Symptom Cluster-Matching Antidepressant Treatment: A Case Series Pilot Study

Despite treating depression with antidepressants, their effectiveness is often insufficient. Comparative effectiveness studies and meta-analyses show the effectiveness of antidepressants; however, they do not provide clear indications as to the choice of a specific antidepressant. The rational choice of antidepressants may be based on matching their mechanisms of action to the symptomatic profiles of depression, reflecting the heterogeneity of symptoms in different patients. The authors presented a series of cases of patients diagnosed with depression in whom at least one previous antidepressant treatment was shown to be ineffective before drug targeted symptom cluster-matching treatment (SCMT). The presented pilot study shows for the first time the effectiveness of SCMT in the different clusters of depressive symptoms. All the described patients obtained recovery from depressive symptoms after introducing drug-targeted SCMT. Once validated in clinical trials, SCMT might become an effective and rational method of selecting an antidepressant according to the individual profile of depressive symptoms, the mechanism of their formation, and the mechanism of drug action. Although the study results are preliminary, SCMT can be a way to personalize treatment, increasing the likelihood of improvement even in patients who meet criteria for treatment-resistant depression.

Cluster Matching for Discrete Data with Multiple Domains with out Alignment of Information

We advocates a Topic methods for unsupervised cluster matching; this is the project of locating matching amongst clusters in first rate domains without correspondence statistics. As an instance, the proposed version famous correspondences among record clusters in English and German without alignment statistics, along with dictionaries and parallel sentences/files. The proposed version assumes that files in all languages have a not unusual latent challenge rely shape, and there are in all likelihood endless numbers of subject matter proportion percent vectors in a latent subject rely region that is shared by means of way of all languages. Each record is generated the use of one of the subject matter percentage percent vectors and language-particular phrase distributions. Via inferring a subject percent vector used for each document, we are able to allocate documents in wonderful languages into commonplace clusters, wherein each cluster is associated with a subject percent vector. Documents assigned into the same cluster are considered to be matched. We extend an green inference method for the proposed version based totally on collapsed Gibbs sampling. The effectiveness of the proposed model is confirmed with real datasets together with multilingual corpora of Wikipedia and product reviews.

Polygon Shape Formation for Multi-Mobile Robots in a Global Knowledge Environment

In coordination of a group of mobile robots in a real environment, the formation is an important task. Multimobile robot formations in global knowledge environments are achieved using small robots with small hardware capabilities. To perform formation, localization, orientation, path planning and obstacle and collision avoidance should be accomplished. Finally, several static and dynamic strategies for polygon shape formation are implemented. For these formations minimizing the energy spent by the robots or the time for achieving the task, have been investigated. These strategies have better efficiency in completing the formation, since they use the cluster matching algorithm instead of the triangulation algorithm.