Explore the vast range of titles available.

The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational spectral imaging methods have resorted to computation power for reduced system volume, but still endure long computation time for iterative spectral reconstructions. Recently, deep learning techniques are introduced into computational spectral imaging, witnessing fast reconstruction speed, great reconstruction quality, and the potential to drastically reduce the system volume. In this article, we review state-of-the-art deep-learning-empowered computational spectral imaging methods. They are further divided into amplitude-coded, phase-coded, and wavelength-coded methods, based on different light properties used for encoding. To boost future researches, we’ve also organized publicly available spectral datasets.This review categorizes deep-learning-based computational spectral imaging methods and provides insight into amplitude, phase, and wavelength-based light encoding strategies for deep-learning spectral reconstruction.

Background Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI resistance, but direct clinical evidence is lacking. Methods Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets. Results Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list ( P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets. Conclusions We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.

Cancer greatly affects the quality of life of humans worldwide and the number of patients suffering from it is continuously increasing. Over the last century, numerous treatments have been developed to improve the survival of cancer patients but substantial progress still needs to be made before cancer can be truly cured. In recent years, antitumor immunity has become the most debated topic in cancer research and the booming development of immunotherapy has led to a new epoch in cancer therapy. In this review, we describe the relationships between tumors and the immune system, and the rise of immunotherapy. Then, we summarize the characteristics of tumor‐associated immunity and immunotherapeutic strategies with various molecular mechanisms by showing the typical immune molecules whose antibodies are broadly used in the clinic and those that are still under investigation. We also discuss important elements from individual cells to the whole human body, including cellular mutations and modulation, metabolic reprogramming, the microbiome, and the immune contexture. In addition, we also present new observations and technical advancements of both diagnostic and therapeutic methods aimed at cancer immunotherapy. Lastly, we discuss the controversies and challenges that negatively impact patient outcomes. In this review, we present a clear view of the major factors and regulators associated with cancer immunotherapy and to provide our point of view on the latest available technologies and treatment methods for resolving clinical problems.

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

Background Precise methods for postoperative risk stratification to guide the administration of adjuvant chemotherapy (ACT) in localized colorectal cancer (CRC) are still lacking. Here, we conducted a prospective, observational, and multicenter study to investigate the utility of circulating tumor DNA (ctDNA) in predicting the recurrence risk. Methods From September 2017 to March 2020, 276 patients with stage II/III CRC were prospectively recruited in this study and 240 evaluable patients were retained for analysis, of which 1290 serial plasma samples were collected. Somatic variants in both the primary tumor and plasma were detected via a targeted sequencing panel of 425 cancer-related genes. Patients were treated and followed up per standard of care. Results Preoperatively, ctDNA was detectable in 154 of 240 patients (64.2%). At day 3–7 postoperation, ctDNA positivity was associated with remarkably high recurrence risk (hazard ratio [HR], 10.98; 95%CI, 5.31–22.72; P  < 0.001). ctDNA clearance and recurrence-free status was achieved in 5 out of 17 ctDNA-positive patients who were subjected to ACT. Likewise, at the first sampling point after ACT, ctDNA-positive patients were 12 times more likely to experience recurrence (HR, 12.76; 95%CI, 5.39–30.19; P  < 0.001). During surveillance after definitive therapy, ctDNA positivity was also associated with extremely high recurrence risk (HR, 32.02; 95%CI, 10.79–95.08; P  < 0.001). In all multivariate analyses, ctDNA positivity remained the most significant and independent predictor of recurrence-free survival after adjusting for known clinicopathological risk factors. Serial ctDNA analyses identified recurrence with an overall accuracy of 92.0% and could detect disease recurrence ahead of radiological imaging with a mean lead time of 5.01 months. Conclusions Postoperative serial ctDNA detection predicted high relapse risk and identified disease recurrence ahead of radiological imaging in patients with stage II/III CRC. ctDNA may be used to guide the decision-making in postsurgical management.

A bstract In this work, we develop conformal bootstrap for Galilean conformal field theory (GCFT). In a GCFT, the Hilbert space could be decomposed into quasiprimary states and its global descendants. Different from the usual conformal field theory, the quasiprimary states in a GCFT constitute multiplets, which are block-diagonized under the Galilean boost operator. More importantly the multiplets include the states of negative norms, indicating the theory is not unitary. We compute global blocks of the multiplets, and discuss the expansion of four-point functions in terms of the global blocks of the multiplets. Furthermore we do the harmonic analysis for the Galilean conformal symmetry and obtain an inversion formula. As the first step to apply the Galilean conformal bootstrap, we construct generalized Galilean free theory (GGFT) explicitly. We read the data of GGFT by using Taylor series expansion of four-point function and the inversion formula independently, and find exact agreement. We discuss some novel features in the Galilean conformal bootstrap, due to the non-semisimpleness of the Galilean conformal algebra and the non-unitarity of the GCFTs.

In recent years, remarkable breakthroughs have been reported on antibody‐drug conjugates (ADCs), with 15 ADCs successfully entering the market over the past decade. This substantial development has positioned ADCs as one of the fastest‐growing domains in the realm of anticancer drugs, demonstrating their efficacy in treating a wide array of malignancies. Nonetheless, there is still an unmet clinical need for wider application, better efficacy, and fewer side effects of ADCs. An ADC generally comprises an antibody, a linker and a payload, and the combination has profound effects on drug structure, pharmacokinetic profile and efficacy. Hence, optimization of the key components provides an opportunity to develop ADCs with higher potency and fewer side effects. In this review, we comprehensively reviewed the current development and the prospects of ADC, provided an analysis of marketed ADCs and the ongoing pipelines globally as well as in China, highlighted several ADC platforms and technologies specific to different pharmaceutical enterprises and biotech companies, and also discussed the new related technologies, possibility of next‐generation ADCs and the directions of clinical research.

A bstract The study of Rényi mutual information (RMI) sheds light on the AdS/CFT correspondence beyond classical order. In this article, we study the Rényi mutual in- formation between two intervals at large distance in two-dimensional holographic warped conformal field theory, which is conjectured to be dual to gravity on AdS 3 or warped AdS 3 spacetimes under Dirichlet-Neumann boundary conditions. By using the operator product expansion of twist operators up to level 3, we read the leading oder and the next-to-leading order RMI in the large central charge and small cross-ratio limits. The leading order result is furthermore confirmed using the conformal block expansion. Finally, we match the next-to-leading order result by a 1-loop calculation in the bulk.

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

Background Previous studies have suggested that exposure to air pollution may increase stroke risk, but the results remain inconsistent. Evidence of more recent studies is highly warranted, especially gas air pollutants. Methods We searched PubMed, Embase, and Web of Science to identify studies till February 2020 and conducted a meta-analysis on the association between air pollution (PM 2.5 , particulate matter with aerodynamic diameter less than 2.5 μm; PM 10 , particulate matter with aerodynamic diameter less than 10 μm; NO 2 , nitrogen dioxide; SO 2 , sulfur dioxide; CO, carbon monoxide; O 3 , ozone) and stroke (hospital admission, incidence, and mortality). Fixed- or random-effects model was used to calculate pooled odds ratios (OR)/hazard ratio (HR) and their 95% confidence intervals (CI) for a 10 μg/m 3 increase in air pollutant concentration. Results A total of 68 studies conducted from more than 23 million participants were included in our meta-analysis. Meta-analyses showed significant associations of all six air pollutants and stroke hospital admission (e.g., PM 2.5 : OR = 1.008 (95% CI 1.005, 1.011); NO 2 : OR = 1.023 (95% CI 1.015, 1.030), per 10 μg/m 3 increases in air pollutant concentration). Exposure to PM 2.5 , SO 2 , and NO 2 was associated with increased risks of stroke incidence (PM 2.5 : HR = 1.048 (95% CI 1.020, 1.076); SO 2 : HR = 1.002 (95% CI 1.000, 1.003); NO 2 : HR = 1.002 (95% CI 1.000, 1.003), respectively). However, no significant differences were found in associations of PM 10 , CO, O 3 , and stroke incidence. Except for CO and O 3 , we found that higher level of air pollution (PM 2.5 , PM 10 , SO 2 , and NO 2 ) exposure was associated with higher stroke mortality (e.g., PM 10 : OR = 1.006 (95% CI 1.003, 1.010), SO 2 : OR = 1.006 (95% CI 1.005, 1.008). Conclusions Exposure to air pollution was positively associated with an increased risk of stroke hospital admission (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ), incidence (PM 2.5 , SO 2 , and NO 2 ), and mortality (PM 2.5 , PM 10 , SO 2 , and NO 2 ). Our study would provide a more comprehensive evidence of air pollution and stroke, especially SO 2 and NO 2 .