Prior Choice Research Articles

Ventrolateral prefrontal cortex in macaques guides decisions in different learning contexts.

Flexibly adjusting our behavioral strategies based on the environmental context is critical to maximize rewards. Ventrolateral prefrontal cortex (vlPFC) has been implicated in both learning and decision-making for probabilistic rewards, although how context influences these processes remains unclear. We collected functional neuroimaging data while rhesus macaques performed a probabilistic learning task in two contexts: one with novel and another with familiar visual stimuli. We found that activity in vlPFC encoded rewards irrespective of the context but encoded behavioral strategies that depend on reward outcome (win-stay/lose-shift) preferentially in novel contexts. Functional connectivity between vlPFC and anterior cingulate cortex varied with behavioral strategy in novel learning blocks. By contrast, connectivity between vlPFC and mediodorsal thalamus was highest when subjects repeated a prior choice. Furthermore, pharmacological D2-receptor blockade altered behavioral strategies during learning and resting-state vlPFC activity. Taken together, our results suggest that multiple vlPFC-linked circuits contribute to adaptive decision-making in different contexts.

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

AbstractIn ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: “Dolphin” (2020), “Nepartak” (2021), and “Meari” (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one.

Localized and global representation of prior value, sensory evidence, and choice in male mouse cerebral cortex.

Adaptive behavior requires integrating prior knowledge of action outcomes and sensory evidence for making decisions while maintaining prior knowledge for future actions. As outcome- and sensory-based decisions are often tested separately, it is unclear how these processes are integrated in the brain. In a tone frequency discrimination task with two sound durations and asymmetric reward blocks, we found that neurons in the medial prefrontal cortex of male mice represented the additive combination of prior reward expectations and choices. The sensory inputs and choices were selectively decoded from the auditory cortex irrespective of reward priors and the secondary motor cortex, respectively, suggesting localized computations of task variables are required within single trials. In contrast, all the recorded regions represented prior values that needed to be maintained across trials. We propose localized and global computations of task variables in different time scales in the cerebral cortex.

High conductive poly(1,8-diaminonaphthalene)/graphene nanocomposite as a cathode for zinc ion batteries

Recently, aqueous zinc ion batteries (AZIBs) are experiencing rapid development benefiting to their high safety and increasing electrochemical performance. Among all kinds of AZIB electrode materials, organic materials have attracted attentions due to their unique advantages such as structural flexibility, small volume expansion, abundant Zn2+ storage sites and environmental friendliness. However, issues involving electrode dissolution, sluggish electrochemical kinetics etc. still hampered the prior choice of organic electrodes. Herein, we designed a poly(1,8-diamino-naphthalene)/ graphene composite aiming to improve the availability of organic electrodes in AZIBs. By means of constructing high conducting graphene network and the synergetic effect between the two components, the electronic transfer kinetics and overall electrochemical performance of PDAN organic electrode have been fully improved. The organic-inorganic composite exhibited an initial specific capacity of 240 mA·h·g−1 and stable galvanostatic cycling after 1000 cycles, exceeding the original polymer.

Understanding Clinician Preferences for Treatment Attributes in Oncology: A Discrete Choice Experiment of Oncologists' and Urologists' Preferences for First-Line Treatment of Locally Advanced/Unresectable Metastatic Urothelial Carcinoma in Five European Countries.

Prior discrete choice experiments (DCE) in oncology found that, on average, clinicians rank survival as the most important treatment attribute. We investigate heterogeneity in clinician preferences within the context of first-line treatment for advanced urothelial carcinoma in Spain, France, Italy, Germany, and the UK. The online DCE included 12 treatment choice tasks, each comparing two hypothetical therapy profiles defined by treatment attributes: grade 3/4 treatment-related adverse events (TRAEs), induction and maintenance administration schedules, progression-free survival, and overall survival (OS). We used a random parameters logit model to estimate attribute relative importance (RI) (0-100%) and generate preference shares for four treatment profiles. Results were stratified by country. Preference heterogeneity was evaluated by latent class analysis. In August and September 2022, 498 clinicians (343 oncologists and 155 urologists) completed the DCE. OS had the strongest influence on clinicians' preferences [RI = 62%; range, 51.6% (Germany) to 63.7% (Spain)] followed by frequency of grade 3/4 TRAEs (RI = 27%). Among treatment profiles, the chemotherapy plus immune checkpoint inhibitor maintenance therapy profile had the largest preference share [51%; range, 38% (Italy) to 56% (UK)]. Four latent classes of clinicians were identified (N = 469), with different treatment profile preferences: survival class (30.1%), trade-off class (22.4%), no strong preference class (40.9%), and aggressive treatment class (6.6%). OS was not the most important attribute for 30.0% of clinicians. While average sample results were consistent with those of prior DCEs, this study found heterogeneity in clinician preferences within and across countries, highlighting the diversity in clinician decision making in oncology.

Comparison of three recent discrete stochastic inversion methods and influence of the prior choice

Groundwater flow depends on subsurface heterogeneity, which often calls for categorical fields to represent different geological facies. The knowledge about subsurface is however limited and often provided indirectly by state variables, such as hydraulic heads of contaminant concentrations. In such cases, solving a categorical inverse problem is an important step in subsurface modeling. In this work, we present and compare three recent inverse frameworks: Posterior Population Expansion (PoPEx), Ensemble Smoother with Multiple Data Assimilation (ESMDA), and DREAM-ZS (a Markov chain Monte Carlo sampler). PoPEx and ESDMA are used with Multiple-point statistics (MPS) as geostatistical engines, and DREAM-ZS is used with a Wasserstein generative adversarial network (WGAN). The three inversion methods are tested on a synthetic example of a pumping test in a fluvial channelized aquifer. Moreover, the inverse problem is solved three times with each method, each time using a different training image to check the performance of the methods with different geological priors. To assess the quality of the results, we propose a framework based on continuous ranked probability score (CRPS), which compares single true values with predictive distributions. All methods performed well when using the training image used to create the reference, but their performances were degraded with the alternative training images. PoPEx produced the least geological artifacts but presented a rather slow convergence. ESMDA showed initially a very fast convergence which reaches a plateau, contrary to the remaining methods. DREAM-ZS was overly confident in placing some incorrect geological features but outperformed the other methods in terms of convergence.

LkGraphene Nano-Derivatives in Pharmaceuticals and Biomedical Advancements: A Comprehensive Review

Abstract: The two-dimensional structure of graphene has a flat single layer of carbon molecules having a honeycomb crystal lattice configuration. Graphene possesses typical physicochemical characteristics such as elevated conductivity, wide-ranging surface area, good biocompatibility, and excellent mechanical properties. Due to their exceptional properties, graphene derivatives have significant implementations in many fields like electronics, environmental, chemical, pharmaceutical, and others. With its distinctive formation and biological characteristics, pharmaceutical and biomedical applications of graphene have gained the impressive interest of researchers and scientists over the past few years. The exceptional properties of graphene, such as its larger surface area, which is four times greater than other nanoparticles, represented it as a prior choice for drug delivery. Graphene derivatives are monolayer graphene, bilayer graphene, reduced Graphene Oxide (rGO), and Graphene Oxide (GO). This review focused on different pharmaceutical applications and the part of the progress made by different researchers on graphene and its derivatives in the distinct field of interest, like in the delivery of drugs, cancer therapy, gene delivery, antibacterial effect, biosensing, bioimaging, tissue engineering, and others.

Frequentist and Bayesian tolerance intervals for setting specification limits for left-censored gamma distributed drug quality attributes.

Tolerance intervals from quality attribute measurements are used to establish specification limits for drug products. Some attribute measurements may be below the reporting limits, that is, left-censored data. When data has a long, right-skew tail, a gamma distribution may be applicable. This paper compares maximum likelihood estimation (MLE) and Bayesian methods to estimate shape and scale parameters of censored gamma distributions and to calculate tolerance intervals under varying sample sizes and extents of censoring. The noninformative reference prior and the maximal data information prior (MDIP) are used to compare the impact of prior choice. Metrics used are bias and root mean square error for the parameter estimation and average length and confidence coefficient for the tolerance interval evaluation. It will be shown that Bayesian method using a reference prior overall performs better than MLE for the scenarios evaluated. When sample size is small, the Bayesian method using MDIP yields conservatively too wide tolerance intervals that are unsuitable basis for specification setting. The metrics for all methods worsened with increasing extent of censoring but improved with increasing sample size, as expected. This study demonstrates that although MLE is relatively simple and available in user-friendly statistical software, it falls short in accurately and precisely producing tolerance limits that maintain the stated confidence depending on the scenario. The Bayesian method using noninformative prior, even though computationally intensive and requires considerable statistical programming, produces tolerance limits which are practically useful for specification setting. Real-world examples are provided to illustrate the findings from the simulation study.

A Dithiin-Linked Covalent Organic Polymer for Ultrahigh Capacity Half-Cell and Symmetric Full-Cell Sodium-Ion Batteries.

Sodium ion-batteries (SIBs) are considered as a class of promising alternatives to lithium-ion batteries (LIBs) to overcome their drawbacks of limited sources and safety problems. However, the lack of high-performance electrode materials hinders the wide-range commercialization of SIBs. Comparing to inorganic counterparts, organic electrode materials, which are benefitted from flexibly designable structures, low cost, environmental friendliness, and high theoretical gravimetric capacities, should be a prior choice. Here, a covalent organic polymer (COP) based material (denoted as CityU-9) is designed and synthesized by integrating multiple redox motifs (benzoquinone and thioether), improved conductivity (sulfur induction), and intrinsic insolubility (rigid skeleton). The half-cell SIBs exhibit ultrahigh specific capacity of 1009mAhg-1 and nearly no capacity drop after 650 cycles. The first all-COP symmetric full-cell shows high specific capacity of 90mAhg-1 and excellent rate capability. This work can extend the selection of redox-active moieties and provide a rational design strategy of high-performance novel organic electrode materials.

Investigating the asymmetric effect of income inequality on financial fragility in South Africa and selected emerging markets: a Bayesian approach with hierarchical priors

PurposeThis study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.Design/methodology/approachIn this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.FindingsThe results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.Originality/valueThe findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.

What affects auditor choice in emerging markets? New evidence on the role of cultural distance

PurposeThis paper aims to examine whether the cultural distance between an international audit firm and target audit clients in emerging countries is associated with auditor choice decisions.Design/methodology/approachBased on a sample of 104,699 firm-year observations from 20 countries over 2009–2020, logit regression analysis is used to investigate the research questions.FindingsThe authors find strong evidence that cultural distance affects the auditor selection decision. The results suggest Big N auditors are more likely to be chosen by target audit clients in emerging countries with less cultural distance. In other words, target audit clients in emerging countries prefer to choose international audit firms whose cultural characteristics are similar. Moreover, results from two-stage least squares regression further suggest that the observed effect of cultural distance on auditor choice is unlikely to be driven by potential endogeneity.Research limitations/implicationsThe auditor choice is limited to companies hiring Big N auditors; the authors exclude any switches to non-Big N auditors or switches between Big N auditors. The study also suffers from the concerns about methodological and conceptual criticism that most studies about national culture have to deal with. Finally, through this paper, the authors carry out the auditor selection process from the target audit clients’ side; the authors do not discuss the supply side of the process.Originality/valueThe authors contribute to the audit choice literature by providing evidence that the cultural distance between the countries of audit firms and target audit clients plays a role in the auditor choice decision. The study complements the prior auditor choice literature, focusing primarily on Western economies, by structuring the sample scope to emerging market economies.

Nucleotide Substitution Model Selection Is Not Necessary for Bayesian Inference of Phylogeny With Well-Behaved Priors.

Model selection aims to choose the most adequate model for the statistical analysis at hand. The model must be complex enough to capture the complexity of the data but should be simple enough not to overfit. In phylogenetics, the most common model selection scenario concerns selecting an adequate substitution and partition model for sequence evolution to infer a phylogenetic tree. Previously, several studies showed that substitution model under-parameterization can bias phylogenetic studies. Here, we explored the impact of substitution model over-parameterization in a Bayesian statistical framework. We performed simulations under the simplest substitution model, the Jukes-Cantor model, and compare posterior estimates of phylogenetic tree topologies and tree length under the true model to the most complex model, the $\text{GTR}+\Gamma+\text{I}$ substitution model, including over-splitting the data into additional subsets (i.e., applying partitioned models). We explored 4 choices of prior distributions: the default substitution model priors of MrBayes, BEAST2, and RevBayes and a newly devised prior choice (Tame). Our results show that Bayesian inference of phylogeny is robust to substitution model over-parameterization and over-partitioning but only under our new prior settings. All 3 current default priors introduced biases for the estimated tree length. We conclude that substitution and partition model selection are superfluous steps in Bayesian phylogenetic inference pipelines if well-behaved prior distributions are applied and more effort should focus on more complex and biologically realistic substitution models.

Flight Delay Prediction With Priority Information of Weather and Non-Weather Features

Flight delay prediction is a major topic in intelligent airport management systems, which emphasizes the use of historical data and potential features to estimate whether a future flight will delay. However, many factors affect flight delays, and these factors can be categorized into weather features (e.g., temperature, humidity, and wind speed) and non-weather features (day-of-month, day-of-week, scheduled departure and arrival time). Moreover, the impacts of weather and non-weather factors on flight delays are different. Weather features play a more important role in adverse weather conditions and are the main reason for long flight delays. When the weather condition changes from severe to non-severe, non-weather features are the main reason for flight delays, and the caused delays are relatively short. Such different impacts on flight delays raise a strong need for considering the priority information of weather and non-weather features in flight delay prediction. In this paper, we design a variant of the Random Forest model to consider the priority information of weather and non-weather features to predict flight delays. A clustering algorithm-based analysis approach is developed to assess the impact of weather and non-weather features on flight delays and draw conclusions on the priority information of weather and non-weather features. A probability sampling method is embedded in the Random Forest at the feature selection stage to perform a prior choice for weather and non-weather features to help select the key influential features. Experiments are carried out on U.S. domestic flights in July 2018, and the comparison results demonstrate that the proposed model can significantly increase flight delay prediction accuracy.

Choice between food and cocaine or fentanyl reinforcers under fixed and variable schedules in female and male rhesus monkeys.

Illicit drugs may be unpredictable in terms of the time and effort required to obtain them, and this can be modeled with variable- (VR) vs. fixed-ratio (FR) schedules. In a recent experiment (Zamarripa et al. 2023), the potency of cocaine to maintain choice was greatest under a VR (compared with a FR) when food was available under a FR schedule. The goal of the current study was to extend prior choice results with VR vs. FR schedules to a more efficient procedure with cocaine or fentanyl vs. food. Furthermore, the FR schedule of food delivery was manipulated to determine whether increased drug choice under a VR (compared with a FR) schedule depends on the size of the schedule of nondrug reinforcement. Adult female (n = 2) and male (n = 4) monkeys chose between cocaine (0-30µg/kg/injection) or fentanyl (0-1.0µg/kg/injection) and food (2 pellets/delivery) under a 5-component procedure. In different conditions, food was available under a FR 25, 50, or 100 and cocaine or fentanyl were available under FR or VR 100 schedules. Cocaine's potency to maintain choice was greatest under a VR 100 (compared with FR 100) when food was available under a FR 50 or 100, and fentanyl's potency to maintain choice was generally greatest under a VR 100 (compared with FR 100) when food was available under a FR 25 or 100. However, outcomes between FR and VR schedules with fentanyl were less robust compared with cocaine. Variability in the time and effort required to obtain illicit drugs could contribute to excessive allocation of behavior toward drug use at the expense of more predictable nondrug alternatives, supporting treatment or policies aimed at making drug access more predictable through agonist medications or a safe supply. The impact of variable requirements on drug choice may be reduced if nondrug reinforcers are relatively less costly, supporting the use of low-cost reinforcers in behavioral therapies like contingency management.

Species boundaries to the limit: Integrating species delimitation methods is critical to avoid taxonomic inflation in the case of the Hajar banded ground gecko (Trachydactylus hajarensis)

With the advent of molecular phylogenetics and the implementation of Multispecies Coalescent-based (MSC) species delimitation methods (SDM), the number of taxonomic studies unveiling and describing cryptic sibling species has greatly increased. However, speciation between early divergent lineages is often defined without evaluating population structure or gene flow, which can lead to false claims of species status and, subsequently, taxonomic inflation. In this study, we focus on the intriguing case of the Arabian gecko Trachydactylus hajarensis (Squamata: Gekkonidae). We generated mitochondrial data (12S rDNA) and genome-wide SNP data (ddRADseq) for 52 specimens to determine phylogenomic relationships, population structure and genetic diversity within this species. Then, we applied a set of different SDMs to evaluate several competing species hypotheses through the MSC. Results show that T. hajarensis is comprised by three well-defined population lineages, two of them in the Hajar Mountains of eastern Arabia, and one in Masirah Island, on the southeastern coast of Oman. Strong mito-nuclear discordances were found between populations inhabiting the Hajar Mountains, but we did not find evidence of current gene flow between them. Surprisingly, discordances in species tree topology were found when different downsampled datasets were used, and especially when linking population sizes, a commonly implemented feature in species tree reconstruction with genomic data. Different SDMs yielded different results, supporting from four species within the group, to T. hajarensis being a single species. With such contrasting results we suggest caution before splitting T. hajarensis. Overall, this study highlights the importance of sample and prior choice and the integration of several SDMs to not incur into taxonomic inflation, providing a set of already available tools to assess population structure, genetic diversity, and SDMs before describing new species.

Specific innovativeness, digital entrepreneurship, and female entrepreneurs

ABSTRACT How does digital entrepreneurship help innovative, women entrepreneurs in emerging economies? Using strategic choice theory, we predict that specific innovativeness improves new venture performance, and that digital entrepreneurship enables innovativeness and increases its effect on performance. We also predict that innovative female entrepreneurs benefit more than male ones from a high degree of digital entrepreneurship. We confirm our hypotheses using primary data of entrepreneurs from Ghana (N = 199) and Vietnam (N = 223). We also conducted a series of robustness tests to validate our findings. We conclude that strategic choices among new ventures yield best results if bounded by prior strategic choice and specific innovativeness, enabled by a high degree of digital entrepreneurship, and enacted by women.

Distributed processing for value-based choice by prelimbic circuits targeting anterior-posterior dorsal striatal subregions in male mice

Fronto-striatal circuits have been implicated in cognitive control of behavioral output for social and appetitive rewards. The functional diversity of prefrontal cortical populations is strongly dependent on their synaptic targets, with control of motor output mediated by connectivity to dorsal striatum. Despite evidence for functional diversity along the anterior-posterior striatal axis, it is unclear how distinct fronto-striatal sub-circuits support value-based choice. Here we found segregated prefrontal populations defined by anterior/posterior dorsomedial striatal target. During a feedback-based 2-alternative choice task, single-photon imaging revealed circuit-specific representations of task-relevant information with prelimbic neurons targeting anterior DMS (PL::A-DMS) robustly modulated during choices and negative outcomes, while prelimbic neurons targeting posterior DMS (PL::P-DMS) encoded internal representations of value and positive outcomes contingent on prior choice. Consistent with this distributed coding, optogenetic inhibition of PL::A-DMS circuits strongly impacted choice monitoring and responses to negative outcomes while inhibition of PL::P-DMS impaired task engagement and strategies following positive outcomes. Together our data uncover PL populations engaged in distributed processing for value-based choice.

Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (with Discussion)

Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks. However, due to the flexibility of these models, the consequences of prior choices can be opaque. And so prior specification can be relatively difficult. At the same time, prior choice can have a substantial effect on posterior inferences. Thus, considerations of robustness need to go hand in hand with nonparametric modeling. In the current paper, we tackle this challenge by exploiting the fact that variational Bayesian methods, in addition to having computational advantages in fitting complex nonparametric models, also yield sensitivities with respect to parametric and nonparametric aspects of Bayesian models. In particular, we demonstrate how to assess the sensitivity of conclusions to the choice of concentration parameter and stick-breaking distribution for inferences under Dirichlet process mixtures and related mixture models. We provide both theoretical and empirical support for our variational approach to Bayesian sensitivity analysis.

Prior choice and data requirements of Bayesian multivariate hierarchical models fit to tag-recovery data: The need for power analyses.

Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect nonzero correlations has rarely been evaluated, and these few studies have not focused on tag-recovery data, which is a common data type. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (Anas platyrhychos) tag-recovery data set and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery data sets than total individuals tagged. Different priors led to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (-1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.

Generalized fiducial factor: An alternative to the Bayes factor for forensic identification of source problems

One formulation of forensic identification of source problems is to determine the source of trace evidence, for instance, glass fragments found on a suspect for a crime. The current state of the science is to compute a Bayes factor comparing the marginal distribution of measurements of trace evidence under two competing propositions for whether or not the unknown source evidence originated from a specific source. The obvious problem with such an approach is the ability to tailor the prior distributions (placed on the features/parameters of the statistical model for the measurements of trace evidence) in favor of the defense or prosecution which is further complicated by the fact that the typical number of measurements of trace evidence is typically sufficiently small that prior choice/specification has a strong influence on the value of the Bayes factor. To remedy this problem of prior specification and choice, we develop an alternative to the Bayes factor, within the framework of generalized fiducial inference, that we term a generalized fiducial factor. Furthermore, we demonstrate empirically, on synthetic and real Netherlands Forensic Institute casework data, deficiencies in Bayes factor and classical/frequentist likelihood ratio approaches.