dataesr/scientific-paragraphs-categorization huggingface dataset:
It includes the following categories. -Urban spices; this category includes inventory of urban spices and habitat with more details on specific spices (witness spices), alien spices and spices activity and health. Data are collected by field visit, by cameras installed at significant locations and from citizens involved in the project. The observation time should be established for each spice considering weather change or major events related to natural or human-made hazards. -Weather conditions; this category concerns the hourly variation of the temperature, humidity, air quality, rainfall intensity and wind speed and direction. -Human activity, with a focus on construction maps, change in land use, pollution of soil and water, noise pollution and light pollution. -Urban hazards such as flood, storms, earthquakes, fire and industrial hazards.
First, we present an updated checklist of the soft-bodied meiofaunal species recorded during the workshop, including a description and a discussion of the main findings. Second, taking advantage of this first comprehensive dataset of soft-bodied meiofauna in an oceanic island, we assess the effects of biological and environmental variables on a set of predictors for meiofaunal diversity at two levels. (1) Regional level: we test the effect of biological variables on the number of soft-bodied meiofaunal endemic species found in the entire island. (2) Local level: we investigate the effect of environmental variables within the island, to test for differences in species richness, percentage of endemic species, and species composition across different types of habitats. At the regional level, we expect for example that groups with higher dispersal capabilities and smaller average size will have a lower percentage of endemic species in Lanzarote than other groups, as these traits are known to be associated to a more widespread distribution  (Curini-Galletti et al.
Acknowledgements We are grateful to  Elena Mateo  and  Leopoldo Moro  for their assistance obtaining the permissions to sample during the workshop. We are in debt to the divers  Luis E. Cañadas ,  Enrique Domínguez ,  Carola D. Jorge , and  Ralf Schoenemark , who assisted us collecting sediments, especially those from  Montaña de Arena. The workshop was funded by  Consejería de Medio Ambiente del Cabildo de Lanzarote and Reserva de la Biosfera. We thank  Aula de la Naturaleza de Lanzarote  for providing housing and space to set up our labs during the workshop. We are in debt to the diving clubs  Pastinaca (Arrecife) and Punta Mujeres (Haria) , and particularly to  Antonio Martín  and  Juan Valenciano , for lending us diving gear and support. Juan Valenciano  kindly sailed us for the collection of samples outside La Corona lava tube and provided some of the pictures used to illustrate this manuscript.
Methods Approval for this study was obtained from the University of KwaZulu-Natal Bio-Ethics Research Committee (BREC BE078/17). Permission was obtained from Grey's Hospital, Pietermaritzburg, KwaZulu-Natal to recruit participants after approval was granted by the Health Research Committee of the KwaZulu-Natal Department of Health (NHRD Ref: KZ_2016RP_297). We conducted a prospective observational study in 100 adult patients receiving general anaesthesia for elective surgery. Patients were eligible for inclusion if they were ≥ 18 years old, planned to have an anaesthetic for longer than 60 min (to allow sufficient time for monitor warm up and increase the number of data points for analysis) during which no body cavity would be opened, this was to limit possible confounders such as fluid and heat loss from exposed bowel or possible need for rapid or large volume fluid administration that are common in emergency setting and open body cavity procedures.
An upper body FAW does cover the skin temperature monitor placed over the sternum. Using a standardized data collection tool (Additional file 1) we collected patient demographics (age, gender, weight, height), type of surgery, use of a FAW, and the temperature reading on the monitors at 15-min intervals. In addition, the data collection tool provided a comments section to provide subjective feedback from the anaesthetic provider regarding the use of the monitoring devices. Eligible study participants were approached and verbally informed about the study. If they were interested in participating, they were provided with written study information in English or isiZulu and written consent was obtained. A written copy of the study information and contact details of the principal investigator were given to the participants.
Participants were assigned study numbers and their identifiable details were separated from the study information to ensure anonymity. Data was analysed using Microsoft Excel. Assuming an effect size difference of 0. 5, an α of 0. 05, and 80% power we required a sample size of 64 participants. However, as suggested by Professor Martin Bland (https://www-users. york. ac. uk/~mb55/meas/sizemeth. htm)  [8] , we decided to include 100 participants using pragmatic sequential sampling. We conducted 5 assessments, first a composite comparison and then each monitor site's performance compared to the oesophageal/pharyngeal monitor. We also examined the impact of FAW on the results. Difference plots, also known as Altman and Bland plots, were used to assess the performance of the skin surface temperature monitor readings as obtained from the four difference sites to the oesophageal/pharyngeal temperature readings.
Statistical Analysis Each experiment was done in triplicate (n = 3). For each replicate 2 samples were analyzed. Analysis of variance (ANOVA), Duncan's multiple range tests for equal variances and Tamhane's test for unequal variances were performed for statistical analysis using SPSS 18. 0 software (SPSS Inc. , Chicago, IL, USA). Differences between means were considered significant when the confidence interval was lower than 5% (p ≤ 0. 05).
Adsorption Isotherms: Nisin A solutions were prepared at concentrations of 500, 1000, 2000, and 3000 µg/ml in 677 mM, pH 2 HCl/KCl buffer. 5 mg of each silicate (MSE, MCM-41, and SBA-15) was added to 1ml of nisin A solution and to 1 ml of buffer (as a control). The tubes containing the silicates and controls of free nisin A at each concentration were incubated at 37°C with shaking of 250 rpm for 5 hours, after which they were centrifuged for 15 minutes at 1,500 rpm. The supernatant was analysed using RP-HPLC to determine the concentration of nisin A remaining in solution. Using the data obtained from the adsorption to the 5mg of silicate, the adsorption study was scaled up, whereby 100 mg of SBA-15, MSE or MCM-41, were weighed into tubes, and 20 ml of a 2,000 µg/ml nisin A solution was added.
08 mg of MCM-41-nisin (1,120 μg/ml nisin A as it had a loading of 0. 282 mg/g), 33. 6 mg SBA-15-nisin (1,820 μg/ml nisin A as it had a loading of 0. 0573 mg/g), and 7. 14 mg MSE-nisin (1,320 μg/ml nisin A as it had a loading of 0. 2277 mg/g), were suspended in 1 ml each of m-FaSSIF and m-FaSSGF with controls comprising of 5. 08 mg of MCM-41, 33. 6 mg SBA-15, and 7. 14 mg MSE, and nisin A solutions of 2,000 μg/ml, 200 μg/ml, and 20 μg/ml, and incubated at 37°C shaking at 275 rpm. Samples were centrifuged at 7,000 rpm for 7 minutes and the supernatant was fully removed at time points of 2, 4, 24, 48 and 72 hours, and replenished with fresh media. The samples were analysed using RP-HPLC to determine the concentration of nisin A present in the supernatant. The release data was fitted to different release rate equations to theoretically determine the mode of release of nisin A from the matrices.
Estimation of molecular dimensions A file corresponding to the structure of pepsin (5PEP) was downloaded from the protein database (http://www. rcsb. org/pdb). The structure for nisin A was not available alone and was only available as its complex with lipid II, a key complex in the active mechanism of antimicrobial peptides, as previously determined by solution NMR (1WCO). Thus the structure was modified and the ligand was removed  [51]. The files were viewed using PyMOL (version 2. 1. 1) and the dimensions were obtained using the Measuring Wizard function in PyMol.
The molecular dimensions of nisin A were estimated to be 4. 76 nm x 2. 32 nm x 0. 9 nm and pepsin was estimated to have the dimensions 5. 60 nm x 5. 54 nm x 4. 14 nm. These dimensions for nisin A as measured using PyMOL simply provide a rough estimate of the molecular size and shape of nisin A. Nisin A most likely adopts a different conformation (Fig. 2 ) in gastrointestinal fluid in the absence of lipid II but the available structural data gives an estimate of the maximum length of the molecule. The estimated surface potential was determined using the 'generate vacuum electrostatic function' in PyMol. The surface potential was estimated to determine the potential for hydrophobic or electrostatic interactions between the enzymes/peptide, and the mesoporous particles. As with the molecular dimensions, the estimated surface potential of nisin A (Fig.
The chemistry of each matrix was characterised by FTIR and SSNMR spectroscopy. The aminefunctionalisation of PMO-PA was confirmed by the presence of the N-H stretch at 3328 cm -1 and the N-H bending at 1641 cm -1 in the FTIR spectrum. The ethylene-functionalisation of MSE was confirmed by the C-H deformation vibrations at 1276 and 1418 cm -1 (data not shown)  [57] -  [59]. 13 C CPMAS SS-NMR analysis of the four mesoporous matrices confirmed the chemical functionalisation of each material (Fig. 3a ). MSE had one peak at a chemical shift (δ) of 5. 3 ppm, corresponding to the ethyl carbons. For the PMO-PA material, three peaks were present at 10. 2, 20. 1 and 50. 8 ppm, corresponding to the three environments for the amino propyl bridging carbons. The peak furthest up field most likely corresponds to the carbons bound to silicon, with the peak at 20.
The adsorption isotherms (Fig. 4 ) indicate that more nisin A binds to MCM-41 than to MSE. In addition, the binding of nisin A to SBA-15 appears to be quite weak at lower nisin A concentrations. From the study carried out, maximum loadings of 0. 0976 + 0. 0002 mg/g onto SBA-15, 0. 258 + 0. 002 mg/g onto MSE, and 0. 391 + 0. 005 mg/g onto MCM-41 were obtained. By fitting the adsorption data to linear models of the Langmuir and Freundlich theories (Table  3 ) it was found that the MCM-41 and MSE could load significantly higher amounts of nisin A compared to SBA-15 with MCM-41 exhibiting the highest adsorption coefficient (K). From the adsorption isotherm theories, it appeared that nisin A adsorbs predominantly through hydrophobic interactionswith adsorption onto the SBA-15 surface being weaker than onto MCM-41 or MSE, most likely due to the scarcity of hydrophobic moieties on its surface.
The Higuchi model describes release kinetics which slow as the diffusion path or the distance that the drug molecules have to travel increases  [50]. This finding supports the hypothesis that the burst release observed from the data sets indicates the presence of surface adsorbed nisin A which dissociated from the surface upon suspension in the m-FaSSGF, followed by a slower diffusion of nisin A from the pores of the mesoporous matrices. No release of nisin A was detected by RP-HPLC analysis from SBA-15, MSE or MCM-41 into m-FaSSIF over this time period, due to the poor solubility of nisin A at the pH of the m-FaSSIF (pH 6. 5). The activity of the supernatants and pellets against S. aureus was studied. During the activity assays, all of the nisin A loaded matrices and their supernatants in m-FaSSGF completely inhibited the growth of S.
Powder The granular size distribution of the unheated freeze-dried powder after crushing was analyzed as described previously in  Schong & Famelart (2019)  using the QICPIC analyzer and the dry disperser RODOS/L module (Sympatec GmbH, Clausthal-Zellerfeld, Germany). Two measurements of the distribution of the "diameter of a circle of equal projection area" of the particles were taken for each sample and the mean volume diameter  (D[4,3] ) was deduced. Two measurements were performed for each unheated sample. Samples of powder particles were transferred to a glass lamella and observed on a light microscope (Olympus BX51, Rungis, France) with a x10 objective. Photographs (10 per sample) were taken using a Qiclick CCD camera (Q Imaging, Microvision Instruments, Evry, France) and the Archimed software program (Microvision Instruments).
Acknowledgments We are grateful to the  Regional councils of Brittany  (grant  N°13008651 ) and  Pays de la Loire  (grant  n°2014-07081 ) for financial support and  INRA  for scientific coordination (J. Leonil) through the interregional project  PROFIL , managed by the  BBA  industrial association, and to  P. Hamon  and  F. Rousseau  for their technical help with HPLC analyses and particle size analyses.
The assemblage of data analyzed in this paper consists of driving and charging data collected between June 2015-June 2018 from 153 PHEVs in California. Five PHEV models are examined in this study: Toyota Prius (11-mile AER), Ford CMax and Fusion Energi (20-mile AER), Chevrolet Volts (35/38 miles and 53 miles AER). The rest of the paper is organized as follows. Section 2 summarizes the aggregate driving and charging data and describes the quantitative methods used. Our analysis and results are detailed in Section 3. Comparative assessment of observed driving and charging behavior with sticker label expectations, followed by the PCA and OLS model results are presented in Section 3. We discuss our findings in Section 4 and conclude this paper in Section 5.
Data and Methods The source of the data used in this paper is from the Advanced PEV Driving and Charging Behavior project, a multi-year study to monitor PEV usage in California  [41, 42]. Online survey was first administered to PEV owners randomly sampled from the California Clean Vehicle Rebate Project  [43]  and vehicle registration records. Sub-sample of respondents were selected and GPS enabled data loggers were installed in the on-board diagnostics (OBD) port and monitored for at least a year. The data loggers report more than two dozen variables related to driving, charging, performance, and comfort. The important vehicle usage parameters relevant to our analysis are: trip and charging session start and ending time stamps and locations; trip and charging session start and end state of charge (SOC); charger level, charging duration, and charged energy; trip distances, duration, and consumption (electricity and gasoline).
Since the scope of this paper is on real-world performance, our analysis strictly focuses on the data from the loggers, and the respondent's home location is the only relevant survey information that is included in our analysis. Five PHEV models with sticker label AER varying from 11-53 miles  [44]  are in the dataset: Toyota Prius (11 miles AER, N = 22), Ford CMax Energi (20 miles AER, N = 28), Ford Fusion Energi (20 miles AER, N = 24), Chevrolet Volts (35/38 miles AER, N = 43; 53 miles AER, N = 36). The 35-and 38-mile AER Chevy Volts were grouped together as Volt-35/38 since there was little difference between their AER capabilities. The PHEV models analyzed in this study accounted for close to 85% of rebates issued to PHEVs under the California Clean Vehicle Rebate Project  [43].
Driving and Charging Data Table  1  presents the driving and charging data which consists of approximately 2 million VMT, 200,000 trips, 52,000 charging sessions, and 260 MWh of charging energy collected over the course of 45,000 driving days (driving and charging or driving only) between June 2015-June 2018 in California. On average, every vehicle in the dataset was driven 292 days, and among the PHEV types it varied between 268 and 315 days during the data collection period. Of the 52,237 charging sessions, 53% were at Level 1(L1), 1. 4 kW rated and the rest were at Level 2 (L2), 3. 3 kW rated  [45]. Throughout the rest of the paper, unless otherwise specified, J2841 UF  [9] , AER and mode specific energy consumption, kWh/mile in CD-EV mode or Miles Per Gallon(MPG) in CS mode, found in EPA fuel economy labelling data  [46]  are collectively addressed as Expectations.
Quantitative Methods Principal Component Analysis (PCA) is a statistical procedure to reduce the dimensionality of the dataset and it falls under the larger umbrella of Exploratory Factor Analysis (EFA). In this paper, we use PCA to reduce the dimensionality of the observed driving and charging data which is highly susceptible to the problem of multicollinearity. Since Utility Factor is the ratio of eVMT to VMT and eVMT is a function of charging behavior, multicollinearity would persist and as such could severely undermine interpreting the statistical significance of driving and/or charging related independent variables (IV) on the dependent variable (DV). Combining both driving and charging related usage metrics and then performing the PCA may not eradicate the problem of multicollinearity.
Validity and Suitability of the Data for PCA The EFA involves four major steps. The first step is to check the appropriateness of the dataset for factor analysis. For this, we used the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA)  [47, 48]  and Bartlett's Test for Sphericity  [49]. The KMO MSA is an index between 0-1 which quantifies the ratio of observed correlations to partial correlations, the higher the better indication of the suitability of PCA  [50, 51]. Literature recommends a empirical rule of thumb of at least 0. 6 as a minimum for the KMO MSA  [52, 53]. Bartlett's Test for Sphericity tests the hypotheses that the correlation matrix is an identify matrix, thereby implying that variables are unrelated and not suitable for PCA. Bartlett's test with a p-value of less than 0.
05 is required for PCA. The KMO and Bartlett's tests together determine whether the underlying structure of the dataset is suitable before proceeding to perform the PCA. The second step is to decide how many factors to retain. The number of factors to extract and retain is typically determined based on the share of variance explained by each of the factors and a suitable threshold for the cumulative total variance captured by the PCA. Scree plot for Eigen Value of greater than one is a widely used method to select the number of factors to retain, which this paper employed. The resultant component matrix shows the factor loadings or correlation between variables used for PCA (row-wise) and the factors (column-wise). Factor loadings outside the interval of ±0. 3 are typically omitted  [53, 54].
The fourth and final step is to suitably name the rotated factors based on the factor loadings. The aggregate driving and charging data of 153 PHEVs was first annualized based on the number of days every PHEV was driven. Using PCA, we identified four dominant driving and charging factors, eight in total. In this paper, the KMO MSA was close to 0. 8 for both the driving and charging related PCA, which is considered "meritorious"  [47, 50, 51]. The p-value of Bartlett's test was extremely low and lower than the significance level of 0. 05 for both the driving and charging related PCA, the data is suitable for PCA. The extracted factors captured 87% of the total variance in the dataset. Variables used for PCA, extracted factors, and their definitions are detailed in Section 3. 2.
OLS Model Development To test the explanatory power of the extracted factors, we built Ordinary Least Squares (OLS) multi-variate linear regression models with the extracted factors as the independent variables (IVs), and the deviation of observed UF from label UF as the dependent variable (DVs). The dataset was divided into two based on the AER: short-range PHEVs (Prius, Ford CMax/Fusion Energi) and long-range PHEVs (Volts). OLS regressions models for short-range PHEVs and long-range PHEVs were developed separately. We carried out a-priori and post-hoc hypothesis tests and validated that the sample size and power are adequate for the given significance level (5%) and sample size for both models. To supplement the insights gathered from the regression models and gauge the practical utility rather than just their statistical significance of the IV, we performed a relative importance analysis of each of these extracted factors by quantifying their main and total effects.
The main effect is the contribution by an IV to the total variance by itself, and the total effect is the contribution by an IV to the total variance in combination with other IVs  [59] [60] [61]. We also examined the effect of including interaction terms in the regression models. The regression model estimates and outcomes of the relative importance analysis are detailed in Section 3. 3. The PCA was done using IBM SPSS and the OLS regression modeling, and relative importance analysis were carried out using JMP Pro 15. 3.
OLS Regression Model Results The eight retained PCs are the IVs and the DV is the difference between observed UF and EPA label city/highway combined UF (∆UF = Observed UF-label UF). The purpose of developing regression models is to understand how well the extracted PCs can explain the difference between observed and label UF, and also identify which PCs contribute the most to the ∆UF and how it varied between short-range and longer-range PHEVs. Using the aggregate annualized dataset, we created OLS regression models for short-range (Prius, CMax, and Fusion) and longer-range PHEVs (Volt35/38 and Volt-53) separately. Statistical tests using G * Power 3 [68] was performed to verify and validate the following:.
Discussion This paper analyzed year-long driving and charging behavior of 153 PHEVs in California and compared it against EPA test cycles and SAE J2841 assumptions. We expanded upon these observations by examining usage pattern differences among the five PHEV models included in this paper. Our findings are summarized below. Observed PHEVs are driven more aggressively and accomplish a higher share of travel in non-urban driving conditions (45 mph or faster or less than 3 stops per mile) compared to standardized dynamometer test cycles. The percentage share of time and distance traveled at highway speeds (60 mph or faster) is noticeably under-represented or excluded in test cycles. Approximately 80% of VMT in the UDDS cycle is at 45 mph or slower, whereas the overall average in our dataset was only 40%.
The cumulative effect of these California specific features were clearly revealed annual VMT and share of long-distance travel (50 miles or more) of the PHEVs observed in this study. The sub-sample of drivers in this dataset are PEV early adopters who purchased or leased a new PHEV and are generally more educated, wealthier, and own a home, compared to mainstream ICE user's driving patterns in the NHTS on which the J2841 relies on  [76, 77]. Rebound effect in the classical sense by which improvements in fuel economy of newer vehicles increase the travel demand  [78] , could also have played a part in higher vehicle usage intensity of all the PHEV models compared to sticker label annual mileage of 15,000 miles, except the Volt-53, which seemed to have faced a slight backfire effect  [79]. Apart from differences among the PHEV models in terms of annual VMT, driving style (aggressive or conservative), and the magnitude of long-distance travel, the distribution of UF (Figure  1 ) indicates that heterogeneity in charging preferences exists among different as well as within the same PHEV model.