Synchronization of Fractional Order Neurons in Presence of Noise.

The firing rate of some biological neurons such as neocortical pyramidal neurons is consistent with fractional order derivative, and the fractional-order neuron models depict the firing rate of neurons more accurately than other integer order neuron models do. For this reason, first, the dynamical characteristics of fractional order Hindmarsh Rose (HR) neuron are investigated, here and then a two coupled neuronal system based on Hindmarsh Rose neuron is presented. The results show several differences in the dynamical cha.racteristics of integer order and fractional order Hindmarsh Rose neuron model. The integer order model shows only one type of firing characteristics when the parameter of the model remained the same. The fractional-order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied with the same parameter values. The firing frequency increases as the order of the fractional operator decreases. The fractional-order is therefore key in determining the firing characteristics of biological neuron models. A linearized model of HR neuron is also given for hardware resource minimizations and to implement this neuronal network on a large scale. A synchronized system of two fractional-order fractional Hindmarsh-Rose (HR) neurons in the presence of noise is also presented. The dynamical characteristics of the modified coupled neuron are determined by the parameters of the neuron model and the coupling function. The robustness of the network in the presence of noise is verified by both amplitude and phase synchronization techniques. A simplification of the coupling function is also presented to reduce the hardware cost. The synchronization results show that the model can produce the desired behavior with acceptable error.

Anomalous nucleation of crystals within amorphous germanium nanowires during thermal annealing.

In this work, germanium nanowires rendered fully amorphous via xenon ion irradiation have been annealed within a transmission electron microscope to induce crystallization. During annealing crystallites appeared in some nanowires whilst others remained fully amorphous. Remarkably, even when nucleation occurred, large sections of the nanowires remained amorphous even though the few crystallites embedded in the amorphous phase were formed at a minimum of 200 °C above the temperature for epitaxial growth and 100 °C above the temperature for random nucleation and growth in bulk germanium. Furthermore, the presence of crystallites was observed to depend on the diameter of the nanowire. Indeed, the formation of crystallites occurred at a higher annealing temperature in thin nanowires compared with thicker ones. Additionally, nanowires with a diameter above 55 nm were made entirely crystalline when the annealing was performed at the temperature normally required for crystallization in germanium (i.e. 500 °C). It is proposed that oxygen atoms hinder both the formation and the growth of crystallites. Furthermore, as crystallites must reach a minimum size to survive and grow within the amorphous nanowires, the instability of crystallites may also play a limited role for the thinnest nanowires.

Weighted dimensionality reduction and robust Gaussian mixture model based cancer patient subtyping from gene expression data.

BACKGROUND: The heterogeneous nature of cancer necessitates subtyping of cancer patients into distinct and well separated subgroups. However, computational issues arise because gene expression data is noisy and contains outliers apart from being high dimensional. As such, an attempt to subtype cancer patients from gene expression data leads to highly overlapping Kaplan-Meier (KM) survival plots and thus clear distinction among the discovered subtypes becomes difficult. Here we attempt to achieve a greater separation among the subtypes through a robust clustering pipeline. METHODS: We propose a robust framework to achieve a better separation among the discovered subtypes. Our framework is based on dimensionality reduction of a weighted gene expression matrix using t-distributed Stochastic Neighbor Embedding (t-SNE) and a robust Gaussian mixture model based clustering approach. Every gene is weighted according to the median absolute deviation (MAD) of the gene before dimensionality reduction. The results are quantified by measuring the minimum pairwise separation among the KM plots and minimum hazard ratio among the subtypes. We also introduce a novel method, called cumulative survival separation, to quantify the separation among the discovered subtypes. RESULTS: To validate the proposed methodology we obtained five cancer gene expression datasets from The Cancer Genome Atlas (TCGA) and comparisons with Consensus Clustering (CC), Consensus non-negative matrix factorization (CNMF), fast density-aware spectral clustering (Spectrum) and Neighborhood based Multi-Omics clustering (NEMO) methodologies show that the proposed method is able to achieve a greater separation compared to the aforementioned methods in literature. For instance, the minimum pairwise life expectancy difference (in days) between the discovered subtypes for GBM is 61 days for the proposed methodology with MAD scores, whereas it is approximately 33, 19, 49 and 33 days only for CC, Spectrum, Nemo and CNMF respectively. Comparisons are also shown for the proposed framework with and without using the MAD scores and it is observed that MAD score significantly improves the subtype separation. Hazard ratio analysis also shows that the proposed methodology performs better. Furthermore, pathway over-representation analyses were carried to identify relevant genetic pathways which can be possible targets for treatment. CONCLUSION: The results suggest that the use of median absolute deviation and a robust clustering methodology are helpful in achieving greater separation among the subtypes with better statistical and clinical significance.

Effect of aluminium concentration on phase formation and radiation stability of Cr2Al x C thin film.

Near-stoichiometric and under-stoichiometric Cr2Al x C (x = 0.9 and 0.75) amorphous compositions were deposited onto a silicon substrate at 330 K in a layer-by-layer fashion using magnetron sputtering from elemental targets. The film thickness was found to be 0.9 µm and 1.2 µm for the near- and under-stoichiometric compositions respectively. A transmission electron microscope (TEM) heating holder was used to heat thin sample lamellae prepared using focused ion beam milling. Near-stoichiometric Cr2AlC thin films consisted of nano MAX phase after crystallization at 873 K. Under-stoichiometric Cr2Al x C (x = 0.75) thin films contained MAX phase along with nanocrystalline chromium aluminides after crystallization at 973 K. Irradiations with 320 keV xenon ions was performed at 623 K using a TEM with an in-situ ion irradiation (MIAMI) facility. Nanocrystalline films of near-stoichiometric Cr2AlC irradiated up to 83 displacements per atom (dpa) showed no observable changes. Also, irradiation of under-stoichiometric nanocrystalline thin films up to 138 dpa did not show any observable amorphization, and recrystallization was observed. This radiation resistance of near- and under-stoichiometric thin films is attributed to the known self-healing property of Cr2Al x C compositions further enhanced by nanocrystallinity.

A topological approach for cancer subtyping from gene expression data.

BACKGROUND: Gene expression data contains key information which can be used for subtyping cancer patients. However, computational methods suffer from 'curse of dimensionality' due to very high dimensionality of omics data and therefore are not able to clearly distinguish between the discovered subtypes in terms of separation of survival plots. METHODS: To address this we propose a framework based on Topological Mapper algorithm. The novelty of this work is that we suggest a method for defining the filter function on which the mapper algorithm heavily depends. Survival analysis of the discovered cancer subtypes is carried out and evaluated in terms of minimum pairwise separation between the Kaplan-Meier plots. Furthermore, we present a method to measure the separation between the discovered subtypes based on hazard ratios. RESULTS: Five cancer genomics datasets obtained from The Cancer Genome Atlas portal have been used for comparisons with Robust Sparse Correlation-Otrimle (RSC-Otrimle) algorithm and Similarity Network Fusion(SNF). Comparisons show that the minimum pairwise life expectancy difference (in days) between the discovered subtypes for lung, colon, breast, glioblastoma and kidney cancers is 107, 204, 20, 88 and 425 days, respectively, for the proposed methodology whereas it is only 69, 43, 6, 61 and 282 days for RSC-Otrimle and 9, 95, 18, 60 and 148 days for SNF. Hazard ratio analysis also shows that the proposed methodology performs better in four of the five datasets. A visual inspection of Kaplan-Meier plots reveals that the proposed methodology achieves lesser overlap in Kaplan-Meier plots especially for lung, breast and kidney cases. Furthermore, relevant genetic pathways for each subtype have been obtained and pathways which can be possible targets for treatment have been discussed. CONCLUSION: The significance of this work lies in individualized understanding of cancer from patient to patient which is the backbone of Precision Medicine.

Enhanced Radiation Tolerance of Tungsten Nanoparticles to He Ion Irradiation.

Materials exposed to plasmas in magnetic confinement nuclear reactors will accumulate radiation-induced defects and energetically implanted gas atoms (from the plasma and transmutations), of which insoluble helium (He) is likely to be the most problematic. The large surface-area-to-volume ratio exhibited by nanoporous materials provides an unsaturable sink with the potential to continuously remove both point defects and He. This property enhances the possibilities for these materials to be tailored for high radiation-damage resistance. In order to explore the potential effect of this on the individual ligaments of nanoporous materials, we present results on the response of tungsten (W) nanoparticles (NPs) to 15 keV He ion irradiation. Tungsten foils and various sizes of NPs were ion irradiated concurrently and imaged in-situ via transmission electron microscopy at 750 °C. Helium bubbles were not observed in NPs with diameters less than 20 nm but did form in larger NPs and the foils. No dislocation loops or black spot damage were observed in any NPs up to 100 nm in diameter but were found to accumulate in the W foils. These results indicate that a nanoporous material, particularly one made up of ligaments with characteristic dimensions of 30 nm or less, is likely to exhibit significant resistance to He accumulation and structural damage and, therefore, be highly tolerant to radiation.

Effects of Starvation and Thermal Stress on the Thermal Tolerance of Silkworm, Bombyx mori: Existence of Trade-offs and Cross-Tolerances.

Organisms, in nature, are often subjected to multiple stressors, both biotic and abiotic. Temperature and starvation are among the main stressors experienced by organisms in their developmental cycle and the responses to these stressors may share signaling pathways, which affects the way these responses are manifested. Temperature is a major factor governing the performance of ectothermic organisms in ecosystems worldwide and, therefore, the thermal tolerance is a central issue in the thermobiology of these organisms. Here, we investigated the effects of starvation as well as mild heat and cold shocks on the thermal tolerance of the larvae of silkworm, Bombyx mori (Linnaeus). Starvation acted as a meaningful or positive stressor as it improved cold tolerance, measured as chill coma recovery time (CCRT), but, at the same time, it acted as a negative stressor and impaired the heat tolerance, measured as heat knockdown time (HKT). In the case of heat tolerance, starvation negated the positive effects of both mild cold as well as mild heat shocks and thus indicated the existence of trade-off between these stressors. Both mild heat and cold shocks improved the thermal tolerance, but the effects were more prominent when the indices were measured in response to a stressor of same type, i.e., a mild cold shock improved the cold tolerance more than the heat tolerance and vice versa. This improvement in thermal tolerance by both mild heat as well as cold shocks indicated the possibility of cross-tolerance between these stressors.

Fuzzy entropy based interactive enhancement of radiographic images.

In certain radiographic images, fuzziness exists due to the vague nature of image characteristics and limitations of visual perception. Accordingly, a gap exists between the information content of an image and the information that can be retrieved. In this paper, a new fuzzy logic based intensification operator has been proposed for enhancement of images. For applying the operator effectively, selection of crossover point is critical. In this paper, the concept of fuzzy entropy has been proposed for objective selection of crossover point. Owing to semantic nature of information content, the methodology has a provision to change levels of enhancement interactively, to help in retrieving the information as required. The effectiveness of the methodology has been demonstrated. Comparison of the results with the Zadeh's INT operator and conventional histogram equalization techniques has established its superiority.

Description of shapes in CT images. The usefulness of time-series modeling techniques for identifying organs.

An attempt has been made to investigate the usefulness of AR and CAR modeling techniques for shape description of substructures in CT images. The efficacy of the models have been checked using two organ shapes (liver and kidney) obtained from abdominal CT sections at nearly identical locations. The contours of the substructures representing the shapes, along with the sequence of coordinates, have been obtained by using edge detection followed by an edge-linking technique, where maximum and minimum lengths of the edge segments could be varied. The modeling techniques thus applied have resulted in feature vectors of the organ shapes corresponding to these models. Validity of the models has been assessed on the basis of lower values and consistency of Euclidean distances within a class, as well as the differences of these values with other classes.

A new fuzzy logic based image enhancement.

In this paper a new fuzzifier (fh) and a new contrast intensification operator (NINT) have been proposed for the enhancement of computerized tomography (CT) images. The performance of the proposed technique has been compared with an existing fuzzy logic based enhancement technique in terms of a new fuzzy contrast measure. The superiority of the proposed technique has been established.

Computed tomography image enhancement using the concept of fuzzy logic.

Diagnostic features in computed tomography (CT) images vary widely due to the vague nature (fuzziness) of functional characteristics in organ pathologies. Classical image enhancement techniques cannot adapt to the characteristics of this nature. A method based on fuzzy logic is given in this paper. In this method the image of interest is transformed into a fuzzy plane using fuzzifiers which can be changed to select a crossover point. A contrast intensification operator (INT) is then applied. The operator increases the grade of membership of those values which lie above the crossover point and decreases the same for those pixels which lie below it, thereby increasing the contrast and reducing the fuzziness. Quantitative measures of fuzziness which reflect a sort of quantitative measure of image quality have also been studied. Using digitized CT images it has been shown that at early stages of a disease, when the contrast of the pathological tissues is very low, the visibility of the disease could be considerably improved using this technique.

Texture analysis of CT-images for early detection of liver malignancy.

In certain medical images e.g., ultrasound it has been seen that the texture conveys useful diagnostic information but in CT and MR images its proper use has not been established. Present study is an attempt to investigate the use of texture analysis for early detection of liver malignancy when the onset of disease is beyond human perception, using CT-images. Using grey level run length as a primitive, five parameters viz., Short Run Emphasis (SRE), Long Run Emphasis (LRE), Grey Level Distribution (GLD), Run Length Distribution (RLD) and Run Percentage (RP) have been studied for this purpose. It has been found that the GLD feature, obtained using Grey Level Run Length Method (GLRLM) conveys useful information about the onset of this disease with a confidence level of above 99 percent. The results have been confirmed on the basis of clinical studies.

Identification of substructures in CT-images.

Identification of substructures is one of the important issues in automated and computer assisted systems used for interpretation of radiographic images because of vague definition of organ shape. In this paper an Autoregressive model has been proposed for shape description of substructures(organs) obtained in the form of outer boundaries from the abdominal CT-images. The results of matching show the effectiveness of the proposed model in identifying substructures from the images.