dataesr/scientific-paragraphs-categorization huggingface dataset:
This model was validated over three weeks using a 1L tubular reactor, fed with various types of wastewater. While it served as a valid foundation for subsequent modeling research, further improvements were necessary to validate it at the pilot-scale level. Specifically, new equations are included to reduce the number of calibrated parameters, and the use of the oxygen mass balance allows for improvement in the accuracy of the model. Equations have been implemented using Python language and Numpy packages while the calibration has been carried out with Scipy library and the optimization tool that uses the "Nelder-Mead" algorithm. The model has been validated over an extensive period (May-November); data have been collected from a demonstrative pilot-scale raceway reactor of 12 m 3 (80 m 2 ) on which the prevailing strain was Scenedesmus sp.
Raceway reactor and inoculum Experimental data were collected from an 80 m 2 (12 m 3 ) raceway reactor working in semi-continuous mode between April and December, with a fixed dilution rate (reverse of the hydraulic retention time time) of 0. 2 day -1 , meaning that every day a 20% of the total volume of the culture have been removed and replaced with wastewater. The raceway was installed in the SABANA Demo Plant located in the IFAPA research centre in La Cañada, Almería, Spain. It was composed of a double channel of 40 m and a sump of 0. 59 m 3 for the gas injections through diffusers and an electric motor connected to a paddlewheel system for culture mixing. The pH was monitored through sensors and controlled through CO 2 injection in the reactor sump. Additionally, an independent airflow allowed reducing the concentration of dissolved oxygen.
Environmental conditions and water composition As previously mentioned, environmental conditions in terms of temperature and radiation were continuously recorded throughout the entire period. The Photosynthetically Active Radiation (PAR) registered were ranged from 2000 µE⋅m -2 ⋅s -1 during the months of April and May, gradually decreasing during the colder seasons with peaks at 1200 µE⋅m - 2 ⋅s -1 , with an average of 620 µE⋅m -2 ⋅s -1 and 350 µE⋅m -2 ⋅s -1 respectevely. Regarding temperature, the highest values were recorded during the summer season (July-August), reaching peaks of 38 • C, while in the spring season (April-May), they ranged between 25-35 • C, and in the autumn season (September-November), between 25-12 • C. Regarding the culture medium, it consisted of wastewater collected from the University of Almería during the entire data collection period, except for August when the water was sourced from the primary water treatment plant in the city of Almeria.
Data collection and analysis Experimental online data were collected every second by a set of sensors, connected to a Programming Logic Controller (PLC) and a Supervisory Control and Data Acquisition (SCADA) system. On the contrary, data coming from laboratory analysis as described in the previous section, were collected once a day. Given the big amount of data available, it was necessary to perform a prior data analysis following a procedure inspired by the "Cross Industry Standard Process Alliance for Data Mining" (CRISP-DM) approach  (Ncr and Clinton, 1999). This methodology is one of the most used among data mining problems and it is composed of six main steps: (i) Business understanding, (ii) Data understanding, (iii) Data preparation, (iv) Modelling, (v) Evaluation, and (vi) Deployment.
In this research, the first five steps have been developed as briefly described below: I. Business understanding: the objective is to develop a model that can describe the evolution of microalgae-bacteria populations in wastewater-related systems. The aim is to develop a tool that allows the simulation of the variation of biomass and nutrient concentration with time as a function of environmental and process variables. II. Data understanding: data have been collected as described in the previous section, studied, and analysed. III. Data preparation: Datasets have been ordered, cleaned, and prepared for the modelling step. IV. Modelling: the biological system has been modelled as described in the next sections. Stepping back to data preparation is often necessary. A part of the experimental data set has been used for the identification of the calibration parameters.
Main changes implemented from ABACO model Despite the ABACO model served as the starting point for the development of ABACO-2, significant modifications have been implemented to enhance prediction accuracy and process understanding. The ABACO model proposed by  Sánchez-zurano et al. , 2021 , can be considered a preliminary study conducted with a limited expertise regarding microalgae cultivation phenomena. Furthermore, this model was developed using a restricted dataset and calibrated using data from laboratory-scale experiments conducted under controlled conditions. In contrast, ABACO-2 aims to calibrate parameters under industrial conditions, utilizing a more extensive dataset that encompasses diverse operational and climatic scenarios. The foremost modification involved the integration of models to account for cell death and respiration, coupled with the variation in BSMO content within the culture, subsequently reduced by heterotrophic bacterial activity.
Calibration procedure Fig. 2  represents the calibration strategy adopted. The experimental dataset used to calibrate the model parameters was selected to include data from different seasons, such as summer, winter, and intermediate seasons (28/04-15/05, 9/08-15/08, 1/11-15/11). In total, the calibration days chosen were 41 (20% of the total amount of data). In this way,  ] + [ (S -S max ) (S -S min ) 2 ( S opt -S min )( ( ( S opt -S min )( S N -S opt )) - ( ( S opt -S max )( S opt + S min -2S ))) ] ( 17 ) it was possible to address the parameters by accounting for various climatic conditions. The final set of parameters was chosen once the objective function described in (19) was minimized. Obj = ∑ ∑ ( y sim -y exp ) 2 σ exp (19) where y sim is the model output, y exp the experimental data and σ exp is the experimental data standard deviation.
The experimental data used for the model calibration regarded X tot , S NH4 , S PO4 , S NO3 , S BSMO , S O2 where X tot was defined as the sum of X alg , X nit and X het and evaluated experimentally as the total dry weight. The model calibration was carried out using Scipy library in Python and "Nelder-Mead" algorithm which is a robust algorithm mainly used for solving unconstrained optimization problems  (Gao and Han, 2012). The list of calibrated parameters with their corresponding values is presented in Table  6.
Conclusions ABACO-2 is a comprehensive model for microalgae-bacteria consortia in wastewater systems. The model was calibrated and validated in a pilot-scale wastewater treatment reactor, exposed to environmental changes and fed with real urban wastewater, which exhibited daily changes in the concentration of nitrogen, phosphorus and organic matter, over an extended period (May-November). The model accurately predict the biomass, dissolved oxygen and nutrient concentration evolution. Its relative simplicity enables accurate predictions and offers advantages in terms of understanding, practicality, efficiency, and versatility. Concluding, the ABACO-2 model can be considered a valuable biological model for the description of algae-bacteria in wastewater systems. To enhance its robustness, future studies should include additional validation across diverse datasets, accounting for different climatic conditions and wastewater types, and explore applications in larger industrial scales.
Acknowledgements This research was funded by the  H2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie grant  agreement (project:  Digitalgaesation ,  955520 ) and by the  H2020 Research and Innovation Framework Programme  (projects:  PRODIGIO ,  101007006 ;  REALM ,  101060991 ). Also, this work was supported by the  Spanish Ministry of Science and Innovation  (project:  HYCO2BIO ,  PID2020-112709RB-C21 ). All the authors would like to thank the  Institute for Agricultural and Fisheries Research and Training (IFAPA).
To do so, the carbon tips are washed with pure water and the ink is scraped using a micropipette tip and 4 μL of a mixture of water and aged catalyst are deposited on a copper or gold TEM grid. The images are analyzed with ImageJ software to determine the size of Pt particles and to plot the particle size distribution. Two types of particle populations are distinguished: well-defined isolated particles and aggregated ones (Fig. 1 ). In this work, the isolated particles are considered as spherical particles. The aggregated particles exhibit different, quite irregular shapes, and it was not possible to determine their "diameter" accurately. At different stages of the electrochemical protocol, two parameters are determined: the diameter of isolated particles and the projected area for aggregated particles.
The diameters of isolated particles are measured using the function "straight", whereas the enclosed area of aggregated particles is measured by the function "freehand selection" of the software ImageJ. Statistical analysis of different TEM images (between 6 and 10) representing more than 300 particles for each experiment (see Tables  S1  and S2  †) leads to a frequency distribution of diameter with a range of 0. 5 nm for isolated particles and a frequency distribution of area with a range of 2 nm 2 for aggregated particles. Since the number of analyzed particles can be different, all the distributions are expressed in frequency, meaning that the number of particles (isolated or aggregated) for all classes is divided by the total number of particles in the distribution. Some parameters are needed to monitor the evolution of particle morphologies and their numbers.
The rrnDB-5. 7 database was used to estimate the actual bacterial amount from 16S rDNA qPCR using theoretical 16S rDNA copies per strain, genus or family  (Stoddard et al. , 2015). Then, the theoretical number of bacteria was used to determine the nitrogen content in the biomass using the theoretical average dry mass of an Escherichia coli cell of 216×10 -15 g/bacterium and with a theoretical relative mass of nitrogen in microbial biomass, ie. 11. 4% according to the average biomass formula CH 1. 8 O 0. 5 N 0. 2  (Heldal et al. , 1985; Loferer-Krößbacher et al. , 1998). The nitrogen present in the biomass was therefore estimated using the formula below: Eq. 6.
To ensure separation of ethylene from CO 2 on the RT-U-Bond column, a CO 2 trap with sodium hydroxide (6M NaOH) was used at the time of sampling. After the ARA method was completed, the headspaces were flushed with N 2 and the gas recycling system was changed. Community sequencing and biomass quantification The microbial communities were quantified using the 16S rDNA qPCR to determine the total bacterial concentration and nifH gene qPCR for N 2 -fixing bacteria. In parallel, 16S rDNA sequencing was performed to identify major members of each community. This sequencing was also necessary to convert the amount of 16S rDNA to total bacteria using the rrnDB-5. 7 database. To analyze the communities present in suspension, 1. 8 mL of sample were collected for qPCR. For the cathodes, 1 cm 2 was recovered at several times.
After quantification, our enriched communities were sequenced according to their 16S rDNA and the results are available on NCBI repository PRJNA838547, Biosample SAMN28447998-SAMN28448066. The V3-V4 region of the 16S rDNA was amplified using universal primers as reported elsewhere  (Carmona-Martínez et al. , 2015). The PCR mixture consisted of MTP Taq DNA Polymerase (Sigma-Aldrich, Germany) (0. 05 u/μL) with enzyme buffer, forward and reverse primers (0. 5 mM), dNTPs (0. 2 mM), sample DNA (5-10 ng/μL), and water to a final volume of 60 μL. 30 cycles of denaturation (95°C, 1 min), annealing (65°C, 1 min), and elongation (72°C, 1 min) were performed in a Mastercycler thermal cycler (Eppendorf, Germany). A final extension step was added for 10 min at 72 °C at the end of the 30th amplification cycle.
nM), 2 μL of DNA, and water were used to a volume of 12 μL. The qPCR cycle was as follows: incubation for 2 min at 95°C and 40 cycles of dissociation (95°C, 10 s) and elongation (61°C, 20 s) steps. The results were then compared to a standard curve to obtain the copy number of the target in the sample. Both the 16S rDNA concentration of the PCE media and the cathodes are considered in the calculation of the total 16S rDNA concentration of the PCE. These concentrations are used as an indicator of the biomass present and the use of a database of the number of 16S operons per bacterial genome was used to estimate the actual amount of bacteria. The presence of N 2 -fixing bacteria was monitored by qPCR of the nifH gene of the Fe-Fe subunit of nitrogenases (Dos Santos et al. , 2012; Gaby & Buckley, 2012).
The most recent example is the Minimum Dietary Diversity for Women of Reproductive Age (MDD-W), a simple population-level dichotomous indicator expressed as the proportion of non-pregnant women of reproductive age who consumed at least 5 out of 10 defined food J o u r n a l P r e -p r o o f groups over the previous 24 hours  (11). MDD-W was validated using nine datasets from 6 distinct LMICs as a proxy for a minimally acceptable level of intake adequacy of 11 micronutrients among non-pregnant women of reproductive age  (12, 13). While the initial MDD-W validation study was able to assess the performance of the indicator for non-pregnant non-lactating women and non-pregnant lactating women, this was not possible for pregnant women due to the lack of data. Recent studies have used the threshold of 5 or more food groups to determine whether pregnant women had more adequate micronutrient intakes but without further validation of this dichotomous indicator in this population group  (14) (15) (16).
Study design and participants Six datasets with quantitative 24-hour recall data collected from rural areas in Bangladesh in 2015  (17) , Burkina Faso in 2017/2019/2020 (BF1,  (18) ), 2020 (BF2,  (19) ) and 2019/2021 (BF3,  (20) ), India in 2019  (21)  and Nepal in 2015  (22)  were selected for analysis. Each dataset is described in more detail in Supplemental table 1, which includes their selection process. Briefly, there were 5 pre-identified datasets (Bangladesh, BF1, BF2, BF3 and India) and we undertook a literature research to identify others, leading to add the dataset from Nepal. The included studies' primary objectives were to assess the feasibility and impact of maternal nutrition packages or integrated agriculture-nutrition interventions (Bangladesh, BF1, BF3 and India), to assess the efficacy of fortified balanced energy-protein supplementation (BF2), or to characterize the status and determinants of intra-household food and nutrient allocation, and test the effect of pregnancy interventions upon dietary intake (Nepal).
Dietary data collection In all studies, dietary data were collected using one to three quantitative multiple-pass 24-hour dietary recalls conducted by enumerators specially trained for this purpose  (26). Participants were asked to describe all foods and beverages consumed during the preceding 24 hours. Recipes were usually collected from the household member who was responsible for cooking. Portion sizes were estimated using methods best suited to local foods and contexts (e. g. previously distributed plates and bowls, common household measures, water volume, rice, images, clay or wooden models, etc. ). Only two datasets had repeated 24-hour dietary recalls on non-consecutive days, with two recalls for 19% of the sample (BF1) and three recalls for 87% of the sample (Nepal). Dietary data were converted into nutrient intakes using country specific food composition tables; the application of yields and nutrient retention factors was done by data providers according to their own practice and information is available from original studies  (17) (18) (19) (21) (22) (23).
This approach is based on information or assumption about both the distribution of nutrient requirements in the population and the day-to-day variations (withinperson) of nutrient intakes. We applied a Box-Cox transformation to the nutrient intake distribution of every micronutrient to obtain normal distributions. For each participant and micronutrient in each separate dataset, we calculated the best linear unbiased predictor (BLUP) of the individual's usual intake  (34)  which was then used to calculate the probability of adequacy for every micronutrient (see Supplemental Methods). All usual nutrient intakes have been calculated solely on the basis of food intakes, excluding intakes from food supplements (e. g. fortified balanced energy-protein supplementation in BF2). When datasets contained repeated 24-hour dietary recalls, the within person variance was defined as the mean of squared intra-individual SDs.
When datasets contained only one 24-hour dietary recall, we used an external within-person variance estimate from a relevant dietary intake survey with two non-consecutive days of recall  (35, 36). We used the external within-person variance to between-person variance ratio multiplied by the between-person variance of our dataset as the within-person variance in the BLUP calculations. A relevant dietary intake survey was defined as a survey conducted in the same geographical and seasonal context among pregnant adolescent girls or women. For Bangladesh, we used the within-person variance estimate from a subsample of the baseline study (~20%) that also participated in the endline study conducted a year later  (37). For BF2 and BF3, we used the within-person variance estimate from BF1 due to the fact that these three surveys were conducted in the same context (Boucle du Mouhoun, Centre-Ouest and Haut-Bassins for BF1, Haut-Bassins for BF2 and Boucle du Mouhoun for BF3) among pregnant adolescent girls and women.
Data analysis Data were analyzed with Stata 17 (Statacorp, College Station, TX) and the Stata syntax that was used for MDD-W validation in non-pregnant women  (12, 13) , with a few minor revisions to match the aims of our analyses. Descriptive statistics are reported as medians (interquartile ranges) due to skewness of the distributions, except for age, height, weight and energy intake, which are reported as means (SDs). Associations between the WDDS-10 and MPA (with or without adjustment for total energy intake) were assessed by fitting simple linear regressions. For the pooled sample, a mixed-effects regression model was used to examine the association between WDDS-10 and MPA, with random effect at dataset level to take into account the within-survey correlation. The MPA variable was previously transformed by BoX-Cox transformation for all the regression models.
We used receiver operating characteristic (ROC) analysis and area under the curve (AUC) to assess the diagnostic performance of WDDS-10 in predicting a MPA >0. 60, with an AUC >0. 70 deemed acceptable for predictive capacity. We estimated sensitivity, specificity and percentage of correct classifications for MDD-W across datasets and in a pooled analysis. The MPA level of 0. 60, as well as the interpretation thresholds, were selected to ensure comparability with the previous analysis used to validate the MDD-W  (12, 13). Sensitivity (i. e. ability to correctly detect a person with an MPA >0. 60) is defined by the ratio between the true positives and the sum of true positives and false negatives. Specificity (i. e. ability to correctly detect a person with an MPA ≤0. 60) is defined by the ratio between the true negatives and the sum of true negatives and false positives.
60. In order to understand the implications of some methodological choices, we conducted additional robustness analyses to estimate sensitivity, specificity and the percentage of correct classifications for MDD-W across datasets and in a pooled analysis according to three distinct scenarios. In the first robustness analysis, we tested 3 scenarios (Sc1, Sc2 and Sc3) where only 1 of the 3 Burkinabe datasets was included in the pooled analysis (BF1, BF2 and BF3, respectively), in order to keep into account the potentially redundant nature of using three surveys from Burkina Faso. In the second robustness analysis, we used the same recommendations from WHO/FAO  (29) , the National Academy of Medicine  (30, 31)  and the IZiNCG (32) but took into account pregnancy trimester, age and level of bioavailability of iron and zinc (see Supplemental table  3 ).
Characteristics of participants Data were available for 4909 pregnant adolescent girls and women (Table  1 ), with sample sizes of the datasets ranging from 452 (BF1) to 1912 (BF3). The mean (SD) age of J o u r n a l P r e -p r o o f participants was 25. 7 (6. 2) years, with participants from Nepal being on average younger than pregnant women from other countries. The inclusion of adolescent girls (15-18 years) across studies varied from none (India) to up to 26% (Bangladesh), and was 7. 1% in the pooled sample. The pregnancy trimester distribution was highly variable across datasets, with a neareven distribution in BF1, whereas almost all participants were in their third trimester in Nepal. Participants in their third trimester represented almost 60% of the pooled sample. Pregnant women in the Burkinabé datasets were on average taller and heavier than participants from other countries.
Dietary diversity The median (interquartile range) WDDS-10 in the pooled sample was 3 (1), with higher median scores in the Bangladeshi, Nepalese, and Indian datasets compared to the three Burkinabe datasets (Table  1 ). Figure  1  shows the percentage of pregnant adolescent girls and women consuming each of the 10 food groups used to construct MDD-W across the six datasets. Consistently across datasets, the diet of all participants was based on starchy staple foods. Most participants consumed other vegetables, but with large variations ranging from 55% in BF1 and BF3 to 91% in Nepal. The prevalence of participants consuming pulses and dairy products greatly differed across datasets: for pulses it was high in Nepal (over 80%), moderate in Bangladesh and India (59 and 46%, respectively), and low in the three Burkinabe datasets (27% in BF1, 14% in BF2 and 15% in BF3).
As for the prevalence of consumption of dairy products, it was very high in India (over 80%), moderate in Nepal and Bangladesh (53 and 33%, respectively), and low in the three Burkinabe datasets (4% in BF1, 3% in BF2 and 11% in BF3). In contrast, the prevalence of participants consuming nuts and seeds, and dark green leafy vegetables was higher in the three Burkinabe datasets. The prevalence of J o u r n a l P r e -p r o o f participants consuming flesh foods, eggs, and other fruits was higher in the Bangladeshi datasets.
Energy and nutrient intakes and the probability of adequacy The mean (SD) energy intake of the pregnant adolescent girls and women was 2068 (969) kcal per day in the pooled sample (Table  2 ), ranging from 1816 (838) kcal in BF3 to 2473 (1482) kcal in BF2. For all micronutrients apart from zinc, median intakes in the pooled sample were below the EAR (Supplemental table 5). However, there were differences between datasets, with median intakes in the Nepalese and Bangladeshi datasets above the EAR for 5 and 4 micronutrients, respectively. Accordingly, PAs varied widely across datasets (Table  2 ). Across surveys, the PAs of vitamin A, riboflavin, folate, vitamin B12, calcium, and iron were <0. 50. The median (IQR) MPA of the participants was 0. 20 (0. 34) in the pooled sample, ranging from 0.