A Global Analysis of the Relationship Between Urbanization and Fatalities in Earthquake-Prone Areas

Urbanization can be a challenge and an opportunity for earthquake risk mitigation. However, little is known about the changes in exposure (for example, population and urban land) to earthquakes in the context of global urbanization, and their impacts on fatalities in earthquake-prone areas. We present a global analysis of the changes in population size and urban land area in earthquake-prone areas from 1990 to 2015, and their impacts on earthquake-related fatalities. We found that more than two thirds of population growth (or 70% of total population in 2015) and nearly three quarters of earthquake-related deaths (or 307,918 deaths) in global earthquake-prone areas occurred in developing countries with an urbanization ratio (percentage of urban population to total population) between 20 and 60%. Holding other factors constant, population size was significantly and positively associated with earthquake fatalities, while the area of urban land was negatively related. The results suggest that fatalities increase for areas where the urbanization ratio is low, but after a ratio between 40 and 50% occurs, earthquake fatalities decline. This finding suggests that the resistance of building and infrastructure is greater in countries with higher urbanization ratios and highlights the need for further investigation. Our quantitative analysis is extended into the future using Shared Socioeconomic Pathways to reveal that by 2050, more than 50% of the population increase in global earthquake-prone areas will take place in a few developing countries (Pakistan, India, Afghanistan, and Bangladesh) that are particularly vulnerable to earthquakes. To reduce earthquake-induced fatalities, enhanced resilience of buildings and urban infrastructure generally in these few countries should be a priority

PARP-1 regulates DNA repair factor availability.

PARP-1 holds major functions on chromatin, DNA damage repair and transcriptional regulation, both of which are relevant in the context of cancer. Here, unbiased transcriptional profiling revealed the downstream transcriptional profile of PARP-1 enzymatic activity. Further investigation of the PARP-1-regulated transcriptome and secondary strategies for assessing PARP-1 activity in patient tissues revealed that PARP-1 activity was unexpectedly enriched as a function of disease progression and was associated with poor outcome independent of DNA double-strand breaks, suggesting that enhanced PARP-1 activity may promote aggressive phenotypes. Mechanistic investigation revealed that active PARP-1 served to enhance E2F1 transcription factor activity, and specifically promoted E2F1-mediated induction of DNA repair factors involved in homologous recombination (HR). Conversely, PARP-1 inhibition reduced HR factor availability and thus acted to induce or enhance BRCA-ness . These observations bring new understanding of PARP-1 function in cancer and have significant ramifications on predicting PARP-1 inhibitor function in the clinical setting

Spatial Mapping of Myeloid Cells and Macrophages by Multiplexed Tissue Staining

An array of phenotypically diverse myeloid cells and macrophages (MC&M) resides in the tumor microenvironment, requiring multiplexed detection systems for visualization. Here we report an automated, multiplexed staining approach, named PLEXODY, that consists of five MC&M-related fluorescently-tagged antibodies (anti - CD68, - CD163, - CD206, - CD11b, and - CD11c), and three chromogenic antibodies, reactive with high- and low-molecular weight cytokeratins and CD3, highlighting tumor regions, benign glands and T cells. The staining prototype and image analysis methods which include a pixel/area-based quantification were developed using tissues from inflamed colon and tonsil and revealed a unique tissue-specific composition of 14 MC&M-associated pixel classes. As a proof-of-principle, PLEXODY was applied to three cases of pancreatic, prostate and renal cancers. Across digital images from these cancer types we observed 10 MC&M-associated pixel classes at frequencies greater than 3%. Cases revealed higher frequencies of single positive compared to multi-color pixels and a high abundance of CD68+/CD163+ and CD68+/CD163+/CD206+ pixels. Significantly more CD68+ and CD163+ vs. CD11b+ and CD11c+ pixels were in direct contact with tumor cells and T cells. While the greatest percentage (~70%) of CD68+ and CD163+ pixels was 0–20 microns away from tumor and T cell borders, CD11b+ and CD11c+ pixels were detected up to 240 microns away from tumor/T cell masks. Together, these data demonstrate significant differences in densities and spatial organization of MC&M-associated pixel classes, but surprising similarities between the three cancer types

Active Mask-Box Scoring R-CNN for Sonar Image Instance Segmentation

Instance segmentation of sonar images is an effective method for underwater target recognition. However, the mismatch among positioning accuracy found by boxIoU and classification confidence, which is used as NMS score in current instance segmentation models; and the high annotation cost of sonar images, are two major problems in the task. To tackle these problems, in this paper, we present a novel instance segmentation method called Mask-Box Scoring R-CNN and embedded it in our proposed deep active learning framework. For the mismatch problem between boxIoU and NMS score, Mask-Box Scoring R-CNN uses a boxIoU head to predict the quality of the bounding boxes. We amend the non-maximum suppression (NMS) score predicted by BoxIoU to preserve high-quality bounding boxes in inference flow. To deal with the annotating problem, we propose a triplets-measure-based active learning (TBAL) method and a balanced-sampling method applicable for deep learning. The TBAL method evaluates the amount of information of unlabeled samples from the aspects of classification confidence, positioning accuracy, and mask quality. The balanced-sampling method selects hard samples from the dataset to train the model to improve performance. The experimental results show that Mask-Box Scoring R-CNN achieves improvements of 1% in boxAP and 1.3% boxAP on our sonar image dataset compared with Mask Scoring R-CNN and Mask R-CNN, respectively. The active learning framework with TBAL and balanced sampling can achieve a competitive performance with less labeled samples than other frameworks, which can better facilitate underwater target recognition

The challenge of population aging for mitigating deaths from PM2.5 air pollution in China

Abstract Estimating the health burden of air pollution against the background of population aging is of great significance for achieving the Sustainable Development Goal 3.9 which aims to substantially reduce the deaths and illnesses from air pollution. Here, we estimated spatiotemporal changes in deaths attributable to PM2.5 air pollution in China from 2000 to 2035 and examined the drivers. The results show that from 2019 to 2035, deaths were projected to decease 15.4% (6.6%–20.7%, 95% CI) and 8.4% (0.6%–13.5%) under the SSP1-2.6 and SSP5-8.5 scenario, respectively, but increase 10.4% (5.1%–20.5%) and 18.1% (13.0%–28.3%) under SSP2-4.5 and SSP3-7.0 scenarios. Population aging will be the leading contributor to increased deaths attributable to PM2.5 air pollution, which will counter the positive gains achieved by improvements in air pollution and healthcare. Region-specific measures are required to mitigate the health burden of air pollution and this requires long-term efforts and mutual cooperation among regions in China

A Large-Scale Dataset of Conservation and Deep Tillage in Mollisols, Northeast Plain, China

One of the primary challenges of our time is to feed a growing and more demanding world population with degraded soil environments under more variable and extreme climate conditions. Conservation tillage (CS) and deep tillage (DT) have received strong international support to help address these challenges but are less used in major global food production in China. Hence, we conducted a large-scale literature search of English and Chinese publications to synthesize the current scientific evidence to evaluate the effects of CS and DT on soil protection and yield maintenance in the Northeast China Plain, which has the most fertile black soil (Mollisols) and is the main agricultural production area of China. As a result, we found that CS had higher soil bulk density, strong soil penetration resistance, greater water contents, and lower soil temperature, and was well-suited for dry and wind erosion-sensitive regions i.e., the southwest areas of the Northeast. Conversely, DT had better performance in the middle belt of the Northeast China Plain, which contained a lower soil temperature and humid areas. Finally, we created an original dataset from papers [dataset 1, including soil physio-chemical parameters, such as soil water, bulk density, organic carbon, sand, silt, clay, pH, total and available nitrogen (N), phosphorus (P), and potassium (K), etc., on crop biomass and yield], by collecting data directly from publications, and two predicted datasets (dataset 2 and dataset 3) of crop yield changes by developing random forest models based on our data

Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate

Abstract Background Immune cell infiltrates (ICI) of tumors are scored by pathologists around tumor glands. To obtain a better understanding of the immune infiltrate, individual immune cell types, their activation states and location relative to tumor cells need to be determined. This process requires precise identification of the tumor area and enumeration of immune cell subtypes separately in the stroma and inside tumor nests. Such measurements can be accomplished by a multiplex format using immunohistochemistry (IHC). Method We developed a pipeline that combines immunohistochemistry (IHC) and digital image analysis. One slide was stained with pan-cytokeratin and CD45 and the other slide with CD8, CD4 and CD68. The tumor mask generated through pan-cytokeratin staining was transferred from one slide to the other using affine image co-registration. Bland-Altman plots and Pearson correlation were used to investigate differences between densities and counts of immune cell underneath the transferred versus manually annotated tumor masks. One-way ANOVA was used to compare the mask transfer error for tissues with solid and glandular tumor architecture. Results The overlap between manual and transferred tumor masks ranged from 20%–90% across all cases. The error of transferring the mask was 2- to 4-fold greater in tumor regions with glandular compared to solid growth pattern (p < 10−6). Analyzing data from a single slide, the Pearson correlation coefficients of cell type densities outside and inside tumor regions were highest for CD4 + T-cells (r = 0.8), CD8 + T-cells (r = 0.68) or CD68+ macrophages (r = 0.79). The correlation coefficient for CD45+ T- and B-cells was only 0.45. The transfer of the mask generated an error in the measurement of intra- and extra- tumoral CD68+, CD8+ or CD4+ counts (p < 10−10). Conclusions In summary, we developed a general method to integrate data from IHC stained slides into a single dataset. Because of the transfer error between slides, we recommend applying the antibody for demarcation of the tumor on the same slide as the ICI antibodies

A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome

Abstract Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture. Here we apply a 2-step machine learning framework for quantitative imaging of tumor vasculature to derive a spatially informed, prognostic gene signature. The trained algorithms classify endothelial cells and generate a vascular area mask (VAM) in H&E micrographs of clear cell renal cell carcinoma (ccRCC) cases from The Cancer Genome Atlas (TCGA). Quantification of VAMs led to the discovery of 9 vascular features (9VF) that predicted disease-free-survival in a discovery cohort (n = 64, HR = 2.3). Correlation analysis and information gain identified a 14 gene expression signature related to the 9VF’s. Two generalized linear models with elastic net regularization (14VF and 14GT), based on the 14 genes, separated independent cohorts of up to 301 cases into good and poor disease-free survival groups (14VF HR = 2.4, 14GT HR = 3.33). For the first time, we successfully applied digital image analysis and targeted machine learning to develop prognostic, morphology-based, gene expression signatures from the vascular architecture. This novel morphogenomic approach has the potential to improve previous methods for biomarker development

Spatial Mapping of Myeloid Cells and Macrophages by Multiplexed Tissue Staining.

An array of phenotypically diverse myeloid cells and macrophages (MC&amp;M) resides in the tumor microenvironment, requiring multiplexed detection systems for visualization. Here we report an automated, multiplexed staining approach, named PLEXODY, that consists of five MC&amp;M-related fluorescently-tagged antibodies (anti - CD68, - CD163, - CD206, - CD11b, and - CD11c), and three chromogenic antibodies, reactive with high- and low-molecular weight cytokeratins and CD3, highlighting tumor regions, benign glands and T cells. The staining prototype and image analysis methods which include a pixel/area-based quantification were developed using tissues from inflamed colon and tonsil and revealed a unique tissue-specific composition of 14 MC&amp;M-associated pixel classes. As a proof-of-principle, PLEXODY was applied to three cases of pancreatic, prostate and renal cancers. Across digital images from these cancer types we observed 10 MC&amp;M-associated pixel classes at frequencies greater than 3%. Cases revealed higher frequencies of single positive compared to multi-color pixels and a high abundance of CD68+/CD163+ and CD68+/CD163+/CD206+ pixels. Significantly more CD68+ and CD163+ vs. CD11b+ and CD11c+ pixels were in direct contact with tumor cells and T cells. While the greatest percentage (~70%) of CD68+ and CD163+ pixels was 0-20 microns away from tumor and T cell borders, CD11b+ and CD11c+ pixels were detected up to 240 microns away from tumor/T cell masks. Together, these data demonstrate significant differences in densities and spatial organization of MC&amp;M-associated pixel classes, but surprising similarities between the three cancer types