dataesr/scientific-paragraphs-categorization huggingface dataset:
Life cycle inventory Background processes were modelled using the Ecoinvent v3. 5 database using the recycled content system model  (Ecoinvent, 2018). Electricity use was modeled using the French grid. Information about foreground processes was collected from farm records, interviews about farm practices, water and energy bills, and technical specifications documents for machinery and purchased supplies. The life cycle inventory for mycelium production was taken from  Leiva et al. (2015b) , using Swiss integrated rye production. The life cycle inventory, showing inputs attributed per kilogram of mushroom, is compiled in Table  1 , and a more detailed inventory with corresponding Ecoinvent process names is included in Table  A1  in the Appendix. To facilitate interpretation of the results, the production system was delineated chronologically into 4 life cycle stages, shown in Figure  1.
, 2012; Paredes et al. , 2009) , the amount of CO2 eq. per kilogram of mushroom that was sequestered in the soil rather than emitted to the atmosphere was calculated (see details in Appendix 1). The amount of CO2 emissions avoided was entered as a negative emission of CO2 to air in the SimaPro modelling software. Climate change results are presented with and without this sequestered carbon term.
Life cycle impact assessment The impacts discussed in this study are climate change (CC), non-renewable energy demand (ED), water depletion (WD), land use (LU), and freshwater eutrophication (FE). These impact categories were chosen because they are important agricultural-related burdens. Additionally, they capture the food-energy-water nexus, which is an increasingly prevalent conceptual framework that highlights the interdependency of these essential resources that have large consumption and are vulnerable in cities  (Garcia and You, 2016). The impacts were modeled as midpoint indicators using SimaPro 9. 0 software and several impact assessment methods, as described below. CC, WD, LU and FE were modeled using the Environmental Footprint 2. 0 method  (European Commission, 2017). The specific methods are the IPCC 2013 100-year model for CC (IPCC, 2013), the EUTREND model for FE (same as the model used in ReCiPe 2008  (Goedkoop et al.
A common and successful material is straw  (Sánchez, 2010). From this perspective, the valorization of SCGs as the bulk substrate material at this case study farm is unique to a commercial farm of this size, and is done because of the farmers' commitment to circular economy and the opportunity of being situated nearby a large city with a high concentration of coffee consumption. A comparison was made to production with a more typical substrate composed largely of straw (43% wheat straw, 53% water, 3% mycelium and 1% CaCO3). The life cycle inventory for wheat straw was taken from the Ecoinvent database, where an economic-based allocation was done to distribute 7-10% of the impacts from wheat grain production  (Nemecek and Kägi, 2007). It was assumed that all other on-farm practices and the final yield remained the same.
Material included impacts from producing the materials themselves. Electricity and gas included their use on the farm, and the background processes embedded in the database. The categories of gas, electricity, transport, and material contributed 43%, 14%, 31%, and 12%, respectively (Figure  3 ). Transport from short supply chains, which here were the SCGs and mushroom delivery, contribute 16% of the CC impacts (7 and 9% respectively). Carbon sequestration from SMS amounted to 0. 19 kg CO2 eq/kg mushroom stored in the soil. This amount was subtracted from the CC impact to give a net CC impact of 2. 99 kg CO2/kg mushroom, which was a 6% abatement. This reduction was rather small because most of the benefits from carbon sequestration were actually allocated to the SMS co-product instead of the mushrooms.
, 2018; Weber and Matthews, 2008). Transport at the mushroom farm incurred significant CC impacts, with a 31% share overall, in which 10% came from the final delivery of the product. Although an emphasis is often placed on the delivery of the final product, impacts from transportation of input materials outweighed product deliveries, as has been found in other studies  (Martin and Molin, 2019). These contribution calculations only consider the transport in the foreground system, and not transport processes embedded in the database representing the background system, so the actual contribution of transport could be even larger. Our findings support claims that proximity alone is not a sufficient indicator of environmental sustainability, and individual attributes and practices of the system can play a more important role  (Edwards-Jones et al.
Considerations for LCA modeling The boundary of the system excluded delivery to the final consumer, which was a limitation because this can be an impactful stage  (Mundler and Rumpus, 2012). Additionally, we used data from the farm for a 2 month period of production, which risks being unrepresentative of the annual production. However we verified that, although this was one of the most productive periods for the farm during 2018, a proportionally large amount of materials and energy were used as well. Finally, any study on sustainability is limited when it only considers one aspect, where here we focused on environmental sustainability. An inclusion of economic and social aspects would be holistic and ideal, but was outside the scope of this study. It should be noted that a system modeling choice likely has a large impact here: the decision to treat SCGs, a recycled input, using  Ekvall and Tillman's (1997)  simple cut off method instead of system expansion and avoided burdens.
Responses from the mushroom farm We partnered with a functioning commercial farm and used data from real cultivation practices, rather than a research farm, pilot project, or relying heavily on data from the literature. In addition to the scientific value of this work, we hoped to provide meaningful insight and decision support for the farmers, who were concerned about the environmental sustainability of their practices and looking for feasible paths to improve. An academic-oriented LCA may not naturally generate results that are most interesting to the farmers. For example, because we were interested in the short supply chain aspect of the farm, we modeled an alternative scenario with reduced delivery frequency that reduces CC impacts by 15%. The farmers quickly rejected this strategy because their oyster mushrooms must be delivered daily, as they are the only provider of this specialty product to the market and are constrained by customer demand.
For each plot, the color of the curves is lighter the larger δ. nm is observed but the energy localization reaches higher values than for 660 nm since higher normalized intensities appear in the PDF. These experimental results have been compared to FDTD simulations performed with the Lumerical software (for details on these numerical simulations see Ref. [38] ). The incident plane wave and the intensity distributions have been deduced from the near field distributions calculated 30 nm above the surface. The results obtained for λ = 660 nm and 730 nm are presented on Fig. 6  (bottom left) and Fig. 6  (bottom right). The simulation predictions first show the same tendencies as the experimental results. The appearance and the increase of a tail toward higher values of the intensities when disorder increases is evidenced.
Background Manual quantification of the metabolic tumor volume (MTV) from whole-body 18 F-FDG PET/CT is time consuming and therefore usually not applied in clinical routine. It has been shown that neural networks might assist nuclear medicine physicians in such quantification tasks. However, little is known if such neural networks have to be designed for a specific type of cancer or whether they can be applied to various cancers. Therefore, the aim of this study was to evaluate the accuracy of a neural network in a cancer that was not used for its training. Methods Fifty consecutive breast cancer patients that underwent 18 F-FDG PET/CT were included in this retrospective analysis. The PET-Assisted Reporting System (PARS) prototype that uses a neural network trained on lymphoma and lung cancer 18 F-FDG PET/CT data had to detect pathological foci and determine their anatomical location.
Statistical analysis MATLAB 2019b (The MathWorks, MA, USA) and Excel (Microsoft, WA, USA) were used for data handling. SPSS v25 (IBM, NY, USA) was used for regressions; Wilcoxon signed-rank test and Mann-Whitney U test. Logarithmic transformation was done for Cox and Pearson regression to correct for skewness of data (base 2; 1 was added to all values prior to transformation)  [19]. For stepwise Cox regression, the forward LR method of SPSS was used with standard settings. Gönen and Heller's concordance index was employed to measure the accuracy of the Cox regression; a bootstrap Gauss test was employed to test for statistical differences  [15, 20, 21]. R v3. 5. 2 (The R Foundation, r-project. org) was used for bootstrapping, descriptive accuracy metrics, and Kaplan Meier plotting. Ninety-five percent confidence intervals (95% CI) of accuracy metrics were determined by bootstrapping with 1000 replicates.
Detection sensitivity and specificity of PERCIST measurable lesions were comparable to the lung cancer results of the initial PARS publication (sensitivity: lung cancer 87% vs. breast cancer 92%, specificity: lung cancer 99%, breast cancer 98%)  [15]. The same was true for the location accuracy (body part: lung cancer 97% vs. breast cancer 98%; region: lung cancer 84% vs. breast cancer 88%)  [15]. The higher accuracy in the breast cancer dataset might partially be explained by the fact that only a Biograph mCT was used for acquisition instead of a combination of the Biograph mCT and the older Biograph 16  [15]. As the metastatic patterns of lymphoma and lung cancer on the one hand and breast cancer on the other are different from each other, the high accuracy in the detection of suspicious FDG foci in breast cancer patients was not obvious.
Fluorescence and Luminescence Decays Time-resolved experiments were performed using, for excitation, a pulsed laser diode emitting at 408 nm (LDH-P-C-400M, FWHM < 70 ps, 1 MHz) coupled with a driver PDL 800-D (both PicoQuant GmbH, Berlin, Germany) and for detection, an avalanche photodiode SPCM-AQR-15 (EG&G, Vaudreuil, Canada) coupled with a 550 nm long-wave pass filter as detection system. The acquisition was performed by a PicoHarp 300 module with a 4-channel router PHR-800 (both PicoQuant GmbH, Berlin, Germany). The fluorescence decays were recorded using the single photon counting method. Data were collected up to 1000 counts accumulated in the maximum channel and analyzed using Time Correlated Single Photon Counting (TCSPC) software Fluofit (PicoQuant GmbH, Berlin, Germany) based on iterative deconvolution using a Levensberg-Marquandt algorithm.
1 O2 lifetime (τΔ) measurements were performed on a TEMPRO-01 spectrophotometer (Horiba Jobin Yvon, Palaiseau, France). The apparatus was composed of a pulsed diode excitation source SpectralLED-415 emitting at 415 nm, a cuvette compartment, a Seya-Namioka type emission monochromator (between 600 and 2000 nm) and a H10330-45 near-infrared photomultiplier tube with a thermoelectric cooler (Hamamatsu, Massy, France) for the detection. The system was monitored by a single-photon counting controller FluoroHub-B and the software DataStation and DAS6 (Horiba Jobin Yvon, Palaiseau, France).
The OV's effectiveness is amplified by its immunostimulatory counterpart,  3  attesting to the need to find a reasonable balance between a multitude of immune mechanisms (such as viral clearance and the antitumor immune response) to achieve ultimate treatment success. Therapeutic cancer vaccines are administered to cancer patients with the goal of eradicating tumor cells through strengthening the patient's own immune response. 4 6] [7]  Currently, there are three clinical trials investigating the efficacy of combining adenovirus and the OV Maraba as an anticancer vaccination treatment,  8 9  two of which are in the USA (NCT02285816; NCT02879760) and one in.
This measurement period was chosen to account for the discernable differences between cell lines after day 12 and the experimental end point at day 18. 19  The implicit parameter r describes the aggressivity of the tumors, with high r corresponding to aggressively growing tumors and low r corresponding to slowly growing tumors. Ordering patients by their tumor growth rate showed a gradual increase in r values across the cohort (figure  2C ). Numerical simulations, the creation of the virtual cohort, all statistical analyses and figure generation were performed using Matlab R2019b.
Thus, it is not necessarily the number of enhancers or VSV lag driving the variation in treatment response, but rather the duration of the treatment. While there are currently no clinical trials combining VV and VSV for the treatment of TNBC, there are other clinical and experimental results that support the findings of our virtual clinical trial. A handful of clinical trials have been conducted with VV or VSV individually,  9 54-56  and there is currently a phase I trial in stage III-IV melanoma using VSV-IFNbetaTYRP1 (NCT03865212). The randomized phase 2 dose-finding trial of Pexa-Vec (an oncolytic VV with a gene encoded to increase expression of GM-CSF) for the treatment of on advanced hepatocellular carcinoma found a clear distinction in survival based on dosage, with the high-dose cohort having an increase in overall survival compared with the low-dose cohort.
Acknowledgements All authors thank  Dr Jean-Simon Diallo  for correspondence regarding the published experiments from  Le Boeuf  et al. Contributors ALJ, TC, M-CB-D and MC conceived of the study. ALJ, KB, TC and MC constructed the model. ALJ performed simulations. ALJ and MC wrote the article. All authors approved the manuscript. Funding ALJ:  Fonds de recherche Santé Québec Programme de bourse de formation postdoctorale pour les citoyens d'autres pays ,  Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM) Postdoctoral  fellowship. TC:  Natural Sciences and Engineering Research Council of Canada (NSERC) PGS-D, U. S. Department of Energy   89233218CNA000001 ,  NIH  grants  R01-AI116868  and  R01-OD011095. M-CB-D :  Institut du cancer de Montréal, Fonds de recherche Québec Santé, Fondation du cancer du sein du Québec.
Note that there are challenges to evaluating such a new model, since global lake datasets remain scarce or incomplete. This is mainly explained by the extensive detailed field measurements required, such as bathymetry profiling and the associated costs  (Hollister and Milstead, 2010). This study tries to overcome these limitations by using inherent CTRIP parameters like the river channel width at the lake outlet, which obviously leads to uncertainties. Sensitivity tests are done by prescribing different outlet width configurations and then studying their impact on both the river streamflow and the lake level amplitude and variability for multiple study sites. After presenting the framework in which the lakes are implemented and the datasets used for evaluation and global estimations, Section 2 describes the method for implementing lakes and the methodology chosen to answer the scientific questions.
CC BY 4. 0 License. properties of soil which is now discretized in 14 layers resulting in a total depth of 12 m. In addition, the scheme can include the effects of soil organics on the thermal and hydrological properties of the soil. The snow is simulated using a multi-layer snow model based on the work of  Boone and Etchevers (2001)  which recent improvements in physics and increased vertical resolution as described in  Decharme et al. (2016). ISBA is fully integrated within the surface modelling platform SURFEX (v8. 1)  (Masson et al. , 2013; Le Moigne et al. , 2020)  developed at the CNRM in order to bring all the models related to the surface parametrisation into one unique software platform. SURFEX allows studies to be performed in offline mode or fully coupled to an atmospheric model extending -de factoits applicability range from local hydrological to large scale climate studies.
The distinction of such land processes in SURFEX comes from the global land cover database ECOCLIMAP-II which dynamically renders the type of vegetation and its cover at the chosen spatial resolution of the model for a given application  (Masson et al. , 2013; Faroux et al. , 2013). ECOCLIMAP-II is a 1 km resolution land-use and land-cover database based on satellite products designed for operational and research numerical weather prediction, climate modelling, hydrological forecasting, and in land surface numerical studies within the SURFEX surface modelling platform  (Le Moigne et al. , 2009). ECOCLIMAP-II details whether a pixel contains one of the four different type of covers: lake, town, land or ocean, and it distinguishes hundreds of plant functional types representing a large variety of ecosystems  (Faroux et al.
, 2013). SURFEX further aggregates the initial covers into upwards of 20 patches which correspond to different land covers or plant functional types. The orography is extracted and upscaled from the 90 m resolution Shuttle Radar Topography Mission to a 1 km-resolution  (Werner, 2001). The ECOCLIMAP-II lake cover scheme provides binary information on the presence or not of a lake in the pixel. No other information is provided thus lake cover information is completed with the Global Lake DataBase (GLDB,  Kourzeneva et al. , 2012; Choulga et al. , 2014)  which has gridded in situ and estimated lake mean depth at 1 km resolution globally. This global database has been developed to gather lake information and retrieve mean depth information for numerical weather prediction. It already serves as input for correcting land cover used by SURFEX for approximately 15,000 lakes on a 1-km resolution grid.
However, a dataset threshold is introduced on lake detection and set at a surface area of 1 km 2 that limits the number of lakes considered in our calculations. In this research, we used continuous mean depth field recently developed at ECMWF  (Choulga et al. , 2019)  to ease aggregation technique from 1-km to 1/12°. Streamflow routing is simulated using CTRIP (Fig. 1 ) which integrates a dynamic resolution of river flows on each cell grid based on the Manning equation which is dependent on the characteristics of the river section. CTRIP is fully coupled to SUR-FEX and considers the interaction between the rivers, the atmosphere and the soil through the input by CTRIP which then computes the river discharge, water table evolution and surface flooded fraction. Moreover, it explicitly accounts for groundwater processes with the integration of a two-dimensional diffusive aquifer scheme connected to rivers and a parameterization of the capillary fluxes within the soil  (Vergnes et al.
As mentioned previously, the outflows are calculated considering an intermediate lake state in order to retrieve the final lake volume. This intermediate state for the time step (s) is defined as an intermediate volume V * lake (kg): V * lake (t) = V (t -∆t) + [P ol (t) -E ol (t) + R S (t) + Q sub (t) + Q in (t)] ∆t ( 5 ) where ∆t is the time step (s) and V (t -∆t) is the lake volume at the previous time step t -∆t (kg. s -1 ). Eq. 6 provides an estimation of the intermediate lake hydraulic head h * lake (m): h * lake (t) = V * lake (t) A ECO (6) with A ECO the lake area in the ECOCLIMAP-II database (m 2 ). The outflow is, by definition, linked to the lake water storage assuming a rating curve relation based on an empirical weir relationship that links the discharge to the water head over the crest (Eq.
485  (Lencastre, 1963) , W weir the width of the outlet equal to the width of the river in the downstream pixel (m), h weir the height of the weir (m) and ρ ω is the volumetric mass of the water (kg. m -3 ). The river width was first determined over France by comparing the mean annual discharge measurements from the Banque Hydro database (www. hydro. eaufrance. fr) and the river width of the "Systeme Relationnel d'Audit de l'Hydromorphologie des Cours d'Eau" (SYRAH) which leads to the empirical equation  (Vergnes et al. , 2014) : (GLWD, http://wp. geog. mcgill. ca/hydrolab/glwd/, (Lehner and Döll, 2004). ω river = αQ β mean ( The initial lake level is equal to the weir height, which results in an initial lake outflow equal to zero. Eq. 7 incorporates the dependence of the depth on the hydraulic head over the weir.
The final lake volume for the time step (t) is derived from the equation: V lake (t) = V * lake (t) -Q out (t)∆t (9) Eq. (  2 ) calculates a change in lake water storage from which the diagnostic variables, such as surface area and lake level, are estimated. Numerous hydrological models assume the lake storage to be a linear function of the surface area and depth. This solution does not take into account the specific lake bathymetry, and it simulates a realistic hypsographic relation, thus the lake surface area is supposed constant. However, knowing how the lake surface area varies with respect to depth is important for improving over-lake evaporation estimations. With regards to the relative scarcity of global-scale datasets on lake bathymetry, implementing appropriate lake hypsometric curves would require extensive developments that will be carried out in further studies.
Initialisation of lakes This step consists in an externalised procedure that creates a map containing physiographic information and initialised variables. This particular part is currently written with a mix of Python and Fortran90 (working with the Gfortran and Intel Fortran compilers). Several aspects of this step are related to the Section 2. 3. 1 and Section 2. 3. 2. NetCDF files are generated from the integration of lake information, aggregated from the ECOCLIMAP database at 1 km resolution and down-scaled to 1/12 o , for the current CTRIP river routing network. Key parameters and variables generated by this pre-processing step are gathered in the Table  2.
On the latitude/longitude dimension, it gives the river discharge, storage, streamflow velocity and stream height on every pixel. On the dimension characterised by the lake ID number, it gives the outflow discharge and the level of lakes. This model tends to be user-friendly with an optimised command interface, allowing users with limited support to operate the code. As soon as the code is compiled, the Fortran option namelist needs to be completed in order to give the desired configuration of the run (input zone, computational time step, diagnostic time step) with the technical support of the SURFEX website (https://www. umr-cnrm. fr/surfex/). Then both the preparatory stage and the master program are run by executing two ready-to-run shell scripts.
These observations cover the time period 1973 to 2013 and are used to monitor the level variations of Lake Geneva on three different shores. Regarding discharge data, a comparison was made with a dataset comprised of data from the Global Runoff Data Center (GRDC; www. bafg. de/GRDC/EN/Home/homepage_node. html), ARCTICNET and the French Banque Hydro databases (www. eaufrance. fr). From these datasets, chosen stations must have a minimum of 3 years of continuous measurements during the simulation period for a drainage area covering at least 1000 km 2. In the validation stage, the most downstream measurement station is chosen for comparison. But if only one station is available for the entire study site, the closest available CTRIP pixel on the river is considered. These datasets remain incomplete and some basins lack data, such as the White Nile watershed.