dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, it includes a simple stepby-step easy procedure making it able to rapidly evolve with changes with no need to deeply modifiy it (for example the addition or removal of pathogens to be monitored by the system). Finally, EPIMIC is currently able to geographically localize the patients according to their villages or the health structures they visited, facilitating the on-site investigations of the validated abnormal events. The local set-up of the software was strongly facilitated by the fact that the two POC-L have been implemented and routinely used from some time (Tables  1  and 2 ) with laboratory technicians trained to fill two preformatted Microsoft Excel databases, avoiding bad quality data and its consequence on syndromic surveillance  [12]. Moreover, the two sites are regularly supplied with reagents necessary to perform the different POC tests, which is crucial for continuous detection and monitoring of pathogens  [8, 9].
Nevertheless, further improvements are needed to enhance our EPIMIC syndromic surveillance network. Indeed, as mentioned before, EPIMIC has been implemented using the Microsoft Excel software, and abnormal events are currently detected using statistical tools based on the historical mean of the data +/-two standard deviations. Although this strategy allows the rapid development of the system and was valuable in terms of human and economic resources  [11] , this is not suitable for the monitoring of big databases including data with seasonal variations in pathogen isolation like flu and P. falciparum, or in case of rare pathogens. These issues are planed to be addressed with the development of a local web-based plateform including more sophisticated statistical tools for the accurate monitoring of abnormal events  [22] [23] [24].
patients per week). 739 deduplicated patients-samples couples were found to be positive for at least one of the tested pathogens. The retrospective analysis of the Dielmo and Ndiop POC laboratory historical database with EPIMIC virtually allowed to identify 443 alarms, 373 before April 2016 and 70 since April 2016. P. falciparum was the pathogen the most cited by alarms emitted by EPIMIC (47 alarms, 2,635 tested samples, and 301 positive samples), followed by R. felis (43 alarms, 2,411 tested samples, and 21 positive samples), flu (39 alarms, 2,237 tested samples, and 135 positive samples), C. burnetii (35 alarms, 2,513 tested samples, and 14 positives), and Borrelia spp. (34 alarms, 2,577 tested samples, and 126 positives) (Table.
Model description The CABLE global land surface model is documented by  Wang et al. (2011) (CABLE1. 4b ) and  Kowalczyk et al. (2013) (CABLE1. 8). Briefly, CABLE consists of five components: (1) the radiation module describes radiation transfer and absorption by sunlit and shaded leaves; (2) the canopy micrometeorology module describes the surface roughness length, zero-plane displacement height, and aerodynamic conductance from the reference height to the air within canopy or to the soil surface; (3) the canopy module includes the coupled energy balance, transpiration, stomatal conductance, and photosynthesis of the sunlit and shaded leaves; (4) the soil module describes heat and water fluxes within soil (six vertical layers) and snow (up to three vertical layers) and at their respective surfaces; and (5) the ecosystem carbon module accounts for the respiration of stem, root, and soil organic carbon decomposition.
CABLE2. 0 includes full biogeochemistry available via the CASA-CNP module  (Wang et al. , 2010) , and differs otherwise from CABLE1. 8 only by small bug fixes and by changes to the vegetation optical properties, as described by  Lorenz et al. (2014). CABLE has been benchmarked off-line (e. g. Best et al. , 2015; Zhang et al. , 2013; Zhou et al. , 2012)  and in coupled environments  (Kowalczyk et al. , 2013).
We have shown that a physically accurate description of soil evaporation available via the SLI soil model significantly enhances predictions of total ET compared to the standard soil model in CABLE, in which supply-limited evaporation is an empirical function of upper layer soil moisture (Eqs. 10-12), and tends to be over-estimated, particularly in the absence of litter effects. We have also shown that when the standard model configuration is adapted to include the new drought response and the effect of litter on soil evaporation, it performs almost as well (at the monthly timescale) as when the full SLI model is implemented. Future work will entail merging the improvements demonstrated here with the new hydrological parameterizations in CABLE  (Decker, 2015) , and a new module for woody vegetation demography and landscape heterogeneity mediated by disturbance  (Haverd et al.
Acknowledgements. This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (U. S. Department of Energy, Biological and Environmental Research,Terrestrial Carbon Program  ( DE-FG02-04ER63917  and  DE-FG02-04ER63911 )),  AfriFlux ,  CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FLUXNET-Canada  (supported by  CFCAS ,  NSERC ,  BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOSSiberia , and  USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEu-ropeIP, FAO-GTOS-TCO, iLEAPS,  Max Planck Institute for Biogeochemistry , the  National Science Foundation ,  Tuscia University, Université Laval and Environment Canada , and the  U. S.
Department of Energy , and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California, Berkeley, and University of Virginia. Vanessa Haverd 's contribution was made possible by funding from the  Australian Climate Change Science Program. Edited by: G. A. Folberth Reviewed by:  E. Blyth ,  H. Zheng , and one anonymous referee.
During the 10 days of field sampling, covering two calendar years and two study sites, translation speeds of between 1 and 15 m s -1 were measured for more than 100 dust devils. Balme et al. (2012)  found that dust devils translated in the same direction as ambient wind, and that the dust devil forward speed correlated well with ambient wind speed. In fact, they found that dust devils travel at about the same speed as the boundary layer winds a few tens of metres above ground. Interestingly, no correlation of translation speed with dust devil diameter was found. Therefore,  Balme et al. (2012)  conclude that dust devil forward motion is a good proxy for the wind field, and that dust devil motions is governed almost solely by local wind patterns-a finding that could prove to be important for Mars, where few meteorology data are available.
It is possible that dedicated LIDAR studies could also gather large quantities of data but to date no such attempts have been made. To date, the largest published study is that of  Ryan and Carroll (1970) , who sampled 80 dust devils. Another more recent large dataset exists (see preliminary report in  Metzger et al. 2011)  and includes more than 50 measurements from mobile in-situ sampling. However, these data are yet to be formally published. Aside from these two examples, individual studies usually report fewer than twenty encounters  (Balme and Greeley 2006). Both in-situ and remote sampling of wind speeds share the second problem-smaller and/or shorter-lived dust devils are harder to sample, and therefore are likely to be under represented in the data. This problem is likely to affect mobile "chase" strategies most significantly, as only the larger and longer-lived dust devils can be caught and penetrated to acquire data.
Dust Load Due to the fast-changing environment within dust devils, the concentrations of airborne dust and larger particles within dust devils are difficult to measure. The most complete study of particle loading in dust devils is that of  Metzger et al. (2011) , who used in-situ sampling on a mobile platform. Few other studies exist for terrestrial dust devils: a preliminary LI-DAR observation at 100 m height  (Renno et al. 2004 ) provided an estimate of dust load of ∼ 100 mg m -3 and there are reports of aircraft in-situ sampling of dust devils at 140 and 300 m height  (Gillette and Sinclair 1990 ), but only flux data are given, not particle load. Other datasets exist for martian dust devils (e. g. Greeley et al. 2006 Greeley et al. , 2010)) , but  Metzger et al. (2011)  provide the main source of dust load data for Earth.
The 'expendable' small pressure loggers advanced by  Lorenz (2012b)  were deployed at Eldorado playa near Boulder City, NV, and showed promise. Three such loggers were operated for a one-month period in June 2012  (Lorenz and Lanagan 2014) , generating the first statistically-robust terrestrial dataset of fixed-station encounters (and allowing comparison of their power-law statistics with those at Mars-see  Lorenz et al. 2016 , in this issue). Note Fig. 9  A solar logger encounter with a large dust devil on Eldorado in summer 2013 (from  Lorenz and Jackson 2015). A broad pressure drop lasting about 20 s is seen; this pressure drop of about 0. 7 mbar is typically encountered not more than once in a few weeks. The solar flux measurement at right shows that the devil was heavily dust-laden, blocking about 30 % of the light: the two prominent troughs are probably the wall of the dust column.
In addition to the ASI/MET's thermal wind sensor, Pathfinder also included three mechanical 'wind socks' mounted at three heights along the ASI/MET mast  (Sullivan et al. 2000). IMP provided images of the solid, metallic, inverted-cone shaped socks and their tilt orientation from which wind speed and direction were derived using pre-flight calibration information. Twelve image sequences obtained over a time period of ∼ 100 seconds several times per sol provided the dataset from which wind vector derivation was attempted. Data from only four of the sampled 42 sols provided imaging indicative of minimum wind speed (few meters per second) necessary to overcome windsock inertia and induce detectable windsock deflection  (Sullivan et al. 2000). Thus, Pathfinder's windsock experiment did not aid in advancing understanding of the dynamic components of martian dust devils.
Methods REMD simulation. We explored the conformational ensemble of Aβ 42 peptide using the REMD technique  60  implemented in the GROMACS molecular modeling package  61, 62. Simulations were performed at the all-atom level with the OPLS-AA force field  63  in explicit solvent using the SPC/E water model  64. The OPLS-AA force field was chosen because previous studies showed that it generated Aβ 42 conformations in good agreement with NMR data  37  and is suitable for simulating the aggregation of several Aβ fragments  65. Although these previous studies used the TIP3P water model, SPC/E was recently shown to generate structures of the fragment Aβ 21-30 similar to those obtained with TIP3P, when using OPLS-AA  66. The crystal structure 1Z0Q  51  was taken from the Protein Data Bank as a starting conformation.
80  to provide the secondary chemical shift values. The Aβ 42 experimental chemical shifts were taken from Table  S1  of the Supporting Information of the article by Wälti et al. , who performed the NMR measurements at 277. 15 K 43. It should be noted that our results extracted from trajectories generated at temperatures around 300 K, cannot strictly be compared with NMR data which were collected at low temperatures (273. 3 and 277. 15 K). However, the Aβ monomer conformations can hardly be characterized by experiments at room temperature, due to its high aggregation propensity, and only experimental data collected at low temperature were available in the literature to assess the extent of our exploration of the peptide conformational space. The Aβ 42 peptide secondary structures were analyzed using the STRIDE program  41, 42.
SCORPION was able to successfully reproduce the quaternary structure of several protein-protein complexes, starting from the bound conformation of the two partners, by using an energy minimization procedure without any bias. PTools performs systematic rigid-body docking, starting from initial regular positions and orientations of the ligand around the receptor surface, at a distance slightly larger than its largest dimension. The docking procedure consists in minimizing the interaction energy between the two partners, using the ligand six translational and rotational degrees of freedom. The minimized complex conformations were finally clustered by similarity and ranked according to their interaction energies. The lowest energy structures of Aβ 42 dimers, obtained by docking, were analyzed regarding the polymeric assemblies that can be generated if the protomer-protomer interface was repeated, using the Heligeom module in PTools  59.
These requirements are: security and privacy, interoperability, adaptation, and personalisation. To elicit a wide spectrum of user reactions, we created these animated videos based on the ContraVision empirical methodology  [23] , which specifically supports the elicitation of end-user requirements for controversial or futuristic technologies. Our deployment of ContraVision presented our pilot study subjects with an equal number of utopian and dystopian scenarios, derived from the food security domain, and described them at different levels of granularity. Our synthesis of the preliminary empirical findings suggests a number of key requirements and software engineering research challenges in this area. We offer these to the research community, together with a rich exemplar and associated scenarios available in both their textual form in the paper and as a series of animated videos (http://sead1.
No more so is this apparent than in the context of the Internet of Things (IoT). The IoT presents a major new opportunity for research in adaptive software engineering. The IoT promises a future where devices and people interact seamlessly with one another, and with their environment, to access targeted, optimised, and adaptive services, due to changes in needs, desires, and context. Realising the IoT requires addressing many challenges, including the support for software adaptation  [1, 10]. In this paper we propose an exemplar, called Feed me, Feed me, to explore the challenges and requirements of modern IoT systems. The purpose of the exemplar is to capture a variety of requirements for engineering adaptive software for the IoT. Feed me, Feed me is an IoT-based ecosystem to support food security; that is to ensure sufficient, safe, and nutritious food to the global population  [13].
To maximise user feedback, we created these animated videos based on the ContraVision empirical methodology  [23]. ContraVision uses two comparable scenarios that highlight the positive and negative aspects of the same situation. The goal of ContraVision is to elicit a wide spectrum of user reactions for potentially controversial or futuristic technologies. We developed an equal number of positive and negative scenarios and presented these scenarios to groups of people in order to help us elicit the requirements and identify the research challenges for engineering adaptive IoT-based software. The initial results of our pilot study confirmed our initial set of identified requirements and research challenges, emphasised some social aspects that need to be considered in the IoT technology when used to support food security, and demonstrated some trade-offs between control and automation necessary for these technologies to be adopted.
FEED ME, FEED ME: A PILOT STUDY The ContraVision empirical methodology  [23]  uses two identical scenarios that highlight the positive and negative aspects of the same situation. The goal of ContraVision is to elicit a wide spectrum of user reactions to potentially controversial or futuristic technologies. To pilot the suitability of our exemplar, we developed an equal number of positive and negative scenarios and presented them to different groups of people in order to help elicit requirements and identify the research challenges of engineering adaptive IoT-based software. In the following subsections we describe the scenarios, how we conducted our preliminary study, and our initial empirical findings.
ContraVision Scenarios Based on the ContraVision methodology, we produced two videos, each of which depicts an end-to-end IoT food security system involving the four levels of granularity described in Figure  1. The main protagonist of the videos is Charlie, a 30 year old father of two children. Charlie and his family use several IoT-based software systems in their daily lives, including sleep monitoring, activity and calorie tracking devices, and smart appliances. We present below the various systems associated with different parts of the scenarios together with a description of the positive and negative scenarios associated with those systems. We elaborate each scenario describing the positive and negative perspectives. Analyse Me. The first scenario highlights Charlie's relationship with the technology and how it may impact his behaviour.
Here we describe a method that we call ClusterViSu, which is based on Voronoi diagrams and tessellation of the individual fluorescence events for visual and quantitative clustering analysis of super-resolution microscopy data. When this manuscript was under review a similar method appeared using the same concept of segmentation based on Voronoi diagrams, called SR-Tesseler  17. While ClusterViSu comprises additional features both studies complement each other; a more detailed comparison of the two methods including similarities and differences is made towards the end of this manuscript. A Voronoi diagram, also known as Dirichlet decomposition, is a tessellation where a tile corresponding to a given data point is a locus of all points of space closest to this data point  18. Applications of Voronoi tessellations are found in various fields from mathematics to natural sciences  19  , e.
Thus, we define a cluster as a collection of neighboring Voronoi zones with areas smaller than a given threshold. To test whether an experimental data set is significantly different from a spatially random distribution that has fluctuations of local density for a finite number of points, we performed Monte-Carlo simulations of randomly distributed points through the same experimental area using the same average density (Fig. 2 ; see methods). The probability distribution functions were calculated for experimental and for randomized data (including the mean value and associated 95% confidence interval; Fig. 2D ). The comparison of distributions of Voronoi cell sizes of clustered and randomly distributed datasets reveals three characteristic regions (Fig. 2D ): (i) small cell areas with an occurrence higher than in the random data set (cells describing clusters), (ii) intermediate regions with lower occurrence (containing Voronoi cell sizes similar to those of randomly distributed data), and (iii) regions of large Voronoi cells with higher occurrence corresponding to low-density event regions between clusters.
30; https://github. com/andronovl/SharpViSu) which includes other standard tools such as corrections for drift and chromatic aberration and resolution estimation by FRC. In the future, the approach could be extended to 3D super-resolution data because Voronoi diagrams are also defined for multi-dimensional cases. To our knowledge, this is the first application of Voronoi tessellation for the clustering analysis of super-resolution microscopy data, together with a related method published by Levet et al. 17  while the present manuscript was under review. To quantify differences between ClusterViSu and SR-Tesseler we run some simulations (see methods; Fig. S6 ). When compared with SR-Tesseler 17 , our method shows a more complete detection of the cluster numbers and retrieval of their size and homogenous shape (Fig.
5). The super-resolution experiments were performed on a Leica SR GSD system built on a base of DMI6000 B inverted wide-field microscope. We used the HCX PL APO 100× /1. 47 Oil CORR TIRF PIFOC objective with a 1. 6× magnification lens that provides an equivalent pixel size of 100 nm on the Andor iXon3 DU-897U-CS0-#BV EMCCD camera with a field of view of 18 × 18 μm in super-resolution mode. Continuous wave fiber lasers (MPBC Inc. , 488 nm 300 mW, 532 nm 1000 mW, 642 nm 500 mW) and a diode laser (405 nm 30 mW) were utilized for excitation. The microscope is also equipped with a suppressed motion (SuMo) sample stage, which reduces drift but does not eliminate it (typical values 20-50 nm over 10 min). The residual drift was corrected by data processing (see below). The samples were first illuminated with the 100% power of the appropriate laser to quickly send the fluorophores into the dark state.
The acquisition started after beginning of observation of single-fluorophore events ("blinking") that corresponded to the drop of the correlation value of consecutive frames to approximately 0. 2 in the corresponding wizard in the LAS AF software. The time of exposition of a frame was 6. 34 ms (H2B and RNA Pol II data) or 50 ms (β -tubulin and TPR data); the electron multiplying gain of the camera was 300 for H2B, RNA Pol II, TPR, 150 for β -tubulin-Alexa 647 and 63 for β -tubulin-Alexa 555; the laser power during the acquisition was 30% (H2B), 50% (TPR) or 100% (RNA Pol II and β -tubulin). After a few minutes, as the number of blinking evens dropped, the sample started to be illuminated additionally by a 405 nm laser with gradual increase of its intensity in order to keep a nearly constant rate of single-molecular returns into the ground state.
Data processing. The localization and fitting of single-molecule events were performed in real time during acquisitions in Leica LAS AF 3. 2. 0. 9652 software with the "center of mass" fitting method. To reduce the number of localizations of the same fluorophore and improve localization precision the data were processed by averaging the coordinates of consecutive events within a radius of 50 nm around each localization. Subsequently, the data were processed in SharpViSu (ref. 30, in press; https://github. com/andronovl/SharpViSu). The drift was detected and corrected using cross-correlation-based approach. Briefly, the dataset was divided on several consecutive subsets, from each of them a histogram image with pixelation of 20-40 nm was build, the shift between these images was detected with subpixel precision and interpolated linearly throughout intermediate frames.
The shift was then subtracted from every frame. The procedure was repeated iteratively 4 times assuring absence of detectable residual drift. For correction for the chromatic aberrations, the aberrations were detected beforehand with Tetraspeck multi-color beads, interpolated through the field of view with 2-order polynomial functions and subtracted from the molecular coordinates obtained from either 488 nm or 532 nm imaging channels. Regions of interest (ROI) for Voronoi analysis were selected manually allowing faster computations and more homogeneous distributions compared to the entire field of view. The following analysis was performed in Matlab using customized code that is included as modules in the ClusterViSu software. Voronoi diagrams (vertices of polygons and connectivity order) were retrieved with either 'voronoi' or 'voronoin' functions.