Climate variability, such as ambient temperature, is crucial for infants' vulnerability to infectious diseases. However, little is known about how climate variability affects infectious disease mortality among infants in high mortality settings. We investigate the association between ambient temperature, seasonality and cause-specific infant mortality. Parish register data from the Sundsvall region in Northern Sweden covering the period 1868-1892 were used in combination with daily temperature data from Härnösand. Mortality due to water- and food-born diseases, airborne infectious diseases, and other causes were modelled as a function of temperature exposure in the previous 14 days using time-series analysis. We found that airborne infectious disease mortality was not related to cold temperatures but rather to seasonality, and that the summer mortality peak due to water- and foodborne infections were associated with high temperatures and not with seasonality.
Climate conditions, such as ambient temperatures, play a crucial role in infants' vulnerability to infectious diseases. However, little is known about how climate conditions, such as temperatures and seasonality, affect infectious disease mortality among infants in high mortality settings. The aim of our study was to investigate the association between cause-specific infant mortality and ambient temperatures and seasonality. We applied a retrospective study design using parish register data from Sweden covering the 1868–1892 period in combination with daily temperature data. Mortality due to water- and foodborne diseases, airborne infectious diseases and other causes was modelled as a function of temperature exposure in the previous 14 days using distributed lagged non-linear models. We found that airborne infectious diseasemortality was not related to cold temperatures, but rather to seasonality. The summer peaks in mortality due to water- and foodborne infections were associated with high temperatures, and not with seasonality. The increased vulnerability of infants to infectious diseases at high temperatures is a significant future risk, given that global temperatures are projected to rise in the coming decades.
Twenty nucleosides occurring in transfer ribonucleic acid (tRNA) have been characterized using 21 experimentally determined (HPLC, TLC, NMR, etc.) and calculated (log P, van der Waals surface area, ionization potential, etc.) variables. Principal component analysis (PCA) was performed on the data set and four statistically significant components or principal properties (PPs) were extracted. The PPs described 68·4% of the variance in the data. The PP values are discussed in terms of similarity and dissimilarity among the nucleosides. The loading vectors from the PCA are used for an interpretation of the nature of the PP vectors. Application of the PPs in sequence-activity modelling is demonstrated with 25 DNA-promoter sequences originating from E. coli.
A novel quantitative multivariate approach for describing and analyse tRNA sequence data is presented. This approach is based on a multivariate chemical description of each nucleoside in the sequence. 30 theoretically calculated descriptors were used to characterize 63 nucleosides, and principal component analysis was used to extract the main variation from this multivariate description. The resulting four principal properties were interpreted as (PPa) size/bulk of the nucleoside, (PPb) polarity/hydrophobicity of the nucleoside, (PPc) electronic properties of the nucleoside and (PPd)polarity and size of the ribose moiety. These principal properties may be used to translate the tRNA letter sequence data into a quantitative chemical representation. We demonstrate the use of this quantitative description with a multivariate analysis of a set of tRNA sequences. This analysis gives models that are interpretable in terms of wich sequence positions, and nucleoside properties that discriminate the different isoacceptors. This approach is applicable on all kinds of RNA sequence data and gives information that is complementary to current sequence analysis techniques.
In this thesis, attention has been focused on the quantitative description of nucleic acids, proteins and peptides. The strategy was to use multivariate chemometrical methods for improving the understanding of the complex structural codes of these kinds of biological molecules. Tools have been developed that enable quantitative modelling of biological molecules, i.e. models based on data that quantitatively describes their properties. The advantage of such models is that they provide interpretations in terms of chemical characteristics for complex features such as similarity, dissimilarity and potency.
By a multivariate physical-chemical characterization of the building blocks of nucleic acids and proteins, i.e. nucleosides and amino acids, descriptive scales have been developed, so called principal properties. The scales give a description of the intrinsic properties of these building blocks. The multivariate characterization results in a multi-property matrix. A principal component analysis of the multi-property matrix gives a small number of latent variables which are considered as the principal properties of the characterized molecules.
The principal property scales may be used for a wide range of different purposes, such as detecting trends and groupings in large sequence data sets, and for analyzing quantitative relationships between structure and function. In statistical experimental design, the descriptors are well suited as design variables to select combinations of amino acids in such a way that they span a wide range of properties.
The use of these principal property descriptors is demonstrated in the quantitative modelling of relationships between structure and activity of various peptide series, DNA-promoters and in the quantitative modelling of transfer ribonucleic acid sequence data (tRNA).
Biopolymer sequences (e.g., DNA, RNA, proteins and polysaccharides) and chemical processes (e.g., a batch or continuous polymer synthesis run in a chemical plant) have close similarities from the modelling point of view. When a set of sequences or processes is characterized by multivariate data, a three-way data matrix is obtained. With sequences the position and with processes the time is one direction in this matrix. The multivariate modelling of this matrix by principal component analysis (PCA) or partial least-squares (PLS) methods for the following purposes is discussed: classification of sequences; quantitative relationships between sequence and biological activity or chemical properties; optimizing a sequence with respect to selected properties; process diagnostics; and quantitative relationships between process variables and product quality variables. To obtain good models, a number of problems have to be adequately dealt with: appropriate characterization of the sequence or process; experimental design (selecting sequences or process settings); transforming the three-way into a two-way matrix; and appropriate modelling and validation (modelling interactions, periodicities, “time series” structures and “neighbour effects”). A multivariate approach to sequence and process modelling using PCA and PLS projections to latent structures is discussed and illustrated with several sets of peptide and DNA promoter data.
Multivariate statistical process control (MSPC) is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an (1) `intuitive' univariate approach, and (2) with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis (PCA) method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking (SMART) chart, is introduced and used.
A multivariate quantitative physicochemical characterization of the five bases adenine (A), cytosine (C), guanine (G), thymine (T) and uracil (U), followed by principal component analysis, shows that the relative dissimilarities between the bases of DNA (A, C, G and T) are almost the same (i.e. balanced). In contrast, mRNA (containing U instead of T) has a considerably larger relative physicochemical similarity between C and U than between all other pairs of bases and is therefore inherently more unbalanced. These results provide a physicochemical explanation of the presence of thymine instead of uracil as an element of DNA. The principal component scores enable a quantitative description of nucleic acid sequence data to be made for structure-activity modelling purposes.
The aim of this study was to develop a structure-property model for membrane partitioningof oligopeptides using statistical design methods and multivariate data analysis. A set of 20tetrapeptides with optional N-methylations at residues 2 and 4 was designed by a D-optimaldesign procedure. After synthesis and purification, the membrane partitioning abilities of thepeptides were tested in two chromatographic systems with phospholipids as the stationaryphase: immobilized artificial membrane chromatography (IAM) and immobilized liposomechromatography (ILC). The relationship between these measures and three different sets ofcalculated descriptors was analyzed by partial least-squares projection to latent structures(PLS). The descriptors used were the molecular surface area, Molsurf parameters, and Volsurfparameters. All three models were of good statistical quality and supported that a largehydrogen-bonding potential and the presence of a negative charge impair membrane partitioning,whereas hydrophobic parameters promote partitioning. The findings are in accordancewith what has been found for absorption of known drugs and have implications for the designof peptide-like drugs with good oral bioavailability.
In this study 87 amino acids (AA.s) have been characterized by 26 physicochemical descriptor variables. These descriptor variables include experimentally determined retention values in seven thin-layer chromatography (TLC) systems, three nuclear magnetic resonance (NMR) shift variables, and 16 calculated variables, namely six semiempirical molecular orbital indices, total, polar, and nonpolar surface area, van der Waals volume of the side chain, log P, molecular weight, and four indicator variables describing hydrogen bond donor and acceptor properties, and side chain charge. In the present study, the data from a previous characterization of 55 AA.s from our laboratory have been extended with data for 32 additional AA.s and 14 new descriptor variables. The new 32 AA.s were selected to represent both intermediate and more extreme physicochemical properties, compared to the 20 coded AA.s. The new extended and updated principal property scales, the z-scales, were calculated and aligned to previously reported z(old)-scales. The appropriateness of the extended z-scales were validated by the use in quantitative sequence-activity modeling (QSAM) of 89 elastase substrate analogues and in a QSAM of 29 neurotensin analogues.