Robust unsupervised small area change detection from SAR imagery using deep learning

Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery

Regal electrochemistry: Sensing of the synthetic cathinone class of new psychoactive substances (NPSs)

© 2015 The Royal Society of Chemistry. In this paper the concept of 'Regal Electrochemistry' is expanded towards the electrochemical sensing of Novel Psychoactive Substances (NPSs) namely synthetic cathinone derivatives where British coinage is used as the electrochemical sensor. In this proof-of-concept approach, the electrochemical sensing of mephedrone (4-MMC) and 4′-methyl-N-ethylcathinone (4-MEC) is shown to be possible using a British 1 pence coin for the first time. This novel electrochemical protocol is validated towards the detection of cathinone derivatives in a seized street sample that has been independently analysed via high performance liquid chromatography demonstrating its potential use as a novel electrochemical sensor for NPSs

Insufficient activity of MAPK pathway is a key monitor of Kidney-Yang Deficiency Syndrome.

OBJECTIVE: To explore the genetic characteristics and molecular regulator of Kidney-Yang Deficiency Syndrome (KDS). DESIGN: A typical KDS family was collected using a questionnaire of cold feeling and a 40-item scoring table of KDS based on Traditional Chinese Medicine (TCM), by single-blind method repeated annually over three years. Their transcriptomes were assayed by microarray and validated by RT-PCR and ELISA. Simultaneously, 10 healthy volunteers were recruited as controls and the same protocols were performed. RESULTS: This typical KDS family has 35 members, of whom 11 were evaluated as having severe KDS and 6 as having common KDS. Results of the cDNA microarray revealed that there were 420 genes/expressed sequence tags differentially expressed in KDS transcriptomes, indicating a global functional impairment in the mass-energy-information carrying network of KDS patients, involving energy metabolism, signal transduction, development, cell cycle, and immunity. Pathway analysis by gene set enrichment assay (GSEA) and other tools demonstrated that mitogenic activated protein kinase (MAPK) is among the most insufficiently activated pathways, while the oxidative phosphorylation and glycolysis/gluconeogenesis pathways, the two main pathways relevant to ATP synthesis, were among the most excessively activated pathways in KDS patients. Results of RT-PCR and ELISA confirmed the status of insufficient activity of the MAPK pathway. CONCLUSION: KDS patients undergo overall attenuated functions in the mass-energy-information carrying network. The marked low level of energy output in KDS may be primarily attributed to the insufficient activity of the MAPK pathway, which may be a key monitor for the abnormal energy metabolism and other impaired activities in KDS.published_or_final_versio

Regal electrochemistry: British 5 pence coins provide useful metallic macroelectrode substrates

The utilisation of British Currency (GBP) as an electrode substrate is demonstrated for the first time. Termed Regal electrochemistry, a 5 pence (5p) coin (GBP) is electrically wired using a bespoke electrochemical cell and is electrochemically characterised using the outer-sphere redox probe hexaammineruthenium(III) chloride. The electroanalytical utility of the 5p coin electrode is demonstrated towards the novel, proof-of-concept sensing of lead(II) ions using square-wave voltammetry in model buffer solutions over the linear range 5-2000 nM exhibiting a limit of detection (3σ) of 1.97 nM. Interestingly, the actual cost of the electrode is 2.5 pence (GBP) since both sides of the coins can be utilised and provide a cheap yet reproducible and disposable metallic electrode substrate that is electrochemically useful

Water sorption studies with mesoporous multivariate monoliths based on UiO-66

Hierarchical linker thermolysis has been used to enhance the porosity of monolithic UiO-66-based metal–organic frameworks (MOFs) containing 30 wt% 2-aminoterephthalic acid (BDC-NH2) linker. In this multivariate (i.e. mixed-linker) MOF, the thermolabile BDC-NH2 linker decomposed at ∼350 °C, inducing mesopore formation. The nitrogen sorption of these monolithic MOFs was probed, and an increase in gas uptake of more than 200 cm3 g−1 was observed after activation by heating, together with an increase in pore volume and mean pore width, indicating the creation of mesopores. Water sorption studies were conducted on these monoliths to explore their performance in that context. Before heating, monoUiO-66-NH2-30%-B showed maximum water vapour uptake of 61.0 wt%, which exceeded that reported for either parent monolith, while the highly mesoporous monolith (monoUiO-66-NH2-30%-A) had a lower maximum water vapour uptake of 36.2 wt%. This work extends the idea of hierarchical linker thermolysis, which has been applied to powder MOFs, to monolithic MOFs for the first time and supports the theory that it can enhance pore sizes in these materials. It also demonstrates the importance of hydrophilic functional groups (in this case, NH2) for improving water uptake in materials

Turnip mosaic potyvirus probably first spread to Eurasian brassica crops from wild orchids about 1000 years ago

Turnip mosaic potyvirus (TuMV) is probably the most widespread and damaging virus that infects cultivated brassicas worldwide. Previous work has indicated that the virus originated in western Eurasia, with all of its closest relatives being viruses of monocotyledonous plants. Here we report that we have identified a sister lineage of TuMV-like potyviruses (TuMV-OM) from European orchids. The isolates of TuMV-OM form a monophyletic sister lineage to the brassica-infecting TuMVs (TuMV-BIs), and are nested within a clade of monocotyledon-infecting viruses. Extensive host-range tests showed that all of the TuMV-OMs are biologically similar to, but distinct from, TuMV-BIs and do not readily infect brassicas. We conclude that it is more likely that TuMV evolved from a TuMV-OM-like ancestor than the reverse. We did Bayesian coalescent analyses using a combination of novel and published sequence data from four TuMV genes [helper component-proteinase protein (HC-Pro), protein 3(P3), nuclear inclusion b protein (NIb), and coat protein (CP)]. Three genes (HC-Pro, P3, and NIb), but not the CP gene, gave results indicating that the TuMV-BI viruses diverged from TuMV-OMs around 1000 years ago. Only 150 years later, the four lineages of the present global population of TuMV-BIs diverged from one another. These dates are congruent with historical records of the spread of agriculture in Western Europe. From about 1200 years ago, there was a warming of the climate, and agriculture and the human population of the region greatly increased. Farming replaced woodlands, fostering viruses and aphid vectors that could invade the crops, which included several brassica cultivars and weeds. Later, starting 500 years ago, inter-continental maritime trade probably spread the TuMV-BIs to the remainder of the world

T helper cell subsets specific for pseudomonas aeruginosa in healthy individuals and patients with cystic fibrosis

Background: We set out to determine the magnitude of antigen-specific memory T helper cell responses to Pseudomonas aeruginosa in healthy humans and patients with cystic fibrosis. Methods: Peripheral blood human memory CD4+ T cells were co-cultured with dendritic cells that had been infected with different strains of Pseudomonas aeruginosa. The T helper response was determined by measuring proliferation, immunoassay of cytokine output, and immunostaining of intracellular cytokines. Results: Healthy individuals and patients with cystic fibrosis had robust antigen-specific memory CD4+ T cell responses to Pseudomonas aeruginosa that not only contained a Th1 and Th17 component but also Th22 cells. In contrast to previous descriptions of human Th22 cells, these Pseudomonal-specific Th22 cells lacked the skin homing markers CCR4 or CCR10, although were CCR6+. Healthy individuals and patients with cystic fibrosis had similar levels of Th22 cells, but the patient group had significantly fewer Th17 cells in peripheral blood. Conclusions: Th22 cells specific to Pseudomonas aeruginosa are induced in both healthy individuals and patients with cystic fibrosis. Along with Th17 cells, they may play an important role in the pulmonary response to this microbe in patients with cystic fibrosis and other conditions