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Recent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single-cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehending the cellular heterogeneity and complex cellular regulatory networks. Multi-omics integration is gaining attention as an important step in joint analysis, and the number of computational tools in this field is growing rapidly. In this paper, we benchmarked 12 multi-omics integration methods on three integration tasks via qualitative visualization and quantitative metrics, considering six main aspects that matter in multi-omics data analysis. Overall, we found that different methods have their own advantages on different aspects, while some methods outperformed other methods in most aspects. We therefore provided guidelines for selecting appropriate methods for specific scenarios and tasks to help obtain meaningful insights from multi-omics data integration. [ABSTRACT FROM AUTHOR]

Concerns raised by Okazaki & Ueda (2022) on the paper by Sambridge et al. (2022) are addressed. Two issues are discussed and some new numerical results presented. The first concerns whether the properties of the Wasserstein time-series misfit introduced in our earlier paper will translate to model space non-uniqueness in a seismic waveform inversion setting. It is argued that this is unlikely, given the special conditions, which must exist between all observed/predicted seismic waveform pairs for non-uniqueness to result. The second issue discussed is the efficacy of using the Sliced Wasserstein algorithm of Bonneel et al. (2015) as an alternate to the marginal Wasserstein algorithm, as proposed by Okazaki & Ueda (2022). It is argued that for optimization-based waveform fitting, the Sliced Wasserstein algorithm is a viable alternate provided care is taken to ensure that conditions arise which do invalidate analytical derivative expressions of the resulting Wasserstein misfit. In practice, this would likely mean recasting the 2D Optimal Transport problem posed in our earlier paper onto unstructured grids. [ABSTRACT FROM AUTHOR]

Background. In April 2008, experts reviewed updates on sexually transmitted disease (STD) prevention and treatment in preparation for the revision of the Centers for Disease Control and Prevention (CDC) STD Treatment Guidelines. This included a review of cervical cancer screening in the STD clinical setting. Methods. Key questions were identified with assistance from an expert panel. Reviews of the literature were conducted using the PubMed computerized database and shared with the panel. Updated information was incorporated in the 2010 CDC STD Treatment Guidelines. Results. We recommend that STD clinics offering cervical screening services screen and treat women according to guidelines by the American College of Obstetrics and Gynecology, the American Cancer Society, the US Preventive Services Task Force, and the American Society for Colposcopists and Cervical Pathologists. New to the 2010 guidelines are higher age for initiating cervical screening (age ≥21 years) and less frequent intervals of screening (at least every 3 years). New recommendations include new technologies, such as liquid-based cytology and high-risk human papillomavirus (HPV) DNA tests. Liquid-based technologies are not recommended over conventional testing. HPV DNA tests are recommended as adjunct tests and with new indications for use in cervical screening and management. Stronger recommendations were issued for STD clinics offering cervical screening services to have protocols in place for follow-up of test results and referral (eg, colposcopy). Conclusions. Important additions to the 2010 STD Treatment Guidelines include information on updated algorithms for screening and management of women and recommendations for use of liquid-based cytology and high-risk HPV testing. [ABSTRACT FROM AUTHOR]

Detecting the cosmic 21 cm signal from epoch of reionization has always been a difficult task. Although the Galactic foreground can be regarded as a smooth power-law spectrum, due to the chromaticity of the antenna, additional structure will be introduced into the global spectrum, making the polynomial fitting algorithm perform poorly. In this paper, we introduce an improved polynomial fitting algorithm – the Vari-Zeroth-Order Polynomial (VZOP) fitting and use it to fit the simulation data. This algorithm is developed for the upcoming low-frequency anechoic chamber experiment, yet it is a general method suitable for application in any single antenna-based global 21 cm signal experiment. VZOP defines a 24-h averaged beam model that brings information about the antenna beam into the polynomial model. Assuming that the beam can be measured, VZOP can successfully recover the 21 cm absorption feature, even if the beam is extremely frequency-dependent. In real observations, due to various systematics, the corrected measured beam contains residual errors that are not completely random. Assuming the errors are frequency-dependent, VZOP is capable of recovering the 21 cm absorption feature even when the error reaches 10 per cent. Even in the most extreme scenario where the errors are completely random, VZOP can at least give a fitting result that is not worse than the common polynomial fitting. In conclusion, the fitting effect of VZOP depends on the structure of the error and the accuracy of the beam measurement. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to a long-standing problem concerning the joint distribution of allelic identity by descent between two individuals at two linked loci. Such distributions have important applications across various fields of genetics, and detailed formulas for selected relationships appear scattered throughout the literature. However, these results were obtained essentially by brute force, with no efficient method available for general pedigrees. The recursive algorithm described in this paper, and its implementation in R, allow efficient calculation of two-locus identity coefficients in any pedigree. As a result, many existing procedures and techniques may, for the first time, be applied to complex and inbred relationships. Two such applications are discussed, concerning the expected likelihood ratio in forensic kinship testing, and variances in realized relatedness. [ABSTRACT FROM AUTHOR]

Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Despite the outstanding advantage of RRS in throughput, the obtained genotype data usually contain a large number of errors. Several error correction methods employing the hidden Markov model (HMM) have been developed to overcome these issues. These methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper, we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum compared to the existing tools in simulation data sets and achieves the most reliable genotype estimation in real data even with error-prone markers. [ABSTRACT FROM AUTHOR]

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale—for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed. [ABSTRACT FROM AUTHOR]

In this paper, we present CALSAGOS : Clustering ALgorithmS Applied to Galaxies in Overdense Systems which is a PYTHON package developed to select cluster members and to search, find, and identify substructures. CALSAGOS is based on clustering algorithms, and was developed to be used in spectroscopic and photometric samples. To test the performance of CALSAGOS , we use the S-PLUS's mock catalogues, and we found an error of 1–6 per cent on member selection depending on the function that is used. Besides, CALSAGOS has a F 1-score of 0.8, a precision of 85 per cent and a completeness of 100 per cent in the identification of substructures in the outer regions of galaxy clusters (r > r 200). The F 1-score, precision, and completeness of CALSAGOS fall to 0.5, 75, and 40 per cent when we consider all substructure identifications (inner and outer) due to the function that searches, finds, and identifies the substructures works in 2D, and cannot resolve the substructures projected over others. [ABSTRACT FROM AUTHOR]

Over the past decades, learning to rank (LTR) algorithms have been gradually applied to bioinformatics. Such methods have shown significant advantages in multiple research tasks in this field. Therefore, it is necessary to summarize and discuss the application of these algorithms so that these algorithms are convenient and contribute to bioinformatics. In this paper, the characteristics of LTR algorithms and their strengths over other types of algorithms are analyzed based on the application of multiple perspectives in bioinformatics. Finally, the paper further discusses the shortcomings of the LTR algorithms, the methods and means to better use the algorithms and some open problems that currently exist. [ABSTRACT FROM AUTHOR]

In gravitational microlensing formalism and for modelling binary light curves, the key step is solving the binary lens equation. Currently, a combination of the Newton's and Laguerre's methods which was first introduced by Skowron & Gould (SG) is used while modelling binary light curves. In this paper, we first introduce a fast root-finding algorithm for univariate polynomials based on the Aberth–Ehrlich (AE) method which was first developed in 1967 as an improvement over the Newton's method. AE algorithm has proven to be much faster than Newton's, Laguerre's, and Durand–Kerner methods and unlike other root-finding algorithms, it is able to produce all the roots simultaneously. After improving the basic AE algorithm and discussing its properties, we will optimize it for solving binary lens equations, which are fifth degree polynomials with complex coefficients. Our method is about 1.8–2.0 times faster than the SG algorithm. Since, for calculating magnification factors for point-like or finite source stars, it is necessary to solve the binary lens equation and find the positions of the produced images in the image plane first, this new method will improve the speed and accuracy of binary microlensing modelling. [ABSTRACT FROM AUTHOR]

In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a "balancing intercept," that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods. [ABSTRACT FROM AUTHOR]

Selecting for multiple traits as opposed to a single trait has become increasingly important in genomic selection. As one of the most popular approaches to multitrait genomic selection, index selection uses a weighted average of all traits as a single breeding objective. Although intuitive and effective, index selection is not only numerically sensitive but also structurally incapable of finding certain optimal breeding parents. This paper proposes a new selection method for multitrait genomic selection, the L-shaped selection, which addresses the limitations of index selection by normalizing the trait values and using an L-shaped objective function to find optimal breeding parents. This algorithm has been proven to be able to find any Pareto optimal solution with appropriate weights. Two performance metrics have also been defined to quantify multitrait genomic selection algorithms with respect to their ability to accelerate genetic gain and preserve genetic diversity. Computational experiments were conducted to demonstrate the improved performance of L-shaped selection over-index selection. [ABSTRACT FROM AUTHOR]

The horizontal-to-vertical spectral ratio (HVSR) of ambient noise is commonly used to infer a site's resonance frequency (⁠|${f_{0,site}}$|⁠). HVSR calculations are performed most commonly using the Fourier amplitude spectrum obtained from a single merged horizontal component (e.g. the geometric mean component) from a three-component sensor. However, the use of a single merged horizontal component implicitly relies on the assumptions of azimuthally isotropic seismic noise and 1-D surface and subsurface conditions. These assumptions may not be justified at many sites, leading to azimuthal variability in HVSR measurements that cannot be accounted for using a single merged component. This paper proposes a new statistical method to account for azimuthal variability in the peak frequency of HVSR curves (⁠|${f_{0,HVSR}}$|⁠). The method uses rotated horizontal components at evenly distributed azimuthal intervals to investigate and quantify azimuthal variability. To ensure unbiased statistics for |${f_{0,HVSR}}$| are obtained, a frequency-domain window-rejection algorithm is applied at each azimuth to automatically remove contaminated time windows in which the |${f_{0,HVSR}}$| values are statistical outliers relative to those obtained from the majority of windows at that azimuth. Then, a weighting scheme is used to account for different numbers of accepted time windows at each azimuth. The new method is applied to a data set of 114 HVSR measurements with significant azimuthal variability in |${f_{0,HVSR}}$|⁠ , and is shown to reliably account for this variability. The methodology is also extended to the estimation of a complete lognormal-median HVSR curve that accounts for azimuthal variability. To encourage the adoption of this statistical approach to accounting for azimuthal variability in single-station HVSR measurements, the methods presented in this paper have been incorporated into hvsrpy , an open-source Python package for HVSR processing. [ABSTRACT FROM AUTHOR]

Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via $\ell_{0} $ optimization. The Annals of Applied Statistics12(4), 2457-2482) that can accurately estimate not just the spike rate, but also the specific times at which the neuron spikes. We develop a much faster algorithm that can be used to deconvolve a fluorescence trace of 100 000 timesteps in less than a second. Furthermore, we present a modification to this algorithm that precludes the possibility of a "negative spike". We demonstrate the performance of this algorithm for spike deconvolution on calcium imaging datasets that were recently released as part of the $\texttt{spikefinder}$ challenge (http://spikefinder.codeneuro.org/). The algorithm presented in this article was used in the Allen Institute for Brain Science's "platform paper" to decode neural activity from the Allen Brain Observatory; this is the main scientific paper in which their data resource is presented. Our $\texttt{C++}$ implementation, along with $\texttt{R}$ and $\texttt{python}$ wrappers, is publicly available. $\texttt{R}$ code is available on $\texttt{CRAN}$ and $\texttt{Github}$, and $\texttt{python}$ wrappers are available on $\texttt{Github}$; see https://github.com/jewellsean/FastLZeroSpikeInference. [ABSTRACT FROM AUTHOR]

Accurate covariance matrices for two-point functions are critical for inferring cosmological parameters in likelihood analyses of large-scale structure surveys. Among various approaches to obtaining the covariance, analytic computation is much faster and less noisy than estimation from data or simulations. However, the transform of covariances from Fourier space to real space involves integrals with two Bessel integrals, which are numerically slow and easily affected by numerical uncertainties. Inaccurate covariances may lead to significant errors in the inference of the cosmological parameters. In this paper, we introduce a 2D-FFTLog algorithm for efficient, accurate, and numerically stable computation of non-Gaussian real-space covariances for both 3D and projected statistics. The 2D-FFTLog algorithm is easily extended to perform real-space bin-averaging. We apply the algorithm to the covariances for galaxy clustering and weak lensing for a Dark Energy Survey Year 3-like and a Rubin Observatory's Legacy Survey of Space and Time Year 1-like survey, and demonstrate that for both surveys, our algorithm can produce numerically stable angular bin-averaged covariances with the flat sky approximation, which are sufficiently accurate for inferring cosmological parameters. The code CosmoCov for computing the real-space covariances with or without the flat-sky approximation is released along with this paper. [ABSTRACT FROM AUTHOR]

Researchers often face the problem of how to address missing data. Multiple imputation is a popular approach, with multiple imputation by chained equations (MICE) being among the most common and flexible methods for execution. MICE iteratively fits a predictive model for each variable with missing values, conditional on other variables in the data. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrectly specified, they may produce biased estimates of the imputed data, yielding inconsistent parameter estimates and invalid inference. Given the set of modeling choices that must be made in conducting multiple imputation, in this paper we propose a data-adaptive approach to model selection. Specifically, we adapt MICE to incorporate an ensemble algorithm, Super Learner, to predict the conditional mean for each missing value, and we also incorporate a local kernel-based estimate of variance. We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. These results suggest that using a flexible machine learning imputation approach can be useful in settings where data are missing at random, especially when the relationships among the variables are complex. [ABSTRACT FROM AUTHOR]

The horizontal-to-vertical spectral ratio (HVSR) of ambient noise measurement is commonly used to estimate a site's resonance frequency (⁠|${f_0}$|⁠). For sites with a strong impedance contrast, the HVSR peak frequency (⁠|${f_{0,\mathrm{ HVSR}}}$|⁠) has been shown to be a good estimate of |${f_0}$|⁠. However, the random nature of ambient noise (both in time and space), in conjunction with variable environmental conditions and sensor coupling issues, can lead to uncertainty in |${f_{0,\mathrm{ HVSR}}}$| estimates. Hence, it is important to report |${f_{0,\mathrm{ HVSR}}}$| in a statistical manner (e.g. as a mean or median value with standard deviation). In this paper, we first discuss widely accepted procedures to process HVSR data and estimate the variance in |${f_{0,\mathrm{ HVSR}}}$|⁠. Then, we propose modifications to improve these procedures in two specific ways. First, we propose using a lognormal distribution to describe |${f_{0,\mathrm{ HVSR}}}$| rather than the more commonly used normal distribution. The use of a lognormal distribution for |${f_{0,\mathrm{ HVSR}}}$| has several advantages, including consistency with earthquake ground motion processing and allowing for a seamless transition between HVSR statistics in terms of both frequency and its reciprocal, period. Second, we introduce a new frequency-domain window-rejection algorithm to decrease variance and enhance data quality. Finally, we use examples of 114 high-variance HVSR measurements and 77 low-variance HVSR measurements collected at two case study sites to demonstrate the effectiveness of the new rejection algorithm and the proposed statistical approach. To encourage their adoption, and promote standardization, the rejection algorithm and lognormal statistics presented in this paper have been incorporated into hvsrpy , an open-source Python package for HVSR processing. [ABSTRACT FROM AUTHOR]

Single-cell technologies provide us new ways to profile transcriptomic landscape, chromatin accessibility, spatial expression patterns in heterogeneous tissues at the resolution of single cell. With enormous generated single-cell datasets, a key analytic challenge is to integrate these datasets to gain biological insights into cellular compositions. Here, we developed a domain-adversarial and variational approximation, DAVAE, which can integrate multiple single-cell datasets across samples, technologies and modalities with a single strategy. Besides, DAVAE can also integrate paired data of ATAC profile and transcriptome profile that are simultaneously measured from a same cell. With a mini-batch stochastic gradient descent strategy, it is scalable for large-scale data and can be accelerated by GPUs. Results on seven real data integration applications demonstrated the effectiveness and scalability of DAVAE in batch-effect removing, transfer learning and cell-type predictions for multiple single-cell datasets across samples, technologies and modalities. Availability: DAVAE has been implemented in a toolkit package "scbean" in the pypi repository, and the source code can be also freely accessible at https://github.com/jhu99/scbean. All our data and source code for reproducing the results of this paper can be accessible at https://github.com/jhu99/davae_paper. [ABSTRACT FROM AUTHOR]

We explore an alternative method to the usual shear correlation function approach for the estimation of aperture mass statistics in weak-lensing survey data. Our approach builds on the direct estimator method. In this paper, we extend our analysis to statistics of arbitrary order and to the multiscale aperture mass statistics. We show that there always exists a linear order algorithm to retrieve any of these generalized aperture mass statistics from shape catalogues when the direct estimator approach is adopted. We validate our approach through application to a large number of Gaussian mock-lensing surveys where the true answer is known and we do this up to 10th-order statistics. We then apply our estimators to an ensemble of real-world mock catalogues obtained from N -body simulations – the SLICS mocks, and show that one can expect to retrieve detections of higher order clustering up to fourth order in a KiDS-1000 like survey. We expect that these methods will be of most utility for future wide-field surveys like Euclid and the Rubin Telescope. [ABSTRACT FROM AUTHOR]

We review the methodology for measurements of two-point functions of the cosmological observables, both power spectra and correlation functions. For pseudo- C ℓ estimators, we will argue that the window-weighted overdensity field can yield more optimal measurements as the window acts as an inverse noise weight, an effect that becomes more important for surveys with a variable selection function. We then discuss the impact of approximations made in the Master algorithm and suggest improvements, the i Master algorithm, which uses the theoretical model to give unbiased results for arbitrarily complex windows provided that the model satisfies weak accuracy conditions. The methodology of i Master algorithm is also generalized to the correlation functions to reconstruct the binned power spectra, for E/B mode separation, or to properly convolve the correlation functions to account for the scale cuts in the Fourier space model. We also show that the errors in the window estimation lead to both additive and multiplicative effects on the overdensity field. Accurate estimation of window power can be required up to scales of ∼2ℓmax or larger. Mis-estimation of the window power leads to biases in the measured power spectra, which scale as |${\delta C_\ell }\sim M^W_{\ell \ell ^{\prime }}\delta W_{\ell ^{\prime }}$|⁠ , where the |$M^W_{\ell \ell ^{\prime }}$| scales as ∼(2ℓ + 1) C ℓ leading to effects that can be important at high ℓ. While the notation in this paper is geared towards photometric galaxy surveys, the discussion is equally applicable to spectroscopic galaxy, intensity mapping, and Cosmic Microwave Background radiation (CMB) surveys. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA). We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms. The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kV–kV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom. Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations. The present study is based on a full simulation environment, which accommodates various situations such as SECT, kV–kV DECT, and even kV–MV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms. [ABSTRACT FROM AUTHOR]

An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW , a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM , npcausal , tmle , and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies. [ABSTRACT FROM AUTHOR]

The paper presents a novel phase-locked loop (PLL)-based resonant frequency tracking method for linear compressors with oscillatory motions. The proposed method achieves the phase-locked control between two signals by using PLL, which includes a multiplier, and a low-pass filter to obtain the phase signal difference. Consequently, based on the phase signal difference, the electrical frequency of the power supply can be adjusted through a PI (proportional-derivative) regulator such that the phase signal difference converges to 90° and the linear compressor works under the resonant frequency conditions. The analytical expression of the resonant frequency condition is proposed for the linear compressor. Then, the detailed design and procedure of the proposed method are presented. The designed method and the resonant frequency characteristics are verified by using the simulation experiment. The simulated results demonstrate that the proposed algorithm has faster response speed and higher convergence accuracy to step load change in comparison with the other commonly used algorithms. [ABSTRACT FROM AUTHOR]

GCA is the most common large vessel vasculitis in the elderly population. In recent years, advanced imaging has changed the way GCA can be diagnosed in many locations. The GCA fast-track clinic approach combined with US examination allows prompt treatment and diagnosis with high certainty. Fast-track clinics have been shown to improve prognosis while being cost effective. However, all diagnostic modalities are highly operator dependent, and in many locations expertise in advanced imaging may not be available. In this paper, we review the current evidence on GCA diagnostics and propose a simple algorithm for diagnosing GCA for use by rheumatologists not working in specialist centres. [ABSTRACT FROM AUTHOR]

Approaches to seismic modelling using integral methods, notably Born–WKBJ (Wentzel–Kramers–Brillouin–Jeffreys), have seen an increase in applications in geophysical prospecting and near-surface exploration. Moreover, due to its linearity characteristic, which can fast and efficiently simulate full-waveform information, the Born–WKBJ based method performs well in seismic inversion. However, the Born approximation is the linearized wavefield simulation by Taylor expansion of the wavefield and omitting the high-order term, which cannot simulate the wavefield accurately, including the amplitude, phase and waveform information. For the seismic inversion, especially the amplitude variation with angle/offset (AVA/AVO) inversion, the amplitude is the most important element for estimating the parameters. In this paper, a Rytov–WKBJ approximation-based method, which can simulate the seismic amplitude information more accurately, is introduced to pre-stack seismic inversion. Besides, in order to improve the resolution of the inversion results, the ℓ1 − 2-norm regularized basis pursuit inversion is introduced to the inversion algorithm. Then, we demonstrate the superiority of the proposed method with the zero-offset and angle-dependent seismic data simulation. The model tests show that the proposed method performs better than the conventional method significantly both on amplitude and phase of the seismic data. Finally, the inversion tests of synthetic and field seismic data indicate that the proposed method can obtain more accurate results with a higher resolution. [ABSTRACT FROM AUTHOR]

Seismic phase detection, identification and first-onset picking are basic but essential routines to analyse earthquake data. As both the number of seismic stations, globally and regionally, and the number of experiments greatly increase due to ever greater availability of instrumentation, automated data processing becomes more and more essential. For example for modern seismic experiments involving 100s to even 1000s instruments, conventional human analyst-based identification and picking of seismic phases is becoming unfeasible, and the introduction of automatic algorithms mandatory. In this paper, we introduce DeepPhasePick, an automatic two-stage method that detects and picks P and S seismic phases from local earthquakes. The method is entirely based on highly optimized deep neural networks, consisting of a first stage that detects the phases using a convolutional neural network, and a second stage that uses two recurrent neural networks to pick both phases. Detection is performed on three-component seismograms. P - and S -picking is then conducted on the vertical and the two-horizontal components, respectively. Systematic hyperparameter optimization was applied to select the best model architectures and to define both the filter applied to pre-process the seismic data as well as the characteristics of the window sample used to feed the models. We trained DeepPhasePick using seismic records extracted from two sets of manually picked event waveforms originating from northern Chile (∼39 000 records for detection and ∼36 000 records for picking). In different tectonic regimes, DeepPhasePick demonstrated the ability to both detect P and S phases from local earthquakes with high accuracy, as well as predict P - and S -phase time onsets with an analyst level of precision. DeepPhasePick additionally computes onset uncertainties based on the Monte Carlo Dropout technique as an approximation of Bayesian inference. This information can then further feed an associator algorithm in an earthquake location procedure. [ABSTRACT FROM AUTHOR]

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case–control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case–control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype. [ABSTRACT FROM AUTHOR]

This paper proposes a morphostatistical characterization of the galaxy distribution through spatial statistical modelling based on inhomogeneous Gibbs point processes. The galaxy distribution is supposed to exhibit two components. The first one is related to the major geometrical features exhibited by the observed galaxy field, here, its corresponding filamentary pattern. The second one is related to the interactions exhibited by the galaxies. Gibbs point processes are statistical models able to integrate these two aspects in a probability density, controlled by some parameters. Several such models are fitted to real observational data via the ABC shadow algorithm. This algorithm provides simultaneous parameter estimation and posterior-based inference, hence allowing the derivation of the statistical significance of the obtained results. [ABSTRACT FROM AUTHOR]

Lucky imaging is a high-angular-resolution astronomical image reconstruction technique which can effectively reduce or remove the effects of atmospheric turbulence. There have been successful real-time lucky imaging algorithms and corresponding implementations in the spatial domain. Although the results of lucky imaging algorithms in the frequency domain are better than in the spatial domain, until now there has been no corresponding real-time algorithm and implementation due to its complexity. This paper proposes a real-time lucky imaging algorithm based on Fourier transform that features real-time processing and dynamic updating and display. The algorithm is implemented on a small- or medium-scale field programmable gate array (FPGA) development board. In this system, a two-dimensional fast Fourier transform of the image is carried out by means of dimension reduction. In order to achieve real-time lucky imaging in the frequency domain, the sum of the squares of the real and imaginary parts are used to replace the amplitude for data selection, and the method of image grouping is used to speed up the data selection. Compared with other real-time lucky imaging algorithms in the spatial domain, this algorithm shows great advantages in image quality. Moreover, the system can carry out real-time lucky imaging processing in the frequency domain for more than 10000 frames of an original image of 512 × 512 pixels continuously, and dynamic display with an image of 128 × 128 pixels. The experimental results illustrate the validity of the proposed algorithm and the feasibility of the proposed implementation techniques for the FPGA-based algorithm. [ABSTRACT FROM AUTHOR]

The recent observation of the shadow of the supermassive compact object M87* by the Event Horizon Telescope (EHT) collaboration has opened up a new window to probe the strong gravity regime. In this paper, we study shadows cast by two viable alternatives to the Kerr black hole, and compare them with the shadow of M87*. The first alternative is a horizonless compact object (HCO) having radius r 0 and exterior Kerr geometry. The second one is a rotating generalization of the recently obtained one parameter (r 0) static metric by Simpson and Visser. This latter metric, constructed using the Newman–Janis algorithm, is a special case of a parametrized rotating non-Kerr geometry obtained by Johannsen. Here, we constrain the parameter r 0 of these alternatives using the results from M87* observation. We find that, for the mass, inclination angle and the angular diameter of the shadow of M87* reported by the EHT collaboration, the maximum value of the parameter r 0 must be in the range 2.54 r + ≤ r 0, max ≤ 3.51 r + for the dimensionless spin range 0.5 ≤ a * ≤ 0.94, with r + being the outer horizon radius of the Kerr black hole at the corresponding spin value. We conclude that these black hole alternatives having r 0 below this maximum range (i.e. r 0 ≤ r 0, max) is consistent with the size and deviation from circularity of the observed shadow of M87*. [ABSTRACT FROM AUTHOR]

In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks. [ABSTRACT FROM AUTHOR]

Motivation mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed. Results In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries retired from electric vehicles can provide considerable economic benefits when they are retired for secondary use. However, retired batteries after screening and restructuring still face the problem of inaccurate battery pack state-of-charge (SOC) estimation due to the existence of extreme inconsistency. To solve this problem, an adaptive fading unscented Kalman filtering (AFUKF) algorithm based on the cell difference model (CDM) is proposed in this paper for improving the accuracy of SOC estimation of retired lithium-ion battery packs. Firstly, an improved CDM based on a hypothetical Rint model is developed based on a second-order resistor/capacitor equivalent circuit model. Secondly, an AFUKF algorithm is developed to improve the adaptability and robustness of local state estimation against process modelling errors. Finally, characteristic data are obtained by conducting discharge tests on the screened retired lithium-ion batteries under specific operating conditions. The proposed method can improve the accuracy of SOC estimation of retired lithium-ion battery packs and provide a new idea for SOC estimation of retired lithium-ion battery packs, as shown by the simulated real experimental data. [ABSTRACT FROM AUTHOR]

Glitches are the observational manifestations of superfluidity inside neutron stars. The aim of this paper is to describe an automated glitch detection pipeline, which can alert the observers on possible real-time detection of rotational glitches in pulsars. Post alert, the pulsars can be monitored at a higher cadence to measure the post-glitch recovery phase. Two algorithms, namely median absolute deviation and polynomial regression, have been explored to detect glitches in real time. The pipeline has been optimized with the help of simulated timing residuals for both the algorithms. Based on the simulations, we conclude that the polynomial regression algorithm is significantly more effective for real time glitch detection. The pipeline has been tested on a few published glitches. This pipeline is presently implemented at the Ooty Radio Telescope. In the era of upcoming large telescopes like SKA, several hundreds of pulsars will be observed regularly and such a tool will be useful for both real-time detection as well as optimal utilization of observation time for such glitching pulsars. [ABSTRACT FROM AUTHOR]

We present a detailed study of the complex time-frequency structure of a sample of previously reported bursts of FRB 121102 detected with the MeerKAT telescope in September 2019. The wide contiguous bandwidth of these observations have revealed a complex bifurcating structure in some bursts at 1250 MHz. When de-dispersed to their structure-optimized dispersion measures (DMs), two of the bursts show a clear deviation from the cold plasma dispersion relationship below 1250 MHz. We find a differential DM of |${\sim }1{-}2~{\rm pc \, cm^{-3}}$| between the lower and higher frequency regions of each burst. We investigate the possibility of plasma lensing by Gaussian lenses of ∼10 au in the host galaxy, and demonstrate that they can qualitatively produce some of the observed burst morphologies. Other possible causes for the observed frequency dependence, such as Faraday delay, are also discussed. Unresolved sub-components in the bursts, however, may have led to an incorrect DM determination. We hence advise exercising caution when considering bursts in isolation. We analyse the presence of two apparent burst pairs. One of these pairs is a potential example of upward frequency drift. The possibility that burst pairs are echoes is also discussed. The average structure-optimized DM is found to be |$563.5\pm 0.2 (\text{sys}) \pm 0.8 (\text{stat})\, {\rm pc \, cm^{-3}}$| – consistent with the values reported in 2018. We use two independent methods to determine the structure-optimized DM of the bursts: the DM_phase algorithm and autocorrelation functions. The latter – originally developed for pulsar analysis – is applied to fast radio bursts for the first time in this paper. [ABSTRACT FROM AUTHOR]

Background in 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) published a sarcopenia definition that aimed to foster advances in identifying and caring for people with sarcopenia. In early 2018, the Working Group met again (EWGSOP2) to update the original definition in order to reflect scientific and clinical evidence that has built over the last decade. This paper presents our updated findings. Objectives to increase consistency of research design, clinical diagnoses and ultimately, care for people with sarcopenia. Recommendations sarcopenia is a muscle disease (muscle failure) rooted in adverse muscle changes that accrue across a lifetime; sarcopenia is common among adults of older age but can also occur earlier in life. In this updated consensus paper on sarcopenia, EWGSOP2: (1) focuses on low muscle strength as a key characteristic of sarcopenia, uses detection of low muscle quantity and quality to confirm the sarcopenia diagnosis, and identifies poor physical performance as indicative of severe sarcopenia; (2) updates the clinical algorithm that can be used for sarcopenia case-finding, diagnosis and confirmation, and severity determination and (3) provides clear cut-off points for measurements of variables that identify and characterise sarcopenia. Conclusions EWGSOP2's updated recommendations aim to increase awareness of sarcopenia and its risk. With these new recommendations, EWGSOP2 calls for healthcare professionals who treat patients at risk for sarcopenia to take actions that will promote early detection and treatment. We also encourage more research in the field of sarcopenia in order to prevent or delay adverse health outcomes that incur a heavy burden for patients and healthcare systems. [ABSTRACT FROM AUTHOR]

Background in 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) published a sarcopenia definition that aimed to foster advances in identifying and caring for people with sarcopenia. In early 2018, the Working Group met again (EWGSOP2) to update the original definition in order to reflect scientific and clinical evidence that has built over the last decade. This paper presents our updated findings. Objectives to increase consistency of research design, clinical diagnoses and ultimately, care for people with sarcopenia. Recommendations sarcopenia is a muscle disease (muscle failure) rooted in adverse muscle changes that accrue across a lifetime; sarcopenia is common among adults of older age but can also occur earlier in life. In this updated consensus paper on sarcopenia, EWGSOP2: (1) focuses on low muscle strength as a key characteristic of sarcopenia, uses detection of low muscle quantity and quality to confirm the sarcopenia diagnosis, and identifies poor physical performance as indicative of severe sarcopenia; (2) updates the clinical algorithm that can be used for sarcopenia case-finding, diagnosis and confirmation, and severity determination and (3) provides clear cut-off points for measurements of variables that identify and characterise sarcopenia. Conclusions EWGSOP2's updated recommendations aim to increase awareness of sarcopenia and its risk. With these new recommendations, EWGSOP2 calls for healthcare professionals who treat patients at risk for sarcopenia to take actions that will promote early detection and treatment. We also encourage more research in the field of sarcopenia in order to prevent or delay adverse health outcomes that incur a heavy burden for patients and healthcare systems. [ABSTRACT FROM AUTHOR]

Cosmic voids and their corresponding redshift-projected mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures enables us to compare the standard model with alternative cosmologies, constrain the dark energy equation of state, and distinguish between different gravitational theories. In this paper, we extend the subspace-constrained mean shift algorithm, a recently introduced method to estimate density ridges, and apply it to 2D weak lensing mass density maps from the Dark Energy Survey Y1 data release to identify curvilinear filamentary structures. We compare the obtained ridges with previous approaches to extract trough structure in the same data, and apply curvelets as an alternative wavelet-based method to constrain densities. We then invoke the Wasserstein distance between noisy and noiseless simulations to validate the denoising capabilities of our method. Our results demonstrate the viability of ridge estimation as a precursor for denoising weak lensing observables to recover the large-scale structure, paving the way for a more versatile and effective search for troughs. [ABSTRACT FROM AUTHOR]

Regularization is necessary for solving non-linear ill-posed inverse problems arising in different fields of geosciences. The base of a suitable regularization is the prior expressed by the regularizer, which can be non-adaptive or adaptive (data-driven), smooth or non-smooth, variational-based or not. Nevertheless, tailoring a suitable and easy-to-implement prior for describing geophysical models is a non-trivial task. In this paper, we propose two generic optimization algorithms to implement arbitrary regularization in non-linear inverse problems such as full-waveform inversion (FWI), where the regularization task is recast as a denoising problem. We assess these optimization algorithms with the plug-and-play block matching (BM3D) regularization algorithm, which determines empirical priors adaptively without any optimization formulation. The non-linear inverse problem is solved with a proximal Newton method, which generalizes the traditional Newton step in such a way to involve the gradients/subgradients of a (possibly non-differentiable) regularization function through operator splitting and proximal mappings. Furthermore, it requires to account for the Hessian matrix in the regularized least-squares optimization problem. We propose two different splitting algorithms for this task. In the first, we compute the Newton search direction with an iterative method based upon the first-order generalized iterative shrinkage-thresholding algorithm (ISTA), and hence Newton-ISTA (NISTA). The iterations require only Hessian-vector products to compute the gradient step of the quadratic approximation of the non-linear objective function. The second relies on the alternating direction method of multipliers (ADMM), and hence Newton-ADMM (NADMM), where the least-squares optimization subproblem and the regularization subproblem in the composite objective function are decoupled through auxiliary variable and solved in an alternating mode. The least-squares subproblem can be solved with exact, inexact, or quasi-Newton methods. We compare NISTA and NADMM numerically by solving FWI with BM3D regularization. The tests show promising results obtained by both algorithms. However, NADMM shows a faster convergence rate than NISTA when using L-BFGS to solve the Newton system. [ABSTRACT FROM AUTHOR]

Owing to the limited size and imperfections of the optical components in a spectrometer, aberrations inevitably make their way into 2D multifibre spectral images in the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST), which leads to obvious spatial variation of the point spread functions (PSFs). However, if spatially variant PSFs are estimated directly, the large storage and intensive computational requirements result in the deconvolution spectrum extraction method becoming intractable. In this paper, we propose a novel method to solve the problem of spatial variation of the PSFs through image aberration correction. When CCD image aberrations are corrected, the convolution kernel can be approximated by only one spatially invariant PSF. Specifically, a novel method based on machine learning is proposed to calibrate the distorted spectral images. The method includes many techniques, such as total least squares (TLS) algorithm, self-supervised learning and multilayer feed-forward neural networksnetworks, and it makes use of a special training set sampling scheme combining 2D distortion features in a flat-field spectrum and calibration lamp spectrum. The calibration experiments on the LAMOST CCD images show that the proposed method is feasible. Furthermore, the spectrum extraction results before and after calibration are compared, and the experimental results show that the characteristics of the extracted 1D waveform are closer to those of an ideal optics system after image correction, and that the PSF of the corrected object spectrum estimated by the blind deconvolution method is nearly centrosymmetric, which indicates that our proposed method can significantly reduce the complexity of spectrum extraction and improve extraction accuracy. [ABSTRACT FROM AUTHOR]

The advent of a new generation of large-scale galaxy surveys is pushing cosmological numerical simulations in an uncharted territory. The simultaneous requirements of high resolution and very large volume pose serious technical challenges, due to their computational and data storage demand. In this paper, we present a novel approach dubbed dynamic zoom simulations – or dzs  – developed to tackle these issues. Our method is tailored to the production of light-cone outputs from N -body numerical simulations, which allow for a more efficient storage and post-processing compared to standard comoving snapshots, and more directly mimic the format of survey data. In dzs , the resolution of the simulation is dynamically decreased outside the light-cone surface, reducing the computational work load, while simultaneously preserving the accuracy inside the light-cone and the large-scale gravitational field. We show that our approach can achieve virtually identical results to traditional simulations at half of the computational cost for our largest box. We also forecast this speedup to increase up to a factor of 5 for larger and/or higher resolution simulations. We assess the accuracy of the numerical integration by comparing pairs of identical simulations run with and without dzs. Deviations in the light-cone halo mass function, in the sky-projected light-cone, and in the 3D matter light-cone always remain below 0.1 per cent. In summary, our results indicate that the dzs technique may provide a highly valuable tool to address the technical challenges that will characterize the next generation of large-scale cosmological simulations. [ABSTRACT FROM AUTHOR]

Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies – almost all of which must be derived from photometry rather than spectroscopy. In this paper, we investigate how using statistical models to understand the populations that make up the colour–magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular, we combine the use of Gaussian mixture models with the high-performing machine-learning photo-z algorithm GPz and show that modelling and accounting for the different colour–magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near-infrared data in two separate deep fields, where training and test data of different colour–magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys. [ABSTRACT FROM AUTHOR]

A catalogue of galaxy clusters was obtained in an area of 414 |$\deg ^2$| up to a redshift z ∼ 0.8 from the Data Release 3 of the Kilo-Degree Survey (KiDS-DR3), using the Adaptive Matched Identifier of Clustered Objects (amico) algorithm. The catalogue and the calibration of the richness–mass relation were presented in two companion papers. Here, we describe the selection of the cluster central galaxy and the classification of blue and red cluster members, and analyse the main cluster properties, such as the red/blue fraction, cluster mass, brightness, and stellar mass of the central galaxy, and their dependence on redshift and cluster richness. We use the Illustris-TNG simulation, which represents the state-of-the-art cosmological simulation of galaxy formation, as a benchmark for the interpretation of the results. A good agreement with simulations is found at low redshifts (z ≤ 0.4), while at higher redshifts the simulations indicate a lower fraction of blue galaxies than what found in the KiDS- amico catalogue: we argue that this may be due to an underestimate of star-forming galaxies in the simulations. The selection of clusters with a larger magnitude difference between the two brightest central galaxies, which may indicate a more relaxed cluster dynamical status, improves the agreement between the observed and simulated cluster mass and stellar mass of the central galaxy. We also find that at a given cluster mass the stellar mass of blue central galaxies is lower than that of the red ones. [ABSTRACT FROM AUTHOR]

This paper presents a catalogue of optimized pointings for differential photometry of 23 779 quasars extracted from the Sloan Digital Sky Survey (SDSS) Catalogue and a Score for each indicating the quality of the Field of View (FoV) associated with that pointing. Observation of millimagnitude variability on a time-scale of minutes typically requires differential observations with reference to an ensemble of reference stars. For optimal performance, these reference stars should have similar colour and magnitude to the target quasar. In addition, the greatest quantity and quality of suitable reference stars may be found by using a telescope pointing which offsets the target object from the centre of the FoV. By comparing each quasar with the stars which appear close to it on the sky in the SDSS Catalogue, an optimum pointing can be calculated, and a figure of merit, referred to as the ' Score ' is calculated for that pointing. Highly flexible software has been developed to enable this process to be automated and implemented in a distributed computing paradigm, which enables the creation of catalogues of pointings given a set of input targets. Applying this technique to a sample of 40 000 targets from the fourth SDSS quasar catalogue resulted in the production of pointings and Scores for 23 779 quasars based on their magnitudes in the SDSS r -band. This catalogue is a useful resource for observers planning differential photometry studies and surveys of quasars to select those which have many suitable celestial neighbours for differential photometry. [ABSTRACT FROM AUTHOR]

We present a time-domain matrix-free elastic Gauss–Newton full-waveform inversion (FWI) algorithm. Our algorithm consists of a Gauss–Newton update with a search direction calculated via elastic least-squares reverse time migration (LSRTM). The conjugate gradient least-squares (CGLS) method solves the LSRTM problem with forward and adjoint operators derived via the elastic Born approximation. The Hessian of the Gauss–Newton method is never explicitly formed or saved in memory. In other words, the CGLS algorithm solves for the Gauss–Newton direction via the application of implicit-form forward and adjoint operators which are equivalent to elastic Born modelling and elastic reverse time migration, respectively. We provide numerical examples to test the proposed algorithm where we invert for P - and S -wave velocities simultaneously. The proposed algorithm performs positively on mid-size problems where we report solutions of slight improvement than those computed using the conventional non-linear conjugate gradient method. In spite of the aforementioned limited gain, the theory developed in this paper contributes to a better understanding of time-domain elastic Gauss–Newton FWI. [ABSTRACT FROM AUTHOR]

Objective. To formulate timely evidence-based guidelines for the management of opioid-induced bowel dysfunction. Setting. Constipation is a major untoward effect of opioids. Increasing prescription of opioids has correlated to increased incidence of opioid-induced constipation. However, the inhibitory effects of opioids are not confined to the colon, but also affect higher segments of the gastrointestinal tract, leading to the coining of the term "opioid-induced bowel dysfunction." Methods. A literature search was conducted using Medline, EMBASE, and EMBASE Classic, and the Cochrane Central Register of Controlled Trials. Predefined search terms and inclusion/exclusion criteria were used to identify and categorize relevant papers. A series of statements were formulated and justified by a comment, then labeled with the degree of agreement and their level of evidence as judged by the Strength of Recommendation Taxonomy (SORT) system. Results. From a list of 10,832 potentially relevant studies, 33 citations were identified for review. Screening the reference lists of the pertinent papers identified additional publications. Current definitions, prevalence, and mechanism of opioidinduced bowel dysfunction were reviewed, and a treatment algorithm and statements regarding patient management were developed to provide guidance on clinical best practice in the management of patients with opioid-induced constipation and opioid-induced bowel dysfunction. Conclusions. In recent years, more insight has been gained in the pathophysiology of this "entity"; new treatment approaches have been developed, but guidelines on clinical best practice are still lacking. Current knowledge is insufficient regarding management of the opioid side effects on the upper gastrointestinal tract, but recommendations can be derived from what we know at present. [ABSTRACT FROM AUTHOR]

Large multiobject spectroscopic surveys require automated algorithms to optimize their observing strategy. One of the most ambitious upcoming spectroscopic surveys is the 4MOST survey. The 4MOST survey facility is a fibre-fed spectroscopic instrument on the VISTA telescope with a large enough field of view to survey a large fraction of the southern sky within a few years. Several Galactic and extragalactic surveys will be carried out simultaneously, so the combined target density will strongly vary. In this paper, we describe a new tiling algorithm that can naturally deal with the large target density variations on the sky and which automatically handles the different exposure times of targets. The tiling pattern is modelled as a marked point process, which is characterized by a probability density that integrates the requirements imposed by the 4MOST survey. The optimal tilling pattern with respect to the defined model is estimated by the tiles configuration that maximizes the proposed probability density. In order to achieve this maximization a simulated annealing algorithm is implemented. The algorithm automatically finds an optimal tiling pattern and assigns a tentative sky brightness condition and exposure time for each tile, while minimizing the total execution time that is needed to observe the list of targets in the combined input catalogue of all surveys. Hence, the algorithm maximizes the long-term observing efficiency and provides an optimal tiling solution for the survey. While designed for the 4MOST survey, the algorithm is flexible and can with simple modifications be applied to any other multiobject spectroscopic survey. [ABSTRACT FROM AUTHOR]

Motivation Third generation sequencing techniques, such as the Single Molecule Real Time technique from PacBio and the MinION technique from Oxford Nanopore, can generate long, error-prone sequencing reads which pose new challenges for fragment assembly algorithms. In this paper, we study the overlap detection problem for error-prone reads, which is the first and most critical step in the de novo fragment assembly. We observe that all the state-of-the-art methods cannot achieve an ideal accuracy for overlap detection (in terms of relatively low precision and recall) due to the high sequencing error rates, especially when the overlap lengths between reads are relatively short (e.g. <2000 bases). This limitation appears inherent to these algorithms due to their usage of q -gram-based seeds under the seed-extension framework. Results We propose smooth q-gram , a variant of q -gram that captures q -gram pairs within small edit distances and design a novel algorithm for detecting overlapping reads using smooth q -gram-based seeds. We implemented the algorithm and tested it on both PacBio and Nanopore sequencing datasets. Our benchmarking results demonstrated that our algorithm outperforms the existing q -gram-based overlap detection algorithms, especially for reads with relatively short overlapping lengths. Availability and implementation The source code of our implementation in C++ is available at https://github.com/FIGOGO/smoothq. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network with highway layers. This algorithm is trained by actual observed cadence and filter combinations such that we can directly input the observed data array without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes. [ABSTRACT FROM AUTHOR]

The K-SVD algorithm has been successfully utilized for adaptively learning the sparse dictionary in 2-D seismic denoising. Because of the high computational cost of many singular value decompositions (SVDs) in the K-SVD algorithm, it is not applicable in practical situations, especially in 3-D or 5-D problems. In this paper, I extend the dictionary learning based denoising approach from 2-D to 3-D. To address the computational efficiency problem in K-SVD, I propose a fast dictionary learning approach based on the sequential generalized K-means (SGK) algorithm for denoising multidimensional seismic data. The SGK algorithm updates each dictionary atom by taking an arithmetic average of several training signals instead of calculating an SVD as used in K-SVD algorithm. I summarize the sparse dictionary learning algorithm using K-SVD, and introduce SGK algorithm together with its detailed mathematical implications. 3-D synthetic, 2-D and 3-D field data examples are used to demonstrate the performance of both K-SVD and SGK algorithms. It has been shown that SGK algorithm can significantly increase the computational efficiency while only slightly degrading the denoising performance. [ABSTRACT FROM AUTHOR]