Autocatalytic Nucleation and Self-Assembly of Inorganic Nanoparticles into Complex Biosimilar Networks.

Self-replication of  molecules and microdroplets have been explored as models in prebiotic chemistry. An analogous process for inorganic nanomaterials would involve the autocatalytic nucleation of  nanoparticles-an area that remains largely uncharted. Demonstrating such systems would be both fundamentally intriguing and practically relevant, especially if the resulting particles self-assemble.

How Likely Is it that I Would Act the Same Way: Modeling Moral Judgment During Uncertainty.

Moral rules come with exceptions, and moral judgments come with uncertainty. For instance, stealing is wrong and generally punished. Yet, it could be the case that the thief is stealing food for their family.

Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.

The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI).

Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.

The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI).

Cortico-hippocampal interactions underlie schema-supported memory encoding in older adults.

Although episodic memory is typically impaired in older adults (OAs) compared to young adults (YAs), this deficit is attenuated when OAs can leverage their rich semantic knowledge, such as their knowledge of schemas. Memory is better for items consistent with pre-existing schemas and this effect is larger in OAs. Neuroimaging studies have associated schema use with the ventromedial prefrontal cortex (vmPFC) and hippocampus (HPC), but most of this research has been limited to YAs.

Reconciling opposing effects of emotion on relational memory: Behavioral, eye-tracking, and brain imaging investigations.

The effects of emotion on memory are wide-ranging and powerful, but they are not uniform. Although there is agreement that emotion enhances memory for individual items, how it influences memory for the associated contextual details (relational memory, RM) remains debated. The prevalent view suggests that emotion impairs RM, but there is also evidence that emotion enhances RM.

Graph Theoretical Description of Phase Transitions in Complex Multiscale Phases with Supramolecular Assemblies.

Phase transitions are typically quantified using order parameters, such as crystal lattice distances and radial distribution functions, which can identify subtle changes in crystalline materials or high-contrast phases with large structural differences. However, the identification of phases with high complexity, multiscale organization and of complex patterns during the structural fluctuations preceding phase transitions, which are essential for understanding the system pathways between phases, is challenging for those traditional analyses. Here, it is shown that for two model systems- thermotropic liquid crystals and a lyotropic water/surfactant mixtures-graph theoretical (GT) descriptors can successfully identify complex phases combining molecular and nanoscale levels of organization that are hard to characterize with traditional methodologies.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 T-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.

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.

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.

Unifying structural descriptors for biological and bioinspired nanoscale complexes.

Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein-protein interactions can serve as a guide for designing protein-nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein-protein interactions are not applicable to inorganic nanoparticles.

Direct feedback and social conformity promote behavioral change via mechanisms indexed by centroparietal positivity: Electrophysiological evidence from a role-swapping ultimatum game.

Our behavior is shaped by multiple factors, including direct feedback (seeing the outcomes of our past actions) and social observation (in part, via a drive to conform to other peoples' behaviors). However, it remains unclear how these two processes are linked in the context of behavioral change. This is important to investigate, as behavioral change is associated with distinct neural correlates that reflect specific aspects of processing, such as information integration and rule updating.