Gradient-free activation maximization for identifying effective stimuli

A fundamental question for understanding brain function is what types of stimuli drive neurons to fire. In visual neuroscience, this question has also been posted as characterizing the receptive field of a neuron. The search for effective stimuli has traditionally been based on a combination of insights from previous studies, intuition, and luck. Recently, the same question has emerged in the study of units in convolutional neural networks (ConvNets), and together with this question a family of solutions were developed that are generally referred to as "feature visualization by activation maximization." We sought to bring in tools and techniques developed for studying ConvNets to the study of biological neural networks. However, one key difference that impedes direct translation of tools is that gradients can be obtained from ConvNets using backpropagation, but such gradients are not available from the brain. To circumvent this problem, we developed a method for gradient-free activation maximization by combining a generative neural network with a genetic algorithm. We termed this method XDream (EXtending DeepDream with real-time evolution for activation maximization), and we have shown that this method can reliably create strong stimuli for neurons in the macaque visual cortex (Ponce et al., 2019). In this paper, we describe extensive experiments characterizing the XDream method by using ConvNet units as in silico models of neurons. We show that XDream is applicable across network layers, architectures, and training sets; examine design choices in the algorithm; and provide practical guides for choosing hyperparameters in the algorithm. XDream is an efficient algorithm for uncovering neuronal tuning preferences in black-box networks using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table

Efficacy of Gene Therapy Is Dependent on Disease Progression in Dystrophic Mice with Mutations in the FKRP Gene

Loss-of-function mutations in the Fukutin-related protein ( ) gene cause limb-girdle muscular dystrophy type 2I (LGMD2I) and other forms of congenital muscular dystrophy-dystroglycanopathy that are associated with glycosylation defects in the α-dystroglycan (α-DG) protein. Systemic administration of a single dose of recombinant adeno-associated virus serotype 9 (AAV9) vector expressing human to a mouse model of LGMD2I at various stages of disease progression was evaluated. The results demonstrate rescue of functional glycosylation of α-DG and muscle function, along with improvements in muscle structure at all disease stages versus age-matched untreated cohorts. Nevertheless, mice treated in the latter stages of disease progression revealed a decrease in beneficial effects of the treatment. The results provide a proof of concept for future clinical trials in patients with -related muscular dystrophy and demonstrate that AAV-mediated gene therapy can potentially benefit patients at all stages of disease progression, but earlier intervention would be highly preferred

Representation Learning for Attributed Multiplex Heterogeneous Network

Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn

Look Twice: A Computational Model of Return Fixations across Tasks and Species

Saccadic eye movements allow animals to bring different parts of an image into high-resolution. During free viewing, inhibition of return incentivizes exploration by discouraging previously visited locations. Despite this inhibition, here we show that subjects make frequent return fixations. We systematically studied a total of 44,328 return fixations out of 217,440 fixations across different tasks, in monkeys and humans, and in static images or egocentric videos. The ubiquitous return fixations were consistent across subjects, tended to occur within short offsets, and were characterized by longer duration than non-return fixations. The locations of return fixations corresponded to image areas of higher saliency and higher similarity to the sought target during visual search tasks. We propose a biologically-inspired computational model that capitalizes on a deep convolutional neural network for object recognition to predict a sequence of fixations. Given an input image, the model computes four maps that constrain the location of the next saccade: a saliency map, a target similarity map, a saccade size map, and a memory map. The model exhibits frequent return fixations and approximates the properties of return fixations across tasks and species. The model provides initial steps towards capturing the trade-off between exploitation of informative image locations combined with exploration of novel image locations during scene viewing

Microtubule affinity-regulating kinase 4 (MARK4) is a component of the ectoplasmic specialization in the rat testis

During the seminiferous epithelial cycle of spermatogenesis, the ectoplasmic specialization (ES, a testis-specific adherens junction, AJ, type) maintains the polarity of elongating/elongated spermatids and confers adhesion to Sertoli cells in the seminiferous epithelium, and known as the apical ES. On the other hand, the ES is also found at the Sertoli-Sertoli cell interface at the blood-testis barrier (BTB) known as basal ES, which together with the tight junction (TJ), maintains Sertoli cell polarity and adhesion, creating a functional barrier that limits paracellular transport of substances across the BTB. However, the apical and basal ES are segregated and restricted to the adluminal compartment and the BTB, respectively. During the transit of preleptotene spermatocytes across the BTB and the release of sperm at spermiation at stage VIII of the seminiferous epithelial cycle, both the apical and basal ES undergo extensive restructuring to facilitate cell movement at these sites. The regulation of these events, in particular their coordination, remains unclear. Studies in other epithelia have shown that the tubulin cytoskeleton is intimately related to cell movement, and MARK [microtubule-associated protein (MAP)/microtubule affinity-regulating kinase] family kinases are crucial regulators of tubulin cytoskeleton stability. Herein MARK4, the predominant member of the MARK protein family in the testis, was shown to be expressed by both Sertoli and germ cells. MARK4 was also detected at the apical and basal ES, displaying highly restrictive spatiotemporal expression at these sites, as well as co-localizing with markers of the apical and basal ES. The expression of MARK4 was found to be stage-specific during the epithelial cycle, structurally associating with α-tubulin and the desmosomal adaptor plakophilin-2, but not with actin-based BTB proteins occludin, β-catenin and Eps8 (epidermal growth factor receptor pathway substrate 8, an actin bundling and barbed end capping protein). More importantly, it was shown that the expression of MARK4 tightly associated with the integrity of the apical ES because a diminished expression of MARK4 associated with apical ES disruption that led to the detachment of elongating/elongated spermatids from the epithelium. These findings thus illustrate that the integrity of apical ES, an actin-based and testis-specific AJ, is dependent not only on the actin filament network, but also on the tubulin-based cytoskeleton

Status of the emittance transfer experiment emtex

In order to improve the injection efficiency of the round UNILAC heavy ion beam into the asymmetric acceptance of the SIS18 it would be of great advantage to decreasethe horizontal emittance by a so called emittance transferto the vertical plane. In this contribution the present statusof the emittance transfer experiment EMTEX at GSI will be reported. A short introduction about the theoretical background of the technique will be given, while the mainpart is dedicated to the practical solutions setting up a testbeam line at GSI. Finally, the results of a first commissioning beam time will be presented. The scheduled beam time to apply the emittance transfer technique foreseen in spring 2014 had to be shifted to calendar week 26 in 2014, just after this conference as some components have not been delivered in time by the contractor. The results and comparison to the theoretical predictions you may find in later publications

Physician and patient use of and attitudes toward complementary and alternative medicine in the treatment of infertility

ObjectiveTo determine use of and attitudes toward complementary and alternative medicine (CAM) among infertility patients and subspecialty physicians.MethodsInfertility patients were asked to complete anonymous written surveys at an academic infertility practice; members of the Society for Reproductive Endocrinology and Infertility were electronically surveyed. Both groups were assessed regarding their use of and attitudes toward CAM.ResultsThe response rate was 32.1% (115/358) among patients and 22.6% (225/995) among physicians (P < 0.05). In total, 105 (91.3%; 95% confidence interval [CI], 85.8–96.2) patients used CAM, and 84 (73.0%; 95% CI, 64.9–81.1) regarded it as beneficial to their fertility treatment. However, only 30 (26.1%; 95% CI, 18.0–34.0) patients reported CAM use to physicians, with the most common reason being that they were “never asked.” Overall, 202 (89.8%; 95% CI, 85.9–93.8) physicians reported inquiring about CAM.ConclusionSignificant discrepancies exist between subfertile patients and physicians in attitudes toward the use of CAM. The current prevalence of CAM use among infertility patients requires greater physician attention and justifies further study on the risks and benefits of integrating CAM into the biomedical treatment of infertility.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135333/1/ijgo253.pd

Understanding Negative Sampling in Graph Representation Learning

Graph representation learning has been extensively studied in recent years. Despite its potential in generating continuous embeddings for various networks, both the effectiveness and efficiency to infer high-quality representations toward large corpus of nodes are still challenging. Sampling is a critical point to achieve the performance goals. Prior arts usually focus on sampling positive node pairs, while the strategy for negative sampling is left insufficiently explored. To bridge the gap, we systematically analyze the role of negative sampling from the perspectives of both objective and risk, theoretically demonstrating that negative sampling is as important as positive sampling in determining the optimization objective and the resulted variance. To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution. With the guidance of the theory, we propose MCNS, approximating the positive distribution with self-contrast approximation and accelerating negative sampling by Metropolis-Hastings. We evaluate our method on 5 datasets that cover extensive downstream graph learning tasks, including link prediction, node classification and personalized recommendation, on a total of 19 experimental settings. These relatively comprehensive experimental results demonstrate its robustness and superiorities.Comment: KDD 202