Do multiple-trial games better reflect prosocial behavior than single-trial games?

Most prior research on the external validity of mixed-motive games has studied only one single game version and/or one specific type of real-life prosocial behavior. The present study employs a different approach. We used multiple game trials - with different payoff structures - to measure participants' behavior in the Prisoner's Dilemma, the Commons Dilemma, and the Public Goods Dilemma We then examined the associations between these aggregated game behaviors and a wide set of self-reported prosocial behaviors such as donations, commuting, and environmental behaviors. We also related these prosocial behavior measures to a dispositional measure of prosociality, social value orientation. We report evidence that the weak statistical relationships routinely observed in prior studies are at least partially a consequence of failures to aggregate. More specifically, our results show that aggregation over multiple game trials was especially effective for the Prisoner's Dilemma, whereas it was somewhat effective for the Public Goods Dilemma Yet, aggregation on the side of the prosocial behaviors was effective for both these games, as well as for social value orientation. The Commons Dilemma, however, turned out to yield invariably poor relationships with prosocial behavior, regardless of the level of aggregation. Based on these findings, we conclude that the use of multiple instances of game behavior and prosocial behavior is preferable to the use of only a single measurement

Liver fibrosis staging by deep learning:a visual-based explanation of diagnostic decisions of the model

OBJECTIVES: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. METHODS: The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. RESULTS: The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2-F4), advanced fibrosis (F3-F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). CONCLUSIONS: Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning-based liver fibrosis staging algorithms. KEY POINTS: • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning

The contribution of teacher, parental and peer support in self-reported school and general well-being among ethnic-cultural minority and majority youth

Social support has been shown to be a crucial element in the well-being of children and adolescents. The present research article investigated how various sources of social support (i.e., parental support, teacher support and peer support) are related to school well-being and general well-being,. A survey was administered to N = 12,215 primary school pupils, pertaining to three ethnic-cultural groups, i.e., the national majority group, the Eastern European minority group and the Middle Eastern minority group. The results showed that perceived teacher support was most strongly and positively related to school well-being, although peer support was also an important determinant of school well-being. All three sources of perceived support were positively related to general well-being. Furthermore, and contrary to previous research, no significant differences were found between both minority groups and the national majority in terms of perceived teacher support. Conversely, both minority groups reported lower perceived parental and peer support. It was further shown that minority status moderated the relationship between the various sources of support and school well-being, although it should be articulated that these effects sizes were fairly small. School diversity, finally, did not yield any relevant effects. Similarities and differences with the existing literature on school well-being are delineated, and potential explanations for these divergences are discussed

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

FDG uptake, a surrogate of tumour hypoxia?

Introduction Tumour hyperglycolysis is driven by activation of hypoxia-inducible factor-1 (HIF-1) through tumour hypoxia. Accordingly, the degree of 2-fluro-2-deoxy-D-glucose (FDG) uptake by tumours might indirectly reflect the level of hypoxia, obviating the need for more specific radiopharmaceuticals for hypoxia imaging. Discussion In this paper, available data on the relationship between hypoxia and FDG uptake by tumour tissue in vitro and in vivo are reviewed. In pre-clinical in vitro studies, acute hypoxia was consistently shown to increase FDG uptake by normal and tumour cells within a couple of hours after onset with mobilisation or modification of glucose transporters optimising glucose uptake, followed by a delayed response with increased rates of transcription of GLUT mRNA. In pre-clinical imaging studies on chronic hypoxia that compared FDG uptake by tumours grown in rat or mice to uptake by FMISO, the pattern of normoxic and hypoxic regions within the human tumour xenografts, as imaged by FMISO, largely correlated with glucose metabolism although minor locoregional differences could not be excluded. In the clinical setting, data are limited and discordant. Conclusion Further evaluation of FDG uptake by various tumour types in relation to intrinsic and bioreductive markers of hypoxia and response to radiotherapy or hypoxia-dependent drugs is needed to fully assess its application as a marker of hypoxia in the clinical setting

Phylogeny of Penicillium and the segregation of Trichocomaceae into three families

Species of Trichocomaceae occur commonly and are important to both industry and medicine. They are associated with food spoilage and mycotoxin production and can occur in the indoor environment, causing health hazards by the formation of β-glucans, mycotoxins and surface proteins. Some species are opportunistic pathogens, while others are exploited in biotechnology for the production of enzymes, antibiotics and other products. Penicillium belongs phylogenetically to Trichocomaceae and more than 250 species are currently accepted in this genus. In this study, we investigated the relationship of Penicillium to other genera of Trichocomaceae and studied in detail the phylogeny of the genus itself. In order to study these relationships, partial RPB1, RPB2 (RNA polymerase II genes), Tsr1 (putative ribosome biogenesis protein) and Cct8 (putative chaperonin complex component TCP-1) gene sequences were obtained. The Trichocomaceae are divided in three separate families: Aspergillaceae, Thermoascaceae and Trichocomaceae. The Aspergillaceae are characterised by the formation flask-shaped or cylindrical phialides, asci produced inside cleistothecia or surrounded by Hülle cells and mainly ascospores with a furrow or slit, while the Trichocomaceae are defined by the formation of lanceolate phialides, asci borne within a tuft or layer of loose hyphae and ascospores lacking a slit. Thermoascus and Paecilomyces, both members of Thermoascaceae, also form ascospores lacking a furrow or slit, but are differentiated from Trichocomaceae by the production of asci from croziers and their thermotolerant or thermophilic nature. Phylogenetic analysis shows that Penicillium is polyphyletic. The genus is re-defined and a monophyletic genus for both anamorphs and teleomorphs is created (Penicillium sensu stricto). The genera Thysanophora, Eupenicillium, Chromocleista, Hemicarpenteles and Torulomyces belong in Penicillium s. str. and new combinations for the species belonging to these genera are proposed. Analysis of Penicillium below genus rank revealed the presence of 25 clades. A new classification system including both anamorph and teleomorph species is proposed and these 25 clades are treated here as sections. An overview of species belonging to each section is presented

Measuring serotonin synthesis: from conventional methods to PET tracers and their (pre)clinical implications

The serotonergic system of the brain is complex, with an extensive innervation pattern covering all brain regions and endowed with at least 15 different receptors (each with their particular distribution patterns), specific reuptake mechanisms and synthetic processes. Many aspects of the functioning of the serotonergic system are still unclear, partially because of the difficulty of measuring physiological processes in the living brain. In this review we give an overview of the conventional methods of measuring serotonin synthesis and methods using positron emission tomography (PET) tracers, more specifically with respect to serotonergic function in affective disorders. Conventional methods are invasive and do not directly measure synthesis rates. Although they may give insight into turnover rates, a more direct measurement may be preferred. PET is a noninvasive technique which can trace metabolic processes, like serotonin synthesis. Tracers developed for this purpose are α-[11C]methyltryptophan ([11C]AMT) and 5-hydroxy-L-[β-11C]tryptophan ([11C]5-HTP). Both tracers have advantages and disadvantages. [11C]AMT can enter the kynurenine pathway under inflammatory conditions (and thus provide a false signal), but this tracer has been used in many studies leading to novel insights regarding antidepressant action. [11C]5-HTP is difficult to produce, but trapping of this compound may better represent serotonin synthesis. AMT and 5-HTP kinetics are differently affected by tryptophan depletion and changes of mood. This may indicate that both tracers are associated with different enzymatic processes. In conclusion, PET with radiolabelled substrates for the serotonergic pathway is the only direct way to detect changes of serotonin synthesis in the living brain