Cryoprotection of Platelets by Grafted Polymers

Unlike red blood cells (RBC) which are stored at 4°C, platelets are stored at 22–24°C (room temperature) due to biophysical and biochemical changes induced by cold temperatures aggregately known as the ‘cold storage lesion’ (CSL). However, 22°C storage greatly increases the risk of microbial growth, thus limiting the safe storage of platelets to only 5–7 days (versus 42 days for RBC). Consequent to the short shelf life of platelets, blood services face chronic shortages of these life-saving cells. To overcome both the risk of microbial contamination and the constrained supplies of platelets, renewed research into attenuating the CSL and/or determining where cold stored platelets are clinically suitable are ongoing. In this chapter, we show that the covalent grafting of methoxypolyethylene glycol (mPEG), a biocompatible polymer, to the membrane of platelets attenuates the CSL. Moreover, the grafted mPEG serves as a potent cryoprotectant allowing platelets to be stored at 4°C, or frozen at −20°C, while retaining normal platelet counts and biologic function. The successful development of platelet PEGylation may provide a means by which the cold storage of platelets can be achieved with a minimal loss of platelet quality while improving both platelet microbial safety and inventory

Adaptive Optical Phase Estimation Using Time-Symmetric Quantum Smoothing

Quantum parameter estimation has many applications, from gravitational wave detection to quantum key distribution. We present the first experimental demonstration of the time-symmetric technique of quantum smoothing. We consider both adaptive and non-adaptive quantum smoothing, and show that both are better than their well-known time-asymmetric counterparts (quantum filtering). For the problem of estimating a stochastically varying phase shift on a coherent beam, our theory predicts that adaptive quantum smoothing (the best scheme) gives an estimate with a mean-square error up to $2\sqrt{2}$ times smaller than that from non-adaptive quantum filtering (the standard quantum limit). The experimentally measured improvement is $2.24 \pm 0.14$

Pore-blockade Times for Field-Driven Polymer Translocation

We study pore blockade times for a translocating polymer of length $N$, driven by a field $E$ across the pore in three dimensions. The polymer performs Rouse dynamics, i.e., we consider polymer dynamics in the absence of hydrodynamical interactions. We find that the typical time the pore remains blocked during a translocation event scales as $\sim N^{(1+2\nu)/(1+\nu)}/E$, where $\nu\simeq0.588$ is the Flory exponent for the polymer. In line with our previous work, we show that this scaling behaviour stems from the polymer dynamics at the immediate vicinity of the pore -- in particular, the memory effects in the polymer chain tension imbalance across the pore. This result, along with the numerical results by several other groups, violates the lower bound $\sim N^{1+\nu}/E$ suggested earlier in the literature. We discuss why this lower bound is incorrect and show, based on conservation of energy, that the correct lower bound for the pore-blockade time for field-driven translocation is given by $\eta N^{2\nu}/E$, where $\eta$ is the viscosity of the medium surrounding the polymer.Comment: 14 pages, 6 figures, slightly shorter than the previous version; to appear in J. Phys.: Cond. Ma

Persistent homology for fast tumor segmentation in whole slide histology images

Automated tumor segmentation in Hematoxylin & Eosin stained histology images is an essential step towards a computer-aided diagnosis system. In this work we propose a novel tumor segmentation approach for a histology whole-slide image (WSI) by exploring the degree of connectivity among nuclei using the novel idea of persistent homology profiles. Our approach is based on 3 steps: 1) selection of exemplar patches from the training dataset using convolutional neural networks (CNNs); 2) construction of persistent homology profiles based on topological features; 3) classification using variant of k-nearest neighbors (k-NN). Extensive experimental results favor our algorithm over a conventional CNN

Fast DNA translocation through a solid-state nanopore

We report translocation experiments on double-strand DNA through a silicon oxide nanopore. Samples containing DNA fragments with seven different lengths between 2000 to 96000 basepairs have been electrophoretically driven through a 10 nm pore. We find a power-law scaling of the translocation time versus length, with an exponent of 1.26 $\pm$ 0.07. This behavior is qualitatively different from the linear behavior observed in similar experiments performed with protein pores. We address the observed nonlinear scaling in a theoretical model that describes experiments where hydrodynamic drag on the section of the polymer outside the pore is the dominant force counteracting the driving. We show that this is the case in our experiments and derive a power-law scaling with an exponent of 1.18, in excellent agreement with our data.Comment: 5 pages, 2 figures. Submitted to PR

Significance of occult neoplastic cells on tumor metastasis: a case report of gastric cancer

<p>Abstract</p> <p>Background</p> <p>Occult neoplastic cells (ONCs) are the tumor cells floating in the lymph node sinuses, distant from the primary tumor, and supposed to be one of most reliable marker of prognosis.</p> <p>Methods</p> <p>We report here the case of a 52-year-old woman with a gastric cancer associated by numerous ONCs.</p> <p>Results</p> <p>Postoperative examination of the stomach disclosed an advanced, poorly differentiated adenocarcinoma with frequent lymph node metastases. In addition to ONCs and occasional micrometastases, focal aggregates of ONCs, one of the possible intermediate lesions between the ONCs and the usual metastases, are also observed.</p> <p>Conclusions</p> <p>In the present case, at least some of ONCs seem to form the microaggregates of tumor cells in lymph nodes, anchor in the sinuses, and grow up to the large tumorous lesion. Even if most of the ONCs were trapped and disappeared under the influence of tumor immunity, the detection of ONCs could be one of the reliable clues to estimate the prognosis.</p

Growth of CrSi2 Nanostructures Using CrCl2 Powder on Si Substrates

Chromium disilicide (CrSi2) nanostructures were grown by the exposure of Si (111) substrates to CrCl2 vapor in an argon gas flow at atmospheric pressure without using any metal catalyst. Dependence of the growth condition on the structural property was investigated. Hexagonal-shaped CrSi2 microrods were grown at 750 &deg;C with 0.05 g of CrCl2. As the quantity of CrCl2 increased to 0.1 g, the bundle of CrSi2 nanowires with microrods and web-liked CrSi2 nanostructure with turning angles were grown at 750 &deg;C and 700 &deg;C, respectively. The preliminary discussion on the growth mechanism of CrSi2 micro- and nanostructures was carried out

Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features

Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on the selection of exemplar image patches from a convolution neural network (CNN) and patch classification by quantifying the divergence between the PHPs of exemplars and the input image patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for image patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperforms competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet and healthy cases. Collectively, the accurate tumor segmentation produces the highest average patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology image analysis

Analysis of interdiffusion between SmFeAsO0.92F0.08 and metals for ex situ fabrication of superconducting wire

We demonstrate the fabrication of superconducting SmFeAsO1-xFx (Sm-1111) wires by using the ex-situ powder-in-tube technique. Sm-1111 powder and a binder composed of SmF3, samarium arsenide, and iron arsenide were used to synthesize the superconducting core. Although the F content of Sm-1111 is reduced in the process of ex-situ fabrication, the binder compensates by sufficiently supplementing the F content, thereby preventing a decrease in the superconducting transition temperature and a shrinking of the superconducting volume fraction. Thus, in the superconducting Sm-1111 wire with the binder, the transport critical current density reaches the highest value of ~4000 A/cm2 at 4.2 K