The relationship between human blood metabolites and preeclampsia-eclampsia: A Mendelian randomization study.

Preeclampsia and eclampsia are serious complications of pregnancy, leading to high rates of maternal and neonatal mortality. During pregnancy, there are changes in relevant serum metabolites in women. However, it remains unclear if these serum metabolites contribute to the development of associated disorders during pregnancy. Therefore, we conducted a Mendelian randomization study to explore the causal relationship between serum metabolites and preeclampsia and eclampsia. We utilized the inverse variance weighted model as our primary analysis approach. We complemented this with sensitivity analyses, including the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis, to ensure the robustness of our findings. Furthermore, we conducted linkage disequilibrium score regression, multivariable Mendelian randomization, and metabolic pathway analysis to further explore the genetic data. The Mendelian randomization analysis has identified γ-glutamylglutamine, inosine, and isoleucine 10 metabolites that are significantly associated with preeclampsia, and γ-glutamylglutamine and phenylacetate 8 metabolites that may potentially contribute to the development of eclampsia. Notably, γ-glutamylglutamine has been found to have a causal relationship with both preeclampsia and eclampsia. In the multivariable Mendelian randomization analysis, our research findings suggest that both isoleucine and X-14304-leucylalanine directly impact preeclampsia within the context of amino acids and peptides. Moreover, our observations reveal that carbohydrates can also have a direct effect on preeclampsia. Importantly, it should be emphasized that only 3-lactate in amino acids has been shown to have a direct influence on eclampsia. This research has the potential to enhance our understanding of the biological variances related to disease status, providing a foundation for future investigations.

Establishment of an Efficient Agrobacterium-Mediated Genetic Transformation System to Enhance the Tolerance of the Paraquat Stress in Engineering Goosegrass (Eleusine Indica L.).

Eleusine indica (goosegrass) is a problematic weed worldwide known for its multi-herbicide tolerance/resistance biotype. However, a genetic transformation method in goosegrass has not been successfully established, making a bottleneck for functional genomics studies in this species. Here, we report a successful Agrobacterium-mediated transformation method for goosegrass. Firstly, we optimized conditions for breaking seed dormancy and increasing seed germination rate. A higher callus induction rate from germinated seeds was obtained in N6 than in MS or B5 medium. Then the optimal transformation efficiency of the gus reporter gene was obtained by infection with Agrobacterium tumefaciens culture of OD600 = 0.5 for 30 min, followed by 3 days of co-cultivation with 300 µmol/L acetosyringone. Concentrations of 20 mg L-1 kanamycin and 100 mg L-1 timentin were used to select the transformed calli. The optimal rate of regeneration of the calli was generated by using 0.50 mg L-1 6-BA and 0.50 mg L-1 KT in the culture medium. Then, using this transformation method, we overexpressed the paraquat-resistant EiKCS gene into a paraquat-susceptible goosegrass biotype MZ04 and confirmed the stable inheritance of paraquat-resistance in the transgenic goosegrass lines. This approach may provide a potential mechanism for the evolution of paraquat-resistant goosegrass and a promising gene for the manipulation of paraquat-resistance plants. This study is novel and valuable in future research using similar methods for herbicide resistance.

Role of household waste, governance quality, and greener energy for public health: Evidence from developed economies.

In the current times, the global economies and international organizations declared that pollution is one of the prominent causes of declined human health. Still, most literature is biased toward economic sustainability and ignores such vital issues. The current study tends to identify the factors affecting public health in the Group of Seven economies except for Italy (G6). Specifically, this study aims to investigate the influence of household waste (HHW), bureaucratic quality (BQ), democratic accountability (DA), urbanization growth (URP), GDP per capita, and renewable energy use (EPR) on public health, throughout 1996-2020. This study uses advanced panel data approaches and finds the heterogeneity of slope coefficients, the dependence of cross-sections, and the persistence of cointegration between the variables. The asymmetric distribution of data leads to employing the novel method of moment quantile regression. The estimated results reveal that URP, GDPPC, and EPR significantly increase domestic general government health expenditures, improving public health. However, HHW and BQ adversely affect public health by reducing health expenditures. The robustness of the results is tested via utilizing the panel quantile regression. Based on the empirical findings, this study suggests policies regarding the improvement in public health expenditure, R&D investment, spending in renewable energy sector, and strengthening of the institutional quality.

Role of Environmental Degradation, Institutional Quality, and Government Health Expenditures for Human Health: Evidence From Emerging Seven Countries.

The current study investigates the association of various economic, non-economic, governance, and environmental indicators on human health for seven emerging economies. Covering the period from 2000Q1 to 2018Q1, this study uses various panel data approaches for empirical estimations. The data is found first-order stationary. Besides, the panel slope is heterogeneous and cross-sectional dependence is present. Further, the cointegration association is found valid among the variables. Therefore, panel quantile regression is used to determine the long-run impact of each explanatory variable on human health at four quantiles (Q25, Q50, Q75, and Q90). The estimated results asserted that economic growth, government health expenditure, and human capital significantly reduce human health disasters like malaria incidences and cases. At the same time, greenhouse gas emissions and regulatory quality are significantly and positively correlated to human health issues in emerging economies. Moreover, mixed (unidirectional and bidirectional) causal associations exist between the variables. This study also provides relevant policy implications based on the empirical results, providing a path for regulating various economic, environmental, and governance sectors. Effective policy implementation and preventive measures can reduce the spread of diseases and mortality rates due to Malaria.

Differences of endogenous polyamines and putative genes associated with paraquat resistance in goosegrass (Eleusine indica L.).

BACKGROUND: Paraquat is one of the most effective herbicides used to control weeds in agricultural management, while the pernicious weed goosegrass (Eleusine indica) has evolved resistance to herbicides, including paraquat. Polyamines provide high-level paraquat resistance in many plants. In the present study, we selected three polyamines, namely, putrescine, spermidine, and spermine, as putative genes to investigate their correlation with paraquat resistance by using paraquat-resistant (R) and paraquat-susceptible (S) goosegrass populations. RESULTS: There was no significant difference in the putrescine nor spermine content between the R and S biotypes. However, 30 and 90 min after paraquat treatment, the spermidine concentration was 346.14-fold and 421.04-fold (P < 0.001) higher in the R biotype than in the S biotype, but the spermidine concentration was drastically reduced to a marginal level after 90 min. Since the transcript level of PqE was low while the spermidine concentration showed a transient increase, the PqE gene was likely involved in the synthesis of the paraquat resistance mechanism, regulation of polyamine content, and synthesis of spermidine and spermine. PqTS1, PqTS2, and PqTS3 encode transporter proteins involved in the regulation of paraquat concentration but showed different transcription patterns with synchronous changes in polyamine content. CONCLUSION: Endogenous polyamines (especially spermidine) play a vital role in paraquat resistance in goosegrass. PqE, PqTS1, PqTS2, and PqTS3 were speculated on the relationship between polyamine metabolism and paraquat resistance. To validate the roles of PqE, PqTS1, PqTS2, and PqTS3 in polyamine transport systems, further research is needed.

A Multi-year Beneficial Effect of Seed Priming with Gibberellic Acid-3 (GA3) on Plant Growth and Production in a Perennial Grass, Leymus chinensis.

Seed priming is a widely used technique in crops to obtain uniform germination and high-quality seedlings. In this study, we found a long-term effect of seed priming with gibberellic acid-3 (GA3) on plant growth and production in Leymus chinensis. Seeds were germinated on agar plates containing 0-200 µM GA3, and the germinated seedlings were transplanted to clay planting pots and grown for about one year. The clonal tillers grown from the mother plants were transplanted to field conditions in the second year. Results showed that GA3 treatment significantly increased seed germination rate by 14-27%. GA3 treatment also promoted subsequent plant growth and biomass production, as shown by a significant increase in plant height, tiller number, and fresh and dry weight in both pot (2016) and field (2017) conditions. It is particularly noteworthy that the growth-promoting effect of a single seed treatment with GA3 lasted for at least two years. In particular, GA3 treatment at 50 µM increased aboveground fresh and dry weight by 168.2% and 108.9% in pot-grown conditions, and 64.5% and 126.2% in field-grown conditions, respectively. These results imply a transgenerational transmission mechanism for the GA-priming effect on clonal offspring growth and biomass production in L. chinensis.

Enrichment of serum biomarkers by magnetic metal-organic framework composites.

Highly efficient extraction of peptides from serum is critical for finding serum biomarkers using mass spectrometry, which still remains a great challenge. Currently, a bottom-up proteomics approach has been applied to discover serum biomarkers. However, the approach was labor intensive, time and cost consuming, and cannot meet the requirements for clinical application. In this work, Fe3O4/C@MIL-100 composites were synthesized to efficiently capture peptides from microwave-assisted formic acid digests of BSA and human serum prior to MALDI-TOF MS analysis. Fe3O4/C@MIL-100 composites exhibited size-selective adsorption performance, thus providing a rapid and convenient approach to enrich low-abundance peptides. Notably, the peptides' mass fingerprinting of serum digestions between type 2 diabetes mellitus (T2DM) and healthy persons were distinguishable, which indicated the potential ability of this technique for T2DM diagnosis and rapid biomarker discovery. Graphical Abstract Efficient extraction and identification of serum biomarkers using Fe3O4/C@MIL-100 composites from acid hydrolysate.

Rapid Isolation of Phenol Degrading Bacteria by Fourier Transform Infrared (FTIR) Spectroscopy.

Phenol is an important chemical engineering material and ubiquitous in industry wastewater, its existence has become a thorny issue in many developed and developing country. More and more stringent standards for effluent all over the world with human realizing the toxicity of phenol have been announced. Many advanced biological methods are applied to industrial wastewater treatment with low cost, high efficiency and no secondary pollution, but the screening of function microorganisms is certain cumbersome process. In our study a rapid procedure devised for screening bacteria on solid medium can degrade phenol coupled with attenuated total reflection fourier transform infrared (ATR-FTIR) which is a detection method has the characteristics of efficient, fast, high fingerprint were used. Principal component analysis (PCA) is a method in common use to extract fingerprint peaks effectively, it couples with partial least squares (PLS) statistical method could establish a credible model. The model we created using PCA-PLS can reach 99. 5% of coefficient determination and validation data get 99. 4%, which shows the promising fitness and forecasting of the model. The high fitting model is used for predicting the concentration of phenol at solid medium where the bacteria were grown. The highly consistent result of two screening methods, solid cultural with ATR-FTIR detected and traditional liquid cultural detected by GC methods, suggests the former can rapid isolate the bacteria which can degrade substrates as well as traditional cumbersome liquid cultural method. Many hazardous substrates widely existed in industry wastewater, most of them has specialize fingerprint peaks detected by ATR-FTIR, thereby this detected method could be used as a rapid detection for isolation of functional microorganisms those can degrade many other toxic substrates.

Biocompatible chiral monolithic stationary phase synthesized via atom transfer radical polymerization for high performance liquid chromatographic analysis.

Novel biocompatible chiral monolithic stationary phase was prepared by reverse and direct atom transfer radical polymerization (ATRP) methods. By taking advantages of the controlled/living property of ATRP method, the chiral monolith was prepared by reverse ATRP (RATRP) firstly. An attractive feature of RATRP is the prepared polymer containing a terminal radically transferable atom that can initiate another post-polymerization reaction by direct ATRP. Then, the biocompatible poly(hydroxyethyl methacrylate) (PHEMA) was grafted on the surface of the chiral monolith by direct ATRP as a diffusion barrier for proteins. This biocompatible chiral monolith was successfully used as restricted access stationary phase for determination of enantiomers in biological samples with direct injection by high-performance liquid chromatography (HPLC).

Emerging enantiomeric resolution materials with homochiral nano-fabrications.

The major scientific challenge of enantiomeric separation is to develop simple, rapid, and sensitive routine analytical methods. Generally, enantio-resolution is still based on "three-point interaction" theory, which indicates that homochiral sites are needed for enantio-selective interaction. However, in recent years, advanced materials with precise homochiral fabrication at the nanoscale have been synthesized, and have shown great potential in development of high-throughput enantio-resolution methods. This tutorial review summarizes fabrication and applications of homochiral materials for enantio-selective recognition and separation. These materials, which include intrinsic and restructured chiral metal surfaces, plasmonic nanostructures, coordination polymers, organic polymer sensors, and molecularly imprinted polymers, have been applied as sensors or chiral stationary phases (CSPs) for efficient enantio-resolution.

Synthesis of magnetic framework composites for the discrimination of Escherichia coli at the strain level.

Rapid and efficient characterization and identification of pathogens at the strain level is of key importance for epidemiologic investigations, which still remains a challenge. In this work, solvothermically Fe3O4-COOH@MIL-101 composites were fabricated by in situ crystallization approach. The composites combine the excellent properties of both chromium (III) terephthalate (MIL-101) and carboxylic-functionalized magnetite (Fe3O4-COOH) particles and possess the efficient peptides/proteins enrichment properties and magnetic responsiveness. Fe3O4-COOH@MIL-101 composites as magnetic solid phase extraction materials were used to increase the discriminatory power of MALDI-TOF MS profiles. BSA tryptic peptides at a low concentration of 0.25 fmol µL(-1) could be detected by MALDI-TOF MS. In addition, Fe3O4-COOH@MIL-101 composites were successfully applied in the selective enrichment of the protein biomarkers from bacterial cell lysates and discrimination of Escherichia coli at the strain level. This work provides the possibility for wide applications of magnetic MOFs to discriminate pathogens below the species level.

Restricted access chiral stationary phase synthesized via reversible addition-fragmentation chain-transfer polymerization for direct analysis of biological samples by high performance liquid chromatography.

Novel hydrophilic microparticles containing ß-cyclodextrin (ß-CD) were prepared via one-pot synthesis using reversible addition-fragmentation chain-transfer (RAFT) precipitation polymerization, a "controlled/living" radical polymerization technique. The polymerization was initiated by hydrophilic macromolecular chain-transfer agent [poly(2-hydroxyethyl methacrylate), PHEMA]. The hydrophilic PHEMA on the surface of microparticles can well improve their surface hydrophilicity and lead to their biological compatibility. As chiral restricted access material (RAM), the hydrophilic microparticles can be used for determination of enantiomers in biological samples with direct injection via HPLC analysis.

[Application of SVR in quantitative analysis of wines].

Fourier transform infrared spectroscopy has been widely used in some related fields, thus induces the rapid development of quantitative analysis method based on Lambert-Beer's Law and chemometrics in recent years. The selection of appropriate pre-processing method and calibration model is extremely crucial to the quantitative analysis. The present paper selected 30 wine samples and used infrared spectroscopy combined with vector regression algorithm SVR quantitative analysis model with standard normal variate, baseline correction and outliers elimination to analyze four representative components of wine. Satisfactory results were gained while the relative errors were limited to less than 5%. This method can be applied to the wine representative quantitative analysis for the material content.

An improved ensemble partial least squares for analysis of near-infrared spectra.

Traditional ensemble regression algorithms such as BAgging Partial Least Squares (BAPLS) and BOosting Partial Least Squares (BOPLS) do not perform very well in the data set that is relatively small or contaminated by random noise. To make the method robust and improve its prediction ability, inspired from bias-variance-covariance decomposition, we propose an improved ensemble partial least squares method based on the diversity. The new method is applied to quantitative analysis of Near InfraRed (NIR) data sets. A comparative study between the proposed method and other previous methods including BAPLS and BOPLS on two NIR data sets is provided. Experimental results show that the proposed method can achieve better performance than other methods.

[SVM-based qualitative analysis of Muscat Hamburg wine produced in Tianjin region].

The purpose was to achieve the identification of Muscat Hamburg wines produced in Tianjin region through scanning and analyzing dry white wine samples of different grape varieties and regions by infrared spectroscopy technology. A support vector machine (SVM) based method was introduced to analyze infrared spectra of dry white wines. The pretreatment processes of the IR spectra were also elaborated, including baseline adjustment, noise Elimination, standard normalization and eliminating the main component of abnormal sample points. The authors selected great quantity of dry white wine samples of different grape regions including 511 Muscat Hamburg wine samples, 438 Italian Riesling wine samples, 307 Chardonnay wine samples, 29 Ugni Blanc wine samples, 44 Rkatsiteli wine samples, 31 longan wine samples and 79 ZeHong wine samples. According to different classification problems, 80% of IR spectra of the wine samples were used to establish discrimination models with SVM-based method, and the remaining 20% of IR spectra were used for the validation of the discrimination models. Experimental results showed that the proposed method is effective, since high classification accuracy, identification rate and rejecting rate were achieved: over 97% for the white wine samples of different grape varieties, meanwhile over 98% for the Muscat Hamburg wine samples produced in different regions. So the method developed in this paper played a good role in the qualitative classification and discrimination of Muscat Hamburg wines produced in Tianjin region. This novel method has a considerable potential and a rosy application future due to the expeditiousness, stability and easy-operation of FTIR method, as well as the veracity and credibility of SVM method.

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis.

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm "Baseline Correction Combined Partial Least Squares (BCC-PLS)", which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7-19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33-68%).

Asymmetric least squares for multiple spectra baseline correction.

In this paper, based on asymmetric least squares smoothing, a new algorithm for multiple spectra baseline correction is proposed. By means of the similarity among the multiple spectra, the algorithm estimates the baselines by penalizing the differences in the baseline corrected signals, which makes the algorithm possible to eliminate scatter effects on the spectra. In addition, a relaxation factor which measures the similarity of the baseline corrected spectra is incorporated into the optimization model and an alternate iteration strategy is used to solve the optimization problem. The proposed algorithm is fast and can output multiple baselines simultaneously. Experimental results on both simulated data and real data demonstrate the effectiveness and efficiency of the algorithm.

[Analysis of liquor flavor spectra and pattern recognition computation].

Chinese liquor is a complex mixture and contains a large amount of microconstituents, which affects the quality and flavor of liquor. In order to discriminate liquor flavors rapidly, the spectra of liquors were obtained by FTIR and employed as the input patterns of pattern classification algorithms, then liquor flavor discrimination models were built. This paper introduces liquor flavor pattern recognition algorithms comprehensively and systematically for the first time, and the algorithms contain statistical classifications (linear discriminant function, quadratic discriminant function, regularized discriminant analysis, and K nearest neighbor), prototype learning algorithm (learning vector quantization), support vector machine and adaboost algorithm. Experimental results show that the liquor flavor classification algorithms demonstrate good performance and achieve high accuracy, recognition rate and rejection rate.