Combined assembly of long and short sequencing reads improve the efficiency of exploring the soil metagenome

Background Advances in DNA sequencing technologies have transformed our capacity to perform life science research, decipher the dynamics of complex soil microbial communities and exploit them for plant disease management. However, soil is a complex conglomerate, which makes functional metagenomics studies very challenging. Results Metagenomes were assembled by long-read (PacBio, PB), short-read (Illumina, IL), and mixture of PB and IL (PI) sequencing of soil DNA samples were compared. Ortholog analyses and functional annotation revealed that the PI approach significantly increased the contig length of the metagenomic sequences compared to IL and enlarged the gene pool compared to PB. The PI approach also offered comparable or higher species abundance than either PB or IL alone, and showed significant advantages for studying natural product biosynthetic genes in the soil microbiomes. Conclusion Our results provide an effective strategy for combining long and short-read DNA sequencing data to explore and distill the maximum information out of soil metagenomics.

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.

Beta-D-Glucan in Patients with Haematological Malignancies

(1-3)-beta-D-glucan (BDG) is an almost panfungal marker (absent in zygomycetes and most cryptococci), which can be successfully used in screening and diagnostic testing in patients with haematological malignancies if its advantages and limitations are known. The aim of this review is to report the data, particularly from the last 5 years, on the use of BDG in haematological population. Published data report mainly on the performance of the Fungitell™ assay, although several others are currently available, and they vary in method and cut-off of positivity. The sensitivity of BDG for invasive fungal disease (IFD) in haematology patients seems lower than in other populations, possibly because of the type of IFD (lower sensitivity was found in case of aspergillosis compared to candidiasis and pneumocystosis) or the use of prophylaxis. The specificity of the test can be improved by using two consecutive positive assays and avoiding testing in the case of the concomitant presence of factors associated with false positive results. BDG should be used in combination with clinical assessment and other diagnostic tests, both radiological and mycological, to provide maximum information. Good performance of BDG in cerebrospinal fluid (CSF) has been reported. BDG is a useful diagnostic method in haematology patients, particularly for pneumocystosis or initial diagnosis of invasive fungal infections.

Semiotics with Style and Situation: Multi-modal Comparative Analysis of Title pages of Native and Non-native English Novels

The communicative function of text can be obtained through the multimodal analysis of the text which contains the interaction and integration of two or more semiotic resources, graphics and text. This study investigated the structure of the titles and graphics of the title pages along with the situation of the content of native and non-native title pages of English novels of modern age. 20 title pages including 10 modern native and non-native English novels are randomly selected from the Google search engine. Multimodal analysis including Jeffries (2016) model to analyse the structure of the text of the title pages, Davy (2013) model to analyse the graphical features of the title pages and Bernstein (2003) to investigate the situational features presented in the title pages of the novels have been followed. A bench mark technique was used to identify the graphics of title pages, structure of the title phrases and situation presented in the title pages. The results generated from qualitative analysis indicated that mostly the native authors observed all of the features mentioned by the great linguists, stylitions and graphic experts while selecting the design of title pages whereas, non-native authors and publisher have not kept these features in mind while selecting the contents of title page of their composition. It causes lack of readership as the readers cannot extract maximum information from the title page. The study has opened new dimensions to the new researchers and it also beneficial for the authors and publisher in the selection of the title pages.Key words: Semiotics, Graphics, Situation, Text, Title-page

Assess the Knowledge of Women on Zinc Supplementation with ORS in the Management of Diarrhea

The assessment is utilized to evaluate and to get a proper information from the mothers or ladies with regard to the utilization of the zinc supplementation with the Oral rehydration salts for the great approach and to see the future aspects of management of diarrhea. With a purpose of inspecting the study was carried out to investigate the 100 mothers. The survey approach was carried out to gather maximum information regarding the advantages of adding zinc with the ORS in the management of diarrhea. This was carried out through the information poll. The data was analysed and visualised using graphs, tables, and descriptive and inferential statistics. The key findings of the current study indicate that the majority of 63 percent of moms received an average score of 41 to 60 percent, while the lowest score was in the 1% of women who had a very excellent score of 81 to 100 percent. Only 2% of moms received a poor mean knowledge score in the range of 21 to 40%, and not a single mother received a score of less than 0%. The overall mean knowledge score of mothers regarding the benefits of supplementing the zinc with the ORS in the management of diarrhea was 17.24 %. According to the study's findings, the mother is the most important factor in enhancing the child's health. Mothers are an important element of the health-care sector because they have the power to change a child's health. She has the potential to make a significant difference in the family's overall health. Mothers need to be well-informed so that they can strive to apply various preventative measures to avoid infections like diarrhoea, for the sake of their children's safety. As a result, the nation's health is dependent on the mother's wisdom. Mother's knowledge of various health-related services needs to be updated and upgraded.

NEW EXHIBITS OF ITEMS WITH CHAMPLEVE ENAMELS IN SUMY REGIONAL MUSEUM (PRELIMINARY REPORT)

During 2018—2020 items with Champleve enamels were presented to Sumy Regional Museum by Sumy residents, S. I. Gutsan and R. A. Bobkov. They were shown at an exhibition dedicated to the 100th anniversary of the Museum in September 2020. The collection consists of more than 70 items (tabl. 1). There are brooches (horseshoe-shaped, triangular, round), pendants (cruciform, lunnula), chains, bracelets, diadems, torques, appliquй, finger-rings, trapezoidal pendants, spurs etc. Because these finds were kept in private collections and were presented to the Museum by their owners, the items could be considered accidental finds except location of them was more or less accurate. Preliminary analysis of things from the collection allowed us to determine the following. The area of finds includes different areas of Sumy region, but those found in Sumy’s suburbs significantly dominate, and by dating they mostly belong to the second stage of development of East European Champleve enamels, according to E. Gorokhovskyi — to the middle and second half of 3rd century. Metal and enamel of things were analyzed by the researchers of The Institute of Applied Physics of NASU using an X-ray fluorescence spectrometer. Situation around archeological monuments requires a rapid reconstruction, adaptation of science to new unfavorable conditions. This publication is prompted by an attempt to obtain maximum information from sources whose reliability is questionable. Moreover, the things analyzed in this publication are of great importance for solving many important questions of archeology, in particular, for the reconstruction of the Slavonic peoples’s genesis.

An Improved Cuckoo Search Clustering Method for Line Loss Data of Transformer District with DGs

For the low-voltage transformer district with distributed generations (DGs), the traditional theoretical calculation method of line loss is not applicable. This paper presents a novel clustering method for line loss data of transformer district with DGs, which combined an improved Cuckoo Search algorithm and K-Means clustering algorithm. Firstly, the influence factors of line loss are screened based on the maximum information coefficient, and the line loss index system is established. Secondly, an improved cuckoo search clustering algorithm is proposed to cluster the sample data set to reduce the dependence on the initial clustering center. Finally, the simulation results of 410 samples from a certain area with photovoltaic power supply show the accuracy and effectiveness of the proposed method. The simulation results show that the proposed method is accurate and effective.

RNIC-A Retrospect Network for image captioning

As cross-domain research combining computer vision and natural language processing, the current image captioning research mainly considers how to improve the visual features, less attention has been paid to utilizing the inherent properties of language to boost captioning performance. Facing this challenge, we proposed a textual attention mechanism, which can obtain semantic relevance between words by scanning all generated words. The Retrospect Network for image captioning(RNIC) proposed in this paper aims to improve input and prediction process by using textual attention. Concretely, the textual attention mechanism is applied to the model simultaneously with the visual attention mechanism to provide the input of the model with the maximum information required for generating captions. In this way, our model can learn to collaboratively attend on both visual and textual features. Moreover, the semantic relevance between words obtained by retrospect is used as the basis for prediction, so that the decoder can simulate the human language system and better make predictions based on the already generated contents. We evaluate the effectiveness of our model on the COCO image captioning datasets and achieve superior performance overthe previous methods.extraction function to extract the hidden unit information of multiple time steps for prediction, to solve the problem of insufficient LSTM prediction information. Experiments have shown that both model significantly improved the various evaluation indicators in the AI CHALLENGER test set.