Isolation,characterization, and quantitative analysis of Microviridin J,a new Microcystis metabolite toxic to Daphnia

This paper describes the purification and characterization of microviridin J, a newly discovered metabolite of Microcystis that causes a lethal molting disruption in Daphnia spp., upon ingestion of living cyanobacterial cells. Microviridin J consists of an acetylated chain of 13 amino acids arranged in three rings and two side chains. Unlike other known isoforms of microviridin, microviridin J contains arginine that imparts a unique solution conformation characterized by proximal hydrophobic interactions between Arg and other regions of the molecule. This eventually results in the formation and stabilization of an additional ring system. Microviridin J potently inhibits porcine trypsin, bovine chymotrypsin, and daphnid trypsin-like proteases. The activity against trypsin is most likely due to Arg and its distinctive conformational interactions. Overall, the data presented for microviridin J emphasize once again the ability of cyanobacteria to produce numerous and potent environmental toxins.     

Fair webs

Fair webs are energy-minimizing curve networks. Obtained via an extension of cubic splines or splines in tension to networks of curves, they are efficiently computable and possess a variety of interesting applications. We present properties of fair webs and their discrete counterparts, i.e., fair polygon networks. Applications of fair curve and polygon networks include fair surface design and approximation under constraints such as obstacle avoidance or guaranteed error bounds, aesthetic remeshing, parameterization and texture mapping, and surface restoration in geometric models.     

Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm. Alexandros Kalousis received the B.Sc. degree in computer science, in 1994, and the M.Sc. degree in advanced information systems, in 1997, both from the University of Athens, Greece. He received the Ph.D. degree in meta-learning for classification algorithm selection from the University of Geneva, Department of Computer Science, Geneva, in 2002. Since then he is a Senior Researcher in the same university. His research interests include relational learning with kernels and distances, stability of feature selection algorithms, and feature extraction from spectral data. Julien Prados is a Ph.D. student at the University of Geneva, Switzerland. In 1999 and 2001, he received the B.Sc. and M.Sc. degrees in computer science from the University Joseph Fourier (Grenoble, France). After a year of work in industry, he joined the Geneva Artificial Intelligence Laboratory, where he is working on bioinformatics and datamining tools for mass spectrometry data analysis. Melanie Hilario has a Ph.D. in computer science from the University of Paris VI and currently works at the University of Geneva’s Artificial Intelligence Laboratory. She has initiated and participated in several European research projects on neuro-symbolic integration, meta-learning, and biological text mining. She has served on the program committees of many conferences and workshops in machine learning, data mining, and artificial intelligence. She is currently an Associate Editor of theInternational Journal on Artificial Intelligence Toolsand a member of the Editorial Board of theIntelligent Data Analysis journal.     

Towards a feminist empowerment model of forgiveness psychotherapy.

In recent years Enright and Fitzgibbon's (2000) process model of forgiveness therapy has received substantial theoretical and empirical attention. However, both the process model of forgiveness therapy and the social-cognitive developmental model on which it is based have received criticism from feminist theorists. The current paper considers feminist criticisms of forgiveness therapy and uses a feminist lens to identify potential areas for growth. Specifically, Worell and Remer's (2003) model of synthesizing feminist ideals into existing theory was consulted, areas of bias within the forgiveness model of psychotherapy were identified, and strategies for restructuring areas of potential bias were introduced. Further, the authors consider unique aspects of forgiveness therapy that can potentially strengthen existing models of feminist therapy. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The developmental trajectories of attention focusing, attentional and behavioral persistence, and externalizing problems during school-age years.

The developmental trajectories of attention focusing (by parents' and teachers' reports) and attentional and behavioral persistence (observed during a laboratory task)--2 indexes of effortful control--and externalizing problems from ages 5 to 10 years were examined for 356 children combined from a pair of 3-wave (2 years apart) longitudinal studies. The authors identified clusters of children with distinct trajectories for these variables and examined the links between the effortful control trajectories and the externalizing problem trajectories. Although attention focusing remained relatively stable, attentional and behavioral persistence continued to show mean-level changes (especially among the children with lower levels of persistence). Children with high and stable trajectories of effortful control tended to exhibit low and stable trajectories of externalizing problems, whereas those with lower and/or less stable trajectories of effortful control showed more elevated and/or fluctuating trajectories of externalizing problems. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Life on the line: Job demands, perceived co-worker support for safety, and hazardous work events.

The present study of 334 United Kingdom trackside workers tested an interaction hypothesis. We hypothesized, drawing on the job demands-resources framework, that perceived support for safety (from senior managers, supervisors, and coworkers) as job resources would weaken the relationship between higher job demands and more frequent hazardous work events. Consistent with social impact theory, we predicted that perceived coworker support for safety would be particularly influential when trackside workers faced higher job demands. Moderated multiple regression showed that, of all three sources of perceived support for safety, perceived coworker support for safety was most important for keeping employees safe in the face of high job demands. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Distributing working memory resources during problem solving.

This study examines how problem solvers distribute working memory demands over internal and external resources. Participants recorded notes while performing an arithmetic task. They recorded a majority of intermediate results and labeled many of those results (e.g., "C?=?10"). When more effort was required to take notes, participants recorded fewer results. Participants with a consistent goal structure recorded fewer results and with practice labeled fewer recorded results than those with varied goal structures. When notes were displayed in a consistent spatial arrangement participants labeled fewer recorded results than when notes appeared in varied locations. These findings indicate that individuals use explicit and implicit strategies for indexing intermediate results. The data support the view that individuals flexibly distribute working memory over internal and external resources in response to situational cost-benefit considerations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Linkage of the NKG2 and CD94 receptor genes to D12S77 in the human natural killer gene complex

Rapid advances in modern gene seeking techniques and the sequence data evolving from related genome research should provide both new targets for drug discovery and new insights into risk factors for many neurological and psychiatric disorders. Coupled with the high speed synthetic capabilities available in many companies, high-throughput screening is identifying potential novel drug candidates at extraordinary rates. This enables the drug discoverer to be more precise in the biological specificity of drugs taken to human trials thereby reducing the potential side-effect profile of clinical candidates. The ability to create large libraries of compounds also allows researchers to focus on metabolism and pharmacokinetics at an earlier stage in the drug development process to minimize drug-drug interactions via common sites of metabolism and optimize duration of action for particular indications. An emerging bottleneck in psychopharmacological drug discovery is the relative paucity of preclinical behavioral models predictive of clinical efficacy and the need to carry out early clinical trials to demonstrate therapeutic utility. However, through the use of recently developed chip technology, coupled with data bases of information about single nucleotide polymorphisms in potential candidate genes or risk factors for psychiatric disorders, it should be possible in the near future to stratify clinical populations genetically for inclusion in specific drug treatment trials. The ultimate goal of this research is to obtain homogeneous populations for trials and to predict risk before the phenotype of the disorder is manifest.     

Model-based control of cathode pressure and oxygen excess ratio of a PEM fuel cell system

For PEM fuel cells supplied with air, pressure and flow control is a key requirement for an efficient and dynamic operation because fuel cells are in risk of starvation when the partial pressure of oxygen at the cathode falls below a critical level. To avoid oxygen starvation and, at the same time, to allow for a dynamic operation of the fuel cell system, both excess ratio of oxygen and cathode pressure need to be adjusted rapidly.