Parity-violating πNN\pi NN coupling constant from the flavor-conserving effective weak chiral Lagrangian

We investigate the parity-violating pion-nucleon-nucleon coupling constant $h^1_{\pi NN}$, based on the chiral quark-soliton model. We employ an effective weak Hamiltonian that takes into account the next-to-leading order corrections from QCD to the weak interactions at the quark level. Using the gradient expansion, we derive the leading-order effective weak chiral Lagrangian with the low-energy constants determined. The effective weak chiral Lagrangian is incorporated in the chiral quark-soliton model to calculate the parity-violating $\pi NN$ constant $h^1_{\pi NN}$. We obtain a value of about $10^{-7}$ at the leading order. The corrections from the next-to-leading order reduce the leading order result by about 20~\%.Comment: 12 page

Outage-based ergodic link adaptation for fading channels with delayed CSIT

Link adaptation in which the transmission data rate is dynamically adjusted according to channel variation is often used to deal with time-varying nature of wireless channel. When channel state information at the transmitter (CSIT) is delayed by more than channel coherence time due to feedback delay, however, the effect of link adaptation can possibly be taken away if this delay is not taken into account. One way to deal with such delay is to predict current channel quality given available observation, but this would inevitably result in prediction error. In this paper, an algorithm with different view point is proposed. By using conditional cdf of current channel given observation, outage probability can be computed for each value of transmission rate $R$. By assuming that the transmission block error rate (BLER) is dominated by outage probability, the expected throughput can also be computed, and $R$ can be determined to maximize it. The proposed scheme is designed to be optimal if channel has ergodicity, and it is shown to considerably outperform conventional schemes in certain Rayleigh fading channel model

Diversity at work matters in times of violent ethnic conflicts

Perceptions that colleagues prefer to work with ethnically similar others can be detrimental to the organisation, especially in regions marred by violence and adversity. Hyun-Jung Lee draws on her research from Sri Lanka to highlight that the level of ethnic diversity in workgroups matters as increasing the opportunities for positive contact can prove beneficial for a given organisation, as well as society more broadly

How can firms manage multi-ethnic workforces in countries with violent ethnic conflicts?

Perceptions of ethnic homophily among colleagues can be detrimental to the firm, writes Hyun-Jung Le

Rocky Road: East Asian International Students‟ Experience of Adaptation to Critical Thinking Way of Learning at U.S. Universities

This roundtable seeks to understand how Confucius-influenced East Asian international students learn to adapt to and participate in the countervailing Western pedagogy that fosters independent critical thinking and reflection and how these Asian students reconcile these seeming polarities as they engage in their doctoral studies at U.S. universities

Immunoinformatic identification of CD8+ T-cell epitopes

Antigen-specific T-cells play a crucial role in the adaptive immune response by providing a defence mechanism against pathogens and maintaining tolerance against self-antigens. This sparked interest in the development of epitope-based vaccines and immunotherapies that elicit antigen-specific T-cell responses. However, screening the antigens driving the response is currently labour-intensive, low-throughput and costly. Due to the limitations of experimental approaches, computational methods for predicting CD8+ T-cells have started to emerge. However, predicting the T-cell recognition potential of MHC-presented peptides has shown to be more challenging than predicting MHC ligands, and the full spectrum of features underlying peptide immunogenicity remains to be explored. Hence, this thesis presents a systems biology approach to study features of peptide immunogenicity and accurately predict CD8+ T-cell epitopes from HLA-I presented pathogenic or cancer peptides. The thesis begins with an immunoinformatic analysis of antigen-specific T-cell profiles in the contexts of autoinflammatory and infectious diseases. In autoinflammatory disease, the multi-modal single-cell sequencing of ulcerative colitis and checkpoint treatment-induced colitis revealed pathology-specific differential expressions of cytotoxic T-cells. The current technologies, however, were unable to identify the source antigen, emphasising the importance of predicting T-cell targets to better understand disease pathology. Moreover, in infectious diseases, CD8+ T-cell epitope prediction algorithms facilitated the understanding of disease heterogeneity and vaccine design during the COVID-19 pandemic, but many existing algorithms were found to be ill-suited for predicting epitopes from emerging pathogens. Therefore, a novel computational workflow was developed for an accurate and robust prediction of source antigens driving the cellular immune response. First, an unbiased evaluation of state-of-the-art algorithms revealed that they perform poorly on both cancer neoepitopes (e.g. glioblastoma) and pathogenic (e.g. SARS-CoV-2) epitopes. After investigating the reasons for low performance, TRAP, a deep learning workflow for context-specific prediction of CD8+ T-cell epitopes, was developed to effectively capture T-cell recognition motifs. The application of TRAP was demonstrated by using it to investigate the immune escape potential of all theoretical SARS-CoV-2 mutants. Thus, this thesis presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of TCR:pMHC interaction and the development of effective clinical therapeutics