dataesr/scientific-paragraphs-categorization huggingface dataset:
ANN, a framework from machine learning, a subfield of artificial intelligence, has seen a rapid explosion in range of applications, from object detection to speech recognition to translation. Rather than traditional software, which relies on a set of human-written rules for image classification, a method explored in other studies  [19] , ANN image processing stacks thousands of images together and uses backpropagation, a recursive algorithm to create its own rules to classify images. A previous study has applied ANN-based systems to diagnostic microscopy of STH with moderate sensitivity, using a device of comparable price to a smartphone to image samples and applying a commercially available artificial intelligence algorithm (Web Microscope) to classify the samples. However, such a device requires internet connection to function and was only validated on 13 samples  [20, 21].
Another study has created and patented an ANN-based system to identify T. trichiura based on a small dataset of sample images (n<100)  [22]. However, there is no precedent in current literature for extensive (n>1,000) ANN-based object detection system training for multiple STH species, nor use in smartphones, nor offline use (disconnected from the internet), nor field testing in specimens. This study developed such a system, named Kankanet from the English word network and the Malagasy word for intestinal worms, kankana. This study also uses a smartphone-compatible mobile microscope, or UVC, with a simple X-Y slide stage. As a proof-of-concept pilot study for ANN-assisted microscopy, this project aimed to address two key obstacles to point-of-care diagnosis of STH in rural Madagascar: (1) the lack of portable and inexpensive microscopy, and (2) the limited capacity and expertise to read microscope images.
Object detection ANN-based system training TensorFlow is an open-source machine learning framework developed by Google Brain. Using the TensorFlow repository, this study developed Kankanet, an ANN-based object detection system built upon a Single Shot Detection meta-architecture and a MobileNet feature extractor, a convolutional neural network developed for mobile vision applications  [24, 25]. Based on a dataset of 2,078 images of STH eggs, Kankanet was trained to recognize three STH species: A. lumbricoides, T. trichiura, and hookworm  [26]. 597 egg pictures were taken by a standard microscope and 1,481 were taken by UVC. The efficacy of Kankanet diagnosis was evaluated with a separate dataset of 186 images with a comparable distribution of species and imaging modalities. The detailed breakdown of the composition of these image sets is shown in Table  1 , which shows percentage distributions by species and imaging modality to show concordance in image distribution between training set and evaluation set.
The following hyperparameters were used: initial learning rate = 0. 004; decay steps = 800720; decay factor = 0. 95, according to the default configuration used to train opensource models released online. To improve the robustness of the model, the dataset was augmented using the default methods of random cropping and horizontal flipping. The loss rate was monitored until it averaged less than 0. 01, as shown in Fig  2 , after which the model was frozen in a format suitable for use in a mobile application. Based on this protocol, two models were trained: 1. Model 1, trained with microscope images only (n = 597).
In SPSS, sensitivity and specificity of the UVC reading were calculated for each species with KK, SSTT, and combined analysis. Separate analyses were calculated for different intensities of infection as classified according to WHO guidelines  [4]. Cohen's Kappa coefficient (K) was calculated for each type of fecal processing method to determine comparability to the modified gold standard reading. Results from Kankanet interpretation were compared to visual interpretation of the same images by a trained parasitologist. The two models were evaluated for sensitivity, specificity, positive predictive value, and negative predictive value using SPSS. There were no samples that had missing results from any of the tests run.
The Kankanet models can be improved by developing a larger image dataset, exploring other object detection meta-architectures, and optimizing file size and computational requirements. A greater number and more even distribution of images of parasite species would improve object detection model sensitivity. Standard laboratory processing and diagnosis of STH is extremely time-consuming and expensive and hence, not often practical for rural low-income communities. As smartphone penetrance will only increase in the coming years, medical technology should leverage smartphones as portable computational equipment, as use and distribution of such software requires no additional cost. Because it is able to be attached to smartphones and requires no external power source than the smartphone itself, UVC is a suitable microscopy option for point-ofcare diagnosis.
Whereas standard microscopy ends up costing around 1. 33 USD per person tested, the Kankanet system costs around 0. 56 USD per person tested. ANN-based object detection systems such as the one introduced here can be useful for screening STH-endemic communities in the context of research, mass drug administrations and STH mapping programs. In addition, Kankanet, rather than replacing human diagnosis, could be a useful diagnostic training aid for healthcare workers and field researchers. With sustained use of such a tool, these workers may more quickly learn how to identify such eggs themselves. Limitations of this study include that the UVC used was of insufficient image quality to produce accurate imaging of T. trichiura and hookworm. The Kankanet models employed used a dataset limited to two imaging modalities: standard microscopy and UVC, and with images of only three species of STH; in addition, images for this dataset were only taken of samples prepared under KK conditions, so the efficacy of this system can only be assessed for those conditions.
We conclude that parasitologist interpretation of UVC imaging of SSTT slides can be a field test comparable to standard microscopy of KK for A. lumbricoides. Second, we conclude that ANN interpretation is a feasible avenue for development of a point-of-care diagnostic aid. With 85. 7% sensitivity and 87. 5% specificity for A. lumbricoides, 100. 0% sensitivity and 100. 0% specificity for T. trichiura, and 66. 7% sensitivity, 100. 0% specificity for hookworm, Kankanet Model 2 has demonstrated stellar results in interpreting UVC images, even though it was trained with a limited proof-of-concept dataset. We hope that continued expansion of the Kankanet image database, improved imaging technology, and improvement of machine learning technology will soon enable Kankanet to achieve rates comparable to those of parasitologists.
Acknowledgments We thank all communities involved in this study: the participants of the two villages investigated, the local authorities, the community health workers and facilitators. We would like to acknowledge the Centre ValBio health team:  Fara Rakotoarison ,  Andri Andriami ,  Marie Solange Razanajaza ,  Marie Lizabelle Zaratambatra ,  Ando Rakotoniaina ,  Francis Daniel Lovasoa. We also thank  Patricia Wright  and  Jesse McKinney  of  Centre Valbio  for their mentorship and logistics coordination. We thank the  Institut Pasteur de Madagascar team :  Augustin Razanajatovo ,  Rado Lalaina Rakotoarison , and  Mamy Donah Andrianatoandro. We thank the team at the  Global Health Institute :  Timothy Kobba ,  Jeanne O'Neil ,  Dr. Peter Small ,  Dr. Joe Brew , and  Dr. Pierce Gardner. We thank  Dr.
Patient data This study included consecutive stage IIIB and IV advanced NSCLC patients, treated at the Georges Francois Leclerc Center with nivolumab anti-PD-1 monotherapy in the second or third line of therapy after approval of nivolumab usage by the French health authorities. First or second line consisted of platinum-based chemotherapy with or without targeted therapy. Patients received nivolumab until progression or unacceptable toxicity. Written informed consent was obtained from all patients before enrollment. The hospital institutional review board approved the study in accordance with the principles of Good Clinical Practice, the Declaration of Helsinki, and other applicable local regulations. This study was declared as a sub-study of an ongoing project registered with ClinicalTrials.
Clinical data collection Included patients received nivolumab at the recommended dose (3 mg/kg q2 weeks). The following data were collected from each patient's medical records: age, sex, smoking status, EGFR and KRAS mutational status, ECOG performance status at the time of nivolumab initiation, nature of first-line platinum-based doublet, number of treatment lines prior to nivolumab initiation, date of nivolumab initiation, best RECIST 1. 1 response to nivolumab, date of progression during or after nivolumab, and death from any cause or last follow-up. The database was opened on 1 May 2015 and closed on 1 February 2017. Two physicians (JDF, AL) reviewed all CTscans to validate response to nivolumab. Whole blood of NSCLC patients was sampled before (D0) and after chemotherapy (D15, D30, and D60) on heparinized tubes for leucocyte phenotyping.
Leucocyte population identification and numeration For leucocyte identification by flow cytometry, whole blood removed to heparinized tubes (100 µL) was stained with different antibody cocktails for 15 min at room temperature. After surface staining, 2 mL of red blood cell lysis solution (BD Biosciences) was added for 10 min, centrifuged (350 g, 5 min), and then resuspended in flow cytometry buffer (eBioscience). Foxp3 staining was carried out according to the manufacturer's protocol using the fixation/permeabilization solution (eBioscience). All events were acquired on a BD LSR-II cytometer equipped with BD FACSDiva software (BD Biosciences), and data were analyzed using FlowJo software (Tree Star). The gating strategy is described in Supplementary Figures  1 2 3 4. For myeloid cells, gated on CD14 + CD15 + cells we select monocytes which are positive for HLA-DR and CD33 as monocytic MDSC (mMDSC), CD14 + CD15 + CD33 + HLA-DR -were considered as granulocytic MDSC (gMDSC).
Anti-CD14-Krome Orange (RMO52) and anti-PD-L1-APC (PD-L1) were purchased from Beckman Coulter. All events were acquired by a CytoFlex cytometer equipped with CytExpert software (Beckman Coulter), and data were analyzed using CytExpert software. The rest of the PBMC (0. 25. 10 6 cells/well) were stimulated with plate-bound antibodies against CD3 (OKT3, 0,5 µg/mL, BioXcell) with or without anti-PD-1 (Nivolumab, 10 µg/mL) or anti-Tim-3 (Fisher Scientific, F38-2E2, 10 µg/mL) in 96-well plate flat bottom. Cells were classically harvested on day five (unless otherwise specified) for detection of cytokines by ELISA and surface staining analysis.
Statistical methods Data analysis was performed using the statistical software R (http://www. R-project. org/) and representations were made with Prism 7 (GraphPad, San Diego, CA, USA). Progression-free survival (PFS) was defined as the time from the first day of treatment to the first recorded evidence of disease progression by the RECIST criteria, clinical evaluation or death. Survivors were censored after 6 months. Overall survival (OS) was calculated as the time from the date of the nivolumab treatment start to the date of death and censored after 24 months. Each distribution of absolute counts of subpopulations of cells between patients and healthy volunteers were compared with Wilcoxon rank-sum test and all boxplots were drawn with median, quartiles and Tukey's whiskers. The distributions of proportions of immunosuppressive markers were compared using the test for the comparison of multicomponent distributions and pie charts were drawn based on the recommendation for graphical display, both developed in SPICE software.
Whole Genome Sequencing The two de novo assemblies are quite similar at first sight, i. e. , 3. 47 and 3. 58 Mbs, 28. 00% and 28. 06% GC-content, 3,085 and 3,286 CDS for isolates 508. 17 and 515. 17, respectively. However, scaffolds from isolate 508. 17 contain only one sequence that is almost identical to pCPPB-1 (accession number UWOV01000018; 99. 8% similarity), whereas those from isolate 515. 17 contain 19 sequences that could be reordered against pCPPB-1 (i. e. , UWOU0100067, 108, 087, 023, 106, 043, 047, 005, 018, 003, 036, 090, 086, 068, 013, 006, 014, 007, 101; 99. 7% identity), and 7 additional sequences showing strong BLAST similarity (e. g. , >99. 7% identity) with pJFP838D and pD13118_cpe (i. e. , UWOU0100008, 009, 016, 022, 024, 026, 034). This indicates that the strain 515. 17 contains two additional plasmids related to pJFP838D and pD13118_cpe, or a plasmid combining these sequences.
5 pA in order not to damage the samples. Synchrotron HRXRD. Diffraction patterns of bleached powdered samples were acquired in the ID-22 beamline at the European Synchrotron Radiation Facility (ESRF), at wavelength of 0. 399 Å and 0. 496 Å. The diffraction patterns were analyzed by Rietveld refinements using GSAS II software  57. Nano-HT. Nano-tomography datasets were obtained at the ID-16B beamline of the ESRF. Bleached samples of dorsal arm plates (DAPs) were sectioned with a scalpel, and small fragments containing several lenses were mounted on the tip of a metal needle and fixed with epoxy. To isolate a single lens, the small DAP fragment was fixed onto an automatic stage equipped with a CO 2 laser that cut the superfluous parts in order to isolate one lens. The samples were positioned at four different distances between the focal plane of the X-ray beam, which focused down to a spot size of 50 × 50 nm 2 using two multilayer-coated Si mirrors in Kirkpatrick-Baez (KB) configuration operated under pink beam mode at 29.
6 KeV and 17. 5 KeV, and the camera. While the sample rotated (360°), 2001 high-resolution images were collected. The datasets were then first processed using a phase retrieval algorithm based on ESRF in-house software using the GNU Octave programming environment  58. In a second step, 3D volumes using the filtered phase maps were reconstructed using back projection with the ESRF python software PyHST2 59. XRF tomography. XRF tomography was also performed at the ID-16B beamline of the ESRF, with the same focusing optics. In this set-up, the sample was mounted on a rotating stage and horizontal line-scans were performed using the focused beam for each rotation interval until a 360°rotation is complete. Measurements were performed at 17. 5 KeV. The XRF-emitted signal was collected by detectors situated perpendicular to the incident beam, then fitted and processed using the Python Multi-Channel Analyzer (PyMCA) program  60.
Elemental sinograms from all detectors were combined and were then further reconstructed by use of the XRDUA software  61  that uses an iterative algorithm. The obtained 2D elemental distribution slices were finally partially corrected for self absorption. FIB. We used the FEI Helios NanoLab G3 UC Dual Beam FIB in our experiment. Ion-milling was performed utilizing high gallium milling currents and accelerating voltage for 1 min 40 s at 0. 23 nA. These conditions allowed ~400 nm milling depth, as measured from close-ups images of the cross-section of the milled box. Samples were embedded in an epoxy resin and polished using a Struers Rotopol-31 + RotoForce-4 polishing machine and successively fine diamond lapping films. Mg-calcite syntheses. Solutions of 0. 2 M Na 2 CO 3 and solutions of 0.
Acknowledgements This project has partially received funding from the  European Union   Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 642976 -NanoHeal Project. We are grateful to  Dr Yaron Kaufman  for his help collecting HRTEM data. We acknowledge the  European Synchrotron Radiation Facility  for provision of synchrotron radiation facilities at  ID22  and  ID16B.
Although the analysis presented in this Letter is expected to be insensitive to such effects, the magnetic-field polarity is reversed periodically during data taking to mitigate the differences of reconstruction efficiencies of particles with opposite charges. Data sets corresponding to about one half of the total integrated luminosity are recorded with each magnetic-field configuration. The online event selection is performed by a trigger, which consists of a hardware stage based on information from the calorimeter and muon systems, followed by two software stages. In the first software stage, events used in this analysis are selected if at least one track has large transverse momentum and is incompatible with originating from any PV, or if any two-track combination forming a secondary vertex, consistent with that of a D 0 decay, is found in the event by a multivariate algorithm  [56, 57].
e. , the minimum distance of its trajectory to the nearest PV; the transverse momenta of the D 0 decay products, the significance of the distance between the D 0 and B decay vertices; the invariant mass m(D 0 µ) and the corrected mass m corr. To suppress background from b-hadron decays to ccπ ± X (ccK ± X), where the cc resonance decays to a pair of muons, D 0 candidates are vetoed if the invariant mass of the µ ∓ π ± (µ ∓ K ± ) pair, where the pion (kaon) is given the muon mass hypothesis, lies within a window of about ±50 MeV/c 2 around the J/ψ or ψ(2S) known masses. The data sample includes events with multiple D * + and B candidates. The majority of these events contain the same reconstructed D 0 meson combined with different tagging pions or muons. When multiple candidates are present in the event, only one is kept randomly.
In particular, the result challenges predictions based on first-principle QCD dynamics  [19, 33]. It complies with predictions based on flavor-SU(3) symmetry, if one assumes a dynamical enhancement of the penguin amplitude  [16, [26] [27] [28] [29] [30] 32]. In the next decade, further measurements with charmed particles, along with possible theoretical improvements, will help clarify the physics picture, and establish whether this result is consistent with the SM or indicates the presence of new dynamics in the up-quark sector. behind the multiple open-source software packages on which we depend. Individual groups or members have received support from AvH Foundation (Germany); EPLANET, Marie Sk lodowska-Curie Actions and ERC (European Union); ANR, Labex P2IO and OCEVU, and Région Auvergne-Rhône-Alpes (France); Key Research Program of Frontier Sciences of CAS, CAS PIFI, and the Thousand Talents Program (China); RFBR, RSF and Yandex LLC (Russia); GVA, XuntaGal and GENCAT (Spain); the Royal Society and the Leverhulme Trust (United Kingdom); Laboratory Directed Research and Development program of LANL (USA).
In the Mediterranean region, drier and hotter summers are leading to more likely and severe wildfires. The authors propose an innovative approach for situational awareness by giving life to maps and exploiting interoperable GIS, hazard models, simulations, and interconnection analysis processes aimed to enhance preparedness and strengthen the resilience of responding organizations. The information related to a virtual city and its countryside has been implemented in the terrain of simulation systems. The TIGER wildfire model software has been adapted to a scenario where districts, refugee camps and critical infrastructures can be impacted by a fire and has been linked to a smoke dispersion model, and associated impacts to the electricity network and roads. The transfer of computed fire propagation and combustion data to the AI-powered SWORD simulation enable more accurate computing of damage and loss.
In SWORD, civil protection, military assets and humanitarian actions can be performed for training and operation preparation. The simulation data about fire and assets' deployments can feed a web app map or a command and control system, thus providing situational awareness for optimal decision-making, and analysis about people in danger, network interconnections and potential service disruption. Disaster managers and commanders can interact with simulated assets performing their chosen courses of action and analyse the outcomes. In conclusion, tests in a wildfire case study demonstrated a high level of interoperability among those systems and the possibility to provide updated situational awareness leading to better emergency preparedness and critical infrastructure resilience building, finally contributing to save more lives.
TIGER combustion/convection/diffusion processes are described by the formula: (3) where the quantities:  T(P,t) is the temperature scalar field;  v(P,t) is the wind vector field, function of space and time;  where k is the air conductivity, V is the volume of the cell, m(P) is the air mass in the cell, c is the specific heat of the air. ( 4 ) is the vertical convection heat transfer coefficient, being the T ∞ ambient temperature f(t,T) is the heat source due to combustion in the cell. TIGER models produce results as time series of standard GIS outputs such as mapable data and kml, which can be displayed in the software user interface using Google Earth technology for visualisation. For the present research, the TIGER model was extended to provide a new output: the maximum heat per unit area (kJ/m 2 ).
Geographic Information System Spatial and network analysis can be integrated with historical data sets, disaster models and simulations  (David et al. , 2017). In addition, availability of data on the distribution of population, during the night (using available census data) and during work time (considering the commuters' flows to reach work places) can provide the ability to support quantitative analysis of disaster's effects on people. The GIS provides a central infrastructure for crisis management in terms of a geospatial database, analytical models and visualization tools, addressing the three dimensions: immediacy, relevancy, and sharing  (Cai et al,. 2006 ) but it is coupled with the dynamic simulation provided by the interoperable simulation systems  (David et al. , 2018). The disaster damage is usually measured in physical units (e.
MASA SWORD Simulation An innovative, Artificial Intelligent (AI) powered constructive simulation platform MASA SWORD, has been chosen, with the ability to be populated by emergency and military assets and show in real-time the impact when a disaster strikes. The propagation of the disaster information is one of the most important aspects and SWORD can work with external disaster propagation models. Since the propagation of the disaster very much depends on the geography, to make its output as accurate as possible, SWORD needs as much as precise geospatial data as possible for generating the simulator's terrain. The "de facto standard" ESRI shapefiles can be imported to represent basic types of terrain. The SWORD terrain generation process regroups vector data layers and delimitation of terrain area and builds the land use (forest, urban, farms, etc.