dataesr/scientific-paragraphs-categorization huggingface dataset:
service apache2 restart Luego se puede ingresar al gestor de usuarios en la dirección web http://192. 168. 10. 1/daloradius con el usuario: administrator y contraseña: radius 5. 3. 1. 2. 5 CoovaChilli. Es un módulo de control de acceso a una red local o pública. Esto se utiliza para acceso a la red inalámbrica antes configurada, permitiendo un ingreso controlado a la red. Para el proceso de instalación se realiza de la siguiente manera, en el terminal se ubica una carpeta especial para CoovaChilli y se procede a descargar el archivo de instalación. git clone https: //github. com/coova/coova-chilli. git Luego de obtener el repositorio se procede a instalar con los siguientes comandos: sh bootstrap. /configure make Al finalizar se realiza un cambio en los parámetros de CoovaChilli, en el cual se inserta la siguiente línea de código, que permite el acceso de la red Wi-Fi a Ethernet.
Domoticz. El software domoticz es un sistema de domótica para controlar varios dispositivos, recibiendo, analizando y mostrando información de diferentes sensores. Este sistema se puede utilizar con sensores como: Interruptores de luces, Sensores de puertas, Timbres, Dispositivos de seguridad, Sensores de tiempo: UV/sensores de lluvia/anemómetros, Sensores de temperatura, Medidores de pulsos y Medidores de Voltaje / AD. ("Manual de Domoticz en Español", s/f) Para este sistema se utiliza el servicio Apache/Web y los puertos analógicos para los sensores como temperatura, humedad, indicadores (leds) y Fans (Servo Motores). Esto con el fin de manejar, recolectar y visualizar la información los sensores. Para instalar domoticz se ejecuta el siguiente comando.
sudo curl -L -k install. domoticz. com | sudo bash Adicionalmente se ejecuta el siguiente comando, donde son complementos necesarios para domoticz. sudo apt-get install cmake make gcc g++libssl-dev git curl libcurl4-openssl-dev libusb-dev wiringpi Otra manera de instalar es. git clone https://github. com/domoticz/domoticz. git carpeta dev-domoticz.
apt-get install g++ apt-get install -y nodejs apt-get install libavahi-compat-libdnssd-dev Para instalar homebridge con sus complementos se ejecutan los siguientes comandos. sudo npm install -g --unsafe-perm homebridge hap-nodejs node-gyp cd /usr/local/lib/node_modules/homebridge/ sudo npm install--unsafe-perm bignum cd/usr/local/lib/node_modules/hapnodejs/node_modules/mdns sudo node-gyp BUILDTYPE=Release rebuild Para la configuración del módulo se realiza los siguientes pasos, Homebridge se ubica en un directorio llamado /home/pi/homebridge y su configuración se ubica en ~/. homebridge# en el archivo config. json, en el cual se muestra los parámetros utilizados para conectar domoticz. El siguiente código es el utilizado para la arquitectura fog computing. { "bridge": { "name": "Homebridge", "username": "CC:22:3D:E3:CE:30", "port": 51826, "pin": "031-45-154" }, "description": "Configuration file for Domoticz platform.
Exclusion criteria were previous cancer diagnosed within the last 5 y, inability to undergo medical follow-up, and inability to read or understand the consent form. Blood samples were collected at baseline (T0) before initiation of therapy (chemotherapy or tyrosine-kinase inhibitor), at first evaluation (6 ± 2 wk; E1), and at time of tumor progression (ToP). Among 109 patients eligible for ctDNA follow-up, 85 underwent their first evaluation at 6 ± 2 wk, 13 received no follow-up evaluation, 7 had their first evaluation either before 1 mo or after 2 mo, and 4 died within the first month. Blood samples were processed within 2 h, and plasma was immediately stored frozen. Clinical data collected prospectively included sex, age, performance status (WHO), smoking history, tumor histological type, TNM tumor stage according to the 7th edition of the Union for International Cancer Control (UICC) classification, description and number of metastatic sites, treatment drugs, dates of initiation and end of treatment, radiological evaluation performed every 2 mo by CT-scan according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.
NGS Analyses and Protocol Sequencing libraries were prepared from tumor FFPE DNA and from circulating free DNA using Ion AmpliSeq Colon and Lung Cancer Research Panel v2 (Life Technologies-Thermo Fisher Scientific), following the manufacturer's recommendations. The multiplex barcoded libraries were generated with the Ion AmpliSeq Library Kit v2 using 6 μL of plasma cell-free DNA as the input, corresponding to a median of 7. 8 ng (range: 1. 38-300 ng). Libraries were normalized using the Ion Library Equalizer Kit. The pooled barcoded libraries (maximum: 96) were processed on an Ion Chef System using an Ion PI Hi-Q Chef Kit and then sequenced on an Ion Proton System using an Ion PI Chip Kit v3. The FASTQ sequencing data were processed and aligned to the human genome (hg19) using the Ion-Torrent Suite v4.
2. 1. The BAM files generated by the Ion Torrent Suite were recalibrated using GATK v3. 4-46 for local realignment around indels and base quality recalibration  [30]. We then applied the BPER method to the recalibrated BAM files as previously described. This method detects high sensitivity mutations in circulating DNA at allele frequencies of as low as 0. 001 for insertions or deletions >2 bp, and at 0. 003 for single nucleotide variations. The BPER method is highly consistent with dPCR for EGFR and KRAS mutations, kappa 0. 90 (0. 73-1. 06)  [27]. It has been implemented within an R package entitled "PlasmaMutationDetector," which is publicly available at https://cran. r-project. org/package=PlasmaMutationDetector. We recommend recalibrating the BAM files with GATK  [30]  before applying "PlasmaMutationDetector.
recommend recalibrating the BAM files with GATK (method section) before applying "PlasmaMutationDetector. " Funding: NP received PhD funding from the  Sites de Recherche Integresur le Cancer  (SIRIC) « CARPEM ». EZ received salary through the  INSERM Physicancer program  (no. PC201423 ). This work was supported by the  Agence Nationale de la Recherche (ANR Nanobiotechnologies ; no. ANR-10-NANO-0002-09 ), the canceropole funding (no. 2011-1-LABEL-UP5-2 ), the  Ligue contre le cancer  ( Program "Equipe labelisee LIGUE "; no. EL2016. LNCC/VaT ) and the  French National Cancer Institute (INCA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. this manuscript have the following competing interests: VT received honorarium from  Boehringer Ingelheim  and is a consultant for RainDance Technologies.
The datasets supporting the conclusions of this article have been deposited in the European Nucleotide Archive (ENA) under the accession code PRJEB17508, http://www. ebi. ac. uk/ena/data/view/PRJEB17508. We developed an R package, which contains functions to analyze BAM files of targeted NGS with the BPER method. The package is publicly available under the R package "PlasmaMutationDetector," https://cran. r-project. org/. We.
( 3 ) Necessary data for Eqs. (  2 ) and (  3 ) can be derived from measurements or from calculations. In the remaining part of this manuscript, Eq. (  1 ), complemented by Eq. (  2 ) and Eq. (3), will be referenced as the RHO_LAMBDA approach. In our RHO_LAMBDA approach, we use the Tanaka (2001) equation for pure water density, ρ w. If the composition of solutes in the water is known, the density of water is calculated by using RHOMV  (Boehrer et al. , 2010)  and finally κ 25 is provided by the algorithm implemented in the PHREEQC code (Parkhust and  Appelo, 1999) , whose description can be found in  Atkins and de Paula (2009)  and on Appelo's webpage of PHREEQC  (Appelo, 2016). This method (re-implemented in Python from the original code) calculates the specific conductance of a solution from the concentration, the activity coefficient and the diffusion coefficient of all the charged species.
, 1999). In order to include also water with a rather unusual composition, we chose two water samples from a meromictic open pit lake, the Waldsee, which contains large amounts of sulfate and dissolved iron (e. g. Dietz et al. , 2008 Dietz et al. , , 2012;; Boehrer et al. , 2009; von Rohden et al. , 2010; Moreira et al. , 2011). Finally, we used seawater, of which the composition is known at high accuracy, as a reference water for a standard comparison. Table 1 presents the original chemical compositions of the different lakes considered in the testing of the RHO_LAMBDA expression. Data were derived from chemical analysis (for experimental details, see Appendix A) or literature (for references, see Table  1 ). We complemented the set using synthetically produced lake water of differing compositions and concentrations from the work by  Gomell and Boehrer (2015)  in order to test systematically the influence of composition and concentration on the values of the coefficients λ 0 and λ 1 (for experimental details, see Appendix A).
Hence, we related the difference to our reference with the contribution of the solutes Rel. Error = (ρ λ -ρ ref ) / (ρ ref -ρ w ). (4) For temperatures in the range of 1-30 • C, usually found in typical limnic conditions, the values of the relative error de-fined by Eq. (  4 ) are displayed in the right column of Fig. 1. On purpose, we obtained the chemical composition from a different source (sample) than the density measurement. In this way, the variability of the water composition within one lake was included in the error determination in our assessment. To judge the accuracy of our approach, we also inserted results from other formulas in common use for transferring CTD data into density: we included UNESCO  (Fofonoff and Millard, 1983) ,  TEOS-10 (IOC et al. , 2010) ,  Chen and Millero (1986)  and  Bührer and Ambühl (1975)  (Fig.
Data availability Webax-Web access to numerical tools of limnology is available at http://www. ufz. de/webax. TEOS-10 original Fortran 90 library and related tools are available at http://www. teos-10. org/software. htm. Density calculator is available at https://sourceforge. net/projects/densitycalc. All the data used in this manuscript to calculate the lambda coefficients and density values are provided in Tables  1  and 2.
02 % PHMB as the least effective formulation against Acanthamoeba polyphaga, with respect to other tested PHMB concentrations (0. 04 %, 0. 06 %, 0. 08 %)  [3]. The safety ocular profiles of three PHMB formulations (0. 08 %, 0. 25 % and 0. 8 %) were assessed in an animal model indicating no relevant treatment-related effects of PHMB ophthalmic solutions at 0. 08 % and 0. 25 %. Only 0. 8 % PHMB eye drops showed moderate/severe ocular toxicity  [2]. The toxicological data supported the plan to investigate PHMB 0. 04 %, 0. 06 %, 0. 08 % (and placebo) eye drops in a Phase I clinical trial. The trial held at three clinical centres in the Netherlands and Belgium in 90 healthy volunteers was successfully completed in Q1 2016. A Phase III clinical trial is scheduled to begin Q4 2016. The ODAK project has faced particular challenges in establishing patient involvement in the drug development pathway due to the nature of the disease.
Infection is unpredictable, disease outcomes vastly differ patient to patient and engaging patients once disease progression is resolved is difficult. There is no established patient group for AK or patient registry. Patient centric information is lacking and patients can feel isolated, without a forum to translate their experience. ODAK is working with patients to address these issues, identifying and engaging with patients for protocol and patient information leaflet design, developing information on preventative care and support materials for the newly diagnosed, and using the Project website as a go to site for patient stories and advice. The research leading to these results has received funding through the European Community's Seventh Framework Programme under grant agreement No. 305661-2.
Projects funded by the BDFA were integral to successful award to the BATCure consortium of a 3-year Horizon2020 research grant. Background Patients with rare diseases often have to endure a late diagnosis, a lack of knowledge among specialists and healthcare workers, a lack of effective treatments, and insufficient attention to scientific research. To provide proper care, it is important to cluster expertise for these diseases. This insight has led to the formation of centres of expertise and also leads to the establishment of European Reference Networks. In the process of identifying and designating expertise in rare diseases at national level, hospitals and healthcare workers generally indicate their expertise. The perception of patients is often not incorporated. The goal of the project 'Expertise Mapped' is to visualise the organisation of care for patients with rare diseases, from patients' perspective.
O8 HAE patient identification and diagnosis: An innovative, 'game changing' collaboration  Thomas J. Croce Jr. (tcroce @shire. com)Head of Global Patient Advocacy, Shire plc, Dublin , Ireland Orphanet Journal of Rare Diseases 2016, 11(Suppl 1): O8 Acknowledgement Thanks  to the national Centers of Expertise for Rare Diseases for cooperation, The  Danish Health Authority  to grant the project and The  Danish Cancer Society  for invaluable feedback. Acknowledgements BATCure consortium is funded under the  EU Horizon2020 research innovation programme  under grant agreement  666918. Project Coordinator  Professor Sara Mole  ( UCL). Institute of Ophthalmology (UCL ) PhD studentship  Professor Robin Ali ,  Professor Sara Mole  ( UCL ). Dr. Claire Russell ,  Royal Veterinary College ,  CLN2  project supported by  Karen  and  Martin Freeman.
Acknowledgements The project ' Expertise Mapped ' is a collaboration between different patient organisations for rare diseases and it is funded by the  Dutch Government (FondsPGO). The  Patient Organisation for Metabolic Diseases (VKS)  is project leader. The project partners for 2016 are the  Patient Organisation for Pulmonary Fibrosis , the  Vasculitis Foundation , the  Pulmonary Hypertension Foundation , the  Patient Organisation for Aplastic Anemia & Paroxysmal Nocturnal Hemoglobinuria, Patient Organisation for Adrenal Gland Diseases (BijnierNET) , the  Patient Organisation for Anorectal Malformation and the Organisation for Tall People.
Acknowledgements The  TAIN  project is funded by the  European Commission  under a  Framework 7 Grant  (No:  281654 -TAIN ) [www. tain-project. org]P20 Bridging the gaps between medical and social care for people living with a rare disease  Raquel Castro  (raquel. castro@eurordis. org) European Organisation for Rare Diseases -EURORDIS, Paris, France Orphanet Journal of Rare Diseases 2016 ,  11(Suppl 1 ): P20.
putida as a chassis for Synthetic Biology constructs. The updated P. putida KT2440 genome sequence is deposited at the International Nucleotide Sequence Data Collaboration (identical accession number: AE015451, version 2). The re-annotated data can also be explored and downloaded using the MicroScope platform (https://www. genoscope. cns. fr/agc/microscope). The curated genomescale metabolic network is available at the MicroCyc repository (http://www. genoscope. cns. fr/agc/microcyc) and can be downloaded using the "Download Data" functionality of the "Search/Export" menu of the MicroScope platform. Finally, the updated metabolic model is available in the Supporting Information (SBML file format).
Curiously, PP_5662 (21 SNPs) and PP_4302 (12 SNPs; encoding urea transporter) gather most of the detected SNPs (72%, either transitions or transversions). The re-annotated genome sequence (see "Experimental Procedures" section) comprises 5,592 CDSs plus 56 fragments of CDS [vs. 5350 CDS stored in the last release of the NCBI GenBank file, NC_002947, or in the Pseudomonas. com database  (Winsor et al. , 2011) ], 22 rRNA genes, and 75 tRNA genes (versus 74). The noncoding regions account for 11. 5% of the P. putida genome and contain 7. 5% of repeated sequences. Only nine noncoding regions of more than 1 kb have been identified (Supporting Information Table  S2 ). Among the annotated CDSs (complete genes and pseudogenes), we identified (i) common gene annotations between the original data and the AMIGene predictions: 5301 genes (94.
8%), the original start codon positions of which were automatically kept, (ii) gene annotation unique to the original GenBank file: 116 genes, and (iii) gene annotation unique to the AMI-Gene prediction: 607 potential new CDSs. Following the manual curation process described in the "Experimental Procedures" section, 311 CDSs unique to the present version and 36 CDSs unique to the original annotation were kept. Moreover, 102 original CDSs were considered false positive predictions and removed from the final set of genes (Supporting Information Table  S3 ). All of them would encode proteins of unknown function and 38 (37. 2%) were found at a position where a new gene has been annotated, generally on the complementary strand  [see M edigue et al. (2002)  for a similar rationale used for annotation of Helicobacter pylori genes] (Supporting Information Tables  S3  and S4 ).
Among those, 197 CDSs were reviewed (Supporting Information Table  S6 ). Most of these proteins were labeled as (putative) enzymes (56%), (putative) transporters (20%) or (putative) regulators (9%). We further annotated 61 genes encoding proteins highly similar to proteins with functions experimentally demonstrated either in Pseudomonas species/genus or in other organisms. This is the case for genes involved in the catabolism of carnitine [PP_0301 to PP_0305;  (Wargo and Hogan, 2009; Bastard et al. , 2014) ], in phenylethylamine degradation [PP_3459 and PP_3460;  (Arias et al. , 2008) ], in gallate degradation [PP_2513, PP_2514 and PP_2515;  (Nogales et al. , 2011) ], and in urate degradation [PP_4287;  (Ramazzina et al. , 2006) ]. In order to provide accurate annotations, the global curation process was directed by the results of the growth phenotype data obtained in this work as well as extracted from experimentally based literature (see next section).
e. , a complete degradation route with reactions connecting the query compound to the central metabolism was present). This observation suggested that the metabolic model and the automatic genome re-annotation were missing catabolic pathways for the remaining 117 compounds. This prompted us to include the full set of 120 compounds as a starting point for a manual metabolic pathway curation process (further described in "Experimental Procedures" section). The outcome of this effort allowed us to identify catabolic pathways for 92 of these compounds (32/43 carbon, 28/43 nitrogen, 29/31 phosphorus and 3/3 carbon and nitrogen sources; see Table  2 ). Some of those metabolic routes, absent from public metabolic pathways databases, are associated to extended substrate specificity of enzymatic activities experimentally described in other organisms.
Wild-type growth predictions indicate whether the GSMM includes any pathways that can convert a specific combination of medium constituents into biomass. In contrast, mutant growth predictions assess the quality of the GPR associations and the appropriate inclusion or exclusion of alternative pathways. The wild-type growth dataset consisted of the full BIOLOG dataset and 12 additional compounds with literature back-up. The mutant growth dataset was made of a combination of two external datasets: the original test-set for the iJP815 model  (Puchałka et al. , 2008)  and experimental data that was published later on  Molina-Henares et al. (2010). As expected, the accuracy of wild-type growth predictions increased marginally when the model was expanded with the automatically predicted reaction set (0.
SNPs/InDels detection strategy High Throughput Sequencing (HTS) data were analyzed using the PALOMA pipeline (Cruveiller S. , unpublished) implemented in the Microscope platform  (Vallenet et al. , 2013). The current pipeline is a "Master" shell script that launches the various modules of the analysis (i. e. , a collection of in-house software written in C) and controls for all tasks having been completed without errors. In a first step, the HTS data quality was assessed by including options like reads trimming or merging/split paired-end/mate-paired reads. In a second step, reads were mapped onto the original sequence of P. putida str. KT2440 (Accession Number NC_002947; AE015451. 1) using the SSAHA2 package  (Ning et al. , 2001). Unique matches having an alignment score equal to at least half of their length were retained as seeds for full Smith-Waterman realignment  (Smith and Waterman, 1981)  keeping at both sides a region of the reference genome extended by five nucleotides.
RNA-Seq Analysis The complete transcriptome high-throughput sequencing data published in  Kim et al. (2013)  was retrieved from the GEO database [  (Barrett et al. , 2013) ; accession no. GSE42491]. Data were then analyzed in the MicroScope platform with the workflow TAMARA  (Vallenet et al. , 2013). The current pipeline is a "Master" shell script that launches the various parts of the analysis (i. e. , a collection of Shell/Perl/R scripts) and checks that all tasks are completed without error. Reads preprocessing and mapping steps are performed in the same way as the PALOMA pipeline (see "SNPs/InDels detection strategy" section for details). After reads were mapped on the newly annotated P. putida strain KT2440 genome, we minimized the false positive discovery rate using  SAMtools [v. 0. 1.
8; (Li et al. , 2009) ] to extract reliable alignments from SAM-formatted files. The number of reads matching each genomic object of the reference genome was then calculated with the Bioconductor-GenomicFeatures package  (Lawrence et al. , 2013). When reads matched several genomic objects, the count number was weighted so as to keep the total number of reads constant. Finally, the Bioconductor-DESeq package  (Anders and Huber, 2010)  was used with default parameters to normalize raw count data based on negative binomial distribution and to determine whether expression levels differed between conditions.
Structural re-annotation of the Pseudomonas putida genome The corrected genome sequence was subsequently processed by the MicroScope pipeline for complete structural and functional annotation  (Vallenet et al. , 2013). Gene prediction was performed using the AMIGene software  (Bocs et al. , 2003)  and the microbial gene finding program Prodigal  (Hyatt et al. , 2010)  known for its capability to locate the translation initiation site with great accuracy. The predicted genes were compared with those listed in the original annotation (AE015451, version: 05-MAR-2010). Manual curation was performed on the two sets of unique genes (see "Results" section) by taking into account transcriptomic information from  (Frank et al. , 2011)  and  (Kim et al. , 2013)  experiments, as well as conservation of sequence similarity and genomic context with homologs in other genomes.
Predicted small CDSs having a coding prediction value inferior to 0. 3 and which are orfans in terms of sequence similarity and not involved in a synteny group were discarded, unless they showed a signal with one of the transcriptomic experiments. A total of 80 unique GenBank genes, and of 296 unique AMIGene CDSs were considered false positive predictions and discarded from the final annotations (artifact status). These genes are kept in our database as "obsolete" genomic objects, but they are removed from the P. putida KT2440 genome annotation deposited at the International Nucleotide Sequence Data Collaboration (identical accession number: AE015451, version 2). The 309 unique AMIGene predictions considered as newly predicted P. putida genes are numbered starting from the last original annotation (PP_5420) (i.