Abstract: Disclosed in the present invention is an anti-Nipah virus monoclonal antibody having neutralization activity. The antibody consists of a monkey-derived variable region and a human constant region, and both light and heavy chains of the monkey-derived variable region have unique CDR regions. The antibody provided by the present invention has an excellent antigen binding capability, and has good binding activity with Bangladesh Nipah virus and Malaysia Nipah virus glycoprotein G. The antibody can effectively neutralize the Nipahpseudovirus. Moreover, the neutralization activity of the antibody is enhanced as the concentration of the antibody increases, and nearly 100% neutralization of the Nipahpseudovirus can be achieved at a concentration of 1 ?g/mL. Also disclosed in the present invention is an application of the monoclonal antibody against the Nipah virus glycoprotein G in preparation of a Nipah virus treatment drug.

Abstract: Embodiments of this application disclose a noise cancellation apparatus and method. The noise cancellation apparatus includes a main control unit and a noise cancellation processing circuit. The main control unit determines a noise cancellation parameter based on a noise cancellation level index or a feature value for determining a matching degree between a headset and an ear canal of a user. The noise cancellation processing unit obtains an inverse phase noise of an ambient noise based on the noise cancellation parameter. After the inverse phase noise is mixed with a played downlink audio signal, the ambient noise can be canceled. In addition, because the noise cancellation parameter is determined from a preset noise cancellation parameter library based on the received or autonomously determined noise cancellation level index, instead of being uniformly configured, a noise cancellation level can be flexibly adjusted, thereby improving a noise cancellation effect and user experience.

Abstract: An analysis method for the causal inference of human physiological network in multiscale time series signals includes the following steps: S1: decomposing physiological signals u1, u2, . . . , um to be analyzed by using a noise-assisted multivariate empirical mode decomposition (NA-MEND) algorithm; S2: carrying out a causal analysis between two different physiological signals ui, uj, where i=1, 2, . . . , m, j=1, 2, . . . , m, and i?j, to obtain a causality between the two signals; and S3: repeating step S2 for any two signals in u1, u2, . . . , um until a causality between each two signals in u1, u2, . . . , um is obtained to form the causal network. The present invention can effectively analyze the causal network of the physiological signals, thereby facilitating the application of the physiological signals.