DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE

Detection and classification of vegetation is a crucial technical task in the management of natural resources since vegetation serves as a foundation for all living things and has a significant impact on climate change such as impacting terrestrial carbon dioxide (CO2). Traditional approaches for acquiring vegetation covers such as field surveys, map interpretation, collateral and data analysis are ineffective as they are time consuming and expensive.  In this paper vegetation regions are automatically detected by applying simple but effective vegetation indices Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) on red(R) and near infrared (NIR) bands of Landsat-8 satellite image. Remote sensing technology makes it possible to analyze vegetation cover across wide areas in a cost-effective manner. Using remotely sensed images, the mapping of vegetation requires a number of factors, techniques, and methodologies. The rapid improvement of remote sensing technologies broadens possibilities for image sources making remotely sensed images more accessible. The dataset used in this paper is the R and NIR bands of Level-1 Tier 1 Landsat-8 optical remote sensing image acquired on 6th September 2013, is processed and made available to users on 2nd May 2017. The pre-processing involving sub-setting operation is performed using the ERDAS Imagine tool on R and NIR bands of Landsat-8 image. The NDVI and SAVI are utilized to extract vegetation features automatically by using python language. Finally by establishing a threshold, vegetation cover of the research area is detected and then classified

Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms

Natural Calamities like floods cause wide-range of damage to human existence as well as substructures. For automatic extraction of flooded area in multi-temporal satellite imagery acquired by Sentinel-1 Synthetic Aperture Radar (SAR), this paper presents two neural network algorithms: Feed-Forward Neural Network, Cascade-forward back-propagation neural network. This work currently focuses on Uttar Pradesh in India, which was affected due to floods during August 2017. The two models are trained, validated and tested using MATLAB R2018b. The models are first trained using a variety of input data until the percentage of error with respect to water body detection is within an acceptable error limit. These models are then used to extract the water features effectively and to detect the flooded regions. Finally, flood area is calculated in sq. km in during flood and post-flood imagery using these algorithms. The results thus obtained are compared with that from the binary thresholding method from previous studies. The results show that the Feed- Forward Neural Network gives better accuracy than the Cascade-forward back propagation neural network. Based on the promising results, the proposed method may assist in our understanding of the role of machine learning in disaster detection

In Silico Model of Critical Cerebral Oxygenation after Traumatic Brain Injury: Implications for Rescuing Hypoxic Tissue.

Cerebral oxygen delivery is central to the modern intensive care of patients with severe traumatic brain injury. Low brain tissue oxygen tension (PbtO2) results from microvascular collapse and diffusion limitation and is associated with adverse outcome. A number of therapies to improve oxygen delivery are known to be effective in improving PbtO2. Their relative effectiveness and microscopic regions of hypoxia, however, may exist/persist even in the presence of normal PbtO2. Unfortunately, there are no methods currently for assessing this quantitatively. We used an in silico (computational) simulation approach to understand the effect of common interventions on the microscopic distribution of brain tissue oxygen tension. We constructed a non-linear mathematical model of cerebral oxygen supply, diffusion, and consumption for a simplified geometry. Model parameters were chosen to agree with clinical parameters. We found that it was possible to create a plausible diffusion-limited scenario with a significant hypoxic fraction by increasing the mean diffusion distance. We found that increasing cerebral blood flow/blood oxygen content or suppressing the cerebral metabolic rate were most effective at improving PbtO2 and reduced the hypoxic fraction. Within the limitations of our modeling assumptions, increasing the arterial oxygen partial pressure was less effective and only improved PbtO2 by creating a region of hyperoxic tissue with no improvement in hypoxic fraction. The in silico simulations can be useful in understanding the likely physiological effect of complex treatments for which measurement techniques do not exist

Importance of the ebp (endocarditis- and biofilm-associated pilus) locus in the pathogenesis of Enterococcus faecalis ascending urinary tract infection.

BACKGROUND: We recently demonstrated that the ubiquitous Enterococcus faecalis ebp (endocarditis- and biofilm-associated pilus) operon is important for biofilm formation and experimental endocarditis. Here, we assess its role in murine urinary tract infection (UTI) by use of wild-type E. faecalis OG1RF and its nonpiliated, ebpA allelic replacement mutant (TX5475). METHODS: OG1RF and TX5475 were administered transurethrally either at an ~1 : 1 ratio (competition assay) or individually (monoinfection). Kidney pairs and urinary bladders were cultured 48 h after infection. These strains were also tested in a peritonitis model. RESULTS: No differences were observed in the peritonitis model. In mixed UTIs, OG1RF significantly outnumbered TX5475 in kidneys (P=.0033) and bladders (P\u3c or =.0001). More OG1RF colony-forming units were also recovered from the kidneys of monoinfected mice at the 4 inocula tested (P=.015 to P=.049), and 50% infective doses of OG1RF for kidneys and bladder (9.1x10(1) and 3.5x10(3) cfu, respectively) were 2-3 log(10) lower than those of TX5475. Increased tropism for the kidney relative to the bladder was observed for both OG1RF and TX5475. CONCLUSION: The ebp locus, part of the core genome of E. faecalis, contributes to infection in an ascending UTI model and is the first such enterococcal locus shown to be important in this site

Adherence to host extracellular matrix and serum components by Enterococcus faecium isolates of diverse origin.

Enterococcus faecium has emerged as an important cause of nosocomial infections over the last two decades. We recently demonstrated collagen type I (CI) as a common adherence target for some E. faecium isolates and a significant correlation was found to exist between acm-mediated CI adherence and clinical origin. Here, we evaluated 60 diverse E. faecium isolates for their adherence to up to 15 immobilized host extracellular matrix and serum components. Adherence phenotypes were most commonly observed to fibronectin (Fn) (20% of the 60 isolates), fibrinogen (17%) and laminin (Ln) (13%), while only one or two of the isolates adhered to collagen type V (CV), transferrin or lactoferrin and none to the other host components tested. Adherence to Fn and Ln was almost exclusively restricted to clinical isolates, especially the endocarditis-enriched nosocomial genogroup clonal complex 17 (CC17). Thus, the ability to adhere to Fn and Ln, in addition to CI, may have contributed to the emergence and adaptation of E. faecium, in particular CC17, as a nosocomial pathogen

Importance of the Collagen Adhesin Ace in Pathogenesis and Protection against Enterococcus faecalis Experimental Endocarditis

Ace is an adhesin to collagen from Enterococcus faecalis expressed conditionally after growth in serum or in the presence of collagen. Here, we generated an ace deletion mutant and showed that it was significantly attenuated versus wild-type OG1RF in a mixed infection rat endocarditis model (P<0.0001), while no differences were observed in a peritonitis model. Complemented OG1RFΔace (pAT392::ace) enhanced early (4 h) heart valve colonization versus OG1RFΔace (pAT392) (P = 0.0418), suggesting that Ace expression is important for early attachment. By flow cytometry using specific anti-recombinant Ace (rAce) immunoglobulins (Igs), we showed in vivo expression of Ace by OG1RF cells obtained directly from infected vegetations, consistent with our previous finding of anti-Ace antibodies in E. faecalis endocarditis patient sera. Finally, rats actively immunized against rAce were less susceptible to infection by OG1RF than non-immunized (P = 0.0004) or sham-immunized (P = 0.0475) by CFU counts. Similarly, animals given specific anti-rAce Igs were less likely to develop E. faecalis endocarditis (P = 0.0001) and showed fewer CFU in vegetations (P = 0.0146). In conclusion, we have shown for the first time that Ace is involved in pathogenesis of, and is useful for protection against, E. faecalis experimental endocarditis

Analysis of clonality and antibiotic resistance among early clinical isolates of Enterococcus faecium in the United States.

BACKGROUND: The Enterococcus faecium genogroup, referred to as clonal complex 17 (CC17), seems to possess multiple determinants that increase its ability to survive and cause disease in nosocomial environments. METHODS: Using 53 clinical and geographically diverse US E. faecium isolates dating from 1971 to 1994, we determined the multilocus sequence type; the presence of 16 putative virulence genes (hyl(Efm), esp(Efm), and fms genes); resistance to ampicillin (AMP) and vancomycin (VAN); and high-level resistance to gentamicin and streptomycin. RESULTS: Overall, 16 different sequence types (STs), mostly CC17 isolates, were identified in 9 different regions of the United States. The earliest CC17 isolates were part of an outbreak that occurred in 1982 in Richmond, Virginia. The characteristics of CC17 isolates included increases in resistance to AMP, the presence of hyl(Efm) and esp(Efm), emergence of resistance to VAN, and the presence of at least 13 of 14 fms genes. Eight of 41 of the early isolates with resistance to AMP, however, were not in CC17. CONCLUSIONS: Although not all early US AMP isolates were clonally related, E. faecium CC17 isolates have been circulating in the United States since at least 1982 and appear to have progressively acquired additional virulence and antibiotic resistance determinants, perhaps explaining the recent success of this species in the hospital environment

EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning

Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decision making and path planning. In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework using evidential learning to directly estimate both classification and regression uncertainties. To employ evidential learning for object detection, we devise a combination of evidential and focal loss functions for the sparse heatmap inputs. We introduce class-balanced weighting for regression and heatmap prediction to tackle the class imbalance encountered by evidential learning. Moreover, we propose a learning scheme to actively utilize the predicted heatmap uncertainties to improve the detection performance by focusing on the most uncertain points. We train our model on the KITTI dataset and evaluate it on challenging out-of-distribution datasets including BDD100K and nuImages. Our experiments demonstrate that our approach improves the precision and minimizes the execution time loss in relation to the base model

Identification and phenotypic characterization of a second collagen adhesin, Scm, and genome-based identification and analysis of 13 other predicted MSCRAMMs, including four distinct pilus loci, in Enterococcus faecium.

Attention has recently been drawn to Enterococcus faecium because of an increasing number of nosocomial infections caused by this species and its resistance to multiple antibacterial agents. However, relatively little is known about the pathogenic determinants of this organism. We have previously identified a cell-wall-anchored collagen adhesin, Acm, produced by some isolates of E. faecium, and a secreted antigen, SagA, exhibiting broad-spectrum binding to extracellular matrix proteins. Here, we analysed the draft genome of strain TX0016 for potential microbial surface components recognizing adhesive matrix molecules (MSCRAMMs). Genome-based bioinformatics identified 22 predicted cell-wall-anchored E. faecium surface proteins (Fms), of which 15 (including Acm) had characteristics typical of MSCRAMMs, including predicted folding into a modular architecture with multiple immunoglobulin-like domains. Functional characterization of one [Fms10; redesignated second collagen adhesin of E. faecium (Scm)] revealed that recombinant Scm(65) (A- and B-domains) and Scm(36) (A-domain) bound to collagen type V efficiently in a concentration-dependent manner, bound considerably less to collagen type I and fibrinogen, and differed from Acm in their binding specificities to collagen types IV and V. Results from far-UV circular dichroism measurements of recombinant Scm(36) and of Acm(37) indicated that these proteins were rich in beta-sheets, supporting our folding predictions. Whole-cell ELISA and FACS analyses unambiguously demonstrated surface expression of Scm in most E. faecium isolates. Strikingly, 11 of the 15 predicted MSCRAMMs clustered in four loci, each with a class C sortase gene; nine of these showed similarity to Enterococcus faecalis Ebp pilus subunits and also contained motifs essential for pilus assembly. Antibodies against one of the predicted major pilus proteins, Fms9 (redesignated EbpC(fm)), detected a \u27ladder\u27 pattern of high-molecular-mass protein bands in a Western blot analysis of cell surface extracts from E. faecium, suggesting that EbpC(fm) is polymerized into a pilus structure. Further analysis of the transcripts of the corresponding gene cluster indicated that fms1 (ebpA(fm)), fms5 (ebpB(fm)) and ebpC(fm) are co-transcribed, a result consistent with those for pilus-encoding gene clusters of other Gram-positive bacteria. All 15 genes occurred frequently in 30 clinically derived diverse E. faecium isolates tested. The common occurrence of MSCRAMM- and pilus-encoding genes and the presence of a second collagen-binding protein may have important implications for our understanding of this emerging pathogen