Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition

In Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed ($\displaystyle i.i.d.$). Contrarily, practical implementations often challenge this notion, manifesting data distribution discrepancies, especially in scenarios such as cross-user HAR. Domain adaptation is the promising approach to address these challenges inherent in cross-user HAR tasks. However, a clear gap in domain adaptation techniques is the neglect of the temporal relation embedded within time series data during the phase of aligning data distributions. Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method. This novel method uniquely recognises and integrates temporal relations during the domain adaptation process. By synergizing the capabilities of generative models with the Temporal Relation Attention mechanism, our method improves the classification performance in cross-user HAR. A comprehensive evaluation has been conducted on three public sensor-based HAR datasets targeting different scenarios and applications to demonstrate the efficacy of the proposed DGDATA method

Detecting Turns And Correcting Headings Using Low-Cost INS

Unlike industrial-grade Inertial Navigation Sensors (INSs) that can provide credible tracking performance, more affordable consumer-grade low-cost INSs produce drifts in heading angles and positions that result in a poor tracking accuracy. Researchers have proposed drift correction methods that attempt to attenuate the drifts when walking straight along the dominant directions is detected. While determining the type of a pedestrian's walk is essential before the heading corrections are made, the current detection techniques heavily rely on thresholding. This paper proposes a novel threshold-less method to detect turns in walking by using pelvic rotation and correct the heading angle based on consumer-grade INSs. The experiments indicate the proposed turn detector and heading correction methods produce very good results which can be applied for future pedestrian tracking, activity recognition or rehabilitation

Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper

Modeling the mitochondrial cardiomyopathy of Barth syndrome with iPSC and heart-on-chip technologies

Studying monogenic mitochondrial cardiomyopathies may yield insights into mitochondrial roles in cardiac development and disease. Here, we combine patient-derived and genetically engineered iPSCs with tissue engineering to elucidate the pathophysiology underlying the cardiomyopathy of Barth syndrome (BTHS), a mitochondrial disorder caused by mutation of the gene Tafazzin (TAZ). Using BTHS iPSC-derived cardiomyocytes (iPSC-CMs), we defined metabolic, structural, and functional abnormalities associated with TAZ mutation. BTHS iPSC-CMs assembled sparse and irregular sarcomeres, and engineered BTHS “heart on chip” tissues contracted weakly. Gene replacement and genome editing demonstrated that TAZ mutation is necessary and sufficient for these phenotypes. Sarcomere assembly and myocardial contraction abnormalities occurred in the context of normal whole cell ATP levels. Excess levels of reactive oxygen species mechanistically linked TAZ mutation to impaired cardiomyocyte function. Our study provides new insights into the pathogenesis of Barth syndrome, suggests new treatment strategies, and advances iPSC-based in vitro modeling of cardiomyopathy

Comparison of a manual and an automated tracking method for Tibetan Plateau vortices

Tibetan Plateau vortices (TPVs) are mesoscale cyclones originating over the Tibetan Plateau (TP) during the extended summer season (April-September). Most TPVs stay on the TP while a small number can move off the TP to the east. TPVs are known to be one of the main precipitation-bearing systems on the TP and moving-off TPVs have been associated with heavy precipitation and flooding downstream of the TP (e.g. Sichuan province, Yangtze River Valley). Identifying and tracking TPVs is difficult both due to their comparatively small horizontal extent (400 – 800 km) and the limited availability of soundings over the TP, which, in turn, constitutes a challenge for short-term predictions of TPV-related impacts and for the climatological study of TPVs. In this study, (i) manual tracking (MT) results using radiosonde data from a network over and downstream of the TP are compared with (ii) results obtained by an automated tracking (AT) algorithm applied to ERA-Interim reanalysis. Ten MT-TPV cases are selected based on method (i) and matched to and compared with the corresponding AT-TPVs identified with method (ii). Conversely, ten AT-TPVs are selected and compared with the corresponding MT-TPVs. In general, the comparison shows good results in cases where the underlying data are in good agreement, but considerable differences are also seen in some cases and explained in terms of differences in the tracking methods, data availability/coverage and disagreement between sounding and ERA-Interim data. Recommendations are given for future efforts in TPV detection and tracking, including in an operational weather forecasting context

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

General Anesthetics Inhibit Erythropoietin Induction under Hypoxic Conditions in the Mouse Brain

Background: Erythropoietin (EPO), originally identified as a hematopoietic growth factor produced in the kidney and fetal liver, is also endogenously expressed in the central nervous system (CNS). EPO in the CNS, mainly produced in astrocytes, is induced under hypoxic conditions in a hypoxia-inducible factor (HIF)-dependent manner and plays a dominant role in neuroprotection and neurogenesis. We investigated the effect of general anesthetics on EPO expression in the mouse brain and primary cultured astrocytes. Methodology/Principal Findings: BALB/c mice were exposed to 10 % oxygen with isoflurane at various concentrations (0.10–1.0%). Expression of EPO mRNA in the brain was studied, and the effects of sevoflurane, halothane, nitrous oxide, pentobarbital, ketamine, and propofol were investigated. In addition, expression of HIF-2a protein was studied by immunoblotting. Hypoxia-induced EPO mRNA expression in the brain was significantly suppressed by isoflurane in a concentration-dependent manner. A similar effect was confirmed for all other general anesthetics. Hypoxia-inducible expression of HIF-2a protein was also significantly suppressed with isoflurane. In the experiments using primary cultured astrocytes, isoflurane, pentobarbital, and ketamine suppressed hypoxia-inducible expression of HIF-2a protein and EPO mRNA. Conclusions/Significance: Taken together, our results indicate that general anesthetics suppress activation of HIF-2 an

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria

Abstract: Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Global, regional, and national burden of osteoarthritis, 1990–2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Background Osteoarthritis is the most common form of arthritis in adults, characterised by chronic pain and loss of mobility. Osteoarthritis most frequently occurs after age 40 years and prevalence increases steeply with age. WHO has designated 2021–30 the decade of healthy ageing, which highlights the need to address diseases such as osteoarthritis, which strongly affect functional ability and quality of life. Osteoarthritis can coexist with, and negatively effect, other chronic conditions. Here we estimate the burden of hand, hip, knee, and other sites of osteoarthritis across geographies, age, sex, and time, with forecasts of prevalence to 2050. Methods In this systematic analysis for the Global Burden of Disease Study, osteoarthritis prevalence in 204 countries and territories from 1990 to 2020 was estimated using data from population-based surveys from 26 countries for knee osteoarthritis, 23 countries for hip osteoarthritis, 42 countries for hand osteoarthritis, and US insurance claims for all of the osteoarthritis sites, including the other types of osteoarthritis category. The reference case definition was symptomatic, radiographically confirmed osteoarthritis. Studies using alternative definitions from the reference case definition (for example self-reported osteoarthritis) were adjusted to reference using regression models. Osteoarthritis severity distribution was obtained from a pooled meta-analysis of sources using the Western Ontario and McMaster Universities Arthritis Index. Final prevalence estimates were multiplied by disability weights to calculate years lived with disability (YLDs). Prevalence was forecast to 2050 using a mixed-effects model. Findings Globally, 595 million (95% uncertainty interval 535–656) people had osteoarthritis in 2020, equal to 7·6% (95% UI 6·8–8·4) of the global population, and an increase of 132·2% (130·3–134·1) in total cases since 1990. Compared with 2020, cases of osteoarthritis are projected to increase 74·9% (59·4–89·9) for knee, 48·6% (35·9–67·1) for hand, 78·6% (57·7–105·3) for hip, and 95·1% (68·1–135·0) for other types of osteoarthritis by 2050. The global age-standardised rate of YLDs for total osteoarthritis was 255·0 YLDs (119·7–557·2) per 100 000 in 2020, a 9·5% (8·6–10·1) increase from 1990 (233·0 YLDs per 100 000, 109·3–510·8). For adults aged 70 years and older, osteoarthritis was the seventh ranked cause of YLDs. Age-standardised prevalence in 2020 was more than 5·5% in all world regions, ranging from 5677·4 (5029·8–6318·1) per 100 000 in southeast Asia to 8632·7 (7852·0–9469·1) per 100 000 in high-income Asia Pacific. Knee was the most common site for osteoarthritis. High BMI contributed to 20·4% (95% UI –1·7 to 36·6) of osteoarthritis. Potentially modifiable risk factors for osteoarthritis such as recreational injury prevention and occupational hazards have not yet been explored in GBD modelling. Interpretation Age-standardised YLDs attributable to osteoarthritis are continuing to rise and will lead to substantial increases in case numbers because of population growth and ageing, and because there is no effective cure for osteoarthritis. The demand on health systems for care of patients with osteoarthritis, including joint replacements, which are highly effective for late stage osteoarthritis in hips and knees, will rise in all regions, but might be out of reach and lead to further health inequity for individuals and countries unable to afford them. Much more can and should be done to prevent people getting to that late stage