Social media uses amongst adolescents: motives, minority stress and eudaimonic well-being / Uso de redes sociales en adolescentes: motivación, estrés de minorías y bienestar eudaimónico

Introduction: The scientific evidence regarding the effects of online social media use on the well-being of adolescents is mixed. In gen-eral, passive uses (receiving, viewing content without interacting) and more screen time are related to lower well-being when compared with active uses (direct interactions and interpersonal exchanges). Objectives:This study ex-amines the types and motives for social media usage amongst adolescents, differentiating them by gender identity and sexual orientation, as well as its effects on eudaimonic well-being and minority stress. Method: A cross-sectional study was conducted with 1259 adolescents, aged 14 to 19 (M= 16.19; SD= 1.08), analysing the Scale of Motives for Using Social Net-working Sites, eudaimonic well-being, the Sexual Minority Adolescent Stress Inventory, screen time and profile type. Results:The results found that longer use time is related to finding partners, social connection and friendships; that gay and bisexual (GB) adolescents perceive more distal stressors online;and that females have higher levels of well-being. Discus-sion: The public profiles of GB males increase self-expression, although minority stress can be related to discrimination, rejection or exclusion. Dif-ferentiated socialization may contribute to a higher level of well-being in females, with both active and passive uses positively effecting eudaimonic well-being in adolescents.(AU)

Introduction: The scientific evidence regarding the effects of online social media use on the well-being of adolescents is mixed. In general, passive uses (receiving, viewing content without interacting) and more screen time are related to lower well-being when compared with active uses (direct interactions and interpersonal exchanges). Objectives: This study examines the types and motives for social media usage amongst adolescents, differentiating them by gender identity and sexual orientation, as well as its effects on eudaimonic well-being and minority stress. Method: A cross-sectional study was conducted with 1259 adolescents, aged 14 to 19 (M = 16.19; SD = 1.08), analysing the Scale of Motives for Using Social Networking Sites, eudaimonic well-being, the Sexual Minority Adolescent Stress Inventory, screen time and profile type. Results: The results found that longer use time is related to finding partners, social connection and friendships; that gay and bisexual (GB) adolescents perceive more distal stressors online; and that females have higher levels of well-being. Discussion: The public profiles of GB males increase self-expression, although minority stress can be related to discrimination, rejection or exclusion. Differentiated socialization may contribute to a higher level of well-being in females, with both active and passive uses positively effecting eudaimonic well-being in adolescents.(AU)

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits. A tutorial.

In this study we focus on two subnetworks common in the circuitry of swim central pattern generators (CPGs) in the sea slugs, Melibe leonina and Dendronotus iris and show that they are independently capable of stably producing emergent network bursting. This observation raises the question of whether the coordination of redundant bursting mechanisms plays a role in the generation of rhythm and its regulation in the given swim CPGs. To address this question, we investigate two pairwise rhythm-generating networks and examine the properties of their fundamental components: cellular and synaptic, which are crucial for proper network assembly and its stable function. We perform a slow-fast decomposition analysis of cellular dynamics and highlight its significant bifurcations occurring in isolated and coupled neurons. A novel model for slow synapses with high filtering efficiency and temporal delay is also introduced and examined. Our findings demonstrate the existence of two modes of oscillation in bicellular rhythm-generating networks with network hysteresis: i) a half-center oscillator and ii) an excitatory-inhibitory pair. These 2-cell networks offer potential as common building blocks combined in modular organization of larger neural circuits preserving robust network hysteresis.

Detection of fake face images using lightweight convolutional neural networks with stacking ensemble learning method.

Images and videos containing fake faces are the most common type of digital manipulation. Such content can lead to negative consequences by spreading false information. The use of machine learning algorithms to produce fake face images has made it challenging to distinguish between genuine and fake content. Face manipulations are categorized into four basic groups: entire face synthesis, face identity manipulation (deepfake), facial attribute manipulation and facial expression manipulation. The study utilized lightweight convolutional neural networks to detect fake face images generated by using entire face synthesis and generative adversarial networks. The dataset used in the training process includes 70,000 real images in the FFHQ dataset and 70,000 fake images produced with StyleGAN2 using the FFHQ dataset. 80% of the dataset was used for training and 20% for testing. Initially, the MobileNet, MobileNetV2, EfficientNetB0, and NASNetMobile convolutional neural networks were trained separately for the training process. In the training, the models were pre-trained on ImageNet and reused with transfer learning. As a result of the first trainings EfficientNetB0 algorithm reached the highest accuracy of 93.64%. The EfficientNetB0 algorithm was revised to increase its accuracy rate by adding two dense layers (256 neurons) with ReLU activation, two dropout layers, one flattening layer, one dense layer (128 neurons) with ReLU activation function, and a softmax activation function used for the classification dense layer with two nodes. As a result of this process accuracy rate of 95.48% was achieved with EfficientNetB0 algorithm. Finally, the model that achieved 95.48% accuracy was used to train MobileNet and MobileNetV2 models together using the stacking ensemble learning method, resulting in the highest accuracy rate of 96.44%.

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes.

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Co-regulatory network analysis of the main secondary metabolite (SM) biosynthesis in Crocus sativus L.

Saffron (Crocus sativus L.) is being embraced as the most important medicinal plant and the commercial source of saffron spice. Despite the beneficial economic and medicinal properties of saffron, the regulatory mechanism of the correlation of TFs and genes related to the biosynthesis of the apocarotenoids pathway is less obvious. Realizing these regulatory hierarchies of gene expression networks related to secondary metabolites production events is the main challenge owing to the complex and extensive interactions between the genetic behaviors. Recently, high throughput expression data have been highly feasible for constructing co-regulation networks to reveal the regulated processes and identifying novel candidate hub genes in response to complex processes of the biosynthesis of secondary metabolites. Herein, we performed Weighted Gene Co-expression Network Analysis (WGCNA), a systems biology method, to identify 11 regulated modules and hub TFs related to secondary metabolites. Three specialized modules were found in the apocarotenoids pathway. Several hub TFs were identified in notable modules, including MADS, C2H2, ERF, bZIP, HD-ZIP, and zinc finger protein MYB and HB, which were potentially associated with apocarotenoid biosynthesis. Furthermore, the expression levels of six hub TFs and six co-regulated genes of apocarotenoids were validated with RT-qPCR. The results confirmed that hub TFs specially MADS, C2H2, and ERF had a high correlation (P < 0.05) and a positive effect on genes under their control in apocarotenoid biosynthesis (CCD2, GLT2, and ADH) among different C. sativus ecotypes in which the metabolite contents were assayed. Promoter analysis of the co-expressed genes of the modules involved in apocarotenoids biosynthesis pathway suggested that not only are the genes co-expressed, but also share common regulatory motifs specially related to hub TFs of each module and that they may describe their common regulation. The result can be used to engineer valuable secondary metabolites of C. sativus by manipulating the hub regulatory TFs.

Iron Homeostatic Regulation in Saccharomyces cerevisiae: Introduction to a Computational Modeling Method.

Over the past 30 years, much has been learned regarding iron homeostatic regulation in budding yeast, S. cerevisiae, including the identity of many of the proteins and molecular-level regulatory mechanisms involved. Most advances have involved inferring such mechanisms based on the analysis of iron-dysregulation phenotypes arising in various genetic mutant strains. Still lacking is a cellular- or system-level understanding of iron homeostasis. These experimental advances are summarized in this review, and a method for developing cellular-level regulatory mechanisms in yeast is presented. The method employs the results of Mössbauer spectroscopy of whole cells and organelles, iron quantification of the same, and ordinary differential equation-based mathematical models. Current models are simplistic when compared to the complexity of iron homeostasis in real cells, yet they hold promise as a useful, perhaps even required, complement to the popular genetics-based approach. The fundamental problem in comprehending cellular regulatory mechanisms is that, given the complexities involved, different molecular-level mechanisms can often give rise to virtually indistinguishable cellular phenotypes. Mathematical models cannot eliminate this problem, but they can minimize it.

Development and evaluation of a deep neural network model for orthokeratology lens fitting.

PURPOSE: To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. The objective was to refine the standardisation of fitting procedures and curtail subjective evaluations, thereby augmenting patient safety in the context of increasing global myopia. METHODS: A retrospective study of successful orthokeratology treatment was conducted on 266 patients, with 449 eyes being analysed. A DNN model with an 80%-20% training-validation split predicted lens parameters (curvature, power and diameter) using corneal topography and refractive indices. The model featured two hidden layers for precision. RESULTS: The DNN model achieved mean absolute errors of 0.21 D for alignment curvature (AC), 0.19 D for target power (TP) and 0.02 mm for lens diameter (LD), with R2 values of 0.97, 0.95 and 0.91, respectively. Accuracy decreased for myopia of less than 1.00 D, astigmatism exceeding 2.00 D and corneal curvatures >45.00 D. Approximately, 2% of cases with unique physiological characteristics showed notable prediction variances. CONCLUSION: While exhibiting high accuracy, the DNN model's limitations in specifying myopia, cylinder power and corneal curvature cases highlight the need for algorithmic refinement and clinical validation in orthokeratology practice.

Changes in the proteome of Apis mellifera acutely exposed to sublethal dosage of glyphosate and imidacloprid.

Uncontrolled use of pesticides has caused a dramatic reduction in the number of pollinators, including bees. Studies on the effects of pesticides on bees have reported effects on both metabolic and neurological levels under chronic exposure. In this study, variations in the differential expression of head and thorax-abdomen proteins in Africanized A. mellifera bees treated acutely with sublethal doses of glyphosate and imidacloprid were studied using a proteomic approach. A total of 92 proteins were detected, 49 of which were differentially expressed compared to those in the control group (47 downregulated and 2 upregulated). Protein interaction networks with differential protein expression ratios suggested that acute exposure of A. mellifera to sublethal doses of glyphosate could cause head damage, which is mainly associated with behavior and metabolism. Simultaneously, imidacloprid can cause damage associated with metabolism as well as, neuronal damage, cellular stress, and impairment of the detoxification system. Regarding the thorax-abdomen fractions, glyphosate could lead to cytoskeleton reorganization and a reduction in defense mechanisms, whereas imidacloprid could affect the coordination and impairment of the oxidative stress response.

Food webs in food webs: the micro-macro interplay of multilayered networks.

Food webs are typically defined as being macro-organism-based (e.g., plants, mammals, birds) or microbial (e.g., bacteria, fungi, viruses). However, these characterizations have limits. We propose a multilayered food web conceptual model where microbial food webs are nested within food webs composed of macro-organisms. Nesting occurs through host-microbe interactions, which influence the health and behavior of host macro-organisms, such that host microbiomes likely alter population dynamics of interacting macro-organisms and vice versa. Here, we explore the theoretical underpinnings of multilayered food webs and the implications of this new conceptual model on food web ecology. Our framework opens avenues for new empirical investigations into complex ecological networks and provides a new lens through which to view a network's response to ecosystem changes.

Guideline "Parkinson's disease" of the German Society of Neurology (Deutsche Gesellschaft für Neurologie): concepts of care.

INTRODUCTION: In 2023, the German Society of Neurology published a new guideline on Parkinson's disease. An important section dealt with PD care concepts, which represent a particularly dynamic field of PD research, including their implementation in clinical practice. Parkinson's disease is the second most common age-associated neurodegenerative disease. Current estimates of the number of cases in the population describe a significant increase in prevalence in Germany by 2030 with higher proportions in rural areas, which also have a lack of sufficient PD care resources. RECOMMENDATIONS: In comparison with other international guidelines, which have so far mentioned palliative care and Parkinson's nurses in particular, the German S2k guideline expands the recommended concepts of PD care to include PD day clinics, inpatient complex treatment, and PD networks. CONCLUSION: Concepts of PD care guidelines are necessary because of the complex and rapidly evolving field of PD care provision. If applied appropriately, the potential for optimized care can be exploited and both the patient burden and the economic burden can be reduced. Given that modern care concepts have so far only been applied in a few regions, it is often impossible to generate broad evidence-based data, so that the evaluation of PD care concepts is partly dependent on expert opinion.

Movement-driven modelling reveals new patterns in disease transmission networks.

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi-host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment. Our objective was to assess whether a more comprehensive approach, using a movement-based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches. The model was built from GPS-collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free-range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model. Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes. Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi-host systems in Mediterranean environments.

Interaction between the prefrontal and visual cortices supports subjective fear.

It has been reported that threatening and non-threatening visual stimuli can be distinguished based on the multi-voxel patterns of haemodynamic activity in the human ventral visual stream. Do these findings mean that there may be evolutionarily hardwired mechanisms within early perception, for the fast and automatic detection of threat, and maybe even for the generation of the subjective experience of fear? In this human neuroimaging study, we presented participants ('fear' group: N = 30; 'no fear' group: N = 30) with 2700 images of animals that could trigger subjective fear or not as a function of the individual's idiosyncratic 'fear profiles' (i.e. fear ratings of animals reported by a given participant). We provide evidence that the ventral visual stream may represent affectively neutral visual features that are statistically associated with fear ratings of participants, without representing the subjective experience of fear itself. More specifically, we show that patterns of haemodynamic activity predictive of a specific 'fear profile' can be observed in the ventral visual stream whether a participant reports being afraid of the stimuli or not. Further, we found that the multivariate information synchronization between ventral visual areas and prefrontal regions distinguished participants who reported being subjectively afraid of the stimuli from those who did not. Together, these findings support the view that the subjective experience of fear may depend on the relevant visual information triggering implicit metacognitive mechanisms in the prefrontal cortex. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Early warning of atrial fibrillation using deep learning.

Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.

An iterative neural network approach applied to human-induced force reconstruction using a non-linear electrodynamic shaker.

Human-induced force analysis plays an important role across a wide range of disciplines, including biomechanics, sport engineering, health monitoring or structural engineering. Specifically, this paper focuses on the replication of ground reaction forces (GRF) generated by humans during movement. They can provide critical information about human-mechanics and be used to optimize athletic performance, prevent and rehabilitate injuries and assess structural vibrations in engineering applications. It is presented an experimental approach that uses an electrodynamic shaker (APS 400) to replicate GRFs generated by humans during movement, with a high degree of accuracy. Successful force reconstruction implies a high fidelity in signal reproduction with the electrodynamic shaker, which leads to an inverse problem, where a reference signal must be replicated with a nonlinear and non-invertible system. The solution presented in this paper relies on the development of an iterative neural network and an inversion-free approach, which aims to generate the most effective drive signal that minimizes the error between the experimental force signal exerted by the shaker and the reference. After the optimization process, the weights of the neural network are updated to make the shaker behave as desired, achieving excellent results in both time and frequency domains.

Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage.

Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction techniques, existing machine-learning and deep-learning approaches are suffering from low classification performance and explainability issues that must be improved. In the current study, we hypothesized that even at the early childhood stage (as early as 3-years), connectivity between brain regions could decode brain states and predict behavioral performance in false-belief tasks. To this end, we proposed an explainable deep learning framework to decode brain states (Theory of Mind and Pain states) and predict individual performance on ToM-related false-belief tasks in a developmental dataset. We proposed an explainable spatiotemporal connectivity-based Graph Convolutional Neural Network (Ex-stGCNN) model for decoding brain states. Here, we consider a developmental dataset, N = 155 (122 children; 3-12 yrs and 33 adults; 18-39 yrs), in which participants watched a short, soundless animated movie, shown to activate Theory-of-Mind (ToM) and pain networs. After scanning, the participants underwent a ToM-related false-belief task, leading to categorization into the pass, fail, and inconsistent groups based on performance. We trained our proposed model using Functional Connectivity (FC) and Inter-Subject Functional Correlations (ISFC) matrices separately. We observed that the stimulus-driven feature set (ISFC) could capture ToM and Pain brain states more accurately with an average accuracy of 94%, whereas it achieved 85% accuracy using FC matrices. We also validated our results using five-fold cross-validation and achieved an average accuracy of 92%. Besides this study, we applied the SHapley Additive exPlanations (SHAP) approach to identify brain fingerprints that contributed the most to predictions. We hypothesized that ToM network brain connectivity could predict individual performance on false-belief tasks. We proposed an Explainable Convolutional Variational Auto-Encoder (Ex-Convolutional VAE) model to predict individual performance on false-belief tasks and trained the model using FC and ISFC matrices separately. ISFC matrices again outperformed the FC matrices in prediction of individual performance. We achieved 93.5% accuracy with an F1-score of 0.94 using ISFC matrices and achieved 90% accuracy with an F1-score of 0.91 using FC matrices.

Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network.

Gliomas observed in medical images require expert neuro-radiologist evaluation for treatment planning and monitoring, motivating development of intelligent systems capable of automating aspects of tumour evaluation. Deep learning models for automatic image segmentation rely on the amount and quality of training data. In this study we developed a neuroimaging synthesis technique to augment data for training fully-convolutional networks (U-nets) to perform automatic glioma segmentation. We used StyleGAN2-ada to simultaneously generate fluid-attenuated inversion recovery (FLAIR) magnetic resonance images and corresponding glioma segmentation masks. Synthetic data were successively added to real training data (n = 2751) in fourteen rounds of 1000 and used to train U-nets that were evaluated on held-out validation (n = 590) and test sets (n = 588). U-nets were trained with and without geometric augmentation (translation, zoom and shear), and Dice coefficients were computed to evaluate segmentation performance. We also monitored the number of training iterations before stopping, total training time, and time per iteration to evaluate computational costs associated with training each U-net. Synthetic data augmentation yielded marginal improvements in Dice coefficients (validation set +0.0409, test set +0.0355), whereas geometric augmentation improved generalization (standard deviation between training, validation and test set performances of 0.01 with, and 0.04 without geometric augmentation). Based on the modest performance gains for automatic glioma segmentation we find it hard to justify the computational expense of developing a synthetic image generation pipeline. Future work may seek to optimize the efficiency of synthetic data generation for augmentation of neuroimaging data.

Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks.

In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that are close to the time of interest. Adaptive lasso weights are introduced to encourage temporal smoothness in edge strength, while a novel elastic fused- l 0 $$ {l}_0 $$ penalty removes spurious edges and encourages temporal smoothness in network structure. Our approach can handle practical complications such as unbalanced visit times. We conduct simulation studies to compare our approach with existing methods. We then apply our method to data from PREDICT-HD, a large prospective observational study of pre-manifest Huntington's disease (HD) patients, to identify symptom and imaging network changes that precede clinical diagnosis of HD.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity.

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

Examining site intervention efficacy and uncertainties with conceptual Bayesian networks: preventing offsite migration of DNAPL and contaminated groundwater.

For contaminated sites, conceptual site models (CSMs) guide the assessment and management of risks, including remediation strategies. Recent research has expanded diagrammatic CSMs with structural causal modeling to develop what are nominally called conceptual Bayesian networks (CBNs) for environmental risk assessment. These CBNs may also be useful for problems of controlling and preventing offsite contaminant migration, especially for sites containing dense nonaqueous phase liquids (DNAPLs). In particular, the CBNs provide greater clarity on the causal relationships between source term, onsite and offsite migration, and remediation effectiveness characterization for contaminated DNAPL sites compared to traditional CSMs. These ideas are demonstrated by the inclusion of modifying variables, causal pathway analysis, and interventions in CBNs. Additionally, several new extensions of the CBN concept are explored including the representation of measurement variables as lines of evidence and alignment with conventional pictorial CSMs for groundwater modeling. Taken as a whole, the CBNs provide a powerful and adaptable knowledge representation tool for remediating subsurface systems contaminated by DNAPL.

A Computational Framework for Memory Engrams.

Memory engrams in mice brains are potentially related to groups of concept cells in human brains. A single concept cell in human hippocampus responds, for example, not only to different images of the same object or person but also to its name written down in characters. Importantly, a single mental concept (object or person) is represented by several concept cells and each concept cell can respond to more than one concept. Computational work shows how mental concepts can be embedded in recurrent artificial neural networks as memory engrams and how neurons that are shared between different engrams can lead to associations between concepts. Therefore, observations at the level of neurons can be linked to cognitive notions of memory recall and association chains between memory items.