The study of interactions between copepods of the genus Acartia and toxic dinoflagellates of the genus Alexandrium has been an important topic during the last four decades. Feeding behavior and physiological responses of copepods have been studied in laboratory and field experiments, sometimes with contradictory results. More recently, an evolutionary adaptive mechanism leading to enhanced tolerance of Alexandrium toxins in a population of Acartia experiencing chronic exposure to these dinoflagellates has been reported. In the present work, we collected data from the existing studies on the effects of Alexandrium on feeding, reproduction and mortality of Acartia. With these data, we performed a systematic review consisting of a secondary analysis employing general or generalized linear models, weighting data from different studies by the reciprocal of their standard deviation. Our first aim was to overcome shortcomings of individual studies: limited ranges of the variables and overlooked variables (experiment length, population adaptation). These shortcomings could have led to inconsistent conclusions by missing heterogeneous patterns in copepod responses and in the interactions between variables. Our second aim was to test the enhanced physiological performance of chronically exposed relative to naïve copepod populations over a wide geographic range. We found that the feeding rate is enhanced by increased food biomass, irrespective of the food type. Toxins do not have a clear effect on egg production and have a bi-phasic effect on egg hatching success, which was negative above a specific threshold. Toxins also increased mortality. Experiment length had a positive effect on egg production and negative on egg hatching. Naïve copepod populations showed consistently lower ingestion of Alexandrium and egg hatching rates, thereby supporting the spread of the aforementioned mechanism across populations over a wide geographic range.

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans\' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

BACKGROUND: Chemoprophylaxis is a prevention method for malaria during travel in malaria-endemic countries. This study aimed to collate and synthesize the evidence of malarial chemoprophylaxis among malaria death cases.
METHODS: Studies documenting malarial chemoprophylaxis related to malaria deaths were searched in PubMed, Scopus, MEDLINE, Embase, and CENTRAL until 3 July 2022. The pooled proportion of malarial chemoprophylaxis among death cases was synthesized using logit transformation and back transformation to a proportion performed using generalized linear mixed models. The pooled log odds ratio (log-OR) with a 95% confidence interval (CI) of malarial chemoprophylaxis in death cases compared to survivors were synthesized.
RESULTS: Fifty-eight studies were included in the systematic review and the meta-analysis. Of 602 pooled malaria death cases, the number of patients who took chemoprophylaxis was 187 (30%) (95% CI 22-40, P < 0.01, 58 studies), and those who took adequate chemoprophylaxis were 24 (5%) (95% CI 2-13, P < 0.01, 42 studies). A comparable log-OR of underwent chemoprophylaxis was observed between malaria death cases and survivors (P = 0.94, pooled log-OR: - 0.02, 95% CI - 0.46-0.42, I2: 0%, 17 studies). Similarly, a comparable log-OR of adequate chemoprophylaxis was identified between malaria death cases and survivors (P = 0.15, pooled log-OR: 0.83, 95% CI - 0.30-1.97, I2: 47.08%, 11 studies).
CONCLUSIONS: Among the studies where malarial chemoprophylaxis was reported, approximately 30% of malaria death cases had taken such prophylaxis. Notably, only 5% of these cases adhered fully or adequately to the recommended chemoprophylactic regimen. However, the analysis did not reveal a significant difference in the odds of malarial chemoprophylaxis between malaria death cases and survivors.

The stepped-wedge cluster randomized trial (SW-CRT), in which clusters are randomized to a time at which they will transition to the intervention condition - rather than a trial arm - is a relatively new design. SW-CRTs have additional design and analytical considerations compared to conventional parallel arm trials. To inform future methodological development, including guidance for trialists and the selection of parameters for statistical simulation studies, we conducted a review of recently published SW-CRTs. Specific objectives were to describe (1) the types of designs used in practice, (2) adherence to key requirements for statistical analysis, and (3) practices around covariate adjustment. We also examined changes in adherence over time and by journal impact factor.
We used electronic searches to identify primary reports of SW-CRTs published 2016-2022. Two reviewers extracted information from each trial report and its protocol, if available, and resolved disagreements through discussion.
We identified 160 eligible trials, randomizing a median (Q1-Q3) of 11 (8-18) clusters to 5 (4-7) sequences. The majority (122, 76%) were cross-sectional (almost all with continuous recruitment), 23 (14%) were closed cohorts and 15 (9%) open cohorts. Many trials had complex design features such as multiple or multivariate primary outcomes (50, 31%) or time-dependent repeated measures (27, 22%). The most common type of primary outcome was binary (51%); continuous outcomes were less common (26%). The most frequently used method of analysis was a generalized linear mixed model (112, 70%); generalized estimating equations were used less frequently (12, 8%). Among 142 trials with fewer than 40 clusters, only 9 (6%) reported using methods appropriate for a small number of clusters. Statistical analyses clearly adjusted for time effects in 119 (74%), for within-cluster correlations in 132 (83%), and for distinct between-period correlations in 13 (8%). Covariates were included in the primary analysis of the primary outcome in 82 (51%) and were most often individual-level covariates; however, clear and complete pre-specification of covariates was uncommon. Adherence to some key methodological requirements (adjusting for time effects, accounting for within-period correlation) was higher among trials published in higher versus lower impact factor journals. Substantial improvements over time were not observed although a slight improvement was observed in the proportion accounting for a distinct between-period correlation.
Future methods development should prioritize methods for SW-CRTs with binary or time-to-event outcomes, small numbers of clusters, continuous recruitment designs, multivariate outcomes, or time-dependent repeated measures. Trialists, journal editors, and peer reviewers should be aware that SW-CRTs have additional methodological requirements over parallel arm designs including the need to account for period effects as well as complex intracluster correlations.

This study aims to further our understanding of prosodic entrainment and its different subtypes by analyzing a single corpus of conversations with 12 different methods and comparing the subsequent results.
Entrainment on three fundamental frequency features was analyzed in a subset of recordings from the LUCID corpus (Baker & Hazan, 2011) using the following methods: global proximity, global convergence, local proximity, local convergence, local synchrony (Levitan & Hirschberg, 2011), prediction using linear mixed-effects models (Schweitzer & Lewandowski, 2013), geometric approach (Lehnert-LeHouillier, Terrazas, & Sandoval, 2020), time-aligned moving average (Kousidis et al., 2008), HYBRID method (De Looze et al., 2014), cross-recurrence quantification analysis (e.g., Fusaroli & Tylén, 2016), and windowed, lagged cross-correlation (Boker et al., 2002). We employed entrainment measures on a local timescale (i.e., on adjacent utterances), a global timescale (i.e., over larger time frames), and a time series-based timescale that is larger than adjacent utterances but smaller than entire conversations.
We observed variance in results of different methods.
Results suggest that each method may measure a slightly different type of entrainment. The complex implications this has for existing and future research are discussed.

Current guidelines recommend using Bland-Altman plots (BA-plots), also called Difference plots, as part of method comparison evaluation in the veterinary clinical pathology laboratory. Analysis of differences can meaningfully augment linear regression techniques and allows fuller summarization of the performance of two methods relative to each other. This work summarizes the current literature on BA-plot composition and evaluation. Model data is used to demonstrate data evaluation approaches based on the observed differences, the combined inherent imprecision of the methods, and clinically relevant performance goals. Common limitations of the approaches, including points of frequent misinterpretation, are presented. BA-plot analysis can be part of an intentionally crafted method comparison study that provides analytically and clinically relevant data.

Reliable quantification in biological systems of endogenous low- and high-molecular substances, drugs and their metabolites, is of particular importance in diagnosis and therapy, and in basic and clinical research. The analytical characteristics of analytical approaches have many differences, including in core features such as accuracy, precision, specificity, and limits of detection (LOD) and quantitation (LOQ). Several different mathematic approaches were developed and used for the comparison of two analytical methods applied to the same chemical compound in the same biological sample. Generally, comparisons of results obtained by two analytical methods yields different quantitative results. Yet, which mathematical approach gives the most reliable results? Which mathematical approach is best suited to demonstrate agreement between the methods, or the superiority of an analytical method A over analytical method B? The simplest and most frequently used method of comparison is the linear regression analysis of data observed by method A (y) and the data observed by method B (x): y = α + βx. In 1986, Bland and Altman indicated that linear regression analysis, notably the use of the correlation coefficient, is inappropriate for method-comparison. Instead, Bland and Altman have suggested an alternative approach, which is generally known as the Bland-Altman approach. Originally, this method of comparison was applied in medicine, for instance, to measure blood pressure by two devices. The Bland-Altman approach was rapidly adapted in analytical chemistry and in clinical chemistry. To date, the approach suggested by Bland-Altman approach is one of the most widely used mathematical approaches for method-comparison. With about 37,000 citations, the original paper published in the journal The Lancet in 1986 is among the most frequently cited scientific papers in this area to date. Nevertheless, the Bland-Altman approach has not been really set on a quantitative basis. No criteria have been proposed thus far, in which the Bland-Altman approach can form the basis on which analytical agreement or the better analytical method can be demonstrated. In this article, the Bland-Altman approach is re-valuated from a quantitative bioanalytical perspective, and an attempt is made to propose acceptance criteria. For this purpose, different analytical methods were compared with Gold Standard analytical methods based on mass spectrometry (MS) and tandem mass spectrometry (MS/MS), i.e., GC-MS, GC-MS/MS, LC-MS and LC-MS/MS. Other chromatographic and non-chromatographic methods were also considered. The results for several different endogenous substances, including nitrate, anandamide, homoarginine, creatinine and malondialdehyde in human plasma, serum and urine are discussed. In addition to the Bland-Altman approach, linear regression analysis and the Oldham-Eksborg method-comparison approaches were used and compared. Special emphasis was given to the relation of difference and mean in the Bland-Altman approach. Currently available guidelines for method validation were also considered. Acceptance criteria for method agreement were proposed, including the slope and correlation coefficient in linear regression, and the coefficient of variation for the percentage difference in the Bland-Altman and Oldham-Eksborg approaches.

Iatrogenic cerebral arterial gas embolism (CAGE) caused by invasive medical procedures may be treated with hyperbaric oxygen therapy (HBOT). Previous studies suggested that initiation of HBOT within 6-8 h is associated with higher probability of favorable outcome, when compared to time-to-HBOT beyond 8 h. We performed a group level and individual patient level meta-analysis of observational studies, to evaluate the relationship between time-to-HBOT and outcome after iatrogenic CAGE.
We systematically searched for studies reporting on time-to-HBOT and outcome in patients with iatrogenic CAGE. On group level, we meta-analyzed the differences between median time-to-HBOT in patients with favorable versus unfavorable outcome. On individual patient level, we analyzed the relationship between time-to-HBOT and probability of favorable outcome in a generalized linear mixed effects model.
Group level meta-analysis (ten studies, 263 patients) shows that patients with favorable outcome were treated with HBOT 2.4 h (95% CI 0.6-9.7) earlier than patients with unfavorable outcome. The generalized linear mixed effects model (eight studies, 126 patients) shows a significant relationship between time-to-HBOT and probability of favorable outcome (p = 0.013) that remains significant after correcting for severity of manifestations (p = 0.041). Probability of favorable outcome decreases from approximately 65% when HBOT is started immediately, to 30% when HBOT is delayed for 15 h.
Increased time-to-HBOT is associated with decreased probability of favorable outcome in iatrogenic CAGE. This suggests that early initiation of HBOT in iatrogenic CAGE is of vital importance.

Geographical and temporal weighted regression (GTWR) model is a local regression linear model, which indirectly reflects the spatio-temporal non-stationary characteristics of the study data by calculating the trends for changes in parameters with space and time. Recently, GTWR model has become one of the hot topics in the study on spatio-temporal heterogeneity of diseases. This review summarizes the basic principles and study methods of the GTWR model, and describes the applications of this model in epidemiology.
［摘要］ 时空地理加权回归(geographical and temporal weighted regression, GTWR)模型是一种局部回归线性模型, 通过 推算参数随空间和时间变化的趋势间接反映研究数据的时空非平稳性特征。近年来, GTWR模型已成为研究疾病时空 异质性的热点之一。本文综述了 GTWR模型基本原理和研究方法, 同时梳理了该模型在流行病学中的应用。.

Altered fractionation concepts and especially moderate hypo-fractionation are evaluated as alternatives to standard treatment for head and neck squamous cell carcinoma (HNSCC), associated with or not concurrent with or sequential to chemotherapy. The calculation of the iso-equivalent dose regimens has as its starting point the linear quadratic (LQ) formalism traditionally based on the \"4Rs\" of radiobiology. The higher rates of therapeutic failure after radiotherapy of HNSCC are associated with the heterogeneity of radio-sensibility. The identification of genetic signatures and radio-resistance scores aims to improve the therapeutic ratio of radiotherapy and to conceptualize personalized fractionation schemes. The new data regarding the involvement of the sixth \"R\" of radiobiology in HNSCC, especially for the HPV-driven subtype, but also for the \"immune active\" minority of HPV-negative HNSCCs, bring to the fore a multifactorial variation of the α/β ratio. The involvement of the antitumor immune response and the dose/fractionation/volume factors as well as the therapeutic sequence in the case of new multimodal treatments including immune checkpoint inhibitors (ICIs) could be included as an additional term in the quadratic linear formalism especially for hypo-fractionation regimens. This term should take into account the dual immunomodulatory effect (immunosuppressant and stimulator of antitumor immunity) of radiotherapy, which varies from case to case and can bring benefit or a detrimental effect.