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This work focuses on the problem of distributed optimization in multiagent cyberphysical systems, where a legitimate agent’s iterates are influenced both by the values it receives from potentially malicious neighboring agents and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. 

Abstract Perisynaptic astroglia provide critical molecular and structural support to regulate synaptic transmission and plasticity in the nanodomain of the axon-spine interface. Three-dimensional reconstruction from serial section electron microscopy (3DEM) was used to investigate relationships between perisynaptic astroglia and dendritic spine synapses undergoing plasticity in the hippocampus of awake adult male rats. Delta-burst stimulation (DBS) of the medial perforant pathway induced long-term potentiation (LTP) in the middle molecular layer and concurrent long-term depression (cLTD) in the outer molecular layer of the dentate gyrus. The contralateral hippocampus received baseline stimulation as a within-animal control. Brains were obtained 30 minutes or 2 hours after DBS onset. An automated 3DEM pipeline was developed to enable unbiased quantification of astroglial coverage at the perimeter of the axon-spine interface. Under all conditions, >85% of synapses had perisynaptic astroglia processes within 120 nm of some portion of the perimeter. LTP broadened the distribution of spine sizes while reducing the presence and proximity of perisynaptic astroglia near the axon-spine interface of large spines. In contrast, cLTD transiently reduced the length of the axon-spine interface perimeter without substantially altering astroglial apposition. The postsynaptic density was discovered to be displaced from the center of the axon-spine interface, with this offset increasing during LTP and decreasing during cLTD. Astroglial access to the postsynaptic density was diminished during LTP and enhanced during cLTD, in parallel with changes in spine size. Thus, access of perisynaptic astroglia to synapses is dynamically modulated during LTP and cLTD alongside synaptic remodeling. Significance StatementPerisynaptic astroglia provide critical molecular and structural regulation of synaptic plasticity underlying learning and memory. The hippocampal dentate gyrus, a brain region crucial for learning and memory, was found to have perisynaptic astroglia at the axon-spine interface of >85% of excitatory synapses measured. Long-term potentiation triggered the retraction of perisynaptic astroglia processes selectively from large synapses. This retraction decreased access of perisynaptic astroglia to the postsynaptic density, which was discovered to be located off-center in the axon-spine interface. Concurrent long-term depression temporarily (< 2 h) decreased spine perimeter and thus increased access of synapses to perisynaptic astroglia. These findings provide new insights into how the structural dynamics of spines and synapses shape access to perisynaptic astroglia. 

Student during-learning data such as think-alouds or writing are often coded for use of strategies or moves, but less often for what knowledge the student is using. However, analyzing the content of such products could yield much valuable information. A promising technique for analyzing the content of student products is semantic network analysis, more widely used in political science, communication, information science, and some other social science disciplines. We reviewed the small literature on semantic network analysis (SemNA) of individuals with relevant outcomes to identify which network analysis metrics might be suitable. The Knowledge Integration (KI) framework from science education is discussed as focusing on amount and structure of student knowledge, and therefore especially relevant for testing with SemNA metrics. We then re-analyze three published think-aloud data sets from undergraduate students learning introductory biology with the metrics found in the literature review. Significant relations with posttest comprehension score are found for number of nodes and edges; degree and betweenness centrality; diameter, and mean distance. Inconsistent results possibly due to text-specific features were found for number of clusters, LCC, and density, and null results were found for PageRank centrality and centralization degree. Basic principles from the KI framework are supported—amount of information (nodes), connections (edges, average degree), key ideas (degree and betweenness centrality) and length of causal chains (mean distance and diameter) are related to posttest comprehension, but not density or LCC. Possible explanations for slight variations across data sets are discussed, and alternative theories and metrics are offered. 

In Africa, many kids get sick from tiny worms called Schistosoma. These worms can slow children’s growth and development; damage the liver, intestines, and bladder; and sometimes lead to cancer or even death. Schistosoma can keep communities poor by reducing people’s ability to work. Over 800 million people are at risk of infection. People get infected when they play or wash in water filled with certain plants and snails. These plants grow fast because fertilizer from farmers’ fields washes into the water when it rains. We found that removing these plants can reduce Schistosoma. Plants that are removed can be turned into food for animals, compost for farms, or gas for cooking and electricity. This solution helps protect kids from getting sick and can even help to slow climate change. By working together, communities can clean their waterbodies and create a healthier, happier future, which is a win-win for people and nature. 

Biomolecular condensates are increasingly recognized as key regulators of chromatin organization, yet how their formation and properties arise from protein sequences remains incompletely understood. Cross-species comparisons can reveal both conserved functions and significant evolutionary differences. Here, we integrate in vitro reconstitution, molecular dynamics simulations, and cell-based assays to examine how Drosophila and human variants of Polyhomeotic (Ph)—a subunit of the PRC1 chromatin regulatory complex— drive condensate formation through their sterile alpha motif (SAM) oligomerization domains. We identify divergent interactions between SAM and the disordered linker connecting it to the rest of Ph. These interactions enhance oligomerization and modulate both the formation and properties of reconstituted condensates. Oligomerization influences condensate dynamics but minimally impacts condensate formation. Linker-SAM interactions also affect condensate formation in Drosophila and human cells and growth in Drosophila imaginal discs. Our findings show how evolutionary changes in disordered linkers can finetune condensate properties, providing insights into sequence-function relationships. 

Biopolymer networks from the intracellular to tissue scale display high rigidity and tensile stress while having coordinations well below the normal threshold for mechanical rigidity. The elastic filaments in these networks are often severed by enzymes in a tension-inhibited manner. The effects of such pruning on the mechanics of prestressed networks have not been studied. We show that networks pruned by a tension-inhibited method remain rigid at much lower coordinations than randomly pruned ones. These findings suggest a possible reason for the repeated evolution of tension-inhibited filament-severing proteins. Published by the American Physical Society2024 

Abstract Marine phytoplankton biomass and chlorophyll-a concentration are often estimated from pigment fluorescence measurements, which have become routine despite known variability in the fluorescent response for a given amount of chlorophyll-a. Here, we present a near-global, monthly climatology of chlorophyll-a fluorescence measurements from profiling floats combined with ocean color satellite estimates of chlorophyll-a concentration to illuminate seasonal biases in the fluorescent response and expand upon previously observed regional patterns in this bias. Global biases span over an order of magnitude, and can vary seasonally by a factor of 10. An independent estimate of chlorophyll-a from light attenuation shows similar global patterns in the chlorophyll-fluorescence bias when compared to biases derived from satellite estimates. Without accounting for these biases, studies or models using fluorescence-estimated chlorophyll-a will inherit the seasonal and regional biases described here. 

Global migrations of diverse animal species often converge along the same routes, bringing together seasonal assemblages of animals that may compete, prey on each other, and share information or pathogens. These interspecific interactions, when energetic demands are high and the time to complete journeys is short, may influence survival, migratory success, stopover ecology, and migratory routes. Numerous accounts suggest that interspecific co-migrations are globally distributed in aerial, aquatic, and terrestrial systems, although the study of migration to date has rarely investigated species interactions among migrating animals. Here, we test the hypothesis that migrating animals are communities engaged in networks of ecological interactions. We leverage over half a million records of 50 bird species from five bird banding sites collected over 8 to 23 y to test for species associations using social network analyses. We find strong support for persistent species relationships across sites and between spring and fall migration. These relationships may be ecologically meaningful: They are often stronger among phylogenetically related species with similar foraging behaviors and nonbreeding ranges even after accounting for the nonsocial contributions to associations, including overlap in migration timing and habitat use. While interspecific interactions could result in costly competition or beneficial information exchange, we find that relationships are largely positive, suggesting limited competitive exclusion at the scale of a banding station during migratory stopovers. Our findings support an understanding of animal migrations that consist of networked communities rather than random assemblages of independently migrating species, encouraging future studies of the nature and consequences of co-migrant interactions. 

We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. For the Two Stage Approach, we assume that the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to simultaneously estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis. We exploit particular structures in the problem to show that this approach remains computationally tractable even with unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions subject to a Sybil attack on a mock-up road network. We extract the trust observations for each robot from communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT algorithms respectively.