A Remote Sensing Technique for Estimating Watershed Runoff

This study was supported in part by the Office of Water Research and Technology, U. S. Department of the Interior under Project A-063-0HI0(print) iii, 53 leaves : ill., map ; 28 cm.Table of Contents -- List of Figures -- List of Tables -- Introduction -- Summary -- Materials and Methods -- Data Analysis -- Results and Discussion -- Conclusions -- Recommendations -- References -- Appendi

High-resolution mapping of Bora winds in the northern Adriatic Sea using synthetic aperture radar

Author Posting. © American Geophysical Union, 2010. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 115 (2010): C04020, doi:10.1029/2009JC005524.The Adriatic Sea is regularly subjected to strong Bora wind events from the northeast during winter. The events have a strong effect on the oceanography in the Adriatic, driving basin-scale gyres that determine the transport of biogeochemical material and extracting large amounts of heat. The Bora is known to have multiple surface wind jets linked to the surrounding orography and have been the focus of many studies, but it has not been possible to describe the detailed spatial structure of these jets by in situ observations. Using high-resolution spaceborne RADARSAT-1 synthetic aperture radar (SAR) images collected during an active Bora period (23 January–16 February 2003), we created a series of high-resolution (300 m) maps of the wind field. The obtained winds show reasonable agreement with several in situ wind observations, with an RMS wind speed error of 3.6 m/s, slightly higher than the 2–3 m/s errors reported in previous studies. These SAR images reveal the spatial structure of the Bora wind in unprecedented detail, showing several new features. In the Senj region of Croatia, several images show rhythmic structure with wavelengths of 2–3 km that may reflect Bora pulsation seen at fixed locations by previous investigators. Along the Italian coast, several images show a wide (20–30 km) band of northwesterly winds that abruptly change to the northeasterly Bora winds further offshore. Meteorological model results suggest that these northwesterly winds are consistent with those of a barrier jet forming along the Italian Apennine mountain chain

On the Possibility of Non-Local and Local Oil Spills Striking the Shores of North Carolina and South Carolina

Oil spills, the releases of liquid petroleum hydrocarbons into the marine environment, have occurred in the Gulf of Mexico (GOM) of the United States (U.S.A). However, no oil spills have ever affected the Eastern Atlantic Seaboard (EAS) of the U.S.A. Nonetheless, we demonstrate from data and numerical modeling that oil spills in the GOM have the potential to reach the U.S.A. EAS via a combination of atmospheric storms, major ocean currents and atmospheric wind driven surface currents. The basis for this hypothesis is that in August of 1987, a Karena Brevis toxin plant outbreak occurred in the GOM, and several weeks hence, showed up on the shores of North Carolina and South Carolina. We recreate that environmental scenario employing atmospheric and oceanic data from 1987, Sea Surface Temperature (SST) images, and via numerical modeling, that an atmospheric cold front, the combination of the Loop Current, the Florida Current, and Gulf Stream Frontal Filaments, created the pathways for the transport of K-Brevis plants from the Gulf to the U.S.A. EAS. Numerical model output of oil spill scenarios, both non-local in the GOM and local to the Carolinas, is presented to prove that this latter hypothesis has credibility and viability

Dynamics of bacteriophages as a promising antibiofilm agents

Pseudomonas aeruginosa is an ubiquitous organism which has emerged as a major threat in the hospital environment. Overuse of antibiotics has also significantly increased the emergence of antimicrobial multiresistant bacteria. P. aeruginosa has an innate ability to adhere to surfaces and form virulent biofilms. Bacteriophage might represent one attractive solution to this problem. In this study, P.aeruginosa phage were utilized to Biofilm inhibition and remove.Sample collected from University sewage. Isolation was done according to Martha.R.J.Clokie protocol. Serial dilution prepared, then equally incubated with bacteria to investigate Biofilm inhibition potential. Biofilm formed base on Microplate Biofilm Assay. The effect of isolated phage investigated on biofilm remove of Pseudomonas putida, E.coli and Acinetobacter baumanii. P.aeruginosa biofilm had OD: 1.688 in 492n.m. Pure phage, 10-2 and 10-3 diluted phage decreased OD to 1.587, 1.341 and 1.461, respectively. Isolated phage dramatically decline OD of Biofilm of all strains.Phages have various affinity to attach to hosts, thereby it is supposed to phages compete for their receptors. Therefore it is supposed phages have most efficiency in optimum concentration to remove biofilm or growth inhibition

Northern Adriatic response to a wintertime bora wind event

The unprecedented suite of research activities that focused on the Adriatic in winter 2003 provided a multi-faceted view of bora events and the resulting oceanic response

An Efficient Algorithm for Face Localization An Efficient Algorithm for Face Localization

Detecting and localizing a face in a single image is the most important part of almost all face recognition systems. Face localization aims to determine the image position of a face for verification purpose of documents such as passport, driving license, ID cards, etc. In this paper an entropy-based method is proposed for detecting the high information region of the image which may include eyes, mouth, nose, etc. The derived regions in this stage of recognition are sent to feature extraction and classification phase. The method has been tested on the ORL database. The results show the effectiveness and robustness of the proposed method for face detection and localization in presence of white additive Gaussian noise up to 25 dbw. We have achieved localization rate 99.75 % for detection of faces in the ORL data set that we had which means 1 miss over 400 ORL faces

[18F]FDG-PET/CT Radiomics and Artificial Intelligence in Lung Cancer: Technical Aspects and Potential Clinical Applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT‐based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes

[¹⁸F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes