dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, GARD ÉNIA was closest to the available national local estimates for the sites especially when discharge was included in the calibration. Thus, the dataset is too limited to draw any firm conclusions and the study should be regarded as the initial steps in identifying the usefulness and precision of these tools for different settings. Additionally, it is found that using long time series (>20 years) is preferable to improve the confidence in the model results. Generally, results points to that the time series models are especially useful for locations were other factors like abstraction or surface waters influence the water table observations, but that very complex and heterogeneous study sites such as karstic areas are problematic/difficult to simulate with all models, and here more detailed integrated modelling is needed.
Several drinking water production techniques are being established to respond immediately to the growing needs of the population. The system of air gap membrane distillation (AGMD) is the best attractive option for the process of water desalination. This thermal process is characterized by its potential to provide drinking water at low energy costs when combined with solar energy. In this paper, the AGMD brackish water desalination unit potentialities coupled with solar energy were investigated. Ghardaïa of the south region has been considered as the field of our study. Mathematical modeling is investigated by employing MATLAB software to develop the prediction of the permeate flux related to the phenomena of heat and mass transfer. Herein, flat plate solar collectors (SFPC) were exploited as a source for heating saline water through free solar energy conversion.
Water molecular weight, gas constant, absolute membrane temperature, and total pressure inside the pores are all represented by M w , R, T m , and P, respectively. D va is the water vapor's thermal diffusivity in the air gap, and τ is the membrane's tortuosity  [8]. B w = εM w PD va RT m δ m τ + δ ag |P a | ln,a (8) The mass flow rate of the AGMD module is given by Equation (  9 ), where J w and S m are the permeate flux and membrane surface, respectively, as shown in Equation (  10 )  [38]. m p = J w. S m (9) The value of the vapor flux is determined by combining the transfer and mass equations developed previously. The MATLAB program was used to calculate the heat transfer and, hence, the permeate flux. Figure  2  shows the flowchart for calculating the permeate mass flow. Water 2023, 15, x FOR PEER REVIEW 6 of 20 the membrane's surface in the hot channel's boundary layer and the air gap at the cooling plate's surface, respectively.
𝑚 = 𝐽. 𝑆 (9) The value of the vapor flux is determined by combining the transfer and mass equations developed previously. The MATLAB program was used to calculate the heat transfer and, hence, the permeate flux. Figure  2  shows the flowchart for calculating the permeate mass flow. Figure  3  presents the strategy for simulating a flat plate collector coupled to an AGMD unit. Initial water conditions must be given for heat transfer calculations. Thus, the inlet temperature of the sea water to the solar collector corresponds to the outlet temperature of the cold channel of the MD cell and the outlet temperature of the water in the solar collector to that of the inlet temperature of the hot channel. Figure  3  presents the strategy for simulating a flat plate collector coupled to an AGMD unit.
Thus, field-scale experiments are very valuable, especially relevant experimental data on high intensity fires that can be of primary interest in understanding the physical characteristics governing the behavior of such fires. In this context, the main objective of this study is to present experimental fires carried out at field-scale on sloped terrains across dense and homogeneous shrubland vegetation, at different time periods (autumn and winter). The first field-scale fire was conducted in the north-western region of Corsica in October 2021, and the second in the south region of this island in March 2022. During these experiments, high intensity fires were obtained and all the detailed data concerning the propagation conditions collected in the sites can be used by the research community as a basis for comparison with numerical simulations carried out using different fire spread models.
The fuel volume fraction β was then obtained by calculating the ratio of the dry fuel load σ, the product of the particle density ρ v and the average vegetation height e as given in Equation  (1). Concerning the ambient weather conditions, relative humidity and air temperature were monitored using a weather meter (Kestrel 5500, Kestrel Instruments, Boothwyn, PA, USA). In addition, a 2D ultrasonic anemometer (WS425, Vaisala Oyj, Vantaa, Finland) located at 3 m above the ground level was used to record the wind speed and direction acting on the fire front. Wind data were recorded using a data logger (CR3000, Campbell Scientific, Logan, UT, USA) at a sampling rate of 1 Hz. All parameters characterizing the vegetation and related to the meteorological and topographical conditions during the two experimental fires are listed in Table  1.
Heat Flux Measurements In order to evaluate the impact of the fire front, the total and radiant heat fluxes were measured during the fire spread using 2 pairs of Medtherm transducers (16H and 64 Series, Medtherm Corp, Huntsville, AL, USA), located at different positions in a free vegetation area ahead of the plot (Figure  1b ). Radiant and total transducers were oriented in the slope direction towards the fire, with view angles of 150 • and 180 • , respectively. The heat flux gauges were plugged into a power-supplied data logger (CR3000, Campbell Scientific) buried 0. 3 m under the ground surface to protect it from fire. The extension cables were also buried into the ground up to the data logger and insulated by Teflon coating and aluminum foil (Figure  3 ). The transducer signals were recorded at a sampling rate of 1 Hz.
Fire Front Impact The fire impact was evaluated at different positions ahead of the fire front. Therefore, radiative and total heat fluxes received by the different transducers located at the end of the plots were recorded for both experiments. During the burn campaign in October, backfires conducted by the firefighters to protect the burning zone were close to the sensors, which affected the measurements during fire. In this case, only heat fluxes measured during the March experiment were investigated, and compared with numerical predictions. Experimental heat fluxes received by the transducers were recorded during the experiment and the data analysis was carried out until the fire front reached the end of the plot. The targets were located at two positions, P1 and P2, as shown in Figure  11.
The in vitro H295R steroidogenesis assay (OECD TG 456) is used to determine a chemical's potential to interfere with steroid hormone synthesis/metabolism. As positive outcomes in this assay can trigger significant higher tiered testing, we compiled a stakeholder database of reference and test item H295R data to characterize assay outcomes. Information concerning whether a Level 5 reproductive toxicity study was triggered due to a positive outcome in the H295R assay was also included. Quality control acceptance criteria were not always achieved, suggesting this assay is challenging to conduct within the guideline specifications. Analysis of test item data demonstrated that pairwise significance testing to controls allowed for overly sensitive statistically significant positive outcomes, which likely contribute to the assay's high positive hit rate.
Furthermore, the Level 4 pubertal assays are considered as insufficiently robust to clearly identify adversity due to altered steroidogenesis. Consequently, according to the EFSA/ECHA ED guidance document  (Andersson et al. , 2018) , a Level 5 reproductive toxicity study is required to contextualize any positive signal identified in the in vitro H295R assay, which would lead to the use of up to ~1800 animals per in vivo study for each test substance concluded to be positive in the H295R assay. The OECD validation report of the H295R assay recommended using a 1. 5-fold change as a cut-off threshold for activity  (Hecker et al. , 2011) ; however, this threshold for activity was not included in the original final test guideline  (OECD TG 456, 2011a). Thus, based on the discrepancies between the validation report and the test guideline, as well as the regulatory consequences of the H295R assay outcomes that could lead to significant animal testing, a database was compiled by stakeholders comprising of ~16,800 H295R assay data points to explore the nature of the positive assay outcomes.
This compendium of data is the first of its kind for the H295R steroidogenesis assay as it allowed a comprehensive evaluation of outcomes using data generated in assays conducted per the test guideline (OECD TG 456). Additional information was also provided for several of the test items included in the database regarding whether a Level 5 reproductive toxicity study was triggered because of a positive outcome in the H295R assay. During the course of these analyses, the concerns raised by industry stakeholders were echoed by members of the OECD validation management group for non-animal testing (VMG-NA) originally tasked with the validation of the H295R test guideline (OECD 456), which were triggered by the reporting of an unusually high occurrence of positive outcomes by some contract research organisations (CROs) and other laboratories  (Jacobs, 2021).
Data sources Test item data obtained from GLP or GLP-like studies conducted in 7 test facilities and according to the OECD TG 456 protocol were provided by 11 participating agrochemical companies. Raw data were submitted using a standardized template for all test item runs and accompanying quality control (QC) plates. In addition to all control chemical and test item data, the conducting laboratory identity, cell passage number, raw hormone levels for T and E2, and cytotoxicity data were requested. The final inventory comprised 16,841 entries, of which 9975 represent test item plates, which amounted to 50 test items, and 6866 represent QC plates. For the analyses conducted, fold change values and all statistical outputs were computed de novo to ensure consistent processing of all data. All analyses were conducted using R v4.
Run interpretation criteria The overall dataset comprised 50 test items with 38 of these substances having data from 2 to 4 runs. The analyses described herein focussed on these 38 test items as the data were generated in-line with OECD 456. The remaining 12 test items were not included in the evaluations as only single runs were performed for them. All runs were analyzed and a run interpretation of equivocal, positive, or negative was determined based on fold changes using four different approaches as described below. The first approach ("Approach 1") was analysis of pairwise concentration-specific significance compared to plate-matched vehicle controls. This was evaluated by ANOVA followed by Dunnett's test as described in the test guideline. In brief, first a Shapiro-Wilks test was used to assess data normality, if data were normal then ANOVA with Dunnett's was run; when data were not normal, Dunn's test was conducted as a non-parametric alternative.
Finally, the fourth approach integrated all three of the evaluated parameters in combination: outcome from Approach 1 (Dunnett's), trend test results, and fold change cutoff. A brief description of interpretation criteria for each approach are listed below. Individual runs for all 50 test items were first evaluated using the above four approaches followed by approach-specific interpretation combining the interpretation of individual runs to give an overall conclusion, determined based on the decision matrices for possible outcomes defined in the relevant OECD TG 456 (ie decision matrix described in the 2011 version of the test guideline was used for Approach 1 whereas the matrix described in the 2022 version was used for Approaches 2-4). As the decision matrices require at least two independent runs for interpretation the outcomes were finally determined for a total of 38 chemicals where data from ≥2 runs for both T and E2 were available.
QC plate performance The OECD TG 456 protocol includes the evaluation of a quality control (QC) plate as part of each test run in order to monitor assay conditions when testing chemicals. In addition to basal production minimums, the performance criteria for the QC plate require that 10 μM forskolin achieve induction of steroidogenesis as measured by ≥ 1. 5-fold induction of T levels compared to solvent control and ≥7. 5-fold induction of E2 levels. Additionally, 1 μM prochloraz should achieve inhibition of hormone levels relative to solvent control of at least 0. 5-fold (e. g. , 2-fold decrease) for both T and E2. The QC plate data accompanying all testing runs were obtained and compiled amounting to ~1600 data points each for forskolin-and for prochloraz-mediated effects. The log2 fold change of T and E2 levels in response to forskolin and prochloraz were evaluated (Fig.
Evaluation of data interpretation outcomes After nearly a decade's experience of performing the OECD TG assay, concerns regarding assay outcomes suggest that consideration of alternative approaches for data interpretation may be warranted. The evaluation of multiple data interpretation criteria was undertaken to determine the impact of efficacy cutoffs and of requiring trend testing for concentration-response. First, all test item runs were analyzed de novo from raw hormone concentration readouts including determination of relative fold change and statistical evaluation. After integration of cytotoxicity data and conduct of statistical analyses, each run was assessed by four different approaches, one of which was the original OECD TG 456 approach (Approach 1) and a second one used the 1. 5-fold threshold cut-off approach (Approach 3), now included in the refined test guideline, to derive an outcome interpretation of positive, negative, or equivocal (Table  1 ).
Of the remaining 10 test items, a conclusion of non-ED (specifically referring to the estrogen, androgen, and steroidogenesis (EAS) modalities) has been proposed by the applicants for 8 test substances and no information was provided for the remaining two compounds (Fig. 3 ; Table  3 ). For the 11/13 compounds considered non-ED (either officially or otherwise) this was due to either the absence of adversity in the extensive database available for each compound (including the new reproductive toxicity study) or due to an absence of a plausible link between the in vitro and in vivo observations. Interestingly, 4 of the 13 test items for which a reproductive toxicity study was triggered were still concluded as being positive in the H295R assay even after application of the fold change cut-off.
H295R outcomes compared with in vivo data: A case study The weight of evidence assessment to determine the ED potential within the regulatory context, according to the EFSA/ECHA ED guidance  (Andersson et al. , 2018) , for a test item was provided for review herein. This example demonstrates how critical the interpretation of the H295R steroidogenesis assay is because follow-up on a positive outcome requires substantial in vivo testing to be conducted. Briefly, the review of ToxCast/Tox21 high-throughput screening data had initially suggested that the test item (ID20 in the blinded compiled dataset) was negative in the estrogen receptor pathway model  (Judson et al. 2015)  and androgen receptor pathway model  (Kleinstreuer et al. 2017). As, according to the EFSA/ECHA ED guidance  (Andersson et al.
The main findings were for effects on body weight, with some secondary organ weight changes, effects on food consumption, and on some hematological parameters. Ultimately, the conclusion from these subchronic studies was that there were no effects relevant to estrogen/ androgen/steroidogenesis (EAS)-modalities and that the criteria for ED were not met. Additional dietary chronic toxicity and carcinogenicity studies were also conducted, in which no effects on weights or histopathology of endocrine organs were observed, resulting in the conclusion that there was no evidence of EAS-mediated endocrine effects in long-term studies in rats and mice. Finally, data available from several reproductive toxicity studies, including the newly conducted extended one generation reproductive toxicity study, were also assessed for endocrine effects, which despite some parental toxicity at higher doses, showed no consistent effects on reproductive efficiency nor on endocrine organ weight or histopathology.
In summary, the in vivo data confirmed that the test item did not provide any consistent findings or any pattern indicating adversity relating to EAS-modalities in the absence of systemic toxicity. The lack of endocrine activity for the test item was confirmed by the absence of effects in the Level 2 (ARTA and aromatase) and Level 3 assays. The additional Level 5 testing initiated based on the marginal efficacy positive call from the H295R steroidogenesis assay in this case did not substantiate the in vitro observation (Table  4 ) and confirmed the lack of account for the combination of results (or an additional run not available in the current dataset which would be required to conclude on an outcome). WP: weak positive; the refined OECD TG 456 (2022) includes a new category of weak positive capturing occurrences of equivocal/negative/(positive or equivocal) or equivocal/equivocal/(equivocal or positive) for each of the three runs or equivocal/positive for each of two runs.
, 2011)  and ultimately led to its inclusion in the refined Test Guideline  (OECD TG 456, 2022). Our analyses, based on a more extensive and broadly sourced database support the refinement and indicate that the criteria for stringent assay conduct and the inclusion of statistical trend analysis proposed herein could help with interpretation in a regulatory context as they can take into account the data-driven characteristics of this assay and help identify truly robust effects that are appropriate for decision making rather than simply characterizing effects for "screening and prioritization purposes". The H295R cells maintain the capacity for inducible production of steroid hormones. However, this is a rather sensitive system and thus stringent assay requirements are in place to ensure performance is meeting the necessary quality.
The database described herein provided insight into the impact that low efficacy, but nonetheless statistically significant, outcomes in the steroidogenesis assay (based on the criteria of the original OECD TG 456 (2011)) could have on additional animal testing. Specifically, new Level 5 reproductive toxicity studies were triggered for 13/36 test items because of a positive or inconclusive outcome in the assay according to the original test guideline criteria. As can be seen in Fig. 3  and Tables  3  and 6  of those 13 compounds would have been classified as negative in the assay according to the recently refined OECD TG 456 (2022). Nevertheless, ~23,400 animals were used to address the relevance of the H295R assay outcomes for the 13 compounds in question. To date only one compound included in our database has been officially concluded as non-ED for HH and two others are considered as non-ED in the DARs written by the RMS.
In addition, a conclusion of non-ED for the EAS modalities has been proposed by the applicants for 8 of the other 10 test substances (no information is available at this time for the remaining two compounds for which the H295R data were voluntarily provided blindly for this project). In all cases these conclusions were based on either an absence of adversity in the extensive in vivo database available for each compound (including the new reproductive toxicity study) or an absence of a plausible link between the in vitro and in vivo observations. The comprehensiveness of the in vitro and particularly the in vivo dataset used to determine whether an agrochemical is an ED was clearly demonstrated with the case study described herein. Test item ID20 was negative in all Level 2 assays except the H295R assay in which it was marginally positive for E2 induction.
The refinement of OECD TG 456 (  2022 ) not only includes the 1. 5fold threshold but also a new final call category of weak positive in the decision matrix. Currently, however, there is no clear guidance concerning what follow-up tests (if any) would be triggered in the case of a molecule being concluded as a weak positive. In the case of test item ID20, the conduct of a third H295R assay would have been necessary to be able to conclude on its potential to interfere with steroidogenesis. Furthermore, depending on the results from this third run, the test item could be classified either as negative or as a weak positive according to the guideline's revised decision matrix. Although not a common occurrence in the extensive database described herein, a number of compounds were classed as weak positives according to the refined decision matrix (3 each for E and T and as an overall conclusion for 4 compounds).
Furthermore, two compounds would warrant additional in vitro testing to finally conclude on steroidogenesis activity with potentially weak positive final calls. Clearly, further retrospective analyses will be required to fully understand the in vitro to in vivo extrapolation, particularly in cases of a weak positive outcome in the H295R assay. In summary, the compendium of data compiled herein is the first of its kind to collate and assess H295R steroidogenesis data generated for regulatory purposes. This dataset was used to evaluate thousands of data points to allow data-driven determination of QC plate trends and assessment of different analysis approaches to determine potentially effective complementary criteria that can aid identification of robust positives and minimize equivocal outcomes.
2b ). We generated four image sets with equal numbers of mitochondria and endobacteria, each dataset having an increasing object density (d = 30, 60, 90, and 120). When a bacterium and a mitochondrion overlapped, we called these cases a "mitochondrion-bacterium intersection" (MBI). We counted the true number of MBIs (NMBI) for each object density d (Fig. 2d , points represent NMBI for each d). After obtaining the true NMBI for each dataset with different object densities, we simulated 2D imaging on the same hypothetical 3D image stacks by taking random 2D slices (Fig. 2c ). We took different numbers of slices (N-slices: 20, 40, 60, 80, 100), and calculated the total NMBIs for each slice set. As more slices were taken from a 3D stack, more intersections were observed, hence, we detected higher NMBI.
Three-dimensional imaging displays the cellular content of I. ricinus oocytes at different maturation stages In order to document the cellular distribution of the mitochondria and bacteria and to accurately quantify free and interacting symbiotic subpopulations in 3D, we used the ZEISS Auriga Crossbeam system (Jena, Germany). FIB-SEM captures volumetric images by irreversibly removing a thin layer from the sample surface and scanning the newly revealed surface in an iterative manner that builds up to a 3D image stack (Fig. 2e, f ). Due to the acquisition of backscattered electrons, FIB-SEM provides poorer resolution compared to conventional TEM (expected maximum resolution is 0. 4 nm compared to 0. 1 nm for TEM in biological samples)  37. Acquiring data for a single volume takes days, allowing for drift, loss of focus, and interruption in milling to compromise the image, that further compromises the image and makes it particularly difficult to follow the mitochondrial membrane.