should be paid to this area

Asymmetric Coordination Induces Electron Localization at Ca Sites for Robust CO2 Electroreduction to CO

Main group single atom catalysts (SACs) are promising for CO2 electroreduction to CO by virtue of their ability in preventing the hydrogen evolution reaction and CO poisoning. Unfortunately, their delocalized orbitals reduce the CO2 activation to *COOH. Herein, an O doping strategy to localize electrons on p-orbitals through asymmetric coordination of Ca SAC sites (Ca-N3O) is developed, thus enhancing the CO2 activation. Theoretical calculations indicate that asymmetric coordination of Ca-N3O improves electron-localization around Ca sites and thus promotes *COOH formation. X-ray absorption fine spectroscopy shows the obtained Ca-N3O features: one O and three N coordinated atoms with one Ca as a reactive site. In situ attenuated total reflection infrared spectroscopy proves that Ca-N3O promotes *COOH formation. As a result, the Ca-N3O catalyst exhibits a state-of-the-art turnover frequency of ≈15 000 per hour in an H-cell and a large current density of −400 mA cm−2 with a CO Faradaic efficiency (FE) ≥ 90% in a flow cell. Moreover, Ca-N3O sites retain a FE above 90% even with a 30% diluted CO2 concentration