Visually encoding quantitative information associated with graph links is an important problem in graph visualization. A conventional approach is to vary the thickness of lines to encode the strength of connections in node-link diagrams. In this paper, we present Sticky Links, a novel visual encoding method that draws graph links with stickiness. Taking the metaphor of links with glues, sticky links represent connection strength using spiky shapes, ranging from two broken spikes for weak connections to connected lines for strong connections. We conducted a controlled user study to compare the efficiency and aesthetic appeal of stickiness with conventional thickness encoding. Our results show that stickiness enables more effective and expressive quantitative encoding while maintaining the perception of node connectivity. Participants also found sticky links to be more aesthetic and less visually cluttering than conventional thickness encoding. Overall, our findings suggest that sticky links offer a promising alternative to conventional methods for encoding quantitative information in graphs.

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.

This paper describes an embedded sinusoidal transform codec that is scalable not only in bit-rate, but also in sampling rate. In a representative implementation, the system produces an embedded bit-stream at 3.2 and 6.4 kbit/s for telephone-bandwidth speech, and scales up to 9.6 kbit/s for 16 kHz sampled wideband speech. The 3.2 kbit/s codec is a sinusoidal transform codec with synthetic phases. The 6.4 kbit/s codec adds resolution to the spectral envelope and transmits measured phases of the eight lowest harmonics. The 9.6 kbit/s codec adds information in the 4 to 8 kHz band to provide higher quality wideband speech.

Sinusoidal transform coding (STC) techniques model speech as the sum of sine-waves whose frequencies, amplitudes and phases are specified at regular intervals. To achieve a low-bit rate representation, only the spectral envelope is encoded and the phases are regenerated according to a minimum phase assumption. In this paper, the inaccuracy of the minimum phase model is demonstrated. It is shown that the phase spectra of decoded speech segments may be corrected using either the parameters of a Rosenberg pulse model or a second order all-pass filter. Experiments have shown that by applying this correction, the phase accuracy increases and the speech quality improves.