Simulations of fluid surrounding a voxelized circle.

<p>In the left 20x20 simulation, due to low resolution, the circle is mapped to a square shaped solid region (red blocks). In the right simulation, 3x3 sub-grid resolution is used, resulting some partially filled fluid grids (transparent red blocks). Notice the round shape of the circle is much better preserved than the left one, which does not use sub-grid resolution.</p

Computer Simulations Imply Forelimb-Dominated Underwater Flight in Plesiosaurs

<div><p>Plesiosaurians are an extinct group of highly derived Mesozoic marine reptiles with a global distribution that spans 135 million years from the Early Jurassic to the Late Cretaceous. During their long evolutionary history they maintained a unique body plan with two pairs of large wing-like flippers, but their locomotion has been a topic of debate for almost 200 years. Key areas of controversy have concerned the most efficient biologically possible limb stroke, e.g. whether it consisted of rowing, underwater flight, or modified underwater flight, and how the four limbs moved in relation to each other: did they move in or out of phase? Previous studies have investigated plesiosaur swimming using a variety of methods, including skeletal analysis, human swimmers, and robotics. We adopt a novel approach using a digital, three-dimensional, articulated, free-swimming plesiosaur in a simulated fluid. We generated a large number of simulations under various joint degrees of freedom to investigate how the locomotory repertoire changes under different parameters. Within the biologically possible range of limb motion, the simulated plesiosaur swims primarily with its forelimbs using an unmodified underwater flight stroke, essentially the same as turtles and penguins. In contrast, the hindlimbs provide relatively weak thrust in all simulations. We conclude that plesiosaurs were forelimb-dominated swimmers that used their hind limbs mainly for maneuverability and stability.</p></div

Two body models.

<p>To test the effect of muscle bulk at the base of the limbs, we built two different body models. The slim model is shown in black, and the modifications to increase the muscle bulk are indicated in red. (A) dorsal view with transverse sections through the neck, body, tail, and flippers, (B) transverse section through the pectoral region, (C) lateral view, (D) transverse section through the pelvic region.</p

Modified sinusoids.

<p>(A) A modified sinusoid that is flat for a given duration, to allow the limb rotation to remain fixed during part of a stroke. (B) Another modified sinusoid that allows for time asymmetry between the downstroke and the upstroke of a limb.</p

3D model construction.

<p>(A) Illustration of the holotype of <i>Meyerasaurus victor</i> (SMNS 12478) (Scale bar = 1m). (B) The three-dimensional model was based on a series of two-dimensional cross sections. (C) Static computer rendering of the final three-dimensional model of <i>Meyerasaurus victor</i> used in our study.</p

Tip traces of the most efficient swimming strokes for each of the three ranges.

<p>(A) narrow, (B) medium, and (C) wide. The best forelimb stroke for the narrow and medium ranges is underwater flight, whereas the best stroke for the wide range is modified flight.</p

Model voxelization.

<p>The voxelized version of the plesiosaur mesh, using 3x3x3 sub-grid voxelization. The model is re-voxelized at each simulated time-step, which further minimizes the effects of voxelization.</p

Additional file 1: of A pivotal role of BEX1 in liver progenitor cell expansion in mice

Figure S1. Genetic ablation of Bex1 in mice. Genomic DNA was isolated, and genotyping of Bex1 performed using PCR. Primers as follows: Bex1–3′, TTCATTTCCCCATCTGAAAGGTCCG; Bex1–5′, TCCCACCTACTCACCCATCCTTCTGG; LTR-5′, AAATGGCGTTACTTAAGCTAGCTTGC. Product size for WT mice is 352 bp, and for Bex1−/− mice is 223 bp (PDF 47 kb

Carte de l'entrée de la rivière de Tréguier : levée sous les ordres de Mr. de Kearney Chevalier de St. louis Capitaine de frégate du Roy chargé par la Cour en 1768, 69 et 70 de la Reconnoissance des côtes du Royaume depuis Bayonne jusqu'a D'unkerque / par Mr. Loupia lieutenant d'infanterie et ingénieur Géographe de sa Majesté

Échelle(s) : Echelle de 1000 toises à six lignes pour 100 toises [= 13,5 cm], echelle d'une Lieu Marine à six lignes pour 100 toises [= 38,5 cm

Image_1_Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.TIF

BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.</p