An extended spatiotemporal exposure index for urban racial segregation

The Segregation Index quantifies the degree of segregation of social groups or classes. Because of the increasing use of fine-grained spatiotemporal activity and flow data, the conventional segregation measurements’ inclusiveness is challenged. We add population flow to the conventional place-based spatial exposure index to identify spatiotemporal segregation changes. Specifically, we considered the population-flow network, hierarchical structure, and time. In Chicago’s demonstration case study, we first used the time-dependent Twitter Origin-Destination flow matrices and their hierarchical structure information to estimate interactions between areal units at the neighborhood level. Then we computed the new population composition of units based on their interactions with other units and estimated the proposed spatiotemporal exposure index for different times. Finally, we systematically compared their differences with the conventional indices at global and local scales to see how population-flow patterns affect the exposure index. The results show that the population-flow patterns reflect valuable information in neighborhood interactions in temporal and spatial dimensions, but it is missing information in the conventional segregation computations. Furthermore, we emphasize that the hierarchical structures of flow patterns and the choice of appropriate parameters are also important factors for a rational segregation evaluation.</p

Table1_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.DOCX

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Image1_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

DataSheet1_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.ZIP

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Review of Strongylogaster Dahlbom (Hymenoptera: Tenthredinidae) from Zhejiang Province, China, with the description of a new species

ABSTRACT Five species of Strongylogaster Dahlbom, 1835 are recorded from Zhejiang Province, China. They are four known species, S. formosana (Rohwer, 1916), S. macula (Klug, 1817), S. takeuchii Naito, 1980 and S. xanthocera (Stephens, 1835), and a new species. Strongylogaster tianmunica sp. nov., collected from Mt. Tianmu in Zhejiang Province, is here described and illustrated. This new species resembles S. nantouensis Naito, 1990, but differs from the latter by the following characters: female body length 10-12 mm, male body length 8-10 mm; tegula brown to dark brown; pronotum largely yellowish-white; trochanters black, apical half of hind femora and of hind tibiae yellowish-white; malar space as long as radius of median ocellus; antennomere 3 as long as antennomere 4; and ovipositor apical sheath with distinct lateral scapes. A key to the five species of Strongylogaster from Zhejiang Province is provided.</div

Image3_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Image4_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Image2_Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma.TIF

Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.</p

Augmenting definitive screening designs: Going outside the box

Definitive screening designs (DSDs) have grown rapidly in popularity since their introduction by Jones and Nachtsheim (2011). Their appeal is that the second-order response surface (RS) model can be estimated in any subset of three factors, without having to perform a follow-up experiment. However, their usefulness as a one-step RS modeling strategy depends heavily on the sparsity of second-order effects and the dominance of first-order terms over pure quadratic terms. To address these limitations, we show how viewing a projection of the design region as spherical and augmenting the DSD with axial points in factors found to involve second-order effects remedies the deficiencies of a stand-alone DSD. We show that augmentation with a second design consisting of axial points is often the Ds-optimal augmentation, as well as minimizing the average prediction variance. Supplemented by this strategy, DSDs are highly effective initial screening designs that support estimation of the second-order RS model in three or four factors.</p