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Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution. [ABSTRACT FROM AUTHOR]

In contrast to the conventional approach of directly comparing genomic sequences using sequence alignment tools, we propose a computational approach that performs comparisons between sequence generators. These sequence generators are learned via a data-driven approach that empirically computes the state machine generating the genomic sequence of interest. As the state machine based generator of the sequence is independent of the sequence length, it provides us with an efficient method to compute the statistical distance between large sets of genomic sequences. Moreover, our technique provides a fast and efficient method to cluster large datasets of genomic sequences, characterize their temporal and spatial evolution in a continuous manner, get insights into the locality sensitive information about the sequences without any need for alignment. Furthermore, we show that the technique can be used to detect local regions with mutation activity, which can then be applied to aid alignment techniques for the fast discovery of mutations. To demonstrate the efficacy of our technique on real genomic data, we cluster different strains of SARS-CoV-2 viral sequences, characterize their evolution and identify regions of the viral sequence with mutations. [ABSTRACT FROM AUTHOR]

Complex biological, neuroscience, geoscience and social networks exhibit heterogeneous self-similar higher order topological structures that are usually characterized as being multifractal in nature. However, describing their topological complexity through a compact mathematical description and deciphering their topological governing rules has remained elusive and prevented a comprehensive understanding of networks. To overcome this challenge, we propose a weighted multifractal graph model capable of capturing the underlying generating rules of complex systems and characterizing their node heterogeneity and pairwise interactions. To infer the generating measure with hidden information, we introduce a variational expectation maximization framework. We demonstrate the robustness of the network generator reconstruction as a function of model properties, especially in noisy and partially observed scenarios. The proposed network generator inference framework is able to reproduce network properties, differentiate varying structures in brain networks and chromosomal interactions, and detect topologically associating domain regions in conformation maps of the human genome. Understanding heterogeneous topological structures in real-world complex networks is challenged by the difficulty of describing their multifractal nature and inferring their generator rules. Here, the authors present a weighted multifractal graph model as a generative approach for studying the structural properties of complex networks in realistic scenarios where only partial observational data is available or the input network is noisy, and demonstrate it on biological networks. [ABSTRACT FROM AUTHOR]

Quantum computers and algorithms can offer exponential performance improvement over some NP-complete programs which cannot be run efficiently through a Von Neumann computing approach. In this paper, we present BayeSyn, which utilizes an enhanced stochastic program synthesis and Bayesian optimization to automatically generate quantum programs from high-level languages subject to certain constraints. We find that stochastic synthesis can comparatively and efficiently generate a program with a lower cost from the high dimensional program space. We also realize that hyperparameters used in stochastic synthesis play a significant role in determining the optimal program. Therefore, BayeSyn utilizes Bayesian optimization to fine-tune such parameters to generate a suitable quantum program. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Throughout their lives, women may experience various breast symptoms. Paget's breast disease is a rare tumour of the nipple and is one of the most serious complications a woman may face. The manifestations of Paget's disease do not make the woman think of breast cancer. Signs of onset may be attributed to a skin problem, as the disease is manifested by itching of the nipple, burning or stinging sensation. Very rarely a white-yellowish or reddish discharge may appear. The patient can cause injuries to herself. This disease is not common, but it is more dangerous than others, being often confused with a dermatological condition (psoriasis or eczema) and treated as such, which is a very serious error. This is why Paget's disease of the breast is difficult to diagnose, often too late, in advanced stages of the cancer. [ABSTRACT FROM AUTHOR]

Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people's quality of life. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Social media have emerged as increasingly popular means and environments for information gathering and propagation. This vigorous growth of social media contributed not only to a pandemic (fast-spreading and far-reaching) of rumors and misinformation, but also to an urgent need for text-based rumor detection strategies. To speed up the detection of misinformation, traditional rumor detection methods based on hand-crafted feature selection need to be replaced by automatic artificial intelligence (AI) approaches. AI decision making systems require to provide explanations in order to assure users of their trustworthiness. Inspired by the thriving development of generative adversarial networks (GANs) on text applications, we propose a GAN-based layered model for rumor detection with explanations. To demonstrate the universality of the proposed approach, we demonstrate its benefits on a gene classification with mutation detection case study. Similarly to the rumor detection, the gene classification can also be formulated as a text-based classification problem. Unlike fake news detection that needs a previously collected verified news database, our model provides explanations in rumor detection based on tweet-level texts only without referring to a verified news database. The layered structure of both generative and discriminative models contributes to the outstanding performance. The layered generators produce rumors by intelligently inserting controversial information in non-rumors, and force the layered discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. On average, in the rumor detection task, our proposed model outperforms state-of-the-art baselines on PHEME dataset by 26.85 % in terms of macro-f1. The excellent performance of our model for textural sequences is also demonstrated by the gene mutation case study on which it achieves 72.69 % macro-f1 score. [ABSTRACT FROM AUTHOR]

Emotional well-being depends on the ability to adaptively cope with various emotional challenges. Most studies have investigated the neural mechanisms of emotion regulation strategies deployed relatively later in the timing of processing that leads to full emotional experiences. However, less is known about strategies that are engaged in earlier stages of emotion processing, such as those involving attentional deployment. We investigated the neural mechanisms associated with self-guided Focused Attention (FA) in mitigating subjective negative emotional experiences. Functional magnetic resonance imaging (fMRI) data were recorded while participants viewed a series of composite negative and neutral images with distinguishable foreground (FG) and background (BG) areas. Participants were instructed to focus either on the FG or BG components of the images, and then rated their emotional experiences. Behavioral results showed that FA was successful in decreasing emotional ratings for negative images viewed in BG Focus condition. At the neural level, the BG Focus was associated with increased activity in regions typically implicated in top-down executive control (dorsolateral prefrontal cortex and lateral parietal cortex) and decreased activity in regions linked to affective processing (amygdala and ventrolateral prefrontal cortex). Dissociable brain activity linked to FA also was identified in visual cortices, including between the parahippocampal and fusiform gyri, showing increased versus decreased activity, respectively, during the BG Focus. These findings complement the evidence from prior FA studies with recollected emotional memories as internal stimuli and further demonstrate the effectiveness of self-guided FA in mitigating negative emotional experiences associated with processing of external unpleasant stimuli. [ABSTRACT FROM AUTHOR]

In his seminal works, Endel Tulving argued that functionally distinct memory systems give rise to subjective experiences of remembering and knowing (i.e., recollection- vs. familiarity-based memory, respectively). Evidence shows that emotion specifically enhances recollection, and this effect is subserved by a synergistic mechanism involving the amygdala (AMY) and hippocampus (HC). In extreme circumstances, however, uncontrolled recollection of highly distressing memories may lead to symptoms of affective disorders. Therefore, it is important to understand the factors that can diminish such detrimental effects. Here, we investigated the effects of Focused Attention (FA) on emotional recollection. FA is an emotion regulation strategy that has been proven quite effective in reducing the impact of emotional responses associated with the recollection of distressing autobiographical memories, but its impact during emotional memory encoding is not known. Functional MRI and eye-tracking data were recorded while participants viewed a series of composite negative and neutral images with distinguishable foreground (FG) and background (BG) areas. Participants were instructed to focus either on the FG or BG content of the images and to rate their emotional responses. About 4 days later, participants' memory was assessed using the R/K procedure, to indicate whether they R ecollected specific contextual details about the encoded images or the images were just familiar to them – i.e., participants only K new that they saw the pictures without being able to remember specific contextual details. First, results revealed that FA was successful in decreasing memory for emotional pictures viewed in BG Focus condition, and this effect was driven by recollection-based retrieval. Second, the BG Focus condition was associated with decreased activity in the AMY, HC, and anterior parahippocampal gyrus for subsequently recollected emotional items. Moreover, correlation analyses also showed that reduced activity in these regions predicted greater reduction in emotional recollection following FA. These results demonstrate the effectiveness of FA in mitigating emotional experiences and emotional recollection associated with unpleasant emotional events. • Emotion control through Focused Attention (FA) reduces emotional experience. • FA also reduces subsequent emotional memory, by impacting recollection. • FA reduces activity in the amygdala and medial-temporal lobe (MTL) memory regions. • Individual differences modulate recollection-based retrieval in amygdala and MTL. • FA is an efficient strategy in reducing unwanted emotional experience and memory. [ABSTRACT FROM AUTHOR]

This paper presents a modeling and experimental framework to characterize the Chemotaxis of Serratia marcescens (S. marcescens) relying on two-dimensional and three-dimensional tracking of individual bacteria. Previous studies mainly characterized bacterial Chemotaxis based on population density analysis. Instead, this study focuses on single-cell tracking and measuring the chemotactic drift velocity Vc from the biased tumble rate of individual bacteria on exposure to a concentration gradient of L-aspartate. The chemotactic response of 5. marcescens is quantified over a range of concentration gradients ( 10-3 to 5 mM/mm) and average concentrations (0.5 x 10~1 to 2.5 mM). Through the analysis of a large number of bacterial swimming trajectories, the tumble rate is found to have a significant bias with respect to the swimming direction. We also verify the relative gradient sensing mechanism in the Chemotaxis of S. marcescens by measuring the change of Vc with the average concentration and the gradient. The applied full pathway model with fitted parameters matches the experimental data. Finally, we show that our measurements based on individual bacteria lead to the determination of the motility coefficient μ (7.25 x 10-6 cm²/s) of a population. The experimental characterization and simulation results for the Chemotaxis of this bacterial species contribute towards using S. marcescens in chemically controlled biohybrid systems. [ABSTRACT FROM AUTHOR]

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. To reconcile these diverging results, we carried out three studies incorporating the following features: (1) testing RM with increased specificity, distinguishing between subjective (recollection based) and objective (item-context match) RM accuracy, (2) accounting for emotion-attention interactions via eye-tracking and task manipulation, and (3) using stimuli with integrated item-context content. Challenging the prevalent view, we identified both enhancing and impairing effects. First, emotion enhanced subjective RM, separately and when confirmed by accurate objective RM. Second, emotion impaired objective RM through attention capturing, but it enhanced RM accuracy when attentional effects were statistically accounted for using eye-tracking data. Third, emotion also enhanced RM when participants were cued to focus on contextual details during encoding, likely by increasing item-context binding. Finally, functional magnetic resonance imaging data recorded from a subset of participants showed that emotional enhancement of RM was associated with increased activity in the medial temporal lobe (MTL) and ventrolateral prefrontal cortex, along with increased intra-MTL and ventrolateral prefrontal cortex-MTL functional connectivity. Overall, these findings reconcile evidence regarding opposing effects of emotion on RM and point to possible training interventions to increase RM specificity in healthy functioning, posttraumatic stress disorder, and aging, by promoting item-context binding and diminishing memory decontextualization. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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. Furthermore, the GT descriptors also reveal the pathways between the different phases. Specifically, centrality parameters and node-based fractal dimension quantify the system behavior preceding the transitions, capturing fluctuation-induced breakup of aggregates and their long-range cooperative interactions. GT parameterization can be generalized for a wide range of chemical systems and be instrumental for the growth mechanisms of complex nanostructures., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

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., (© 2023. The Psychonomic Society, Inc.)

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., (© 2023. The Psychonomic Society, Inc.)

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., (© 2023. The Author(s).)

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.

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., (© 2023 Cognitive Science Society LLC.)

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., Competing Interests: Declaration of interests Z.J.G. is an advisor and/or board member of Scribe Biosciences, Provenance Bio, and Serotiny. Z.J.G. is an author on patents related to the topic of this commentary. E.L. reports research grants from the American Cancer Society (RSG-22-022-01-CDP) 2022–2026, and the Minnesota Ovarian Cancer Alliance in 2019, 2021, and 2022; honorarium and travel expenses for a research talk at GlaxoSmithKline in 2016; honoraria and travel expenses for lab-based research talks 2018–21, and equipment for laboratory-based research 2018–present, Novocure, Ltd; honorarium for panel discussion organized by Antidote Education for a CME module on diagnostics and treatment of HER2+ gastric and colorectal cancers, funded by Daiichi-Sankyo, 2021 (honorarium donated to lab); compensation for scientific review of proposed printed content, Elsevier Publishing and Johns Hopkins Press; consultant, Nomocan Pharmaceuticals (no financial compensation); Scientific Advisory Board Member, Minnetronix, LLC, 2018–2019 (no financial compensation); consultant and speaker honorarium, Boston Scientific US, 2019; Institutional Principal Investigator for clinical trials sponsored by Celgene, Novocure, Intima Biosciences, and the National Cancer Institute, and University of Minnesota membership in the Caris Life Sciences Precision Oncology Alliance (no financial compensation). D.P. is a member of the advisory board of Insitro. G.L.S. is a current employee of Seagen and is an inventor on patents related to immunotherapies for treatment of cancer., (Published by Elsevier Inc.)

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.

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 1 -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 t 32  =  - 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 t 108  = 0.78, p > 0.78), the statistical test is underpowered. BAs estimated at acute baseline versus chronic follow-up do not differ significantly (t 264  = 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., (© 2022. The Author(s), under exclusive licence to American Aging Association.)

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. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the machine-learning algorithms trained on protein-protein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative 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 Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)

The global rise of COVID-19 health risk has triggered the related misinformation infodemic. We present the first analysis of COVID-19 misinformation networks and determine few of its implications. Firstly, we analyze the spread trends of COVID-19 misinformation and discover that the COVID-19 misinformation statistics are well fitted by a log-normal distribution. Secondly, we form misinformation networks by taking individual misinformation as a node and similarity between misinformation nodes as links, and we decipher the laws of COVID-19 misinformation network evolution: (1) We discover that misinformation evolves to optimize the network information transfer over time with the sacrifice of robustness. (2) We demonstrate the co-existence of fit get richer and rich get richer phenomena in misinformation networks. (3) We show that a misinformation network evolution with node deletion mechanism captures well the public attention shift on social media. Lastly, we present a network science inspired deep learning framework to accurately predict which Twitter posts are likely to become central nodes (i.e., high centrality) in a misinformation network from only one sentence without the need to know the whole network topology. With the network analysis and the central node prediction, we propose that if we correctly suppress certain central nodes in the misinformation network, the information transfer of network would be severely impacted.

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus.

This paper presents a modeling and experimental framework to characterize the chemotaxis of Serratia marcescens (S. marcescens) relying on two-dimensional and three-dimensional tracking of individual bacteria. Previous studies mainly characterized bacterial chemotaxis based on population density analysis. Instead, this study focuses on single-cell tracking and measuring the chemotactic drift velocity V(C) from the biased tumble rate of individual bacteria on exposure to a concentration gradient of l-aspartate. The chemotactic response of S. marcescens is quantified over a range of concentration gradients (10^{-3} to 5 mM/mm) and average concentrations (0.5 × 10(-3) to 2.5 mM). Through the analysis of a large number of bacterial swimming trajectories, the tumble rate is found to have a significant bias with respect to the swimming direction. We also verify the relative gradient sensing mechanism in the chemotaxis of S. marcescens by measuring the change of V(C) with the average concentration and the gradient. The applied full pathway model with fitted parameters matches the experimental data. Finally, we show that our measurements based on individual bacteria lead to the determination of the motility coefficient μ (7.25 × 10(-6) cm(2)/s) of a population. The experimental characterization and simulation results for the chemotaxis of this bacterial species contribute towards using S. marcescens in chemically controlled biohybrid systems.

Healthcare costs in the US are among the highest in the world. Chronic diseases such as diabetes significantly contribute to these extensive costs. Despite technological advances to improve sensing and actuation devices, we still lack a coherent theory that facilitates the design and optimization of efficient and robust medical cyber-physical systems for managing chronic diseases. In this paper, we propose a mathematical model for capturing the complex dynamics of blood glucose time series (e.g., time dependent and fractal behavior) observed in real world measurements via fractional calculus concepts. Building upon our time dependent fractal model, we propose a novel model predictive controller for an artificial pancreas that regulates insulin injection. We verify the accuracy of our controller by comparing it to conventional non-fractal models using real world measurements and show how the nonlinear optimal controller based on fractal calculus concepts is superior to non-fractal controllers in terms of average risk index and prediction accuracy.