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Haplotype-level allelic characterization facilitates research on the functional, evolutionary and breeding-related features of extremely large and complex plant genomes. We report a 21.7-Gb chromosome-level haplotype-resolved assembly in Pinus densiflora. We found genome rearrangements involving translocations and inversions between chromosomes 1 and 3 of Pinus species and a proliferation of specific long terminal repeat (LTR) retrotransposons (LTR-RTs) in P. densiflora. Evolutionary analyses illustrated that tandem and LTR-RT-mediated duplications led to an increment of transcription factor (TF) genes in P. densiflora. The haplotype sequence comparison showed allelic imbalances, including presence–absence variations of genes (PAV genes) and their functional contributions to flowering and abiotic stress-related traits in P. densiflora. Allele-aware resequencing analysis revealed PAV gene diversity across P. densiflora accessions. Our study provides insights into key mechanisms underlying the evolution of genome structure, LTR-RTs and TFs within the Pinus lineage as well as allelic imbalances and diversity across P. densiflora. 

This paper presents design and control innovations of wearable robots that tackle two barriers to widespread adoption of powered exoskeletons, namely restriction of human movement and versatile control of wearable co-robot systems. First, the proposed quasi-direct drive actuation comprising of our customized high-torque density motors and low ratio transmission mechanism significantly reduces the mass of the robot and produces high backdrivability. Second, we derive a biomechanics model-based control that generates biological torque profile for versatile control of both squat and stoop lifting assistance. The control algorithm detects lifting postures using compact inertial measurement unit (IMU) sensors to generate an assistive profile that is proportional to the biological torque produced from our model. Experimental results demonstrate that the robot exhibits low mechanical impedance (1.5 Nm resistive torque) when it is unpowered and 0.5 Nm resistive torque with zero-torque tracking control. Root mean square (RMS) error of torque tracking is less than 0.29 Nm (1.21% error of 24 Nm peak torque). Compared with squatting without the exoskeleton, the controller reduces 87.5%, 80% and 75% of the of three knee extensor muscles (average peak EMG of 3 healthy subjects) during squat with 50% of biological torque assistance.