Investigating the suitability of online eye tracking for psychological research: Evidence from comparisons with in-person data using emotion-attention interaction tasks.

The future is bound to bring rapid methodological changes to psychological research. One such promising candidate is the use of webcam-based eye tracking. Earlier research investigating the quality of online eye-tracking data has found increased spatial and temporal error compared to infrared recordings. Our studies expand on this work by investigating how this spatial error impacts researchers' abilities to study psychological phenomena. We carried out two studies involving emotion-attention interaction tasks, using four participant samples. In each study, one sample involved typical in-person collection of infrared eye-tracking data, and the other involved online collection of webcam-based data. We had two main findings: First, we found that the online data replicated seven of eight in-person results, although the effect sizes were just 52% [42%, 62%] the size of those seen in-person. Second, explaining the lack of replication in one result, we show how online eye tracking is biased toward recording more gaze points near the center of participants' screen, which can interfere with comparisons if left unchecked. Overall, our results suggest that well-powered online eye-tracking research is highly feasible, although researchers must exercise caution, collecting more participants and potentially adjusting their stimulus designs or analytic procedures.

GAHLS: an optimized graph analytics based high level synthesis framework.

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

What does a population-level mediation reveal about individual people?

Mediation analysis investigates the covariation of variables in a population of interest. In contrast, the resolution level of psychological theory, at its core, aims to reach all the way to the behaviors, mental processes, and relationships of individual persons. It would be a logical error to presume that the population-level pattern of behavior revealed by a mediation analysis directly describes all, or even many, individual members of the population. Instead, to reconcile collective covariation with theoretical claims about individual behavior, one needs to look beyond abstract aggregate trends. Taking data quality as a given and a mediation model's estimated parameters as accurate population-level depictions, what can one say about the number of people properly described by the linkages in that mediation analysis? How many individuals are exceptions to that pattern or pathway? How can we bridge the gap between psychological theory and analytic method? We provide a simple framework for understanding how many people actually align with the pattern of relationships revealed by a population-level mediation. Additionally, for those individuals who are exceptions to that pattern, we tabulate how many people mismatch which features of the mediation pattern. Consistent with the person-oriented research paradigm, understanding the distribution of alignment and mismatches goes beyond the realm of traditional variable-level mediation analysis. Yet, such a tabulation is key to designing potential interventions. It provides the basis for predicting how many people stand to either benefit from, or be disadvantaged by, which type of intervention.

The role of long-term power-law memory in controlling large-scale dynamical networks.

Controlling large-scale dynamical networks is crucial to understand and, ultimately, craft the evolution of complex behavior. While broadly speaking we understand how to control Markov dynamical networks, where the current state is only a function of its previous state, we lack a general understanding of how to control dynamical networks whose current state depends on states in the distant past (i.e. long-term memory). Therefore, we require a different way to analyze and control the more prevalent long-term memory dynamical networks. Herein, we propose a new approach to control dynamical networks exhibiting long-term power-law memory dependencies. Our newly proposed method enables us to find the minimum number of driven nodes (i.e. the state vertices in the network that are connected to one and only one input) and their placement to control a long-term power-law memory dynamical network given a specific time-horizon, which we define as the 'time-to-control'. Remarkably, we provide evidence that long-term power-law memory dynamical networks require considerably fewer driven nodes to steer the network's state to a desired goal for any given time-to-control as compared with Markov dynamical networks. Finally, our method can be used as a tool to determine the existence of long-term memory dynamics in networks.

Emotional dissociations in temporal associations: opposing effects of arousal on memory for details surrounding unpleasant events.

Research targeting emotion's impact on relational episodic memory has largely focused on spatial aspects, but less is known about emotion's impact on memory for an event's temporal associations. The present research investigated this topic. Participants viewed a series of interspersed negative and neutral images with instructions to create stories linking successive images. Later, participants performed a surprise memory test, which measured temporal associations between pairs of consecutive pictures where one picture was negative and one was neutral. Analyses focused on how the order of negative and neutral images during encoding influenced retrieval accuracy. Converging results from a discovery study (N = 72) and pre-registered replication study (N = 150) revealed a "forward-favouring" effect of emotion in temporal memory encoding: Participants encoded associations between negative stimuli and subsequent neutral stimuli more strongly than associations between negative stimuli and preceding neutral stimuli. This finding may reflect a novel trade-off regarding emotion's effects on memory and is relevant for understanding affective disorders, as key clinical symptoms can be conceptualised as maladaptive memory retrieval of temporal details.

A unified approach of detecting phase transition in time-varying complex networks.

Deciphering the non-trivial interactions and mechanisms driving the evolution of time-varying complex networks (TVCNs) plays a crucial role in designing optimal control strategies for such networks or enhancing their causal predictive capabilities. In this paper, we advance the science of TVCNs by providing a mathematical framework through which we can gauge how local changes within a complex weighted network affect its global properties. More precisely, we focus on unraveling unknown geometric properties of a network and determine its implications on detecting phase transitions within the dynamics of a TVCN. In this vein, we aim at elaborating a novel and unified approach that can be used to depict the relationship between local interactions in a complex network and its global kinetics. We propose a geometric-inspired framework to characterize the network's state and detect a phase transition between different states, to infer the TVCN's dynamics. A phase of a TVCN is determined by its Forman-Ricci curvature property. Numerical experiments show the usefulness of the proposed curvature formalism to detect the transition between phases within artificially generated networks. Furthermore, we demonstrate the effectiveness of the proposed framework in identifying the phase transition phenomena governing the training and learning processes of artificial neural networks. Moreover, we exploit this approach to investigate the phase transition phenomena in cellular re-programming by interpreting the dynamics of Hi-C matrices as TVCNs and observing singularity trends in the curvature network entropy. Finally, we demonstrate that this curvature formalism can detect a political change. Specifically, our framework can be applied to the US Senate data to detect a political change in the United States of America after the 1994 election, as discussed by political scientists.

Social Expectations are Primarily Rooted in Reciprocity: An Investigation of Fairness, Cooperation, and Trustworthiness.

Social expectations guide people's evaluations of others' behaviors, but the origins of these expectations remain unclear. It is traditionally thought that people's expectations depend on their past observations of others' behavior, and people harshly judge atypical behavior. Here, we considered that social expectations are also influenced by a drive for reciprocity, and people evaluate others' actions by reflecting on their own decisions. To compare these views, we performed four studies. Study 1 used an Ultimatum Game task where participants alternated Responder and Proposer roles. Modeling participants' expectations suggested they evaluated the fairness of received offers via comparisons to their own offers. Study 2 replicated these findings and showed that observing selfish behavior (lowball offers) only promoted acceptance of selfishness if observers started acting selfishly themselves. Study 3 generalized the findings, demonstrating that they also arise in the Public Goods Game, emerge cross-culturally, and apply to antisocial punishment whereby selfish players punish generosity. Finally, Study 4 introduced the Trust Game and showed that participants trusted players who reciprocated their behavior, even if it was selfish, as much as they trusted generous players. Overall, this research shows that social expectations and evaluations are rooted in drives for reciprocity. This carries theoretical implications, speaking to a parallel in the mechanisms driving both decision-making and social evaluations, along with practical importance for understanding and promoting cooperation.

ConnSearch: A framework for functional connectivity analysis designed for interpretability and effectiveness at limited sample sizes.

Functional connectivity studies increasingly turn to machine learning methods, which typically involve fitting a connectome-wide classifier, then conducting post hoc interpretation analyses to identify the neural correlates that best predict a dependent variable. However, this traditional analytic paradigm suffers from two main limitations. First, even if classifiers are perfectly accurate, interpretation analyses may not identify all the patterns expressed by a dependent variable. Second, even if classifiers are generalizable, the patterns implicated via interpretation analyses may not replicate. In other words, this traditional approach can yield effective classifiers while falling short of most neuroscientists' goals: pinpointing the neural correlates of dependent variables. We propose a new framework for multivariate analysis, ConnSearch, which involves dividing the connectome into components (e.g., groups of highly connected regions) and fitting an independent model for each component (e.g., a support vector machine or a correlation-based model). Conclusions about the link between a dependent variable and the brain are based on which components yield predictive models rather than on interpretation analysis. We used working memory data from the Human Connectome Project (N = 50-250) to compare ConnSearch with four existing connectome-wide classification/interpretation methods. For each approach, the models attempted to classify examples as being from the high-load or low-load conditions (binary labels). Relative to traditional methods, ConnSearch identified neural correlates that were more comprehensive, had greater consistency with the WM literature, and better replicated across datasets. Hence, ConnSearch is well-positioned to be an effective tool for functional connectivity research.

Poloxamer/Carboxymethyl Pullulan Aqueous Systems-Miscibility and Thermogelation Studies Using Viscometry, Rheology and Dynamic Light Scattering.

Thermally-induced gelling systems based on Poloxamer 407 (PL) and polysaccharides are known for their biomedical applications; however, phase separation frequently occurs in mixtures of poloxamer and neutral polysaccharides. In the present paper, the carboxymethyl pullulan (CMP) (here synthesized) was proposed for compatibilization with poloxamer (PL). The miscibility between PL and CMP in dilute aqueous solution was studied by capillary viscometry. CMP with substitution degrees higher than 0.5 proved to be compatible with PL. The thermogelation of concentrated PL solutions (17%) in the presence of CMP was monitored by the tube inversion method, texture analysis and rheology. The micellization and gelation of PL in the absence or in the presence of CMP were also studied by dynamic light scattering. The critical micelle temperature and sol-gel transition temperature decrease with the addition of CMP, but the concentration of CMP has a peculiar influence on the rheological parameters of the gels. In fact, low concentrations of CMP decrease the gel strength. With a further increase in polyelectrolyte concentration, the gel strength increases until 1% CMP, then the rheological parameters are lowered again. At 37 °C, the gels are able to recover the initial network structure after high deformations, showing a reversible healing process.

Modeling collective cell behavior in cancer: Perspectives from an interdisciplinary conversation.

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.

Fractional Dynamics Foster Deep Learning of COPD Stage Prediction.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages-from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.

Temperature/pH-Sensitive Double Cross-Linked Hydrogels as Platform for Controlled Delivery of Metoclopramide.

Novel double cross-linked (DC) hydrogels with pH-/temperature-sensitive properties were designed and developed. Therefore, linear pH-sensitive poly(methyl vinyl ether-alt-maleic acid) (P(VME/MA)) macromolecules were absorbed within a thermosensitive poly(N-isopropylacrylamide-co-hydroxyethylacrylamide)-hydrogel (PNH) and, subsequently, cross-linked together through a solvent-free thermal method. As a novelty, double cross-linked hydrogels were obtained from previously purified polymers in the absence of any solvent or cross-linking agent, which are generally harmful for the body. The new DC structures were characterized by FT-IR spectroscopy, SEM, swelling kinetic measurements, and mechanical tests. The resulting scaffolds exhibited interconnected pores and a flexible pattern, compared to the brittle structure of conventional PNH. The swelling kinetics of DC hydrogels were deeply affected by temperature (25 and 37 °C) and pH (7.4 and 1.2). Furthermore, the hydrogels absorbed a great amount of water in a basic environment and displayed improved mechanical properties. Metoclopramide (Met) was loaded within DC hydrogels as a model drug to investigate the ability of the support to control the drug release rate. The results obtained recommended them as convenient platforms for the oral administration of drugs, with the release of the largest part of the active principle occurring in the colon.

Brain age estimation reveals older adults' accelerated senescence after traumatic brain injury.

Adults aged 60 and over are most vulnerable to mild traumatic brain injury (mTBI). Nevertheless, the extent to which chronological age (CA) at injury affects TBI-related brain aging is unknown. This study applies Gaussian process regression to T1-weighted magnetic resonance images (MRIs) acquired within [Formula: see text]7 days and again [Formula: see text]6 months after a single mTBI sustained by 133 participants aged 20-83 (CA [Formula: see text] = 42.6 ± 17 years; 51 females). Brain BAs are estimated, modeled, and compared as a function of sex and CA at injury using a statistical model selection procedure. On average, the brains of older adults age by 15.3 ± 6.9 years after mTBI, whereas those of younger adults age only by 1.8 ± 5.6 years, a significant difference (Welch's t32 = - 9.17, p ≃ 9.47 × 10-11). For an adult aged [Formula: see text]30 to [Formula: see text]60, the expected amount of TBI-related brain aging is [Formula: see text]3 years greater than in an individual younger by a decade. For an individual over [Formula: see text]60, the respective amount is [Formula: see text]7 years. Despite no significant sex differences in brain aging (Welch's t108 = 0.78, p > 0.78), the statistical test is underpowered. BAs estimated at acute baseline versus chronic follow-up do not differ significantly (t264 = 0.41, p > 0.66, power = 80%), suggesting negligible TBI-related brain aging during the chronic stage of TBI despite accelerated aging during the acute stage. Our results indicate that a single mTBI sustained after age [Formula: see text]60 involves approximately [Formula: see text]10 years of premature and lasting brain aging, which is MRI detectable as early as [Formula: see text]7 days post-injury.

Inferring functional communities from partially observed biological networks exploiting geometric topology and side information.

Cellular biological networks represent the molecular interactions that shape function of living cells. Uncovering the organization of a biological network requires efficient and accurate algorithms to determine the components, termed communities, underlying specific processes. Detecting functional communities is challenging because reconstructed biological networks are always incomplete due to technical bias and biological complexity, and the evaluation of putative communities is further complicated by a lack of known ground truth. To address these challenges, we developed a geometric-based detection framework based on Ollivier-Ricci curvature to exploit information about network topology to perform community detection from partially observed biological networks. We further improved this approach by integrating knowledge of gene function, termed side information, into the Ollivier-Ricci curvature algorithm to aid in community detection. This approach identified essential conserved and varied biological communities from partially observed Arabidopsis protein interaction datasets better than the previously used methods. We show that Ollivier-Ricci curvature with side information identified an expanded auxin community to include an important protein stability complex, the Cop9 signalosome, consistent with previous reported links to auxin response and root development. The results show that community detection based on Ollivier-Ricci curvature with side information can uncover novel components and novel communities in biological networks, providing novel insight into the organization and function of complex networks.

Spanning Network Gels from Nanoparticles and Graph Theoretical Analysis of Their Structure and Properties.

Gels self-assembled from colloidal nanoparticles (NPs) translate the size-dependent properties of nanostructures to materials with macroscale volumes. Large spanning networks of NP chains provide high interconnectivity within the material necessary for a wide range of properties from conductivity to viscoelasticity. However, a great challenge for nanoscale engineering of such gels lies in being able to accurately and quantitatively describe their complex non-crystalline structure that combines order and disorder. The quantitative relationships between the mesoscale structural and material properties of nanostructured gels are currently unknown. Here, it is shown that lead telluride NPs spontaneously self-assemble into a spanning network hydrogel. By applying graph theory (GT), a method for quantifying the complex structure of the NP gels is established using a topological descriptor of average nodal connectivity that is found to correlate with the gel's mechanical and charge transport properties. GT descriptions make possible the design of non-crystalline porous materials from a variety of nanoscale components for photonics, catalysis, adsorption, and thermoelectrics.

Biological Networks across Scales-The Theoretical and Empirical Foundations for Time-Varying Complex Networks that Connect Structure and Function across Levels of Biological Organization.

Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, that is, dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations-an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales.

Deep Learning for Reintegrating Biology.

The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning (DL), could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place. Using these conceptualizations, fragmentation of biology can be seen as the result of too few and especially too short-ranged connections. Reintegrating biology could then be accomplished by establishing more, longer ranged connections. DL methods appear to be very well suited for addressing this particular need at this particular time. Notwithstanding the numerous unsubstantiated claims regarding the capabilities of AI, DL networks represent a major advance in the ability to find complex relationships inside large data sets that would have not been accessible with traditional data analytic methods or to a human observer. In addition, ongoing advances in the automation of taking measurements from phenomena on all levels of biological organization continue to increase the number of large quantitative data sets that are available. These increasingly common data sets could serve as anchor points for making long-range connections by virtue of DL. However, connections within the domain of life are likely to be structured in a highly nonuniform fashion and hence it is necessary to develop methods, for example, theoretical, computational, and experimental, to determine linkage of biological data sets most likely to provide useful insights on a biological problem using DL. Finally, specific DL approaches and architectures should be developed to match the needs of reintegrating biology.

The impact of focused attention on emotional evaluation: An eye-tracking investigation.

Emotional well-being depends on the ability to successfully engage a variety of coping strategies to regulate affective responses. Most studies have investigated the effectiveness of emotion regulation (ER) strategies that are deployed relatively later in the timing of processing that leads to full emotional experiences (i.e. reappraisal and suppression). Strategies engaged in earlier stages of emotion processing, such as those involved in attentional deployment, have also been investigated, but relatively less is known about their mechanisms. Here, we investigate the effectiveness of self-guided focused attention (FA) in reducing the impact of unpleasant pictures on the experienced negative affect. Participants viewed a series of composite images with distinguishable foreground (FG, either negative or neutral) and background (BG, always neutral) areas and were asked to focus on the FG or BG content. Eye-tracking data were recorded while performing the FA task, along with participants' ratings of their experienced emotional response following the presentation of each image. First, proving the effectiveness of self-guided FA in down-regulating negative affect, focusing away from the emotional content of pictures (BG focus) was associated with lower emotional ratings. Second, trial-based eye-tracking data corroborated these results, showing that spending less time gazing within the negative FG predicted reductions in emotional ratings. Third, this reduction was largest among subjects who habitually use suppression to regulate their emotions. Overall, the present findings expand the evidence regarding the FA's effectiveness in controlling the impact of emotional stimuli and inform the development of training interventions emphasizing attentional control to improve emotional well-being. (PsycInfo Database Record (c) 2022 APA, all rights reserved).