dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods and results data were extracted using data extraction sheets designed a priori for the study (including reference, year of publication, geographical location of the study, birth cohorts included, study method, data sources, definition of CP and diagnostic criteria used, age at diagnosis/confirmation of diagnosis, definition and inclusion of postneonatal CP, and numerator and denominator definitions). The corresponding author was contacted, as required, to clarify information or data, and authors were asked to provide data from 1995 only. We preferentially extracted case and denominator data for live births, rather than children living in the region. When multiple prevalence rates were reported for children at different ages, data were extracted for the age group closest to age 5 years, when a diagnosis of CP is usually confirmed/verified.
Quality and risk of bias assessment CP registers/surveillance systems and included publications were critically appraised for quality and risk of bias. The JBI checklist for prevalence studies  17  was used, which includes nine quality items (marked as yes, no, unclear, or not applicable) and an overall appraisal to 'include' or 'exclude' the study for meta-analysis. Sets of two reviewers independently assessed each data source; discrepancies were resolved with an independent third reviewer (SG, SM, HSS, SJH, GH, KH, KM).
Objective 2a: most recent prevalence in each region Recent prevalence of pre-/perinatal, postneonatal, and overall CP were calculated for each region, with 95% confidence intervals (CI). Data for the two most recent birth years were used for pre-/perinatal CP, while data for any number of birth years from 2010 were used for postneonatal CP given the small number of individuals with postneonatal CP. The 95% CIs were calculated using approximation to the normal distribution after proportions were transformed using Freeman-Tukey double arcsine transformation. 17 Results are presented as prevalence rates after the pooled estimates were back transformed.
Objective 2b: current combined global prevalence Meta-analyses of 'current' prevalence of pre-/perinatal CP per 1000 live births, postneonatal CP per 10 000 live births, and overall CP per 1000 live births were performed for regions with prevalence data for at least two consecutive birth years from 2010. The current birth prevalences of CP (pre-/perinatal, postneonatal, overall) were derived using univariate meta-analysis of proportions methods. Random effects meta-analyses using the DerSimonian and Laird method were preferred to fixed effects meta-analysis because of the anticipated heterogeneity of CP prevalence between regions. Heterogeneity between regional estimates was assessed using the coefficients τ 2 and I 2. χ 2 tests of heterogeneity were also performed. These tests were only used descriptively.
R E SU LTS Data from 41 regions of 27 countries were included. CP registers contributed data representing 19 regions from 15 countries, all from Europe and Australia, and classified as regions from HICs. Data from two register networks of Australia and Europe were also received (ACPR and SCPE). Published literature provided data from an additional 22 regions from 12 countries: Africa (n = 2 regions from LMICs), Asia (n = 4 regions from LMICs and n = 5 regions from HICs), Europe (n = 1 region from LMICs and n = 3 from HICs), North America (n = 7 regions from HICs) (Table  1  and Figure  S1 ). No registers or studies were assessed as having a high risk of bias; therefore, all were included in at least one analysis (Tables 1, S1, and S2). Data sources not from registers ranged from face-to-face clinical assessments to administrative data linkages (Table  1 ).
Most recent birth years were included in the analysis; however, they ranged from 1995 to 2014. Variation across regions reflect different birth years, size of the denominator population, and World Bank income levels. Two regions from LMIC had high birth prevalence estimates of 3 and 3. 4 per 1000 live births, one of these regions also had a high postneonatal CP estimate. Meta-analysis was restricted to regions with data since 2010 to obtain an estimate for current birth prevalence. A total of 17 regions were included in the analysis for both pre-/perinatal CP, postneonatal CP, and overall CP, all of which were HICs and from CP registers. Heterogeneity does exist between regions; however, for pre-/perinatal CP there was a combined estimate of 1. 5 per 1000 (95% CI 1. 4-1. 6) live births (τ 2 < 0.
DISCUS SION Before this paper, the most recent international CP birth prevalence study was published in 2013. 3 As such, the great majority of data included were from HICs and birth years from the 1980s and 1990s. At that time the overall birth prevalence was stable, and the estimate was 2. 1 per 1000 live births. Since then, several studies have been published that suggest declines in birth prevalence have occurred in the 2000s. 4, 6 This study was therefore undertaken to update our understanding of the global prevalence of CP by using contemporary data from CP registers and surveillance systems as well as published literature. Our study confirmed that pre-/perinatal CP is declining in high-income regions in Europe and Australia. The trend was similar for individual regions and for the two major CP register networks, SCPE and ACPR.
3), a CoM admittance controller (Eq. 4) and a Lexicographic QP solving the inverse kinematics of the robot (see Fig. 2 ). The authors have implemented this scheme in an open-source package GEPETTO Team LAAS-CNRS (2021c), based on the QP in  Mansard et al. (2009) , adding the DCM and CoM admittance controllers. The tasks used during the simulations are (the priority 0 is the highest one) : • Feet tracking (priority 0) • CoM height tracking (priority I) • CoM lateral-sagittal tracking (priority II) • Waist orientation (priority III) • Posture regularization in half-sitting (priority IV) The respective task gains are defined in Table  1.
(2014)  and has been successfully used on HRP-2 robot in Del Prete et al. (  2016 ). Inverse Dynamics WQP: TSID Position -This control scheme is based on a DCM controller (Eq. 3), a CoM admittance controller (Eq. 4) and a WQP solving the inverse dynamics of the robot, see Fig. 2. Compared to the previous controller, this one implements an AM task, which regulates the angular momentum to 0, using the formulation of Eq. 5. The authors have implemented this controller using the TSID Del Prete (2021) library in the same package than the controller TSID Torque, with the DCM and CoM admittance controllers. The tasks considered during the simulations are: • Feet tracking (priority 0) • Feet contacts (priority 0) • CoM height tracking (priority I, weight 10 3 ) This is a provisional file, not the final typeset article • CoM lateral-sagittal tracking (priority I, weight 10 3 ) • Waist orientation (priority I, weight 1) • Posture regularization in half-sitting (priority I, weight 0.
1) • AM velocity-acceleration regularization (priority I, weight 2 × 10 -2 ) The respective task gains are defined in Table  1. The weights and gains have been chosen through trials and errors with an apriori heuristic. Inverse Dynamics WQP: TSID Torque -This control scheme is based on a WQP solving the inverse dynamics of the robot (with an AM regularization task, using the formulation of Eq. 5), as shown in Fig. 3. From the desired acceleration computed by the QP, TSID retrieves the associated torque by using the robot equation of the dynamics. The authors have implemented this controller using the TSID Del Prete (2021) library in the open-source package GEPETTO Team LAAS-CNRS (2020). The tasks considered in the simulations are the same as TSID position, with different gains (see Table  1 ).
PAL robotics low-level controller To achieve torque control on the real robot, it is needed to transform the joint torque commands to motor currents. We decided to use the PAL robotics constructor low-level controller, which computes new commands respecting the robot actuators dynamics. This low-level controller is a proprietary black-box, which use a ros-control hardware interface to communicate with the robot. To interface our control scheme (based on the SoT with the WPG), we had to create a new version of the roscontrol-sot package. Indeed, our control scheme needs no more to communicate directly with the robot but with the PAL robotics controller, which implements different functions and formulations. One of the major difficulty is that the proprietary code source is not available, we only had access to its C++ headers and some basic tutorials.
Developing this interface to keep all the functionalities implemented in the roscontrol-sot package (for instance to keep the recording of the logs and creating all the necessary signals needed by the SoT in the dynamic-graph structure), take us months of work (including the following remark). [50, 50, 50, 50, 50, 50, 50, 50] [10, 10]  Table  10. Tasks gains of the torque control scheme for the posture task. Moreover, as the robot has a modified operating system called ferrum (equivalent to ubuntu), we created a Docker Merkel (2014) container to have exactly the same environment as the one on the robot to test our codes. Installing the SoT packages on this environment was not trivial as some packages had conflicting dependencies with the PAL robotics packages. Finally, we succeeded to test in this Docker container our interface and our torque controller using the PAL simulator available on ferrum.
RNA-seq Total RNA was extracted using the RNeasy mini kit (Qiagen, Hilden, Germany) following the manufacturer's protocol. RNA-seq libraries were sequenced on Illumina HiSeq instruments, resulting in approximately 30 million reads per sample. The STAR aligner was used to map sequencing reads to transcriptome in the homo sapiens hg19 reference genome. [16] Read counts for individual genes were produced using the unstranded count function in HTSeq v. 0. 6. 0,  [17] followed by the estimation of expression values and detection of differentially expressed transcripts using EdgeR and including only the genes with count per million reads > 1 for one or more samples. [18, 19] ifferentially expressed genes were defined by at least 2-fold change with false discovery rate < 0. 01. The Database for Annotation, Visualization, and Integrated Discovery (DAVID; https://david.
ncifc rf. gov/) was used to analyze the functions and signaling pathways of the differentially expressed genes (DEGs). Biological processes that DEGs were involved in were identified through gene ontology (GO) analysis in DAVID with a cutoff adjusted p value < 0. 05. For Gene-Set Enrichment Analysis (GSEA), DEGs modulated by each statin were ranked by fold change, and then the enrichment of DEGs in the gene set (YAPrelated genes up-regulated by HCV infection) were calculated by GSEAPreranked analysis of GSEA software.
PLS score Experimental modulation of the PLS was quantitatively measured as a PLS score as follows. For each sample in the expression data set, nearest template prediction (NTP)  [20]  was performed to determine prognostic risk level based on the PLS gene-expression profiles as previously described,  [21]  and a PLS score was calculated as -log 10 (NTP prediction p value) × (+ 1 for high risk prediction; -1 for low risk prediction).
Quantitative reverse-transcription polymerase chain reaction Complementary DNA was synthesized from messenger RNA (mRNA) using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) following the manufacturer's protocol. The mRNA expression levels were analyzed by quantitative reverse-transcription polymerase chain reaction (PCR) with glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as a reference gene (Table  S1 ), using PowerUP SYBR Green Master Mix (Thermo Fisher Scientific, Waltham, MA) and CFX Manager Software 3. 1 (Bio-Rad Laboratories, Hercules, CA) following the manufacturer's protocol.
These findings suggest that atorvastatin is possibly the most potent statin to reduce HCC risk in patients with chronic liver disease of any etiology. We have embarked on a clinical trial to confirm the PLS score-lowering effect of atorvastatin in patients with advanced liver disease of any etiology at high risk of HCC (NCT05028829). We expect that the clinical trial will prove the chemopreventive effect of atorvastatin for.
Many marine megafauna taxa are tied to the sea surface for breathing which makes them vulnerable to vessel collisions. Sea turtles have developed efficient mechanisms to reduce surface time for breathing to a few seconds, but they can extend their surface periods to rest or to rewarm after diving into deep and colder waters. However, knowledge of collision occurrences is limited to data of turtles stranded along the coastline worldwide, whereas events occurring offshore go likely underestimated due to the sinking of carcasses. Here we performed a spatially explicit assessment to identify, for the first time, oceanic areas of higher exposure for sea turtles from maritime traffic in the Tyrrhenian Sea, Western Mediterranean. Satellite-tracking data were used to estimate utilization distributions of loggerhead turtles using Brownian bridge kernel density estimation.
Maritime traffic density maps based on Automatic Identification System (AIS) data were extracted from open-access data layers, provided by the European Maritime Safety Agency, summarized, and used for the exposure analysis. Turtle occurrences were also investigated in response to vessel densities and seasonal patterns by fitting a generalized additive model to the data. Our results demonstrated that loggerhead turtles are potentially exposed to maritime traffic across the entire basin, especially in the easternmost part. The exposure varies among spring/summer and autumn/ winter months. Highest turtle occurrences were found in regions primarily subjected to cargo, tanker, and passenger transportation. This study represents the first-ever effort to characterize the exposure of oceanic loggerhead turtles to maritime traffic and highlights oceanic areas of higher exposure where Frontiers in Marine Science frontiersin.
When the ship hull or propeller hits a sea turtle, the collisions usually cause major, life-threatening injuries such as massive carapace fractures or deep cuts on the head, flippers, and carapace  (Hazel and Gyuris, 2006; Work et al. , 2010). To date, information regarding the impact of vessel collisions on sea turtles is restricted to data derived from stranding events  (Cannon, 1998; Jepson, 2005; Oroś et al. , 2005; Hazel and Gyuris, 2006; Chaloupka et al. , 2008; Tomaś et al. , 2008; Work et al. , 2015; Meager, 2016; Foley et al. , 2019; Belmahi et al. , 2020). In the Mediterranean Sea, a study based on 30-year stranding records reported injuries possibly due to vessel collisions in 6. 4% of turtles recovered/stranded along the Italian coast  (Casale et al. , 2010a). Furthermore, according to the mentioned study, this trend increases during warm seasons when traffic is generally higher, and sea turtles spend more time at the surface and are more susceptible to vessel encounters.
, 2018). Furthermore, the Tyrrhenian Sea (Western Mediterranean) has recently been recognized as an important foraging and overwintering area for Mediterranean loggerhead turtles  (Luschi et al. , 2018; Chimienti et al. , 2020; Chatzimentor et al. , 2021). In these studies, turtles were tracked while moving in the oceanic waters for extended periods, often close to seamounts and volcanic islands  (Fiori et al. , 2016)  or in the proximity of fishing areas  (Blasi et al. , 2016; Blasi and Mattei, 2017). In the current study, we analyzed satellite tracking data collected over a 15-year period on loggerhead turtles primarily moving within the oceanic habitats of the Tyrrhenian Sea to determine their main patterns of movement in the oceanic realm. Then, we performed a spatially explicit assessment of exposure for the species to maritime traffic by using published vessel density data.
Preparing tracking data and standardizing locations Prior to any analysis, locations were visually inspected and the first 24 h after the release of the turtle was omitted. For ARGOS tags, locations with no estimable location accuracy (i. e. , location class Z) were excluded. In order to also eliminate the low-accuracy locations (location classes A and B) associated with unrealistic speeds, a 200-km/day speed filter  (Dujon et al. , 2017)  was also applied to raw ARGOS datasets. Filtering was performed by using the function "vmask" (algorithm by  McConnell et al. , 1992)  within the "argosfilter" package  (Freitas, 2012). For QFP positioning, only the quality classes Loggerhead turtles showing a marked preference for coastal habitats and with a tracking duration of less than 28 consecutive tracking days in the TYS (n = 6) were discarded and not included in the following analysis.
Utilization distribution estimates Animals' utilization distributions (UDs) were estimated using the Brownian bridge approach of the Kernel method for autocorrelated relocations  (Bullard, 1991; Horne et al. , 2007)  applying the function "kernelbb" within the AdehabitatHR R package  (Calenge, 2006; Calenge, 2019). Instead of simply smoothing the relocation pattern as the classic kernel function does, the method considers that the animal has moved through a continuous path, which is not necessarily linear, between two successive relocations. The Brownian bridge model estimates the probability density that this path passed through any point of the study area, given that the animal was located at the point r1 at time t1 and at the point r2 at time t2, with a certain amount of inaccuracy. The estimation relies on two smoothing parameters, sig1 and sig2: the first measure is based on the inaccuracy of the relocations, and the second was computed following the maximum likelihood approach described in  Horne et al.
(2007)  by using the function "liker" of the package. The function's output is the probability density to relocate the animal at a given place (UD) which was then multiplied by cell area (1 km 2 ) to standardize it over the area of interest, so that the volume under the UD is equal to 1. The function "getvolumeUD" was then used to estimate the home range distributions (HRs) of individuals and function "rasterize" of the raster package to compute the mean HR and the mean UD for all animals. The same procedure was followed to estimate densities over different periods of the year, partitioning data in two semesters in order to allow the comparison with ship density data: from October to March (autumn/winter, n. relocations: 2,904) and from April to September (spring/summer, n. relocations: 1,781).
Summarizing maritime traffic AIS-based data Vessel density maps were provided by the European Maritime Safety Agency (EMSA) and retrieved at the EMODnet Human Activities website. Here, data are provided as the density (h/km 2 ) of vessels of certain type (cargo, passenger, fishing, etc. ) in a given period (month or year) within a grid cell of 1-km 2 resolution, from January 2017 to 2020. Vessel density maps were based on Automatic Identification Systems (AIS) data. AIS is an automatic tracking system that uses transceivers installed on vessels by automatically broadcasting critical information, including the vessel's identity and real-time position, course, and speed, using VHF frequencies. European law requires AIS to be fitted aboard all ships of 300 gross tonnage and upward engaged on international voyages, cargo ships of 500 gross tonnage and upward not engaged on international voyages, and all passenger ships irrespective of size.
Furthermore, since May 2014, all European fishing vessels exceeding 15-m length overall are required to carry and operate an AIS device (EU Dir 2011/ 15/EU). Traffic densities were gathered and manipulated with the "raster" package to obtain both total and seasonal (October-March and April-September) average maps for the period 2017-2020 sorted by four selected vessel types (Cargo, Fishing, Passenger, Tanker, and an additional category combining "All traffic"). Due to the lack of data anterior to 2017, it was not possible to match the entire tracking period  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020) (2021). Yearly vessel densities were compared to understand the degree of the yearly variation. Kruskal-Wallis tests were performed to identify significant differences in (i) total ship density among subareas, (ii) total ship density among two seasons, and (iii) total densities among ship categories.
Generalized Additive Model (GAM) analysis The aim of the GAM was to investigate relationships between loggerhead turtles and vessel occurrence in the oceanic TYS. In this regard, we fit the GAM to our data using the "mgcv" package  (Wood, 2011). The dataset was obtained by rescaling from 1-to 15-km 2 resolution the raster of turtles' seasonal UDs and vessel densities and setting the new values as the sum of the values in the original cells using the "r. resamp. stat" function. A grid of points of 15 km 2 was then used to sample raster values using the "Point Sampling Tool" in QGIS. This grid size was chosen among other resolutions (5 km 2 , 10 km 2 , and 30 km 2 ) as the best option to reduce spatial autocorrelation effects to acceptable level and maintaining the finest possible scale. The model used the seasonal UD as the response variable (UD_season) and the logtransformed vessel density (logVD_season) as the explanatory variable.
dev. ) days in total or 74 ( ± 41 std. dev. ) days during the spring/summer and 132 ( ± 51 std. dev. ) during the autumn/winter season. The average home range distribution referring to the overall dataset indicated three main core areas (75% HR) in the southeastern and partially the central-eastern parts of the TYS (Figure  2 ) sized 909, 488, and 261 km 2. Seasonal mean home ranges indicated that the overall area used is greater  over autumn/winter, with core areas identified again in the eastern TYS (the largest sized 1,247 km 2 ), whereas during the spring/summer (Figure  3A ) turtles seem to use smaller patches and disperse more widely over the basin, without a common core area (Figure  3B ).
Indeed, looking at the detailed traffic composition (Figure  S4 ), cargo was the vessel type contributing most (38%) to all-traffic densities, presenting the highest values in the northern and southeastern parts of the basin due to the important global route passing through the Messina Strait that connects the western and eastern areas of the Mediterranean. Fishing can also contribute considerably to the overall traffic in the oceanic TYS (28%, in our dataset)  (Coomber et al. , 2016). However, we acknowledge that fishing activity is not represented completely in our analysis, as we excluded areas on the continental shelf where most of the fishing activity, including artisanal fishery, is expected to occur  (Mangano et al. , 2014).
Indeed, higher speeds potentially reduce the chance for the animal to notice the approaching vessel and dive or move away from its course and imply heavier consequences in case of collision  (Schoeman et al. , 2020). Studies in coastal regions suggested that green turtles are unlikely to take effective evasive action when approached by small-medium vessels at speeds exceeding 4 km/h  (Hazel et al. , 2007) , and that vessels had to slow down to decrease the probability of severe injuries also on loggerhead turtles  (Work et al. , 2010). It is possible to expect that passenger ships, cargo, and tankers, which travel approximately around 28, 21, and 16 km/h, respectively  (Coomber et al. , 2016) , have a greater likelihood for collision events compared to other vessels. Additionally, larger vessels have deeper drafts which may exceed 10 m (for cargo vessels, data from Marine Traffic-Global Ship Tracking Intelligence), implying a large potential collision zone for turtles, which might be easily pulled underneath the vessel by the suction of the huge propeller.