Wireless-Connection Drop Prediction, Warning, and Protection

This publication describes systems and techniques directed to wireless-connection drop prediction, warning, and protection. A wireless-communication device, such as a smartphone, includes a wireless-connection manager application. The wireless-communication device, having a wireless connection to an access point of a wireless network, performs operations under the direction of the wireless-connection manager application to predict, warn, and protect a user of instances where the wireless connection may drop. The operations include determining that a quality metric of a signal supporting the wireless connection to the access point does not meet a threshold, determining that another access point to the wireless network is not available, and, in response, presenting a warning to the user to protect the user and allow the user to make decisions so that the wireless-communication device maintains the wireless connection to the access point

Service-Oriented Mobile Network Tracking and Guiding

This publication describes techniques for a User Equipment device (UE) (e.g., a mobile device, a tablet, a wireless-communication device) to guide a user to an area with a requested network service. Mobile device users often encounter situations where the wireless network they are connected to does not provide a requested network service. With no other available means to find a wireless network with the requested network service, users will often wander in attempt to access the network services they desire. Thus, a wireless-connection network manager (WCNM) that can aid the user by guiding them to a physical location where the user previously received access to a requested service is desirable

VIGAN: Missing View Imputation with Generative Adversarial Networks

In an era when big data are becoming the norm, there is less concern with the quantity but more with the quality and completeness of the data. In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. The missing data problem has been challenging to address in multi-view data analysis. Especially, when certain samples miss an entire view of data, it creates the missing view problem. Classic multiple imputations or matrix completion methods are hardly effective here when no information can be based on in the specific view to impute data for such samples. The commonly-used simple method of removing samples with a missing view can dramatically reduce sample size, thus diminishing the statistical power of a subsequent analysis. In this paper, we propose a novel approach for view imputation via generative adversarial networks (GANs), which we name by VIGAN. This approach first treats each view as a separate domain and identifies domain-to-domain mappings via a GAN using randomly-sampled data from each view, and then employs a multi-modal denoising autoencoder (DAE) to reconstruct the missing view from the GAN outputs based on paired data across the views. Then, by optimizing the GAN and DAE jointly, our model enables the knowledge integration for domain mappings and view correspondences to effectively recover the missing view. Empirical results on benchmark datasets validate the VIGAN approach by comparing against the state of the art. The evaluation of VIGAN in a genetic study of substance use disorders further proves the effectiveness and usability of this approach in life science.Comment: 10 pages, 8 figures, conferenc

Human beta defensin 2 selectively inhibits HIV-1 in highly permissive CCR6+CD4+ T cells

Chemokine receptor type 6 (CCR6)+CD4+ T cells are preferentially infected and depleted during HIV disease progression, but are preserved in non-progressors. CCR6 is expressed on a heterogeneous population of memory CD4+ T cells that are critical to mucosal immunity. Preferential infection of these cells is associated, in part, with high surface expression of CCR5, CXCR4, and α4β7. In addition, CCR6+CD4+ T cells harbor elevated levels of integrated viral DNA and high levels of proliferation markers. We have previously shown that the CCR6 ligands MIP-3α and human beta defensins inhibit HIV replication. The inhibition required CCR6 and the induction of APOBEC3G. Here, we further characterize the induction of apolipoprotein B mRNA editing enzyme (APOBEC3G) by human beta defensin 2. Human beta defensin 2 rapidly induces transcriptional induction of APOBEC3G that involves extracellular signal-regulated kinases 1/2 (ERK1/2) activation and the transcription factors NFATc2, NFATc1, and IRF4. We demonstrate that human beta defensin 2 selectively protects primary CCR6+CD4+ T cells infected with HIV-1. The selective protection of CCR6+CD4+ T cell subsets may be critical in maintaining mucosal immune function and preventing disease progression

Text Data Embedded into a Voice Call

This publication describes systems and techniques directed to embedding text data into a voice call. A first wireless-communication device, such as a smartphone, includes a text-voice manager application. The first wireless-communication device performs the voice call with a second wireless-communication device that includes a peer text-voice manager application. The first wireless-communication device, by executing instructions of the text-voice manager application, performs operations that include receiving text data for a text message, converting the text data to a voice message, detecting silent periods in a stream of a voice transmission, and embedding the voice message into the detected silent periods in the stream of the voice transmission. The second wireless-communication device receives the stream of the voice transmission, detects a pattern in the stream of the voice transmission that indicates a presence of the voice message, de-converts the voice message to the text data, and embeds the text data into a text message displayed on the second wireless-communication device

COSE: A Consistency-Sensitivity Metric for Saliency on Image Classification

We present a set of metrics that utilize vision priors to effectively assess the performance of saliency methods on image classification tasks. To understand behavior in deep learning models, many methods provide visual saliency maps emphasizing image regions that most contribute to a model prediction. However, there is limited work on analyzing the reliability of saliency methods in explaining model decisions. We propose the metric COnsistency-SEnsitivity (COSE) that quantifies the equivariant and invariant properties of visual model explanations using simple data augmentations. Through our metrics, we show that although saliency methods are thought to be architecture-independent, most methods could better explain transformer-based models over convolutional-based models. In addition, GradCAM was found to outperform other methods in terms of COSE but was shown to have limitations such as lack of variability for fine-grained datasets. The duality between consistency and sensitivity allow the analysis of saliency methods from different angles. Ultimately, we find that it is important to balance these two metrics for a saliency map to faithfully show model behavior

A case study in open source innovation: developing the Tidepool Platform for interoperability in type 1 diabetes management.

OBJECTIVE:Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management. MATERIALS AND METHODS:An interdisciplinary team decided to establish a nonprofit company, Tidepool, and build open-source software. RESULTS:Through a user-centered design process, the authors created a software platform, the Tidepool Platform, to upload and host T1D device data in an integrated, device-agnostic fashion, as well as an application ("app"), Blip, to visualize the data. Tidepool's software utilizes the principles of modular components, modern web design including REST APIs and JavaScript, cloud computing, agile development methodology, and robust privacy and security. DISCUSSION:By consolidating the currently scattered and siloed T1D device data ecosystem into one open platform, Tidepool can improve access to the data and enable new possibilities and efficiencies in T1D clinical care and research. The Tidepool Platform decouples diabetes apps from diabetes devices, allowing software developers to build innovative apps without requiring them to design a unique back-end (e.g., database and security) or unique ways of ingesting device data. It allows people with T1D to choose to use any preferred app regardless of which device(s) they use. CONCLUSION:The authors believe that the Tidepool Platform can solve two current problems in the T1D device landscape: 1) limited access to T1D device data and 2) poor interoperability of data from different devices. If proven effective, Tidepool's open source, cloud model for health data interoperability is applicable to other healthcare use cases

Assisted Nerve Regeneration Utilizing Novel Nerve Conduits with Wall-Encapsulated Cells

Peripheral nerves possess an innate ability to regenerate, but following complete transection or segmental damage to the nerve, surgical intervention is required to re-approximate the ends of a nerve for a chance at functional recovery. Among the treatment options, synthetic nerve conduits are a promising tissue engineering approach to effect peripheral nerve regeneration across functionally debilitating segmental defects. An essential step in this restoration is the formation of a “nerve bridge” pioneered by Schwann cells that migrate to the center of the conduit in response to chemokine gradients. This work in this dissertation focuses on the creation of a chemokine gradient in nerve conduits through the use of stem cell-secreted neurotrophic factors. While cell incorporation inside conduits is a widely applied technique and has demonstrated some beneficial effects, conventional cell-seeding methods fail to produce a directional signal for invading Schwann cells. A large part of the challenge to providing this signal is the ability to localize cells to a desired region. The work herein elaborates a versatile, single-step method to encapsulate neurotrophically active cells within the walls of a conduit through the use of a composite nanofibrous scaffold, allowing for strict control of cell number and spatial distribution along the length of the conduit. The resulting structure significantly enhances dorsal root ganglion outgrowth in vitro, and is flexible and mechanically suitable for in vivo implantation. Utilizing stem cells encapsulated within the central third of the conduit, markedly different cell distribution (Gaussian vs. quadratic) and retention are observed over the course of 6 weeks in a 10 mm rat sciatic nerve transection model when compared to standard cell injection method. This drives Schwann cell migration into the center of the regenerating nerve bridge, and at 16 weeks rats presented with significantly enhanced function and axon myelin over control. Taken together, the work in this dissertation demonstrates that this method of utilizing a spatially restricted cell secretome, which is a departure from conventional homogeneous or uncontrolled cell loading, presents a new paradigm for studying and maximizing the potential of cell application in peripheral nerve repair