dataesr/scientific-paragraphs-categorization huggingface dataset:
It is essential to consider these two factors, because the city is inhabited by more than 7. 5 million people where subsidence directly impacts on urban structures and infrastructure. Further works should be carried out by applying the PS/DS technique using new coming SAR data, e. g. , ALOS PALSAR 2 and Sentinel-1, to continuously monitor the ground subsidence phenomena. The detailed information of the ALOS PALSAR L band data descending stack is reported in Table  A2.
Acknowledgments This work was supported in part by  European Space Agency (ESA) ,  Centre National d'Etudes Spatiales/Terre Ocean Surfaces Continentales, Atmosphere  ( CNES/TOSCA ) and IRSTEA, UMR TETIS. ALOS PALSAR images were provided by the  Japan Aerospace Exploration Agency  under the framework of Kyoto and Carbon Phase 3, and in particular thank to  Masanobu Shimada. The authors would like to thank the entire  Geomatics Center team at International Technology Park, HCMC National University , and particularly,  Msc. Vuong Quoc Viet  for providing ground data. We thank  Alan Murray  for English corrections. We gratefully acknowledge  Ngo Yen Nhi  for discussions on HCMC water nuisance and for allowing us to use the photograph in figure 10. We are grateful to  Tran Thi Thu Luong  for discussions on the urbanization of HCMC.
Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data-based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary β-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing.
Study Setting Marseille is the second-most populous French city (estimated population 850,726 persons in 2010). All data analyzed in this article came from the 4 university hospitals of Marseille (North, South, Conception, and Timone hospitals). Cumulatively, these hospitals represent ≈3,700 beds, including ≈1,500 beds for the Timone Hospital, ≈600 beds for the North Hospital, ≈700 beds for the Conception Hospital, and ≈900 beds for the South Hospital. The AP-HM clinical microbiology laboratory is located at Timone Hospital; the laboratory performed ≈145,000 serologic tests and ≈200,000 PCRs and cultures of microorganisms from 220,000 samples in 2012  (14). This amount of data allowed us to implement our own laboratory-databased syndromic surveillance system.
Organization of Surveillance Activity on Tools of AP-HM The AP-HM laboratory-based surveillance consists of 3 following syndromic surveillance tools founded on Excel software (Mircosoft Corp. , Redmond, WA, USA): 1 previously described system called EPIMIC (EPIdemiological biosurveillance and alert based on MICrobiologic data)  (15, 16 ), 1 surveillance system implemented for the surveillance of bacterial antibiotic resistance (MARSS, Marseille Antibiotic Resistance Surveillance System), and BALYSES (BActerial real-time LaboratorY-based SurveillancE System), which was developed for the surveillance of the number of patients infected by each bacteria species identified at least once in our laboratory. Our surveillance systems are defined as syndromic surveillance systems because no surveillance data are specifically collected for their use.
The flow of information needed for each of the 3 surveillance systems is summarized in Figure  1. However, only BALYSES and MARSS are further described. All of the data routinely used for the 2 surveillance systems are manually collected from the Timone Hospital laboratory information management systems and processed by using Microsoft Excel software (2007 version). Data are then entered in the 2 surveillance systems according to their nature. The 2 systems automatically compare the entered data with their specific thresholds. Alarms are emitted by the systems if the entered values exceed thresholds. The emitted alarms are analyzed weekly during a specific thematic epidemiology meeting with laboratory staff. If alarms are validated, further investigations are immediately conducted by biologists, clinicians, and medical residents.
Historical Databases The detection of abnormal events necessitates the calculation of expected references, previously called historical thresholds. To define the expected references, 2 historical databases were built by using data extracted from the laboratory information management systems of the 4 university hospitals of Marseille. The first historical database consisted of all of the bacterial identifications obtained from January 2002 to December 2013 (excluding December 2002, data unavailable), including those described in a previous work  (17) , and a second database consisted of most antimicrobial resistance profiles obtained from October 2012 through March 2013. These data were then processed with Microsoft Excel software (2007 version) and sorted. The first database was then sorted, and only samples from which bacterial species were properly identified were conserved.
Databases and Surveillance Systems The first version of the 11-year historical BALYSES database contained 161,374 bacterial identifications corresponding to 568 different bacterial species. The 10 most numerous bacterial species were E. coli (37,560 patients), S. aureus  (23,   1 ). In parallel, MARSS detected 31 alarms (16 confirmed events and 15 unconfirmed events, ≈0. 6 alarms/week), which led to 15 official reports to the ARS of the PACA region, France (Table  4 , http://wwwnc. cdc. gov/EID/ article/21/8/14-1419-T4htm; Figure  4 ). The positive predictive value for the study period was 0. 52. Thirteen bacterial species triggered alarms in MARSS. Here, the bacterial species, in order according to the number of alarms triggered, were K. pneumoniae (13 alarms), E. cloacae (3   4 , http://wwwnc.
This facilitates a rapid public health response to counter possible epidemics. As an example, EPIMIC, our third surveillance system not described here (Figure  1 )  (15, 16) , allowed us to detect a nosocomial outbreak of the hypervirulent Clostridium difficile ribotype O27 that started in March 2013  (19). As we continue to fight this major public health problem, a list of recommended containment measures, such as systematic isolation of infected patients in special care units or systematic screening of patients at risk, is being published and transmitted to our institutional and regional health care providers. Our 2 surveillance systems have been implemented by using Microsoft Excel software. This strategy makes the  systems easy to handle and allows rapid modifications and improvements without the need for in-depth computer skills.
These advantages may not be the case for fully designed website surveillance systems such as the Swiss Antibiotic Resistance Surveillance database  (20)  or the Real-Time Outbreak and Disease Surveillance (RODS)  (21). These aspects  are key factors for the optimal long-term use at the hospital level because surveillance systems can be considered complex socio-technical systems with the objective of assisting users during abnormal epidemic events  (22). The implementation of our 2 surveillance systems required 1 full-time PhD student for 4 months and a computer with standard configuration equipped with Microsoft Office version 2003 or 2007. In France, the national research agency requires that the minimum salary of a PhD student is 33,000€ per year. Considering that the average price for a basic computer equipped with Microsoft Office is ≈500€ and that the PhD student's salary for the 4 months was 11,000€, plus the administrative and management costs, the total consolidated cost of these surveillance systems was ≈13,800€ (US $17,000).
Poor quality data were also observed in the emergency department syndromic surveillance system in New York, primarily because of the lack of human resources  (25). However, our surveillance systems have 2 main limitations. The first limitation is the statistical analysis used for the detection of abnormal events. As described before, our surveillance systems compared entered data with the historical means ± 2 SDs. For our purposes, this tool was simple to develop and was used effectively to detect abnormal events. However, these statistics do not consider seasonal variations in pathogen isolation, especially for rare bacterial species. To address this problem, Enki et al. improved the detection algorithms according to the frequency of isolation of the 3,303 pathogens included in the 20-year LabBase surveillance database recovered from the UK Health Protection Agency  (26).
Acknowledgments We thank  American Journal Experts  for English corrections. This work was partly funded by the  Centre National de la Recherche Scientifique  and the  Institut Hospitalo-Universitaire Méditerranée Infection. Mr. Abat  is a PhD student at the  Institut Hospitalo-Universitaire Méditerranée Infection, Aix-Marseille Université. His research interest is the implementation of computer tools for real-time epidemiologic surveillance of abnormal events based on clinical microbiology laboratory data.
P ulmonary tuberculosis remains one of most deadly infectious diseases  (1). The responsible agent, Mycobacterium tuberculosis, is a genetically monomorphic species, owing to its low DNA diversity  (2)  and clonal evolution (3), but with a host adaptation characterized by specific clones in some geographic areas  (4) (5) (6). Among 34 M. tuberculosis isolates obtained from French Polynesian patients (7), we recently observed two isolates, MT11 and MT14, the peptidic spectra of which were clustered by matrixassisted laser desorption ionization-time of flight mass spectrometry. Of note, the 16S rRNA gene sequences of both isolates showed 99% sequence similarity to that of M. tuberculosis H37Rv (GenBank accession no. AL123456) and a unique mutation at position 1247. This mutation differed from all 16S rRNA genes previously reported in the other M.
tuberculosis complex members. The partial rpoB gene sequences of MT11 and MT14 showed 100% similarity with that of M. tuberculosis H37Rv (GenBank accession no. AL123456)  (8). Twenty-four mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) genotyping loci  (9)  and spoligotyping  (10)  identified the two isolates as belonging to the Haarlem lineage (O. D. Aboubaker, M. Phelippeau, M. Drancourt, and D. Musso, unpublished data). We thought that analyzing the whole-genome sequence of M. tuberculosis MT11 would help define the phylogenetic relationships within the M. tuberculosis complex and design tools for its advanced detection and identification. Chromosomal DNA was isolated as previously described (11) and sequenced on the MiSeq Technology (Illumina, Inc. , San Diego, CA, USA) through four runs using two mate-pair libraries with insert sizes of 10 and 3.
3 kb in a 2 ϫ 250-bp run for each barcoded library. The whole set of reads was trimmed using Trimmomatic  (12)  and assembled using the assembler software SPAdes  (13, 14). Contigs were combined using SSPACE  (15)  and Opera  (16) , helped by GapFiller  (17) , and homemade tools in Python were used to refine the set. The draft genome of M. tuberculosis MT11 consists of 16 con-tigs without gap containing 4,110,293 bp, which is the second smallest genome among M. tuberculosis, and a 65. 15% GϩC content. Noncoding genes and miscellaneous features were predicted using RNAmmer  (18) , Aragorn (. Coding DNA sequences (CDSs) were predicted using Prodigal  (23) , and functional annotation was achieved using BLASTϩ  (24)  and HMMER3  (25)  against the UniProtKB database  (26). The genome was shown to contain at least 85 predicted RNAs, including 3 rRNAs, 51 tRNAs, one transfer-messenger RNA (tmRNA), and 30 miscellaneous RNAs.
A total of 3,949 genes were also identified, representing a coding capacity of 3,682,833 bp (89. 6% coding density). Among these genes, 281 (7. 12%) encode putative proteins, and 542 (13. 72%) encode hypothetical proteins. Moreover, 2,776 genes matched a least one sequence in the Clusters of Orthologous Groups (COGs) database  (27, 28)  using BLASTp default parameters. Nucleotide sequence accession numbers. The M. tuberculosis MT11 strain annotated genome sequence has been deposited at EMBL under the accession numbers CVMX01000001 to CVMX01000016.
As discussed above, a very informative DSB repair protein is the H2AX, which under a DSB turns into its phosphorylated form (γ-H2AX). γ-H2AX forms foci that can be microscopically observed as fluorescent spots after immunofluorescence staining. The γ-H2AX assay is considered as the gold standard of DSB detection, since a linear dependence of foci number on radiation dose has been verified and there is a "1:1" correlation between foci number and DSBs. Since the importance of γ-H2AX is given, it is a worthwhile task to look for sites of colocalization of γ-H2AX foci with potent foci of other DDR proteins. Both epifluorescence and confocal microscopy can be utilized. Although confocal is by definition more informative, epi-fluorescence could be approved as more time-effective. γ-H2AX foci detection can be carried out through epifluorescence using a high throughput image analysis software, making foci imaging and scoring almost fully automated (like the Metasystems Metafer 4 system).
A recent publication by  Asaithamby et al. (2011)  utilized the idea of detecting clustered DNA lesions in human cells using the foci colocalization approach for low and high-LET ions. Generally speaking, colocalization is referred to the detection of two different color foci (each one corresponding to a different DDR protein) that coexist in the same cell area. For calculating the level of co-localization, highly specific software like the Imaris (Bitplane) can be used. Usually the criterion for foci colocalization is the coverage of a minimum percentage (e. g. , 70%) of focus A area by the focus of protein B and vice versa. This is useful for estimation of co-occurrence between foci of similar size. As an example and using a freely available software (JCount), colocalization of APE1 and γ-H2AX foci in HepG2 cells irradiated with argon ions (Ar-36, FIGURE 5 | (A) Principles of colocalization between foci of dissimilar size.
Colocalization exists if the ratio of B foci number per A nucleus area is greater than the ratio of B foci number per cell nucleus area. (B) Realistic example of colocalization between APE1 and γ-H2AX foci in human HepG2 cells irradiated with high-LET Ar heavy ions (Ar-36, LET 269. 4 keV/µm). In our case γ-H2AX forms large and bright red foci with clear boundaries, while APE1 gives a punctuated and diffused staining, forming numerous small "foci," even in case of non-irradiated cells. In an attempt of using a freely available software, we have performed the analysis using the Jcount software (courtesy of Dr. Pavel Lobachevsky group, Peter McCallum Institute, Australia). As expected a significant increase in the colocalization of APE1 foci with the DSB focus is observed for all irradiated samples compared to non-irradiated (0 Gy).
Evaluation of Meta-Analysis Results by Manual Searching in Quertle and other Bibliographic Data Bases In order to more efficiently scrutinize our results we performed for all of the above found genes (Table  1 ) a manual search in Quertle (LLC, 2014) and also using databases GeneCards  (Safran et al. , 2010) , Uniprot  (Apweiler et al. , 2004) , Entrez Gene  (Maglott et al. , 2005)  and PANTHER  (Moussavi Nik et al. , 2012). No Quertle search result overlap was found for any of these genes between each of them and the other two categories (columns). For example, ionizing radiation genes (column 1) with the terms "oxidative or replication" stress suggesting a possible "uniqueness" for each of these markers. As a result, these genes or their encoded proteins are potential biomarkers, but they need to be validated in further studies.
According to Gene Ontology, PSEN1 is a gene belonging in the DDR family of genes and especially damage resulting from oxidative stress inducers like hypoxia  (Moussavi Nik et al. , 2012). The encoded protein belongs to gamma secretase complex, which is a cellular component. Gamma-secretase cleaves several transmembrane proteins including the cell surface receptor Notch and the beta-amyloid precursor protein. Presenilin is a protein which forms a complex with Aph1, Nicastrin and others to cause intramembranous proteolysis of Notch subsequent to its extracellular cleavage by TACE after ligand binding. Presenilin is the actual peptidase in the complex. Its name derives from the fact that it is separately also the peptidase involved in cleavage of the β-amyloid precursor protein or β-APP, and in this role, mutations in Presenilin1 and Presenilin 2 in humans have been linked to familial early onset Alzheimer's disease as researched in PANTHER database database  (Mi et al.
Acknowledgments This work was supported by an  EU   Marie Curie Reintegration Grant   MC-CIG-303514 ,  Greek National  funds through the  Operational Program  'Educational and  Lifelong Learning  of the National Strategic Reference Framework (NSRF)-Research Funding Program: THALES (Grant number  MIS 379346 ),  Labex PRIMES  ( ANR-11-LABX-0063 ). The sponsorship of the  COST Action CM1201  " Biomimetic Radical Chemistry " is gratefully acknowledged. The argon ion beamtime at GANIL was supported by the  European Community  through the  European Nuclear Science and Applications Research (ENSAR)  contract in the framework of  FP7 Integrated Infrastructure Initiative  (Grant Agreement n • 262010).
The present study was hence designed to address this lack of information. Our goal was to propose an assessment of different smartphone sensors and different manufacturer's algorithm performances. It evaluates three best-selling smartphones (Apple iPhone 4, Apple iPhone 5S and Samsung Galaxy Nexus), which are still widely used in research, teaching, and in industrial applications. Their sensors and embedded software are also available in a wide variety of other smartphones on the market. To perform angle measurement, we used some algorithms that are state-of-the art and are available for all scientific community  [13]  but also the algorithms that are given by the two major manufacturers of smartphone inside the Software Development Kit (SDK) of their Operating System. Such evaluations are crucially needed to allow or not the use of smartphones internal sensors and manufacturer's or Attitude and Heading Reference System (AHRS) algorithms implemented on smartphones to perform clinical angle measurement compared to specific tool, which are more precise than standard clinical tools.
Smartphones The smartphone market can be complex because it includes different manufacturers and different software providers. In order to cover the majority (in terms of number of units sold) of this market, we have chosen to select three different and representative smartphones from the two largest sellers (Apple and Samsung) and which contains different sensors and software. The first Smartphone tested is an Apple iPhone 4 equipped with (1) a 3D accelerometer (ST-Microelectronics, LIS331DLH, Geneva, Switzerland), (2) an integrated 3D gyroscope (ST-Microelectronics, L3G200D, Geneva, Switzerland), and (3) a 3D magnetometer (Asahi Kasei Microdevices, AKM8975, Nobeoka, Japan). This device runs, for these tests, iOS 7 operating system with our homemade software. This software is nothing more than a simple graphical interface that collects the data using different algorithms (embedded or given by the OS) using selected frequency and conditions.
All data are saved in a Comma Separated Values (CSV)-like file for future use and statistics. The second Smartphone tested is an Apple iPhone 5S equipped with (1) a 3D accelerometer (Bosch Sensortec BMA220, Gerlingen, Germany), (2) an integrated 3D gyroscope (ST-Microelectronics, L3G4200DH, Geneva, Switzerland), and (3) a 3D magnetometer (AKM, AK8963, Tokyo, Japan). The device also runs iOS 7 operating system and our homemade software. The third Smartphone tested is a Samsung Galaxy Nexus equipped with (1) a 3D accelerometer (Bosch Sensortec BMA220, Gerlingen, Germany), (2) an integrated 3D gyroscope (InvenSens, MPU-3050, San Jose, CA, USA), and (3) a 3D magnetometer (Yamaha, YAS530, Shizuoka, Japan). The device runs Android 4. 3 Jelly Bean operating system with our homemade software.
Xsens Xsens IMU are commonly used in motion sensing applications, and as the gold standard for much scientific research  [14] [15] [16] [17]. Motion Trackers MTx are the selected devices for this experiment. It contains all solid-state miniature MEMS inertial sensors inside (accelerometer, magnetometer and gyroscope). Their static accuracy for roll and pitch is under 0. 5° and for yaw under 1° according to the manufacturer. Data are collected using the given Xsens MT Software.
Filters Implementation Algorithms In order to collect data from the different smartphones, we built, for each operating system, a dedicated application to collect, at a 50 Hz frequency, acceleration force in m/s² , geomagnetic field in µT and angular rate in rad/s for the three physical axes of device (x, y, z). In addition, we collected, at the same frequency, Euler angles values from filters provided by manufacturers, using the methodology described in the respective documentations. Other filters (Madgwick and Mahony) are then calculated from the raw values and converted from quaternion to Euler angles following algorithms described in the next section. Thereby, our dedicated application is able to collect orientation data from three different filters: manufacturer filter, Madgwick algorithm and Mahony filter.
Mahony Filter To implement Mahony Filter on iOS or Android operating systems, sample code provided by Madgwick in C and MatLab languages were used and converted. In both systems, we created a method that takes as arguments all raw data from gyroscope, accelerometer and magnetometer and the previous estimate of orientation. This method is executed at the sampling rate of 50 Hz using the dedicated methods to obtain precision timers for each OS. The following formulas used the same notation as Madgwick  [13]. q B A ˆ describes the orientation of frame B relative to frame A and v A ˆ is a vector described in frame A. Algorithm begins with the normalization of accelerometer measurement and magnetometer measurement. To perform normalization and keep efficiency of computation, the fast inverse square root is used to product our normalized unit vector for both measurements as Equation (1): 2 z 2 y x v + v + v = v 2 / 1 ˆ (1) where x v , y v and z v are axis of frames from sensors.
Madgwick Filter To implement the Madgwick filter on Android and iOS operating systems, sample code provided by Madgwick in C and MatLab languages are also used. A method that takes as arguments all raw data from gyroscope, accelerometer and magnetometer and the previous estimate of orientation was created. This method is executed at the sampling rate of 50 Hz, as Mahony and manufacturer's methods. The algorithm begins with the normalization of accelerometer and magnetometer measurements, using the same methodology as Section 3. 3. 1. The measured direction of the Earth magnetic field in the earth frame and the effect of an erroneous inclination of the measured direction Earth magnetic field are calculated using this same methodology, too. Then, a gradient descent algorithm corrective step is used, as described in  [13].