Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations.

BACKGROUND: The application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle's milk production into low ([Formula: see text]4500 kg), medium (4500-7500 kg), and high ([Formula: see text]7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD). RESULTS: The comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA's 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA. CONCLUSION: The findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

Understanding the implications of a complete case analysis for regression models with a right-censored covariate.

Despite its drawbacks, the complete case analysis is commonly used in regression models with incomplete covariates. Understanding when the complete case analysis will lead to consistent parameter estimation is vital before use. Our aim here is to demonstrate when a complete case analysis is consistent for randomly right-censored covariates and to discuss the implications of its use even when consistent. Across the censored covariate literature, different assumptions are made to ensure a complete case analysis produces a consistent estimator, which leads to confusion in practice. We make several contributions to dispel this confusion. First, we summarize the language surrounding the assumptions that lead to a consistent complete case estimator. Then, we show a unidirectional hierarchical relationship between these assumptions, which leads us to one sufficient assumption to consider before using a complete case analysis. Lastly, we conduct a simulation study to illustrate the performance of a complete case analysis with a right-censored covariate under different censoring mechanism assumptions, and we demonstrate its use with a Huntington disease data example.

Transformations in O Online: Group Process in the Virtual Realm.

The authors describe their experiences as members of an international online study group, initiated before the COVID-19 pandemic to read aloud and discuss Bion's (1965) Transformations. The three separately authored essays and commentary included here reflect the multifaceted phenomena in which images and voices in Zoom rectangles are transformed into shared emotional experience, the O of the group in Bion's language. These observations show how group members translate online experience into a felt sense of being with others, and suggest that oscillations in the sense of being inside or outside the group demonstrate the dialectical and constantly changing nature of the analytic field in an online group.

Generalized nonlinearity in animal ecology: Research, review, and recommendations.

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point-count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out-performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species-specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision-making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.

Using theoretical constraints and the TASI taxonomy to delineate predictably replicable findings.

The focus of the present article is not on failures to replicate but on the more optimistically framed and more fruitful question: What stable findings can be reproduced reliably and can be trusted by decision makers, managers, health agents, or politicians? We propagate the working hypothesis that a twofold key to stable and replicable findings lies in the existence of theoretical constraints and, no less important, in researchers' sensitivity to metatheoretical, auxiliary assumptions. We introduce a hierarchy of four levels of theoretical constraints-a priori principles, psychophysical, empirical, and modelling constraints-combined with the TASI taxonomy of theoretical, auxiliary, statistical, and inferential assumptions Trafimow, Journal for the Theory of Social Behaviour, 52, 37-48, (2022). Although theoretical constraints clearly facilitate stable and replicable research findings, TASI reminds us of various reasons why even perfectly valid hypotheses need not always be borne out. The presented framework should help researchers to operationalize conditions under which theoretical constraints render empirical findings most predictable.

Causal inference in the absence of positivity: The role of overlap weights.

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.

Analysis of Missingness Scenarios for Observational Health Data.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis. We apply this framework to the case study of observational data in healthcare facilities. We identify ten fundamental missingness scenarios and show how they influence the identification step for missing data graphical models, inverse probability weighting estimation, and exponential tilting sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis: complete-case analysis, Missforest imputation, and inverse probability weighting estimation. The experiments are conducted for two objectives: variable mean estimation and classification accuracy. We advocate for our analysis approach as a reference for the observational health data analysis. Beyond that, we also posit that the proposed analysis framework is applicable to other medical domains.

Chinese college students' PGD symptoms and their relationship to cognitive variables: a latent profile analysis.

Bereavement is a commonly experienced grief event; however, bereavement can also trigger a number of psychological consequences, such as prolonged grief disorder (PGD). At present, the differences in prolonged grief disorder symptoms (PGD symptoms) among various individuals and how those symptoms relate to cognitive variables are unclear. In the present study, 817 Chinese college students with bereavement experience were selected as participants. Based on the evaluation results of their irrational beliefs, bereavement-related irrational beliefs, basic world assumptions, and PGD symptoms, an individual-centered latent profile analysis was used to divide a group with PGD symptoms into several subgroups and comprehensively examine the relationships between these subgroups and cognitive variables. (1) The severity of PGD symptoms among Chinese college students can be categorized into three subgroups: mild, moderate, and severe. (2) Cognitive variables such as irrational beliefs and basic world assumptions were all found to correlate with the severity of PGD symptoms; bereavement-related irrational beliefs was the variable with the largest correlation. However, for the first time, this study found that different dimensions of basic world assumptions had different directions of correlation, based on the severity of the PGD symptoms. Justice, control, randomness, and self-control had significantly positive correlations. Conversely, benevolence of the world, benevolence of people, and worthiness of the self had significantly negative correlations. These results have important reference value for cognitive behavioral therapy (CBT) treatment and interventions for PGD issues in Chinese college students.

The Hill function is the universal Hopfield barrier for sharpness of input-output responses.

The Hill functions, [Formula: see text], have been widely used in biology for over a century but, with the exception of [Formula: see text], they have had no justification other than as a convenient fit to empirical data. Here, we show that they are the universal limit for the sharpness of any input-output response arising from a Markov process model at thermodynamic equilibrium. Models may represent arbitrary molecular complexity, with multiple ligands, internal states, conformations, coregulators, etc, under core assumptions that are detailed in the paper. The model output may be any linear combination of steady-state probabilities, with components other than the chosen input ligand held constant. This formulation generalizes most of the responses in the literature. We use a coarse-graining method in the graph-theoretic linear framework to show that two sharpness measures for input-output responses fall within an effectively bounded region of the positive quadrant, [Formula: see text], for any equilibrium model with [Formula: see text] input binding sites. [Formula: see text] exhibits a cusp which approaches, but never exceeds, the sharpness of [Formula: see text], but the region and the cusp can be exceeded when models are taken away from thermodynamic equilibrium. Such fundamental thermodynamic limits are called Hopfield barriers, and our results provide a biophysical justification for the Hill functions as the universal Hopfield barriers for sharpness. Our results also introduce an object, [Formula: see text], whose structure may be of mathematical interest, and suggest the importance of characterizing Hopfield barriers for other forms of cellular information processing.

The normativist-naturalist puzzle: Functions and assumptions of health assessment tools.

While there is no shortage in discussions of health assessment tools, little is known about health professionals' experience of their practical uses. However, these tools rely on assumptions that have significant impacts on the practice of health assessment. In this study, we explore health professionals' experiences with health assessment tools, that is, how they define, use, and understand these tools, and whether they take them to measure health and wellbeing. We combine a qualitative, interview-based study of the uses and understandings of health assessment tools among Danish health professionals with a philosophical analysis of these applications and perceptions. Our study shows that contrary assumptions are involved in the use of the tools, to the extent that one can speak of a normativist-naturalist puzzle: health professionals generally apply a normativist conception of health, find health assessment useful and valuable for their clinical practice, but believe that what the tools measure is basically not health proper but some proximal entity of a more naturalist kind. This result demonstrates the complexity of health assessment tools and suggests that they are used with care to ensure both that particular tools are used for the kinds of tasks they are most apt for, and that they are put to use in awareness of their limitations.

The Hill function is the universal Hopfield barrier for sharpness of input-output responses.

The Hill functions, ℋh(x)=xh/1+xh, have been widely used in biology for over a century but, with the exception of ℋ1, they have had no justification other than as a convenient fit to empirical data. Here, we show that they are the universal limit for the sharpness of any input-output response arising from a Markov process model at thermodynamic equilibrium. Models may represent arbitrary molecular complexity, with multiple ligands, internal states, conformations, co-regulators, etc, under core assumptions that are detailed in the paper. The model output may be any linear combination of steady-state probabilities, with components other than the chosen input ligand held constant. This formulation generalises most of the responses in the literature. We use a coarse-graining method in the graph-theoretic linear framework to show that two sharpness measures for input-output responses fall within an effectively bounded region of the positive quadrant, Ωm⊂ℝ+2, for any equilibrium model with m input binding sites. Ωm exhibits a cusp which approaches, but never exceeds, the sharpness of ℋm but the region and the cusp can be exceeded when models are taken away from thermodynamic equilibrium. Such fundamental thermodynamic limits are called Hopfield barriers and our results provide a biophysical justification for the Hill functions as the universal Hopfield barriers for sharpness. Our results also introduce an object, Ωm, whose structure may be of mathematical interest, and suggest the importance of characterising Hopfield barriers for other forms of cellular information processing.

When indices disagree: facing conceptual and practical challenges.

Hypothesis testing requires meaningful ways to quantify biological phenomena and account for alternative mechanisms that could explain the same pattern. Researchers combine experiments, statistics, and indices to account for these confounding mechanisms. Key concepts in ecology and evolution, such as niche breadth (NB) or fitness, can be represented by several indices, which often provide uncorrelated estimates. Is this because the indices use different types of noisy data or because the targeted phenomenon is complex and multidimensional? We discuss implications of these scenarios and propose five steps to aid researchers in identifying and combining indices, experiments, and statistics. Building on prior efforts to construct databases of hypotheses and indices and document assumptions, these steps help provide a formal strategy to reduce self-confirmatory bias.

Observational Dose-Response Meta-Analysis Methods May Bias Risk Estimates at Low Consumption Levels: The Case of Meat and Colorectal Cancer.

Observational studies of foods and health are susceptible to bias, particularly from confounding between diet and other lifestyle factors. Common methods for deriving dose-response meta-analysis (DRMA) may contribute to biased or overly certain risk estimates. We used DRMA models to evaluate the empirical evidence for colorectal cancer (CRC) association with unprocessed red meat (RM) and processed meats (PM), and the consistency of this association for low and high consumers under different modeling assumptions. Using the Global Burden of Disease project's systematic reviews as a start, we compiled a data set of studies of PM with 29 cohorts contributing 23,522,676 person-years and of 23 cohorts for RM totaling 17,259,839 person-years. We fitted DRMA models to lower consumers only [consumption < United States median of PM (21 g/d) or RM (56 g/d)] and compared them with DRMA models using all consumers. To investigate impacts of model selection, we compared classical DRMA models against an empirical model for both lower consumers only and for all consumers. Finally, we assessed if the type of reference consumer (nonconsumer or mixed consumer/nonconsumer) influenced a meta-analysis of the lowest consumption arm. We found no significant association with consumption of 50 g/d RM using an empirical fit with lower consumption (relative risk [RR] 0.93 (0.8-1.02) or all consumption levels (1.04 (0.99-1.10)), while classical models showed RRs as high as 1.09 (1.00-1.18) at 50g/day. PM consumption of 20 g/d was not associated with CRC (1.01 (0.87-1.18)) when using lower consumer data, regardless of model choice. Using all consumption data resulted in association with CRC at 20g/day of PM for the empirical models (1.07 (1.02-1.12)) and with as little as 1g/day for classical models. The empirical DRMA showed nonlinear, nonmonotonic relationships for PM and RM. Nonconsumer reference groups did not affect RM (P = 0.056) or PM (P = 0.937) association with CRC in lowest consumption arms. In conclusion, classical DRMA model assumptions and inclusion of higher consumption levels influence the association between CRC and low RM and PM consumption. Furthermore, a no-risk limit of 0 g/d consumption of RM and PM is inconsistent with the evidence.

Profiles of posttraumatic stress disorder and negative world assumptions in treatment-seeking refugees.

Background: Refugees often suffer from trauma-related psychopathology, specifically posttraumatic stress disorder (PTSD). Negative world assumptions are strongly correlated with the development, course, and severity of PTSD.Objective: This study aimed to investigate whether there are distinct profiles of PTSD and negative world assumptions (NWA) and examine whether trauma load, torture, and gender differentially predict such symptom profiles.Method: In a sample of 225 treatment-seeking refugees who had resettled in the Netherlands, latent profile analysis was used to identify subgroups of patients sharing the same profile of PTSD and NWA symptoms. Predictors of profile membership were analyzed via multinomial logistic regression.Results: A three-profile solution yielded the best model fit: a low PTSD/low NWA profile (23.6%), a high PTSD/high NWA profile (41.8%), and a high PTSD/low NWA profile (34.7%). Participants who reported a higher trauma load, were more likely to be part of the high PTSD/high NWA profile or the high PTSD/low NWA profile in comparison to low PTSD/low NWA profile. Participants who reported having experienced torture were more likely to be part of the high PTSD/high NWA profile in comparison to low PTSD/low NWA profile. Gender did not differentiate between the profiles.Conclusions: This study reveals that among treatment-seeking refugees resettled in the Netherlands, there are distinct profiles of PTSD and NWA. These profiles indicate that PTSD and NWA are not uniformly experienced among refugees, emphasizing the diversity in their psychological responses to trauma. Among individuals experiencing severe PTSD symptoms, a subgroup was identified of individuals who additionally exhibited negative assumptions about themselves, others, and the world. Recognizing this heterogeneity is crucial in both research and clinical practice, particularly in the context of refugee mental health. Directions for future research are discussed.

Three profiles of PTSD and negative world assumptions were identified in a group of treatment-seeking refugees.Directions for future research and the importance of recognizing heterogeneity in psychological responses to traumatic experiences in refugees are discussed.

Impact of anthropogenic emission control in reducing future PM2.5 concentrations and the related oxidative potential across different regions of China.

Affected by both future anthropogenic emissions and climate change, future prediction of PM2.5 and its Oxidative Potential (OP) distribution is a significant challenge, especially in developing countries like China. To overcome this challenge, we estimated historical and future PM2.5 concentrations and associated OP using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. Considering different future socio-economic pathways and emission scenario assumptions, we quantified how the contribution from various anthropogenic emission sectors will change under these scenarios. Results show that compared to the CESM_SSP2-4.5_CLE scenario (based on moderate radiative forcing and Current Legislation Emission), the CESM_SSP1-2.6_MFR scenario (based on sustainability development and Maximum Feasible Reductions) is projected to yield greater environmental and health benefits in the future. Under the CESM_SSP1-2.6_MFR scenario, annual average PM2.5 concentrations (OP) are expected to decrease to 30 (0.8 nmolmin-1m-3) in almost all regions by 2030, which will be 65 % (67 %) lower than that in 2010. From a long-term perspective, it is anticipated that OP in the Fen-Wei Plain region will experience the maximum reduction (82.6 %) from 2010 to 2049. Largely benefiting from the effective control of PM2.5 in the region, it has decreased by 82.1 %. Crucially, once emission reduction measures reach a certain level (in 2040), further reductions become less significant. This study also emphasized the significant role of secondary aerosol formation and biomass-burning sources in influencing OP during both historical and future periods. In different scenarios, the reduction range of OP from 2010 to 2049 is estimated to be between 71 % and 85 % by controlling precursor emissions involved in secondary aerosol formation and emissions from biomass burning. Results indicate that strengthening the control of anthropogenic emissions in various regions are key to achieving air quality targets and safeguarding human health in the future.

Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15 % to 40 %. GSI effectiveness also varies considerably, resulting in differences in simulated percent of CSO volume reduced from 8 % to 35 %, depending on the GSI type and modeled flow or process. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible, and uncertainty and assumptions should always be considered.

Changing your mind about the data: Updating sampling assumptions in inductive inference.

When people use samples of evidence to make inferences, they consider both the sample contents and how the sample was generated ("sampling assumptions"). The current studies examined whether people can update their sampling assumptions - whether they can revise a belief about sample generation that is discovered to be incorrect, and reinterpret old data in light of the new belief. We used a property induction task where learners saw a sample of instances that shared a novel property and then inferred whether it generalized to other items. Assumptions about how the sample was selected were manipulated between conditions: in the property sampling frame condition, items were selected because they shared a property, while in the category sampling frame condition, items were selected because they belonged to a particular category. Experiment 1 found that these frames affected patterns of property generalization regardless of whether they were presented before or after the sample data was observed: in both cases, generalization was narrower under a property than a category frame. In Experiments 2 and 3, an initial category or property frame was presented before the sample, and was later retracted and replaced with the complementary frame. Learners were able to update their beliefs about sample generation, basing their property generalization on the more recent correct frame. These results show that learners can revise incorrect beliefs about data selection and adjust their inductive inferences accordingly.

The Course of Anxiety-Specific Cognitive Bias Following Daycare/Inpatient Treatment in Youths with Social Phobia and School Absenteeism.

Social phobia (SP) is a common mental disorder in youth often accompanied by absence from school, which may require daycare or inpatient intervention (DC/IN). Objective: The present explorative study investigates changes in anxiety-specific implicit assumptions and interpretation bias following DC/IN. Methods: The study included 16 youths with SP (M age = 15.8 [SD = 1.24], females: 62.5 %) participating in DC/IN. We assessed the main outcomes using the Implicit Association Test and Affective Misattribution Procedure. Results: A large effect was shown for reducing implicit assumptions of feeling anxious (p = .142; η2p = .171) and for reducing the implicit interpretation bias (p = .137; η2p = .162). No change was indicated by effect size in implicit assumptions of feeling socially rejected (p = .649; η2p = .016). Social phobia symptoms initially correlated with changes in implicit assumptions of feeling anxious (r = .45). Conclusion: Effect sizes indicate that implicit anxiety-specific assumptions and interpretation bias descriptively improved following DC/IN. Thus, DC/IN may lead to meaningful improvements of anxiety-specific cognition in some individuals with high SP symptoms, emphasizing the relevance of cognitive behavioral approaches in the treatment of SP. Several limitations are discussed.

Top 10 Tips Palliative Care Clinicians Should Know About Messaging for the Public.

When speaking to public audiences, palliative care advocates often reach for personal experiences of great meaning and significance in their own lives, and often distill those experiences to a key message. However, this approach may not be the most effective way to engage a public audience whose closest experience with palliative care is based on social media or third-hand stories. Research demonstrates that the lay public often starts with inaccurate assumptions about palliative care, including that it is only for people at end of life. These misunderstandings can lead people with serious illness to decline palliative care services that are backed by evidence and demonstrate real benefit. This phenomenon of "declines based on inaccurate assumptions" is widely seen in clinical practice and palliative care demonstration projects. Public messaging is an evidence-based approach to engage more effectively with the public when doing outreach for palliative care. The 10 tips provided are based on a multiyear and multiorganizational project focused on improving the messaging of palliative care for the public. As palliative care services are increasingly expanded and integrated into health systems, public messaging can provide a new approach for building partnerships with the public by offering messages that consistently meet their needs based on their current perceptions. Incorporating public-informed messaging strategies could enable palliative care clinicians and advocates to address the lay public with greater confidence and clarity about how palliative care can serve them, their families, and their communities.