DNA-based ForceChrono probes for deciphering single-molecule force dynamics in living cells.

Understanding cellular force transmission dynamics is crucial in mechanobiology. We developed the DNA-based ForceChrono probe to measure force magnitude, duration, and loading rates at the single-molecule level within living cells. The ForceChrono probe circumvents the limitations of in vitro single-molecule force spectroscopy by enabling direct measurements within the dynamic cellular environment. Our findings reveal integrin force loading rates of 0.5-2 pN/s and durations ranging from tens of seconds in nascent adhesions to approximately 100 s in mature focal adhesions. The probe's robust and reversible design allows for continuous monitoring of these dynamic changes as cells undergo morphological transformations. Additionally, by analyzing how mutations, deletions, or pharmacological interventions affect these parameters, we can deduce the functional roles of specific proteins or domains in cellular mechanotransduction. The ForceChrono probe provides detailed insights into the dynamics of mechanical forces, advancing our understanding of cellular mechanics and the molecular mechanisms of mechanotransduction.

Hydrogel-based molecular tension fluorescence microscopy for investigating receptor-mediated rigidity sensing.

Extracellular matrix (ECM) rigidity serves as a crucial mechanical cue impacting diverse biological processes. However, understanding the molecular mechanisms of rigidity sensing has been limited by the spatial resolution and force sensitivity of current cellular force measurement techniques. Here we developed a method to functionalize DNA tension probes on soft hydrogel surfaces in a controllable and reliable manner, enabling molecular tension fluorescence microscopy for rigidity sensing studies. Our findings showed that fibroblasts respond to substrate rigidity by recruiting more force-bearing integrins and modulating integrin sampling frequency of the ECM, rather than simply overloading the existing integrin-ligand bonds, to promote focal adhesion maturation. We also demonstrated that ECM rigidity positively regulates the pN force of T cell receptor-ligand bond and T cell receptor mechanical sampling frequency, promoting T cell activation. Thus, hydrogel-based molecular tension fluorescence microscopy implemented on a standard confocal microscope provides a simple and effective means to explore detailed molecular force information for rigidity-dependent biological processes.

Machine Learning Assisted Self-Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security.

Identity recognition as the first barrier of intelligent security plays a vital role, which is facing new challenges that are unable to meet the need of intelligent era due to low accuracy, complex configuration and dependence on power supply. Here, a finger temperature-driven intelligent identity recognition strategy is presented based on a thermogalvanic hydrogel (TGH) by actively discerning biometric characteristics of fingers. The TGH is a dual network PVA/Agar hydrogel in an H2O/glycerol binary solvent with [Fe(CN)6]3-/4- as a redox couple. Using a concave-arranged TGH array, the characteristics of users can be distinguished adequately even under an open environment by extracting self-existent intrinsic temperature features from five typical sites of fingers. Combined with machine learning, the TGH array can recognize different users with a high average accuracy of 97.6%. This self-powered identity recognition strategy is further applied to a smart lock, attaining a more reliable security protection from biometric characteristics than bare passwords. This work provides a promising solution for achieving better identity recognition, which has great advantages in intelligent security and human-machine interaction toward future Internet of everything.

Vehicle Occupant Detection Based on MM-Wave Radar.

With the continuous development of automotive intelligence, vehicle occupant detection technology has received increasing attention. Despite various types of research in this field, a simple, reliable, and highly private detection method is lacking. This paper proposes a method for vehicle occupant detection using millimeter-wave radar. Specifically, the paper outlines the system design for vehicle occupant detection using millimeter-wave radar. By collecting the raw signals of FMCW radar and applying Range-FFT and DoA estimation algorithms, a range-azimuth heatmap was generated, visually depicting the current status of people inside the vehicle. Furthermore, utilizing the collected range-azimuth heatmap of passengers, this paper integrates the Faster R-CNN deep learning networks with radar signal processing to identify passenger information. Finally, to test the performance of the detection method proposed in this article, an experimental verification was conducted in a car and the results were compared with those of traditional machine learning algorithms. The findings indicated that the method employed in this experiment achieves higher accuracy, reaching approximately 99%.

Molecular Force Imaging Reveals That Integrin-Dependent Mechanical Checkpoint Regulates Fcγ-Receptor-Mediated Phagocytosis in Macrophages.

Macrophages are a type of immune cell that helps eliminate pathogens and diseased cells. Recent research has shown that macrophages can sense mechanical cues from potential targets to perform effective phagocytosis, but the mechanisms behind it remain unclear. In this study, we used DNA-based tension probes to study the role of integrin-mediated forces in FcγR-mediated phagocytosis. The results showed that when the phagocytic receptor FcγR is activated, the force-bearing integrins create a "mechanical barrier" that physically excludes the phosphatase CD45 and facilitates phagocytosis. However, if the integrin-mediated forces are physically restricted at lower levels or if the macrophage is on a soft matrix, CD45 exclusion is significantly reduced. Moreover, CD47-SIRPα "don't eat me" signaling can reduce CD45 segregation by inhibiting the mechanical stability of the integrin barrier. These findings demonstrate how macrophages use molecular forces to identify physical properties and combine them with biochemical signals from phagocytic receptors to guide phagocytosis.

Oxytocin and the Punitive Hub-Dynamic Spread of Cooperation in Human Social Networks.

Human society operates on large-scale cooperation. However, individual differences in cooperativeness and incentives to free ride on others' cooperation make large-scale cooperation fragile and can lead to reduced social welfare. Thus, how individual cooperation spreads through human social networks remains puzzling from ecological, evolutionary, and societal perspectives. Here, we identify oxytocin and costly punishment as biobehavioral mechanisms that facilitate the propagation of cooperation in social networks. In three laboratory experiments (n = 870 human participants: 373 males, 497 females), individuals were embedded in heterogeneous networks and made repeated decisions with feedback in games of trust (n = 342), ultimatum bargaining (n = 324), and prisoner's dilemma with punishment (n = 204). In each heterogeneous network, individuals at central positions (hub nodes) were given intranasal oxytocin (or placebo). Giving oxytocin (vs matching placebo) to central individuals increased their trust and enforcement of cooperation norms. Oxytocin-enhanced norm enforcement, but not elevated trust, explained the spreading of cooperation throughout the social network. Moreover, grounded in evolutionary game theory, we simulated computer agents that interacted in heterogeneous networks with central nodes varying in terms of cooperation and punishment levels. Simulation results confirmed that central cooperators' willingness to punish noncooperation allowed the permeation of the network and enabled the evolution of network cooperation. These results identify an oxytocin-initiated proximate mechanism explaining how individual cooperation facilitates network-wide cooperation in human society and shed light on the widespread phenomenon of heterogeneous composition and enforcement systems at all levels of life.SIGNIFICANCE STATEMENT Human society operates on large-scale cooperation. Yet because cooperation is exploitable by free riding, how cooperation in social networks emerges remains puzzling from evolutionary and societal perspectives. Here we identify oxytocin and altruistic punishment as key factors facilitating the propagation of cooperation in human social networks. Individuals played repeated economic games in heterogeneous networks where individuals at central positions were given oxytocin or placebo. Oxytocin-enhanced cooperative norm enforcement, but not elevated trust, explained cooperation spreading throughout the social network. Evolutionary simulations confirmed that central cooperators' willingness to punish noncooperation allowed the permeation of the network and enabled the evolution of cooperation. These results identify an oxytocin-initiated proximate mechanism explaining how individual cooperation facilitates network-wide cooperation in human social networks.

The Golgi microtubules regulate single cell durotaxis.

Current understandings on cell motility and directionality rely heavily on accumulated investigations of the adhesion-actin cytoskeleton-actomyosin contractility cycles, while microtubules have been understudied in this context. Durotaxis, the ability of cells to migrate up gradients of substrate stiffness, plays a critical part in development and disease. Here, we identify the pivotal role of Golgi microtubules in durotactic migration of single cells. Using high-throughput analysis of microtubule plus ends/focal adhesion interactions, we uncover that these non-centrosomal microtubules actively impart leading edge focal adhesion (FA) dynamics. Furthermore, we designed a new system where islands of higher stiffness were patterned within RGD peptide coated polyacrylamide gels. We revealed that the positioning of the Golgi apparatus is responsive to external mechanical cues and that the Golgi-nucleus axis aligns with the stiffness gradient in durotaxis. Together, our work unveils the cytoskeletal underpinning for single cell durotaxis. We propose a model in which the Golgi-nucleus axis serves both as a compass and as a steering wheel for durotactic migration, dictating cell directionality through the interaction between non-centrosomal microtubules and the FA dynamics.

Silencing GmATG7 Leads to Accelerated Senescence and Enhanced Disease Resistance in Soybean.

Autophagy plays a critical role in nutrient recycling/re-utilizing under nutrient deprivation conditions. However, the role of autophagy in soybeans has not been intensively investigated. In this study, the Autophay-related gene 7 (ATG7) gene in soybeans (referred to as GmATG7) was silenced using a virus-induced gene silencing approach mediated by Bean pod mottle virus (BPMV). Our results showed that ATG8 proteins were highly accumulated in the dark-treated leaves of the GmATG7-silenced plants relative to the vector control leaves (BPMV-0), which is indicative of an impaired autophagy pathway. Consistent with the impaired autophagy, the dark-treated GmATG7-silenced leaves displayed an accelerated senescence phenotype, which was not seen on the dark-treated BPMV-0 leaves. In addition, the accumulation levels of both H2O2 and salicylic acid (SA) were significantly induced in the GmATG7-silenced plants compared with the BPMV-0 plants, indicating an activated immunity. Consistently, the GmATG7-silenced plants were more resistant against both Pseudomonas syringae pv. glycinea (Psg) and Soybean mosaic virus (SMV) compared with the BPMV-0 plants. However, the activated immunity in the GmATG7-silenced plant was not dependent upon the activation of MPK3/MPK6. Collectively, our results demonstrated that the function of GmATG7 is indispensable for autophagy in soybeans, and the activated immunity in the GmATG7-silenced plant is a result of impaired autophagy.

Exploring Integrin-Mediated Force Transmission during Confined Cell Migration by DNA-Based Tension Probes.

Mechanical forces have profound effects on the morphology and migration of cells in a two-dimensional environment. However, cells in vivo mostly migrate in three-dimensional space while physically constrained, and the mechanism by which cellular dynamic forces drive migration in this confined environment is unclear. Here, we present a method of fabricating microfluidic chips with integrated DNA-based tension probes to measure spatiotemporal variations in integrin-mediated force exerted during confined cell migration. Using this developed device, we measured the spatial locations, magnitudes, and temporal characteristics of integrin-ligand tension signals in motile cells in different microchannels and found that cells exerted less force and underwent increasingly transitory integrin-ligand interactions when migrating in confined spaces. This study demonstrates that the described method provides insights into understanding the migratory machinery of cells in geometrically confined environment that better mimics physiological conditions.

Place cells and geometry lead to a flexible grid pattern.

Place cells and grid cells are important neurons involved in spatial navigation in the mammalian brain. Grid cells are believed to play an important role in forming a cognitive map of the environment. Experimental observations in recent years showed that the grid pattern is not invariant but is influenced by the shape of the spatial environment. However, the cause of this deformation remains elusive. Here, we focused on the functional interactions between place cells and grid cells, utilizing the information of location relationships between the firing fields of place cells to optimize the previous grid cell feedforward generation model and expand its application to more complex environmental scenarios. Not only was the regular equilateral triangle periodic firing field structure of the grid cells reproduced, but the expected results were consistent with the experiment for the environment with various complex boundary shapes and environmental deformation. Even in the field of three-dimensional spatial grid patterns, forward-looking predictions have been made. This provides a possible model explanation for how the coupling of grid cells and place cells adapt to the diversity of the external environment to deepen our understanding of the neural basis for constructing cognitive maps.

Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering.

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

Emergence of communities and diversity in social networks.

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

Biological conservation law as an emerging functionality in dynamical neuronal networks.

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking.

Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver's location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver's motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.

Robust reconstruction of complex networks from sparse data.

Reconstructing complex networks from measurable data is a fundamental problem for understanding and controlling collective dynamics of complex networked systems. However, a significant challenge arises when we attempt to decode structural information hidden in limited amounts of data accompanied by noise and in the presence of inaccessible nodes. Here, we develop a general framework for robust reconstruction of complex networks from sparse and noisy data. Specifically, we decompose the task of reconstructing the whole network into recovering local structures centered at each node. Thus, the natural sparsity of complex networks ensures a conversion from the local structure reconstruction into a sparse signal reconstruction problem that can be addressed by using the lasso, a convex optimization method. We apply our method to evolutionary games, transportation, and communication processes taking place in a variety of model and real complex networks, finding that universal high reconstruction accuracy can be achieved from sparse data in spite of noise in time series and missing data of partial nodes. Our approach opens new routes to the network reconstruction problem and has potential applications in a wide range of fields.

Graph embedding-based heterogeneous domain adaptation with domain-invariant feature learning and distributional order preserving.

Heterogeneous domain adaptation (HDA) methods leverage prior knowledge from the source domain to train models for the target domain and address the differences in their feature spaces. However, incorrect alignment of categories and distribution structure disruption may be caused by unlabeled target samples during the domain alignment process for most existing methods, resulting in negative transfer. Additionally, the previous works rarely focus on the robustness and interpretability of the model. To address these issues, we propose a novel Graph embedding-based Heterogeneous domain-Invariant feature learning and Distributional order preserving framework (GHID). Specifically, a bidirectional robust cross-domain alignment graph embedding structure is proposed to globally align two domains, which learns the domain-invariant and discriminative features simultaneously. In addition, the interpretability of the proposed graph structures is demonstrated through two theoretical analyses, which can elucidate the correlation between important samples from a global perspective in heterogeneous domain alignment scenarios. Then, a heterogeneous discriminative distributional order preserving graph embedding structure is designed to preserve the original distribution relationship of each domain to prevent negative transfer. Moreover, the dynamic centroid strategy is incorporated into the graph structures to improve the robustness of the model. Comprehensive experimental results on four benchmarks demonstrate that the proposed method outperforms other state-of-the-art approaches in effectiveness.

[Silencing GmWRKY33B genes leads to reduced disease resistance in soybean].

The WRKYs are a group of plant-specific transcription factors that play important roles in defense responses. In this study, we silenced 2 GmWRKY33B homologous genes using a bean pod mosaic virus (BPMV) vector carrying a single fragment from the conserved region of the GmWRKY33B genes. Silencing GmWRKY33B did not result in morphological changes. However, significantly reduced resistances to Pseudomonas syringae pv. glycinea (Psg) and soybean mosaic virus (SMV) were observed in the GmWRKY33B-silenced plants, indicating a positive role of the GmWRKY33B genes in disease resistance. Kinase assay showed that silencing the GmWRKY33B genes significantly reduced the activation of GmMPK6, but not GmMPK3, in response to flg22 treatment. Reverse transcriptase PCR (RT-PCR) analysis of the genes encoding prenyltransferases (PTs), which are the key enzymes in the biosynthesis of glyceollin, showed that the Psg-induced expression of these genes was significantly reduced in the GmWRKY33B-silenced plants compared with the BPMV-0 empty vector plants, which correlated with the presence of the W-boxes in the promoter regions of these genes. Taken together, our results suggest that GmWRKY33Bs are involved in soybean immunity through regulating the activation of the kinase activity of GmMPK6 as well as through regulating the expression of the key genes encoding the biosynthesis of glyceollins.

[GmAGB1 plays a positive regulatory role in soybean defense responses].

Heterotrimeric GTP-binding protein (G-proteins) complex, which consists of Gα, Gß and Gγ subunits, plays critical roles in defense signaling. Arabidopsis genome contains only a single Gß-encoding gene, AGB1. Loss function of AGB1 in Arabidopsis results in enhanced susceptibility to a wide range of pathogens. However, the function of soybean AGB1 in immunity has not been previously interrogated. Bioinformatic analysis indicated that there are four GmAGB1 homologous genes in soybean genome, sharing homology of 86%-97%. To overcome the functional redundancy of these GmAGB1 homologs, virus-induced gene silencing (VIGS) mediated by the bean pod mottle virus (BPMV) was used to silence these four genes simultaneously. As expected, these four GmAGB1 homologous genes were indeed silenced by a single BPMV-VIGS vector carrying a conserved fragments among these four genes. A dwarfed phenotype was observed in GmAGB1s-silenced soybean plants, suggesting that GmAGB1s play a crucial role in growth and development. Disease resistance analysis indicated that silencing GmAGB1s significantly compromised the resistance of soybean plants against Xanthomonas campestris pv. glycinea (Xag). This reduced resistance was correlated with the decreased accumulation of pathogen-induced reactive oxygen species (ROS) and the reduced activation of GmMPK3 in response to flg22, a conserved N-terminal peptide of flagellin protein. These results indicate that GmAGB1 functions as a positive regulator in disease resistance and GmAGB1 is indispensable for the ROS production and GmMPK3 activation induced by pathogen infection. Yeast two hybrid assay showed that GmAGB1 interacted with GmAGG1, suggesting that an evolutionary conserved heterotrimeric G protein complex similarly functions in soybean.

Distance and grid-like codes support the navigation of abstract social space in the human brain.

People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a 'social cognitive map' organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.