dataesr/scientific-paragraphs-categorization huggingface dataset:
Planned Statistical Analyses General descriptive statistics for continuous variables will include number of observations (n), mean, SD, median, interquartile range (IQR), minimum and maximum values. Categorical data will be summarized by frequency and percentages. The primary endpoint as the proportion of transplantation success (according to the two-steps rule) at 12 months will be presented together with the two-sided 95% CI. The analysis of the proportion of transplantation successes will be performed in the Full Analysis Set (FAS; as primary analysis) at transplantation level, that is, all transplantations of all patients who undergo at least one application of the investigational product and in the Per Protocol Set (as sensitivity analysis) at transplantation level. The proportion of patients with one or more transplantation success (according to the two-steps rule) will be presented together with the two-sided 95% CI.
This analysis will be performed on the FAS set at patient level. In addition to descriptive statistics, when applicable, appropriate non-parametric statistical tests will be provided for secondary efficacy variables. An overview of AEs with the number of patients affected, as well as the number of events, with any treatment emergent events (TEAEs), serious TEAEs, drug-related TEAEs, serious drug-related TEAEs, TEAEs leading to withdrawal, and TEAEs leading to death will be presented. Treatment emergent events will be tabulated by system organ class (SOC) and preferred term (PT) using the MedDRA dictionary. Vital signs (actual values and change from baseline, if applicable) will be presented by time point. Safety laboratory data (actual values and change from baseline, if applicable) will be presented by time point.
Thus, residues that are highly conserved in orthologs but not in paralogs of a ligand are likely related to the ligand's specific function. Unlike other existing methods for prediction of specificity determining residues (a review can be found in (  24 )), we included interprotein coevolution measures to narrow down those residues that are responsible for a specific interaction. The DIRpred score is calculated as the sum of the four components (I: orthologs conservation, II: complement of the paralog's conservation, III: ligand-receptor coevolution, and IV: complement of ligand internal coevolution). Optionally, the analysis can be conducted using a structural alignment of the paralogs (multiple structural alignment [MSTA]) instead of the multiple sequence alignment (MSA). An implementation of the DIRpred analysis was done using Python.
To observe the long-term effects of the mutants on the cellular phenotype, we performed a cell growth assay using an IncuCyte live-cell analysis system on two fibroblast cell lines (Bj5-tα and Albino Swiss mouse 3T3). With the same platform, we also performed an apoptotic assay on A431 cells by measuring the reduction in cell population, using the Annexin V green reagent to confirm the induction of apoptosis. After 1 day of initial incubation, we subjected the cells to 1, 10, or 100 nM concentration of growth factors. Treating Bj5-tα fibroblasts with 100 nM WT EGF resulted in the highest reduction in the proliferation rate compared with the control. The reduction in proliferation was accompanied by a change in cellular morphology that could be a signal of differentiation (Data S1). However, treatment with any of the mutants decreased the ability of the ligand to suppress growth (Fig.
In the WT EGF simulation, the EGFR dimer had an average of 15 H-bonds, higher than any other simulation (Fig. 6D ). However, the number of bonds between the receptor and ligand was not altered (Fig. 6E ). In addition, during the course of our simulations, we also noted differences in the RMSF specifically located at the dimerization arm domain (Fig. 6B ). The conformational space sampled by the dimerization arm of K48T simulation was much wider than the WT simulation (Fig. 6C ). To analyze the temporal distribution of these motion, we measured the distance between Pro-272 and Gly-288 of different EGFR monomers. This distance was chosen because it was able to discriminate between the EGFR dimer in complex with EREG (PDB ID: 5WB7) and EGF (PDB ID: 1IVO). In 5WB7, one of the two distances is much bigger (~1.
Discussion The prediction of functional residues is a well-developed field  (34) , where conservation measures are considered a key factor to rely on. Tools such as ConSurf  (35)  and the evolutionary trace-like methods  (36)  are able to identify slowly evolving positions that are involved in folding, interaction, or catalytic activity  (34). However, the specific reason why a residue is conserved often remains unclear. In this work, we show a new method to identify residues that affect specific functions in a system of interest. Our approach combines a conservation score calculated from the structural alignment of paralogs and among orthologs, with an intramolecular and intermolecular coevolution score with previously known   S3. Band intensity estimates were calculated using Bio-Rad Image Lab software (Bio-Rad).
Plots and statistics were performed using PRISM software (GraphPad). EGF, epidermal growth factor; EGFR, epidermal growth factor receptor. interactors. Conservation and coevolution give a complementary signal, thus improving the overall predictive performance  (37). The coevolution score was introduced to DIRpred to highlight residues that are directly responsible of an interaction with the receptor, at the expenses of those that interact intramolecularly within the ligand. Although the first part efficiently identifies Asn-32 and Lys-48 as putative interactors, the second part does not properly give a penalty to residue Tyr-29. The interaction of this position with His-10 is also conserved in the EGF but with reversed positioning (Fig. S12 ), resulting in a low paralog conservation (therefore high contribution to the DIRpred score).
A431 cells constitutionally express EGFRs at high levels. Treatment with the EGF has been observed to promote STAT1dependent apoptosis  (40). This pathway is dependent on the internalization of the ligand-receptor complex by the endocytosis process, a key factor in the ligand-induced biased signaling  (16). In our experiments, A431 cells treated with mutant EGF ligands show a higher growth rate, therefore a decreased rate of apoptosis compared with the WT. Given the setting of the experiment, modularity in the endocytosis pathway is a straightforward explanation to the observed differences in the growth rate and in protein levels and phosphorylation. Interestingly, D46T-and   S3. Band intensity estimates were calculated using Bio-Rad Image Lab software (Bio-Rad). Plots and statistics were performed using PRISM software (GraphPad).
Phylogenetic analysis From the MSA of the EGF from different species, nearly identical sequences were removed. The Drosophila melanogaster EGF sequence was added as an outgroup in the EGF phylogeny, while Caenorhabditis elegans EGF was used as outgroup for the ligand phylogeny. MSA and phylogenetic tree images were created using Unipro UGENE software  (51). Three phylogenetic trees were made using the neighborjoining, maximum likelihood, and Bayesian methods in IQ-TREE  (52) , using ModelFinder to scan for the best-fit evolutionary model and parameters  (53).
7 ). Conservation scores were calculated using three different formulations: (1) IDENTITY, (2) BLOSUM, and (3) JSDw. The IDENTITY score measures the frequency of appearance of a reference residue in the MSA. The BLOSUM score takes the amino acid substitution frequency into account using the BLOSUM62 matrix, normalized by the maximum and minimum scores for the BLOSUM matrix. The JSDw score is based on Jensen-Shannon divergence with a window of residues  (54). The FASTA sequences were imported using Biopython package, while analysis and plots were performed with Python 3 package seaborn. EGF index positions from the paralogs MSA and MSTA were, respectively, used to align the MSAand MSTA-based scores. A schematic representation of how the four scores were obtained is shown in Figure  S1. The code used in the analysis of the DIRpred score and plots and the data used in this article are shared on GitHub: https:// github.
Statistical validation of EGF DIRpred The statistical analysis of EGF DIRpred scores was done as in the study by  Mirny and Gelfand (55)  with some modifications. The key point is that a null-hypothesis dataset should take into account the higher similarity found between orthologs, rather than between paralogs. Such a dataset was obtained through a simulated evolution performed using the Python package Pyvolve  (25). The sequences in the output dataset were required to have a relationship akin to those of the EGF homologs. Using the WAG model (  56 ), a random 53amino acid-long protein sequence was evolved from the root of the EGF homolog tree until each leaf node, simulating a scenario of neutral evolution. Then, the sequences were divided in orthologs and paralogs using the original tree classification.
In I, the ortholog MSA of each paralog is used to compute a conservation score (as exemplified from the histograms on the alignments). Alignment images were produced using Unipro software  (51). In II, the human paralog sequences or structures were aligned, and the same conservation score function was used. In the purple box, the conservation score functions are represented. In III, the coevolution score between the EGF and each receptor site was computed using mutual information (MI). The yellow lines on the structure connect the highest scoring coevolving residues between the ligand and receptor. In IV, the MI coevolution score was computed from the combined ligand ortholog alignment. The circular plot was performed using MISTIC2  (72). DIRpred, divergence-inducing residue prediction; EGF, epidermal growth factor; MSA, multiple sequence alignment.
Cell proliferation assay We measured cell proliferation using a label-free, noninvasive, cellular confluence assay with IncuCyte Live-Cell Imaging Systems (Essen BioScience). Human Bj5-tα (2500 cells/well) and Mouse Swiss Albino 3T3 (1000 cells/well) were seeded overnight on a 96-well plate (Corning) at 37 C in an incubator. The next day, cells were treated with WT EGF and mutants at 1 nM, 10 nM, and 100 nM concentrations and placed in an XL-3 incubation chamber maintained at 37 C. The plate was scanned using a 4× objective at 2-h intervals over 3 days. Cell confluence was measured using IncuCyte Analysis Software. The IncuCyte Analyzer gives real-time confluence data based on segmentation of high-definition phase-contrast images. Cell proliferation is shown as an increase in the confluence rate relative to the control.
Apoptosis assay Experiments were performed with the A431 human cancer cell line. 5000 cells/well were seeded on a 96-well plate (Corning) and incubated at 37 C for 24 h. Media were replaced with fresh DMEM containing WT EGF, or EGF mutants at 1, 10, and 100 nM concentrations, and fluorescent annexin V green reagent. Plates were prewarmed before data acquisition to avoid condensation and expansion of the plate, which affect autofocus. Images were captured every 2 h (4×) for 3 days in the IncuCyte system. Cell proliferation is reported as in the previous assay.
Statistics Proliferation and apoptosis experiments were performed in duplicates. All results are shown as the mean ± SD. Raw data were analyzed by two-way ANOVA with 95% confidence level. The multiple comparison test was corrected using the Bonferroni post hoc test. Prism 8 software was used for statistical analysis. Asterisks in the pictures show p-values using GraphPad convention: 0. 1234 > (ns), 0. 0332 > (*), 0. 0021 > (**), 0. 0002 > (***), 0. 0001 > (****).
MD All the simulations were performed with GROMACS version 2020 (61) using charmm36-mar2019 force field (62) and SPC216 for water. PDB ID 5wb7 was used as a model and template. EGF structure model was extracted from PDB ID 1ivo and superimposed to the ligand using UCSF Chimera Matchmaker algorithm  (63). Mutants were generated using SWISS-MODEL webserver (64), while missing atoms were compensated using Scwrl4 (65). A system was composed of the EGFR dimer in complex with one or two EGF WT, N32R, D46T, K48T, or W50Y was solvated and neutralized using NaCl ions in a dodecahedral box. Then, energy, temperature, and pressure equilibrations were performed to the system following the guidelines in the study by  Lemkul, 2019 (66). A 100-ns production simulation was run using the Verlet cut-off scheme (67) for nonbonded interactions and LINCS as the constraint algorithm  (68).
Images were captured every 2 h (4× magnification) for 3 days in the IncuCyte system. The number on the top of the bars shows the p-values of a two-way ANOVA (A) or one-way ANOVA (B) multiple comparison to the control lane, corrected for multiple sampling using the Bonferroni correction. Details of the ANOVA statistics can be found in TableS3. Band intensity estimates were calculated using Bio-Rad Image Lab software (Bio-Rad). Plots and statistics were performed using PRISM software (GraphPad). EGF, epidermal growth factor.
Acknowledgments-We thank  Tadashi Yamamoto  and  Ichiro Maruyama  for providing the antibodies and  Keiko Kono  for the technical help with the WB experiments. We are grateful for the help and support provided by the  Scientific Computing section of Research Support Division  and in particular  Jan Moren  at  Okinawa Institute of Science and Technology. We thank  Madhuri Gade ,  Mirco Dindo ,  Harry Wilson , and  Benjamin Clifton  for critical reading. We thank  Pierre Matricon  for the discussion on the molecular dynamics data,  Russel Alenton  for the discussion on the cell proliferation assay, and  Christine Guzman  for the discussion on the Western blot analyses, and  Saacnicteh Toledo Patino  for the help with the NanoTemper instrument. Author contributions-S. P. , D. M. , and P. L. conceptualization; S.
This is not to be confused with the deformable mirror used by the CANARY AO system itself. For designing the waveguides of the pupil remapper, we employed a MATLAB custom routine implementing a recursive cubic spline interpolation. This software allowed the equalization of the geometrical paths of the injected light beams  [29]  with an error lower than 1 nm, thus preserving their coherence before reaching the DBC. The minimum radius of curvature was set at 30 mm for not introducing additional losses. After the beam combiner, a fan-out region was added to remap (in other words, reformat) the output section from a zig-zag configuration to a linear one [Fig. 1(c) ]. This approach makes the output modes more distinguishable since they can be arranged with higher separations. Moreover, it would make a spectral measurement easier, since the output light could be vertically dispersed without the risk of modal cross talk.
B. Altair We recorded only one continuous shot for Altair with a total of 1500 frames. As shown in Table  5 , the photon number from Altair is comparable to the case of Vega, which suggests that the error analysis and potential impact of noise sources in low photon regime may apply to the Altair data as well. We show the visibility amplitudes and CPs for Altair in Figs. 7  and 8 , respectively, where contributions from 94% of the frames are shown for the analysis of the visibilities with the selection criteria defined above. The mean, standard deviation, and median values of the visibilities for all combinations from Altair are shown in Table  4.
Huby et al. [9]  operated in the visible range a pupil remapping experiment in which they were able to measure precise CPs, but could not measure visibility amplitudes. Jovanovic et al. [10]  tested in the H-band a multi-axial combination scheme using a ULI written beam combiner. They show that they were able to detect fringes on a bright source and set constraints on the achievable precision on the CPs. In comparison, our retrieval of the CPs is not satisfactory to a level comparable to these authors, but it is not possible with this data to conclude if this is intrinsic to the DBC retrieval process since we are strongly impacted by other sources of noise. On the other side, the retrieval of the visibility amplitude delivered a better outcome, even though we are impacted by fringe smearing.
Acknowledgment. We thank the anonymous referees for their valuable comments that helped to improve the quality of the paper. We thank  Mr. Norberto Gonzales , the telescope operator, and  Dr. Chris Benn , the support astronomer, for their technical assistance during the on-sky tests at the  William Herschel Telescope. We also thank  Dr. Eloy Hernandez  and  Momen Diab  for their input regarding the detector noise and AO. Disclosures. The authors declare no conflict of interests. Data Availability. Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
The rapid advancements in digital technologies have prompted organizations to embrace digital transformations (DTs) in order to enhance efficiency, gain a competitive advantage, and achieve long-term sustainability objectives. However, the successful adoption of innovative digital technologies necessitates the careful consideration of various factors, such as stakeholder engagement, resource allocation, risk mitigation, and the availability of resources and implementation support. This study examines the sustainable adoption of innovative digital technologies (DTs) within digital transformations. The data for this study were collected from 760 stakeholders through a questionnaire survey and analyzed using SPSS software (Version 27). This study's results underscore the significance of considering the efficiency of the transformation process and the long-term sustainability outcomes for organizations.
Research Methods This methodology section describes the methods used to carry out the suggested study. So, the methodology used in the current study is quantitative. SPSS software has been used in the study, i. e. , IBM SPSS 29 version. This software is used for a reliability test, ANOVA test, as well as Chi-square test. SPSS software is used in the methods because it is helpful in performing statistical and quantitative data analysis. In the study conducted by  Bao and Zhang (2023)   [58] , they discussed the impact of digital transformation on enterprise innovation performance under the background of information management based on SPSS statistical software. From the inspiration of that study, we have taken the SPSS software methodology to gain the desired results. Data were gathered using a primary-instrument questionnaire survey to analyze the study's purpose.
Data Collection The data used in this study were gathered between 8 January and 17 March 2023. A questionnaire survey was conducted online among stakeholders over the age of 25 in Saudi Arabia who were sustainability practitioners, IT professionals, industry experts, ICT users, and stakeholders. A total of 760 individuals ultimately took part in the survey. In Table  1 , the demographic traits are listed. Gender shows that 50. 53% of respondents were male, and 49. 47% of respondents were female, thus the majority of respondents were male. According to the age group, 13. 95% of respondents were between the ages of 25 and 34, 59. 61% were between the ages of 35 and 44, and 26. 45% were over the age of 45. In terms of profession, 25. 26% of the respondents were sustainability practitioners, 25.
987, p < 0. 01) and positive correlations between resources and support and sustainability goals (r = 0. 982, p < 0. 01). All values of Pearson's correlation coefficient are statistically significant, according to the correlation analysis findings. The data were collected in an online mode with the help of Google Forms; the data were collected from 760 respondents. The sample questionnaire was collected and is mentioned in Appendix A. Similar to that, there were 50 questions asked, but we cannot show all 50 questions in the paper as the data become huge. As the study contains 760 respondents, the results will vary, and it will be a large number. The statements that are considered as hypotheses in Table  3  consist of the majority of the commonly stated responses to the questions asked. In Table  4 , the factors regarding the questionnaire are explained with an ANOVA, which helps in analyzing variances across the means, and Chi-square, which is used to compare the results with the expected results.
Discussion Digital transformation involves the collection and processing of vast amounts of data. Companies must prioritize data privacy and security to build trust with their customers and stakeholders  [14]. The sustainable adoption of innovative digital technologies in DT means considering environmental, social, and ethical impacts throughout the entire life cycle of technology implementation. By prioritizing sustainability and responsible practices, companies can achieve long-term success while minimizing negative impacts on the environment and society  [15]. The purpose of the current study is to examine the significance of the long-term adoption of innovative digital technologies in DT. Data were collected through an online questionnaire survey from 760 people over 25 years of age in Saudi Arabia who were sustainability practitioners, IT professionals, professionals, ICT users, and stakeholders.
Conclusions This study examines the sustainable adoption of innovative digital technologies in digital transformations and its implications for organizations. The data were assembled from 760 stakeholders through a questionnaire survey, which was then analyzed using SPSS software. The result of the analysis indicated that the combination of DT and sustainability principles positively influences the efficiency of the transformation which can be measured by environmental, social, and economic performance indicators. The study findings demonstrated that aligning DT goals with SDGs increases long-term sustainability outcomes for organizations. The research identified that digital technologies focusing on sustainability gain organizations a competitive advantage. The analysis revealed that sustainable adoption in DT projects benefits from effective governance by enhancing stakeholder engagement, resource allocation, and risk mitigation for successful implementation and outcomes.
Sample and data collection The hypotheses are tested on a sample of 111 German family firms. The firms eligible for our study were identified entering the following search criteria on the Bureau van Dijk Amadeus database, in the 2011: (1) German firms (Germany has a two-tier governance system, strictly separating the management board from the supervisory board); (2) with no other holding above them (global ultimate owner-GUO); (3) with an annual turnover at least equals to V50 million; (4) with at least 50. 1% of the shares owned by one family. This last query helped us in doing a first screening of family firms. However, we also put a question in the questionnaire in order to catch the self-perception; and checked firms' Website and press releases looking for a specific mentioning of the firm as being a family firm.
We obtained a first sample of 781 firms, to which CEOs we asked to participate in an online survey during the summer 2011. The questionnaire contained 124 items. After a follow-up mailing, we reached a final sample of 144 firms (leading to a response rate of 18. 4%). An analysis of variance (ANOVA) between firms that responded before and after the follow-up mailing yield no significant differences on the JSBED variables of interest, controlling for a potential non-response bias  (Armstrong and Overton, 1977). For those 144 firms we also searched for collecting secondary data (performance measures 2006-2013) on Amadeus, Bureau Van Dijk database. Based on the available secondary data the final sample is 111 firms. The responding firms are of varying sizes, with a majority of medium and large-sized firms (6.