Inorganic phosphate nanorods are a novel fluorescent label in cell biology

We report the first use of inorganic fluorescent lanthanide (europium and terbium) ortho phosphate [LnPO(4)·H(2)O, Ln = Eu and Tb] nanorods as a novel fluorescent label in cell biology. These nanorods, synthesized by the microwave technique, retain their fluorescent properties after internalization into human umbilical vein endothelial cells (HUVEC), 786-O cells, or renal carcinoma cells (RCC). The cellular internalization of these nanorods and their fluorescence properties were characterized by fluorescence spectroscopy (FS), differential interference contrast (DIC) microscopy, confocal microscopy, and transmission electron microscopy (TEM). At concentrations up to 50 μg/ml, the use of [(3)H]-thymidine incorporation assays, apoptosis assays (TUNEL), and trypan blue exclusion illustrated the non-toxic nature of these nanorods, a major advantage over traditional organic dye

Neural Techniques for Improving the Classification Accuracy of Microarray Data Set using Rough Set Feature Selection Method

Abstract---Classification, a data mining task is an effective method to classify the data in the process of Knowledge Data Discovery. Classification method algorithms are widely used in medical field to classify the medical data for diagnosis. Feature Selection increases the accuracy of the Classifier because it eliminates irrelevant attributes. This paper analyzes the performance of neural network classifiers with and without feature selection in terms of accuracy and efficiency to build a model on four different datasets. This paper provides rough feature selection scheme, and evaluates the relative performance of four different neural network classification procedures such as Learning Vector Quantisation (LVQ) -LVQ1, LVQ3, optimizedlearning-rate LVQ1 (OLVQ1), and The Self-Organizing Map (SOM) incorporating those methods. Experimental results show that the LVQ3 neural classification is an appropriate classification method makes it possible to construct high performance classification models for microarray data

Differentiation Of Mesenchymal Stem Cells (Mscs) To Functional Neuron On Graphene-Polycaprolactone Nanoscaffolds

Spinal cord is an important part of the central nervous system that controls all activities of the body. It is a tubular bundle of nerve fibers and tissues connecting brain to nearly all parts of the body. Nerve cells in an adult human body do not divide and make copies of themselves. Therefore, in case of an injury or damage to any part of spinal cord causes permanent changes to strength, sensation and other body functions. The field of tissue engineering and regenerative medicine which aims to replace and repair damaged tissues, organs or cells entails for effective methods for fabricating biological scaffolds. Here we present synthesis of fibrous scaffolds by a process called electrospinning that can provide a microenvironment in-vitro for differentiation and proliferation of functional neurons from mesenchymal stem cells. These nanofibrous PCL scaffolds with graphene as filler materials are engineered in such a way so as to provide topological, biochemical as well as electrical cues that can enhance neurite extension and penetration. Poly(ε-caprolactone) (PCL) is a FDA approved synthetic biodegradable polyester extensively used in biomedical applications. Graphene, a single layer carbon crystal, based nanomaterials have recently gained considerable interest for tissue engineering applications including osteogenic, neural and differentiation in other lineages due to their favorable chemical, electrical and mechanical properties. Our final aim is that the functional tissues or organs developed in vitro shall be implanted inside body to rehabilitate the biological function that was lost due to injury, abnormality or loss

Luminescence of Ce3+ in Y2SiO5 nanocrystals: Role of crystal structure and crystal size

Here, we report the role of crystal structure and crystal size on the photoluminescence properties of Ce3+ ions in Y2SiO5 nanocrystals. The emission at 430 nm (5d(1) —> 4f(1)) and lifetime of the excited state of Ce3+ ion doped Y2SiO5 nanocrystals are found to be sensitive to the crystal structure, crystal size, and dopant concentration. It is found that the overall lifetime of 0.5 mol % Ce doped Y2SiO5 nanocrystals are 8.78 and 3.45 ns for 1000 and 1100 degreesC heat-treated samples with the same crystal structure (X-1-Y2SiO5 phase), respectively. However, a significant increase in the overall lifetime (35.21 ns) is observed for the 1300 degreesC annealed 0.5 mol % Ce doped Y2SiO5 sample having a different crystal structure (X-2-Y2SiO5 phase). We found that the decay kinetic is biexponential. It is explained that the fast component arises due to sequential hole-electron capture on the luminescent ions and the slow component arises from isolated ions. Our analysis suggests that modifications of radiative and nonraditive relaxation mechanisms are due to local symmetry structure of the host lattice and crystal size, respectively

Luminescence of Ce<SUP>3+</SUP> in Y<SUB>2</SUB>SiO<SUB>5</SUB> Nanocrystals: Role of Crystal Structure and Crystal Size

Here, we report the role of crystal structure and crystal size on the photoluminescence properties of Ce<SUP>3+</SUP> ions in Y<SUB>2</SUB>SiO<SUB>5</SUB> nanocrystals. The emission at 430 nm (5d<SUP>1</SUP> → 4f<SUP>1</SUP>) and lifetime of the excited state of Ce<SUP>3+</SUP> ion doped Y<SUB>2</SUB>SiO<SUB>5</SUB> nanocrystals are found to be sensitive to the crystal structure, crystal size, and dopant concentration. It is found that the overall lifetime 〈τ〉 of 0.5 mol % Ce doped Y<SUB>2</SUB>SiO<SUB>5</SUB> nanocrystals are 8.78 and 3.45 ns for 1000 and 1100 °C heat-treated samples with the same crystal structure (X<SUB>1</SUB>−Y<SUB>2</SUB>SiO<SUB>5</SUB> phase), respectively. However, a significant increase in the overall lifetime (35.21 ns) is observed for the 1300 °C annealed 0.5 mol % Ce doped Y<SUB>2</SUB>SiO<SUB>5</SUB> sample having a different crystal structure (X<SUB>2</SUB>−Y<SUB>2</SUB>SiO<SUB>5</SUB> phase). We found that the decay kinetic is biexponential. It is explained that the fast component arises due to sequential hole−electron capture on the luminescent ions and the slow component arises from isolated ions. Our analysis suggests that modifications of radiative and nonraditive relaxation mechanisms are due to local symmetry structure of the host lattice and crystal size, respectively

The role of semiconducting hosts on photoluminescence efficiency of Eu-complex

The energy transfer from the semiconducting hosts, poly (N-vinylcarbazole) (PVK), 2-(4-biphenylyl)-5-(4-tert-butylphenyl)-1,3,4-oxadiazole (PBD) and a mixture of PVK and PBD blend matrix to Eu-Complex [Eu (TTA)<SUB>3</SUB>(Phen)] is investigated by steady state and time resolved photoluminescence spectroscopy. The emission intensity of the peak at 612 nm (<SUP>5</SUP>D<SUB>0</SUB> → <SUP>7</SUP>F<SUB>2</SUB>) of Eu-ions and the photoluminescence efficiency are found to be sensitive to the nature of hosts. We found that the luminescence lifetimes of <SUP>5</SUP>D<SUB>0</SUB> → <SUP>7</SUP>F<SUB>2</SUB> transition (612 nm) of Eu-ions are 580, 634 and 427 μs for Eu-complex (0.11 wt%) doped PVK:PBD, PBD and PVK matrix, respectively. The analysis suggests that the energy transfer from PBD to europium complex is most efficient than PVK and PVK:PBD matrix

Influence of nanoenvironment on luminescence of Eu<SUP>3+</SUP> activated SnO<SUB>2</SUB> nanocrystals

The emission intensity of the peak at 612 nm (<SUP>5</SUP>D<SUB>0</SUB>→<SUP>7</SUP>F<SUB>2</SUB>) of the Eu<SUP>3+</SUP> ions activated SnO<SUB>2</SUB> nanocrystals (doped and coated) is found to be sensitive to the nanoenvironment. We have compared the luminescence efficiencies of the nanocrystals of SnO<SUB>2</SUB> doped by Eu<SUB>2</SUB>O<SUB>3</SUB> with those of SnO<SUB>2</SUB> coated by Eu<SUB>2</SUB>O<SUB>3</SUB> and we found that the intensities are significantly higher in coated nanocrystals. Furthermore, it is clear from luminescence intensity measurements that Eu<SUP>3+</SUP> ions occupy low symmetry sites in the Eu<SUB>2</SUB>O<SUB>3</SUB> coated SnO<SUB>2</SUB> nanocrystal. The analysis suggests that the radiative relaxation rate is higher in Eu<SUB>2</SUB>O<SUB>3</SUB> coated SnO<SUB>2</SUB> nanocrystals than Eu<SUB>2</SUB>O<SUB>3</SUB> doped SnO<SUB>2</SUB> nanocrystals due to the asymmetric environment of Eu<SUP>3+</SUP> ions in coated samples

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Feature Weighted Attention—Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images

In remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long Short-Term Memory (BiLSTM) method to reduce the overfitting problem and increase the performance of classification in change detection applications. Additionally, data usage and accuracy in remote sensing activities, particularly CD, can be significantly improved by a large number of training models based on BiLSTM. Normalization techniques are applied to input images in order to enhance the quality and reduce the difference in pixel value. The AlexNet and VGG16 models were used to extract useful features from the normalized images. The extracted features were then applied to the FWA-BiLSTM model, to give more weight to the unique features and increase the efficiency of classification. The attention layer selects the unique features that help to distinguish the changes in the remote sensing images. From the experimental results, it was clearly shown that the proposed FWA-BiLSTM model achieved better performance in terms of precision (93.43%), recall (93.16%), and overall accuracy (99.26%), when compared with the existing Difference-enhancement Dense-attention Convolutional Neural Network (DDCNN) model