The Influence of Electron Beam Sterilization on In Vivo Degradation of β-TCP/PCL of Different Composite Ratios for Bone Tissue Engineering.

We evaluated the effect of electron beam (E-beam) sterilization (25 kGy, ISO 11137) on the degradation of β-tricalcium phosphate/polycaprolactone (β-TCP/PCL) composite filaments of various ratios (0:100, 20:80, 40:60, and 60:40 TCP:PCL by mass) in a rat subcutaneous model for 24 weeks. Volumes of the samples before implantation and after explantation were measured using micro-computed tomography (micro-CT). The filament volume changes before sacrifice were also measured using a live micro-CT. In our micro-CT analyses, there was no significant difference in volume change between the E-beam treated groups and non-E-beam treated groups of the same β-TCP to PCL ratios, except for the 0% β-TCP group. However, the average volume reduction differences between the E-beam and non-E-beam groups in the same-ratio samples were 0.76% (0% TCP), 3.30% (20% TCP), 4.65% (40% TCP), and 3.67% (60% TCP). The E-beam samples generally had more volume reduction in all experimental groups. Therefore, E-beam treatment may accelerate degradation. In our live micro-CT analyses, most volume reduction arose in the first four weeks after implantation and slowed between 4 and 20 weeks in all groups. E-beam groups showed greater volume reduction at every time point, which is consistent with the results by micro-CT analysis. Histology results suggest the biocompatibility of TCP/PCL composite filaments

TransNets: Learning to Transform for Recommendation

Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender Systems (RecSys 2017

Do Narcissists Enjoy Visiting Social Networking Sites? It Depends on How Adaptive They Are

Previous evidence suggests that narcissistic people tend to visit social networking sites (SNS) frequently, but the emotions accompanying their engagement on such sites has not been a significant subject of study. Therefore, we examined the relationship between narcissism and the affective experience on SNS in two different samples. To do so, we not only examined narcissism as a whole but also distinguished between adaptive and maladaptive narcissism. Results of the two studies consistently showed that: (1) narcissism as a whole was not correlated with the SNS affective experience; (2) maladaptive narcissism was predictive of a worse affective experience on SNS; and (3) partly due to a positive correlation with self-esteem, adaptive narcissism was associated with a better SNS affective experience. In addition, these findings held with SNS activities considered in simultaneity. The present research extends our understanding of the relationship between narcissism and social networking as well as that between emotion and social networking

GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness.Comment: Accepted at MICCAI 202

TrkA+ Neurons Induce Pathologic Regeneration After Soft Tissue Trauma

Heterotopic ossification (HO) is a dynamic, complex pathologic process that often occurs after severe polytrauma trauma, resulting in an abnormal mesenchymal stem cell differentiation leading to ectopic bone growth in soft-tissues including tendons, ligaments, and muscles. The abnormal bone structure and location induce pain and loss of mobility. Recently, we observed that NGF (Nerve growth factor)-responsive TrkA (Tropomyosin receptor kinase A)-expressing nerves invade sites of soft-tissue trauma, and this is a necessary feature for heterotopic bone formation at sites of injury. Here, we assayed the effects of the partial TrkA agonist Gambogic amide (GA) in peritendinous heterotopic bone after extremity trauma. Mice underwent HO induction using the burn/tenotomy model with or without systemic treatment with GA, followed by an examination of the injury site via radiographic imaging, histology, and immunohistochemistry. Single-cell RNA Sequencing confirmed an increase in neurotrophin signaling activity after HO-inducing extremity trauma. Next, TrkA agonism led to injury site hyper-innervation, more brisk expression of cartilage antigens within the injured tendon, and a shift from FGF to TGF beta signaling activity among injury site cells. Nine weeks after injury, this culminated in higher overall levels of heterotopic bone among GA-treated animals. In summary, these studies further link injury site hyper-innervation with increased vascular ingrowth and ultimately heterotopic bone after trauma. In the future, modulation of TrkA signaling may represent a potent means to prevent the trauma-induced heterotopic bone formation and improve tissue regeneration

User Diverse Preference Modeling by Multimodal Attentive Metric Learning

Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's varying preferences on all items, especially when considering the diverse characteristics of various items. To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items. In particular, for each user-item pair, we propose an attention neural network, which exploits the item's multimodal features to estimate the user's special attention to different aspects of this item. The obtained attention is then integrated into a metric-based learning method to predict the user preference on this item. The advantage of metric learning is that it can naturally overcome the problem of dot product similarity, which is adopted by matrix factorization (MF) based recommendation models but does not satisfy the triangle inequality property. In addition, it is worth mentioning that the attention mechanism cannot only help model user's diverse preferences towards different items, but also overcome the geometrically restrictive problem caused by collaborative metric learning. Extensive experiments on large-scale real-world datasets show that our model can substantially outperform the state-of-the-art baselines, demonstrating the potential of modeling user diverse preference for recommendation.Comment: Accepted by ACM Multimedia 2019 as a full pape

Standing Enokitake-like Nanowire Films for Highly Stretchable Elastronics

Stretchable electronics may enable electronic components to be part of our organs-ideal for future wearable/implantable biodiagnostic systems. One of key challenges is failure of the soft/rigid material interface due to mismatching Young’ s moduli, which limits stretchability and durability of current systems. Here, we show that standing enokitake-like gold-nanowire-based films chemically bonded to an elastomer can be stretched up to 900% and are highly durable, with >93% conductivity recovery even after 2000 stretching/releasing cycles to 800% strain. Both experimental and modeling reveal that this superior elastic property originates from standing enokitake-like nanowire film structures. The closely packed nanoparticle layer sticks to the top of the nanowires, which easily cracks under strain, whereas the bottom part of the nanowires is compliant with substrate deformation. This leads to tiny V-shaped cracks with a maintained electron transport pathway rather than large U-shaped cracks that are frequently observed for conventional metal films. We further show that our standing nanowire films can serve as current collectors in supercapacitors and second skin-like smart masks for facial expression detection