Visualizing Hydropower Across the Himalayas: Mapping in a time of Regulatory Decline

This paper introduces the busy field of hydropower development in the Himalayan region of the GBM basin to press the urgency for greater information and data exchange. The paper provides an example of a mapping method and a database that will add to the existing online sources of information and analysis offered by nongovernmental agencies and some government departments. This project contributes to the general aim of many citizen groups to limit, monitor and regulate the practices of hydropower companies and the management of their infrastructure in the GBM. The monitoring pressure from citizen groups and science projects continues to serve as an important replacement to the weak functioning of the country environment ministries and corrects the corruptions of the license raj that plaque project deals and environmental clearances. These citizen motivated knowledge exchanges, especially through online portals and social media, can even push for better transnational instruments for formal governmental data sharing

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning

Urban climate and resiliency: A synthesis report of state of the art and future research directions

The Urban Climate and Resiliency-Science Working Group (i.e., The WG) was convened in the summer of 2018 to explore the scientific grand challenges related to climate resiliency of cities. The WG leveraged the presentations at the 10th International Conference on Urban Climate (ICUC10) held in New York City (NYC) on 6–10 August 2018 as input forum. ICUC10 was a collaboration between the International Association of Urban Climate, American Meteorological Society, and World Meteorological Organization. It attracted more than 600 participants from more than 50 countries, resulting in close to 700 oral and poster presentations under the common theme of “Sustainable & Resilient Urban Environments”. ICUC10 covered topics related to urban climate and weather processes with far-reaching implications to weather forecasting, climate change adaptation, air quality, health, energy, urban planning, and governance. This article provides a synthesis of the analysis of the current state of the art and of the recommendations of the WG for future research along each of the four Grand Challenges in the context of urban climate and weather resiliency; Modeling, Observations, Cyber-Informatics, and Knowledge Transfer & Applications

Meta-analysis of archived DNA microarrays identifies genes regulated by hypoxia and involved in a metastatic phenotype in cancer cells

<p>Abstract</p> <p>Background</p> <p>Metastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines.</p> <p>Methods</p> <p>Affymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch <it>t </it>test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest.</p> <p>Results</p> <p>Three complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known <it>JUNB</it>, <it>FOS </it>and <it>TP63</it>, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as <it>SERPINE1 </it>and <it>MMP7</it>, are known to regulate metastasis. Twenty-one genes including <it>VEGFA </it>and <it>ID2 </it>have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis.</p> <p>Conclusion</p> <p>The proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.</p

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Using "local climate zones" to detect urban heat island on two small cities in Alabama

Classifying “urban” and “rural” environments is a challenge in understanding urban climate, specifically urban heat islands (UHIs). Stewart and Oke developed the “local climate zone” (LCZ) classification system to clarify these distinctions using 17 unique groups. This system has been applied to many areas around the world, but few studies have attempted to utilize them to detect UHI effects in smaller cities. Our aim was to use the LCZ classification system 1) to detect UHI in two small cities in Alabama and 2) to determine whether similar zones experienced similar intensity or magnitude of UHIs. For 1 week, we monitored hourly temperature in two cities, in four zones: compact low-rise, open low-rise, dense forests, and water. We found that urban zones were often warmer for overall, daytime, and nighttime temperatures relative to rural zones (from −0.1° to 2.8°C). In addition, we found that temperatures between cities in similar zones were not very similar, indicating that the LCZ system does not predict UHI intensity equally in places with similar background climates. We found that the LCZ classification system was easy to use, and we recognize its potential as a tool for urban ecologists and urban planners

Identification and classification of geographically isolated wetlands in North Alabama using geographic object based image analysis (GeOBIA)

Due to recent Supreme Court rulings, there has been an increased interest in the isolated wetlands of the United States. These types of wetlands serve vital ecological roles such as water quality regulation and as a habitat of biological diversity. This study focuses specifically on mapping of geographically isolated wetlands, or those that are separated from traditional wetlands by a given spatial extent, using Geographic Object-Based Image Analysis (GeOBIA). GeOBIA is a type of remote sensing analysis that identifies objects and features in data-sets via automated methodologies. This type of analysis offers the opportunity to increase the efficiency of what has traditionally been a very labour intensive process of manual photo-interpretation. This analysis resulted in the delineation of 26,424 areas as geographically isolated wetlands. These results were assessed for accuracy through both manual inspection of aerial imagery and field verification which yielded accuracies of 83.7 and 87.7%, respectively

Multi-approach synergic investigation between land surface temperature and land-use land-cover

Rapid urban expansion and associated land-use land-cover (LULC) change in India have emerged as a serious environmental threat that accelerates the impacts of urban heat island intensity (UHII). Three independent investigations have been conducted in this study using a series of Landsat data. The objectives of this work are: (1) To predict the near-future LULC scenario using an integrated model; (2) To understand the connection between band mean for particular LULC class with LST; (3) To analyze the temporal relationship between different types of built-up clusters and LST. The LULC and LST maps reveal that LST increases from 27.01° to 33.86°C, whereas built-up areas rise from 6.93% to 27.10% during 1988–2018, respectively. We observed that the near-future LULC scenario of KMA shows a huge expansion of built-up areas paid by decreased vegetation and open spaces. A clear significant correlation has been found between band mean and LST in all three Landsat sensors with the R2 = 0.84; p\u3c0.02 for Landsat 5 TM, R2 = 0.91 and 0.99; p\u3c0.01 and 0.00 for Landsat 7 ETM+, and R2 = 0.88; p\u3c0.01 for Landsat 8 OLI in connection to our second objective. However, no agreement has been found between different built-up clusters and LST over 30 years of observation. For the first time, this study established the interconnectivity between bands of Landsat sensors and LST. The temporal relationship between different built-up clusters and LST have reviled also for the first time. Beside this, the rising rate of built-up areas was observed by the integrated model. Such alarming condition demands immediate attention to sustainable, and scientific land use regulations under new urbanism policy