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ABSTRACT Diatoms and Chlorophyta are two major phyla of phytoplankton in marine ecosystems. The quantitative detection of the population succession and the interaction between them in natural marine ecosystems is a key challenge that ecologists face. In this study, using high‐throughput sequencing (HTS) analysis, a negative correlation was found between Diatoms and Chlorophyta near the Dafeng River Estuary (DRE) and the Sanniang Bay (SNB) located in the Beibu Gulf, China. To clarify the underlying mechanism, a co‐occurrence network was employed to scrutinize the interspecific relationships between the two phytoplankton groups, and the Mantel test was used to evaluate their relationships with environmental factors. The results indicated that the negative correlation between Diatoms and Chlorophyta was independent of interspecies interactions. Moreover, the effects of environmental factors on Diatoms and Chlorophyta were complex, being both positive and negative across seasons, and thus, they failed to explain this correlation satisfactorily. The partial least squares‐path modeling (PLS‐PM) analysis was performed using six latent variables, including seawater properties, nutrients, biomass, alpha diversity, Chlorophyta, and Diatoms. According to the results, the mechanisms behind the negative correlation between Chlorophyta and Diatoms varied across different seasons. Overall, both the differing responses of Chlorophyta and Diatoms to changes in temperature and nutrients and the complex hydrodynamic characteristics of the estuary and the bay in the study area were the main factors causing this negative correlation. This study offers a new approach to understand the succession of some phyla in phytoplankton. Using high‐throughput sequencing (HTS) analysis, a negative correlation was found between Bacillariophyta and Chlorophyta near the Dafeng River Estuary (DRE) and the Sanniang Bay (SNB) located in the Beibu Gulf, China. The partial least squares‐path modeling (PLS‐PM) analysis showed that both the differing responses of Chlorophyta and Bacillariophyta to changes in temperature and nutrients and the complex hydrodynamic characteristics of the estuary and the bay were the main factors causing this negative correlation.

The fourth joint inter-agency report on integrated analysis of antimicrobial consumption (AMC) and the occurrence of antimicrobial resistance (AMR) in bacteria from humans and food-producing animals (JIACRA) addressed data obtained by the Agencies' EU-wide surveillance networks for 2019-2021. The analysis also sought to identify whether significant trends in AMR and AMC were concomitant over 2014-2021. AMC in both human and animal sectors, expressed in mg/kg of estimated biomass, was compared at country and European level. In 2021, the total AMC was assessed at 125.0 mg/kg of biomass for humans (28 EU/EEA countries, range 44.3-160.1) and 92.6 mg/kg of biomass for food-producing animals (29 EU/EEA countries, range 2.5-296.5). Between 2014 and 2021, total AMC in food-producing animals decreased by 44%, while in humans, it remained relatively stable. Univariate and multivariate analyses were performed to study associations between AMC and AMR for selected combinations of bacteria and antimicrobials. Positive associations between consumption of certain antimicrobials and resistance to those substances in bacteria from both humans and food-producing animals were observed. For certain combinations of bacteria and antimicrobials, AMR in bacteria from humans was associated with AMR in bacteria from food-producing animals which, in turn, was related to AMC in animals. The relative strength of these associations differed markedly between antimicrobial class, microorganism and sector. For certain antimicrobials, statistically significant decreasing trends in AMC and AMR were concomitant for food-producing animals and humans in several countries over 2014-2021. Similarly, a proportion of countries that significantly reduced total AMC also registered increasing susceptibility to antimicrobials in indicator from food-producing animals and originating from human invasive infections (i.e., exhibited 'complete susceptibility' or 'zero resistance' to a harmonised set of antimicrobials). Overall, the findings suggest that measures implemented to reduce AMC in food-producing animals and in humans have been effective in many countries. Nevertheless, these measures need to be reinforced so that reductions in AMC are retained and further continued, where necessary. This also highlights the importance of measures that promote human and animal health, such as vaccination and better hygiene, thereby reducing the need for use of antimicrobials.

Purpose This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM. Design/methodology/approach This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models. Findings This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables. Research limitations/implications Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously. Practical implications To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables. Originality/value This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

The fourth joint inter‐agency report on integrated analysis of antimicrobial consumption (AMC) and the occurrence of antimicrobial resistance (AMR) in bacteria from humans and food‐producing animals (JIACRA) addressed data obtained by the Agencies' EU‐wide surveillance networks for 2019–2021. The analysis also sought to identify whether significant trends in AMR and AMC were concomitant over 2014–2021. AMC in both human and animal sectors, expressed in mg/kg of estimated biomass, was compared at country and European level. In 2021, the total AMC was assessed at 125.0 mg/kg of biomass for humans (28 EU/EEA countries, range 44.3–160.1) and 92.6 mg/kg of biomass for food‐producing animals (29 EU/EEA countries, range 2.5–296.5). Between 2014 and 2021, total AMC in food‐producing animals decreased by 44%, while in humans, it remained relatively stable. Univariate and multivariate analyses were performed to study associations between AMC and AMR for selected combinations of bacteria and antimicrobials. Positive associations between consumption of certain antimicrobials and resistance to those substances in bacteria from both humans and food‐producing animals were observed. For certain combinations of bacteria and antimicrobials, AMR in bacteria from humans was associated with AMR in bacteria from food‐producing animals which, in turn, was related to AMC in animals. The relative strength of these associations differed markedly between antimicrobial class, microorganism and sector. For certain antimicrobials, statistically significant decreasing trends in AMC and AMR were concomitant for food‐producing animals and humans in several countries over 2014‐2021. Similarly, a proportion of countries that significantly reduced total AMC also registered increasing susceptibility to antimicrobials in indicator E. coli from food‐producing animals and E. coli originating from human invasive infections (i.e., exhibited ‘complete susceptibility’ or ‘zero resistance’ to a harmonised set of antimicrobials). Overall, the findings suggest that measures implemented to reduce AMC in food‐producing animals and in humans have been effective in many countries. Nevertheless, these measures need to be reinforced so that reductions in AMC are retained and further continued, where necessary. This also highlights the importance of measures that promote human and animal health, such as vaccination and better hygiene, thereby reducing the need for use of antimicrobials.

Purpose People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches. Design/methodology/approach The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches. Findings Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach. Research limitations/implications Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations. Originality/value This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

Partial least squares path modeling (PLS-PM) has become very popular in recent years, for measuring concepts that depend on different aspects and that are based on different types of relationships. PLS-PM represents a useful tool to explore relationships and to analyze the influence of the different aspects on the complex phenomenon analyzed. In particular, the use of higher-order constructs has allowed researchers to extend the application of PLS-PM to more advanced and complex models. In this work, our attention is focused on higher-order constructs that include reflective or formative relationships. Even if the dispute between formative models and reflective models is not exactly recent, it is still alive in current literature, for the most part within the context of structural equation models. This paper focuses attention on theoretical and mathematical differences between formative and reflective measurement models within the context of the PLS-PM approach. A simulation study is proposed in order to show how these approaches fit well in different modeling situations. The approaches have been compared using empirical application in a sustainability context. The findings from the simulation and the empirical application can help researchers to estimate and to use the higher-order PLS-PM approach in reflective and formative type models.

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.

Purpose As technology in tourism and hospitality (TTH) develops technical artifacts according to visitors’ demands, it must deal with both behavioral and design constructs in the context of structural equation modeling (SEM). While behavioral constructs are typically modeled as common factors, the study at hand introduces the composite into TTH to model artifacts. To deal with both kinds of constructs, this paper aims to exploit partial least squares path modeling (PLS-PM) as a confirmatory approach to estimate models containing common factors and composites. Design/methodology/approach The study at hand presents PLS-PM in its current form, i.e. as a full-fledged approach for confirmatory purposes. By introducing the composite to model artifacts, TTH scholars can use PLS-PM to answer research questions of the type “Is artifact xyz useful?”, contributing to a further understanding of TTH. To demonstrate the composite model, an empirical example is used. Findings PLS-PM is a promising approach when the model contains both common factors and composites. By applying the test for overall model fit, empirical evidence can be obtained for latent variables and artifacts. In doing so, researchers can statistically test whether a developed artifact is useful. Originality/value To the best of the authors’ knowledge, this is the first study to discuss the practical application of composite and common factor models in TTH research. Besides introducing the composite to model artifacts, the study at hand also guides scholars in the assessment of PLS-PM results. 研究目的 因为旅游酒店科技（TTH）根据游客需求而定制科技产品, TTH必须在结构方程模型（SEM）下结合游客行为和设计等变量。一般行为变量在模型中是常见因子, 本研究将这些变量编入TTH结构成为模块。本研究采用PLS-PM方法来预估含有隐性变量和模块的模型。. 研究设计/方法/途径 本研究设计PLS-PM模式, 即确定性全变量方法。TTH学者们通过引进结构形成模型模块, 使用PLS-PM研究方法, 以回答研究问题“模块xyz有用吗？”, 因此对TTH进一步理解。为了展示复合模型, 本论文采用实际验证。. 研究结果 PLS-PM在面对模块内存在常见因子和复合模块的结构时是有力方法。实际验证结果通过整体最佳模型参数, 得到隐性变量和模块。为此, 研究者们能够在统计方法上测量是否开发的模型模块是否有用。. 研究原创性/研究价值 据作者所知, 本论文是首个研究在TTH领域上应用模块和常见因子模型。本研究引进显性变量在模型模块中, 以指导学者评估PLS-PM结果报告。.

Although eco-friendly (pro-environmental) behaviour in tourism has attracted interest among practitioners and scholars, little is known about the influence of these attitudes on the choice of eco-friendly destinations, especially in the context of emerging tourist markets such as India. Thus, this article aims to verify a model of the relationships between attitudes towards the environment and eco-friendly tourism, social and personal norms regarding environmentally responsible behaviour, perceived behavioural control, behavioural intentions regarding eco-friendly destinations and the willingness to pay for such trips using the theory of planned behaviour. The study used an online survey conducted with 598 Indians. The relationships between the variables were analysed using PLS-PM. The most important results indicated that (1) there are significant relationships between the attitude towards the environment, the attitude towards an eco-friendly destination, social and personal norms and behavioural control and intentions regarding travelling to eco-destinations and (2) well-educated young Indian consumers expressed a positive attitude towards eco-friendly destinations; however, there was only a very weak relationship between this attitude and willingness to pay more for trips to them. These findings are valuable for pro-environmental planning and the growing green market/economy, as well as for the discussion on the future of pro-environmental tourism development.

The second ECDC/EFSA/EMA joint report on the integrated analysis of antimicrobial consumption (AMC) and antimicrobial resistance (AMR) in bacteria from humans and food-producing animals addressed data obtained by the Agencies' EU-wide surveillance networks for 2013-2015. AMC in both sectors, expressed in mg/kg of estimated biomass, were compared at country and European level. Substantial variations between countries were observed in both sectors. Estimated data on AMC for pigs and poultry were used for the first time. Univariate and multivariate analyses were applied to study associations between AMC and AMR. In 2014, the average AMC was higher in animals (152 mg/kg) than in humans (124 mg/kg), but the opposite applied to the median AMC (67 and 118 mg/kg, respectively). In 18 of 28 countries, AMC was lower in animals than in humans. Univariate analysis showed statistically-significant (p < 0.05) associations between AMC and AMR for fluoroquinolones and in both sectors, for 3rd- and 4th-generation cephalosporins and in humans, and tetracyclines and polymyxins and in animals. In humans, there was a statistically-significant association between AMC and AMR for carbapenems and polymyxins in . Consumption of macrolides in animals was significantly associated with macrolide resistance in in animals and humans. Multivariate analyses provided a unique approach to assess the contributions of AMC in humans and animals and AMR in bacteria from animals to AMR in bacteria from humans. Multivariate analyses demonstrated that 3rd- and 4th-generation cephalosporin and fluoroquinolone resistance in from humans was associated with corresponding AMC in humans, whereas resistance to fluoroquinolones in spp. and spp. from humans was related to consumption of fluoroquinolones in animals. These results suggest that from a 'One-health' perspective, there is potential in both sectors to further develop prudent use of antimicrobials and thereby reduce AMR.