An immune-related prognostic model predicts neoplasm-immunity interactions for metastatic nasopharyngeal carcinoma

BackgroundThe prognosis of nasopharyngeal carcinoma (NPC) has been recognized to improve immensely owing to radiotherapy combined with chemotherapy. However, patients with metastatic NPC have a poor prognosis. Immunotherapy has dramatically prolonged the survival of patients with NPC. Hence, further research on immune-related biomarkers is imperative to establish the prognosis of metastatic NPC.Methods10 NPC RNA expression profiles were generated from patients with or without distant metastasis after chemoradiotherapy from the Fujian Cancer Hospital. The differential immune-related genes were identified and validated by immunohistochemistry analysis. The method of least absolute shrinkage and selection operator (LASSO)was used to further establish the immune-related prognostic model in an external GEO database (GSE102349, n=88). The immune microenvironment and signal pathways were evaluated in multiple dimensions at the transcriptome and single-cell levels.Results1328 differential genes were identified, out of which 520 were upregulated and 808 were downregulated. Notably, most of the immune genes and pathways were down-regulated in the metastasis group. A prognostic immune model involving nine hub genes. Patients in low-risk group were characterized by survival advantage, hot immune phenotype and benefit from immunotherapy. Compared with immune cells, malignant cell exhibited the most active levels of risk score by ssGSEA. Accordingly, intercellular communications including LT, CD70, CD40 and SPP1, and the like, between high-risk and low-risk were explored by the R package “Cellchat”.ConclusionWe have constructed a model based on immunity of metastatic NPC and determined its prognostic value. The model identified the level of immune cell infiltration, cell-cell communication, along with potential immunotherapy for metastatic NPC

Synergistic Association of PTGS2 and CYP2E1 Genetic Polymorphisms with Lung Cancer Risk in Northeastern Chinese

BACKGROUND: Lung cancer is the most common cause of cancer-related deaths worldwide. The aim of this study was to investigate the association of five extensively-studied polymorphisms in PTGS2 (rs689466, rs5275, rs20417) and CYP2E1 (rs2031920, rs6413432) genes with lung cancer risk in a large northeastern Chinese population. METHODOLOGY/PRINCIPAL FINDINGS: This is a hospital-based case-control study involving 684 patients with lung cancer and 604 cancer-free controls. Genotyping was performed using the PCR-LDR method. Data were analyzed using Haplo.stats and MDR programs. There were significant differences between patients and controls in allele/genotype distributions of rs5275 (P = 0.002/0.003) and rs6413432 (P = 0.037/0.044), as well as in genotype distributions of rs689466 (P = 0.02). The risk for lung cancer associated with the rs5275-C mutant allele was decreased by 60% (95% CI [confidence interval]: 0.21-0.74; P = 0.004) under the recessive model. Carriers of rs689466-G mutant allele had a 28% (95% CI: 0.57-0.92; P = 0.008) reduced risk of developing lung cancer relative to the AA genotype carriers. In haplotype analysis, haplotype G-C-C-T (in order of rs689466, rs5275, rs2031920 and rs6413432) decreased the odds of lung cancer by 28% (95% CI: 0.51-0.93; P = 0.019) after adjusting for confounding factors, whereas haplotype A-T-T-T had 1.49-fold (95% CI: 1.21-1.79; P = 0.012) increased risk for lung cancer. Using MDR method, the overall best model including rs5275, rs689466 and rs6413432 polymorphisms was identified with a maximal testing accuracy of 66.1% and a maximal cross-validation consistency of 10 out of 10 (P = 0.003). CONCLUSIONS/SIGNIFICANCE: Our findings demonstrated a potentially synergistic association of PTGS2 and CYP2E1 polymorphisms with the underlying cause of lung cancer in northeastern Chinese

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology

Treatment of Phenol-Containing Coal Chemical Biochemical Tailwater by Catalytic Ozonation Using Mn-Ce/γ-Al₂O₃

In this study, a Mn-Ce/γ-Al2O3 catalyst with multiple active components was prepared through the doping–calcination method for advanced treatment of coal chemical biochemical treatment effluent and characterized by X-ray diffraction, X-ray fluorescence spectroscopy, scanning electron microscopy, and BET analysis. In addition, preparation and catalytic ozonation conditions were optimized, and the mechanism of catalytic ozonation was discussed. The Mn-Ce/γ-Al2O3 catalyst significantly enhanced COD and total phenol removal in reaction with ozone. The characterization results suggested that the pore structure of the optimized Mn-Ce/γ-Al2O3 catalyst was significantly improved. After calcination, the metallic elements Mn and Ce existed in the form of the oxides MnO2 and CeO2. The best operating conditions in the study were as follows: (1) reaction time of 30 min, (2) initial pH of 9, (3) ozone dosage of 3.0 g/h, and (4) catalyst dosage of 30 g/L. The removal efficiency of COD and total phenol from coal chemical biochemical tail water was reduced with the addition of tert-butanol, which proves that hydroxyl radicals (•OH) played a leading role in the Mn-Ce/γ-Al2O3 catalytic ozonation treatment process of biochemical tailwater. Ultraviolet absorption spectroscopy analysis indicated that some conjugated structures and benzene ring structures of organics in coal chemical biochemical tail water were destroyed. This work proposes the utilization of the easily available Mn-Ce/γ-Al2O3 catalyst and exhibits application prospects for the advanced treatment of coal chemical biochemical tailwater

Intelligent Optimization Mechanism Based on an Objective Function for Efficient Home Appliances Control in an Embedded Edge Platform

In recent years the ever-expanding internet of things (IoT) is becoming more empowered to revolutionize our world with the advent of cutting-edge features and intelligence in an IoT ecosystem. Thanks to the development of the IoT, researchers have devoted themselves to technologies that convert a conventional home into an intelligent occupants-aware place to manage electric resources with autonomous devices to deal with excess energy consumption and providing a comfortable living environment. There are studies to supplement the innate shortcomings of the IoT and improve intelligence by using cloud computing and machine learning. However, the machine learning-based autonomous control devices lack flexibility, and cloud computing is challenging with latency and security. In this paper, we propose a rule-based optimization mechanism on an embedded edge platform to provide dynamic home appliance control and advanced intelligence in a smart home. To provide actional control ability, we design and developed a rule-based objective function in the EdgeX edge computing platform to control the temperature states of the smart home. Compared to cloud computing, edge computing can provide faster response and higher quality of services. The edge computing paradigm provides better analysis, processing, and storage abilities to the data generated from the IoT sensors to enhance the capability of IoT devices concerning computing, storage, and network resources. In order to satisfy the paradigm of distributed edge computing, all the services are implemented as microservices. The microservices are connected to each other through REST APIs based on the constrained IoT devices to provide all the functionalities that accomplish a trade-off between energy consumption and occupant-desired environment setting for the smart home appliances. We simulated our proposed system to control the temperature of a smart home; through experimental findings, we investigated the application against the delay time and overall memory consumption by the embedded edge system of EdgeX. The result of this research work suggests that the implemented services operated efficiently in the raspberry pi 3 hardware of IoT devices

Design and Implementation of a Sensor-Cloud Platform for Physical Sensor Management on CoT Environments

The development of the Internet of Things (IoT) has increased the ubiquity of the Internet by integrating all objects for interaction via embedded systems, leading to a highly distributed network of devices communicating with human beings as well as other devices. In recent years, cloud computing has attracted a lot of attention from specialists and experts around the world. With the increasing number of distributed sensor nodes in wireless sensor networks, new models for interacting with wireless sensors using the cloud are intended to overcome restricted resources and efficiency. In this paper, we propose a novel sensor-cloud based platform which is able to virtualize physical sensors as virtual sensors in the CoT (Cloud of Things) environment. Virtual sensors, which are the essentials of this sensor-cloud architecture, simplify the process of generating a multiuser environment over resource-constrained physical wireless sensors and can help in implementing applications across different domains. Virtual sensors are dynamically provided in a group which advantages capability of the management the designed platform. An auto-detection approach on the basis of virtual sensors is additionally proposed to identify the accessible physical sensors nodes even if the status of these sensors are offline. In order to assess the usability of the designed platform, a smart-space-based IoT case study was implemented, and a series of experiments were carried out to evaluate the proposed system performance. Furthermore, a comparison analysis was made and the results indicate that the proposed platform outperforms the existing platforms in numerous respects

Effects of environmental variables on seedling distribution of rare and endangered Dacrydium pierrei

Because growth environment is affected by climate change, Dacrydium pierrei resources are becoming less and less. Therefore, understanding the effects of environmental variables on seedling-sapling distributions can help gain insight into changes in population recruitment in the context of climate change. The seedling-saplings distribution and variability of Dacrydium pierrei in environmental variables at Bawangling, Hainan, China, was surveyed over a 3-year period. In addition, laboratory experiments measuring the effects of soil moisture on seedling emergence were conducted to identify seedling development characteristics; principal component analysis (PCA) and Gaussian mixture model (GMM) were used to assess how different factors influenced Dacrydium pierrei seedlings-saplings distribution. The results demonstrated that the influence degree of seedling-sapling distribution is soil temperature>litter thickness>available phosphorus>canopy density> available potassium>nitrate nitrogen; a large number of seedling-saplings occurring at altitudes 1140-1300 m; a GMM trained with a C2-L3-H4-A5-I6 combination yielded an accuracy of 72.23% in simulating seedling-saplings distribution; temperature and precipitation have strong impact on seedling-sapling distribution, with increasing soil moisture, seedling emergence shows a positive relationship. This study focuses more on developing a new method for research on the seedling-sapling distribution of Dacrydium pierrei to get reference for its adaptive management with the intense extreme climate change

Simultaneous determination of phenolic metabolites in Chinese citrus and grape cultivars

Background As the major bioactive compounds in citrus and grape, it is significant to use the contents of flavonoids and phenolic acids as quality evaluation criteria to provide a better view of classifying the quality and understanding the potential health benefits of each fruit variety. Methods A total of 15 varieties of citrus and 12 varieties of grapes were collected from Fujian, China. High-performance liquid chromatography method was used for the simultaneous determination of 17 phenolic compounds, including gallic acid, chlorogenic acid, caffeic acid, syringic acid, ρ-coumaric acid, ferulic acid, benzoic acid, salicylic acid, catechin, epicatechin, resveratrol, rutin, naringin, hesperidin, quercetin, nobiletin and tangeritin in the peels of citrus and grape cultivars. Further, the cultivars of citrus and grape were classified using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Results A thorough separation of the 17 compounds was achieved within 100 min. The tested method exhibited good linearity (the limits of detection and limits of quantification were in the range of 0.03–1.83 µg/mL and 0.09–5.55 µg/mL, respectively), precision (the relative standard deviations of repeatability were 1.02–1.97%), and recovery (92.2–102.82%) for all the compounds, which could be used for the simultaneous determination of phenolic compounds in citrus and grape. Hesperidin (12.93–26,160.98 µg/g DW) and salicylic acid (5.35–751.02 µg/g DW) were the main flavonoids and phenolic acids in 15 citrus varieties, respectively. Besides, the hesperidin (ND to 605.48 µg/g DW) and salicylic acid (ND to 1,461.79 µg/g DW) were found as the highest flavonoid and the most abundant phenolic acid in grapes, respectively. A total of 15 citrus and 12 grape samples were classified into two main groups by PCA and HCA with strong consistency