Challenges and Opportunities to Conduct Cancer Care Research in China: Experience from a Pilot Project

Background: Cancer has become the leading cause of death in China. Effective cancer control and population science research programs are desperately needed in China. The China Medical Board (CMB) funding has provided us with an opportunity to build a research team specializing in cancer care utilization and access research and demonstrate the usefulness of the accrued data. The CMB-funded project will describe patterns of cancer screening, incidence, and treatment in Shandong Province in China and enable the researchers to understand possible causes of disparities in cancer control in China. Findings: Although CMB projects do not provide salary support for affiliated American faculty, they do provide Chinese scholars in the U.S. an excellent opportunity to help improve health care in China. There are many challenges and opportunities in health care service and utilization research in China. For example, public data for cancer care research does not exist. We had to acquire secondary data from several governmental organizations andreconciled regional variations in data management. After acquiring all the data, we could create the most comprehensive cancer access, utilization, and outcomes research database to date in China and possibly expand this research in Shandong and other provinces. Students and analysts need to be trained to ensure the confidentiality of data linked to personal identifiers of patients and providers. At the same time, users need to learn how to manipulate and analyze large scale, messy, secondary data. Discussion: We hope that the key findings will identify innovative scientific opportunities to improve cancer control and reduce inequities in communities. We intend to prepare manuscripts and reports in Chinese to disseminate findings to communities, policy makers, health care providers, and  the scientific community. From the policy perspective, this study is a demonstration project drawing policy makers’ attention to the importance of comprehensive cancer prevention and control data collection, both for accurate assessment and informed decision making with a high likelihood to effect desired change

Evolutionary dynamics under periodic switching of update rules on regular networks

Microscopic strategy update rules play an important role in the evolutionary dynamics of cooperation among interacting agents on complex networks. Many previous related works only consider one \emph{fixed} rule, while in the real world, individuals may switch, sometimes periodically, between rules. It is of particular theoretical interest to investigate under what conditions the periodic switching of strategy update rules facilitates the emergence of cooperation. To answer this question, we study the evolutionary prisoner's dilemma game on regular networks where agents can periodically switch their strategy update rules. We accordingly develop a theoretical framework of this periodically switched system, where the replicator equation corresponding to each specific microscopic update rule is used for describing the subsystem, and all the subsystems are activated in sequence. By utilizing switched system theory, we identify the theoretical condition for the emergence of cooperative behavior. Under this condition, we have proved that the periodically switched system with different switching rules can converge to the full cooperation state. Finally, we consider an example where two strategy update rules, that is, the imitation and pairwise-comparison updating, are periodically switched, and find that our numerical calculations validate our theoretical results

Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data

The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impressive capabilities for addressing tasks in familiar environments, they encounter limitations in adapting to unfamiliar environment settings. In this study, we propose a general-purpose, language-conditioned approach that combines base skill priors and imitation learning under unstructured data to enhance the algorithm's generalization in adapting to unfamiliar environments. We assess our model's performance in both simulated and real-world environments using a zero-shot setting. In the simulated environment, the proposed approach surpasses previously reported scores for CALVIN benchmark, especially in the challenging Zero-Shot Multi-Environment setting. The average completed task length, indicating the average number of tasks the agent can continuously complete, improves more than 2.5 times compared to the state-of-the-art method HULC. In addition, we conduct a zero-shot evaluation of our policy in a real-world setting, following training exclusively in simulated environments without additional specific adaptations. In this evaluation, we set up ten tasks and achieved an average 30% improvement in our approach compared to the current state-of-the-art approach, demonstrating a high generalization capability in both simulated environments and the real world. For further details, including access to our code and videos, please refer to our supplementary materials

Expression of CXCR4 and non-small cell lung cancer prognosis: a meta-analysis

PURPOSE: The prognostic value of aberrant C-X-C chemokine receptor type 4 (CXCR4) levels in NSCLC has been described in empirical studies. This meta-analysis evaluates the value of CXCR4 as a prognostic marker for NSCLC and determines the relationship between CXCR4 and clinicopathological features of NSCLC. METHODS: A comprehensive search of the English-language literature in PubMed, Embase, Google Scholar and Web of Science was performed. Articles containing sufficient published data to determine an estimate of the hazard ratio (HR) and a 95% confidence interval (95% CI) for over survival (OS) or disease-free survival (DFS) were selected. Of 417 potentially relevant studies, 10 eligible studies (1,334 NSCLC patients) met the inclusion criteria. RESULTS: Overall, high CXCR4 expression was significantly associated with a poor OS rate (HR=1.59, 95% CI=1.36-1.87, P \u3c 0.001) while the association with DFS was not statistically significant (HR=1.00, 95% CI=0.37-2.69, P=0.993). Stratified analysis by subcellular localization found that CXCR4 overexpression in the non-nucleus predicts poor OS (HR=1.65, 95% CI=1.40-1.95, P \u3c 0.001) and DFS (HR=3.06, 95% CI=2.15-4.37, P \u3c 0.001), but elevated CXCR4 expression in the nucleus was positively associated with DFS (HR=0.44, 95% CI=0.26-0.75, P=0.002). NSCLC patients with CXCR4 expression were more likely to be diagnosed with adenocarcinoma cancer (OR=1.45, 95% CI=1.07-1.95, P=0.016), lymph node involvement (OR=0.69, 95% CI=0.50-0.96, P=0.027), and distant metastasis (OR=0.36, 95% CI=0.14-0.93, P=0.035). CONCLUSION: Aberrant overexpression of CXCR4 is associated with worse overall survival, adenocarcinoma histology, distant metastasis, lymph node involvement in NSCLC

Natural Proteasome Inhibitor Celastrol Suppresses Androgen-Independent Prostate Cancer Progression by Modulating Apoptotic Proteins and NF-kappaB

Celastrol is a natural proteasome inhibitor that exhibits promising anti-tumor effects in human malignancies, especially the androgen-independent prostate cancer (AIPC) with constitutive NF-κB activation. Celastrol induces apoptosis by means of proteasome inhibition and suppresses prostate tumor growth. However, the detailed mechanism of action remains elusive. In the current study, we aim to test the hypothesis that celastrol suppresses AIPC progression via inhibiting the constitutive NF-κB activity as well as modulating the Bcl-2 family proteins.We examined the efficacy of celastrol both in vitro and in vivo, and evaluated the role of NF-κB in celastrol-mediated AIPC regression. We found that celastrol inhibited cell proliferation in all three AIPC cell lines (PC-3, DU145 and CL1), with IC₅₀ in the range of 1-2 µM. Celastrol also suppressed cell migration and invasion. Celastrol significantly induced apoptosis as evidenced by increased sub-G1 population, caspase activation and PARP cleavage. Moreover, celastrol promoted cleavage of the anti-apoptotic protein Mcl-1 and activated the pro-apoptotic protein Noxa. In addition, celastrol rapidly blocked cytosolic IκBα degradation and nuclear translocation of RelA. Likewise, celastrol inhibited the expression of multiple NF-κB target genes that are involved in proliferation, invasion and anti-apoptosis. Celastrol suppressed AIPC tumor progression by inhibiting proliferation, increasing apoptosis and decreasing angiogenesis, in PC-3 xenograft model in nude mouse. Furthermore, increased cellular IκBα and inhibited expression of various NF-κB target genes were observed in tumor tissues.Our data suggest that, via targeting the proteasome, celastrol suppresses proliferation, invasion and angiogenesis by inducing the apoptotic machinery and attenuating constitutive NF-κB activity in AIPC both in vitro and in vivo. Celastrol as an active ingredient of traditional herbal medicine could thus be developed as a new therapeutic agent for hormone-refractory prostate cancer

Forest stand spectrum reconstruction using spectrum spatial feature gathering and multilayer perceptron

IntroductionThree-dimensional spectral distributions of forest stands can provide spatial information on the physiological and biochemical status of forests, which is vital for forest management. However, three-dimensional spectral studies of forest stands are limited.MethodsIn this study, LiDAR and multispectral data were collected from Masson pine stands in southern Fujian Province, China, and a method was proposed for inverting forest spectra using point clouds as a unit. First, multispectral values were mapped to a point cloud, and the isolated forest algorithm combined with K-means clustering was applied to characterize fusion data. Second, five deep learning algorithms were selected for semantic segmentation, and the overall accuracy (oAcc) and mean intersection ratio (mIoU) were used to evaluate the performance of various algorithms on the fusion data set. Third, the semantic segmentation model was used to reconfigure the class 3D spectral distribution, and the model inversion outcomes were evaluated by the peaks and valleys of the curve of the predicted values and distribution gaps.ResultsThe results show that the correlations between spectral attributes and between spatial attributes were both greater than 0.98, while the correlation between spectral and spatial attributes was 0.43. The most applicable method was PointMLP, highest oAcc was 0.84, highest mIoU was 0.75, peak interval of the prediction curve tended to be consistent with the true values, and maximum difference between the predicted value and the true value of the point cloud spectrum was 0.83.DiscussionExperimental data suggested that combining spatial fusion and semantic segmentation effectively inverts three-dimensional spectral information for forest stands. The model could meet the accuracy requirements of local spectral inversion, and the NIR values of stands in different regions were correlated with the vertical height of the canopy and the distance from the tree apex in the region. These findings improve our understanding of the precise three-dimensional spectral distribution of forests, providing a basis for near-earth remote sensing of forests and the estimation of forest stand health

Detection and exposure assessment of pesticide residues in leek in He’nan Province

ObjectiveTo evaluate the health risk of pesticide exposure from leek， the pesticide residue in leek from Henan market was investigated.MethodsThe residues of 16 pesticides in leek sold on Henan market in 2020 were detected and analyzed. According to health guidance values such as food consumption data of the World Health Organization， acute reference dose formulated by Joint Meeting on Pesticide Residues and adaptable daily intake in “National food safety standard-Maximum residue limits for pesticides in food”， the acute and chronic exposure risks of pesticide residues in leek were evaluated by point assessment method， and the cumulative exposure was evaluated by hazard index method.ResultsThere were many types of pesticide residues in leek samples and 93.81% （424/452） of the samples were positive. 7 of the 14 pesticides exceeded their MRLs， and the violation rate of all samples was 16.15%. The detection of multiple pesticides was relatively serious， and 56.42% of the samples contained more than two pesticide residues. In the acute exposure assessment， the acute risks of carbofuran， procymidone and phorate exceeded the acceptable level. In the chronic exposure assessment， the chronic risk of omethoate exceeded the acceptable level. And insecticide pesticides had cumulative poisoning risk.ConclusionThe situation of pesticide residues in leek in Henan province was relatively prominent. To ensure the safety of agricultural products， it was recommended that the routine monitoring and use of pesticide， especially high-risk pesticides such as omethoate， carbofuran， procymidone and phorate should be strengthened