More than just changeability: Specific factors are used to explain the changeability of personality traits.

Implicit theories and psychological essentialism are valuable frameworks used to model how changeable many traits are perceived to be. These frameworks, however, characterise changeability as a broad and generalised construct and do not fully capture the nuance and specificity involved in personality changeability. We therefore sought to deconstruct implicit theories about changeability into underlying more specific aspects of changeability. We measured how changeable people theorise personality traits to be according to three underlying specific factors: volitional control, context and age. We investigated how specific implicit theories about changeability vary across different personality traits. We show that two personality traits (agreeableness and conscientiousness) are linked to dissociable patterns of specific factors used to explain how changeable they are. These findings yield new insight into the way people explain why personality traits are changeable and demonstrate that different types of reasons are used to explain the changeability of agreeableness and conscientiousness.

De novo detection of somatic variants in long-read single-cell RNA sequencing data.

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, possibly leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), copy-number alterations (CNAs), and gene fusions to reconstruct the tumor clonal heterogeneity. For SNV calling, LongSom distinguishes somatic SNVs from germline polymorphisms by reannotating marker gene expression-based cell types using called variants and applying strict filters. Applying LongSom to ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against single-cell and bulk panel DNA-seq data and could not be detected with short-read (SR) scRNA-seq. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo SNVs, CNAs, and fusions detection, thus enabling the study of cancer evolution, clonal heterogeneity, and treatment resistance.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution.

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

Happiness Maximization Is a WEIRD Way of Living.

Psychological science tends to treat subjective well-being and happiness synonymously. We start from the assumption that subjective well-being is more than being happy to ask the fundamental question: What is the ideal level of happiness? From a cross-cultural perspective, we propose that the idealization of attaining maximum levels of happiness may be especially characteristic of Western, educated, industrial, rich, and democratic (WEIRD) societies but less so for others. Searching for an explanation for why "happiness maximization" might have emerged in these societies, we turn to studies linking cultures to their eco-environmental habitat. We discuss the premise that WEIRD cultures emerged in an exceptionally benign ecological habitat (i.e., faced relatively light existential pressures compared with other regions). We review the influence of the Gulf Stream on the Northwestern European climate as a source of these comparatively benign geographical conditions. We propose that the ecological conditions in which WEIRD societies emerged afforded them a basis to endorse happiness as a value and to idealize attaining its maximum level. To provide a nomological network for happiness maximization, we also studied some of its potential side effects, namely alcohol and drug consumption and abuse and the prevalence of mania. To evaluate our hypothesis, we reanalyze data from two large-scale studies on ideal levels of personal life satisfaction-the most common operationalization of happiness in psychology-involving respondents from 61 countries. We conclude that societies whose members seek to maximize happiness tend to be characterized as WEIRD, and generalizing this across societies can prove problematic if adopted at the ideological and policy level.

Enhancing Patient Communication With Chat-GPT in Radiology: Evaluating the Efficacy and Readability of Answers to Common Imaging-Related Questions.

PURPOSE: To assess ChatGPT's accuracy, relevance, and readability in answering patients' common imaging-related questions and examine the effect of a simple prompt. METHODS: A total of 22 imaging-related questions were developed from categories previously described as important to patients, as follows: safety, the radiology report, the procedure, preparation before imaging, meaning of terms, and medical staff. These questions were posed to ChatGPT with and without a short prompt instructing the model to provide an accurate and easy-to-understand response for the average person. Four board-certified radiologists evaluated the answers for accuracy, consistency, and relevance. Two patient advocates also reviewed responses for their utility for patients. Readability was assessed using the Flesch Kincaid Grade Level. Statistical comparisons were performed using χ2 and paired t tests. RESULTS: A total of 264 answers were assessed for both unprompted and prompted questions. Unprompted responses were accurate 83% of the time (218 of 264), which did not significantly change for prompted responses (87% [229 of 264]; P = .2). The consistency of the responses increased from 72% (63 of 88) to 86% (76 of 88) when prompts were given (P = .02). Nearly all responses (99% [261 of 264]) were at least partially relevant for both question types. Fewer unprompted responses were considered fully relevant at 67% (176 of 264), although this increased significantly to 80% when prompts were given (210 of 264; P = .001). The average Flesch Kincaid Grade Level was high at 13.6 [CI, 12.9-14.2], unchanged with the prompt (13.0 [CI, 12.41-13.60], P = .2). None of the responses reached the eighth-grade readability level recommended for patient-facing materials. DISCUSSION: ChatGPT demonstrates the potential to respond accurately, consistently, and relevantly to patients' imaging-related questions. However, imperfect accuracy and high complexity necessitate oversight before implementation. Prompts reduced response variability and yielded more-targeted information, but they did not improve readability. ChatGPT has the potential to increase accessibility to health information and streamline the production of patient-facing educational materials; however, its current limitations require cautious implementation and further research.

Dose length product to effective dose coefficients in adults.

OBJECTIVES: The most accurate method for estimating patient effective dose (a principal metric for tracking patient radiation exposure) from computed tomography (CT) requires time-intensive Monte Carlo simulation. A simpler method multiplies a scalar coefficient by the widely available scanner-reported dose length product (DLP) to estimate effective dose. We developed new adult effective dose coefficients using actual patient scans and assessed their agreement with Monte Carlo simulation. METHODS: A multicenter sample of 216,906 adult CT scans was prospectively assembled in 2015-2020 from the University of California San Francisco International CT Dose Registry and the University of Florida library of computational phantoms. We generated effective dose coefficients for eight body regions, stratified by patient sex, diameter, and scanner manufacturer. We applied the new coefficients to DLPs to calculate effective doses and assess their correlations with Monte Carlo radiation transport-generated effective dose. RESULTS: Effective dose coefficients varied by body region and decreased in magnitude with increasing patient diameter. Coefficients were approximately twofold higher for torso scans in smallest compared with largest diameter categories. For example, abdomen and pelvis coefficients decreased from 0.027 to 0.013 mSv/mGy-cm between the 16-20 cm and 41+ cm categories. There were modest but consistent differences by sex and manufacturer. Diameter-based coefficients used to estimate effective dose produced strong correlations with the reference standard (Pearson correlations 0.77-0.86). The reported conversion coefficients differ from previous studies, particularly in neck CT. CONCLUSIONS: New effective dose coefficients derived from empirical clinical scans can be used to easily estimate effective dose using scanner-reported DLP. CLINICAL RELEVANCE STATEMENT: Scalar coefficients multiplied by DLP offer a simple approximation to effective dose, a key radiation dose metric. New effective dose coefficients from this study strongly correlate with gold standard, Monte Carlo-generated effective dose, and differ somewhat from previous studies. KEY POINTS: • Previous effective dose coefficients were derived from theoretical models rather than real patient data. • The new coefficients (from a large registry/phantom library) differ from previous studies. • The new coefficients offer reasonably reliable values for estimating effective dose.

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging.

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be "hardwired" to the brain's neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level.

All You Nonconformists Are (Not) All Alike: Dissociable Social Stereotypes of Mavericks and Contrarians.

While some people easily align themselves with others, others find themselves less aligned with sociocultural norms (e.g., nonconformists). Though people outside the mainstream tend to capture societies' attention, very little is known regarding how nonconformists are construed. In these studies, we investigated how different types of nonconformists are stereotyped. We sought to elucidate common and dissociable social stereotypes of two types of nonconformity; mavericks and contrarians, driven toward independence versus being different, respectively. We found that mavericks are construed as highly competent and conscientious, well suited for leadership roles, and more likely to be male, older, and satisfied with their life. Contrarians are construed as highly social, low in warmth and agreeableness, highly neurotic, well suited for roles involving creativity and self-expression, and more likely to be female, younger, and less satisfied with their lives. We situate these findings within models linking cultural context with conformity.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector.

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that although developers recognize the utility of analyzing data related to the impact or usage of their software, they struggle to find the time or funding to support such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of the evaluations of their software so that they can more fully benefit from such assessments.

What referring clinicians value most: Accuracy of radiology results and personal interactions with radiologists.

PURPOSE: We sought to identify which aspects of the referring clinician experience are most strongly correlated with overall satisfaction, and hence of greatest relevant importance to referring clinicians. METHODS: A survey instrument assessing referring clinician satisfaction throughout 11 domains of the radiology process map was distributed 2720 clinicians. The survey contained sections assessing each process map domain, with each section including a question about satisfaction overall in that domain and multiple more granular questions. The final question on the survey was overall satisfaction with the department. Univariate logistic regression and multivariate logistic regression were performed to assess the association between individual survey questions and overall satisfaction with the department. RESULTS: 729 referring clinicians (27%) completed the survey. Using univariate logistic regression nearly every question was associated with overall satisfaction. Amongst the 11 domains of the radiology process map multivariate logistic regression identified the following as mostly strongly associated with overall satisfaction: results/reporting overall (odds ratio 4.71; 95% confidence interval 2.15-10.23), section with which work most closely overall (3.39; 1.28-8.64), and inpatient radiology overall (2.39; 1.08-5.08). Other survey questions associated with overall satisfaction on multivariate logistic regression were attending radiologist interactions (odds ratio 3.71; 95% confidence interval 1.54-8.69), timeliness of inpatient radiology results (2.91; 1.01-8.09), technologist interactions (2.15; 0.99-4.40), appointment availability for urgent outpatient studies (2.01; 1.08-3.64), and guidance for selecting correct imaging study (1.88; 1.04-3.34). CONCLUSION: Referring clinicians value most the accuracy of the radiology report and their interactions with attending radiologists, particularly within the section they work most closely.

Dose length product to effective dose coefficients in children.

BACKGROUND: The most accurate method for estimating effective dose (the most widely understood metric for tracking patient radiation exposure) from computed tomography (CT) requires time-intensive Monte Carlo simulation. A simpler method multiplies a scalar coefficient by the widely available scanner-reported dose length product (DLP) to estimate effective dose. OBJECTIVE: Develop pediatric effective dose coefficients and assess their agreement with Monte Carlo simulation. MATERIALS AND METHODS: Multicenter, population-based sample of 128,397 pediatric diagnostic CT scans prospectively assembled in 2015-2020 from the University of California San Francisco International CT Dose Registry and the University of Florida library of highly realistic hybrid computational phantoms. We generated effective dose coefficients for seven body regions, stratified by patient age, diameter, and scanner manufacturer. We applied the new coefficients to DLPs to calculate effective doses and assessed their correlations with Monte Carlo radiation transport-generated effective doses. RESULTS: The reported effective dose coefficients, generally higher than previous studies, varied by body region and decreased in magnitude with increasing age. Coefficients were approximately 4 to 13-fold higher (across body regions) for patients <1 year old compared with patients 15-21 years old. For example, head CT (54% of scans) dose coefficients decreased from 0.039 to 0.003 mSv/mGy-cm in patients <1 year old vs. 15-21 years old. There were minimal differences by manufacturer. Using age-based conversion coefficients to estimate effective dose produced moderate to strong correlations with Monte Carlo results (Pearson correlations 0.52-0.80 across body regions). CONCLUSIONS: New pediatric effective dose coefficients update existing literature and can be used to easily estimate effective dose using scanner-reported DLP.

Cultural Differences in Susceptibility to the End of History Illusion.

The End of History Illusion (EoHI) is the tendency to report that a greater amount of change occurred in the past than is predicted to occur in the future. We investigated if cultural differences exist in the magnitude of the EoHI for self-reported life satisfaction and personality traits. We found an effect of culture such that the difference between reported past and predicted future change was greater for U.S. Americans than Japanese, and that individual differences in two aspects of the self (self-esteem and self-concept clarity) mediated the link between culture and the magnitude of the EoHI. We also found a robust cultural difference in perceptions of past change; U.S. Americans tended to think about the past more negatively than their Japanese counterparts. These findings yield new insight onto the link between cultural context and the way people remember the past and imagine the future.

Gene Fusions Create Partner and Collateral Dependencies Essential to Cancer Cell Survival.

Gene fusions frequently result from rearrangements in cancer genomes. In many instances, gene fusions play an important role in oncogenesis; in other instances, they are thought to be passenger events. Although regulatory element rearrangements and copy number alterations resulting from these structural variants are known to lead to transcriptional dysregulation across cancers, the extent to which these events result in functional dependencies with an impact on cancer cell survival is variable. Here we used CRISPR-Cas9 dependency screens to evaluate the fitness impact of 3,277 fusions across 645 cell lines from the Cancer Dependency Map. We found that 35% of cell lines harbored either a fusion partner dependency or a collateral dependency on a gene within the same topologically associating domain as a fusion partner. Fusion-associated dependencies revealed numerous novel oncogenic drivers and clinically translatable alterations. Broadly, fusions can result in partner and collateral dependencies that have biological and clinical relevance across cancer types. SIGNIFICANCE: This study provides insights into how fusions contribute to fitness in different cancer contexts beyond partner-gene activation events, identifying partner and collateral dependencies that may have direct implications for clinical care.

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Multiplexed mRNA assembly into ribonucleoprotein particles plays an operon-like role in the control of yeast cell physiology.

Prokaryotes utilize polycistronic messages (operons) to co-translate proteins involved in the same biological processes. Whether eukaryotes achieve similar regulation by selectively assembling and translating monocistronic messages derived from different chromosomes is unknown. We employed transcript-specific RNA pulldowns and RNA-seq/RT-PCR to identify yeast mRNAs that co-precipitate as ribonucleoprotein (RNP) complexes. Consistent with the hypothesis of eukaryotic RNA operons, mRNAs encoding components of the mating pathway, heat shock proteins, and mitochondrial outer membrane proteins multiplex in trans, forming discrete messenger ribonucleoprotein (mRNP) complexes (called transperons). Chromatin capture and allele tagging experiments reveal that genes encoding multiplexed mRNAs physically interact; thus, RNA assembly may result from co-regulated gene expression. Transperon assembly and function depends upon histone H4, and its depletion leads to defects in RNA multiplexing, decreased pheromone responsiveness and mating, and increased heat shock sensitivity. We propose that intergenic associations and non-canonical histone H4 functions contribute to transperon formation in eukaryotic cells and regulate cell physiology.

Investigating the neural substrates of Antagonistic Externalizing and social-cognitive Theory of Mind: an fMRI examination of functional activity and synchrony.

Recently developed quantitative models of psychopathology (i.e., Hierarchical Taxonomy of Psychopathology) identify an Antagonistic Externalizing spectrum that captures the psychological disposition toward criminal and antisocial behavior. The purpose of the present study was to examine relations between Antagonistic psychopathology (and associated Five-Factor model Antagonism/Agreeableness) and neural functioning related to social-cognitive Theory of Mind using a large sample (N = 973) collected as part of the Human Connectome Project (Van Essen et al., 2013a). No meaningful relations between Antagonism/Antagonistic Externalizing and Theory of Mind-related neural activity or synchrony were observed (p < .005). We conclude by outlining methodological considerations (e.g., validity of social cognition task and low test-retest reliability of functional biomarkers) that may account for these null results, and present recommendations for future research.

In vivo comparison of rotating platform and fixed bearing knee replacements during lunge and pivot activities.

BACKGROUND: The objective of this study was to discover whether notable differences in mobile and fixed-bearing kinematics occur during activity that promotes tibial rotation, and to compare these results with normal healthy kinematics. We hypothesized that rotating-platform knee replacements would exhibit greater rotation of the tibia relative to the fixed-bearing knee replacements. MATERIALS AND METHODS: The in vivo motion of the tibia relative to the femur was measured in subjects with posterior stabilized fixed-bearing (FB) and rotating-platform (RP) total knee arthroplasties using a high-speed stereo radiography system during a lunge and gait with a change in direction (pivot). RESULTS: The in vivo internal/external (IE) rotation and anterior/posterior translation of the tibia relative to the femur was similar between mobile and fixed-bearing total knee prostheses during two activities of daily living that included an activity that challenged tibial IE rotation. Measurements of IE rotation in participants with RP had higher variability and significantly greater range between maximum internal and external rotation compared with FB participants. The greater amount of variability of RP was not unlike the healthy knee. CONCLUSION: The pattern of IE rotation and AP translation for both RP and FB designs were similar to healthy kinematics but with less IE rotation. The RP implants more closely replicated the asymmetrical posterior condylar translation and range of IE rotation of the healthy knee during activity that challenged tibial IE rotation.