Copper complexes of new benzodioxotetraaza macrocycles with potential applications in nuclear medicine

Two novel benzodioxotetraaza macrocycles [2,9-dioxo-1,4,7,10-tetraazabicyclo[10.4.0]1,11-hexadeca-1(11),13,15-triene (H(2)L1) and 2,10-dioxo-1,4,8,11-tetraazabicyclo[11.4.0]1,12-heptadeca-1(12),14,16-triene (H(2)L2)] were synthesized by a [1 + 1] crablike cyclization. The protonation constants of both ligands were determined by H-1 NMR titration and by potentiometry at 25.0 degrees C in 0.10 M ionic strength in KNO3. The latter method was also used to ascertain the stability constants of their copper(II) complexes. These studies showed that the CuL1 complex has a much lower thermodynamic stability than the CuL2, and the H(2)L2 displays an excellent affinity for copper(II), due to the good fit of copper(II) into its cavity. The copper complexes of the novel ligands were characterized by electronic spectroscopy in solution and by crystal X-ray diffraction. These studies indicated that the copper center in the CuL1 complex adopts a square-pyramidal geometry with the four nitrogen atoms of the macrocycle forming the equatorial plane and a water molecule at axial position, and the copper in the CuL2 complex is square-planar. Several labeling conditions were tested, and only H(2)L2 could be labeled with Cu-67 efficiently (> 98%) in mild conditions (39 degrees C, 15 min) to provide a slightly hydrophilic radioligand (log D = -0.19 +/- 0.03 at pH 7.4). The in vitro stability was studied in the presence of different buffers or with an excess of diethylenetriamine-pentaethanoic acid. Very high stability was shown under these conditions for over 5 days. The incubation of the radiocopper complex in human serum showed 6% protein binding.

Mechanisms of alkylpyrazine formation in a potato model system containing added glycine

The use of glycine to limit acrylamide formation during the heating of a potato model system was also found to alter the relative proportions of alkylpyrazines. The addition of glycine increased the quantities of several alkylpyrazines, and labeling studies using [2-C-13]glycine showed that those alkylpyrazines which increased in the presence of glycine had at least one C-13-labeled methyl substituent derived from glycine. The distribution of C-13 within the pyrazines suggested two pathways by which glycine, and other amino acids, participate in alkylpyrazine formation, and showed the relative contribution of each pathway. Alkylpyrazines that involve glycine in both formation pathways displayed the largest relative increases with glycine addition. The study provided an insight into the sensitivity of alkylpyrazine formation to the amino acid composition in a heated food and demonstrated the importance of those amino acids that are able to contribute an alkyl substituent. This may aid in estimating the impact of amino acid addition on pyrazine formation, when amino acids are added to foods for acrylamide mitigation.

Investigation of cooperativity in the binding of ligands to the D-2 dopamine receptor

The D 2 dopamine receptor exists as dimers or as higher-order oligomers, as determined from data from physical experiments. In this study, we sought evidence that this oligomerization leads to cooperativity by examining the binding of three radioligands ([H-3] nemonapride, [H-3] raclopride, and [H-3] spiperone) to D 2 dopamine receptors expressed in membranes of Sf9 cells. In saturation binding experiments, the three radioligands exhibited different B-max values, and the B-max values could be altered by the addition of sodium ions to assays. Despite labeling different numbers of sites, the different ligands were able to achieve full inhibition in competition experiments. Some ligand pairs also exhibited complex inhibition curves in these experiments. In radioligand dissociation experiments, the rate of dissociation of [H-3] nemonapride or [H-3] spiperone depended on the sodium ion concentration but was independent of the competing ligand. Although some of the data in this study are consistent with the behavior of a cooperative oligomeric receptor, not all of the data are in agreement with this model. It may, therefore, be necessary to consider more complex models for the behavior of this receptor.

A role for Notch signaling in human corneal epithelial cell differentiation and proliferation

PURPOSE. To identify the role of Notch signaling in the human corneal epithelium. METHODS. Localization of Notch1, Notch2, Delta1, and Jagged1 in the human corneal epithelium was observed with the use of indirect immunofluorescence microscopy. Gene and protein expression of Notch receptors and ligands in human corneal epithelial cells was determined by RT-PCR and Western blot analysis, respectively. The effects of Notch inhibition (by {gamma}-secretase inhibition) and activation (by recombinant Jagged1) on epithelial cell proliferation (Ki67) and differentiation (CK3) were analyzed after Western blotting and immunocytochemistry. RESULTS. Immunofluorescent labeling localized Notch1 and Notch2 to suprabasal epithelial cell layers, whereas Delta1 and Jagged1 were observed throughout the corneal epithelium. Notch1, Notch2, Delta1, and Jagged1 genes and proteins were expressed in human corneal epithelial cells. {gamma}-Secretase inhibition resulted in decreased Notch1 and Notch2 expression, with an accompanying decrease in Ki67 and increased CK3 expression. The activation of Notch by Jagged1 resulted in the upregulation of active forms of Notch1 and 2 proteins (P < 0.05), with a concurrent increase in Ki67 (P < 0.05) and a decrease in CK3 (P < 0.05) expression. Interestingly, {gamma}-secretase inhibition in a three-dimensional, stratified corneal epithelium equivalent had no effect on Ki67 or CK3 expression. In contrast, Jagged1 activation resulted in decreased CK3 expression (P < 0.05), though neither Notch activation nor inhibition affected cell proliferation in the 3D tissue equivalent. CONCLUSIONS. Notch family members and ligands are expressed in the human corneal epithelium and appear to play pivotal roles in corneal epithelial cell differentiation.

Agonist-dependent internalization of D2 receptors: Imaging quantification by confocal microscopy

In positron emission tomography and single photon emission computed tomography studies using D2 dopamine (DA) receptor radiotracers, a decrease in radiotracer binding potential (BP) is usually interpreted in terms of increased competition with synaptic DA. However, some data suggest that this signal may also reflect agonist (DA)-induced increases in D2 receptor (D2R) internalization, a process which would presumably also decrease the population of receptors available for binding to hydrophilic radioligands. To advance interpretation of alterations in D2 radiotracer BP, direct methods of assessment of D2R internalization are required. Here, we describe a confocal microscopy-based approach for the quantification of agonist-dependent receptor internalization. The method relies upon double-labeling of the receptors with antibodies directed against intracellular as well as extracellular epitopes. Following agonist stimulation, DA D2R internalization was quantified by differentiating, in optical cell sections, the signal due to the staining of the extracellular from intracellular epitopes of D2Rs. Receptor internalization was increased in the presence of the D2 agonists DA and bromocriptine, but not the D1 agonist SKF38393. Pretreatment with either the D2 antagonist sulpiride, or inhibitors of internalization (phenylarsine oxide and high molarity sucrose), blocked D2-agonist induced receptor internalization, thus validating this method in vitro. This approach therefore provides a direct and streamlined methodology for investigating the pharmacological and mechanistic aspects of D2R internalization, and should inform the interpretation of results from in vivo receptor imaging studies.

Using what we know: consequences of intentionally retrieving gist versus item-specific information

The effect of a prior gist-based versus item-specific retrieval orientation on recognition of objects and words was examined. Prior item-specific retrieval increased item-specific recognition of episodically related but not previously tested objects relative to both conceptual- and perceptual-gist retrieval. An item-specific retrieval advantage also was found when the stimuli were words (synonyms) rather than objects but not when participants overtly named objects during gist-based recognition testing, which suggests that they did not always label objects under general gist-retrieval instructions. Unlike verbal overshadowing, labeling objects during recognition attenuated (but did not eliminate) test- and interference-related forgetting. A full understanding of how retrieval affects subsequent memory, even for events or facts that are not themselves retrieved, must take into account the specificity with which that retrieval occurs.

Unsupervised genetic algorithm deployed for intrusion detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.

Rat brain serotonin neurones that express neuronal nitric oxide synthase have increased sensitivity to the substituted amphetamine serotonin toxins 3,4-methylenedioxymethamphetamine and p-chloroamphetamine

Substituted amphetamines such as p-chloroamphetamine and the abused drug methylenedioxymethamphetamine cause selective destruction of serotonin axons in rats, by unknown mechanisms. Since some serotonin neurones also express neuronal nitric oxide synthase, which has been implicated in neurotoxicity, the present study was undertaken to determine whether nitric oxide synthase expressing serotonin neurones are selectively vulnerable to methylenedioxymethamphetamine or p-chloroamphetamine. Using double-labeling immunocytochemistry and double in situ hybridization for nitric oxide synthase and the serotonin transporter, it was confirmed that about two thirds of serotonergic cell bodies in the dorsal raphe nucleus expressed nitric oxide synthase, however few if any serotonin transporter immunoreactive axons in striatum expressed nitric oxide synthase at detectable levels. Methylenedioxymethamphetamine (30 mg/kg) or p-chloroamphetamine (2 x 10 mg/kg) was administered to Sprague-Dawley rats, and 7 days after drug administration there were modest decreases in the levels of serotonin transporter protein in frontal cortex, and striatum using Western blotting, even though axonal loss could be clearly seen by immunostaining. p-Chloroamphetamine or methylenedioxymethamphetamine administration did not alter the level of nitric oxide synthase in striatum or frontal cortex, determined by Western blotting. Analysis of serotonin neuronal cell bodies 7 days after p-chloroamphetamine treatment, revealed a net down-regulation of serotonin transporter mRNA levels, and a profound change in expression of nitric oxide synthase, with 33% of serotonin transporter mRNA positive cells containing nitric oxide synthase mRNA, compared with 65% in control animals. Altogether these results support the hypothesis that serotonin neurones which express nitric oxide synthase are most vulnerable to substituted amphetamine toxicity, supporting the concept that the selective vulnerability of serotonin neurones has a molecular basis.

Gene-chip technology and its applications

Gene Chips are finding extensive use in animal and plant science. Generally microarrays are of two kind, cDNA or oligonucleotide. cDNA microarrays were developed at Stanford University, whereas oligonucleotide were developed by Affymetrix. The construction of cDNA or oligonucleotide on a glass slide helps to compare the gene expression level of treated and control samples by labeling mRNA with green (Cy3) and red (Cy5) dyes. The hybridized gene chip emit fluorescence whose intensity and colour can be measured. RNA labeling can be done directly or indirectly. Indirect method involves amino allyle modified dUTP instead of pre-labelled nucleotide. Hybridization of gene chip generally occurs in a minimum volume possible and to ensure the hetroduplex formation, a ten fold more DNA is spotted on slide than in the solutions. A confocal or semi confocal laser technologies coupled with CCD camera are used for image acquisition. For standardization, house keeping genes are used or cDNA are spotted in gene chip that are not present in treated or control samples. Moreover, statistical analysis (image analysis) and cluster analysis softwares have been developed by Stanford University. The gene-chip technology has many applications like expression analysis, gene expression signatures (molecular phenotypes) and promoter regulatory element co-expression.

Quantitative plant proteomics

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.

Sustainable building certification and the rent premium: a panel data approach

This paper investigates whether obtaining sustainable building certification entails a rental premium for commercial office buildings and tracks its development over time. To this aim, both a difference-in-differences and a fixed-effects model approach are applied to a large panel dataset of office buildings in the United States in the 2000–2010 period. The results indicate a significant rental premium for both ENERGY STAR and LEED certified buildings. Controlling for confounding factors, this premium is shown to have increased steadily from 2006 to 2008, followed by a moderate decline in the subsequent periods. The results also show a significant positive relationship between ENERGY STAR labeling and building occupancy rates.

A survey of terahertz applications in cultural heritage conservation science

The authors present a review of the advances that have been made to establish terahertz applications in the cultural heritage conservation sector over the last several years. This includes material spectroscopy, 2D and 3D imaging and tomographic studies, using a broad range of terahertz sources demonstrating the breadth and application of this burgeoning community.

Policy interventions to promote healthy eating: a review of what works, what does not and what is promising

Unhealthy diets can lead to various diseases, which in turn can translate into a bigger burden for the state in the form of health services and lost production. Obesity alone has enormous costs and claims thousands of lives every year. Although diet quality in the European Union has improved across countries, it still falls well short of conformity with the World Health Organization dietary guidelines. In this review, we classify types of policy interventions addressing healthy eating and identify through a literature review what specific policy interventions are better suited to improve diets. Policy interventions are classified into two broad categories: information measures and measures targeting the market environment. Using this classification, we summarize a number of previous systematic reviews, academic papers, and institutional reports and draw some conclusions about their effectiveness. Of the information measures, policy interventions aimed at reducing or banning unhealthy food advertisements generally have had a weak positive effect on improving diets, while public information campaigns have been successful in raising awareness of unhealthy eating but have failed to translate the message into action. Nutritional labeling allows for informed choice. However, informed choice is not necessarily healthier; knowing or being able to read and interpret nutritional labeling on food purchased does not necessarily result in consumption of healthier foods. Interventions targeting the market environment, such as fiscal measures and nutrient, food, and diet standards, are rarer and generally more effective, though more intrusive. Overall, we conclude that measures to support informed choice have a mixed and limited record of success. On the other hand, measures to target the market environment are more intrusive but may be more effective.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.