dataesr/scientific-paragraphs-categorization huggingface dataset:
71. These comparisons are presented in  Menut et al. (2013)  and  Mailler et al. (2017). The CHIMERE aerosol module is responsible for the simulation of physical and chemical processes that influence the size distribution and chemical speciation of aerosols  (Bessagnet et al. , 2008). This module distributes aerosols in a number of size bins; here 10 bins range from 40 nm to 40 µm, in a logarithmic sectional distribution, each bin spanning over a size range of a factor of 2  (40-20, 20-10 µm, etc. ). The module also addresses coagulation, nucleation, condensation, and dry and wet deposition processes. The basic chemical speciation includes elemental carbon (EC), sulfate, nitrate, ammonium, SOA, dust, salt, and PPM (primary particulate matter other than ones mentioned above).
This instrument has the same general structure of an AMS (aerosol mass spectrometer), with the difference that it was developed specifically for long-term monitoring. A high-resolution time-of-flight AMS (HR-ToF-AMS, Aerodyne Research Inc. ;  Decarlo et al. , 2006 ) operated under standard conditions (i. e. , temperature of the vaporizer set at 600 • C, electronic ionization (EI) at 70 eV) was deployed with a temporal resolution of 8 min to determine the bulk chemical composition of the non-refractory fraction of the aerosol for the Es Pinar site. AMS data were processed and analyzed using the HR-ToF-AMS analysis software SQUIRREL (SeQUential Igor data RetRiEvaL) v. 1. 52L and PIKA (Peak Integration by Key Analysis) v. 1. 11L for the IGOR Pro software package (Wavemetrics, Inc. , Portland, OR, USA).
Other measurements For the validation of meteorological parameters, along with the meteorological surface measurements performed at ChArMEx stations, radiosonde data for three stations in the western Mediterranean basin were used for simulationobservation comparisons for meteorological parameters. The radiosondes are performed by Météo France at the two stations of Ajaccio, France (41 • 55 5 , 8 • 47 38 ), and Nîmes, France (43 • 51 22 , 4 • 24 22 ), and by AEMet at Palma, Spain (39 • 36 21 , 2 • 42 24 ). Each day two balloons at about 00:00 and 12:00 UTC are available for each station; a total of 96 balloons are included in the comparisons. Ajaccio and Palma are coastal stations, but Nîmes is farther from the coast compared to the other two stations. Each day two balloons were launched at about 00:00 and 12:00 UTC at each station, and a total of 96 balloons are included in the comparisons for an altitude between the surface and 10 km.
Meteorology evaluation Meteorological output of the mesoscale WRF model at different resolutions has been used as input to the CHIMERE CTM. The meteorological data used by CHIMERE were compared to various meteorological observations such as radiosondes and surface observations at the measurement sites. Detailed results of these comparisons are given in the Supplement (Sect. SI4). Here, a short overview of the results and the implications for the model ability to simulate transport to the measurement sites is given. Comparisons for temperature, a basic variable to control the quality of meteorological simulations, show a good correlation for radiosonde comparisons (typically from 0. 60 to 0. 85 for hourly values) and a low bias (typically from -1. 16 to -0. 39 • C) for the three sites of Palma, Nîmes, and Ajac-cio.
Wind speed shows a good correlation at higher altitudes, and also near the surface for Nîmes and Palma for radiosonde stations, while for the Ajaccio station the sea-land breezes are probably not well represented in the model. For Es Pinar, the coastal feature of the site is difficult to take into account in a 10 km horizontally resolved simulation and leads to larger errors. Ground-based meteorological measurements were also compared at both sites (SI4). At Ersa, the correlation in finer domains is better than that of D10 for wind speed (typically around 0. 66 versus around 0. 60). For the E-OBS network (surface data sets provided by European Climate Assessment and Dataset, ECAD) project for monitoring and analyzing climate extremes,  (Haylock et al. , 2008; Hofstra et al. , 2009)  comparisons (SI4) also show a good correlation and a low bias for temperature (correlation of 0.
WRF meteorological model. E-OBS data sets are acknowledged. The thesis work of Arineh Cholakian is supported by ADEME, INERIS (with the support of the French ministry in charge of ecology), and via the ANR SAF-MED project. Eric Hamonou and the ChArMEx team are acknowledged for their great help in organizing the measurement campaign at Ersa, as well as the MISTRALS management and accounting team. Jorge Pey is currently granted with a Ramón y Cajal research contract (RYC-2013-14159) from the Spanish Ministry of Economy, Industry and Competitiveness. This work was performed using HPC resources from GENCI-CCRT (grant 2017-t2015017232). Edited by: Jean-Luc Attié Reviewed by: two anonymous referees.
The Orchard Field Test Site The Orchard Field Test Site in south-western Idaho is used for long-term simulations of soil water dynamics  (Flerchinger & Hardegree, 2004 ), receiving about 293 mm of precipitation annually. The comparison at the Orchard site is seen first as a test of SLI under realistic freeze-thaw conditions and second as a test of the influence of snow cover on the dynamics of soil temperature and moisture. Here we use data from an experimental bare soil plot with loamy sand from winter 1997 to spring 1998 as presented in  Flerchinger et al. (2012). The data exhibits four freeze-thaw cycles that penetrate up to 30 cm into the soil, followed by a period with relatively little change in near surface temperatures (Figure  4a ). This was due to insulation by a light snow cover at the beginning of 1998.
SLI coupled to CABLE was used in these simulations so that SLI interacts with the aerodynamic behavior of CABLE under stable, winter-time conditions. Aerodynamic formulations for the stable atmosphere were recently improved in CABLE  (Haverd et al. , 2016)  and influence greatly the exchanges of heat between the snow surface and the atmosphere. Soil properties were provided by G. N. Flerchinger (personal communication, 2013). CABLE-SLI reproduces very well the freezing depth of about 30 cm (data not shown). But here we present the results of the uppermost two centimeters of soil to illustrate the physical behavior of the freeze-thaw and snow formulations (Figure  4 ). Installed sensors measured liquid not total soil moisture under freezing conditions. Liquid soil moisture is changing quickly in 2 cm depth when temperatures reach the freezing point just below 08C.
SLI solves the coupled equations for snow and soil simultaneously while also solving explicitly for the surface conditions (Appendix D). It does, therefore, not split the grid cell into snow covered and uncovered fractions in order to preserve energy. So all available energy has to melt the snow and cannot heat some uncovered part of the grid cell. Snow water equivalent SWE also follows the observed interannual variations but the absolute value seems to be underestimated (Figure  5b ). This could be due to the formulation of fresh snow density. Existing formulations of the snow density of new snow rely heavily on data sets at single sites and do not take into account density differences with, for example, continentality  (Schmidt & Gluns, 1991). However, thermal insulation of the snow pack works very well (Figure  5a ).
Snow Distribution on the Northern Hemisphere Figure  6  shows the yearly maxima of snow water equivalent SWE and snow depth on the Northern Hemisphere above 458N. Estimated SWE are based on satellite data (ESA DUE GlobSnow,  Takala et al. , 2011)  so that comparison can be done on the actual time span. Snow depth climatology is estimated from groundbased observations starting in the 1950s until the mid-1980s  (Foster & Davy, 1988). Satellite-based and database-derived observations of SWE and snow depth, respectively, are hence not as tightly linked as the model results. SWE and snow depth show very similar patterns in the CABLE-SLI simulations down to local snow spots such as the west coast of Canada or the Kamchatka Peninsula. CABLE-SLI compares favorably with the independent estimates in that it shows the same patterns across the hemisphere.
The magnitudes are very similar but exhibit local differences. CABLE-SLI compares better with the satellite-based SWE estimates than with the snow depth climatology. There is no clear trend of low SWE in CABLE-SLI as suggested by the comparison in the Valdai watershed (Figure  5b ). It must be noted though that the pattern is strongly determined by incoming snow. The CABLE-SLI runs were forced with the WFDEI data set  (Weedon et al. , 2014). In contrast, comparisons of the above snow depth and SWE estimates with CABLE-SLI forced by GSWP-2 were rather poor (results not shown) because of large biases in input precipitation  (Decharme & Douville, 2006).
, 2013; Law et al. , 2017; Ziehn et al. , 2017) , providing it an implicit numerical solution. Individual parts of the model, such as the soil freeze-thaw formulation, as well as interactions between model components, such as the insulation of snow upon soil temperatures, were tested with a large variety of data from theoretical predictions (section 3. 1), laboratory experiments (section 3. 2), and field observations (sections 3. 3, 3. 4 and 3. 5). Performance of stand-alone SLI highlighted the detailed and physically accurate behavior of SLI in all test cases. A CABLE-SLI model application on continental scale was demonstrated for the Northern hemisphere, which illustrated also the limits of the data-model comparison. CABLE-SLI calculates very good permafrost extent (section 3. 7), especially in comparison with other CMIP5 models  (Koven et al.
Coupling CABLE-SLI in the future with a carbon cycle model that vertically resolves soil carbon production will allow detailed studies of couple carbon-permafrost dynamics in a changing climate. Appendix A: Nomenclature Subscripts and superscripts used here have the following meanings: a 5 atmosphere; adv 5 advection; i 5 ice; j 5 j-th soil layer; l 5 liquid; melt 5 melt water; par 5 soil particles; prec 5 precipitation; sat 5 saturation; soil 5 soil; sol 5 solute; snow 5 snow; t 5 at time t; v 5 vapor; H 5 heat; 0 5 snow layer; 1 5 first soil layer incl. pond vertical heat flux within soil column, J m 22 s 21 q evap evaporative flux from soil or snow surface to atmosphere, m s 21 q evap,pot atmospheric demand for evaporative flux, m s 21 q H vertical conductive heat flux within soil column, J m 22 s 21 q H,0 vertical conductive heat flux at snow/soil interface, J m 22 s 21 q H,0,adv combined heat flux due to both conduction and advection at snow/soil interface, J m 22 s 21 q r H;adv;j combined heat flux due to both conduction and advection at the interface of soil layers j and j11, and at the fraction r through the time-step, J m 22 s 21 q l liquid phase flux of soil moisture, m s 21 q r l liquid phase flux of soil moisture at the fraction r through the time-step, m s 21 q l,supply potential supply to evaporation of liquid water, m s 21 q melt melt water flux, m s 21 q prec,l liquid precipitation input, m s 21 q prec,i frozen precipitation input, m s 21 q v vapor phase flux of soil moisture, m s 21 q v,0 vertical vapor flux between snow and soil surface, m s 21 q v,supply potential supply to evaporation by vapor transport, m s 21 q w,0 water flux (liquid and vapor) at snow/soil interface, m s 21 q w,21 c water flux  (snow, liquid and vapor)   Appendix B: Extension of Soil-Litter-Iso for Frozen Soil We adopt the heat flow theory of  Fuchs et al.
Acknowledgments We thank  U Wollschl€ ager  and  S Weism€  uller for providing the Tibetan observational data,  GN Flerchinger  for the Orchard observations including soil properties,  K Luojus  and all contributors to GlobSnow for snow height (http://www. globsnow. info),and  A Robock  and colleagues for making available the data of the Valdai watersheds (http://climate. envsci. rutgers. edu/soil_moisture/valdai. html). C Koven  kindly provided his digital version of permafrost extent, available from NSIDC:  National Snow  and  Ice Data Center, Boulder, CO, USA  (http:// nsidc. org/data/ggd318). The Mizoguchi freezing experiment used digitized data from Hansson et al. (2004). CABLE-SLI source code can be accessed after registration at https:// trac. nci. org. au/trac/cable. Simulations in this work used revision no.
g. glucose). Interestingly, enzyme-gated electron transfer displays useful thermodynamic similarities with current flow in electronics and behaves as elementary biomolecular transistors wired through binding kinetics determining logic, signal amplification, and memory  (Eyring et al, 1981; Mehta et al, 2015). Proteins of specific functionality also offer the advantage to be identified through mining of databases of natural biochemical networks, and synthetic biochemical circuits can be easily designed to integrate catalytic activities that depend on specific molecular biomarkers, enabling the coupling of biosensing with decision-making algorithms in situ. Here, biochemical information processing under the digital domain to implement decision-making algorithms requires the definition of thresholds for concentration parameters, where a signal at a node of a biochemical circuit is encoded as the continuous valuation or absence of a particular species.
Therefore, a primary key step was to develop a systematic in silico framework supporting design automation of synthetic biochemical logic circuits. For this purpose, we developed a computational tool, NetGate, a part of the Silicell Maker software suite, which enables us to mine curated metabolic network databases for biochemical parts, devices, and circuits performing specific Boolean functions, and automates the search for more complex biochemical algorithms  (Bouffard et al, 2015). In this context, we define mining as the A specific biotechnological problem, such as assaying for the presence of pathological biomarkers in a clinical sample, can be solved by an appropriate set of combined biosensing/biocomputing operations performed by biochemical species. An abstract Boolean function can be formalized along with a set of kinetic specifications, corresponding to the logic to perform on molecular components in situ to solve an analytical and decision problem.
, and spatial features of microscale structures for realistic physics-based simulation of three-dimensional complex environments. In order to model the selective permeation of small molecules' inputs through the a-hemolysin pores of the protosensors membrane, we implemented in HSIM Fick's equations of diffusion (see Appendix for details). In this study, we use HSIM to perform assessment of kinetically and functionally suitable logic devices circuits and verify the behavior of protosensors. The software environment BIOCHAM  (Calzone et al, 2006; Soliman, 2012)  (Biochemical Abstract Machine) then provides model checking, automated exploration of a multidimensional design space, and optimization of experimental parameters according to temporal logic specifications. Specifically, BIOCHAM supports sensitivity and robustness analysis of the biochemical parameters (e.
g. enzyme concentrations) that have to be finely tuned with respect to each other to maximize robustness with respect to specific temporal logic behaviors (Fig  1 ). To generate the synthetic biochemical circuits described in this study, we performed an organism agnostic search of all the sets of natural metabolic networks of the BRENDA database with overlapping enzymes, substrates, or products related to the inputs and outputs of the circuits we aimed at designing. SBML files of these networks were then downloaded and merged into a large SBML network  (Appendix Fig S1 and Code EV1). This large network was used as input and mined using the program NetGate to identify 775 biochemical logic gates solutions of <2 reactions. The program NetBuild was then used to find unique biochemical implementations satisfying the Boolean logic specifications of protosensors that could execute the particular acute diabetes diagnostic algorithm (Fig  2C,  Appendix Figs S1  and S2 ).
The medical algorithm is distributed through three distinct and orthogonal protosensors, each processing two biomarkers as inputs, which were named for convenience GluONe (Glucose and Acetone as inputs, Code EV2), LacOH (Lactate and Ethanol as inputs, Code EV3), and GluNOx (Glucose and Nitric oxides as inputs, Code EV4). The biochemical implementation for these three systems required 6, 5, and 4 different biochemical entities, comprising 4, 3, and 2 different enzymes, respectively. Biomolecular signal processing occurring in these circuits leads to the synthesis of the following measurable output signals molecules: NADH (output 1, 340 nm absorbance), Resorufin (output 2, 571-600 nm fluorescence), ABTS (output 3, 420 nm absorbance), and DAF (output 4, 488-515 nm fluorescence). The Boolean formalism and truth tables corresponding to the medical algorithm, as well as the biochemical implementation, are depicted in Fig 2C  (Detailed in Appendix Fig S2) , and SBML models of the synthetic circuits can be found in SI.
Feeding HSIM with biochemical knowledge extracted from the BRENDA database, stochastic simulations were then performed to evaluate the behavior of the three circuits (See Appendix Table  S1  and S2, Appendix Figs S2  and S3  for more detail). As a first step, we studied models of non-encapsulated synthetic circuits, where initial conditions (i. e. species concentrations) were determined empirically and non-optimized. Predictions of the evolution of these three biochemical circuits after induction with variable concentrations of input biomarkers are represented as computed molecular output signals heat maps  (Fig 3A). The relation between computed molecular concentrations and experimental measured signal was calibrated beforehand  (See Appendix and Appendix Fig S4). In silico models showed that the de novo biochemical circuits behaved according to Boolean logic specifications with large signal fold change and neardigital responses.
Concentration parameters were determined empirically, and kinetic parameters were extracted from BRENDA database. The mathematical relation between concentration of outputs and related absorption or fluorescence was calibrated beforehand  (Appendix Fig S4). Heat maps were generated by plotting the mean of five simulations trajectories for each point. B In vitro biochemical circuits implementations and experimental measurements of truth tables with photograph of tubes at 60 min showing human-readable outputs (left), and kinetic behavior compared to HSIM predictions (right). The concentrations of inputs used to induce the circuits were 1 mM acetone, 1 mM glucose, 20 mM ethanol, 0. 5 mM lactate, 5 mM glucose, and 5 mM NOx. The experiments were carried out at 25°C, and the truth protosensors, as compared with positive controls  (Appendix Fig S11).
When comparing these data to HSIM model simulations, experimentally measured switching thresholds were found to show very good agreement with predictions, along with near-digital responses. In order to further validate the spatial and analytical digitization of output signals, confocal microscopy was used to quantitatively measure and precisely visualize output signals generation within single induced protosensors (Fig  5D ). Bright images with high SNRs and important response fold changes were obtained, strongly corroborating previous flow cytometry data. Molecular output signals appeared well localized in the interior of protosensors, although possible leakage was not quantified. Although satisfying for analytical applications, greater background noise was found compared to non-encapsulated synthetic biochemical circuits.
The experiments were carried out at 25°C, and the truth table reflects the values obtained at 60 min. From top to bottom, two-sided Student's t-test of induced versus highest non-induced condition P = 3. 3627 E-06, P = 1. 1701 E-04, P = 4. 2909 E-06, P = 1. 3397 E-05 C Experimental validation of computer prediction at the single (proto)cell level using flow cytometry. We measured the main fluorescence output of each different protosensor logic systems 60 min after induction by the respective biomarkers at discrete inducer concentrations as indicated for each corresponding contour plot. For LacOH, in order to get a fluorescent output signal measurable with a flow cytometer, we exchanged ABTS output with Resorufin, which both undergo analogous reduction process. Flow cytometry data collection was carried on 10,000 individual events.
We proceeded to incubation of GluONe protosensors with either diabetic urine samples or non-diabetic control urines and as previously described measured output signal responses (Fig  6B ). Glycosuria analysis using the clinical point-of-care gold standard was also concomitantly performed (i. e. urinary dipsticks). We found high correlation between output signals from protosensors and visual examination of dipsticks. Moreover, receiver operating characteristic (ROC) analysis of the data showed that the assay reliably detected glycosuria in samples from diabetic patients, with a near ~100% sensitivity and specificity, and an area under curve of ~0. 9981, which defines GluONe protosensors as high performance diagnostic test holding comparison to gold standard. Taken together, these data demonstrate that protosensors can discriminate between normal and diabetic patients with excellent diagnostic accuracy.
Therefore, we conclude that rational biomolecular programming of protosensors may be used to generate clinicalgrade assays to detect endogenous biomarkers of disease in patient samples. A Flow cytometry evaluation of protosensors structural robustness in urines. GluONe protosensors were induced and incubated for 2 h at 25°C in human urine and analyzed using flow cytometry while recording Resorufin fluorescence and forward-scattered light. B Left: Dot histogram of data used for statistical analysis. Dots of the same color (white, gray, black) correspond to one repetition of the experiment, which was performed in triplicates in different patient samples. Above each data point is depicted a photograph of a urinary dipstick measurement performed on the sample. We then plotted a dot histogram of data comparing the non-diabetic to diabetic set.
Right: Receiver operating characteristic (ROC) analysis curve depicting statistic sensitivity versus (1-specificity). A set of 72 measurement performed in non-pathological urine were compared to 72 measurements performed in urine from diabetic patients. The data were processed using the software SigmaPlot (Systat Software Inc. ) with a computed P < 0. 0001 using a Mann-Whitney-Wilcoxon test. We used GluONe protosensors for this assay and measured Out1 signal (NADH absorbance) after 60 min at room temperature. ª 2018 The Authors Molecular Systems Biology 14: e7845 | 2018.
Most biological parts available for the bottom-up engineering of synthetic biochemical circuits remain insufficiently characterized in increasing context complexity. Moreover, a lack of orthogonal parts may still limit the scaling up of in vitro synthetic circuits. For instance, some input biomarkers may in certain case lack biochemical sensors interfacing with signal processing modules. In order to overcome these limits, our current efforts focus on eliminating the remaining steps of human intervention and further automate classification, standardization, and robustness assessment of biological parts in context. This initiative is underway through automated refinement of databases, which stores parts, abstract modules such as sensors, switches, or Boolean logic gates extracted from biological networks  (Bouffard et al, 2015) , which we hope will lead to constant improvement in bottom-up design capabilities.