Discriminant Function Analysis Model Research Articles

Quantifying the contribution of dust sources in the arid area of northwest China using multivariate statistical techniques and Bayesian mixing models

In this study, topsoil samples and dust samples during dust storms were collected from the main dust source in northwest China, the geochemical elements were measured, and the contribution of dust sources was quantified using a discriminant function analysis (DFA) model and a MixSIAR model. Finally, the accuracy of the model was evaluated using an artificial mixture sample. The results showed that the dust sources and their contributions were significantly heterogeneous due to the natural and anthropogenic influences on the source-transport-sink process. DFA and MixSIAR models showed that the main dust sources in Dunhuang and Jiuquan were the Western Gobi and Western oasis, and Zhangye was mainly influenced by the Western Gobi, Western oasis, Middle Gobi, and Central oasis, with the influence of the Middle oasis being more pronounced. The Eastern Gobi, Eastern oasis, Badain Jaran desert, and Tengger desert have a greater influence on Jinchang, Wuwei, Baiyin, and Lanzhou. The contribution of each dust source in the Hexi region was found to show a significant distance effect. The accuracy of the models varied depending on the number of dust sources. The study provides new ideas for quantifying the contribution of dust source and for global dust storm control.

Rapid identification of raw and sulfur-fumigated Paeoniae Radix Alba based on Heracles NEO ultra-fast gas phase electronic nose

Since the current identification method for Paeoniae Radix Alba is complex in operation and long time-consuming with high requirements for technicians, the present study employed Heracles NEO ultra-fast gas phase electronic nose(E-nose) technology to identify raw and sulfur-fumigated Paeoniae Radix Alba decoction pieces in order to establish a rapid identification method for sulfur-fumigated Paeoniae Radix Alba. The odors of raw Paeoniae Radix Alba and its sulfur-fumigated products were analyzed by Heracles NEO ultra-fast gas phase E-nose to obtain the odor chromatographic information. The chemometric model was established, and the data were processed by principal component analysis(PCA), discriminant function analysis(DFA), soft independent modeling of class analogy(SIMCA), and partial least squares discriminant analysis(PLS-DA). The differential compounds of raw and sulfur-fumigated samples were qualitatively analyzed based on the Kovats retention index and Arochembase. As revealed by the comparison of gas chromatograms of raw and sulfur-fumigated Paeoniae Radix Alba, the heights of several peaks in the chromatograms before and after sulfur fumigation changed significantly. The peak(No.8) produced by ethylbenzene disappeared completely due to sulfonation reaction in the process of sulfur fumigation, indicating that ethylbenzene may be the key component in the identification of Paeoniae Radix Alba and its sulfur-fumigated products. In PCA, DFA, SIMCA, and PLS-DA models, the two types of samples were separated into two different regions, indicating that the established models can clearly distinguish between raw and sulfur-fumigated Paeoniae Radix Alba. The results showed that Heracles NEO ultra-fast gas phase E-nose technology could realize the rapid identification of raw and sulfur-fumigated Paeoniae Radix Alba, which provides a new method and idea for the rapid identification of sulfur-fumigated Chinese medicine.

Detection and Classification of Bacterial Cells After Centrifugation and Filtration of Liquid Specimens Using Laser-Induced Breakdown Spectroscopy.

Five species of bacteria including Escherichia coli, Mycobacterium smegmatis, Pseudomonas aeruginosa, Staphylococcus epidermidis, and Enterobacter cloacae were deposited from suspensions of various titers onto disposable nitrocellulose filter media for analysis by laser-induced breakdown spectroscopy (LIBS). Bacteria were concentrated and isolated in the center of the filter media during centrifugation using a simple and convenient sample preparation step. Summing all the single-shot LIBS spectra acquired from a given bacterial deposition provided perfectly sensitive and specific discrimination from sterile water control specimens in a partial least squares discriminant analysis (PLS-DA). Use of the single-shot spectra provided only a 0.87 and 0.72 sensitivity and specificity, respectively. To increase the statistical validity of chemometric analyses, a library of pseudodata was created by adding Gaussian noise to the measured intensity of every emission line in an averaged spectrum of each bacterium. The normally distributed pseudodata, consisting of 4995 spectra, were used to compare the performance of the PLS-DA with a discriminant function analysis (DFA) and an artificial neural network (ANN). For the highly similar bacterial data, no algorithm showed significantly superior performance, although the PLS-DA performed least accurately with a classification error of 0.21 compared to 0.16 and 0.17 for ANN and DFA, respectively. Single-shot LIBS spectra from all of the bacterial species were classified in a DFA model tested with a tenfold cross-validation. Classification errors ranging from 20% to 31% were measured due to repeatability limitations in the single-shot data.

Hissing of geese: caller identity encoded in a non-vocal acoustic signal.

Non-vocal, or unvoiced, signals surprisingly have received very little attention until recently especially when compared to other acoustic signals. Some sounds made by terrestrial vertebrates are produced not only by the larynx but also by the syrinx. Furthermore, some birds are known to produce several types of non-syrinx sounds. Besides mechanical sounds produced by feathers, bills and/or wings, sounds can be also produced by constriction, anywhere along the pathway from the lungs to the lips or nostrils (in mammals), or to the bill (in birds), resulting in turbulent, aerodynamic sounds. These noises often emulate whispering, snorting or hissing. Even though hissing sounds have been studied in mammals and reptiles, only a few studies have analyzed hissing sounds in birds. Presently, only the hissing of small, nesting passerines as a defense against their respective predators have been studied. We studied hissing in domestic goose. This bird represents a ground nesting non-passerine bird which frequently produces hissing out of the nest in comparison to passerines producing hissing during nesting in holes e.g., parids. Compared to vocally produced alarm calls, almost nothing is known about how non-vocal hissing sounds potentially encode information about a caller’s identity. Therefore, we aimed to test whether non-vocal air expirations can encode an individual’s identity similar to those sounds generated by the syrinx or the larynx. We analyzed 217 hissing sounds from 22 individual geese. We calculated the Potential for Individual Coding (PIC) comparing the coefficient of variation both within and among individuals. In addition, we conducted a series of 15 a stepwise discriminant function analysis (DFA) models. All 16 acoustic variables showed a higher coefficient of variation among individuals. Twelve DFA models revealed 51.2–54.4% classification result (cross-validated output) and all 15 models showed 60.8–68.2% classification output based on conventional DFA in comparison to a 4.5% success rate when classification by chance. This indicates the stability of the DFA results even when using different combinations of variables. Our findings showed that an individual’s identity could be encoded with respect to the energy distribution at the beginning of a signal and the lowest frequencies. Body weight did not influence an individual’s sound expression. Recognition of hissing mates in dangerous situations could increase the probability of their surviving via a more efficient anti-predator response.

Fisher's Linear Discriminant Function Analysis and its Potential Utility as a Tool for the Assessment of Health-and-Wellness Programs in Indigenous Communities.

Diabetes mellitus is a growing public health problem affecting persons in both developed and developing nations. The prevalence of type 2 diabetes mellitus (T2DM) is reported to be several times higher among Indigenous populations compared to their non-Indigenous counterparts. Discriminant function analysis (DFA) is a potential tool that can be used to quantitatively evaluate the effectiveness of Indigenous health-and-wellness programs (e.g., on-the-land programs, T2DM interventions), by creating a type of pre-and-post-program scoring system. As the communities of the Eeyou Istchee territory, subarctic Quebec, Canada, have varying degrees of isolation, we derived a DFA tool for point-of-contact evaluations to aid in monitoring and assessment of health-and-wellness programs in rural and remote locations. We developed several DFA models to discriminate between those with and without T2DM status using age, fasting blood glucose, body mass index, waist girth, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, and total cholesterol in participants from the Eeyou Istchee. The models showed a ~97% specificity (i.e., true positives for non-T2DM) in classification. This study highlights how varying risk factor models can be used to discriminate those without T2DM with high specificity among James Bay Cree communities in Canada.

Sex estimation from radiogrametric linear dimensions of the metacarpals and phalanges in a Nigerian population

The study evaluated the sex-estimative capabilities of the digital radiogrametric linear dimensions of the metacarpals, proximal, middle, and distal phalanges of a Nigerian population. One hundred adults (50 male and 50 female) computed x-ray images of the right hand, obtained from 7 radiologic centres in different hospitals in Kwara and Lagos State were used for the study. Linear dimensions (length alone) of each bone were digitally measured (in millimetres; mm) from the apex to the base using MicroDicom Viewer (Version 3.1.4). The data were entered and managed into Microsoft excel (2016) and imported into SPSS Version 23 (IBM® Armonk, USA) for analysis. Discriminant Function Analysis (DFA) was used to evaluate the sex-discriminatory characteristics of the different bone dimensions (confidence level was set at 95 %; p < 0.05 was taken to be significant). The result showed that only the metacarpals (M_1D - M_5D) and Proximal phalanges (PP_3D - PP_5D) exhibited significant sex differences (P < 0.05), and was entered into the DFA model. (P> 0.05). The canonical correlations (CC) were 0.523 ([R2[M] = 27.3 %]; with Wilks’ Lambda Λ = 0.727, P = 0.000) and 0.428 ([R2[PP] = 18.3 %]; with Wilks’ Lambda Λ = 0.817, P = 0.002) for the metacarpals and proximal phalanges respectively. After cross-validation, accurate classification into group membership (sex) was achieved with an accuracy of 69.0 % using the metacarpals and 66.0 % using the proximal phalanges. Therefore, it can be concluded that the segregated skeletal finger dimensions are not very reliable as a stand-alone anatomical structure for sex-discrimination, but could serve as an adjunct during biological profiling in human identification.

Application of Bayesian model and discriminant function analysis to the estimation of sediment source contributions

Bayesian and discriminant function analysis (DFA) models have recently been used as tools to estimate sediment source contributions. Unlike existing multivariate mixing models, the accuracy of these two models remains unclear. In the current study, four well-distinguished source samples were used to create artificial mixtures to test the performance of Bayesian and DFA models. These models were tested against the Walling-Collins model, a credible model used in estimation of sediment source contributions estimation, as a reference. The artificial mixtures were divided into five groups, with each group consisting of five samples with known source percentages. The relative contributions of the sediment sources to the individual and grouped samples were calculated using each of the models. The mean absolute error (MAE) and standard error of (SE) MAE were used to test the accuracy of each model and the robustness of the optimized solutions. For the individual sediment samples, the calculated source contributions obtained with the Bayesian (MAE = 7.4%, SE = 0.6%) and Walling-Collins (MAE = 7.5%, SE = 0.7%) models produced results which were closest to the actual percentages of the source contributions to the sediment mixtures. The DFA model produced the worst estimates (MAE = 18.4%, SE = 1.4%). For the grouped sediment samples, the Walling-Collins model (MAE = 5.4%) was the best predictor, closely followed by the Bayesian model (MAE = 5.9%). The results obtained with the DFA model were similar to the values for the individual sediment samples, with the accuracy of the source contribution value being the poorest obtained with any of the models (MAE = 18.5%). An increase in sample size improved the accuracies of the Walling-Collins and Bayesian models, but the DFA model produced similarly inaccurate results for both the individual and grouped sediment samples. Generally, the accuracy of the Walling-Collins and Bayesian models was similar (p > 0.01), while there were significant differences (p < 0.01) between the DFA model and the other models. This study demonstrated that the Bayesian model could provide a credible estimation of sediment source contributions and has great practical potential, while the accuracy of the DFA model still requires considerable improvement.

Combining E-Nose and Lateral Flow Immunoassays (LFIAs) for Rapid Occurrence/Co-Occurrence Aflatoxin and Fumonisin Detection in Maize.

The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory limits, single-contaminated, and co-contaminated samples were classified according to the detection ranges established for commercial lateral flow immunoassays (LFIAs) for mycotoxin determination. Correspondence between methods was evaluated by discriminant function analysis (DFA) procedures using IBM SPSS Statistics 22. Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated percentage of samples correctly classified by the eight-variate DFA model for aflatoxin was 81%. The overall leave-out-one cross-validated percentage of samples correctly classified by the seven-variate DFA model for fumonisin was 85%. The overall leave-out-one cross-validated percentage of samples correctly classified by the nine-variate DFA model for the three classes of contamination (below the regulatory limits, single-contaminated, co-contaminated) was 65%. Therefore, even though an exhaustive evaluation will require a larger dataset to perform a validation procedure, an electronic nose (e-nose) seems to be a promising rapid/screening method to detect contamination by aflatoxin, fumonisin, or both in maize kernel stocks.

Differentiating bipolar disorders from unipolar depression by applying the Brief Assessment of Cognition in Affective Disorders.

Scholars continue to argue about whether bipolar disorders (BD) and unipolar depression (UD) are distinguishable with regard to neurocognitive function. This study aims to explore the cognitive profiles of UD and BD by applying the Brief Assessment of Cognition in Affective Disorders (BAC-A) for neuropsychological assessment. This cross-sectional study included 68 patients with UD, 67 patients with BD, and 135 healthy control subjects. We evaluated the participants' cognitive functions at euthymic status using the BAC-A, which is made up of six traditional cognitive subtests and the Affective Processing Test. We then used a discriminant function analysis (DFA) to determine whether cognitive performance can be used to distinguish these participant groups. Healthy controls demonstrated better performance in all subtests of the BAC-A than both the UD and BD patients, with the exception of delayed recognition of affective interference. Compared with the BD group, the UD group exhibited better performance in working memory and emotion inhibition. Furthermore, using all BAC-A indexes, a total of 70% of participants could be correctly classified using a DFA model, and the discriminating validity between UD and BD was superior to using either the traditional cognitive domains or the Affective Processing Test alone. We have found that UD patients may exhibit an intermediate performance between healthy subjects and BD patients in working memory and emotional inhibition tests. The BAC-A can potentially assist in differentiating BD patients from UD patients at euthymic status in clinical settings.

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.

Evaluation of sexual dimorphism by discriminant function analysis of toe length (1T-5T) of adult Igbo populace in Nigeria.

Background:Sex determination is an important and one of the foremost criteria in establishing the identity of an individual, and this is achieved by investigating various anatomical structures to establish sex discriminatory features. The present study conducted baseline data for the toe with a view of finding discriminatory sex characteristics.Materials and Methods:A total of 420 subjects were studied by direct linear measurements of the toe length (big toe [1T] to the fifth toes [5T]) of both feet using a digital Vernier caliper with accuracy of 0.01 mm. Statistical Package for Social Sciences (IBM, version 23, Armonk, New York, USA), Levene's ANOVA outcome informed the use of t-tests to compare mean differences. Discriminant function analysis (DFA) was used to evaluate the possibility of sex categorization. The significance level was set at 95%.Results:The mean ± standard deviation values of the right (R) toes for the males were 49.63 ± 4.43 mm (1T), 36.92 ± 5.14 mm (2T), 30.35 ± 4.95 mm (3T), 25.55 ± 3.97 mm (4T) and 22.21 ± 2.94 mm (5T), whereas the female values were 45.73 ± 4.07 mm (1T), 33.31 ± 4.66 mm (2T), 26.63 ± 4.02 mm (3T), 22.89 ± 3.43 mm (4T), and 19.77 ± 2.70 mm (5T). The left male values were 49.16 ± 4.32 mm (1T), 36.82 ± 5.16 mm (2T), 30.88 ± 4.91 mm (3T), 26.13 ± 3.99 mm (4T), and 22.46 ± 3.24 mm (5T), whereas the female values were 45.33 ± 4.05 mm (1T), 33.05 ± 4.70 mm (2T), 27.27 ± 4.29 mm (3T), 23.10 ± 3.36 mm (4T), 19.81 ± 2.59 mm (5T). From the results, males displayed significantly higher mean values than females in all measured parameters (t = 2.405, P = 0.018) with no asymmetry (P > 0.05); although T3 and T4 were larger on the left foot. The DFA model when tested with the present data derived a significant F likelihood ratio test (P < 0.001), a Wilks’ lambda predictability value of 0.759 having a model accuracy of 69.5% with a better prediction for female (70%) than males (69%).Conclusion:The use of toe length alone may not be effective for sex differentiation; however, it can serve as an adjunct in forensic investigation involving sex identification.

Growing on rocky ground: Microhabitat predictors for site-occupancy of Aloe peglerae, an Endangered endemic species with a restricted range

Succulent aloes of savanna and grassland ecosystems have adaptations to fire but favourable local-scale microhabitat conditions can further support plant growth, reproduction, and survival. However, due to the heterogeneity in habitat structure, it may be unclear what type of microhabitat predictors are important for aloe conservation, and hence mismanagement of plants with restricted ranges may cause declines and/or local extinctions. This study compared the seasonal differences of aloe-occupied (n=100) and aloe-vacant (n=100) microhabitats for Aloe peglerae, an Endangered endemic of the Magaliesberg and Witwatersberg mountain ranges in South Africa. The aim was to determine key microhabitat predictors of aloe persistence in a fire-mediated savanna during the wet and dry seasons. Twelve microhabitat variables were measured within circular plots around each aloe-occupied and aloe-vacant microsite (0.5m radius). Aloe-occupied microsites in both seasons were strongly associated with typical ‘safe site’ microhabitat conditions comprising a higher average rock cover (aloe-occupied site=56%; aloe-vacant site=42%), lower bare ground cover (12%; 21%), lower grass cover (25%; 32%), lower above-ground biomass (1,079kg.ha-1; 1,616kg.ha-1), and higher forb cover (4.8%; 3.3%). A discriminant function analysis model for summer indicated the strongest microhabitat predictors of site discrimination were rock, grass, and bare ground cover. The strongest predictors of site discrimination for the winter model were bare ground cover, plant litter cover, and above-ground biomass. Thus, Aloe peglerae persists in rocky safe sites of low surrounding vegetation, providing increased fire-protection. Fire exclusion and the resultant increase in biomass and woody vegetation (bush encroachment) may thus be detrimental to the long-term persistence of Aloe peglerae.

Strontium isotope analyses (87Sr/86Sr) of otoliths from anadromous Bering cisco (Coregonus laurettae) to determine stock composition

AbstractA commercial fishery targeting the anadromous Bering cisco (Coregonus laurettae) is occurring in the Yukon River, Alaska, USA. All three of the known global spawning populations occur in Alaska. Managers believed that two of the three populations were being harvested in the fishery. To determine the likelihood of a mixed-stock fishery, we used 87Sr/86Sr values from the freshwater region of otoliths, from spawning adult Bering cisco of known origin (n = 82), to create a baseline. A 10-fold cross-validated, quadratic discriminant function analysis (DFA) of the three baseline population 87Sr/86Sr values (Yukon River, n = 27; South Fork Kuskokwim River [Kuskokwim River], n = 25; and Susitna River, n = 30) correctly reclassified 98.8% of the fish analysed. The baseline DFA model was then used to classify the 87Sr/86Sr values from a set of otoliths removed from commercially harvested Bering cisco (n = 139). Using a posterior probability threshold of 90%, we found that &amp;gt;97% of the commercial samples were classified as originating in the Yukon River. The remainder of the commercial samples were classified as originating in the Kuskokwim River (0.7%) or from the Susitna River (1.5%). The presence of 87Sr/86Sr values consistent with the Susitna River discovered in the Yukon River baseline (n = 1) and commercial samples (n = 2) suggested either multiple isotope signatures within the Yukon River population or straying among populations. Strontium isotope data provide an effective tool to monitor the movements and stock composition of Bering cisco.

Determination of trace metal concentrations in ginseng (Panax Quinquefolius (American)) roots for forensic comparison using Inductively Coupled Plasma Mass-Spectrometry

The trace metal content of roots of samples of the American ginseng natural herbal plant species (Panax quinquefolius) was investigated as a means of differentiating between this species grown on Wisconsin and New Zealand farms, and from Canadian and Chinese sources. ICP-MS measurements were undertaken by ashing samples of the roots and then digestion with conc. HNO3 and H2O2. There was considerable variation in the concentrations of 28 detectable elements along the length of a root, between different roots, between different farms/sources and between different countries. Statistical processing of the log-transformed concentration data was undertaken using principal component analysis (PCA) and discriminant function analysis (DFA). Although PCA showed some differentiation between samples, a much clearer discrimination of the Panax quinquefolius species of ginseng from the four countries was observed using DFA. 88% of the variation between countries could be accounted for by only using discriminant function 1 while 80% of the remaining 12% of the variation between countries is accounted for by discriminant function 2. The Fisher Classification Functions classify 98% of the 87 samples to the correct country of origin with 97% of the cross-validated cases correctly classified. The predictive ability of this DFA model was further tested by constructing 100 discriminant models each using a random selection of the data for two thirds of the 87 sampled ginseng root tops, and then using the resulting classification functions to determine correctly the country of origin of the remaining third of the cases. The mean success rate of the 100 classifications was 92%. These results suggest that measurement and statistical analysis of just the trace metal content of the roots of Panax quinquefolius promises to be an excellent predictor of the country of origin of this ginseng species.

Differential diagnosis of children with suspected childhood apraxia of speech.

The gold standard for diagnosing childhood apraxia of speech (CAS) is expert judgment of perceptual features. The aim of this study was to identify a set of objective measures that differentiate CAS from other speech disorders. Seventy-two children (4-12 years of age) diagnosed with suspected CAS by community speech-language pathologists were screened. Forty-seven participants underwent diagnostic assessment including presence or absence of perceptual CAS features. Twenty-eight children met two sets of diagnostic criteria for CAS (American Speech-Language-Hearing Association, 2007b; Shriberg, Potter, & Strand, 2009); another 4 met the CAS criteria with comorbidity. Fifteen were categorized as non-CAS with phonological impairment, submucous cleft, or dysarthria. Following this, 24 different measures from the diagnostic assessment were rated by blinded raters. Multivariate discriminant function analysis was used to identify the combination of measures that best predicted expert diagnoses. The discriminant function analysis model, including syllable segregation, lexical stress matches, percentage phonemes correct from a polysyllabic picture-naming task, and articulatory accuracy on repetition of /pətəkə/, reached 91% diagnostic accuracy against expert diagnosis. Polysyllabic production accuracy and an oral motor examination that includes diadochokinesis may be sufficient to reliably identify CAS and rule out structural abnormality or dysarthria. Testing with a larger unselected sample is required.

A tiered method for discriminant function analysis models for the Reference Condition Approach: model performance and assessment

Abstract: Reference Condition Approach (RCA) predictive models are used to assess a test site against reference sites probabilistically matched based on habitat. These models are the basis of several major national stream bioassessment programs in the UK, Australia, and Canada. In the usual approach to developing predictive models, discriminant function analysis (DFA) is used to assign a test site to a group of matched reference sites. These groups typically are established by classification of a macroinvertebrate assemblage and matched to the habitat attributes in a single-step DFA model. We examined an alternative to standard DFA in which a series of tiered models are used. This tiered method constructs a model for the 1st division in a hierarchical classification, and then develops models for each further step in the hierarchical classification. We examined the method with 3 training and validation data sets. Validation data consisted of data from reference sites and those same sites after they underwe...

Modeling and mapping isotopic patterns in the Northwest Atlantic derived from loggerhead sea turtles

Stable isotope analysis can be used to infer geospatial linkages of highly migratory species. Identifying foraging grounds of marine organisms from their isotopic signatures is becoming de rigueur as it has been with terrestrial organisms. Sea turtles are being increasingly studied using a combination of satellite telemetry and stable isotope analysis; these studies along with those from other charismatic, highly vagile, and widely distributed species (e.g., tuna, billfish, sharks, dolphins, whales) have the potential to yield large datasets to develop methodologies to decipher migratory pathways in the marine realm. We collected tissue samples (epidermis and red blood cells) for carbon (δ13C) and nitrogen (δ15N) stable isotope analysis from 214 individual loggerheads (Caretta caretta) in the Northwest Atlantic Ocean (NWA). We used discriminant function analysis (DFA) to examine how well δ13C and δ15N classify loggerhead foraging areas. The DFA model was derived from isotopic signatures of 58 loggerheads equipped with satellite tags to identify foraging locations. We assessed model accuracy with the remaining 156 untracked loggerheads that were captured at their foraging locations. The DFA model correctly identified the foraging ground of 93.0% of individuals with a probability greater than 66.7%. The results of the external validation (1) confirm that assignment models based on tracked loggerheads in the NWA are robust and (2) provide the first independent evidence supporting the use of these models for migratory marine organisms. Additionally, we used these data to generate loggerhead‐specific δ13C and δ15N isoscapes, the first for a predator in the Atlantic Ocean. We found a latitudinal trend of δ13C values with higher values in the southern region (20–25 °N) and a more complex pattern with δ15N, with intermediate latitudes (30–35 °N) near large coastal estuaries having higher δ15N‐enrichment. These results indicate that this method with further refinement may provide a viable, more spatially‐explicit option for identifying loggerhead foraging grounds.

Factors Associated with High-Frequency Cannabis Use and Driving among a Multi-site Sample of University Students in Ontario

Cannabis use and driving (CUD) is a growing public health concern. This study’s main objective was to identify distinguishing characteristics associated with high-frequency CUD (HFCUD) activity (i.e., CUD &gt; 12 times) in a multi-site sample of university students who had self-identified as having driven a car within 4 hours of cannabis use in the past year. Participants for the study (n = 248; age 18–28 years) were recruited by mass advertising at five universities in Ontario. Participants were screened for eligibility and assessed by an anonymous interview between April 2005 and March 2006. Bivariate analyses determined factors associated with HFCUD (i.e., &gt; 12 times) vs. a low frequency of CUD (LFCUD); significant factors were subsequently entered into a discriminant function analysis model. HFCUD was associated with several variables, including frequent (i.e., at least weekly) cannabis use; daily driving; perception of own ability to drive not being impaired by cannabis use; and expectation of CUD in the next 12 months (all p &lt; 0.0001). CUD among young drivers is an important health and safety risk requiring effective interventions. Given the strong association of HFCUD with frequent cannabis use, these phenomena need to be addressed conjointly. Furthermore, preventive interventions responsive to the specific socio-cultural contexts of possible CUD need to be developed and implemented.

Identification of the maternal source of young‐of‐the‐year Arctic charr in Lake Hazen, Canada

Summary A discriminant function analysis model based on carbon and nitrogen stable isotope values was used to identify offspring of piscivorous large‐form and non‐piscivorous small‐form Arctic charr, Salvelinus alpinus, morphotypes from Lake Hazen, Nunavut, Canada. The ability to distinguish between morphotypes in Lake Hazen was based mainly on the separation of δ15N signatures because of adult occupation of significantly different trophic levels. A smaller difference between adult morphotype δ13C values, most likely combined with a potential increase in variation among individuals at the egg stage, a faster turnover rate in carbon relative to nitrogen and the size at which fish can be sampled in the open‐water season in Lake Hazen, probably contributes to the limited use of δ13C in distinguishing between morphotype offspring. Based on young‐of‐the‐year (YOY) origin estimates, the adult morphotypes were estimated to contribute approximately equally to the YOY population; however, the morphotype offspring were differentially distributed among sampled nursery sites. Unequal distribution corresponds with prerequisites suggested for the evolution of trophic specialists within a single population that experiences assortative mating based on trophic specialisation. Differential use of spawning areas and the more or less equal importance of both Lake Hazen forms found in this study, taken alongside previously noted morphological variation and available genetic evidence, suggest that Lake Hazen may be an example of early divergence relative to other Arctic charr populations described in the literature.

Detecting Response Bias on the MindStreams Battery

The most effective tests for detection of negative response bias (NRB) are based on the forced choice paradigm, but patients coached prior to their administration may easily discern their purpose. Algorithms for NRB detection based upon parameters from standard neuropsychological tests may be more difficult for patients to discern and thus more effective in detecting NRB. The current study aimed to uncover abnormal performance patterns associated with NRB on the MindStreams computerized battery. Simulators, cognitively healthy controls, and genuinely cognitively impaired patients participated in the study. A repeated measures analysis was applied to delineate anomalous patterns on Verbal Memory, Non-Verbal Memory, Staged Information Processing Speed, and Stroop tests. The variables derived from this analysis were entered into a multivariate discriminant function analysis (DFA). The DFA model, based on accuracy, reaction time (RT) and accuracy/RT measures, differentiated efficiently between simulators and the two control groups. Various cut-offs were provided based on this model. It is concluded that computerized cognitive testing may be valuable for malingering assessment using detection strategies based on performance patterns.