Clavicle

Ultrasonography was performed by the same trained operator (DL) using an LogiQ7 (GE Healthcare, Little Chalfont, UK) equipped with a high resolution 10-MHz linear probe and a 7.5-MHz convex phased-array probe. Images were recorded for subsequent computer-assisted quantitative analysis performed by a trained investigator (AG), unaware of the ventilatory condition.
The convex probe was placed below the right costal margin along the mid-clavicular line, so that the ultrasound beam was perpendicular to the posterior third of the corresponding hemi-diaphragm, as previously described [13 (link)]. Patients were scanned along the long axis of the intercostal spaces, with the liver serving as an acoustic window. M-mode was then used to display diaphragm excursion, and three subsequent measurements were averaged. The values of diaphragm excursion in healthy individuals were reported to be 1.8 ± 0.3 cm during quiet breathing [13 (link)].
Diaphragm thickness was assessed in the zone of apposition of the diaphragm to the rib cage. The linear probe was placed above the right 10th rib in the mid-axillary line, as previously described [27 (link)]. The inferior border of the costophrenic sinus was identified as the zone of transition from the artifactual representation of normal lung to the visualization of the diaphragm and liver. In this area, the diaphragm is observed as a three-layered structure: a non-echogenic central layer bordered by two echogenic layers - the peritoneum and the diaphragmatic pleurae [27 (link)]. Three subsequent measures were averaged. The thickening fraction (TF) was calculated as follows: $\mathrm{T}\mathrm{F}=\left(\mathrm{End}-\mathrm{inspiratory}\mathrm{thickness}–\mathrm{End}-\mathrm{expiratory}\mathrm{thickness}\right)/\mathrm{End}-\mathrm{expiratory}\mathrm{thickness}*100\text{.}$

The muscle activations estimated from the musculoskeletal model during the upper-extremity isometric force task were used to calculate synergies. The muscle activations from all force directions were combined into an m × t matrix, V, where m was the number of muscles (i.e., 30) and t was the number of force directions (i.e., 1000). The activations for each muscle were normalized to unit-variance to ensure that the synergies were not biased toward high-variance muscles (Roh et al., 2012 (link)). NNMF was used to calculate synergies (Lee and Seung, 1999 (link); Tresch et al., 1999 (link), 2006 (link)) such that V = W^*C where W is the m × n matrix with n synergies and C is the n × t matrix of synergy activation coefficients. Thus, each column of W represents the weights of each muscle for one synergy, and each row of C represents how much the corresponding synergy was activated or used to generate force in each direction. The number of synergies, n, was set at four to compare to the prior experimental study. The NNMF algorithm was implemented within an iterative optimization which tested random initial estimates of W and C and selected the muscle weights and activation timings that minimized the sum of squared error between V and the muscle activations.
To demonstrate that our simulation was consistent with experimental observation, we first compared the synergies estimated from the musculoskeletal model to the synergies from the experimental protocol reported by Roh et al. (2012 (link)). The experimental protocol included EMG from eight muscles: the brachioradialis, biceps brachii, triceps brachii (long and lateral heads), deltoid (anterior, medial, and posterior fibers), and pectoralis major (clavicular fibers). Thus, for this comparison, we used the activations from the musculoskeletal model for the eight muscles with EMG to calculate synergies using NNMF. We compared the synergies from the musculoskeletal model to the experimental synergies from eight unimpaired subjects. We calculated the similarity of the synergies as the average correlation coefficient. To evaluate if the synergies from the simulation were within the inter-subject variability, we compared the synergies from the musculoskeletal model to the experimental synergies of each subject. We calculated the similarity of the experimental synergies from each subject to one another to evaluate the inter-subject variability. Each subject's synergies were then compared to the simulated synergies to evaluate the similarity between the experimental and simulated synergies. We used an equivalence test to determine if the similarity of the experimental and simulated synergies were within the inter-subject similarity with a significance level of 0.05. For both the inter-subject similarity and similarity between experimental and simulated, we report the 95% confidence intervals.

The skull along with a fragmentary clavicle, ISI A 202, is poorly preserved (Figs. 3, 4, 5 and 6). Only the left half of the skull is preserved and the specimen is heavily eroded. Thus, the ornaments are not well observed in all the areas. The upper part of the parietal and postfrontal have coarse ridges and grooves preserved in them. The skull, its fragments and the clavicle, all have been excavated from the same point in the location and were present together with the same individual as the skull.

The new skull material along with the clavicle (ISI A 202) excavated by the authors and the specimen RH01/Pal/CHQ/Tiki/15 previously described as metoposaurid (Kumar & Sharma, 2019 ) has been studied in detail and referred to in this article. The map of the temnospondyl-bearing localities of the Tiki Formation has been modified here with faunal boundaries (hypothetical faunal boundary demarcated in a red dotted line, after Mukherjee & Ray, 2012 ) (Fig. 1). The temnospondyl bearing (metoposaurid and chigutisaurid) localities of the Maleri Formation have also been extensively mapped and modified (after Dasgupta, Ghosh & Gierlowski-Kordesch, 2017 (link); Kutty & Sengupta, 1989 ) (Fig. 2). Some distinct sections have been logged in the Tiki Formation and has been compared with the existing and modified logs of the Late Triassic Tiki and Maleri Formation.