Soft γ-ray repeaters (SGRs) are 'magnetars', a small class of slowly spinning neutron stars with extreme surface magnetic fields, B 1015 gauss (refs 1
, 2
-3). On 27 December 2004, a giant flare was ...detected from the magnetar SGR 1806 - 20 (ref. 2), only the third such event recorded. This burst of energy was detected by a variety of instruments and even caused an ionospheric disturbance in the Earth's upper atmosphere that was recorded around the globe. Here we report the detection of a fading radio afterglow produced by this outburst, with a luminosity 500 times larger than the only other detection of a similar source. From day 6 to day 19 after the flare from SGR 1806 - 20, a resolved, linearly polarized, radio nebula was seen, expanding at approximately a quarter of the speed of light. To create this nebula, at least 4 × 1043 ergs of energy must have been emitted by the giant flare in the form of magnetic fields and relativistic particles.
Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for ...all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way.
The new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles Formula: see text, Formula: see text, Formula: see text, and Formula: see text. Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results.
SAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss .
•A scatter search algorithm for mixed blocking permutation flowshop scheduling.•A new and effective NEH-based heuristic is used in initial solution generation.•A greedy job selection within insert ...and swap operators are used in local search.•Outperforms state-of-the-art algorithms on well-known benchmark problem sets.
Empty or limited storage capacities between machines introduce various types of blocking constraint in the industries with flowshop environment. While large applications demand flowshop scheduling with a mix of different types of blocking, research in this area mainly focuses on using only one kind of blocking in a given problem instance. In this paper, using makespan as a criterion, we study permutation flowshops with zero capacity buffers operating under mixed blocking conditions. We present a very effective scatter search (SS) algorithm for this. At the initialisation phase of SS, we use a modified version of the well-known Nawaz, Enscore and Ham (NEH) heuristic. For the improvement method in SS, we use an Iterated Local Search (ILS) algorithm that adopts a greedy job selection and a powerful NEH-based perturbation procedure. Moreover, in the reference set update phase of SS, with small probabilities, we accept worse solutions so as to increase the search diversity. On standard benchmark problems of varying sizes, our algorithm very significantly outperforms well-known existing algorithms in terms of both the solution quality and the computing time. Moreover, our algorithm has found new upper bounds for 314 out of 360 benchmark problem instances.
Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the ...on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more complex neural networks then just could counterbalance the noise. From artificial intelligence and machine learning perspectives, problem representations and solution approaches do mutually interact and thus affect performance. We also argue that comparatively simpler predictors can more easily be reconstructed than the more complex ones. With these arguments in mind, we present a deep learning method named Simpler Angle Predictor (SAP) to train simpler DNN models that enhance protein backbone angle prediction. We then empirically show that SAP can significantly outperform existing state-of-the-art methods on well-known benchmark datasets: for some types of angles, the differences are 6-8 in terms of mean absolute error (MAE). The SAP program along with its data is available from the website https://gitlab.com/mahnewton/sap .
•Studying the prize-collecting arc routing problems (PARPs) with real-time scenarios.•Proposing constructive heuristics and metaheuristic algorithm to solve the problem.•Outperforming the ...best-existing methods in the literature.
Time-dependent prize-collecting arc routing problems (TD-PARPs) generalise the regular prize-collecting arc routing problems (PARPs). PARPs have arcs associated with collectable prizes along with travelling costs. TD-PARPs allow travel times to vary at the travelling horizon so that real-life uncertain factors such as traffic and weather conditions can be taken into account. A TD-PARP is to find a travelling route that maximises the profit i.e. total collected prizes minus total travelling costs. TD-PARPs have two facets: selecting a subset of arcs to be travelled and scheduling the selected arcs in the travelling route. TD-PARPs have not been studied much although they are more realistic and generic. In this paper, we first propose a set of deterministic heuristic search algorithms that range from a simple procedure producing quite good results in a fraction of a CPU second to a more extensive procedure producing high-quality results but at the expense of slightly extra CPU time. In this paper, we then propose a meta-heuristic based scatter search (SS) algorithm for TD-PARPs. For the improvement method in the SS algorithm, we propose a multi-operator algorithm that incorporates various neighbourhood operators to diversify the local exploration. For the combination method in the SS algorithm, we propose a 2-level path relinking procedure, which explores combinations of visited and unvisited arcs using two different operators. To control the diversity of the solutions in the SS algorithm, we propose a new effective distance measurement. The experimental results on standard benchmark problems indicate that the proposed SS algorithm significantly outperforms the state-of-the-art existing methods.
Conjunctive normal forms (CNF) of structured satisfiability problems contain logic gate patterns. So Boolean circuits (BC) by and large can be obtained from them and thus structural information that ...is lost in the CNF can be recovered. However, it is not known which logic gates are useful for local search on BCs or which logic gates in particular help local search the most and why. In this article, we empirically show that exploitation of xor, xnor, eq, and not gates is a key factor behind the performance of local search algorithms using single variable flips when adapted to logic gate constraints. Moreover, controlled experiments and investigations into the variables selected for flipping further elucidates these findings. To achieve these conclusions, we have adapted the AdaptNovelty+ and CCANr algorithms to cope with logic gate-based constraint models. These are two prominent families of local search algorithms for satisfiability. We performed our experiments using a large set of benchmark instances from SATLib, SAT2014, and SAT2020. We have also presented techniques to eliminate cycles among logic gates that are detected from CNF and to propagate equivalence of variables statically through the logic gate dependency relationships.
Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during ...conformational search. In this context, prediction of large inter-residue distances and also prediction of distances between residues separated largely in the protein sequence remain challenging. To deal with these challenges, state-of-the-art inter-residue distance prediction algorithms have used large sets of coevolutionary and non-coevolutionary features. In this paper, we argue that the more the types of features used, the more the kinds of noises introduced and then the deep learning model has to overcome the noises to improve the accuracy of the predictions. Also, multiple features capturing similar underlying characteristics might not necessarily have significantly better cumulative effect. So we scrutinise the feature space to reduce the types of features to be used, but at the same time, we strive to improve the prediction accuracy. Consequently, for inter-residue real distance prediction, in this paper, we propose a deep learning model named scrutinised distance predictor (SDP), which uses only 2 coevolutionary and 3 non-coevolutionary features. On several sets of benchmark proteins, our proposed SDP method improves mean Local Distance Different Test (LDDT) scores at least by 10% over existing state-of-the-art methods. The SDP program along with its data is available from the website https://gitlab.com/mahnewton/sdp .
In contrast to the honey bee gut, which is colonized by a few characteristic bacterial clades, the hive of the honey bee is home to a diverse array of microbes, including many lactic acid bacteria ...(LAB). In this study, we used culture, combined with sequencing, to sample the LAB communities found across hive environments. Specifically, we sought to use network analysis to identify microbial hubs sharing nearly identical operational taxonomic units, evidence which may indicate cooccurrence of bacteria between environments. In the process, we identified interactions between noncore bacterial members (Fructobacillus and Lactobacillaceae) and honey bee-specific "core" members. Both Fructobacillus and Lactobacillaceae colonize brood cells, bee bread, and nectar and may serve the role of pioneering species, establishing an environment conducive to the inoculation by honey bee core bacteria. Coculture assays showed that these noncore bacterial members promote the growth of honey bee-specific bacterial species. Specifically, Fructobacillus by-products in spent medium supported the growth of the Firm-5 honey bee-specific clade in vitro. Metabolic characterization of Fructobacillus using carbohydrate utilization assays revealed that this strain is capable of utilizing the simple sugars fructose and glucose, as well as the complex plant carbohydrate lignin. We tested Fructobacillus for antibiotic sensitivity and found that this bacterium, which may be important for establishment of the microbiome, is sensitive to the commonly used antibiotic tetracycline. Our results point to the possible significance of "noncore" and environmental microbial community members in the modulation of honey bee microbiome dynamics and suggest that tetracycline use by beekeepers should be limited.
In falciparum malaria sequestration of erythrocytes containing mature forms of Plasmodium falciparum in the microvasculature of vital organs is central to pathology, but quantitation of this hidden ...sequestered parasite load in vivo has not previously been possible. The peripheral blood parasite count measures only the circulating, relatively non-pathogenic parasite numbers. P. falciparum releases a specific histidine-rich protein (PfHRP2) into plasma. Quantitative measurement of plasma PfHRP2 concentrations may reflect the total parasite biomass in falciparum malaria.
We measured plasma concentrations of PfHRP2, using a quantitative antigen-capture enzyme-linked immunosorbent assay, in 337 adult patients with falciparum malaria of varying severity hospitalised on the Thai-Burmese border. Based on in vitro production rates, we constructed a model to link this measure to the total parasite burden in the patient. The estimated geometric mean parasite burden was 7 x 10(11) (95% confidence interval CI 5.8 x 10(11) to 8.5 x 10(11)) parasites per body, and was over six times higher in severe malaria (geometric mean 1.7 x 10(12), 95% CI 1.3 x 10(12) to 2.3 x 10(12)) than in patients hospitalised without signs of severity (geometric mean 2.8 x 10(11), 95% CI 2.3 x 10(11) to 3.5 x 10(11); p < 0.001). Parasite burden was highest in patients who died (geometric mean 3.4 x 10(12), 95% CI 1.9 x 10(12) to 6.3 x 10(12); p = 0.03). The calculated number of sequestered parasites increased with disease severity and was higher in patients with late developmental stages of P. falciparum present on peripheral blood smears. Comparing model and laboratory estimates of the time of sequestration suggested that admission to hospital with uncomplicated malaria often follows schizogony-but in severe malaria is unrelated to stage of parasite development.
Plasma PfHRP2 concentrations may be used to estimate the total body parasite biomass in acute falciparum malaria. Severe malaria results from extensive sequestration of parasitised erythrocytes.
Summary
Background
Screening overweight and obese children for non‐alcoholic fatty liver disease (NAFLD) is recommended by paediatric and endocrinology societies. However, gastroenterology societies ...have called for more data before making a formal recommendation.
Aim
To determine whether the detection of suspected NAFLD in overweight and obese children through screening in primary care and referral to paediatric gastroenterology resulted in a correct diagnosis of NAFLD.
Methods
Information generated in the clinical evaluation of 347 children identified with suspected NAFLD through screening in primary care and referral to paediatric gastroenterology was captured prospectively. Diagnostic outcomes were reported. The diagnostic performance of two times the upper limit of normal (ULN) for alanine aminotransferase (ALT) was assessed.
Results
Non‐alcoholic fatty liver disease was diagnosed in 55% of children identified by screening and referral. Liver disease other than NAFLD was present in 18% of those referred. Autoimmune hepatitis was the most common alternative diagnosis. Children with NAFLD had significantly (P < 0.05) higher screening ALT (98 ± 95) than children with liver disease other than NAFLD (86 ± 74). Advanced fibrosis was present in 11% of children. For the diagnosis of NAFLD, screening ALT two times the clinical ULN had a sensitivity of 57% and a specificity of 71%.
Conclusions
Screening of overweight and obese children in primary care for NAFLD with referral to paediatric gastroenterology has the potential to identify clinically relevant liver pathology. Consensus is needed on how to value the risk and rewards of screening and referral, to identify children with liver disease in the most appropriate manner.
PDF
