Ancient biases in phenotype production drove the functional evolution of a protein family.

Biological systems may be biased in the phenotypes they can access by mutation, but the extent of these biases and their causal role in the evolution of extant phenotypic diversity remains unclear. There are three major challenges: it is difficult to isolate the effect of bias in the genotype-phenotype (GP) map from that of natural selection in producing natural diversity, the universe of possible genotypes and phenotypes is so vast and complex that a direct characterization has been impossible, and most extant phenotypes evolved long ago in species whose GP maps cannot be recovered. Here we develop exhaustive multi-phenotype deep mutational scanning to experimentally characterize the complete GP maps of two reconstructed ancestral steroid receptor proteins, which existed during an ancient phylogenetic interval when a new phenotype-specific binding of a new DNA response element-evolved.

Primary neuraxial anesthetic for elective total knee arthroplasty in patient with left ventricular assist device.

Background: There is a rising number of patients with left ventricular assist devices (LVADs) undergoing non-cardiac procedures, both emergent and elective. Historically, anesthetic options for these patients have been limited to general anesthesia. Limited data exists for the use of neuraxial anesthesia in patients with LVADs despite its common use in orthopedic procedures for non-LVAD patients.

Total versus robotic-assisted unicompartmental knee replacement (TRAKER) for medial compartment osteorthritis: a randomized controlled trial.

Aims: Unicompartmental knee arthroplasty (UKA) is associated with an accelerated recovery, improved functional outcomes, and retention of anatomical knee kinematics when compared to manual total knee arthroplasty (mTKA). UKA is not universally employed by all surgeons as there is a higher revision risk when compared to mTKA. Robotic arm-assisted (ra) UKA enables the surgeon to position the prosthesis more accurately when compared to manual UKA, and is associated with improved functional outcomes and a lower early revision risk.

Error fields: personalized robotic movement training that augments one's more likely mistakes.

Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined to accommodate a person's individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely.