dataesr/scientific-paragraphs-categorization huggingface dataset:
2020) using the Molecular Signatures Database v7. 0 (Msigdb, Broad Institute, https://www. gsea-msigdb. org/gsea/msigdb/index. jsp). The Lund expression data (Gene Expression Omnibus GSE65904) of 214 melanomas  (Cirenajwis et al. 2017) , includes 66 metastatic patients with long term survival data. We tested the differential expression between aged long and short-term survivors (Limma package (version 3. 44. 1) in R). MCPCounter package generated cell population scores  (Ritchie et al. 2015). Cell population scores were generated using the MCPCounter R package (version 1. 2. 0  (Becht et al. 2016 )) in the TCGA SKCM RSEM normalised RNA-seq expression data. CD8 T cell scores were compared between long and short-term aged metastases using Mann-Whitney in GraphPad Prism (version 7. 01). Response to Immunotherapy: Data from MSK-IMPACT  (Samstein et al.
)  was downloaded from cBioPortal (http://www. cbioportal. org/study/summary?id=msk_impact_2017) and reanalyzed  (Zehir et al. 2017 ). To determine high vs low TMB we investigated the centile groupings within the melanoma group and used the third decile (i. e. those in the highest 30% TMB) as high. As did the original MSK-IMPACT, we tested all deciles  (Samstein et al. ). 30% was the inflection point at which the response curve separation began to accelerate. Any cutoff from 30% to 10% results in significance (with increasing magnitude as the cutoff becomes more stringent) -we chose a cutoff at 30% to include as many patients as possible. Data availability: Anonymized data are available without restriction. The analytical code is available from the authors without restriction. IMPACT study stratified by age and the 4 gene driver signature.
Tables institutions in both countries approved molecular research and all human participants gave written informed consent. The validation cohorts are unselected, clinical cases prospectively collected without specific targeted gene signatures planned to minimise confirmation bias. Both cohorts had comprehensive clinical outcome data (supplemental table  S1 ) and both were subjected to targeted deep sequencing at a depth of x100 of 40 melanoma genes from primary melanoma samples. The melanoma panel includes genes found mutated. in >10% of TCGA samples or genes mutated in less than <10% of samples that are known cancer drivers, including BRAF, NRAS, CDKN2A and IDH1. Exomes were captured with Agilent SureSelect ALL Exon (V6, V6+UTRs r2) and sequenced on Illumina HiSeq 2000. Sequences were aligned with Burrow-Wheeler Alignment (BWA 0.
7. 15) with MEM option onto human reference genome, hg19. Base quality was measured with GATK 3. 7 tools BaseRecalibrator and PrintReads. Samtools used for variant calling. Somatic mutations were annotated with Oncotator 1. 9. 0. 0. Somatic variants were detected using MuTect2 (GATK 3. 7) in the A cohort, and with VarScan 2 in the B cohort. Both sets of variants were filtered to eliminate common germline variants. The data in Table  1  show the median and range TMB of elderly melanoma samples, which demonstrate overlapping ranges with TCGA samples. Critically, the proportion of the 4-gene driver mutations in the primary tumour B cohort is not statistically different (p=0. 09) to the A validation cohort. The 4-gene signature is significantly less frequent in the primary cohorts (p= 0. 0008 B cohort, p=0.
• Identification of driver genes To test the hypothesis that melanoma development in older patients is driven by accumulation of mutations in specific risk genes, we analysed the mutation data using the oncodriveCLUST algorithm  (Tamborero et al. 2013)  as implemented in the maftools package in R to discover the key driver mutations. This algorithm is a powerful and well-described method validated in the literature and has been used extensively to successfully identify driver mutations/genes in numerous cancer contexts. OncodriveCLUST considers the background mutation rate, gene length and the clustering characteristics of mutations identified in order to derive a confidence value for labelling a gene as a driver as opposed to a passenger. We make no claims regarding function or mechanistic biology behind these mutations but used this bioinformatic tool as a discriminator to identify genes that are likely to be important in the older patient population, but not the younger one.
• Gene expression For the TCGA data, differential gene expression analysis was performed using the DESeq2 (version. 28. 1)  (Love et al. 2014 ) package in R (version 4. 0. 0, RStudio v1. 2. 5003, RStudio Inc). RNA-seq count data for the TCGA SKCM cohort was acquired using the TCGABiolinks package (version 2. 16. 0  (Colaprico et al. 2015) ). The count dataset was filtered to remove any genes that had less than 1000 counts across all samples. Differential expression analysis was run between long term and short-term survivors in the aged (>59 years) metastatic cohort. Long-term survivors were defined as any patient whose overall survival time was 2000 days or greater, and these were compared to patients who died before 2000 days. For pathway enrichment analysis genes that were significantly differentially expressed (FDR p-value < 0.
1) were compared against the Reactome Database  (Jassal et al. 2020)   The Lund cohort comprises microarray expression data (Gene Expression Omnibus GSE65904) of 214 melanomas  (Cirenajwis et al. 2017) , of which 66 are metastatic, with long term survival data. We tested the differential expression analysis between long term and shortterm survivors in the aged cohort as described above. Analysis was performed using the Limma package (version 3. 44. 1) in R. MCPCounter packaged was used as above to generate cell population scores for each tumor. Where a marker gene had more than one probe quantified on microarray, the most abundantly expressed transcript was used in MCPCounter, that is the probe that had the highest mean value across all samples  (Ritchie et al. 2015).
• Response to Immunotherapy To investigate the effects of the driver genes and tumor mutation burden in immunotherapy treated patients data from the MSK-IMPACT study  (Samstein et al. )  was downloaded from the cBioPortal database (http://www. cbioportal. org/study/summary?id=msk_impact_2017) and reanalyzed. The melanoma cohort included 320 patients, 181 of whom were older than 59 at diagnosis, all of whom had been treated with immunotherapy agents and whose sequencing data included the mutation status of the four driver genes. The sequencing methodology and analysis pipeline for these samples is described in the previous relevant literature  (Zehir et al. 2017). To determine high vs low tumor mutation burden in this cohort we investigated the centile groupings within the melanoma group and used the third decile (i.
Acknowledgments AV is a  Wellcome Beit Fellow  and personally funded by a  Wellcome Trust   Intermediate Fellowship  ( 110078/Z/15/Z ),  Cancer Research UK  ( A27412 ),  Leo Pharma Gold Award  and  Royal Society  ( RGS\R1\201222 ). SPS is funded by the  Medical Research Council UK  ( MR/R001146/1 ). EN is funded by  Fondo de Investigación en Salud (FIS)   PI15/01860 ,  Instituto Carlos III, Spain. RK is funded by  TRANSCAN  ( 01KT15511 ),  German Ministry of Education and Research (BMBF)  and  DKFZ. RM is funded by  Cancer Research UK  ( A27412 ) and  Wellcome Trust  ( 100282/Z/12/Z ). CG-M is funded by the  French Dermatology Society, Collège des Enseignants en Dermatologie de France (CEDEF)  and  UNICANCER France. We acknowledge the generous contribution of  APHM Biobank (France)  and  IVO Biobank (Spain).
| Sample Processing and kSORT Testing The kSORT test was performed at Immucor's CLIA/CAP accredited laboratory Idx (Immucor Dx, Grand Rapids, MI), in a blinded fashion without knowledge or information about the sample's phenotype. Total RNA was extracted using the Qiagen PAXgene Blood RNA kit (Qiagen, Hilden, Germany). All RNA samples were quantified (NanoDrop Spectrophotometer, ThermoFisher Scientific, US A), and analyzed for RNA integrity (Bioanalyzer 2100, Agilent Inc, Santa Clara, CA). RNA of sufficient high quality (RNA integrity number (RIN) >7) was reverse transcribed into complementary DNA (cDNA) (ABI Veriti thermal cycler) using random primers and SuperScript Vilo Mastermix (Fisher Scientific, Invitrogen, CA), and subsequently analyzed for kSORT gene expression (DUSP1, CFLAR, ITGAX, NAMPT, MAPK9, PSEN1, RYBP, NKTR, SLC25A37, CEACAM4, RARA, RXRA, EPOR, GZMK, and RHEB) together with 18S ribosomal RNA as housekeeping gene on the ABI ViiA 7 real-time PCR instrument using TaqMan chemistry (ABI Life Technologies, CA, USA).
Relative gene expression levels 9 were evaluated using the kSORT software kSAS v. 1. 06. As described in detail in the original article, 5 kSORT risk categories were calculated based on sample correlation of kSORT gene expression with kSORT gene expression in established reference AR and NoAR profiles across 13 unique kSORT gene models. In an aggregated risk analysis, kSORT scores >9 generated an overall High Risk (HR) for rejection category, kSORT scores <-9 generated an overall Low Risk (LR) for rejection category; and samples that were not classified as HR or LR were categorized as Indeterminate (IND) at the time. The results from all 1790 samples were transferred from ImmucorDx to the independent statistical center for integration with the phenotype data and subsequent statistical analysis.
| Role of the funding source Immucor, Inc provided the funding to conduct kSORT analysis and was involved in study design and performed the laboratory execution of the kSORT assay blinded to the clinical or histological data. The clinical centers collected and compiled clinical and histological data but remained blinded to the results of the kSORT assay. The kSORT assay results and the clinical/histological data were sent separately to and analyzed by an independent statistical center (L-BioStat). Only the clinical centers and the statistical center had access to the full data.
Renal allograft BXs were graded according to the 2015 Banff classification 8 by the local pathologists. Acute rejection was defined as antibody-mediated rejection (ABMR) and/or T cell-mediated rejection (TCMR), not taking into account borderline changes (BL-TCMR) or changes suspicious for ABMR (sABMR) cases. Any rejection was defined as any combination of ABMR, TCMR, BL-TCMR, or sABMR. Screening for donor-specific HLA antibodies (DSAs) was performed with solid phase assay for all patients at time of a renal allograft biopsy, according to local center practice. The clinical data from the French centers were extracted from the prospectively collected DIVAT database [Données Informatiques Validées en Transplantation; French Research Ministry: RC12_0452, last agreement No 13 334, No CNIL for the cohort: 891735; http://www.
In mobile networks, sensitive information is transmitted over the radio link between the Base Station (BS) and the Mobile Station (MS). Therefore, a security mechanism based on confidentiality and integrity functions is designed to protect the anonymity of users'. More precisely, the confidentiality function was standardized by the Third Generation Partnership Project (3GPP) to protect users' data and the integrity function to protect control (signaling) data. However, both functions are based on the same kernel algorithm. For example, the functions used in the Universal Mobile Telecommunications System (UMTS) network are based on the algorithm, namely the KASUMI block cipher. In this work, we proposed an optimized function that combines the confidentiality and integrity functions in one Running-Block-Cipher and ensures the same functionalities as the basic functions (UMTS F8 and F9 functions).
Introduction Today, mobile networks and Internet applications (e-mail, ecommerce, e-learning, etc. ) occupy an important part of our lives and transfer a lot of personal information via a radio link that is not protected physically. Consequently, service providers must protect all data exchanged on the channel used (wireless) to preserve the anonymity of users despite the difficulties encountered. For this reason, security mechanisms based on encryption algorithms are used to ensure the security of data transmitted over mobile networks. ( Liu et al. , 2008; Madani and Taougast, 2018 ) , principally KA-SUMI algorithm  ( Dai et al. , 2010; Kitsos et al. , 2004; Madani et al. , 2017c ). Secondly, we simplified the implementation of the internal functions (FL, FO, and FI) of the KASUMI block cipher (the core of standard F8 and F9 functions) to increase the encryption throughput.
A general vision on KASUMI block cipher KASUMI is a block cipher with a Feistel structure for eight rounds and developed on 3GPP. It operates in 64-bits block data and controlled by 128-bits Ciphering Key (CK). To generate output key-stream at each round, it executes two internal functions, FL and FO. It executes the FL then FO function in the odd rounds ((1, 3, 5, and 7), and the FO then FL function in the even rounds 2, 4, 6, and 8). The process begins by dividing the 64-bits input data into two 32-bits strings, left L0 and right R0. The outputs of the odd rounds are produced according to Eq. (1) for i = 1 , 3, 5, and 7, and the outputs of the even rounds are produced according to Eq. (  2 ) for i = 2 , 4, 6, and 8. L (i + 1) = F O (F L (L (i ) , KLi ) , KOi, K I i ) xor R (i ) ; R (i + 1) = L (i ) (1) L (i + 1) = F L (F O (L (i ) , KOi, K I i ) , K Li ) xor R (i ) ; R (i + 1) = L (i ) (2) We not that CK is used at each round to derive 8 sub-keys of size 16-bits: KLi 1 and KLi 2 (used by FL function), KOi 1 ,KOi 2 , and KOi 3 (used by FO function), K I i 1 ,K I i 2 , and K I i 3 (used by FI function).
Proposed running-block In this section, we present the proposed Running-Block architecture. The purpose is to reduce the required logic resources to hardware parallel processing on limited systems. To achieve this objective, we have developed an architecture that allows confidentiality F8 and integrity F9 functions to be executed using a single common kernel (KASUMI block cipher). Data routing and switching between the confidentiality and integrity operating modes in the proposed architecture is provided by a control block encoded as an FSM. The proposed architecture is illustrated in Fig. 6  , and the processing steps are presented as follows.
FSM control unit To realize the Running-Block, we opt for a hardware behavior description based on FSM. This system describes the numerical resolution based on a control unit designed to manage the inputs (key and value) and output of the KASUMI block cipher. Thereby, the description is implemented in a digital way suitable for FPGA using the FSM system coded in VHDL language and detailed in Fig. 7. This figure describes the FSM system corresponding to the internal states of the Running-Block. Detailed description at each state of the FSM system is presented as follows. • Sate-init: It is the initialization state. All outputs are set to zero, and the confidentiality and integrity vectors (VecConf and VecInt) are initialized with the corresponding input parameters, as follow. Note that parameter is used to design a bitwise XOR operation.
Implementation results The proposed architecture has been described using the VHDL language and has been implemented on Xilinx Virtex FPGA technology Virtex devices specification , Xilinx (2014) , Virtex 5 FPGA Configuration User Guide. Integrated Synthesis Environment (ISE) 13. 2 of Xilinx tools have been used for this digital implementation allowing us to obtain the logic resource requirements and the associated real-time constraints. The proposed Running-Block synthesis results after a place-and-route are presented in Table  1. Note that the throughput is calculated using Eq. (  3 ). Throughput = Block size × Clock f requency Lat ency cycl e (3) From the obtained results, we remark that good performances are achieved by the proposed implementation in terms of throughput (7953. 387 Mbps and 3 latency cycles with the mode confidentiality, 1084.
In the automotive and rail domains, vehicles are entering the era of full automation thanks to wireless sensors and communication systems, shifting control functions from a human driver to computers. High data rate, robustness, high reliability and ultra-low latency wireless communications are required in the context of autonomous train and safety critical applications. Today, the Future Railway Mobile Communication System (FRMCS) is under development at European level within the International Union of Railways (UIC). This system will answer all the current and future needs of rail. It will be IP based, multi-bearer and resilient to technology evolution. In the context of the development of different FRMCS prototypes by industry, it is crucial to be able to test them in representative Railway radio environments thanks to laboratory tools.
Second, the paper describes the hardware and software components of the Emulradio4Rail platform and gives results of the proof of concept of the LTE emulation using the Open Air Interface (OAI) tool  [8]. The paper is organised as follows. Section II gives generalities on mobile radio channel models. Mathematical representation and Tapped Delay Line (TDL) models are recalled. In section III, we describe the main railway radio environments such as open air, viaduct, cutting, hilly terrain and tunnel. In section IV, TDL models from the literature derived mainly from high-speed lines (HSL) measurements are detailed. Each environment is considered. Section V presents the selection of a set of railway TDL channel models that have been considered for implementation in the Emulradio4Rail platform.
First, the USRP 2901 is very sensitive to power changes and it needs to be calibrated properly. This behavior makes the system more vulnerable to power changes, such as the ones emulated during the tests (fadings and Doppler), as we observed in the UL. Second, the Open Air Interface implementation version chosen was selected at the very beginning of the project. It does not seem to be prepared for high performance testing like the one carried out with IPERF. LTE processing (from baseband signal, up to network layer) is a high demanding task, which requires a soft-real-time behavior. When implemented mainly in software, like in OAI, it is a very CPU consuming task. So, when intensively testing with IPERF, any bug or not protected overflow makes the system lose the real-time behavior and fail.
VIII. ACKNOWLEDGMENT This work has been supported by the H2020 Emulradio4Rail project, under Grant 826152 within Shift2Rail program and the ELSAT2020 project, co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts de France Region Council. The authors also would like to thank warmly Keysight for giving them the possibility to realise the tests with the powerful PROPSIM equipment. We also would like to thank Nathalie Rolland and Rédha Kassi for the help in the final integration of the platform at IRCICA (Research Institute on software and hardware devices for information and Advanced communication, a Service and Research unit (USR-3380) associated to the CNRS and University of Lille).
Tool description AkvaVis is a decision support tool based on GIS that processes data relating to aquaculture management. The AkvaVis tool performs suitability analysis on proposed aquaculture areas through the utilization of a series of indicators and can create virtual farm objects to display and interact with models and environmental data. The tool provides a userfriendly interface and can produce reports of the analysis undertaken for use by a variety of users with differing requirements with regards to aquaculture management, for example governing bodies, farmers and researchers. AkvaVis follows the Web Map Service (WMS) protocol providing geo-referenced map images within a web application. When implementing the tool, different programming languages were employed, including Java on the server side and Adobe flash (using action scripts), HTML and JavaScript on the client side.
Normandy CS SISAQUA was designed to share information between a large panel of stakeholders and enlighten public debate. For scientists, it is a way to transfer data and knowledge. Both data and indicators utilized within SISAQUA were defined through a consultation process set up with main stakeholders involved in the case study area (Table  A1 ). Two workshops were successively organized with the support of national authorities in charge of the management of marine and coastal activities in the region. The objective of the first workshop (which was attended by 33 people) was to identify and prioritise information required to plan aquaculture activities. The second workshop (which was attended by 22 people) was dedicated to determining the indicators required to help with planning for both shellfish and fish culture sites.
Currently, 134 data layers are integrated into SISAQUA. These layers have been grouped into the following categories; i) environmental data for terrestrial, coastline and marine domains, ii) regulations on protected areas, iii) information on other usages, iv) water quality and v) information related to aquaculture activities (Table  A4  and Fig  shell length, dry flesh mass and condition index (ratio between total individual mass and flesh mass) obtained after a rearing cycle (March 1 st -December 31 st for oysters, August 1 st year n -August 1 st year n+1) and time to reach commercial size of 9. 5 cm and 5. 5 cm for oysters and mussels, respectively. Three indicators were developed which are all based on the same approach (Table  A4  and Fig. 4C, D ). Several criteria, (fixed by the application), are combined with thresholds chosen by the user.
From tools to management The four case studies represent decision support tools developed for areas with very different scales of aquaculture and a complex series of issues (Fig. 1 ). These tools differ in In line with our own experience and lessons learned, we consider that the following four issues must be examined to determine a tools usefulness for management, namely: data access and management; analysis tools and functions; stakeholder involvement; and tool applicability, implementation and maintenance (Fig. 1 ). Below we analyse the main features of AkvaVis, SISAQUA and APDSS with respect to these issues, and highlight some differences between our tools and existing operational decision-making tools developed for aquaculture planning. The AquaSpace tool displays a spatial representation of opportunities and risks of a proposed aquaculture site in marine areas exposed to multiple human activities and their respective pressures  (Gimpel et al.
, 2018). It has been utilised within the German portion of the North Sea to assess the potential expansion of aquaculture of the European seabass and blue mussels. Bricker et al. (2016)  described how they coupled a local carrying capacity model to an existing web-based mapping tool developed for decision-making to address the question of the potential for growth of the American oyster in predefined suitable areas. The BLUEFARM-2 tool was developed to identify suitable sites for shellfish culture in the Adriatic Sea  (Brigolin et al. , 2017). Different data were aggregated in a GIS and several scenarios of aquaculture development were tested using a Spatial Multi-Criteria Evaluation framework. Puniwai et al. (2014)  developed a web-based tool for siting various aquaculture systems within the Hawaii archipelago.