Stepwise Discriminant Analysis Research Articles

Fisher discriminant analysis of multimodal ultrasound in diagnosis of cervical metastatic lymph nodes in papillary thyroid cancer.

The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher's stepwise discriminant analysis. The model's effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. The multimodal ultrasound diagnostic model, established through Fisher's stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Diagnosis of schizophrenia by integrated saccade scores and associations with psychiatric symptoms, and functioning.

Eye movement as a neurobiological biomarker of schizophrenia. We aim to estimate diagnostic accuracy of integrated pro/antisaccade eye movement measurements to discriminate between healthy individuals and schizophrenic patients. We compared the eye movement performance of 85 healthy individuals and 116 schizophrenia-stable patients during prosaccade and antisaccade tasks. The difference eye movement measurements were accumulated by stepwise discriminant analysis to produce an integrated score. Finally, the diagnostic value of the integrated score was calculated by the receiver operating characteristic (ROC) area under the curve (AUC), and the best sensitivity and specificity were calculated based on the given cutoff values. Using discriminant analysis, an integrated score included the residual gain and latency (step) during the prosaccade test, the error rate, and the corrected error rate during the antisaccade test. We found that the integrated score could well classify schizophrenia patients and healthy individuals with an accuracy of 80.6%. In the ROC, Youden's index was 0.634 (sensitivity = 81.0%, specificity = 82.4%) and AUC was 0.871. There were significant difference patterns of correlation between the severity of psychiatric symptoms and daily functioning and diagnostic eye movement measurements. Using only 2 saccade tasks to discriminate well between schizophrenia patients and healthy controls, suggesting that abnormalities in saccade behavior is a potential biomarker and efficient diagnostic tool for identifying schizophrenia. The underlying neuropathologic mechanisms associated with abnormal saccades may provide insights into the intervention and diagnosis of schizophrenia.

Sodium Bicarbonate Tolerance During Seedling Stages of Maize (Zea mays L.) Lines

ABSTRACT(1) Soil alkalinization and salinization represent a growing global challenge. Maize (Zea mays L.), with its relatively low tolerance to salt and alkali, is increasingly vulnerable to saline‐alkali stress. Identifying maize genotypes that can withstand salinity and alkalinity is crucial to broaden the base of salt‐alkali‐tolerant maize germplasm. (2) In this study, we screened 65 maize germplasm resources for alkali stress using a 60 mM NaHCO3 solution. We measured fifteen morphological and physiological indices, including seedling height, stem thickness, and leaf area. Various analytical methods—correlation analysis, principal component analysis, subordinate function analysis, cluster analysis, stepwise discriminant analysis, and ridge regression analysis—were used to assess the seedling alkali tolerance of these maize germplasm resources. The physiological indices of six tested maize varieties were analyzed in greater detail. (3) The findings revealed complex correlations among traits, particularly strong negative associations between conductivity and root traits such as length, volume, surface area, diameter, and number of branches. The 15 evaluation indices were reduced to 7 principal components, explaining 77.89% of the variance. By applying affiliation functions and weights, we derived a comprehensive evaluation of maize seedling alkali tolerance. Notably, three germplasms—Liang Yu 99, Bi Xiang 638, and Gan Xin 2818—demonstrated significant comprehensive seedling alkali tolerance. Cluster analysis grouped the 65 maize germplasm resources into four distinct categories (I, II, III, and IV). The results of the cluster analysis were confirmed by multiclass stepwise discriminant analysis, which achieved a correct classification rate of 92.3% for 60 maize genotypes regarding alkalinity tolerance. Using principal component and ridge regression analyses, we formulated a regression equation for alkali tolerance: D‐value = −1.369 + 0.002 * relative root volume + 0.003 * relative number of root forks + 0.006 * relative chlorophyll SPAD + 0.005 * relative stem thickness + 0.005 * relative plant height + 0.001 * relative conductivity + 0.002 * relative dry weight of underground parts. Under sodium bicarbonate stress, morphological indices and germination rates were significantly reduced, germination was inhibited, photosynthetic pigment levels in maize leaves decreased to varying degrees, and the activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) significantly increased. Alkali stress markedly enhanced the antioxidant enzyme activities in maize varieties, with alkali‐resistant varieties exhibiting a greater increase in antioxidant enzyme activities than alkali‐sensitive varieties under such stress. (4) By screening for alkali tolerance in maize seedlings, the identified alkali‐tolerant genotypes can be further utilized as suitable donor parents, thereby enhancing the use of alkali‐tolerant germplasm resources and providing theoretical guidance for maize cultivation in saline‐alkaline environments.

Sex estimation using Magnetic Resonance Imaging measurements of hand and foot in Egyptian population

BackgroundThe identification of skeletal remains begins with a sex evaluation since other biological profile elements, such as size and age, are sex-specific. The pelvis is the favored element for sex assessment since it is very sexually dimorphic.Aim of the studyDelivering hand and foot bones sex estimate criteria for Egyptian population identification was the goal of this work.Subject and methodsEighty-two Egyptian adult subjects (41 males and 41 females) aged ≥ 21 years old were subjected to MRI scan on the right hand and foot to assess 9 measurements: hand length, hand width, four measurements of middle metacarpal bone (length, breadth, head breadth and base breadth), first metatarsal bone length, first metatarsal bone width and (first metatarsal bone length x first metatarsal bone width).ResultsAll the measured parameters in the right hand and foot were higher in males than females. First metatarsal bone length x first metatarsal bone width in the foot and middle metacarpal bone breadth in the hand were the most sexually dimorphic parameters, with accuracy of 86.6 percent and 84.1 percent, respectively. In stepwise discriminant analysis, three of nine measurements were selected: First metatarsal bone length x first metatarsal bone width, middle metacarpal bone breadth and hand width for the sex prediction equation in the Egyptian population, with a cross-validated sex classification accuracy of 90.2%.ConclusionSex can be assessed from hand and foot metric parameters measured by MRI with high accuracy.

Predicting Indian SME financial distress: an ex-ante approach

Purpose The purpose of this study is to propose a financial distress prediction (FDP) model and method suitable for listed Indian small and medium enterprises (SMEs). Design/methodology/approach A three-part screening criteria similar to Platt and Platt (2006) was tested independently and jointly on Indian SMEs using statistically significant financial variables. Five stepwise multiple discriminant analysis (MDA) models were developed and the best-performing model was further compared against seminal models to check for robustness. Findings Model C2 developed under Criterion C which stated “if net income before special items is negative in any given year the firm is considered as ‘unwell’” proved robust and effective. Originality/value The proposed model identifies the importance of profitability and efficiency ratios over leverage ratios in determining financial distress and therefore, has implications for SME owners/managers and shareholders.

Effect of Needle Age and Location on the Volatile Composition of Essential Oils from Juniperus communis L.: A Chemometric Study

AbstractProfiles of the volatile organic compound (VOC) content of essential oils (EOs) extracted from young and old Juniperus communis L. needles were generated via gas chromatography combined with mass spectrometry (GC‐MS) and flame ionization detection (GC‐FID) and subsequently processed using multivariate statistical methods. These were then used to differentiate the EOs according to needle age and geographical origin. From a total 49 of VOCs identified, statistically significant differences (p&lt;0.05) between young‐ and old‐needle EOs were found for 21 VOCs. Most of the EOs from the young needles showed higher (p&lt;0.05) contents of α‐ and β‐pinene and sesquiterpenes (β‐ and γ‐elemene, germacrene D and B, α‐muurolene, and δ‐cadinene) and their oxygenated derivatives (α‐ and τ‐cadinol), while most of the monoterpenes (α‐thujene, sabinene, terpinolene, β‐myrcene, β‐phellandrene, and p‐cymene) were present in higher quantities (p&lt;0.05) in the EOs from the old needles. The volatiles germacrene D, α‐ and β‐pinene, α‐cadinol, and sabinene were identified via stepwise and canonical discriminant analysis as the most valuable for EO discrimination according to needle age and geographical origin. Recognition and prediction ability tests of the EOs achieved 100 % accuracy in EO classification according to the above mentioned criterion.

A novel discriminant algorithm for differential diagnosis of mild to moderate thalassemia and iron deficiency anemia.

Mild to moderate thalassemia trait (TT) and iron deficiency anemia (IDA) are the most common conditions of microcytic hypochromic anemia (MHA) and they exhibit highly similar clinical and laboratory features. It is sometimes difficult to make a differential diagnosis between TT and IDA in clinical practice. Therefore, a simple, effective, and reliable index is needed to discriminate between TT and IDA. Data of 598 patients (320 for TT and 278 for IDA) were enrolled and randomly assigned to training set (278 of 598, 70%) and validation set (320 of 598, 30%). Stepwise discriminant analysis was used to define the best diagnostic formula for the discrimination between TT and IDA in training set. The accuracy and diagnostic performance of formula was tested and verified by receiver operating characteristic (ROC) analysis in validation set and its diagnostic performance was compared with other published indices. A novel formula, Thalassemia and IDA Discrimination Index (TIDI) = -13.932 + 0.434 × RBC + 0.033 × Hb + 0.025 ×MCHC + 53.593 × RET%, was developed to discriminate TT from IDA. TIDI showed a high discrimination performance in ROC analysis, with the Area Under the Curve (AUC) = 0.936, Youden' s index = 78.7%, sensitivity = 89.5%, specificity = 89.2%, respectively. Furthermore, the formula index also obtained a good classification performance in distinguishing 5 common genotypes of TT from IDA (AUC from 0.854-0.987). The new, simple algorithm can be used as an effective and robust tool for the differential diagnosis of mild to moderate TT and IDA in Guangxi region, China.

Assessing the reliability and accuracy of sex estimation models utilizing sternal morphometry derived from computed tomography in the Ghanaian population

Sex estimation models are specific to populations and cannot be generalized due to genetic and environmental variabilities. This retrospective cross-sectional study, conducted between January and September 2023 at the Tamale Teaching Hospital, included 119 (50.9 %) females and 115 (49.1 %) males aged 23–82 years. Measurements, including manubrium length (M), manubrium width (MW), sternal body length (B), combined manubrium and sternal body lengths (CL), corpus sterni width at first sternebrae (CSWS1), and corpus sterni width at third sternebrae (CSWS3), were obtained from Computerized Tomographic (CT) images of the sternum using DICOM Viewer, accurate to 0.1 cm. Subsequently, sternal area (SA) and sternal index (SI) were calculated. Univariable and stepwise multivariable discriminant function analysis (DFA) and logistic regression (LR) models were developed using a training sample (70 %), and cross-validation was performed on a holdout sample (30 %). Results showed that the linear measurements, excluding M, and sternal area were higher in males than females (P<0.001), while the sternal index was <50 % in males but >50 % in females (P<0.001). Univariable sex estimation accuracies, in cross-validation, ranged from 43.7 % to 92.9 % in DFA and 50.0–92.9 % in LR. For multivariable models, the accuracy ranges were 92.9–94.3 % in DFA and 91.6–93.0 % in LR. The sternal body length was the most accurate at 90.1 % in DFA and 90.2 % in LR, with lower sex bias (male-female) in LR than in DFA (-0.1 vs. 8.7). The sternum proves valuable for sex estimation, with sternal body length as the most accurate linear measurement. However, multivariable models, particularly LR, demonstrate higher accuracy compared to DFA.

Lauric acid content in intramuscular fat is a reliable indicator of black soldier fly larvae meal consumption in Muscovy ducks

The present research investigated if intramuscular fatty acid (FA) profile could distinguish meat from ducks fed with black soldier fly larvae meal (BSFLM) during fattening. By using stepwise linear discriminant analysis on FA profiles of 96 meat samples, lauric acid (C12:0) was found to be the best predictor, accurately differentiating samples with only two misclassifications. The Fisher classification functions indicated that breast samples with lauric acid content above 0.222 % in intramuscular fat would be classified as BSFLM-fed. The Fisher classification functions were also effective in other poultry species fed BSFLM, with only two misclassified samples out of 42 samples from a dataset compiled from published papers. Misclassifications were linked to an unexpected lauric acid content in the intramuscular fat. In conclusion, given that BSFLM is the only relevant source of lauric acid in the diet, this fatty acid could identify meat from BSFLM-fed ducks and possibly other poultry species, warranting further exploration of other FA as potential identifiers of BSFLM-fed poultry.

68Ga]DOTATOC PET-derived radiomics to predict genetic background of head and neck paragangliomas: a pilot investigation.

To investigate the [68Ga]DOTATOC PET radiomic profile of head and neck paragangliomas (HNPGLs) and identify radiomic characteristics useful as predictors of succinate dehydrogenase genes (SDHx) pathogenic variants. Sporadic and SDHx HNPGL patients, who underwent [68Ga]DOTATOC PET/CT, were retrospectively included. HNPGLs were analyzed using LIFEx software, and extracted features were harmonized to correct for batch effects and confronted testing for multiple comparison. Stepwise discriminant analysis was conducted to remove redundancy and identify best discriminating features. ROC analysis was used to define optimal cut-offs. Multivariate decision-tree analysis was performed using CHAID method. 34 patients harboring 60 HNPGLs (51 SDHx in 25 patients) were included. Three sporadic and nine SDHx HNPGLs were metastatic. At stepwise discriminant analysis, both GLSZM-Zone Size Non-Uniformity (ZSNU, reflecting tumor heterogeneity) and IB-TLSRE (total lesion somatostatin receptor expression) were independent predictors of genetic status, with 96.4% of lesions and 91.6% of patients correctly classified after cross validation (p < 0.001). Among non-metastatic patients, GLSZM-ZSNU and IB-TLSRE were significantly higher in sporadic than SDHx HNPGLs (p < 0.001). No differences were revealed in metastatic patients. Decision-tree analysis highlights multifocality and IB-TLSRE as useful variables, correctly identifying 6/9 sporadic and 24/25 SDHx patients. Model failed to classify one SDHA and three sporadic patients (2 metastatic). Radiomics features GLSZM-ZSNU and IB-TLSRE appear to reflect HNPGLs SDHx status and tumor behavior (metastatic vs. non-metastatic). If validated, especially IB-TLSRE might represent a simple and time-efficient radiomic index for SDHx variants early screening and prediction of tumor behavior in HNPGL cases.

Kidney and Liver Predictors of Adults Hospitalized with COVID-19 Infection

SARS-CoV-2 damages not only the lungs, but also the liver and kidney. Most critically ill COVID-19 patients have liver and kidney dysfunctions. The early identification of patients with COVID-19 who will develop severe or critical disease symptoms is important for delivering proper and early treatment. This research studies the correlation of liver and kidney function indexes and COVID-19 outcomes. Electronic medical record data from 391 patients diagnosed with COVID-19 in the COVID-19 Department, Galilee Medical Center, Nahariya, Israel were collected. Epidemiological, clinical, laboratory, and imaging variables were analyzed. The liver and kidney enzyme indexes were measured upon admission and discharge. A correlation between laboratory levels and severity and mortality of COVID-19 patients was undertaken. This study included 391 COVID-19 patients, 258 mild patients and 133 severe patients. Multivariate stepwise regression analyses and discriminant analyses were used to identify and validate powerful predictors. The main outcome was death or invasive ventilation. Three factors, namely higher urea nitrogen (BUN) and IL-6, and lower albumin levels, were the most powerful predictors of mortality, and classified the results (survival vs. death) correctly in 85% of cases (diagnostic accuracy) with a sensitivity of 88% and a specificity 55%. Compared with mild patients, severe patients had lower albumin (ALB), higher alanine aminotransferase (ALT), aspartate aminotransferase (AST) and BUN (all p &lt; 0.001). COVID-19 patients, especially severe patients, have damage to liver and kidney function. BUN, IL-6 and albumin are factors predicting mortality while fibrinogen and AST could be independent factors for predicting the severity of COVID-19.

Morphological Differences and Contour Visualization of Statoliths in Different Geographic Populations of Purpleback Flying Squid (Sthenoteuthis oualaniensis)

Statoliths are important hard tissues in cephalopods. Significant differences are found in the external morphology of statoliths in different groups or species. In this study, stepwise discriminant analysis was used to investigate the external morphological differences in purpleback flying squid statoliths in three different marine regions, comprising the East Indian Ocean (5° S–2° N, 82°–92° E), Central East Pacific Ocean (02°37′ S–0°59′ N, 99°44′ W–114°19′ W), and Northwest Indian Ocean (17°04′ N–17°18′ N, 61°05′ E–61°32′ E). The contours of statoliths were reconstructed visually by using Fourier analysis and the landmark method. The results obtained by stepwise discriminant analysis showed that the accuracy of identification was 84.4% for the traditional measurement method, 82.9% for the Fourier analysis method, and 87.3% for the landmark method. The contour visualization results showed that the purpleback flying squid statoliths were small in the Central East Pacific Ocean, and the curvature of the side region was the most obvious. The radian differentiation of statoliths was most gentle in the East Indian Ocean. In the Northwest Indian Ocean, the rostral region of statoliths was shorter and the dorsal region was smoother. The reconstruction results detected significant differences in the outer morphology of statoliths in different marine regions. The results obtained in this study show that all three methods are effective for identifying populations, but the landmark method is better than the traditional measurement method. The reconstruction of statolith contours using the Fourier transform and landmark methods provides an important scientific basis for conducting taxonomy, according to statolith morphology.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

A study on the development and the application strategy of FT-MIRS-based models for the diagnosis of subclinical mastitis and milk quality classification in buffaloes

Buffalo mastitis detection and buffalo milk quality are of great importance to the world dairy industry. Somatic cell count (SCC) can be employed to assess mammary gland health and milk quality in dairy cows, but SCC detection is costly, and the detection instrument is expensive. The purpose of this study was to develop high and low SCC identification models using Fourier-transform mid-infrared spectrum (FT-MIRS), which could be used to diagnose subclinical mastitis (SCM) in buffalo and to determine the milk quality grade at a low cost. The dataset contained 899 buffalo milk samples collected from two regions in China. Firstly, the samples were divided into positive group above the threshold (SCM or unqualified milk) and negative group below the threshold (healthy or qualified milk) with SCC = 200 × 103 cells/mL (SCM in buffalo), 400 × 103 cells/mL (EU standard for raw cow milk, ES), 500 × 103 cells/mL (Indian standard for raw buffalo milk, IS), and 750 × 103 cells/mL (US standard for raw cow milk, US) as thresholds, respectively. Then, with FT-MIRS as predictive variables, predictive models were developed using Partial Least Squares Discriminant Analysis (PLSDA), Random Forest (RF), and Gradient Boosting Machine (GBM). The main results were as follows: the AUCval of the diagnostic model of SCM in buffalo using SCM criteria as a threshold was 0.84 (PLSDA). The AUCval values of the three buffalo milk quality classification models with ES, IS, and US as thresholds were 0.76 (PLSDA), 0.78 (GBM), and 0.84 (PLSDA), respectively. The predictive models established in this paper had a weak predictive ability for positive samples, and the “stepwise discriminant analysis” was recommended to improve the model application effect: The models were applied to classify samples as positive and negative, and then the samples with higher predictive probability were selected. Finally, the remaining samples were further differentiated using the reference methods of SCC detection. To conclude, FT-MIRS has the potential to predict buffalo mammary gland health status and buffalo milk quality grade, reduce the cost of SCC detection, and improve work efficiency, which will lay the foundation for rapid buffalo milk quality classification by raw milk regulatory authorities and rapid diagnosis of SCM in buffalo by farms.

“Back to Braak”: Role of Nucleus Reuniens and Subcortical Pathways in Alzheimer’s Disease Progression

BackgroundPatients with Alzheimer’s Disease (AD) exhibit structural alterations of the thalamus that correlate with clinical symptoms. However, given the anatomical complexity of this brain structure, it is still unclear whether atrophy affects specific thalamic nuclei and modulates the clinical progression from a prodromal stage, known as Mild Cognitive Impairment (MCI), to full-fledged AD.ObjectivesTo characterize the structural integrity of distinct thalamic nuclei across the AD spectrum, testing whether MCI patients who convert to AD (c-MCI) show a distinctive pattern of thalamic structural alterations compared to patients who remain stable (s-MCI).DesignInvestigating between-group differences in the volumetric features of distinct thalamic nuclei across the AD spectrum.SettingProdromal and clinical stages of AD.ParticipantsWe analyzed data from 84 healthy control subjects (HC), 58 individuals with MCI, and 102 AD patients. The dataset was obtained from the AD Neuroimaging Initiative (ADNI-3) database. The MCI group was further divided into two subgroups depending on whether patients remained stable (s-MCI, n=22) or progressed to AD (s-MCI, n=36) in the 48 months following the diagnosis.MeasurementsA multivariate analysis of variance (MANOVA) assessed group differences in the volumetric features of distinct thalamic nuclei obtained from magnetic resonance (MR) images. A stepwise discriminant function analysis identified which feature most effectively predicted the conversion to AD. The corresponding predictive performance was evaluated through a Receiver Operating Characteristic approach.ResultsAD and c-MCI patients showed generalized atrophy of thalamic nuclei compared to HC. In contrast, no significant structural differences were observed between s-MCI and HC subjects. Compared to s-MCI, c-MCI individuals displayed significant atrophy of the nucleus reuniens and a trend toward significant atrophy in the anteroventral and laterodorsal nuclei. The discriminant function analysis confirmed the nucleus reuniens as a significant predictor of AD conversion, with a sensitivity of 0.73 and a specificity of 0.69.ConclusionsIn line with the pathophysiological relevance of the nucleus reuniens proposed by seminal post-mortem studies on patients with AD, we confirm the pivotal role of this nucleus as a critical hub in the clinical progression to AD. We also propose a theoretical model to explain the evolving dysfunction of subcortical brain networks in the disease process.

Predicting feed efficiency of Angus steers using the gastrointestinal microbiome

Microbial composition of the gastrointestinal tracts is an important factor affecting the variation in feed efficiency in ruminants. Several studies have investigated the composition of the ruminal and fecal microbiotas, as well as their impacts on feed efficiency and digestion. In addition, next-generation DNA sequencing techniques have allowed us to gain a better understanding of such microbiomes. In this study, the beef cattle microbiome data were analyzed using both a multivariate and a univariate approach and the results were compared. Moreover, a statistical procedure to classify calves in two groups with extreme Residual Feed Intake (RFI) values, using their microbiota profile, was developed. Both fecal and ruminal samples were collected from 63 Angus steers at two different time points for evaluation of their microbiomes: at the beginning and at the end of the feedlot. An additional fecal sample was collected at weaning. A total of 149 and 119 bacterial families (BFs) were retrieved from the ruminal and fecal samples, respectively. A Canonical Discriminant Analysis (CDA) was used to investigate whether BFs were able to distinguish between rumen and fecal samples. A sub-sample of 28 steers was divided in two groups based on their feed efficiency status: positive or negative for RFI. Fecal samples collected at weaning were used to assign the positive and negative RFI animals to their corresponding groups using both Stepwise Discriminant Analysis and CDA. Results revealed that CDA was able to distinguish between rumen and fecal samples. Peptostreptococcaceae was the family most associated with the fecal samples, whereas Prevotellaceae the most associated with the ruminal samples. The CDA using 19 BFs selected from the stepwise was able to correctly assign all animals to the proper RFI groups (negative or positive). Rhizobiaceae was the family most associated with negative RFI, whereas Comamonadacea was the family most linked with positive RFI. The results from this study showed that the multivariate approach can be used to improve microbiome data analysis, as well as to predict feed efficiency in beef cattle using information derived from the fecal microbiome.

Copper Stress Causes Shell Morphology Changes in Early Juvenile Anodonta woodiana Based on Geometric-Morphometric Analysis.

In this study, the morphological characteristics of early juvenile shells of Anodonta woodiana, which were exposed to different concentrations of aqueous copper, were analyzed using 10 landmarks to determine morphological changes in the shells. Morphological changes mainly occurred at the top of the shell and front and back ends of the central axis. Stepwise discriminant analysis proved shape differences among experimental and control groups. The results of this study demonstrate for the first time that environmentally relevant copper concentrations cause stress-related morphological changes in A. woodiana in the vulnerable early juvenile stage.

Identifying cognitive profiles in children with neurodevelopmental disorders using online cognitive testing.

Children with neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) tend to exhibit similar deficits in attention and memory ability. Early screening of cognitive deficits in children with NDDs, particularly in preschool children, is fundamental to improving cognitive and academic outcomes. In order to determine cognitive profiles in children with ASD and ADHD, we developed accessible audiovisual instructions for an online battery of 13 cognitive tests. Children ages 4-16 who were diagnosed with ADHD (n = 83), or ASD (n = 37), or who were typically developing children (TD) (n = 86) were recruited. Data were analyzed using a stepwise Discriminant Analysis to determine which cognitive tasks were the strongest discriminators between the diagnostic groups. Results revealed four tasks reflective of working memory, reasoning, and attentional processes, which correctly classified approximately 53-60% of each group. The ADHD group had lower scores on attentional tasks compared to TD, while ASD group had lower scores on reasoning tasks compared to the TD children, and made more attempts across all four tasks. The results from this study stress the need for cognitive screening assessments that include domain-specific items to improve the characterization of executive function deficits and promote academic achievement in all children with NDDs.

Stepwise canonical discriminant analysis for morphometric characterization of three strains of broiler chicken

Stepwise canonical discriminant analysis of quantitative traits was performed to characterize Arbor Acre (n = 29), Marshall (n = 29) and Ross 308 (n = 30) strains of broiler chicken at two weeks of age. Average body weight of 322.11 ? 51.33 g, 310.70 ? 51.20 g and 310.24 ? 42.37 g were obtained for Arbor Acre, Marshall and Ross 308 strains, respectively. Correlation coefficients between traits was positive (0.13-0.80). Out of the eight traits used, shank length (SL), body length (BL), wing length (WL), breast girth (BG) and thigh circumference (TC) were selected by stepwise procedure as discriminating variables with respective tolerance of 0.75, 0.84, 0.86, 0.57 and 0.66. The Mahalanobis distance indicated that Arbor Acre and Marshall were closely related by SL (2.006), WL (5.704), BG (6.002) and TC (6.314) while Arbor Acre and Ross 308 were related by BL (4.212). Two canonical discriminant functions were generated. Function 1, with smaller Wilk's Lambda (0.15), stronger canonical correlation coefficient (0.88), highly significant Chi-square (p&lt;0.0001), greater eigenvalue (3.14) and percentage variance (87.28%), possessed higher discriminatory power than function 2.72.4%, 82.8% and 90.0% of the cross validated cases were correctly classified as Arbor Acre, Marshall and Ross broilers, respectively. The study revealed that Ross 308 was genetically distinct from Arbor Acre and Marshall, which were more closely related, using shank length, body length, wing length, breast girth and thigh circumference as discriminating traits. Stepwise canonical discriminant analysis is therefore relevant in classification genetics.

A Stepwise Discriminant Analysis and FBCSP Feature Selection Strategy for EEG MI Recognition

A Stepwise Discriminant Analysis and FBCSP Feature Selection Strategy for EEG MI Recognition