Ammonium-derived nitrous oxide is a global source in streams.

Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Aptamer-based nanointerferometer enables amplification-free ultrasensitive detection and differentiation of SARS-CoV-2 variants.

The state-of-the-art SARS-CoV-2 detection methods include qRT-PCR and antibody-based lateral flow assay (LFA) point-of-care tests. Despite the high sensitivity and selectivity, qRT-PCR is slow, expensive and needs well-trained operators. On the other extreme, LFA suffers from low sensitivity albeit its fast detection speed, low detection cost and ease of use. Therefore, the continuing COVID-19 pandemic calls for a SARS-CoV-2 detection method that is rapid, convenient and cost-effective without compromise in sensitivity. Here we provide a proof-of-principle demonstration of an optimized aptamer-based nanointerferometer that enables rapid and amplification-free detection of SARS-CoV-2 spike protein-coated pseudovirus directly from human saliva with the limit of detection (LOD) of about 400 copies per mL. This LOD is on par with that of qRT-PCR, making it 1000 to 100,000-fold more sensitive than commercial LFA tests. Using various combinations of negative selections during the screens for the aptamer targeting the receptor binding domain of the spike protein of SARS-CoV-2, we isolated two aptamers that can distinguish the Omicron and Delta variants. Integrating these two aptamers with LFA strips or the nanointerferometer sensors allows both detection and differentiation of the Omicron and Delta variants which has the potential to realize rapid triage of patients infected different SARS-CoV-2 variants.

PUNDIT: Pulmonary nodule detection with image category transformation.

BACKGROUND: Convolutional neural networks (CNNs) have achieved great success in pulmonary nodules detection, which plays an important role in lung cancer screening. PURPOSE: In this paper, we proposed a novel strategy for pulmonary nodule detection by learning it from a harder task, which was to transform nodule images into normal images. We named this strategy as pulmonary nodule detection with image category transformation (PUNDIT). METHODS: There were two steps for nodules detection, nodule candidate detection and false positive (FP) reduction. In nodule candidate detection step, a segmentation-based framework was built for detection. We designed an image category transformation (ICT) task to translate nodule images into pixel-to-pixel normal images and share the information of detection and transformation tasks by multitask learning. As for references of transformation tasks, we proposed background consistency losses into standard cycle-consistent adversarial networks, which can solve the problem of background uncontrolled changing. A three-dimensional network was used in FP reduction step. RESULTS: PUNDIT was evaluated in two datasets, cancer screening dataset (CSD) with 1186 nodules for cross-validation and (CTD) with 3668 nodules for external test. Results were mainly evaluated by competition performance metric (CPM), the average sensitivity at seven predefined FP rates. The CPM was improved from 0.906 to 0.931 in CSD, and from 0.835 to 0.848 in CTD. CONCLUSIONS: Experimental results showed that PUNDIT can improve the performance of pulmonary nodules detection effectively.

Corncob-pyrite bioretention system for enhanced dissolved nutrient treatment: Carbon source release and mixotrophic denitrification.

Solid biomass waste amendment and substrates modification in bioretention systems have been increasingly used to achieve effective dissolved nutrients pollution control in stormwater runoff. However, the risk of excess chemical oxygen demand (COD) leaching from organic carbon sources is often overlooked on most occasions. Pyrite is an efficient electron donor for autotrophic denitrification, but little is known about the efficacy of autotrophic-heterotrophic synergistic effect between additional carbon source and pyrite in bioretention. Here, four bioretention columns (i.e., corncob column (C), pyrite column (P), the corncob-pyrite layered column (L-CP), and the corncob-pyrite mixed column (M-CP)) were designed and filled with soil, quartz sand, and modified media to reveal the synergistic effects. The results showed that the corncob-pyrite layered bioretention could maintain low COD effluent concentration with high stability and efficiency in treating dissolved nutrients. When the influent nitrogen and phosphorus concentrations were 8.46 mg/L and 0.94 mg/L, the average removal rates of ammonia nitrogen, total inorganic nitrogen, and phosphate were 83.6%, 70.52%, and 76.35%, respectively. The scouring experiment showed that placing the corncob in the mulch layer was beneficial to the sustained release of dissolved organic carbon (DOC). Erosion pits were found in the SEM images of used pyrite, indicating that autotrophic denitrifying bacteria in the bioretention could react with pyrite as an electron donor. The relative abundance of Thiobacillus in the submerged zone of the corncob-pyrite layered bioretention reached 38.39%, indicating that the carbon source in the mulch layer increased the relative abundance of Thiobacillus. Coexisting heterotrophic and autotrophic denitrification in this bioretention created a more abundant microbial community structure in the submerged zone. Overall, the corncob-pyrite layered bioretention is highly promising for stormwater runoff treatment, with effective pollution removal and minimal COD emission.

Distinguishing benign and malignant lesions on contrast-enhanced breast cone-beam CT with deep learning neural architecture search.

PURPOSE: To utilize a neural architecture search (NAS) approach to develop a convolutional neural network (CNN) method for distinguishing benign and malignant lesions on breast cone-beam CT (BCBCT). METHOD: 165 patients with 114 malignant and 86 benign lesions were collected by two institutions from May 2012 to August 2014. The NAS method autonomously generated a CNN model using one institution's dataset for training (patients/lesions: 71/91) and validation (patients/lesions: 20/23). The model was externally tested on another institution's dataset (patients/lesions: 74/87), and its performance was compared with fine-tuned ResNet-50 models and two breast radiologists who independently read the lesions in the testing dataset without knowing lesion diagnosis. RESULTS: The lesion diameters (mean ± SD) were 18.8 ± 12.9 mm, 22.7 ± 10.5 mm, and 20.0 ± 11.8 mm in the training, validation, and external testing set, respectively. Compared to the best ResNet-50 model, the NAS-generated CNN model performed three times faster and, in the external testing set, achieved a higher (though not statistically different) AUC, with sensitivity (95% CI) and specificity (95% CI) of 0.727, 80% (66-90%), and 60% (42-75%), respectively. Meanwhile, the performances of the NAS-generated CNN and the two radiologists' visual ratings were not statistically different. CONCLUSIONS: Our preliminary results demonstrated that a CNN autonomously generated by NAS performed comparably to pre-trained ResNet models and radiologists in predicting malignant breast lesions on contrast-enhanced BCBCT. In comparison to ResNet, which must be designed by an expert, the NAS approach may be used to automatically generate a deep learning architecture for medical image analysis.

Burden and etiology of moderate and severe diarrhea in children less than 5 years of age living in north and south of China: Prospective, population-based surveillance.

BACKGROUND: Diarrhea remains the leading cause of childhood illness in China. Better understanding of burden and etiology of diarrheal diseases is important for development of effective prevention measures. METHODS: Population-based diarrhea surveillance was conducted in Sanjiang (southern China) year-round and Zhengding (northern China) in autumn/winter. Stool specimens were collected from children < 5 years of age experiencing diarrhea. The TaqMan Array Card (TAC), based on multiplex real-time PCR, was applied to detect multiple enteric microbial agents simultaneously. Results using these methods were compared to those derived from conventional PCR assays. RESULTS: During the study period, 6,380 children in Zhengding and 3,581 children in Sanjiang < 5 years of age participated. Three hundred and forty (31.2%) and 279 (22.9%) diarrhea episodes were identified as moderate-to-severe in the two counties, with incidence of 60.4 and 88.3 cases per 1,000 child-years in Zhengding and Sanjiang, respectively. The five most frequently detected bacterial and viral agents in Sanjiang were adenovirus, enterovirus, enteroaggregative Escherichia coli (EAEC), rotavirus, and sapovirus all the year round, while the most common viral agents in Zhengding were rotavirus, followed by astrovirus and adenovirus during the cool season. Compared to conventional PCR assay, the average incremental detection via the TAC method was twofold. CONCLUSION: Our study demonstrated high diversity and prevalence of multiple major bacterial and viral agents, including rotavirus and calicivirus, among children in China. Further studies are needed to define the public health significance of neglected but frequently detected pathogens such as EAEC, enterotoxigenic E. coli, Campylobacter, adenovirus, and enterovirus.

Genetic susceptibility to rotavirus infection in Chinese children: a population-based case-control study.

Rotaviruses (RVs) are the leading cause of acute gastroenteritis in children, while histo-blood group antigens (HBGAs) are believed to be host attachment and susceptibility factors of RVs. A large case-control study nested in a population-based diarrhea surveillance targeting children <5 y of age was performed in rural Hebei province, north China. Saliva and serum samples were collected from all participants to determine HBGA phenotyping, FUT2 mutations, and RV IgG antibody titers. A logistic model was employed to assess the association between host HBGA secretor status and risk of RV infection. Among 235 RV cases and 680 non-diarrhea controls studied, 82.4% of participants were IgG positive by an average age of 77 months. Out of the 235 RV cases, 216 (91.9%) were secretors, whereas the secretor rate was 76.3% in the non-diarrhea controls, resulted in an adjusted OR of 3.0 (95%CI: 1.9-4.7, P < .0001) between the two groups. Our population-based case-control study indicated a strong association between host HBGA secretor status and risk of RV infection in Chinese children. The high prevalence of Lewis-positive secretor status strongly suggests that Chinese children may be genetically susceptible to current co-circulating RV strains, and thus, a universal childhood immunization program against RV disease should be successful in China.

Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.

PURPOSE: The clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for high quality contrast-enhancement in medical images. We aimed to develop a machine-learning algorithm for Quality Control of Contrast-Enhancement on CT-scan (CECT-QC). METHOD: Multicenter data from four independent cohorts [A, B, C, D] of patients with measurable liver lesions were analyzed retrospectively (patients:time-points; 503:3397): [A] dynamic CTs from primary liver cancer (60:2359); [B] triphasic CTs from primary liver cancer (31:93); [C] triphasic CTs from hepatocellular carcinoma (121:363); [D] portal venous phase CTs of liver metastasis from colorectal cancer (291:582). Patients from cohort A were randomized to training-set (48:1884) and test-set (12:475). A random forest classifier was trained and tested to identify five contrast-enhancement phases. The input was the mean intensity of the abdominal aorta and the portal vein measured on a single abdominal CT scan image at a single time-point. The output to be predicted was: non-contrast [NCP], early-arterial [E-AP], optimal-arterial [O-AP], optimal-portal [O-PVP], and late-portal [L-PVP]. Clinical utility was assessed in cohorts B, C, and D. RESULTS: The CECT-QC algorithm showed performances of 98 %, 90 %, and 84 % for predicting NCP, O-AP, and O-PVP, respectively. O-PVP was reached in half of patients and was associated with a peak in liver malignancy density. Contrast-enhancement quality significantly influenced radiomics features deciphering the phenotype of liver neoplasms. CONCLUSIONS: A single CT-image can be used to differentiate five contrast-enhancement phases for radiomics-based precision medicine in the most common liver neoplasms occurring in patients with or without liver cirrhosis.

Immune response to different types of hepatitis B vaccine booster doses 2-32 years after the primary immunization schedule and its influencing factors.

OBJECTIVE: To assess the immune effect of different types of hepatitis B vaccine (HepB) booster doses 2-32 years after primary immunization, explore the influencing factors, and offer guidance regarding the necessity and timing of boosters. METHODS: In total, 1163 participants who were born from 1986 to 2015, received the HepB full-course primary vaccination, were hepatitis B surface antigen (HBsAg) and hepatitis B core antibody (anti-HBc) negative, and had hepatitis B surface antibody (anti-HBs) <10 mIU/mL were enrolled. Individuals were randomly divided into two groups and received a booster dose of HepB. Venous blood samples were collected 30 days later and tested for anti-HBs. RESULTS: In total, 595 and 568 individuals received a single dose of HepB (CHO) and HepB (SC), respectively. Venous blood samples were obtained from 1079 vaccinees (CHO: 554, SC: 525). The seroconversion rates were 93.68% (519/554) and 86.67% (455/525) (p < 0.05), with geometric mean concentrations (GMCs) of 426.58 mIU/ml and 223.8 mIU/ml, respectively. This result indicated that BMI, smoking status, vaccine types of booster and prebooster anti-HBs concentration significantly influenced anti-HBs levels. Only BMI, prebooster anti-HBs concentrations and booster types were different between the anti-HBs positive and negative groups. CONCLUSIONS: Participants boostered with HepB (CHO) had a relatively higher seroconversion rate than those boostered with HepB (SC). The high seroconversion rates in the two groups suggested that the subjects remained protected despite low circulating antibodies, so there is currently no urgent need for booster immunization. Factors including BMI ≥ 25 and prebooster anti-HBs concentration <2.5 mIU/mL, which contributed to lower responses to a booster dose, might indicate a greater risk of breakthrough infection.

Burden of viral gastroenteritis in children living in rural China: Population-based surveillance.

BACKGROUND: Despite the considerable disease burden caused by the disease, rotavirus vaccine has not been introduced into routine national immunization schedule, and norovirus vaccines are being developed without a comprehensive understanding of gastroenteritis epidemiology. To bridge this knowledge gap, we investigated the disease burden of viral gastroenteritis in rural China. METHODS: Between October 2011 and December 2013, population-based surveillance was conducted in Zhengding and Sanjiang counties in China. Stool samples were collected from children <5 years of age with diarrhea. All specimens were tested for rotaviruses, noroviruses, sapoviruses, enteric adenoviruses, and astroviruses. RESULTS: The most common pathogen causing diarrhea was rotavirus (54.7 vs 45.6 cases/1,000 children/year in Zhengding and Sanjiang, respectively), followed by norovirus (28.4 vs 19.3 cases/1,000 children/year in Zhengding and Sanjiang, respectively). The highest incidence of these viruses was observed in children 6-18 months of age. Among the 5 viral pathogens, rotaviruses caused the most severe illness, followed by noroviruses. CONCLUSION: Rotavirus and norovirus are the 2 most important viral pathogens causing childhood diarrhea in both northern and southern China; they should be the major targets for viral gastroenteritis prevention strategies among children in China.

Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks.

OBJECTIVES: To develop a deep learning-based algorithm to automatically identify optimal portal venous phase timing (PVP-timing) so that image analysis techniques can be accurately performed on post contrast studies. METHODS: 681 CT-scans (training: 479 CT-scans; validation: 202 CT-scans) from a multicenter clinical trial in patients with liver metastases from colorectal cancer were retrospectively analyzed for algorithm development and validation. An additional external validation was performed on a cohort of 228 CT-scans from gastroenteropancreatic neuroendocrine cancer patients. Image acquisition was performed according to each centers' standard CT protocol for single portal venous phase, portal venous acquisition. The reference gold standard for the classification of PVP-timing as either optimal or nonoptimal was based on experienced radiologists' consensus opinion. The algorithm performed automated localization (on axial slices) of the portal vein and aorta upon which a novel dual input Convolutional Neural Network calculated a probability of the optimal PVP-timing. RESULTS: The algorithm automatically computed a PVP-timing score in 3 seconds and reached area under the curve of 0.837 (95% CI: 0.765, 0.890) in validation set and 0.844 (95% CI: 0.786, 0.889) in external validation set. CONCLUSION: A fully automated, deep-learning derived PVP-timing algorithm was developed to classify scans' contrast-enhancement timing and identify scans with optimal PVP-timing. The rapid identification of such scans will aid in the analysis of quantitative (radiomics) features used to characterize tumors and changes in enhancement with treatment in a multitude of settings including quantitative response criteria such as Choi and MASS which rely on reproducible measurement of enhancement.

Long-term protection at 20-31 years after primary vaccination with plasma-derived hepatitis B vaccine in a Chinese rural community.

Background: To assess the long-term protection conferred by plasma-derived hepatitis B vaccine at 20-31y after primary immunization during infancy in Chinese rural community.Method: Participants born between 1986 and 1996, who received a full course of primary vaccination with plasma-derived hepatitis B vaccine and had no experience with booster vaccination were enrolled. An epidemiological investigation was performed, and blood samples were collected to detect hepatitis B surface antigen (HBsAg), hepatitis B surface antibody (anti-HBs), and hepatitis B core antibody (anti-HBc). The positive rate of HBsAg, anti-HBs, and anti-HBc were calculated to evaluate the long-term protection of the plasma-derived hepatitis B vaccine.Results: A total of 949 participants were enrolled in the final analysis. Six subjects were detected to be HBsAg-positive, resulting in a HBsAg carrier rate of 0.63% (6/949). A total of 468 (52.41%) participants maintained a level of anti-HBs antibody ≥10 mIU/mL, with a GMC of 112.20 mIU/mL (95%CI: 97.72 ~ 128.82 mIU/mL). A significant downtrend was observed in the anti-HBs positive rate (P < .001). The average anti-HBc positive rate was 5.90% (56/949), increased with prolongation of immunization (P < .001).Conclusions: The plasma-derived hepatitis B vaccine maintained satisfactory protection at 20-31 y after primary immunization. These results indicate that a booster dose is not necessary. Further studies on the immune memory induced by the plasma-derived hepatitis B vaccine are needed.

Immune persistence 17 to 20 years after primary vaccination with recombination hepatitis B vaccine (CHO) and the effect of booster dose vaccination.

BACKGROUND: To assess the immune persistence conferred by a Chinese hamster ovary (CHO)-derived hepatitis B vaccine (HepB) 17 to 20 years after primary immunization during early life. METHODS: Participants born between 1997 and 1999 who received a full course of primary vaccination with HepB (CHO) and who had no experience with booster vaccination were enrolled. Blood samples were required from each participant for measurement of hepatitis B surface antibody (anti-HBs), surface antigen and core antibody levels. For those who possessed an anti-HBs antibody < 10 mIU/mL, a single dose of HepB was administered, and 30 days later, serum specimens were collected to assess the booster effects. RESULTS: A total of 1352 participants were included in this study. Of these, 1007 (74.5%) participants could retain an anti-HBs antibody ≥10 mIU/mL, with a geometric mean concentration (GMC) of 57.4 mIU/mL. HBsAg was detected in six participants, resulting in a HBsAg carrier rate of 0.4% (6/1352). Of those participants with anti-HBs antibodies < 10 mIU/mL, after a challenge dose, 231 (93.1%) presented an anti-HBs antibody ≥10 mIU/mL, with a GMC of 368.7 mIU/mL. A significant increase in the anti-HBs positive rate (≥ 10 mIU/mL) after challenge was observed in participants with anti-HBs antibodies between 2.5 and 10 mIU/mL and participants boosted with HepB (CHO), rather than those with anti-HBs antibodies < 2.5 mIU/mL and those boosted with HepB (SC). CONCLUSION: Since satisfactory immune protection against HBV infection conferred by primary vaccination administered 17-20 years ago was demonstrated, there is currently no urgent need for booster immunization.

Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling.

OBJECTIVES: The tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models. METHODS: Five hundred three lung adenocarcinoma patients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point. CONCLUSION: Our results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool. KEY POINTS: • Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status. • In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions. • The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.

Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography.

Computer-aided detection (CAD) systems can assist radiologists in reducing the interpretation time and improving the detection results in computed tomographic colonography (CTC). However, existing false positives (FPs) impair the advantages of CAD systems. This study aims to develop new morphological features for the FP reduction while maintaining high detection sensitivity. Volumetric feature maps are computed for each polyp candidate by using three-dimensional (3-D) geodesic distance transformation, circular transformation (CcT), and quantized convergence index (QCI) filters. Then, new morphological features are developed based on the curvature, fractal dimension, and volumetric feature maps. To the best of our knowledge, we are also the first to develop 3-D CcT and QCI filters specifically for colonic polyps. The new morphological features were evaluated to reduce the FPs by using 456 oral contrast-enhanced CT scans from 228 patients with 130 polyps ≥5 mm. For comparison, the well-defined features from our previous work were used to generate a baseline reference. The additional use of the new morphological features reduced the FP rate from 4.2 to 2.0 FPs per scan (i.e., 52.4% FP reduction percentage) at 96.2% by-polyp sensitivity and from 4.5 to 2.1 FPs per scan (i.e., 53.3% FP reduction percentage) at 93.9% per-scan sensitivity for polyps ≥5 mm. Experimental results indicate that the new morphological features can effectively reduce the FP rate without sacrificing detection sensitivity. We believe that the newly developed morphological features would advance the CAD systems to assist radiologists in interpreting CTC images.

The Role of PET-Based Radiomic Features in Predicting Local Control of Esophageal Cancer Treated with Concurrent Chemoradiotherapy.

This study was designed to evaluate the predictive performance of 18F-fluorodeoxyglucose positron emission tomography (PET)-based radiomic features for local control of esophageal cancer treated with concurrent chemoradiotherapy (CRT). For each of the 30 patients enrolled, 440 radiomic features were extracted from both pre-CRT and mid-CRT PET images. The top 25 features with the highest areas under the receiver operating characteristic curve for identifying local control status were selected as discriminative features. Four machine-learning methods, random forest (RF), support vector machine, logistic regression, and extreme learning machine, were used to build predictive models with clinical features, radiomic features or a combination of both. An RF model incorporating both clinical and radiomic features achieved the best predictive performance, with an accuracy of 93.3%, a specificity of 95.7%, and a sensitivity of 85.7%. Based on risk scores of local failure predicted by this model, the 2-year local control rate and PFS rate were 100.0% (95% CI 100.0-100.0%) and 52.2% (31.8-72.6%) in the low-risk group and 14.3% (0.0-40.2%) and 0.0% (0.0-40.2%) in the high-risk group, respectively. This model may have the potential to stratify patients with different risks of local failure after CRT for esophageal cancer, which may facilitate the delivery of personalized treatment.

Investigation of NO conversion by different types of sewage sludge chars under low temperature.

Different types of sludge chars, i.e. the original (S1), HNO3 washed (S2), KOH activated (S3), and H2 reduced chars, were prepared to investigate their performances for NO conversion under low temperature. Results indicated that the surface area of the sludge chars did not play key role on NO conversion. S1 showed the higher NO conversion performance than S2 and S3, due to the reducing effect of iron components in S1 (mainly FeS and Fe2P). Both H2 reduction at high temperature and the followed cooling atmosphere (H2 or N2) have great influences on the activity of S1. H2 reduction can promote the reducing capacity of the iron components, thus the performance of S1 for NO conversion was also significantly enhanced. However, NO conversion over S1 decreased in the presence of O2, because the iron components were more quickly oxidized in O2 atmosphere. Results of duration tests showed that the activity of S1 decreased with time, but it can be recovered by calcination in N2. The profiles of the temperature programmed desorption (TPD) of CO and CO2 indicated that CO and CO2 emissions are mainly from the decomposition of oxygen functional groups (OFGs), including anhydrides, phenols, esters, and carboxylic acid. Furthermore, mechanisms of NO adsorption and conversion by S1-S3 were explored by an in-situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). It was found that NO adsorbed on the sludge chars mainly transferred into NO2 species at 50 °C, and then NO2 species were gradually oxidized to nitrate species with the increase of temperature. Finally, the possible reaction routes for NO conversion were proposed based on experimental data.