Methylation status of individual CpG sites within Alu elements in the human genome and Alu hypomethylation in gastric carcinomas

<p>Abstract</p> <p>Background</p> <p><it>Alu </it>methylation is correlated with the overall level of DNA methylation and recombination activity of the genome. However, the maintenance and methylation status of each CpG site within <it>Alu </it>elements (<it>Alu</it>) and its methylation status have not well characterized. This information is useful for understanding natural status of <it>Alu </it>in the genome and helpful for developing an optimal assay to quantify <it>Alu </it>hypomethylation.</p> <p>Methods</p> <p>Bisulfite clone sequencing was carried out in 14 human gastric samples initially. A <it>Cac</it>8I COBRA-DHPLC assay was developed to detect methylated-<it>Alu </it>proportion in cell lines and 48 paired gastric carcinomas and 55 gastritis samples. DHPLC data were statistically interpreted using SPSS version 16.0.</p> <p>Results</p> <p>From the results of 427 <it>Alu </it>bisulfite clone sequences, we found that only 27.2% of CpG sites within <it>Alu </it>elements were preserved (4.6 of 17 analyzed CpGs, A ~ Q) and that 86.6% of remaining-CpGs were methylated. Deamination was the main reason for low preservation of methylation targets. A high correlation coefficient of methylation was observed between <it>Alu </it>clones and CpG site J (0.963), A (0.950), H (0.946), D (0.945). Comethylation of the sites H and J were used as an indicator of the proportion of methylated-<it>Alu </it>in a <it>Cac</it>8I COBRA-DHPLC assay. Validation studies showed that hypermethylation or hypomethylation of <it>Alu </it>elements in human cell lines could be detected sensitively by the assay after treatment with 5-aza-dC and M.<it>Sss</it>I, respectively. The proportion of methylated-<it>Alu </it>copies in gastric carcinomas (3.01%) was significantly lower than that in the corresponding normal samples (3.19%) and gastritis biopsies (3.23%).</p> <p>Conclusions</p> <p>Most <it>Alu </it>CpG sites are deaminated in the genome. 27% of <it>Alu </it>CpG sites represented in our amplification products. 87% of the remaining CpG sites are methylated. <it>Alu </it>hypomethylation in primary gastric carcinomas could be detected with the <it>Cac</it>8I COBRA-DHPLC assay quantitatively.</p

Advances in Modern Clinical Ultrasound

Advances in modern clinical ultrasound include developments in ultrasound signal processing, imaging techniques and clinical applications. Improvements in ultrasound processing include contrast and high-fidelity ultrasound imaging to expand B-mode imaging and microvascular (or microluminal) discrimination. Similarly, volumetric sonography, automated or intelligent ultrasound, and fusion imaging developed from the innate limitations of planar ultrasound, including user-operator technical dependencies and complex anatomic spatial prerequisites. Additionally, ultrasound techniques and instrumentation have evolved towards expanding access amongst clinicians and patients. To that end, portability of ultrasound systems has become paramount. This has afforded growth into the point-of-care ultrasound and remote or tele-ultrasound arenas. In parallel, advanced applications of ultrasound imaging have arisen. These include high frequency superficial sonograms to diagnose dermatologic pathologies as well as various intra-cavitary or lesional interrogations by contrast-enhanced ultrasound. Properties such as real­time definition and ease-of-access have spumed procedural and interventional applications for vascular access. This narrative review provides an overview of these advances and potential future directions of ultrasound

Polycomb CBX7 Directly Controls Trimethylation of Histone H3 at Lysine 9 at the p16 Locus

BACKGROUND: H3K9 trimethylation (H3K9me3) and binding of PcG repressor complex-1 (PRC1) may play crucial roles in the epigenetic silencing of the p16 gene. However, the mechanism of the initiation of this trimethylation is unknown. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we found that upregulating the expression of PRC1 component Cbx7 in gastric cancer cell lines MGC803 and BGC823 led to significantly suppress the expression of genes within the p16-Arf-p15 locus. H3K9me3 formation was observed at the p16 promoter and Regulatory Domain (RD). CBX7 and SUV39H2 binding to these regions were also detectable in the CBX7-stably upregulated cells. CBX7-SUV39H2 complexes were observed within nucleus in bimolecular fluorescence complementation assay (BiFC). Mutations of the chromodomain or deletion of Pc-box abolished the CBX7-binding and H3K9me3 formation, and thus partially repressed the function of CBX7. SiRNA-knockdown of Suv39h2 blocked the repressive effect of CBX7 on p16 transcription. Moreover, we found that expression of CBX7 in gastric carcinoma tissues with p16 methylation was significantly lower than that in their corresponding normal tissues, which showed a negative correlation with transcription of p16 in gastric mucosa. CONCLUSION/SIGNIFICANCE: These results demonstrated for the first time, to our knowledge, that CBX7 could initiate H3K9me3 formation at the p16 promoter

Thermal environment and thermal comfort in a passive residential building in the severe cold area of China

Purpose / Context - The outdoor climate in Harbin is more severely colder than German. Therefore, it is important to study indoor thermal environment and human thermal comfort in Harbin passive buildings by applying Germany technology. However, few studies were reported on this topic. Methodology / Approach - A field measurement on thermal environment was carried out in a passive residential building in Harbin, as well as a subjective survey on residents’ thermal response. 25 residents in 21 apartments volunteered as the participants in this study. Among them, a continuous monitoring was conducted in 7 apartments. Results - The results show that the mean indoor temperature was 26.2℃, which was over higher than the upper limit of ASHRAE 55-2013. The average relative humidity was 35.9%, close to the lower limit. There was a small temperature difference between the indoor air temperature and the inner surface temperature of the exterior wall, which indicates a good insulation performance and reduces discomfort induced by cold radiation. 50% residents confided that the indoor environment was over warm, and they usually adjusted clothes to the environment. The neutral temperature was 24.2℃, and 90% acceptability temperature was 23.2~25.2℃, a width of 2 ℃, which indicates a weak adaptation and tolerability for the residents. Key Findings / Implications - A lower indoor temperature was recommended in operation. Not only could residents’ thermal comfort be improved, but also the energy consumption was reduced further. Originality – Thermal environment and thermal comfort in a Harbin passive residential building was researched

INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

Enhancement of farming management relies heavily on enhancing farmer knowledge. In the past, both the direct learning approach and the personnel extension system for improving fertilization practices of smallholders has proven insufficiently effective. Therefore, this article proposes an interactive knowledge learning approach using artificial intelligence as a promising alternative. The system consists of two parts. The first is a dialog interface that accepts information from farmers about their current farming practices. The second part is an intelligent decision system, which categorizes the information provided by farmers in two categories. The first consists of on-farm constraints, such as fertilizer resources, split application times and seasons. The second comprises knowledge-based practices by farmers, such as nutrient in- and output balance, ratios of different nutrients and the ratios of each split nutrient amount to the total nutrient input. The interactive knowledge learning approach aims to identify and rectify incorrect practices in the knowledge-based category while considering the farmer&#8217;s available finance, labor, and fertilizer resources. Investigations show that the interactive knowledge learning approach can make a strong contribution to prevention of the overuse of nitrogen and phosphorus fertilizers, and mitigating agricultural non-point source pollution

Genetic divergence and gene flow among Mesorhizobium strains nodulating the shrub legume Caragana

Although the biogeography of rhizobia has been investigated extensively, little is known about the adaptive molecular evolution of rhizobia influenced by soil environments and selected by legumes. In this study, microevolution of Mesorhizobium strains nodulating Caragana in a semi-fixing desert belt in northern China was investigated. Five core genes-atpD, glnII, gyrB, recA, and rpoB, six heat-shock factor genes-clpA, clpB, dnaK, dnaf, grpE, and hlsU, and five nodulation genes-nodA, nodC, nodD, nodG, and nodP, of 72 representative mesorhizobia were studied in order to determine their genetic variations. A total of 21 genospecies were defined based on the average nucleotide identity (ANI) of concatenated core genes using a threshold of 96% similarity, and by the phylogenetic analyses of the core/heat-shock factor genes. Significant genetic divergence was observed among the genospecies in the semi-fixing desert belt (areas A-E) and Yunnan province (area F), which was closely related to the environmental conditions and geographic distance. Gene flow occurred more frequently among the genospecies in areas A-E, and three sites in area B, than between area F and the other five areas. Recombination occurred among strains more frequently for heat-shock factor genes than the other genes. The results conclusively showed that the Caragana-associated mesorhizobia had divergently evolved according to their geographic distribution, and have been selected not only by the environmental conditions but also by the host plants. (C) 2015 Elsevier GmbH. All rights reserved

The Practice of Deep Learning Methods in Biodiversity Information Collection

Deep learning is one machine learning method based on the layers used in artificial neural networks. The breakthrough of deep learning in classification led to its rapid application in speech recognition, natural language understanding, and image processing and recognition. In the field of biodiversity informatics, deep learning efforts are being applied in rapid species identification and counts of individuals identified based on image, audio, video, and other data types. However, deep learning methods hold great potential for application in all aspects of biodiversity informatics. We present a case study illustrating how to increase data collection quality and efficiency using well-established technology such as optical character recognition (OCR) and some image classification. Our goal is to image data from the scanned documents of various butterfly atlases, add species, specimens, collections, photographs and other relevant information, and build a database of butterfly images. Information collection involves image annotation and text-based descritpion input. Although the work of image annotation is simple, this process can be accelerated by deep learning-based target segmentation to make the selection process easier, such as changing box select to a double click. The process of information collection is complicated, involving input of species names, specimen collection, specimen description, and other information. Generally, there are many images in atlases, the text layout is rather messy, and overall OCR success is poor. Therefore, the measures we take are as follows: Step A: select the screenshot of the text and then call the OCR interface to generate the text material; Step B: proceed with NLP- (natural language processing) related processing; Step C: perform manual operations on the results, and introduce the NLP function again to this process; Step D: submit the result. The deep learning applications we integrated in our client tool include: target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. Some simple machine learning methods such as k-nearest neighbor can be used to automatically determine gender, pose, and so on. While complex information such as collection place, time, and collector can be analyzed by deep learning-based NLP methods in the future. In our infomation collection process, ten fields are required to submit one single record. Of those, about 4-5 input fields can be dealt with the AI-assistant. It can thus be seen from the above process that deep learning has reduced the workload of manual information annotation by at least 30%. With improvements in accuracy, the era of using automatic information extraction robots to replace manual information annotation and collection is just around the corner

Lithium sulfide-based cathode for lithium-ion/sulfur battery: Recent progress and challenges

It has been more than a quarter century since lithium-ion (Li-ion)battery technology was first developed. The current Li-ion batteries represent a market of approximately $77 billion, and by 2030, it will reach$100 billion. The technological advancement of the portable electronics industry, however, along with vehicle electrification and grid energy storage necessitates more energy that can be easily supplied by the Li-ion battery due to the capacity limitations of its intrinsic materials. Fully-lithiated sulfur or lithium sulphide (Li2S)has been considered as promising cathode candidate that shows three times higher capacity (∼1166 mAh/g)than the current Li-ion technology. Many researchers put Li2S ahead of elemental sulfur as a cathode material (even though elemental sulfur has a higher theoretical capacity than Li2S), as it can be coupled with other anode materials and can form a Li-ion/Sulfur battery that reduces the safety concerns for using metallic lithium anode due to dendrite formation. Like sulfur, however, Li2S as a cathode material is also electronically and ionically insulating, and on top of that, its high activation potential and moisture sensitivity are the notable hindrances to making this cathode material marketable. This review paper comprehensively discusses the current progress towards addressing the aforementioned key challenges, as well as providing a detailed discussion of all attempts to develop different anodes and electrolytes for making Li2S cathode-based full cells.</p

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements