Targeting alternative splicing in cancer immunotherapy

Tumor immunotherapy has made great progress in cancer treatment but still faces several challenges, such as a limited number of targetable antigens and varying responses among patients. Alternative splicing (AS) is an essential process for the maturation of nearly all mammalian mRNAs. Recent studies show that AS contributes to expanding cancer-specific antigens and modulating immunogenicity, making it a promising solution to the above challenges. The organoid technology preserves the individual immune microenvironment and reduces the time/economic costs of the experiment model, facilitating the development of splicing-based immunotherapy. Here, we summarize three critical roles of AS in immunotherapy: resources for generating neoantigens, targets for immune-therapeutic modulation, and biomarkers to guide immunotherapy options. Subsequently, we highlight the benefits of adopting organoids to develop AS-based immunotherapies. Finally, we discuss the current challenges in studying AS-based immunotherapy in terms of existing bioinformatics algorithms and biological technologies

Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models

Recent studies of the emergent capabilities of transformer-based Natural Language Understanding (NLU) models have indicated that they have an understanding of lexical and compositional semantics. We provide evidence that suggests these claims should be taken with a grain of salt: we find that state-of-the-art Natural Language Inference (NLI) models are sensitive towards minor semantics preserving surface-form variations, which lead to sizable inconsistent model decisions during inference. Notably, this behaviour differs from valid and in-depth comprehension of compositional semantics, however does neither emerge when evaluating model accuracy on standard benchmarks nor when probing for syntactic, monotonic, and logically robust reasoning. We propose a novel framework to measure the extent of semantic sensitivity. To this end, we evaluate NLI models on adversarially generated examples containing minor semantics-preserving surface-form input noise. This is achieved using conditional text generation, with the explicit condition that the NLI model predicts the relationship between the original and adversarial inputs as a symmetric equivalence entailment. We systematically study the effects of the phenomenon across NLI models for $\textbf{in-}$ and $\textbf{out-of-}$ domain settings. Our experiments show that semantic sensitivity causes performance degradations of $12.92\%$ and $23.71\%$ average over $\textbf{in-}$ and $\textbf{out-of-}$ domain settings, respectively. We further perform ablation studies, analysing this phenomenon across models, datasets, and variations in inference and show that semantic sensitivity can lead to major inconsistency within model predictions.Comment: EACL 202

Estimate of Saturation Pressures of Crude Oil by Using Ensemble-Smoother-Assisted Equation of State

The equation of state (EOS) has been extensively used to evaluate the saturation pressures of petroleum fluids. However, the accurate determination of empirical parameters in the EOS is challenging and time-consuming, especially when multiple measurements are involved in the regression process. In this work, an ensemble smoother (ES) -assisted EOS method has been proposed to compute the saturation pressure by intelligently optimizing the to-be-tuned parameters. To be specific, the to-be-tuned parameters for the Peng–Robinson EOS (PR EOS) are integrated into a model input matrix and the measured saturation pressures are collected into a model output matrix. The model input matrix is then integrally and iteratively updated with respect to the model output matrix by using the iterative ES algorithm. For convenience, an in-house module is compiled to implement the ES-assisted EOS for determining the saturation pressures of crude oils. Subsequently, the experimentally measured saturation pressures of 45 mixtures of heavy oil and solvents are used to validate the performance of the in-house module. In addition, 130 measured saturation pressures of worldwide light oil samples are collected to verify the applicability of the developed ES-assisted EOS method. The in-house module is found to be competent by not only matching 45 measured saturation pressures with a better agreement than a commercial simulator but also providing a quantitative means to analyze the uncertainties associated with the estimated model parameters and the saturation pressure. Moreover, the application of the ES-assisted EOS to 130 light oil samples distinctly demonstrates that the new method greatly improves the accuracy and reliability of the EOS regression. Consequently, the in-house module representing the ES-assisted EOS is proven as an efficient and flexible tool to determine the saturation pressure under various conditions and implement uncertain analyses associated with the saturation pressure

Sex differences in the combined influence of inflammation and nutrition status on depressive symptoms: insights from NHANES

BackgroundBoth nutrition and inflammation are associated with depression, but previous studies have focused on individual factors. Here, we assessed the association between composite indices of nutrition and inflammation and depression.MethodsAdult participants selected from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018 were chosen. The exposure variable was the Advanced Lung Cancer Inflammation Index (ALI) integrating nutrition and inflammation, categorized into low, medium, and high groups. The outcome variable was depression assessed using the Patient Health Questionnaire-9 (PHQ-9). A multivariable logistic regression model was employed to evaluate the relationship between ALI and the risk of depression.ResultsAfter extensive adjustment for covariates, in the overall population, participants with moderate and high levels of ALI had a decreased prevalence of depression compared to those with low ALI levels, with reductions of 17% (OR, 0.83; 95% CI: 0.72–0.97) and 23% (OR, 0.77; 95% CI: 0.66–0.91), respectively. Among females, participants with moderate and high ALI levels had a decreased prevalence of depression by 27% (OR, 0.73; 95% CI: 0.60–0.88) and 21% (OR, 0.79; 95% CI: 0.64–0.98), respectively, compared to those with low ALI levels, whereas no significant association was observed among males. Subgroup analyses based on females and males yielded consistent results.ConclusionIn this study, we observed a negative correlation between moderate to high levels of ALI and the prevalence of depression, along with gender differences. Specifically, in females, greater attention should be given to the nutritional and inflammatory status

Photo-Otto engine with quantum correlations

We theoretically prose and investigate a photo-Otto engine that is working with a single-mode radiation field inside an optical cavity and alternatively driven by a hot and a cold reservoir, where the hot reservoir is realized by sending one of a pair of correlated two-level atoms to pass through the optical cavity, and the cold one is made of a collection of noninteracting boson modes. In terms of the quantum discord of the pair of atoms, we derive the analytical expressions for the performance parameters (power and efficiency) and stability measure (coefficient of variation for power). We show that quantum discord boosts the performance and efficiency of the quantum engine, and even may change the operation mode. We also demonstrate that quantum discord improves the stability of machine by decreasing the coefficient of variation for power which satisfies the generalized thermodynamic uncertainty relation. Finally, we find that these results can be transferred to another photo-Otto engine model, where the optical cavity is alternatively coupled to a hot thermal bosonic bath and to a beam of pairs of the two correlated atoms that play the role of a cold reservoir

Regional-based strategies for municipality carbon mitigation: a case study of Chongqing in China

Different CO carbon mitigation strategies are required due to the uneven development of regions. In China, the western region is rich in natural resources, but its industrial technology is not as advanced as other regions. In addition, a few studies have attempted to explore the CO carbon mitigation strategies for a municipality of this region. In terms of modeling, the current studies often focus on the low-carbon potentials at the country, province, city and sector levels, while the carbon flows and their integration in neighboring regions are not well studied. In this paper, to explore the impact of regional-difference factors on CO reduction, we propose regional-based CO mitigation for a municipality and use Chongqing as a case study. In our methodology, the hierarchical structure analysis is conducted to identify the primary contradictions of regional CO emissions. Then, using system dynamics, CO emission systems of major industries, including cement, power and transportation, are modeled. Through simulations of baseline and low-carbon scenarios, key influencing factors in each industry are analyzed. They are then generalized to identify the important aspects of CO emission reduction for this region. Finally, the low-carbon development strategy covering three sub-pathways, i.e., the industrial system, energy structure and socio development is discussed to help the local government for policy-making