Patterns Of Bias Research Articles

Seasonal biases in fluorescence-estimated chlorophyll-a derived from biogeochemical profiling floats

Marine phytoplankton biomass and chlorophyll-a concentration are often estimated from pigment fluorescence measurements, which have become routine despite known variability in the fluorescent response for a given amount of chlorophyll-a. Here, we present a near-global, monthly climatology of chlorophyll-a fluorescence measurements from profiling floats combined with ocean color satellite estimates of chlorophyll-a concentration to illuminate seasonal biases in the fluorescent response and expand upon previously observed regional patterns in this bias. Global biases span over an order of magnitude, and can vary seasonally by a factor of 10. An independent estimate of chlorophyll-a from light attenuation shows similar global patterns in the chlorophyll-fluorescence bias when compared to biases derived from satellite estimates. Without accounting for these biases, studies or models using fluorescence-estimated chlorophyll-a will inherit the seasonal and regional biases described here.

Long-run measurement of income-related inequalities in health care under universal coverage: evidence from longitudinal analysis in Korea

BackgroundMany countries have sought to promote well-being for their entire populations through the implementation of universal health coverage (UHC). To identify the extent to which UHC has been attained, it is necessary to evaluate equity of access to use of needed care and the cost burden of health services for the country’s entire population. This study considers income-related inequalities in health care utilization and spending in a long-term perspective for the case of the Republic of Korea.MethodsExploiting longitudinal data from a nationally representative health survey from 2008 to 2018, this study investigates how income-related inequalities in health care in Korea have varied over time and examines the extent to which need and non-need factors contribute those inequalities, using an in‐depth decomposition analysis, allowing for heterogeneous responses across income groups.ResultsThe empirical results show that overall health care utilization is disproportionately concentrated among the poor over both the short and long run. Income-group differences and non-need determinants, such as marital status and private health insurance, make larger pro-poor contributions to inequality in inpatient care use, while chronic disease prevalence greatly pushes outpatient care utilization in a pro-poor direction. The results regarding inpatient care expenses indicate a similar pattern of pro-poor bias. Long-run inequality favors the better-off in terms of outpatient care expenses, where the contribution of income-group differences has the largest impact.ConclusionMy findings suggest that it is important for health care policy in Korea to focus on improvements in the health status and well-being of low-income groups, as poor people are likely to be in poorer health. Non-need contributors could worsen pro-poor inequalities if the economic disparity across households were to increase due to the demographic transition. Higher spending on inpatient care may be a heavier financial burden for low-income people. Additional supportive measures should be provided to prevent them from suffering economic hardship. By contrast, people in high-income groups may spend most on costly services in outpatient care, including uninsured services, with the help of private health insurance. Nevertheless, the expansion of income disparity should be alleviated even from a health care policy perspective.

The perceptual and mnemonic effects of ensemble representation on individual size representation.

Our visual world consists of multiple objects, necessitating the identification of individual objects. Nevertheless, the representation of visual objects often exerts influence on each other. Even when we selectively attend to a subset of visual objects, the representations of surrounding items are encoded and influence the processing of the attended item(s). However, it remains unclear whether the effect of group ensemble representation on individual item representation occurs at the perceptual encoding phase, during the memory maintenance period, or both. Therefore, the current study conducted visual psychophysics experiments to investigate the contributions of perceptual and mnemonic bias on the observed effect of ensemble representation on individual size representation. Across five experiments, we found a consistent pattern of repulsive ensemble bias, such that the size of an individual target circle was consistently reported to be smaller than it actually was when presented alongside other circles with larger mean size, and vice versa. There was a perceptual component to the bias, but mnemonic factors also influenced its magnitude. Specifically, the repulsion bias was strongest with a short retention period (0-50 ms), then reduced within a second to a weaker magnitude that remained stable for a longer retention period (5,000 ms). Such patterns of results persisted when we facilitated the processing of ensemble representation by increasing the set size (Experiment 1B) or post-cueing the target circle so that attention was distributed across all items (Experiment 2B).

Attentional bias to social media stimuli ismoderated by fear of missing out among problematic social media users.

Previous evidence has indicated that problematic social media use (PSMU) is characterized by an attentional bias to social media icons (such as Facebook icons), but not to social webpages (such as Facebook webpages). They suggest that there may be other factors influencing attentional bias like fear of missing out (FoMO). But it remains unclear how FoMO moderates attentional bias in PSMU. This study aims to investigate whether PSMU show attentional bias for stimuli associated with social media, and how FoMO moderates on attentional bias among PSMU through experimental methods. Based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study explored mechanisms of attentional bias to social media icons (such as WeChat) related to PSMU and further examined the role of FoMO in this relationship. Specifically, attentional bias patterns to social media icons of 62 participants (31 PSMU and 31 control group) were explored during a dot-probe paradigm combined with eye-tracking in Experiment 1, and attentional bias patterns to social media icons of another 61 individuals with PSMU with different FoMO levels was explored during a dot-probe paradigm combined with eye-tracking in Experiment 2. Results revealed that individuals with PSMU had an attentional bias toward social media icons, demonstrated by attentional maintenance, and such bias such bias was moderated by FoMO negatively, demonstrated by attentional vigilance and maintenance in PSMU/high FoMO. These results suggest that attentional bias is a common mechanism associated with PSMU, and FoMO is a key factor on the development of PSMU.

Mediation-adjusted multivariable Mendelian randomisation study identified novel metabolites related to mental health

BackgroundFrom the pathway perspective, metabolites have the potential to improve knowledge about the aetiology of psychiatric diseases. Previous studies suggested a link between specific blood metabolites and mental disorders, but...

Don't jump the gun quite yet: aiming for the true target in plant neurobiology research.

In their recent paper, Kingsland and Taiz argue that proponents of plant intelligence and plant neurobiology misuse historical sources to support their claims, suggesting a pattern of bias. They critique the reliance on subjective judgments and the systematic misuse of past literature by notable scientists. This response addresses their criticisms while adhering to Rapoport's rules to foster constructive academic dialogue. We emphasize the importance of evidence-based research and highlight areas of agreement, including the fallacy of appealing to authority and the necessity for more robust empirical evidence. However, we also challenge their selective citation practices and argue that their narrative itself is subject to the same criticisms they levy. By examining recent works and pointing out overlooked rebuttals, we aim to clarify misconceptions and advocate for a more nuanced understanding of plant intelligence research. This dialogue underscores the need for rigorous, respectful scientific discourse to advance the field.

Stochastic Publics: The Emergence and Ethics of AI-Generated Publics in Technical Communication

The concept of a public—a group of strangers drawn together through their mutual attention to a text—has historically been tied to the notion of human intentionality. The recent popularization of artificial intelligence (AI) large language models (such as ChatGPT) destabilizes this connection. When large language models generate text, they may inadvertently form stochastic publics—groups pulled together through the randomization of biased data patterns drawn from AI training material. This exploratory study draws on a three-phase dialogue with OpenAI's ChatGPT 4 to identify the risks of stochastic publics and suggest human-originated interventions grounded in feminist care ethics.

Genetic analysis of tomato brown rugose fruit virus reveals evolutionary adaptation and codon usage bias patterns

Tomato brown rugose fruit virus (ToBRFV) poses a significant threat to tomato production worldwide, prompting extensive research into its genetic diversity, evolutionary dynamics, and adaptive strategies. In this study, we conducted a comprehensive analysis of ToBRFV at the codon level, focusing on codon usage bias, selection pressures, and evolutionary patterns across multiple genes. Our analysis revealed distinct patterns of codon usage bias and selection pressures within the ToBRFV genome, with varying levels of genetic diversity and evolutionary constraints among different genes. We observed a transition/transversion bias of 2.07 across the entire ToBRFV genome, with the movement protein (MP) gene exhibiting the highest transition/transversion bias and SNP density, suggesting potential evolutionary pressures or a higher mutation rate in this gene. Furthermore, our study identified episodic positive selection primarily in the MP gene, highlighting specific codons subject to adaptive changes in response to host immune pressures or environmental factors. Comparative analysis of codon usage bias in the coat protein (CP) and RNA-dependent RNA polymerase (RdRp) genes revealed gene-specific patterns reflecting functional constraints and adaptation to the host's translational machinery. Our findings provide valuable insights into the molecular mechanisms driving ToBRFV evolution and adaptation, with implications for understanding viral pathogenesis, host-virus interactions, and the development of control strategies. Future research directions include further elucidating the functional significance of codon usage biases, exploring the role of episodic positive selection in viral adaptation, and leveraging these insights to inform the development of effective antiviral strategies and crop protection measures.

Genetic Sex and Origin Identification Suggests Differential Migration of Male and Female Atlantic Bluefin Tuna (Thunnus thynnus) in the Northeast Atlantic

ABSTRACTKnowledge about sex‐specific difference in life‐history traits—like growth, mortality, or behavior—is of key importance for management and conservation as these parameters are essential for predictive modeling of population sustainability. We applied a newly developed molecular sex identification method, in combination with a SNP (single nucleotide polymorphism) panel for inferring the population of origin, for more than 300 large Atlantic bluefin tuna (ABFT) collected over several years from newly reclaimed feeding grounds in the Northeast Atlantic. The vast majority (95%) of individuals were genetically assigned to the eastern Atlantic population, which migrates between spawning grounds in the Mediterranean and feeding grounds in the Northeast Atlantic. We found a consistent pattern of a male bias among the eastern Atlantic individuals, with a 4‐year mean of 63% males (59%–65%). Males were most prominent within the smallest (< 230 cm) and largest (> 250 cm) length classes, while the sex ratio was close to 1:1 for intermediate sizes (230–250 cm). The results from this new, widely applicable, and noninvasive approach suggests differential occupancy or migration timing of ABFT males and females, which cannot be explained alone by sex‐specific differences in growth. Our findings are corroborated by previous traditional studies of sex ratios in dead ABFT from the Atlantic, the Mediterranean, and the Gulf of Mexico. In concert with observed differences in growth and mortality rates between the sexes, these findings should be recognized in order to sustainably manage the resource, maintain productivity, and conserve diversity within the species.

Codon Usage Analysis Reveals Distinct Evolutionary Patterns and Host Adaptation Strategies in Duck Hepatitis Virus 1 (DHV-1) Phylogroups.

Duck hepatitis virus 1 (DHV-1) is a major threat to the global poultry industry, causing significant economic losses due to high mortality rates in young ducklings. To better understand the evolution and host adaptation strategies of DHV-1, we conducted a comprehensive codon usage analysis of DHV-1 genomes. Our phylogenetic analysis revealed three well-supported DHV-1 phylogroups (Ia, Ib, and II) with distinct genetic diversity patterns. Comparative analyses of the codon usage bias and dinucleotide abundance uncovered a strong preference for A/U-ended codons and a biased pattern of dinucleotide usage in the DHV-1 genome, with CG dinucleotides being extremely underrepresented. Effective number of codons (ENC) analysis indicated a low codon usage bias in the DHV-1 ORF sequences, suggesting adaptation to host codon usage preferences. PR2 bias, ENC plot, and neutrality analyses revealed that both mutation pressure and natural selection influence the codon usage patterns of DHV-1. Notably, the three DHV-1 phylogroups exhibited distinct evolutionary trends, with phylogroups Ia and Ib showing evidence of neutral evolution accompanied by selective pressure, while the phylogroup II evolution was primarily driven by random genetic drift. Comparative analysis of the codon usage indices (CAI, RCDI, and SiD) among the phylogroups highlighted significant differences between subgroups Ia and Ib, suggesting distinct evolutionary pressures or adaptations influencing their codon usage. These findings contribute to our understanding of DHV-1 evolution and host adaptation, with potential implications for the development of effective control measures and vaccines.

ChatGPT as Research Scientist: Probing GPT’s capabilities as a Research Librarian, Research Ethicist, Data Generator, and Data Predictor

How good a research scientist is ChatGPT? We systematically probed the capabilities of GPT-3.5 and GPT-4 across four central components of the scientific process: as a Research Librarian, Research Ethicist, Data Generator, and Novel Data Predictor, using psychological science as a testing field. In Study 1 (Research Librarian), unlike human researchers, GPT-3.5 and GPT-4 hallucinated, authoritatively generating fictional references 36.0% and 5.4% of the time, respectively, although GPT-4 exhibited an evolving capacity to acknowledge its fictions. In Study 2 (Research Ethicist), GPT-4 (though not GPT-3.5) proved capable of detecting violations like p-hacking in fictional research protocols, correcting 88.6% of blatantly presented issues, and 72.6% of subtly presented issues. In Study 3 (Data Generator), both models consistently replicated patterns of cultural bias previously discovered in large language corpora, indicating that ChatGPT can simulate known results, an antecedent to usefulness for both data generation and skills like hypothesis generation. Contrastingly, in Study 4 (Novel Data Predictor), neither model was successful at predicting new results absent in their training data, and neither appeared to leverage substantially new information when predicting more vs. less novel outcomes. Together, these results suggest that GPT is a flawed but rapidly improving librarian, a decent research ethicist already, capable of data generation in simple domains with known characteristics but poor at predicting novel patterns of empirical data to aid future experimentation.

Near‐surface wind profiles from numerical model predictions. Part II: Verifications against Australia‐wide surface wind observations

AbstractThe new scheme for deriving the near‐surface wind profiles discussed in Ma (in review) is applied to an Australian Bureau of Meteorology operational convective scale model over various domains. Both the new and conventional schemes' diagnostic 10‐m winds are then verified against Australia‐wide automatic weather station observations. Analyses of bulk statistics reveal that the new scheme's 10‐m wind forecasts have generally better accuracy than the current conventional scheme with a consistent reduction of biases over all domains. A widely recognised diurnal bias pattern of surface wind speed over the land is substantially reduced, and the inclusion of Ekman spiral effect on the 10‐m wind marginally improves statistics of the wind direction during the nighttime. The new scheme introduces no systemic bias, given the histogram of a bulk mean bias is analogiased to a Gaussian distribution, and moves the distribution of diagnostic wind speed closer to that observed.

Drivers of the mean biases of the tropical atmospheric circulation in a moist static energy framework

In this study, we apply the moist static energy for first baroclinic mode (MSEB) model to examine the drivers of the mean tropical atmospheric circulation biases over oceanic regions. The model diagnoses the vertical motion in an air column of the tropical regions based on net energy heat flux and advection of moisture or heat into the air column in relation to the stability of the air column due to the gradients in moist static energy. Analysis of Coupled Model Intercomparison Project (CMIP) and Atmospheric Model Intercomparison Project (AMIP) simulations helped to identified errors intrinsic to the atmospheric models or errors due to atmosphere–ocean coupling process. Despite some limitations of the MSEB model, our multi-model mean analysis over the entire tropical ocean reveals that the primary drivers of the tropical circulation biases mostly result from intrinsic atmospheric model errors in top of the atmosphere longwave radiation and surface latent heats fluxes, suggesting a link to biases in the hydrological cycle. Oceanic coupling significantly enhanced some of the biases. Biases in the advection of moist static energy also play an important role, while biases in the gross moist stability profiles play only a minor role. Further, we examine the inter-model variations in four main regional large-scale biases (double-ITCZ, Pacific cold tongue, southward shift of ITCZ over the Atlantic, and dipole bias over the Indian Ocean). The analysis suggests that regional bias patterns across general circulation models are primarily driven by coupling errors, except for the bias in the Indian Ocean, which is intrinsic to the atmospheric model but amplified by coupling. Notably, longwave radiation biases at the top of the atmosphere are prevalent among the four bias patterns, as well as biases in moisture advection over the Atlantic. Our results underscore the significant role of net longwave radiation at the top of the atmosphere, an aspect not sufficiently emphasized in previous studies.

Evaluating predictive equations for energy requirements throughout breast cancer trajectory: A comparative study

Background & AimsAccurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors). MethodsData from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N=46) or a whole-room indirect calorimeter (WRIC-REE N=44). Agreement between methods was evaluated using Bland-Altman and Lin's concordance coefficient correlation (Lin's CCC). Results90 participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼ 300kcal for both methods), including the most commonly used ones, such as Harris-Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin's CCC analysis (< 0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer. ConclusionMost equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation.

First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).

Mitigating implicit and explicit bias in structured data without sacrificing accuracy in pattern classification

AbstractUsing biased data to train Artificial Intelligence (AI) algorithms will lead to biased decisions, discriminating against certain groups or individuals. Bias can be explicit (one or several protected features directly influence the decisions) or implicit (one or several protected features indirectly influence the decisions). Unsurprisingly, biased patterns are difficult to detect and mitigate. This paper investigates the extent to which explicit and implicit against one or more protected features in structured classification data sets can be mitigated simultaneously while retaining the data’s discriminatory power. The main contribution of this paper concerns an optimization-based bias mitigation method that reweights the training instances. The algorithm operates with numerical and nominal data and can mitigate implicit and explicit bias against several protected features simultaneously. The trade-off between bias mitigation and accuracy loss can be controlled using parameters in the objective function. The numerical simulations using real-world data sets show a reduction of up to 77% of implicit bias and a complete removal of explicit bias against protected features at no cost of accuracy of a wrapper classifier trained on the data. Overall, the proposed method outperforms the state-of-the-art bias mitigation methods for the selected data sets.

Patterns in Genome-Wide Codon Usage Bias in Representative Species of Lycophytes and Ferns.

The latest research shows that ferns and lycophytes have distinct evolutionary lineages. The codon usage patterns of lycophytes and ferns have not yet been documented. To investigate the gene expression profiles across various plant lineages with respect to codon usage, analyze the disparities and determinants of gene evolution in primitive plant species, and identify appropriate exogenous gene expression platforms, the whole-genome sequences of four distinct species were retrieved from the NCBI database. The findings indicated that Ceratopteris richardii, Adiantum capillus-veneris, and Selaginella moellendorffii exhibited an elevated A/U content in their codon base composition and a tendency to end with A/U. Additionally, S. capillus-veneris had more C/G in its codons and a tendency to end with C/G. The ENC values derived from both ENC-plot and ENC-ratio analyses deviated significantly from the standard curves, suggesting that the codon usage preferences of these four species were primarily influenced by genetic mutations and natural selection, with natural selection exerting a more prominent influence. This finding was further supported by PR2-Plot, neutrality plot analysis, and COA. A combination of RSCU and ENC values was used as a reference criterion to rank the codons and further identify the optimal codons. The study identified 24 high-frequency codons in C. richardii, A. capillus-veneris, and Diphasiastrum complanatum, with no shared optimal codons among the four species. Arabidopsis thaliana and Ginkgo biloba exhibited similar codon preferences to the three species, except for S. moellendorffii. This research offers a theoretical framework at the genomic codon level for investigating the phylogenetic relationships between lycophytes and ferns, shedding light on gene codon optimization and its implications for genetic engineering in breeding.

Measurement Biases in Ocean Temperature Profiles from Marine Mammal Dataloggers

Abstract The study focuses on biases in ocean temperature profiles obtained by means of Satellite Relay Data Loggers (SRDL recorders) and time–depth recorder (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based conductivity–temperature–depth (CTD) profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of Valeport 3 (VP3) and conductivity–temperature–fluorescence (CTF) sensors, the bias is negative except for the layer 100–200 m. The vertical bias structure suggests a link to the upper-ocean thermal structure within the upper 200-m layer. Accounting for a time lag which might remain in the postprocessed data reduces the bias variability throughout the water column. Below 200-m depth, the bias remains negative with the overall mean of −0.027° ± 0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08°–0.14°C below 100 m primarily due to the systematic error in pressure. Significance Statement The purpose of this work is to improve the consistency of the data from the specific instrumentation type used to measure ocean water temperature, namely, the data from miniature temperature sensors attached to marine mammals. As mammals dive during their route to and from their feeding areas, these sensors measure water temperature and dataloggers send the measured temperature data to oceanographic data centers via satellites as soon as the mammals return to the sea surface. We have shown that these data exhibit small systematic instrumental errors and suggested the respective corrections. Taking these corrections into account is important for the assessment of the ocean climate change.

Identification and Genomic Characterization of Two Novel Hepatoviruses in Shrews from Yunnan Province, China.

Hepatitis A virus (HAV), a member of the genus Hepatovirus (Picornaviridae HepV), remains a significant viral pathogen, frequently causing enterically transmitted hepatitis worldwide. In this study, we conducted an epidemiological survey of HepVs carried by small terrestrial mammals in the wild in Yunnan Province, China. Utilizing HepV-specific broad-spectrum RT-PCR, next-generation sequencing (NGS), and QNome nanopore sequencing (QNS) techniques, we identified and characterized two novel HepVs provisionally named EpMa-HAV and EpLe-HAV, discovered in the long-tailed mountain shrew (Episoriculus macrurus) and long-tailed brown-toothed shrew (Episoriculus leucops), respectively. Our sequence and phylogenetic analyses of EpMa-HAV and EpLe-HAV indicated that they belong to the species Hepatovirus I (HepV-I) clade II, also known as the Chinese shrew HepV clade. Notably, the codon usage bias pattern of novel shrew HepVs is consistent with that of previously identified Chinese shrew HepV. Furthermore, our structural analysis demonstrated that shrew HepVs differ from other mammalian HepVs in RNA secondary structure and exhibit variances in key protein sites. Overall, the discovery of two novel HepVs in shrews expands the host range of HepV and underscores the existence of genetically diverse animal homologs of human HAV within the genus HepV.

Testing the validity of online psychophysical measurement of body image perception.

This body image study tests the viability of transferring a complex psychophysical paradigm from a controlled in-person laboratory task to an online environment. 172 female participants made online judgements about their own body size when viewing images of computer-generated female bodies presented in either in front-view or at 45-degrees in a method of adjustment (MOA) paradigm. The results of these judgements were then compared to the results of two laboratory-based studies (with 96 and 40 female participants respectively) to establish three key findings. Firstly, the results show that the accuracy of online and in-lab estimates of body size are comparable, secondly that the same patterns of visual biases in judgements are shown both in-lab and online, and thirdly online data shows the same view-orientation advantage in accuracy in body size judgements as the laboratory studies. Thus, this study suggests that that online sampling potentially represents a rapid and accurate way of collecting reliable complex behavioural and perceptual data from a more diverse range of participants than is normally sampled in laboratory-based studies. It also offers the potential for designing stratified sampling strategies to construct a truly representative sample of a target population.