This study investigated the effectiveness of Solve It! instruction on students’ knowledge of math problem-solving strategies. Solve It! is a cognitive strategy intervention designed to improve the math problem solving of middle school students with learning disabilities (LD). Participants included seventh- and eighth-grade students with LD (n = 77) and average-achieving students (n = 77). We examined treatment effects of the intervention, as well as differential effects of treatment across ability levels, on students’ knowledge of problem-solving strategies using the Math Problem-Solving Assessment. Results showed that students across ability levels who received Solve It! instruction reported using significantly more strategies than students in the comparison group. Implications for instruction are discussed as well as directions for future research.

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Pattern recognition-informed (PRI) feedback using channel current cheminformatics (CCC) software is shown to be possible in "real-time" experimental efforts. The accuracy of the PRI classification is shown to inherit the high accuracy of our offline classifier: 99.9% accuracy in distinguishing between terminal base pairs of two DNA hairpins. The pattern recognition software consists of hidden Markov model (HMM) feature extraction software, and support vector machine (SVM) classification/ clustering software that is optimized for data acquired on a nanopore channel detection system. For general nanopore detection, the distributed HMM and SVM processing used here provides a processing speedup that allows pattern recognition to complete within the time frame of the signal acquisition - where the sampling is halted if the blockade signal is identified as not in the desired subset of events (or once recognized as nondiagnostic in general). We demonstrate that Nanopore Detection with PRI offers significant advantage when applied to data acquisition on antibody-antigen system, or other complex biomolecular mixtures, due to the reduction in wasted observation time on eventually rejected "junk" (nondiagnostic) signals.

Objective. The purpose of this paper is to provide evidence for the relationship between personality disorders (PDs), obsessive compulsive disorder (OCD), and other anxiety disorders different from OCD (non-OCD) symptomatology. Method. The sample consisted of a group of 122 individuals divided into three groups (41 OCD; 40 non-OCD, and 41 controls) matched by sex, age, and educational level. All the individuals answered the IPDE questionnaire and were evaluated by means of the SCID-I and SCID-II interviews. Results. Patients with OCD and non-OCD present a higher presence of PD. There was an increase in cluster C diagnoses in both groups, with no statistically significant differences between them. Conclusions. Presenting anxiety disorder seems to cause a specific vulnerability for PD. Most of the PDs that were presented belonged to cluster C. Obsessive Compulsive Personality Disorder (OCPD) is the most common among OCD. However, it does not occur more frequently among OCD patients than among other anxious patients, which does not confirm the continuum between obsessive personality and OCD. Implications for categorical and dimensional diagnoses are discussed.

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