Does expert psychological testimony inform or influence juror decision making? A social cognitive analysis.

The authors examined whether expert testimony serves an educational or a persuasive function. Participants watched a simulated sexual abuse trial in which the child witness had been prepared for her testimony (i.e., she was calm, composed, and confident) or unprepared (i.e., emotional, confused, and uncertain). The trial contained different levels of expert testimony: none, standard (i.e., a summary of the research), repetitive (i.e., standard testimony plus a 2nd summary of the research), or concrete (i.e., standard testimony plus a hypothetical scenario linking the research to the case facts) testimony. Repetitive testimony bolstered the child's testimony, whereas concrete and standard testimony did not. Concrete testimony sensitized jurors to behavioral correlates of sexual victimization; standard and repetitive testimony desensitized jurors to these correlates. Implications for the use of procedural innovations in sexual abuse trials are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Isometric Piecewise Polynomial Curves

The main preoccupations of research in computer-aided geometric design have been on shape-specification techniques for polynomial curves and surfaces, and on the continuity between segments or patches. When modelling with such techniques, curves and surfaces can be compressed or expanded arbitrarily. There has been relatively little work on interacting with direct spatial properties of curves and surfaces, such as their arc length or surface area. As a first step, we derive families of parametric piecewise polynomial curves that satisfy various positional and tangential constraints together with arc-length constraints. We call these curves isometric curves. A space curve is defined as a sequence of polynomial curve segments, each of which is defined by the familiar Hermite or Bézier constraints for cubic polynomials; as well, each segment is constrained to have a specified arc length. We demonstrate that this class of curves is attractive and stable. We also describe the numerical techniques used that are sufficient for achieving real time interaction with these curves on low-end workstations.     

An optimal control method for aggregate production planning in large-scale manufacturing systems with capacity expansion and deterioration

An optimal control approach to continuous-time aggregate production planning problems is presented. The proposed approach describes the production and capacity evolution (expansion, sell and deterioration) processes in the form of differential equations with regular production, subcontracting and capacity change rates controllable on one hierarchical level. In this way, the traditional disadvantages of the two-level problem consideration (one level for strategic capacity planning and the other for production smoothing) are avoided. Analytical properties for optimal production and capacity control regimes and conditions for their changeover are derived by the maximum principle. Based on these results, an insight into the optimal behaviour of the production system is gained and a fast numerical method is developed to identify and sequence the optimal regimes for arbitrary demand profiles. A computational example illustrates the effectiveness of the approach.     

Anger reduction in early adolescents.

High-anger 6–8th graders received cognitive-relaxation coping skills (CRCS), social skills training (SST), or no treatment. Compared to the control, CRCS and SST were equally effective in reducing trait, general, and personal–situational anger and outward negative anger expression, as well as increasing controlled anger expression. On other variables, however, CRCS showed some superiority. Although both interventions led to equivalent reductions on one measure of anxiety, CRCS lowered depression, shyness, general deviance, and another measure of anxiety, whereas SST did not and was not significantly different from other groups. CRCS lowered school deviance more than SST. No between-group differences were found on self-esteem, alcohol consumption, or intoxication. Possible explanations of these findings were discussed, along with methodological issues. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Temporal dynamics and drivers of lake ecosystem metabolism using high resolution observations in a shallow,tropical, eutrophic lake (Laguna Lake,Philippines)

The physical, chemical, and biological dynamics under changing atmospheric conditions of Laguna Lake, Philippines were analyzed from intensive observations. Diel measurements were conducted for 48 continuous hours for both dry and wet tropical seasons in addition to fine resolution long-term monitoring. Results revealed significant vertical and diel variations of lake variables in spite of a shallow water depth (2.5 m) caused by the intense surface heating from solar irradiance (~800 W m^?2) and accentuated by the lake's high turbidity (16–32 ftu). Late afternoon land-lake breeze (~5–7 m s^?1) regularly breaks daytime thermal stratification, and convective cooling at night maintains isothermal condition until dawn of the next day. The stratified condition demonstrated a wind-driven, density-induced 2-layer current pattern with a windward moving epilimnion (~4 folds faster) and a compensating hypolimnetic flow in the general lake circulation direction. Laguna Lake was observed to have a dominating diel cycle but also undergoes significant seasonal limnological variations brought primarily by climate, hydrology, and its interaction with the adjacent sea. Significantly correlated variations of pH, chlorophyll-a and DO in the dry season were indicative of the higher biological activity associated with the intrusion of polluted waters from Metro Manila. The non-occurrence of thermal over-turn was observed to be regularly followed by bottom hypoxic conditions (2–4 mg L^?1), indicative of the eutrophic condition of the lake and the importance of diel wind-induced mixing in the bottom supply of DO. Laguna Lake was found to be predominantly net heterotrophic (GPP:R < 1, NEP < 0).     

Morse set classification and hierarchical refinement using Conley index

Morse decomposition provides a numerically stable topological representation of vector fields that is crucial for their rigorous interpretation. However, Morse decomposition is not unique, and its granularity directly impacts its computational cost. In this paper, we propose an automatic refinement scheme to construct the Morse Connection Graph (MCG) of a given vector field in a hierarchical fashion. Our framework allows a Morse set to be refined through a local update of the flow combinatorialization graph, as well as the connection regions between Morse sets. The computation is fast because the most expensive computation is concentrated on a small portion of the domain. Furthermore, the present work allows the generation of a topologically consistent hierarchy of MCGs, which cannot be obtained using a global method. The classification of the extracted Morse sets is a crucial step for the construction of the MCG, for which the Poincare′ index is inadequate. We make use of an upper bound for the Conley index, provided by the Betti numbers of an index pair for a translation along the flow, to classify the Morse sets. This upper bound is sufficiently accurate for Morse set classification and provides supportive information for the automatic refinement process. An improved visualization technique for MCG is developed to incorporate the Conley indices. Finally, we apply the proposed techniques to a number of synthetic and realworld simulation data to demonstrate their utility.     

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.     

The GNAQ T96S Mutation Affects Cell Signaling and Enhances the Oncogenic Properties of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the most common malignant tumor in the liver, grows and metastasizes rapidly. Despite advances in treatment modalities, the five-year survival rate of HCC remains less than 30%. We sought genetic mutations that may affect the oncogenic properties of HCC, using The Cancer Genome Atlas (TCGA) data analysis. We found that the GNAQ T96S mutation (threonine 96 to serine alteration of the Gαq protein) was present in 12 out of 373 HCC patients (3.2%). To examine the effect of the GNAQ T96S mutation on HCC, we transfected the SK-Hep-1 cell line with the wild-type or the mutant GNAQ T96S expression vector. Transfection with the wild-type GNAQ expression vector enhanced anchorage-independent growth, migration, and the MAPK pathways in the SK-Hep-1 cells compared to control vector transfection. Moreover, cell proliferation, anchorage-independent growth, migration, and the MAPK pathways were further enhanced in the SK-Hep-1 cells transfected with the GNAQ T96S expression vector compared to the wild-type GNAQ-transfected cells. In silico structural analysis shows that the substitution of the GNAQ amino acid threonine 96 with a serine may destabilize the interaction between the regulator of G protein signaling (RGS) protein and GNAQ. This may reduce the inhibitory effect of RGS on GNAQ signaling, enhancing the GNAQ signaling pathway. Single nucleotide polymorphism (SNP) genotyping analysis for Korean HCC patients shows that the GNAQ T96S mutation was found in only one of the 456 patients (0.22%). Our data suggest that the GNAQ T96S hotspot mutation may play an oncogenic role in HCC by potentiating the GNAQ signal transduction pathway.     

Utilizing Radiant Nanoparticles in Silicon for Balanced White Color Adapters

Silicon - This work fully depends on the silicon nanoparticles. It is represented as SiNPs. This depends on the transparent LEDs color converters. The spectrum obtained is fully white, so it is...