Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Near-term tropical cyclone risk and coupled Earth system model biases.

Most current climate models predict that the equatorial Pacific will evolve under greenhouse gas-induced warming to a more El Niño-like state over the next several decades, with a reduced zonal sea surface temperature gradient and weakened atmospheric Walker circulation. Yet, observations over the last 50 y show the opposite trend, toward a more La Niña-like state. Recent research provides evidence that the discrepancy cannot be dismissed as due to internal variability but rather that the models are incorrectly simulating the equatorial Pacific response to greenhouse gas warming. This implies that projections of regional tropical cyclone activity may be incorrect as well, perhaps even in the direction of change, in ways that can be understood by analogy to historical El Niño and La Niña events: North Pacific tropical cyclone projections will be too active, North Atlantic ones not active enough, for example. Other perils, including severe convective storms and droughts, will also be projected erroneously. While it can be argued that these errors are transient, such that the models' responses to greenhouse gases may be correct in equilibrium, the transient response is relevant for climate adaptation in the next several decades. Given the urgency of understanding regional patterns of climate risk in the near term, it would be desirable to develop projections that represent a broader range of possible future tropical Pacific warming scenarios-including some in which recent historical trends continue-even if such projections cannot currently be produced using existing coupled earth system models.

Large language models show human-like content biases in transmission chain experiments.

As the use of large language models (LLMs) grows, it is important to examine whether they exhibit biases in their output. Research in cultural evolution, using transmission chain experiments, demonstrates that humans have biases to attend to, remember, and transmit some types of content over others. Here, in five preregistered experiments using material from previous studies with human participants, we use the same, transmission chain-like methodology, and find that the LLM ChatGPT-3 shows biases analogous to humans for content that is gender-stereotype-consistent, social, negative, threat-related, and biologically counterintuitive, over other content. The presence of these biases in LLM output suggests that such content is widespread in its training data and could have consequential downstream effects, by magnifying preexisting human tendencies for cognitively appealing and not necessarily informative, or valuable, content.

HiConfidence: a novel approach uncovering the biological signal in Hi-C data affected by technical biases.

The chromatin interaction assays, particularly Hi-C, enable detailed studies of genome architecture in multiple organisms and model systems, resulting in a deeper understanding of gene expression regulation mechanisms mediated by epigenetics. However, the analysis and interpretation of Hi-C data remain challenging due to technical biases, limiting direct comparisons of datasets obtained in different experiments and laboratories. As a result, removing biases from Hi-C-generated chromatin contact matrices is a critical data analysis step. Our novel approach, HiConfidence, eliminates biases from the Hi-C data by weighing chromatin contacts according to their consistency between replicates so that low-quality replicates do not substantially influence the result. The algorithm is effective for the analysis of global changes in chromatin structures such as compartments and topologically associating domains. We apply the HiConfidence approach to several Hi-C datasets with significant technical biases, that could not be analyzed effectively using existing methods, and obtain meaningful biological conclusions. In particular, HiConfidence aids in the study of how changes in histone acetylation pattern affect chromatin organization in Drosophila melanogaster S2 cells. The method is freely available at GitHub: https://github.com/victorykobets/HiConfidence.

Quantification of biases in predictions of protein-protein binding affinity changes upon mutations.

Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.

Mutational biases favor complexity increases in protein interaction networks after gene duplication.

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

Why postmortems fail.

Most high-profile disasters are followed by demands for an investigation into what went wrong. Even before they start, calls for finding the missed warning signs and an explanation for why people did not "connect the dots" will be common. Unfortunately, however, the same combination of political pressures and the failure to adopt good social science methods that contributed to the initial failure usually lead to postmortems that are badly flawed. The high stakes mean that powerful actors will have strong incentives to see that certain conclusions are-and are not-drawn. Most postmortems also are marred by strong psychological biases, especially the assumption that incorrect inferences must have been the product of wrong ways of thinking, premature cognitive closure, the naive use of hindsight, and the neglect of the comparative method. Given this experience, I predict that the forthcoming inquiries into the January 6, 2021, storming of the US Capitol and the abrupt end to the Afghan government will stumble in many ways.

A framing effect in the judgment of discrimination.

Discrimination is not only an objective fact but also a subjective judgment. While extensive research has studied discrimination as an objective fact, we study the judgment of discrimination and show that it is malleable while holding objective discrimination constant. We focus on a common situation in real life: the constituent groups in a candidate pool are unequal (e.g., fewer female candidates than male candidates for tech jobs), and observers (e.g., the public) see only one side of the decision outcome (e.g., only the hired applicants, not the rejected ones). Ten experiments reveal a framing effect: people judge the decision-maker (e.g., the tech firm) as more discriminatory against the minority in the candidate pool if people see the composition of the accepted candidates than if they see the composition of the rejected candidates, even though the information in the two frames is equivalent (i.e., knowing the information in one frame is sufficient to infer the information in the other). The framing effect occurs regardless of whether the decision-maker is objectively discriminatory, replicates across diverse samples (Americans, Asians, and Europeans) and types of discrimination (e.g., gender, race, political orientation), and has significant behavioral consequences. We theorize and show that the framing effect arises because, when judging discrimination, people overlook information that they could infer but is not explicitly given, and they expect equality in the composition of the constituent groups in their given frame. This research highlights the fallibility of judged discrimination and suggests interventions to reduce biases and increase accuracy.

The functional role of spatial anisotropies in ensemble perception.

BACKGROUND: The human brain can rapidly represent sets of similar stimuli by their ensemble summary statistics, like the average orientation or size. Classic models assume that ensemble statistics are computed by integrating all elements with equal weight. Challenging this view, here, we show that ensemble statistics are estimated by combining parafoveal and foveal statistics in proportion to their reliability. In a series of experiments, observers reproduced the average orientation of an ensemble of stimuli under varying levels of visual uncertainty. RESULTS: Ensemble statistics were affected by multiple spatial biases, in particular, a strong and persistent bias towards the center of the visual field. This bias, evident in the majority of subjects and in all experiments, scaled with uncertainty: the higher the uncertainty in the ensemble statistics, the larger the bias towards the element shown at the fovea. CONCLUSION: Our findings indicate that ensemble perception cannot be explained by simple uniform pooling. The visual system weights information anisotropically from both the parafovea and the fovea, taking the intrinsic spatial anisotropies of vision into account to compensate for visual uncertainty.

Multiple and Dissociable Effects of Sensory History on Working-Memory Performance.

Behavioral reports of sensory information are biased by stimulus history. The nature and direction of such serial-dependence biases can differ between experimental settings; both attractive and repulsive biases toward previous stimuli have been observed. How and when these biases arise in the human brain remains largely unexplored. They could occur either via a change in sensory processing itself and/or during postperceptual processes such as maintenance or decision-making. To address this, we tested 20 participants (11 female) and analyzed behavioral and magnetoencephalographic (MEG) data from a working-memory task in which participants were sequentially presented with two randomly oriented gratings, one of which was cued for recall at the end of the trial. Behavioral responses showed evidence for two distinct biases: (1) a within-trial repulsive bias away from the previously encoded orientation on the same trial, and (2) a between-trial attractive bias toward the task-relevant orientation on the previous trial. Multivariate classification of stimulus orientation revealed that neural representations during stimulus encoding were biased away from the previous grating orientation, regardless of whether we considered the within-trial or between-trial prior orientation, despite opposite effects on behavior. These results suggest that repulsive biases occur at the level of sensory processing and can be overridden at postperceptual stages to result in attractive biases in behavior.SIGNIFICANCE STATEMENT Recent experience biases behavioral reports of sensory information, possibly capitalizing on the temporal regularity in our environment. It is still unclear at what stage of stimulus processing such serial biases arise. Here, we recorded behavior and neurophysiological [magnetoencephalographic (MEG)] data to test whether neural activity patterns during early sensory processing show the same biases seen in participants' reports. In a working-memory task that produced multiple biases in behavior, responses were biased toward previous targets, but away from more recent stimuli. Neural activity patterns were uniformly biased away from all previously relevant items. Our results contradict proposals that all serial biases arise at an early sensory processing stage. Instead, neural activity exhibited mostly adaptation-like responses to recent stimuli.

Improving Orthologous Signal and Model Fit in Datasets Addressing the Root of the Animal Phylogeny.

There is conflicting evidence as to whether Porifera (sponges) or Ctenophora (comb jellies) comprise the root of the animal phylogeny. Support for either a Porifera-sister or Ctenophore-sister tree has been extensively examined in the context of model selection, taxon sampling, and outgroup selection. The influence of dataset construction is comparatively understudied. We re-examine five animal phylogeny datasets that have supported either root hypothesis using an approach designed to enrich orthologous signal in phylogenomic datasets. We find that many component orthogroups in animal datasets fail to recover major lineages as monophyletic with the exception of Ctenophora, regardless of the supported root. Enriching these datasets to retain orthogroups recovering ≥3 major lineages reduces dataset size by up to 50% while retaining underlying phylogenetic information and taxon sampling. Site-heterogeneous phylogenomic analysis of these enriched datasets recovers both Porifera-sister and Ctenophora-sister positions, even with additional constraints on outgroup sampling. Two datasets which previously supported Ctenophora-sister support Porifera-sister upon enrichment. All enriched datasets display improved model fitness under posterior predictive analysis. While not conclusively rooting animals at either Porifera or Ctenophora, we do see an increase in signal for Porifera-sister and a decrease in signal for Ctenophore-sister when data are filtered for orthologous signal. Our results indicate that dataset size and construction as well as model fit influence animal root inference.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

Pupil dilation reflects effortful action invigoration in overcoming aversive Pavlovian biases.

"Pavlovian" or "motivational" biases describe the phenomenon that the valence of prospective outcomes modulates action invigoration: Reward prospect invigorates action, whereas punishment prospect suppresses it. The adaptive role of these biases in decision-making is still unclear. One idea is that they constitute a fast-and-frugal decision strategy in situations characterized by high arousal, e.g., in presence of a predator, which demand a quick response. In this pre-registered study (N = 35), we tested whether such a situation-induced via subliminally presented angry versus neutral faces-leads to increased reliance on Pavlovian biases. We measured trial-by-trial arousal by tracking pupil diameter while participants performed an orthogonalized Motivational Go/NoGo Task. Pavlovian biases were present in responses, reaction times, and even gaze, with lower gaze dispersion under aversive cues reflecting "freezing of gaze." The subliminally presented faces did not affect responses, reaction times, or pupil diameter, suggesting that the arousal manipulation was ineffective. However, pupil dilations reflected facets of bias suppression, specifically the physical (but not cognitive) effort needed to overcome aversive inhibition: Particularly strong and sustained dilations occurred when participants managed to perform Go responses to aversive cues. Conversely, no such dilations occurred when they managed to inhibit responses to Win cues. These results suggest that pupil diameter does not reflect response conflict per se nor the inhibition of prepotent responses, but specifically effortful action invigoration as needed to overcome aversive inhibition. We discuss our results in the context of the "value of work" theory of striatal dopamine.

Integrating perspectives in developing mycorrhizal trait databases: a call for inclusive and collaborative continental efforts.

Global assessments of mycorrhizal symbiosis present large sampling gaps in rich biodiversity regions. Filling these gaps is necessary to build large-scale, unbiased mycorrhizal databases to obtain reliable analyses and prevent misleading generalizations. Underrepresented regions in mycorrhizal research are mainly in Africa, Asia, and South America. Despite the high biodiversity and endemism in these regions, many groups of organisms remain understudied, especially mycorrhizal fungi. In this Viewpoint, we emphasize the importance of inclusive and collaborative continental efforts in integrating perspectives for comprehensive trait database development and propose a conceptual framework that can help build large mycorrhizal databases in underrepresented regions. Based on the four Vs of big data (volume, variety, veracity, and velocity), we identify the main challenges of constructing a large mycorrhizal dataset and propose solutions for each challenge. We share our collaborative methodology, which involves employing open calls and working groups to engage all mycorrhizal researchers in the region to build a South American Mycorrhizal Database. By fostering interdisciplinary collaborations and embracing a continental-scale approach, we can create robust mycorrhizal trait databases that provide valuable insights into the evolution, ecology, and functioning of mycorrhizal associations, reducing the geographical biases that are so common in large-scale ecological studies.

Do metacognitive therapies for schizophrenia-spectrum disorders work? A meta-analytic investigation.

Recent reviews and meta-analyses of metacognitive therapy for schizophrenia-spectrum disorder (SSD) have included uncontrolled studies, single-session interventions, and/or analyses limited to a single form of metacognitive therapy. We sought to evaluate the efficacy of metacognitive therapies more broadly based on controlled trials (CT) of sustained treatments. We conducted a pre-registered meta-analysis of controlled trials that investigated the effects of meta-cognitive therapies on primary positive symptom outcomes, and secondary symptom, function and/or insight measures. Electronic databases were searched up to March 2022 using variants of the keywords, 'metacognitive therapy', 'schizophrenia', and 'controlled trial'. Studies were identified and screened according to PRISMA guidelines. Outcomes were assessed with random effects models and sample, intervention, and study quality indices were investigated as potential moderators. Our search identified 44 unique CTs with usable data from 2423 participants. Data were extracted by four investigators with reliability >98%. Results revealed that metacognitive therapies produced significant small-to-moderate effects on delusions (g = 0.32), positive symptoms (g = 0.30) and psychosocial function (g = 0.31), and significant, small effects on cognitive bias (g = 0.25), negative symptoms (g = 0.24), clinical insight (g = 0.29), and social cognition (g = 0.27). Findings were robust in the face of sample differences in age, education, gender, antipsychotic dosage, and duration of illness. Except for social cognition and negative symptoms, effects were evident even in the most rigorous study designs. Thus, results suggest that metacognitive therapies for SSD benefit people, and these benefits transfer to function and illness insight. Future research should modify existing treatments to increase the magnitude of treatment benefits.

Exploring the lack of articular ends at the Middle Pleistocene site of Qesem Cave, Israel.

Biased skeletal part representation is a key element for making inferences about transport decisions, carcass procurement, and use patterns in anthropogenic accumulations. In the absence of destructive taphonomic processes, it is often assumed that the abundance of different anatomical portions represents selective transport and discard patterns of human groups. Because body parts may be transported for specific products such as meat, marrow or grease, a pattern that usually attracts attention in many archaeological sites is the low proportions of appendicular epiphyses. Here we present the case of faunal assemblages from the lower stratigraphic sequence of Qesem Cave, Israel, dated to ca. 430 to 300 ka. All bone accumulations are characterized by a biased skeletal profile including mainly long-limb bones and a virtual absence of epiphyses. The assemblages also show density-mediated attrition not linked to fossil-diagenetic processes, a targeted specific destruction to the most greasy articular ends and an almost total absence of carnivore intervention. Our goal here is to explore the processes that entail the destruction of appendicular epiphyses at Qesem Cave, as well as propose viable hypotheses to explain their underrepresentation on-site. Our results shed light on the domestic activities linked to the processing of bones at the site and support the importance of animal grease in the caloric intake of Middle Pleistocene humans.

The association between perceived social functioning and heart rate variability is mediated by subclinical depressive symptomatology and moderated by gender.

Chronic loneliness and low perceived social support have been recognized as risk factors for both mental and cardiovascular disorders. It has been proposed that their link to psychophysiological problems may involve changes in parasympathetic activity. However, the exact underlying psychopathological mechanisms and the moderating effects of gender are still not thoroughly examined. Thus, the present study investigated associations between perceived social functioning and resting vagal tone in the context of potential cognitive and subclinical mediators and gender differences. Three hundred twenty-five young adults (aged 18-35, 180 women) underwent an electrocardiogram measurement of 6-minute resting heart rate variability (HRV). They also completed questionnaires assessing loneliness, perceived social support, social cognitive biases, depressive and social anxiety symptoms, and general mental health. In men, HRV was significantly and negatively associated with poorer perceived social functioning, depressive symptoms, and self-reported social cognitive biases, while in women, there was a quadratic link between HRV and depressive symptoms and HRV and general mental health. Moderated mediation analysis revealed that depressive symptoms fully mediated the relationship between perceived social functioning and HRV in men. The results suggest that decreased resting vagal tone in lonely individuals is linked to depressive symptomatology rather than to specific social cognitive biases and that this association is significant only in men.

Physical origin of the peak tailing of monoclonal antibodies in size-exclusion chromatography using bio-compatible systems and columns.

The analysis of mixtures containing monoclonal antibody (mAb) (approximately 150 kDa molecular weight) and sub-unit impurities (approximately 100 kDa) is challenging, even when adopting the latest ultra-high-pressure liquid chromatography (UHPLC) columns (4.6 mm [Formula: see text] 150 mm coated hardware, 1.7 [Formula: see text]m 250 BEH[Formula: see text] Surface-modified Particles) and systems (ACQUITY[Formula: see text] UPLC[Formula: see text] I-class Bio Plus). The main issue still encountered is a persistent tail of the mAb peak. Here, the physical origin(s) of such peak tailing in size-exclusion chromatography (SEC) are investigated from both fundamental and practical approaches. Up to five relevant physical origins are analyzed: sample heterogeneity (glycoforms), UHPLC system dispersion, strong residual binding of the mAb to the SEC particles (via hydrophobic and/or electrostatic interactions) and to the stainless steel column/system hardware, slow escape kinetics of the mAb from the SEC particles, and flow heterogeneity caused by the non-ideal slurry packing of SEC columns. Experiments (testing sample heterogeneity, system dispersion, and strong residual interactions) and calculations (predicting the transient absorption/escape kinetics in a single SEC particle and the two-dimensional peak concentration profiles) altogether unambiguously demonstrate that the observed mAb peak tailing is caused primarily by the long-range velocity biases across the SEC column combined with the slow transverse dispersion of mAbs. Therefore, improvement in the resolution between mAb and sub-unit fragment impurities can only be achieved by increasing the column length, e.g., by applying recycling chromatography at acceptable pressures.

How Can Debiasing Research Aid Efforts to Reduce Discrimination?

PUBLIC ABSTRACT: Scientists studying intergroup biases are often concerned with lessening discrimination (unequal treatment of one social group versus another), but many interventions for reducing such biased behavior have weak or limited evidence. In this review article, we argue one productive avenue for reducing discrimination comes from adapting interventions in a separate field-judgment and decision-making-that has historically studied "debiasing": the ways people can lessen the unwanted influence of irrelevant information on decision-making. While debiasing research shares several commonalities with research on reducing intergroup discrimination, many debiasing interventions have relied on methods that differ from those deployed in the intergroup bias literature. We review several instances where debiasing principles have been successfully applied toward reducing intergroup biases in behavior and introduce other debiasing techniques that may be well-suited for future efforts in lessening discrimination.

The effects of life experiences and polygenic risk for depression on the development of positive and negative cognitive biases across adolescence: The CogBIAS hypothesis.

The Cognitive Bias (CogBIAS) hypothesis proposes that cognitive biases develop as a function of environmental influences (which determine the valence of biases) and the genetic susceptibility to those influences (which determines the potency of biases). The current study employed a longitudinal, polygenic-by-environment approach to examine the CogBIAS hypothesis. To this end, measures of life experiences and polygenic scores for depression were used to assess the development of memory and interpretation biases in a three-wave sample of adolescents (12-16 years) (N = 337). Using mixed effects modeling, three patterns were revealed. First, positive life experiences (PLEs) were found to diminish negative and enhance positive forms of memory and social interpretation biases. Second, and against expectation, negative life experiences and depression polygenic scores were not associated with any cognitive outcomes, upon adjusting for psychopathology. Finally, and most importantly, the interaction between high polygenic risk and greater PLEs was associated with a stronger positive interpretation bias for social situations. These results provide the first line of polygenic evidence in support of the CogBIAS hypothesis, but also extend this hypothesis by highlighting positive genetic and nuanced environmental influences on the development of cognitive biases across adolescence.