Combination and integration to redirect NK cells for cancer immunotherapy

Cancer is a disease with still a high mortality. The number of new cases is still increasing globally despite current treatments. Therefore, new therapies with high specificity and efficiency to control the progress of cancer are urgently needed in the clinic. Nowadays, new cancer treatments emerged that are based on the immune system that have achieved encouraging outcomes. Together these modalities are called tumour immunotherapy. In this current PhD thesis, we have continued our group’s previous research on developing novel approaches for cancer immunotherapies are based on natural killer (NK) cells. This research demonstrated multiple methods to enhance the anti-tumour capacities of NK cells: 1) a combination with an anti-tumour antibody, 2) introduced an activation chimeric antigen receptor (CAR), 3) CRISPR/Cas9 genetic deletion of an inhibitory signal. These NK cell combinatorial approaches are ready for scale-up to be implemented into clinical treatments to the ultimate benefit of patients

Sloppy Identity in Chinese Sluicing-like Constructions

Concerning the nature of sluicing-like constructions in Chinese, this thesis examines two competing analyses proposed in the literature: the pseudo-sluicing analysis and the PF-deletion (sluicing) analysis. It shows that both analyses fail to reconcile with the presence of the copula shi as well as the sloppy reading found in Chinese sluicing-like constructions. It also observes that the sloppy reading and the copula shi are in complementary distribution—constructions with the copular shi are unable to be associated with sloppy readings. With respect to such facts, this thesis suggests to divide Chinese sluicing-like constructions into two distinct syntactic structures, namely pseudo-sluicing (for those with the copula shi) and sluicing (for those without the copula shi). Further, this thesis manages to fix the difficulty in creating the environment for PF-deletion in sluicing by suggesting that the wh-phrase moves to the left periphery through topicalization. Discussions on problems proposed in the literature are also included

Learning Active Basis Models by EM-Type Algorithms

EM algorithm is a convenient tool for maximum likelihood model fitting when the data are incomplete or when there are latent variables or hidden states. In this review article we explain that EM algorithm is a natural computational scheme for learning image templates of object categories where the learning is not fully supervised. We represent an image template by an active basis model, which is a linear composition of a selected set of localized, elongated and oriented wavelet elements that are allowed to slightly perturb their locations and orientations to account for the deformations of object shapes. The model can be easily learned when the objects in the training images are of the same pose, and appear at the same location and scale. This is often called supervised learning. In the situation where the objects may appear at different unknown locations, orientations and scales in the training images, we have to incorporate the unknown locations, orientations and scales as latent variables into the image generation process, and learn the template by EM-type algorithms. The E-step imputes the unknown locations, orientations and scales based on the currently learned template. This step can be considered self-supervision, which involves using the current template to recognize the objects in the training images. The M-step then relearns the template based on the imputed locations, orientations and scales, and this is essentially the same as supervised learning. So the EM learning process iterates between recognition and supervised learning. We illustrate this scheme by several experiments.Comment: Published in at http://dx.doi.org/10.1214/09-STS281 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Glueball relevant study on isoscalars from Nf=2N_f=2 lattice QCD

We perform a glueball-relevant study on isoscalars based on anisotropic $N_f=2$ lattice QCD gauge configurations. In the scalar channel, we identify the ground state obtained through gluonic operators to be a single-particle state through its dispersion relation. When $q\bar{q}$ operator is included, we find the mass of this state does not change, and the $q\bar{q}$ operator couples very weakly to this state. So this state is most likely a glueball state. For pseudoscalars, along with the exiting lattice results, our study implies that both the conventional $q\bar{q}$ state $\eta_2$ (or $\eta'$ in flavor $SU(3)$) and a heavier glueball-like state with a mass of roughly 2.6 GeV exist in the spectrum of lattice QCD with dynamical quarks.Comment: 8 pages, 3 figures, 3 tables, talk presented at the 35th International Symposium on Lattice Field Theory, 18-24 June 2017, Granada, Spai

Assessing Trust in Online Collaboration in E-government during the COVID-19 pandemic: An Employee Perspective

Due to the outbreak of the COVID-19 pandemic, firms and institutions have to shift to work from home to prevent the spreading of the pandemic. As a public sector, employees in government institutions also collaborate online during the lockdown. Collaboration online has been identified as a challenge for employees. While our understanding of how employees’ perception and trust of the e-government is still limited. To address this research gap, this study intends to investigate the antecedents of employees’ trust in e-government during their work process in the new normal. By conducting a qualitative study with 14 in-depth interviews with employees with e-government experience during the COVID-19 pandemic, we extracted several key antecedents of employees’ trust in e-government. Based on the qualitative data analysis, a theoretical model of trust antecedents was proposed. Our study provides a deep understanding of the specific antecedents of employees’ trust in the e-government context