Using different techniques to derive dietary patterns (DP) could evaluate real-world diet behaviours and provide DP recommendations. Therefore, we identified DP using hybrid methodologies and examined the associations of DP with all-cause and CVD mortality among older Chinese. Using data from the Guangzhou Biobank Cohort Study, dietary intake was assessed using a validated FFQ. DP were derived using hybrid methods including reduced rank regression (RRR) and partial least squares (PLS), focusing on nutrients commonly insufficient in Asian diets. Associations of the DP with mortality and CVD risk factors were examined using Cox regression and generalised linear models, respectively. Of 19 598 participants with an average follow-up of 15·8 years, 4966 deaths occurred. Two DP were derived based on the riboflavin-density, K:Na ratio and vitamin C-density. The DP derived from both RRR and PLS featured high intakes of green vegetables, yellow/orange fruits and whole grains and low intakes of refined grains and plant oils, with additional high intakes of fish identified by RRR and milk by PLS. These DP were associated with lower all-cause and CVD mortality risks. Compared with the lowest quartile, the highest quartiles showed lower risks of all-cause (hazard ratio (HR): 0·89–0·91, all P < 0·01) and CVD mortality (HR: 0·79–0·82, all P < 0·01). Moreover, both DP were associated with favourable cardiometabolic profiles, including lower systolic blood pressure, TAG and high-sensitivity C-reactive protein levels, and higher HDL-cholesterol levels. These findings suggest that nutrient-rich DP using hybrid methods may support the development of dietary recommendations to reduce mortality among older Chinese.

We propose an alternative method to partial least squares for path analysis with components, called generalized structured component analysis. The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method avoids the principal limitation of partial least squares (i.e., the lack of a global optimization procedure) while fully retaining all the advantages of partial least squares (e.g., less restricted distributional assumptions and no improper solutions). The method is also versatile enough to capture complex relationships among variables, including higher-order components and multi-group comparisons. A straightforward estimation algorithm is developed to minimize the criterion.

Team psychological safety — a non-threatening and safe climate — allows team members to express and share each other's opinions freely, and this sharing may produce more useful perspectives to induce team creativity. In a psychologically safe climate, transactive memory systems (TMSs) may be constructed for describing the specialised division of cognitive labour for solving information problems and thereby enabling team members to quickly gain and use knowledge across domains. As a consequence, further ideas may be generated within teams, increasing team creativity. Our research model is assessed using data from a sample of 110 team members from 40 research and development (R&D) teams in a leading technology company in Taiwan and analysed using the partial least squares method. The results of this study reveal that: (1) team psychological safety did not directly affect team creativity, (2) team psychological safety affects TMSs, (3) TMSs affect team creativity, and (4) TMSs fully mediate the relationship between team psychological safety and team creativity. This study also discusses the implications for team creativity.

R&D is uncertain work that involves the knowledge, skills, or perspectives of team members. When R&D teams develop new products or technologies, the need for psychological safety within the teams is increasingly emphasized. If R&D team members perceive that team psychological safety exists, they may be willing to offer knowledge or perspectives during the development process because they are not afraid of being rejected or embarrassed for speaking up. However, the application of the theory of team psychological safety to R&D teams is considerably limited. This study explores the antecedents and consequences of team psychological safety in R&D teams. Our research model is assessed using data from a sample of 245 team members from sixty technology R&D teams at a leading R&D institute and is analyzed using the partial least squares (PLS) method. The results of this study suggest that: (1) social capital exerts a positive and significant effect on team psychological safety; (2) team psychological safety has a positive and significant impact on team performance; (3) knowledge sharing and team learning positively and significantly mediate the relationship between team psychological safety and team performance; and (4) knowledge sharing exhibits a positive and significant effect on team learning. This study also discusses the implications of team psychological safety for R&D teams.

The aim of this study was to investigate the effects of numerous milk compositional factors on milk coagulation properties using Partial Least Squares (PLS). Milk from herds of Jersey and Holstein-Friesian cattle was collected across the year and blended (n=55), to maximise variation in composition and coagulation. The milk was analysed for casein, protein, fat, titratable acidity, lactose, Ca2+, urea content, micelles size, fat globule size, somatic cell count and pH. Milk coagulation properties were defined as coagulation time, curd firmness and curd firmness rate measured by a controlled strain rheometer. The models derived from PLS had higher predictive power than previous models demonstrating the value of measuring more milk components. In addition to the well-established relationships with casein and protein levels, CMS and fat globule size were found to have as strong impact on all of the three models. The study also found a positive impact of fat on milk coagulation properties and a strong relationship between lactose and curd firmness, and urea and curd firmness rate, all of which warrant further investigation due to current lack of knowledge of the underlying mechanism. These findings demonstrate the importance of using a wider range of milk compositional variables for the prediction of the milk coagulation properties, and hence as indicators of milk suitability for cheese making.

This study investigated the role of visuospatial tasks in identifying cognitive decline in patients with Alzheimer's disease (AD), by correlating neuropsychological performance with cerebral perfusion measures. There were 157 participants: 29 neurologically healthy controls (age: 70.3 ± 6.6, MMSE ≥ 27), 86 patients with mild AD (age: 69.18 ± 8.28, MMSE ≥ 21) and 42 patients moderate/severe AD (age: 68.86 ± 10.69, MMSE 8–20). Single Photon Emission Computerized Tomography (SPECT) was used to derive regional perfusion ratios, and correlated using partial least squares (PLS) with neuropsychological test scores from the Benton Line Orientation (BLO) and the Rey-Osterrieth Complex Figure (RO). Cross-sectional analysis demonstrated that mean scores differed in accordance with disease status: control group (BLO 25.5, RO 33.3); mild AD (BLO 20.1, RO 25.5); moderate/severe AD (BLO 10.7, RO 16). Correlations were observed between BLO/RO and right parietal SPECT regions in the AD groups. Visuospatial performance, often undersampled in cognitive batteries for AD, is clearly impaired even in mild AD and correlates with functional deficits as indexed by cerebral perfusion ratios on SPECT implicating right hemisphere circuits. Furthermore, PLS reveals that usual spatial tasks probe a distributed brain network in both hemispheres including many areas targeted by early AD pathology. (JINS, 2008, 14, 1034–1045.)

Visible (Vis) and near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive technique that has found many applications in assessing the quality of agricultural commodities, including wool. In this study, Vis and NIR spectroscopy combined with multivariate data analysis was investigated regarding its feasibility in predicting a range of fibre characteristics in raw alpaca wool samples. Mid-side samples (n = 149) were taken from alpacas from a range of colours and ages at shearing time over 4 years (2000 to 2004) and subsequently analysed for fibre characteristics such as mean fibre diameter (MFD) and standard deviation (and coefficient of variation), spin fineness, curvature degree (and standard deviation), comfort factor, medullation percentage (by weight and number in white samples only) using traditional reference laboratory testing methods. Samples were scanned in a large cuvette using a FOSS NIRSystems 6500 monochromator instrument in reflectance mode in the Vis and NIR regions (400 to 2500 nm). Partial least squares (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Mathematical pre-treatment of the spectra (second derivative) as well as various combinations of wavelength range were used in model development. The best calibration model was found when using the NIR region (1100 to 2500 nm) for the prediction of MFD, which had a coefficient of determination in cross-validation (R2) of 0.88 with a root mean square standard error of cross validation (RMSECV) of 2.62 μm. The results show the NIR technique to have promise as a semi-quantitative method for screening purposes. The lack of grease in alpaca wool samples suggests that the technique might find ready application as a rapid measurement technique for preliminary classing of shorn fleeces or, if used directly on the animal, the technology might offer an objective tool to assist in the selection of animals in breeding programmes or shows.

One challenge in the analysis of event-related potentials (ERPs) is to identify task-related differences in scalp topography. The multivariate Partial Least Squares (PLS) analysis was used to identify the spatiotemporal distribution of ERP differences related to experimental manipulations. Two simulations included latency shifts and amplitude changes at peaks with temporal overlap. PLS identified effects only at modeled timepoints and electrodes. In contrast, principal components analysis identified differences at most timepoints. We also demonstrated that PLS identified combinations of waveform differences, not isolated sources. ERP components in an auditory oddball task were also assessed with PLS. The primary distinction was between ERPs on hit and correct rejection trials, expressed at multiple timepoints and electrodes. PLS provides a mechanism to describe experimental differences in ERP waveforms, simultaneously across the head.