Monitoring soil moisture in winter wheat with crop water stress index based on canopy-air temperature time lag effect.

Crop water stress index (CWSI) has been widely used in soil moisture monitoring. However, the influence of the time lag effect between canopy temperature and air temperature on the accuracy of soil moisture monitoring with different CWSI models has not been further investigated. Therefore, based on the continuous record of canopy temperature and air temperature, this study explored the influence of canopy-air temperature hysteresis on the diagnosis of soil moisture with three CWSI models (CWSIT-theoretical, CWSIE-empirical, CWSIH-hybrid). The results show (1) the peak time of canopy temperature was ahead of that of air temperature, and the lag time varied under different soil moisture conditions. When the soil moisture was seriously deficient, the lag time decreased. However, from jointing-heading period to filling-ripening period, the lag time became longer. (2) The values of CWSIT, CWSIE, and CWSIH decreased when the time lag effect was considered. In jointing-heading period, heading-filling period, and filling-ripening period, CWSIT had the highest accuracy in soil moisture monitoring without the consideration of the time lag effect. When the time lag effect was considered, the monitoring accuracy of CWSIE and CWSIH was greatly improved and higher than that of CWSIT, while that of CWSIT was reduced. The findings provided a basis for further improving the accuracy of soil moisture monitoring with CWSI models.

Radar remote sensing-based inversion model of soil salt content at different depths under vegetation.

Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0-10, 10-20, 0-20, 20-40, 0-40, 40-60 and 0-60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10-20 cm was the optimal inversion depth for all the four models, followed by 20-40 and 0-40 cm. Among the four models, SVM was higher in accuracy than the other three at 10-20 cm (RP 2 = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.

Sputum microbiota as a potential diagnostic marker for multidrug-resistant tuberculosis.

The prevalence of drug-resistant Mycobacterium tuberculosis (Mtb) strains makes disease control more complicated, which is the main cause of death in tuberculosis (TB) patients. Early detection and timely standard treatment are the key to current prevention and control of drug-resistant TB. In recent years, despite the continuous advancement in drug-resistant TB diagnostic technology, the needs for clinical rapid and accurate diagnosis are still not fully met. With the development of sequencing technology, the research of human microecology has been intensified. This study aims to use 16 rRNA sequencing technology to detect and analyze upper respiratory flora of TB patients with anti-TB drug sensitivity (DS, n = 55), monoresistance isoniazide (MR-INH, n = 33), monoresistance rifampin (MR-RFP, n = 12), multidrug resistance (MDR, n = 26) and polyresistance (PR, n = 39) in southern China. Potential microbial diagnostic markers for different types of TB drug resistance are searched by screening differential flora, which provides certain guiding significance for drug resistance diagnosis and clinical drug use of TB. The results showed that the pulmonary microenvironment of TB patients was more susceptible to infection by external pathogens, and the infection of different drug-resistant Mtb leads to changes in different flora. Importantly, seven novel microorganisms (Leptotrichia, Granulicatella, Campylobacter, Delfitia, Kingella, Chlamydophila, Bordetella) were identified by 16S rRNA sequencing as diagnostic markers for different drug resistance types of TB. Leptotrichia, Granulicatella, Campylobacter were potential diagnostic marker for TB patients with INH single-resistance. Delftia was a potential diagnostic marker for TB patients with RFP single drug-resistance. Kingella and Chlamydophila can be used as diagnostic markers for TB patients with PR. Bordetella can be used as a potential diagnostic marker for identification of TB patients with MDR.

Aberrant expression of miR-16, B12 and CD272 in peripheral blood mononuclear cells from patients with active tuberculosis.

Tuberculosis (TB) immunity is affected by complex immune regulation processes, which involve various immune cells, immune molecules, and cytokines. Here, we evaluated the expression of B12, CD272 and miR-16 in peripheral blood mononuclear cells (PBMC) of patients with active pulmonary tuberculosis. The results showed that monocytes expressing CD272 or B12 were down-regulated in patients with tuberculosis. The expression of B12 and CD272 in T cells and monocytes is related to tuberculosis. In TB patients, the up-regulation of miR-16 was negatively correlated with B12 mRNA expression, miR-16 was mainly expressed in CD14+ monocytes, and CD272 mRNA was mainly expressed in CD19+ B cells. It is worth noting that the overexpression of miR-16 inhibits the expression of CD272 and B12 in monocytes of TB patients. After BCG stimulation, miR-16 expression of CD14+ monocytes was up-regulated and B12 mRNA and CD272 mRNA expressions were down-regulated in TB patients. Finally, we found that miR-16 may participate in the TB immunization process through targeted regulation of B12 expression. These studies indicate that the expression of B12, CD272 and miR-16 in PBMC may be related to tuberculosis.

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms.

Soil salinization is a global problem closely related to the sustainable development of social economy. Compared with frequently-used satellite-borne sensors, unmanned aerial vehicles (UAVs) equipped with multispectral sensors provide an opportunity to monitor soil salinization with on-demand high spatial and temporal resolution. This study aims to quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery, and explore the deep mining of multispectral data. For this purpose, a total of 60 soil samples (0-20 cm) were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. Meanwhile, from the UAV sensor we obtained the multispectral data, based on which 22 spectral covariates (6 spectral bands and 16 spectral indices) were constructed. The sensitive spectral covariates were selected by means of gray relational analysis (GRA), successive projections algorithm (SPA) and variable importance in projection (VIP), and from these selected covariates estimation models were built using back propagation neural network (BPNN) regression, support vector regression (SVR) and random forest (RF) regression, respectively. The performance of the models was assessed by coefficient of determination (R 2), root mean squared error (RMSE) and ratio of performance to deviation (RPD). The results showed that the estimation accuracy of the models had been improved markedly using three variable selection methods, and VIP outperformed GRA and GRA outperformed SPA. However, the model accuracy with the three machine learning algorithms turned out to be significantly different: RF > SVR > BPNN. All the 12 SSC estimation models could be used to quantitatively estimate SSC (RPD > 1.4) while the VIP-RF model achieved the highest accuracy (R c 2 = 0.835, R P 2 = 0.812, RPD = 2.299). The result of this study proved that UAV-borne multispectral sensor is a feasible instrument for SSC estimation, and provided a reference for further similar research.

Estimating salt content of vegetated soil at different depths with Sentinel-2 data.

The accurate and timely monitoring of the soil salt content (SSC) at different depths is the prerequisite for the solution to salinization in the arid and semiarid areas. Sentinel-2 has demonstrated significant superiority in SSC inversion for its higher temporal, spatial and spectral resolution, but previous research on SSC inversion with Sentinel-2 mainly focused on the unvegetated surface soil. Based on Sentinel-2 data, this study aimed to build four machine learning models at five depths (0∼20 cm, 20∼40 cm, 40∼60 cm, 0∼40 cm, and 0∼60 cm) in the vegetated area, and evaluate the sensitivity of Sentinel-2 to SSC at different depths and the inversion capability of the models. Firstly, 117 soil samples were collected from Jiefangzha Irrigation Area (JIA) in Hetao Irrigation District (HID), Inner Mongolia, China during August, 2019. Then a set of independent variables (IVs, including 12 bands and 32 spectral indices) were obtained based on the Sentinel-2 data (released by the European Space Agency), and the full subset selection was used to select the optimal combination of IVs at five depths. Finally, four machine learning algorithms, back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to build inversion models at each depth. The model performance was assessed using adjusted coefficient of determination (R2 adj ), root mean square error (RMSE) and mean absolute error (MAE). The results indicated that 20∼40 cm was the optimal depth for SSC inversion. All the models at this depth demonstrated a good fitting (R2 adj ≈ 0.6) and a good control of the inversion errors (RMSE < 0.16%, MAE < 0.12%). At the depths of 40∼60 cm and 0∼20 cm the inversion performance showed a slight and a great decrease respectively. The sensitivity of Sentinel-2 to SSC at different depths was as follows: 20∼40 cm > 40∼60 cm > 0∼40 cm > 0∼60 cm > 0∼20 cm. All four machine learning models demonstrated good inversion performance (R2 adj > 0.46). RF was the best model with high fitting and inversion accuracy. Its R2 adj at five depths were between 0.5 to 0.68. The SSC inversion capabilities of all the four models were as follows: RF model > ELM model > SVM model > BPNN model. This study can provide a reference for soil salinization monitoring in large vegetated area.

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP.

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl-, Mg2+ and SO4 2- was very high, that of CO3 2- was high and K+ was relatively lower, but HCO3 - failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.

[Mass spectrometry-based identification of new serum biomarkers in patients with multidrug resistant pulmonary tuberculosis].

OBJECTIVE: To screen new serum metabolic biomarkers for different drug resistance profiles of pulmonary tuberculosis (TB) and explore their mechanisms and functions. METHODS: We collected serum samples from TB patients with drug sensitivity (DS), monoresistance to isoniazid (MR-INH), monoresistance to rifampin (MR-RFP), multidrug resistance (MDR), and polyresistance (PR). The metabolites in the serum samples were extracted by oscillatory and deproteinization for LC-MS/MS analysis, and the results were normalized by Pareto-scaling method and analyzed using Metaboanalyst 4.0 software to identify the differential metabolites. The differential metabolites were characterized by function enrichment and co-expression analysis to explore their function and possible pathological mechanisms. RESULTS: Compared with the DS group, 286 abnormally expressed metabolites were identified in MR-INH group, 362 in MR-RPF group, 277 in MDR group and 1208 in PR group by LC-MS/MS analysis. Acetylagmatine (P < 0.05), aminopentol (P < 0.05), and tetracosanyl oleate (P < 0.05) in MR-INH group; Ala His Pro Thr (P < 0.001) and glycinoprenol-9 (P < 0.05) in MR-RFP group; trimethylamine (P < 0.05), penaresidin A (P < 0.05), and verazine (P < 0.05) in MDR group; and PIP (18:1(11Z)/ 18:3(6Z, 9Z, 12Z)) (P < 0.001), Pro Arg Trp Tyr (P < 0.001), N-methyldioctylamine (P < 0.001), and phytolaccoside E (P < 0.05) in PR group all showed significant differential expressions. Significant differential expressions of phthalic acid mono-2-ethylhexyl ester (P < 0.05) and eicosanoyl-EA (P < 0.05) were found in all the drug resistant groups as compared with DS group. CONCLUSIONS: Acetylagmatine, aminopentol, tetracosanyl oleate, Ala His Pro Thr, glycinoprenol-9, trimethylamine, penaresidin A, verazine, PIP(18:1(11Z)/18:3(6Z, 9Z, 12Z)), Pro Arg Trp Tyr, N-methyldioctylamine, phytolaccoside E, phthalic acid mono-2-ethylhexyl ester, and eicosanoyl-EA are potentially new biomarkers that indicate monoresistance, multi-drug resistance and polyresistance of Mycobacterium tuberculosis. The combined use of these biomarkers potentially allows for assessment of drug resistance in TB and enhances the diagnostic sensitivity and specificity.

Upregulation of miR-196b-5p attenuates BCG uptake via targeting SOCS3 and activating STAT3 in macrophages from patients with long-term cigarette smoking-related active pulmonary tuberculosis.

BACKGROUND: Cigarette smoking (CS) triggers an intense and harmful inflammatory response in lungs mediated by alveolar and blood macrophages, monocytes, and neutrophils and is closely associated with prevalence of tuberculosis (TB). The risk of death in patients with long-term cigarette smoking-related pulmonary tuberculosis (LCS-PTB) is approximately 4.5 times higher than those with nonsmoking pulmonary tuberculosis (N-PTB). However, the mechanisms underlying the harmful inflammatory responses in the setting of LCS-PTB have not been well documented. METHODS: 28 cases LCS-PTB patients, 22 cases N-PTB patients and 20 cases healthy volunteers were enrolled in this study. Monocytes were isolated from peripheral blood mononuclear cells. Differentiated human MDM and U937 cell were prepared with M-CSF and PMA stimulation, respectively. The miR-196b-5p, STAT1, STAT3, STAT4, STAT5A, STAT5B, STAT6, SOCS1 and SOCS3 mRNA expression were detected by qRT-PCR. Western blot was performed according to SOCS1, SOCS3, and pSTAT3 expression. The mycobacterial uptake by MDMs from different groups of patients after Bacillus Calmette-Guérin (BCG) infection and agomir-196b-5p or antagomir-196b-5p transfection were used by flow cytometry analysis. Human IL-6, IL-10 and TNF-α levels on the plasma and cell culture supernatant samples were measured using ELISA. For dual-luciferase reporter assay, the SOCS3 3'-UTR segments, containing the binding elements of miR-196b-5p or its mutant versions were synthesized as sense and antisense linkers. RESULTS: In this study, we found that IL-6, TNF-α production, SOCS3 mRNA expression were downregulated, while miR-196b-5p and STAT3 mRNA expression were upregulated in monocytes from LCS-PTB patients as compared to N-PTB patients. Meanwhile, we demonstrated that miR-196b-5p could target SOCS3 and activate STAT3 signaling pathway, which may possibly contribute to attenuation of BCG uptake and decrease in IL-6 and TNF-α production in macrophages. CONCLUSIONS: Our findings revealed that CS exposure regulates inflammatory responses in monocyte/macrophages from LCS-PTB patients via upregulating miR-196b-5p, and further understanding of the specific role of miR-196b-5p in inflammatory responses mightfacilitate elucidating the pathogenesis of LCS-PTB, thus leading to the development of new therapeutic strategies for PTB patients with long-term cigarette smoking.

Evaluation of enhanced soil washing process and phytoremediation with maize oil, carboxymethyl-ß-cyclodextrin, and vetiver grass for the recovery of organochlorine pesticides and heavy metals from a pesticide factory site.

An innovative ex situ soil washing technology was developed in this study to remediate organochlorine pesticides (OCPs) and heavy metals in a mixed contaminated site. Elevated temperature (60 °C) combined with ultrasonication (40 kHz, 20 min) at 50 mL L(-1) maize oil and 45 g L(-1) carboxylmethyl-ß-cyclodextrin were effective in extracting pollutants from the soil. After two successive washing cycles, the removal efficiency rates for total OCPs, mirex, endosulfans, chlordanes, Cd, and Pb were approximately 94.7%, 87.2%, 98.5%, 92.3%, 91.6%, and 87.3%, respectively. Cultivation of vetiver grass and addition of nutrients for 3 months further degraded 34.7% of the residual total OCPs and partially restored the microbiological functions of the soil. This result was indicated by the significant increase in the number, biomass C, N, and functioning diversity of soil microorganisms (p < 0.05). After the treatment, the residual OCPs and heavy metals existed as very slowly desorbing fraction and residual fraction, as evaluated by Tenax extraction combined with a first-three-compartment model and sequential extraction. Moreover, the secondary environmental risk of residual pollutants in the remediated soil was at an acceptable level. The proposed combined cleanup strategy proved to be effective and environmentally friendly.

Role of cosubstrate and bioaccessibility played in the enhanced anaerobic biodegradation of organochlorine pesticides (OCPs) in a paddy soil by nitrate and methyl-ß-cyclodextrin amendments.

The present study was conducted to investigate the anaerobic biodegradation potential of biostimulation by nitrate (KNO3) and methyl-ß-cyclodextrin (MCD) addition on an aged organochlorine pesticide (OCP)-contaminated paddy soil. After 180 days of incubation, total OCP biodegradation was highest in soil receiving the addition of nitrate and MCD simultaneously and then followed by nitrate addition, MCD addition, and control. The highest biodegradation of chlordanes, hexachlorocyclohexanes, endosulfans, and total OCPs was 74.3, 63.5, 51.2, and 65.1%, respectively. Meanwhile, MCD addition significantly increased OCP bioaccessibility (p < 0.05) evaluated by Tenax TA extraction and a three-compartment model method. Moreover, the addition of nitrate and MCD also obtained the highest values of soil microbial activities, including soil microbial biomass carbon and nitrogen, ATP production, denitrifying bacteria count, and nitrate reductase activity. Such similar trend between OCP biodegradation and soil-denitrifying activities suggests a close relationship between OCP biodegradation and N cycling and the indirect/direct involvement of soil microorganisms, especially denitrifying microorganisms in the anaerobic biodegradation of OCPs.

Anaerobic degradation of purified terephthalic acid wastewater using a novel, rapid mass-transfer circulating fluidized bed.

The anaerobic treatability of purified terephthalic acid (PTA) wastewater in a novel, rapid mass-transfer fluidized bed reactor using brick particles as porous carrier materials was investigated. The reactor operation was stable after a short 34 day start-up period, with chemical oxygen demand (COD) removal efficiency between 65 and 75%, terephthalate (TA) removal efficiency between 60% and 70%, and system organic loading rate (OLR) increasing from 7.37 to 18.52 kg COD/m(3) d. The results demonstrate that the reactor is very efficient, and requires a low hydraulic retention time (HRT) of 8 h to remove both TA and COD from the high-concentration PTA wastewater. The system also has high resistance capacity to varied OLR.