Cellulose-binding domains: potential for purification of complex proteins

The endoglucanase CenA and the exoglucanase Cex from Cellulomonasfimi each contain a discrete cellulose-binding domain (CBD),at the amino-terminus or carboxyl-terminus respectively. Thegene fragment encoding the CBD can be fused to the gene of aprotein of interest. Using this approach hybrid proteins canbe engineered which bind reversibly to cellulose and exhibitthe biological activity of the protein partner. Alkaline phosphatase(PhoA) from Escherichia coli, and a ß-glucosidase(Abg) from an Agrobacterium sp. are dimeric proteins. The fusionpolypeptides CenA-PhoA and Abg-CBC_cex are sensitive to proteolysisat the junctions between the fusion partners. Proteolysis resultsin a mixture of homo- and heterodimers; these bind to celluloseif one or both of the monomers carry a CBD, e.g. CenA-PhoA/CenA-PhoAand CenA-PhoA/PhoA. CBD fusion polypeptides could be used inthis way to purify polypeptides which associate with the fusionpartner.     

High-Temperature Creep Behavior of Mixed Conducting La0.5Sr0.5Fe1−xCoxO3-δ (0.5≤x≤1) Materials

Steady-state compressive creep rate of La_0.5Sr_0.5Fe_0.5Co_0.5O_3−δ (LSFC) and La_0.5Sr_0.5CoO_3−δ (LSC) is reported in the temperature region 900°–1050°C and stress range 5–28 MPa. The stress exponents for the two materials were 1.71±0.18 and 1.24±0.15, respectively. The activation energy for creep was considerably higher for LSC (619±56 kJ/mol) than for LSFC (392±28 kJ/mol). The grain size exponent for LSC was 1.28±0.14. Considerably higher creep rates were observed for both materials in N₂ compared with air. Relaxation by creep of chemical-induced stresses in oxygen-permeable membranes is addressed, especially at low partial pressure of oxygen.     

Exploiting Ontology for Software License Agreements

Software end-users need to sign licenses to seal an agreement with the product providers. Habitually, users agree with the license (i.e. terms and conditions) without fully understanding the agreement. To address this issue, an ontological model is developed to formulate the user requirements and license agreements formally. This paper, introduces ontological model that includes the abstract license ontology of common features found in di?erent license agreements. The abstract license ontology is then extended to a few real world license agreements. The resulting model can be used for di?erent purposes such as querying the appropriate licenses for a speciˉc requirement or checking the license terms and conditions with user requirements.     

A scalable solution to the nearest neighbor search problem through local-search methods on neighbor graphs

Pattern Analysis and Applications - Nearest neighbor search is a powerful abstraction for data access; however, data indexing is troublesome even for approximate indexes. For intrinsically...     

Assessing task understanding in remote ultrasound diagnosis via gesture analysis

Pattern Analysis and Applications - This work presents a gesture-based approach to estimate task understanding and performance during remote ultrasound tasks. Our approach is comprised of...     

Tensor Clustering for Rendering Many‐Light Animations

Rendering animations of scenes with deformable objects, camera motion, and complex illumination, including indirect lighting and arbitrary shading, is a long‐standing challenge. Prior work has shown that complex lighting can be accurately approximated by a large collection of point lights. In this formulation, rendering of animation sequences becomes the problem of efficiently shading many surface samples from many lights across several frames. This paper presents a tensor formulation of the animated many‐light problem, where each element of the tensor expresses the contribution of one light to one pixel in one frame. We sparsely sample rows and columns of the tensor, and introduce a clustering algorithm to select a small number of representative lights to efficiently approximate the animation. Our algorithm achieves efficiency by reusing representatives across frames, while minimizing temporal flicker. We demonstrate our algorithm in a variety of scenes that include deformable objects, complex illumination and arbitrary shading and show that a surprisingly small number of representative lights is sufficient for high quality rendering. We believe out algorithm will find practical use in applications that require fast previews of complex animation.     

Interpretation time of serial chest CT examinations with stacked-metaphor workstation versus film alternator

PURPOSE: Interpretation time of serial staging chest CT cases, which each contained current and previous examinations, with a simple prototype workstation called filmstack was experimentally compared with interpretation time with a film alternator. MATERIALS AND METHODS: The filmstack displayed a "stack" of sections for each examination; user controls allowed rapid selection of preset attenuation windows and both synchronized and unsynchronized scrolling. Eight radiologists were timed as they used the filmstack and the film alternator to interpret four ergonomically complex serial CT cases. RESULTS: All reports dictated on the basis of findings with filmstack and film were of acceptable clinical accuracy. The time to examine a case with filmstack was significantly faster than the time with film, including the time to load and unload the alternator (99% confidence [P = .01]). There was no statistically significant difference in interpretation time between filmstack and prehung film. CONCLUSION: Use of a low-cost stacked CT workstation with a single 1,024 x 1,024 monitor is an effective means of interpreting cases that require comparison of multiple CT examinations.     

Technologies and logistics for phosphorus recovery from livestock waste

Phosphorus (P) runoff from livestock waste can trigger algal blooms that adversely affect aquatic life and human health. One strategy to mitigate this problem is to install nutrient recovery technologies that concentrate and mobilize nutrients from nutrient-rich regions to nutrient-deficient ones. We present supply chain design formulations to identify optimal types and locations for P recovery technologies. The formulations capture trade-offs in transportation costs, technology efficiency, investment/operational costs, revenue collected from different recovered products (struvite and nutrient cakes), and environmental impact. Our computational framework is used to analyze the impact of different scenarios for market prices of recovered products, recovery yields, and remediation costs. We find that transportation of waste alone (without any processing) can achieve significant reductions in environmental impact at low cost, but cannot achieve economic sustainability in the long run due to the lack of direct revenue streams. Mechanical separation technologies that recover P in the form of nutrient cakes are low-cost solutions that can achieve high environmental benefits and reduced transportation costs, but revenues are also limited due to low values of the cakes. Struvite crystallization in fluidized beds is found to be a highly attractive option under current struvite prices, but economic sustainability is strongly dependent on yield recoveries (which are currently highly uncertain).     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.