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Background: Previous studies of emotion recognition abilities of people with eating disorders used accuracy to identify performance deficits for these individuals. The current study examined eating disorder symptom severity as a function of emotion categorization abilities, using a visual cognition paradigm that offers insights into how emotional faces may be categorized, as opposed to just how well these faces are categorized., Methods: Undergraduate students (N = 87, 50 women, 34 men, 3 non-binary) completed the Bubbles task and a standard emotion categorization task, as well as a set of questionnaires assessing their eating disorder symptomology and comorbid disorders. We examined the relationship between visual information use (assessed via Bubbles) and eating disorder symptomology (EDDS) while controlling for anxiety (STAI), depression (BDI-II), alexithymia (TAS), and emotion regulation difficulties (DERS-sf)., Results: Overall visual information use (i.e. how well participants used facial features important for accurate emotion categorization) was not significantly related to eating disorder symptoms, despite producing interpretable patterns for each emotion category. Emotion categorization accuracy was also not related to eating disorder symptoms., Conclusions: Results from this study must be interpreted with caution, given the non-clinical sample. Future research may benefit from comparing visual information use in patients with an eating disorder and healthy controls, as well as employing designs focused on specific emotion categories, such as anger., (© 2024. The Author(s).)

Material depictions in artwork are useful tools for revealing image features that support material categorization. For example, artistic recipes for drawing specific materials make explicit the critical information leading to recognizable material properties (Di Cicco, Wjintjes, & Pont, 2020) and investigating the recognizability of material renderings as a function of their visual features supports conclusions about the vocabulary of material perception. Here, we examined how the recognition of materials from photographs and drawings was affected by the application of the Portilla-Simoncelli texture synthesis model. This manipulation allowed us to examine how categorization may be affected differently across materials and image formats when only summary statistic information about appearance was retained. Further, we compared human performance to the categorization accuracy obtained from a pretrained deep convolutional neural network to determine if observers' performance was reflected in the network. Although we found some similarities between human and network performance for photographic images, the results obtained from drawings differed substantially. Our results demonstrate that texture statistics play a variable role in material categorization across rendering formats and material categories and that the human perception of material drawings is not effectively captured by deep convolutional neural networks trained for object recognition.

The N190 is a body-sensitive ERP component that responds to images of human bodies in different poses. In natural settings, bodies vary in posture and appear within complex, cluttered environments, frequently with other people. In many studies, however, such variability is absent. How does the N190 response change when observers see images that incorporate these sources of variability? In two experiments (N = 16 each), we varied the natural appearance of upright and inverted bodies to examine how the N190 amplitude, latency, and the Body-Inversion Effect (BIE) were affected by natural variability. In Experiment 1, we varied the number of people present in upright and inverted naturalistic scenes such that only one body, a subitizable number of bodies, or a "crowd" was present. In Experiment 2, we varied the natural body appearance by presenting bodies either as silhouettes or with photographic detail. Further, we varied the natural background appearance by either removing it or presenting individual bodies within a rich environment. Using component-based analyses of the N190, we found that the number of bodies in a scene reduced the N190 amplitude, but didn't affect the BIE (Experiment 1). Naturalistic body and background appearance (Experiment 2) also affected the N190, such that component amplitude was dramatically reduced by naturalistic appearance. To complement this analysis, we examined the contribution of spatiotemporal features (i.e., electrode × time point amplitude) via SVM decoding. This technique allows us to examine which timepoints across the entire waveform contribute the most to successful decoding of body orientation in each condition. This analysis revealed that later timepoints (after 300ms) contribute most to successful orientation decoding. These results demonstrate that natural appearance variability affects body processing at the N190 and that later ERP components may make important contributions to body processing in natural scenes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Nudnou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Face images depicting the same individual can differ substantially from one another. Ecological variation in pose, expression, lighting, and other sources of appearance variability complicates the recognition and matching of unfamiliar faces, but acquired familiarity leads to the ability to cope with these challenges. Among the many ways that face of the same individual can vary, some images are judged to be better likenesses of familiar individuals than others. Simply put, these images look more like the individual under consideration than others. But what does it mean for an image to be a better likeness than another? Does likeness entail typicality, or is it something distinct from this? We examined the relationship between the likeness of face images and the similarity of those images to average images of target individuals using a set of famous faces selected for reciprocal familiarity/unfamiliarity across US and UK participants. We found that though likeness judgments are correlated with similarity-to-prototype judgments made by both familiar and unfamiliar participants, this correlation was smaller than the correlation between similarity judgments made by different participant groups. This implies that while familiarity weakens the relationship between likeness and similarity-to-prototype judgments, it does not change similarity-to-prototype judgments to the same degree., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Introduction: Digital patient monitoring (DPM) tools can enable more effective clinical care and improved patient outcomes in cancer. However, their broad adoption requires ease of use and demonstration of real-world clinical utility/impact. ORIGAMA (MO42720) is an interventional, open-label, multicountry platform study investigating the clinical utility of DPM tools and specific treatments. ORIGAMA will begin with two cohorts that aim to assess the impact of the atezolizumab-specific Roche DPM Module (hosted on the Kaiku Health DPM platform (Helsinki, Finland)) on health outcomes and healthcare resource usage, and its feasibility to support at-home treatment administration, in participants receiving systemic anticancer treatment. Other digital health solutions may be added to future cohorts., Methods and Analysis: In Cohort A, participants with metastatic non-small cell lung cancer (NSCLC), extensive-stage SCLC or Child Pugh A unresectable hepatocellular carcinoma will be randomised to a locally approved anticancer regimen containing intravenous atezolizumab (TECENTRIQ, F. Hoffmann-La Roche Ltd/Genentech) and local standard-of-care support, with/without the Roche DPM Module. Cohort B will assess the feasibility of the Roche DPM Module in supporting administration of three cycles of subcutaneous atezolizumab (1875 mg; Day 1 of each 21-day cycle) in the hospital, followed by 13 cycles at home by a healthcare professional (ie, flexible care), in participants with programmed cell-death ligand 1-positive, early-stage NSCLC. The primary endpoints are the mean difference in change of the participant-reported Total Symptom Interference Score at Week 12 from baseline (Cohort A) and flexible care adoption rate at Cycle 6 (Cohort B)., Ethics and Dissemination: This study will be conducted according to the Declaration of Helsinki, and/or the applicable laws and regulations of the country in which the research is conducted, whichever affords the greater protection to the individual. The study received its first Ethics Committee approval in Spain in October 2022. Participants will provide written informed consent in a face-to-face setting. The results of this study will be presented at national and/or international congresses and disseminated via publication in peer-reviewed journals., Trial Registration Number: NCT05694013., Competing Interests: Competing interests: SI has acted in a consultancy and advisory role for Bristol-Myers Squibb, Roche and Merck Sharp & Dohme; has participated in a speaker bureau or provided expert testimony for Boehringer Ingelheim; is an employee at an institution that has received a research grant or funding from Roche; and has received travel and accommodation expenses from Boehringer Ingelheim, Merck Sharp & Dohme, Roche, Novartis and Kaiku Health. A-MB: has received honoraria for participation in an advisory board for Roche (Ireland), has received institutional research funding to Lung Cancer Europe (LuCE) from Amgen, AstraZeneca, Bayer, Blueprint Medicines, BMS, Boehringer Ingelheim, Daiichi Sankyo, Eli Lilly, Merck, MSD, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda and Thermo Fisher, has received institutional honoraria to LuCE from Janssen, and has participated in various meetings, presentations and advisory boards, on behalf of LuCE. BB, AB, and AYCS are employees of and have stocks/other ownership in F. Hoffmann-La Roche Ltd. ME has acted in a consultancy and advisory role for Roche, is an employee at an institution that has received research grants from Novartis, Roche, BMS and Kaiku Health, and has received travel and accommodation expenses from Vifor. SG and MM-O are employees of and have stocks/other ownership in F. Hoffmann-La Roche Ltd. MR has received grants or contracts from Bayer and Ipsen, has received consulting fees from Bayer, BMS, Roche, Ipsen, AstraZeneca, Eli Lilly, BTG and Universal DX, and has received payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events, from Bayer, BMS, Gilead Sciences, Eli Lilly, Roche and Eisai. MW has received research funding from Roche for conduct of the study. JA is an employee of and has stocks/other ownership in F. Hoffmann-La Roche Ltd., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Alectinib is an anaplastic lymphoma kinase (ALK) inhibitor approved for treatment of ALK-positive non-small cell lung cancer. Population pharmacokinetic (PK) models were developed for alectinib and its major active metabolite M4 using phase I/II PK data in crizotinib-failed patients (N = 138). The PK profiles were best described by two separate models with similar structure for both entities: open one-compartment models with sequential zero/first-order input and first-order elimination rate. Body weight with fixed allometric scaling factor on clearance and volume of both entities was the only significant covariate. Bayesian feedback analyses of the PK data collected from Japanese and global treatment-naïve patients in phase III studies (N = 334) confirmed the body weight effect. Landmark Cox proportional hazards analyses of progression-free survival in treatment-naïve patients identified the average molar concentrations of both entities alectinib and M4 during the first 6 weeks of treatment as a significant covariate, with an optimal response achieved for concentrations above 1040 nmol/L. With 600 mg twice daily (b.i.d.), 92% of global patients are above this threshold concentration, compared with only 43% of patients with 300 mg b.i.d. In Japan, where the body weight distribution is lower, the approved 300 mg b.i.d. dose brings about 70% of Japanese patients above this threshold. Logistic regression analyses found no significant relationship between the combined alectinib-M4 molar concentration and first occurrence of adverse events. These pharmacometric results were used to expedite and facilitate regulatory approvals of 600 mg b.i.d. for first-line ALK-positive NSCLC in the United States and European Union in 2017 and in China in 2018., (© 2021 F.Hoffmann-La Roche Ltd. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)