Deformability Measurement of Single-Cells at High-Throughput With Imaging Flow Cytometry

Disease conditions like malaria, sickle cell anemia, diabetes mellitus, cancer, etc., are known to significantly alter the deformability of certain types of cells (red blood cells, white blood cells, circulating tumor cells, etc.). To determine the cellular deformability, techniques like micropipette aspiration, atomic force microscopy, optical tweezers, quantitative phase imaging have been developed. Many of these techniques have an advantage of determining the single cell deformability with ultrahigh precision. However, the suitability of these techniques for the realization of a deformability based diagnostic tool is questionable as they are expensive and extremely slow to operate on a huge population of cells. In this paper, we propose a technique for high-throughput (800 cells/s) determination of cellular deformability on a single cell basis. This technique involves capturing the image(s) of cells in flow that have undergone deformation under the influence of shear gradient generated by the fluid flowing through the microfluidic channels. Deformability indices of these cells can be computed by performing morphological operations on these images. We demonstrate the applicability of this technique for examining the deformability index on healthy, diabetic, and sphered red blood cells. We believe that this technique has a strong role to play in the realization of a potential tool that uses deformability as one of the important criteria in disease diagnosis.

A semi-automated, field-portable microscopy platform for clinical diagnostic applications

Clinical microscopy is a versatile diagnostic platform used for diagnosis of a multitude of diseases. In the recent past, many microfluidics based point-of-care diagnostic devices have been developed, which serve as alternatives to microscopy. However, these point-of-care devices are not as multi-functional and versatile as clinical microscopy. With the use of custom designed optics and microfluidics, we have developed a versatile microscopy-based cellular diagnostic platform, which can be used at the point of care. The microscopy platform presented here is capable of detecting infections of very low parasitemia level (in a very small quantity of sample), without the use of any additional computational hardware. Such a cost-effective and portable diagnostic device, would greatly impact the quality of health care available to people living in rural locations of the world. Apart from clinical diagnostics, it's applicability to field research in environmental microbiology has also been outlined. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

Spectral intensity distribution of trapped

To calculate static response properties of a many-body system, local density approximation (LDA) can be safely applied. But, to obtain dynamical response functions, the applicability of LDA is limited bacause dynamics of the system needs to be considered as well. To examine this in the context of cold atoms, we consider a system of non-interacting spin4 fermions confined by a harmonic trapping potential. We have calculated a very important response function, the spectral intensity distribution function (SIDF), both exactly and using LDA at zero temperature and compared with each other for different dimensions, trap frequencies and momenta. The behaviour of the SIDF at a particular momentum can be explained by noting the behaviour of the density of states (DoS) of the free system (without trap) in that particular dimension. The agreement between exact and LDA SIDFs becomes better with increase in dimensions and number of particles.

Fusing the Navigation Information of Dual Foot-Mounted Zero-Velocity-Update-Aided Inertial Navigation Systems

A range constraint method viz. centroid method is proposed to fuse the navigation information of dual (right and left) foot-mounted Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation Systems (INSs). Here, the range constraint means that the distance of separation between the position estimates of right and left foot ZUPT-aided INSs cannot be greater than a quantity known as foot-to-foot maximum separation. We present the experimental results which illustrate the applicability of the proposed method. The results show that the proposed method significantly enhances the accuracy of the navigation solution when compared to using two uncoupled foot-mounted ZUPT-aided INSs. Also, we compare the performance of the proposed method with the existing data fusion methods.

Spectral solutions to the Korteweg-de-Vries and nonlinear Schrodinger equations

In this paper, we present the solutions of 1-D and 2-D non-linear partial differential equations with initial conditions. We approach the solutions in time domain using two methods. We first solve the equations using Fourier spectral approximation in the spatial domain and secondly we compare the results with the approximation in the spatial domain using orthogonal functions such as Legendre or Chebyshev polynomials as their basis functions. The advantages and the applicability of the two different methods for different types of problems are brought out by considering 1-D and 2-D nonlinear partial differential equations namely the Korteweg-de-Vries and nonlinear Schrodinger equation with different potential function. (C) 2015 Elsevier Ltd. All rights reserved.

Simulation of Effective Thermal Conductivity of Metal Hydride Packed Beds

Several mathematical models are available for estimation of effective thermal conductivity of nonreactive packed beds. Keeping in view the salient differences between metal hydride beds in which chemisorption of hydrogen takes place and conventional nonreactive packed beds, modified models are proposed here to predict the effective thermal conductivity. Variation in properties such as solid thermal conductivity and porosity during hydrogen absorption and desorption processes are incorporated. These extended models have been applied to simulate the effective thermal conductivity of the MmNi(4.5)Al(0.5) hydride bed and are compared with the experimental results. Applicability of the extended models for estimation of the effective thermal conductivity at different operating conditions such as pressure, temperature, and hydrogen concentration is discussed.

Macroporous three-dimensional graphene oxide foams for dye adsorption and antibacterial applications

Several reports illustrate the wide range applicability of graphene oxide (GO) in water remediation. However, a few layers of graphene oxide tend to aggregate under saline conditions thereby reducing its activity. The effects of aggregation can be minimized by having a random arrangement of GO layers in a three dimensional architecture. The current study emphasizes the potential benefits of highly porous, ultralight graphene oxide foams in environmental applications. These foams were prepared by a facile and cost effective lyophilization technique. The 3D architecture allowed the direct use of these foams in the removal of aqueous pollutants without any pretreatment such as ultrasonication. Due to its macroporous nature, the foams exhibited excellent adsorption abilities towards carcinogenic dyes such as rhodamine B (RB), malachite green (MG) and acriflavine (AF) with respective sorption capacities of 446, 321 and 228 mg g(-1) of foam. These foams were also further investigated for antibacterial activities against E. coli bacteria in aqueous and nutrient growth media. The random arrangement of GO layers in the porous foam architecture allowed it to exhibit excellent antibacterial activity even under physiological conditions by following the classical wrapping-perturbation mechanism. These results demonstrate the vast scope of GO foam in water remediation for both dye removal and antibacterial activity.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

High-throughput miniaturized microfluidic microscopy with radially parallelized channel geometry

In this article, we present a novel approach to throughput enhancement in miniaturized microfluidic microscopy systems. Using the presented approach, we demonstrate an inexpensive yet high-throughput analytical instrument. Using the high-throughput analytical instrument, we have been able to achieve about 125,880 cells per minute (more than one hundred and twenty five thousand cells per minute), even while employing cost-effective low frame rate cameras (120 fps). The throughput achieved here is a notable progression in the field of diagnostics as it enables rapid quantitative testing and analysis. We demonstrate the applicability of the instrument to point-of-care diagnostics, by performing blood cell counting. We report a comparative analysis between the counts (in cells per mu l) obtained from our instrument, with that of a commercially available hematology analyzer.

High Loading of Pd Nanoparticles by Interior Functionalization of MOFs for Heterogeneous Catalysis

In this report, the issue related to nanoparticle (NP) agglomeration upon increasing their loading amount into metal-organic frameworks (MOFs) has been addressed by functionalization of MOFs with alkyne groups. The alkynophilicity of the Pd2+ (or other noble metals) ions has been utilized successfully for significant loading of Pd NPs into alkyne functionalized MOFs. It has been shown here that the size and loading amount of Pd NPs are highly dependent on the surface area and pore width of the MOFs. The loading amount of Pd NPs was increased monotonically without altering their size distribution on a particular MOF. Importantly, the distinct role of alkyne groups for Pe(2+) stabilization has also been demonstrated by performing a control experiment considering a MOF without an alkyne moiety. The preparation of NPs involved two distinct steps viz. adsorption of metal ions inside MOFs and reduction of metal ions. Both of these steps were monitored by microscopic techniques. This report also demonstrates the applicability of Pd@MOF NPs as extremely efficient heterogeneous catalysts for Heck-coupling and hydrogenation reactions of aryl bromides or iodides and alkenes, respectively.