Search

Abstract Background Immune checkpoint therapy (ICT) provides durable responses in select cancer patients, yet resistance remains a significant challenge, prompting the exploration of underlying molecular mechanisms. Tyrosylprotein sulfotransferase-2 (TPST2), known for its role in protein tyrosine O-sulfation, has been suggested to modulate the extracellular protein-protein interactions, but its specific role in cancer immunity remains largely unexplored. Methods To explore tumor cell-intrinsic factors influencing anti-PD1 responsiveness, we conducted a pooled loss-of-function genetic screen in humanized mice engrafted with human immune cells. The responsiveness of cancer cells to interferon-γ (IFNγ) was estimated by evaluating IFNγ-mediated induction of target genes, STAT1 phosphorylation, HLA expression, and cell growth suppression. The sulfotyrosine-modified target gene of TPST2 was identified by co-immunoprecipitation and mass spectrometry. The in vivo effects of TPST2 inhibition were evaluated using mouse syngeneic tumor models and corroborated by bulk and single-cell RNA sequencing analyses. Results Through in vivo genome-wide CRISPR screening, TPST2 loss-of-function emerged as a potential enhancer of anti-PD1 treatment efficacy. TPST2 suppressed IFNγ signaling by sulfating IFNγ receptor 1 at Y397 residue, while its downregulation boosted IFNγ-mediated signaling and antigen presentation. Depletion of TPST2 in cancer cells augmented anti-PD1 antibody efficacy in syngeneic mouse tumor models by enhancing tumor-infiltrating lymphocytes. RNA sequencing data revealed TPST2’s inverse correlation with antigen presentation, and increased TPST2 expression is associated with poor prognosis and altered cancer immunity across cancer types. Conclusions We propose TPST2’s novel role as a suppressor of cancer immunity and advocate for its consideration as a therapeutic target in ICT-based treatments.

Purpose – The purpose of this study is to compare the competition and productivity of the US freight rail transportation industry for the past 41 years (1980 ∼ 2020), which consists of the two periods, before and after the abolishment of the Interstate Commerce Commission (ICC) in 1995. Design/methodology/approach – This study investigates any relationships between the market concentration index values and labor productivity values in the separate two periods, and how the existence of a regulatory body in the freight transportation market impacted the productivity of the freight rail transportation industry by using a Cobb–Douglas production function on annual financial statement data from the US stock exchange market. Findings – This study found that, after the abolishment of the ICC: (1) the rail industry became less competitive, (2) even if the rail industry had an increasing labor productivity trend, there was a strong negative correlation between the market concentration index and labor productivity and (3) the rail industry’s total factor productivity was decreased. Originality/value – This study is to find empirical evidence of the effect of the ICC abolishment on the competition and productivity levels in the US freight rail transportation industry using a continuous data set of 41-year financial statements, which is unique compared to previous studies.

Purpose – The purpose of this paper is to identify the factors affecting user acceptance for NFC mobile wallets in both Korean and US markets. Design/methodology/approach – The proposed model extends the UTAUT2 model with new constructs of credibility (CR) and service smartness (SS). This study was analyzed using partial least square structural equation modeling on data collected from 701 college students between the ages of 18 and 29. Findings – The results of this study demonstrate that performance expectancy (PE), effort expectancy (EE), CR, SS and habit (HB) have strong positive relationships with a user’s behavioral intention to use NFC mobile wallets. Comparing the results of the USA and South Korea, there are different results regarding PE and CR. Research limitations/implications – This study shows that all factors except social influence (SI) have significant positive relationships with the intention to adopt NFC mobile wallets: Among the original UTUAT2 factors, PE, EE and HB are important determinants of NFC mobile wallet adoption and the new constructs, CR and SS, are significant determinants that influence BI. However, the target respondents are limited to college students of South Korea and the USA Thus, caution should be used when applying the results of this study towards less ICT developed countries and towards different age groups. Practical implications – This study provides multiple practical contributions. First, this study emphasizes HB as the strongest factor for adopting NFC mobile wallets in both South Korea and the USA Second, this study also highlights the importance of SS. Third, this study reveals that SI is not associated with the adoption of NFC mobile wallets. Fourth, nationality differences between the USA and South Korea account for the differences in consumer behaviors. Originality/value – This study has two main contributions: First, this study introduces a modified UTAUT2 model with two new variables (CR and SS) useful for NFC mobile wallets. Second, this study compares the results of partial least square structured equation models (PLS-SEM) of the two nationality groups, South Korea and the USA

Recently, edge computing is getting attention as a new computing paradigm that is expected to achieve short-delay and high-throughput task offloading for large scale Internet-of-Things (IoT) applications. In edge computing, workload distribution is one of the most critical issues that largely influences the delay and throughput performance of edge clouds, especially in distributed Function-as-a-Service (FaaS) over networked edge nodes. In this paper, we propose the Resource Allocation Control Engine with Reinforcement learning (RACER), which provides an efficient workload distribution strategy to reduce the task response slowdown with per-task response time Quality-of-Service (QoS). First, we present a novel problem formulation with the per-task QoS constraint derived from the well-known token bucket mechanism. Second, we employ a problem relaxation to reduce the overall computation complexity by compromising just a bit of optimality. Lastly, we take the deep reinforcement learning approach as an alternative solution to the workload distribution problem to cope with the uncertainty and dynamicity of underlying environments. Evaluation results show that RACER achieves a significant improvement in terms of per-task QoS violation ratio, average slowdown, and control efficiency, compared to AREA, a state-of-the-art workload distribution method.

Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.