Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p

Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity.

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than once

Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than once

Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than once

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Searching for stochastic gravitational waves using data from the two colocated LIGO Hanford detectors

Searches for a stochastic gravitational-wave background (SGWB) using terrestrial detectors typically involve cross-correlating data from pairs of detectors. The sensitivity of such cross-correlation analyses depends, among other things, on the separation between the two detectors: the smaller the separation, the better the sensitivity. Hence, a colocated detector pair is more sensitive to a gravitational-wave background than a noncolocated detector pair. However, colocated detectors are also expected to suffer from correlated noise from instrumental and environmental effects that could contaminate the measurement of the background. Hence, methods to identify and mitigate the effects of correlated noise are necessary to achieve the potential increase in sensitivity of colocated detectors. Here we report on the first SGWB analysis using the two LIGO Hanford detectors and address the complications arising from correlated environmental noise. We apply correlated noise identification and mitigation techniques to data taken by the two LIGO Hanford detectors, H1 and H2, during LIGO’s fifth science run. At low frequencies, 40–460 Hz, we are unable to sufficiently mitigate the correlated noise to a level where we may confidently measure or bound the stochastic gravitational-wave signal. However, at high frequencies, 460–1000 Hz, these techniques are sufficient to set a 95% confidence level upper limit on the gravitational-wave energy density of Ω(f) < 7.7 × 10[superscript -4](f/900  Hz)[superscript 3], which improves on the previous upper limit by a factor of ~180. In doing so, we demonstrate techniques that will be useful for future searches using advanced detectors, where correlated noise (e.g., from global magnetic fields) may affect even widely separated detectors.National Science Foundation (U.S.)United States. National Aeronautics and Space AdministrationCarnegie TrustDavid & Lucile Packard FoundationAlfred P. Sloan Foundatio

Machine Learning for Species Identification: The HebelomaProject from database to website

Attempts to use machine learning (ML) for species identification of macrofungi have usually involved the use of image recognition to deduce the species from photographs, sometimes combining this with collection metadata. Our approach is different: we use a set of quantified morphological characters (for example, the average length of the spores) and locality (GPS coordinates). Using this data alone, the machine can learn to differentiate between species.Our case study is the genus Hebeloma, fungi within the order Agaricales, where species determination is renowned as a difficult problem. Whether it is as a result of recent speciation, the plasticity of the species, hybridization or stasis is a difficult question to answer. What is sure is that this has led to difficulties with species delimitation and consequently a controversial taxonomy.The Hebeloma Project—our attempt to solve this problem by rigorously understanding the genus—has been evolving for over 20 years. We began organizing collections in a database in 2003. The database now has over 10,000 collections, from around the world, with not only metadata but also morphological descriptions and photographs, both macroscopic and microscopic, as well as molecular data including at least an internal transcribed spacer (ITS) sequence (generally, but not universally, accepted as a DNA barcode marker for fungi (Schoch et al. 2012)), and in many cases sequences of several loci. Included within this set of collections are almost all type specimens worldwide. The collections on the database have been analysed and compared. The analysis uses both the morphological and molecular data as well as information about habitat and location. In this way, almost all collections are assigned to a species. This development has been enabled and assisted by citizen scientists from around the globe, collecting and recording information about their finds as well as preserving material.From this database, we have built a website, which updates as the database updates. The website (hebeloma.org) is currently undergoing beta testing prior to a public launch. It includes up-to-date species descriptions, which are generated by amalgamating the data from the collections of each species in the database. Additional tools allow the user to explore those species with similar habitat preferences, or those from a particular biogeographic area. The user is also able to compare a range of characters of different species via an interactive plotter.The ML-based species identifier is featured on the website. The standardised storage of the collection data on the database forms the backbone for the identifier. A portion of the collections on the database are (almost) randomly selected as a training set for the learning phase of the algorithm. The learning is “supervised” in the sense that collections in the training set have been pre-assigned to a species by expert analysis. With the learning phase complete, the remainder of the database collections may then be used for testing. To use the species identifier on the website, a user inputs the same small number of morphological characters used to train the tool and it promptly returns the most likely species represented, ranked in order of probability.As well as describing the neural network behind the species identifier tool, we will demonstrate it in action on the website, present the successful results it has had in testing to date and discuss its current limitations and possible generalizations

Machine Learning for Species Identification: The HebelomaProject from database to website

Attempts to use machine learning (ML) for species identification of macrofungi have usually involved the use of image recognition to deduce the species from photographs, sometimes combining this with collection metadata. Our approach is different: we use a set of quantified morphological characters (for example, the average length of the spores) and locality (GPS coordinates). Using this data alone, the machine can learn to differentiate between species.Our case study is the genus Hebeloma, fungi within the order Agaricales, where species determination is renowned as a difficult problem. Whether it is as a result of recent speciation, the plasticity of the species, hybridization or stasis is a difficult question to answer. What is sure is that this has led to difficulties with species delimitation and consequently a controversial taxonomy.The Hebeloma Project—our attempt to solve this problem by rigorously understanding the genus—has been evolving for over 20 years. We began organizing collections in a database in 2003. The database now has over 10,000 collections, from around the world, with not only metadata but also morphological descriptions and photographs, both macroscopic and microscopic, as well as molecular data including at least an internal transcribed spacer (ITS) sequence (generally, but not universally, accepted as a DNA barcode marker for fungi (Schoch et al. 2012)), and in many cases sequences of several loci. Included within this set of collections are almost all type specimens worldwide. The collections on the database have been analysed and compared. The analysis uses both the morphological and molecular data as well as information about habitat and location. In this way, almost all collections are assigned to a species. This development has been enabled and assisted by citizen scientists from around the globe, collecting and recording information about their finds as well as preserving material.From this database, we have built a website, which updates as the database updates. The website (hebeloma.org) is currently undergoing beta testing prior to a public launch. It includes up-to-date species descriptions, which are generated by amalgamating the data from the collections of each species in the database. Additional tools allow the user to explore those species with similar habitat preferences, or those from a particular biogeographic area. The user is also able to compare a range of characters of different species via an interactive plotter.The ML-based species identifier is featured on the website. The standardised storage of the collection data on the database forms the backbone for the identifier. A portion of the collections on the database are (almost) randomly selected as a training set for the learning phase of the algorithm. The learning is “supervised” in the sense that collections in the training set have been pre-assigned to a species by expert analysis. With the learning phase complete, the remainder of the database collections may then be used for testing. To use the species identifier on the website, a user inputs the same small number of morphological characters used to train the tool and it promptly returns the most likely species represented, ranked in order of probability.As well as describing the neural network behind the species identifier tool, we will demonstrate it in action on the website, present the successful results it has had in testing to date and discuss its current limitations and possible generalizations