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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. 

Marine net community production (NCP), a metric of ecosystem functionality, is often estimated as the residual term in a mass balance equation that aims to describe upper ocean variations in the time series of a chemical tracer. The advent of biogeochemical (BGC) Argo profiling floats equipped with nitrate, pH, and oxygen sensors has enabled such NCP estimation across vast ocean regions. Floats typically drift at 1,000 m depth between profiling from ∼2,000 m to the surface every 10 days, resulting in quasi‐Lagrangian time series that can reflect different upper ocean water masses over time. However, limited information about real‐time horizontal tracer gradients often leads to lateral processes being omitted during tracer budget closure, which can bias the residual‐term NCP estimates. To determine the potential magnitude of such biases, we developed a method to quantify and adjust for the impact of lateral float movement across horizontal tracer gradients using dissolved inorganic carbon (DIC) as our case study. We evaluated the method by extracting artificial float profiles from a depth‐resolved observation‐based DIC product to generate an artificial DIC time series. We then estimated NCP before and after accounting for horizontal gradient effects and compared the results to NCP estimates from an artificial DIC time series extracted at a fixed location along the float trajectory. Testing 10 biogeographical domains with moderate to substantial horizontal DIC gradients, our method significantly improved the precision (by ∼50 to ∼80%) and accuracy (by ∼10 to ∼100%) of regional NCP estimates. This method can be applied to other tracers with multi‐month‐long residence times. 

Abstract Profiles of oxygen measurements from Argo profiling floats now vastly outnumber shipboard profiles. To correct for drift, float oxygen data are often initially adjusted to deployment casts, ship‐based climatologies, or, recently, measurements of atmospheric oxygen for in situ calibration. Air calibration enables accurate measurements in the upper ocean but may not provide similar accuracy at depth. Using a quality controlled shipboard data set, we find that the entire Argo oxygen data set is offset relative to shipboard measurements (float minus ship) at pressures of 1,450–2,000 db by a median of −1.9 μmol kg⁻¹(mean ± SD of −1.9 ± 3.9, 95% confidence interval around the mean of {−2.2, −1.6}) and air‐calibrated floats are offset by −2.7 μmol kg⁻¹(−3.0 ± 3.4 (CI_95%{−3.7, −2.4}). The difference between float and shipboard oxygen is likely due to offsets in the float oxygen data and not oxygen changes at depth or biases in the shipboard data set. In addition to complicating the calculation of long‐term ocean oxygen changes, these float oxygen offsets impact the adjustment of float nitrate and pH measurements, therefore biasing important derived quantities such as the partial pressure of CO₂(pCO₂) and dissolved inorganic carbon. Correcting floats with air‐calibrated oxygen sensors for the float‐ship oxygen offsets alters float pH by a median of 3.0 mpH (3.1 ± 3.7) and float‐derived surfacepCO₂by −3.2 μatm (−3.2 ± 3.9). This adjustment to floatpCO₂represents half, or more, of the bias in float‐derivedpCO₂reported in studies comparing floatpCO₂to shipboardpCO₂measurements. 

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. 

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