Classical Target Research Articles

Ship detection and tracking method for satellite video based on multiscale saliency and surrounding contrast analysis

In port surveillance, monitoring based on satellite video is a valuable supplement to a ground monitoring system because of its wide monitoring range. Therefore, automatic ship detection and tracking based on satellite video is an important research field. However, because of the small size of ships without texture and the interference of sea noise, it is also a challenging subject. An approach of automatic detection and tracking moving ships of different sizes using satellite video is presented. First, motion compensation between two frames is realized. Then, saliency maps of multiscale differential image are combined to create dynamic multiscale saliency map (DMSM), which is more suitable for the detection of ships of different sizes. Third, candidate motion regions are segmented from DMSM, and moving ships can be detected after the false alarms are removed based on the surrounding contrast. Fourth, important elements such as centroid distance, area ratio, and histogram distance from moving ships are used to perform ship matching. Finally, ship association and tracking are realized by using the intermediate frame in every three adjacent frames. Experimental results on satellite sequences indicate that our method can effectively detect and track ships and obtain the target track, which is superior in terms of the defined recall and precision compared with other classical target tracking methods.

Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection

Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of “cascaded detection”, “random averaging” and “multi-scale scanning” are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.

Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees

Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5–98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.

Maximal representations of cocompact complex hyperbolic lattices, a uniform approach

We complete the classification of maximal representations of cocompact complex hyperbolic lattices in Hermitian Lie groups by dealing with the exceptional groups E 6(-14) and E 7(-25) . We prove that if ρ is a maximal representation of a cocompact complex hyperbolic lattice Γ⊂SU(1,n), n>1, in an exceptional Hermitian group G ℝ , then n=2 and G ℝ =E 6(-14) , and we describe completely the representation ρ. The case of classical Hermitian target groups was treated by Vincent Koziarz and the second named author [KM17]. However we do not focus immediately on the exceptional cases and instead we provide a more unified perspective, as independent as possible of the classification of the simple Hermitian Lie groups. This relies on the study of the cominuscule representation of the complexification G of the target group G ℝ . As a by-product of our methods, when the target Hermitian group G ℝ has tube type, we obtain an inequality on the Toledo invariant of the representation ρ:Γ→G ℝ which is stronger than the Milnor-Wood inequality (thereby excluding maximal representations in such groups).

SAT-204 Silencing of Maternal Hepatic Glucocorticoid Receptor Is Essential for Normal Fetal Development

Excessive or chronic stress can lead to a variety of diseases due to aberrant activation of the glucocorticoid receptor (GR), a ligand activated transcription factor. Pregnancy represents a particular window of sensitivity in which excessive stress can have adverse outcomes, particularly on the developing fetus. Models of maternal stress have historically relied on the use of psychosocial stressors or chronic administration of glucocorticoids leading to systemic increases in glucocorticoids, making it near impossible to understand the tissue-specific effects of stress during pregnancy in contributing to maternal and fetal diseases. We have previously demonstrated an antagonistic relationship between reproductive axis activity and hepatic glucocorticoid sensitivity in a mouse model of menopause. However, if stress/sex hormone crosstalk exists in the liver during pregnancy, and if so, what the physiological purpose it serves is completely unknown. We have discovered hepatic stress hormone responsiveness is diminished during pregnancy. Utilizing whole transcriptome array we profiled the maternal hepatic transcriptome and found a substantial overlap between the pregnancy-regulated transcriptome and glucocorticoid regulated transcriptome. Of the overlapping genes and pathways, we observed the direction of regulation to be largely opposite between the pregnancy-regulated transcriptome and stress hormone regulated transcriptome. Furthermore, we observed reduced basal expression of classical GR target genes in the pregnant liver and impaired induction of GR target genes in response to dexamethasone treatment in the liver of pregnant versus virgin mice. The reduced glucocorticoid sensitivity in the liver during pregnancy is due to impaired expression of GR, which is mediated via epigenetic silencing. Provocatively, we found reinstallation of GR to hepatocytes during pregnancy via adeno-associated viral transduction dysregulates genes involved in proliferation, ultimately culminating in impaired pregnancy-induced hepatomegaly. Furthermore. disruption of the maternal hepatic adaptation to pregnancy by GR reinstallation results in impaired in utero growth of the fetus. These data demonstrate the tissue-specific effects of stress hormone signaling in the maternal compartment is regulated in a dynamic fashion to ultimately support the healthy development of embryos.

Target Dictionary Construction-Based Sparse Representation Hyperspectral Target Detection Methods

Hyperspectral imagery (HSI) with a high spectral resolution contains hundreds and even thousands of spectral bands, and conveys abundant spectral information, which provides a unique advantage for target detection. A number of classical target detectors have been proposed based on the linear mixing model (LMM) and sparsity-based model. Compared with the LMM, sparsity-based detectors present a better performance on dealing with the spectral variability. Despite the great success of the sparsity-based model in recent years, one problem with all state-of-the-art sparsity-based models still exist: the target dictionary is formed via the target training samples that are selected from the global image scene. This is an improper way to construct target dictionary for hyperspectral target detection since the priori information is usually a given target spectrum obtained from a spectral library. Besides, target training samples selected from the global image scene are usually insufficient, which results in the problem that the target training samples and background training samples are unbalanced in the data volume, causing a deteriorated detection model. To tackle these problems, this paper constructs a target dictionary construction-based method, then proposes the constructed target dictionary-based sparsity-based target detection model and the constructed target dictionary-based sparse representation-based binary hypothesis model, which are called TDC-STD and TDC-SRBBH, respectively. Both of the proposed algorithms only need a given target spectrum as the input priori information. By using the given target spectrum for pre-detection via constrained energy minimization, we choose the pixels that have large output values as target training samples to construct the target dictionary. The proposed algorithms were tested on three benchmark HSI datasets and the experimental results show that the proposed algorithms demonstrate outstanding detection performances when compared with other state-of-the-art detectors.

Aspergillus flavus squalene synthase as an antifungal target: Expression, activity, and inhibition

Invasive aspergillosis (IA) is a life-threatening disease impacting immunocompromised individuals. Standard treatments of IA, including polyenes and azoles, suffer from high toxicity and emerging resistance, leading to the need to develop new antifungal agents with novel mechanisms of action. Ergosterol biosynthesis is a classic target for antifungals, and squalene synthase (SQS) catalyzes the first committed step in ergosterol biosynthesis in Aspergillus spp. making SQS of interest in the context of antifungal development. Here, we cloned, expressed, purified and characterized SQS from the pathogen Aspergillus flavus (AfSQS), confirming that it produced squalene. To identify potential leads targeting AfSQS, we tested known squalene synthase inhibitors, zaragozic acid and the phosphonosulfonate BPH-652, finding that they were potent inhibitors. We then screened a library of 744 compounds from the National Cancer Institute (NCI) Diversity Set V for inhibition activity. 20 hits were identified and IC50 values were determined using dose-response curves. 14 compounds that interfered with the assay were excluded and the remaining 6 compounds were analyzed for drug-likeness, resulting in one compound, celastrol, which had an AfSQS IC50 value of 830 nM. Enzyme inhibition kinetics revealed that celastrol binds to AfSQS in a noncompetitive manner, but did not bind covalently. Since celastrol is also known to inhibit growth of the highly virulent Aspergillus fumigatus by inhibiting flavin-dependent monooxygenase siderophore A (SidA, under iron starvation conditions), it may be a promising multi-target lead for antifungal development.

Chemical Screening Approaches Enabling Drug Discovery of Autophagy Modulators for Biomedical Applications in Human Diseases.

Autophagy is an intracellular degradation pathway for malfunctioning aggregation-prone proteins, damaged organelles, unwanted macromolecules and invading pathogens. This process is essential for maintaining cellular and tissue homeostasis that contribute to organismal survival. Autophagy dysfunction has been implicated in the pathogenesis of diverse human diseases, and therefore, therapeutic exploitation of autophagy is of potential biomedical relevance. A number of chemical screening approaches have been established for the drug discovery of autophagy modulators based on the perturbations of autophagy reporters or the clearance of autophagy substrates. These readouts can be detected by fluorescence and high-content microscopy, flow cytometry, microplate reader and immunoblotting, and the assays have evolved to enable high-throughput screening and measurement of autophagic flux. Several pharmacological modulators of autophagy have been identified that act either via the classical mechanistic target of rapamycin (mTOR) pathway or independently of mTOR. Many of these autophagy modulators have been demonstrated to exert beneficial effects in transgenic models of neurodegenerative disorders, cancer, infectious diseases, liver diseases, myopathies as well as in lifespan extension. This review describes the commonly used chemical screening approaches in mammalian cells and the key autophagy modulators identified through these methods, and highlights the therapeutic benefits of these compounds in specific disease contexts.

Probing Quantum Dynamical Couple Correlations with Time-Domain Interferometry.

Time-domain interferometry (TDI) is a promising method to characterize spatial and temporal correlations at x-ray energies, via the so-called intermediate scattering function and the related dynamical couple correlations. However, so far, it has only been analyzed for classical target systems. Here, we provide a quantum analysis, and suggest a scheme that allows us to access quantum dynamical correlations. We further show how TDI can be used to exclude classical models for the target dynamics, and illustrate our results using a single particle in a double well potential.

A cuckoo search algorithm: Effects of coevolution and application in the development of distributed layouts

This article presents a cuckoo search algorithm, via Lévy flight, considering the effects of coevolution on the displacement dynamics of animal communities, and applies this algorithm to the development of maximally distributed physical arrangements. Some variables are introduced such as egg weight and the acquired learning of host birds. The results show that the proposed algorithm prevails, in the majority of the interviewed instances, in the degree of distribution in comparison with the classic TARGET and ALVO methods, and with the Genetic Algorithm. The proposed algorithm was shown to be able to obtain a low degree of distribution in a satisfactory computational time, especially in large problems.

Non-Genomic Effects of Aldosterone.

Encouraging changes in the steroid hormone receptor field from initially questioning the role of non-genomic actions of steroid hormones to acceptance of the concept that the acute, membrane-centric actions are linked and/or regulate the nuclear actions. The focus of this chapter is how the non-genomic effects are linked to the longer lasting, genomic actions of aldosterone. By non-genomic we refer to the rapid actions that occur within minutes do not require transcription or translation and occur in both classical MR target organs (kidney and colon) and non-epithelial tissues (blood vessels, heart, and adipose). The mechanism of rapid non-genomic actions of aldosterone varies between tissues. As a result, this chapter is viewed through the lens of how the non-genomic and genomic actions of aldosterone are linked in cardiovascular disease. Specifically, regulation of sodium flux in the myocardium has an important role in pathogenesis of cardiac arrhythmia. Since there are now recognized gender differences in cardiovascular disease, we also include preliminary studies to investigate the interaction of sex steroid hormones with the ligand binding pocket of the mineralocorticoid receptor. Overall, we aim to showcase how the non-genomic effects of aldosterone potentially modulate the genomic effects and represent additional targets for intervention.

Multi-Target Detection and Adaptive Waveform Design for Cognitive MIMO Radar

Multi-target detection is the main function of radar. Cognitive radar, which can adaptively investigate the radar scene and determine the next action based on previous measurements, has better performance. In this paper, a multi-target detection method and an adaptive waveform design algorithm for cognitive MIMO radar are proposed. In this method, the multi-target detection is modeled as a multi-hypothesis testing. The multi-hypothesis testing is investigated according to sequentially received data. Along with the multi-target detection method, an adaptive waveform design algorithm based on information theory is proposed to improve the efficiency of the multi-hypothesis testing. We adopt semi-definite relaxation technique and semi-definite programming to tackle the nonconvex design problem. Numerical examples demonstrate that the proposed multi-target detection method has better performance than the classical target detection method, and the proposed adaptive waveform design algorithm can significantly improve the performance of the proposed multi-target detection method.

Graft-Vs-Host Reactivity Against the Bone Marrow Is Directed Against the Hematopoietic and Non-Hematopoietic Compartments in Mice

Hematopoietic dysfunction is frequently observed following allogeneic hematopoietic cell transplantation (allo-HCT) and contributes to substantial morbidity. Various factors, e.g. viral infections, poor graft-function, and certain drugs can cause cytopenias, but usually, graft-vs-host disease (GVHD) of the bone marrow (BM) is not taken into account as a major cause of hematopoietic dysfunction, particularly when no other signs of GVHD in classical target organs are present.Here, we studied in an MHC-matched, minor antigen mismatched HCT model (C3H.SWàC57BL/6 (B6)) the effects of lethal irradiation and allo-HCT of HSC (cKIT+Sca1+Lin-) alone or in combination with T cells (TC) on the hematopoietic compartment (HC) and non-hematopoietic compartment (NHC) of the BM. FACS analysis was used to assess relative and absolute cell counts of hematopoietic and non-hematopoietic populations. Confocal microscopy was used to establish 3D images of structural integrity vs. disruption, and qRT-PCR was used to examine functional alterations of gene expression within NHC populations.In this HCT model mice did not develop overt signs of acute GVHD. In recipients of HSC+TC recovery of hemoglobin (Hb) and neutrophils (Nph) was significantly delayed beyond d+56, while Hb and Nph were normalized by d+21 in recipients of pure HSC. Recovery of BM cell counts was also faster in HSC recipients, whereas addition of TC to the graft delayed reconstitution of total BM counts, BM B cells and BM neutrophils significantly. Not only the HC was affected by alloreactive TC, but also non-hematopoietic populations, including CXCL12-abundant reticular (CAR) cells (CD45-Ter119-CD31-CD140b+) and endothelial cells (EC; CD45-Ter119-CD31+) declined post-HCT, and showed a significantly slower recovery in mice given allogeneic TC within their HCT. 3D-confocal microscopy confirmed the alloreactive damage to the NHC, as imaging revealed rapid recovery of CAR cells in B6.CXCL12-GFP recipients of pure HSC, whereas regeneration of CAR cells was impaired in mice given HSC+TC. Likewise, the extracellular matrix and sinusoidal vascular structures recovered promptly in the HSC group, whereas HSC+TC recipients displayed severe disruption of the structural integrity with impaired recovery of the BM microvessels. Increased microvascular permeability was shown in all transplanted (and irradiated) mice on d+7 post-HCT by Evans blue dye extravasation assay. Recovery from leakiness was faster in mice transplanted with HSC alone compared with those given alloreactive TC. Of note, confocal microscopy revealed that in recipients of alloreactive TC hematopoietic cell content was replaced by an abundance of adipocytes for a prolonged period of time (Figure 1A). To further elucidate functional alterations of the NHC in the context of HCT with/without TC we FACS-isolated CAR cells and EC from HSC and HSC+TC recipients on d+14 and d+28 post-HCT and performed qRT-PCR of candidate genes in these populations. In HSC+TC recipients both CAR cells and EC showed significantly lower relative expression levels of SCF (stem cell factor, ligand to cKIT), CXCL12 (C-X-C-motif chemokine 12, = SDF-1, a cytokine with various functions in hematopoiesis and lymphopoiesis), and IL-7 (a growth factor that stimulates the differentiation of pluripotent HSC into lymphoid progenitors) compared with mice transplanted with HSC only. Rather, in CAR cells of HSC+TC recipients adiponectin and PPARγ (peroxisome proliferator-activated receptor γ, stimulates lipid uptake and adipogenesis) were significantly higher compared with HSC only recipients (Figure 1B). These findings suggest that alloreactive TC promote adipogenesis while at the same time suppressing hematopoiesis via deprivation of essential cytokines and growth factors required for HSC function and maturation.Delayed hematopoietic regeneration post allo-HCT represents a major problem in clinical patient care. GVH reactions of the BM have been poorly characterized. Our experiments help to further characterize “marrow GVHD” and its impact on hematopoietic output. We illustrate the damage that alloreactive TC can have on both hematopoiesis directly but also on the nonhematopoietic compartment and its critical functions in stimulating and supporting hematopoietic regeneration. DisclosuresNo relevant conflicts of interest to declare.

Painless Nerve Growth Factor: A TrkA biased agonist mediating a broad neuroprotection via its actions on microglia cells.

Nerve Growth Factor (NGF) is a therapeutic candidate for Alzheimer's disease, based on its well known actions on basal forebrain cholinergic neurons. However, because of its pro-nociceptive activity, in current clinical trials NGF has to be administered intraparenchymally into the brain by neurosurgery via cell or gene therapy approaches. To prevent the NGF pain-inducing collateral effects, thus avoiding the necessity for local brain injection, we developed painless NGF (hNGFp), based on the human genetic disease Hereditary Sensory and Autonomic Neuropathy type V (HSAN V). hNGFp has similar neurotrophic activity as wild type human NGF, but its pain sensitizing activity is tenfold lower. Pharmacologically, hNGFp is a biased receptor agonist of NGF TrkA receptor. The results of recent studies shed new light on the neuroprotective mechanism by hNGFp and are highly relevant for the planning of NGF-based clinical trials. The intraparenchymal delivery of hNGFp, as used in clinical trials, was simulated in the 5xFAD mouse model and found to be inefficacious in reducing Aβ plaque load. On the contrary, the same dose of hNGFp administered intranasally, which was rather widely biodistributed in the brain and did not induce pain sensitization, blocked APP processing into amyloid and restored synaptic plasticity and memory in this aggressive neurodegeneration model. This potent and broad neuroprotection by hNGFp was found to be mediated by hNGFp actions on glial cells. hNGFp increases inflammatory proteins such as the soluble TNFα receptor II and the chemokine CXCL12. Independent work has shown that NGF has a potent anti-inflammatory action on microglia and steers them towards a neuroprotective phenotype. These studies demonstrate that microglia cells are a new target cell of NGF in the brain and have therapeutic significance: i) they establish that the neuroprotective actions of hNGFp relies on a widespread exposure of the brain, ii) they identify a new anti-neurodegenerative pathway, linking hNGFp to inflammatory chemokines and cytokines via microglia, a common target for new therapeutic opportunities for neurodegenerative diseases, iii) they extend the neuroprotective potential of hNGFp beyond its classical cholinergic target, thereby widening the range of neurological diseases for which this neurotrophic factor might be used therapeutically, iv) they help interpreting the results of current NGF clinical trials in AD and the design of future trials with this new potent therapeutic candidate.

Asteroseismic age estimates of RGB stars in open clusters

Context.Open clusters (OCs) provide a classical target to calibrate the age scale and other stellar parameters. Despite their wide use, some issues remain to be explored in detail.Aims.We performed a theoretical investigation focused on the age estimate of red giant branch (RGB) stars in OCs based on mixed classical surface (Teffand [Fe/H]) and asteroseismic (Δνandνmax) parameters. We aimed to evaluate the performances of three widely adopted fitting procedures, that is, a pure geometrical fit, a maximum likelihood approach, and a single stars fit, in recovering stellar parameters.Methods.A dense grid of stellar models was computed, covering different chemical compositions and different values of the mixing-length parameter. Artificial OCs were generated from these data by means of a Monte Carlo procedure for two different ages (7.5 and 9.0 Gyr) and two different choices of the number of stars in the RGB evolutionary phase (35 and 80). The cluster age and other fundamental parameters were then recovered by means of the three methods previously mentioned. A Monte Carlo Markov chain approach was adopted for estimating the posterior densities of probability of the estimated parameters.Results.The geometrical approach overestimated the age by about 0.3 and 0.2 Gyr for true ages of 7.5 and 9.0 Gyr, respectively. The value of the initial helium content was recovered unbiased within the large random errors on the estimates. The maximum likelihood approach provided similar biases (0.1 and 0.2 Gyr) but with a variance reduced by a factor of between two and four with respect to geometrical fit. The independent fit of single stars showed a very large variance owing to its neglect of the fact that the stars came from the same cluster. The age of the cluster was recovered with no biases for 7.5 Gyr true age and with a bias of −0.4 Gyr for 9.0 Gyr. The most important difference between geometrical and maximum likelihood approaches was the robustness against observational errors. For the first fitting technique, we found that estimations starting from the same sample but with different Gaussian perturbations on the observables suffer from a variability in the recovered mean of about 0.3 Gyr from one Monte Carlo run to another. This value was as high as 45% of the intrinsic variability due to observational errors. On the other hand, for the maximum likelihood fitting method, this value was about 65%. This larger variability led most simulations – up to 90% – to fail to include the true parameter values in their estimated 1σcredible interval. Finally, we compared the performance of the three fitting methods for single RGB-star age estimation. The variability owing to the choice of the fitting method was minor, being about 15% of the variability caused by observational uncertainties.Conclusions.Each method has its own merits and drawbacks. The single star fit showed the lowest performances. The higher precision of the maximum likelihood estimates is partially negated by the lower protection that this technique shows against random fluctuations compared to the pure geometrical fit. Ultimately, the choice of the fitting method has to be evaluated in light of the specific sample and evolutionary phases under investigation.

Drug induced mucosal erythema multiforme

<p class="abstract">Erythema multiforme (EM) is a hypersensitivity reaction to different antigenic stimuli, commoner being infection followed by drugs. It occurs predominantly in younger age group. EM has benign course but frequent recurrences are common especially secondary to HSV infection. Erythema multiforme present as classical cutaneous target/ targetoid lesions and mucosal bullae/erosions. Though almost 70% of patients suffer from mucosal involvement, isolated mucosal affection is very rare. Fuchs syndrome/ectodermosis pluriorificialis is a rare variant characterized by severe involvement of two or three mucosal sites in the absence of skin lesions commonly triggered by Mycoplasma pneumoniae. Here we are reporting a rare case of mucosal erythema multiforme secondary to drug and highlighting the importance of distinguishing it from other similarly presenting mucosal disorders.</p>

Interactive processing of radar target detection and tracking

PurposeAn interactive processing scheme is proposed to improve the target detection probability as well as the tracking performance of the radar system.Design/methodology/approachFirstly, with the spatial-correlated features extracted from the foreground and background statistical models, the thresholds were adapted to distinguish the dim small targets from clutters in the complex incoherent radar images. Then, the target trajectories were constructed with the target tracking algorithm. According to the temporal correlation with the target life cycle, the thresholding values were modified in the neighbourhood of the predicted positions to improve the detection sensitivity in these areas during the tracking process. Finally, the temporal-correlated features of the remained clutters were used to further reduce the false alarm rate.FindingsThe proposed algorithm was applied on the simulated data, as well as the image sequences obtained with the incoherent marine radars. The detection results demonstrated that the interactive algorithm could detect and track the dim small targets with relatively low false alarm rate.Practical implicationsThe interactive processing scheme could be applied for low-altitude airspace surveillance with incoherent marine radar.Originality/valueThe proposed scheme outperforms the classical radar target detection algorithms and the state-of-the-art image processing algorithms for video-based surveillance.

BTK modulates p73 activity to induce apoptosis independently of p53

Bruton’s tyrosine kinase (BTK) is a key component of B cell receptor signalling. Because of this, BTK plays an important role in cell proliferation and survival in various B cell malignancies. However, in certain contexts, BTK can also have tumour suppressor functions. We have previously shown that BTK activates the p53 transcriptional activity by binding to and phosphorylating p53, as well as acting on MDM2 to reduce its inhibitory effects. This results in increased p53 functions, including enhanced cell death. Here, we report that BTK can also induce cell death and increase responses to DNA damage independently of p53. This is concomitant to the induction of p21, PUMA and MDM2, which are classic target genes of the p53 family of proteins. Our results show that these p53-independent effects of BTK are mediated through p73. Similar to what we observed in the p53 pathway, BTK can upregulate p73 after DNA damage and induce expression of its target genes, suggesting that BTK is a modulator of p73 functions and in the absence of p53. This effect allows BTK to have pro-apoptotic functions independently of its effects on the p53 pathway and thus play an important role in the DNA damage-related induction of apoptosis in the absence of p53. This provides a novel role of BTK in tumour suppression and contributes to the understanding of its complex pleiotropic functions

Hyperspectral Target Detection via Adaptive Information—Theoretic Metric Learning with Local Constraints

By using the high spectral resolution, hyperspectral images (HSIs) provide significant information for target detection, which is of great interest in HSI processing. However, most classical target detection methods may only perform well based on certain assumptions. Simultaneously, using limited numbers of target samples and preserving the discriminative information is also a challenging problem in hyperspectral target detection. To overcome these shortcomings, this paper proposes a novel adaptive information-theoretic metric learning with local constraints (ITML-ALC) for hyperspectral target detection. The proposed method firstly uses the information-theoretic metric learning (ITML) method as the objective function for learning a Mahalanobis distance to separate similar and dissimilar point-pairs without certain assumptions, needing fewer adjusted parameters. Then, adaptively local constraints are applied to shrink the distances between samples of similar pairs and expand the distances between samples of dissimilar pairs. Finally, target detection decision can be made by considering both the threshold and the changes between the distances before and after metric learning. Experimental results demonstrate that the proposed method can obviously separate target samples from background ones and outperform both the state-of-the-art target detection algorithms and the other classical metric learning methods.

Abstract B033: Characterizing the global function of NCOR2 in prostate cancer cells and its contribution to PCa progression

Abstract The nuclear receptor corepressors (NCORs) serve as critical mediators between transcription factors and epigenetic regulators, and thus their functions are critical in the maintenance of epigenetic and transcriptional states. As such, NCORs have important developmental and homeostatic roles, and disruptions of their functions are implicated in various cancers. In hormonally driven cancers, such as breast and prostate cancer (PCa), NCORs are implicated in de novo and acquired changes to steroid hormone signaling and therapeutic resistance. Supporting the importance of NCOR function in the prostate, inactivating genomic aberrations to NCORs have been identified in androgen deprivation therapy (ADT) resistant disease. However, to date, the global functions of NCORs in prostate cancer cells and their overall contributions to PCa progression remain enigmatic. To characterize the global functions of NCOR2/SMRT in prostate cancer we have undertaken efforts to map the NCOR2 dependent transcriptome (RNA-seq), miRnome (miRNA-seq), methylome (EPIC methylation array) and cistrome (ChIP-seq) in both androgen-sensitive (LNCaP) and ADT-resistant (LNCaP-C42) prostate cancer cells upon shRNA targeted knockdown. NCOR2 loss in LNCaP-C42 cells revealed a striking degree of global hypermethylation (87,078 CpGs > 10% gain, adj.pval < 0.01), while relatively little hypomethylation was observed (1,742 CpGs > 10% loss). Hypermethylation was largely exclusive of TSS loci and CpG islands, occurring at distal regions. Conversely, the NCOR2 associated transcriptome was more divergent in its expression changes by comparable criteria (1,491 upregulated, 1,195 downregulated). Similar patterns were observed in LNCaP cells, albeit to a lesser extent, suggesting broader regulatory roles in ADT resistant cells. NCOR2 knockdown deregulated a number of miRNA including let-7e and miR-200a with target binding motifs significantly enriched among the NCOR2 regulated transcriptome, and several of these miRNA stratified survival in PCa patient data. Furthermore, NCOR2 binding at candidate loci distinguished normal from malignant prostate cells, and global binding profiles revealed significant overlap with androgen receptor and pioneering factor FOXA1. To examine the effect of NCOR2 loss in the context of androgen signaling, similar experimental conditions were performed in the presence of dihydrotestosterone (DHT). NCOR2 control of androgen sensitivity was complex, resulting in amplified signaling of some classic AR target genes (KLK3, HERC3) while dampening others (TMPRSS2, TIPARP). Ongoing data integration efforts will dissect novel NCOR2 methylome-cistrome-transcriptome relationships within and across models to define target gene networks that are epigenetically governed through NCOR2 function. To determine whether NCOR2 loss alters PCa progression we have utilized stable introduction of NCOR2 targeting shRNA in the CWR22 xenograft model that simulates androgen-dependent primary growth, regression upon ADT, and subsequent recurrence at established proportions. NCOR2 loss had no effect on primary tumor growth rate or size of tumor at ADT. However, shNCOR2 expressing tumors had significantly reduced regression in response to ADT, and furthermore at 180 days post injection, a significantly higher proportion of shNCOR2 expressing tumors (17/49) had reached recurrence compared to shCTL tumors (7/46). In total, these findings place NCOR2 as an important epigenetic regulator in prostate cancer cells, the dysfunction of which contributes to PCa progression. Citation Format: Mark D. Long, Prashant K. Singh, Gerard Llimos, Spencer Rosario, Dominic J. Smiraglia, Moray J. Campbell. Characterizing the global function of NCOR2 in prostate cancer cells and its contribution to PCa progression [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B033.