Stepwise Discriminant Function Research Articles

Using GLUE to pull apart the provenance of atmospheric dust

Abstract Identifying the sources of aeolian dust is a crucial step in mitigating the associated hazards. We apply a Generalized Likelihood Uncertainty Estimation (GLUE) model to constrain the uncertainties associated with sediment fingerprinting of atmospheric dust in the Sistan region on the Iran-Afghanistan border, one of the world’s dustiest places. 57 dust samples were collected from the rooftop of the Zabol Department of Environmental Protection during a summer dusty period from June to October 2014, in addition to 31 surface soil samples collected from potential sources nearby, including cultivated land (n = 8), uncultivated rangeland (n = 7), and two dry lakes: Hamoun Puzak (n = 10) and Hamoun Saberi (n = 6). Dust and soil samples were analyzed for 24 tracers including 16 geochemical elements and 8 water-soluble ions. Five optimum composite fingerprints (Fe, Sr, Mn, Cr and Pb) were selected for discriminating sources by a two-stage statistical process involving a Kruskal-Wallis test and stepwise discriminant function analysis (DFA). Uncertainty ranges for source contributions of dust determined by the GLUE model showed that the dry lake Hamoun Puzak is the dominant source for all dust samples from Zabol and cultivated land is a secondary source. We found marked spatial variance in the importance of regional dry lake beds as dust sources, and temporal persistence in dust emissions from Hamoun Puzak, despite very large areas of adjacent lake beds drying during the study period. Aeolian sediment fingerprinting studies can benefit considerably from the constraints provided by modelling frameworks, such as GLUE, for quantifying the uncertainty in dust provenance data.

Combined use of spectral and structural characteristics for improved red band needle blight detection in pine plantation stands

Current trends in research for detection of infections in forests almost exclusively involve the use of a single imaging sensor. However, combining information from a range of sensors could potentially enhance the ability to diagnose and quantify the infection. This study investigated the potential of combining hyperspectral and LiDAR data for red band needle blight detection. A comparative study was performed on the spectral signatures retrieved for two plots established in lodgepole pine stands and on a range of LiDAR metrics retrieved at individual tree-level. Leaf spectroscopy of green and partially chlorotic needles affected by red band needle blight highlighted the green, red and short near-infrared parts of the electromagnetic spectrum as the most promising. A good separation was found between the two pine stands using a number of spectral indices utilising those spectral regions. Similarly, a distinction was found when intra-canopy distribution of LiDAR returns was analysed. The percentage of ground returns within canopy extents and the height-normalised 50th percentile (height normalisation was performed to each tree’s canopy extents) were identified as the most useful features among LiDAR metrics for separation of trees between the plots. Analysis based on those metrics yielded an accuracy of 80.9%, indicating a potential for using LiDAR metrics to detect disease-induced defoliation. Stepwise discriminant function analysis identified Enhanced Vegetation Index, Normalised Green Red Difference Index, percentage of ground returns, and the height-normalised 50th percentile to be the best predictors for detection of changes in the canopy resulting from red band needle blight. Using a combination of these variables led to a substantial decrease of unexplained variance within the data and an improvement in discrimination accuracy (96.7%). The results suggest combining information from different sensors can improve the ability to detect red band needle blight.

Methods to Predict Beaver Dam Occurrence in Coastal Oregon

Pools provided by beaver (Castor canadensis) contribute to critical habitat requirements of salmonids in fluvial systems of the Pacific Northwest, therefore more land managers are interested in managing watersheds that include beavers or engaging in beaver-related restoration projects. We evaluated the utility of applying an existing beaver habitat suitability model to better understand beaver dam site characteristics in coastal Oregon, identify optimum dam site locations, and guide future beaver-related restoration efforts. We used a combination of t-tests, Wilcoxon rank sum tests, and a stepwise discriminant function analysis to examine stream habitat associations with field data collected at known and predicted dam sites at reach and pool/riffle levels. We found bank-full width, valley floor width, and channel gradient performed well in predicting dam locations across the Alsea River Basin. Known dam sites had wider valley floors, shallower shoreline slopes, and fewer larger, deeper pools than predicted sites. Overall, our results suggest the beaver habitat suitability model combined with a digital elevation model can be used to guide where beaver dams may occur within the Alsea River Basin, yet they do not capture fine scale habitat associations that may lead to a settling response in beavers. For example, presence of large deep pools may be necessary for beavers to escape predation before and during dam building. Results from our study may be used to prioritize potential dam sites in other coastal basins that have similar geomorphic characteristics.

Early Maladaptive Schemas Characterizing Different Types of Adolescents

The goal of this study was to find out whether early maladaptive schemas (EMS) can be differentiated between the gifted adolescents and delinquent adolescents. Two groups of adolescents were recruited as participants to be surveyed. 144 gifted adolescents were taken from a gifted science and math education center, and 115 delinquent adolescents who had committed crime were taken from 4 police stations in the area of Gyungnam province in Korea. The Korean version of the Schema Inventory for Children was used to measure the level of the early maladaptive schemas (EMS). Stepwise discriminant function analysis yielded a function containing 5 maladaptive schemas (failure, unrelenting standards, vulnerability to harm and illness, loneness/mistrust/abuse, and subjugation), classifying 75.29 accurately into either gifted adolescents or delinquent adolescents. These results suggested that the types of adolescents (gifted adolescents, and delinquent adolescents) can be predicted based on early maladaptive schemas. The findings are discussed from the perspective of Schema Therapy and school counseling.

OSTEOMETRIC ESTIMATION OF SEX FROM MASTOID TRIANGLE IN MALAYSIAN POPULATION

Objective: Determination of sex is one of the most important parameters for conducting biological profile of unknown skeletal remains in anatomical anthropology and forensic medicine. Mastoid bone is useful for the identification of sex, as it is the most protected bone and is resistant to damage, due to its anatomical position at the base of the skull. The aim of this study was to develop new equations for the estimation of sex from mastoid triangle in the Malaysian population.Methods: About 10 parameters were studied on 388 computed tomography scans of crania in 231 males and 157 females. The parameters comprised three sides of mastoid triangle, its perimeter and area on both sides. T-test was used to compare between the right and left sides and between males and females. Stepwise discriminant function was used to reveal the best discriminatory parameter and its classification accuracy.Results: Comparison of means by T-test revealed no difference between the right and left sides in both sexes. T-test showed a significant difference between males and females for all parameters. Perimeter of mastoid triangle was found to be the best parameter by stepwise discriminant analysis. The equation based on perimeter of mastoid triangle was developed with 84.4% classification accuracy.Conclusion: The developed equation could be used to assess sexual dimorphism of fragmented Malaysian crania with intact mastoid region. The achieved cross-validated classification was relatively high compared to that in other previous studies.

Brain Connectivity and Cognitive Flexibility in Nonirradiated Adult Survivors of Childhood Leukemia.

This study aimed to assess functional and structural brain connectivity in adult childhood leukemia survivors and the link with cognitive functioning and previously identified risk factors such as intrathecal methotrexate dose and age at start of therapy. Thirty-one nonirradiated adult childhood leukemia survivors and 35 controls underwent cognitive testing and multimodal magnetic resonance imaging (resting state functional MRI, T1-weighted, diffusion-weighted, and myelin water imaging [MWI]). Analyses included dual regression, voxel-based morphometry, advanced diffusion, and MWI modeling techniques besides stepwise discriminant function analysis to identify the most affected executive cognitive domain. Correlations with discrete intrathecal MTX doses and (semi)continuous variables were calculated using Spearman's rank and Pearson's correlation, respectively. All correlation tests were two-sided. Positive and negative T-contrasts in functional and structural MRI analysis were one-sided. Survivors demonstrated lower functional connectivity between the default mode network (DMN) and inferior temporal gyrus (ITG; P < .008). Additionally, we observed higher fractional anisotropy (FA; P = .04) and lower orientation dispersion index (ODI; P = .008) at the left centrum semiovale, which could-given that several fiber bundles cross this region-suggest selective reduced integrity of the respective white matter tracts. Set shifting reaction time, a measure of cognitive flexibility, was mostly impaired and correlated with lower FA (r = -0.53, P = .003) and higher ODI (r = 0.40, P = .04) in survivors but not with DMN-ITG connectivity. There were no statistically significant differences between survivors and controls in WM or GM volume, nor was there a statistically significant correlation between imaging measurements and age at start of therapy or intrathecal methotrexate dose. Adult, nonirradiated childhood leukemia survivors show altered brain connectivity, which is linked with cognitive flexibility.

Three-dimensionally derived interlandmark distances for sex estimation in intact and fragmentary crania

The skull is the element most frequently presented to forensic anthropologists for analysis yet weathering, corpse maiming, and scavenger activity often result in damage and fragmentation. This fragmentation results in a reduction in the number of traditional calliper derived measurements that can be obtained and subjected to discriminant based analyses for sex estimation. In this investigation, we employed three-dimensional geometric morphometric methods to derive novel interlandmark distance measures across six regions of the cranium including the basicranium, basipalate, zygoma, orbits and the cranium globally to create functions to discriminate sex with high efficacy, even in the event of fragmentation. Forty-five homologous landmarks were digitised across each of 227 (114 males and 113 females) South African crania of European descent (white) sampled from the Raymond A Dart Collection of Human Skeletons, housed in the School of Anatomical Sciences, University of the Witwatersrand, South Africa. A total of 990 interlandmark distances (ILDs) were mathematically derived using Pythagorean geometry. These ILDs were then filtered by region and subjected to both direct and stepwise discriminant function analyses. Discriminant equations where derived for each region and achieved the following average cross-validated sex estimation accuracies: basicranium—74%; basipalate—80.2%; zygomatic—82.4; orbits—71.8%; nasomaxilla—83.7%; global cranium—88.2%. A large number of the ILDs used to derive the discriminant functions are novel, demonstrating the efficacy of geometric morphometric methods and illustrating the need to reassess old methods of data collection using modern methods to determine whether they best capture biological differences. The results of this study provide an invaluable contribution to forensic anthropology in South Africa as it provides an accurate, practical means of assessing sex using fragmentary material that may otherwise have been disregarded. These will undeniable aid in accurate sex estimation and ultimately, victim identification.

An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from healthy controls.

Dysarthrophonia is a predominant symptom in many neurological diseases, affecting the quality of life of the patients. In this study, we produced a discriminant function equation that can differentiate MS patients from healthy controls, using electroglottographic variables not analyzed in a previous study. We applied stepwise linear discriminant function analysis in order to produce a function and score derived from electroglottographic variables extracted from a previous study. The derived discriminant function's statistical significance was determined via Wilk's λ test (and the associated p value). Finally, a 2 × 2 confusion matrix was used to determine the function's predictive accuracy, whereas the cross-validated predictive accuracy is estimated via the "leave-one-out" classification process. Discriminant function analysis (DFA) was used to create a linear function of continuous predictors. DFA produced the following model (Wilk's λ= 0.043, χ2 = 388.588, p< 0.0001, Tables3 and 4): D (MS vs controls) = 0.728*DQx1 mean monologue + 0.325*CQx monologue + 0.298*DFx1 90% range monologue + 0.443*DQx1 90% range reading - 1.490*DQx1 90% range monologue. The derived discriminant score (S1) was used subsequently in order to form the coordinates of a ROC curve. Thus, a cutoff score of - 0.788 for S1 corresponded to a perfect classification (100% sensitivity and 100% specificity, p= 1.67e-22). Consistent with previous findings, electroglottographic evaluation represents an easy to implement and potentially important assessment in MS patients, achieving adequate classification accuracy. Further evaluation is needed to determine its use as a biomarker.

Multivariate Profiles of Selected versus Non-Selected Elite Youth Brazilian Soccer Players.

This study determined whether a multivariate profile more effectively discriminated selected than non-selected elite youth Brazilian soccer players. This examination was carried out on 66 youth soccer players (selected, n = 28, mean age 16.3 ± 0.1; non-selected, n = 38, mean age 16.7 ± 0.4) using objective instruments. Multivariate profiles were assessed through anthropometric characteristics, biological maturation, tactical-technical skills, and motor performance. The Student’s t-test identified that selected players exhibited significantly higher values for height (t = 2.331, p = 0.02), lean body mass (t = 2.441, p = 0.01), and maturity offset (t = 4.559, p < 0.001), as well as performed better in declarative tactical knowledge (t = 10.484, p < 0.001), shooting (t = 2.188, p = 0.03), dribbling (t = 5.914, p < 0.001), speed – 30 m (t = 8.304, p < 0.001), countermovement jump (t = 2.718, p = 0.008), and peak power tests (t = 2.454, p = 0.01). Forward stepwise discriminant function analysis showed that declarative tactical knowledge, running speed –30 m, maturity offset, dribbling, height, and peak power correctly classified 97% of the selected players. These findings may have implications for a highly efficient selection process with objective measures of youth players in soccer clubs.

Development and Psychometric Evaluation of the Brief Adolescent Gambling Screen (BAGS).

The purpose of this study was to develop and evaluate the initial reliability, validity and classification accuracy of a new brief screen for adolescent problem gambling. The three-item Brief Adolescent Gambling Screen (BAGS) was derived from the nine-item Gambling Problem Severity Subscale (GPSS) of the Canadian Adolescent Gambling Inventory (CAGI) using a secondary analysis of existing CAGI data. The sample of 105 adolescents included 49 females and 56 males from Canada who completed the CAGI, a self-administered measure of DSM-IV diagnostic criteria for Pathological Gambling, and a clinician-administered diagnostic interview including the DSM-IV diagnostic criteria for Pathological Gambling (both of which were adapted to yield DSM-5 Gambling Disorder diagnosis). A stepwise multivariate discriminant function analysis selected three GPSS items as the best predictors of a diagnosis of Gambling Disorder. The BAGS demonstrated satisfactory estimates of reliability, validity and classification accuracy and was equivalent to the nine-item GPSS of the CAGI and the BAGS was more accurate than the SOGS-RA. The BAGS estimates of classification accuracy include hit rate = 0.95, sensitivity = 0.88, specificity = 0.98, false positive rate = 0.02, and false negative rate = 0.12. Since these classification estimates are preliminary, derived from a relatively small sample size, and based upon the same sample from which the items were selected, it will be important to cross-validate the BAGS with larger and more diverse samples. The BAGS should be evaluated for use as a screening tool in both clinical and school settings as well as epidemiological surveys.

Geometric Morphometric Differentiation of Two Western USA Lizards (Phrynosomatidae: Squamata):Uta stansburianaandUrosaurus ornatus, with Implications for Fossil Identification

Squamate fossil identification has been challenging due to the incomplete understanding and sometimes complete lack of osteological research of extant species. Here we compared the maxilla of two similar species of phrynosomatids: Uta stansburiana (Common Side-blotched Lizard) and Urosaurus ornatus (Ornate Tree Lizard). Through landmark-based geometric morphometric analyses, we determined which characters significantly separated the two species. A principle component analysis (PCA) and a stepwise discriminant function analysis (DFA) were conducted, in which we compared 15 landmarks between U. stansburiana and U. ornatus. Both the PCA and stepwise DFA showed separation between the two species. The stepwise DFA selected five of the 15 characters as statistically significant, three of which are considered apomorphies and show promise for fossil identification. The first character is in the ventral region of the posterior maxilla process; U. ornatus has a defined notch, whereas U. stansburiana does not. The second and third characters are in the anterior portion of the maxilla, which is curved dorsally in U. stansburiana, whereas U. ornatus shows no curving. The results of this study are used to identify fossil Uta vs Urosaurus, but more analyses need to be conducted on other phrynosomatid species for comprehensive identification.

Arceuthobium tsugense (Viscaceae): four subspecies with contrasting morphologies and host distributions

There have been four subspecies of hemlock dwarf mistletoe (Arceuthobium tsugense: Viscaceae) described by various investigators of this complex group of parasitic flowering plants: subsp. tsugense, subsp. amabilae, subsp. contortae, and subsp. mertensianae. As suggested by their subspecific epithets, these taxa differ in their host affinities; parasitizing different and the same hosts to varying degrees. Although these taxa also have morphological differences, their classifica-tion has been under debate for many years. Therefore, we compared the morphological characteristics of each subspecies using both univariate and multivariate statistical analyses in order to better assess their differences. Because some investigators have grouped hemlock dwarf mistletoe with western dwarf mistletoe (A. campylopodum), we also compared the subspecies of hemlock dwarf mistletoe with western dwarf mistletoe. Our morphometric analyses demonstrated that all of the subspecies are morphologically distinct from western dwarf mistletoe and that subsp. contortae is the most morphologically differentiated of the subspecies. Overlap in the morphological characters across two of the other three subspecies was evident; yet, subsp. amabilae and subsp. mertensianae were also consistently delimited using female and male plant morphologies. Statistical comparisons of female or male plants via standard and stepwise discriminant function analyses demonstrated that without consideration of host plant, female and male A. tsugense subsp. tsugense are morphologically similar to corresponding plants of subsp. amabilae and mertensianae but not subsp. contortae or A. campylopodum.

Novel candidate genes of the PARK7 interactome as mediators of apoptosis and acetylation in multiple sclerosis: An in silico analysis

Backgroundcurrently only 4 studies have explored the potential role of PARK7's dysregulation in MS pathophysiology Currently, no study has evaluated the potential role of the PARK7 interactome in MS. ObjectiveThe aim of our study was to assess the differential expression of PARK7 mRNA in peripheral blood mononuclears (PBMCs) donated from MS versus healthy patients using data mining techniques. MethodsThe PARK7 interactome data from the GDS3920 profile were scrutinized for differentially expressed genes (DEGs); Gene Enrichment Analysis (GEA) was used to detect significantly enriched biological functions. Results27 differentially expressed genes in the MS dataset were detected; 12 of these (NDUFA4, UBA2, TDP2, NPM1, NDUFS3, SUMO1, PIAS2, KIAA0101, RBBP4, NONO, RBBP7 AND HSPA4) are reported for the first time in MS. Stepwise Linear Discriminant Function Analysis constructed a predictive model (Wilk's λ = 0.176, χ2 = 45.204, p = 1.5275e−10) with 2 variables (TIDP2, RBBP4) that achieved 96.6% accuracy when discriminating between patients and controls. Gene Enrichment Analysis revealed that induction and regulation of programmed / intrinsic cell death represented the most salient Gene Ontology annotations. Cross-validation on systemic lupus erythematosus and ischemic stroke datasets revealed that these functions are unique to the MS dataset. ConclusionsBased on our results, novel potential target genes are revealed; these differentially expressed genes regulate epigenetic and apoptotic pathways that may further elucidate underlying mechanisms of autorreactivity in MS.

Aeolian sediment fingerprinting using a Bayesian mixing model

AbstractIdentifying sand provenance in depositional aeolian environments (e.g. dunefields) can elucidate sediment pathways and fluxes, and inform potential land management strategies where windblown sand and dust is a hazard to health and infrastructure. However, the complexity of these pathways typically makes this a challenging proposition, and uncertainties on the composition of mixed‐source sediments are often not reported. This study demonstrates that a quantitative fingerprinting method within the Bayesian Markov Chain Monte Carlo (MCMC) framework offers great potential for exploring the provenance and uncertainties associated with aeolian sands.Eight samples were taken from dunes of the small (~58 km2) Ashkzar erg, central Iran, and 49 from three distinct potential sediment sources in the surrounding area. These were analyzed for 61 tracers including 53 geochemical elements (trace, major and rare earth elements (REE)) and eight REE ratios. Kruskal–Wallis H‐tests and stepwise discriminant function analysis (DFA) allowed the identification of an optimum composite fingerprint based on six tracers (Rb, Sr, 87Sr, (La/Yb)n, Ga and δCe), and a Bayesian mixing model was applied to derive the source apportionment estimates within an uncertainty framework.There is substantial variation in the uncertainties in the fingerprinting results, with some samples yielding clear discrimination of components, and some with less clear fingerprints. Quaternary terraces and fans contribute the largest component to the dunes, but they are also the most extensive surrounding unit; clay flats and marls, however, contribute out of proportion to their small outcrop extent. The successful application of these methods to aeolian sediment deposits demonstrates their potential for providing quantitative estimates of aeolian sediment provenances in other mixed‐source arid settings, and may prove especially beneficial where sediment is derived from multiple sources, or where other methods of provenance (e.g. detrital zircon U–Pb dating) are not possible due to mineralogical constraints. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Applying regularized logistic regression (RLR) for the discrimination of sediment facies in reservoirs based on composite fingerprints

Soils and sediments can be distinguished based on “composite fingerprints”, i.e., sets of physical and chemical properties that are suitable for discrimination. At present, statistical stepwise variable selection methods are frequently applied to identify composite fingerprints, although they have been seriously criticized. Here, we test regularized logistic regression (RLR) as an alternative approach in the context of a reservoir siltation study where the post-dam facies is to be distinguished from the pre-dam facies. The pre- and post-dam facies of four reservoirs located in the Kruger National Park were examined with respect to grain size composition, color, and content of calcium-lactate leachable phosphorus (P CAL). A composite fingerprint was identified applying RLR to training data. The fitted regression model was used for the classification of samples not involved in the training dataset. For comparison, variable selection was performed with stepwise discriminant function analysis (DFA) and samples were classified by applying linear discriminant analysis (LDA). Both approaches were validated by comparing field interpretation and classification results. The analysis was extended based on Monte Carlo simulations and synthetic datasets to quantify uncertainties and to enhance the method comparison. RLR and stepwise DFA identify grain size parameters and P CAL content to be particularly useful for the facies discrimination. Neglecting and taking into account a potential sampling bias, both approaches lead to ≤3 and 5% misclassifications, respectively. RLR outperforms stepwise DFA/LDA in Monte Carlo simulations, although misclassification rates do not significantly differ (p = 0.84). RLR uses on average 12% less fingerprint properties. Moreover, RLR-derived probabilities of group membership represent a more reliable measure for classification conclusiveness than probabilities calculated from LDA, which is evident in significantly lower (p < 0.001) probability residuals for misclassified samples. Stepwise DFA/LDA reveals lower misclassification rates than RLR when data fulfill multivariate normality in each group and equal within-group covariance matrices. RLR is an innovative tool for the discrimination of sediment facies in reservoirs and, more generally, for studies requiring the discrimination of soils and sediments. Although stepwise procedures will in practice often perform similarly well, we discourage their use for the identification of composite fingerprints due to the risk of suboptimal variable selection involving variables with spurious discriminatory power.

Gender determination by odontometric method.

Context:Gender determination is central in establishing personal identification from human skeletal remains. The study was conducted to find out the accuracy with which gender can be determined by odontometric methods.Aims:To investigate the mesiodistal (MD) and buccolingual (BL) dimensions of all the teeth of permanent dentition to find new parameters to differentiate between male and female teeth and to assess whether each type of linear measurement can be used independently in odontometric sex differentiation.Materials and Methods:The study was conducted at a dental college on a composite group of 500 individuals comprising 250 males and 250 females. Impressions of upper and lower jaws were made with alginate impression material and casts prepared with dental stone. A digital Vernier calliper was used to measure the BL and MD dimensions of all the upper teeth except the third molars.Statistical Analysis Used:The results were subjected to statistical analysis using univariate analysis and linear stepwise discriminant function analysis to find the variables which discriminate gender significantly.Results:The MD and BL dimensions between males and females were statistically significant. The predicted value for correct classification of gender was also statistically significant.Conclusions:The ability to differentiate gender in the population using stepwise discriminant functions was found to be very high with 99.8% accuracy with males showing statistically larger teeth than females. This is similar to the near 100% success in gender determination using pelvic and skull bones.

Potential use of ionic species for identifying source land-uses of stormwater runoff

Identifying critical land-uses or source areas is important to prioritize resources for cost-effective stormwater management. This study investigated the use of information on ionic composition as a fingerprint to identify the source land-use of stormwater runoff. We used 12 ionic species in stormwater runoff monitored for a total of 20 storm events at five sites with different land-use compositions during the 2012-2014 wet seasons. A stepwise forward discriminant function analysis (DFA) with the jack-knifed cross validation approach was used to select ionic species that better discriminate the land-use of its source. Of the 12 ionic species, 9 species (K+, Mg2+, Na+, NH4+, Br-, Cl-, F-, NO2-, and SO42-) were selected for better performance of the DFA. The DFA successfully differentiated stormwater samples from urban, rural, and construction sites using concentrations of the ionic species (70%, 95%, and 91% of correct classification, respectively). Over 80% of the new data cases were correctly classified by the trained DFA model. When applied to data cases from a mixed land-use catchment and downstream, the DFA model showed the greater impact of urban areas and rural areas respectively in the earlier and later parts of a storm event.

Variation of facial features among three African populations: Body height match analyses

Body height is one of the variables that show a correlation with facial craniometry. Here we seek to discriminate the three populations (Nigerians, Ugandans and Kenyans) using facial craniometry based on different categories of body height of adult males. A total of 513 individuals comprising 234 Nigerians, 169 Ugandans and 110 Kenyans with mean age of 25.27, s=5.13 (18–40 years) participated. Paired and unpaired facial features were measured using direct craniometry. Multivariate and stepwise discriminate function analyses were used for differentiation of the three populations. The result showed significant overall facial differences among the three populations in all the body height categories. Skull height, total facial height, outer canthal distance, exophthalmometry, right ear width and nasal length were significantly different among the three different populations irrespective of body height categories. Other variables were sensitive to body height. Stepwise discriminant function analyses included maximum of six variables for better discrimination between the three populations. The single best discriminator of the groups was total facial height, however, for body height >1.70m the single best discriminator was nasal length. Most of the variables were better used with function 1, hence, better discrimination than function 2. In conclusion, adult body height in addition to other factors such as age, sex, and ethnicity should be considered in making decision on facial craniometry. However, not all the facial linear dimensions were sensitive to body height.

Call-based identification as a potential tool for monitoring Great Spotted Kiwi

The ability to discriminate between individuals on the basis of call features has proven to be a useful tool for monitoring rare, nocturnal and cryptic bird species. In this study, vocal individuality was assessed in a population of Great Spotted Kiwi (Apteryx haastii) in the Hawdon Valley, New Zealand. Acoustic recorders were installed near the nest-sites of seven pairs between November 2012 and March 2013. Recorders were again installed at the new nesting sites for three of these same pairs in the following season (September-December 2013). Temporal and spectral parameters of calls were measured and stepwise discriminant function analysis was used to determine whether these parameters were effective in discriminating between individuals. The analyses indicated that Great Spotted Kiwi vocalisations were highly individualised. Male individuals were classified with an accuracy of 95.7%, on the basis of seven parameters, and females with an accuracy of 90%, on the basis of five call parameters. Individuals could also be accurately identified between years, suggesting that the individual features of calls are temporally stable. These findings suggest that Great Spotted Kiwi vocalisations could be used for acoustic identification purposes.

Application of RP-18 thin-layer chromatography and quantitative structure–activity relationship analysis for the prediction of the blood–brain barrier permeation

In this study, a number of computed or chromatographically measured (RP-18 thin-layer chromatography [TLC]) descriptors are presented. The relationships between these descriptors and the observed (BBobs) and calculated (B2) BBB bioavailability were studied by stepwise multiple regression analysis and discriminant function analysis on a group of 34 compounds of diverse structures. Useful models of the blood—brain distribution given by the equations: BBobs = −1.19 + 2.05 B2 + 3.89 RF − 62.31 RF/PSA + 0.290 log D (R2 = 0.85, n = 24) and B2 = 4.06 − 1.61 HA − 1.95 HD + 105.49 RF/PSA (R2 = 0.95, n = 34) were developed and validated. Models for discrimination between CNS+ and CNS− compounds were built on the basis of RF, RF/PSA, HD, and B2 descriptors. The diagnostic power of important parameters was evaluated by cluster analysis. Thirty-four compounds examined throughout this study were successfully assigned to two clusters: CNS+ and CNS−. Analysis of variances for 6 descriptors (HD, HA, RF, RF/PSA, DM, and B2...