Directional acoustic antennas based on Valley-Hall topological insulators

Realizing directional acoustic signal transmittance and reception robust against surrounding noise and competing signals is crucial in many areas such as communication, navigation, and detection for medical and industrial purposes. The fundamentally wide‐angled radiation pattern of most current acoustic sensors and transducers displays a major limitation of the performance when it comes to precise targeting and probing of sound particular of interest in human speaking and hearing. Here, it is shown how topological acoustic valley transport can be designed to enable a unique beamforming mechanism that renders a superdirective needle‐like sound radiation and reception pattern. The strategy rests on out‐coupling valley‐polarized edge states, whose beam is experimentally detected in the far‐field with 10° width and a sound‐intensity enhancement factor ≈10. Furthermore, anti‐interference communication is proposed where sound is received from desired directions, but background noise from other directions is successfully suppressed. This type of topological acoustic antenna offers new ways to control sound with improved performance and functionalities that are highly desirable for versatile applications.Z.Z. and Y.T. contributed equally to this work. This work was supported by National Key R&D Program of China (2017YFA0303702), NSFC (11674172, 11574148, and 11474162), Jiangsu Provincial NSF (BK20160018), the Fundamental Research Funds for the Central Universities (020414380001) and Nanjing University Innovation and Creative Program for Ph.D. candidate (CXCY17-11). J.C. acknowledges the support from the European Research Council (ERC) through the Starting Grant 714577 PHONOMETA and from the MINECO through a Ramón y Cajal grant (Grant No. RYC-2015-17156)

Identification of Parkinsons disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data.

Parkinsons disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine

Clinical and biological progress over 50 years in Rett syndrome

In the 50 years since Andreas Rett first described the syndrome that came to bear his name, and is now known to be caused by a mutation in the methyl-CpG-binding protein 2 (MECP2) gene, a compelling blend of astute clinical observations and clinical and laboratory research has substantially enhanced our understanding of this rare disorder. Here, we document the contributions of the early pioneers in Rett syndrome (RTT) research, and describe the evolution of knowledge in terms of diagnostic criteria, clinical variation, and the interplay with other Rett-related disorders. We provide a synthesis of what is known about the neurobiology of MeCP2, considering the lessons learned from both cell and animal models, and how they might inform future clinical trials. With a focus on the core criteria, we examine the relationships between genotype and clinical severity. We review current knowledge about the many comorbidities that occur in RTT, and how genotype may modify their presentation. We also acknowledge the important drivers that are accelerating this research programme, including the roles of research infrastructure, international collaboration and advocacy groups. Finally, we highlight the major milestones since 1966, and what they mean for the day-to-day lives of individuals with RTT and their families