bluuebunny/crossref_metadata_2025_split huggingface dataset:
HiFLEx—A Highly Flexible Package to Reduce Cross-dispersed Echelle Spectra
Authors: Errmann Ronny , Cook Neil , Anglada-Escudé Guillem , Sithajan Sirinrat , Mkrtichian David , Semenko Eugene , Martin William , Tanvir Tabassum S. , Feng Fabo , Collett James L. , Jones Hugh R. A. 
Published: May 2020

Abstract
We describe a flexible data reduction package for high resolution cross-dispersed echelle data. This open-source package is developed in Python and includes optional GUIs for most of the steps. It does not require any pre-knowledge about the form or position of the echelle-orders. It has been tested on cross-dispersed echelle spectrographs between 13k and 115k resolution (bifurcated fiber-fed spectrogaph ESO-HARPS and single fiber-fed spectrograph TNT-MRES). HiFLEx can be used to determine radial velocities and is designed to use the TERRA package but can also control the radial velocity packages such as CERES and SERVAL to perform the radial velocity analysis. Tests on HARPS data indicates radial velocities results within ±3 m s−1 of the literature pipelines without any fine tuning of extraction parameters.
Mapping the evidence of climate change adaptation policy instruments in Europe
Authors: Biesbroek Robbert , Delaney Aogan 
Published: May 2020

Abstract
With climate change impacts being felt across Europe, governments have started to invest in designing and implementing adaptation actions. The means through which governments adapt is generally referred to as policy instruments. Although there is a large body of adaptation policy literature emerging, our comprehensive understanding of policy instruments is limited nor do we know much about how scholarship on adaptation is addressing critical questions about policy instrument choice and effectiveness to support policy practice. In this article we map academic scholarship on climate change adaptation policy instruments in Europe. Using systematic approaches, we identify 184 relevant articles published 2014–2019. Our findings show that research is heavily concentrated on a limited number of western-European countries, with hardly any insights from eastern Europe and smaller countries. Most studies do not connect climate change impacts and risks with policy instruments, making assessment of policy effectiveness difficult, if not impossible. We argue that expanding the geographical scope of future research and enhancing the diversity of study types across Europe is critical for advancing theories on climate change adaptation policy, as well as providing useful recommendations for policy makers to strengthen the solution space and accelerate climate change adaptation.
Electrospun poly(lactic acid) nanofiber mats for controlled transdermal delivery of essential oil from Zingiber cassumunar Roxb
Authors: Wongkanya Ratchada , Teeranachaideekul Veerawat , Makarasen Arthit , Chuysinuan Piyachat , Yingyuad Peerada , Nooeaid Patcharakamon , Techasakul Supanna , Chuenchom Laemthong , Dechtrirat Decha 
Published: May 2020

Abstract
A controlled release system of Plai (Zingiber cassumunar Roxb.) oil based on electrospun poly(lactic) acid (PLA) nanofiber mat was successfully developed. The physicochemical properties of the nanofibers loaded with select amounts of oil (15%, 20%, and 30% wt) were characterized using various techniques, including a morphological study using scanning electron microscopy (SEM), structural determination using Fourier transform infrared spectrometry (FTIR) and x-ray diffraction (XRD), as well as thermal properties study using differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The loading content and the entrapment efficiency of Plai oil within the fiber mats were evaluated and were found to be remarkably high, ensuring that PLA was an appropriate material for Plai oil loading. The ability of the nanofiber mats to release (E)-1-(3,4-dimethoxyphenyl) butadiene (DMPBD) was also examined and the fiber mats showed controlled release characteristics. As the nanofiber mats have particularly high specific surface area with fully accessible and interconnected pore structures, a liquid medium with active ingredients will not be trapped in blind pores but can be fully released out of the fiber matrix. Furthermore, in vitro skin permeation of the active compound as well as a skin irritation were assessed using reconstructed human epidermis (EpiSkinTM). It was found that DMPBD could efficiently penetrate through the skin model. Moreover, the nanofiber mats containing Plai oil also showed no skin irritation, indicating them as promising prototypes for medical applications.
Efficient unitary method for simulation of driven quantum dot systems
Authors: Talkington Spenser , Jiang HongWen 
Published: May 2020

Abstract
Density matrices evolved according the von Neumann equation are commonly used to simulate the dynamics of driven quantum systems. However, computational methods using density matrices are often too slow to explore the large parameter spaces of solid state quantum systems. Here we develop a unitary computation method to quickly perform simulations for closed quantum systems, where dissipation to the environment can be ignored. We use three techniques to optimize simulations, apply them to six time-dependent pulses for a semiconductor quantum dot qubit system, and predict the dynamic evolutions. We compare computational times between our unitary method and the density matrix method for a variety of image sizes. As an example, we implement our unitary method for a realistic four-state system (Z. Shi et al, Nat. Commun. 5, 3020 (2014)), and find that it is over two orders of magnitude faster than the corresponding density matrix method implemented in the popular quantum simulation software QuTiP.
Numerical methodology for treating static and dynamic dislocation problems near a free surface
Authors: Siddique Abu Bakar , Khraishi Tariq 
Published: May 2020

Abstract
Simulation of dislocation dynamics enables researchers and scientists to explore the plastic behavior of crystalline materials under loading. Analytic solutions for the stress field due to a linear dislocation segment near a free surface are case-specific, e.g. dealing with either a horizontal segment or a vertical segment, and therefore hard to implement in time-dependent dislocation dynamics simulations as different dislocation segments could be differently oriented. This article presents a generalized numerical framework to find the stress field beneath a free surface due to the presence of a dislocation segment. The framework can be expanded to non-flat surfaces. Also, three-dimensional discrete dislocation dynamics simulations are performed here, which clearly show the effect of free surfaces on the flow stress of a material.
Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets
Authors: Wei Zhuohui , Zhang Yue , Weng Wanlin , Chen Jiazhou , Cai Hongmin 
Published: May 2020

Abstract
The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.
Weighted minimizer sampling improves long read mapping
Authors: Jain Chirag , Rhie Arang , Zhang Haowen , Chu Claudia , Walenz Brian P , Koren Sergey , Phillippy Adam M 
Published: May 2020

Abstract

Motivation
In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g. Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions.

Results
We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering a weight for each k-mer; i.e. the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes.

Availability and implementation
Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap.
Unsupervised topological alignment for single-cell multi-omics integration
Authors: Cao Kai , Bai Xiangqi , Hong Yiguang , Wan Lin 
Published: May 2020

Abstract

Motivation
Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging.

Results
In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex non-linear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features.

Availability and implementation
UnionCom software is available at https://github.com/caokai1073/UnionCom.

Supplementary information
Supplementary data are available at Bioinformatics online.
Chromatin network markers of leukemia
Authors: Malod-Dognin N , Pancaldi V , Valencia A , Pržulj N 
Published: May 2020

Abstract

Motivation
The structure of chromatin impacts gene expression. Its alteration has been shown to coincide with the occurrence of cancer. A key challenge is in understanding the role of chromatin structure (CS) in cellular processes and its implications in diseases.

Results
We propose a comparative pipeline to analyze CSs and apply it to study chronic lymphocytic leukemia (CLL). We model the chromatin of the affected and control cells as networks and analyze the network topology by state-of-the-art methods. Our results show that CSs are a rich source of new biological and functional information about DNA elements and cells that can complement protein–protein and co-expression data. Importantly, we show the existence of structural markers of cancer-related DNA elements in the chromatin. Surprisingly, CLL driver genes are characterized by specific local wiring patterns not only in the CS network of CLL cells, but also of healthy cells. This allows us to successfully predict new CLL-related DNA elements. Importantly, this shows that we can identify cancer-related DNA elements in other cancer types by investigating the CS network of the healthy cell of origin, a key new insight paving the road to new therapeutic strategies. This gives us an opportunity to exploit chromosome conformation data in healthy cells to predict new drivers.

Availability and implementation
Our predicted CLL genes and RNAs are provided as a free resource to the community at https://life.bsc.es/iconbi/chromatin/index.html.

Supplementary information
Supplementary data are available at Bioinformatics online.
Geometric potentials from deep learning improve prediction of CDR H3 loop structures
Authors: Ruffolo Jeffrey A , Guerra Carlos , Mahajan Sai Pooja , Sulam Jeremias , Gray Jeffrey J 
Published: May 2020

Abstract

Motivation
Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo.

Results
When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark.

Availability and Implementation
DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations.

Supplementary information
Supplementary data are available at Bioinformatics online.
Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach
Authors: Wu Yufeng 
Published: May 2020

Abstract

Motivation
Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they do not fully utilize the information [e.g. linkage disequilibrium (LD)] contained in population genetic data.

Results
In this article, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled haplotypes and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred local genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up the likelihood computation on the MSC model and the optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze population genetic datasets of current interests.

Availability and implementation
The program GTmix is available for download at: https://github.com/yufengwudcs/GTmix.

Supplementary information
Supplementary data are available at Bioinformatics online.
Inference attacks against differentially private query results from genomic datasets including dependent tuples
Authors: Almadhoun Nour , Ayday Erman , Ulusoy Özgür 
Published: May 2020

Abstract

Motivation
The rapid decrease in the sequencing technology costs leads to a revolution in medical research and clinical care. Today, researchers have access to large genomic datasets to study associations between variants and complex traits. However, availability of such genomic datasets also results in new privacy concerns about personal information of the participants in genomic studies. Differential privacy (DP) is one of the rigorous privacy concepts, which received widespread interest for sharing summary statistics from genomic datasets while protecting the privacy of participants against inference attacks. However, DP has a known drawback as it does not consider the correlation between dataset tuples. Therefore, privacy guarantees of DP-based mechanisms may degrade if the dataset includes dependent tuples, which is a common situation for genomic datasets due to the inherent correlations between genomes of family members.

Results
In this article, using two real-life genomic datasets, we show that exploiting the correlation between the dataset participants results in significant information leak from differentially private results of complex queries. We formulate this as an attribute inference attack and show the privacy loss in minor allele frequency (MAF) and chi-square queries. Our results show that using the results of differentially private MAF queries and utilizing the dependency between tuples, an adversary can reveal up to 50% more sensitive information about the genome of a target (compared to original privacy guarantees of standard DP-based mechanisms), while differentially privacy chi-square queries can reveal up to 40% more sensitive information. Furthermore, we show that the adversary can use the inferred genomic data obtained from the attribute inference attack to infer the membership of a target in another genomic dataset (e.g. associated with a sensitive trait). Using a log-likelihood-ratio test, our results also show that the inference power of the adversary can be significantly high in such an attack even using inferred (and hence partially incorrect) genomes.

Availability and implementation
https://github.com/nourmadhoun/Inference-Attacks-Differential-Privacy
On specification tests for composite likelihood inference
Authors: Huang Jing , Ning Yang , Reid Nancy , Chen Yong 
Published: May 2020

SummaryComposite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite likelihood inference. The test statistics are motivated by the fact that the second Bartlett identity holds for each component of the composite likelihood function when these components are correctly specified. We construct the test statistics based on the discrepancy between the so-called composite information matrix and the sensitivity matrix. As an illustration, we study three important cases of the proposed tests and establish their limiting distributions under both null and local alternative hypotheses. Finally, we evaluate the finite-sample performance of the proposed tests in several examples.
Individualized Treatment of Multidrug-resistant Tuberculosis Using Whole-Genome Sequencing and Expanded Drug-Susceptibility Testing
Authors: Dookie Navisha , Padayatchi Nesri , Lessells Richard J , Naicker Cherise L , Chotoo Sunitha , Naidoo Kogieleum 
Published: May 2020

Abstract
A case of multidrug-resistant tuberculosis is presented. It highlights the role of whole-genome sequencing, expanded phenotypic drug susceptibility testing, and enhanced case management, offering a more complete understanding of drug susceptibility to Mycobacterium tuberculosis. This approach guides an effective individualized treatment strategy that results in rapid sustained culture conversion.