When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Preceding rule induction with instance reduction methods

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy

Neighborhood Typology and Cardiometabolic Pregnancy Outcomes in the Maternal Adiposity Metabolism and Stress Study.

ObjectiveThis study aimed to assess associations between neighborhood typologies classified across multiple neighborhood domains and cardiometabolic pregnancy outcomes and determine variation in effectiveness of a mindfulness-based stress-reduction intervention on outcomes across neighborhood types.MethodsNeighborhoods of participants in the Maternal Adiposity Metabolism and Stress (MAMAS) intervention (n = 208) were classified across dimensions of socioeconomic, food, safety, and service/resource environments using latent class analysis. The study estimated associations between neighborhood type and three cardiometabolic pregnancy outcomes-glucose tolerance (GT) during pregnancy, excessive gestational weight gain, and 6-month postpartum weight retention (PPWR)-using marginal regression models. Interaction between neighborhood type and intervention was assessed.ResultsFive neighborhood types differing across socioeconomic, food, and resource environments were identified. Compared with poor, well-resourced neighborhoods, middle-income neighborhoods with low resources had higher risk of impaired GT (relative risk [RR]: 4.1; 95% confidence Interval [CI]: 1.1, 15.5), and wealthy, well-resourced neighborhoods had higher PPWR (beta: 3.9 kg; 95% CI: 0.3, 7.5). Intervention effectiveness varied across neighborhood type with wealthy, well-resourced and poor, moderately resourced neighborhoods showing improvements in GT scores. PPWR was higher in intervention compared with control groups within wealthy, well-resourced neighborhoods.ConclusionsConsideration of multidimensional neighborhood typologies revealed important nuances in intervention effectiveness on cardiometabolic pregnancy outcomes

Applying the COM-B model to creation of an IT-enabled health coaching and resource linkage program for low-income Latina moms with recent gestational diabetes: the STAR MAMA program.

BACKGROUND:One of the fastest growing risk groups for early onset of diabetes is women with a recent pregnancy complicated by gestational diabetes, and for this group, Latinas are the largest at-risk group in the USA. Although evidence-based interventions, such as the Diabetes Prevention Program (DPP), which focuses on low-cost changes in eating, physical activity and weight management can lower diabetes risk and delay onset, these programs have yet to be tailored to postpartum Latina women. This study aims to tailor a IT-enabled health communication program to promote DPP-concordant behavior change among postpartum Latina women with recent gestational diabetes. The COM-B model (incorporating Capability, Opportunity, and Motivational behavioral barriers and enablers) and the Behavior Change Wheel (BCW) framework, convey a theoretically based approach for intervention development. We combined a health literacy-tailored health IT tool for reaching ethnic minority patients with diabetes with a BCW-based approach to develop a health coaching intervention targeted to postpartum Latina women with recent gestational diabetes. Current evidence, four focus groups (n = 22 participants), and input from a Regional Consortium of health care providers, diabetes experts, and health literacy practitioners informed the intervention development. Thematic analysis of focus group data used the COM-B model to determine content. Relevant cultural, theoretical, and technological components that underpin the design and development of the intervention were selected using the BCW framework. RESULTS:STAR MAMA delivers DPP content in Spanish and English using health communication strategies to: (1) validate the emotions and experiences postpartum women struggle with; (2) encourage integration of prevention strategies into family life through mothers becoming intergenerational custodians of health; and (3) increase social and material supports through referral to social networks, health coaches, and community resources. Feasibility, acceptability, and health-related outcomes (weight loss, physical activity, consumption of healthy foods, breastfeeding, and glucose screening) will be evaluated at 9 months postpartum using a randomized controlled trial design. CONCLUSIONS:STAR MAMA provides a DPP-based intervention that integrates theory-based design steps. Through systematic use of behavioral theory to inform intervention development, STAR MAMA may represent a strategy to develop health IT intervention tools to meet the needs of diverse populations. TRIAL REGISTRATION:ClinicalTrials.gov NCT02240420

MicroRNA-466 inhibits tumor growth and bone metastasis in prostate cancer by direct regulation of osteogenic transcription factor RUNX2.

MicroRNAs (miRNAs) have emerged as key players in cancer progression and metastatic initiation yet their importance in regulating prostate cancer (PCa) metastasis to bone has begun to be appreciated. We employed multimodal strategy based on in-house PCa clinical samples, publicly available TCGA cohorts, a panel of cell lines, in silico analyses, and a series of in vitro and in vivo assays to investigate the role of miR-466 in PCa. Expression analyses revealed that miR-466 is under-expressed in PCa compared to normal tissues. Reconstitution of miR-466 in metastatic PCa cell lines impaired their oncogenic functions such as cell proliferation, migration/invasion and induced cell cycle arrest, and apoptosis compared to control miRNA. Conversely, attenuation of miR-466 in normal prostate cells induced tumorigenic characteristics. miR-466 suppressed PCa growth and metastasis through direct targeting of bone-related transcription factor RUNX2. Overexpression of miR-466 caused a marked downregulation of integrated network of RUNX2 target genes such as osteopontin, osteocalcin, ANGPTs, MMP11 including Fyn, pAkt, FAK and vimentin that are known to be involved in migration, invasion, angiogenesis, EMT and metastasis. Xenograft models indicate that miR-466 inhibits primary orthotopic tumor growth and spontaneous metastasis to bone. Receiver operating curve and Kaplan-Meier analyses show that miR-466 expression can discriminate between malignant and normal prostate tissues; and can predict biochemical relapse. In conclusion, our data strongly suggests miR-466-mediated attenuation of RUNX2 as a novel therapeutic approach to regulate PCa growth, particularly metastasis to bone. This study is the first report documenting the anti-bone metastatic role and clinical significance of miR-466 in prostate cancer

Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

A Transit Oriented Development Proposal for the Fourth and King Caltrain Station in San Francisco

The South of Market and Mission Bay neighborhoods have seen an explosion in growth over the last decade because of the high cluster of technology company opportunities in the area and the Bay Area’s high housing cost. These two neighborhoods are home to a public transportation hub with two San Francisco Muni metro and bus lines, Caltrain service, and Amtrak intercity bus service to Oakland and other parts of California. The existing Caltrain Fourth and King Station and railyards have massive potential for prime real estate development. San Francisco has seen a 10% increase in population over the last ten years. With the median cost of rent in San Francisco being between $2,000 to$3,000 in 2020 depending on the number of bedrooms, new homes are needed to lower the cost of rent (McLean, 2020), Furthermore, Caltrain’s ridership has been on the rise since 2010 with San Francisco being the most used station in the network with 15,000 riders in 2019 (Caltrain, 2019). A new station is necessary as the original 1970s station building approaches 50 years of service. With the high cost of living in the Bay Area, Caltrain ridership at its highest levels, and the need to build more housing and a new station, the Fourth and King Station can be a viable place for new development. This project addresses how a new Caltrain terminus station could be built while also accounting for Caltrain expansion, high-speed rail connectivity, new market-rate and affordable housing, and urban design of the South of Market and Mission Bay Neighborhoods