Predation scars provide a new method to distinguish native and invasive crab predation on mollusc prey.

Crab species are increasingly important socioeconomic resources that are threatened by human exploitation, climate change, and invasive species, such as European green crabs (Carcinus maenas). However, the continued health of their populations is often uncertain given the limited long-term population data, necessitating alternate approaches to ensure their continued viability. Furthermore, C. maenas are one of the most highly invasive and destructive marine species globally, posing a threat to local ecosystems and species, including socioeconomically important crabs and their mollusc prey. Improved understanding of C. maenas invasions and their impacts on local crab and mollusc resources is therefore vitally important. Here, we present a new method for identifying species-level presence and relative abundances of important crab species, including invasive C. maenas, from the scars they leave on their prey. We conducted controlled manipulative feeding experiments in which individuals of Dungeness crabs (Metacarcinus magister), red rock crabs (Cancer productus), and C. maenas, were allowed to attack snails (Tegula funebralis) and produce sublethal shell damage. Resulting shell damage was photographed and landmarked for geometric morphometric analyses to determine any differences in the shape of shell damage between crab species. There were statistically significant differences between the shape of shell damage created by all three crab species (p < .0001). Shell damage formed a gradient from narrow/deep (C. productus) to shallow/wide (C. maenas) with M. magister as an intermediate form. Our method provides a novel, cost-effective tool for long-term species-specific reconstructions of crab populations and assessing the broader ecological impacts of C. maenas invasions that can inform management and mitigation for these three important crab species.

Identification of amnestic mild cognitive impairment among Black and White community-dwelling older adults using NIH Toolbox Cognition tablet battery.

OBJECTIVES: Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori "Norm Adjusted" scores versus "Unadjusted" standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined. METHODS: Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60-85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78). RESULTS: Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted). CONCLUSIONS: Racial differences were noted despite the use of normalized scores or demographic covariates-highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.

Three-dimensional geometric morphometric analyses of humerus ecomorphology: New perspectives for paleohabitat reconstruction in carnivorans and ungulates.

Long bone ecomorphology has proven effective for paleohabitat reconstructions across a wide range of mammalian clades. Still, there is no comprehensive framework to allow interpretation of long bone morphological variation within and between different monophyletic groups. Here, we investigated the use of humerus morphometry to classify living members of the orders Carnivora and ungulates based on their preferred habitats. Using geometric morphometrics, we extracted three different kinds of humerus shape data describing interspecific variation with and without accounting for evolutionary allometry and phylogenetic signal. The traditional a priori categorization of species in open, mixed, and closed habitats was employed in combination with selected subsets of shape variables to identify the best-predictive models for habitat adaptation. These were identified based on the statistical performance of phylogenetic and non-phylogenetic discriminant analyses and then applied to predict habitats on a subsample of fossil species. Size-free shape data combined with phylogenetic discriminant analyses showed the highest rate of accuracy in habitat classification for a combined sample of carnivorans and ungulates. Conversely, when the two groups were investigated separately, traditional shape data analyzed with phylogenetic discriminant function analyses provided models with the greatest predictive power. By combining carnivorans and ungulates within the same methodological framework we identified common adaptive features in closed habitat-adapted species that show compressed epiphyses, while open habitat-adapted species have expanded epiphyses. These morphologies evolved to allow significant degree of direction switches during locomotion in closed habitats compared to open habitat-adapted species whose forelimb joints evolved to stabilize articulations for increasing speed.

Utilising Discriminant Function Analysis (DFA) for Classifying Osteoarthritis (OA) Patients and Volunteers Based on Biomarker Concentration.

Osteoarthritis (OA) is a degenerative joint disease characterised by the breakdown of cartilage, causing pain, stiffness, and limited movement. Early diagnosis is crucial for effective management but remains challenging due to non-specific early symptoms. This study explores the application of Discriminant Function Analysis (DFA) to classify OA patients and healthy volunteers based on biomarker concentrations of Interleukin-6 (IL-6), Tumour necrosis factor-alpha (TNF-α), and Myeloperoxidase (MPO). DFA was employed to analyse biomarker data from 86 participants (58 patients, 28 volunteers) to evaluate the discriminatory power of these biomarkers in predicting OA. Significant differences were observed in MPO and TNF-α levels between groups, while IL-6 did not show a significant distinction. The iterative classification process improved model assumptions and classification accuracy, achieving a pre-classification accuracy of 71.8%, which adjusted to 57.1% post-classification. The results highlight DFA's potential in OA diagnosis, suggesting its utility in managing complex data and aiding personalised treatment strategies. The study underscores the need for larger sample sizes and additional biomarkers to enhance diagnostic robustness and provides a foundation for integrating DFA into clinical practice for early OA detection.

Morphological variation in Schizothorax oconnori, Schizothorax waltoni (Teleostei: Cyprinidae: Schizothoracinae), and their natural hybrids from the middle Yarlung Zangbo River, Tibet.

The morphological variation in Schizothorax oconnori, Schizothorax waltoni, and their natural hybrids was examined using conventional and image-based analysis approaches. In total, 38 specimens of S. oconnori, 35 of S. waltoni, and 37 natural hybrids were collected from the Shigatse to the Lhasa section of the Yarlung Zangbo River during June and July 2021. A total of 21 morphometric, 4 meristic, and 27 truss variables were employed for the classification of S. oconnori, S. waltoni, and natural hybrids. Principal component analysis (PCA) and factor analysis (FA), as well as discriminant function analysis (DFA) and cluster analysis (CA), were conducted to identify differences based on traditional and truss measurements. Four principal components explained 75.92% of the variation among the morphometric characters, while five principal components accounted for 79.69% of the variation among the truss distances. FA results showed that factor 1 was associated with head shape, and factor 2 was associated with fins based on morphometric characters. Among the truss characters, factor 1 was related to head shape, and factor 2 was related to chest shape. In DFA, morphometric measurements achieved higher accuracy (100%) compared to truss distances (94.55%). The head morphology of hybrids exhibited intermediate traits between S. oconnori and S. waltoni. Both morphometry-based and truss-based clustering indicated that the morphology of natural hybrids leaned toward S. oconnori. In conclusion, the combination of morphometric and truss analysis is beneficial for classifying S. oconnori, S. waltoni, and their natural hybrids. The presence of natural hybrids could be considered an evolutionary response to the differentiation of nutritional and spatial niches in the middle Yarlung Zangbo River.

Selection of mungbean (Vigna radiata L.) mutants with respect to seasonal variation of summer and spring using discriminant function analysis.

The current study was carried out at Nuclear Institute of Agriculture, Tandojam to assess ten mungbean mutants together with one check cultivar in two separate cropping seasons. The findings revealed that all mutants, with the exception of the branches per plant, had significantly different examined traits. By characterizing ten variables, including plant height (cm), number of branches/plant, number of seeds per pod, grain yield per plot (g/plot), grain weight per plant, pod length (cm), pods per plant, days to flowering, above ground biological weight per plot (g/plot) and days to maturity, the results could considerably differentiate between low and high producing mutants. Discriminant analysis was used to choose high-yielding genotypes. The discriminant score demonstrated a significant canonical correlation of 0.994** and could account for 98.8 % of differences in mungbean production. According to the results of discriminant function analysis, the most significant features are pod length, days to flowering, plant height and above ground biological weight. The highest discriminant scores were displayed by the genotypes AEM66, AEM27, AEM25 and AEM14, identified as high yielding mutants. The low yielding mungbean mutants, designated Viz, AEM20, AEM30, AEM35, AEM-96, AEM29, AEM40 and AEM32 are those that exhibit the lowest values of the discriminant score. Mungbean cultivation is more successful in the summer than it is in the spring.

Sexual dimorphism of the twelfth thoracic vertebra for sex determination in the Central Thai population.

Analyzing skeletal remains is crucial for identifying individuals, and forensic anthropologists use this analysis to determine biological characteristics, particularly sex, aiding criminal investigations. Among thoracic vertebrae, the twelfth thoracic vertebra (T12) is highly sexually dimorphic in various populations. This study aims to establish a discriminant function equation (DFE) for sex determination based on T12 in the Central Thai population. A total of 15 parameters of T12 were examined in 69 bone samples (43 males and 26 females). Among the 15 parameters, 14 were significantly different between males and females. The discriminant function equation (DFE) was generated as DFE = -19.578 + 0.376(i) BDsm + 0.254(l) PW + 0.081TDm, with a cutoff value of -0.296 for males and females, showing 92.8 % accuracy. The evaluation of the DFE using 10 blind samples showed 90 % accuracy. These findings may offer an additional method for sex determination through T12, complementing the examination of other skeletal elements.

Estimation of sex using dimensions around the metatarsal diaphyseal nutrient foramen: Application of discriminant function analysis and logistic regression models.

Sex estimation equations are population-specific, and a wider use of multiple bones to generate equations will increase the accuracy of sex estimation in forensic settings. The metatarsal bones have been used previously, however the dimensions around the diaphyseal nutrient foramen have not been utilised in sex estimation. The current study aimed to determine the utility of the dimensions around the nutrient foramen of metatarsal bones in estimating sex in the South Africans of European descent (SAED). Five measurements around the nutrient foramen were taken from a total of 876 metatarsal bones (first to fifth) from 186 individual skeletons (99 males, 87 females) obtained from the Raymond A. Dart Modern Skeletal Collection. Measurements subjected to direct and stepwise discriminant function (DFA) and logistic regression (LRA) analyses included total length, distance from proximal end to nutrient foramen, circumference, and mediolateral and dorsoplantar diameters at the level of the nutrient foramen. The original classification accuracies for multivariable functions of the stepwise and direct DFA ranged from 83.1-88.3% to 85.5-88.3%, respectively. The original classification accuracies for multivariable functions of the stepwise and direct LRA ranged from 83.3%-88.7% to 86.2%-88.3%, respectively. The cross-validation classifications showed a drop of 0-2.4% for DFA and 0.2-1.1% for LRA. The width measurements were better predictors of sex than length. The dimensions around the metatarsal bone nutrient foramen exhibit sexual dimorphism in the SAED. The generated DFA and LRA functions produced high average classification accuracies which are useful in sex estimation during forensic human identification.

Evaluation of morphological traits using multivariate techniques in Kotdhar goat native to Shivalik hill region of Himachal Pradesh.

The objective of this study was to analyze the diverse morphological attributes of Kotdhar goat native to Shivalik hill region of Himachal Pradesh. A total number of 180 goats, comprising 150 females and 30 males aged between 1 and 3 years, were randomly selected to estimate15 distinct morphological traits and ten body indices. Mean values of 17.66 cm, 11.68 cm, 8.73 cm, 71.94 cm, 69.83 cm, 74.48 cm, 38.10 cm, 73.38 cm, 20.55 cm, 16.48 cm, 14.89 cm, 13.57 cm, 19.20 cm, 7.71 cm and 26.12 kg were estimated for head length, head width, face length, body length, wither height, heart girth, chest depth, rump height, rump length, rump width, haunch width, bicoastal width, shoulder width, cannon bone and body weight respectively. In addition, morphological indices viz. body index, length index, body ratio, thoracic development, pelvic index, longitudinal pelvic index, transverse pelvic index, proportionality index, dactyl thorax index and relative depth of thorax were estimated from morphological traits. Results of morphological indices indicated that Kotdhar goat are medium bodied, longilinear, tall animals and well-suited for meat production. Phenotypic correlation among most of traits was positive and significant. Among all correlations, correlation between head length and head width was found lowest (- 0.270), while the correlation between heart girth and chest depth was observed highest (0.861). Canonical discriminant function analysis showed that there was no considerable level of intermixing for morphological traits in Kotdhar goat. Employing principal component analysis with varimax rotation, three principal components were observed, accounting for 68.47% of the total variation. First principal component (PC1) encapsulated the highest proportion of variance (44.39), revealing the high loadings on head length (0.777), body length (0.747), wither height (0.782), heart girth (0.785), chest depth (0.706), rump height (0.762), haunch width (0.734) and bicoastal width (0.793). Hence, it is possible to utilize these eight variables out of the 15 morphological traits for the characterization of the Kotdhar goat.

Sex estimation from the hyoid bone measurements in an adult Eastern Turkish population using 3D CT images, discriminant function analysis, support vector machines, and artificial neural networks☆.

The hyoid bone is one of the bones in the human body that shows sexual dimorphism. The anthropological and anthropometric characteristics that determine sexual dimorphism are influenced by demographic differences. The aim of this study was to investigate the rate of sexual dimorphism of the hyoid bone in the adult Eastern Turkish population from the examination of the 3D computed tomography images of 240 patients, using discriminant function analysis (DFA), support vector machines (SVM), and artificial neural networks (ANN). These evaluations were based on eight hyoid measurements that have been frequently used in previous CT studies. The results showed that all eight measurements were higher in males than in females (p = 0.000). It was determined that sex could be estimated accurately at up to 93.3 % using DFA, 93.8 % using SVM and 95.4 % using ANN. The maximum accuracy rate achieved to 94.2 % in males using SVM, and 95.8 % in females using ANN. These high rates of sexual dimorphism found using DFA, SVM, and ANN in this study indicate that characteristics of the hyoid bone can be utilized to determine sex in the Eastern Turkish population.