DHX9 SUMOylation is required for the suppression of R-loop-associated genome instability.

RNA helicase DHX9 is essential for genome stability by resolving aberrant R-loops. However, its regulatory mechanisms remain unclear. Here we show that SUMOylation at lysine 120 (K120) is crucial for DHX9 function.

Investigation of Immunogenicity Assessment of Biosimilar Monoclonal Antibodies in the United States.

Immunogenicity is critical for biologics. However, reference biologics labeling documents do not necessarily mention immunogenicity impact, rendering the development of biosimilars more challenging. We aimed to investigate the comparative assessment of immunogenicity profiles between biosimilars and their respective reference biologics in the review reports of the biosimilar monoclonal antibody applications approved by the Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA) as of March 13, 2022, covering 22 applications approved between April 5, 2016, and December 17, 2021.

Effectiveness of Tai Chi on fibromyalgia patients: A meta-analysis of randomized controlled trials.

Objective: To identify empirical evidence on the effectiveness of Tai Chi in treating fibromyalgia (FM).

Method: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to compare the effectiveness of Tai Chi and standard care or conventional therapeutic exercise in patients with FM. PubMed, Medline, and Physiotherapy Evidence Database were searched for relevant studies published before May 2019.

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space.

Learning the Inverse Dynamics of Robotic Manipulators in Structured Reproducing Kernel Hilbert Space.

We investigate the modeling of inverse dynamics without prior kinematic information for holonomic rigid-body robots. Despite success in compensating robot dynamics and friction, general inverse dynamics models are nontrivial. Rigid-body models are restrictive or inefficient; learning-based models are generalizable yet require large training data.