dataesr/scientific-paragraphs-categorization huggingface dataset:
One stained image in our dataset was chosen by an expert according to the quality of its coloring to be a target image and we normalized the other images into its color appearance. After evaluation of the prediction, we observed that both normalizations slightly improve the results, specifically the epithelium regions (Table  2 ). Does a large spectrum of colors contribute or mislead the learning process ? Because of the contrast that appears more strongly in the grayscale, we wanted to evaluate how well the H&E grayscale images can improve the performance of our model. Because gray levels can facilitate the differentiation of epithelium tissue structures from non-epithelium structures. However, based on our experiments (Table 2), we found that our framework improves the identification of tissues more on raw RGB normalized images than on grayscale images.
The reason could be that grayscale images miss some relevant information that might be helpful for discriminating between different tissues with similar nuclei distribution, for example invasive and in situ classes. What is the minimum of necessary H&E images to represent the diversity of the characterized tissues ? In this section, we answer the following question: what the minimum amount of data to solve a semantic segmentation problem by training CNN from scratch is ? This crucial question was not explored in the recent deep learning based medical image studies and in particular in image pathology publications. To address this question, we evaluated both, SegNet and U-Net, by varying the number of training images and randomly picking images from our dataset for each run. During the training phase, we observed two different behaviors: a consistent DC improvement for SegNet, as the number of training images increases whereas U-Net seems to converge faster (see Figure.
3). On the opposite, during the prediction evaluation on WSIs, SegNet converges very fast to the almost optimal result, whereas U-Net needs at least 6 images to get there. The main conclusion of this observation which can be applied to any dataset for probably various domains, reveals the importance of the chosen model according to the number of inputs which is consistent with results of similar work  (Ronneberger et al. , ). In this study we decided to keep SegNet as the optimal model as the baseline for other experiments considering that it gives better results after a training phase on 9 images.
What the minimum amount of labeled data is? We evaluated two correlated parameters which are the size S p and the number N p of randomly extracted patches per WSI. Regarding the size of patches, we looked at a large range from 96 to 384 pixels, using R as a ratio where S p = 96 * R with R ∈ {1, 2, 3, 4}. Figure. 4 shows the train and prediction DC of our 2 chosen networks. Obviously, both models demonstrate different behavior when S p and N p vary. Seg-Net shows a constant increase of training curve because its architecture includes batch normalization. But, starting fromN p = 2500, the DC remains constant. A larger random samples could lead to a lot of redundancy due to overlapping patches. Secondly, there is a trade-off between localization accuracy and the use of context. Larger patches require more maxpooling layers that reduce the localization accuracy, while small patches allow the network to see more details but only little context.
RESULTS Among the 11 WSIs in our data set, we choose two representative WSIs for test (Figure. 1) to evaluate the final prediction performance of our framework. Table  3  shows global as well as class-wise performance on the test images of the four networks predictions for the 8 classes as presented in Section 3. Even if the global score of the entire images do not vary much, SegNet slightly outperforms the other networks. Thus, giving the diversity and the number of tissue categories, it is more interesting to analyze the classes-wise metrics (Table  3 ) to capture the difference between the evaluated models. The classwise accuracy clearly shows that larger classes have reasonable accuracy and smaller classes have lower accuracy. Epithelium classes and, in particular, the two carcinoma subtypes are more challenging for the models to segment than the non-epithelium classes, many of which occupy a small part of the whole image and appear infrequently as shown in Figure.
CONCLUSIONS & DISCUSSION We proposed an end-to-end framework for a medical multi-classes segmentation task. We first introduced a dataset of 11 H&E stained breast cancer images captured at intermediate resolution (20x magnification). We annotated WSIs into 7 tissues plus background categories that an expert pathologist determined important for the medical task. We proposed a deep analysis of network settings for image segmentation in order to determine the optimal configuration that can be used in similar task. The final results was evaluated using pixel-wise metrics. Results of U-Net, SegNet, FCN and DeepLab got comparable scores with DC of 0. 86, 0. 87, 0. 86 and 0. 86 respectively. The current study retains several limitations that we want to address in future work: Epithelium classes and artifacts remains a challenge to be detected due to the huge tissue variability among the WSIs.
Leaders of such development are Docker 1   [29]  and Linux Containers (LXC  [17] ). Nevertheless its implications for scientific computing including HPC are still on doubt. Containers are proving to be an extremely valuable technology for science delivering portability and reproducibility to the users. Containers can provide the requirements of a program and execute it directly, without the overhead that comes with hypervisor-based approaches. "Singularity-containers" from  [23]  is a container-based approach which focuses on providing portable environments which could leverage the migration of computational science to the cloud. Singularity integrates seamlessly with existing workload managers such as Slurm, HTCondor or Torque; fact that could ease its adoption of HPC facilities. At the distributed systems and networks laboratory, at Universidad del Valle, we are working on the deployment of container-based software infrastructures to support the research process on different areas of knowledge.
On 2005 OpenVZ was announced as a COS implementation for Linux systems. Despite it was an open source project there was not too much interest in the Linux community then it was barely included into the Kernel main stream. OpenVZ never gets enough track amongst Linux community. LXC (Linux Containers) took advantage of the namespace concept. Different from previous approaches where file system isolation was provided, LXC extended the isolation property to users, processes and networking. On 2001, Linux supported the first file system namespace known as the mount namespace. Since then, other namespaces have been supported, UTS, IPC, PID, user and network namespaces. In addition to isolation, on 2006, Google project (process containers) implemented a functionality to limit the resource usage, e.
g. CPU, memory, disk I/O, network). This project was later merged into the Linux kernel and it was named cgroups (control groups). From that, cgroups capabilities have been extended to firewalling and unified hierarchy, amongst others. Docker released on 2013, was basically an additional layer on top of LXC exposing additional features such as mounted storage, network port redirection, and container catalog management. These features made Docker a prime time product for the industry. Singularity is a project developed at Lawrence Berkeley National Laboratory (LBNL) and it is mainly focused on experimental reproducibility and isolation.
II-A. LXC LXC is built on top of kernel namespaces which is a Linux kernel feature that isolates and virtualizes system resources such as processes, network, filesystems, network stack, among others 2. These capabilities allow a fully operational container-based environments exhibiting interesting features such as exposure of network services from containers, containers live migration, and a complete set of accountability mechanisms  [43]. In the networking context, LXC supports route-and bridge-based networking which allow the communication with the outside world but these features add a virtual network layer over the host which imposes an overhead to the network performance. Mechanisms based on cgroups are used for restraining the amount of resources that a container can consume, e. g. CPU, memory, number of opened files and so on.
II-B. Docker Docker basically extends LXC with a kernel-and application level API  [8]  and mainly focusing on network service virtualization. Through the libcontainer library, Docker provides access to virtualization facilities provided by the Linux kernel along with some abstracted virtualized interfaces such as libvirt  3  , LXC and systemd-nspawn  4. The control over host's resources is provided through Control Groups (cgroups) thus it limits the amount of resources used by a container such as memory, disk space and I/O  [42]. Docker features a layered filesystem called AuFS (Advanced Multi-Layered Unification Filesystem) which allows to overlay one or more existing filesystems. When a process needs to create a copy, AuFS creates a copy of that file. This feature provides image versioning management and exposing base images to more specialized virtualized systems  [29].
Docker has emerged as a key player in the virtualization field since it has being widely adopted in the industry and academy because it leverages infrastructure consolidation and exhibits a low resource footprint. Docker has boosted the adoption of service oriented architectures (e. g. microservices  [15] ) because it ease the deployment of self-contained modules which are able to independently interact with third parties using well-known and widely adopted network protocols (e. g. web services). These service oriented architectures encourage the adoption of adaptable and extensible computational environments (e. g. workflows) accelerating the pace of scientific progress  [16].
II-D. Comparing LXC, Docker and Singularity Three COS technologies have been discussed. LXC has evolved in the meantime. One big evolution is LXD which is an API for LXC. For instance, it allows the live migration of containers between different LXD hosts. Therefore, users of LXC/LXD are not the same than the docker users (e. g. LXC is provided by the Proxmox virtualization server solution  5  ). Indeed, state full containers and full environment (like singularity) could be quite interesting features. On the other hand, Singularity exhibits a limited number of properties because it is mostly conceived for code mobility and high availability of resources. In next section, HPC benchmarks were run against these COS technologies and preliminary results exhibit that the absence of some features positively affects the performance of these benchmarks.
Searching for the problem size that can deliver peak performance is extensive; instead, we used the same problem size 10 times (10 N, 115840 Ns) for performance analysis. Figure  4  shows the performance of HPL-Benchmark. The Y axis is demonstrating the differences in technologies (that is why it doesn't goto zero). The LXC was not able to achieve native performance presenting an average overhead of 7. 76 %, Docker overhead was 2. 89 %, this could be probably caused by the default CPU use restrictions set on the daemon which by default each container is allowed to use a node's CPU for a predefined amount of time. Singularity was able to achieve a better performance than native with 5. 42 % because is not emulating a full hardware level virtualization (only the mount namespace) paradigm and as the image itself is only a single metadata lookup this can yield in very high performance benefits.
III-D. Memory performance The Memory performance on single node was evaluated with the STREAM application benchmark  [27]. It is a simple synthetic benchmark program that measures sustainable memory bandwidth (in MB/s) and the corresponding computation rate for simple vector kernels  [26]. The STREAM benchmark is specifically designed to work with datasets much larger than the available cache on any given system, so that the results are (presumably) more indicative of the performance of very large, vector style applications. Performance evaluation is tight to the memory bandwidth of the system. Performance is therefore gated by memory bandwidth and not latency. The benchmark has four components: COPY, SCALE, ADD and TRIAD. Results are presented in fig 7. Figure  7  presents different performance for COS and native systems, for vector operation.
III-F. GPU performance The performance studies were executed on a Dell Po-werEdge R720, with 2*Intel(R) Xeon(R) CPU E5-2603 @ 1. 80GHz (8 cores) and a NVIDIA Tesla K20M. 7. From a system point of view, we used Ubuntu 16. 04. 2 (64-bit), with NVIDIA cuda 8. 0  [21]  and the NVIDIA driver version 375. 26. The virtualization technologies and their versions are given in Table  III. In order to evaluate COS technologies for GPU-HPC, we used the NAMD (NAnoscale Molecular Dynamics)  [20]  program, as a benchmark tool. We ran those GPU benchmarks on a Tesla K20m with "NAMD x86 64 multicore CUDA version 2017-03-16" [on the stmv dataset ( 1066628 Atoms )], using the 8 cores and the GPU card, without any specific additional configuration, except the use of the "gpu4singularity" 8 code for Singularity and the "nvidiadocker" 9 tool for Docker.
III-G. Source Code The scripts to run the experiments from this paper are available at https://github. com/ArangoGutierrez/containersbenchs 7 Kepler architecture  [25] , GK110 Graphics processors, 2496 CORES, 208 TMUS, 40 Rops, 5120 MB Memory size, GDDR5 Memory type, 320 bit Bus width 8 https://github. com/NIH-HPC/gpu4singularity 9 https://github. com/NVIDIA/nvidia-docker Fig. 10. Tesla K20m benchmarks on NAMD. IV. RELATED WORK Some papers have explored the overhead of containerbased virtualization tools as presented by  [13] ,  [43] ,  [22]. Mostly they compare the performance overhead of COS versus classic Virtual machine technologies (e. g KVM, LinuxVserver). They all agree that the current resource management implementation for LXC and Docker, lead to poor isolation and security. Containers are proving to be an extremely valuable technology for scientific research, delivering benefits such as portability and reproducibility to scientific users.
CUDA accelerated machine learning projects have already started offering Dockerfiles in order to run those applications over a container ready system. [5]  For I/O-intensive workloads, Container images can be much more optimal then running against shared storage (even when the container image exists on that remote storage). That is normal, as this is the same principle as cache or even the HPC scratch, that is to say a way to have data close to the process. Moreover, we would avoid the use of the standard Docker-based solutions (AUFS), due to overhead issues. Concerning small tasks like "Hello World", or even a more consistent memory job (see STREAM results), one more time, we would avoid the use of Docker, except if the container is already running on the host. That could lead to a real problem in a HPC system where the image needs to be downloaded everywhere and then, started, before being executed.
Prior to the study of Meseguer et al. [14] , Erwinia had only been found in aphids as a gut associate (in a laboratory strain of the pea aphid), and named Erwinia aphidicola  [20, 21]. The association of Erwinia in Cinara is therefore of particular interest because of its possible recent transition from a free-living to an obligate endosymbiotic life-style. Its study represents a unique opportunity to understand the mechanisms by which free-living bacteria establish themselves, become integrated as obligate partners, and persist in multi-partner endosymbioses. Here we use fluorescence in situ hybridisation (FISH) and whole-genome sequence data of the endosymbiotic partners of nine Erwinia-associated Cinara (Aphididae: Lachninae) species to answer the following questions. Are these obligate symbionts derived from a single acquisition of a free-living Erwinia lineage? Have they since been integrated in specialised cells and vertically transmitted with their aphid hosts along with Buchnera aphidicola? What metabolic functions do these new symbionts bring to the system? Do Erwinia provide novel functions unavailable in Buchnera? This would suggest that their acquisition could represent an ecological innovation.
Alternatively do they merely compensate Buchnera's deficiencies? This could imply that symbiont dynamics is only a succession of bacteria providing similar benefits to their insect host. We used phylogenomics to shed light on the history of the integration of Erwinia as an obligate symbiont and test whether it has been vertically transmitted alongside with Buchnera. We then reconstructed the genome-based metabolic complementation of the Buchnera-Erwinia pairs and explored whether Erwinia can confer new metabolic functions to their aphid hosts. These approaches revealed that Erwinia endosymbionts have evolved from a single event of lifestyle shift and have then co-diverged with aphids and Buchnera. Our data also revealed the existence of a third obligate endosymbiont (Hamiltonella sp. ) in two closely related aphid species.
0], 0. 9 M NaCl, 0. 01% SDS, and 30% formamide) and then washed (20 mM Tris-HCl [pH 8. 0], 5 mM EDTA, 0. 1 M NaCl, and 0. 01% SDS) before slide preparation. The embryos of up to 10 individuals were viewed under a ZEISS LSM700 confocal microscope. We used two competitive specific probes for Buchnera (BLach-FITC [5′-FITC-CCCGTTYGCCGCTCGCCGTCA-FITC-3′], ref. [18] ) and Erwinia symbionts in Cinara (ErCinara-Cy3 [5′-ErCinara-Cy3-CCCGTTCGCCACTCGTCGBCA-3′], this study). TIFF-formatted images were exported using the ZEN v2. 3 SP1 software from ZEISS automatically setting the intensity minimum to "black" and the intensity maximum to "white" value (Auto Min/Max option). Exported images were imported into Inkscape v0. 92. 4 for building the published figures.
Genome assembly and annotation Illumina reads were right-tail clipped (minimum quality threshold of 20), using FASTX-Toolkit v0. 0. 14 (http://ha nnonlab. cshl. edu/fastx_toolkit/). Reads shorter than 75 base pairs (bp) were dropped. Additionally, PRINSEQ v0. 20. 4  [25]  was used to remove reads containing undefined nucleotides as well as those left without a pair after the filtering and clipping process. The resulting reads were assembled, using SPAdes v3. 10. 1  [26] , with the options --only-assembler option and k-mer sizes of 33, 55, 77, 99 and 127. From the resulting contigs, those that were shorter than 200 bp were dropped. The remaining contigs were binned, using results from a BLASTX  [27]  search (best hit per contig) against a database consisting of the pea aphid's proteome and a selection of aphid's symbiotic bacteria and free-living relatives' proteomes (supplementary Table  S2 , Supplementary Material online).
When no genome was available for a certain lineage, closely related bacteria were used. The assigned contigs were manually screened using the BLASTX web server (searching against the nr database) to insure correct assignment. No scaffold was found to belong to a Sodalisrelated bacterium nor contained a 16 SrRNA gene from such bacterial taxa. This binning process confirmed the presence of the previously reported putative Erwinia and Hamiltonella co-obligate symbionts, as inferred from 16S rRNA gene fragment sequencing from several specimens of the nine Cinara species analysed in this study  [14, 23] , as well as other additional symbionts. For all samples, we identified an additional circular molecule in the Erwinia bin representing a putative plasmid. The resulting contigs were then used as reference for read mapping and individual genome assembly using SPAdes, as described above, with read error correction.
The resulting genomes were annotated using Prokka v1. 12  [28]. In order to validate start codons, ribosomal binding sites were predicted with RBSfinder  [29]. This was followed by non-coding RNA prediction with infernal v1. 1. 2 [30]  (against the Rfam v12. 3 database  [31] ), tRNAscan-SE v1. 3. 1  [32] , and ARAGORN v1. 2. 36  [33]. This annotation was followed by manual curation of the genes on UGENE v1. 28. 1  [34]  through on-line BLASTX searches of the intergenic regions as well as through BLASTP and DELTA-BLAST  [35]  searches of the predicted ORFs against NCBI's nr database. Priority for the BLAST searches was as follows: (1) against Escherichia coli K-12 substrain MG1655 (for both Buchnera and Erwinia), (2) against Erwinia amylovora CFBP1430 (for Erwinia symbionts) and last (3) against the whole nr database.
This prediction was performed using a combination of sequence alignment with m-coffee  [36]  and BLASTX searches against the NCBI's nr database (restricted to Erwinia or Buchnera taxon ID). This allowed the identification of pseudogenes missed by the previous searches. Metabolic reconstruction for Buchnera and Erwinia was performed in Pathway Tools v22. 5  [37]  and manually curated, using EcoCyc  [38]  and BioCyc  [39]. Visual plotting of inferred metabolisms was done by hand in Inkscape v0. 92. 4. The annotated genomes of Buchnera and Erwinia, as well as the unannotated ones of Hamiltonella and sequencing reads are available at the European Nucleotide Archive with project numbers PRJEB15506, PRJEB31183, PRJEB31187, PRJEB31188, PRJEB31190, PRJEB31191, PRJEB31194, PRJEB31195 and PRJEB31197.
Model selection for all nucleotide alignments was done in jModelTest v2. 1. 10 [51, 52]. To infer the origin of the Tn3 mobile elements found in Erwinia symbionts' plasmids, we performed on-line BLASTP vs. NCBI's nr database and collected the top 50 non-redundant hits. We aligned the sequences with MAFFT v7. 271 (L-INS-i algorithm) and then removed divergent and ambiguously aligned blocks woth Gblocks v0. 91b. Bayesian inference was conducted in MrBayes v3. 2. 5, using the LG+G substitution model, and ran as described above. All resulting trees were visualised and exported with FigTree v1. 4. 1 (http://tree. bio. ed. ac. uk/software/figtree/) and edited in Inkscape v0. 92. 4. All alignments, NEXUS files, and NEWICK-/NEXUS-formatted trees can be found in https://doi. org/10. 5281/zenodo. 2566355.
Serial horizontal gene transfer underlies multipartner co-obligate mutualistic association Both the biotin-and thiamin-biosynthetic genes of the Erwinia endosymbiont's plasmids (bioA, bioB, thiC, thiE, thiF, thiS, thiG, thiH, and thiD) encoded in this molecule consistently showed top BLASTP hits against Sodalis and Sodalis-like bacteria. Surprisingly, the thiamin-biosynthetic genes present in the Hamiltonella's symbiont genome showed similar results. These results contrasted with what we observed for the plasmid-encoded nupC, apbE, and gpmA, where the top BLASTP hits were consistently against Erwinia bacteria. To test for HGT events across the Erwinia genome, we ran BLASTP similarity searches of the proteins of Erwinia endosymbionts vs. a database built from the proteomes of Erwinia and Sodalis species.
'Candidatus Erwinia haradaeae' sp. nov We propose the specific name 'Candidatus Erwinia haradaeae' for the monophyletic lineage of enterobacterial endosymbionts exclusively found affiliated as co-obligate symbionts in the monophyletic group of Cinara aphid species analysed in this study, although its presence in other closely related species cannot be discarded. The closest relative of the symbiont of C. pseudotaxifoliae by 16S rRNA gene sequence identity is the Erwinia pyrifoliae strain EpK1/15 (INSDC accession number CP023567. 1) with which it shares 94% sequence identity (2350 bitscore). The specific epithet 'haradaeae' is in honour of Hosami Harada, who performed research suggesting the origin of the aphid's obligate Buchnera symbiont as derived from a habitant of the plant on which the host fed  [20, 56].