While acknowledging that peer review is imperfect, we argue that preprint servers, which disseminate non-peer-reviewed research, represent a significant risk to society. Preprints can lead to the spread of misinformation, undermine public trust in science, and contribute to harmful public health outcomes, as occurred during the COVID-19 pandemic. We challenge the premise that preprints accelerate discovery and we argue that the absence of pre-publication peer review compromises the reliability of scientific findings. We contend that scientists have a duty of care to society and that the lack of pre-publication vetting, and the minimal oversight associated with preprinting, neglects this responsibility. Because of these harms, and to safeguard both public health and trust in science, we propose that journals should not publish manuscripts posted on preprint servers.
A symbiotic protozoan in the hindgut of termites was accidentally discovered. Its head and body continuously rotated in opposite directions without stopping or reversing directions. Rotations included the plasma membrane of each part of the cell, and the membrane was continuous across the surface shear zone. This protozoan offers direct visual evidence for the fluid nature of cell membranes, and is the first known example of a rotary motor in eukaryotic cells. The rotary motor is an axostyle that runs through the protozoan like a driveshaft and generates relative torque along its length. The entire plasma membrane of the cell is actually fluid and adheres to the underlying cytoplasm/cytoskeleton, passively following its movements. Cell membranes clearly do not determine the shape or movements of cells. Ectosymbiotic rod bacteria attached to the cell surface are flagellated and propel the protozoan, the first known case of prokaryotic flagella driving locomotion of eukaryotic cells.
Nowadays, single-step genomic best linear unbiased prediction (ssGBLUP) is the most popular methodology for livestock genetic evaluation. It simultaneously evaluates genotyped and nongenotyped animals using an augmented genotype and pedigree relationship matrix. In most livestock species, a small proportion of the population is genotyped. A small model limited to genotyped animals (genomic best linear unbiased prediction, GBLUP) incorporating information from nongenotyped animals would be of interest, because it reduces the dimension of equations and computational costs and produces solutions equivalent to ssGBLUP. Two methods are considered in this study. First, genotyped animals, whether phenotyped or nonphenotyped, receive phenotype contributions from their nongenotyped relatives. Calculating those pseudo-phenotypes and weights for the introduced residual heterogeneity is challenging. The second method involves absorbing equations of nongenotyped animals into the equations of genotyped animals. The solutions from the resulting GBLUP model were equivalent to those of the full ssGBLUP model. However, two main reasons prevent the introduced GBLUP model from being adopted and widely implemented. First, such implementation enforces (pedigree-based) additive genetic covariances among nongenotyped animals into their residual covariance matrix. That introduces nonzero off-diagonal elements to the diagonal residual covariance matrix for nongenotyped animals, making its inversion cumbersome. Also, the inverted matrix is dense, increasing the multiplication costs to other matrices and the vector of phenotypes. Second, the breeding values of nongenotyped animals ($a_1$) are not conditional to those of genotyped animals ($a_2$), making back-solving of $a_1$ from $a_2$ challenging.
The blood-brain barrier (BBB) is hypothesized to play a role in the pathophysiology of psychosis. However, studying this structure in living humans is challenging due to limitations in current methods that fail to identify subtle changes or dysfunctions. Moreover, obtaining a biopsy is too invasive. To address these challenges, we aimed to isolate BBB-derived extracellular vesicles as potential biomarkers for neuropsychiatric conditions, providing a “liquid biopsy of the BBB”. Individuals with psychosis and healthy controls were recruited for this study. In the absence of single specific proteins for brain endothelial cells, we targeted three distinct proteins for immunolabelling. Proteomics and flow cytometry confirmed the presence of EV with the signature CD45-/CD31+/GLUT1+. Our efforts to generate adequate material for confirming proteomic analysis of EV with this signature were terminated due to excessive time and resource demands. This setback led us to recognize that our current approach was not feasible due to low abundance of EVs with the target signature. Moreover, the relative abundance of putative blood-derived EVs in comparison to putative brain endothelial cells (BEC)-derived EVs contradicted previously published data, raising concerns about the specificity of our approach.
Affective polarization, people’s emotional attachment to their own party and dislike of rival parties, has been a growing area of interest in recent decades. However, studies on affective polarization have tended to use difference score measures to capture affective polarization. Specifically, it is common to use the difference between people’s rating of their own party, and their ratings of a rival party (or parties). It has long been known that such methods are problematic for measuring any variable and the extensive conceptual, statistical, and theoretical problems with using difference scores are here reviewed at length. Then, a tutorial for polyvariate regression is provided. Polyvariate regression is an analytic approach that allows the researcher to test how a predictor is associated with affective polarization without using difference scores. This is then demonstrated by testing the association of people’s stances on government defense spending with feeling thermometer ratings of the Republican and Democratic parties in the United States (U.S.), both to help researchers in this area to use it in their work and to demonstrate further how using difference scores may obscure possibly interesting findings. The same association is tested with a difference score measure as the outcome variable. The outcomes of both analyses are compared, and show the superiority of using polyvariate regression over difference score measures. The application of polyvariate regression in multiparty contexts and under other conditions is discussed, as are limitations of polyvariate regression. Polynomial regression is also recommended as an approach to investigating affective polarization when polarization is the predictor rather than the outcome.
