Explore the vast range of titles available.

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.

Objective: Emotional divorce refers to a state of emotional disengagement from one's spouse, which can lead to marital dissatisfaction. Gender role conflict is a predictor of marital dissatisfaction and ultimately, divorce. The literature suggests that rigid adherence to traditional gender roles may contribute to emotional divorce. In this article, the authors aim to investigate the moderating effect of gender roles in the relationship between emotional divorce and marital satisfaction by using two multivariate methods in statistical analysis. Materials and methods: This cross-sectional study was conducted on 539 women aged 18-65 years. The standard questionnaires used include 1) Bem Sex Role Inventory (Short form), 2) Gutman’s Emotional Divorce Questionnaire, and 3) Evaluation and Nurturing Relationship Issues, Communication and Happiness (ENRICH) Marital Satisfaction (EMS) Scale. To achieve the purpose of the study, PLS and PLSc methods have been used. Results: The mean age and time of marriage were 33.88 ± 6.6 and 13.03 ± 7.29 years. There is a significant negative relationship between emotional divorce and marital satisfaction. The gender role of most participants in the study has been androgenic and feminine, which has increased the moderating effect of this relationship. Also, when there is a common (reflective) factor model, Consistent Partial Least Square is more likely to provide a better fit than Partial Least Square. Conclusion: The results showed that higher women's marital satisfaction would be associated with lower emotional divorce. On the other hand, the role of gender is a mediating factor in marital satisfaction and emotional divorce. Having good male and female characteristics can have a positive impact on marital satisfaction, so increasing couples' knowledge of gender roles and trying to reduce traditional extreme roles can help increase marital satisfaction and reduce emotional divorce.

Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc), has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are of interest to the field. Such evidence is direly needed before a complete approach to the estimation of SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc.

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near‐infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825‐1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000–4,000 cm‐1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm‐1. Principal components analysis (PCA), partial least‐squares discriminant analysis (PLS‐DA), and partial least‐squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS‐DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R‐square and root mean square error (RMSE) values obtained for the PLS‐DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave‐one‐out cross‐validation procedure was used as an internal cross‐validation criteria and the R‐square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples. Generalized flow chart of partial least‐squares regression analysis.

• Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth’s biomes, an efficient, globally generalizable approach to predict LMA is still lacking. • We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 gm–2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. • Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R² = 0.89; root mean square error (RMSE) = 15.45 gm–2). • Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, M area ), and isotopic composition (δ 15 N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7-4.4% for nitrogen ( N mass ), 42-54% for carbon ( C mass ), 17-58% for fiber (acid-digestible fiber, ADF), 7-44% for lignin (acid-digestible lignin, ADL), 3-31% for cellulose, 17-265 g/m 2 for M area , and −9.4‰ to 0.8‰ for δ 15 N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R 2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for N mass , R 2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for C mass , R 2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R 2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R 2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R 2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ 15 N, and R 2 = 0.88 (0.87) with RMSE = 17.2 g/m 2 (22.8 g/m 2 ) for M area . This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.

• Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait–Vc,max relationships are robust across different forest types remains unclear. • Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (Vc,max), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. • We found weak and forest type-specific relationships between Vc,max and the four morphological and biochemical traits (R² ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait–Vc,max relationships across three forest biomes into a single robust model for Vc,max (R² = 0.77), and also accurately estimated the four traits (R² = 0.75–0.94). • These findings challenge the traditional use of the empirical trait–Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Due to the coupled or interconnected relationships among frequent climate extremes, unique geological conditions, discontinuous soil distribution, rugged geomorphology, and highly heterogeneous landscapes in different karst watersheds, few studies were conducted to decouple the relative magnitudes of the climate, lithology, soil, topography, and landscape on soil erosion in karst regions. The objective of this study was to quantify the relative importance of these influencing factors on runoff and sediment yield (SY) in 40 typical karst watersheds in southwest China. To address this issue, the Pearson correlation and random forest were first to select the dominant factors influencing runoff and SY. Subsequently, the partial least squares‐structural equation model (PLS‐SEM) was used to decouple the complex relationships among runoff, SY and their potential influencing factors. Results showed that climate, lithology, soil, topography and landscape could explain 79% of the runoff variation, and only climate factors have significant impact on runoff for heterogeneous karst watersheds (P < 0.01, path coefficient (β) = 0.589). The explanation of five factors to SY variability is 59%, and the landscape has the greatest impact on SY (P < 0.01, β = −0.458). Different from runoff, climatic factors have no significant influence on SY. By elucidating a complex coupled relationship framework, this study can provide a scientific basis for the formulation of soil and water loss program, and the optimization of land resources and ecological environment sustainable development in karst watersheds. Plain Language Summary The variations of runoff and sediment yield (SY) among heterogeneous watersheds are expected to be influenced by climate, lithology, soil, topography and landscape, which are generally interconnected and coupled. Therefore, it is important to decouple the complex relationship between runoff, SY and their potential influencing factors, especially in karst areas where ecologically fragile regions experience severe soil erosion. The results show that only climate factors play a significant influence on runoff. Unlike runoff, all factors have significant effects on SY except climate factors. Interestingly, landscape factors have the greatest influence on SY. Our findings are conducive to better understanding the hydrological and sediment transport characteristics of karst watersheds, and provide scientific basis for soil erosion control and sustainable development of ecological environment. Key Points We decoupled the effects of climate, lithology, soil, topography and landscape on runoff and sediment yield (SY) Climate and landscape exerted the largest influence on runoff and SY, respectively Lithology, soil, topography, and landscape significantly affected SY rather than runoff variability

More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non‐destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least‐squares regression to build models linking pressed‐leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh‐ or ground‐leaf spectra of the same samples. The traits our pressed‐leaf models could estimate best were LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water‐related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514–0.790; %RMSE = 12.8–19.6). Remaining elements were predicted poorly (R2 < 0.5, %RMSE > 20). For most chemical traits, pressed‐leaf models performed better than fresh‐leaf models, but worse than ground‐leaf models. Pressed‐leaf models were worse than fresh‐leaf models for estimating LMA and LDMC, but better than ground‐leaf models for LMA. Finally, in a subset of samples, we used partial least‐squares discriminant analysis to classify specimens among 10 species with near‐perfect accuracy (>97%) from pressed‐ and ground‐leaf spectra, and slightly lower accuracy (>93%) from fresh‐leaf spectra. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed‐leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far‐reaching implications for capturing the wide range of functional and taxonomic information in the world’s preserved plant collections. Résumé Plus que jamais, les écologistes cherchent à utiliser des collections d'herbiers pour estimer les traits fonctionnels des plantes dans le passé et à travers des biomes. Cependant, plusieurs mesures de traits sont destructives et pourraient ne pas être effectuées sur des spécimens de grande valeur. De plus en plus, les chercheuses et chercheurs utilisent la spectroscopie de réflectance pour estimer des traits des feuilles fraîches ou broyées, et pour délimiter ou identifier les espèces. Nous étendons ici ces travaux en réalisant des mesures non‐destructives avec des feuilles entières et pressées. À partir de 618 échantillons provenant de 68 espèces, nous avons utilisé la régression des moindres carrés partiels pour construire des modèles liant les spectres de réflectance des feuilles pressées avec un large ensemble de traits, incluant la masse foliaire spécifique (‘leaf mass per area,’ LMA), la teneur en matière sèche des feuilles (‘leaf dry matter content,’ LDMC), l'épaisseur d'eau équivalente, les fractions de carbone, des pigments et douze éléments. Nous avons comparé ces modèles à ceux entraînés sur les spectres des feuilles fraîches ou broyées provenant des mêmes échantillons. Les traits les mieux estimés par nos modèles sur des feuilles pressées étaient la LMA (R2 = 0.932; %REQM = 6.56), le carbone (R2 = 0.855; %REQM = 9.03) et la cellulose (R2 = 0.803; %REQM = 12.2), suivis des traits liés à l'eau, de certains éléments nutritifs (Ca, Mg, N et P), des autres fractions de carbone et des pigments (tous les R2 = 0.514–0.790; %REQM = 12.8–19.6). Les autres éléments nutritifs ne pouvaient pas être bien estimés (R2 < 0.5, %RMSE >20). Pour la plupart des traits chimiques, les modèles sur des feuilles pressées étaient plus performants que ceux de feuilles fraîches, mais moins performants que ceux à partir de feuilles broyées. Les modèles sur des feuilles pressées performaient moins bien que ceux sur des feuilles fraîches pour estimer la LMA et la LDMC, mais performaient mieux que ceux sur des feuilles broyées pour la LMA. Finalement, pour un sous‐ensemble d'échantillons, nous avons utilisé l'analyse discriminante des moindres carrés partiels et réussi à classifier les spécimens parmi 10 espèces avec une précision presque parfaite (>97%) à partir des spectres des feuilles pressées ou broyées, et avec une précision légèrement plus basse (>93%) à partir de feuilles fraîches. Nos résultats démontrent que l'application de la spectroscopie sur des feuilles pressées est une approche non‐destructive prometteuse pour estimer des traits fonctionnels et pour identifier des espèces. Les spectres des feuilles pressées semblent combiner les avantages des feuilles fraîches et de celles broyées: comme les feuilles fraîches, elles conservent une partie de l'expression spectrale de la structure foliaire; comme les feuilles broyées, elles contournent l'effet masquant de l'absorption par l'eau. Notre étude a des implications importantes pour l'acquisition de données fonctionnelles et taxonomiques à partir des collections de plantes préservées à travers le monde.