Editorial: Next generation γδ T cell-based tumor immunotherapy

The discovery of the gd T-cell receptor (TCR) and its ability to confer potent cytotoxic activity in CD3+ cells some 35 years ago sparked the initial proliferation in research that garnered widespread interest in the biology and function of gd T cells

Disparity in Checkpoint Inhibitor Utilization Among Commercially Insured Adult Patients With Metastatic Lung Cancer

BACKGROUND: There is a lack of evidence from nationwide samples on the disparity of initiating immune checkpoint inhibitors (ICIs) after metastatic lung cancer diagnosis. METHODS: We identified metastatic lung cancer patients diagnosed between 2015 and 2020 from a large, nationwide commercial claims database. We analyzed the time from metastatic lung cancer diagnosis to ICI therapy using Cox proportional hazard models. Independent variables included county-level measures (quintiles of percentage of racialized population, quintiles of percentage of population below poverty, urbanity, and density of medical oncologists) and patient characteristics (age, sex, Charlson comorbidity index, Medicare Advantage, and year of diagnosis). All tests were 2-sided. RESULTS: A total of 17 022 patients were included. Counties with a larger proportion of racialized population appeared to be more urban, have a greater percentage of its residents in poverty, and have a higher density of medical oncologists. In Cox analysis, the adjusted hazard ratio of the second, third, fourth, and highest quintile of percentage of racialized population were 0.89 (95% confidence interval [CI] = 0.82 to 0.98), 0.85 (95% CI = 0.78 to 0.93), 0.78 (95% CI = 0.71 to 0.86), and 0.71 (95% CI = 0.62 to 0.81), respectively, compared with counties in the lowest quintile. The slower ICI therapy initiation was driven by counties with the highest percentage of Hispanic population and other non-Black racialized groups. CONCLUSIONS: Commercially insured patients with metastatic lung cancer who lived in counties with greater percentage of racialized population had slower initiation of ICI therapy after lung cancer diagnosis, despite greater density of oncologists in their neighborhood

Rupture Heterogeneity and Directivity Effects in Back-Projection Analysis

The back projection method is a tremendously powerful technique for investigating the time dependent earthquake source, but its physical interpretation is elusive. We investigate how earthquake rupture heterogeneity and directivity can affect back-projection results (imaged location and beam power) using synthetic earthquake models. Rather than attempting to model the dynamics of any specific real earthquake, we use idealized kinematic rupture models, with constant or varying rupture velocity, peak slip rate, and fault-local strike orientation along unilateral or bilateral rupturing faults, and perform back-projection with the resultant synthetic seismograms. Our experiments show back-projection can track only heterogeneous rupture processes;homogeneous rupture is not resolved in our synthetic experiments. The amplitude of beam power does not necessarily correlate with the amplitude of any specific rupture parameter (e.g., slip rate or rupture velocity) at the back-projected location. Rather, it depends on the spatial heterogeneity around the back-projected rupture front, and is affected by the rupture directivity. A shorter characteristic wavelength of the source heterogeneity or rupture directivity toward the array results in strong beam power in higher frequency. We derive an equation based on Doppler theory to relate the wavelength of heterogeneity with synthetic seismogram frequency. This theoretical relation can explain the frequency- and array-dependent back-projection results not only in our synthetic experiments but also to analyze the 2019 M7.6 bilaterally rupturing New Ireland earthquake. Our study provides a novel perspective to physically interpret back-projection results and to retrieve information about earthquake rupture characteristics

Identifying Hendra Virus Diversity in Pteropid Bats

Hendra virus (HeV) causes a zoonotic disease with high mortality that is transmitted to humans from bats of the genus Pteropus (flying foxes) via an intermediary equine host. Factors promoting spillover from bats to horses are uncertain at this time, but plausibly encompass host and/or agent and/or environmental factors. There is a lack of HeV sequence information derived from the natural bat host, as previously sequences have only been obtained from horses or humans following spillover events. In order to obtain an insight into possible variants of HeV circulating in flying foxes, collection of urine was undertaken in multiple flying fox roosts in Queensland, Australia. HeV was found to be geographically widespread in flying foxes with a number of HeV variants circulating at the one time at multiple locations, while at times the same variant was found circulating at disparate locations. Sequence diversity within variants allowed differentiation on the basis of nucleotide changes, and hypervariable regions in the genome were identified that could be used to differentiate circulating variants. Further, during the study, HeV was isolated from the urine of flying foxes on four occasions from three different locations. The data indicates that spillover events do not correlate with particular HeV isolates, suggesting that host and/or environmental factors are the primary determinants of bat-horse spillover. Thus future spillover events are likely to occur, and there is an on-going need for effective risk management strategies for both human and animal health

Evaluating Self-supervised Speech Models on a Taiwanese Hokkien Corpus

Taiwanese Hokkien is declining in use and status due to a language shift towards Mandarin in Taiwan. This is partly why it is a low resource language in NLP and speech research today. To ensure that the state of the art in speech processing does not leave Taiwanese Hokkien behind, we contribute a 1.5-hour dataset of Taiwanese Hokkien to ML-SUPERB's hidden set. Evaluating ML-SUPERB's suite of self-supervised learning (SSL) speech representations on our dataset, we find that model size does not consistently determine performance. In fact, certain smaller models outperform larger ones. Furthermore, linguistic alignment between pretraining data and the target language plays a crucial role.Comment: Accepted to ASRU 202