dataesr/scientific-paragraphs-categorization huggingface dataset:
IODs, fork asymmetry, fork velocity and GIFD were measured as described  [34]. 26 and 17 IODs, 32 and 29 fork asymmetries, 64 and 59 fork velocities, as well as 42 and 32 Forks, were measured for HCT116 shCTR and shNF-YA cells, respectively. Fork asymmetry has been calculated by the formula [(Long green track / short green track) -1] × 100  [35]. The non-parametric test of Mann-Whitney was applied to compare data sets from HCT116 shCTR and shNF-YA.
Acknowledgments We warmly thank  G. S. Chadha  for his technical assistance. This work was supported by the  Associazione Italiana per la Ricerca sul Cancro  to CI (IG No. 14210 ) and RM (IG No. 14130 ). BM is supported by  Marie Curie IRG fellowship  ( PIRG07-GA-2010-268448 ). JJB acknowledges support from  Cancer Research UK  (grant  C303/A14301 ). PB is the recipient of a fellowship from the  Fondazione Italiana per la Ricerca sul Cancro (FIRC).
Hybridization signals were detected with NeutrAvidin-FITC. Chromosomes were mounted in antifade medium (ProLong Gold antifade reagent with DAPI; Invitrogen) and covered with a glass coverslip. Chromosome slides were analyzed under a Leica DM 6000 B microscope. Images were taken with a Leica DFC 345 FX camera using Leica Application Suite 3. 7 software with an Image Overlay module.
Acknowledgements The complete financial support for this study was provided by the grant from the  Russian Science Foundation  no. 14-14-00541  to the  Zoological Institute of the Russian Academy of Sciences. We thank  Dr. E. V. Ilyina  ( Dagestan Scientific Center RAS, Makhachkala ) for help in collection and  Dr. V. A. Krivokhatsky  ( Zoological Institute RAS, St. Petersburg ) for help in identification of species, studied in the present paper.
For this, we performed macrophage infection assays with Salmonella strains containing the fadBA-gfp, fadD-gfp, fadL-gfp, fadE-gfp, or aceBAgfp reporters. The bacteria were grown in M9 during 3. 5 hours in order to maintain a low induction of the reporters, and used to infect activated RAW 264. 7 macrophages grown in DMEM medium. Intracellular bacteria were extracted after different times of infection, and their fluorescence levels monitored by flow cytometry. Single peak of GFP histograms were recorded for the different Salmonella strains extracted from macrophages, indicating that the entire population of intracellular bacteria was responding to the vacuolar environment (data not shown). The mean GFP levels of the four strains, carrying the fad reporters, decrease significantly soon after infection (Fig.
For flow cytometric analysis, bacteria were gated in FL2 and analyzed for the expression of GFP in FL1. Data were acquired with a FACScalibur (BD Biosciences) equipped with blue argon laser (488 nm) and analyzed with the FlowJo software (Tree Star Inc. , Ashland, OR) on 2000 to 10000 events finally identified as S. Typhimurium particles. Statistical analyses were performed using Prism (GraphPad, San Diego, CA, USA).
Interestingly, ATM displayed a gene expression profile sharing similarities with human tumour-associated macrophages (TAM)  (10, 11). In line, ATM were found to produce a repertoire of growth factors, cytokines, chemokines and proteolytic enzymes involved in the regulation of tumour growth, angiogenesis, invasion and/or promotion of cancer metastasis, as observed in TAM  (12). Finally, we show that the ATM phenotype is programmed by the cellular environment as demonstrated by indirect co-culture experiments applying the pre-adipocyte-conditioned media to MDM from lean subjects, resulting in an ATMlike phenotype. These data indicate that the TAM-like human ATM phenotype is directed by surrounding cell types and suggest that factors released by ATM may contribute to cancer initiation and progression in obese patients.
Sample preparation for gene expression and data analysis of Affymetrix oligonucleotide arrays Total RNA was checked for quantity and quality using the Agilent 2100 Bioanalyzer (Agilent Biotechnologies). ATM, MDM and TAM RNA samples were processed using HG-U133 plus 2. 0 (ATM and MDM) and HG-U133A (TAM) Affymetrix Genechips. The MatchProbes package (PMID: 14988118) was used to match the probe sequences between the GeneChips. Quality assurance of the hybridization was assessed by constructing a virtual image of the chip by plotting each residual obtained with each probe at the corresponding position using the affyPLM package. Raw gene expression data were processed and normalized with the Affymetrix GCOS 1. 4 software and by multiarray analysis (RMA)  (17).
Functional classification, visualisation and pathway analysis Selected gene lists were uploaded in the DAVID (Database for Annotation, Visualization, and Integrated Discovery) website  (19). Bioinformatics resource and the functional annotation and classification gene tools were used to generate clusters of related genes  (20). Integrative data analysis, including multiple graphical visualizations was performed using GenespringGX10 (Agilent Biotechnologies) and pathway analysis by the Kyoto Encyclopedia of Genes and Genomes (KEGG). The profile of functional pathways was analysed using ProfCom software  (21). This software allows comparing the proportion of genes related to specific gene ontology (GO) categories (biological process, cellular component and molecular function) among the ATM higher expressed genes to the proportion of genes related to the same category within the GO reference set of all genes from homo sapiens  (21).
Our results indicate that the expression level of CXCL1, CXCL10 and CXCL11 was much lower in CD68+ atherosclerotic plaque macrophages compared to ATM. Expression of CXCL9 and CXCL12 was highly variable between samples, also because of the relatively low expression levels of these two chemokines (supplemental figure  4 ). Moreover, CXCL5 mRNA expression was not detectable in CD68+ atherosclerotic plaque macrophages. Overall, these data indicate that the expression profile of CXCL members in ATM is different from those obtained in atherosclerotic plaque inflammatory macrophages. A large number of highly expressed genes in ATM encode predicted secreted proteins. SignalIP 3. 0 and SecretomeP 2. 0 softwares were used to predict secreted proteins from ATM (supplemental table 3). 57 genes of the 1066 genes over-expressed in ATM were not identified by the software and are thus marked as unknown (supplemental figure  5 ).
By contrast, the expression of the same genes was generally lower in MDM compared to TAM (figure  6 ). These data demonstrate that human ATM display a TAM-like phenotype. Serum concentrations of CXCL1, 2, 3, 10 and 11 are elevated in obese subjects. To determine whether circulating concentrations of CXCL chemokines and some factors belonging to the CCRI pathways, such as sgp130 and TGFβ1, which are over-represented in ATM, are altered in vivo in obese individuals, serum from obese and lean subjects was analyzed. The serum levels of total CXCL1, 2, 3 (Gro), CXCL 10, 11 and sgp130 were significantly elevated in obese compared to lean individuals, whereas the level of the anti-proliferative factor TGFβ1 was significantly decreased in obese individuals (figure  7 ). Altogether these results indicate that the expression of several factors highly expressed in ATM and related to cancer correlates with circulating levels in obese subjects without malignancies.
Our study also reveals that circulating levels of Gro (CXCL1, 2, 3), CXCL9, 10, 11 (CXCR3 ligands) and sgp130, key players in the maintenance and amplification of autoimmunity-related inflammatory processes  (35) , are elevated in obese subjects, while antiproliferative TGFβ1 concentrations are lower  (41). ATM over-expressed both OSM family members (IL-6, LIF, OSM, IL-11) and their receptors. Indirect co-culture experiments on MDM indicated that the phenotype of ATM is specifically related to factors released by the preadipocytes, whereas lipids released from mature adipocytes do not induce these effects. These data are in line with previous observations, suggesting the high pro-inflammatory potential of preadipocytes  (5, 6). Collectively, our data indicate that ATM may contribute to the link between obesity and cancer and revealed for these cells a gene expression profile comparable to those of TAM.
FOOTNOTES We are grateful to the patients participating in this study. Members of F. Pattou's surgery team, particularly MF. Six, C. Eberle and L. Arnalsteen are acknowledged. We are grateful to P. Gelé (Centre d'Investigation Clinique, CHRU, Lille) for blood and serum collection and to J. Berthout, A. Blondy, E. Vallez, C. Rommens, A. Lucas, C. Copin, A. Patrice, V. Gmyr, S. Deledicque, D. Hot and L. Huot for technical assistance. The research leading to these results has received funding from the "Nouvelle Societé Française d'Athérosclérose / Sanofi-Aventis" (to Mayi T. H. ), the "Societé Française de Cardiologie / Sanofi-Aventis", the COST Action BM0602 and the European Community's 7th Framework Programme (FP7/2007-2013) under grant agreement n° 201608. G. Chinetti-Gbaguidi is a recipient of a Contrat d'Interface from the CHRU de Lille.
The fasta and quality files from each sample have been submitted to the Sequence Read Archive (www. ncbi. nlm. nih. gov/sra; accession SRX981059). Processing of pyrosequencing data. The two fungal sequence datasets (ITS1 and ITS2) were processed using the SCATA pipeline (http://scata. mykopat. slu. se refs 52,59) whereas the pipeline provided by the Ribosomal Database Project 60 was used for both bacterial and archaeal sequences. Adaptor and primer sequences were trimmed, and sequences of low quality (exponential quality score lower than 10) or shorter than 200 bp were removed. The 6 bp DNA barcode attached to the primers was used to assign sequences to samples. Operational taxonomic units (OTUs) were defined using single linkage clustering at the 98. 5% identity level for the fungal sequences according to Wallander et al.
61  and Blaalid et al. 62. For bacterial and archaeal sequences, OTUs were defined using complete linkage clustering at the 97% identity level  63. After removing all the singletons, a total of 641 230, 182 365 and 624 321 sequences were obtained for the bacterial, fungal and archaeal communities, respectively. These corresponded to 41 418, 2 295, 2 062 and 3 497 bacterial, fungal (ITS1 and ITS2) and archaeal OTUs respectively, distributed across the 21 environmental samples replicated three times. The OTUs were taxonomically identified using the RDPII taxonomy tool  63  and the SILVA-ARB database  64  for the bacterial and archaeal OTUs, respectively (see Table  S1  for details). The fungal ITS sequences were phylogenetically assigned using MEGAN v. 4. (MEtaGenome Analyzer, Center for bioinformatics, Tübingen, Germany  65  ) according to their best matches from the GenBank database  66.
Community and statistical analysis. After checking that the Bray-Curtis distance within the three replicates were significantly lower than between depths or sites (vegdist function in the Vegan library program implemented in R (http://www. r-project. org/); P < 0. 001, t-test; Fig. S2 ), data from the different sample replicates were pooled to increase the amount of sequences and the representativeness of each sample. The pooled data were then clustered according to the dissimilarity (Bray-Curtis) of their microbial community composition. The main OTUs (relative abundance per sample higher than 1‰ for each dataset -fungi, bacteria and archaea) in the different environmental samples, the habitat generalists detected in at least 16 of the 21 samples and the habitat specialists detected in only one sample type were visualised using the Perl script bubble.
pl (available at http://hallam. microbiology. ubc. ca/). To explore co-occurrence patterns, a network analysis was performed in R by calculating all possible Spearman's rank correlations between OTUs with a relative abundance per sample higher than 1%. This filtering step removed poorly represented OTUs and reduced network complexity. A valid co-occurrence event was considered to be a robust correlation if the Spearman's correlation coefficient (ρ ) was both > 0. 6 and highly statistically significant (P < 0. 001). Global descriptors of the network were obtained using Network Analyzer in Cytoscape v3. 0. 1  67  and the network of coexisting microorganisms was visualised using the Gephi v0. 8. 2-beta software  68.
With this in mind, the following hardware peripherals were selected:  Two constraints were defined for the hardware selection: (1) use the maximum amount of COTS hardware and minimize possible modifications, so that the development time is minimized; and (2) choose peripherals with USB connections to ease hardware development and testing. With this in mind, the following hardware peripherals were selected: Embedded Linux Platform: Our choice was the Raspberry Pi  [43]  due to its wide availability and community support. Other platforms were evaluated, namely the Kontron M2M  [44]  and the Intel Galileo (Gen 1)  [45] , but they were discarded because of: (1) higher price than the Raspberry Pi, and (2) less support, with the Galileo having, at the time, minimal support due to its early market release, while on the Kontron it was more complex to create and manage Linux images.
Software Architecture The software architecture was developed to be flexible so that it supports different versions of the CRIP, i. e. , support changes/upgrades to the hardware configuration during the development stages, as well as when installing the platform where different versions could be deployed in order to reduce costs. The chosen operating system was Linux, namely the official Raspberry Pi distribution Raspbian, and the software was written as a Linux daemon in C++ language. The software architecture of the daemon is depicted in Figure  4.
 Embedded Linux Platform: Our choice was the Raspberry Pi  [43]  due to its wide availability and community support. Other platforms were evaluated, namely the Kontron M2M  [44]  and the Intel Galileo (Gen 1)  [45] , but they were discarded because of: (1) higher price than the Raspberry Pi, and (2) less support, with the Galileo having, at the time, minimal support due to its early market release, while on the Kontron it was more complex to create and manage Linux images.
Module Middleware This module contains all classes that implement the main functionalities of the CRIP. The main class is the CripRegisterDaemon, responsible for initializing all hardware and communications of the CRIP, namely Bluetooth, biometric and NFC, respecting the hardware, and the HTTP server respecting the communications. The initialization of the CRIP is successful if all hardware is available and the HTTP server starts successfully. When a user interacts with the hardware, an internal interrupt from the respective module (biometric or NFC) is started and the data from the hardware is read. A user interaction can be either of the three: (a) place a finger on the biometric reader; (b) place a smartcard on the NFC reader; or (c) receive an HTTP request from the CAALHP (the user interacted with the CAALHP).
Module Communications The class CripHttpServer implements a more constrained HTTP server, based on the libmicrohttpd library  [46] , to handle the REST requests registered when the server starts. When a method is registered it is associated an URL, so that when a REST request is received the respective method is invoked. The class CripHttpClient is a simple HTTP client, based on the libcurl library  [47] , that allows to perform simple HTTP GET and POST requests to other systems, such as the CAALHP. All data exchanges are made in JavaScript Object Notation (JSON). The class to handle JSON data transformation is also on this module, although not represented on the diagram for brevity and easier understanding.
Module Hardware The access to hardware peripherals, namely Bluetooth, biometric and NFC reader/writer is abstracted, respectively, by the interfaces ICripBluetooth, ICripBiometric and ICripNfc, and must be accessed through a hardware factory defined by the interface ICripHardwareFactory. The use of interfaces allows implementing and using different hardware, because it abstracts the peripheral's internal behavior and unifies the access to it with common methods. The access to the instances of the peripherals is made through the class CripHardwareFactory, an implementation of the ICripHardwareFactory interface. This class abstracts and manages internal configurations of the hardware peripherals, avoiding unnecessary complexity when accessing them. CripBluetooth provides access to the Bluetooth peripherals.
It is based on the BlueZ stack  [48]  and on the Antidote library  [49] , that handles the reception and parsing of IEEE 11073 messages from Continua Alliance-compliant personal health devices. CripBiometricSfm3520 provides access to the biometric peripheral; the class is based on a custom library where all the actions supported by the peripheral are implemented, since Suprema does not offer official Linux support. The class CripNfcUsb provides access to the NFC peripheral and it is based on the open-source library libnfc  [50]. Also on this module, although not represented, are also the classes that permit paring Bluetooth devices using NFC tags. More details on this are presented in Section 2. 3. 5. The classes CripCredentials, CripBluetoothDevice and CripDeviceMeasure are data containers for, respectively, handling users' credentials, Bluetooth information used for configuring the device and handling data readings from the personal health devices.
Software States Overview The behavior of CRIP can be described on a simple state machine diagram, as depicted on Figure  5. When the CRIP starts it is always waiting for an interrupt (Wait state), which can either receive a HTTP request or an interrupt from the hardware. When an interrupt is being attended, other incoming requests/interrupts are not attended until the current one is finished. When a HTTP request is received, CRIP attends it (AttendRequest) and then goes back to the Wait state. A hardware interrupt can occur either when a finger is placed on the biometric reader, or when a NFC tag/smartcard is placed over the NFC reader. If the former occurs, the user's biometric template is read (ReadTemplate), validated against templates previously stored templates on the hardware (ValidateTemplate) and, if the user is valid, its ID is sent to CAALHP (SendUserIdCaalhp); otherwise the template reading is ignored.
The devices we tested do not natively support any NFC exchange, therefore they were retrofitted with a NFC sticker tag that contained the information need to pair the device, namely the MAC address, pin code and a friendly name for the device. From the point of view of the NDEF protocol, this is considered a static handover, i. e. , the communication is unidirectional (the CRIP reads the device's tag), as opposed to a negotiated handover that can occur when two devices are NFC enabled. At the time the CRIP was developed a library supporting these data exchanges was not available. Therefore, a custom library was implemented to support them. Details on the library will not be presented here, as it only reflects the data formats defined by the standard. Also new libraries such as the open-source Linux*NFC  [52] , from Intel, or Open NFC  [53] , are now available and we recommend their use for future implementations.
When this process is finished, the nurse would go to the next room, where the same process of identifying a device and acquiring vital data is repeated. This entire process would be similar for a citizen's home deployment with a visiting nurse taking its devices to acquire health measurements. A second evaluation reviewed a home deployment scenario, where a citizen has access to a CAALHP connected to a CRIP and some health devices, and it is responsible for its health measurements. A deployment such as the one presented on Figure  6  was used for the evaluation, with the CAALHP executing Dummy CAALHP. The NFC tag that identifies a device was placed on the CRIP in order to identify it and if necessary also pair it. After they were paired, the Dummy CAALHP application was used to acquire vital data from each device.
This identification and data acquisition procedure was repeated five times for each device. A third evaluation to assess the systems seamless device configuration was performed in the Aarhus University laboratory by three of the authors, using a custom health device, namely an intelligent bed. Specifically, the CRIP's device identification functionality using NFC tags and the device profile installation and configuration on the CAALHP are evaluated. A setup similar to the one depicted in Figure  6  was used, with the CAALHP running both CAALHP and Dummy CAALHP applications. First a NFC card was written with the device's information, using the Dummy CAALHP application. After, the user must authenticate on the system, via biometrics, so it unlocks and allows the installation of new devices. On successful authentication, the NFC tag containing the bed's profile was placed on the CRIP and the profile information is sent to the CAALHP.
CRIP Prototypes Two CRIP prototypes were developed during the project. The first prototype was a quick assembly to mainly test integration with the CAALHP, while the second prototype resolved some issues that appeared during the first one and received some hardware upgrades. In the first prototype the hardware was distributed in three levels, as depicted in Figure  7. The first level is solely an USB hub, the second one a Raspberry Pi Model B and the third one is composed of the NFC and biometric modules; the Bluetooth module was attached to the front panel of the box. The NFC and Bluetooth hardware modules where connected to the USB hub, while this and the biometric module where connected to the Raspberry Pi's USB ports. On this prototype a USB hub was used because this Raspberry Pi model only had two USB ports available to connect all hardware peripherals.
For the second evaluation, the devices were always recognized and it was possible to acquire health data from them. The identification and configuration of the custom device was successful, i. e. , the CAALHP received the device's profile information from the CRIP and was able to download, install and configure the device driver and application of the intelligent bed on the CAALHP, without any intervention by the facilitators. The protocol was tested for both authorized and unauthorized users and worked as expected, i. e. , authorized users could install the device's software, while unauthorized ones failed to do so.
Some of the IoT aspects that were met by the CRIP that we would like to highlight are availability, scalability, privacy and security. Regarding the availability and scalability of IoT solutions, with the growth of personal health devices, boosted especially by the number of fitness and health wearables, it is important to support them in a seamless way in order to enhance the technological user experience and well-being. We believe that that comes from platform independency and continuously available services and applications that can involve citizens on health related routines. While this is difficult to achieve, it can be easier, from the technological point of view, if the device manufacturers can be involved on this integration. As part of the CareStore strategy, that could be possible through the development of medical applications for the CAALHP and creation of device drivers that support communication and data handling with the devices.