There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including latent class analysis, machine learning, and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks, and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information, and treelet transform. Fourteen studies assessed associations between dietary patterns characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs.

In contrast to Thurstone's Law of Comparative Judgment, a model in which a comparison pair and its complement are assumed to give rise to two different distributions of differences is considered. The consequences of this revised model on scaling problems is developed.

Formulas are derived by which, given the factor loadings and the internal reliability of a test of unit length, the following estimates can be made: (1) the common-factor loadings for a similar (homogeneous) test of length n; (2) the number of times (n) that a test needs to be lengthened homogeneously to achieve a factor loading of a desired magnitude; and (3) the correlation between two tests, either or both of which have been altered in length, as a function of (a) the new factor loadings in the altered tests or (b) the original loadings in the unit-length tests. The appropriate use of the derived formulas depends upon the fulfillment of four assumptions enumerated.

Two current methods of deriving common-factor scores from tests are briefly examined and rejected. One of these estimates a score from a multiple-regression equation with as many terms as there are tests in the battery. The other limits the equation to a few tests heavily saturated with the desired factor, with or without tests used to suppress the undesired factors. In the proposed methods, the single best test for each common factor is the starting point. Such a test ordinarily has a very few undesired factors to be suppressed, frequently only one. The suppression test should be univocal, or nearly so. Fortunately, there are relatively univocal tests for factors that commonly require suppression. Equations are offered by which the desired-factor test and a single suppression test can be weighted in order to achieve one or more objectives. Among the objectives are (1) maximizing the desired factor variance, (2) minimizing the undesired factor variance, (3) a compromise, in which the undesired variance is materially reduced without loss in desired variance, and (4) a change to any selected ratio of desired to undesired variance. A more generalized solution is also suggested. The methods can be extended in part to the suppression of more than one factor. Equations are derived for the suppression of two factors.

Even amidst a decline in religious affiliation, nearly half of the U.S. population still attends religious services at least once a month, and congregations remain the single largest non-profit organizational type across the nation. Therefore, congregational influence on political attitudes and behavior is a crucial line of inquiry. We analyze interviews of 94 congregational leaders to better understand why they address or avoid political issues when preaching. Our research reveals that clergy use theological and pragmatic reasoning to determine whether they explicitly include political discourse in their sermons. Our findings are noteworthy in that clergy from a wide range of religious traditions use similar reasoning, and the same rationale can lead different clergy to adopt contrasting approaches to political content in sermons. Thus, this paper provides nuanced insight into the relationship between religion and politics and may help foster greater mutual understanding in a deeply divided political and social climate.

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

Scholarly and practitioner interest in authentic leadership has grown at an accelerating rate over the last decade, resulting in a proliferation of publications across diverse social science disciplines. Accompanying this interest has been criticism of authentic leadership theory and the methods used to explore it. We conducted a systematic review of 303 scholarly articles published from 2010 to 2023 to critically assess the conceptual and empirical strengths and limitations of this literature and map the nomological network of the authentic leadership construct. Results indicate that much of the extant research does not follow best practices in terms of research design and analysis. Based on the findings obtained, an agenda for advancing authentic leadership theory and research that embraces a signaling theory perspective is proposed.

Sex biases in prevalence of disease are often attributed to intrinsic factors, such as physiological differences while a proximate role of extrinsic factors such as behavioural or ecological differences may be more difficult to establish. We combined large-scale screening for the presence and lineage identity of avian malaria (haemosporidian) parasites, in 1234 collared flycatchers (Ficedula albicollis) with life-history information from each bird to establish the location and timing of infection. We found an overall infection rate of 36.2% ± 0.03 (95% CI) with 25 distinct malaria lineages. Interestingly, first-year breeding males and females had similar infection prevalence while females accrued a significantly higher infection rate than males later in life. The sex difference in infection rate was driven by the most abundant Haemoproteus, lineage, hPHSIB1, while the infection rate of Plasmodium lineages was similar in males and females. Furthermore, when infections were assigned to an apparent transmission location, we found that the sex difference in infection rate trend was driven by lineages transmitted in Europe, more specifically by one lineage (the hPHSIB1), while no similar pattern was found in African lineages. We deduce that the observed infection patterns are likely to be caused by differences in breeding behaviour, with incubating females (and nestling individuals of both sexes) being easy targets for the biting insects that are the vectors of avian malaria parasites. Overall, our results are most consistent with ecological factors rather than intrinsic factors underlying the observed sex-biased infection rate of avian malaria in collared flycatchers.

The functioning and richness of marine systems (and biological interactions such as parasitism) are continuously influenced by a changing environment. Using hierarchical modelling of species communities (HMSC), the presence and abundance of multiple parasite species of the black-spotted croaker, Protonibea diacanthus (Sciaenidae), was modelled against environmental measures reflecting seasonal change. Protonibea diacanthus were collected in three seasons across 2019–2021 from four locations within the waters of the Northern Territory, Australia. The length of P. diacanthus proved to have a strong positive effect on the abundance of parasite taxa and overall parasitic assemblage of the sciaenid host. This finding introduces potential implications for parasitism in the future as fish body size responds to fishing pressure and climate changes. Of the various environmental factors measured during the tropical seasons of northern Australia, water temperature and salinity changes were shown as potential causal factors for the variance in parasite presence and abundance, with changes most influential on external parasitic organisms. As environmental factors like ocean temperature and salinity directly affect parasite–host relationships, this study suggests that parasite assemblages and the ecological functions that they perform are likely to change considerably over the coming decades in response to climate change and its proceeding effects.

Staphylococcus aureus nasal carriers were randomized (1:1) to XF-73 or placebo nasal gel, administered 5x over ∼24hrs pre-cardiac surgery. S. aureus burden rapidly decreased after 2 doses (–2.2log10 CFU/mL; placebo –0.01log10 CFU/mL) and was maintained to 6 days post-surgery. Among XF-73 patients, 46.5% received post-operative anti-staphylococcal antibiotics versus 70% in placebo (P = 0.045).