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Multiple myeloma (MM) is a plasma cells neoplasm. The overexpression of Bcl-2 family proteins, particularly myeloid cell leukemia 1 (Mcl-1), plays a critical role in the pathogenesis of MM. The overexpression of Mcl-1 is associated with drug resistance and overall poor prognosis of MM. Thus, inhibition of the Mcl-1 protein considered as a therapeutic strategy to kill the myeloma cells. Over the last decade, the development of selective Mcl-1 inhibitors has seen remarkable advancement. This review presents the critical role of Mcl-1 in the progression of MM, the most prominent BH3 mimetic and semi-BH3 mimetic that selectively inhibit Mcl-1, and could be used as single agent or combined with existing therapies. 

Lip‐reading provides an effective speech communication interface for people with voice disorders and for intuitive human–machine interactions. Existing systems are generally challenged by bulkiness, obtrusiveness, and poor robustness against environmental interferences. The lack of a truly natural and unobtrusive system for converting lip movements to speech precludes the continuous use and wide‐scale deployment of such devices. Here, the design of a hardware–software architecture to capture, analyze, and interpret lip movements associated with either normal or silent speech is presented. The system can recognize different and similar visemes. It is robust in a noisy or dark environment. Self‐adhesive, skin‐conformable, and semi‐transparent dry electrodes are developed to track high‐fidelity speech‐relevant electromyogram signals without impeding daily activities. The resulting skin‐like sensors can form seamless contact with the curvilinear and dynamic surfaces of the skin, which is crucial for a high signal‐to‐noise ratio and minimal interference. Machine learning algorithms are employed to decode electromyogram signals and convert them to spoken words. Finally, the applications of the developed lip‐reading system in augmented reality and medical service are demonstrated, which illustrate the great potential in immersive interaction and healthcare applications.

 

Despite increasing conflict at human–wildlife interfaces, there exists little research on how the attributes and behavior of individual wild animals may influence human–wildlife interactions. Adopting a comparative approach, we examined the impact of animals’ life-history and social attributes on interactions between humans and (peri)urban macaques in Asia. For 10 groups of rhesus, long-tailed, and bonnet macaques, we collected social behavior, spatial data, and human–interaction data for 11–20 months on pre-identified individuals. Mixed-model analysis revealed that, across all species, males and spatially peripheral individuals interacted with humans the most, and that high-ranking individuals initiated more interactions with humans than low-rankers. Among bonnet macaques, but not rhesus or long-tailed macaques, individuals who were more well-connected in their grooming network interacted more frequently with humans than less well-connected individuals. From an evolutionary perspective, our results suggest that individuals incurring lower costs related to their life-history (males) and resource-access (high rank; strong social connections within a socially tolerant macaque species), but also higher costs on account of compromising the advantages of being in the core of their group (spatial periphery), are the most likely to take risks by interacting with humans in anthropogenic environments. From a conservation perspective, evaluating individual behavior will better inform efforts to minimize conflict-related costs and zoonotic-risk.