Search for: All records

Here we provide percent contribution of mineral associated (i.e., heavy fraction - HF) and relatively more labile (i.e., light fraction - LF) organic matter through soil profiles and along hillslope catena within sites in the Critical Zone Network (CZNet) Geomicrobiology cluster. Each sample is separated into a HF an a LF utilizing a 1.85 g cm-3 sodium polytungstate (3Na2WO4·9WO3·H2O or Na6 [H2W12O40]) solution. The resultant fractions are run for percent carbon (C) and nitrogen (N) and their associated stable isotopes (δ13C and δ15N) to offer novel insights in soil organic matter processes. Samples that were either too small for analytical analysis or below instrument detection limit are labeled with BDL. 

Recent work characterized shifts in preparatory activity of the motor cortex during motor learning. The specific shift geometry during learning, washout, and relearning blocks was hypothesized to implement the acquisition, retention, and retrieval of motor memories. We sought to train recurrent neural network (RNN) models that could be used to study these motor learning phenomena. We built an environment for a curl field (CF) motor learning task and trained RNNs with reinforcement learning (RL) with novel regularization terms to perform behaviorally realistic reaching trajectories over the course of learning. Our choice of RL over supervised learning was motivated by the idea that motor adaptation, in the absence of demonstrations, is a process of reoptimization. We find these models, despite lack of supervision, reproduce many behavioral findings from monkey CF adaptation experiments. These models also captured key neurophysiological findings.We found that the model’s preparatory activity existed in a force-predictive subspace that remained stable across learning, washout, and relearning. Additionally, preparatory activity shifted uniformly, independently of the distance to the CF trained target. Finally, we found that the washout shift became more orthogonal to the learning shift, and hence more brain-like, when the RNNs were pretrained to have prior experience with CF dynamics. We argue the increased fit to neurophysiological recordings is driven by more generalizable and structured dynamical motifs in the model with more prior experience. This suggests that prior experience could organize preparatory neural activity underlying motor memory to have more orthogonal characteristics, by forming structured dynamical motifs in the motor cortex circuitry. 

Abstract Increasing fine root carbon (FRC) inputs into soils has been proposed as a solution to increasing soil organic carbon (SOC). However, FRC inputs can also enhance SOC loss through priming. Here, we tested the broad-scale relationships between SOC and FRC at 43 sites across the US National Ecological Observatory Network. We found that SOC and FRC stocks were positively related with an across-ecosystem slope of 7 ± 3 kg SOC m⁻²per kg FRC m⁻², but this relationship was driven by grasslands. Grasslands had double the across-ecosystem slope while forest FRC and SOC were unrelated. Furthermore, deep grassland soils primarily showed net SOC accrual relative to FRC input. Conversely, forests had high variability in whether FRC inputs were related to net SOC priming or accrual. We conclude that while FRC increases could lead to increased SOC in grasslands, especially at depth, the FRC-SOC relationship remains difficult to characterize in forests. 

Different actin nucleation-promoting factors (NPFs) orchestrate different patterns of cell protrusions, likely reflecting their distinct patterns of self-organization. Here, we leveraged in vivo biochemical approaches to investigate how the WAVE complex instructs the formation of sheet-like lamellipodia. We show that the WAVE complex is a core constituent of a linear multilayered protein array at the plasma membrane, expected for an NPF that builds sheet-like actin-based protrusions. Negative membrane curvature is both necessary and sufficient for WAVE complex linear membrane association in the presence of upstream activators (Rac, Arf1/6, and PIP3) and the PRDs of both WAVE2 and Abi2, providing a potential mechanistic basis for templating of lamellipodia and their emergent behaviors, including barrier avoidance. Through computational modeling, we demonstrate that WAVE complex’s linear organization and preference for negative curvature both play important roles in robust lamellipodia formation. Our data reveal key features of mesoscale WAVE complex patterning and highlight an integral relation between NPF self-organization and cell morphogenesis. 

This paper introduces a method to synthesize a 3D tensor field within a constrained geometric domain represented as a tetrahedral mesh. Whereas previous techniques optimize forisotropicfields, we focus onanisotropictensor fields that are smooth and aligned with the domain boundary or user guidance. The key ingredient of our method is a novel computational design framework, built on top of thesymmetric orthogonally decomposable(odeco) tensor representation, to optimize the stretching ratios and orientations for each tensor in the domain. In contrast to past techniques designed only forisotropictensors, we demonstrate the efficacy of our approach in generating smooth volumetric tensor fields with highanisotropyand shape conformity, especially for the domain with complex shapes. We apply these anisotropic tensor fields to various applications, such as anisotropic meshing, structural mechanics, and fabrication. 

Abstract We present a novel counterexample-guided, sketch-based method for the synthesis of symbolic distributed protocols in TLA⁺. Our method’s chief novelty lies in a new search space reduction technique called interpretation reduction, which allows to not only eliminate incorrect candidate protocols before they are sent to the verifier, but also to avoid enumerating redundant candidates in the first place. Further performance improvements are achieved by an advanced technique for exact generalization of counterexamples. Experiments on a set of established benchmarks show that our tool is almost always faster than the state of the art, often by orders of magnitude, and was also able to synthesize an entire TLA⁺protocol “from scratch” in less than 3 minutes where the state of the art timed out after an hour. Our method is sound, complete, and guaranteed to terminate on unrealizable synthesis instances under common assumptions which hold in all our benchmarks.