dataesr/scientific-paragraphs-categorization huggingface dataset:
The steady-state minimum observed concentration (C min ) was reached by the second dose  [12]. In a phase 1 RSV-A human challenge study, lumicitabine inhibited viral replication, reduced viral load, and enhanced viral clearance in healthy volunteers  [11]. Patel et al. (2019)   [12]  developed a pharmacokinetic-pharmacodynamic (PK-PD) model that described the antiviral effect of lumicitabine in the adult challenge population during active RSV infection, with the aim of modeling efficacious plasma exposures and guiding dose selection in the adult and pediatric populations. Overall, lumicitabine was well-tolerated in both studies with no changes reported in hematological parameters. Here, we present the results of ALS-8176-503 (Study 1; NCT02202356), a phase 1b study conducted to evaluate the safety, PK, PD (absolute viral load and effect of exposure on viral kinetics), and the emergence of viral resistance after single-and multiple-ascending doses of lumicitabine in hospitalized infants and neonates with RSV infection.
Except the neonate cohort, each dosing cohort was further stratified by age (�1 to <2 months, �2 to <6 months and �6 to �12 months), with planned enrollment of approximately 8 patients in each stratum. An age de-escalation approach was used, such that infants between age �6 to �12 months were enrolled first, followed by those aged �2 to <6 months, and then �1 to <2 months. A sentinel group of 3 infants was enrolled initially in each age stratum and randomized (2:1) to receive either lumicitabine or placebo. An Independent Data Monitoring Committee (IDMC) reviewed the safety and PK data for the sentinel group prior to randomization (4:1 lumicitabine or placebo) of the remaining 5 infants in the age stratum. Following this assessment, enrollment in the next age stratum's sentinel cohort(s) was initiated.
Infants received the single oral dose of the study drug after randomization and were evaluated over a 7-day period. Infants discharged prior to day 7 were asked to return as an outpatient for assessment on day 7. The Part 2 (MAD part) commenced after evaluation of the PK and safety data of Part 1 by the IDMC. Each infant received a total of 10 doses which included an initial loading dose (LD) on day 1 followed by 9 maintenance doses (MD) commencing 8 to 18 hours later, administered twice daily. This LD/MD regimen consisted of multiple-ascending doses of lumicitabine (4. 1/1. 37 mg/kg, 10/2 mg/kg, 30/6 mg/kg, 30/10 mg/kg, 40/20 mg/kg, and 60/40 mg/kg), or placebo. Each dosing cohort was further stratified into 3 age strata (�1. 0 to <2. 0 months, �2 to <6 months and �6 to �12 months) and randomized (3:1) to receive either lumicitabine or placebo.
The enrollment of neonates (<28 days old) occurred following the IDMC's evaluation of the safety and PK data in 79 infants aged 1-12 months. The neonates were to be randomized (3:1) to receive either lumicitabine or placebo as 10 mg/kg LD on day 1, followed by 9 MD of 2 mg/kg administered twice daily. Only a single neonate was enrolled; subsequent enrollment of neonates was discontinued following a health authority request to generate more safety data in infants prior to the enrollment of additional neonates. Infants were evaluated over a 28-day period from the time of randomization. All received standard supportive care during the study. Throughout both the SAD and MAD parts of the study, the IDMC reviewed unblinded PK of metabolites (ALS-008112 and ALS-008144) and the safety data on a regular basis and approved each dose escalation decision.
Patient safety was monitored by regular assessment of the results of clinical laboratory tests, electrocardiograms, physical examinations, vital signs, and adverse events (AEs). In December 2016, the sponsor voluntarily halted the study due to a serious AE of severe neutropenia. The sponsor conducted a comprehensive review of the hematologic data from the lumicitabine development program. Following review by the IDMC, who determined the benefit-risk remained favorable, the sponsor resumed the study after notification/agreement of Health Authority/ ethics committees. Phase 2b study of lumicitabine in infants and children hospitalized with RSV (Study 2). Study 2 was a randomized, double-blind, placebo-controlled, phase 2b study conducted in infants and children (aged 28 days to 36 months) hospitalized with RSV infection.
Statistical analysis In Study 1, all statistical analyses were performed using SAS Version 9. 4 (SAS Institute, Inc. , Cary, NC, USA). Based on clinical experience, in Study 1, a sample size of up to 260 patients was to be enrolled to enable the assessment of safety and PK parameters. In Study 2, a sample size of 180 patients was to be randomized to offer approximately 97% power to detect a positive dose-response relationship. However, considering the early termination of the study, statistical analysis was not performed as planned. Therefore, the data are presented descriptively. All analyses were presented by dose groups and the combined age strata. In the final analysis, placebo data from the dose groups and/or age strata (being summarized) were pooled to form a larger comparative group.
The semi-mechanistic pharmacokinetic model developed in adults was extrapolated to infant populations. However, the potential for reduced drug distribution in infant lungs, or lower intracellular active triphosphate formation (not featured in the model) cannot be excluded. Higher than anticipated doses might be required to achieve clinical benefit in children. However, a study of lumicitabine in hospitalized adults with natural RSV infection failed to demonstrate RSV antiviral activity, despite exposures exceeding those in the human challenge study (NCT02935673). There are several plausible reasons for the discrepancy in the antiviral response seen between the RSV-A human challenge study in adults  [11]  and those in infants with natural RSV infection. In the human challenge study, treatment was initiated as soon as the virus was detectable.
This association was illustrated within Study 1 and Study 2. Graded neutropenia was observed at the study entry prior to treatment, with emergent graded neutropenia in some patients assigned to placebo. When the dose-related increase in reversible neutropenia became apparent, recruitment in Study 1 had already been completed. However, Study 2 was halted by the sponsor. Following consultation with independent experts, new non-clinical studies were undertaken to further evaluate and assess these safety findings. Additional animal studies determined that hematopoietic and clastogenic effects could occur at lower blood concentrations of lumicitabine with non-human primates as compared to the other animal species previously tested (data to be published separately). Subsequently, the sponsor terminated all lumicitabine studies and decided to discontinue the development of lumicitabine for all indications, including RSV and the human metapneumovirus infection.
Acknowledgments We acknowledge  Hein Fennema, PhD  for statistical analysis. Vaibhav Deshpande, PhD  ( SIRO Clinpharm Pvt. Ltd. ) provided medical writing assistance. Bradford Challis, PhD  and  Robert Achenbach  ( Janssen Global Services, LLC ) provided additional editorial support for this manuscript. The authors thank the study participants and their parents, without whom this study would not have been accomplished, as well as the investigators and study coordinators for their contributions to this study. RSV503 and RSV2004 Study Group Members:  Mina Pastagia ,  Leen Vijgen :  Janssen Research & Development, LLC ;  Keith Nieforth :  Certara Strategic Consulting, Parsippany, NJ, USA ;  Jeff Sorbel :  Triangle, Biostatistics LLC, Raleigh, NC, USA ;  John P DeVincenzo MD :  University of Tennessee Health Sciences Center, Memphis, TN, USA ;  Jolanta Bernatoniene :  Upper Maudlin Street, Bristol BS2 8AE, UK.
, 1996). According to more recent simulation studies  (Wang, 2016) , the sibship frequency based estimate (SF)  (Wang, 2009)  seemed to provide more accurate results than the previous ones. No comparison was ever made with an alternative method based on one and two locus identity measures (1&2LI)  (Vitalis & Couvet, 2001c, b) , implemented by the software Estim 1. 2. 2  (Vitalis, 2002) , updated from Estim 1  (Vitalis & Couvet, 2001a). Based on simulations, the 1&2LI method provided accurate (though slightly underestimated) results, especially when more than four loci were used  (Vitalis & Couvet, 2001b). Again, no simulation study exhaustively compared all available one-sample estimates. This would require replicated simulations of different scenarios of population structure (Island or stepping stone models with varying subpopulation number, sub-population sizes and immigration rates), different kinds of loci (microsatellite like or SNP like loci) with varying number of loci, number of alleles and mutation rates, and with or without amplification problems (null alleles, stuttering, short allele dominance or allelic dropouts), and varying sampling strategies.
Acknowledgements Thierry de Meeûs  would like to thank  François Rousset ,  François Ziegler  and members of the Maxima-discuss list for recurrently answering to my queries and for giving me tips that really unblocked the advances of the present work. Special thanks to  Joseph Felsenstein  who gave the idea of using Taylor-MacLaurin series, which triggered the transformation of a small paragraph of a book into a full article. We also are grateful to  Robin Waples ,  Jay Taylor  and two anonymous referees, whose comments considerably helped improving this manuscript, as  Myriam Heuertz  for her significant editorial inputs. This work was entirely financed by the  Institut de Recherche pour le Développement (IRD)  (recurring subsidies of UMR Intertryp).
Data availability Simulations used for the graphic of Figure  3  are available in the file "Simulations-1st-pop. zip" in Zenodo. Maxima scripts are provided in the section Scripts at the end of the manuscript. The template "TemplateRhipicephalusFstatResNeFiveMethods. xlsx", with the corresponding dataset "BoophilusAdultsDataCattle. txt", and the file VarDif. pdf (computation of the variance of a difference) are also available in Zenodo.
Molecule Gap Gap Lower Bound Gate Reduction  [4]   [4]. For these molecules in the cc-pVDZ basis, we can estimate the energy gaps using EOM-CCSD calculations with ORCA software  [23, 24]. The target accuracy considered in  [4]  was ϵ = 1 mHa. The standard approach to quantum phase estimation (ignoring the cost due to imperfect ground state preparation) uses a circuit with 2/ϵ applications of c-exp (2πiH) to achieve an ϵ accurate estimate with high probability. We include the various logarithmic factors (c. f. Algorithm 2) and set an input state overlap value of η = 1/1000, which is conservative (i. e. lower) relative to the values found in  [25]. In the last column we give cost reductions relative to recent methods  [12] , which use 2/πϵ applications of c-exp (2πiH). Using the costs established in Theorem 1.
For our purposes, the filter function is used as a tool for organizing the time signal data from a limited time window into useful information about the spectrum of H. We briefly explain how to evaluate (or, rather, estimate) the complex number (f * p)(x) for any given value x using low-depth Hadamard test circuits. Three key features make low-depth convolution estimation possible. First, the convolution can be expressed as a linear combination of p(t) = tr ρe -2πiHt , (f * p)(x) = ∞ -∞ f (t)p(t)e 2πixt dt, ( 8 ) where f (t) denotes the Fourier transform of f (x). Second, these traces can be estimated from the Hadamard test data as shown in Eq. 6. Third, the circuit depth of each Hadamard test is proportional to t. We can limit the circuit depth used in the algorithm and still obtain an accurate estimate of the convolution by judiciously truncating the integral approximation ∞ -∞ f (t)p(t)e 2πixt dt ≈ T -T f (t) tr ρe -2πiHt e 2πixt dt.
The tools of classical signal processing have been designed to handle such noisy signals and can aid in the design and analysis of robust quantum algorithms  [28, 29, 30]. One requirement of the algorithm is that a lower bound on the energy gap must be specified. There exist quantum chemistry methods for estimating the gap (e. g. using the ORCA software  [23, 24]  as we did for our numerical comparisons). However, such estimates can become inaccurate for large systems. It may be helpful to incorporate a step into the quantum algorithm that estimates this gap. Although this estimation is computationally hard in general  [31] , many physical systems of interest have structure that make the estimation feasible. In this work, we did not consider the impact of implementation error on the performance of the algorithm.
Acknowledgements This work was done while R. Z. and S. Z. were research interns at  Zapata Computing Inc. We especially thank  Alex Kunitsa  for carrying out the gap estimations for the EC and PF - 6 molecules and providing quantum chemistry expertise on the topic and we thank  Mario Berta  for the suggestion to include such estimations in the manuscript. We thank  Chong Sun  for developing the software used to generate Figure 2. We also thank  Yudong Cao ,  Phillip Jensen ,  Artur Izmaylov ,  Yu Tong , and  Katerina Gratsea  for feedback on the manuscript. Finally, we thank  Aram Harrow  and  Andrew Childs  for discussions and suggestions on future directions for this line of research.
Acknowledgments This publication was produced with the nancial support of the  European Union  within the framework of the  ENI Cross-Border Cooperation Programme Italy-Tunisia   2014-2020  through the  INTEMAR project -IS_2. 1_073  Innovations in the integrated control of  insect pests and pathogens recently introduced on vegetable crops. Its content is the sole responsibility of the project bene ciary and does not necessarily re ect the opinions of the  European Union  and those of the  Managing Authority. This study used the facilities provided by the  High Agronomic Institute of Chott-Mariem, University of Sousse, Tunisia. We think the student  H. Mannai  that contributed to measuring D. frontalis life history traits during here internship.
Single Crystal X-ray Diffraction Analysis Slow evaporation of a hexane-CH 2 Cl 2 solution of compound 1 and 4 allowed us to grow crystals which are suitable for XRD analysis. Table  2  provides crystallographic and structural refinement data for 1 and 4. A Bruker D8 VENTURE diffractometer with PHOTON II detector at 296(2) K was used to collect and integrate crystal diffraction data of 1. Further, a Bruker APEX-II CCD diffractometer was used to collect as well as integrate the crystal data of 4. A dual-space algorithm using SHELXT program  [51]  was used to solve the structures and refinement was achieved utilizing full-matrix least-squares methods based on F 2 using SHELXL-97 software  [52]. Olex2-1. 3  [53]  was used to draw molecular structures. The refinement of non-hydrogen atoms was aided by anisotropic displacement parameters (ADPs), while the introduction of hydrogen atoms, except for bridging hydrogens, was achieved through analysis of Fourier difference maps.
Computational Details Geometry optimizations of all model clusters were carried out using the Gaussian 09 software package  [54]. In order to reduce the computational efforts, the computations were carried out with the Cp ligands instead of Cp* (for optimized model clusters, see Figures  S14-S17 ). The Becke-Lee-Yang-Parr (B3LYP) hybrid functional  [55] , along with the def2-TZVP basis set  [56]  from the EMSL (Environmental Molecular Sciences Laboratory) Basis Set Exchange Library  [57]  for all atoms. Optimizations were carried out without any solvent effects or symmetry constraints. To ensure that the optimized geometries represented genuine minima on the potential energy hypersurface, frequency calculations were conducted for all structures. These calculations were used to examine the nature of the stationary states and verify that there were no imaginary frequencies present.
Acknowledgments: S. M. thanks  IIT Madras  for research fellowship. S. G. thanks  CSIR, India  and S. S. thanks  UGC, India  for research fellowship. Department of Chemistry, IIT Madras  is thankfully acknowledged for X-ray support. The authors express their gratitude to  IIT Madras  for providing computational facilities. Additionally, they extend their appreciation to  P. K. Sudhadevi Antharjanam  and  Venkatachalam Ramkumar  for their valuable contributions in analyzing the single crystal X-ray diffraction data.
Cells Ten primary human MM cell lines (5 epithelioid and 5 sarcomatoid), obtained during diagnostic thoracoscopies, were collected from the S. Antonio e Biagio e Cesare Arrigo Hospital Biological Bank of Malignant Mesothelioma (Alessandria, Italy) after obtaining written informed consent. The local Ethics Committees approved the study (#9/11/2011; #126/2016). Primary MM cells were used until passage 10. Table  1  contains clinical and pathological data of the MM patients. Primary MM cells were cultured in HAM's F12 medium and supplemented with 10% v/v fetal FBS and 100 U/mL penicillin-100 µg/mL streptomycin.
Real Time PCR (RT-PCR) Total RNA was extracted using VWR Life Science RiboZol™ RNA Extraction Reagent (VWR Life Science, Radnor, PA, USA) and reverse-transcribed using the iScriptTM cDNA Synthesis Kit (Bio-Rad Laboratories). qRT-PCR was carried out using SYBR Green Supermix (Bio-Rad Laboratories). qPrimerDepot software (http://primerdepot. nci. nih. gov/, accessed on 13 september 2022) was used to obtain the desired PCR primers (Supplementary Table  S1 ). Gene Expression Quantitation software (Bio-Rad Laboratories) was used to assess relative gene expression levels.
Acknowledgments: We thank Costanzo Costamagna for the technical support. We are grateful to all the staff at the Animal Facility (UTE-IRS UN) at Nantes University for the excellent care of the rats during the experiments. We also acknowledge the IBISA MicroPICell facility (Biogenouest), a member of the national infrastructure France-Bioimaging supported by the French national research agency (ANR-10-INBS-04).
The issue with existing rockfall databases is that they often have low spatial accuracy, poor information about the rockfall event, and lack information on the rock masses' dimensions or volumes  [14] , which are usually only recorded at the maximum runout areas. Therefore, field collection is required to obtain truthful and reliable data on past events  [8, 14]. However, field collection can be challenging  [19] , mainly because rockfalls occur in mountainous areas that are remote and difficult to access. The collection of data, e. g. , the dimensions of deposited rock masses, is labor-intensive and time-consuming since measurements are performed by hand using a measuring tape and are dangerous since new rockfall events during field collection operations might occur again  [8, 14, 20, 21].
The data were implemented in RockPro3D software, which was used to obtain the travelling distance of the rocks. Spreafico et al. [38]  used the results of the UAV photogrammetric survey for the block identification and classification of their surfaces and further compared the rock-scattering results with the results of a discrete fracture network (DFN) model. Vanneschi et al. [7]  used UAV photogrammetric 3D point clouds to identify and measure rocks in order to use them in the 3D modeling of rockfall trajectories using Rockyfor3D. Robiati et al. [39]  extracted a digital surface model from SfM and Multiview Stereopsis (MVS) and rock sizes to model rockfall runout zones using two models: Rocfall and Rockyfor3D. Francioni et al. [40]  used UAV photogrammetric point clouds to extract rock volumes as inputs for rockfall simulations.
They digitalized over 600 rocks on orthophotographs with volumes larger than 1 m 3 to calibrate rockfall modeling with Rockyfor3D. Gallo et al. [41]  derived a digital elevation model from UAV photogrammetry, in combination with photos identified, and they measured more than 600 brocks in the GIS software. The measured rocks were further used in modeling rockfall runout areas with RocPro3D. To overcome the issues related to the measurement of rock deposits on the field and to obtain a representative sample in order to model rockfall trajectories and potential runout zones, this study aims to gather the required data with UAVs. In particular, different sensors mounted on UAVs can be used for obtaining input parameters for rockfall modeling. The purpose of this study was to obtain the locations of the rockfall deposits and their dimensions (length, width, and height) in order to perform a reconstructive analysis of rockfall events.
The scanner weighs 1. 32 kg and has a size of 16 × 10. 3 × 13. 8 cm. It enables a laser range of 140 m (at 10% of target reflectivity), 640k shots, and 3 echoes per second. The UAV surveys were carried out on the following dates: 15 July 2021 at Kekec rockfall, 20 July 2021 at Krnica rockfall, and 21 July 2021 at Mangart rockfall. UAV missions were planned with UgCS Ground Station Software  [48]  so that the UAV was following the terrain. The terrain raster was derived from the LiDAR data of Slovenia in resolution 1 × 1 m  [49]. The flights were planned with 80% frontal and 60% side overlap at the flying height above the terrain of 80 m. The missions were planned in a parallel and cross-grid flight pattern (Figure  4 ). Pictures were taken at two angles, 65°, and 85°, in order to obtain The Krnica rockfall occurred in 2007 from a steep rock wall and is situated approximately at 1465 m of altitude.
Processing of UAV Data Images obtained with the photogrammetric UAV survey were processed with Pix4DMapper (Version 4. 6. 4) by Pix4D  [51]. Bundle block adjustments were carried out by GCPs; the coordinate system for the horizontal geodetic datum was the Slovenia 1996/Slovene National grid with Slovenian Geodetic Datum 1996, while it was the Slovenian system SLOVRP2016 for the vertical geodetic datum. The configuration of the processing was as follows: initial processing was performed in full mode, and point cloud densification was made out of a 1/2 image scale with high point densities. The LiDAR data were processed in the same horizontal and vertical system. The postprocessing of trajectory was performed in the POSPac UAV  [52] , and the point cloud was further processed in the YellowScan CloudStation  [53].
The strip adjustment of the flight lines was performed in TerraMatch by Terrasolid  [54]. The densities of both photogrammetric and LiDAR point clouds are available in Table  1. The UAV surveys were carried out on the following dates: 15 July 2021 at Kekec rockfall, 20 July 2021 at Krnica rockfall, and 21 July 2021 at Mangart rockfall. UAV missions were planned with UgCS Ground Station Software  [48]  so that the UAV was following the terrain. The terrain raster was derived from the LiDAR data of Slovenia in resolution 1 × 1 m  [49]. The flights were planned with 80% frontal and 60% side overlap at the flying height above the terrain of 80 m. The missions were planned in a parallel and cross-grid flight pattern (Figure  4 ). Pictures were taken at two angles, 65 • , and 85 • , in order to obtain a more accurate representation of terrain and individual rock deposits  [50].
Processing of UAV Data Images obtained with the photogrammetric UAV survey were processed with Pix4DMapper (Version 4. 6. 4) by Pix4D  [51]. Bundle block adjustments were carried out by GCPs; the coordinate system for the horizontal geodetic datum was the Slovenia 1996/Slovene National grid with Slovenian Geodetic Datum 1996, while it was the Slovenian system SLOVRP2016 for the vertical geodetic datum. The configuration of the processing was as follows: initial processing was performed in full mode, and point cloud densification was made out of a 1/2 image scale with high point densities. The LiDAR data were processed in the same horizontal and vertical system. The post-processing of trajectory was performed in the POSPac UAV  [52] , and the point cloud was further processed in the YellowScan CloudStation  [53].