Canonical Scores Research Articles

Pattern Recognition of Alkaloids by Inhibiting the Catalytic Activity of Dopzymes for Dopamine.

Exploiting the specific recognition probe for all of the biomolecules is difficult in "lock-and-key" biosensors. The cross-reaction or the semispecific probe in pattern recognition mode is an alternative strategy through extracting a multidimensional signal array from recognition elements. Here, we design a pattern recognition sensor array based on the alkaloid-inhibited catalytic activity of dopzymes for the discrimination and determination of six alkaloids. In this sensor array, three different G-rich sequences, i.e., G-triplex (G3), G-quadruplex (GQ1), and the G-quadruplex dimer (2GQ1) possessing various peroxidase activities, conjugated with a dopamine aptamer and the dopzymes (G3-d-apt, GQ1-d-apt, and 2GQ1-d-apt) were obtained with an enhanced catalytic performance for the substrate. Through the interactions between six target alkaloids and G3, GQ1, and 2GQ1 regions, the pattern signal (6 alkaloids × 3 dopzymes × 5 replicates) was obtained from the diverse inhibited effect for the dopzyme activity. In virtue of the statistical method principal component analysis (PCA), the data array was projected into a new dimensional space to acquire the three-dimensional (3D) canonical scores and grouped into their respective clusters. The sensor array exhibited an outstanding discrimination and classification capability for six alkaloids with different concentrations with 100% accuracy. In addition, the nonspecific recognition elements of the sensor array showed high selectivity even though other alkaloids with similar structures to targets existed in the samples. Importantly, the levels of the six targets can be analyzed by the most influential discrimination factor, which represented the vector with the highest variance, evidencing that the sensor array has potential in drug screening and clinical treatment.

Towards Personally Relevant Navigation: The Differential Effects of Cognitive Style and Map Orientation on Spatial Knowledge Development

Under emergencies such as floods and fires or during indoor navigation where cues from local landmarks and a Global Positioning System (GPS) are no longer available, the acquisition of comprehensive environmental representation becomes particularly important. Several studies demonstrated that individual differences in cognitive style might play an important role in creating a complete environmental representation and spatial navigation. However, this relationship between cognitive style and spatial navigation is not well researched. This study hypothesized that a specific type of map orientation (north-up vs. forward-up) might be more efficient for individuals with different cognitive styles. Forty participants were recruited to perform spatial tasks in a virtual maze environment to understand how cognitive style may relate to spatial navigation abilities, particularly the acquisition of survey and route knowledge. To measure survey knowledge, pointing direction tests and sketch map tests were employed, whereas, for route knowledge, the landmark sequencing test and route retracing test were employed. The results showed that both field-dependent and field-independent participants showed more accurate canonical organization in their sketch map task with a north-up map than with a forward-up map, with field-independent participants outperforming field-dependent participants in canonical organization scores. The map orientation did not influence the performance of Field-Independent participants on the pointing direct test, with field-dependent participants showing higher angular error with north-up maps. Regarding route knowledge, field-independent participants had more accurate responses in the landmark sequencing tests with a north-up map than with a forward-up map. On the other hand, field-dependent participants had higher accuracy in landmark sequencing tests in the forward-up map condition than in the north-up map condition. In the route retracing test, however, the map orientation had no statistically significant effect on different cognitive style groups. The results indicate that cognitive style may affect the relationship between map orientation and spatial knowledge acquisition.

Geometric morphometry enables species hypotheses testing and the assignment of historical type specimens in the genus Callirhinus Blanchard (Coleoptera: Scarabaeidae: Anomalini)

Abstract Assignation of old type specimens is important for nomenclatural stability, but when it comes to ancient specimens, the use of certain techniques is sometimes not appropriate, due to the condition or age of the specimen. In this study, the supposedly polymorphic and monotypic genus Callirhinus Blanchard was analysed with geometric morphometric techniques and photographs of the pronotum, with the purpose of identifying the locality of the old type specimen (19th century) and to test a priori defined hypothetical species by cumulation of evidence within an integrative taxonomic protocol using geometric morphometry, complemented by morphology and geographical discontinuity. Five morphotypes were defined a priori, and principal component, canonical variates analysis, and distances tests for the assignment of the type specimen based on canonical scores and Procrustes distances between means of groups were carried out. Statistical significance was found in four axes validating the species hypotheses, discovering that the type specimen belongs to the morphotype from Jalisco state. Additionally, a taxonomic treatment, the description of four new species, C. choperi Ramírez-Ponce & Zaragoza sp. nov., C. nanduRamírez-Ponce & Zaragoza sp. nov., C. huiinisRamírez-Ponce & Zaragoza sp. nov., and C. veemeRamírez-Ponce & Zaragoza sp. nov. , keys for the species determination, new distributional records, and a map are provided. The present work demonstrates that Geometric Morphometry is an adequate analytical tool that supports taxonomic identification at low cost and without damaging historical specimens.

Colorimetric sensor array for discriminating and determinating phenolic pollutants basing on different ratio of ligands in Cu/MOFs

The high toxicity and low biodegradability of the phenolic pollutants destroyed the balance of the environment and influenced human health seriously. Here, we developed a three-dimensional coloremetric sensor array for discriminating and determinating phenolic pollutants basing on the distinct Cu/nucleotides MOFs. Firstly, three laccase-mimic Cu/MOFs (Cu/AMP, Cu/CMP, and Cu/GMP) were obtained by regulating the molar ratio of Cu2+ and nucleotides. Then the Cu/MOFs as the recognition elements of the sensor array catalyzed the pollutants-4-AAP-H2O2 system, obtaining the colored benzoquinone products. Subsequently, the data array obtaining from the combined training matrix (3 Cu/MOFs × 6 pollutants × 5 replicates) was projected into a new dimensional space to obtain the 3D canonical scores, and classified into individual clusters by introducing LDA method. No overlap in their respective LDA plots for the six phenolic pollutants with different concentrations suggested the prominent discriminating performance of the sensor array. Furthermore, the sensor array exhibited high selectivity compared to the “lock-and-key” sensors even other active matrices coexisting in water samples. Importantly, the most influential discrimination factor was used to monitor the levels of the six targets, evidencing the potential application in assessing water pollution and maintaining human health.

Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data.

Multi-source functional block-wise missing data arise more commonly in medical care recently with the rapid development of big data and medical technology, hence there is an urgent need to develop efficient dimension reduction to extract important information for classification under such data. However, most existing methods for classification problems consider high-dimensional data as covariates. In the paper, we propose a novel multinomial imputed-factor Logistic regression model with multi-source functional block-wise missing data as covariates. Our main contribution is to establishing two multinomial factor regression models by using the imputed multi-source functional principal component scores and imputed canonical scores as covariates, respectively, where the missing factors are imputed by both the conditional mean imputation and the multiple block-wise imputation approaches. Specifically, the univariate FPCA is carried out for the observable data of each data source firstly to obtain the univariate principal component scores and the eigenfunctions. Then, the block-wise missing univariate principal component scores instead of the block-wise missing functional data are imputed by the conditional mean imputation method and the multiple block-wise imputation method, respectively. After that, based on the imputed univariate factors, the multi-source principal component scores are constructed by using the relationship between the multi-source principal component scores and the univariate principal component scores; and at the same time, the canonical scores are obtained by the multiple-set canonial correlation analysis. Finally, the multinomial imputed-factor Logistic regression model is established with the multi-source principal component scores or the canonical scores as factors. Numerical simulations and real data analysis on ADNI data show the proposed method works well.

Floral and reproductive biology of Commelina erecta L. (Commelinaceae) in a coastal dune system on the Gulf of Mexico

The most common sexual systems found in Commelinaceae species are hermaphroditic and andromonoecious, and although enantiostyly is also recognized in the family, it has only been recorded in a few species. The presence of different sexual systems (v.gr., andromonoecious and enantiostyly) in the same species is still unnoted. Commelina erecta is a common andromonoecious-enantiostylous species (i.e., it bears male, left-styled and right-styled flowers) of the coastal dunes of La Mancha in Veracruz, Mexico. Our aims were to determine: (1) the type of enantiostyly, (2) the floral morph-ratios in a population, (3) the differences in size of flowers structures among floral morphs, to (4) characterize the floral visitor community in the different floral morphs, and (5) evaluate differences in fruit set in the hermaphroditic flowers. We found that C. erecta in La Mancha is characterized by non-reciprocal monomorphic enantiostyly. The proportion of floral morphs varied among sampling months. Discriminant analysis and a Wilcoxon test using canonical scores indicated that the anther area and filament length of male flowers are larger in size than those in hermaphroditic flowers. In total, we recorded 220 visits to the flowers of C. erecta by 18 species of insects belonging to five orders. Our study shows that the main floral visitor was the halictid bee Augoclorella sp. Left- and right-styled flowers were equally successful in terms of producing fruits and seeds of the same weight and size, but overall reproductive success was low, with fruit set of only 29%.

The combined use of organochlorine contaminant profiles and molecular genetics for stock discrimination of white whales (Delphinapterus leucas) hunted in three communities on southeast Baffin Island

Putative stock differences in white whales (Delphinapterus leucas) landed by hunters between 1992 and 1996 from the southeast Baffin Island communities of Kimmirut (KI), Iqaluit (IQ) and Pangnirtung (PA) were examined using organochlorine contaminant (OC) profiles of 124 whales, the molecular genetics of 270 whales and both types of data from 97 whales. OC concentrations were generally lower in whales hunted in PA than those hunted in KI and IQ, and many OCs were lower in KI than IQ. In canonical discriminant function (CDA) using 13 OC predictor variables (10 OC groups, mirex, octachlorostyrene and endosulfan), the first canonical function accounted for 77% of the variance and separated whales from PA with those from IQ and KI; the second canonical function separated whales from KI with those from IQ. A previous study of the molecular genetics of white whales showed that whales hunted in the three communities were significantly differentiated on the basis of haplotype and/or microsatellite allele frequencies (de March et al., 2002). When the results of two studies were combined, many whales were slightly more strongly associated with a particular source hunting community than they were in the component studies. Using a posteriori crossvalidation probabilities in an analysis with variables from both studies, 72% of white whales were correctly crossvalidated to their source hunting community; 82.5% from PA; 56.5% from IQ; and 58.8% from KI. The highest misclassification rates were KI to IQ (23.5%), IQ to KI and IQ to PA (21.7% in both cases) and the lowest rates were PA to KI (3.5%), PA to IQ (14.0%) and KI to PA (17.6%). This pattern of assignments was not significantly different from those in the genetics or contaminants studies alone. However, the crossvalidation probabilities to the most likely source communities were approximately 20% larger in the combined analysis than in the component studies. Canonical scores in the combined analysis were more strongly correlated with variables from the OC Study than with variables from the genetics study. Whales placed to PA and IQ could be identified primarily by their OC signatures, however many whales from PA also had a strong PA genetics signature. Whales from IQ were identifiable only by their OC signatures. Both a strong KI genetics and OC signature described approximately half of whales from KI. We believe that at least three stocks were sampled from the three communities. Some whales in PA were very distinct, confirming previous beliefs that a separate stock occurs in Cumberland Sound. Whales hunted in IQ and KI differed to a lesser degree, and may be from stocks subject to a gradient or from a mixture of stocks. Some whales from PA are more likely to have genotypes and OC signatures that are also found in IQ and KI than the reverse. It is possible that summering areas of the stocks that were identified in KI and IQ are not consistent from year to year or across generations. The main problems in combining results for individuals used in several studies, particularly when there are many measurements for relatively few individuals, is to find a limited number of relevant predictor variables that can be used in the combined analysis, while avoiding both overparameterisation and results blurred by meaningless variables.

Abstract WP222: Temporally Differentially Expressed MikroRNAs In Serum Predict Recovery In Different Acute Brain Injuries

Background: Brain plasticity is important processes in recovering after different types of acute brain injuries such as aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS) and traumatic brain injury (TBI). Knowledge gaps still exist which miRNAs contribute to recovery of the acute brain injuries. Hypotheses: Temporally differentially expressed (DE) miRNAs after acute brain injuries may have association to outcome irrespective the type of the acute brain injury. MiRNAs that are DE across different type of brain injuries may reveal important conserved associations that can reflect important biological mechanisms and serve as a biomarker. Methods: Prospective cohort (n=24) consisted of IS (n=8), aSAH (n=8) and TBI (n=8) patients. Two serum samples were collected per patient (early 24-48h and late 120-192h after injury). Outcome was measured 90 days after injury (mRS favorable 0-3 (n=15), unfavorable 4-6 (n=9). MiRNAs were extracted from the samples (total n = 48 samples). Sequencing was performed and DESeq2 was used to identify DE miRNAs. The miRNA putative target genes were predicted with miRWalk. Normalized expression values of identified DE miRNAs were used and linear canonical discriminant analysis (LDA) was performed. Canonical scores were used to build combinatory biomarker with logistic modelling predicting outcome. Results: We identified 22 temporally DE miRNAs (p<0.05) that were in common across all acute brain injury types when compared between favorable and unfavorable groups. From this pool miRWalk target analysis identified 4 miRNAs (hsa-miR-146b-3p, hsa-miR-485-3p, hsa-miR-5010-5p, hsa-miR-485-5p) that targeted to known plasticity mechanism. LDA of these four miRNAs resulted an equation with canonical scores: 0.636 x [hsa-miR-146b-3p] + 0.576 x [hsa-miR-485-3p] + 0.652 x [hsa-miR-5010-5p] + 0.372 x [hsa-miR-485-5p]. The receiver operating characteristic curve generated showed AUC = 94.1%, 95% CI = (0.849, 1.00), p = 0.016 Conclusions: Results show that combinatory biomarker of identified miRNAs perform well across different type of brain injuries. Validation in larger cohort is justified. Identified common miRNAs also provide new targets for mechanistical validation in disease models of TBI and stroke.

Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting.

Sensor Array Based Determination of Edman Degradated Amino Acids Using Poly(p‐phenyleneethynylene)s

A cross‐reactive optical sensor array based on poly(p‐phenyleneethynylene)s (PPEs) determines Edman degraded amino acids. We report a sensor array composed of three anionic PPEs P1–P3, and their electrostatic complexes with metal ions (Fe2+, Cu2+, Co2+). We recorded distinct fluorescence intensity response patterns as “fingerprints” of this chemical tongue toward standard phenylthiohydantoin (PTH) amino acids—degradation products of the Edman process. These “fingerprints” were converted into canonical scores by linear discrimination analysis (LDA), which differentiates all of the PTH‐amino acids. This array discriminates PTH‐amino acid residues degraded from an oligopeptide through Edman sequencing. This approach is complementary to chromatography approaches which rely on mass spectrometry; our array offers the advantage of simplicity.

Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics.

Background: Glycemic variability (GV) is an important component of glycemic control for patients with type 1 diabetes (T1D). The inadequacy of existing measurements lies in the fact that they view the variability from different aspects, so that no consensus has been reached among physicians as to which metrics to use in practice. Moreover, although GV, from 1 day to another, can show very different patterns, few metrics have been dedicated to daily evaluations. Materials and Methods: A reference (stable glycemia) statistical model is built based on a combination of daily computed canonical glycemic control metrics including variability. The metrics are computed for subjects from the TRIMECO islet transplantation trial, selected when their β-score (composite score for grading success) is ≥6 after a transplantation. Then, for any new daily glycemia recording, its likelihood with respect to this reference model provides a multimetric score of daily GV severity. In addition, determining the likelihood value that best separates the daily glycemia with β-score = 0 from that with β-score ≥6, we propose an objective decision rule to classify daily glycemia into "stable" or "unstable." Results: The proposed characterization framework integrates multiple standard metrics and provides a comprehensive daily GV index, based on which, long-term variability evaluations and investigations on the implicit link between variability and β-score can be carried out. Evaluation, in a daily GV classification task, shows that the proposed method is highly concordant to the experience of diabetologists. Conclusion: A multivariate statistical model is proposed to characterize the daily GV of subjects with T1D. The model has the advantage to provide a single variability score that gathers the information power of a number of canonical scores, too partial to be used individually. A reliable decision rule to classify daily variability measurements into stable or unstable is also provided.

A soil quality index for evaluation of degradation under land use and soil erosion categories in a small mountainous catchment, Iran

Soil erosion and land use type have long been viewed as being particularly important drivers of soil degradation. The objectives of this study, therefore, were to select a new soil quality index (SQI) which varies significantly with land use/soil erosion, and to evaluate the new SQI using expert opinion. In total, 18 soil physical, chemical, and biochemical properties (indicators) were measured on 56 soil samples collected from four land use/soil erosion categories (rangeland/surface erosion, rangeland/subsurface erosion, cultivated land/surface erosion and dry-farming land/surface erosion). Principal component and classification analysis (PCCA) identified five PCs that explained 77.7% of the variation in soil properties with the biochemical PC varying significantly with land use/soil erosion. General discriminant analysis (GDA) selected urease and clay as the most sensitive properties distinguishing the land use/soil erosion categories. The GDA canonical scores for the new SQI were significantly correlated with expert opinion soil surface summed scores (for soil movement, surface litter, pedestalling, rills and flow pattern) derived using the U.S. Department of the Interior Bureau of Land Management (BLM) method. A forward stepwise general regression model revealed that the new SQI values were explained by soil movement, surface litter, and the summed values of the soil surface factors. Overall, this study confirmed that soil quality in the study area in Iran is controlled by land use and corresponding soil erosion.

On the connection between large-scale atmospheric circulation and winter GPCP precipitation over the Mediterranean region for the period 1980-2017

Abstract The influence of the large-scale atmospheric circulation on winter precipitation over the Mediterranean region is studied with the use of monthly 2.5° × 2.5° ERA-Interim 500 and 1000 hPa geopotential heights and GPCP-v2.3 precipitation, for the period 1980–2017. At first, Principal Component Analysis (PCA) is applied both to geopotential heights and precipitation datasets and the main modes of inter-annual variations are found. Then, these modes are investigated through Canonical Correlation Analysis (CCA) to identify the centers of action of the lower and the middle troposphere that control precipitation variability over the Mediterranean region. Three statistically significant canonical pairs are found. The first one, in the North Atlantic, controls precipitation over western Asia Minor, northern Libya and the western Iberian Peninsula. Τhe second mode corresponds to the see-saw between Greenland and central Europe and controls precipitation over the northern Mediterranean region. The third one is located north of Britain and controls precipitation over NW Africa and northern France. For each canonical pair, the mean geopotential heights and precipitation anomaly patterns for the four (10%) years with the highest and the lowest canonical scores are constructed, confirming the results of CCA. Moreover, the correlation between the canonical scores and the indices of the large-scale atmospheric oscillations, namely the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the North Sea – Caspian Pattern (NCP) and the East Atlantic/West Russia Pattern (EA/WR), was studied.

An Immune Cells Constructed Model to Identify Immunoparalysis

Background: Sepsis is a highly heterogeneous syndrome with diverse immune status. Among diverse immune status, immunoparalysis is well known as the prominently detrimental status. Due to lack of tool to identify immunoparalysis in clinics, previous randomised control trials (RCT) investigating the immunoregulator have all yielded paradoxical conclusions. Therefore, we aimed to establish an immune model to classify sepsis into different immune endotypes via bioinformatics for better patient selection and more appropriate targeting of previously unsuccessful therapeutic approaches. Methods: Transcriptomics data on blood leukocyte in septic adult within 48 hours after ICU admission in the Gene Expression Omnibus (GEO) were searched up to June 2019. Cell type identification by estimating relative subset of known RNA transcripts (CIBERSORT) were utilized to transform the gene by sample matrix into canonical pathways enrichment scores and absolute fractions of 22 human immune cell phenotypes. Univariate Cox’s proportional-hazards models were conducted to screen prognostic immune cells into the final immune model constructed by multivariable Cox proportional hazards regression. Patients were divided into immunity-A endotype and immunity-B endotype through medians of immune score estimated from the risk model established. The discriminating ability of immune model was evaluated at levels of immune cells, immune biomarkers and immunity-related signal transduction pathways through differential expression analysis and Gene Set Enrichment Analysis(GSEA). Kaplan-Meier (KM) curves and principal component analysis (PCA) were performed to evaluate calibration ability of risk model. External dataset of sepsis was utilized for model validation. Findings: A total of 479 patients from GSE65682 are enrolled in train cohort, and 106 patients from GSE63042 are set as external validation cohorts. Univariate Cox regression analyses indicate that the percentage of 4 immune cells (Macrophages M0, Macrophages M2, B cells naive, T cells CD4 naive) are significantly associated with 28-day cumulative mortality, P < 0.05. Subsequently, the 4 prognostic immune cells constructed immune model could classify sepsis into two endotypes(immunity-A and immunity-B). Three dimensional results (immune cells, immune molecules and immunity-related pathways) demonstrate that patients in immunity-A endotype suffered from immunoparalysis, and patients in immunity-B suffer from relative normal host response, P < 0.05. KM curves indicate that patients immunity-A had significantly shorter survival days than patients in immunity-B, with Hazard Ratio (95% CI) as 2.32 (1.53 to 3.46) and P value as 0.00. PCA indicated that immunity-A and immunity-B could be distinguished by current immune model. External validation furthermore demonstrates that this model would precisely and efficiently categorize sepsis into immunoparalysis endotype and normal host response endotype. Interpretation: The present study explored an immune model to classify sepsis into immunity-A endotype (immunoparalysis) and immunity-B(normal host response). Funding Statement: Supported in part by grants from the National Natural Science Foundation of China (grant numbers: 81571847, 81930058), the projects of Jiangsu Provincial Medical Key Discipline (ZDXKA2016025) and Jiangsu Provincial Special Program of Medical Science (BE2018743, BE2018743). Declaration of Interests: The authors declare that they have no competing interests.

A comprehensive classification of edible oils according to their radical scavenging spectral profile evaluated by advanced chemometrics

A comprehensive study concerning the characterization and classification of 30 cold-pressed edible oils according to their UV-Vis spectra and radical scavenging profiles using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay is presented. Considering the principal component analysis (PCA) and fuzzy-principal component analysis (FPCA) loadings profiles, the characteristic spectral regions with a significant influence in oil samples classification were identified and associated with characteristic factors in each group. Much more, the oils with high antiradical capacity were revealed. The scores corresponding to the first principal component and the canonical scores corresponding to the first discriminant function derived from radical scavenging spectral profiles allowed a relevant classification of oils in well-defined groups associated with their high, medium and low radical scavenging capacity. The FPCA-LDA method applied on DPPH radical scavenging spectral profiles of edible oils appeared to be the most efficient method with a correct classification rate of 96.7%.

Wing morphometric variability in Aedes aegypti (Diptera: Culicidae) from different urban built environments

BackgroundAedes aegypti is the main vector of the dengue, Zika and several other arboviruses. It is highly adapted to urbanized environments and can be found worldwide. Mosquito population control is considered the best strategy for fighting mosquito-borne diseases, making an understanding of their population dynamics vital for the development of more effective vector control programs. This study therefore sought to investigate how different levels of urbanization affect Aedes aegypti populations and modulate population structure in this species with the aid of wing geometric morphometrics.MethodsSpecimens were collected from eleven locations in three areas with distinct levels of urbanization in the city of São Paulo, Brazil: conserved, intermediate and urbanized. The right wings of female mosquitoes collected were removed, and photographed and digitized. Canonical variate analysis and Mahalanobis distance were used to investigate the degree of wing-shape dissimilarity among populations. Thin-plate splines were calculated by regression analysis of Canonical Variation Analysis scores against wing-shape variation, and a cross-validated reclassification was performed for each individual; a neighbor-joining tree was then constructed.ResultsMetapopulation and individual population analysis showed a clear segregation pattern in the Canonical Variation Analysis. Pairwise cross-validated reclassification yielded relatively high scores considering the microgeographical scale of the study and the fact that the study populations belong to the same species. The neighbor-joining tree showed that mosquitoes in the intermediate urban area segregated in the metapopulation and individual population analyses. Our findings show significant population structuring in Aedes aegypti mosquitoes in the areas studied. This is related to the different degrees of urbanization in the areas where the specimens were collected along with their geographical location.ConclusionsUrbanization processes in the study areas appear to play an important role in microevolutionary processes triggered by man-made modifications in the environment, resulting in a previously unknown population structuring pattern of major epidemiological importance.

Geographical variation in the skeletal morphology of red jungle fowl

ABSTRACT1. The skulls and postcranial skeletons of the red jungle fowl (Gallus gallus) were compared osteometrically between the populations from North and South Vietnam, North and Central Laos and Southeast Bangladesh. The populations include the three subspecies of G. g. spadiceus, G. g. gallus and G. g. murghi and were sampled to reveal the geographical morphological variations among populations in G. gallus.2. The morphometric characteristics of subspecies murghi could be clearly distinguished from those of the other subspecies using a canonical discriminant analysis. However, the size and shape of the skull of the gallus population from South Vietnam were not statistically different from that of the subspecies spadiceus from North Laos. The canonical discriminant scores also clearly indicated that there were morphological similarities in the skulls of the populations from North Laos and South Vietnam.3. From the results, therefore, it is concluded that red jungle fowls do not exhibit high levels of osteometric variation between geographical localities at least within the Indochinese Peninsula.4. This contrasts with previous studies which have described these subspecies as having various external morphological differences and have argued that zoogeographical barriers exist between the north and south areas of the Indochinese Peninsula.

Noninvasive Strategy Based on Real-Time in Vivo Cataluminescence Monitoring for Clinical Breath Analysis.

The development of noninvasive methods for real-time in vivo analysis is of great significant, which provides powerful tools for medical research and clinical diagnosis. In the present work, we described a new strategy based on cataluminescence (CTL) for real-time in vivo clinical breath analysis. To illustrate such strategy, a homemade real-time CTL monitoring system characterized by coupling an online sampling device with a CTL sensor for sevoflurane (SVF) was designed, and a real-time in vivo method for the monitoring of SVF in exhaled breath was proposed. The accuracy of the method was evaluated by analyzing the real exhaled breath samples, and the results were compared with those obtained by GC/MS. The measured data obtained by the two methods were in good agreement. Subsequently, the method was applied to real-time monitoring of SVF in exhaled breath from rat models of the control group to investigate elimination pharmacokinetics. In order to further probe the potential of the method for clinical application, the elimination pharmacokinetics of SVF from rat models of control group, liver fibrosis group alcohol liver group, and nonalcoholic fatty liver group were monitored by the method. The raw data of pharmacokinetics of different groups were normalized and subsequently subjected to linear discriminant analysis (LDA). These data were transformed to canonical scores which were visualized as well-clustered with the classification accuracy of 100%, and the overall accuracy of leave-one-out cross-validation procedure is 88%, thereby indicating the utility of the potential of the method for liver disease diagnosis. Our strategy undoubtedly opens up a new door for real-time clinical analysis in a pain-free and noninvasive way and also guides a promising development direction for CTL.

Application of multivariate techniques in the evaluation of pure lines of beans

ABSTRACT: The objective of this study was to apply multivariate techniques, canonical discriminant analysis, and multivariate contrasts, indicating the most favorable inferences in the evaluation of pure lines of beans. The study was conducted at the experimental field of the Institute for Breeding and Molecular Genetics, in Lages, SC, Brazil. The experiment was composed of 24 pure lines of beans from the Santa Catarina test of cultivars. Plant height, numbers of pods and grains per plant, and stem diameter were the variables measured. The complete randomized block design was used with four replications. The data were subjected to multivariate analysis of variance, canonical discriminant analysis, multivariate contrasts and univariate contrasts. The first canonical discriminant function has captured 81% of the total variation in the data. The Scott-Knott test showed two groups of inbred lines at the average -of scores of the first canonical discriminant function. It was considered that testing hypotheses with the canonical scores may result in loss of information obtained from the original data. Multivariate contrasts indicated differences within the group formed by the Scott-Knott test. The canonical discriminant analysis and multivariate contrasts are excellent techniques to be combined in the multivariate assessment, being used to explore and test hypotheses, respectively.

Forearm articular proportions and the antebrachial index in Homo sapiens, Australopithecus afarensis and the great apes

When hominin bipedality evolved, the forearms were free to adopt nonlocomotor tasks which may have resulted in changes to the articular surfaces of the ulna and the relative lengths of the forearm bones. Similarly, sex differences in forearm proportions may be more likely to emerge in bipeds than in the great apes given the locomotor constraints in Gorilla, Pan and Pongo. To test these assumptions, ulnar articular proportions and the antebrachial index (radius length/ulna length) in Homo sapiens (n=51), Gorilla gorilla (n=88), Pan troglodytes (n=49), Pongo pygmaeus (n=36) and Australopithecus afarensis A.L. 288-1 and A.L. 438-1 are compared. Intercept-adjusted ratios are used to control for size and minimize the effects of allometry. Canonical scores axes show that the proximally broad and elongated trochlear notch with respect to size in H. sapiens and A. afarensis is largely distinct from G. gorilla, P. troglodytes and P. pygmaeus. A cluster analysis of scaled ulnar articular dimensions groups H. sapiens males with A.L. 438-1 ulna length estimates, while one A.L. 288-1 ulna length estimate groups with Pan and another clusters most closely with H. sapiens, G. gorilla and A.L. 438-1. The relatively low antebrachial index characterizing H. sapiens and non-outlier estimates of A.L. 288-1 and A.L. 438-1 differs from those of the great apes. Unique sex differences in H. sapiens suggest a link between bipedality and forearm functional morphology.