Models of human category learning have predominately assumed that both the structure in the world and the analogous structure of the internal cognitive representations are best modeled by hierarchies of disjoint categories. Strict taxonomies do, in fact, capture important structure of the world. However, there are realistic situations in which systems of overlapping categories can engender more accurate inferences than can taxonomies. Two preliminary models for learning overlapping categories are presented and their benefit is illustrated. The models are discussed with respect to their potential implications for theory-based category learning and conceptual combination.

Causal models describe some part of the world to allow an information system to perform complex tasks such as diagnosis. However, as many researchers have discovered, such models are rarely complete or consistent. As well, the world may change slightly, making a previously complete model incomplete. A computational theory of the use of causal models must allow for completion and correction in the face of new evidence. This paper discusses these issues with respect to the evolution of a causal model in a diagnosis task. The reasoner's goal is to diagnose a fault in a malfunctioning automobile, and it improves its diagnostic model by comparing it with an instructor's. A general process model is presented with two implementations. Related work in explanation based learning and in incorrect causal models is discussed

Poplar grow throughout the West coast & are adapted to extremely variable conditions. To examine what allows for this wide range of growth conditions, Jerry Tuskan's team has collected 1000 different individuals from British Columbia to California. In 2009, three Common Gardens were established where each individual was cloned in triplicate. Nearly all of these trees have been sequenced using short read technology, revealing a huge degree of variation in genotype. Correlating this genomic variation to phenotype would be greatly be strengthened if the variants could be phased into long haplotype blocks.

Dorsal thoracic arachnoid webs are rare clinical entities caused by a thickened intradural extramedullary band of arachnoid tissue that compresses the spinal cord, and often present with progressive back pain, paresthesias, and lower extremity weakness. In this report, we review the radiographic features of the "Scalpel Sign" and describe the case of a 47-year-old male that failed conservative therapy and was found to have dorsal thoracic arachnoid web. The patient underwent laminectomy and microsurgical release of the compressing arachnoid band. Postoperatively, the patient had complete resolution of his pain. Intraoperatively, the somatosensory evoked potentials were improved once the band was released. The prompt diagnosis of dorsal arachnoid webs remains critical because surgical treatment arrests and potentially reverses the pathology.

A new caridean shrimp, Alvinocaris costaricensis, is described from methane seeps in the eastern Pacific off Costa Rica. The new species is the 16th described species of the genus, and by molecular analysis appears closest to Alvinocaris komaii from the Lau Basin, southwestern Pacific, but shares certain morphological characters with A. lusca from the Galapagos Rift and A. muricola from the West Florida Escarpment, as well as with A. kexueae from the Manus Basin in the Southwest Pacific.

Metallosphaera sedula is a thermoacidophilic crenarchaeote with a 2.19-Mb genome. Here, we report the genome sequences of several evolved derivatives of M. sedula generated through adaptive laboratory evolution for enhanced arsenate resistance.

Objective

Adaptive laboratory evolution (ALE) is an effective approach to study the evolution behavior of bacterial cultures and to select for strains with desired metabolic features. In this study, we explored the possibility of evolving Thermotoga sp. strain RQ7 for cellulose-degrading abilities.

Results

Wild type RQ7 strain was subject to a series of transfers over six and half years with cellulose filter paper as the main and eventually the sole carbon source. Each transfer was accompanied with the addition of 50 μg of Caldicellulosiruptor saccharolyticus DSM 8903 genomic DNA. A total of 331 transfers were completed. No cellulose degradation was observed with the RQ7 cultures. Thirty three (33) isolates from six time points were sampled and sequenced. Nineteen (19) of the 33 isolates were unique, and the rest were duplicated clones. None of the isolates acquired C. saccharolyticus DNA, but all accumulated small-scale mutations throughout their genomes. Sequence analyses revealed 35 mutations that were preserved throughout the generations and another 15 mutations emerged near the end of the study. Many of the affected genes participate in phosphate metabolism, substrate transport, stress response, sensory transduction, and gene regulation.

Abstract Background Clinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs). Methods ExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study. Results We evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (first stage) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (second stage) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers. Conclusions Our experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols).