Functional Inactivation of Drosophila GCK Orthologs Causes Genomic Instability and Oxidative Stress in a Fly Model of MODY-2

Maturity-onset diabetes of the young (MODY) type 2 is caused by heterozygous inactivating mutations in the gene encoding glucokinase (GCK), a pivotal enzyme for glucose homeostasis. In the pancreas GCK regulates insulin secretion, while in the liver it promotes glucose utilization and storage. We showed that silencing the Drosophila GCK orthologs Hex-A and Hex-C results in a MODY-2-like hyperglycemia. Targeted knock-down revealed that Hex-A is expressed in insulin producing cells (IPCs) whereas Hex-C is specifically expressed in the fat body. We showed that Hex-A is essential for insulin secretion and it is required for Hex-C expression. Reduced levels of either Hex-A or Hex-C resulted in chromosome aberrations (CABs), together with an increased production of advanced glycation end-products (AGEs) and reactive oxygen species (ROS). This result suggests that CABs, in GCK depleted cells, are likely due to hyperglycemia, which produces oxidative stress through AGE metabolism. In agreement with this hypothesis, treating GCK-depleted larvae with the antioxidant vitamin B6 rescued CABs, whereas the treatment with a B6 inhibitor enhanced genomic instability. Although MODY-2 rarely produces complications, our data revealed the possibility that MODY-2 impacts genome integrity.     

Secondary Metabolic Profile as a Tool for Distinction and Characterization of Cultivars of Black Pepper (Piper nigrum L.) Cultivated in Pará State,Brazil

This study evaluated the chemical compositions of the leaves and fruits of eight black pepper cultivars cultivated in Pará State (Amazon, Brazil). Hydrodistillation and gas chromatography–mass spectrometry were employed to extract and analyze the volatile compounds, respectively. Sesquiterpene hydrocarbons were predominant (58.5–90.9%) in the cultivars “Cingapura”, “Equador”, “Guajarina”, “Iaçará”, and “Kottanadan”, and “Bragantina”, “Clonada”, and “Uthirankota” displayed oxygenated sesquiterpenoids (50.6–75.0%). The multivariate statistical analysis applied using volatile composition grouped the samples into four groups: γ-Elemene, curzerene, and δ-elemene (“Equador”/“Guajarina”, I); δ-elemene (“Iaçará”/“Kottanadan”/“Cingapura”, II); elemol (“Clonada”/“Uthirankota”, III) and α-muurolol, bicyclogermacrene, and cubebol (“Bragantina”, IV). The major compounds in all fruit samples were monoterpene hydrocarbons such as α-pinene, β-pinene, and limonene. Among the cultivar leaves, phenolics content (44.75–140.53 mg GAE·g⁻¹ FW), the enzymatic activity of phenylalanine-ammonia lyase (20.19–57.22 µU·mL⁻¹), and carotenoids (0.21–2.31 µg·mL⁻¹) displayed significant variations. Due to black pepper’s susceptibility to Fusarium infection, a molecular docking analysis was carried out on Fusarium protein targets using each cultivar’s volatile components. F. oxysporum endoglucanase was identified as the preferential protein target of the compounds. These results can be used to identify chemical markers related to the susceptibility degree of black pepper cultivars to plant diseases prevalent in Pará State.     

Superabsorbent hydrogel based on modified polysaccharide for removal of Pb2+ and Cu2+ from water with excellent performance

This contribution describes the absorption percentage of Pb²⁺ and Cu²⁺ from water by a superabsorbent hydrogel matrix (SH) made from an anionic polysaccharide copolymerized with acrylic acid (AAc) and acrylamide (AAm). Metal‐absorption tests, upon sequential pH variation, indicated that the SH has pH‐sensitivity for the absorption of both metals from solution, attributed to the functional ionic groups (? COOH) present in the AAc and arabic gum (AG) segments. At the pH 5.0, the SH exhibited good absorption capacity: 73.10% for Pb²⁺, 81.99% for Cu²⁺ in water and 63.64% for Pb²⁺, and 76.67% for Cu²⁺ in saline water with 0.1 mol kg^?1 ionic strength. A replicated 2² full factorial design with a central point was built to evaluate the maximum absorption capacity of the metals into the SH. It was found that both the interaction and main effects of the pH and the initial concentration of metal solution on absorption percentage of the metals were statistically significant. Surface response plots indicated that the absorption capacity of both metals into the SH may be appreciably improved by using the solutions with lower initial concentration of metal and with higher pH values. Metal‐absorption results demonstrated that the SH is a convenient material for absorption of Pb²⁺ and Cu²⁺ from pure aqueous and saline aqueous environments. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Dielectric behavior of PVDF/POMA blends that have a low doped POMA content

The real (ε′) and imaginary (ε″) components of the complex permittivity of blends of PVDF [poly(vinylidene fluoride)] with POMA [poly(o‐methoxyaniline)] doped with toluenosulfonic acid (TSA) containing 1, 2.5, and 5 wt % POMA–TSA were determined in the frequency interval between 10² and 3 × 10⁶ Hz and in the temperature range from ?120 up to 120°C. It was observed that the values of ε′ and ε″ had a greater increase with the POMA–TSA content and with a temperature in the region of frequencies below 10 kHz. This effect decreased with frequency and it was attributed to interfacial polarization. This polarization was caused by the blend heterogeneity, formed by conductive POMA–TSA agglomerates dispersed in an insulating matrix of PVDF. The equation of Maxwell–Garnett, modified by Cohen, was used to evaluate the permittivity and conductivity behavior of POMA–TSA in the blends. A strong decrease was observed in POMA–TSA conductivity in the blend, which was bigger the lower the POMA–TSA content in the blend. This decrease could have been caused either by the POMA dedoping during the blend preparation process or by its dispersion into the insulating matrix. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 752–758, 2003     

Master batch approach for developing PVDF/EVA/CNT nanocomposites with co-continuous morphology and improved electrical conductivity

Conducting polymer composites constituted by co-continuous poly (vinylidene fluoride) (PVDF)/ ethylene- vinyl acetate copolymer (EVA) blends with multiwalled carbon nanotube (CNT) were prepared by melt mixing using different procedures. The effect of the master batch approach on the conductivity, morphology, mechanical, thermal and rheological properties of PVDF/EVA/CNT nanocomposites was compared with that based on one step mixing strategy. The selective extraction experiments revealed that CNT was preferentially localized in the EVA phase in all situations, even when PVDF@CNT master batch was employed. Nanocomposites prepared with EVA@CNT master batch displayed higher conductivity, whose value reached around 10⁻¹ S m⁻¹ with the addition of 0.56 vol% of CNT. The better electrical performance was attributed to the better distribution of the filler, as indicated by transmission electron microscopy and rheological behavior. The electrical and rheological behavior were also investigated as a function of the CNT content.     

On the evaluation of unsupervised outlier detection: measures,datasets, and an empirical study

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data mining research. Little is known regarding the strengths and weaknesses of different standard outlier detection models, and the impact of parameter choices for these algorithms. The scarcity of appropriate benchmark datasets with ground truth annotation is a significant impediment to the evaluation of outlier methods. Even when labeled datasets are available, their suitability for the outlier detection task is typically unknown. Furthermore, the biases of commonly-used evaluation measures are not fully understood. It is thus difficult to ascertain the extent to which newly-proposed outlier detection methods improve over established methods. In this paper, we perform an extensive experimental study on the performance of a representative set of standard k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results.     

A Genre-Aware Approach to Focused Crawling

Focused crawlers have as their main goal to crawl Web pages that are relevant to a specific topic or user interest, playing an important role for a great variety of applications. In general, they work by trying to find and crawl all kinds of pages deemed as related to an implicitly declared topic. However, users are often not simply interested in any document about a topic, but instead they may want only documents of a given type or genre on that topic to be retrieved. In this article, we describe an approach to focused crawling that exploits not only content-related information but also genre information present in Web pages to guide the crawling process. This approach has been designed to address situations in which the specific topic of interest can be expressed by specifying two sets of terms, the first describing genre aspects of the desired pages and the second related to the subject or content of these pages, thus requiring no training or any kind of preprocessing. The effectiveness, efficiency and scalability of the proposed approach are demonstrated by a set of experiments involving the crawling of pages related to syllabi of computer science courses, job offers in the computer science field and sale offers of computer equipments. These experiments show that focused crawlers constructed according to our genre-aware approach achieve levels of F1 superior to 88%, requiring the analysis of no more than 65% of the visited pages in order to find 90% of the relevant pages. In addition, we experimentally analyze the impact of term selection on our approach and evaluate a proposed strategy for semi-automatic generation of such terms. This analysis shows that a small set of terms selected by an expert or a set of terms specified by a typical user familiar with the topic is usually enough to produce good results and that such a semi-automatic strategy is very effective in supporting the task of selecting the sets of terms required to guide a crawling process.     

Design Optimization of Composite Prosthetic Tubes Using GA-ANN Algorithm Considering Tsai-Wu Failure Criteria

The investigation of possible failures in composite materials is a matter of very great importance, and the Tsai-Wu criterion is an effective criterion for analyzing those flaws in anisotropic materials and defining whether the material at a given load will or will not suffer structural failure. In this study, an optimization procedure is proposed to minimize the maximum value of Tsai-Wu of laminated composite tubes subject to axial loading. Artificial neural networks and genetic algorithms are chosen as optimization tools. The results of this study show that the developed algorithm converges faster. Then, the maximum Tsai-Wu value is used as the objective function and the fiber orientations are the constraints in the optimization process. The results yielded by them are compared and discussed. Optimal results are compared with respect to the usual initial design. The design approach is recommended for structures where composites are the key load-carrying members such as orthopedic prosthesis.