Assessment of the origin and stability of Nifedipine polymorph IV by differential scanning calorimetry (DSC)

Purpose. To examine the thermal transition(s) between different polymorphic forms of Nifedipine and to define experimental conditions that lead to the generation of polymorph IV. Methods. Experiments were performed using a DSC 823e (Mettler Toledo). Nifedipine exists in four polymorphic forms, as well as an amorphous state. Examination of Nifedipine was conducted using the following method(s): cycle 1: 25ºC to 190ºC, 190ºC to 25ºC (formation of amorphous Nifedipine); cycle 2: 25ºC to X (60,70,80...150ºC), X to 25ºC; cycle 3: 25ºC to 190ºC and holding isothermally for 5 min between cycles (heating/cooling rate of 10ºC/min). Results. The amorphous state Nifedipine can sustain heating up to 90ºC without significant changes in its composition. Cycle 2 of amorphous material heated up to 90ºC shows only the glass transition at ~44ºC. In cycle 3 of the same material, a glass transition has been recorded at ~44ºC, followed by two exotherms (~100 and ~115ºC (crystallisation of polymorph III and II, respectively) and an endotherm (169ºC (melting of polymorphs I/II)). Samples that have been heated to temperatures between 100ºC and 120ºC in the second cycle showed a glass transition at ~44ºC and an additional exotherm at ~95ºC (crystallisation of polymorph III) on cooling a exotherm was observed at ~40ºC (crystallisation of polymorph IV). The same material showed no glass transition in cycle 3 but an endotherm at around 62ºC (melting of polymorph IV) an exotherm (~98ºC) and an endotherm (169ºC) melting of polymorph I/II. Heating the sample to a temperatures greater than 130ºC in cycle two results in a glass transition at ~44ºC, and two exotherms at ~102 and 125ºC (crystallisation of polymorphs III and I, respectively). Conclusions. DSC data suggests that polymorph IV can only be produced from amorphous or polymorph III samples. The presence of polymorph I or II drives the conversion of the less stable polymorphic form IV into the most stable form, I. Although form IV of Nifedipine can easily be created, following defined experimental conditions, it may only coexist with amorphous or polymorph III states. When polymorphs I and II are present in the sample polymorph IV cannot be etected.

A revised classification for direct tooth-colored restorative materials

Composite resins and glass-ionomer cements were introduced to dentistry in the 1960s and 1970s, respectively. Since then, there has been a series of modifications to both materials as well as the development other groups claiming intermediate characteristics between the two. The result is a confusion of materials leading to selection problems. While both materials are tooth-colored, there is a considerable difference in their properties, and it is important that each is used in the appropriate situation. Composite resin materials are esthetic and now show acceptable physical strength and wear resistance. However, they are hydrophobic, and therefore more difficult to handle in the oral environment, and cannot support ion migration. Also, the problems of gaining long-term adhesion to dentin have yet to be overcome. On the other hand, glass ionomers are water-based and therefore have the potential for ion migration, both inward and outward from the restoration, leading to a number of advantages. However, they lack the physical properties required for use in load-bearing areas. A logical classification designed to differentiate the materials was first published by McLean et al in 1994, but in the last 15 years, both types of material have undergone further research and modification. This paper is designed to bring the classification up to date so that the operator can make a suitable, evidence-based, choice when selecting a material for any given situation.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.