Comparing Privacy Control Methods for Smartphone Platforms

Abstract-Nowadays, many important applications are performed through mobile phones. It is essential to ensure that users' private information is not leaked through those applications. In this paper, we perform a comparison on privacy control methods implemented on the Android and iOS platforms based on the Bellotti and Sellen's framework. The comparison helps understand the discrepancies existent between the users' expectations for privacy and the privacy control methods currently implemented in Android and iOS. To better address users' privacy concerns, we propose a programming model for platform designers to improve privacy. Our initial study on 60 privacy bugs show that using the proposed programming models, 14 Android and 5 iOS privacy bugs can be eliminated

Social context of adherence in an open-label 1% tenofovir gel trial : gender dynamics and disclosure in KwaZulu-Natal, South Africa.

CAPRISA, 2016.Abstract available in PDF file

High variety of known and new RNA and DNA viruses of diverse origins in untreated sewage

Deep sequencing of untreated sewage provides an opportunity to monitor enteric infections in large populations and for high-throughput viral discovery. A metagenomics analysis of purified viral particles in untreated sewage from the United States (San Francisco, CA), Nigeria (Maiduguri), Thailand (Bangkok), and Nepal (Kathmandu) revealed sequences related to 29 eukaryotic viral families infecting vertebrates, invertebrates, and plants (BLASTx E score, <10(−4)), including known pathogens (>90% protein identities) in numerous viral families infecting humans (Adenoviridae, Astroviridae, Caliciviridae, Hepeviridae, Parvoviridae, Picornaviridae, Picobirnaviridae, and Reoviridae), plants (Alphaflexiviridae, Betaflexiviridae, Partitiviridae, Sobemovirus, Secoviridae, Tombusviridae, Tymoviridae, Virgaviridae), and insects (Dicistroviridae, Nodaviridae, and Parvoviridae). The full and partial genomes of a novel kobuvirus, salivirus, and sapovirus are described. A novel astrovirus (casa astrovirus) basal to those infecting mammals and birds, potentially representing a third astrovirus genus, was partially characterized. Potential new genera and families of viruses distantly related to members of the single-stranded RNA picorna-like virus superfamily were genetically characterized and named Picalivirus, Secalivirus, Hepelivirus, Nedicistrovirus, Cadicistrovirus, and Niflavirus. Phylogenetic analysis placed these highly divergent genomes near the root of the picorna-like virus superfamily, with possible vertebrate, plant, or arthropod hosts inferred from nucleotide composition analysis. Circular DNA genomes distantly related to the plant-infecting Geminiviridae family were named Baminivirus, Nimivirus, and Niminivirus. These results highlight the utility of analyzing sewage to monitor shedding of viral pathogens and the high viral diversity found in this common pollutant and provide genetic information to facilitate future studies of these newly characterized viruses

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel’s sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for BER=10 −1.9 BER=10−1.9, the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity

Workshop Report for Cancer Research: Defining the Shades of Gy: Utilizing the Biological Consequences of Radiotherapy in the Development of New Treatment Approaches—Meeting Viewpoint

The ability to physically target radiotherapy using image-guidance is continually improving with photons and particle therapy that include protons and heavier ions such as carbon. The unit of dose deposited is the gray (Gy); however, particle therapies produce different patterns of ionizations, and there is evidence that the biological effects of radiation depend on dose size, schedule, and type of radiation. This National Cancer Institute (NCI)–sponsored workshop addressed the potential of using radiation-induced biological perturbations in addition to physical dose, Gy, as a transformational approach to quantifying radiation

COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic

During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals’ experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy. © 2022, The Author(s).U.S. Department of Education, ED: P031S190304; Texas A and M International University, TAMIU; National Research University Higher School of Economics, ВШЭThe COVIDiSTRESS Consortium would like to acknowledge the contributions of friends and collaborators in translating and sharing the COVIDiSTRESS survey, as well as the study participants. Data analysis was supported by Texas A&M International University (TAMIU) Research Grant, TAMIU Act on Ideas, and the TAMIU Advancing Research and Curriculum Initiative (TAMIU ARC) awarded by the US Department of Education Developing Hispanic-Serving Institutions Program (Award # P031S190304). Data collection by Dmitrii Dubrov was supported within the framework of the Basic Research Program at HSE University, RF

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Electromagnetic Levitation Refining of Silicon–Iron Alloys for Generation of Solar Grade Silicon

At present, expensive semiconductor grade silicon (SEG-Si) is used for the manufacture of cells to convert solar energy into electricity. This results in a high cost for photovoltaic electricity compared to electricity derived from conventional sources. The processing of inexpensive metallurgical silicon, or ferrosilicon alloys to solar grade silicon, is a potentially economical refining route to produce photovoltaic (PV) silicon. With phosphorus being one of the most difficult impurities to remove by conventional techniques, this project investigated the use of electromagnetic levitation (EML) of ferrosilicon (75Fe-25Si) in a hydrogen-argon gas stream (48.5% H2), to assist with dephosphorization. The experimental results demonstrated that dephosphorization was achieved through increased processing time in the EML system, with approximately 72% of phosphorus removed from ferrosilicon after 40 minutes. Effects of changing system pressure, thermodynamic and kinetic factors on dephosphorization were also evaluated.Appreciation is expressed to the Natural Sciences and Engineering Research Council of Canada for providing funding for this project through a Strategic Research Grant