The Penn Electrophysiology of Encoding and Retrieval Study.

The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) aimed to characterize the behavioral and electrophysiological (EEG) correlates of memory encoding and retrieval in highly practiced individuals. Across five PEERS experiments, 300+ subjects contributed more than 7,000 memory testing sessions with recorded EEG data. Here we tell the story of PEERS: its genesis, evolution, major findings, and the lessons it taught us about taking a big scientific approach in studying memory and the human brain. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The influence of emotion on temporal context models.

Temporal context models (TCMs) have been influential in understanding episodic memory and its neural underpinnings. Recently, TCMs have been extended to explain emotional memory effects, one of the most clinically important findings in the field of memory research. This review covers recent advances in hypotheses for the neural representation of spatiotemporal context through the lens of TCMs, including their ability to explain the influence of emotion on episodic and temporal memory. In recent years, simplifying assumptions of "classical" TCMs - with exponential trace decay and the mechanism by which temporal context is recovered - have become increasingly clear. The review also outlines how recent advances could be incorporated into a future TCM, beyond classical assumptions, to integrate emotional modulation.

The Impact of Expectation Management and Model Transparency on Radiologists' Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study.

BACKGROUND: Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems. OBJECTIVE: We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists' trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans. METHODS: In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists' trust in their assessments had changed based on the AI recommendations. RESULTS: Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists' confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists' confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs. CONCLUSIONS: Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists' trust in the AI-CAD system can be impaired. Radiologists' confidence in their assessments was improved by using the AI recommendations.

Enhancing the Preanesthesia Evaluation: Consensus Development of the Cannabis Use and Behaviors Assessment Tool (CUBAT).

PURPOSE: This project aimed to develop an evidence-based preanesthesia cannabis use assessment tool to acquire complete and accurate patient history and develop a best-informed, individualized anesthesia and analgesia care plan. DESIGN: Modified Delphi. METHODS: Using an evidence synthesis and multistage, modified Delphi process, eight experts from across the United States developed a consensus-based tool to aid in developing a best-informed, individualized plan for anesthesia and analgesia care. FINDINGS: Two survey rounds integrated informed evidence-based tool revisions. The final tool included instructions for use, a glossary of terms, and seven key assessment items aimed at gathering the most influential information regarding cannabis use. CONCLUSIONS: The Cannabis Use and Behaviors Assessment Tool is a first-of-its-kind tool providing an essential framework for preanesthesia cannabis use assessment.

Association between nutrition self-efficacy, health locus of control and food choice motives in consumers in nine European countries.

We investigated associations between food choice motives and psychological determinants of dietary health behaviour change (nutrition self-efficacy, NS-E, and health locus of control, HLoC) among 9381 participants (18-65 years, 49.4% females) from nine European countries. Price was the highest rated food choice motive. Higher importance of all motives was associated with higher NS-E and with higher Internal HLoC. Relationships between food choice motives and External HLoC were also in the expected direction in showing negative associations with Health, Natural Content, Weight Control, Mood and Sensory Appeal. Higher External HLoC was also associated with perceived greater importance of 'external' motives Ethical Concern, Familiarity and Convenience. Relationships between External HLoC and food choice motives were not all in the expected direction. Price was unrelated to External HLoC. Females rated the importance of all motives higher than males. People with less education ascribed greater importance to Price in motivating food choices. Together, these findings imply that self-efficacy and health locus of control should be considered along with motivations for food choice in dietary health promotion.

Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies.

OBJECTIVES: To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). METHODS: This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. RESULTS: A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001). CONCLUSION: Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. CLINICAL RELEVANCE STATEMENT: Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. KEY POINTS: • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

Building Synthetic Cells─From the Technology Infrastructure to Cellular Entities.

The de novo construction of a living organism is a compelling vision. Despite the astonishing technologies developed to modify living cells, building a functioning cell "from scratch" has yet to be accomplished. The pursuit of this goal alone has─and will─yield scientific insights affecting fields as diverse as cell biology, biotechnology, medicine, and astrobiology. Multiple approaches have aimed to create biochemical systems manifesting common characteristics of life, such as compartmentalization, metabolism, and replication and the derived features, evolution, responsiveness to stimuli, and directed movement. Significant achievements in synthesizing each of these criteria have been made, individually and in limited combinations. Here, we review these efforts, distinguish different approaches, and highlight bottlenecks in the current research. We look ahead at what work remains to be accomplished and propose a "roadmap" with key milestones to achieve the vision of building cells from molecular parts.

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools.

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

Heterobimetallic Base Pair Programming in Designer 3D DNA Crystals.

Metal-mediated DNA (mmDNA) presents a pathway toward engineering bioinorganic and electronic behavior into DNA devices. Many chemical and biophysical forces drive the programmable chelation of metals between pyrimidine base pairs. Here, we developed a crystallographic method using the three-dimensional (3D) DNA tensegrity triangle motif to capture single- and multi-metal binding modes across granular changes to environmental pH using anomalous scattering. Leveraging this programmable crystal, we determined 28 biomolecular structures to capture mmDNA reactions. We found that silver(I) binds with increasing occupancy in T-T and U-U pairs at elevated pH levels, and we exploited this to capture silver(I) and mercury(II) within the same base pair and to isolate the titration points for homo- and heterometal base pair modes. We additionally determined the structure of a C-C pair with both silver(I) and mercury(II). Finally, we extend our paradigm to capture cadmium(II) in T-T pairs together with mercury(II) at high pH. The precision self-assembly of heterobimetallic DNA chemistry at the sub-nanometer scale will enable atomistic design frameworks for more elaborate mmDNA-based nanodevices and nanotechnologies.

How financial mechanisms can incentivize provision of ecosystem services from land restoration: A systematic review protocol.

The current food chain both contributes to, and is affected by, climate change. While GHG emissions and emissions to water and soil are a problem for the whole food chain, the majority of such emissions and the major solutions to them can be found in the farming and land use sector. The farming system needs to reduce its greenhouse-gas emissions and adapt its supply chain to cope with climate change. A broad variety of payment tools have been proposed to motivate farmers and landowners to take certain actions to reduce greenhouse gas emissions and encourage the protection or restoration of natural resources. The protocol described here (OSF preregistration https://doi.org/10.17605/OSF.IO/STGQ6) outlines the methodology for a systematic review to explore how financial mechanisms such as green bonds can provide incentives to agri-food sector to support environmental sustainability and ecosystem service delivery through land-use change. Our primary research question is: how do financial mechanisms incentivize land restoration? Studies will be categorized according to the types of financial mechanisms, their characteristics, methods of land restoration and their impact on mitigating agri-food footprint. The results are expected to increase our understanding about the design of financing tools currently used to accelerate nature restoration. Moreover, they will inform us about the effectiveness of deploying such tools on rural communities, food companies and landowners.

AAV Serotypes and Their Suitability for Retinal Gene Therapy.

Throughout the last 25 years, exceptional progress in retinal gene therapy was achieved. The major breakthrough was realized in 2017 when the FDA approved the adeno-associated virus (AAV)-based gene therapy for treatment of the monogenetic disorder Leber congenital amaurosis type 2 (LCA2). Since then, many therapies for inherited retinal diseases (IRD) reached phase I/II clinical trials, targeting diseases like achromatopsia, choroideremia, retinitis pigmentosa, Stargardt disease, and many more (reviewed in (Trapani and Auricchio, Trends Mol Med 24:669-681, 2018)). Advanced vector and capsid design technologies as well as improved gene transfer and gene editing methods may lead to refined therapies for various eye diseases. Many research departments worldwide focus on optimizing transgene expression by designing novel AAV serotypes. Besides serotype tropism, the method of injection (intravitreal, subretinal, or suprachoroidal) (Han et al., Hum Gene Ther 31:1288-1299, 2020) defines the efficiency outcome along with the use of tissue-specific promotors which play a critical role for cell targeting.

Conserved functions of prion candidates suggest a primeval role of protein self-templating.

Amyloid-based prions have simple structures, a wide phylogenetic distribution, and a plethora of functions in contemporary organisms, suggesting they may be an ancient phenomenon. However, this hypothesis has yet to be addressed with a systematic, computational, and experimental approach. Here we present a framework to help guide future experimental verification of candidate prions with conserved functions to understand their role in the early stages of evolution and potentially in the origins of life. We identified candidate prions in all high-quality proteomes available in UniProt computationally, assessed their phylogenomic distributions, and analyzed candidate-prion functional annotations. Of the 27 980 560 proteins scanned, 228 561 were identified as candidate prions (~0.82%). Among these candidates, there were 84 Gene Ontology (GO) terms conserved across the three domains of life. We found that candidate prions with a possible role in adaptation were particularly well-represented within this group. We discuss unifying features of candidate prions to elucidate the primeval roles of prions and their associated functions. Candidate prions annotated as transcription factors, DNA binding, and kinases are particularly well suited to generating diverse responses to changes in their environment and could allow for adaptation and population expansion into more diverse environments. We hypothesized that a relationship between these functions and candidate prions could be evolutionarily ancient, even if individual prion domains themselves are not evolutionarily conserved. Candidate prions annotated with these universally occurring functions potentially represent the oldest extant prions on Earth and are therefore excellent experimental targets.

Confidence of Life Detection: The Problem of Unconceived Alternatives.

Potential biosignatures that offer the promise of extraterrestrial life (past or present) are to be expected in the coming years and decades, whether from within our own solar system, from an exoplanet atmosphere, or otherwise. With each such potential biosignature, the degree of our uncertainty will be the first question asked. Have we really identified extraterrestrial life? How sure are we? This paper considers the problem of unconceived alternative explanations. We stress that articulating our uncertainty requires an assessment of the extent to which we have explored the relevant possibility space. It is argued that, for most conceivable potential biosignatures, we currently have not explored the relevant possibility space very thoroughly at all. Not only does this severely limit the circumstances in which we could reasonably be confident in our detection of extraterrestrial life, it also poses a significant challenge to any attempt to quantify our degree of uncertainty. The discussion leads us to the following recommendation: when it comes specifically to an extraterrestrial life-detection claim, the astrobiology community should follow the uncertainty assessment approach adopted by the Intergovernmental Panel on Climate Change (IPCC).

Metal-Mediated DNA Nanotechnology in 3D: Structural Library by Templated Diffraction.

DNA double helices containing metal-mediated DNA (mmDNA) base pairs are constructed from Ag+ and Hg2+ ions between pyrimidine:pyrimidine pairs with the promise of nanoelectronics. Rational design of mmDNA nanomaterials is impractical without a complete lexical and structural description. Here, the programmability of structural DNA nanotechnology toward its founding mission of self-assembling a diffraction platform for biomolecular structure determination is explored. The tensegrity triangle is employed to build a comprehensive structural library of mmDNA pairs via X-ray diffraction and generalized design rules for mmDNA construction are elucidated. Two binding modes are uncovered: N3-dominant, centrosymmetric pairs and major groove binders driven by 5-position ring modifications. Energy gap calculations show additional levels in the lowest unoccupied molecular orbitals (LUMO) of mmDNA structures, rendering them attractive molecular electronic candidates.

The Skin of Color Society's Meeting the Challenge Summit, 2022: Diversity in Dermatology Clinical Trials Proceedings.

Importance: Clinical trials remain the cornerstone for determining the safety and efficacy of an intervention. A diverse participant pool in dermatology clinical trials is critical to ensure that results are generalizable among the patient population who will ultimately depend on the efficacy of the intervention. The Skin of Color Society hosted the inaugural Meeting the Challenge Summit: Diversity in Dermatology Clinical Trials in Washington, DC, from June 10 to 11, 2022. The summit was an interactive and collaborative effort to advance discussions regarding the need for broader inclusion of racial and ethnic minority patients in dermatology clinical trials. Observations: The summit focused on 3 principal areas: (1) understanding the current clinical trials landscape; (2) breaking down patient, clinician, industry, and regulatory barriers; and (3) effecting change through a diversity-focused strategy. The program hosted thought-provoking panel talks and discussions with various stakeholder groups, including a keynote presentation from the family of Henrietta Lacks. Conclusions and Relevance: Panel discussions and insightful presentations from physicians, industry leaders, community trailblazers, and patients fostered new collaborations. The summit provided recommendations and suggested strategies for future initiatives designed to increase the representation of minority individuals in dermatology clinical trials.

Consumers' Preferences for Apple Production Attributes: Results of a Choice Experiment.

Various food safety and environmental problems in China have raised consumer awareness of food safety issues and negative environmental impacts in various supply chains. This research assessed consumer preferences and willingness to pay (WTP) for food safety and ecosystem delivery attributes associated with apples, demonstrated through the application of different traceability systems. Research participants were recruited in Beijing (N = 384) and Shanghai (N = 320). Choice experiment methodology was applied. The data were analyzed using conditional logit, random parameter logit, and latent class models; the results indicated significant consumer preferences for traceability information, including in relation to lower pesticide usage and application of organic fertilizer during primary production. The results also indicated that participants in this research had a significant willingness-to-pay premium for apple products that had production information traceability, had reduced pesticide use, and were grown with organic fertilizers. The models demonstrated heterogeneous preferences among participants such that consumers could be divided into three classes: non-price-sensitive (53.5%), pesticide-sensitive (21.7%), and price-sensitive (24.8%).

Orbital character of the spin-reorientation transition in TbMn6Sn6.

Ferromagnetic (FM) order in a two-dimensional kagome layer is predicted to generate a topological Chern insulator without an applied magnetic field. The Chern gap is largest when spin moments point perpendicular to the kagome layer, enabling the capability to switch topological transport properties, such as the quantum anomalous Hall effect, by controlling the spin orientation. In TbMn6Sn6, the uniaxial magnetic anisotropy of the Tb3+ ion is effective at generating the Chern state within the FM Mn kagome layers while a spin-reorientation (SR) transition to easy-plane order above TSR = 310 K provides a mechanism for switching. Here, we use inelastic neutron scattering to provide key insights into the fundamental nature of the SR transition. The observation of two Tb excitations, which are split by the magnetic anisotropy energy, indicates an effective two-state orbital character for the Tb ion, with a uniaxial ground state and an isotropic excited state. The simultaneous observation of both modes below TSR confirms that orbital fluctuations are slow on magnetic and electronic time scales < ps and act as a spatially-random orbital alloy. A thermally-driven critical concentration of isotropic Tb ions triggers the SR transition.

Modeling inducible neuropathologies of the retina with differential phenotypes in organoids.

Neurodegenerative diseases remain incompletely understood and therapies are needed. Stem cell-derived organoid models facilitate fundamental and translational medicine research. However, to which extent differential neuronal and glial pathologic processes can be reproduced in current systems is still unclear. Here, we tested 16 different chemical, physical, and cell functional manipulations in mouse retina organoids to further explore this. Some of the treatments induce differential phenotypes, indicating that organoids are competent to reproduce distinct pathologic processes. Notably, mouse retina organoids even reproduce a complex pathology phenotype with combined photoreceptor neurodegeneration and glial pathologies upon combined (not single) application of HBEGF and TNF, two factors previously associated with neurodegenerative diseases. Pharmacological inhibitors for MAPK signaling completely prevent photoreceptor and glial pathologies, while inhibitors for Rho/ROCK, NFkB, and CDK4 differentially affect them. In conclusion, mouse retina organoids facilitate reproduction of distinct and complex pathologies, mechanistic access, insights for further organoid optimization, and modeling of differential phenotypes for future applications in fundamental and translational medicine research.

Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI.

Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) features for classification justification. Materials and Methods This retrospective study included consecutive patients with histopathologic analysis-proven prostatic lesions who underwent biparametric MRI and biopsy between January 2012 and December 2017. After image annotation by two radiologists, a deep learning model was trained to detect the index lesion; classify PCa, clinically significant PCa (Gleason score ≥ 7), and benign lesions (eg, prostatitis); and justify classifications using PI-RADS features. Lesion- and patient-based performance were assessed using fivefold cross validation and areas under the receiver operating characteristic curve. Clinical feasibility was tested in a multireader study and by using the external PROSTATEx data set. Statistical evaluation of the multireader study included Mann-Whitney U and exact Fisher-Yates test. Results Overall, 1224 men (median age, 67 years; IQR, 62-73 years) had 3260 prostatic lesions (372 lesions with Gleason score of 6; 743 lesions with Gleason score of ≥ 7; 2145 benign lesions). XAI reliably detected clinically significant PCa in internal (area under the receiver operating characteristic curve, 0.89) and external test sets (area under the receiver operating characteristic curve, 0.87) with a sensitivity of 93% (95% CI: 87, 98) and an average of one false-positive finding per patient. Accuracy of the visual and textual explanations of XAI classifications was 80% (1080 of 1352), confirmed by experts. XAI-assisted readings improved the confidence (4.1 vs 3.4 on a five-point Likert scale; P = .007) of nonexperts in assessing PI-RADS 3 lesions, reducing reading time by 58 seconds (P = .009). Conclusion The explainable AI model reliably detected and classified clinically significant prostate cancer and improved the confidence and reading time of nonexperts while providing visual and textual explanations using well-established imaging features. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.

A happy accident: a novel turfgrass reference genome.

Poa pratensis, commonly known as Kentucky bluegrass, is a popular cool-season grass species used as turf in lawns and recreation areas globally. Despite its substantial economic value, a reference genome had not previously been assembled due to the genome's relatively large size and biological complexity that includes apomixis, polyploidy, and interspecific hybridization. We report here a fortuitous de novo assembly and annotation of a P. pratensis genome. Instead of sequencing the genome of a C4 grass, we accidentally sampled and sequenced tissue from a weedy P. pratensis whose stolon was intertwined with that of the C4 grass. The draft assembly consists of 6.09 Gbp with an N50 scaffold length of 65.1 Mbp, and a total of 118 scaffolds, generated using PacBio long reads and Bionano optical map technology. We annotated 256K gene models and found 58% of the genome to be composed of transposable elements. To demonstrate the applicability of the reference genome, we evaluated population structure and estimated genetic diversity in P. pratensis collected from three North American prairies, two in Manitoba, Canada and one in Colorado, USA. Our results support previous studies that found high genetic diversity and population structure within the species. The reference genome and annotation will be an important resource for turfgrass breeding and study of bluegrasses.