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1. Erroneous inference based on a lack of preference within one group: Autism, mice, and the social approach task

   https://doi.org/10.1002/aur.2154  

   Nygaard, Kayla R. ; Maloney, Susan E. ; Dougherty, Joseph D. ( June 2019 , Autism Research)

   The Social Approach Task is commonly used to identify sociability deficits when modeling liability factors for autism spectrum disorder (ASD) in mice. It was developed to expand upon existing assays to examine distinct aspects of social behavior in rodents and has become a standard component of mouse ASD‐relevant phenotyping pipelines. However, there is variability in the statistical analysis and interpretation of results from this task. A common analytical approach is to conduct within‐group comparisons only, and then interpret a difference insignificancelevels as if it were a group difference, without any direct comparison. As an efficient shorthand, we named this approach EWOCs: Erroneous Within‐group Only Comparisons. Here, we examined the prevalence of EWOCs and used simulations to test whether this approach could produce misleading inferences. Our review of Social Approach studies of high‐confidence ASD genes revealed 45% of papers sampled used only this analytical approach. Through simulations, we then demonstrate how a lack of significant difference within one group often does not correspond to a significant difference between groups, and show this erroneous interpretation increases the rate of false positives up to 25%. Finally, we define a simple solution: use an index, like a social preference score, with direct statisticalmore »comparisons between groups to identify significant differences. We also provide power calculations to guide sample size in future studies. Overall, elimination of EWOCs and adoption of direct comparisons should result in more accurate, reliable, and reproducible data interpretations from the Social Approach Task across ASD liability models.Autism Res2019, 12: 1171–1183. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.

   The Social Approach Task is widely used to assess social behavior in mice and is frequently used in studies modeling autism. However, reviewing published studies showed nearly half do not use correct comparisons to interpret these data. Using simulated and original data, we argue the correct statistical approach is a direct comparison of scores between groups. This simple solution should reduce false positives and improve consistency of results across studies.

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2. Constant spacing in filament bundles

   https://doi.org/10.1088/1367-2630/ab1c2d  

   Atkinson, Daria W. ; Santangelo, Christian D. ; Grason, Gregory M. ( June 2019 , New Journal of Physics)

   Assemblies of one-dimensional filaments appear in a wide range of physical systems: from biopolymer bundles, columnar liquid crystals, and superconductor vortex arrays; to familiar macroscopic materials, like ropes, cables, and textiles. Interactions between the constituent filaments in such systems are most sensitive to thedistance of closest approachbetween the central curves which approximate their configuration, subjecting these distinct assemblies to common geometric constraints. In this paper, we consider two distinct notions of constant spacing in multi-filament packings in${\mathbb{R}}^3$:equidistance, where the distance of closest approach is constant along the length of filament pairs; andisometry, where the distances of closest approach between all neighboring filaments are constant and equal. We show that, although any smooth curve in${\mathbb{R}}^3$permits one dimensional families of collinear equidistant curves belonging to a ruled surface, there are only two families of tangent fields with mutually equidistant integral curves in${\mathbb{R}}^3$. The relative shapes and configurations of curves in these families are highly constrained: they must be either (isometric) developable domains, which can bend, but not twist; or (non-isometric) constant-pitch helical bundles, which can twist, but not bend. Thus, filament textures that are simultaneously bent and twisted, suchmore »as twisted toroids of condensed DNA plasmids or wire ropes, are doubly frustrated: twist frustrates constant neighbor spacing in the cross-section, while non-equidistance requires additional longitudinal variations of spacing along the filaments. To illustrate the consequences of the failure of equidistance, we compare spacing in three ‘almost equidistant’ ansatzes for twisted toroidal bundles and use our formulation of equidistance to construct upper bounds on the growth of longitudinal variations of spacing with bundle thickness.

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3. Recognition and Reconfiguration of Lattice-Based Cellular Structures by Simple Robots

   https://doi.org/10.1109/ICRA40945.2020.9196700  

   Niehs, Eike ; Schmidt, Arne ; Scheffer, Christian ; Biediger, Daniel E. ; Yannuzzi, Michael ; Jenett, Benjamin ; Abdel-Rahman, Amira ; Cheung, Kenneth C. ; Becker, Aaron T. ; Fekete, Sandor P. ( May 2020 , 2020 IEEE International Conference on Robotics and Automation (ICRA))

   We consider recognition and reconfiguration of lattice-based cellular structures by very simple robots with only basic functionality. The underlying motivation is the construction and modification of space facilities of enormous dimensions, where the combination of new materials with extremely simple robots promises structures of previously unthinkable size and flexibility; this is also closely related to the newly emerging field of programmable matter. Aiming for large-scale scalability, both in terms of the number of the cellular components of a structure, as well as the number of robots that are being deployed for construction requires simple yet robust robots and mechanisms, while also dealing with various basic constraints, such as connectivity of a structure during reconfiguration. To this end, we propose an approach that combines ultra-light, cellular building materials with extremely simple robots. We develop basic algorithmic methods that are able to detect and reconfigure arbitrary cellular structures, based on robots that have only constant-sized memory. As a proof of concept, we demonstrate the feasibility of this approach for specific cellular materials and robots that have been developed at NASA.
4. A novel stochastic optimization method for handling misalignments of proton and photon doses in combined treatments

   https://doi.org/10.1088/1361-6560/ac858f  

   Fabiano, Silvia ; Torelli, Nathan ; Papp, Dávid ; Unkelbach, Jan ( September 2022 , Physics in Medicine & Biology)

   Objective.Combined proton–photon treatments, where most fractions are delivered with photons and only a few are delivered with protons, may represent a practical approach to optimally use limited proton resources. It has been shown that, when organs at risk (OARs) are located within or near the tumor, the optimal multi-modality treatment uses protons to hypofractionate parts of the target volume and photons to achieve near-uniform fractionation in dose-limiting healthy tissues, thus exploiting the fractionation effect. These plans may be sensitive to range and setup errors, especially misalignments between proton and photon doses. Thus, we developed a novel stochastic optimization method to directly incorporate these uncertainties into the biologically effective dose (BED)-based simultaneous optimization of proton and photon plans.Approach.The method considers the expected value$E\left(b\right)$and standard deviation$\sigma \left(b\right)$of the cumulative BED$b$in every voxel of a structure. For the target, a piecewise quadratic penalty function of the form${\left[b^{\min }-\left(E\left(b\right)-2\sigma \left(b\right)\right)\right]}_{+}^2$is minimized, aiming for plans in which the expected BED minus two times the standard deviation exceeds the prescribed BED$b^{\min }.$Analogously,close=')' open='(' separators=''>Eb+2σb−bmax+2is considered for OARs.Main results.Using a spinal metastasis case and a liver cancer patient, it is demonstrated that the novel stochastic optimization method yields robust combined treatment plans. Tumor coverage and a good sparing of the main OARs are maintained despite range and setup errors, and especially misalignments between proton and photon doses. This is achieved without explicitly considering all combinations of proton and photon error scenarios.Significance.Concerns about range and setup errors for safe clinical implementation of optimized proton–photon radiotherapy can be addressed through an appropriate stochastic planning method.

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5. Toward a generalizable deep CNN for neural drive estimation across muscles and participants

   https://doi.org/10.1088/1741-2552/acae0b  

   Wen, Yue ; Kim, Sangjoon J. ; Avrillon, Simon ; Levine, Jackson T. ; Hug, François ; Pons, José L. ( January 2023 , Journal of Neural Engineering)

   Objective.High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading to a wide range of neural-machine interface applications. However, current online methods need offline retraining when applied to the same muscle on a different day or to a different person, which limits their applications in a real-time neural-machine interface. We proposed a deep convolutional neural network (CNN) framework for neural drive estimation, which takes in frames of HD-EMG signals as input, extracts general spatiotemporal properties of MU action potentials, and outputs the number of spikes in each frame. The deep CNN can generalize its application without retraining to HD-EMG data recorded in separate sessions, muscles, or participants.Approach.We recorded HD-EMG signals from the vastus medialis and vastus lateralis muscles from five participants while they performed isometric contractions during two sessions separated by ∼20 months. We identified MU spike trains from HD-EMG signals using a convolutive blind source separation (BSS) method, and then used the cumulative spike train (CST) of these MUs and the HD-EMG signals to train andmore »validate the deep CNN.Main results.On average, the correlation coefficients between CST from the BSS and that from deep CNN were$0.983\pm 0.006$for leave-one-out across-sessions-and-muscles validation and$0.989\pm 0.002$for leave-one-out across-participants validation. When trained with more than four datasets, the performance of deep CNN saturated at$0.984\pm 0.001$for cross validations across muscles, sessions, and participants.Significance.We can conclude that the deep CNN is generalizable across the aforementioned conditions without retraining. We could potentially generate a robust deep CNN to estimate neural drive to muscles for neural-machine interfaces.

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