UDP-glycosyltransferases act as key determinants of host plant range in generalist and specialist Spodoptera species.

Phytophagous insects have evolved sophisticated detoxification systems to overcome the antiherbivore chemical defenses produced by many plants. However, how these biotransformation systems differ in generalist and specialist insect species and their role in determining insect host plant range remains an open question. Here, we show that UDP-glucosyltransferases (UGTs) play a key role in determining the host range of insect species within the Spodoptera genus. Comparative genomic analyses of Spodoptera species that differ in host plant breadth identified a relatively conserved number of UGT genes in generalist species but high levels of UGT gene pseudogenization in the specialist Spodoptera picta. CRISPR-Cas9 knockouts of the three main UGT gene clusters of Spodoptera frugiperda revealed that UGT33 genes play an important role in allowing this species to utilize the poaceous plants maize, wheat, and rice, while UGT40 genes facilitate utilization of cotton. Further functional analyses in vivo and in vitro identified the UGT SfUGT33F32 as the key mechanism that allows generalist S. frugiperda to detoxify the benzoxazinoid DIMBOA (2,4-dihydroxy-7-methoxy-2H-1,4-benzoxazin-3(4H)-one), a potent insecticidal phytotoxin produced by poaceous plants. However, while this detoxification capacity is conserved in several generalist Spodoptera species, Spodoptera picta, which specializes on Crinum plants, is unable to detoxify DIMBOA due to a nonfunctionalizing mutation in SpUGT33F34. Collectively, these findings provide insight into the role of insect UGTs in host plant adaptation, the mechanistic basis of evolutionary transitions between generalism and specialism and offer molecular targets for controlling a group of notorious insect pests.

Subacute thyroiditis during early pregnancy: a case report and literature review.

BACKGROUND: Subacute thyroiditis (SAT) is rarely diagnosed in pregnant women, and only 7 cases have been reported to date. Thyroid dysfunction, especially hyperthyroidism, during pregnancy has been associated with both maternal and neonatal complications. Thus, the early diagnosis and treatment of SAT during pregnancy may be beneficial. We present a case report and literature review to complement the diagnostic evaluation and management of SAT during pregnancy. CASE PRESENTATION: A 27-year-old woman presented in gestational week 17 of her first pregnancy and had a negative prior medical history. She presented to the Endocrinology Department complaining of neck pain for one month that had intensified in the last five days. Physical examination revealed a diffusely enlarged thyroid gland that was firm and tender on palpation. The patient also had an elevated temperature and heart rate. The increasing and long-lasting pain coupled with a decreased level of thyroid-stimulating hormone indicated hyperthyroidism. Ultrasound findings were indicative of SAT. Importantly, the pain was so severe that 10 mg of oral prednisone per day was administered in gestational week 18, which was increased to 15 mg/d after 10 days that was discontinued in week 28. Levothyroxine was started in gestational week 24 and administered throughout the pregnancy. The patient responded well to the treatments, and her neck pain disappeared in gestational week 21. She gave birth to a healthy male in gestational week 41. CONCLUSION: SAT can be diagnosed and effectively managed during pregnancy, thus benefiting mothers and infants.

Study on Defective T Junction-Mediated Strand Displacement Amplification and Its Application in Microchip Electrophoretic Detection of Longer Bacterial 16S rDNA.

Current strand displacement amplification (SDA)-based nucleic acid sensing methods generally rely on a ssDNA template that involves complementary bases to the endonuclease recognition sequence, which has the limitation of detecting only short nucleic acids. Herein, a new SDA method in which the defective T junction structure is first used to support SDA (dT-SDA) was proposed and applied in longer DNA detection. In dT-SDA, an auxiliary probe and a primer were designed to specifically identify the target gene, following the formation of a stable defective T junction structure through proximity hybridization, and the formation of defective T junctions could further trigger cascade SDA cycling to produce numerous ssDNA products. The quantity of these ssDNA products was detected through microchip electrophoresis (MCE) and could be transformed to the concentration of the target gene. Moreover, the applicability of this developed strategy in detecting long genomic DNA was verified by detecting bacterial 16S rDNA. This proposed dT-SDA strategy consumes less time and has satisfactory sensitivity, which has great potential for effective bacterial screening and infection diagnosis.

A case report of spontaneous staphylococcal meningitis in a cynomolgus monkey.

This report describes a suppurative meningitis in a young cynomolgus. The animal had neutrophil aggregation in the subarachnoid space and hemorrhage in bilateral adrenal glands. Staphylococcus was identified by FISH in brain. To our knowledge, this is the first case of staphylococcal meningitis with Waterhouse-Friderichsen syndrome in a cynomolgus monkey.

Occupational Carbon Monoxide Poisoning Associated with Diesel Motor Operation in an Air-raid Shelter in June 2015, Jinan City, China-An Epidemiological Investigation.

Occupational carbon monoxide (CO) poisoning related to diesel motor fumes in an air-raid shelter (ARS) was first identified in Jinan City, China, in June 2015. A total of 17 cases were identified, including 14 possible cases of firemen and 3 confirmed cases of water channel clean-up workers. The overall attack rate (AR) of firemen was 42% (14/33). The firemen had a significantly higher AR with a longer exposure and more protracted time of rescue in the ARS (P < 0.05). All the cases stated that they did not realize the potentially high level of exposure to CO in the ARS. CO poisoning posed a risk to both patients and service providers. Occupational safety and health education should be promoted and enforced in all workplaces where CO sources exist.

The HepaRG cell line, a superior in vitro model to L-02, HepG2 and hiHeps cell lines for assessing drug-induced liver injury.

Drug-induced liver injury (DILI) is a leading cause of discontinuation of new drug approval or withdrawal of marketed medicine based on safety due to organ vulnerability. The aim of this research is to investigate the potential abilities of four different in vitro cell models (L-02, HepG2, HepaRG, and hiHeps cell lines) in assessing marketed drugs labeled with apparently different types of liver injury. A total of 17 drugs with versatile pharmacological profiles were chosen, of which, 14 drugs are recognized as DILI agents and 3 drugs are DILI irrelevant. Preliminary cellular screening assays indicated that the HepaRG cell line had an advantage over other cell lines in predicting drugs associated with DILI in vitro as it had the highest Youden's index (71.4%). A multi-parametric screening assay showed that oxidative stress, mitochondrial damage, and disorders of neutral lipid metabolism were changed notably in the HepaRG cell line after DILI-related drugs exposure, accounting for its high sensitivity in comparison with other three cell lines. In addition, aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and malate dehydrogenase (MDH) all correlated with the cytotoxic effects of diclofenac sodium (p < 0.05), buspirone hydrochloride (p < 0.01), and danazol (p < 0.01) in the HepaRG cell line. We conclude that the HepaRG cell line is a superior in vitro cell model to other three cell lines for evaluating drugs with DILI potential.

Polygonumnolides C1-C4; minor dianthrone glycosides from the roots of Polygonum multiflorum Thunb.

Four new dianthrone glycosides, named polygonumnolides C1-C4 (1-4), were isolated from the dried roots of Polygonum multiflorum Thunb, together with two known emodin dianthrones (5-6). Their hepatotoxicities were evaluated against L-02 cell lines. Compounds 1-4 showed weak hepatotoxicity against L-02 cell lines with IC50 values of 313.05, 205.20, 294.20, and 207.35 µM, respectively.

[Soil Salinity Estimation Based on Near-Ground Multispectral Imagery in Typical Area of the Yellow River Delta].

This study chooses the core demonstration area of 'Bohai Barn' project as the study area, which is located in Wudi, Shandong Province. We first collected near-ground and multispectral images and surface soil salinity data using ADC portable multispectral camera and EC110 portable salinometer. Then three vegetation indices, namely NDVI, SAVI and GNDVI, were used to build 18 models respectively with the actual measured soil salinity. These models include linear function, exponential function, logarithmic function, exponentiation function, quadratic function and cubic function, from which the best estimation model for soil salinity estimation was selected and used for inverting and analyzing soil salinity status of the study area. Results indicated that all models mentioned above could effectively estimate soil salinity and models using SAVI as the dependent variable were more effective than the others. Among SAVI models, the linear model (Y = -0.524x + 0.663, n = 70) is the best, under which the test value of F is the highest as 141.347 at significance test level, estimated R2 0.797 with a 93.36% accuracy. Soil salinity of the study area is mainly around 2.5 per thousand - 3.5 per thousand, which gradually increases from southwest to northeast. The study has probed into soil salinity estimation methods based on near-ground and multispectral data, and will provide a quick and effective technical soil salinity estimation approach for coastal saline soil of the study area and the whole Yellow River Delta.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

[Land salinization information extraction method based on HSI hyperspectral and TM imagery].

This paper chose the typical salinization area in Kenli County of the Yellow River Delta as the study area, selected HJ-1A satellite HSI image at March 15, 2011 and TM image at March 22, 2011 as source of information, and pre-processed these data by image cropping, geometric correction and atmospheric correction. Spectral characteristics of main land use types including different degree of salinization lands, water and shoals were analyzed to find distinct bands for information extraction Land use information extraction model was built by adopting the quantitative and qualitative rules combining the spectral characteristics and the content of soil salinity. Land salinization information was extracted via image classification using decision tree method. The remote sensing image interpretation accuracy was verified by land salinization degree, which was determined through soil salinity chemical analysis of soil sampling points. In addition, classification accuracy between the hyperspectral and multi-spectral images were analyzed and compared. The results showed that the overall image classification accuracy of HSI was 96.43%, Kappa coefficient was 95.59%; while the overall image classification accuracy of TM was 89.17%, Kappa coefficient was 86.74%. Therefore, compared to multi-spectral TM data, the hyperspectral imagery could be more accurate and efficient for land salinization information extraction. Also, the classification map showed that the soil salinity distinction degree of hyperspectral image was higher than that of multi-spectral image. This study explored the land salinization information extraction techniques from hyperspectral imagery, extracted the spatial distribution and area ratio information of different degree of salinization land, and provided decision-making basis for the scientific utilization and management of coastal salinization land resources in the Yellow River Delta.

[Hyperspectral estimation of kalium content in apple florescence canopy based on fuzzy recognition].

The objective of the present paper is fast and nondestructive estimate of kalium content using ASD FieldSpec3 spectrometer determined hyperspectral data in apple florescence canopy. According to detection of hyperspectral data of the apple florescence canopy and kalium content data at laboratory in Qixia city of experimental orchards in 2008 and 2009, the correlation analysis of hyperspectral reflectance and its eleven transforms with kalium content was proceeded. The biggest correlation coefficient as independent variable and the estimation model of kalium content were established based on fuzzy recognition algorithms. The model was tested by sample inspection in 2008 and verified by data in 2009. The results showed that the correlation is less for the original spectral reflectance (R) and its reciprocal(1/R), logarithm (lgR), square root (R1/2) and the kalium content, but it is enhanced obviously for their first derivative and second derivative. The correlation coefficient(r) of kalium content estimating model y = 11.344 5h + 1.309 7 is 0.985 1, the total root mean square difference (RMSE) is 0.355 7 and F statistics is 3 085.6. The average relative error of measured values and estimated values for 24 inspection sample is 9.8%, estimation accuracy is 90.2% and verification accuracy is 83.3% utilizing test data in 2009. It was showed that this model is more stable by estimating apple florescence canopy of kalium content and the model precision is able to meet the needs of production.

[Estimation of chlorophyll content in apple tree canopy based on hyperspectral parameters].

The hyperspectral reflectance of apple tree canopy during spring shoots stopping growth period was measured using ASD FieldSpec3 field spectrometer. Original spectral data were processed in deviation forms, and significant spectrum parameters correlated with chlorophyll content were found out with correlation analysis. The best vegetation indices were chosen and the apple canopy chlorophyll content estimation model was established by analyzing vegetation index of two-band combination in the sensitive region 400-1 350 nm. The result showed that (1) The sensitive band region of apple canopy chlorophyll content is 400-1 350 nm. (2) The vegetation index CCI(D(794)/D(763)) can commendably estimate the apple canopy chlorophyll content. (3) The model with CCI(D(794)/D(763)) as the independent variables was determined to be the best for chlorophyll content prediction of apple tree canopy. Therefore, using hyperspectral technology can estimate apple canopy chlorophyll content more rapidly and accurately, and provides a theoretical basis for rapid apple tree canopy nutrition diagnosis and growth monitoring.

[Mapping environmental vulnerability from ETM + data in the Yellow River Mouth Area].

The environmental vulnerability retrieval is important to support continuing data. The spatial distribution of regional environmental vulnerability was got through remote sensing retrieval. In view of soil and vegetation, the environmental vulnerability evaluation index system was built, and the environmental vulnerability of sampling points was calculated by the AHP-fuzzy method, then the correlation between the sampling points environmental vulnerability and ETM + spectral reflectance ratio including some kinds of conversion data was analyzed to determine the sensitive spectral parameters. Based on that, models of correlation analysis, traditional regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the spectral reflectance and the environmental vulnerability. With this model, the environmental vulnerability distribution was retrieved in the Yellow River Mouth Area. The results showed that the correlation between the environmental vulnerability and the spring NDVI, the September NDVI and the spring brightness was better than others, so they were selected as the sensitive spectral parameters. The model precision result showed that in addition to the support vector model, the other model reached the significant level. While all the multi-variable regression was better than all one-variable regression, and the model accuracy of BP neural network was the best. This study will serve as a reliable theoretical reference for the large spatial scale environmental vulnerability estimation based on remote sensing data.

[Grain yield estimation of wheat-maize rotation cultivated land based on Sentinel-2 multi-spectral image: A case study in Caoxian County, Shandong, China]. / 基于Sentinel-2å¤šå ‰è°±å½±åƒçš„å°éº¦-玉米轮作耕地粮食产量估测­­ä»¥æ›¹åŽ¿ä¸ºä¾‹.

Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.

Gut microbiota-derived nicotinamide mononucleotide alleviates acute pancreatitis by activating pancreatic SIRT3 signalling.

BACKGROUND AND PURPOSE: Gut microbiota dysbiosis induced by acute pancreatitis (AP) exacerbates pancreatic injury and systemic inflammatory responses. The alleviation of gut microbiota dysbiosis through faecal microbiota transplantation (FMT) is considered a potential strategy to reduce tissue damage and inflammation in many clinical disorders. Here, we aim to investigate the effect of gut microbiota and microbiota-derived metabolites on AP and further clarify the mechanisms associated with pancreatic damage and inflammation. EXPERIMENTAL APPROACH: AP rat and mouse models were established by administration of caerulein or sodium taurocholate in vivo. Pancreatic acinar cells were exposed to caerulein and lipopolysaccharide in vitro to simulate AP. KEY RESULTS: Normobiotic FMT alleviated AP-induced gut microbiota dysbiosis and ameliorated the severity of AP, including mitochondrial dysfunction, oxidative damage and inflammation. Normobiotic FMT induced higher levels of NAD+ (nicotinamide adenine dinucleotide)-associated metabolites, particularly nicotinamide mononucleotide (NMN). NMN administration mitigated AP-mediated mitochondrial dysfunction, oxidative damage and inflammation by increasing pancreatic NAD+ levels. Similarly, overexpression of the NAD+ -dependent mitochondrial deacetylase sirtuin 3 (SIRT3) alleviated the severity of AP. Furthermore, SIRT3 deacetylated peroxiredoxin 5 (PRDX5) and enhanced PRDX5 protein expression, thereby promoting its antioxidant and anti-inflammatory activities in AP. Importantly, normobiotic FMT-mediated NMN metabolism induced SIRT3-PRDX5 pathway activation during AP. CONCLUSION AND IMPLICATIONS: Gut microbiota-derived NMN alleviates the severity of AP by activating the SIRT3-PRDX5 pathway. Normobiotic FMT could be served as a potential strategy for AP treatment.

Corn oil as a vehicle in drug development exerts a dose-dependent effect on gene expression profiles in rat thymus.

The purpose of this study was to investigate the effects of corn oil (CO), which is widely used as a vehicle for water-insoluble agents in drug development, on the gene expression profiles in rat thymus with microarray technique. Female Wistar rats were administered daily with normal saline (NS) and CO 2, 5 and 10 ml kg⁻¹ per day for 14 days, respectively. Then, the thymus samples of rats were collected for microarray test and histopathology examination. CD4⁺ and CD8⁺ lymphocytes in peripheral blood were also numerated to assess the effects on lymphocyte subpopulations. The microarray data showed that the numbers of differentially expressed genes in the 2, 5 and 10 ml kg⁻¹ CO groups were 0, 40 and 458, respectively, compared with the NS control group. The altered genes were mainly associated with immune response, cellular response to organic cyclic substance and regulation of fatty acid ß-oxidation. However, no abnormal changes in thymus weight, CD4⁺ and CD8⁺ lymphocytes counts and histopathological examination were observed in the three CO groups. These data showed that 10 ml kg⁻¹ CO, the usually recommended dosing volume as a vehicle in drug safety assessment, caused obvious dysregulated genes in rat thymus. Our study suggests that the appropriate dosing volume of CO gavage as a vehicle for water-insoluble agents in drug development should be 2 ml kg⁻¹ per day, if agent effects on thymus will be assessed in gene levels.

[Hyperspectral estimation models of chlorophyll content in apple leaves].

The present study chose the apple orchard of Shandong Agricultural University as the study area to explore the method of apple leaf chlorophyll content estimation by hyperspectral analysis technology. Through analyzing the characteristics of apple leaves' hyperspectral curve, transforming the original spectral into first derivative, red edge position and leaf chlorophyll index (LCI) respectively, and making the correlation analysis and regression analysis of these variables with the chlorophyll content to establish the estimation models and test to select the high fitting precision models. Results showed that the fitting precision of the estimation model with variable of LCI and the estimation model with variable of the first derivative in the band of 521 and 523 nm was the highest. The coefficients of determination R2 were 0.845 and 0.839, the root mean square errors RMSE were 2.961 and 2.719, and the relative errors RE% were 4.71% and 4.70%, respectively. Therefore LCI and the first derivative are the important index for apple leaf chlorophyll content estimation. The models have positive significance to guide the production of apple cultivation.

Highly sensitive detection of MUC1 by microchip electrophoresis combining with target recycling amplification and strand displacement amplification.

Mucin 1 (MUC1) is usually overexpressed in a variety of malignant tumors, and quantitative analysis of MUC1 plays an important role in the early diagnosis of cancer. In this work, a highly sensitive MUC1 assay was developed by integrating microchip electrophoresis (MCE) with target recycling amplification (TRA) and strand displacement amplification (SDA). Specifically, the presence of MUC1 can trigger the exposure of the designed hairpin probe (HP) to initiate SDA and an amplified amount of ssDNA is produced finally. The amount of these ssDNA can be detected by MCE, then the concentration of MUC1 can be obtained through the correlation between MUC1 concentration and ssDNA concentration. The experimental results show that the MCE signal had a good linear relationship with MUC1 concentration in the range of 1.0 pg/mL - 1.0 × 103 pg/mL with a low limit of detection of 0.23 pg/mL under the optimal conditions (S/N = 3). Additionally, the assay had been successfully applied to detect MUC1 in biological samples with satisfactory results, providing an alternative assay for the detection of other tumor markers owing to the high sensitivity, high selectivity, simple operation and low sample consumption.

Biosensing bacterial 16S rDNA by microchip electrophoresis combined with a CRISPR system based on real-time crRNA/Cas12a formation.

The accurate, simple and sensitive detection of bacterial infections at the early stage is highly valuable in preventing the spread of disease. Recently, CRISPR-Cas12a enzyme-derived nucleic acid detection methods have emerged along with the discovery of the indiscriminate single-stranded DNA (ssDNA) cleavage activity of Cas12a. These nucleic acid detection methods are made effective and sensitive by combining them with isothermal amplification technologies. However, most of the proposed CRISPR-Cas12a strategies involve Cas-crRNA complexes in the preassembled mode, which result in inevitable nonspecific background signals. Besides, the signal ssDNA used in these strategies needs tedious pre-labeling of the signal molecules. Herein, a post-assembly CRISPR-Cas12a method has been proposed based on target-induced transcription amplification and real-time crRNA generation for bacterial 16S rDNA biosensing. This strategy is label-free through the combination of microchip electrophoresis (MCE) detection. In addition, this method eliminates the need for a protospacer adjacent motif (PAM) on the target sequences, and has the potential to be an effective and simple method for nucleic acid detection and infectious disease diagnosis.