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TDP-43 is a predominantly nuclear DNA/RNA binding protein that is often mislocalized into insoluble cytoplasmic inclusions in post-mortem patient tissue in a variety of neurodegenerative disorders, most notably, Amyotrophic Lateral Sclerosis (ALS), a fatal and progressive neuromuscular disorder. The underlying causes of TDP-43 proteinopathies remain unclear, but recent studies indicate the formation of these protein assemblies is driven by aberrant phase transitions of RNA deficient TDP-43. Technical limitations have prevented our ability to understand how TDP-43 proteinopathy relates to disease pathogenesis. Current animal models of TDP-43 proteinopathy often rely on overexpression of wild-type TDP-43 to non-physiological levels that may initiate neurotoxicity through nuclear gain of function mechanisms, or by the expression of disease-causing mutations found in only a fraction of ALS patients. New technologies allowing for light-responsive control of subcellular protein crowding provide a promising approach to drive intracellular protein aggregation, as we have previously demonstrated in vitro. Here we present a model for the optogenetic induction of TDP-43 aggregation in Drosophila that recapitulates key biochemical features seen in patient pathology, most notably light-inducible persistent insoluble species and progressive motor dysfunction. These data describe a photokinetic in vivo model that could be as a future platform to identify novel genetic and pharmacological modifiers of diseases associated with TDP-43 neuropathology.

Cyber Physical Systems (CPS) and Internet of Things (IoT) communities are often asked to test devices regarding their effects on underlying infrastructure. Usually, only one or two devices are given to the testers, but hundreds or thousands are needed to really test IoT effects. This proposition makes IoT Test & Evaluation (T&E) cost and management prohibitive. One possible approach is to develop a digital twin of the IoT device and employ many replicas of the twin in a simulation environment comprised of various simulators that mimic the IoT device’s operational environment. Cyber attack experimentation is a critical aspect of IoT T&E and without such a virtual T&E environment, it is almost impossible to study large scale effects. This paper will present a digital twin engineering methodology as applicable to IoT device T&E and cyber experimentation.

The neuromuscular disorder spinal muscular atrophy (SMA), the most common inherited killer of infants, is caused by insufficient expression of survival motor neuron (SMN) protein. SMA therapeutics development efforts have focused on identifying strategies to increase SMN expression. We identified a long non-coding RNA (lncRNA) that arises from the antisense strand of SMN, SMN-AS1, which is enriched in neurons and transcriptionally represses SMN expression by recruiting the epigenetic Polycomb repressive complex-2. Targeted degradation of SMN-AS1 with antisense oligonucleotides (ASOs) increases SMN expression in patient-derived cells, cultured neurons, and the mouse central nervous system. SMN-AS1 ASOs delivered together with SMN2 splice-switching oligonucleotides additively increase SMN expression and improve survival of severe SMA mice. This study is the first proof of concept that targeting a lncRNA to transcriptionally activate SMN2 can be combined with SMN2 splicing modification to ameliorate SMA and demonstrates the promise of combinatorial ASOs for the treatment of neurogenetic disorders.

WiFi effectively has two extremes: low power consumption and high latency, or low latency and high power consumption. WiFi Power Save Mode saves energy by trading added latency for less power consumption. Minimal latency but maximum power, on the other hand, is consumed with WiFi Active Mode. While research has advanced in mitigating these extremes, certain types of network traffic such as constant bitrate streaming make the contrast unavoidable. We introduce Bluesaver, which provides low latency and low energy by maintaining a Bluetooth and WiFi connection simultaneously. Bluesaver is designed at the MAC layer and is able to opportunistically select the most efficient connection for packets while still assuring acceptable latency. We implement Bluesaver on an Android phone and Access Point and show that we can save more than 25 $\%$ energy over existing solutions and attain the capability of quickly adapting to changes in network traffic.

TDP-43 proteinopathy is a pathological hallmark of amyotrophic lateral sclerosis and frontotemporal dementia where cytoplasmic TDP-43 inclusions are observed within degenerating regions of patient postmortem tissue. The mechanism by which TDP-43 aggregates has remained elusive due to technological limitations, which prevent the analysis of specific TDP-43 interactions in live cells. We present an optogenetic approach to reliably induce TDP-43 proteinopathy under spatiotemporal control. We show that the formation of pathologically relevant inclusions is driven by aberrant interactions between low-complexity domains of TDP-43 that are antagonized by RNA binding. Although stress granules are hypothesized to be a conduit for seeding TDP-43 proteinopathy, we demonstrate pathological inclusions outside these RNA-rich structures. Furthermore, we show that aberrant phase transitions of cytoplasmic TDP-43 are neurotoxic and that treatment with oligonucleotides composed of TDP-43 target sequences prevent inclusions and rescue neurotoxicity. Collectively, these studies provide insight into the mechanisms that underlie TDP-43 proteinopathy and present a potential avenue for therapeutic intervention.

Little known about changes in immune responses to TB that occur with aging. We identified pulmonary TB index cases and enrolled their heavily TB exposed household contacts in Uganda. We studied 26 older (mean age 58, range 50–78) and 17 younger (mean age 23, range 18–30) household contacts. None had active TB. 11 Index cases with active TB were examined also. PBMC were studied at time 0 by RNA-seq transcriptomic analysis. Not surprisingly, pathway-based analysis of young subjects with active TB versus contacts showed upregulation of IL6, complement, and type I interferon response gene (IRGs such as OAS1/2, IFI6/27, IFIT2/3 and IRF7) pathways signifying a coordinated innate immune response. Interestingly, many of the same IRGs expressed in active TB in younger individuals were also upregulated in older highly exposed versus younger highly exposed. However, this IRG expression was in the context of type II IRG (IFNG), IL12 and other proinflammatory cytokine signaling consistent with higher activation of the inflammasome upon TB exposure in the aged. PBMC were also stimulated with TB and CMV for study by multiparameter flow cytometry. Surprisingly the magnitude and polyfunctional quality of memory CD4+ T cells responding to TB antigens was similar between the age groups measuring TNF, IFN, and IL-2. The polyfunctional profile however was different between T cells responding to TB and CMV supporting a specific phenotype to TB responding cells. Several inhibitory receptors associated with exhaustion (CD160, 2B4,PD-1) have a trend or are higher in CD4+ T cell memory subsets with age.

Background Epidemiological data shows a bimodal pattern of mortality in lupus. The most deadly complications are associated with systemic lupus erythematosus (SLE) disease activities, including kidney disease and infections, in the first 10 years; subsequently, the primary cause of morbidity and mortality is cardiovascular disease (CVD). Previously, we used cardiac magnetic resonance imaging (CMR) with quantitative T2 mapping in human SLE and observed enhanced T2 signals in active patients during flares, which suggested sub-clinical levels of cardiac inflammation. Objectives Since patients may significantly benefit from non-invasive monitoring of CVD, we used CMR to examine the NZM2410 animal model of lupus nephritis (LN), which develops severe lupus-like glomerulonephritis at 22–40 weeks of age. Methods Myocardial edema was measured with T2 CMR mapping on the BioSpec 94/30 microMRI imaging system. Mice were monitored for cardiac abnormalities and physiological cardiac function using a high-frequency ultrasound echocardiogram (EC). Blood urea nitrogen (BUN) levels were measured to assess kidney function and ELISA was performed on cardiac tissue homogenates and serum. Heart tissue was collected for H&E, immunhistochemistry, and Masson9s trichrome staining to measure tissue fibrosis. Results As NZM2410 mice aged, a significantly enhanced expression of IL-1β, IL-2, IL-6, IL-10, CXCL1, and TNF-α was detected in both the serum and cardiac tissue. CMR T2 mapping was performed at 20–25 weeks of age and EC was used to monitor for cardiac abnormalities. When ECs indicated increased left ventricular diameter and residual volumes as well as significantly decreased ejection fractions relative to baseline, CMR with T2 mapping was performed again. Despite BUN levels and weight measurements showing no signs of end-stage renal disease, T2 signals were elevated and the myocardium thickened significantly to produce a prominent left ventricular hypertrophy upon MRI analysis. In addition, expression of IL-10, IL-2, IL-6, CXCL1, and TNF-α was significantly up-regulated in the serum of these mice. Histopathological examination revealed robust infiltrates in cardiac tissue with significant fibrosis. While immunohistochemical staining showed little to no detection of B-cells, neutrophils, or macrophages, IL-17 and CD3+ T-cells were observed in cardiac infiltrates. Conclusions Our results demonstrate that myocarditis is associated with pro-inflammatory cytokine expression and LN disease progression in NZM2410 mice prior to end-stage renal disease. Consequently, our data establish CMR as a novel non-invasive technique to explore the contribution of SLE-mediated inflammation on CVD and to develop therapeutic strategies to pharmacologically inhibit cardiac damage. Future work will identify biomarkers associated with cardiac damage and will further develop CMR with quantitative T2 mapping as a clinical tool to assess CVD in patients with SLE. References Ardoin, S, et al. CMR with Quantitative T2 Mapping in Patients with Active SLE [abstract]. Arthritis Rheumatol. October 2014; 66, S10. DOI: 10.1002/art.38914 Acknowledgement The Ohio State University Wexner Medical Center. Disclosure of Interest None declared

Effective WiFi power management can strongly impact the energy consumption on Smartphones. Through controlled experiments, we find that WiFi power management on a wide variety of Smartphones is a largely autonomous process that is processed completely at the driver level. Driver level implementations suffer from the limitation that important power management decisions can be made only by observing packets at the MAC layer. This approach has the unfortunate side effect that each application has equal opportunity to impact WiFi power management to consume more energy, since distinguishing between applications is not feasible at the MAC layer. The power cost difference between WiFi power modes is high (a factor of 20 times when idle), therefore determining which applications are permitted to impact WiFi power management is an important and relevant problem. In this paper we propose SAPSM: Smart Adaptive Power Save Mode. SAPSM labels each application with a priority with the assistance of a machine learning classifier. Only high priority applications affect the client's behavior to switch to CAM or Active mode, while low priority traffic is optimized for energy efficiency. Our implementation on an Android Smartphone improves energy savings by up to 56% under typical usage patterns.

The vast array of small wireless sensors is a boon to body sensor network applications, especially in the context awareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendly hardware and software, accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone. We provide an accurate and efficient classification approach through the use of ensemble learning. We explore the properties of different sensors and sensor data to further improve classification efficiency and reduce reliance on user annotated ground truth. We evaluate our PBN system with multiple subjects over a two week period and demonstrate that the system is easy to use, accurate, and appropriate for mobile devices.

Since one-third of a smart phone's battery energy is consumed by its WiFi interface, it is critical to switch the WiFi radio from its active or Constantly Awake Mode (CAM), which draws high power (726mW with screen off), to its sleep or Power Save Mode (PSM), which consumes little power (36mW). Applications like VoIP do not perform well under PSM mode however, due to their real-time nature, so the energy footprint is quite high. The challenge is to save energy while not affecting performance. In this paper we present SiFi: Silence prediction based WiFi energy adaptation. SiFi examines audio streams from phone calls and tracks when silence periods start and stop. This data is stored in a prediction model. Using this historical data, we predict the length of future silence periods and place the WiFi radio to sleep during these periods. We implement the design on an Android Smart phone and acheive 40% energy savings while maintaining high voice fidelity.

Body sensor networks (BSNs) have been developed for a set of performance-critical applications, including smart healthcare, assisted living, emergency response, athletic performance evaluation, and interactive controls. Many of these applications require stringent performance assurance in terms of communication throughput and bounded time delay. While solutions exist in literature for providing joint throughput and time delay assurance by proposing specific MAC protocols or extensions, we provide this joint assurance in a novel radio-agnostic manner. In our approach, the underlying MAC and PHY layers can be heterogeneous and their details do not need to be known to upper layers like the resource management. Such a radio-agnostic performance assurance is critical because a range of radio platforms are adopted for practical body sensor usage. Our approach is based on a group-polling scheme that is essential for radio-agnostic BSN design. Through theoretical analysis, we prove that with the group-polling scheme, achieving joint throughput and time delay assurance is an NP-hard problem. For practical system deployment, we propose the BodyT2 framework that assures throughput and time delay performance in a heterogeneous BSN. Through both TelosB mote lab tests and real body experiments in an Android phone-centric BSN, we demonstrate that BodyT2 achieves superior performance over existing solutions.

SummaryForty-six days after successful inoculation of L. donovani into hamsters, there appeared edema associated with amyloidosis. There was marked anasarca at 60 and 77 days after infection with extensive deposition of amyloid in the glomeruli and the adrenal cortex. The hypoalbuminemia, which coincided with the edema and a proteinuria, is believed to have been caused by the impairment of glomerular function.