Suicide attempters within a delinquent population.

Contrasted 30 13–15 yr old delinquents who had attempted suicide with 120 delinquent controls. Multiple regression analyses showed that suicidal ideation, depression, gender, a tendency to act out, conflict with parents, childhood hyperactivity, and the interactions of gender with depression and with acting out accounted for 49% of the variance in suicide attempts. Females were found to attempt suicide 2? times as often as males. Females were also more likely to attempt suicide when depressed than were similarly depressed males. Male and female delinquents who engaged in a high level of acting out were equally likely to attempt suicide, but females who did not act out were more likely to attempt suicide than males who did not act out. Similarities between these adolescent attempters and adult suicide attempters described in the literature are discussed. (34 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Screening for Cytochrome P450 Reactivity by Harnessing Catalase as Reporter Enzyme

The big screen : We have devised a high‐throughput screening method for organic peroxide‐dependent P450 reactivity by taking advantage of a previously undescribed activity of catalase, which was used as reporter enzyme. This two‐step assay, followed by liquid chromatography/mass spectrometry analyses, allowed the facile identification of several new substrates for bacterial P450 enzymes.

     

Combinatorial Strategies by Marine Cyanobacteria: Symplostatin 4, an Antimitotic Natural Dolastatin 10/15 Hybrid that Synergizes with the Coproduced HDAC Inhibitor Largazole

Combinatorial biosynthesis meets combinatorial pharmacology, cyanobacterial style : A new antimitotic natural product with features of both dolastatins 10 and 15 was isolated from the same Floridian Symploca sp. sample that produced the histone deacetylase inhibitor largazole. Both agents in combination are more effective in inhibiting cancer cell proliferation than either agent alone.

     

Determination of Preferred pH for Root-knot Nematode Aggregation Using Pluronic F-127 Gel

Root-knot nematodes (Meloidogyne spp.) are obligate endoparasites of a wide range of plant species. The infective stage is attracted strongly to and enters host roots at the zone of elongation, but the compounds responsible for this attraction have not been identified. We developed a simple assay to investigate nematode response to chemical gradients that uses Pluronic F-127, a synthetic block copolymer that, as a 23% aqueous solution, forms a liquid at low temperature and a gel at room temperature. Test chemicals are put into a modified pipette tip, or ‘chemical dispenser,’ and dispensers are inserted into the gel in which nematodes have been dispersed. Meloidogyne hapla is attracted to pH gradients formed by acetic acid and several other Brønsted acids and aggregates between pH 4.5 and 5.4. While this pH range was attractive to all tested root-knot nematode strains and species, the level of aggregation depended on the species/strain assessed. For actively growing roots, the pH at the root surface is most acidic at the zone of elongation. This observation is consistent with the idea that low pH is an attractant for nematodes. Root-knot nematodes have been reported to be attracted to carbon dioxide, but our experiments suggest that the observed attraction may be due to acidification of solutions by dissolved CO₂ rather than to CO₂ itself. These results suggest that Pluronic F-127 gel will be broadly applicable for examining responses of a range of organisms to chemical gradients or to each other.     

Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change

This paper is motivated by the analysis of serial structural magnetic resonance imaging (MRI) data of the brain to map patterns of local tissue volume loss or gain over time, using registration-based deformation tensor morphometry. Specifically, we address the important confound of local tissue contrast changes which can be induced by neurodegenerative or neurodevelopmental processes. These not only modify apparent tissue volume, but also modify tissue integrity and its resulting MRI contrast parameters. In order to address this confound we derive an approach to the voxel-wise optimization of regional mutual information (RMI) and use this to drive a viscous fluid deformation model between images in a symmetric registration process. A quantitative evaluation of the method when compared to earlier approaches is included using both synthetic data and clinical imaging data. Results show a significant reduction in errors when tissue contrast changes locally between acquisitions. Finally, examples of applying the technique to map different patterns of atrophy rate in different neurodegenerative conditions is included.     

Thermoplastic encapsulations of a sensor platform by high‐temperature injection molding up to 360°C

For bioprotective encapsulated Internet of Things (IoT) electronics for the medical market, especially for cost‐effective, single or multiple use, injection molding will be of major importance. The stresses from the production process pose special challenges for electronics. The aim of the study was the simulative and experimental characterization of the load spectrum from the injection molding process and its effects on electronics. General recommendations for the encapsulation of IoT electronics in polymer matrices were derived. A microcontroller‐controlled temperature sensor board and a suitable injection mold were developed. Eight circumferential NTC‐sensors measured the prevailing thermal load directly on the component during overmolding. A simulation model allowed statements on the loads to be made about individual components, validated in experimental tests. In addition to PP and PMMA, PSU with a melt temperature of 360°C was tested. The temperature influence of the melt could be described as a function of the component position. The loss of adhesion, shrinkage, distortion, cavities, and delamination were analyzed using micrograph analysis. Complete functionality of the electronics was ensured after encapsulation. Optimization strategies were developed for the design of both IoT electronics and the injection mold. Novel concepts such as interlayer or backfill vias could be derived for future research approaches. POLYM. ENG. SCI., 59:1315–1331 2019. © 2019 Society of Plastics Engineers     

Blueberries and Their Anthocyanins: Factors Affecting Biosynthesis and Properties

Abstract: Blueberry is one of the most popular fruits in North America and rich in anthocyanins. Its content in anthocyanins contributes to the health‐beneficial effects of blueberry against several chronic diseases including cardiovascular disorders, neurodegenerative diseases, diabetes, and cancer. This paper summarizes various facts presenting blueberry as a fruit with huge potential for increased future consumption as a health‐enhancing food. Factors affecting the biosynthesis of the various anthocyanins in blueberries, including agronomic and genetic factors, and the possible pathways of biosynthesis of the major anthocyanins present in this plant are discussed. The important health‐beneficial effects associated with blueberry anthocyanins, properties of these anthocyanins leading to the beneficial effects, and food processing parameters leading to the depletion of the amounts of anthocyanins present in the final processed products are also briefly discussed. Furthermore, the general methods of extraction and analyses that have been reported for being successfully applied to blueberry anthocyanins are also reviewed. Practical Application: Blueberries are well known for their nutritional and beneficial health effects, however, information concerning the physiology behind the blueberry beneficial effects is still lacking. There is little or no information on the characterization of growing conditions on anthocyanins in blueberries and research is lagging behind on advanced methods of extracting blueberry anthocyanins.     

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.