Modeling and Analysis of the Soil Vapor Extraction Equipment for Soil Remediation

Soil vapor extraction (SVE) is one of the most commonly used technologies for soil remediation of contaminated sites, and the use of models to accurately predict and evaluate the operational effectiveness of SVE is a necessary part of site contamination treatment projects. A pneumatic model-based equipment model is proposed to comprehensively describe the SVE operation process. Though the numerical simulation, the influence of fan frequency, air valve opening, pressure, and total flow was analyzed, and an optimal extraction strategy was validated. Then, field experiments were carried out to verify the validity of the model. The proposed model and experimental results can provide a theoretical basis for the design and duration evaluation of SVE

Performance analysis of global HYCOM flow field using Argo profiles

Flow field data generated by ocean models are important for simulating ocean currents and circulation patterns, which are essential components in digital Earth construction. To evaluate the accuracy of model-simulated flow fields, Array for Real-time Geostrophic Oceanography (Argo) float observations can be considered benchmarks. In this study, a novel method for comparing Argo profiles with 3-dimensional trajectories obtained by simulating Argo floats in Hybrid Coordinate Ocean Model (HYCOM)-provided flow fields was proposed. Surface and subsurface trajectories were calculated, and their spatial matching characteristics were analyzed. The results demonstrated that (1) the HYCOM surface and subsurface flow fields generally conform to the basic characteristics and trends of ocean currents; (2) the HYCOM sea surface current field error pattern exhibits a symmetrical distribution centered on the equator in the Northern and Southern Hemispheres and increases with increasing latitude; and (3) the HYCOM subsurface flow field exhibits regional differences, with the largest differences in the Gulf Stream, North Atlantic Warm Current, and Westerly Wind Drift region. Through analysis of the disparities between HYCOM and Argo data, the effectiveness of using model simulation data can be enhanced, and the accuracy and dependability of ocean models can be improved

Anomaly prediction of CT equipment based on IoMT data

Abstract Background Large-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model. Methods We extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models. Results The results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset. Conclusions The proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases

SMRT sequencing analysis reveals the full-length transcripts and alternative splicing patterns in Ananas comosus var. bracteatus

Background Ananas comosus var. bracteatus is an herbaceous perennial monocot cultivated as an ornamental plant for its chimeric leaves. Because of its genomic complexity, and because no genomic information is available in the public GenBank database, the complete structure of the mRNA transcript is unclear and there are limited molecular mechanism studies for Ananas comosus var. bracteatus. Methods Three size fractionated full-length cDNA libraries (1–2 kb, 2–3 kb, and 3–6 kb) were constructed and subsequently sequenced in five single-molecule real-time (SMRT) cells (2 cells, 2 cells, and 1 cell, respectively). Results In total, 19,838 transcripts were identified for alternative splicing (AS) analysis. Among them, 19,185 (96.7%) transcripts were functionally annotated. A total of 9,921 genes were identified by mapping the non-redundant isoforms to the reference genome. A total of 10,649 AS events were identified, the majority of which were intron retention events. The alternatively spliced genes had functions in the basic metabolism processes of the plant such as carbon metabolism, amino acid biosynthesis, and glycolysis. Fourteen genes related to chlorophyll biosynthesis were identified as having AS events. The distribution of the splicing sites and the percentage of conventional and non-canonical AS sites of the genes categorized in pathways related to the albino leaf phenotype (ko00860, ko00195, ko00196, and ko00710) varied greatly. The present results showed that there were 8,316 genes carrying at least one poly (A) site, which generated 21,873 poly (A) sites. These findings indicated that the quality of the gene structure and functional information of the obtained genome was greatly improved, which may facilitate further genetic study of Ananas comosus var. bracteatus

Screening and characterization of long noncoding RNAs involved in the albinism of Ananas comosus var. bracteatus leaves.

Long noncoding RNAs (lncRNAs) have been reported to play key regulatory roles in plant growth, development, and biotic and abiotic stress physiology. Revealing the mechanism of lncRNA regulation in the albino portions of leaves is important for understanding the development of chimeric leaves in Ananas comosus var. bracteatus. In this study, a total of 3,543 candidate lncRNAs were identified, among which 1,451 were differentially expressed between completely green (CGr) and completely white (CWh) leaves. LncRNAs tend to have shorter transcripts, lower expression levels, and greater expression specificity than protein-coding genes. Predicted lncRNA targets were functionally annotated by the Gene Ontology (GO), Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. A lncRNA-mRNA interaction network was constructed, and 36 target mRNAs related to chlorophyll metabolism were predicted to interact with 86 lncRNAs. Among these, 25 significantly differentially expressed lncRNAs putatively interacted with 16 target mRNAs. Based on an expression pattern analysis of the lncRNAs and their target mRNAs, the lncRNAs targeting magnesium chelatase subunit H (ChlH), protochlorophyllide oxidoreductase (POR), and heme o synthase (COX10) were suggested as key regulators of chlorophyll metabolism. This study provides the first lncRNA database for A. comosus var. bracteatus and contributes greatly to understanding the mechanism of epigenetic regulation of leaf albinism

Secretome profiling identifies neuron-derived neurotrophic factor as a tumor-suppressive factor in lung cancer

© 2019, American Society for Clinical Investigation. Clinical and preclinical studies show tissue-specific differences in tumorigenesis. Tissue specificity is controlled by differential gene expression. We prioritized genes that encode secreted proteins according to their preferential expression in normal lungs to identify candidates associated with lung cancer. Indeed, most of the lung-enriched genes identified in our analysis have known or suspected roles in lung cancer. We focused on the gene encoding neuron-derived neurotrophic factor (NDNF), which had not yet been associated with lung cancer. We determined that NDNF was preferentially expressed in the normal adult lung and that its expression was decreased in human lung adenocarcinoma and a mouse model of this cancer. Higher expression of NDNF was associated with better clinical outcome of patients with lung adenocarcinoma. Purified NDNF inhibited proliferation of lung cancer cells, whereas silencing NDNF promoted tumor cell growth in culture and in xenograft models. We determined that NDNF is downregulated through DNA hypermethylation near CpG island shores in human lung adenocarcinoma. Furthermore, the lung cancer–related DNA hypermethylation sites corresponded to the methylation sites that occurred in tissues with low NDNF expression. Thus, by analyzing the tissue-specific secretome, we identified a tumor-suppressive factor, NDNF, which is associated with patient outcomes in lung adenocarcinoma

Large-area transfer of two-dimensional materials free of cracks, contamination and wrinkles via controllable conformal contact

Reliable transfer techniques are critical for the integration of 2D materials with arbitrary substrates. Here, the authors describe a method to transfer 4-inch and A4-sized defect-free graphene films onto rigid and flexible substrates with controllable conformal contact, leading to improved electrical properties and uniformity. The availability of graphene and other two-dimensional (2D) materials on a wide range of substrates forms the basis for large-area applications, such as graphene integration with silicon-based technologies, which requires graphene on silicon with outperforming carrier mobilities. However, 2D materials were only produced on limited archetypal substrates by chemical vapor deposition approaches. Reliable after-growth transfer techniques, that do not produce cracks, contamination, and wrinkles, are critical for layering 2D materials onto arbitrary substrates. Here we show that, by incorporating oxhydryl groups-containing volatile molecules, the supporting films can be deformed under heat to achieve a controllable conformal contact, enabling the large-area transfer of 2D films without cracks, contamination, and wrinkles. The resulting conformity with enhanced adhesion facilitates the direct delamination of supporting films from graphene, providing ultraclean surfaces and carrier mobilities up to 1,420,000 cm(2) V-1 s(-1) at 4 K.</p