Convolutional neural network for automated mass segmentation in mammography.

Background: Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features.

Deep convolutional neural networks for mammography: advances, challenges and applications.

Background: The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks.

A molecular structure matching approach to efficient identification of endogenous mammalian biochemical structures.

Metabolomics is the study of small molecules, called metabolites, of a cell, tissue or organism. It is of particular interest as endogenous metabolites represent the phenotype resulting from gene expression. A major challenge in metabolomics research is the structural identification of unknown biochemical compounds in complex biofluids.

Synthesis, spectral and quantum chemical studies on NO-chelating sulfamonomethoxine-cyclophosph(V)azane and its Er(III) complex.

Computational studies have been carried out at the DFT-B3LYP/6-31G(d) level of theory on the structural and spectroscopic properties of novel ethane-1,2-diol-dichlorocyclophosph(V)azane of sulfamonomethoxine (L), and its binuclear Er(III) complex. Different tautomers of the ligand were optimized at the ab initio DFT level. Keto-form structure is about 15.

Integrating Clonal Selection and Deterministic Sampling for Efficient Associative Classification.

Traditional Associative Classification (AC) algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier.

The use of graph matching algorithms to identify biochemical substructures in synthetic chemical compounds: Application to metabolomics.

Metabolomics is a rapidly growing field studying the small-molecule metabolite profile of a biological organism. Studying metabolism has a potential to contribute to biomedical research as well as drug discovery. One of the current challenges in metabolomics is the identification of unknown metabolites as existing chemical databases are incomplete.

Disposable screen-printed sensors for determination of duloxetine hydrochloride.

A screen-printed disposable electrode system for the determination of duloxetine hydrochloride (DL) was developed using screen-printing technology. Homemade printing has been characterized and optimized on the basis of effects of the modifier and plasticizers. The fabricated bi-electrode potentiometric strip containing both working and reference electrodes was used as duloxetine hydrochloride sensor.

Novel bluetongue virus serotype from Kuwait.

Sheep and goats sampled in Kuwait during February 2010 were seropositive for bluetongue virus (BTV). BTV isolate KUW2010/02, from 1 of only 2 sheep that also tested positive for BTV by real-time reverse transcription-PCR, caused mild clinical signs in sheep. Nucleotide sequencing identified KUW2010/02 as a novel BTV serotype.

Improved algorithms for parsing ESLTAGs: a grammatical model suitable for RNA pseudoknots.

Formal grammars have been employed in biology to solve various important problems. In particular, grammars have been used to model and predict RNA structures. Two such grammars are Simple Linear Tree Adjoining Grammars (SLTAGs) and Extended SLTAGs (ESLTAGs).

New dimeric cyclodiphosph(V)azane complexes of Cr(III), Co(II), Ni(II), Cu(II), and Zn(II): preparation, characterization and biological activity studies.

The complexes of type [CrLCl](2).3.5H(2)O and [ML(H(2)O)](2).