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Dysregulation of the PTEN-mTORC1 signaling pathway disrupts cellular metabolism and contributes to neurodevelopmental disorders, including autism spectrum disorder (ASD). In cerebellar Purkinje cells (PCs), PTEN deficiency leads to hyperactivation of mTORC1, impaired energy homeostasis, and progressive neuronal degeneration. Given that physical exercise is a potent modulator of metabolic signaling, we investigated whether voluntary running could restore metabolic balance and ameliorate cellular and behavioral deficits in a mouse model of PC-specific PTEN deficiency. UsingPtenconditional knockout (cKO) mice, we evaluated the effects of voluntary running on motor coordination, metabolic signaling, neuronal morphology and preservation, and social and non-social behaviors. Behavioral analyses revealed that running significantly improved motor performance inPtencKO mice. At the cellular level, voluntary running increased phosphorylated AMP-activated protein kinase (pAMPK) and restored mitochondrial and lysosomal content in PC dendrites fromPtencKO mice. Unexpectedly, running further enhanced mTORC1 activity inPtencKO mice, as indicated by increased pS6R immunoreactivity, suggesting a complex interplay between anabolic and catabolic pathways. Behavioral analyses further revealed that voluntary running reduced sex-dependent differences in social and non-social behaviors observed in sedentaryPtencKO mice. Despite persistent dendritic hypertrophy and synaptic VGLUT2 alterations, running reduced PC loss and partially normalized microglial morphology and activation. Together, these findings demonstrate that voluntary running improves metabolic homeostasis and neuronal preservation inPtencKO mice, even in the presence of sustained mTORC1 activation. Our results highlight the therapeutic potential of physical activity in modulating metabolic pathways and mitigating neurodevelopmental pathology. 

Liquid-phase exfoliation (LPE) of emergent materials composed of weakly bound one-dimensional (1D) and quasi-1D (q-1D) building blocks presents a straightforward route not only for the discovery of confined physical states in 1D but also for the realization of scalable functional devices. However, compared to the more established routes in two-dimensional (2D) crystals, the nature of LPE in 1D and q-1D crystals presents a more random process. This distinction arises from the various available interchain directions across several crystallographic facets unique to 1D and q1D solids, from which the chains can be cleaved apart into a stochastic combination of nanowires, nanoribbons, and nanosheets. Using the 1D ionic phase comprised of ∼4.3 Å thin chains, (NbSe4)3I, we demonstrate herein the profound influence of crystal morphology, exposed facets, and their degree of wettability, passivation, and surface roughness in directing the LPE behavior of 1D crystals. Through the growth of bulk crystals as long needles with exposed (hk0) facets or as quasi-2D flakes with exposed (00l) facets susceptible to passivation, we show that these two distinct precursor morphologies display divergent behaviorboth in solvent preference and quality of resulting nanostructures. Under optimal conditions involving bulk needles and tetrahydrofuran as solvent, we show that the LPE of (NbSe4)3I results in ultrathin nanoribbons with high aspect ratios bearing lengths >5 μm, thicknesses down to 7.2 ± 2.6 nm, and widths of 26.4 ± 10.9 nm. The nanoribbons, solution processable as thin films, retain their native crystal structure and semiconducting character. Moreover, the nanoribbons also manifest pronounced degrees of bending and substrate-driven twisting at the nanoscale while maintaining long-range order. These results highlight a means to understand the fundamental chemical and physical behavior of noncovalently bound 1D solids through the realization of solution-processable 1D nanoribbons and nanowires that also have the potential as components for next-generation devices that approach the atomic scale. 

Accurate modeling of conformational energies is key to the crystal structure prediction of conformational polymorphs. Focusing on molecules XXXI and XXXII from the seventh blind test of crystal structure prediction, this study employs various electronic structure methods up to the level of domain-local pair natural orbital coupled cluster singles and doubles with perturbative triples [DLPNO-CCSD(T1)] to benchmark the conformational energies and to assess their impact on the crystal energy landscapes. Molecule XXXI proves to be a relatively straightforward case, with the conformational energies from generalized gradient approximation (GGA) functional B86bPBE-XDM changing only modestly when using more advanced density functionals such as PBE0-D4, ωB97M-V, and revDSD-PBEP86-D4, dispersion-corrected second-order Møller–Plesset perturbation theory (SCS-MP2D), or DLPNO-CCSD(T1). In contrast, the conformational energies of molecule XXXII prove difficult to determine reliably, and variations in the computed conformational energies appreciably impact the crystal energy landscape. Even high-level methods such as revDSD-PBEP86-D4 and SCS-MP2D exhibit significant disagreements with the DLPNO-CCSD(T1) benchmarks for molecule XXXII, highlighting the difficulty of predicting conformational energies for complex, drug-like molecules. The best-converged predicted crystal energy landscape obtained here for molecule XXXII disagrees significantly with what has been inferred about the solid-form landscape experimentally. The identified limitations of the calculations are probably insufficient to account for the discrepancies between theory and experiment on molecule XXXII, and further investigation of the experimental solid-form landscape would be valuable. Finally, assessment of several semi-empirical methods findsr²SCAN-3c to be the most promising, with conformational energy accuracy intermediate between the GGA and hybrid functionals and a low computational cost. 

Functional electrical stimulation is a promising technique for restoring arm function to those with paralysis from a high spinal cord injury. While simple controllers are easy to implement, model-based controllers are likely better equipped to leverage the arm’s kinematic and dynamic complexity, particularly for the high variations associated with functional arm movement. One modelling technique for a model-based controller is Gaussian Process Regression. Previous simulation work has shown promise leveraging whole-arm error data to identify the arm’s various subsystems, but used perfect simulated data. We asked caregivers to correct a robotic arm’s movement as simulated muscles generated torque. The simulated muscles were controlled as if they were electrically stimulated human arm muscles. This study demonstrates non-expert caregivers’ ability to collect this error data via whole-arm corrections, and provides insight into their ability to improve arm subsystem models made with Gaussian Process Regression. Despite significant error in caregivers’ ability to provide force corrections to hold the robot in a static configuration, these corrections were leveraged to significantly improve muscle models; the muscles that improved the most were the ones primarily used to move the physiologically actuated robot. 

Abstract This paper explores the use of Gaussian process regression for system identification in control engineering. It introduces two novel approaches that utilize the data from a measured global system error. The paper demonstrates these approaches by identifying a simulated system with three subsystems, a one degree of freedom mass with two antagonist muscles. The first approach uses this whole-system error data alone, achieving accuracy on the same order of magnitude as subsystem-specific data ( $9.28\pm 0.87\text{N\hspace{0.17em}}\text{vs.\hspace{0.17em}}6.96\pm 0.32\text{N}$of total model errors). This is significant, as it shows that the same data set can be used to identify unique subsystems, as opposed to requiring a set of data descriptive of only a single subsystem. The second approach demonstrated in this paper mixes traditional subsystem-specific data with the whole system error data, achieving up to 98.71% model improvement.