Gut microbial fatty acid isomerization modulates intraepithelial T cells.

The human gut microbiome constantly converts natural products derived from the host and diet into numerous bioactive metabolites1-3. Dietary fats are essential micronutrients that undergo lipolysis to release free fatty acids (FAs) for absorption in the small intestine4. Gut commensal bacteria modify some unsaturated FAs-for example, linoleic acid (LA)-into various intestinal FA isomers that regulate host metabolism and have anticarcinogenic properties5. However, little is known about how this diet-microorganism FA isomerization network affects the mucosal immune system of the host. Here we report that both dietary factors and microbial factors influence the level of gut LA isomers (conjugated LAs (CLAs)) and that CLAs in turn modulate a distinct population of CD4+ intraepithelial lymphocytes (IELs) that express CD8αα in the small intestine. Genetic abolition of FA isomerization pathways in individual gut symbionts significantly decreases the number of CD4+CD8αα+ IELs in gnotobiotic mice. Restoration of CLAs increases CD4+CD8αα+ IEL levels in the presence of the transcription factor hepatocyte nuclear factor 4γ (HNF4γ). Mechanistically, HNF4γ facilitates CD4+CD8αα+ IEL development by modulating interleukin-18 signalling. In mice, specific deletion of HNF4γ in T cells leads to early mortality from infection by intestinal pathogens. Our data reveal a new role for bacterial FA metabolic pathways in the control of host intraepithelial immunological homeostasis by modulating the relative number of CD4+ T cells that were CD4+CD8αα+.

Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development.

The brain is a complex tissue whose function relies on coordinated anatomical and molecular features. However, the molecular annotation of the spatial organization of the brain is currently insufficient. Here, we describe microfluidic indexing-based spatial assay for transposase-accessible chromatin and RNA-sequencing (MISAR-seq), a method for spatially resolved joint profiling of chromatin accessibility and gene expression. By applying MISAR-seq to the developing mouse brain, we study tissue organization and spatiotemporal regulatory logics during mouse brain development.

Growth Factor FGF2 Cooperates with Interleukin-17 to Repair Intestinal Epithelial Damage.

The intestinal epithelial barrier plays a critical role in the mucosal immunity. However, it remains largely unknown how the epithelial barrier is maintained after damage. Here we show that growth factor FGF2 synergized with interleukin-17 (IL-17) to induce genes for repairing of damaged epithelium. FGF2 or IL-17 deficiency resulted in impaired epithelial proliferation, increased pro-inflammatory microbiota outgrowth, and consequently worse pathology in a DSS-induced colitis model. The dysregulated microbiota in the model induced transforming growth factor beta 1 (TGFß1) expression, which in turn induced FGF2 expression mainly in regulatory T cells. Act1, an essential adaptor in IL-17 signaling, suppressed FGF2-induced ERK activation through binding to adaptor molecule GRB2 to interfere with its association with guanine nucleotide exchange factor SOS1. Act1 preferentially bound to IL-17 receptor complex, releasing its suppressive effect on FGF2 signaling. Thus, microbiota-driven FGF2 and IL-17 cooperate to repair the damaged intestinal epithelium through Act1-mediated direct signaling cross-talk.

Comprehensive Similarity Algorithm and Molecular Dynamics Simulation-Assisted Terahertz Spectroscopy for Intelligent Matching Identification of Quorum Signal Molecules (N-Acyl-Homoserine Lactones).

To investigate the mechanism of aquatic pathogens in quorum sensing (QS) and decode the signal transmission of aquatic Gram-negative pathogens, this paper proposes a novel method for the intelligent matching identification of eight quorum signaling molecules (N-acyl-homoserine lactones, AHLs) with similar molecular structures, using terahertz (THz) spectroscopy combined with molecular dynamics simulation and spectral similarity calculation. The THz fingerprint absorption spectral peaks of the eight AHLs were identified, attributed, and resolved using the density functional theory (DFT) for molecular dynamics simulation. To reduce the computational complexity of matching recognition, spectra with high peak matching values with the target were preliminarily selected, based on the peak position features of AHL samples. A comprehensive similarity calculation (CSC) method using a weighted improved Jaccard similarity algorithm (IJS) and discrete Fréchet distance algorithm (DFD) is proposed to calculate the similarity between the selected spectra and the targets, as well as to return the matching result with the highest accuracy. The results show that all AHL molecular types can be correctly identified, and the average quantization accuracy of CSC is 98.48%. This study provides a theoretical and data-supported foundation for the identification of AHLs, based on THz spectroscopy, and offers a new method for the high-throughput and automatic identification of AHLs.

Nanoporous Graphene Oxide-Based Quartz Crystal Microbalance Gas Sensor with Dual-Signal Responses for Trimethylamine Detection.

This paper presents a straightforward method to develop a nanoporous graphene oxide (NGO)-functionalized quartz crystal microbalance (QCM) gas sensor for the detection of trimethylamine (TMA), aiming to form a reliable monitoring mechanism strategy for low-concentration TMA that can still cause serious odor nuisance. The synthesized NGO material was characterized by transmission electron microscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy to verify its structure and morphology. Compared with the bare and GO-based QCM sensors, the NGO-based QCM sensor exhibited ultra-high sensitivity (65.23 Hz/µL), excellent linearity (R2 = 0.98), high response/recovery capability (3 s/20 s) and excellent repeatability (RSD = 0.02, n = 3) toward TMA with frequency shift and resistance. Furthermore, the selectivity of the proposed NGO-based sensor to TMA was verified by analysis of the dual-signal responses. It is also proved that increasing the conductivity did not improve the resistance signal. This work confirms that the proposed NGO-based sensor with dual signals provides a new avenue for TMA sensing, and the sensor is expected to become a potential candidate for gas detection.

High-Precision Automatic Identification of Fentanyl-Related Drugs by Terahertz Spectroscopy with Molecular Dynamics Simulation and Spectral Similarity Mapping.

Fentanyl is a potent opioid analgesic with high bioavailability. It is the leading cause of drug addiction and overdose death. To better control the abuse of fentanyl and its derivatives, it is crucial to develop rapid and sensitive detection methods. However, fentanyl-related substrates undergo similar molecular structures resulting in similar properties, which are difficult to be identified by conventional spectroscopic methods. In this work, a method for the automatic identification of 8 fentanyl-related substances with similar spectral characteristics was developed using terahertz (THz) spectroscopy coupled with density functional theory (DFT) and spectral similarity mapping (SSM). To characterize the THz fingerprints of these fentanyl-related samples more accurately, the method of baseline estimation and denoising with sparsity was performed before revealing the unique molecular dynamics of each substance by DFT. The SSM method was proposed to identify these fentanyl analogs based on weighted spectral cosine-cross similarity and fingerprint discrete Fréchet distance, generating a matching list by stepwise searching the entire spectral database. The top matched list returned the identification results of the target fentanyl analogs with accuracies of 94.48~99.33%. Results from this work provide algorithms' increased reliability, which serves as an artificial intelligence-based tool for high-precision fentanyl analysis in real-world samples.

Transcriptome Analysis Reveals Genes and Pathways Associated with Salt Tolerance during Seed Germination in Suaeda liaotungensis.

Seed germination is susceptible to external environmental factors, especially salt stress. Suaeda liaotungensis is a halophyte with strong salt tolerance, and the germination rate of brown seeds under 1000 mM NaCl treatment still reached 28.9%. To explore the mechanism of salt stress response during brown seed germination in Suaeda liaotungensis, we conducted transcriptomic analysis on the dry seeds (SlD), germinated seeds under the control condition (SlG_C), and salt treatment (SlG_N). Transcriptome analysis revealed that 13314 and 755 differentially expressed genes (DEGs) from SlD vs. SlG_C and SlG_C vs. SlG_N were detected, respectively. Most DEGs were enriched in pathways related to transcription regulation and hormone signal transduction, ROS metabolism, cell wall organization or biogenesis, and carbohydrate metabolic process in two contrasting groups. Compared with the control condition, POD and CAT activity, H2O2, soluble sugar, and proline contents were increased during germinated seeds under salt stress. Furthermore, functional analysis demonstrated that overexpression of SlNAC2 significantly enhanced salt tolerance during the germination stage in Arabidopsis. These results not only revealed the tolerant mechanism of brown seed germination in response to salinity stress but also promoted the exploration and application of salt-tolerant gene resources of Suaeda liaotungensis.

A history of opioid exposure in females increases the risk of metabolic disorders in their future male offspring.

Worldwide consumption of opioids remains at historic levels. Preclinical studies report intergenerational effects on the endogenous opioid system of future progeny following preconception morphine exposure. Given the role of endogenous opioids in energy homeostasis, such effects could impact metabolism in the next generation. Thus, we examined diet-induced modifications in F1 male progeny of morphine-exposed female rats (MORF1). When fed a high fat-sugar diet (FSD) for 6 weeks, MORF1 males display features of emerging metabolic syndrome; they consume more food, gain more weight, and develop fasting-induced hyperglycemia and hyperinsulinemia. In the hypothalamus, proteins involved in energy homeostasis are modified and RNA sequencing revealed down-regulation of genes associated with neuronal plasticity, coupled with up-regulation of genes associated with immune, inflammatory, and metabolic processes that are specific to FSD-maintained MORF1 males. Thus, limited preconception morphine exposure in female rats increases the risk of metabolic syndrome/type 2 diabetes in the next generation.

Novel insights into the role of BRD4 in fine particulate matter induced airway hyperresponsiveness.

Epidemiological research has identified that exposure to fine particulate matter (PM2.5) can increase airway hyperresponsiveness (AHR) which is considered a typical characteristic of asthma. Although the effect of PM2.5 on AHR has been elucidated to a certain degree, its exact mechanism remains unclear. Bromodomain-containing protein 4 (BRD4) is recognized as a member of the bromodomain and extraterminal (BET) family, with the ability to maintain higher-order chromatin configuration and regulate gene expression programs. The primary objective of our study was to examine the role of BRD4 in AHR triggered by PM2.5, and to elucidate its possible molecular mechanism. A mouse model with AHR was established using a nose-only PM2.5 exposure system. We observed that PM2.5 enhanced AHR in the experimental group compared to the control group, and this alteration was accompanied by increased lung inflammation and BRD4 expression in bronchi-lung tissue. However, the BRD4 inhibitor (ZL0420) could alleviate the aforementioned alterations in the mouse model with PM2.5 exposure. To explore the exact molecular mechanism, we further examined the role of BRD4 in human airway smooth muscle cells (hASMCs) after exposure to PM2.5 DMSO extracts. We found that PM2.5 DMSO extracts, which promoted the contraction and migration of hASMCs, was accompanied by an increase in the levels of BRD4, kallikrein 14 (KLK14), bradykinin 2 receptor (B2R), matrix metalloproteinases2(MMP-2), matrix metalloproteinases9(MMP-9), vimentin and bradykinin (BK) secretion, while ZL0420 and BRD4 gene silencing could reverse this response. In summary, these results demonstrate that BRD4 is an important player in AHR triggered by PM2.5, and BRD4 inhibition can ameliorate AHR induced by PM2.5. In addition, PM2.5 DMSO extracts can promote the contraction and migration of hASMCs by increasing BRD4 expression.

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning.

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L-1) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2-2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.

Disseminated Cryptococcosis revealed by transverse myelitis in Immunocompetent patient: a case report and review of the literature.

BACKGROUND: Transverse myelitis (TM) is due to inflammatory spinal cord injury with bilateral neurologic involvement, which is sensory, motor, or autonomic in nature. It may be associated with autoimmune disease, vaccination, intoxication and infections. The most common infection cause of TM is Coxsackie virus and Mycoplasma pneumoniae. The cryptococcosis is rare. We present the case of disseminated cryptococcosis revealed by transverse myelitis in an immunocompetent 55-year-old male patient. The literature review is also stated. CASE PRESENTATION: The 55-year-old man suffered from gradual numbness, weakness in both lower limbs and finally paralyzed in the bed. The thoracic spine Computed tomography (CT) was normal, but multiple nodules in the lung were accidentally discovered. Thoracic Magnetic Resonance Imaging (MRI) showed diffused thoracic spinal cord thickening and extensively intramedullary T2 hyper intensity areas. Gadolinium contrast enhanced T1WI showed an intramedullary circle-enhanced nodule at 9th thoracic level. Diagnosis was made by histological examination of the bilateral lung biopsy. The patient was treated successfully with systemic amphotericin B liposome and fluconazole and intrathecal dexamethasone and amphotericin B liposome. CONCLUSIONS: This is a patient with disseminated cryptococcosis involving the lung, spinal cord and adrenal glands, which is rare in the absence of immunodeficiency.

The effect and burden modification of heating on adult asthma hospitalizations in Shijiazhuang: a time-series analysis.

BACKGROUND: Previous studies have found associations between asthma morbidity and air pollution especially in young population, (PLoS One 12:e0180522, 2017; Can J Public Health 103:4-8, 2012; Environ Health Perspect 118:449-57, 2010; Am J Respir Crit Care Med 182:307-16, 2010; J Allergy Clin Immunol 104:717-22, 2008; J Allergy Clin Immunol 104:717-22, 1999; Environ Res 111:1137-47, 2011) but most of them were conducted in areas with relatively low air pollutant level. Moreover, very few studies have investigated the effect and burden modification of heating season during which the ambient air pollution level is significantly different from that during non-heating season in north China. OBJECTIVES: This study aimed to evaluate the effect and burden modification of heating on short-term associations between adult asthma hospitalizations and ambient air pollution in the north China city of Shijiazhuang. METHODS: Generalized additive models combined with penalized distributed lag nonlinear models were used to model associations between daily asthma hospitalizations and ambient air pollutants from 1 January 2013 to 16 December 2016 in Shijiazhuang city, adjusting for long-term and seasonality trend, day of week, statutory holiday, daily mean air pressure and temperature. Attributable risks were calculated to evaluate the burden of asthma hospitalizations due to air pollutants exposure. The effect of pollutants on hospitalization and the attributable measures were estimated in heating and non-heating season separately and the comparisons between the two seasons were conducted. RESULTS: All pollutants demonstrated positive and significant impacts on asthma hospitalizations both in heating season and non-heating season, except for O3 in heating season where a negative association was observed. However, the differences of the pollutant-specific effects between the two seasons were not significant. SO2 and NO2 exposure were associated with the heaviest burden among all pollutants in heating season; meanwhile, PM10 and PM2.5 were associated with the heaviest burden in heating season. CONCLUSIONS: In conclusion, we found evidence of the effect of ambient air pollutants on asthma hospitalizations in Shijiazhuang. The central heating period could modify the effects in terms of attributable risks. The disease burden modification of heating should be taken into consideration when planning intervention measures to reduce the risk of asthma hospitalization.

Resistance to docetaxel in prostate cancer is associated with androgen receptor activation and loss of KDM5D expression.

The androgen receptor (AR) plays an essential role in prostate cancer, and suppression of its signaling with androgen deprivation therapy (ADT) has been the mainstay of treatment for metastatic hormone-sensitive prostate cancer for more than 70 y. Chemotherapy has been reserved for metastatic castration-resistant prostate cancer (mCRPC). The Eastern Cooperative Oncology Group-led trial E3805: ChemoHormonal Therapy Versus Androgen Ablation Randomized Trial for Extensive Disease in Prostate Cancer (CHAARTED) showed that the addition of docetaxel to ADT prolonged overall survival compared with ADT alone in patients with metastatic hormone-sensitive prostate cancer. This finding suggests that there is an interaction between AR signaling activity and docetaxel sensitivity. Here we demonstrate that the prostate cancer cell lines LNCaP and LAPC4 display markedly different sensitivity to docetaxel with AR activation, and RNA-seq analysis of these cell lines identified KDM5D (lysine-specific demethylase 5D) encoded on the Y chromosome as a potential mediator of this sensitivity. Knocking down KDM5D expression in LNCaP leads to docetaxel resistance in the presence of dihydrotestosterone. KDM5D physically interacts with AR in the nucleus, and regulates its transcriptional activity by demethylating H3K4me3 active transcriptional marks. Attenuating KDM5D expression dysregulates AR signaling, resulting in docetaxel insensitivity. KDM5D deletion was also observed in the LNCaP-derived CRPC cell line 104R2, which displayed docetaxel insensitivity with AR activation, unlike parental LNCaP. Dataset analysis from the Oncomine database revealed significantly decreased KDM5D expression in CRPC and poorer prognosis with low KDM5D expression. Taking these data together, this work indicates that KDM5D modulates the AR axis and that this is associated with altered docetaxel sensitivity.

Rapid and Quantitative Determination of Sildenafil in Cocktail Based on Surface Enhanced Raman Spectroscopy.

Sildenafil (SD) and its related compounds are the most common adulterants found in herbal preparations used as sexual enhancer or man's virility products. However, the abuse of SD threatens human health such as through headache, back pain, rhinitis, etc. Therefore, it is important to accurately detect the presence of SD in alcoholic beverages. In this study, the Opto Trace Raman 202 (OTR 202) was used as a surface-enhanced Raman spectroscopy (SERS) active colloids to detect SD. The results demonstrated that the limit of detection (LOD) of SD was found to be as low as 0.1 mg/L. Moreover, 1235, 1401, 1530, and 1584 cm-1 could be qualitatively determined as SD characteristic peaks. In a practical application, SD in cocktail could be easily detected using SERS based on OTR 202. Also, there was a good linear correlation between the intensity of Raman peaks at 1235, 1401, 1530, and 1584 cm-1 and the logarithm of SD concentration in cocktail was in the range of 0.1-10 mg/L (0.9822 < R2 < 0.9860). The relative standard deviation (RSD) was less than 12.7% and the recovery ranged from 93.0%-105.8%. Moreover, the original 500-1700 cm-1 SERS spectra were pretreated and the partial least squares (PLS) was applied to establish the prediction model between SERS spectra and SD content in cocktail and the highest determination coefficient (Rp2) reached 0.9856. In summary, the SD in cocktail could be rapidly and quantitatively determined by SERS, which was beneficial to provide a rapid and accurate scheme for the detection of SD in alcoholic beverages.

Rapid Determination of Thiabendazole Pesticides in Rape by Surface Enhanced Raman Spectroscopy.

Thiabendazole is widely used in sclerotium blight, downy mildew and black rot prevention and treatment in rape. Accurate monitoring of thiabendazole pesticides in plants will prevent potential adverse effects to the Environment and human health. Surface Enhanced Raman Spectroscopy (SERS) is a highly sensitive fingerprint with the advantages of simple operation, convenient portability and high detection efficiency. In this paper, a rapid determination method of thiabendazole pesticides in rape was conducted combining SERS with chemometric methods. The original SERS were pretreated and the partial least squares (PLS) was applied to establish the prediction model between SERS and thiabendazole pesticides in rape. As a result, the SERS enhancing effect based on silver Nano-substrate was better than that of gold Nano-substrate, where the detection limit of thiabendazole pesticides in rape could reach 0.1 mg/L. Moreover, 782, 1007 and 1576 cm−1 could be determined as thiabendazole pesticides Raman characteristic peaks in rape. The prediction effect of thiabendazole pesticides in rape was the best ( R p 2 = 0.94, RMSEP = 3.17 mg/L) after the original spectra preprocessed with 1st-Derivative, and the linear relevance between thiabendazole pesticides concentration and Raman peak intensity at 782 cm−1 was the highest (R² = 0.91). Furthermore, five rape samples with unknown thiabendazole pesticides concentration were used to verify the accuracy and reliability of this method. It was showed that prediction relative standard deviation was 0.70–9.85%, recovery rate was 94.71–118.92% and t value was −1.489. In conclusion, the thiabendazole pesticides in rape could be rapidly and accurately detected by SERS, which was beneficial to provide a rapid, accurate and reliable scheme for the detection of pesticides residues in agriculture products.

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy.

Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm-1 could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm-1: y = 672.26x + 5748.4, R² = 0.9948; 1008 cm-1: y = 1155.4x + 8740.2, R² = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300⁻1700 cm-1 SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (Rp² = 0.9769, RMSEP = 0.556 mg/L, RPD = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil.

Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy.

Deltamethrin is widely used in pest prevention and control such as red spiders, aphids, and grubs in strawberry. It is important to accurately monitor whether the deltamethrin residue in strawberry exceeds the standard. In this paper, density functional theory (DFT) was used to theoretically analyze the molecular structure of deltamethrin, gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were used to enhance the surface enhanced Raman spectroscopy (SERS) detection signal. As a result, the theoretical Raman peaks of deltamethrin calculated by DFT were basically similar to the measured results, and the enhancing effects based on AuNPs was better than that of AgNPs. Moreover, 554, 736, 776, 964, 1000, 1166, 1206, 1593, 1613, and 1735 cm−1 could be determined as deltamethrin characteristic peaks, among which only three Raman peaks (736, 1000, and 1166 cm−1) could be used as the deltamethrin characteristic peaks in strawberry when the detection limit reached 0.1 mg/L. In addition, the 500⁻1800 cm−1 SERS of deltamethrin were analyzed by the partial least squares (PLS) and backward interval partial least squares (BIPLS). The prediction accuracy of deltamethrin in strawberry (Rp2 = 0.93, RMSEp = 4.66 mg/L, RPD = 3.59) was the highest when the original spectra were pretreated by multiplicative scatter correction (MSC) and then modeled by BIPLS. In conclusion, the deltamethrin in strawberry could be qualitatively analyzed and quantitatively determined by SERS based on AuNPs enhancement, which provides a new detection scheme for deltamethrin residue determination in strawberry.

Spectral Characterization and Molecular Dynamics Simulation of Pesticides Based on Terahertz Time-Domain Spectra Analyses and Density Functional Theory (DFT) Calculations.

This work provides the experimental and theoretical fundamentals for detecting the molecular fingerprints of six kinds of pesticides by using terahertz (THz) time-domain spectroscopy (THz-TDS). The spectra of absorption coefficient and refractive index of the pesticides, chlorpyrifos, fipronil, carbofuran, dimethoate, methomyl, and thidiazuron are obtained in frequencies of 0.1⁻3.5 THz. To accurately describe the THz spectral characteristics of pesticides, the wavelet threshold de-noising (WTD) method with db 5 wavelet fucntion, 5-layer decomposition, and soft-threshold de-noising was used to eliminate the spectral noise. The spectral baseline correction (SBC) method based on asymmetric least squares smoothing was used to remove the baseline drift. Spectral results show that chlorpyrifo had three characteristic absorption peaks at 1.47, 1.93, and 2.73 THz. Fipronil showed three peaks at 0.76, 1.23, and 2.31 THz. Carbofuran showed two peaks at 2.72 and 3.06 THz. Dimethoate showed three peaks at 1.05, 1.89, and 2.92 THz. Methomyl showed five peaks at 1.01, 1.65, 1.91, 2.72, and 3.20 THz. Thidiazuron showed four peaks at 0.99, 1.57, 2.17, and 2.66 THz. The density functional theory (DFT) of B3LYP/6-31G+(d,p) was applied to simulate the molecular dynamics for peak analyzing of the pesticides based on isolated molecules. The theoretical spectra are in good agreement with the experimental spectra processed by WTD + SBC, which implies the validity of WTD + SBC spectral processing methods and the accuracy of DFT spectral peak analysis. These results support that the combination of THz-TDS and DFT is an effective tool for pesticide fingerprint analysis and the molecular dynamics simulations.

Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms.

Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application.

Detection of Water Content in Rapeseed Leaves Using Terahertz Spectroscopy.

The terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated. The transmission and absorption spectra in the range of 0.3-2 THz were measured by using THz time-domain spectroscopy. The mean transmittance and absorption coefficients were applied to analyze the change regulation of WC. In addition, the Savitzky-Golay method was performed to preprocess the spectra. Then, the partial least squares (PLS), kernel PLS (KPLS), and Boosting-PLS were conducted to establish models for predicting WC based on the processed transmission and absorption spectra. Reliable results were obtained by these three methods. KPLS generated the best prediction accuracy of WC. The prediction coefficient correlation (Rval) and root mean square error (RMSEP) of KPLS based on transmission were Rval = 0.8508, RMSEP = 0.1015, and that based on absorption were Rval = 0.8574, RMSEP = 0.1009. Results demonstrated that THz spectroscopy combined with modeling methods provided an efficient and feasible technique for detecting plant physiological information.