140 research outputs found

    Why Gujarat Needs Much Better Higher Education & Research to Succeed in Knowledge Economy & What We Can Do About It?

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    This white paper distills the deliberations on the role of higher education and research as a key enabler of a Knowledge based Society. In particular it discusses (a) the importance of higher quality PhDs for building a knowledge society, (b) the initiatives and progress in competing economies in higher education and research, (c) where Gujarat stands in comparison, and (d) some recommendations on what Gujarat can do to enable timely progress towards building a knowledge based society and economy. These deliberations were conducted in conjunction with the International Conference on \u27Reconnecting Gujarati Diaspora with its Homeland: Contribution to its Development with focus on Building a Knowledge Society

    Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network

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    As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability—including environmental determinants of COVID-19 infection—into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for “what-if” analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications

    Three Conservation Applications of Astronaut Photographs of Earth: Tidal Flat Loss (Japan), Elephant Impacts on Vegetation (Botswana), and Seagrass and Mangrove Monitoring (Australia)

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    NASA photographs taken from low Earth orbit can provide information relevant to conservation biology. This data source is now more accessible due to improvements in digitizing technology, Internet file transfer, and availability of image processing software. We present three examples of conservation-related projects that benefited from using orbital photographs. (1) A time series of photographs from the Space Shuttle showing wetland conversion in Japan was used as a tool for communicating about the impacts of tidal flat loss. Real-time communication with astronauts about a newsworthy event resulted in acquiring current imagery. These images and the availability of other high resolution digital images from NASA provided timely public information on the observed changes. (2) A Space Shuttle photograph of Chobe National Park in Botswana was digitally classified and analyzed to identify the locations of elephant-impacted woodland. Field validation later confirmed that areas identified on the image showed evidence of elephant impacts. (3) A summary map from intensive field surveys of seagrasses in Shoalwater Bay, Australia was used as reference data for a supervised classification of a digitized photograph taken from orbit. The classification was able to distinguish seagrasses, sediments and mangroves with accuracy approximating that in studies using other satellite remote sensing data. Orbital photographs are in the public domain and the database of nearly 400,000 photographs from the late 1960s to the present is available at a single searchable location on the Internet. These photographs can be used by conservation biologists for general information about the landscape and in quantitative applications

    Spatiotemporal Variations in Heat-Related Health Risk in Three Midwestern U.S. Cities Between 1990 and 2010

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    Mortality from extreme heat is a leading cause of weather-related fatality, which is expected to increase in frequency with future climate scenarios. This study examines the spatiotemporal variations in heat-related health risk in three Midwestern cities in the United States between the years 1990 to 2010; cities include Chicago, Illinois, Indianapolis, IN, and Dayton, OH. In order to examine these variations we utilize the recently developed Extreme Heat Vulnerability Index (EHVI) that uses a principal components solution to vulnerability. The EHVI incorporates data from the U.S. Decadal Census and remotely sensed variables to determine heat-related vulnerability at an intra-urban level (census block group). The results demonstrate significant spatiotemporal variations in heat-health risk within the cities involved

    Proton and molecular permeation through the basal plane of monolayer graphene oxide

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    Two-dimensional (2D) materials offer a prospect of membranes that combine negligible gas permeability with high proton conductivity and could outperform the existing proton exchange membranes used in various applications including fuel cells. Graphene oxide (GO), a well-known 2D material, facilitates rapid proton transport along its basal plane but proton conductivity across it remains unknown. It is also often presumed that individual GO monolayers contain a large density of nanoscale pinholes that lead to considerable gas leakage across the GO basal plane. Here we show that relatively large, micrometer-scale areas of monolayer GO are impermeable to gases, including helium, while exhibiting proton conductivity through the basal plane which is nearly two orders of magnitude higher than that of graphene. These findings provide insights into the key properties of GO and demonstrate that chemical functionalization of 2D crystals can be utilized to enhance their proton transparency without compromising gas impermeability

    Proton and molecular permeation through the basal plane of monolayer graphene oxide

    Get PDF
    Two-dimensional (2D) materials offer a prospect of membranes that combine negligible gas permeability with high proton conductivity and could outperform the existing proton exchange membranes used in various applications including fuel cells. Graphene oxide (GO), a well-known 2D material, facilitates rapid proton transport along its basal plane but proton conductivity across it remains unknown. It is also often presumed that individual GO monolayers contain a large density of nanoscale pinholes that lead to considerable gas leakage across the GO basal plane. Here we show that relatively large, micrometer-scale areas of monolayer GO are impermeable to gases, including helium, while exhibiting proton conductivity through the basal plane which is nearly two orders of magnitude higher than that of graphene. These findings provide insights into the key properties of GO and demonstrate that chemical functionalization of 2D crystals can be utilized to enhance their proton transparency without compromising gas impermeability

    Targeting the Conserved Stem Loop 2 Motif in the SARS-CoV-2 Genome.

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    RNA structural elements occur in numerous single-stranded positive-sense RNA viruses. The stem-loop 2 motif (s2m) is one such element with an unusually high degree of sequence conservation, being found in the 3' untranslated region (UTR) in the genomes of many astroviruses, some picornaviruses and noroviruses, and a variety of coronaviruses, including severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2. The evolutionary conservation and its occurrence in all viral subgenomic transcripts imply a key role for s2m in the viral infection cycle. Our findings indicate that the element, while stably folded, can nonetheless be invaded and remodeled spontaneously by antisense oligonucleotides (ASOs) that initiate pairing in exposed loops and trigger efficient sequence-specific RNA cleavage in reporter assays. ASOs also act to inhibit replication in an astrovirus replicon model system in a sequence-specific, dose-dependent manner and inhibit SARS-CoV-2 replication in cell culture. Our results thus permit us to suggest that the s2m element is readily targeted by ASOs, which show promise as antiviral agents. IMPORTANCE The highly conserved stem-loop 2 motif (s2m) is found in the genomes of many RNA viruses, including SARS-CoV-2. Our findings indicate that the s2m element can be targeted by antisense oligonucleotides. The antiviral potential of this element represents a promising start for further research into targeting conserved elements in RNA viruses.ERC, BBSR

    Aldehyde dehydrogenase inhibition as a pathogenic mechanism in Parkinson disease

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    Parkinson disease (PD) is a neurodegenerative disorder particularly characterized by the loss of dopaminergic neurons in the substantia nigra. Pesticide exposure has been associated with PD occurrence, and we previously reported that the fungicide benomyl interferes with several cellular processes potentially relevant to PD pathogenesis. Here we propose that benomyl, via its bioactivated thiocarbamate sulfoxide metabolite, inhibits aldehyde dehydrogenase (ALDH), leading to accumulation of the reactive dopamine metabolite 3,4-dihydroxyphenylacetaldehyde (DOPAL), preferential degeneration of dopaminergic neurons, and development of PD. This hypothesis is supported by multiple lines of evidence. (i) We previously showed in mice the metabolism of benomyl to S-methyl N-butylthiocarbamate sulfoxide, which inhibits ALDH at nanomolar levels. We report here that benomyl exposure in primary mesencephalic neurons (ii) inhibits ALDH and (iii) alters dopamine homeostasis. It induces selective dopaminergic neuronal damage (iv) in vitro in primary mesencephalic cultures and (v) in vivo in a zebrafish system. (vi) In vitro cell loss was attenuated by reducing DOPAL formation. (vii) In our epidemiology study, higher exposure to benomyl was associated with increased PD risk. This ALDH model for PD etiology may help explain the selective vulnerability of dopaminergic neurons in PD and provide a potential mechanism through which environmental toxicants contribute to PD pathogenesis
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