Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Confounds in pictorial sets: The role of complexity and familiarity in basic-level picture processing

Complexity is conventionally defined as the level of detail or intricacy contained within a picture. The study of complexity has received relatively little attention-in part, because of the absence of an acceptable metric. Traditionally, normative ratings of complexity have been based on human judgments. However, this study demonstrates that published norms for visual complexity are biased. Familiarity and learning influence the subjective complexity scores for nonsense shapes, with a significant training x familiarity interaction [F(1,52) = 17.53, p <.05]. Several image-processing techniques were explored as alternative measures of picture and image complexity. A perimeter detection measure correlates strongly with human judgments of the complexity of line drawings of real-world objects and nonsense shapes and captures some of the processes important in judgments of subjective complexity, while removing the bias due to familiarity effects.

Superimposed vibration confers no additional benefit compared with resistance training alone

Eighteen participants (22-43 years) were randomly allocated to one of two groups: resistance training combined with vibration (VIB; five males, four females) or resistance training alone (CON; five males, four females). Each participant trained three sessions per week (three sets of 10 seated calf raises against a load, which was increased progressively from 75% of one repetition maximum (1RM) to 90% 1RM for 4 weeks. For the VIB group, a vibratory stimulus (30 Hz, 2.5 mm amplitude) was applied to the soles of the feet by a vibration platform. The two groups did not differ significantly with respect to the total amount of work performed during training. Both groups showed a significant increase in maximum voluntary contraction and 1RM (P

Evaluation of extended training for general practice in Northern Ireland: qualitative study.

Objective To evaluate participants' perceptions of the impact on them of an additional six months' training beyond the standard 12 month general practice vocational training scheme. Design Qualitative study using focus groups. Setting General practice vocational training in Northern Ireland. Participants 13 general practitioner registrars, six of whom participated in the additional six months' training, and four trainers involved in the additional six months' training. Main outcome measures: Participants' views about their experiences in 18 month and 12 month courses. Results Participants reported that the 12 month course was generally positive but was too pressurised and focused on examinations, and also that it had a negative impact on self care. The nature of the learning and assessment was reported to have left participants feeling averse to further continuing education and lacking in confidence. In contrast, the extended six month component was reported to have restimulated learning by focusing more on patient care and promoting self directed learning. It developed confidence, promoted teamwork, and gave experience of two practice contexts, and was reported as valuable by both ex-registrars and trainers. However, both the 12 and 18 month courses left participants feeling underprepared for practice management and self care. Conclusions 12 months' training in general practice does not provide doctors with the necessary competencies and confidence to enter independent practice. The extended period was reported to promote greater professional development, critical evaluation skills, and orientation to lifelong learning but does not fill all the gaps.

Habit reversal training as a treatment for refractory OCD - A case study

Nearly 4 million American men and women from all geographic, ethnic, or economic backgrounds are diagnosed with obsessive-compulsive disorder (OCD). While a combination of cognitive behaviour therapy (CBT) and psycho-pharmaca seems successful for 50% to 60% of patients, for intractable cases the typical recommendation is more medication or more CBT, however there is little evidence that the intensified treatment regimen is successful. In this paper, habit reversal training, including awareness training, competing/other response training, self-monitoring, social support, and generalisation, was implemented with a long-term treatment-refractory OCD patient. Treatment gains and long-term maintenance indicate the potential of habit reversal procedures with these patients.

Can childhood neglect be prevented through parenting skills training?

Child neglect continues to be the most prevalent form of child maltreatment, yet it has received less specific research attention than other forms of maltreatment (Zuravin, 1999). It is only in recent years that neglect has been seen as a phenomenon that needs to be conceptualised separately to other forms of abuse (Gershater- Molko et al., 2002). Although the term ‘neglect’ is used generally when children do not receive minimal physical and/or emotional care, there is no single agreed definition; one possible reason for this is the lack of consensus about minimally adequate standards of childcare either within professional groups or existing research (Rose and Meezan, 1996; Stone, 1998).