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ObjectiveTo investigate the incidence of myocardial injury in children with critically ill children without primary cardiac disease and the association between elevated cardiac troponin I (cTnl) and creatine kinase MB (CK-MB) concentrations and disease progression and prognosis to guide early treatment.MethodsThe serum cTnI and CK-MB concentrations of 292 children with critically ill children without primary cardiac disease in Yantai Yuhuangding Hospital between January 2021 and January 2024 were retrospectively analyzed within 24 h after entering the Pediatric Intensive Care Unit (PICU). The children were divided into normal and abnormal groups according to the myocardial marker results. The abnormal group was further divided into the cTnI-elevated, CK-MB-elevated, single-elevated (cTnI- or CK-MB-elevated) and double-elevated (cTnI- and CK-MB-elevated) groups. The differences in the clinical indicators and their relationships with prognosis for the groups were compared.ResultsThe incidence of myocardial injury among the critically ill children without primary cardiac disease was 55.1%. The incidence of myocardial injury in children with infectious diarrhea combined with moderate and severe dehydration reached 85.19%. The pediatric critical illness score; frequency of use of vasoactive drugs; hypotension, shock, heart failure, respiratory failure, and multiple organ dysfunction syndrome; and mortality indexes differed significantly for the normal and abnormal myocardial marker groups (P

BackgroundKnee Osteoarthritis (KOA) is a prevalent and debilitating condition affecting millions worldwide, yet its underlying etiology remains poorly understood. Recent advances in neuroimaging and genetic methodologies offer new avenues to explore the potential neuropsychological contributions to KOA. This study aims to investigate the causal relationships between brain-wide morphometric variations and KOA using a genetic epidemiology approach.MethodLeveraging data from 36,778 UK Biobank participants for human brain morphometry and 487,411 UK Biobank participants for KOA, this research employed a two-sample Mendelian Randomization (TSMR) approach to explore the causal effects of 83 brain-wide volumes on KOA. The primary method of analysis was the Inverse Variance Weighted (IVW) and Wald Ratio (WR) method, complemented by MR Egger and IVW methods for heterogeneity and pleiotropy assessments. A significance threshold of p < 0.05 was set to determine causality. The analysis results were assessed for heterogeneity using the MR Egger and IVW methods. Brain-wide volumes with Q_pval < 0.05 were considered indicative of heterogeneity. The MR Egger method was employed to evaluate the pleiotropy of the analysis results, with brain-wide volumes having a p-value < 0.05 considered suggestive of pleiotropy.ResultsOur findings revealed significant causal associations between KOA and eight brain-wide volumes: Left parahippocampal volume, Right posterior cingulate volume, Left transverse temporal volume, Left caudal anterior cingulate volume, Right paracentral volume, Left paracentral volume, Right lateral orbitofrontal volume, and Left superior temporal volume. These associations remained robust after tests for heterogeneity and pleiotropy, underscoring their potential role in the pathogenesis of KOA.ConclusionThis study provides novel evidence of the causal relationships between specific brain morphometries and KOA, suggesting that neuroanatomical variations might contribute to the risk and development of KOA. These findings pave the way for further research into the neurobiological mechanisms underlying KOA and may eventually lead to the development of new intervention strategies targeting these neuropsychological pathways.

Abstract The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

In response to the challenge of noise filtering for the impulsive vibration signals of rolling bearings, this paper presented a novel filtering method based on the improved Morlet wavelet, which has clear physical meaning and is more conducive to parameter optimization through employing Gaussian waveform width to replace the traditional Morlet wavelet shape factor. Simultaneously, the marine predation algorithm was employed and the minimum Shannon entropy was used as the parameter optimization index while optimizing the shape width and center frequency of the improved Morlet wavelet. The vibration waveform of the rolling bearing was matched perfectly by using the optimized Morlet wave. Shannon entropy was used as the evaluation index of noise filtering, and the quantitative analysis of noise filtering was realized. Through experimental validation, this method was proved to be effective in noise elimination for rolling bearing. It is significance to preprocessing of vibration signal, feature extraction and fault recognition of rolling bearing.

Cottonseed is a by-product of cotton industry, but its broad application in human diets has not been fully elucidated. This study aims to analyse the nutritional composition of cotton sprouts and investigate metabolic and transcriptomic differences under light and darkness. The findings revealed that the glanded ZM41 yellow cotton sprouts were rich in protein (2.8 %), vitamin E (0.443 mg/100 g), and minerals, with < 0.01 % free gossypol. Moreover, protein, aspartic acid (1,026 mg/100 g), and serine (136 mg/100 g) reached their highest levels in nine-day-old sprouts, while no significant differences were observed in the other nutrients on the 7th, 9th, or 11th day. The average quantitative values of 64 cottonseed varieties found gossypol (27.9 mg/kg), protein (3.6 %), potassium (1,487.3 mg/kg), calcium (506.2 mg/kg) and magnesium (330.2 mg/kg). Integrated metabolome and transcriptome analyses revealed that 317 out of 1,880 metabolites were differentially accumulated between light and dark conditions, wherein the downregulation of structural genes encoding CHI, F3H, FLS, DFR, F3′H, ANS, LAR and ANR resulted in decreased flavonoid levels in dark-cultivated sprouts, while the upregulation of HMGR and 2-ODD-1 enhanced the biosynthesis of gossypol. This study provides insights into the nutritional and metabolic aspects of cotton sprouts, offering a reference for potential commercial applications.

Abstract Objective This study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management. Methods Genetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. Results The MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44–0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. Conclusion The study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver metastasis (CRLM) patients, and the study highlights the importance of MR as a powerful epidemiological tool for investigating causal associations between exposures and outcomes.

In cluster analysis, determining the number of clusters is an important issue because there is less information about the most appropriate number of clusters in the real problem. Automatic clustering is a clustering method that automatically finds the most appropriate number of clusters and divides instances into the corresponding clusters. In this paper, a novel automatic clustering algorithm based on the improved marine predator algorithm (IMPA) and K-means algorithm is proposed. The new IMPA utilizes refracted opposition-based learning in population initialization, generates opposite solutions to improve the diversity of the population and produces more accurate solutions. In addition, the sine-cosine algorithm is incorporated to balance global exploration and local development of the algorithm for dynamic updating of the predator and prey population positions. At the same time, the Gaussian-Cauchy mutation is combined to improve the probability of obtaining the globally optimal solution. The proposed IMPA is validated with some benchmark data sets. The calculation results show that IMPA is superior to the original MPA in automatic clustering. In addition, IMPA is also used to solve the problem of fault classification of Xi*an Jiaotong University bearing data. The results show that the IMPA has better and more stable results than other algorithms such as the original MPA, whale optimization algorithm, fuzzy C-means and K-means in automatic clustering.

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC.Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated.Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets.Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.

Cotton (Gossypium hirsutum L.) is a significant fiber crop. Being a major contributor to the textile industry requires continuous care and attention. Cotton is subjected to various biotic and abiotic constraints. Among these, biotic factors including cotton leaf curl virus (CLCuV) are dominant. CLCuV is a notorious disease of cotton and is acquired, carried, and transmitted by the whitefly (Bemisia tabaci). A cotton plant affected with CLCuV may show a wide range of symptoms such as yellowing of leaves, thickening of veins, upward or downward curling, formation of enations, and stunted growth. Though there are many efforts to protect the crop from CLCuV, long-term results are not yet obtained as CLCuV strains are capable of mutating and overcoming plant resistance. However, systemic-induced resistance using a gene-based approach remained effective until new virulent strains of CLCuV (like Cotton Leaf Curl Burewala Virus and others) came into existence. Disease control by biological means and the development of CLCuV-resistant cotton varieties are in progress. In this review, we first discussed in detail the evolution of cotton and CLCuV strains, the transmission mechanism of CLCuV, the genetic architecture of CLCuV vectors, and the use of pathogen and nonpathogen-based approaches to control CLCuD. Next, we delineate the uses of cutting-edge technologies like genome editing (with a special focus on CRISPR-Cas), next-generation technologies, and their application in cotton genomics and speed breeding to develop CLCuD resistant cotton germplasm in a short time. Finally, we delve into the current obstacles related to cotton genome editing and explore forthcoming pathways for enhancing precision in genome editing through the utilization of advanced genome editing technologies. These endeavors aim to enhance cotton’s resilience against CLCuD.

In order to overcome the low accuracy of traditional Extreme Learning Machine (ELM) network in the performance evaluation of Rotate Vector (RV) reducer, a pattern recognition model of ELM based on Ensemble Empirical Mode Decomposition (EEMD) fusion and Improved artificial Jellyfish Search (IJS) algorithm was proposed for RV reducer fault diagnosis. Firstly, it is theoretically proved that the torque transmission of RV reducer has periodicity during normal operation. The characteristics of data periodicity can be effectively reflected by using the test signal periodicity characteristics of rotating machinery and EEMD. Secondly, the Logistic chaotic mapping of population initialization in JS algorithm is replaced by tent mapping. At the same time, the competition mechanism is introduced to form a new IJS. The simulation results of standard test function show that the new algorithm has the characteristics of faster convergence and higher accuracy. The new algorithm was used to optimize the input layer weight of the ELM, and the pattern recognition model of IJS-ELM was established. The model performance was tested by XJTU-SY bearing experimental data set of Xi'an Jiaotong University. The results show that the new model is superior to JS-ELM and ELM in multi-classification performance. Finally, the new model is applied to the fault diagnosis of RV reducer. The results show that the proposed EEMD-IJS-ELM fault diagnosis model has higher accuracy and stability than other models.

BackgroundAlthough subclinical hypothyroidism (SCH) is related to abnormalities in left ventricular diastolic function, the use of levothyroxine as a regular treatment remains debatable. This meta-analysis aimed to determine whether thyroid hormone replacement therapy affects cardiac diastolic function in patients with SCH as measured by echocardiography.MethodsThis meta-analysis included a search of the EMBASE, PubMed, Web of Science, and Cochrane Library databases from their inception to May 18, 2023, for studies analyzing cardiac morphology and functional changes in patients with SCH before and after thyroid hormone replacement. The outcome measures were cardiac morphology and diastolic and overall cardiac function, as assessed using ultrasound parameters (including ventricular wall thickness, chamber size, mitral wave flow, tissue Doppler, and speckle tracking). The quality of the studies was assessed using the Newcastle–Ottawa Scale. The standard mean differences (MDs) and 95% confidence intervals (CI) were calculated using fixed- or random-effects models.ResultsSeventeen studies met the inclusion criteria. A total of 568 patients participated and completed the follow-up. All studies specifically stated that serum thyrotropin levels returned to normal by the end of the study period. Compared with baseline levels, no significant morphological changes were observed in the heart. In terms of diastolic function, we discovered that the ratios of E-velocity to A-velocity (E/A) had greatly improved after thyroid hormone replacement therapy, whereas the ratios of the mitral inflow E wave to the tissue Doppler e’ wave (E/e’) had not. Global longitudinal strain (GLS) increased significantly after treatment with levothyroxine.ConclusionIn adult patients with SCH, thyroid hormone supplementation can partially but not completely improve parameters of diastolic function during the observation period. This meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement, an updated guideline for reporting systematic reviews (11) and was registered with INPLASY (INPLASY202320083).Systematic review registrationhttps://inplasy.com/inplasy-2023-2-0083.

The genetic relationships between the stratigraphic textures, thickness changes, burial depths, and the characteristics of the geothermal zoning of the Cenozoic in the northwestern Shandong region were analyzed in this study. Methods involving segmented water temperature measurements of geothermal well drilling, wellhead hydrological surveys, geothermal reservoirs, and caprock thickness measurements and statistics were adopted. The following findings were revealed in this study's research results: (1) The Paleogene and Neogene reservoir types in the northwestern Shandong region were determined to be mainly water-bearing fine sandstone and medium-fine sandstone pores, with thick layered, interbedded, and zoned stratigraphic structures. The layered and zoned geothermal reservoirs were found to be primarily distributed in a zonal manner on the bedding plane and characterized by good regional continuity. The fine sandstone and medium-fine sandstone sections with well-developed pores and high water content levels were geothermal reservoirs, while mudstone sections were geothermal barriers. The reservoirs and barriers were characterized by interlayer structures; (2) The boundary between the sag basin and the uplifting was taken as the dividing line of the geothermal fields, and the geothermal areas in the northwestern Shandong region were divided into different geothermal fields, all belonging to the sedimentary basin's conductive geothermal resources; (3) The major geothermal reservoirs included the lower members of the Neogene Minghuazhen Formation, Guantao Formation, and Dongying Formation. The Quaternary argillaceous sediment and the mudstone in the upper member of the Minghuazhen Formation formed the caprocks in the study area. In this study, the macroscopic distribution laws of geothermal resources in the northwestern Shandong region were proposed and were considered to have practical significance for further exploration and development.

Knowledge about the optical features of benthic objects is essential for quantifying spectral signatures, remote sensing-based mapping, and ecological monitoring in coral reefs. However, the spectral identification of benthic species and the accurate measurement of the in situ reflectance spectra of relevant research objects remain underexplored. An underwater radiation measuring system suitable for coral reef environments was specifically designed to obtain in situ reflectance spectra and match benthic photographs of various substrate targets. This instrument has the advantages of obtaining hyperspectral, dual-channel simultaneous measurements, and automatically adjusting the integration time according to the light intensity. Based on in situ hyperspectral datasets, the linear discriminant analysis (LDA) was used for exploring and discriminating spectral characteristics from three taxonomic ranks, which include typical substrates of six community groups, nine coral families, and six Acroporidae species. In situ full-resolution (1-nm) spectra provided the best discrimination ability with mean accuracies of 97.5%, 90.9%, and 91.6% for typical substrates, coral families, and coral species, respectively. The spectral abilities of remote sensors were assessed by applying the spectral response functions of three multispectral sensors (Landsat 8 OLI, Sentinel-2A, and World View-2) to the full-resolution spectra. Discrimination analyses of the simulated spectra demonstrated that the spectral separations of typical substrates might be apparent, with overall classification accuracies of 89.6%, 88.2%, and 90.4% for the Landsat 8 OLI, Sentinel-2A, and World View-2 sensors, respectively. The spectral separation for different corals, however, may not be effective when using multispectral sensors. The discrimination analyses of families and species produced overall classification accuracies of 67.1% and 69.6%, respectively, for the Landsat 8 OLI, 56.0% and 56.0% for the Sentinel-2A sensor, and 64.5% and 61.8% for the World View-2 sensor. In summary, this method has the potential for identifing substrate targets in communities and taxonomic coral groups by applying in situ hyperspectral datasets. Furthermore, multispectral satellite sensors are currently inadequate for spectrally separate corals, while spectral discrimination is possible and practical for different substrate targets with visual spectral differences.

Abstract Purpose Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory loss and cognitive dysfunction. Quinone oxidoreductase 1 (NQO1) is an antioxidant enzyme that plays an important role in controlling cellular redox state, whose expression is altered in the brain tissues of AD patients. In addition to its traditional antioxidant effects, NQO1 also acts as a multifunctional RNA‐binding protein involved in posttranscriptional regulation. Whether the RNA‐binding activity of NQO1 influences AD pathology has not been investigated yet. Methods The RNA‐binding functions of NQO1 in rat pheochromocytoma (PC12) cells were investigated using siRNA knockdown followed by total RNA sequencing. Reverse transcription quantitative polymerase chain reaction was performed to explore the impact of NQO1 on the transcription and alternative splicing of apoptotic genes. Results NQO1 knockdown led to a significant increase in cellular apoptosis. Genes involved in certain apoptosis pathways, such as positive regulation of apoptotic processes and mitogen‐activated protein kinase signaling, were under global transcriptional and alternative splicing regulation. NQO1 regulated the transcription of apoptotic genes Cryab, Lgmn, Ngf, Apoe, Brd7, and Stat3, as well as the alternative splicing of apoptotic genes BIN1, Picalm, and Fyn. Conclusion Our findings suggest that NQO1 participates in the pathology of AD by regulating the expression and alternative splicing of the genes involved in apoptosis. These results extend our understanding of NQO1 in apoptotic pathways at the posttranscriptional level in AD.

Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM). There are a variety of techniques for real-time robust drone guidance, but numerous of them solve in discretized airspace and control, which would require an additional path smoothing step to provide flexible commands for UAS. To deliver safe and computationally efficient guidance for UAS operations, we explore the use of a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to lead autonomous UAS to their destinations while bypassing obstacles through continuous control. The proposed scenario state representation and reward function can map the continuous state space to continuous control for both heading angle and speed. To verify the effectiveness of the proposed learning framework, we conducted numerical experiments with static and moving obstacles. Uncertainties associated with the environments and safety operation bounds are investigated in detail. Results show that the proposed model is able to provide accurate and robust guidance and resolve conflict with a success rate of over 99%.

Abstract Background The tricarboxylic acid (TCA) cycle is crucial for energy supply in animal, plant, and microbial cells. It is not only the main pathway of carbohydrate catabolism but also the final pathway of lipid and protein catabolism. Some TCA genes have been found to play important roles in the growth and development of tomato and potato, but no comprehensive study of TCA cycle genes in Solanaceae crops has been reported. Results In this study, we analyzed TCA cycle genes in four important Solanaceae vegetable crops (potato (Solanum tuberosum), tomato (Solanum lycopersicum), eggplant (Solanum melongena), and pepper (Capsicum annuum)) based on comparative genomics. The four Solanaceae crops had a total of 180 TCA cycle genes: 43 in potato, 44 in tomato, 40 in eggplant, and 53 in pepper. Phylogenetic analysis, collinearity analysis, and tissue expression patterns revealed the conservation of and differences in TCA cycle genes between the four Solanaceae crops and found that there were unique subgroup members in Solanaceae crops that were independent of Arabidopsis genes. The expression analysis of potato TCA cycle genes showed that (1) they were widely expressed in various tissues, and some transcripts like Soltu.DM.01G003320.1(SCoAL) and Soltu.DM.04G021520.1 (SDH) mainly accumulate in vegetative organs, and some transcripts such as Soltu.DM.12G005620.3 (SDH) and Soltu.DM.02G007400.4 (MDH) are preferentially expressed in reproductive organs; (2) several transcripts can be significantly induced by hormones, such as Soltu.DM.08G023870.2 (IDH) and Soltu.DM.06G029290.1 (SDH) under ABA treatment, and Soltu.DM.07G021850.2 (CSY) and Soltu.DM.09G026740.1 (MDH) under BAP treatment, and Soltu.DM.02G000940.1 (IDH) and Soltu.DM.01G031350.4 (MDH) under GA treatment; (3) Soltu.DM.11G024650.1 (SDH) can be upregulated by the three disease resistance inducers including Phytophthora infestans, acibenzolar-S-methyl (BTH), and DL-β-amino-n-butyric acid (BABA); and (4) the levels of Soltu.DM.01G045790.1 (MDH), Soltu.DM.01G028520.3 (CSY), and Soltu.DM.12G028700.1 (CSY) can be activated by both NaCl and mannitol. The subcellular localization results of three potato citrate synthases showed that Soltu.DM.01G028520.3 was localized in mitochondria, while Soltu.DM.12G028700.1 and Soltu.DM.07G021850.1 were localized in the cytoplasm. Conclusions This study provides a scientific foundation for the comprehensive understanding and functional studies of TCA cycle genes in Solanaceae crops and reveals their potential roles in potato growth, development, and stress response.

Abstract A novel technique was proposed to prepare micronized fly ash (FA), broadening the application of fly ash in rubber compounds. This facile method of FA micronization is carried out by microbial corrosion to produce a loose structure which is easily destroyed by the following ball milling step. Only 20 min of the ball milling treatment after microbial corrosion is needed to obtain FA particles with an average size of 194 nm, representing a remarkable reduction compared to several days that are typically required to achieve such a small size through the ball milling treatment. Due to the favourable FA particle sizes and interface compatibility, the natural rubber compounds achieved a good reinforcement effect at 20.7 MPa. The improved mechanical properties of the simple FA micronization method explored in this work are highly favourable for expanding the use of FA as reinforcement fillers to partially replace the carbon black fillers that are currently widely used in the rubber industry.

BackgroundInterleukin-6 (IL-6) is essential for maintaining intestinal epithelial homeostasis. Although cold water-immersion restraint (CWIR) stress is commonly used to induce in vivo gastric injury, it also affects intestinal epithelial permeability. Although IL-6 is increased in response to acute physiological and psychological stress, its exact effects on the pathophysiology of the intestinal epithelium in response to acute CWIR stress remain unknown.MethodsWe used IL-6 knockout (KO) mice with acute CWIR modeling to investigate the effect of IL-6 deficiency on intestinal epithelial morphology and pathological damage using histological staining assays under the acute stress. We detected jejunal epithelial apoptosis using TUNEL and standard molecular experiments.ResultsCWIR caused intestinal epithelial damage, which was alleviated by the absence of IL-6, as evidenced by morphological changes and goblet cell and intestinal permeability alteration. IL-6 KO also reduced CWIR-mediated inflammatory levels and improved stress defense. Meanwhile, IL-6 deficiency decreased the intestinal epithelial apoptosis induced by CWIR administration. This IL-6 KO-led effect depended more on mitochondrial AIF signaling rather than the traditional caspase pathway.ConclusionAs a result, we concluded that acute CWIR-induced severe intestinal damage and jejunal epithelium apoptosis could be alleviated by IL-6 deficiency, implying a protective effect of IL-6 deficiency on the intestines under acute stress. The findings shed new light on treating CWIR-induced intestinal disorders by inhibiting IL-6 signaling.

In the recent past, the production of wastewater from domestic and industrial sources steadily increased through population growth, urbanization, the Industrial Revolution, and economic development. In the world, 80% of wastewater consists of several harmful substances and hazardous chemicals that cause many deadly effects on human beings as well as ecosystems. So, the elimination of this toxic substance before discarding it into landfills is utilized as an alternative source of water which is an emerging need. Using treated wastewater for agricultural purposes is an excellent approach to rendering wastewater beneficial. As the quantity of wastewater grows, it becomes necessity to redistribute the water in a beneficial way. The rapidly increasing world population will undoubtedly increase the food demand, which directly requires more water for irrigation purposes. The rapidly increasing world population rate will undoubtedly demand an increased food production rate, which directly impacts agricultural water usage. In order to achieve sustainability in terms of agricultural water usage, alternative water resources should be explored. In this review, we tried to focus on summarizing all the leading studies in the field of wastewater utilization, the most prominent treatment methods, and a benchmarking of their technical efficiency in agriculture with special emphasis on agriculture in the marginal lands, with special emphasis on the United Arab Emirates.

According to BESO’s principle of binarizing continuous design variables and the excellent performance of the standard HPO algorithm in terms of solving continuous optimization problems, a discrete binary Hunter-prey optimization algorithm is introduced to construct an efficient topology optimization model. It was used to solve the problems that the BESO method of topology optimization has, such as easily falling into the local optimal value and being unable to obtain the optimal topology configuration; the metaheuristic algorithm was able to solve the topology optimization model’s low computational efficiency and could easily produce intermediate elements and unclear boundaries. Firstly, the BHPO algorithm was constructed by discrete binary processing using the s-shape transformation function. Secondly, BHPO-BESO topology optimization theory was established by combining the BHPO algorithm with BESO topology optimization. Using the sensitivity information of the objective function and the updated principle of the meta-heuristic of the BHPO algorithm, a semi-random search for the optimal topology configuration was carried out. Finally, numerical simulation experiments were conducted by using the three typical examples of the cantilever beam, simply supported beam, and clamping beam as optimization objects and the results were compared with the solution results of BESO topology optimization. The experimental results showed that compared with BESO, BHPO-BESO could find the optimal topology configuration with lower compliance and maximum stiffness, and it has higher computational efficiency, which can solve the above problems.

In recent years, pretrained models have been widely used in various fields, including natural language understanding, computer vision, and natural language generation. However, the performance of these language generation models is highly dependent on the model size and the dataset size. While larger models excel in some aspects, they cannot learn up-to-date knowledge and are relatively difficult to relearn. In this paper, we introduce EvoText, a novel training method that enhances the performance of any natural language generation model without requiring additional datasets during the entire training process (although a prior dataset is necessary for pretraining). EvoText employs two models: G, a text generation model, and D, a model that can determine whether the data generated by G is legitimate. Initially, the fine-tuned D model serves as the knowledge base. The text generated by G is then input to D to determine whether it is legitimate. Finally, G is fine-tuned based on D’s output. EvoText enables the model to learn up-to-date knowledge through a self-escalation process that builds on a priori knowledge. When EvoText needs to learn something new, it simply fine-tunes the D model. Our approach applies to autoregressive language modeling for all Transformer classes. With EvoText, eight models achieved stable improvements in seven natural language processing tasks without any changes to the model structure.

Abstract Background Brassica oleracea includes several morphologically diverse, economically important vegetable crops, such as the cauliflower and cabbage. However, genetic variants, especially large structural variants (SVs), that underlie the extreme morphological diversity of B. oleracea remain largely unexplored. Results Here we present high-quality chromosome-scale genome assemblies for two B. oleracea morphotypes, cauliflower and cabbage. Direct comparison of these two assemblies identifies ~ 120 K high-confidence SVs. Population analysis of 271 B. oleracea accessions using these SVs clearly separates different morphotypes, suggesting the association of SVs with B. oleracea intraspecific divergence. Genes affected by SVs selected between cauliflower and cabbage are enriched with functions related to response to stress and stimulus and meristem and flower development. Furthermore, genes affected by selected SVs and involved in the switch from vegetative to generative growth that defines curd initiation, inflorescence meristem proliferation for curd formation, maintenance and enlargement, are identified, providing insights into the regulatory network of curd development. Conclusions This study reveals the important roles of SVs in diversification of different morphotypes of B. oleracea, and the newly assembled genomes and the SVs provide rich resources for future research and breeding.

The logistical and practical challenges of supplying food for long-term space missions are substantial. In this comment, the authors discuss potential biotechnological approaches that could be used to aid the production of food crops in space.

This article introduces multiangular imagery into physics-based bathymetry in order to compensate for the shortage of bathymetric spectral bands caused by the low spectral resolution of current high-spatial-resolution satellite multispectral imagery. The focus is to propose a selective bathymetric retrieval method to eliminate the negative effect of nonoptimal image data on depth retrieval in multiangular imagery-based bathymetry. The elimination of the negative effect is implemented by excluding nonoptimal pixels in every individual image from bathymetric retrieval. An empirical criterion is designed for the determination of nonoptimal pixels. The proposed method can use multiangular image data selectively, avoiding situations where bathymetric retrieval results from the whole multiangular imagery are poorer than that from a part of the individual images. The method was tested in two typical areas within the Xisha (Paracel) Islands of the South China Sea using two-angle WorldView-2 multispectral images. The test showed that the derived depths of the method (i.e., depths derived from the selective image data) provided a better fit to the validation depths than those from the entirety of both images. The underestimation of depths derived from the entirety of both images was also improved to some extent.

The log-ratio method (LRM) proposed by Stumpf et al. has been widely used to map bathymetry from multispectral imagery for oligotrophic waters, while the selection criteria of bands for the LRM have been subject to tradeoffs between maximum detectable depth and sensitivity. In this article, we first applied a method for global sensitivity analysis to a semianalytical forward model of optically shallow waters with the WorldView-2 band-set. The results show that the sensitive wavelength band in water-leaving reflectance for water depth varies from the longer wavelength band to the shorter wavelength band with increasing water depth. Then, we developed an adaptive blended algorithm approach (ABAA) to seamlessly map bathymetry from the shallower region to the deeper region. The LRM with different band combinations was selected for the sub-algorithms of the ABAA. The subalgorithms and depth range used for each subalgorithm of the ABAA were automatically determined by the proposed applicable depth range analysis that considers logarithmic regression for the LRM. The ABAA was applied to WorldView-2 and Landsat-8 imagery of the Xisha Qundao. When the in situ bathymetry data are available, compared with the LRM with the blue and green bands, the ABAA significantly improves the accuracy of the estimated depth, especially for waters shallower than 6 m (root-mean- square error (RMSE) = 0.31 to 0.94 m for WorldView-2 data, RMSE= 0.25 to 1.42 m for Landsat-8 data). When the in situ bathymetry data are absent, the ABAA performs better than the LRM with a single band ratio and an optimization-based method overall.

With the increase of service time, the rotation period of rotating machinery may become irregular, and the Ensemble Empirical Mode Decomposition (EEMD)can effectively reflect its periodic state. In order to accurately evaluate the working state of the Rotate Vector (RV) reducer, the torque transfer formula of the RV reducer is first derived to theoretically prove periodicity of torque transfer in normal operation. Then, EEMD is able to effectively reflect the characteristics of data periodicity. A fault diagnosis model based on EEMD-MPA-KELM was proposed, and a bearing experimental dataset from Xi‘an Jiaotong University was used to verify the performance of the model. In view of the characteristics of the industrial robot RV reducer fault was not obvious and the sample data is few, spectrum diagram was used to diagnose the fault from the RV reducer measured data. The EEMD decomposition was performed on the data measured by the RV reducer test platform to obtain several Intrinsic Mode Functions (IMF). After the overall average checking and optimization of each IMF, several groups of eigenvalues were obtained. The eigenvalues were input into the Kernel Extreme Learning Machine (KELM) optimized by the Marine Predators Algorithm (MPA), and the fault diagnosis model was established. Finally, compared with other models, the prediction results showed that the proposed model can judge the working state of RV reducer more effectively.

Structural Health Monitoring requires the continuous assessment of a structure’s operational conditions, which involves the collection and analysis of a large amount of data in both spatial and temporal domains. Conventionally, both data-driven and physics-based models for structural damage detection have relied on handcrafted features, which are susceptible to the practitioner’s expertise and experience in feature selection. The limitations of handcrafted features stem from the potential for information loss during the extraction of high-dimensional spatiotemporal data collected from the sensing system. To address this challenge, this paper proposes a novel, automated structural damage detection technique called Simplicial Complex Enhanced Manifold Embedding (SCEME). The key innovation of SCEME is the reduction of dimensions in both the temporal and spatial domains for efficient and information-preserving feature extraction. This is achieved by constructing a simplicial complex for each signal and using the resulting topological invariants as key features in the temporal domain. Subsequently, curvature-enhanced topological manifold embedding is performed for spatial dimension reduction. The proposed methodology effectively represents both intra-series and inter-series correlations in the low-dimensional embeddings, making it useful for classification and visualization. Numerical simulations and two benchmark experimental datasets validate the high accuracy of the proposed method in classifying different damage scenarios and preserving useful information for structural identification. It is especially beneficial for structural damage detection using complex data with high spatial and temporal dimensions and large uncertainties in reality.

Bearing is an important part of rotating machinery, and its early fault diagnosis and accurate classification have always been difficult in engineering application. At present, the models based on the fusion of various optimization algorithms and neural networks have become one of the emerging techniques for accurate fault identification. Firstly, an improved antlion optimizer (ALO) algorithm based on estimation of distribution algorithm (EDA) and variable-step Lévy flight strategy, abbreviated as ELALO, is proposed as a new bionic intelligence. During the initialization of population, the individuals with poor fitness are redistributed by the Gaussian probability model. In view of the stagnation of iteration, Lévy flight strategy is introduced and the adaptive change of disturbance step length is controlled. Experimental results on 4 benchmark functions show that the novel ELALO can effectively improve the solution accuracy and convergence speed, compared with the original ALO. Secondly, in order to solve the disadvantage that extreme learning machine (ELM) network is easy to fall into local optimization, this ELALO algorithm is used to initialize the weights and thresholds of its network and to form the new pattern recognition model, ELALO-ELM. Finally, the bearing data of 8 patterns from Western Reserve University are decomposed by local mean decomposition (LMD), and then the symbolic entropy (SE) of the first three product function (PF) components signals is extracted and used as the input eigenvectors. Compared with the standard ELM and ALO-ELM models, the ELALO-ELM model has better generalization and stronger robustness and it can effectively improve the efficiency of network training and the accuracy of early fault pattern classification in bearing fault diagnosis. The new ELALO-ELM model can also be used for other difficult classification problems.

Abstract Background Leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) is a collagen receptor belonging to the immunoglobulin superfamily. Although previous studies have evaluated the biological role of LAIR in solid tumors, the precise mechanisms underlying the functions of LAIR-1 as a regulator of tumor biological functions remain unclear. Methods LAIR-1 expression was evaluated by immunohistochemical analysis using an osteosarcoma (OS) tissue microarray. Wound healing and transwell migration assays were performed to evaluate tumor cell migration. Quantitative real-time polymerase chain reaction (qPCR) and western blotting were conducted to detect the expression of epithelial–mesenchymal transition (EMT)-related molecules. RNA-sequencing (RNA-seq) was conducted to evaluate the mRNA expression profiles after overexpressing LAIR-1 in OS cells. Glucose transporter (Glut)1 expression in OS cells was evaluated by western blotting. Results LAIR-1 expression was significantly different between the T1 and T2 stages of OS tumors, and it inhibited OS cell migration. LAIR-1 expression was inversely correlated with the expression of Twist1, an EMT-associated transcription factor, via the Forkhead box O1 signal transduction pathway. Furthermore, RNA-seq and qPCR demonstrated that the expression of EMT energy metabolism-related molecules was significantly reduced after LAIR-1 overexpression. Conclusions LAIR-1 overexpression decreased the expression of Glut1 and inhibited the expression of EMT-related molecules in OS cells. These findings provide new insights into the molecular mechanism underlying OS progression.

Abstract Background Chromosomal rearrangements in addition to t(15;17) have been reported in 25-40% of APL patients, with a large predominance of trisomy 8. Other abnormalities are far less frequent, particularly as ider(17), and the prognostic significance is still unclear. Case presentation We present the case of a patient with t(15;17)(q22;q21), der(15)t(15;17) and ider(17)(q10)t(15;17)(q22;q21). In particular, the RT-PCR result for PML-RARA of this patient was a false negative and mutational analysis of AML-related genes showed SNP rs2454206 in the TET2 gene and yielded negative findings in other genes including AML1, ASXL1, CEBPA, DNMT3A, FLT3, KIT, NPM1, TP53, and U2AF1. After the early usage of arsenic trioxide combinated with ATRA and vigorous supportive treatment to maintain PLT ≥30×109/L and FIB >1500 mg/L, this patient was under MMR and HCR without any clinical symptoms or signs until now. Conclusion False negative reslults of RT-PCR analysis for PML-RARA are rare in APL and ider(17) is even more infrequent. To our knowledge, this is the first reported case of APL with ider(17) and false negative RT-PCR analysis results. The role of ider(17) in APL is still an ongoing investigation and limited by the small number of published cases. The patient reported here benefited from vigorous supportive treatment during the combination of ATRA and arsenic trioxide in induction chemotherapy and the clinical outcome was favorable.

Abstract Background The tricarboxylic acid (TCA) cycle is crucial for cellular energy metabolism and carbon skeleton supply. However, the detailed functions of the maize TCA cycle genes remain unclear. Results In this study, 91 TCA genes were identified in maize by a homology search, and they were distributed on 10 chromosomes and 1 contig. Phylogenetic results showed that almost all maize TCA genes could be classified into eight major clades according to their enzyme families. Sequence alignment revealed that several genes in the same subunit shared high protein sequence similarity. The results of cis-acting element analysis suggested that several TCA genes might be involved in signal transduction and plant growth. Expression profile analysis showed that many maize TCA cycle genes were expressed in specific tissues, and replicate genes always shared similar expression patterns. Moreover, qPCR analysis revealed that some TCA genes were highly expressed in the anthers at the microspore meiosis phase. In addition, we predicted the potential interaction networks among the maize TCA genes. Next, we cloned five TCA genes located on different TCA enzyme complexes, Zm00001d008244 (isocitrate dehydrogenase, IDH), Zm00001d017258 (succinyl-CoA synthetase, SCoAL), Zm00001d025258 (α-ketoglutarate dehydrogenase, αKGDH), Zm00001d027558 (aconitase, ACO) and Zm00001d044042 (malate dehydrogenase, MDH). Confocal observation showed that their protein products were mainly localized to the mitochondria; however, Zm00001d025258 and Zm00001d027558 were also distributed in the nucleus, and Zm00001d017258 and Zm00001d044042 were also located in other unknown positions in the cytoplasm. Through the bimolecular fluorescent complimentary (BiFC) method, it was determined that Zm00001d027558 and Zm00001d044042 could form homologous dimers, and both homologous dimers were mainly distributed in the mitochondria. However, no heterodimers were detected between these five genes. Finally, Arabidopsis lines overexpressing the above five genes were constructed, and those transgenic lines exhibited altered primary root length, salt tolerance, and fertility. Conclusion Sequence compositions, duplication patterns, phylogenetic relationships, cis-elements, expression patterns, and interaction networks were investigated for all maize TCA cycle genes. Five maize TCA genes were overexpressed in Arabidopsis, and they could alter primary root length, salt tolerance, and fertility. In conclusion, our findings may help to reveal the molecular function of the TCA genes in maize.

Growing evidence has demonstrated that stress triggers gastrointestinal (GI) disorders. This study aimed to investigate how the acute cold water-immersion restraint (CWIR) stress affects intestinal injury and gut microbiota (GM) distribution. Male C57BL/6 mice were used to establish a CWIR animal model. Hematoxylin–eosin and periodic acid–Schiff staining were performed to assess intestinal histopathological changes. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis and immunofluorescence staining were used to evaluate the expression of inflammatory cytokines and immune cell infiltration in the intestinal tissues. The gut permeability and intestinal occludin protein expression were determined through fluorescein isothiocyanate-dextran detection and western blot, respectively. GM profiles were analyzed via high-throughput sequencing of the fecal bacterial 16S rRNA genes. Results showed that CWIR induced more severe intestinal mucosal injury compared to the control, leading to a significant increase in tumor necrosis factor-α expression, but no infiltration of neutrophil and T cells. CWIR also resulted in GI disruption and increased the permeability of the intestinal mucosa. GM profiles showed that CWIR reduced GM diversity of mice compared with the control group. Specifically, aerobic and gram-negative bacteria significantly increased after CWIR, which was associated with the severity of gut injury under stress. Therefore, acute CWIR leads to severe intestinal damage with inflammation and disrupts the GM homeostasis, contributing to decreased GM diversity. Our findings provide the theoretical basis for the further treatment of intestinal disorders induced by CWIR.

In order to design an active deformation mirror for projection objective aberration imaging quality control, a topology optimization design method of active deformation mirrors based on discrete orthogonal Zernike polynomials is proposed in this paper. Firstly, in order to solve the problem that continuous Zernike polynomials do not have orthogonality on the discrete coordinates inside the unit circle, which causes the instability of topology optimization results, discrete orthogonal Zernike polynomials are used to characterize the active deformation mirror wave aberrations. Then, the optical and structural deformations are combined to establish an optical-mechanical coupling topology optimization model with the help of the variable density method to derive the sensitivity of the mathematical model. Finally, a wave aberration corrected deformation mirror in an optical machine system is used as an arithmetic example for topology optimization, and the results show that the absolute value of the Zernike coefficient Z4 after optimization is improved by nearly one order of magnitude compared with the value before optimization, and the vibration characteristics of the optimized structure meet the design requirements. The optimization effect is significant, which improves the optical performance of the deformed mirror and provides a new scheme for the design of the deformed mirror structure which has a certain practical value for engineering.

Platelet-rich plasma (PRP) is a platelet-based application used to treat osteoarthritis (OA) clinically. The co-stimulatory molecule CD226 is expressed in T cells, NK cells, and also platelets. However, exact effects of CD226 on platelets and whether its expression level influences PRP efficacy are largely unknown. Here, CD226fl/flPF4-Cre mice were obtained from mating CD226 fl/fl mice with PF4-Cre mice. Blood samples and washed platelets were collected from the mice eyeballs to undergo routine blood tests and transmission electron microscopy. Differentially expressed proteins were detected by iTRAQ-based proteomics analysis. Animal OA models were established through surgical destabilization of the medial meniscus (DMM) for C57BL/6 wildtype mice, followed by PRP injection to evaluate the effects of platelet CD226 on PRP efficacy. The results showed that deletion of platelet CD226 increased the number of megakaryocytes (MKs) in bone marrow (BM) but reduced MKs in spleen, combined with significantly decreased platelet amounts, α-granule secretion, and reduced immature platelets; indicating that absence of platelet CD226 may disrupt MK/platelet homeostasis and arrested platelet release from MKs. Sequencing analysis showed abnormal ribosomal functions and much downregulated proteins in the absence of platelet CD226. Autophagy-related proteins were also reduced in the CD226-absent MKs/platelets. Moreover, deletion of platelet CD226 diminished the protective effects of PRP on DMM-induced cartilage lesions in mice, and PDGF restored it. Therefore, deficiency of platelet CD226 inhibited platelet maturation, secretion, and normal ribosomal functions, which may lead to depressed PRP efficacy on OA, suggesting that CD226 is required to regulate platelet growth, functions, and its application.

With advanced manufacturing processes, many modern structural designs need to include microscale uncertainties of material properties and microstructures that can affect overall performances, such as microelectromechanical systems, micro–opto–electro–mechanical systems, and micro-optical electronics systems. Topology design optimization at this scale must consider uncertainties from material microstructures as the scale of the structure and material microstructure is comparable. Very few studies consider microstructure uncertainties in topology design due to their scales are much different in classical mechanical/civil engineering. A novel framework of reliability-based topology optimization is proposed to specifically address this gap for the design. The microscale uncertainties of heterogeneous materials are quantified by the explicit mixture random field model. Then, the material distribution is optimized by a heuristic updating scheme, and sensitivities of reliability constraints are calculated using the adjoint design-point-based importance sampling method. The proposed methodology simultaneously considers microscale hierarchical uncertainties in microstructures and material property variations for each phase. The feasibility of the framework is demonstrated by several numerical examples with different multi-phase materials. Compared with deterministic topology optimization and classical topology optimization with first-order reliability method approximations, the optimal design obtained from the proposed method can achieve accurate target reliability with minimized limit state function evaluations.

In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching preprocessor-LSTM. First, aiming at the problem of low accuracy of remaining life prediction for small samples of mechanical products caused by multiple time scales and multiple fault states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation are used as the remaining life prediction criteria to intuitively reflect The possibility of failure of a component or system at a certain point in time. Considering the demand of adaptive small-sample residual life prediction data, this paper establishes the adaptive matching pre-processor model of life characteristics. On this basis, the LSTM neural network is used to establish a small-sample adaptive residual life prediction model. Then, the XJTU-SY bearing life data set and the test data of the small-sample life characteristics measured by the RV reducer are used as the research objects, and a small amount of the data set is randomly selected. The remaining life expectancy is predicted from the sample data and compared with its standard remaining life, respectively. The comparison results show that the overall prediction error is small. This study shows that the remaining life prediction model established can better predict the remaining life of mechanical product sub-sample data and provides a feasible method for predicting the remaining life of mechanical product sub-samples.

Context: Huoxin formula is a Traditional Chinese Medicine for coronary heart disease (CHD) treatment. Objective: To explore the therapeutic mechanism of the Huoxin formula on arterial functions in CHD patients. Materials and methods: Fifty-eight CHD patients receiving cardiovascular drugs including β-receptor blocker, statins, and antiplatelet medications or others were randomized into intervention [additionally 13.5 g Huoxin formula granules dissolved in 150 mL warm water per time, twice a day (n = 30)] and control [only cardiovascular drugs (n = 28)] groups. Serum biomarkers (hs-CRP, IL-18, IL-17, TNF-α, MMP-9), and cardiovascular indicators of the common and internal carotid arteries (ICAs) were monitored before and after the treatments. Results: After 3 months of treatment, the increases of intima-media thicknesses (IMT) of the left and right common carotid arteries (CCAs) as well as of the left and right ICAs and the increases of the left and right cardio-ankle vascular index were all significantly (all p

Rf4 is one of the dominant restorer genes for maize C-type cytoplasmic male sterility (CMS-C), which has significant value in hybrid maize seed production. However, the highly complex fertility restoration mechanism of CMS-C makes it difficult to screen Rf4-restorer lines, and insufficient Rf4-restorer lines limit its use in current agricultural production. To search for Rf4-restorer lines, in this study, the genotypes of eighteen inbred maize lines at the Rf4 locus were analyzed based on the male fertility investigation of hybrid F1, the genetic analysis of F2 populations, molecular marker mapping, allelic tests, and Rf4 genomic sequence analysis. Our results indicated that of the eighteen maize inbred lines, ten were able to completely rescue CMS-C line CHuangzaosi (CHZS) male sterility. A genetic analysis showed that DAN598, PHT77, 78551S, and LH212Ht only contained one dominant restorer gene each, and the molecular-marker mapping indicated that their restorer genes were located at the short arm of chromosome 8. The allelic testing of the fertility of the restorer (Rf) demonstrated that the restorer gene of twelve inbred lines, including DAN598, PHT77, 78551S, and LH212Ht, was allelic to one restorer gene of A619. Furthermore, the genomic sequence alignment of Rf4 revealed that there were two different amino acids in the coding sequence between the A619 (Rf4Rf4) restorer lines and four CMS-C lines (rf4rf4). For the crucial S1596 site variation (TTT/TAC), DAN598, PHT77, 78551S, and LH212Ht shared the same bases (TTT) with A619 and encoded phenylalanine, while the four CMS-C sterile lines had the TAC and encoded tyrosine. Our results revealed that these tester lines, DAN598, PHT77, 78551S, and LH212Ht, were the Rf4-restorer lines. Additionally, derived from the sequence variants of Rf4, 39 possible Rf4-restorer lines from 129 inbred maize lines were detected. Furthermore, we developed a Cleaved Amplified Polymorphism Sequences (CAPS) marker based on the S1596 variations. The PCR amplification product of S1596 (TAC) was digested by the TatI endonuclease into two bands with sizes of ~260 bp and ~100 bp. In comparison, when S1596 was TTT, the PCR product could not be digested. In conclusion, in this study, we identified various Rf4-restorer lines for maize CMS-C and developed a molecular marker for Rf4. The reported results will contribute to the popularization and application of Rf4 in hybrid maize-seed production.

Although genetic transformation has opened up a new era for cotton molecular breeding, it still suffers from the limitation problem of long transformation periods, which slows down the generation of new cotton germplasms. In this study, LT gene (SV40 large T antigen), which promotes the transformation efficiency of animal cells, was codon-optimized. Its overexpression vector was transformed into cotton. It was observed that EC (embryogenic callus) formation period was 33% shorter and transformation efficiency was slightly higher in the LT T0 generation than that of control. RNA-seq data of NEC (non-embryonic callus) and EC from LT and control revealed that more DEGs (differential expression genes) in NEC were identified than that of EC, indicating LT mainly functioned in NEC. Further KEGG, GO, and transcription factor analyses showed that DEGs were significantly enriched in brassinosteroid biosynthesis pathways and that bHLH, MYB, and AP2/ERF were the top three gene families, which are involved in EC formation. In addition, the key genes related to the auxin pathway were differentially expressed only in LT overexpression NEC, which caused early response, biosynthesis, and transportation of the hormone, resulting in EC earlier formation. In summary, the results demonstrated that LT can promote somatic embryogenesis in cotton, which provides a new strategy for improving cotton transformation and shortening EC formation time.

BackgroundOsteosarcoma (OS) is a highly malignant and aggressive bone tumor. This study was performed to explore the mechanisms of HuR (human antigen R) in the progression of OS.MethodsHuR expression levels in OS tissues and cells were detected by immunohistochemistry and western blotting. HuR siRNA was transfected into SJSA-1 OS cells to downregulate HuR expression, and then cell proliferation, migration, and epithelial-mesenchymal transition (EMT) were evaluated. RNA immunoprecipitation was performed to determine the association of the long non-coding RNA (lncRNA) XIST and argonaute RISC catalytic component (AGO) 2 with HuR. Fluorescence in situ hybridization analysis was performed to detect the expression of lncRNA XIST. Western blotting and immunofluorescence assays were performed to observe AGO2 expression after HuR or/and lncRNA XIST knockdown.ResultsKnockdown of HuR repressed OS cell migration and EMT. AGO2 was identified as a target of HuR and silencing of HuR decreased AGO2 expression. The lncRNA XIST was associated with HuR-mediated AGO2 suppression. Moreover, knockdown of AGO2 significantly inhibited cell proliferation, migration, and EMT in OS.ConclusionOur findings indicate that HuR knockdown suppresses OS cell EMT by regulating lncRNA XIST/AGO2 signaling.

Aiming at the problem that the traditional reliability models of mechanical products are used to predict the reliability of hydraulic automatic transmission and the expected result is relatively large, firstly, the empirical distribution model line is used to statistically analyze the failure distribution law of the hydraulic automatic transmission; then, the Fourier transform is used to perform frequency domain analysis on experience distribution; on this basis, comprehensively consider the characteristics of experience distribution and frequency domain characteristics of experience distribution, constructs the reliability model of exponential decay oscillation distribution and the corresponding reliability, failure efficiency and average life calculation model; meanwhile, studies the influence of attenuation coefficient, oscillation amplitude, oscillation angle frequency, and other parameters on the probability distribution characteristics. On this basis, the established probability distribution models are adopted to fit the failure time data of hydraulic automatic gearbox carried by a forklift, and the fitting results are compared with exponential distribution models, three-parameter Weibull models, and “bathtub curve” models. The comparing results show that the established exponential decayed oscillation distribution model can better describe the probability distribution characteristics of the fault-free working time of automatic transmission, and the use of this model can obtain a smaller root mean square error. Simultaneously, the research conclusions of this paper can provide meaningful guidance and reference for the analysis of the life distribution model of mechanical products with exponentially attenuated oscillation probability density change law.

This paper reports an experimental and theoretical investigation on the fatigue performance enhancement of damaged steel structures by bonded carbon fiber reinforced polymer (CFRP) systems considering the temperature effects. First, thermomechanical properties of the epoxy adhesive and CFRPs were examined via dynamic mechanical analysis (DMA) and tensile tests. The appropriate curing procedure for the structural adhesive was obtained. Next, the fatigue behavior of notched steel plates strengthened by bonded CFRPs was investigated at different temperatures. During the fatigue tests, beach marking and back-face strain techniques were used to monitor the fatigue crack growth on steel components and the damage evolution within the bonding interface. The results show that the high-temperature resistance of the epoxy adhesive can be effectively improved by increasing the curing temperature and duration. The properly cured bonded CFRP patches can effectively enhance the fatigue performance of damaged steel structures. However, and the elevated temperatures significantly degrade the fatigue behavior of the CFRP-strengthened steel components.

Abstract Background C-type cytoplasmic male sterility (CMS-C) is one of the three major types of cytoplasmic male sterility (CMS) in maize. Rf4 is a dominant restorer gene for CMS-C and has great value in hybrid maize breeding, but little information concerning its functional mechanism is known. Results To reveal the functional mechanism of Rf4, we developed a pair of maize near-isogenic lines (NILs) for the Rf4 locus, which included a NIL_rf4 male-sterile line and a NIL_Rf4 male fertility-restored line. Genetic analysis and molecular marker detection indicated that the male fertility of NIL_Rf4 was controlled by Rf4. Whole-genome sequencing demonstrated genomic differences between the two NILs was clustered in the Rf4 mapping region. Unmapped reads of NILs were further assembled to uncover Rf4 candidates. RNA-Seq was then performed for the developing anthers of the NILs to identify critical genes and pathways associated with fertility restoration. A total of 7125 differentially expressed genes (DEGs) were identified. These DEGs were significantly enriched in 242 Gene Ontology (GO) categories, wherein 100 DEGs were involved in pollen tube development, pollen tube growth, pollen development, and gametophyte development. Homology analysis revealed 198 male fertility-related DEGs, and pathway enrichment analysis revealed that 58 DEGs were enriched in cell energy metabolism processes involved in glycolysis, the pentose phosphate pathway, and pyruvate metabolism. By querying the Plant Reactome Pathway database, we found that 14 of the DEGs were involved in the mitochondrial tricarboxylic acid (TCA) cycle and that most of them belonged to the isocitrate dehydrogenase (IDH) and oxoglutarate dehydrogenase (OGDH) enzyme complexes. Transcriptome sequencing and real-time quantitative PCR (qPCR) showed that all the above TCA cycle-related genes were up-regulated in NIL_Rf4. The results of our subsequent enzyme-linked immunosorbent assay (ELISA) experiments pointed out that the contents of both the IDH and OGDH enzymes accumulated more in the spikelets of NIL_Rf4 than in those of NIL_rf4. Conclusion The present research provides valuable genomic resources for deep insight into the molecular mechanism underlying CMS-C male fertility restoration. Importantly, our results indicated that genes involved in energy metabolism, especially some mitochondrial TCA cycle-related genes, were associated with maize CMS-C male fertility restoration.

Ducks, one of the most common domestic fowls, originated from mallards. Here, the authors perform whole-genome sequencing of mallards, indigenous-breed ducks, and Pekin ducks, as well as 1026 ducks from a population generated by wild × domestic crosses to identify selection signals and map variants associated with body size and plumage color.

Abstract Background To identify optimization of dose distributions of target volumes and decrease of radiation doses to normal tissues during stereotactic body radiation therapy (SBRT) for pancreatic cancer with dose-limiting auto-shells. Methods With the same prescription dose, dose constraints of normal organs and calculation algorithm, treatment plans of each eligible patient were re-generated with 3 shells, 5 shells and 7 shells, respectively. The prescription isodose line and beam number of each patient in 3-shell, 5-shell and 7-shell plan remained the same. Hence, a triplet data set of dosimetric parameters was generated and analyzed. Results As the increase of shell number, the conformal index, volumes encompassed by 100% prescription isodose line and 30% prescription isodose line significantly decreased. The new conformal index was higher in 3-shell group than that in 5-shell and 7-shell group. A sharper dose fall-off was found in 5-shell and 7-shell group compared to 3-shell group. And the tumor coverage in 7-shell was better than that of 3-shell and 5-shell. Lower D5cc of the intestine, D10cc of the stomach, Dmax of the spinal cord and smaller V10 of the spleen was confirmed in 7-shell group compared to 3-shell group. Conclusions More conformal dose distributions of target volumes and lower radiation doses to normal organs could be performed with the increase of dose-limiting auto-shells, which may be more beneficial to potential critical organs without established dose constraints.

Producing food via vertical farming (VF) is an efficient method since it requires less space with increased yield per unit area. Such a system can assist in solving major food-shortage problems since it presents a higher crop yield per unit area compared to conventional farming. Thus, VF can be seen as a production method that can cope with the challenge of the constantly growing population, making it also possible to cultivate crops in regions with adverse climate conditions. However, the public might be concerned about the sustainability of VF systems since plants are produced in an unconventional setting. Therefore, there is a need to consider and evaluate the consumers’ acceptance of VF. The particular study attempts to both analyse consumer acceptance of VF in the Nordic areas and offer insights into VF acceptance among young customers in a comparative analysis. The results indicated that VF is not widely accepted by young Nordic consumers. The concept of sustainability is one of the principles driving forces behind consumer acceptance of vertical farms. The more cases of vertical farms in European cities, the better seems to be the level of acceptance among young customers and their willingness to purchase their products.

The transcription factor ZmbHLH16, the maize ortholog of OsTIP2 (OsbHLH142), was isolated in the present study. Tissue expression analysis showed that ZmbHLH16 is preferentially expressed in male reproductive organs. Subcellular location analysis of ZmbHLH16 via rice protoplast indicated that it is located in the nucleus. Through nucleotide variation analysis, 36 polymorphic sites in ZmbHLH16, including 23 single nucleotide polymorphisms and 13 InDels, were detected among 78 maize inbred lines. Neutrality tests and linkage disequilibrium analysis showed that ZmbHLH16 experienced no significant evolutionary pressure. Yeast one-hybrid experiment showed that the first 80 residues in the N-terminus of ZmbHLH16 had transactivation activity, whereas the full length did not. Genome-wide coexpression analysis showed that 395 genes were coexpressed with ZmbHLH16. Among these genes, the transcription factor ZmbHLH51 had similar expression pattern and identical subcellular localization to those of ZmbHLH16. Subsequently, the interaction between ZmbHLH51 and ZmbHLH16 was verified by yeast two-hybrid experiment. Through yeast two-hybrid analysis of series truncated ZmbHLH16 fragments, we found not only the typical bHLH domain [175-221 amino acids (a.a.)], but also that the 81-160 a.a. and 241-365 a.a. of ZmbHLH16 could interact with ZmbHLH51. All these results lay the foundation for further understanding the functions of ZmbHLH16.

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.