Western blotting readily identifies specific proteins amidst complex biological backgrounds [1, 2]. Nevertheless, immunob-lotting suffers from tremendous labor-intensive and time-intensive requirements [3]. The slab-g...
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ISBN:
(纸本)9781618395955
Western blotting readily identifies specific proteins amidst complex biological backgrounds [1, 2]. Nevertheless, immunob-lotting suffers from tremendous labor-intensive and time-intensive requirements [3]. The slab-gel assays require 1-2 days for completion with multiple hands-on "blotting" steps and yield semi-quantitative information. Recently, our group has introduced new approaches for completing Western blotting. The microfluidic integration strategies introduced and used allow rapid results reporting, full assay automation, and limited sample consumption (1-10 uL). Our integration strategies use spatial, temporal, and spatiotemporal modulation of separation mechanisms in fully electrophoretic systems. The present study reports on recapitulation of immunoaffinity in previously sized proteins, using novel in-transit electrophoretic removal of SDS from SDS-protein complexes. Early results show both the length- and timescales for protein 'renaturation' are compatible with on-chip operation. Further, substantial binding affinity is recapitulated using this streamlined and promising approach.
Tactile sensation is a complex manifestation of mechanical stimuli applied to the skin. At the most fundamental level of the somatosensory system is the cutaneous mechanoreceptor. The objective here was to establish a...
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This paper describes an analog-to-digital converter (ADC) array for an implantable neural sensor which digitizes neural signals sensed by a microelectrode array. The ADC array consists of 96 variable resolution ADC ba...
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Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable prom...
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Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, *** and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the applic
It is important that eye care professionals have access to tools that accurately and efficiently convey the results of an eye exam to students and patients. This paper describes work-in-progress on a software system t...
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Microstructural and electrical properties of Gd-doped CeO2 (GDC; Ce0.9Gd0.1O1.95) thin films prepared by pulsed laser deposition as an electrolyte in solid-oxide fuel cells (SOFCs) were investigated. The GDC thin film...
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Microstructural and electrical properties of Gd-doped CeO2 (GDC; Ce0.9Gd0.1O1.95) thin films prepared by pulsed laser deposition as an electrolyte in solid-oxide fuel cells (SOFCs) were investigated. The GDC thin films were prepared on various substrates including single-crystal yttria-stabilized zirconia (YSZ) and magnesium oxide (MgO) substrates. The GDC thin-film electrolytes with different grain sizes and grain morphologies were prepared by varying the deposition parameters, such as substrate temperature, oxygen partial pressure, target repetition rate, and laser ablation energy. The microstructural properties of these films were examined using X-ray diffraction (XRD), transmission electron microscopy (TEM), and atomic force microscopy (AFM). Alternating-current (AC) and direct-current (DC) electrical measurements through in-plane method show that the electrical property of the GDC thin film strongly depends on grain size, e.g., the total conductivity of the films deposited at 700 °C (7.3 × 10−3 S/cm) is about 20 times higher than the ones deposited at room temperature (3.6 × 10−4 S/cm) at the measurement temperature of 600 °C.
The overall goal of this study was to compare the accuracy of various data analysis techniques to quantify tremor severity (TS) in a clinical context, with the aim of improving the reliability (context consistency and...
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The overall goal of this study was to compare the accuracy of various data analysis techniques to quantify tremor severity (TS) in a clinical context, with the aim of improving the reliability (context consistency and inter-rater agreement) of tremor evaluation in patients with Parkinson's disease (PD) or essential tremor (ET). Ten patients with either PD or ET were asked to perform several tasks used in the clinical practice for the characterization of tremor. Three-axis gyroscopes in a Shimmer device measured angular velocities of the wrist of each subject for postural, kinetic, spiral tracing, and resting scenarios, and a digital pen recorded subjects' tracings of an Archimedes spiral printed on paper. Gyroscope data were used for training and testing a supervised machine learning algorithm to classify TS and for root mean squared (RMS) numerical rating of TS, while digital pen data were analyzed numerically to quantify tracing deviations from the spiral and obtain a tremor rating. We evaluated the performance of our proposed methods compared to clinicians' diagnostic rating. The machine learning method matched the clinical rating with 82% accuracy, the digital pen with 78% accuracy, and RMS with 42% accuracy. We obtained the best accuracy of 82% using the decision tree machine learning approach with gyroscope data measured with the Shimmer.
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