Draft genome sequence of Rhodococcus opacus strain 04-OD7, which can mobilize phosphate

Rhodococcus opacus strain 04-OD7 (=CCTCC AB 2017148) is a Gram-positive bacterium showing inorganic phosphate solubilization capacity for the first time in the genus Rhodococcus. We present here the draft genome description of R. opacus 04-OD7 along with multiple phosphorus (P) mobilization-related genes, supporting its inorganic phosphate solubilization

Evaluation and optimization of parameters in the measurement for airborne scanner using response surface method

This paper aims to evaluate the working parameters and try to make an optimized use of the parameters which affect the measurement accuracy of airborne scanner. First, based on response surface method, three levels of configuration values of each parameter are selected, respectively, and 53 response surface experiments are designed. Second, three-dimensional coordinate errors of the scan points in each response surface experiment are calculated by comparing the coordinates measured by airborne scanner and common measuring apparatus. Third, by analyzing the experimental error through response surface method, the optimum configuration values of the parameters are determined. Meanwhile, the configuration characteristics and change laws of each parameter on three-dimensional coordinate errors are also realized. Results show that the most influencing parameters are flight height, flight speed, ground feature, aspect angle, scan frequency, and course angle. The optimum values for these parameters are found to be 46.14 m/s for flight speed, type 2 for ground feature, 88 Hz for scan frequency, 54.4° for course angle, 24.12° for aspect angle, and 215.92 m for flight height. The verification experiments showed that the predicted values from the response surface method are quite close to the experimental values, which validate the proposed approach

Data on Quantitative Microbial Elemental Cycling (QMEC) primer design and validation

This work included the primer design details of Quantitative Microbial Elemental Cycling (QMEC) method, a high-throughput qPCR method for microbial carbon (C), nitrogen (N), phosphorus (P), sulfur (S) and methane metabolism potential detection and assessment. We designed 36 novel primers based on their amino acid sequences. Via illumina sequencing technology, their phylogenetic taxonomy was identified and analyzed to validate the primer specificity.Peer reviewe

Deep learning based real-time facial mask detection and crowd monitoring

During the Covid pandemic, the importance of wearing mask has been noted globally. Additionally, crowded human clusters facilitated the transmission of the virus, which brings up the need for new systems for monitoring such situations. To address such issues, this research proposes an object recognition visual system based on deep learning to monitor the wearing of masks in a certain space and the control of the number of people indoors as an important tool during an epidemic. This research mainly investigates two types of identification. The first is to monitor whether people entering the site wear a mask at the entrance and exit of the field, and the second is to count the number of people entering a specific area. Experimental results show that by utilising the visual sensor, it is possible to detect and identify the people who frequently enter and exit in real-time. An advanced transfer learning approach has been employed to achieve the best discrimination performance. The actual training results prove that the migration learning Mask R-CNN algorithm produced by this method and the original Mask R-CNN algorithm have increased the mAP by 3%, reaching a mAP of 96%. In addition, the accuracy of the random sampling and identification in actual scenes has reached 92.1%. The developed deep learning vision system has an enhanced identification ability for the verification and analysis of actual scenes and has great application potential

Public perceptions of digital fashion: An analysis of sentiment and Latent Dirichlet Allocation topic modeling

Since digital technology has had a significant impact on the fashion industry, digital fashion has become a hot topic in today’s society. Currently, research on digital fashion is focused on the transformation of enterprise marketing strategies and the discussion of digital technology. Despite this, the current study does not include an analysis of the audience’s emotional and cognitive responses to digital fashion on social networking platforms. A comprehensive analysis and discussion of 52,891 posts about digital fashion and virtual fashion published on social networking sites was conducted using k-means clustering analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis in this study. The study examines the public’s perception and hot topics about digital fashion, as well as the industry’s development situation and trends. According to the findings, both positive and neutral emotions accompany the public’s attitude toward digital fashion. There is a wide range of topics covered in the discussion. Innovations in digital technology have impacted the creation of jobs, talent demand, marketing strategies, profit forms, and industrial chain innovation of fashion-related businesses. Researchers in related fields will find this study useful not only as a reference for research methods and directions, but also as a source of references for research methodology. A case study and data reference will also be provided to industry practitioners

3-(2-Thioxo-1,3-dithiol-4-ylsulfan­yl)­propane­nitrile

The title compound, C6H5NS4, consists of a planar 2-thioxo-1,3-dithiol-4-ylsulfanyl unit [maximum deviation from the ring plane = 0.0325 (2) Å], with a cyano­ethyl­sulfanyl substituent in the 4-position. In the crystal structure, weak inter­molecular C—H⋯S hydrogen bonds together with S⋯N inter­actions [3.260 (5) Å] form two-dimensional layers in the bc plane

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the area of computer science. How to glean papers worth reading from the massive literature to do a quick survey or keep up with the latest advancement about a specific research topic has become a challenging task. Existing academic search engines such as Google Scholar return relevant papers by individually calculating the relevance between each paper and query. However, such systems usually omit the prerequisite chains of a research topic and cannot form a meaningful reading path. In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. To serve as a research benchmark, we further propose SurveyBank, a dataset consisting of large quantities of survey papers in the field of computer science as well as their citation relationships. Each survey paper contains key phrases extracted from its title and multi-level reading lists inferred from its references. Furthermore, we propose a graph-optimization-based approach for reading path generation which takes the relationship between papers into account. Extensive evaluations demonstrate that our approach outperforms other baselines. A Real-time Reading Path Generation System (RePaGer) has been also implemented with our designed model. To the best of our knowledge, we are the first to target this important research problem. Our source code of RePaGer system and SurveyBank dataset can be found on here.Comment: 16 pages, 12 figure

Penicillamine Increases Free Copper and Enhances Oxidative Stress in the Brain of Toxic Milk Mice

Wilson disease (WD) is characterized by the accumulation of copper arising from a mutation in the ATP7B gene. Penicillamine (PA) makes 10–50% of the patients with neurologic symptoms neurologically worse at the early stage of administration. The aim of this study was to determine how the copper metabolism changes and whether the change impairs the brain of toxic milk (tx) mice, an animal model of WD, during the PA administration. The free copper and protein-bound copper concentrations in the serum, cortex and basal ganglia of tx mice with PA administration for 3 days, 10 days and 14 days, respectively, were investigated. The expression of copper transporters, ATP7A and CTR1,was analyzed by real-time quantitative PCR, immunofluorescence and Western blot. Then SOD, MDA and GSH/GSSG were detected to determine whether the oxidative stress changed correspondingly. The results revealed the elevated free copper concentrations in the serum and brain, and declined protein-bound copper concentrations in the brain of tx mice during PA administration. Meanwhile, transiently increased expression of ATP7A and CTR1 was observed generally in the brain parenchyma by immunofluorescence, real-time quantitative PCR and Western blot. Additionally, ATP7A and CTR1 were observed to locate mainly at Golgi apparatus and cellular membrane respectively. Intense staining of ATP7A in the choroid plexus was found in tx mice on the 3rd and 10th day of PA treatment, but rare staining of ATP7A and CTR1 in the blood-brain barrier (BBB). Decreased GSH/GSSG and increased MDA concentrations were also viewed in the cortex and basal ganglia. Our results suggested the elevated free copper concentrations in the brain might lead to the enhanced oxidative stress during PA administration. The increased free copper in the brain might come from the copper mobilized from brain parenchyma cells but not from the serum according to the ATP7A and CTR1 expression analysis