Machine Learning and Pedometers: An Integration-Based Convolutional Neural Network for Step Counting and Detection

This thesis explores a machine learning-based approach to step detection and counting for a pedometer. Our novelty is to analyze a window of time containing an arbitrary number of steps, and integrate the detected count using a sliding window technique. We compare the effectiveness of this approach against classic deterministic algorithms. While classic algorithms perform well during regular gait (e.g. walking or running), they can perform significantly worse during semi-regular and irregular gaits that still contribute to a person’s overall step count. These non-regular gaits can make up a significant portion of the daily step count for people, and an improvement to measuring these gaits can drastically improve the performance of the overall pedometer. Using data collected by 30 participants performing 3 different activities to simulate regular, semi-regular, and irregular gaits, a training and testing strategy was implemented using a sliding window algorithm of pedometer accelerometer data. Data was cut in rows representative of the sliding window, normalized according to the minimum and maximum values of the corresponding sensor-axis combination, and finally collated in specific training and holdout groups for validation purposes. Nine models were trained to predict a continuous count of steps within a given window, for each fold of our five-fold validation process. These nine models correspond to each gait and sensor combination from the collected data set. Once models are trained, they are evaluated against the holdout validation set to test for both run count accuracy (RCA), a measure of the pedometers detected step to actual step count, and step detection accuracy (SDA), a measure of how well the algorithm can predict the time of an actual step. These are obtained through an additional post-processing step that integrates the predicted steps per window over time in order to find the total count of steps within a given training data set. Additionally, an algorithm estimates the times when predicted steps occur by using the running count of total steps. Once testing is performed on all nine models, the process is repeated across all five folds to verify model architecture consistency throughout the entire data set. A window size test was implemented to vary the window size of the sliding window algorithm between 1 and 10 seconds to discover the effect of the sliding window size on the convolutional neural network\u27s step count and detection performance. Again, these tests were run across five different folds to ensure an accurate average measure of each model\u27s performance. By comparing the metrics of RCA and SDA between the machine-learning approach and other algorithms, we see that the method introduced in this thesis performs similarly or better than both a consumer pedometer device, as well as the three classic algorithms of peak detection, thresholding, and autocorrelation. It was found that with a window size of two seconds, this novel approach can detect steps with an overall average RCA of 0.99 and SDA of 0.88, better than any individual classic algorithm

Government expenditure and economic growth in the European Union countries: New evidence.

This paper provides new evidence of the impact of government spending on economic growth in the European Union countries. Governments can adjust their levels of spending in order to influence their economies, although the relationship between these variables can be positive or negative, depending on the countries included in the sample, the period of estimation and the variables which reflect the size of the public sector. The results obtained based on regression and panel techniques suggest that government expenditure is not clearly related with economic growth in the European Union countries over the period 1994-2012

Automated DNA mutation detection using universal conditions direct sequencing: application to ten muscular dystrophy genes

<p>Abstract</p> <p>Background</p> <p>One of the most common and efficient methods for detecting mutations in genes is PCR amplification followed by direct sequencing. Until recently, the process of designing PCR assays has been to focus on individual assay parameters rather than concentrating on matching conditions for a set of assays. Primers for each individual assay were selected based on location and sequence concerns. The two primer sequences were then iteratively adjusted to make the individual assays work properly. This generally resulted in groups of assays with different annealing temperatures that required the use of multiple thermal cyclers or multiple passes in a single thermal cycler making diagnostic testing time-consuming, laborious and expensive.</p> <p>These factors have severely hampered diagnostic testing services, leaving many families without an answer for the exact cause of a familial genetic disease. A search of GeneTests for sequencing analysis of the entire coding sequence for genes that are known to cause muscular dystrophies returns only a small list of laboratories that perform comprehensive gene panels.</p> <p>The hypothesis for the study was that a complete set of universal assays can be designed to amplify and sequence any gene or family of genes using computer aided design tools. If true, this would allow automation and optimization of the mutation detection process resulting in reduced cost and increased throughput.</p> <p>Results</p> <p>An automated process has been developed for the detection of deletions, duplications/insertions and point mutations in any gene or family of genes and has been applied to ten genes known to bear mutations that cause muscular dystrophy: DMD; CAV3; CAPN3; FKRP; TRIM32; LMNA; SGCA; SGCB; SGCG; SGCD. Using this process, mutations have been found in five DMD patients and four LGMD patients (one in the FKRP gene, one in the CAV3 gene, and two likely causative heterozygous pairs of variations in the CAPN3 gene of two other patients). Methods and assay sequences are reported in this paper.</p> <p>Conclusion</p> <p>This automated process allows laboratories to discover DNA variations in a short time and at low cost.</p

The dynamic transcriptome during maturation of biofilms formed by methicillin-resistant Staphylococcus aureus

BackgroundMethicillin-resistant Staphylococcus aureus (MRSA), a leading cause of chronic infections, forms prolific biofilms which afford an escape route from antibiotic treatment and host immunity. However, MRSA clones are genetically diverse, and mechanisms underlying biofilm formation remain under-studied. Such studies form the basis for developing targeted therapeutics. Here, we studied the temporal changes in the biofilm transcriptome of three pandemic MRSA clones: USA300, HEMRSA-15, and ST239.MethodsBiofilm formation was assessed using a static model with one representative strain per clone. Total RNA was extracted from biofilm and planktonic cultures after 24, 48, and 72 h of growth, followed by rRNA depletion and sequencing (Illumina Inc., San Diego, CA, United States, NextSeq500, v2, 1 × 75 bp). Differentially expressed gene (DEG) analysis between phenotypes and among early (24 h), intermediate (48 h), and late (72 h) stages of biofilms was performed together with in silico co-expression network construction and compared between clones. To understand the influence of SCCmec and ACME on biofilm formation, isogenic mutants containing deletions of the entire elements or of single genes therein were constructed in USA300.ResultsGenes involved in primarily core genome-encoded KEGG pathways (transporters and others) were upregulated in 24-h biofilm culture compared to 24-h planktonic culture. However, the number of affected pathways in the ST239 24 h biofilm (n = 11) was remarkably lower than that in USA300/EMRSA-15 biofilms (USA300: n = 27, HEMRSA-15: n = 58). The clfA gene, which encodes clumping factor A, was the single common DEG identified across the three clones in 24-h biofilm culture (2.2- to 2.66-fold). In intermediate (48 h) and late (72 h) stages of biofilms, decreased expression of central metabolic and fermentative pathways (glycolysis/gluconeogenesis, fatty acid biosynthesis), indicating a shift to anaerobic conditions, was already evident in USA300 and HEMRSA-15 in 48-h biofilm cultures; ST239 showed a similar profile at 72 h. Last, SCCmec+ACME deletion and opp3D disruption negatively affected USA300 biofilm formation.ConclusionOur data show striking differences in gene expression during biofilm formation by three of the most important pandemic MRSA clones, USA300, HEMRSA-15, and ST239. The clfA gene was the only significantly upregulated gene across all three strains in 24-h biofilm cultures and exemplifies an important target to disrupt early biofilms. Furthermore, our data indicate a critical role for arginine catabolism pathways in early biofilm formation

Biocompatibility of implantable materials: an oxidative stress viewpoint

Oxidative stress occurs when the production of oxidants surpasses the antioxidant capacity in living cells. Oxidative stress is implicated in a number of pathological conditions such as cardiovascular and neurodegenerative diseases but it also has crucial roles in the regulation of cellular activities. Over the last few decades, many studies have identified significant connections between oxidative stress, inflammation and healing. In particular, increasing evidence indicates that the production of oxidants and the cellular response to oxidative stress are intricately connected to the fate of implanted biomaterials. This review article provides an overview of the major mechanisms underlying the link between oxidative stress and the biocompatibility of biomaterials. ROS, RNS and lipid peroxidation products act as chemo-attractants, signalling molecules and agents of degradation during the inflammation and healing phases. As chemo-attractants and signalling molecules, they contribute to the recruitment and activation of inflammatory and healing cells, which in turn produce more oxidants. As agents of degradation, they contribute to the maturation of the extracellular matrix at the healing site and to the degradation of the implanted material. Oxidative stress is itself influenced by the material properties, such as by their composition, their surface properties and their degradation products. Because both cells and materials produce and react with oxidants, oxidative stress may be the most direct route mediating the communication between cells and materials. Improved understanding of the oxidative stress mechanisms following biomaterial implantation may therefore help the development of new biomaterials with enhanced biocompatibility

Report from Working Group 3: Beyond the standard model physics at the HL-LHC and HE-LHC

This is the third out of five chapters of the final report [1] of the Workshop on Physics at HL-LHC, and perspectives on HE-LHC [2]. It is devoted to the study of the potential, in the search for Beyond the Standard Model (BSM) physics, of the High Luminosity (HL) phase of the LHC, defined as $3$ ab$^{-1}$ of data taken at a centre-of-mass energy of 14 TeV, and of a possible future upgrade, the High Energy (HE) LHC, defined as $15$ ab$^{-1}$ of data at a centre-of-mass energy of 27 TeV. We consider a large variety of new physics models, both in a simplified model fashion and in a more model-dependent one. A long list of contributions from the theory and experimental (ATLAS, CMS, LHCb) communities have been collected and merged together to give a complete, wide, and consistent view of future prospects for BSM physics at the considered colliders. On top of the usual standard candles, such as supersymmetric simplified models and resonances, considered for the evaluation of future collider potentials, this report contains results on dark matter and dark sectors, long lived particles, leptoquarks, sterile neutrinos, axion-like particles, heavy scalars, vector-like quarks, and more. Particular attention is placed, especially in the study of the HL-LHC prospects, to the detector upgrades, the assessment of the future systematic uncertainties, and new experimental techniques. The general conclusion is that the HL-LHC, on top of allowing to extend the present LHC mass and coupling reach by $20-50\%$ on most new physics scenarios, will also be able to constrain, and potentially discover, new physics that is presently unconstrained. Moreover, compared to the HL-LHC, the reach in most observables will, generally more than double at the HE-LHC, which may represent a good candidate future facility for a final test of TeV-scale new physics

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p&lt;0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (&lt;1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (&lt;1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline