Optimizing public transit quality and system access: the multiple-route, maximal covering/shortest-path problem

Public transit service is a promising travel mode because of its potential to address urban sustainability. However, current ridership of public transit is very low in most urban regions -- particularly those in the United States. Low transit ridership can be attributed to many factors, among which poor service quality is key. Transit service quality may potentially be improved by decreasing the number of service stops, but this would be likely to reduce access coverage. Improving transit service quality while maintaining adequate access coverage is a challenge facing public transit agencies. In this paper we propose a multiple-route, maximal covering/shortest-path model to address the trade-off between public transit service quality and access coverage in an established bus-based transit system. The model is applied to routes in Columbus, Ohio. Results show that it is possible to improve transit service quality by eliminating redundant or underutilized service stops.

Impacts of climate change and urban growth on the streamflow characteristics of the Milwaukee River (Wisconsin, USA)

Hydrological impact studies of climate change increasingly take land use changes into account. However, the Midwestern USA is still understudied in this context. This study investigated the impacts of potential climate change and urban growth on the streamflow characteristics of the Milwaukee River located in southeastern Wisconsin. The Hydrological Simulation Program-Fortran (HSPF) was set up for the catchment and calibrated against observed streamflow data. The calibrated HSPF model was run with a series of climate and urban growth scenarios generated from nine global climate models (GCMs) and a land use simulation model, respectively. The outcomes from the GCMs, statistically downscaled at 10-km grid spacing, generally indicated a warmer and wetter climate by the mid-21st century, and the land use simulation model projected moderate urban growth by the time. Major findings from the study include: (1) land use changes alone resulted in negligible streamflow changes; (2) low flows showed more sensitivity than mean streamflow to climate change; (3) streamflow variability increased with both land use and climate changes, and (4) uncertainty in simulated streamflow among GCMs was larger than uncertainty among the GCM output themselves. The findings suggest that the current pace of urban growth would not pose much threat to the water resources in the area. Considering that low flow indices responded more sensitively than mean streamflow to climate change, measures to improve resilience to drought conditions are recommended. Because land use change impacts were quite small, considering the impact of both climate and land use scenarios did not produce a significantly different result

MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis

Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently observed images, the accuracies are often low, with mixed pixels in coarse-resolution imagery. In this paper, we used the eight-day, 500 m MODIS products (MOD09A1) to test the feasibility of crop unmixing in the U.S. Midwest, an important bioenergy land use region. With all MODIS images acquired in 2007, the 46-point Normalized Difference Vegetation Index (NDVI) time series was extracted in the study region. Assuming the phenological pattern at a pixel is a linear mixture of all crops in this pixel, a spatially constrained phenological mixture analysis (SPMA) was performed to extract crop percent covers with endmembers selected in a dynamic local neighborhood. The SPMA results matched well with the USDA crop data layers (CDL) at pixel level and the Crop Census records at county level. This study revealed more spatial details of energy crops that could better assist bioenergy decision-making in the Midwest

Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China

Identification of specific prognostic markers for lung squamous cell carcinoma based on tumor progression, immune infiltration, and stem index

IntroductionLung squamous cell carcinoma (LUSC) is a unique subform of nonsmall cell lung cancer (NSCLC). The lack of specific driver genes as therapeutic targets leads to worse prognoses in patients with LUSC, even with chemotherapy, radiotherapy, or immune checkpoint inhibitors. Furthermore, research on the LUSC-specific prognosis genes is lacking. This study aimed to develop a comprehensive LUSC-specific differentially expressed genes (DEGs) signature for prognosis correlated with tumor progression, immune infiltration,and stem index.MethodsRNA sequencing data for LUSC and lung adenocarcinoma (LUAD) were extracted from The Cancer Genome Atlas (TCGA) data portal, and DEGs analyses were conducted in TCGA-LUSC and TCGA-LUAD cohorts to identify specific DEGs associated with LUSC. Functional analysis and protein–protein interaction network were performed to annotate the roles of LUSC-specific DEGs and select the top 100 LUSC-specific DEGs. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were performed to select prognosis-related DEGs.ResultsOverall, 1,604 LUSC-specific DEGs were obtained, and a validated seven-gene signature was constructed comprising FGG, C3, FGA, JUN, CST3, CPSF4, and HIST1H2BH. FGG, C3, FGA, JUN, and CST3 were correlated with poor LUSC prognosis, whereas CPSF4 and HIST1H2BH were potential positive prognosis markers in patients with LUSC. Receiver operating characteristic analysis further confirmed that the genetic profile could accurately estimate the overall survival of LUSC patients. Analysis of immune infiltration demonstrated that the high risk (HR) LUSC patients exhibited accelerated tumor infiltration, relative to low risk (LR) LUSC patients. Molecular expressions of immune checkpoint genes differed significantly between the HR and LR cohorts. A ceRNA network containing 19 lncRNAs, 50 miRNAs, and 7 prognostic DEGs was constructed to demonstrate the prognostic value of novel biomarkers of LUSC-specific DEGs based on tumor progression, stemindex, and immune infiltration. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index.ConclusionOur study demonstrated the potential clinical implication of the 7- DEGs signature for prognosis prediction of LUSC patients based on tumor progression, immune infiltration, and stem index. And the FGG could be an independent prognostic biomarker of LUSC promoting cell proliferation, migration, invasion, THP-1 cell infiltration, and stem cell maintenance

Activin A and BMP4 Signaling Expands Potency of Mouse Embryonic Stem Cells in Serum-Free Media.

Inhibitors of Mek1/2 and Gsk3β, known as 2i, and, together with leukemia inhibitory factor, enhance the derivation of embryonic stem cells (ESCs) and promote ground-state pluripotency (2i/L-ESCs). However, recent reports show that prolonged Mek1/2 suppression impairs developmental potential of ESCs, and is rescued by serum (S/L-ESCs). Here, we show that culturing ESCs in Activin A and BMP4, and in the absence of MEK1/2 inhibitor (ABC/L medium), establishes advanced stem cells derived from ESCs (esASCs). We demonstrate that esASCs contributed to germline lineages, full-term chimeras and generated esASC-derived mice by tetraploid complementation. We show that, in contrast to 2i/L-ESCs, esASCs display distinct molecular signatures and a stable hypermethylated epigenome, which is reversible and similar to serum-cultured ESCs. Importantly, we also derived novel ASCs (blASCs) from blastocysts in ABC/L medium. Our results provide insights into the derivation of novel ESCs with DNA hypermethylation from blastocysts in chemically defined medium

The Wnt Antagonist Frzb-1 Regulates Chondrocyte Maturation and Long Bone Development during Limb Skeletogenesis

AbstractThe Wnt antagonist Frzb-1 is expressed during limb skeletogenesis, but its roles in this complex multistep process are not fully understood. To address this issue, we determined Frzb-1 gene expression patterns during chick long bone development and carried out gain- and loss-of-function studies by misexpression of Frzb-1, Wnt-8 (a known Frzb-1 target), or different forms of the intracellular Wnt mediator LEF-1 in developing limbs and cultured chondrocytes. Frzb-1 expression was quite strong in mesenchymal prechondrogenic condensations and then characterized epiphyseal articular chondrocytes and prehypertrophic chondrocytes in growth plates. Virally driven Frzb-1 misexpression caused shortening of skeletal elements, joint fusion, and delayed chondrocyte maturation, with consequent inhibition of matrix mineralization, metalloprotease expression, and marrow/bone formation. In good agreement, misexpression of Frzb-1 or a dominant-negative form of LEF-1 in cultured chondrocytes maintained the cells at an immature stage. Instead, misexpression of Wnt-8 or a constitutively active LEF-1 strongly promoted chondrocyte maturation, hypertrophy, and calcification. Immunostaining revealed that the distribution of endogenous Wnt mediator β-catenin changes dramatically in vivo and in vitro, from largely cytoplasmic in immature proliferating and prehypertrophic chondrocytes to nuclear in hypertrophic mineralizing chondrocytes. Misexpression of Frzb-1 prevented β-catenin nuclear relocalization in chondrocytes in vivo or in vitro. The data demonstrate that Frzb-1 exerts a strong influence on limb skeletogenesis and is a powerful and direct modulator of chondrocyte maturation, phenotype, and function. Phases of skeletogenesis, such as terminal chondrocyte maturation and joint formation, appear to be particularly dependent on Wnt signaling and thus very sensitive to Frzb-1 antagonistic action

Role and Clinical Utility of Cancer/Testis Antigens in Head and Neck Squamous Cell Carcinoma

Cancer/testis (CT) antigens exhibit selective expression predominantly in immunoprivileged tissues in non-pathological contexts but are aberrantly expressed in diverse cancers. Due to their expression pattern, they have historically been attractive targets for immunotherapies. A growing number of studies implicate CT antigens in almost all hallmarks of cancer, suggesting that they may act as cancer drivers. CT antigens are expressed in head and neck squamous cell carcinomas. However, their role in the pathogenesis of these cancers remains poorly studied. Given that CT antigens hold intriguing potential as therapeutic targets and as biomarkers for prognosis and that they can provide novel insights into oncogenic mechanisms, their further study in the context of head and squamous cell carcinoma is warranted

Incorporating Remote Sensing Information in Modeling House Values: a Regression Tree Approach

This paper explores the possibility of incorporating remote sensing information in modeling house values in the City of Milwaukee, Wisconsin, U.S.A. In particular, a Landsat ETM+ image was utilized to derive environmental characteristics, including the fractions of vegetation, impervious surface, and soil, with a linear spectral mixture analysis approach. These environmental characteristics, together with house structural attributes, were integrated to house value models. Two modeling techniques, a global OLS regression and a regression tree approach, were employed to build the relationship between house values and house structural and environmental characteristics. Analysis of results indicates that environmental characteristics generated from remote sensing technologies have strong influences on house values, and the addition of them improves house value modeling performance significantly. Moreover, the regression tree model proves as a better alternative to the OLS regression models in terms of predicting accuracy. In particular, based on the testing dataset, the mean average error (MAE) and relative error (RE) dropped from 0.202 and 0.434 for the OLS model to 0.134 and 0.280 for the regression tree model, while the correlation coefficient between the predicted and observed values increased from 0.903 to 0.960. Further, as a nonparametric and local model, the regression tree method alleviates the problems with the OLS techniques and provides a means in delineating urban housing submarkets

A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability

Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the most important step. Thanks to the spatial heterogeneity of natural and urban landscapes, the variability of endmember class and corresponding spectra has been widely considered as the profound error source in SMA. To address the challenging problems, we proposed a geographic information-assisted temporal mixture analysis (GATMA). Specifically, a logistic regression analysis was applied to analyze the relationship between land use/land covers and surrounding socio-economic factors, and a classification tree method was used to identify the present status of endmember classes throughout the whole study area. Furthermore, an ordinary kriging analysis was employed to generate a spatially varying endmember spectra at all pixels in the remote sensing image. As a consequence, a fully constrained temporal mixture analysis was conducted for examining the fractional land use land covers. Results show that the proposed GATMA achieved a promising accuracy with an RMSE of 6.81%, SE of 1.29% and MAE of 2.6%. In addition, comparative analysis result illustrates that a significant accuracy improvement has been found in the whole study area and both developed and less developed areas, and this demonstrates that the variability of endmember class and endmember spectra is essential for unmixing analysis