Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements.

Vocal production learning ("vocal learning") is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.

Influence of Preoperative T1-Slope and Cervical Sagittal Vertical Axis on Postoperative Cervical Sagittal Alignment Following Posterior Cervical Laminoplasty.

BACKGROUND: Assess correlation between preoperative cervical sagittal alignment (T1 slope [T1S] and C2-C7 cervical sagittal vertical axis [cSVA]) and postoperative cervical sagittal balance after posterior cervical laminoplasty. METHODS: Consecutive patients who underwent laminoplasty at a single institution with >6 weeks postoperative follow-up were divided into 4 groups based on preoperative cSVA and T1S (Group 1: cSVA <4 cm/T1S <20°; Group 2: cSVA ≥4 cm/T1S ≥20°; Group 3: cSVA <4 cm/T1S ≥20°; Group 4: cSVA <4 cm/T1S <20°). Radiographic analyses were conducted at 3 timepoints, and changes in cSVA, C2-C7 cervical lordosis (CL), and T1S -CL were compared. RESULTS: A total of 214 patients met inclusion criteria (28 patients had cSVA <4 cm/T1S <20° [Group 1]; 47 patients had cSVA ≥4 cm/T1S ≥20° [Group 2]; 139 patients had cSVA <4 cm/T1S ≥20° [Group 3]). No patients had cSVA ≥4 cm/T1S <20° (Group 4). Patients either had a C4-C6 (60.7%) or C3-C6 (39.3%) laminoplasty. Mean follow-up was 1.6 ± 1.32 years. For all patients, mean cSVA increased 6 mm postoperatively. cSVA significantly increased postoperatively for both groups with a preoperative cSVA <4 cm (ie, Groups 1 and 3 [P < 0.01]). For all patients, mean CL decreased 2° postoperatively. Groups 1 and 2 had significant differences in preoperative CL but nonsignificant differences at 6 weeks (P = 0.41) and last follow-up (P = 0.06). CONCLUSION: Cervical laminoplasty resulted in a mean decrease in CL. Patients with high preoperative T1S, irrespective of cSVA, were at risk of loss of CL postoperatively. While patients with low preoperative T1S and cSVA <4 cm experienced a decrease in global sagittal cervical alignment, CL was not jeopardized. CLINICAL RELEVANCE: The results of this study may facilitate preoperative planning for patients undergoing posterior cervical laminoplasty.

Unsupervised machine learning discovers classes in aluminium alloys.

Aluminium (Al) alloys are critical to many applications. Although Al alloys have been commercially widespread for over a century, their development has predominantly taken a trial-and-error approach. Furthermore, many discrete studies regarding Al alloys, often application specific, have precluded a broader consolidation of Al alloy classification. Iterative label spreading (ILS), an unsupervised machine learning approach, was used to identify the different classes of Al alloys, drawing from a specifically curated dataset of 1154 Al alloys (including alloy composition and processing conditions). Using ILS, eight classes of Al alloys were identified based on a comprehensive feature set under two descriptors. Further, a decision tree classifier was used to validate the separation of classes.

Power of an Adaptive Trial Design for Endovascular Stroke Studies: Simulations Using IMS (Interventional Management of Stroke) III Data.

BACKGROUND AND PURPOSE: Adaptive trial designs that allow enrichment of the study population through subgroup selection can increase the chance of a positive trial when there is a differential treatment effect among patient subgroups. The goal of this study is to illustrate the potential benefit of adaptive subgroup selection in endovascular stroke studies. METHODS: We simulated the performance of a trial design with adaptive subgroup selection and compared it with that of a traditional design. Outcome data were based on 90-day modified Rankin Scale scores, observed in IMS III (Interventional Management of Stroke III), among patients with a vessel occlusion on baseline computed tomographic angiography (n=382). Patients were categorized based on 2 methods: (1) according to location of the arterial occlusive lesion and onset-to-randomization time and (2) according to onset-to-randomization time alone. The power to demonstrate a treatment benefit was based on 10 000 trial simulations for each design. RESULTS: The treatment effect was relatively homogeneous across categories when patients were categorized based on arterial occlusive lesion and time. Consequently, the adaptive design had similar power (47%) compared with the fixed trial design (45%). There was a differential treatment effect when patients were categorized based on time alone, resulting in greater power with the adaptive design (82%) than with the fixed design (57%). CONCLUSIONS: These simulations, based on real-world patient data, indicate that adaptive subgroup selection has merit in endovascular stroke trials as it substantially increases power when the treatment effect differs among subgroups in a predicted pattern.