Synthesis and in vitro evaluation of a novel radioligand for αvβ3 integrin receptor imaging: [(18)F]FPPA-c(RGDfK).

The development of RGD-based antagonist of αvβ3 integrin receptor has enhanced the interest in PET probes to image this receptor for the early detection of cancer, to monitor the disease progression and the response to therapy. In this work, a novel prosthetic group (N-(4-fluorophenyl)pent-4-ynamide or FPPA) for the (18)F-labeling of an αvβ3 selective RGD-peptide was successfully prepared. [(18)F]FPPA was obtained in three steps with a radiochemical yield of 44% (decay corrected). Conjugation to c(RGDfK(N3)) by the Cu(II) catalyzed Huisgen azido alkyne cycloaddition provided the [(18)F]FPPA-c(RGDfK) with a radiochemical yield of 29% (decay corrected), in an overall synthesis time of 140min.

Synthesis of a non-peptidic PET tracer designed for α5β1 integrin receptor.

Arginine-glycine-aspartic acid (RGD)-containing peptides have been traditionally used as PET probes to noninvasively image angiogenesis, but recently, small selective molecules for α5 β1 integrin receptor have been developed with promising results. Sixty-one antagonists were screened, and tert-butyl (S)-3-(2-((3R,5S)-1-(3-(1-(2-fluoroethyl)-1H-1,2,3-triazol-4-yl)propanoyl)-5-((pyridin-2-ylamino)methyl)pyrrolidin-3-yloxy)acetamido)-2-(2,4,6-trimethylbenzamido)propanoate (FPMt) was selected for the development of a PET tracer to image the expression of α5 β1 integrin receptors. An alkynyl precursor (PMt) was initially synthesized in six steps, and its radiolabeling was performed according to the azide-alkyne copper(II)-catalyzed Huisgen's cycloaddition by using 1-azido-2-[(18)F]fluoroethane ([(18)F]12). Different reaction conditions between PMt and [(18)F]12 were investigated, but all of them afforded [(18)F]FPMt in 15 min with similar radiochemical yields (80-83%, decay corrected). Overall, the final radiopharmaceutical ([(18)F]FPMt) was obtained after a synthesis time of 60-70 min in 42-44% decay-corrected radiochemical yield.

Synthesis of medium-chain-length polyhydroxyalkanoates in arabidopsis thaliana using intermediates of peroxisomal fatty acid beta-oxidation.

Polyhydroxyalkanoate (PHA) is a family of polymers composed primarily of R-3-hydroxyalkanoic acids. These polymers have properties of biodegradable thermoplastics and elastomers. Medium-chain-length PHAs (MCL-PHAs) are synthesized in bacteria by using intermediates of the beta-oxidation of alkanoic acids. To assess the feasibility of producing MCL-PHAs in plants, Arabidopsis thaliana was transformed with the PhaC1 synthase from Pseudomonas aeruginosa modified for peroxisome targeting by addition of the carboxyl 34 amino acids from the Brassica napus isocitrate lyase. Immunocytochemistry demonstrated that the modified PHA synthase was appropriately targeted to leaf-type peroxisomes in light-grown plants and glyoxysomes in dark-grown plants. Plants expressing the PHA synthase accumulated electron-lucent inclusions in the glyoxysomes and leaf-type peroxisomes, as well as in the vacuole. These inclusions were similar to bacterial PHA inclusions. Analysis of plant extracts by GC and mass spectrometry demonstrated the presence of MCL-PHA in transgenic plants to approximately 4 mg per g of dry weight. The plant PHA contained saturated and unsaturated 3-hydroxyalkanoic acids ranging from six to 16 carbons with 41% of the monomers being 3-hydroxyoctanoic acid and 3-hydroxyoctenoic acid. These results indicate that the beta-oxidation of plant fatty acids can generate a broad range of R-3-hydroxyacyl-CoA intermediates that can be used to synthesize MCL-PHAs.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Imaging liver and brain glycogen metabolism at the nanometer scale.

In mammals, glycogen synthesis and degradation are dynamic processes regulating blood and cerebral glucose-levels within a well-defined physiological range. Despite the essential role of glycogen in hepatic and cerebral metabolism, its spatiotemporal distribution at the molecular and cellular level is unclear. By correlating electron microscopy and ultra-high resolution ion microprobe (NanoSIMS) imaging of tissue from fasted mice injected with (13)C-labeled glucose, we demonstrate that liver glycogenesis initiates in the hepatocyte perinuclear region before spreading toward the cell membrane. In the mouse brain, we observe that (13)C is inhomogeneously incorporated into astrocytic glycogen at a rate ~25 times slower than in the liver, in agreement with prior bulk studies. This experiment, using temporally resolved, nanometer-scale imaging of glycogen synthesis and degradation, provides greater insight into glucose metabolism in mammalian organs and shows how this technique can be used to explore biochemical pathways in healthy and diseased states. FROM THE CLINICAL EDITOR: By correlating electron microscopy and ultra-high resolution ion microprobe imaging of tissue from fasting mice injected with (13)C-labeled glucose, the authors demonstrate a method to image glycogen metabolism at the nanometer scale.

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

PIDD orchestrates translesion DNA synthesis in response to UV irradiation.

PIDD has been implicated in survival and apoptotic pathways in response to DNA damage, and a role for PIDD was recently identified in non-homologous end-joining (NHEJ) repair induced by γ-irradiation. Here, we present an interaction of PIDD with PCNA, first identified in a proteomics screen. PCNA has essential functions in DNA replication and repair following UV irradiation. Translesion synthesis (TLS) is a process that prevents UV irradiation-induced replication blockage and is characterized by PCNA monoubiquitination and interaction with the TLS polymerase eta (polη). Both of these processes are inhibited by p21. We report that PIDD modulates p21-PCNA dissociation, and promotes PCNA monoubiquitination and interaction with polη in response to UV irradiation. Furthermore, PIDD deficiency leads to a defect in TLS that is associated, both in vitro and in vivo, with cellular sensitization to UV-induced apoptosis. Thus, PIDD performs key functions upon UV irradiation, including TLS, NHEJ, NF-κB activation and cell death.

MSight: an image analysis software for liquid chromatography-mass spectrometry.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.