Using sample qualification techniques to classify dsc records

The Similarity Match, Distance Match, Discriminant Analysis, Search Standards, and QC Compare Search techniques of statistical analysis are applied to DSC records in order to distinguish between two forms or classes of a new pharmaceutical active component. The classes are defined according to its therapeutic activity as 'positive' and 'negative'. Excellent results are obtained using the QC Compare Search Method. This revised version was published online in July 2006 with corrections to the Cover Date.     

Using random forest to classify T-cell epitopes based on amino acid properties and molecular features

T-lymphocyte (T-cell) is a very important component in human immune system. T-cell epitopes can be used for the accurately monitoring the immune responses which activation by major histocompatibility complex (MHC), and rationally designing vaccines. Therefore, accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. In current study, two types peptide features, i.e., amino acid properties and chemical molecular features were used for the T-cell epitopes peptide representation. Based on these features, random forest (RF) algorithm, a powerful machine learning algorithm, was used to classify T-cell epitopes and non-T-cell epitopes. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for proposed method are 97.54%, 97.22%, 97.60%, 0.9193, and 0.9868, respectively. These results indicate that current method based on the combined features and RF is effective for T-cell epitopes prediction.     

A method to determine and classify the chirality of the water medium

A new numerical method to determine the chirality of water configurations is developed. It consists in the comparison of matrices composed for both initial configuration and its mirror image based on the information of four bound water molecules. The method developed enables the rapid and unambiguous determination of the chirality of an aqueous system.     

Neural networks as a tool to classify compounds according to aromaticity criteria

Aromaticity is a fundamental concept in chemistry, with many theoretical and practical implications. Although most organic compounds can be categorized as aromatic, non-aromatic, or antiaromatic, it is often difficult to classify borderline compounds as well as to quantify this property. Many aromaticity criteria have been proposed, although none of them gives an entirely satisfactory solution. The inability to fully arrange organic compounds according to a single criterion arises from the fact that aromaticity is a multidimensional phenomenon. Neural networks are computational techniques that allow one to treat a large amount of data, thereby reducing the dimensionality of the input set to a bidimensional output. We present the successful applications of Kohonen's self-organizing maps to classify organic compounds according to aromaticity criteria, showing a good correlation between the aromaticity of a compound and its placement in a particular neuron. Although the input data for the training of the network were different aromaticity criteria (stabilization energy, diamagnetic susceptibility, NICS, NICS(1), and HOMA) for five-membered heterocycles, the method can be extended to other organic compounds. Some useful features of this method are: 1) it is very fast, requiring less than one minute of computational time to place a new compound in the map; 2) the placement of the different compounds in the map is conveniently visualized; 3) the position of a compound in the map depends on its aromatic character, thus allowing us to establish a quantitative scale of aromaticity, based on Euclidean distances between neurons, 4) it has predictive power. Overall, the results reported herein constitute a significant contribution to the longstanding debate on the quantitative treatment of aromaticity.     

Using biofunctional magnetic nanoparticles to capture gram-negative bacteria at an ultra-low concentration

After conjugation to vancomycin (Van), chemically stable and highly magnetic anisotropic FePt magnetic nanoparticles (approximately 4 nm) become water-soluble and capture E. coli at 15 cfu mL(-1).     

Using biofunctional magnetic nanoparticles to capture vancomycin-resistant enterococci and other gram-positive bacteria at ultralow concentration

Covalently linked to vancomycin (Van), chemically stable and highly magnetic anisotropic FePt magnetic nanoparticles (3-4 nm) become water-soluble and capture vancomycin-resistant enterococci (VRE) and other Gram-positive bacteria at concentrations approximately 10(1) cfu/mL via polyvalent ligand-receptor interactions. When a pyramidal end of a magnet "focuses" the nanoparticles into approximately 1 mm(2) area, the bacteria can be observed by an optical microscope and further identified by electron micrograph (EM). Compared to the conventional use of magnetic particles (with the sizes of 1-5 microm) in biological separation or drug delivery, magnetic nanoparticles, combined with specific receptor-ligand interactions, promise a sensitive and rapid protocol to detect pathogens.     

Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines

Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.     

Shot-noise limited single-molecule FRET histograms: comparison between theory and experiments

We describe a simple approach and present a straightforward numerical algorithm to compute the best fit shot-noise limited proximity ratio histogram (PRH) in single-molecule fluorescence resonant energy transfer diffusion experiments. The key ingredient is the use of the experimental burst size distribution, as obtained after burst search through the photon data streams. We show how the use of an alternated laser excitation scheme and a correspondingly optimized burst search algorithm eliminates several potential artifacts affecting the calculation of the best fit shot-noise limited PRH. This algorithm is tested extensively on simulations and simple experimental systems. We find that dsDNA data exhibit a wider PRH than expected from shot noise only and hypothetically account for it by assuming a small Gaussian distribution of distances with an average standard deviation of 1.6 A. Finally, we briefly mention the results of a future publication and illustrate them with a simple two-state model system (DNA hairpin), for which the kinetic transition rates between the open and closed conformations are extracted.     

How to control the scaling of CaCO3: a "fingerprinting technique" to classify additives

A titration set-up coupling ion selective electrodes with pH adjustment was used to analyze the effects of additives present during precipitation of calcium carbonate. Besides industrially well-established antiscalants (sodium triphosphate, citrate, polyacrylate and poly(aspartic acid)), also functional polymers being active in morphosynthesis (polystyrene sulfonate and poly(styrene-alt-maleic acid)) were analyzed. Interestingly each additive acts in its specific way, suggesting the notation "fingerprinting" for a complex interplay of up to five "solution modes" of influencing CaCO(3) precipitation and crystallisation. The results provide new insights into the modes of additive controlled crystallisation, and in the long run, the insights may facilitate the design of precipitation systems that yield complex and tailor-made crystals.     

Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.     

An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data

It is common that imbalanced datasets are often generated from high-throughput screening (HTS). For a given dataset without taking into account the imbalanced nature, most classification methods tend to produce high predictive accuracy for the majority class, but significantly poor performance for the minority class. In this work, an efficient algorithm, GLMBoost, coupled with Synthetic Minority Over-sampling TEchnique (SMOTE) is developed and utilized to overcome the problem for several imbalanced datasets from PubChem BioAssay. By applying the proposed combinatorial method, those data of rare samples (active compounds), for which usually poor results are generated, can be detected apparently with high balanced accuracy (Gmean). As a comparison with GLMBoost, Random Forest (RF) combined with SMOTE is also adopted to classify the same datasets. Our results show that the former (GLMBoost + SMOTE) not only exhibits higher performance as measured by the percentage of correct classification for the rare samples (Sensitivity) and Gmean, but also demonstrates greater computational efficiency than the latter (RF + SMOTE). Therefore, we hope that the proposed combinatorial algorithm based on GLMBoost and SMOTE could be extensively used to tackle the imbalanced classification problem.     

Immunological responses to gut bacteria

The integral nature of interactions between the gut microbiota and host is especially evident with respect to effects on the immune system and host defenses. Host-microbiota interactions are increasingly being revealed as complex and dynamic, with far-reaching effects on varied aspects of host health. This review focuses on adaptive and innate immune responses to the gut microbiota and the bidirectional nature of these host-microbe interactions.     

Additional studies on shape selectivity by using the carotenoid test to classify C18 bonded silica

Numerous chromatographic tests are applied to study the C18 bonded phases, either to classify these phases, or to better determine their properties or their chromatographic behaviours. Because the carotenoid test is developed in supercritical fluid chromatography (SFC), many correlations with high performance liquid chromatography (HPLC) results have been necessary to ensure the trueness of the classification reached in these analytical conditions. Consequently, the analytical conditions of the carotenoid test were chosen to fit with the TbN/BaP values from the NIST 869a test, which describe the shape selectivity of the alkyl bonded phases. Additional studies performed in this paper by using well standardized silica (J'Sphere, YMC Pack ODS A, Wakosil II), which varied from their surface coverage (J'Sphere), their pore shape (YMC), or their bonding type and carbon content (Wakosil II). The use of these C18 stationary phases allows to reach more accurate conclusion on the comparison of the shape selectivity values provided either by the cis/trans β-carotene selectivity or by the TRI/oTER or TbN/BaP ones. The extension of the studies to many other C18 stationary phases allows clarifying the relationships between the carotenoid test and the tests based on the use of PAH, as well as the usage limit of TRI/oTER and TBN/BaP selectivity in regards of the bonding density of the stationary phases for both monomeric and polymeric phases. By checking other aromatic compounds, another selectivity (anthracene/oTER) display greater correlation with the carotenoid test, that suggest an improvement of the relevance of this new compound couple for the steric selectivity study.     

Introduction to measurement of color of fluorescent materials

Fluorescent pigments and dyes add brightness and color to our lives, but brightness and light to the consumer can spell difficulties in color matching and formulation for a manufacturer. While fluorescent pigments have been used for many years, the proper formulation and quality control of colored materials containing them has been a challenge, primarily due to lack of proper instrumentation to make the necessary measurements. Another difficulty has been a general lack of understanding of how such materials achieve fluorescence. This paper, and those also published in this section, are intended to present an overview of the general principles associated with measuring fluorescent color. It addresses the chemistry and physics of what causes fluorescent pigments to work and gives some examples of applications for the pigments. Considerations as to why standard spectrophotometers and colorimeters fail to give completely accurate results in measuring such materials are discussed. After an overview of the effects of different illuminants on fluorescent pigments, I will discuss how geometry of measurement can affect results, and how several national laboratories are making such measurements.