Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in gastric cancer.

Metabolic reprogramming has been defined as a key hall mark of human tumors. However, metabolic heterogeneity in gastric cancer has not been elucidated. Here we separated the TCGA-STAD dataset into two metabolic subtypes. The differences between subtypes were elaborated in terms of transcriptomics, genomics, tumor-infiltrating cells, and single-cell resolution. We found that metabolic subtype 1 is predominantly characterized by low metabolism, high immune cell infiltration. Subtype 2 is mainly characterized by high metabolism and low immune cell infiltration. From single-cell resolution, we found that the high metabolism of subtype 2 is dominated by epithelial cells. Not only epithelial cells, but also various immune cells and stromal cells showed high metabolism in subtype 2 and low metabolism in subtype 1. Our study established a classification of gastric cancer metabolic subtypes and explored the differences between subtypes from multiple dimensions, especially the single-cell resolution.

Critical Path-Based Backdoor Detection for Deep Neural Networks.

Backdoor attack to deep neural networks (DNNs) is among the predominant approaches to bring great threats into artificial intelligence. The existing methods to detect backdoor attacks focus on the perspective of distributions in DNNs, however, limited by its ability of generalization across DNN models. In this article, a critical-path-based backdoor detector (CPBD) is proposed, which approaches to detect backdoor attacks via DNN's interpretability. CPBD is designed to efficiently discover the characteristics of backdoors, which distinguish the critical paths in the attacked DNNs. To deal with the intractably large number of neurons, we propose to simplify the neurons, and the preserved key nodes are integrated into a set of critical paths. Thus, a DNN model can be formulated as a combination of several critical paths. Afterward, the detection of backdoors is performed based on the analysis of critical paths corresponding to different classes. Then, combining all the above steps, the CPBD algorithm is integrated to present the results in a standard and systematic manner. In addition, CPBD is able to locate neurons associated with malicious triggers, the combination of which is named as trigger propagation path. Extensive experiments are conducted, which testify the efficiency of the proposed method on multiple DNNs and different trigger sizes.

Ti3C2Tx MXene/urchin-like PANI hollow nanosphere composite for high performance flexible ammonia gas sensor.

Ammonia (NH3) has been used as a typical indicator to monitor food spoilage, human health, and air quality. However, the development of flexible NH3 sensors with high response, excellent selectivity and low cost remains a huge challenge. Herein, a high performance NH3 sensor based on Ti3C2Tx MXene nanosheet/urchin-like PANI hollow nanosphere composite (MP) was fabricated through template method and in situ polymerization. The NH3 sensor is fabricated with no high cost electrodes through directly depositing this composite on flexible polyethylene terephthalate (PET) during polymerization. This optimized MP film sensor exhibits high response of 3.70 to 10 ppm NH3 at room temperature, which is 4.74-fold in comparison with urchin-like PANI hollow nanosphere (u-PANI). It also shows excellent selectivity, good repeatability, satisfactory flexibility, air stability and low detection limit of 30 ppb. The effective morphology control and heterojunction construction of MP composite are responsible for superior sensing performance. Moreover, the application of this film sensor in the monitoring of the spoilage process of fresh pork is demonstrated. This study offers a new strategy for fabricating high performance flexible room-temperature NH3 sensors, which may be scale fabrication and application in daily life.

Integrated proteomic and metabolomic analyses reveal significant changes in chloroplasts and mitochondria of pepper (Capsicum annuum L.) during Sclerotium rolfsii infection.

Infection by Sclerotium rolfsii will cause serious disease and lead to significant economic losses in chili pepper. In this study, the response of pepper during S. rolfsii infection was explored by electron microscopy, physiological determination and integrated proteome and metabolome analyses. Our results showed that the stomata of pepper stems were important portals for S. rolfsii infection. The plant cell morphology was significantly changed at the time of the fungal hyphae just contacting (T1) or surrounding (T2) the pepper. The chlorophyll, carotenoid, and MDA contents and the activities of POD, SOD, and CAT were markedly upregulated at T1 and T2. Approximately 4129 proteins and 823 metabolites were clearly identified in proteome and metabolome analyses, respectively. A change in 396 proteins and 54 metabolites in pepper stem tissues was observed at T1 compared with 438 proteins and 53 metabolites at T2. The proteins and metabolites related to photosynthesis and antioxidant systems in chloroplasts and mitochondria were disproportionally affected by S. rolfsii infection, impacting carbohydrate and amino acid metabolism. This study provided new insights into the response mechanism in pepper stems during S. rolfsii infection, which can guide future work on fungal disease resistance breeding in pepper.

Simultaneous removal of sulphur dioxide and nitric oxide at different oxygen concentrations in a thermophilic biotrickling filter (BTF): Evaluation of removal efficiency, intermediates interaction and characterisation of microbial communities.

Simultaneous flue gas desulphurisation and denitrification in biotrickling filter was investigated under different O2 concentrations (0%, 3%, 5%, 8% and 10%) at 45 °C. NO and SO2 removal efficiency, intermediates (NO3-, NO2-, NO2, SO42- and S2-) interaction and accumulation, S0 recovery and microbial community structure were investigated. Results indicated the highest NO removal efficiency was 96.5% at 5% O2. Maximum SO2 removal efficiency was 95.6% at 3% O2. Moreover, N intermediates accumulation increased when O2 concentration increased from 0% to 10%. The lowest S2- concentration of 61 mg/L and the maximum S0 recovery of 76.9% were achieved at 5% O2. The bioreactor at 10% O2 contained less bacterial OTUs richness and evenness compared with other conditions. Illumina analysis indicated Proteobacteria, Firmicutes and Bacteroidetes were the dominant members. Overall, microbial community structure differs significantly under different O2 concentrations.

Simultaneous removal of nitric oxide and sulfur dioxide in a biofilter under micro-oxygen thermophilic conditions: Removal performance, competitive relationship and bacterial community structure.

The efficiency of a biofilter to simultaneously remove nitric oxide (NO) and sulfur dioxide (SO2) was investigated under thermophilic (48 ±â€¯2 °C) micro-oxygen (3 vol%) conditions. After the start-up stage (Days 0-14), the stable operation period was divided into three stages. SO2 inlet concentration remained 500 mg/m3, NO inlet concentrations were 300 mg/m3 (Days 15-40), 500 mg/m3 (Days 41-70) and 700 mg/m3 (Days 71-100). In each stable stage, the removal efficiency of NO and SO2 exceeded 90%, the maximum removal rates of NO and SO2 were 98.08% and 99.61%, respectively. The final products of SO2 were mostly sulphur. Nitrate-reducing bacteria inhibited sulphate-reducing bacteria. Illumina high-throughput sequencing confirmed that the relative abundance of nitrate-reducing bacteria was positively correlated with NO removal efficiency, the relative abundance of sulphate-reducing bacteria was related to the conversion rate of sulphur.

Effects of 2,4,6-trichlorophenol on simultaneous nitrification and denitrification: Performance, possible degradation pathway and bacterial community structure.

This study aimed to investigate the effect of different 2,4,6-trichlorophenol (TCP) concentrations on the performance of simultaneous nitrification and denitrification processes established in a sequential batch biofilm reactor. And the degradation and the possible degradation pathway of 2,4,6-TCP and microbial community structure were also explored. Results indicated that 2,4,6-TCP inhibited the nitrification with the decrease in ammonium nitrogen removal. However, 2,4,6-TCP had different effects on denitrification. Nitrate accumulation showed the tendency to decrease first and then increase, whilst nitrite accumulation showed the opposite with a small change. The adaptation and recovery time of 25 mg/l 2,4,6-TCP was longest. In addition, the process had a good degradation effect on 2,4,6-TCP. Comparing the degradation of 2,4,6-TCP under different concentrations, the result showed that 2,4,6-TCP was mainly reduced to 2,4-dichlorophenol. With the increase in 2,4,6-TCP concentration, the differences in the bacterial community in the reactor were significant.

Evaluation of microbial p-chloroaniline degradation in bioelectrochemical reactors in the presence of easily-biodegrading cosubstrates: Degradation efficiency and bacterial community structure.

This study aimed to illustrate p-Chloroaniline (p-CIA) biodegradation efficiencies in bioelectrochemical reactors under stimulation by a low-voltage electric field (0.2 V versus Ag/AgCl) in the presence of easily-degrading cosubstrates including glucose and acetate. The biodegradation efficiencies of closed-circuit bioreactors were compared with those of open-circuit reactors. Experimental results showed that the six different bioreactors provided different p-CIA biodegradation efficiencies. The highest biodegradation efficiency of 38.5 ±â€¯10.3 mg/l was obtained in a closed-circuit bioreactor with acetate and the lowest biodegradation efficiency of 15.7 ±â€¯9.4 mg/l was obtained in an open-circuit bioreactor. This difference may be attributed to the presence of electrical stimulation and acetate. The results for generated current and biodegradation efficiency indicated that acetate is a better cosubstrate than glucose. High-throughput sequencing technologies were used to characterise the bacterial community structure of the six bioreactors and revealed that different bacterial communities resulted in different treatment efficiencies.

Carbon footprint assessment for a local branded pure milk product: a lifecycle based approach

Abstract This paper provides a simplified life cycle based assessment for a local branded pure milk product, to measure its related carbon footprint, including production of raw milk, dairy processing, transportation of milk product and disposal of packaging waste. The results show that the total carbon footprint of the pure milk is 1120g CO2/L. The production of raw milk is identified as the major contributor to the carbon footprint. This contribution has amounted to 843 g of CO2 per liter of pure milk, accounted for 75.27% of the total carbon footprint. The carbon footprint of product transportation is 38 g of CO2 per liter, which accounts for 3.39% of the total. The carbon footprint related to the dairy processing and disposal of waste packaging is 173 g of CO2 per liter and 66 g of CO2 per liter, accounting for 15.45% and 5.89% of the total, respectively. The carbon footprint assessment intends to help dairy enterprises identify the intensive sectors of carbon emissions, and provides insight into improvement of product environmental performances.