dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Set-up The code for training and testing the CNNs model is developed on the MATLAB 2019b platform, using a PC equipped with 32 GB memory, Intel Core i7-8700k CPU , GeForce RTX 2080Ti graphic card with 32 GB memory and 10. 1 Cuda edition. During the training, the network weight is updated by the Adam optimizer  [63]  with learning rate selected at 0. 0001 and minibatch size equal to 16. Using patch extraction, total training images generated from NCI-ISBI 2013 and UKMMC dataset are 9200 and 5280, respectively. To ensure that the trained CNNs are well generalized, 5-fold cross validation strategy is utilized  [64]. Here, the data are divided into 80% for training and 20% for testing the networks  [64, 65]. The performance of the CNNs is obtained from an average value of the 5-fold cross-validation.
Quantitative and Qualitative Performance The segmentation performance by the CNNs is quantitatively evaluated in terms of DSC values, averaged over all test images. The DSC values for datasets NCI-ISBI 2013 and UKMMC are shown in Tables  1  and 2 , respectively. The results are presented for both without patch extraction and with patch extraction. Subsequent paragraphs provide an analysis of segmentation results for both datasets. 1  indicate that the patch-wise method will result in better segmentation mainly because it has increased the amount of training data hence, enriching the deep neural network with more data. In particular, using patch-wise implementation, average DSC gain, calculated from the four CNNs is 2. 5% and 2. 1% for PZ and CG, respectively. In addition, the results clearly show that DeepLabV3+ gives the best prostate segmentation with the highest DSC values of 78.
9% and 92. 8% for PZ and CG, respectively. In general, the better performance for the segmentation of CG compared to PZ by the CNNs is due to the higher ratio of CG volume to PZ volume in the NCI-ISBI 2013 dataset. As for qualitative analysis, we present a sample of segmented images as shown in Figure  6  for both without and with the patch-wise method for PZ and CG regions. Visual comparison between without patch and with patch-wise indicates that more accurate segmentation can be achieved by patch-wise method. This highlights the significance of patch extraction which has enriched the CNNs with more data and improves its accuracy. It is notable from Figure  6  that the mask generated by the best CNN, DeepLabV3+ is closely similar in shape and size to the ground truth mask. In addition, DeepLabV3+ can accurately segment the region around the prostate boundary.
On the other hand, FCN, SegNet, and U-Net are also able to segment prostate into PZ and CG regions but with less accurate segmentation, especially around the pixels close to the prostate boundary. Similar trends of performance are seen in the 2-class labeled UKMMC dataset, as given in Table  2. Here, the ground truth mask is only available for the whole prostate gland (WG). Again, the best DSC value of 91. 9%, is generated by DeepLabV3+. Overall, the average improvement of the patch-wise method over without patch is by 8. 7%. For qualitative analysis, visual inspection on the segmented image as shown in Figure  7  is performed. From Figure  7 , it is clear that DeepLabV3+ is better than FCN, SegNet, and U-Net in segmenting the WG. Notably, FCN, SegNet, and U-Net are not able to accurately segment the WG, especially around the upper and lower sections of the WG.
To further confirm the better performance of patch-wise DeepLabV3+ over three other CNNs, a significance test is performed using the Wilcoxon signed-rank test generating p-values for the DeepLabV3+ versus the rest. The significance level is set at 0. 05 and a null hypothesis is that the DeepLabV3+ versus three other CNNs will not improve the DSC values of the segmented images. The p-values tabulated in Table  3  for both NCI-ISBI 2013 and UKMMC datasets are all less than 0. 05 which imply that we can reject the null hypothesis. In other words, this gives the indication that patch-wise DeepLabV3+ can better perform prostate segmentation than U-Net, SegNet, and FCN when measured in terms of DSC values. The better performance of the DeepLabV3+ can be attributed to its architecture that uses the Xception model as the backbone and making use of atrous separable convolution for retaining the spatial resolution of the resultant feature maps.
This allows the network to extract denser feature map by controlling the field view for precise localization and assimilation of context. In addition, utilizing atrous convolution enables the network to precisely define area with arbitrary scale by rebuilding them at various scales in atrous spatial pyramid pooling (ASPP). This has essentially resulted in a faster and efficient encoder-decoder network that covers better spatial information at multiple scales as compared to other encoder-decoder networks. Performance comparison of DeepLabV3+ in terms of DSC values with some selected techniques including the state-of-the-art technique is summarized in Table  4. Direct comparison of DeepLabV3+ with other published techniques is difficult due to different in datasets, scanning protocols as well as numbers of images.
Rough Comparison with other prostate segmentation techniques proposed in the past are listed in Table  4. If we consider the segmentation of WG by  [19, 20, 30, 31] , the best DSC value is 89. 4% by Yu et al. [31] , which employed residual connection in U-Net. Our patch-wise DeepLabV3+ using a UKMMC dataset has resulted in a DSC value of 91. 9%, which is 2. 5% better than  [31]. This may not be a completely fair comparison since it was not using the same dataset. However, this comparison may provide valuable insight on what is the best encoder-decoder segmentation method. Now, let's consider segmentation of PZ and CG proposed by Litjens et al. [23]  and Rundo et al. [32]. Then, comparing the DSC values indicate that our deep-wise DeepLabV3+ gave better DSC by 4. 8% (PZ) and 3. 7% (CG) than the multi atlas-based segmentation  [23].
Conclusions In this paper, the evaluation of four encoder-decoder CNNs, including FCN, SegNet, U-Net, and DeepLabV3+ for segmentation of prostate T2W MRI is presented. The proposed methodology for the investigation includes methods for image pre-processing, patch extraction, data augmentation, and class weight balancing. These techniques have resulted in the normalized dataset, increased training images as well as its spatial variation, and reduced the effect of imbalance class. The selection of appropriate techniques prior to training the network is essential to achieve a well-trained network that gives good segmentation accuracy. Comparison with atlas-based, deformable model based, CNN-based, and state-of-the-art techniques has proven that patch-wise DeepLabV3+ gave better performance in segmentation of prostate WG, PZ, or CG.
DSC values (in percentage) of prostate whole gland (WG) segmentation by FCN, SegNet, U-Net, and DeepLabV3+ for test images from the UKMMC dataset. The encoder-decoder CNNs are trained and tested in two scenarios: (1) without patch extraction using (256 × 256)-pixel input images and (2) patch-wise extraction using (192 × 192)-pixel patch size input images. The DSC values for NCI-ISBI 2013 dataset for FCN, SegNet, U-Net, and DeepLabV3+ are presented in Table 1. The NCI-ISBI 2013 is a 3-class labeled data where the prostate regions are divided into two regions, peripheral zone (PZ) and central gland (CG). The DSC values in Table.
Funding: This research is supported by two research grants: (1)  International Collaborative Research Fund between Universiti Teknologi PETRONAS (UTP)  and  Universitas Muhammadiyah Purwokerto (UMP)  under grant number  015ME0-098 , and (2) the  Yayasan Universiti Teknologi PETRONAS  under grant number  YUTP-FRG 015LC0-292.
International Conference "Bioinformatics: from Algorithms to Applications" (BiATA) is one of the few international conferences that bring together both the programmers and algorithm developers creating tools for a wide spectrum of modern bioinformatics studies and the researchers conducting those experiments interested in finding reliable and easy to use tools for data analysis. COVID-19 this year forced conferences online. Virtual conferences offer new experience that on a positive side allowed more participants to attend meetings that would be too hard or too expensive to attend in person. That's exactly what happened to BiATA-2020 that we had had to run remotely this year: we had a pleasure to welcome 475 scientists from all over the world versus 100 participants in previous years! Follow our traditions, BiATA2020 promotes active application of bioinformatics in numerous fields of research, identifies new trends in the fields of bioinformatics, computational genomics and transcriptomics, as well as in discovery of biologically active molecules.
Materials and methods Benchmark bioinformatic tools were compared in order to define the most appropriate tools for the genus Enterococcus spp. To ensure data analysis reproducibility, JAMIRA workflow was designed using the Snakemake framework. Bioinformatic tools were installed in isolated environments with Anaconda package manager to encapsulate all software dependencies. JAMIRA includes the following bioinformatic tools: Abricate for virulence gene prediction, RGI for antimicrobial resistance gene prediction, Plas-midFinder for plasmid prediction, IslandPath-DIMOB for genomic islands prediction, PhiSpy for prophage prediction and Prokka for genome annotation. A web application of JAMIRA is being implemented using the PHP Laravel framework for the elaboration of the program's internal structure and JavaScript, HTML, and CSS for graphical user interface.
O6 Black cat in a dark room: search for new viruses in metagenomes Yulia Yakovleva #1,2* , Alexey Zabelkin #3 , Maria Skazina 2,4 , Artyom Kaltovich 2 , Dmitry Antipov 5,6 , Mikhail Rayko Detection of hidden viral diversity is a challenging task, which goes beyond the standard protocol of processing metagenomic data. Meanwhile, publicly available databases contain a large amount of metagenomic data-the promising source of novel viral genomes, which remains largely understudied. Here we present the new pipeline for detecting full-length viral genomes from assembled metagenomes. Viral genomes represent cyclic or linear molecules with the ends containing repeated sequences. Both types could be recognized as cyclic sequences. We detect such contigs by searching repeats ranging from 50 to 200 bp using Knuth-Morris-Pratt algorithm.
This algorithm takes linear time depending on the maximum length of the allowed repeat, which permits to process large amounts of data and reduce its dimensionality. We classify cyclic sequences as viral or non-viral based on predicted gene content using viralVerify tool. For each selected viral contig we identify the capsid and terminase genes based on HMM profiles. We aligned found protein sequences against the NCBI nr database with Diamond. The protein sequences, both queries and hits, belonging to each HMM profile were clustered with CD-HIT v4. 8. 1 (span 80%, identity 50%). The resulting centroid sequences were aligned using MAFFT v7. 310 with default parameters, followed by phylogeny reconstruction using UPGMA and RAxML v8. 2. 11 separately. Clusters that do not contain any hits were classified as previously unknown.
The completeness of viral contigs was inspected with viralComplete and CheckV. We tested our pipeline on assembled metagenomes from NCBI Assembly database. More than 170 Gb of data representing about 1300 metagenomes derived from seawater, soil and biofilms habitats were analyzed. Our analysis revealed that the diversity of viruses is much greater than we know up to date. Hundreds of new viruses clusters were detected. For example, we identified 3 new representatives of the Siphoviridae and Podoviridae bacteriophage families from 10 biofilm-derived metagenomes. Our approach allows us to detect full-length viral genomes with a lower chance of false-positive results. In the future, the user of our pipeline can submit metagenome assemblies or raw reads to the input and receive annotated viral genomes from the data.
We created Pannopi-a scalable, easy-to-use assembly and annotation pipeline based on a hierarchical pangenome graph. The program performs a large-scale analysis of the nucleic acid sequence of bacteria from preparation to functional annotation. The process runs from the preparation of sequence reads to genome assembly, through cleaning up the genome from external contamination to structural and functional annotation. Quality control is carried out throughout the process. Pannopi has tests and benchmarks not only for genome assembly, but also for its annotation using eight genomes from different taxonomic groups. This allows new annotation methods to be tested and benchmarking quickly. Pipeline includes the most advanced and effective tools for genomic annotation and allows for flexible customization of their use.
So that the user can select between a few genome assemblers and annotators or even to run all of the tools for subsequent compare. Also, Pannopi allows users to select the taxons for pan-genome comparative genomics and required modules; it can be used on a separate command-line program or through a web interface. Pannopi output includes includes: raw data quality control; assembly; cleaned from contamination assembly; assembly quality control; structural annotation; functional annotation; pangenome-based comparative annotation; lists of antibiotic resistance and virulence genes, plasmids, phages, IS elements, tandem repeats, mlst-type, and serotype.
O9 Assembly and Annotation of Ashkenazi Reference genome Aleksey V. Zimin 1,2,* † , Alaina Shumate 1,2, † , Rachel M. Sherman 1,3 , Daniela Puiu As the sequencing technologies advance and costs decrease, it is now becoming possible to produce individual Reference genomes that rival the quality of the well-established References. In this project we produced new population-specific human Reference genome. For the initial assembly we used publicly available Illumina, Oxford Nanopore ultralong and PacBio HiFi data for Human Genome Project HG002 individual from Ashkenazi jewish trio. These data are available from Genome In A Bottle (GIAB) project. We assembled the reads with MaSuRCA genome assembler version 3. 3. 1, and then used MaSuRCA chromosome scaffolder to validate, order and orient the assembled contigs based on their alignments to the human Reference genome GRCh38.
Data sets were deposited at DDBJ/ENA/GenBank BioProject ID PRJNA504923 under the accession VFQP0000000000, VFQQ000000000, VFQR000000000, VFQS000000000, VFQT000000000 and VFQU0000000. Protein-coding sequences output from Prokka  [7]  that were evaluated using eggNOG  [8]  revealed genes conferring stress response to Cu 2+ , Zn 2+ , Pb 2+ , Cd 2+ , Ca 2+ metal ions and smt metallothionein. These genes are reported to be responsible for the efflux/transport functions and heavy metal resistance that can be major attributes of cyanobacterial species for their survival to extreme metal conditions. This is the first report of cyanobacteria in a copper mining site in Benguet Province that were analyzed using shotgun metagenomics.
Note that this grammar describes only the classical base pairs and cannot express pseudoknots. The result of a matrix-based parsing algorithm for some sequence is a boolean matrix that represents all the theoretically possible stems in terms of grammar, but the real secondary structure cannot contain all of them at once and, besides, there can be more complex, not expressible in given grammar elements. Therefore, parsing matrices require further processing and we propose to use a neural network to handle them in order to generate an actual secondary structure. For experimental research we took sequences from RnaCentral  [7]  database and as Reference data for network training we used the output of CentroidFold tool  [8] : contact matrices that represent connections between nucleotides in secondary structure.
We transformed parsing matrices and contact maps to blackand-white images. These images were used for training the generative neural network which takes a parsingprovided image as an input and transforms it to the maximal approximation of the considered contact map. We applied deep residual networks with the local alignment algorithm at the end of the sequence of layers. We trained models with and without alignment on several datasets with fixed sequence length interval and estimated them by precision, recall and F1 score metrics calculated for numbers of correctly and incorrectly guessed contacts for each image. All models showed F1 score up to 70% and we discovered that the smaller the window size, the more accurate the model, moreover, alignment significantly improves precision of neural networks due to removing the contacts that break the secondary structure.
Unfortunately, an emerging resistance of insects to these toxins due to mutations in hosts' receptors retards efficient pest management. The two strategies that could contribute to solving this issue are the search for novel toxins and the construction of artificial toxins through the domain shuffling. To discover new 3-D Cry toxins in genomic data, we have recently developed an HMM-based tool called CryProcessor that retrieves sequences of 3-D Cry toxins from large datasets and provides an opportunity to get the layout of individual domains  [1]. This tool outperforms its analogs in terms of accuracy, speed, and throughput. Cry toxins' domain layout provided by CryProcessor could facilitate the development of chimeric toxins by accelerating in silico construction of chimeric toxins. Considering the diversity of Cry toxins, one generally accepted yet not lucidly validated hypothesis links this diversification of the 3-D Cry toxins with domains' exchanges between them.
To fulfill this gap, here we conducted a large-scale phylogenetic study of the 3-D Cry toxins. Using CryProcessor, we screened the IPG and Genbank databases and identified 600 novel toxins, which were then merged with toxins from the Bt Nomenclature. We constructed phylogenetic trees based both on full sequences and separate domains. The evaluation of topological differences between the trees revealed a dissimilarity between the topology of the full sequence-based tree and the domain-only trees. We then screened sequences for signals of recombination events. As a result, we revealed 50 recombination events that belonged to each of the domains. Our results indicate that recombination events represent a pivotal mechanism for the evolution and diversification of 3-D Cry toxins. A more in-depth look into the history of recombination events would allow us to understand evolutionary mechanisms originating the Cry toxins diversity, and to develop new toxins precisely and efficiently.
Despite very harsh climatic conditions, the diversity of soil bacterial communities of the Arctic is as high as in other biomes and still underestimated. This work is aimed at the assessment of microbial communities as well as identifying the main environmental factors affecting their structure and diversity in permafrost soils of Yamal peninsula, Russian Arctic. Materials and methods DNA isolation was performed using the PowerSoil ® DNA Isolation Kit (Mobio Laboratories, Solana Beach, CA, USA). The purified DNA templates were amplified with universal multiplex primers F515 5′-GTG CCA GCMGCC GCG GTAA-3′ and R806 5′-GGA CTA CVSGGG TAT CTAAT-3′  [1]  targeting the variable region V4 of bacterial and archaeal 16S rRNA genes. Amplicon libraries sequencing was performed by ILLUMINA MiSeq. Sequence data processing was carried out using "Trimmomatic"  [2]  and "QIIME"  [3].
Also in this river area we observed increasing OTU abundance of anaerobic methanotrophs belonging to Candidatus Methanoperedens. Further North (64-67ºN) we observed enrichment of δ13C-CH 4 and increasing in methanogenic community archaeons belonging to Methanosarcina and Methanoregula. We think that NGS sequencing data can clarify taxonomical composition of metanotrophic and metanogenic communities of sediments, their activity and impact on geochemical methane-driving processes, and reveal active participants in those communities. BMC Bioinformatics 2020, 21(Suppl 20):567 undergoes developmental retardation at the pre-storage phase of development followed by developmental acceleration after 10 DAP. Consistent with this notion, transcripts associated with storage compound accumulation and acquisition of desiccation tolerance were found to be expressed earlier in Sprint-2.
Background Microbial genomics has seen rapid improvements in the past decade primarily due to the development of novel algorithms capable of assembling the data generated by a variety of next-generation sequencing technologies, into a high-quality genome. Depending on the sequencing technology, type of libraries, and the complexity of the genome, this has most often resulted in the generation of draft genomes. The completion of these microbial genomes however, have remained a challenge. Even with the advancement of technologies which produce long reads, the cost-effectiveness of short-read technologies has resulted in the deposition of 468,154 (as of December 2019) permanent-draft genomes (i. e. , genomes unlikely to be ever completed) in the NCBI database, while the number of complete genomes is only 16,814.
Materials and methods The complete genome of Escherichia coli (NZ_CP027599. 1) was selected as the standard assembly for our study. Short reads data sets of varying read lengths (75 bp to 250 bp) were simulated using the programs ART  [1] , DWGSIM  [2] , NEAT  [3] , pIRS  [4] , and Wgsim  [5]. These reads were Reference mapped to the standard assembly (NZ_CP027599. 1) using BWA  [6] , Bowtie  [7] , Novoalign (https ://novoc raft. com/), and SMALT (https ://www. sange r. ac. uk/tool/smalt -0/). Fifteen different genomes from the genus Citrobacter, Enterobacter, Salmonella, Shigella, and Yersinia at varying evolutionary distance to Escherichia coli were used as References, for mapping the aforementioned simulated reads. The best resulting assemblies were selected as input for the software GFinisher  [8].
Numerous genetic polymorphisms are believed to increase the risk of IS. The advent of genome-wide genotyping caused a wave of genome-wide association studies (GWAS). At least ten candidate-genes associated with IS were previously described. Here we present the first round findings of single nucleotide polymorphisms (SNPs) associated with the development of IS in individuals of the Eastern Slavic ancestry. The results were obtained with GWAS and machine learning (ML) approaches. The case and control groups consisted of 1051 and 421 individuals, correspondingly. They were genotyped with several types of DNA-microarrays. Upon meeting the requirement of quality control, we obtained about 82,000 autosomal SNPs in the merged dataset. The GWAS included a conventional chi-square test, an exact Fisher test and the Bayes factor method.
References 1 Almodaresi, F. , Sarkar, H. , Srivastava, A. and Patro, R. , 2018. A space and time-efficient index for the compacted colored de Bruijn graph. Bioinformatics, 34(13), pp. i169-i177. 2 Holley, G. and Melsted, P. , 2019. Bifrost-Highly parallel construction and indexing of colored and compacted de Bruijn graphs. BioRxiv, p. 695338. 3 Marchet, C. , Kerbiriou, M. and Limasset, A. , 2019, April. Indexing De Bruijn graphs with minimizers. In Recomb seq. 4 Marchet, C. , Iqbal, Z. , Gautheret, D. , Salson, M. and Chikhi, R. , 2020. REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets. ISMB. 5 Bray, N. L. , Pimentel, H. , Melsted, P. and Pachter, L. , 2016. Near-optimal probabilistic RNA-seq quantification. Nature biotechnology, 34(5), pp. 525-527. 6 Marçais, G.