Intra- and interindividual variability in fasted gastric content volume.

BACKGROUND: Gastric fluid plays a key role in food digestion and drug dissolution, therefore, the amount of gastric fluid present in a fasted state may influence subsequent digestion and drug delivery. We aimed to describe intra- and interindividual variation in fasted gastric content volume (FGCV) and to determine the association with age, sex, and body size characteristics. METHODS: Data from 24 MRI studies measuring FGCV in healthy, mostly young individuals after an overnight fast were pooled. The analysis included 366 participants who had up to 6 repeated measurements, with a total of 870 measurements. Linear mixed model analysis was performed to calculate intra- and interindividual variability and to assess the effects of age, sex, weight, height, weight*height as a proxy for body size, and body mass index (BMI). RESULTS: FGCV ranged from 0 to 156 mL, with a mean (± SD) value of 33 ± 25 mL. The overall coefficient of variation within the study population was 75.6%, interindividual SD was 15 mL, and the intraindividual SD was 19 mL. Age, weight, height, weight*height, and BMI had no effect on FGCV. Women had lower volumes compared to men (MD: -6 mL), when corrected for the aforementioned factors. CONCLUSION: FGCV is highly variable, with higher intraindividual compared to interindividual variability, indicating that FGCV is subject to day-to-day and within-day variation and is not a stable personal characteristic. This highlights the importance of considering FGCV when studying digestion and drug dissolution. Exact implications remain to be studied.

High economic inequality is linked to greater moralization.

Throughout the 21st century, economic inequality is predicted to increase as we face new challenges, from changes in the technological landscape to the growing climate crisis. It is crucial we understand how these changes in inequality may affect how people think and behave. We propose that economic inequality threatens the social fabric of society, in turn increasing moralization-that is, the greater tendency to employ or emphasize morality in everyday life-as an attempt to restore order and control. Using longitudinal data from X, formerly known as Twitter, our first study demonstrates that high economic inequality is associated with greater use of moral language online (e.g. the use of words such as "disgust", "hurt", and "respect'). Study 2 then examined data from 41 regions around the world, generally showing that higher inequality has a small association with harsher moral judgments of people's everyday actions. Together these findings demonstrate that economic inequality is linked to the tendency to see the world through a moral lens.

The impact of generative artificial intelligence on socioeconomic inequalities and policy making.

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

Observing the earth from space: Does a virtual reality overview effect experience increase pro-environmental behaviour?

Astronauts (and recently businessmen) often express a renewed sense of responsibility for taking care of the environment, after observing the overwhelming beauty of Earth from space. Despite recent attention for this "overview effect", it is unclear whether experiencing the effect directly impacts pro-environmental behaviour. Using a virtual reality experience, the current research tests in two experimental studies the direct impact of an immersive overview effect experience on both short-term and longer term subsequent pro-environmental behaviours (donating to an environmental NGO, consuming less diary and meat). Furthermore, it investigates whether the technological immersiveness of the VR experience amplifies the effect, and the mediating role of connectedness to nature. Results show no effects of the short (7 minutes) overview effect VR video on pro-environmental behaviour (Study 1). For the longer video (15 minutes, Study 2), the results showed that the most immersive experience (video featuring meditative music and voice-over) appeared to increase connection with nature and higher donation amounts to an eco-NGO, but not significantly. No effects were found for subsequent meat and dairy consumption behaviours (measured on day 2, 4, and 6). These findings contribute to a deeper understanding of the specific features determining the effectiveness of the overview effect experiences on actual pro-environmental behaviour, providing important insights to businesses and educational institutions.

Correction: Gastric coagulation and postprandial amino acid absorption of milk is affected by mineral composition: a randomized crossover trial.

Correction for 'Gastric coagulation and postprandial amino acid absorption of milk is affected by mineral composition: a randomized crossover trial' by Elise J. M. van Eijnatten et al., Food Funct., 2024, 15, 3098-3107, https://doi.org/10.1039/D3FO04063A.

Love and hate do not modulate the attentional blink but improve overall performance.

How may feelings of love and hate impact people's attention? We used a modified Attentional Blink (AB) task in which 300 participants were asked to categorise a name representing a person towards whom they felt either hate, love, or neutral (first target) plus identify a number word (second target), both embedded in a rapidly presented stream of other words. The lag to the second target was systematically varied. Contrary to our hypothesis, results revealed that both hated and loved names resulted in higher accuracy for the second target than neutral names, which was largely independent of lag. Also, there we observed no sustained transfer effects of love and hate onto neutral name trials. The findings differ from prior research on attentional blink and transient, non-personal, stimulus-driven emotions, suggesting that interpersonal feelings activate different attention-relevant mechanisms. Relevant to future research, we speculate that love and hate are motivators of goal-directed behaviour that facilitate subsequent information processing.

DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images.

BACKGROUND AND PURPOSE: Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network. METHODS: Thirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel. RESULTS: SNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important. CONCLUSIONS: The deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available.

First comprehensive untargeted metabolomics study of suramin-treated Trypanosoma brucei: an integrated data analysis workflow from multifactor data modelling to functional analysis.

INTRODUCTION: Human African trypanosomiasis, commonly known as sleeping sickness, is a vector-borne parasitic disease prevalent in sub-Saharan Africa and transmitted by the tsetse fly. Suramin, a medication with a long history of clinical use, has demonstrated varied modes of action against Trypanosoma brucei. This study employs a comprehensive workflow to investigate the metabolic effects of suramin on T. brucei, utilizing a multimodal metabolomics approach. OBJECTIVES: The primary aim of this study is to comprehensively analyze the metabolic impact of suramin on T. brucei using a combined liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) approach. Statistical analyses, encompassing multivariate analysis and pathway enrichment analysis, are applied to elucidate significant variations and metabolic changes resulting from suramin treatment. METHODS: A detailed methodology involving the integration of high-resolution data from LC-MS and NMR techniques is presented. The study conducts a thorough analysis of metabolite profiles in both suramin-treated and control T. brucei brucei samples. Statistical techniques, including ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA), ANOVA 2 analysis, and bootstrap tests, are employed to discern the effects of suramin treatment on the metabolomics outcomes. RESULTS: Our investigation reveals substantial differences in metabolic profiles between the control and suramin-treated groups. ASCA and PCA analysis confirm distinct separation between these groups in both MS-negative and NMR analyses. Furthermore, ANOVA 2 analysis and bootstrap tests confirmed the significance of treatment, time, and interaction effects on the metabolomics outcomes. Functional analysis of the data from LC-MS highlighted the impact of treatment on amino-acid, and amino-sugar and nucleotide-sugar metabolism, while time effects were observed on carbon intermediary metabolism (notably glycolysis and di- and tricarboxylic acids of the succinate production pathway and tricarboxylic acid (TCA) cycle). CONCLUSION: Through the integration of LC-MS and NMR techniques coupled with advanced statistical analyses, this study identifies distinctive metabolic signatures and pathways associated with suramin treatment in T. brucei. These findings contribute to a deeper understanding of the pharmacological impact of suramin and have the potential to inform the development of more efficacious therapeutic strategies against African trypanosomiasis.

Gastric coagulation and postprandial amino acid absorption of milk is affected by mineral composition: a randomized crossover trial.

Background: In vitro studies suggest that casein coagulation of milk is influenced by its mineral composition, and may therefore affect the dynamics of protein digestion, gastric emptying and appearance of amino acids (AA) in the blood, but this remains to be confirmed in vivo. Objective: This study aimed to compare gastrointestinal digestion between two milks with the same total calcium content but different casein mineralization (CM). Design: Fifteen males (age 30.9 ± 13.8 years, BMI 22.5 ± 2.2 kg m-2) participated in this randomized cross-over study with two treatments. Participants underwent gastric magnetic resonance imaging (MRI) scans at the baseline and every 10 min up to 90 min after consumption of 600 ml milk with low or high CM. Blood samples were taken at the baseline and up to 5 hours postprandially. Primary outcomes were postprandial plasma AA concentrations and gastric emptying rate. Secondary outcomes were postprandial glucose and insulin levels, gastric coagulation as estimated by image texture metrics, and appetite ratings. Results: Gastric content volume over time was similar for both treatments. However, gastric content image analysis suggested that the liquid fraction emptied quicker in the high CM milk, while the coagulum emptied slower. Relative to high CM, low CM showed earlier appearance of AAs that are more dominant in casein, such as proline (MD 4.18 µmol L-1, 95% CI [2.38-5.98], p < 0.001), while there was no difference in appearance of AAs that are more dominant in whey protein, such as leucine. The image texture metrics homogeneity and busyness differed significantly between treatments (MD 0.007, 95% CI [0.001, 0.012], p = 0.022; MD 0.005, 95% CI [0.001, 0.010], p = 0.012) likely because of a reduced coagulation in the low CM milk. Conclusions: Mineral composition of milk can influence postprandial serum AA kinetics, likely due to differences in coagulation dynamics. The clinical trial registry number is NL8959 (https://clinicaltrials.gov).

Context matters: Neural processing of food-flavored e-cigarettes and the influence of smoking.

E-cigarettes are harmful, addictive, and popular. In e-cigarettes, nicotine is often paired with food-flavors. How this pairing of nicotine and food cues influences neural processing warrants investigation, as in smokers, both types of cues activate similar brain regions. Additionally, while most e-cigarettes are sweet, savory e-cigarettes are seemingly absent, although savory flavors are commonly liked in food. To understand how smoking status and type of flavor modulate reactions to food-flavored e-cigarettes, in comparison to actual food, neural and subjective responses to food odors were measured in a 2 (sweet vs. savory odor) x2 (food vs. e-cigarette context) x2 (smokers vs. non-smokers) design in 22 occasional/light smokers and 25 non-smokers. During fMRI scanning, participants were exposed to sweet and savory odors and pictures creating the two contexts. Liking and wanting were repeatedly measured on a 100-unit visual-analogue-scale. Results show that sweet e-cigarettes were liked (Δ = 14.2 ± 1.7) and wanted (Δ = 39.5 ± 3.1) more than savory e-cigarettes, and their cues activated the anterior cingulate more (cluster-level qFDR = 0.003). Further, we observed context-dependent variations in insula response to odors (cluster-level qFDR = 0.023, and = 0.030). Savory odors in an e-cigarette context were wanted less than the same odors in a food-context (Δ = 32.8 ± 3.1). Smokers and non-smokers reacted similarly to flavored product cues. Our results indicate that the principles of flavor preference in food cannot directly be applied to e-cigarettes and that it is challenging to design sweet and savory e-cigarettes to appeal to smokers only.

Milk coagulation and gastric emptying in women experiencing gastrointestinal symptoms after ingestion of cow's milk.

BACKGROUND: Gastrointestinal symptoms after drinking milk are often attributed to lactose intolerance or cow's milk allergy. However, some individuals without either condition still report gastrointestinal symptoms after drinking milk. This may be caused by gastric emptying (GE) rate or gastric protein coagulation. This study aimed to compare GE rate and protein coagulation after milk consumption between individuals reporting gastrointestinal symptoms and those without symptoms using a novel gastric MRI approach. METHODS: Thirty women were included in this case-control study, of whom 15 reported gastrointestinal symptoms after drinking milk and 15 were controls. Participants underwent gastric MRI before and up to 90 min after consumption of 250 mL cow's milk. Gastric content volume and image texture of the stomach contents were used to determine GE and changes in the degree of coagulation. KEY RESULTS: GE half-time did not differ between the groups (gastrointestinal symptom group 66 ± 18 min; control group 61 ± 14 min, p = 0.845). The gastrointestinal symptom group reported symptoms from 30 min onwards and rated pain highest at 90 min. The control group reported no symptoms. Image texture analyses showed a significantly higher percentage of coagulum and lower percentage of liquid in the group in the GI symptom group (MD 11%, 95% CI [3.9, 17], p = 0.003). In vitro data suggests that pH and proteolytic enzyme activity influence the coagulum structure. CONCLUSIONS AND INFERENCES: Gastric milk coagulation and emptied fraction of stomach content may differ between individuals experiencing symptoms after milk consumption, possibly due to differences in pH and proteolytic enzyme activity.

A synthesis of evidence for policy from behavioural science during COVID-19.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Adaptations to infer fitness interdependence promote the evolution of cooperation.

The evolution of cooperation is a major question in the biological and behavioral sciences. While most theoretical studies model cooperation in the context of an isolated interaction (e.g., a Prisoner's Dilemma), humans live in heterogeneous social environments, characterized by large variations in fitness interdependence-the extent to which one's fitness is affected by others. Theoretical and experimental work indicates that humans can infer, and respond to, variations in interdependence. In a heterogeneous ancestral environment, these psychological mechanisms to infer fitness interdependence could have provided a selective advantage, allowing individuals to maximize their fitness by deciding when and with whom to cooperate. Yet, to date, the link between cognitive inference, variation in fitness interdependence, and cooperation remains unclear. Here we introduce a theoretical framework to study the evolution of inference and cooperation in heterogeneous social environments, where individuals experience interactions with varying levels of corresponding interests. Using a combination of evolutionary game theory and agent-based modeling, we model the evolution of adaptive agents, who incur a cost to infer interdependence, in populations of fixed-behavior agents who always cooperate or defect. Our results indicate that natural selection could promote the evolution of psychological mechanisms to infer fitness interdependence, provided that there is enough variation in fitness interdependence to offset the cost of inference. Under certain conditions, the fixation of adaptive agents results in higher levels of cooperation. This depends crucially on the type of inference performed and the features of the interdependence landscape.

How much (ATP) does it cost to build a trypanosome? A theoretical study on the quantity of ATP needed to maintain and duplicate a bloodstream-form Trypanosoma brucei cell.

ATP hydrolysis is required for the synthesis, transport and polymerization of monomers for macromolecules as well as for the assembly of the latter into cellular structures. Other cellular processes not directly related to synthesis of biomass, such as maintenance of membrane potential and cellular shape, also require ATP. The unicellular flagellated parasite Trypanosoma brucei has a complex digenetic life cycle. The primary energy source for this parasite in its bloodstream form (BSF) is glucose, which is abundant in the host's bloodstream. Here, we made a detailed estimation of the energy budget during the BSF cell cycle. As glycolysis is the source of most produced ATP, we calculated that a single parasite produces 6.0 x 1011 molecules of ATP/cell cycle. Total biomass production (which involves biomass maintenance and duplication) accounts for ~63% of the total energy budget, while the total biomass duplication accounts for the remaining ~37% of the ATP consumption, with in both cases translation being the most expensive process. These values allowed us to estimate a theoretical YATP of 10.1 (g biomass)/mole ATP and a theoretical [Formula: see text] of 28.6 (g biomass)/mole ATP. Flagellar motility, variant surface glycoprotein recycling, transport and maintenance of transmembrane potential account for less than 30% of the consumed ATP. Finally, there is still ~5.5% available in the budget that is being used for other cellular processes of as yet unknown cost. These data put a new perspective on the assumptions about the relative energetic weight of the processes a BSF trypanosome undergoes during its cell cycle.

A Trypanosoma cruzi phosphoglycerate kinase isoform with a Per-Arnt-Sim domain acts as a possible sensor for intracellular conditions.

Per-ARNT-Sim (PAS) domains constitute a family of domains present in a wide variety of prokaryotic and eukaryotic organisms. They form part of the structure of various proteins involved in diverse cellular processes. Regulation of enzymatic activity and adaptation to environmental conditions, by binding small ligands, are the main functions attributed to PAS-containing proteins. Recently, genes for a diverse set of proteins with a PAS domain were identified in the genomes of several protists belonging to the group of kinetoplastids, however, until now few of these proteins have been characterized. In this work, we characterize a phosphoglycerate kinase containing a PAS domain present in Trypanosoma cruzi (TcPAS-PGK). This PGK isoform is an active enzyme of 58 kDa with a PAS domain located at its N-terminal end. We identified the protein's localization within glycosomes of the epimastigote form of the parasite by differential centrifugation and selective permeabilization of its membranes with digitonin, as well as in an enriched mitochondrial fraction. Heterologous expression systems were developed for the protein with the N-terminal PAS domain (PAS-PGKc) and without it (PAS-PGKt), and the substrate affinities of both forms of the protein were determined. The enzyme does not exhibit standard Michaelis-Menten kinetics. When evaluating the dependence of the specific activity of the recombinant PAS-PGK on the concentration of its substrates 3-phosphoglycerate (3PGA) and ATP, two peaks of maximal activity were found for the complete enzyme with the PAS domain and a single peak for the enzyme without the domain. Km values measured for 3PGA were 219 ± 26 and 8.8 ± 1.3 µM, and for ATP 291 ± 15 and 38 ± 2.2 µM, for the first peak of PAS-PGKc and for PAS-PGKt, respectively, whereas for the second PAS-PGKc peak values of approximately 1.1-1.2 mM were estimated for both substrates. Both recombinant proteins show inhibition by high concentrations of their substrates, ATP and 3PGA. The presence of hemin and FAD exerts a stimulatory effect on PAS-PGKc, increasing the specific activity by up to 55%. This stimulation is not observed in the absence of the PAS domain. It strongly suggests that the PAS domain has an important function in vivo in T. cruzi in the modulation of the catalytic activity of this PGK isoform. In addition, the PAS-PGK through its PAS and PGK domains could act as a sensor for intracellular conditions in the parasite to adjust its intermediary metabolism.

Antimicrobial Peptides (AMPs): Potential Therapeutic Strategy against Trypanosomiases?

Trypanosomiases are a group of tropical diseases that have devastating health and socio-economic effects worldwide. In humans, these diseases are caused by the pathogenic kinetoplastids Trypanosoma brucei, causing African trypanosomiasis or sleeping sickness, and Trypanosoma cruzi, causing American trypanosomiasis or Chagas disease. Currently, these diseases lack effective treatment. This is attributed to the high toxicity and limited trypanocidal activity of registered drugs, as well as resistance development and difficulties in their administration. All this has prompted the search for new compounds that can serve as the basis for the development of treatment of these diseases. Antimicrobial peptides (AMPs) are small peptides synthesized by both prokaryotes and (unicellular and multicellular) eukaryotes, where they fulfill functions related to competition strategy with other organisms and immune defense. These AMPs can bind and induce perturbation in cell membranes, leading to permeation of molecules, alteration of morphology, disruption of cellular homeostasis, and activation of cell death. These peptides have activity against various pathogenic microorganisms, including parasitic protists. Therefore, they are being considered for new therapeutic strategies to treat some parasitic diseases. In this review, we analyze AMPs as therapeutic alternatives for the treatment of trypanosomiases, emphasizing their possible application as possible candidates for the development of future natural anti-trypanosome drugs.

Advances in Image Processing for Epileptogenic Zone Detection with MRI.

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

The potential for clinical application of automatic quantification of olfactory bulb volume in MRI scans using convolutional neural networks.

The olfactory bulbs (OBs) play a key role in olfactory processing; their volume is important for diagnosis, prognosis and treatment of patients with olfactory loss. Until now, measurements of OB volumes have been limited to quantification of manually segmented OBs, which is a cumbersome task and makes evaluation of OB volumes in large scale clinical studies infeasible. Hence, the aim of this study was to evaluate the potential of our previously developed automatic OB segmentation method for application in clinical practice and to relate the results to clinical outcome measures. To evaluate utilization potential of the automatic segmentation method, three data sets containing MR scans of patients with olfactory loss were included. Dataset 1 (N = 66) and 3 (N = 181) were collected at the Smell and Taste Center in Ede (NL) on a 3 T scanner; dataset 2 (N = 42) was collected at the Smell and Taste Clinic in Dresden (DE) on a 1.5 T scanner. To define the reference standard, manual annotation of the OBs was performed in Dataset 1 and 2. OBs were segmented with a method that employs two consecutive convolutional neural networks (CNNs) that the first localize the OBs in an MRI scan and subsequently segment them. In Dataset 1 and 2, the method accurately segmented the OBs, resulting in a Dice coefficient above 0.7 and average symmetrical surface distance below 0.3 mm. Volumes determined from manual and automatic segmentations showed a strong correlation (Dataset 1: r = 0.79, p < 0.001; Dataset 2: r = 0.72, p = 0.004). In addition, the method was able to recognize the absence of an OB. In Dataset 3, OB volumes computed from automatic segmentations obtained with our method were related to clinical outcome measures, i.e. duration and etiology of olfactory loss, and olfactory ability. We found that OB volume was significantly related to age of the patient, duration and etiology of olfactory loss, and olfactory ability (F(5, 172) = 11.348, p < 0.001, R2 = 0.248). In conclusion, the results demonstrate that automatic segmentation of the OBs and subsequent computation of their volumes in MRI scans can be performed accurately and can be applied in clinical and research population studies. Automatic evaluation may lead to more insight in the role of OB volume in diagnosis, prognosis and treatment of olfactory loss.

Reputation Reminders: When do Eye Cues Promote Prosocial Behavior?

The watching eyes effect has gained significant attention in recent years both from scientists and from policy makers and professionals in the field. The phenomenon posits that the mere presence of eye cues can promote prosocial behavior. However, there is a growing debate about the generality of the effect across various measures and contexts. This review seeks to combine various distinct -and formerly isolated- perspectives by identifying four key components for effective interventions based on the watching eyes effect: Anonymity, crowdedness, costs, and exposure. Eye cues need to reduce perceived anonymity, be placed in non-crowded places, target low-cost prosocial acts and appear for a short amount of time. Next to these conditions, we discuss implications for other cues to reputation and recommend directions that will stimulate further research and applications in society.

Evolution: 'Millefoglie' origin of mitochondrial cristae.

Striated intracytoplasmic membranes in alphaproteobacteria are often reminiscent of millefoglie pastries. A new study reveals a protein complex homologous to that responsible for mitochondrial cristae formation drives intracytoplasmic membrane formation, thereby establishing bacterial ancestry for the biogenesis of mitochondrial cristae.