High-resolution AFM imaging of the CA125 protein and its aptamer-based complexes.

With the advances in medical imaging devices, large volumes of high-resolution 3D medical image data have been produced. These high-resolution 3D data are very large in size, and severely stress storage systems and networks. Most existing Web-based 3D medical image interactive applications therefore deal with only low- or medium-resolution image data. While it is possible to download the whole 3D high-resolution image data from the server and perform the image visualization and analysis at the client site, such an alternative is infeasible when the high-resolution data are very huge, and many users concurrently access the server. In this paper, we propose a novel framework for Web-based interactive applications of high-resolution 3D medical image data. Specifically, we first partition the whole 3D data into buckets, and then compress each bucket separately. We also propose an indexing structure for these buckets to efficiently support typical queries such as 3D slicer and region of interest (ROI), and only the relevant buckets are transmitted instead of the whole high-resolution 3D medical image data. Furthermore, in order to better support concurrent accesses and to improve the average response time, we also propose some techniques for bucket group access on the server side and incremental transmission. Our experimental study based on a human brain MRI data set indicates that the proposed framework can significantly reduce storage and communication requirements, and can enable real-time interaction with remote high-resolution 3D medical image data for many concurrent users

We introduce a simple model to describe the interplay between specific and non-specific interactions. We study the influence of various physical factors on the static and dynamic properties of the specific interactions of our model and show that contrary to intuitive expectations, non-specific interactions can assist in the formation of specific complexes and increase their stability. We then discuss the relevance of these results for biological systems.

The task of drilling and completing horizontal development wells in carbonate reservoirs can prove to be quite difficult, especially in the well placement and completion design stages. These stages of well construction can be particularly challenging when the reservoir is characterized by natural fracture systems which can result in drilling fluid losses, or when drilling with narrow TVD targets. The utilization of high resolution borehole images to optimize petrophysical evaluation, well placement, and completion design becomes a necessity for reservoir evaluation team. This paper will present a case study of how this new LWD technology optimized well placement, and completion design. During the pre-job planning stage, numerous scenarios are considered to assist in determining which "bundle" of logging technology is best suited to achieve the objectives of drilling the well. Optimizing logging design specifications required to meet the objectives may not be a simple job. For instance, placing the lateral near the roof of the reservoir while getting resistivity measurements that are least affected by overlying beds would seem to be irreconcilable. Similarly, acquiring high quality resolution image data in the event of borehole deterioration associated with total drilling fluid losses is a difficult task to achieve. In this case study, the technology is shown to successfully "geo-steer" within a single thin layer of the reservoir and achieved a 100% net-to-gross while simultaneously placing the wellbore near the roof. The real-time high resolution image was of sufficient quality used to identify sub-seismic faults, natural conductive fractures, karst dissolution features, healed fractures and drilling induced fractures as the well was being drilled. Once the well was drilled to the planned total depth, the high resolution image data could be used for the completion design, thereby saving the time, expense, and risk of an additional logging run using pipe-conveyed wireline logging tools. In addition to the completion design, high confidence dip data were used to update the geological models, fracture porosity and aperture were computed from the images and the shallow focused resistivity measurements were used for more accurate saturation calculations.

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy.

In this paper, to solve the problem of lacking of road information caused by ground object occlusion, a registration and fusion method of high-resolution satellite image and vehicle point cloud data is proposed. Firstly, the road surface and crash barrier are extracted by using the filtering algorithm of joint gradient and elevation. Secondly, the Canny algorithm is used to extract road boundary on satellite images, and the plane is selected to extract linear points according to the elevation features of crash barrier and road boundary. Thirdly, the nearest neighbor point cloud iteration method is used to realize the matching of linear points with the same name. Finally, the high-resolution image and DEM are combined to generate a 3D model, and the vehicle point cloud data is registered with the three-dimensional model according to a rotation matrix, so as to improve the efficiency of high-precision map construction. The experimental results show that this method can effectively realize the registration of high resolution image and vehicle lidar data, combine the complementary advantages of high resolution image and point cloud data, improving the integrity of vehicle point cloud data, and alleviate the problem of high-precision map construction caused by occlusion to a certain extent.

The undeveloped sublayers of the prolific offshore Arab reservoir were recently targeted for appraisal drilling, aligning with the ever-increasing efforts to expand production and book new reserves. The first extended reach horizontal well was drilled in this field to evaluate the economic potential of four different reservoir sublayers. Reservoir characterization and the evaluation of lateral permeability changes were the primary objectives for these low permeability layers. Maximizing reservoir contact while maintaining minimum borehole tortuosity presented substantial geosteering challenges. Another challenge is that the bottomhole assembly (BHA) must be free from radioactive chemical sources. A well placement workflow was developed that honors the structural geological setting, based on the existing field knowledge and offset petrophysical data. The optimized BHA consists of a point-the-bit rotary steerable system (RSS) and logging-while-drilling (LWD) sensors. These LWD sensors include high-resolution microresistivity imaging, laterolog resistivity, azimuthal multipole acoustic, nuclear magnetic resonance (NMR), ultrasonic calliper, and near-bit azimuthal gamma ray sensors. High-resolution microresistivity imaging and the near-bit azimuthal gamma ray sensor were used to geosteer in the thin reservoir subunits and to facilitate fracture identification. NMR was used to help remain in sweet zones in real time and to provide pore size distribution, based on T1 measurements for permeability evaluation. Acoustic and high-resolution image data were used to derive empirical permeabilities. The 8,000-ft horizontal section was successfully geosteered with 100% reservoir contact, tapping into four thin reservoir sublayers. Real-time high-resolution microresistivity images, dip picks, and near-bit azimuthal gamma ray data helped to maintain the wellbore attitude parallel to the stratigraphy within each sublayer; they also facilitated a smooth transition from one sublayer to the next with minimum borehole tortuosity, aided by the point-the-bit RSS and at-bit inclination measurements. Fracture evaluation from high-resolution images, NMR, acoustic, and image-based permeabilities are integrated with production log results to enable a better understanding of the field, to benchmark flow unit identification in these undeveloped reservoirs, and to optimize future geosteering and petrophysical data acquisition requirements. The traditional reactive geosteering concept is challenged by placing the 8,000-ft extended reach section in four different sublayers that are as thin as 3 ft true stratigraphic thickness (TST) without penetrating any boundaries. The multidisciplinary approach helped to assess the economic potential of these undeveloped layers within the local reservoir sector and to formulate plans for a future field development program.

Permeability is a fundamental petrophysical attribute required to accurately evaluate recoverable reserves and design an appropriate field-development strategy. Because logging tools do not measure absolute permeability, minimizing uncertainty in the evaluation of log-derived permeabilities remains one of the most critical petrophysical challenges in the oil industry. Horizontal development in laterally heterogeneous carbonate reservoirs also requires evaluation of lateral permeability variations to optimize completion design, while maximizing reservoir exposure via precise well placement in real time. This paper demonstrates innovative methods to evaluate lateral permeability variations in heterogenous carbonate reservoirs. The workflow for log-derived permeability predictions is based on empirical relationships using nuclear magnetic resonance (NMR), acoustic, and high-resolution imaging tool measurements. These are normalized in an integrated multi-disciplinary approach using core, well test, production logs, and formation-tester mobility data where available. Traditionally, formation-tester tools have been used to obtain single pressure and mobility values at each test station. The logging-while-drilling (LWD) formation tester can be oriented azimuthally to help evaluate permeability anisotropy, which is a key factor for reservoir characterization in laterally heterogeneous reservoir layers. The oriented data can also be used to adjust the well plan in real time to maximize reservoir exposure in the desired "sweet spot." Variations in the oriented LWD formation tester measurements at each depth station exhibited favorable correlations to azimuthal changes observed in the LWD high-resolution micro resistivity image. Detailed image analysis further helped to understand the mechanism that governs the azimuthal permeability profile. The combination of oriented LWD formation-tester and high-resolution image data also aided in making better real-time geosteering decisions, as well as in the planning and design of a future field-development program within the local reservoir sector. Operational considerations to maximize data quality rely on an optimized bottomhole assembly (BHA) design, accurate depth control, and robust orientation techniques based on best practices and lessons learned. This paper presents an integrated approach for well placement and an improved understanding of flow-unit characterization via first-time use of oriented formation-tester data in conjunction with corresponding high-resolution images in a laterally heterogeneous reservoir.

High-Speed AFM Reveals the Dynamics of Single Biomolecules at the Nanometer Scale

Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the storm, PlanetScope data might be rapidly available due to its high temporal resolution, while the acquisition of high-resolution airborne data often takes weeks to a month and is, therefore, used in a second step for more detailed mapping. The study area is located in Bavaria, Germany (ca. 165 km2), and labels for damaged areas were provided by the Bavarian State Institute of Forestry (LWF). Modifications of a U-Net architecture were compared to other approaches using transfer learning (e.g., VGG19) to find the most efficient architecture for the task on both datasets while keeping the computational time low. A custom implementation of U-Net proved to be more accurate than transfer learning, especially on medium (3 m) resolution PlanetScope imagery (intersection over union score (IoU) 0.55) where transfer learning completely failed. Results for transfer learning based on VGG19 on high-resolution aerial image data are comparable to results from the custom U-Net architecture (IoU 0.76 vs. 0.73). When using both architectures on a dataset from a different area (located in Hesse, Germany), however, we find that the custom implementations have problems generalizing on aerial image data while VGG19 still detects most damage in these images. For PlanetScope data, VGG19 again fails while U-Net achieves reasonable mappings. Results highlight the potential of Deep Learning algorithms to detect damaged areas with an IoU of 0.73 on airborne data and 0.55 on Planet Dove data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign followed by detailed mapping in a second stage.

Permeability is a fundamental petrophysical attribute required to accurately evaluate recoverable reserves and design an appropriate field-development strategy. Because logging tools do not measure absolute permeability, minimizing uncertainty in the evaluation of log-derived permeabilities remains one of the most critical petrophysical challenges in the oil industry. Horizontal development in laterally heterogeneous carbonate reservoirs also requires evaluation of lateral permeability variations to optimize completion design while maximizing reservoir exposure via precise well placement in real time. This paper demonstrates innovative methods to evaluate lateral permeability variations in heterogeneous carbonate reservoirs. The workflow for log-derived permeability predictions is based on empirical relationships using nuclear magnetic resonance (NMR) and high-resolution imaging tool measurements. These are normalized in an integrated multidisciplinary approach using core, well test, production logs, and formation-tester mobility data where available. Traditionally, formation-tester tools have been used to obtain single-pressure and mobility values at each test station. The logging-while-drilling (LWD) formation tester can be oriented azimuthally to help evaluate permeability anisotropy, which is a key factor for reservoir characterization in laterally heterogeneous reservoir layers. The oriented data can also be used to adjust the well plan in real time to maximize reservoir exposure in the desired “sweet spot.” Variations in the oriented LWD formation-tester measurements at each depth station exhibited favorable correlations to azimuthal changes observed in the LWD high-resolution microresistivity image. Detailed image analysis further helped to understand the mechanism that governs the azimuthal permeability profile. The combination of oriented LWD formation-tester and high-resolution image data also aided in making better real-time geosteering decisions, as well as in the planning and design of a future field-development program within the local reservoir sector. Operational considerations to maximize data quality rely on an optimized bottomhole assembly (BHA) design, accurate depth control, and robust orientation techniques based on best practices and lessons learned. This paper presents an integrated approach for well placement and an improved understanding of flow-unit characterization via the first-time use of oriented formation-tester data in conjunction with corresponding high-resolution images in a laterally heterogeneous reservoir.

In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data.

Off-road vehicles can have a devastating impact on vegetation and soil. Here, we sought to quantify, through a combination of field vegetation, bulk soil, and image analyses, the impact of off-road vehicles on the vegetation and soils of Rawdat Al Shams, which is located in central Saudi Arabia. Soil compaction density was measured in the field, and 27 soil samples were collected for bulk density analysis in the lab to quantify the impacts of off-road vehicles. High spatial resolution images, such as those obtained by the satellites GeoEye-1 and IKONOS-2, were used for surveying the damage to vegetation cover and soil compaction caused by these vehicles. Vegetation cover was mapped using the Normalized Difference Vegetation Index (NDVI) technique based on high-resolution images taken at different times of the year. Vehicle trails were derived from satellite data via visual analysis. All damaged areas were determined from high-resolution image data. In this study, we conducted quantitative analyses of vegetation cover change, the impacts of vehicle trails (hereafter "trail impacts"), and a bulk soil analysis. Image data showed that both vegetation cover and trail impacts increased from 2008 to 2015, with the average percentage of trail impacts nearly equal to that of the percentage of vegetation cover during this period. Forty-six species of plants were found to be present in the study area, consisting of all types of life forms, yet trees were represented by a single species, Acacia gerrardii. Herbs composed the largest share of plant life, with 29 species, followed by perennial herbs (12 species), grasses (5 species), and shrubs (3 species). Analysis of soil bulk density for Rawdat Al Shams showed that off-road driving greatly impacts soil density. Twenty-two plant species were observed on the trails, the majority of which were ephemerals. Notoceras bicorne was the most common, with a frequency rate of 93.33%, an abundance value of 78.47%, and a density of 0.1 in transect 1, followed by Plantago ovata.

The local change in the three different structures of restriction enzyme BamHI, which include DNA-free dimer and non-specific and specific complexes with DNA, were detected by the fluorescence from a site-selectively introduced solvatochromic fluorophore Nbeta-L-alanyl-5-(N,N-dimethylamino)naphthalene-1-sulfonamide (DanAla). According to the crystal structure, alpha-helices of the non-specific complex containing Ile82, Glu86 and Trp206 residues are converted into random coil by the formation of specific complex with a substrate. To understand the microenvironmental change caused by the structural transition around these positions, the DanAla probe was site-specifically introduced into the positions, and steady-state and time-resolved fluorescence was observed. The steady-state fluorescence gave us information that the rigidity of the polypeptide chains would be enhanced by the formation of the specific complex. The time-resolved fluorescence supported that the change in a water molecule-accessible space was induced by DNA-binding. We revealed that the change in rigidity and solvation around the specific positions was detected by the characteristic fluorescence using the combination of steady-state and time-resolved fluorescence techniques.

Sirex noctilio is causing considerable mortality in commercial pine plantations in KwaZulu-Natal, South Africa. The ability to remotely detect variable (for example, low, medium and high) S. noctilio infestation levels remains crucial for monitoring of the actual spread of the disease and for the effective deployment of suppression activities. Although high resolution image data can detect and monitor S.noctilio infestations there are no guidelines to the appropriate spatial resolutions that are suitable for detection and monitoring purposes. This study examines the use of minimum variance to analyze S. noctilio infestations in an effort to determine an optimal spatial resolution of remotely sensed data for forest health monitoring purposes. High resolution (0.5 m) image data was collected using a four band airborne sensor and infestation levels were derived using the normalized difference vegetation index (NDVI) and Gaussian maximum likelihood classifier. It was determined that the appropriate spatial resolution for the detection and monitoring of S.noctilio infestations as estimated by the minimum variance of sub samples narrowly differed based on the level of localized infestations present in the study area. Pixel sizes larger than 2.3 m will not provide adequate information for high infestation levels, while using pixel sizes smaller than the 1.75 m for detecting low to medium infestation levels will yield inappropriate results. The results of this study establish the necessary spatial resolution guidelines needed for the operational detection and monitoring of S.noctilio.

Road network is an important part of ground remote sensing image, and extraction of road network information plays an important role in image matching and urban planning. In recent years, high-resolution remote sensing images have attracted more and more attention of people, and object detection and recognition from high-resolution image data has become an important research topic. In this paper, the basic features of roads in high-resolution remote sensing images are briefly described, and two methods of road network extraction are analyzed and compared. It is of great significance to make better use of road features to effectively remove the interference of various noises in high-resolution images and accurately extract road information.