Evaluation of drug information retrieval services for selected investigational antineoplastic agents

The availability of drug information that is useful to clinicians is an important need for those responsible for medication use in patients. Physicians, pharmacists, nurses, and patients routinely require access to relevant information related to rational drug therapy. The need for rapid access to relevant information has become increasingly important as the science, technology, and specialization within health care expand. Because of this expansion the literature has increased not only in size but also in complexity. The term drug information (e.g., used in drug information service, drug information center or drug information specialist) is defined as knowledge of facts or circumstances acquired through reading, study or practical experience concerning the chemical substance intended for use in diagnosis, prevention, treatment or cure of disease or otherwise to enhance the physical or mental well-being of men or animals (1). This definition may be expanded to include the ability to provide information to the user in a special manner known as a drug information service. Drug information service is defined as the activities involved with accumulating, organizing, and retrieving drug information and may include provision of documents and bibliographic compilations or other medical library functions (1)

PRIIME: A generic framework for interactive personalized interesting pattern discovery

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial proportion are not much significant for many data analysis endeavors. Discovery of a small number of personalized interesting patterns from the large output set according to a particular user's interest is an important as well as challenging task. Existing works on pattern summarization do not solve this problem from the personalization viewpoint. In this work, we propose an interactive pattern discovery framework named PRIIME which identifies a set of interesting patterns for a specific user without requiring any prior input on the interestingness measure of patterns from the user. The proposed framework is generic to support discovery of the interesting set, sequence and graph type patterns. We develop a softmax classification based iterative learning algorithm that uses a limited number of interactive feedback from the user to learn her interestingness profile, and use this profile for pattern recommendation. To handle sequence and graph type patterns PRIIME adopts a neural net (NN) based unsupervised feature construction approach. We also develop a strategy that combines exploration and exploitation to select patterns for feedback. We show experimental results on several real-life datasets to validate the performance of the proposed method. We also compare with the existing methods of interactive pattern discovery to show that our method is substantially superior in performance. To portray the applicability of the framework, we present a case study from the real-estate domain

Six-coordinate oxime-imine cobalt(III) complexes with amino acid co-ligands; synthesis and characterisation

In this publication, several six coordinate Co(III)-complexes are reported. The reaction of 2,3-butanedione monoxime with ethylenediamine or o-phenylenediamine in mole ratios of 2:1 gave the tetradentate imine-oxime ligands diaminoethane-N,N`-bis(2-butylidine-3-onedioxime) H2L1 and o-phenylenediamine-N,N`-bis(2-butylidine-3-onedioxime), respectively. The reaction of H2L1 and H2L2 with Co(NO3)2, and the amino acid co-ligands (glycine or serine) resulted in the formation of the required complexes. Upon complex formation, the ligands behave as a neutral tetradantate species, while the amino acid co-ligand acts as a monobasic species. The mode of bonding and overall geometry of the complexes were determined through physico-chemical and spectroscopic methods. These studies revealed octahedral geometry about Co(III) complexes in which the co-ligands bound through the amine and the carboxylate groups. Molecular structure for the complexes have been optimised by CS Chem 3D Ultra Molecular Modelling and Analysis Program and supported six coordinate geometry

Pengaruh Kualitas Laporan Keuangan, Penyajian Laporan Keuangan Dan Aksesibilitas Laporan Keuangan Terhadap Akuntabilitas Pengelolaan Keuangan Daerah (Studi Empiris Pada Satuan Kerja Perangkat Daerah Kota Pekanbaru)

This study aims to describe the influence of Quality of Financial Reporting, Financial Statements Effect, and Effect Against Accountability Accessibility Financial Statements of Financial Management At Work Unit of City of Pekanbaru. The population covers all aspects related to the preparation of financial statements in government agencies located in the city of Pekanbaru. The population in this study consisted of all SKPD contained in Pekanbaru city as much as 32 SKPD. Each SKPD will be spread as much as 3 questionnaire consisted of Chief SKPD, Head of Finance and Head of the planning of each SKPD contained in Pekanbaru, bringing the total respondents were 96 respondents. Further analysis of data using multiple regression analysis through SPSS 17.0 software msi. The result of the calculation as described is known that affect the quality of financial statements Financial Management Accountability. The result of the calculation as described can be seen that the effect on the financial statements of Financial Management Accountability. The result of the calculation as described can be seen that the effect on the accessibility of Financial Statements Financial Management Accountability in the SKPD Pekanbaru

Pengaruh Pelayanan, Produktivitas, Kompensasi, Dan Sumber Daya Manusia Terhadap Kinerja Pdam Tirta Kampar – Kota Bangkinang

This study aimed to examine the impact of the service, productivity, compensation and human resources to the performance of PDAM Tirta Kampar - City Bangkinang with multiple regression method. The aim of this study was PDAM Tirta Kampar - Bangkinang City. This research is a survey method. Respondents in this study were employees of PDAM Tirta Kampar - Bangkinang City. The number of samples was 61 employees. There are five variables in this study consisted of four independent variables such as service, productivity, compensation and human resources, and the dependent variable is the performance of PDAM Tirta Kampar - Bangkinang City. According to the research, it can be concluded that the productivity and human resources significantly affect the performance of PDAM Tirta Kampar - City Bangkinang, while service and compensation did not significantly affect the performance of PDAM Tirta Kampar - Bangkinang City. The coefficient of determination shows that the service, productivity, compensation and human resources affect the dependent variable (performance PDAM Tirta Kampar - City Bangkinang) of 50.30%, while the remaining 49.70% influenced by other factors

EEG-based image classification using an efficient geometric deep network based on functional connectivity

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method's efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data. The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users' visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently availabl