dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements W. P. , J. P. C. , and A. J. P. were supported by the  Swedish Research Council  (project no. 2016-03441 ) and the  Swedish National Infrastructure for Computing (SNIC)  through the center for parallel computing (PDC), project number  2019-3-500. N. P. , V. T. , M. K. , M. F. , and G. P. acknowledge support by the project  MIS 5002567 , implemented under the "Action for the Strategic Development on the Research and Technological Sector", funded by the  NSRF   2014-2020  and co-financed by the  European Union and Greece. Part of the DFT work was performed using computational resources of the  Research Computing Department at Khalifa University. X. L. , K. P. , G. P. , and Y. A. would like to acknowledge the support of  Khalifa University of Science and Technology  Award No.
Reactobiome uncovered environment, host and microbiome interactions As diet is prone to seasonal changes 30 , we examined the reactobiome over a year in the longitudinal Swedish cohort. The reaction richness was similar across the four seasons (Extended Data Fig. 7a ), but the abundances of reactions related to energy metabolism varied (Extended Data Fig. 7b , Supplementary Table  19 ). Reaction abundances of central carbon metabolism were highest in the spring and lowest in the summer (two-way ANOVA test, pvalue < 0. 001). Similarly, Acyl-CoA oxidase for fatty acid degradation and alpha amylase for starch degradation increased from winter to spring and decreased in summer. An increase in the proportion of calories from carbohydrates in the spring was reported for a US cohort 30 ; and it may be that cohort consumed more fats and starch in the spring, leading to an increases in microbes particularly adapted to use them.
Metagenomics species pan-genome (MSP) and completeness estimation The updated Integrated Gene Catalogue of the human gut microbiome (IGC2) 21 was used to generate the generic metabolic model. The updated MSP information was used here to reconstruct the species-specific models 18. All the metagenome samples have been previously mapped to the IGC2 and profiled for the MSP abundances. MSP completeness was estimated using two conserved single-copy marker genes (SCM) sets that have been previously determined for all bacterial and archaeal finished genomes 41. The sets consist of 139 (bacteria) and 162 (archaea) SCM that were found to occur only once in at least 90% of all genomes. Species completeness score was estimated as the proportion of SCM genes found in the MSP using hmmsearch and HMMER 42 formatted databases for archaeal and bacterial SCM.
Diet allocation for GEMs Diet allocation for GEMs was built using the USDA food composition database (https://ndb. nal. usda. gov/ndb/). Based on the molecular weight for each micronutrient, the mmol/gDW was calculated and divided per 24 hours (mmol/gDW*hour). Food items that had a missing value for an amino acid but contained protein were replaced by the average of the amino acid for the corresponding food group. This value was then divided by the total average of all foods in the database. Four different diets (high-protein-plant based, high-protein omnivorous, high fibre-plant based and high fibre omnivorous) were formulated based on macronutrient composition  47, 48. Each diet is an average diet based on a three-day meal plan in which each day consisted of three main meals and a snack.
Clustering of reactobiome profile, modelling and features of Reactotypes Dirichlet multinomial mixtures (DMM) method 23 was performed using "DirichletMultinomial" R package for clustering of reactobiome profile. The number of clusters (reactotypes) were determined by fitted Dirichlet mixtures and Laplace approximation to the model using dmm and laplace functions. The robustness of the reactotypes were investigated using supervised machine learning models and cross validation using "caret", "e1071" and "randomForest" R packages. The parameters (50 folds repeat 10 times) for train function were set using rfeControl function. The collation of resampling results was performed by resamples function. We subsequently used wellness cohort as a test set and trained random forest based prediction model for reactobiome profile.
Microbiome-host and seasonal changes association studies In order to investigate the association for each reaction, we filtered the reactions <0. 01% of reactobiome composition and prevalence > 10%. The multiple covariates between reactobiome profile and host clinical and plasma metabolites in the population-scale longitudinal study were subjected to multivariate regression, considering random effects of individuals by linear mixedeffect models (multivariate random effects models) using MaAsLin2 R package (https://huttenhower. sph. harvard. edu/maaslin). Significant associations (adjusted p-value <0. 01) were visualized using circlize R packages 49 for circus plot and Cytoscape 3. 7. 2 50 for network representation. The abundance of the reactions was analysed using two-way ANOVA to detect seasonal changes in individuals (n=86) in a combination of two independent variables i.
Acknowledgements This study was supported by  Engineering and Physical Sciences Research Council (EPSRC) ,  EP/S001301/1 ,  Biotechnology Biological Sciences Research Council (BBSRC)   BB/S016899/1 ,  Science for Life Laboratory , the  Knut and Alice Wallenberg Foundation , the  Erling Persson Foundation and King's College London. We thank the  Swedish National Infrastructure  for Computing at SNIC through  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  under Project  SNIC 2019/ 3-226 ,  SNIC 2020/6-153 ,  SNIC 2020/5-222.
Code availability The MIGRENE toolbox used to reconstruct MAGMA, quality control, generating reactobiome, reaction abundance, calculating reaction richness, personalized and community modelling, pRSE and diet allocation, with several tutorials and testing datasets, can be found at our GitHub repository https://github. com/sysbiomelab/MIGRENE. The toolbox can be applied to any microbial gene catalogues and metagenome species for GEMs reconstruction. In addition, any public microbial GEMs, together with the abundance, can be utilized for reactobiome, reaction richness and abundance, and community modeling.
FOFS allows online recording of deviations in chlorophyll and dissolved oxygen concentrations induced by the study organism. Transparent data processing through Python scripts provides the possibility to adjust procedures to needs when working in different environmental contexts (e. g. , temperature vs. pH, salinity, oxygen, biological cues) and with different filter-feeding species. We successfully demonstrate the functionality of the method through recording responses of Baltic Sea blue mussels (Mytilus) during one-day thermal cycles. This method practically provides a tool to help researchers exposing organisms to environmental variability for some weeks or months, to relate the observed long-term performance responses to short-term energy budget responses, and to explain their findings with the potential to generalize patterns.
1% O 2 at 20. 9% O 2 or ± 0. 04 mg L -1 at 9. 1 mg L -1 ) attached to the inner surface of the incubation chambers (Fig. 1a ,b; Fig. S1 ). Sensor spots are read out by an oximeter (OXY-4 mini, PreSens Precision Sensing) through optical fibers connected to the cylinders' outer surface. Configuration and data logging are achieved using the corresponding software. Sensor spots are calibrated based on the two-point calibration protocol (PreSens 2017). The reference measurements were conducted in anoxic water (prepared by dissolving 10 g of sodium sulfite in 1000 mL water) and water-vapor saturated air. Food concentration is measured using fluorometers (Cyclops 7f, Turner Designs; application: chlorophyll in vivo, blue excitation; minimum detection limit: 0. 00003 mg L -1 ; linear range: 0-0. 5 mg L -1 ) in dark and well-mixed conditions inside the fluorometry chambers (Fig.
1 ; Fig. S1 ). Fluorometers are set up and data are recorded using the Cyclops-explorer connectors and software (Turner Designs). The size of incubation (oximetry) chambers must be large enough to satisfy the space requirements for the species' normal activities (i. e. , related to the size of study specimens). The volume of the fluorometry chamber is chosen to provide a distance of 8 cm between the fluorometer's optical face and the chamber floor while the optical face and shade caps of the fluorometer are entirely submerged (Turner Designs 2020). The source-water tank (600 liters) is equipped with a control system (Profilux 3. 1TeX; GHL Advanced Technology) automating thermal simulations. A temperature profile (in. csv format) is submitted via the GHL-controller software to the ProfiLux computer, which then adjusts the source-water temperature.
Data processing through python scripts Here, an overview of different steps of the data processing is provided with a focus on the techniques used to correct and convert measurements and calculate the response variables. The associated Python scripts can be found in the Supplementary Information Scripts. Notes and explanatory remarks provided throughout the scripts clarify how the steps and commands in the scripts work and how one can use and adopt them. Dissolved-oxygen concentration calculator "DO_calculator. py" (Supplementary Information Script S1) transforms the phase angle data (phi) collected via PreSens Pts3 sensor spots (and Oxi4-mini oximeter) to the dissolved-oxygen concentration in % air-saturation considering the temperaturesensitivity of the phase angle and Stern-Volmer constant.
The % air-saturation data are then converted to μmolO 2 L -1 considering ambient temperature and salinity. The ambient temperature data used in the processing are recorded by the logger placed within FOFS. The equations used in this calculator are based on the Oxi4-mini instruction manual (PreSens 2004). Future users applying oximeters lacking the automatic temperature-correction and unit conversion can revise the script based on the specifications of their device (sensors). Trial-by-trial analysis "FOFS_trial-by-trial_processing. py" (Supplementary Information Script S2) can be applied to process raw data and generate outputs including data frames and time series plots of raw, corrected, and converted versions of measurements and calculated data of the response variables for each experimental trial.
Any change in the respiration or filtration activities of the study specimen almost instantly alters the dissolved oxygen or food concentration in the oximetry chamber and in the inflow to the fluorometry chamber. The inflow is being mixed with the solution in the larger fluorometry chamber; therefore, any measured change in food concentration is a dampened (temporally lagged and weakened) version of a change in the inflow food concentration. The script uses a linear differential equation  (Campbell and Haberman 2008 ; Supporting Information Text S3) to improve the estimation of rapid changes in the measured food concentration (Fig. 3c, d ). These rapid changes in food concentration can occur because of filtration shutdown or recovery of the study organism. Notably, if the measured food concentration follows a consistent trend with no rapid changes, no correction is done (Supplementary Information Fig.
For example, if the difference between measurements of sensor S1 and sensor C is 1% at a real ambient concentration of 100% air saturation, after the correction the two values will depart < 0. 1% per 10% decrease in the ambient concentration. The final version of main-trial data (named "con-trol_ymol_per_l_C" and "corrected_ymol_per_l_Sn" in the script) are then applied to Eq. 3 to calculate the respiration rate. resp Sn μmolO 2 min -1 À Á = oxyg C μmol L -1 À Á -oxyg Sn μmol L -1 À Á À Á × flow rate L min -1 À Á ð3Þ Step 3 (scope for growth) The principal outputs of data processing are filtration and respiration records. The script also provides a very simplistic estimation of surplus of energy available for growth based on the assimilated energy minus the respired energy (the basic definition of the SFG;  Widdows 1976 ).
FOFS integrative processing The data frames created through the trial-by-trial processing can be integrated using "FOFS_integrative_processing. py" (Supplementary Information Script S3). Importantly, a data sheet containing dry weights and shell lengths of study specimens (e. g. , Supplementary Information Table  S1 ) must be manually added to the experimental folder, before executing the script. The script first merges post-stage data frames, including all estimated cumulative random effects ("%_cumulative_ random_effects_Sn" in the script). Then, it concatenates maintrial data frames one by one, plus defining size-standardized rates of filtration, feeding, respiration, and SFG for the replicates. All responses are standardized to shell length and dry tissue weight as proxies for gill surface area and tissue volume  (Hamburger et al.
1983; Riisgård 2001). Any change to the complete experimental data frame ("experiment_df"), such as excluding a broken part, can be done through "manual imposition of changes. " The script produces line plots, each aggregating over replicated values of a specific variable at each time point and shows estimates of the averages with the respective 95% confidence intervals. Finally, thermal variations in responses are described through Generalized Additive Models (pyGAM module; Servén and Brummitt 2018) using data of the whole experiment or a specific phase of it (e. g. , the warming or cooling phase of a thermal fluctuation treatment). The best fit GAM is selected using a grid search over multiple values of the regularization parameter and n-spline values seeking the lowest Generalized Cross-Validation score (for more details on GAM, refer to Wood 2017).
Demonstration experiment In a few studies, flow-through setups were applied for simultaneous measurement of filtration and respiration of aquatic organisms under static experimental conditions  (Widdows 1973; Haure et al. 2003). Besides, guidelines are available for the design of flow-through setups proper for measuring the filtration rate under static conditions  (Filgueira et al. 2006; Larsen and Riisgård. 2011 ). Here, we describe the design of a FOFS and provide the protocols used for semiautomated data processing through Python scripts. The method described here allows for high-resolution monitoring of filtration and respiration rates in response to dynamic environmental conditions, ultimately enabling the detection of the ecological limits of benthic filter feeders facing climate change.
During all demonstration trials, seawater salinity was ca. 21 PSU, and the flow rates of Pump1 and Pump2 were constantly 16 and 2 mL min -1 , respectively. The food tank was refilled with concentrated food-solution after each trial. The sensitivity of the fluorometers was set to X10, which is suitable for measuring in the range of 1 to 5 μg Chl L -1 , comprising the concentration range of our experiment. Readout frequencies of the fluorometers, the oximeter, and the temperature logger were set to 30 s. All data of the demonstration experiment, including blank and mussel trials' data, are archived and accessible in Pangaea (www. pangaea. de; Vajedsamiei et al. 2020).
Challenges and solutions provided by FOFS and the suggested data processing In FOFS, we successfully used submersible fluorometers that are more affordable and easier to handle due to their small size compared to previously applied laboratory fluorometers  (Haure et al. 2003; Pleissner et al. 2013). We provide the procedure for temperature correction and unit conversion of Chlorophyll data, which was successfully tested in the demonstration experiment. The Python scripts explicitly facilitate all steps of data processing, making our method more understandable and adaptable for future studies. It applies a robust modeling technique for denoising measurements. It includes the dampening-effect correction which can be applied to data obtained through similar flow-through setups (including aquarium or mesocosm-based systems) in which the sensors are inevitably positioned in a series of chambers of different sizes.
Acknowledgments The authors would like to acknowledge  Ulrike Panknin  for providing the Rhodomonas culture and technical assistance. We also thank  Dakeishla Mary Diaz Morales  for her comments on the final manuscript and  Prof. Martin Wahl  for his supports throughout this project. This work and JV were funded through the  Deutsche Forschungsgemeinschaft (DFG)  project: The neglected role of environmental fluctuations as modulator of stress and driver of rapid evolution (Grant Number:  PA 2643/2/348431475 ) and through  GEOMAR. The project was supported by the  Cluster of Excellence "The Future Ocean ," funded within the framework of the Excellence Initiative by the  DFG  on behalf of the German federal and state governments. CP was funded by the postdoc program of the  Helmholtz-Gemeinschaft Deutscher Forschungszentren  and by  GEO-MAR.
RK was supported by the collaborative research center 754 " Climate-Biogeochemistry Interactions in the Tropical Ocean " (www. sfb754. de)which was supported by the  DFG. RK also acknowledges support from the  Make Our Planet Great Again program  of the  French National Research Agency  within the  Programme d'Investissements d'Avenir ; reference  ANR-19-MPGA-0012. MK was funded through the  PhD program  of  "studienstiftung des deutschen volkes". Open Access  funding enabled and organized by ProjektDEAL.
Numerical results show an excellent agreement with metrology even for hundreds of layers  [11, 43]. The calculation considers the structural features of the multilayer (thickness of each layer, materials and roughness of each interface), the conditions of illumination (angle of incidence, wavelength and polarization) and the measurement conditions (scattering angle). It outputs the values of the scattering function ARS (Angle Resolved Scattering). This calculation is far more complex than the specular case. As an illustration, consider a structure with N layers of respective thicknesses e p∈[  [1;N] ] and materials n p∈  [[1;N] ] , where n p can be extracted from a database of at least two materials. The algorithms described in  [29]  for the computation of the specular spectral transmittance T(λ, i), reflectance R(λ, i) and ARS functions in transmission ARS T (θ, λ, i) and in reflection ARS R (θ, λ, i) require the following numbers of numerical operations: N specular = [N + 2] × N λ × N i N scattering = [[7 × N + 2] × N θ + N] × N λ × N i where N λ is the number of wavelengths λ, N i the number of angles of incidence i and N θ the number of scattering angles θ.
Logarithm (-log 10 (y)) is taken on the output label data y to help the model to differentiate between low output values; the resulting output labels typically sit between 0 and n, with 10 -n the lowest level for the TIS. Typically, n = 5 or 6 for classical filtering applications. The whole model is implemented in MATLAB. of the input vector parameters to minimize the MSE of the prediction compared with the target and converges to the solution x sol. This design approach benefits from the high computational speed of the DNN and the gradients computed by backpropagation. A training of 1024 epochs is usually enough to converge to a suitable solution. Finally, the solution is assessed using an exact analytical computation of the spectral response, called ground truth, to ensure that it complies with the prediction of the designer.
At the end of the optimization process, the ratio between the final solution and the initial solution are respectively around 1/3 and 1/9. The reduction of the TIS T is less than a decade but still shows some improvements. Figure  7  presents the Quarter Wavelength Optical Thickness of the solution obtained after optimization, where we observe that the regular quarter wavelength structure is lost but the general pattern and the cavities remain. Further refinements of the optimized structure have not been successful. For that purpose, we have used a standard refinement procedure using the Optilayer software and have tried to correct the spectral oscillations on the specular response in the bandwidth. However, as our solution is unconventional, the classical means used to refine thin-film structures were inefficient.
our first implementation, we consider structures of 20 alternating layers of SiO 2 and Nb 2 O 5. The alternation is fixed and the layer on the top (interfacing the superstrate and illuminated by the incident field) is always of high index material (i. e. , Nb 2 O 5 ). The 20 input features are the 20 thickness values of the layers, varying from 60 nm to 120 The training of the predictor uses a dataset of 50,000 examples, the input vectors are randomly generated and the corresponding scattering response output values are uniformly sampled over 201 points between 400 nm and 800 nm. Generating this dataset takes about 4 hours on a 24-core Intel Xeon Silver processor. The set is split into a training set (80% of the examples) and a validation set (20%). The predictor network has 5 fully connected hidden layers (with respectively 500, 500, 500, 201 and 201 units) separated by ReLU layers.
For our first implementation, we consider structures of 20 alternating layers of SiO 2 and Nb 2 O 5. The alternation is fixed and the layer on the top (interfacing the superstrate and illuminated by the incident field) is always of high index material (i. e. , Nb 2 O 5 ). The 20 input features are the 20 thickness values of the layers, varying from 60 nm to 120 The training of the predictor uses a dataset of 50,000 examples, the input vectors are randomly generated and the corresponding scattering response output values are uniformly sampled over 201 points between 400 nm and 800 nm. Generating this dataset takes about 4 hours on a 24-core Intel Xeon Silver processor. The set is split into a training set (80% of the examples) and a validation set (20%). The predictor network has 5 fully connected hidden layers (with respectively 500, 500, 500, 201 and 201 units) separated by ReLU layers.
Introduction The NEUBIAS Training Schools (the "TSs" hereafter) have been developed as a series of 15 training events around Bioimage Analysis (BIA). These events took place between 2016-2020 over eight different venues throughout Europe (Figure  1 ) and brought together four communities of experts: life-scientists, microscopists, software tool developers and bioimage analysts with the aim to train the researchers and the prospective trainers, and to promote the exchange of knowledge and experience between the four communities of experts. The TSs were highly successful and well praised by the over 400 trainees, and by the recruited community of over 100 invited organisers, trainers and speakers. The series led to the creation of numerous training contents on bioimage analysis from basic to advanced topics, which are archived in repositories and are being made accessible as "open access" via the "BioImage informatics index" tool (http://biii.
eu), also developed by the NEUBIAS community. The TS venues also led to the growth and strengthening of NEUBIAS as a professional network composed of bioimage analysts, software developers, instrumentalists and life scientists, with over 250 active members and over 5000 listed contacts. The interaction of the different professionals has enriched the bioimaging community hence fostering different disciplines to converge towards a common framework for BIA challenges and their research and technological needs. Here we review the conceptual design of the TSs content, how NEUBIAS tailored the level of the courses to the different needs of the aforementioned communities, and how the training programme has organically evolved over the years to cope with the tremendous pace at which BIA is developing.