dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and Future Research Directions This study systematically analyzed the driving and charging patterns of 153 PHEVs operating in California. The purpose of this study was to investigate why the observed performance of PHEVs deviated from their expectations and what the influential factors were that contributed to these disparities. We first compared observed and expected PHEV usage patterns. We also compared the usage patterns of the five PHEV models (Prius, CMax/Fusion Energi, first and second generation Chevrolet Volts) that were analyzed in this study at time-scales varying from trip-level to annual estimates. We utilized principal components analysis to reduce the dimensionality of the dataset while capturing at least 87% of the variance in dataset using just four driving and four charging related factors.
The probability of Type I and Type II error is α and β respectively. From Tables A1 and A2 we can see that there are sufficient number of short-range and long-range PHEVs in the dataset to detect a "true" effect of a predictor. Table  A2  is a post-hoc test which calculates the power achieved given the significance level, sample size and effect size. From Table  A2 , we can see that the achieved power of the OLS regression models is more than sufficient. Table  A3  presents the regression model estimates and the model fit summaries with interaction effects. When we compare the main effects only model results (Tables  9  and 10 ) and the model results with main and interaction effects in Table  A3 , the estimates changed slightly. However, their statistical significance almost remained identical to the main effects only model, even after the inclusion of interaction effects across all the four models in Table  A3 , the only change was observed when interacting PC2.
Data Analysis Longitudinal data in both experiments, RGR, and the physiological parameters NBI, chlorophyll, flavonols, and anthocyanin were analyzed on the basis of repeated measures. Whenever data met parametric test assumptions, a linear mixed model with a split-plot design for repeated measures was applied, including each plant as a random factor; otherwise data were analyzed by means of non-parametric testing  (Naguchi et al. , 2012). We double checked that data met the parametric test assumptions graphically and by means of the Shapiro-Wilk test (residuals normality) and Breusch-Pagan test (homoscedasticity test). A type III ANOVA model was used to test hypothesis in parametric repeated measures analyses to RGR data in both experiments and NBI, chlorophyll, and flavonols in Experiment 2.
Whenever the interaction between treatment effect and sampling time was significant, a post hoc pairwise comparison was performed among treatments for each sampling period individually. We used t-test comparisons for parametric datasets and Mann-Whitney-Wilcoxon test for non-parametric datasets; in both cases, p's were subsequently corrected by means of Benjamini-Hochberg False Discovery Rate test for multiple comparisons with a 10% acceptance level  (Benjamini and Yekutieli, 2001). The effects of the leachate source upon total mass and root/ stem mass ratio were tested with a one-way ANOVA test, the model II, owing to an imbalance among the number of plants in the treatments in the two experiments. The root/stem mass ratio was in each occasion transformed with logit transformation to improve normality and homoscedasticity of residuals.
Total biomass log transformation was needed only for the L. hexapetala dataset in the first experiment. In both experiments, the effects of the leachate origin upon the number of branches, including plants without branches in the datasets, were analyzed with a generalized linear model following a Poisson error distribution. The effect of the leachate origin upon the total length of branches was tested with a one-way ANOVA, type II, excluding from the analyzed dataset the plants without branches. All analyses were performed with R software (R Core Team, 2016).
Acknowledgements On behalf of all authors, the corresponding author states that there is no conflict of interest. The authors would like to thank the Icam students,  Olexandr BAL ,  Baptiste GENAUZEAU ,  William LE GOFF ,  Mathilde CLEMENT ,  Simon PASQUIER ,  Luc BLEUZEN ,  Léo BEDERE ,  Laurent DESFLANS ,  Quentin SAVINA ,  Samuel GOBIN , and  Alexandra ANSELIN  for their precious participations to the study. This work was partially funded by  ADEME  and  TOTAL in the frame of RTD program BEfficacité énergétique dans l'industrie^.
0783)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 5. 04% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 11 nucleotide sequences. All positions containing gaps and missing data were eliminated. There was a total of 3117 positions in the final dataset. Evolutionary analyses were conducted in MEGA7  [8]. Alphacoronavirus: Human coronavirus 229E (HCoV-229E; NC_002645); Deltacoronavirus: Wigeon coronavirus HKU20 (NC_016995); Gammacoronavirus: Beluga Whale coronavirus SW1 (NC_010646); BCoV: Bovine coronavirus (NC_003045); CRCoV: Canine respiratory coronavirus (JX860640); HCoV-OC43: Human coronavirus OC43 (NC_006213); HCoV-HKU1: Human coronavirus HKU1 (NC_006577); Hibecovirus: Bat Hp-betacoronavirus/Zhejiang2013 (NC_025217); Merbecovirus: Middle East respiratory syndrome coronavirus (NC_019843); Nobecovirus: Bat coronavirus HKU9 (NC_009021); Sarbecovirus: SARS coronavirus (NC_004718).
Data Analysis All experiments were performed at least three times in triplicate. All graphs presented in this work were created using GraphPad Prism 6 software. Distribution of values was tested using Shapiro-Wilk normality test and equality of group variances was examined with Browne-Forsythe test. Depending on the results of these tests, one-way ANOVA with Dunnett's multiple comparisons test or Kruskal-Wallis test with Dunn's multiple comparisons test or unpaired t test with Welch's correction were used. p values < 0. 05 were considered significant. One asterisk (*) identifies adjusted p values between 0. 01 and 0. 05, two asterisks (**) identify adjusted p values between 0. 01 and 0. 001, three asterisks (***) identify adjusted p values between 0. 001 and 0. 0001, four asterisks (****) identify adjusted p values below 0.
04% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 11 nucleotide sequences. All positions containing gaps and missing data were eliminated. There was a total of 3117 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 [8]. Alphacoronavirus: Human coronavirus 229E (HCoV-229E; NC_002645); Deltacoronavirus: Wigeon coronavirus HKU20 (NC_016995); Gammacoronavirus: Beluga Whale coronavirus SW1 (NC_010646); BCoV: Bovine coronavirus (NC_003045); CRCoV: Canine respiratory coronavirus (JX860640); HCoV-OC43: Human coronavirus OC43 (NC_006213); HCoV-HKU1: Human coronavirus HKU1 (NC_006577); Hibecovirus: Bat Hp-betacoronavirus/Zhejiang2013 (NC_025217); Merbecovirus: Middle East respiratory syndrome coronavirus (NC_019843); Nobecovirus: Bat coronavirus HKU9 (NC_009021); Sarbecovirus: SARS coronavirus (NC_004718).
0783)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 5. 04% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 11 nucleotide sequences. All positions containing gaps and missing data were eliminated. There was a total of 3117 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 [8]. Alphacoronavirus: Human coronavirus 229E (HCoV-229E; NC_002645); Deltacoronavirus: Wigeon coronavirus HKU20 (NC_016995); Gammacoronavirus: Beluga Whale coronavirus SW1 (NC_010646); BCoV: Bovine coronavirus (NC_003045); CRCoV: Canine respiratory coronavirus (JX860640); HCoV-OC43: Human coronavirus OC43 (NC_006213); HCoV-HKU1: Human coronavirus HKU1 (NC_006577); Hibecovirus: Bat Hp-betacoronavirus/Zhejiang2013 (NC_025217); Merbecovirus: Middle East respiratory syndrome coronavirus (NC_019843); Nobecovirus: Bat coronavirus HKU9 (NC_009021); Sarbecovirus: SARS coronavirus (NC_004718).
Statistical analysis The results are represented as the main average of taro plants corms and shoots in each of the three control vs three drought individual rows, expressed per dry weight basis. All samples were statistically evaluated with IBM SPSS Statistics v. 24 for Mac, for Pearson correlations and Analysis of Variance (ANOVA, p ≤ 0. 05). The MVSP v. 3. 1 for Windows was used for principal component analysis (PCA). δ 15 N nitrogen isotopic composition (‰); δ 13 C carbon isotopic composition (‰), [N] total nitrogen (mg, DW); a significant differences between accessions (ANOVA, p ≤ 0. 01), b significant differences between control and drought stress conditions (ANOVA, p ≤ 0. 01). Control is well-watered, drought is severe stress. Variation is the difference between control and drought per trait.
Acknowledgements This work was supported by the  Programa Operacional da Região Autónoma da Madeira -PO Madeira   14-20  [grant number  M1420-01-0145-FEDER-000011 , CASBio] and the  Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação -ARDITI  [grant number  M1420-09-5369-FSE-000001 ].
An additional guideline regarding the validation of analytical methods is also available  [16]. Therefore, at the time of MAA, each applicant is required to define a suitable validation assay with a cut-off value for release criteria. It became clear that this was a massive challenge, given the scarce experience with these innovative products. Importantly, variability in the product manufacturing process makes this task even more difficult  [17]. Issues with release criteria were noted at the drug substance level (unacceptable specification of potency) and drug product level [unacceptable process-related impurities and replication competent adenovirus (RCA)]. The EMA quality data certification is part of a set of initiatives promoted by CAT to foster the development of ATMPs. The quality data certification procedure involves the scientific evaluation of these data and intends to identify any potential issues early on so that these can be addressed before the submission of a MAA.
In general, there has been a clear trend regarding quality data acceptability by CAT/CHMP. This could either be a result of the increased experience of regulators with GTMP assessment or the submission of more robust quality data by the applicants. This could be verified, for instance, by considering the overall difference between the assessments of Cerepro in 2007 compared with 2010. Here, there was a clear improvement, to the point where there were no deficiencies precluding GTMP approval in the 2010 MAA submission for Cerepro as far as quality data were concerned.
This study was also able to resolve the issue on reproduction toxicity. For the assessment of toxicology, deficiencies were noted regarding repeat-dose toxicity studies in two out of four unsuccessful MAAs (Advexin and CLG). Safety data limitations as well as study design not adequately reflecting the intended clinical use resulted in an unsatisfactory regulatory opinion. Assessment of insertional mutagenesis risk was not applicable to products using non-integrating vectors, such as Advexin, CLG, and Cerepro (adenoviral vector) as well as Imlygic (herpes simplex vector). The risk was higher in products using integrating vectors, such as Glybera (AAV vector) and Strimvelis (retroviral vector). The tumorigenic risk of Glybera was associated with two elements: (i) potential for insertional mutagenesis; and (ii) inclusion of woodchuck post-transcriptional elements.
The applicant highlighted that there were no further practical methods to assess the risk of tumorigenicity and the available evidence suggested that the risk was low. Theoretically, the product could integrate and cause a tumor. However, both CAT and CHMP agreed with the applicant that no further animal testing or experiments could usefully address these concerns. For Strimvelis, even though it theoretically exhibited a higher insertional mutagenic potential of all the GTMPs included in this analysis, because of the nature of the vector used, carcinogenicity studies have not been conducted because no adequate animal model was available to evaluate the tumourigenic potential. The main reason was the inability to achieve long-term engraftment of transduced cells in mice. Similarly to quality assessment, applicants are able to use the certification procedure for a regulatory and scientific evaluation of nonclinical data already collected before MAA, along with a request for scientific advice or protocol assistance.
Clinical safety data assessment All MAAs reported at least one deficiency regarding safety assessment, and unsurprisingly similar results were obtained for RMP, taking into account that most safety concerns were addressed through this tool (Table  1 ). The most common observations were limited or incomplete safety database (three out of four unsuccessful GTMPs and in two out of three successful GTMPs), as well as specific safety concerns over immunogenicity (two out of four unsuccessful GTMPs and all three successful GTMPs). The risk of immunogenicity was previously reported as an important hurdle in GTMP development  [24]. Immunogenicity safety concerns regarding Advexin and CLG were noted because the local immune response risk described in the literature was not adequately assessed by the applicant.
g. , Glybera). Concerning environmental risk assessments, deficiencies were noted for Advexin and CLG. For all other MAAs, the data presented were considered satisfactory to support the claim that the risks to human health (other than patients) and the environment were negligible. The main issues concerned immunocompromised individuals who were at high risk because of the presence of RCA in the medicinal product (which can be transferred, in a potentially sustained fashion, if immunocompromised individuals came into contact with treated patients), shedding of the Ad5-p53 vector, and possible horizontal transmission. Risk management strategies were considered insufficient to address this concern in both MAAs.
Concluding remarks Here, we have provided valuable insights for use in future GTMP MAAs. Clearly, the benefit-risk assessment with subsequent issuing of a successful MAA is a complex and multifactorial exercise. Strimvelis is the only gene therapy included in this analysis administered as ex vivo gene therapy, as opposed to the other GTMPs, which are intended to be used in vivo. This feature represents an added complexity regarding the review of quality, nonclinical, and clinical data upon MAA request. Each individual GTMP component might impact the efficacy and safety profile of a product, including the vector, inserted sequence(s), target cells modified by the vector, or the encoded protein  [18]. Although at the initiation of CAT, quality data were noted as a significant deficiency, over the years there have been substantial improvements.
Clinical assessment is undoubtedly where the regulator tends to encounter unacceptable issues. In particular, clinical efficacy demonstration and safety have a key role in GTMP approval. Our analysis suggests that lack or insufficient efficacy demonstration, change or use of a nonvalidated pEP, and application of post-hoc and subgroup analysis constitute the most predominant objections. Safety wise, limited databases and inadequately addressing immunogenicity concerns are highlighted as the most frequently raised objections. In the case of successful GTMPs, most deficiencies were addressed through oral explanation or written answers or by the submission of additional data (either during a MAA review or post marketing). In this context, RMP updates were noted in almost all GTMPs. Although quantitative data on the request or use of EMA initiatives to support ATMP development, particularly for ATMP certification and classification, were not analyzed here, these are acknowledged to be an advantage.
The tree size was selected using a cross validation procedure. Thus, the tree was pruned to the smallest structure, for which the error rate was within one standard error of the minimum (De'ath 2002). CART analysis was performed using the mvpart package  (Therneau, Atkinson 2013)  in the open-source statistical software environment R 3. 1. 0 (R Development Core Team 2013). Redundancy Analysis (RDA) was used to examine the insect relationships with 18 sampling sites and 23 environmental variables. Before the analysis, the abundance of insects was transformed using log (n + 1). Forward selection was employed to test which of the environmental variables explained a significant (p < 0. 05) proportion of taxa variance. The significance of explanatory variables was tested against 500 Monte-Carlo permutations.
Instrumentation Solid phases were identied by powder X-ray diffraction (PXRD) "PANalyticalX'Pert Pro" diffract meter with ltered (Ni lter) Cu radiation from 2q ¼ 5 to 90 at a count rate of 23. 6 s per step of 0. 017 at room temperature. The PXRD was rened by the Rietveld method 40 using the Full proof program (version 1. 9 C-May 2016-LLB-JRC). Background, scale factors, zero-point positions, cell parameters, atomic positions, and prole coefficients for the Pseudo-Voight/FCJ asymmetric peak-shape function were rened until achieving convergence. The microstructure studies were performed using scanning electron microscopy (SEM) along with EDX. The pictures were taken at room temperature on a Phillips XL30 equipped with a eld emission gun at 20 KV. The UV-Vis DRS spectra in a wavelength of 300-1800 nm were recorded through a UV-Vis spectrophotometer (SHIMADZU UV 3100S) with an integrated sphere for determining the band gap.
3  The W-H analysis of ZnO and Pr 0. 01 Zn 0. 97 O assuming (UDM) analysis. Y ¼ " h 2 þ ðh þ 2kÞ 2 3 þ al c 2 # 2 S 11 h 2 þ ðh þ 2kÞ 2 3 ! 2 þ S 33 al c 4 þ ð2S 13 þ S 44 Þ ðh þ 2kÞ 2 3 al c Here S 11 , S 13 , S 33 and S 44 are the elastic compliances of ZnO with the values of (7. 858 Â 10 À12 ) (À2. 206 Â 10 À12 ), (6. 94 Â 10 À12 ) and (  23. 57 Â 10 À12 ) m 2  N À1 , respectively. 58. Morever, a graph is drawn between 4 sin q hkl and b hkl cos q hkl and tted as straight line. The uniform deformation stress and crystallite size are estimated from the slope and y-intercept of the graph respectively. Thus, the lattice strain extracted from the crystal is measured by using the Y value. From Fig. 3 , it can be seen that athoughthere is a small scattering of experimental data points closer to the linear t, it allows the uniformity of the stress to a reasonable approximation.
The response variable was the proportion of individuals present after 24 h in each experimental trial and predictors were method (standard vs. ACM), species (WCM vs. TSSM) and their interaction. Effects were tested using type II likelihood-ratio chi-square tests  42. We used pairwise post-hoc comparisons to determine significant differences between method-species combinations using estimated marginal means implemented in the emmeans R package  43. We estimated the overall cost and time needed to apply the compared methods. For this purpose the cost of all elements and ingredients needed to prepare each method for 10 experimental trials was calculated: the price of Plexiglas elements for standard methods (rearing in cages and on cotton); the price of 6-well Plexiglas plates and the price of ingredients needed to prepare ACM.
However, one limitation of the ACM method is that the use of cut plant fragments cannot account for a whole plant's organismal response to the microherbivore's feeding. This would be particularly important in studies in which both plant and herbivore data are recorded, rather than only the herbivore's behaviour or life-history traits. Measurements of changes to a cut plant fragment in response to microherbivore feeding could address part of this concern but we have not explored this avenue to date. Due to their economic importance, eriophyid mites are subjects of various ecological studies investigating mite characteristics that can be useful in predicting their distribution and outbreaks but these are often conducted using cohorts of specimens infesting whole plants  21, 23, 71. However, due to their minute size, eriophyids can be difficult to detect and monitor when reared on whole plants.
Acknowledgements The authors give special thanks to  Sara Magalhães  ( Centre for Ecology, Evolution and Environmental Changes, Faculty of Science, University of Lisbon ) for fruitful discussion and inspiring ideas,  Karolina Wleklik  for mites' drawings, the company  DANKO Hodowla Roślin Sp. z o. o. for the Triticum aestivum seeds. The study was funded by the  National Science Centre, Poland (NSC) ; research grant no. 2016/21/B/NZ8/00786 , and supported by  NSC  PhD scholarship no. 2019/32/T/NZ8/00151  awarded to A. L. A. L. is a scholarship holder from the  Adam Mickiewicz University Foundation  awarded in  2019/2020.
The powder X-Ray Diffraction (PXRD) patterns were recorded with a Panalytical Empyrean Diffractometer (Cu Kα radiation, λ = 0. 154 nm). Powder patterns were analysed according to the Rietveld method 32 using the MAUD software  33. N 2 adsorption-desorption isotherms were registered at 77 K using a Quantachrome Nova 2200 Multi-Station High Speed Gas Sorption Analyzer. Samples were outgassed for 12 h at room temperature in the degas station. Adsorbed nitrogen volumes were normalized to the standard temperature and pressure. The specific surface area (S BET ) was calculated according to the standard BET method (based on the Brunauer-Emmett-Teller theory) in the relative absorption pressure (P/P 0 ) range from 0. 045 to 0. 250  34. Attenuated Total Reflection (ATR) Infrared Spectra were acquired between 600 and 4000 cm -1 by using a Perkin Elmer Spectrum 65 Spectrometer equipped with an ATR setup.
The assistance of urea in creating a reducing atmosphere during the YAG formation is somehow confirmed by the XRD patterns of sample prepared at R = 2, where the formation of the YAG phase cannot be observed even at T = 900 °C (only traces of yttria can be seen). On the other end, in agreement with the IR data, these findings evidence the role of urea, which probably partially bond the aluminium and affect its availability to participate to the crystallization process, involving the formation of yttria, as previously reported in the case of YAG nanoparticles prepared in microemulsion  25, 26. The Rietveld refinement  32  has been applied to the XRD pattern of the samples treated at 800 and 900 °C by using the MAUD software  33. The Rietveld best fit reproduces well the diffraction pattern of each sample.
Acknowledgements M. L. S. and F. A. thank the  University of Palermo  for supporting this research through the  CORI2016  ( Bando per la concessione di contributi per l'avvio e lo sviluppo di collaborazioni dell ' Ateneo  2016 -Azione D -CORI-2016-D-D15-180463 ) and through the  PJ-DR-CONTR-ESTERO-DA00. 05  ( Premi e contributi a studenti meritevoli o svantaggiati ) projects, respectively. C. G. acknowledges the  Stranski-Laboratorium für Physikalische und Theoretische Chemie (Institute of Chemistry ) of the  Technical University of Berlin  for financial and lab support. C. G. and C. D. acknowledge  QMUL  for financial support. Authors would like to thank  Prof. Wieslaw Strek  for his valuable remarks and  Mr. Robert Tomala  for his help in QE measurements ( Institute of Low Temperature and Structure Research, Polish Academy of Sciences of Wrocław, Poland ).