Genetic algorithms to determine the optimal parameters of an ensemble local mean decomposition

An optimization method for an ensemble local mean decomposition (ELMD) parameters selection using genetic algorithms is proposed. The execution of this technique depends heavily on the correct choice of the parameters of its model as pointed out in previous works. The effectiveness of the proposed method was evaluated using synthetic signals, discussed by several authors. The resulting algorithm obtained similar results to OELMD, but with an 82% reduction in processing time. Actual vibration signals were also analysed, presenting satisfactory results

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

Background Musculoskeletal disorders include more than 150 different conditions affecting joints, muscles, bones, ligaments, tendons, and the spine. To capture all health loss from death and disability due to musculoskeletal disorders, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) includes a residual musculoskeletal category for conditions other than osteoarthritis, rheumatoid arthritis, gout, low back pain, and neck pain. This category is called other musculoskeletal disorders and includes, for example, systemic lupus erythematosus and spondylopathies. We provide updated estimates of the prevalence, mortality, and disability attributable to other musculoskeletal disorders and forecasted prevalence to 2050. Methods Prevalence of other musculoskeletal disorders was estimated in 204 countries and territories from 1990 to 2020 using data from 68 sources across 23 countries from which subtraction of cases of rheumatoid arthritis, osteoarthritis, low back pain, neck pain, and gout from the total number of cases of musculoskeletal disorders was possible. Data were analysed with Bayesian meta-regression models to estimate prevalence by year, age, sex, and location. Years lived with disability (YLDs) were estimated from prevalence and disability weights. Mortality attributed to other musculoskeletal disorders was estimated using vital registration data. Prevalence was forecast to 2050 by regressing prevalence estimates from 1990 to 2020 with Socio-demographic Index as a predictor, then multiplying by population forecasts. Findings Globally, 494 million (95% uncertainty interval 431–564) people had other musculoskeletal disorders in 2020, an increase of 123·4% (116·9–129·3) in total cases from 221 million (192–253) in 1990. Cases of other musculoskeletal disorders are projected to increase by 115% (107–124) from 2020 to 2050, to an estimated 1060 million (95% UI 964–1170) prevalent cases in 2050; most regions were projected to have at least a 50% increase in cases between 2020 and 2050. The global age-standardised prevalence of other musculoskeletal disorders was 47·4% (44·9–49·4) higher in females than in males and increased with age to a peak at 65–69 years in male and female sexes. In 2020, other musculoskeletal disorders was the sixth ranked cause of YLDs globally (42·7 million [29·4–60·0]) and was associated with 83100 deaths (73 600–91600). Interpretation Other musculoskeletal disorders were responsible for a large number of global YLDs in 2020. Until individual conditions and risk factors are more explicitly quantified, policy responses to this burden remain a challenge. Temporal trends and geographical differences in estimates of non-fatal disease burden should not be overinterpreted as they are based on sparse, low-quality data.Peer ReviewedPostprint (published version

A Ranking System for Reference Libraries of DNA Barcodes: Application to Marine Fish Species from Portugal

BACKGROUND: The increasing availability of reference libraries of DNA barcodes (RLDB) offers the opportunity to the screen the level of consistency in DNA barcode data among libraries, in order to detect possible disagreements generated from taxonomic uncertainty or operational shortcomings. We propose a ranking system to attribute a confidence level to species identifications associated with DNA barcode records from a RLDB. Here we apply the proposed ranking system to a newly generated RLDB for marine fish of Portugal. METHODOLOGY/PRINCIPAL FINDINGS: Specimens (n = 659) representing 102 marine fish species were collected along the continental shelf of Portugal, morphologically identified and archived in a museum collection. Samples were sequenced at the barcode region of the cytochrome oxidase subunit I gene (COI-5P). Resultant DNA barcodes had average intra-specific and inter-specific Kimura-2-parameter distances (0.32% and 8.84%, respectively) within the range usually observed for marine fishes. All specimens were ranked in five different levels (A-E), according to the reliability of the match between their species identification and the respective diagnostic DNA barcodes. Grades A to E were attributed upon submission of individual specimen sequences to BOLD-IDS and inspection of the clustering pattern in the NJ tree generated. Overall, our study resulted in 73.5% of unambiguous species IDs (grade A), 7.8% taxonomically congruent barcode clusters within our dataset, but awaiting external confirmation (grade B), and 18.7% of species identifications with lower levels of reliability (grades C/E). CONCLUSION/SIGNIFICANCE: We highlight the importance of implementing a system to rank barcode records in RLDB, in order to flag taxa in need of taxonomic revision, or reduce ambiguities of discordant data. With increasing DNA barcode records publicly available, this cross-validation system would provide a metric of relative accuracy of barcodes, while enabling the continuous revision and annotation required in taxonomic work

Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of SARS-CoV-2 genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three available genomic nomenclature systems for SARS-CoV-2 to all sequence data from the WHO European Region available during the COVID-19 pandemic until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation. We provide a comparison of the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.Peer reviewe

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing

We present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model- or machine learning-based photometric redshift methods—ANNZ2, BPZ calibrated against BCC-Ufig simulations, SKYNET, and TPZ—are analyzed. For training, calibration, and testing of these methods, we construct a catalogue of spectroscopically confirmed galaxies matched against DES SV data. The performance of the methods is evaluated against the matched spectroscopic catalogue, focusing on metrics relevant for weak lensing analyses, with additional validation against COSMOS photo-z’s. From the galaxies in the DES SV shear catalogue, which have mean redshift 0.72 0.01 over the range 0.3 < z < 1.3, we construct three tomographic bins with means of z ¼ f0.45; 0.67; 1.00g. These bins each have systematic uncertainties δz ≲ 0.05 in the mean of the fiducial SKYNET photo-z nðzÞ. We propagate the errors in the redshift distributions through to their impact on cosmological parameters estimated with cosmic shear, and find that they cause shifts in the value of σ8 of approximately 3%. This shift is within the one sigma statistical errors on σ8 for the DES SV shear catalogue. We further study the potential impact of systematic differences on the critical surface density, Σcrit, finding levels of bias safely less than the statistical power of DES SV data. We recommend a final Gaussian prior for the photo-z bias in the mean of nðzÞ of width 0.05 for each of the three tomographic bins, and show that this is a sufficient bias model for the corresponding cosmology analysis

Integrated monitoring of mola mola behaviour in space and time

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of finescale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) videorecorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (r(s) = 0.184, p < 0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator's finescale behaviour observed over a two weeks in May 2014

The Dark Energy Survey : more than dark energy – an overview

This overview paper describes the legacy prospect and discovery potential of the Dark Energy Survey (DES) beyond cosmological studies, illustrating it with examples from the DES early data. DES is using a wide-field camera (DECam) on the 4 m Blanco Telescope in Chile to image 5000 sq deg of the sky in five filters (grizY). By its completion, the survey is expected to have generated a catalogue of 300 million galaxies with photometric redshifts and 100 million stars. In addition, a time-domain survey search over 27 sq deg is expected to yield a sample of thousands of Type Ia supernovae and other transients. The main goals of DES are to characterize dark energy and dark matter, and to test alternative models of gravity; these goals will be pursued by studying large-scale structure, cluster counts, weak gravitational lensing and Type Ia supernovae. However, DES also provides a rich data set which allows us to study many other aspects of astrophysics. In this paper, we focus on additional science with DES, emphasizing areas where the survey makes a difference with respect to other current surveys. The paper illustrates, using early data (from ‘Science Verification’, and from the first, second and third seasons of observations), what DES can tell us about the Solar system, the Milky Way, galaxy evolution, quasars and other topics. In addition, we show that if the cosmological model is assumed to be +cold dark matter, then important astrophysics can be deduced from the primary DES probes. Highlights from DES early data include the discovery of 34 trans-Neptunian objects, 17 dwarf satellites of the Milky Way, one published z > 6 quasar (and more confirmed) and two published superluminous supernovae (and more confirmed)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio