Analyses Of Individual Datasets Research Articles

Integrating distance sampling and presence-only data to estimate species abundance.

Integrated models combine multiple data types within a unified analysis to estimate species abundance and covariate effects. By sharing biological parameters, integrated models improve the accuracy and precision of estimates compared to separate analyses of individual data sets. We developed an integrated point process model to combine presence-only and distance sampling data for estimation of spatially explicit abundance patterns. Simulations across a range of parameter values demonstrate that our model can recover estimates of biological covariates, but parameter accuracy and precision varied with the quantity of each data type. We applied our model to a case study of black-backed jackals in the Masai Mara National Reserve, Kenya, to examine effects of spatially varying covariates on jackal abundance patterns. The model revealed that jackals were positively affected by anthropogenic disturbance on the landscape, with highest abundance estimated along the Reserve border near human activity. We found minimal effects of landscape cover, lion density, and distance to water source, suggesting that human use of the Reserve may be the biggest driver of jackal abundance patterns. Our integrated model expands the scope of ecological inference by taking advantage of widely available presence-only data, while simultaneously leveraging richer, but typically limited, distance sampling data.

Selection and validation of reliable reference genes for Tolypocladium guangdongense gene expression analysis under differentially developmental stages and temperature stresses

Tolypocladium guangdongense, formerly known as Cordyceps guangdongensis, is a widely cultivated fungus of the Cordyceps s.l. species that has been investigated over the last 12 years. It has the potential to be used in a number of applications in the health and pharmaceutical industries for it has shown its high nutritional and medicinal values according to previous animal studies. qRT-PCR (quantitative reverse transcription polymerase chain reaction) is extensively used to analyze the expression pattern and molecular mechanisms of functional genes under differentially experimental conditions. The expression stability of reference genes used for normalization determines the reliability of qRT-PCR results, indicating the importance of selection and validation of reference genes before gene expression analysis. In the present study, three statistical algorithms, geNorm, NormFinder and BestKeeper, were used for analyzing the expression stability of nineteen candidate reference genes (CRGs) in T. guangdongense. Investigation were carried out under differentially experimental conditions, which included differentially developmential stages (mycelia, primordia, young and mature fruiting bodies), different carbon sources, cold and heat stresses. The results showed that histone H4 and tubulin beta chain 2 (β-tub2) were the most and least stable genes, respectively, across all the experimental samples. Moreover, analysis of individual data sets exhibited different stability and expression profiles of reference genes. The vacuolar protein sorting gene VPS was the most stable gene expressed under the differentially developmental stages and temperature stresses, whereas H4 was the most stably expressed gene under different carbon sources. Therefore, it can be proposed that VPS and H4 are the preferred reference genes for normalization of gene expression under different experimental conditions. The results of our present study will enable more accurate evaluation of gene expression in T. guangdongense using the optimal reference gene for qRT-PCR analysis.

Taxonomy and systematics of Hyaloscyphaceae and Arachnopezizaceae.

The circumscription and composition of the Hyaloscyphaceae are controversial and based on poorly sampled or unsupported phylogenies. The generic limits within the hyaloscyphoid fungi are also very poorly understood. To address this issue, a robust five-gene Bayesian phylogeny (LSU, RPB1, RPB2, TEF-1α, mtSSU; 5521 bp) with a focus on the core group of Hyaloscyphaceae and Arachnopezizaceae is presented here, with comparative morphological and histochemical characters. A wide representative sampling of Hyaloscypha supports it as monophyletic and shows H. aureliella (subgenus Eupezizella) to be a strongly supported sister taxon. Reinforced by distinguishing morphological features, Eupezizella is here recognised as a separate genus, comprising E. aureliella, E. britannica, E. roseoguttata and E. nipponica (previously treated in Hyaloscypha). In a sister group to the Hyaloscypha-Eupezizella clade a new genus, Mimicoscypha, is created for three seldom collected and poorly understood species, M. lacrimiformis, M. mimica (nom. nov.) and M. paludosa, previously treated in Phialina, Hyaloscypha and Eriopezia, respectively. The Arachnopezizaceae is polyphyletic, because Arachnoscypha forms a monophyletic group with Polydesmia pruinosa, distant to Arachnopeziza and Eriopezia; in addition, Arachnopeziza variepilosa represents an early diverging lineage in Hyaloscyphaceae s.str. The hyphae originating from the base of the apothecia in Arachnoscypha are considered anchoring hyphae (vs a subiculum) and Arachnoscypha is excluded from Arachnopezizaceae. A new genus, Resinoscypha, is established to accommodate Arachnopeziza variepilosa and A. monoseptata, originally described in Protounguicularia. Mimicoscypha and Resinoscypha are distinguished among hyaloscyphoid fungi by long tapering multiseptate hairs that are not dextrinoid or glassy, in combination with ectal excipulum cells with deep amyloid nodules. Unique to Resinoscypha is cyanophilous resinous content in the hairs concentrated at the apex and septa. Small intensely amyloid nodules in the hairs are furthermore characteristic for Resinoscypha and Eupezizella. To elucidate species limits and diversity in Arachnopeziza, mainly from Northern Europe, we applied genealogical concordance phylogenetic species recognition (GCPSR) using analyses of individual datasets (ITS, LSU, RPB1, RPB2, TEF-1α) and comparative morphology. Eight species were identified as highly supported and reciprocally monophyletic. Four of these are newly discovered species, with two formally described here, viz. A. estonica and A. ptilidiophila. In addition, Belonium sphagnisedum, which completely lacks prominent hairs, is here combined in Arachnopeziza, widening the concept of the genus. Numerous publicly available sequences named A. aurata represent A. delicatula and the confusion between these two species is clarified. An additional four singletons are considered to be distinct species, because they were genetically divergent from their sisters. A highly supported five-gene phylogeny of Arachnopezizaceae identified four major clades in Arachnopeziza, with Eriopezia as a sister group. Two of the clades include species with a strong connection to bryophytes; the third clade includes species growing on bulky woody substrates and with pigmented exudates on the hairs; and the fourth clade species with hyaline exudates growing on both bryophytes and hardwood. A morphological account is given of the composition of Hyaloscyphaceae and Arachnopezizaceae, including new observations on vital and histochemical characters. Citation: Kosonen T, Huhtinen S, Hansen K. 2021. Taxonomy and systematics of Hyaloscyphaceae and Arachnopezizaceae. Persoonia 46: 26-62. https://doi.org/10.3767/persoonia.2021.46.02.

The accelerating rotation of the magnetic He-weak star HD 142990

ABSTRACT HD 142990 (V 913 Sco; B5 V) is a He-weak star with a strong surface magnetic field and a short rotation period (Prot ∼ 1 d). Whilst it is clearly a rapid rotator, recent determinations of Prot are in formal disagreement. In this paper, we collect magnetic and photometric data with a combined 40-yr baseline in order to re-evaluate Prot and examine its stability. Both period analysis of individual data sets and O − C analysis of the photometric data demonstrate that Prot has decreased over the past 30 yr, violating expectations from magnetospheric braking models, but consistent with behaviour reported for 2 other hot, rapidly rotating magnetic stars, CU Vir and HD 37776. The available magnetic and photometric time series for HD 142990 can be coherently phased assuming a spin-up rate $\dot{P}$ of approximately −0.6 s yr−1, although there is some indication that $\dot{P}$ may have slowed in recent years, possibly indicating an irregular or cyclic rotational evolution.

Cross platform analysis of methylation, miRNA and stem cell gene expression data in germ cell tumors highlights characteristic differences by tumor histology

BackgroundAlterations in methylation patterns, miRNA expression, and stem cell protein expression occur in germ cell tumors (GCTs). Our goal is to integrate molecular data across platforms to identify molecular signatures in the three main histologic subtypes of Type I and Type II GCTs (yolk sac tumor (YST), germinoma, and teratoma).MethodsWe included 39 GCTs and 7 paired adjacent tissue samples in the current analysis. Molecular data available for analysis include DNA methylation data (Illumina GoldenGate Cancer Methylation Panel I), miRNA expression (NanoString nCounter miRNA platform), and stem cell factor expression (SABiosciences Human Embryonic Stem Cell Array). We evaluated the cross platform correlations of the data features using the Maximum Information Coefficient (MIC).ResultsIn analyses of individual datasets, differences were observed by tumor histology. Germinomas had higher expression of transcription factors maintaining stemness, while YSTs had higher expression of cytokines, endoderm and endothelial markers. We also observed differences in miRNA expression, with miR-371-5p, miR-122, miR-302a, miR-302d, and miR-373 showing elevated expression in one or more histologic subtypes. Using the MIC, we identified correlations across the data features, including six major hubs with higher expression in YST (LEFTY1, LEFTY2, miR302b, miR302a, miR 126, and miR 122) compared with other GCT.ConclusionsWhile prognosis for GCTs is overall favorable, many patients experience resistance to chemotherapy, relapse and/or long term adverse health effects following treatment. Targeted therapies, based on integrated analyses of molecular tumor data such as that presented here, may provide a way to secure high cure rates while reducing unintended health consequences.

Diverse Data Sets Can Yield Reliable Information through Mechanistic Modeling: Salicylic Acid Clearance.

This is a practical example of a powerful research strategy: putting together data from studies covering a diversity of conditions can yield a scientifically sound grasp of the phenomenon when the individual observations failed to provide definitive understanding. The rationale is that defining a realistic, quantitative, explanatory hypothesis for the whole set of studies, brings about a “consilience” of the often competing hypotheses considered for individual data sets. An internally consistent conjecture linking multiple data sets simultaneously provides stronger evidence on the characteristics of a system than does analysis of individual data sets limited to narrow ranges of conditions. Our example examines three very different data sets on the clearance of salicylic acid from humans: a high concentration set from aspirin overdoses; a set with medium concentrations from a research study on the influences of the route of administration and of sex on the clearance kinetics, and a set on low dose aspirin for cardiovascular health. Three models were tested: (1) a first order reaction, (2) a Michaelis-Menten (M-M) approach, and (3) an enzyme kinetic model with forward and backward reactions. The reaction rates found from model 1 were distinctly different for the three data sets, having no commonality. The M-M model 2 fitted each of the three data sets but gave a reliable estimates of the Michaelis constant only for the medium level data (Km = 24±5.4 mg/L); analyzing the three data sets together with model 2 gave Km = 18±2.6 mg/L. (Estimating parameters using larger numbers of data points in an optimization increases the degrees of freedom, constraining the range of the estimates). Using the enzyme kinetic model (3) increased the number of free parameters but nevertheless improved the goodness of fit to the combined data sets, giving tighter constraints, and a lower estimated Km = 14.6±2.9 mg/L, demonstrating that fitting diverse data sets with a single model improves confidence in the results. This modeling effort is also an example of reproducible science available at html://www.physiome.org/jsim/models/webmodel/NSR/SalicylicAcidClearance

Gene Set Based Integrated Data Analysis Reveals Phenotypic Differences in a Brain Cancer Model

A key challenge in the data analysis of biological high-throughput experiments is to handle the often low number of samples in the experiments compared to the number of biomolecules that are simultaneously measured. Combining experimental data using independent technologies to illuminate the same biological trends, as well as complementing each other in a larger perspective, is one natural way to overcome this challenge. In this work we investigated if integrating proteomics and transcriptomics data from a brain cancer animal model using gene set based analysis methodology, could enhance the biological interpretation of the data relative to more traditional analysis of the two datasets individually. The brain cancer model used is based on serial passaging of transplanted human brain tumor material (glioblastoma - GBM) through several generations in rats. These serial transplantations lead over time to genotypic and phenotypic changes in the tumors and represent a medically relevant model with a rare access to samples and where consequent analyses of individual datasets have revealed relatively few significant findings on their own. We found that the integrated analysis both performed better in terms of significance measure of its findings compared to individual analyses, as well as providing independent verification of the individual results. Thus a better context for overall biological interpretation of the data can be achieved.

Extracting insights from the shape of complex data using topology

This paper applies topological methods to study complex high dimensional data sets by extracting shapes (patterns) and obtaining insights about them. Our method combines the best features of existing standard methodologies such as principal component and cluster analyses to provide a geometric representation of complex data sets. Through this hybrid method, we often find subgroups in data sets that traditional methodologies fail to find. Our method also permits the analysis of individual data sets as well as the analysis of relationships between related data sets. We illustrate the use of our method by applying it to three very different kinds of data, namely gene expression from breast tumors, voting data from the United States House of Representatives and player performance data from the NBA, in each case finding stratifications of the data which are more refined than those produced by standard methods.

Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

BackgroundIn the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required.MethodologyThe main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established.ConclusionsWe find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.

Seasonal variation of fine particulate composition in the centre of a UK city

In order to better project the impacts of air quality control measures, it is necessary to study the behaviour of particulates beyond mass-based metrics. To study the long term and seasonal trends of particulate chemical composition in Manchester, UK, seven Aerosol Mass Spectrometer (AMS) datasets obtained from ambient sampling in central Manchester over a seven year period have been combined and compared. The size-resolved submicron nitrate, sulphate, ammonium and organic matter data were examined to assess the relative importance of season, local meteorology and synoptic transport in determining ambient concentrations. Systematic trends were observed that were not previously possible through the analysis of individual datasets in isolation. The results obtained here indicate that prevailing westerly winds tend to bring relatively consistent low levels of pollutants, whereas Easterly and Northerly winds bring much more variable pollutant concentrations, whose chemical compositions are strongly dependent on season. Specifically, continental air in the summer brings with it high levels of sulphates whereas in winter similar synoptic conditions and cold ground temperatures tend to induce strong inversions, trapping local pollution and leading to elevated organic matter. Local temperature was also found to be important, with the very low temperatures in winter and very high temperatures in summer giving the greatest concentrations of all components except ammonium nitrate in summer, which began to evaporate at the higher temperatures. These results will assist in evaluating the chemical nature of air pollution episodes and also provide a phenomenological basis for testing chemical transport and exposure models. While the results focus on Manchester, similar phenomena could be expected of a number of UK cities.

Phylogenetic synthesis of morphological and molecular data reveals new insights into the higher-level classification of Tipuloidea (Diptera)

Tipuloidea, the crane flies, are a diverse lineage of true flies (Insecta: Diptera) whose phylogenetic classification and taxonomy remain a challenge. Here we present the results of a quantitative phylogenetic analysis of Tipuloidea based on combined morphological characters (adult, larvae and pupae) and nuclear gene sequence data (28S rDNA and CAD). Forty-five species, from 44 genera and subgenera, were sampled, representing the four putative families of Tipuloidea (Cylindrotomidae, Limoniidae, Pediciidae and Tipulidae sensu stricto). Analyses of individual datasets, although differing in overall topology, support the monophyly of several major lineages within Tipuloidea. Parsimony and Bayesian analyses using individual morphological and molecular datasets resulted in incongruent topologies. Increased resolution and tree support was obtained when both datasets (morphology and genes) were combined, in both combined evidence parsimony and Bayesian analyses, than when analysed separately. The recovered consensus phylogeny was not consistent with any previously proposed Tipuloidea classification, with previous importance assigned to character states shown here to represent losses and reversals seen as a major factor influencing erroneous classification. The results provided here, together with evidence from previous analyses, were used to append the Tipuloidea classification to supported evolutionary lineages. Tipuloidea is presented as two families: Pediciidae and Tipulidae. Pediciidae is recovered as the sister group to all remaining Tipuloidea. Our current phylogenetic hypothesis is not consistent with the existing subfamilial classification of the ‘Limoniidae’, which is paraphyletic with respect to a well-supported Tipulinae + Cylindrotominae clade, whereas the three ‘limoniid’ subfamilies are para- or polyphyletic. The recognition of ‘Limoniidae’ as a valid monophyletic family is discouraged and the subfamilies of ‘Limoniidae’ are amended and placed within Tipulidae. A revised phylogenetic classification is proposed for the crane flies based on a synthesis of evidence from multiple genes and morphology.

Hidden Likelihood Support in Genomic Data: Can Forty-Five Wrongs Make a Right?

Combined analysis of multiple phylogenetic data sets can reveal emergent character support that is not evident in separate analyses of individual data sets. Previous parsimony analyses have shown that this hidden support often accounts for a large percentage of the overall phylogenetic signal in cladistic studies. Here, reanalysis of a large comparative genomic data set for yeast (genus Saccharomyces) demonstrates that hidden support can be an important factor in maximum likelihood analyses of multiple data sets as well. Emergent signal in a concatenation of 106 genes was responsible for up to 64% of the likelihood support at a particular node (the difference in log likelihood scores between optimal topologies that included and excluded a supported clade). A grouping of four yeast species (S. cerevisiae, S. paradoxus, S. mikatae, and S. kudriavzevii) was robustly supported by combined analysis of all 106 genes, but separate analyses of individual genes suggested numerous conflicts. Forty-eight genes strictly contradicted S. cerevisiae + S. paradoxus + S. mikatae + S. kudriavzevii in separate analyses, but combined likelihood analyses that included up to 45 of the "wrong" data sets supported this group. Extensive hidden support also emerged in a combined likelihood analysis of 41 genes that each recovered the exact same topology in separate analyses of the individual genes. These results show that isolated analyses of individual data sets can mask congruence and distort interpretations of clade stability, even in strictly model-based phylogenetic methods. Consensus and supertree procedures that ignore hidden phylogenetic signals are, at best, incomplete.

A confirmatory analysis of four acoustic correlates of timbre space

Exploratory multidimensional scaling studies of musical instrument timbres generally yield two- to four-dimensional perceptual spaces. Acoustic parameters have been derived that correlate moderately to highly with the perceptual dimensions. In a confirmatory study, two three-dimensional sets of synthetic, harmonic sounds equalized for fundamental frequency, loudness, and perceived duration were designed. The first two dimensions corresponded to attack time and spectral centroid in both sound sets. The third dimension corresponded to spectral flux (variation of the spectral centroid over time) in the first set and to the energy ratio of odd to even harmonics in the second set. Group analyses of dissimilarity judgments for all pairs of sounds homogeneously distributed in each space revealed a two-dimensional solution for the first set and a three-dimensional solution for the second set. Log attack time and spectral centroid were confirmed as perceptual dimensions in both solutions. The even/odd energy ratio was confirmed as a third dimension in the second set. Spectral flux was not confirmed in the first set, suggesting that this parameter should be re-examined. Analyses of individual data sets tested for differences across listeners in the mapping of acoustic parameters to perceptual dimensions. [Work supported by the CTI program of the CNRS.]

A Total-Evidence Phylogeny of Ticks Provides Insights into the Evolution of Life Cycles and Biogeography

We inferred the phylogeny of 33 species of ticks from the subfamilies Rhipicephalinae and Hyalomminae from analyses of nuclear and mitochondrial DNA and morphology. We used nucleotide sequences from 12S rRNA, cytochrome c oxidase I, internal transcribed spacer 2 of the nuclear rRNA, and 18S rRNA. Nucleotide sequences and morphology were analyzed separately and together in a total-evidence analysis. Analyses of the five partitions together (3303 characters) gave the best-resolved and the best-supported hypothesis so far for the phylogeny of ticks in the Rhipicephalinae and Hyalomminae, despite the fact that some partitions did not have data for some taxa. However, most of the hidden conflict (lower support in the total-evidence analyses compared to that in the individual analyses) was found in those partitions that had taxa without data. The partitions with complete taxonomic sampling had more hidden support (higher support in the total-evidence analyses compared to that in the separate-partition analyses) than hidden conflict. Mapping of geographic origins of ticks onto our phylogeny indicates an African origin for the Rhipicephalinae sensu lato (i.e., including Hyalomma spp.), the Rhipicephalus–Boophilus lineage, the Dermacentor–Anocentor lineage, and the Rhipicephalus–Booophilus–Nosomma–Hyalomma–Rhipicentor lineage. The Nosomma–Hyalomma lineage appears to have evolved in Asia. Our total-evidence phylogeny indicates that (i) the genus Rhipicephalus is paraphyletic with respect to the genus Boophilus, (ii) the genus Dermacentor is paraphyletic with respect to the genus Anocentor, and (iii) some subgenera of the genera Hyalomma and Rhipicephalus are paraphyletic with respect to other subgenera in these genera. Study of the Rhipicephalinae and Hyalomminae over the last 7 years has shown that analyses of individual datasets (e.g., one gene or morphology) seldom resolve many phylogenetic relationships, but analyses of more than one dataset can generate well-resolved phylogenies for these ticks.

Accuracy and precision of NMR relaxation experiments and MD simulations for characterizing protein dynamics

Model-free parameters obtained from nuclear magnetic resonance (NMR) relaxation experiments and molecular dynamics (MD) simulations commonly are used to describe the intramolecular dynamical properties of proteins. To assess the relative accuracy and precision of experimental and simulated model-free parameters, three independent data sets derived from backbone 15N NMR relaxation experiments and two independent data sets derived from MD simulations of Escherichia coli ribonuclease HI are compared. The widths of the distributions of the differences between the order parameters for pairs of NMR data sets are congruent with the uncertainties derived from statistical analyses of individual data sets; thus, current protocols for analyzing NMR data encapsulate random uncertainties appropriately. Large differences in order parameters for certain residues are attributed to systematic differences between samples for intralaboratory comparisons and unknown, possibly magnetic field-dependent, experimental effects for interlaboratory comparisons. The widths of distributions of the differences between the order parameters for two NMR sets are similar to widths of distributions for an NMR and an MD set or for two MD sets. The linear correlations between the order parameters for an MD set and an NMR set are within the range of correlations observed between pairs of NMR sets. These comparisons suggest that the NMR and MD generalized order parameters for the backbone amide N—H bond vectors are of comparable accuracy for residues exhibiting motions on a fast time scale (<100 ps). Large discrepancies between NMR and MD order parameters for certain residues are attributed to the occurrence of “rare” motional events over the simulation trajectories, the disruption of an element of secondary structure in one of the simulations, and lack of consensus among the experimental data sets. Consequently, (easily detectable) severe distortions of local protein structure and infrequent motional events in MD simulations appear to be the most serious artifacts affecting the accuracy and precision, respectively, of MD order parameters relative to NMR values. In addition, MD order parameters for motions on a fast (<100 ps) timescale are more precisely determined than their NMR counterparts, thereby permitting more detailed dynamic characterization of biologically important residues by MD simulation than is sometimes possible by experimental methods. Proteins 28:481–493, 1997. © 1997 Wiley-Liss, Inc.

Accuracy and precision of NMR relaxation experiments and MD simulations for characterizing protein dynamics

Model-free parameters obtained from nuclear magnetic resonance (NMR) relaxation experiments and molecular dynamics (MD) simulations commonly are used to describe the intramolecular dynamical properties of proteins. To assess the relative accuracy and precision of experimental and simulated model-free parameters, three independent data sets derived from backbone 15N NMR relaxation experiments and two independent data sets derived from MD simulations of Escherichia-coli ribonuclease HI are compared. The widths of the distributions of the differences between the order parameters for pairs of NMR data sets are congruent with the uncertainties derived from statistical analyses of individual data sets; thus, current protocols for analyzing NMR data encapsulate random uncertainties appropriately. Large differences in order parameters for certain residues are attributed to systematic differences between samples for intralaboratory comparisons and unknown, possibly magnetic field-dependent, experimental effects for interlaboratory comparisons. The widths of distributions of the differences between the order parameters for two NMR sets are similar to widths of distributions for an NMR and an MD set or for two MD sets. The linear correlations between the order parameters for an MD set and an NMR set are within the range of correlations observed between pairs of NMR sets. These comparisons suggest that the NMR and MD generalized order parameters for the backbone amide N-H bond vectors are of comparable accuracy for residues exhibiting motions on a fast time scale (< 100 ps). Large discrepancies between NMR and MD order parameters for certain residues are attributed to the occurrence of "rare" motional events over the simulation trajectories, the disruption of an element of secondary structure in one of the simulations, and lack of consensus among the experimental data sets. Consequently, (easily detectable) severe distortions of local protein structure and infrequent motional events in MD simulations appear to be the most serious artifacts affecting the accuracy and precision, respectively, of MD order parameters relative to NMR values. In addition, MD order parameters for motions on a fast (< 100 ps) timescale are more precisely determined than their NMR counterparts, thereby permitting more detailed dynamic characterization of biologically important residues by MD simulation than is sometimes possible by experimental methods. Proteins 28:481-493, 1997.

Class-level relationships in the phylum Cnidaria: molecular and morphological evidence.

The evolutionary history of cnidarian life cycles has been debated since the 1880s, with different hypotheses favored even by current textbooks. Contributing to the disagreement is the fact that the systematic relationships of the four cnidarian classes have received relatively little examination using modern systematic methods. Here we present analyses of class-level relationships based on 18S ribosomal DNA (rDNA) sequence, mitochondrial 16S rDNA sequence, mitochondrial genome structure, and morphological characters. DNA sequences were aligned using a repeatable parsimony-based approach incorporating a range of alignment parameters. Analyses of individual data sets and of all data combined are unanimous in grouping the classes possessing a medusa stage, leaving the holobenthic Anthozoa basal within the phylum.

The closure of a long-stay psychiatric hospital: a longitudinal study of patients' behavior.

This paper summarises longitudinal data based on repeat assessments of the behavioural functioning of 90 long-stay psychiatric patients. Data were gathered over a period of 4-5 years that spanned a hospital closure and the resettlement of patients in various alternative settings. Time-series analyses of individual data sets showed that resettlement led to no significant behavioural change in the majority of patients. However, some significant behavioural improvements were evidenced for approximately 20% of the group, while behavioural deterioration occurred in approximately 12%. Supplementary findings showed that following resettlement the quality of care provided significantly improved in many respects. However, access to social, recreational or vocational activities was reduced. In addition, the level of physical health symptomatology was shown to increase for this group of patients.