dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). The average interocular macular thickness asymmetry was only 5. 6 μm (95% CI 4. 6 to 6. 5 μm) suggesting macular thickness to be remarkably symmetrical between right and left eyes of healthy subjects, similar to the findings of Sullivan-Mee and colleagues  (Sullivan-Mee et al. , 2013). However, asymmetry increased with age (0. 04 μm per year, 95% CI: 0. 02-0. 06 μm) and was greater in men compared to women so these factors may need to be taken into account when determining if asymmetry is normal or not. The greatest asymmetry was in the superior and inferior nasal cells, probably due to the location of the vascular arcades in this region, which are included in the segmentation by the Spectralis OCT software. Further research into the relationship between ocular dominance and retinal thickness may also necessitate modifications to current OCT normative databases, for example, if dominant eyes are found to have a thicker cpRNFL than non-dominant eyes, the normal cpRNFL ranges may need to be adjusted depending on which eye is imaged.
The Beaver Dam Eye Study used all eyes in their analysis, confirming that "Analyses were done in each eye separately, and relationships between each covariate and retinal thickness measure were modelled using generalized estimating equations with an exchangeable working correlation structure to account for correlation between the 2 eyes from a single participant"  (Myers et al. , 2015). Using one eye data per patient avoids this potential for bias, however has the disadvantage of wasting half of the measured data. A UK Biobank study examining associations with macular thickness used one random eye from 32,062 subjects in their analysis (P. J. Patel et al. , 2016). Bunce and colleagues suggest we use one random eye from each participant for initial analysis, and the paired eye data as a repeat analysis for validity  (Bunce et al.
Terms such as 'asymmetry index' have an established role in studies of fingerprint asymmetry  (Buchwald and Grubska, 2012)  and facial attractiveness  (Hume and Montgomerie, 2001) , where quantitative indices of asymmetry are used to explore relationships with other biological factors, as well as an outcome measure in randomised trials. Ultimately, we cannot continue to assume symmetry within our eyes and visual pathways. Such assumptions are not only now invalid, but have perhaps been obscuring important findings in eye and brain research. We need to revisit the development and use of normative data of retinal measures, that are used within OCT devices and similar. Larger sample sizes as well as recognition of interocular asymmetry will provide more meaningful and scientifically valid reference datasets.
Equally, the assumption of symmetry cannot be used as a marker of validation or success of new technologies for quantitative imaging of the retina, such as retinal vasculature morphology. As imaging modalities become more sophisticated in generating long-term data derived from an ever-expanding repertoire of non-invasive tools, it is timely to consider the relevance of both single and paired eye datasets in clinical academic enquiry. In dissecting the determinants of interocular symmetry and neurovascular patterning in development and disease it is clear that there are unanswered questions in how laterality differences might be utilised in research and clinical practice. Researchers should avoid the potential pitfall of forcing an expectation of symmetry on paired structures and clinicians should be aware of both the benefits and limitations in extrapolating single eye data to the fellow eye in diagnosis and prognosis.
Future Directions At the heart of understanding biological symmetry is the complex genetic initiation of patterning in development. Biological pruning is a necessary process in neural and vascular morphogenesis in early development and continues throughout life  (Watson et al. , 2016). This appreciation for the biological processes involved in shaping structural symmetry can underpin the development of ophthalmic imaging devices, that are now so ubiquitous within clinical practice as well research environments. Greater use of captured data will facilitate improved normative datasets, which will in turn provide greater clinical reference. Finally, from a computational point of view, we see deep learning techniques increasingly making their way into large collections of clinical data  (Fauw et al.
, 2016). Our discussion has concentrated on morphology, and ignored a wealth of other and arguably not always independent factors, e. g. haemodynamic and physiological parameters. We have today the tools to take an increasingly holistic view of symmetry, as well as of biomarkers, including a large variety of feature types in the pool of independent variables. Strategies are under way to extract the maximum information from a single patient-imaging episode, providing multi-modal imaging data, and guiding us towards the potential for compound metrics  (Cameron et al. , 2016). And with large-scale datasets and images now being imported into artificial intelligence systems  (Armstrong, 2016) , this is only going to grow.
Acknowledgements JC is supported by a  Rowling Scholarship  from the  Anne Rowling Regenerative Neurology Clinic, University of Edinburgh. AT is supported with an  NHS Research Scotland (NRS)  Fellowship. FD is supported by a joint  Stroke Association/Garfield Weston Foundation Senior Clinical Fellowship. The VAMPIRE team acknowledges support from  ESPRC  grant  SIDD EP/M005976/1  "Multimodal retinal biomarkers for vascular dementia".
In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease.
Quantitative kinetic modelling approaches using differential or stochastic equations can provide a detailed analysis of a network's dynamics, but the large number of parameters required make them less appropriate for large scale signaling networks. In order to address the lack of kinetic data, discrete logical modelling can be used as an alternative way to study the system's qualitative dynamic behavior  [7, 8]. In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA), a multifactorial autoimmune disease that causes chronic inflammation of the synovial joints with an etiology that still remains unclear. With the use of the software CellDesigner  [9] , we are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts  [10]  and most importantly experts' advice (Figure  1 ).
In 2010 Wu et al. published a detailed molecular map concerning rheumatoid arthritis using the software CellDesigner. We decided to use this map as a basis, and expand. The map has been updated with information published after 2010 by exhaustive manual curation and the help of data mining tools. Only experimentally validated interactions in at least two peer reviewed scientific publications are kept. Due to the fact that the initial map was based on high throughput gene expression data from 28 studies and interactions inferred from KEGG database, all nodes and interactions are re-evaluated carefully in an effort to limit false positives. When validation with small scale experiments is not possible, we keep nodes that appear in at least two different high throughput studies. Detailed annotation including PubMED IDs and HUGO names is also added in the MIRIAM section of the CellDesigner file.
As far as context representation and overall structure of the map, expert's advice has been taken into account along with an effort to comply with SBGN standards. The RA map will be web-published in the coming months (a full length manuscript is under preparation) in the form of an interactive map, using the platform MINERVA  [11] , allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledge base for the disease. The user will have access to all literature used, with detailed annotations for every component and reaction, including PubMed IDs, and a list of identifiers such as Uniprot, EntrezGene, Ensembl, HGNC and RefSeq. As the map is constructed using information from various experimental studies, the user will also be able to opt for visualization of data with specific cell origin, highlighting cell-specific sub-networks within the global one.
Moreover, the user will have the possibility to spot all known drug targets, and the corresponding drugs up to date for RA. Detailed view of an element will allow the search for drugs, chemicals and miRNAs targeting this particular element. Additionally, user-provided omic datasets could be displayed as overlay, giving a first estimation of affected pathways and components. Lastly, the map will provide feedback about the unmapped molecules from the dataset, allowing for better understanding of the experimental results and for further development of the map's contents. We have used public datasets from proteomic and transcriptomic studies  [12] [13] [14]  to demonstrate how the map can be used as a template for separate or simultaneous visualization of different experimental results. The map will also be used for the mapping of in-house data concerning the transcriptome analysis of ten individuals that developed RA (measurements before the onset of the disease and early after) (Teixeira et al.
, under preparation). The RA map so far includes information derived from more than 100 scientific papers. It has six distinct compartments, namely extracellular space (with extracellular proteins), plasma membrane (with membrane receptors and ligand proteins), cytoplasm (with proteins, miRNAs, small molecules and the sub-compartments of mitochondrion, Golgi apparatus and endoplasmic reticulum), nucleus (with genes, RNAs and transcription factors), a compartment for the secreted molecules and a phenotype compartment including more than ten cellular fates. It comprises more than 400 components and a total of 324 reactions. Each component and reaction in the map is referenced with at least two PubMed IDs or database identifiers if inferred from a specific database. Topological analysis of the RA map using the software Cytoscape  [15]  and relevant plugins reveals unconnected or loosely connected parts that reflect our fragmented knowledge about physical and/or genetic interactions, posing thus obstacles in the subsequent derivation of a reliable dynamical model.
To improve connectivity we use dedicated PPI databases (through http://www. imexconsortium. org), pathway databases (e. g. KEGG, SIGNOR or REACTOME) and the commercial software Ingenuity Pathway Analysis (IPA, http:// www. ingenuity. com) in order to investigate potential co-players of the proteins of interest. For the time being, we do not make use of simulated/computationally inferred interactions or interactions inferred from other species (i. e. mice), restricting our search to experimentally validated data of human origin. Characteristic features of RA include synovial inflammation that can lead to bone erosion and permanent deformity. It is broadly recognized that in RA, synovial inflammation results from complex interactions between haematopoietic and stromal cells. Recent studies have shown that RA synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease  [16].
The aim is to predict the system's response to single or combined perturbations, and identify novel targets for pharmacological intervention. The Boolean model will be made publicly available in Cell Collective for further contributions, simulations, and analyses by the research community, hopefully within 2018. The web based platform Cell Collective allows real time simulations without the need for software installation making the model more accessible to a wider audience. Moreover, the platform supports annotation, so the user can have simultaneous access to the model description, the rules and the literature used for the rules' inference. Lastly, the resulting logical model for RA fibroblasts could be further analyzed with the software GINsim  [19]  and also serve as a template for the derivation of a continuous model using the software MaBoSS  [20]  allowing the computation of phenotype probabilities.
The building of a logical model is an iterative multistep process. The assembly of a molecular map comprising biological pathways of interest and integrating information from literature and public databases could serve as the first step. Experts' feedback assures the quality of the map and the accuracy of the knowledge represented, along with strict curation criteria and standards for the graphical representation. Web publication facilitates community feedback and transforms the map in a powerful data analysis and visualization tool. The network can be further exploited using graph analysis tools to identify important nodes and pathways, or it can serve as a scaffold for dynamical models allowing simulations. Interesting predictions can then be experimentally tested, contributing to the validation and refinement of the map.
To this end, females from Kisumu and MRS strains were exposed to three different treatments in WHO test tubes: 1 h with PBO (4%), 1 h with deltamethrin (0. 05%), and 1 h with PBO (4%) followed by 1 h with deltamethrin (0. 05%). The number of mosquitoes knocked down during each treatment was recorded at 5, 10, 15 and 20 minutes, then every 10 minutes until 1 h for KdT determination. Biochemical assays. Cytochrome P450 monooxygenase levels, and GST and NSE activity were determined using, respectively, tetramethylbenzidine, chlorodinitrobenzene, and αand β-napthyl acetate substrates, as described in the WHO manual  32. Absorbances were read by spectrophotometry using a TECAN Sunrise TM microplate absorbance reader. Data layout was managed with the Magellan software version 7. 0 coupled with the spectrophotometer.
The thicknesses of the different layers (endocuticle, mesocuticle and exocuticle) were also measured separately where picture sharpness was sufficient to distinguish between them. Three to five different partition pictures were taken per leg cross-section. 39 basically set for LC 50 and LC 95 determination was modified to R script BioAssay 6. 2 (http://www. isem. univmontp2. fr/recherche/equipes/evolution-vecteurs-adaptation-et-symbioses/ personnel/labbe-pierrick/) for KdT 50 and KdT 95 determination with a 95% confidence interval. The script created a generalized linear model (GLM) with binomial errors in the response variable. A probit link was then fitted to the data where the probit knockdown was a function of the logarithm of times for each strain. This allowed RR 50  and RR 95 to be computed and comparisons between two or more populations to be carried out.
The levels of enzymatic activity in Kisumu and MRS were compared using a Mann-Whitney test. Relative expressions of target genes were determined according to the ΔΔCt methods described by Schmittgen & Livak  40  and implemented in the Light Cycler ® 480. A Mann-Whitney test was also used to compare the fold changes between the MRS unex- posed to insecticides and MRS survivors. The difference between the cuticle thicknesses of Kisumu and MRS, and the penetration of deltamethrin and DP were analysed using a Wilcoxon rank sum test. All statistical analyses were performed with the R statistical software  41  and differences were considered significant with a p-value < 0. 05. The graphs were drawn with the ggplot2 package  42  implemented in R and GraphPad Prism 43 for Windows.
Acknowledgements G. A. Y. was supported by the  Mediterranean Infection Foundation. We thank the  LabEx CeMEB  for supporting G. A. Y. 's  Scientific Immersion at Heraklion , and  INFRAVEC2  for funding the cuticular analysis. Very special thanks go to  Philippe Clair  for excellent advice on qPCR, the Treilles foundation: « La  fondation des treilles, créée par  Anne Gruner Schlumberger , a notamment pour vocation d' ouvrir et de nourrir le dialogue entre les sciences et les arts afin de faire progresser la création et la recherche contemporaines. Elle accueille également des chercheurs et des écrivains dans le domaine des Treilles (Var) www. les-treilles. com». V. B. was supported by a  Scholarship for Strengthening Post-Doctoral Research  from  The Greek State Scholarships Foundation (IKY)  within the framework of the  Operational Programme "Human Resources Development Program, Education and Life-Long Learning ".
Introduction During the past few years, cryptographic algorithms have been extensively used to fight security threats  (Stallings, 2010). Secure systems are of tremendous importance nowadays. Secure transmission and storage of data are needed for all types of solutions, ranging from area sensitive embedded devices to massively parallel high performance computing devices. Such diversified area/performance requirements motivated us to explore different implementation techniques for Advanced Encryption Standard (AES) cryptographic algorithm. AES is an example of symmetric key cryptographic algorithm and today it is used in many cryptographic applications. AES algorithm (also known as Rijndael cipher algorithm) was accredited as the new commercial cryptographic algorithm in 2001 to replace aging Data Encryption Standard (DES) algorithm  (Advanced Encryption Standard, 2001 ).
Our results using Xilinx Spartan-6 (Xilinx, 2014b), Virtex-5 devices show that by carefully optimizing the algorithm implementation and by efficiently mapping the hardware resources, small area and high throughput can be achieved for different techniques. Furthermore, when compared with recent state-ofthe-art results, our optimal technique gives better or comparable throughput while using significantly less FPGA resources. The remainder of the paper is organized as follows. In Section 2, an overview of AES encryption algorithm is provided. In this section, brief description of different modules of AES algorithm is presented. Section 3 presents key optimization and resource mapping techniques for AES algorithm. Further, a discussion is presented in this section on how these techniques can result in good area and delay trade-offs.
Key expansion module Main purpose of this function is to calculate round key for each round of the cipher based on original key. As shown in Fig. 1 , this module comprises of three simple sub-modules SubWord, RotWord, and RoundConst. SubWord is a function that takes a four byte input word and applies S-box to each of four bytes to produce output word. RotWord takes a word, performs cyclic permutation, and returns the word. RoundConst function contains a round constant array that performs a bitwise XOR function. Round constant array contains values given by ½x iÀ1 ; f00g; f00g; f00g with x iÀ1 being powers of x (x is denoted as {02}) in the field GF(2 8 ). KeyExpansion is an important function and depending upon the target architecture its implementation can be performed either by using computing resources or by using on-chip memory resources that eventually leads to different results.
Experimental setup In this work, we have implemented 128 bit key length AES algorithm using five different exploration techniques (described in Section 3. 3). Implementation of AES algorithm is performed on xc6s1x150-3-fgg900 device which is a member of Xilinx Spartan-6 family. The VHDL core of the design is synthesized, placed and routed using Xilinx ISE 13. 2 release where explicit directives were used to determine the mapping of S-box and loop unrolling was achieved through parallel processes for cipher and key expansion module. The Xilinx tool was used to measure number of slice registers, number of slice LUTs, maximum frequency, and critical path delay for each exploration technique. From these values, we also calculated theoretical throughput and efficiency of each design using Eqs. (  2 ) and (  3 ) respectively.
In other words, this accumulator is much more accurate than the data that is thrown to it. The results are reported in Table  I  for simple and double precision. The Accuracy line of the table reports the number of correct bits of each implementation, after the result has been rounded to a float. All the data in this table was obtained by generating VHDL from C synthesis using Vivado HLS followed by place and route from Vivado v2015. 4, build 1412921. This table also reports synthesis results for the corresponding FloPoCo-generated VHDL, which doesn't include the array management. Vivado HLS uses DSPs to implement the shifts in its floating-point adders. Even if the shifts were implemented in LUTs, the first column would remain well below 500 LUTs: it has the best resource usage. However the latency of one iteration is 7 cycles, hence 100K iterations takes 700K cycles.
C. Evaluation of the toy example of Listing 5 The proposed transformations work on non-trivial examples such as the one represented in Listing 5. Table  II  shows how resource consumption depends on epsilon, all the other parameters being those given in the pragma of Listing 5. All these versions where synthesised for 100 MHz. Compared to the classical IEEE-754 implementation, the transformed code uses more LUTs for less DSPs. This is due to Vivado implementing shifters using DSPs within the floatingpoint IP, but not in the transformed code. In all cases, on this example, the transformed code has its latency reduced by a factor 20.
B. Benchmarks improved by the proposed transformation Enhanced Livermore Loops: This program contains 16 kernels of loops that compute numerical equations. Among these kernels, there is one that performs a sum-of-product (banded linear equations). This kernel computes 20000 sumsof-products. The values accumulated are pre-computed. This is a perfect candidate for the proposed transformations. For this benchmark, the optimal accumulation parameters were found as: MaxAcc=50000. 0 epsilon=1e-5 MaxInput=22000. 0 Synthesis results of both codes (before and after transformation) are given in Table  III  Both of these programs accumulate values from registers or memory that are already computed. It makes these programs good candidates for the proposed transformations. Vivado HLS is unable to predict a latency for these designs due to their non-constant loop trip counts.
C. Benchmarks that exposed the limitations of HLS tools Black Scholes: This program contains an accumulation that sums 200 terms. The result of this computation is divided by a constant (that could be optimized by using transformations based on  [3] ). This process is performed 5000 times. Here the optimal accumulator parameters are the following: MaxAcc=245000. 0 epsilon=1e-4 MaxInput=278. 0 This gives us an accumulator that uses 19 bits for the integer part and 10 bits for the fractional part. The result of the synthesis are provided in Table  III. For comparable area, accuracy is vastly improved but latency could not be obtained from Vivado HLS. Indeed, the Black Scholes algorithm uses the mathematical function power. Such a function is not natively supported by Vivado HLS, and was therefore implemented by hand using a data dependant trip count loop.