Single-molecule observation of nucleotide induced conformational changes in basal SecA-ATP hydrolysis

11 pages ; illustrationsSecA is the critical adenosine triphosphatase that drives preprotein transport through the translocon, SecYEG, in Escherichia coli. This process is thought to be regulated by conformational changes of specific domains of SecA, but real-time, real-space measurement of these changes is lacking. We use single-molecule atomic force microscopy (AFM) to visualize nucleotide-dependent conformations and conformational dynamics of SecA. Distinct topographical populations were observed in the presence of specific nucleotides. AFM investigations during basal adenosine triphosphate (ATP) hydrolysis revealed rapid, reversible transitions between a compact and an extended state at the ~100-ms time scale. A SecA mutant lacking the precursor-binding domain (PBD) aided interpretation. Further, the biochemical activity of SecA prepared for AFM was confirmed by tracking inorganic phosphate release. We conclude that ATP-driven dynamics are largely due to PBD motion but that other segments of SecA contribute to this motion during the transition state of the ATP hydrolysis cycle.Funding: This work was supported by the National Science Foundation (CAREER award number 1054832 to G.M.K.) and a Burroughs Wellcome Fund Career Award at the Scientific Interface (to G.M.K.)Nagaraju Chada1*, Kanokporn Chattrakun1, Brendan P. Marsh1†, Chunfeng Mao2, Priya Bariya2, Gavin M. King1,2‡: 1Department of Physics and Astronomy, University of Missouri–Columbia, Columbia, MO 65211, USA. 2Department of Biochemistry, University of Missouri–Columbia, Columbia, MO 65211, USA. *Present address: Department of Biology, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA. †Present address: Department of Applied Physics, Stanford University, Stanford, CA 94305 USA. ‡Corresponding author.Nagaraju Chada (1*), Kanokporn Chattrakun (1), Brendan P. Marsh (1†), Chunfeng Mao (2), Priya Bariya (2), Gavin M. King (1,2‡) -- References: 1) Department of Physics and Astronomy, University of Missouri–Columbia, Columbia,MO 65211, USA ; 2) Department of Biochemistry, University of Missouri–Columbia, Columbia, MO 65211, USA ; *) Present address: Department of Biology, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA ; †) Present address: Department of Applied Physics, Stanford University, Stanford, CA 94305 USA ; ‡) Corresponding author

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed

A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes

During the last years a wide range of algorithms and devices have been made available to easily acquire range images. The increasing abundance of depth data boosts the need for reliable and unsupervised analysis techniques, spanning from part registration to automated segmentation. In this context, we focus on the recognition of known objects in cluttered and incomplete 3D scans. Locating and fitting a model to a scene are very important tasks in many scenarios such as industrial inspection, scene understanding, medical imaging and even gaming. For this reason, these problems have been addressed extensively in the literature. Several of the proposed methods adopt local descriptor-based approaches, while a number of hurdles still hinder the use of global techniques. In this paper we offer a different perspective on the topic: We adopt an evolutionary selection algorithm that seeks global agreement among surface points, while operating at a local level. The approach effectively extends the scope of local descriptors by actively selecting correspondences that satisfy global consistency constraints, allowing us to attack a more challenging scenario where model and scene have different, unknown scales. This leads to a novel and very effective pipeline for 3D object recognition, which is validated with an extensive set of experiment

Demarcation of Potential Groundwater Recharge Zones Through REMOTE Sensing, GIS, and MCDA: A Case Study of the Aji River Basin in Saurashtra, Gujarat, India

Groundwater is an important and valuable natural resource. Due to extensive agricultural practices and industrial developments, groundwater resources have recently been overexploited and depleted. Therefore, there is a need to adopt sustainable water resource management practices to augment groundwater recharge. This study uses advanced geospatial technologies to identify potential groundwater recharge zones in the Aji River Basin in the Saurashtra region of Gujarat, India. The primary objective was to enhance potential groundwater recharge zones in the Aji River basin region. Therefore, various parameters affecting groundwater availability, including lineament, geology, soil, density, slope, rainfall, land use/land cover, geomorphology, and drainage density, were considered for delineating potential groundwater recharge zones. The integrated approach, using remote sensing (RS), geographical information system (GIS), and multi-criteria decision analysis (MCDA), was employed to achieve this. The influence factor was determined through Satty`s analytic hierarchy process (AHP) method. The sub-parameters were ranked according to the AHP scale, and a weighted overlay analysis tool in ArcGIS software was utilized to map the potential groundwater recharge zones in the basin. The concluding map was categorized into five distinct zones based on potential groundwater recharge zones: ‘Excellent’ (50.50 km2, 2.36 %), ‘Good’ (1376.78 km2, 64.56 %), ‘Moderate’ (599.14 km2, 28.09 %), ‘Poor’ (90.67 km2, 4.25 %), and ‘Very Poor’ (15.32 km2,0.718 %). The SCS-Curve number method was employed to assess the potential for rainfall-induced runoff. The Aji River basin exhibited a weighted mean rainfall and runoff of 622.68 mm and 241.07 mm, respectively. The estimated weighted runoff for the Aji River basin was 514.07 million cubic meters (MCM), with a 75 % exceedance probability, resulting in a runoff of 152.97 MCM. This study concludes that integrating remote sensing, GIS, and MCDA techniques effectively identifies and delineates potential groundwater recharge zones, which is crucial for sustainable water resource management

Performance evaluation of 3D local feature descriptors

A number of 3D local feature descriptors have been proposed in literature. It is however, unclear which descriptors are more appropriate for a particular application. This paper compares nine popular local descriptors in the context of 3D shape retrieval, 3D object recognition, and 3D modeling. We first evaluate these descriptors on six popular datasets in terms of descriptiveness. We then test their robustness with respect to support radius, Gaussian noise, shot noise, varying mesh resolution, image boundary, and keypoint localization errors. Our extensive tests show that Tri-Spin-Images (TriSI) has the best overall performance across all datasets. Unique Shape Context (USC), Rotational Projection Statistics (RoPS), 3D Shape Context (3DSC), and Signature of Histograms of OrienTations (SHOT) also achieved overall acceptable results