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We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient—a critical requirement for the processing of future petabyte-sized datasets. During segmentation of neurons in electron microscopy datasets, auxiliary learning via the prediction of local shape descriptors increases efficiency, which is important for the processing of datasets of ever-increasing size.

We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.A deep-learning-based approach enables automatic identification of synaptically connected neurons in electron microscopy datasets of the fly brain.

We present a machine learning approach for the inverse design of organic semiconductor materials from benzene and thiophene-based polycyclic aromatic compounds (PACs). Inverse design is an efficient approach to materials discovery that aims to design materials with preset properties. However, it is complex due to the non-uniqueness and nonlinearity of property-to-structure relationships. We demonstrate the potential of this approach through the inverse design of PACs to achieve target HOMO-LUMO gaps, a key property for organic semiconductors, ranging from 1.36 eV to 4.37 eV with an error of 0.15 eV within Density Functional Theory uncertainty. The model uses goal-conditioned reinforcement learning with chemical domain knowledge, allowing addressing design goals directly. To incorporate practical aspects such as chemical accessibility, the model can include soft constraints, such as minimizing ring count to favor smaller structures. Thus, our framework addresses key inverse design challenges while allowing prioritization of more optimal or diverse candidates.

Volumetric muscle loss (VML) is the acute loss of muscle mass due to trauma. Such injuries occur primarily in the extremities and are debilitating, as there is no clinical treatment to restore muscle function. Pro-inflammatory advanced glycation end-products (AGEs) and the soluble receptor for advanced glycation end-products (RAGE) are known to increase in acute trauma patient’s serum and are correlated with increased injury severity. However, it is unclear whether AGEs and RAGE increase in muscle post-trauma. To test this, we used decellularized muscle matrix (DMM), a pro-myogenic, non-immunogenic extracellular matrix biomaterial derived from skeletal muscle. We delivered adipose-derived stromal cells (ASCs) and primary myoblasts to support myogenesis and immunomodulation (N = 8 rats/group). DMM non-seeded and seeded grafts were compared to empty defect and sham controls. Then, 56 days after surgery muscle force was assessed, histology characterized, and protein levels for AGEs, RAGE, p38 MAPK, and myosin heavy chains were measured. Overall, our data showed improved muscle regeneration in ASC-treated injury sites and a regulation of RAGE and p38 MAPK signaling, while myoblast-treated injuries resulted in minor improvements. Taken together, these results suggested that ASCs combined with DMM provides a pro-myogenic microenvironment with immunomodulatory capabilities and indicates further exploration of RAGE signaling in VML.

In this study, a modified version of electrospun nylon 66 nanofibers by silica particles were blended into ordinary Portland cement to investigate the microstructure and some mechanical properties of cementitious material. The addition of silica into the nanofibers improved the tensile and compressive properties of the hardened cement pastes. The observations from the mechanical strength tests showed an increase of 41%, 33% and 65% in tensile strength, compressive strength, and toughness, respectively, when modifying the cement pastes with the proposed nanofibers. The observations from scanning electron microscopy and transmission electron microscopy showed the morphology and microstructure of the fibers as well as their behaviors inside the cement matrix. Additionally, X-ray diffraction and thermal gravimetric analysis clarified the occurrence of the extra pozzolanic reaction, as well as the calcium hydroxide consumption by the attached silica inside the cement matrix. Finally, the observations from this study showed the successful fabrication of the modified nanofibers and the feasibility of improving the tensile and compressive behaviors of cement pastes using the proposed electrospun nanofibers.

The hybrid retrofit system using FRP and concrete overlay applied on the top of slabs has proven effective in strengthening and overcoming logistical constraints, compared with conventional strengthening techniques using externally bonded composite materials to the underside of the slabs. Nevertheless, the performance of retrofitted slabs is governed by debonding failure due to the low bond strength between CFRP and concrete overlay. Thus, this study investigates the behavior of flexural strengthened slabs with FRP retrofit systems and the effect of bond–slip laws on debonding failure. Firstly, two full-scale RC slabs with and without a retrofit system were tested in a four-point bending setup as the control specimens. Then, the same retrofitted slab was simulated by utilizing the commercial program ABAQUS. A sensitivity analysis was conducted to consider the influence of bond–slip laws to predict the failure mechanism of the retrofitted slabs based on load–deflection relationships. The results showed that the strengthened slab enhanced the load-carrying capacity by 59%, stiffness by 111%, and toughness by 29%. The initial stiffness of 0.1K0 and maximum shear stress of 0.13τmax, compared with the corresponding values of Neubauer’s and Rostasy’s bond–slip law, can be used to simulate the global response of the retrofitted slab validated by experiment results.

Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.Kuan, Phelps, et al. used synchrotron X-ray imaging and deep learning to map dense neuronal wiring in fly and mouse tissue, enabling examination of individual cells and connectivity in circuits governing motor control and perceptual decision-making.

The cerebellum is thought to help detect and correct errors between intended and executed commands 1 , 2 and is critical for social behaviours, cognition and emotion 3 – 6 . Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise 7 . Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network’s first layer 8 – 13 . However, maximizing encoding capacity reduces the resilience to noise 7 . To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks. Mapping of the mouse cerebellar cortex using 3D reconstruction from electron microscopy, as well as numerical simulation of neuronal activity, shows non-random redundancy of connectivity that may favour resilient learning over encoding capacity.

Sprouty1 (Spry1) is a conserved antagonist of FGF signaling. The goal of this study was to further explore the downstream mechanisms governing Spry1 inhibition of endothelial cell proliferation. Up-regulation of Spry1 in HUVECs inhibited tube formation on Matrigel (n = 6, P < 0.001). This was associated with decreased proliferation as measured by BrdU incorporation (n = 6, P < 0.001) and increased protein expression of the cyclin-dependent kinase inhibitor 1A (CDKN1A), p21 and cyclin-dependent kinase inhibitor 1B (CDKN1B), p27. A transcriptional analysis using a targeted human angiogenesis array following up-regulation of Spry1 demonstrated a >2-fold increase in an anti-angiogenic factor, serpin peptidase inhibitor, clad F (Serpinf1), and a >2-fold decrease in pro-angiogenic factors fms-related tyrosine kinase 1 (FLT1), angiopoietin2 (Ang-2), and placental growth factor (PGF) (n = 2). To define upstream mechanisms that may regulate endogenous Spry1, we performed a search for responsive elements upstream of the promoter region. This search resulted in the identification of multiple degenerate hypoxia responsive elements. Exposure to hypoxia resulted in a significant increase in Spry1 expression (n = 8, P < 0.01). These findings shed new light on downstream signaling pathways associated with Spry1 anti-proliferative responses, and provide new evidence that hypoxia stimulates Spry1 expression.

This study presents a numerical investigation of the elastic critical lateral-torsional buckling of a steel beam subjected to simultaneous transverse loading at the top flange and negative end moments. Here, the elastic critical buckling of the steel beam was estimated by utilizing the finite element software ABAQUS. In addition, the influence of the length-to-height ratio was taken into account. Additionally, the predicted values for elastic critical buckling when applying existing design codes and a previous study were also analyzed and compared to the numerical results of the finite element analysis. The result of the comparison revealed that the projected values from the design codes and the study are conservative for the majority of cases and have a tendency to be too conservative when the length-to-height ratio increases. Furthermore, a new equation with a factor considering the influence of the length-to-height ratio and transverse loading on the top flange is proposed, and the proposed equation shows sufficient accuracy and less conservative values for most cases.