bluuebunny/crossref_metadata_2025_split huggingface dataset:
One-pot phytosynthesis of nano-silver from Mentha longifolia L.: their characterization and evaluation of photodynamic potential
Authors: Javed Bilal , Nadhman Akhtar , Razzaq Abdul , Mashwani Zia-ur-Rehman 
Published: May 2020

Abstract
The present study deals with the ecofriendly one-pot synthesis and stabilization of silver nanoparticles (AgNPs) by using aqueous extract of Mentha longifolia branches. Spectrophotometric analysis of different ratios of reactants revealed that a 1 to 9 ratio of plant extract and silver salt solution respectively is the most suitable proportion for synthesis. Synthesis of AgNPs was confirmed initially by the observation of change in the color of the reaction mixture which was carried out at 60 °C by using 3 mM of silver salt and the pH of the reaction medium was maintained at 5.22. A characteristic surface plasmon resonance (SPR) band was observed at 495 nm of light wavelength. SEM images revealed that the nanoparticles are in ∼20–80 nm and are anisotropic and nearly spherical while EDX analysis showed the presence of elemental Ag with ∼90% signal intensity. Size distribution analysis of AgNPs was performed by dynamic light scattering technique and AgNPs were found in the range of ∼8–30 nm. ROS quantification revealed that the AgNPs have a quantum yield of 0.09 Φ which provides them the ability to proteolytically treat cancer and other microbial pathogenic cells. AgNPs did not report any photothermal activity to be used as photodynamic agents. These findings explain the redox potential of M. longifolia to bio-fabricate AgNPs and their abilities to generate ROS may help to curb dreading diseases.
Finite element analysis of the effect of porosity on residual stress in 2024 aluminium alloy GTAW
Authors: Poolperm Pattarawadee , Nakkiew Wasawat , Naksuk Nirut 
Published: May 2020

Abstract
Simulations of the welding process for butt joints using finite element analysis (FEA) of the effect of porosity are presented. The metal used was aluminium alloy (grade 2024), and the filler material was alloy ER5356. The simulations were performed using the commercial software ANSYS, considering a double ellipsoid heat source, temperature-dependent material properties, material deposits, mechanical analysis, transient heat transfer, and defects (porosity). In this study, the FEA simulations were constructed for two types of heat source (single- and double-ellipsoid) used in gas tungsten arc welding (GTAW), and the calculated residual stress results were compared with the experimental values. Two double ellipsoid models were constructed for cases with and without porosity. The porosity was measured by three-dimensional (3D) computed tomography (CT), and the size and location of pores were mapped onto the weld bead created by the birth-and-death technique.
Equivalence between Scalar-Tensor theories and f(R)-gravity: from the action to cosmological perturbations
Authors: Velásquez Joel , Castañeda Leonardo 
Published: May 2020

Abstract
In this paper we calculate the field equations for Scalar-Tensor from a variational principle, taking into account the Gibbons-York-Hawking type boundary term. We do the same for the theories f(R), following (Guarnizo (2010), Gen. Rel. Grav.
42, 2713–2728). Then, we review the equivalences between both theories in the metric formalism. Thus, starting from the perturbations for Scalar-Tensor theories, we find the perturbations for f(R) gravity under the equivalences. Working with two specific models of f(R), we explore the equivalences between the theories under conformal-Newtonian gauge. Further, we show the perturbations for both theories under the sub-horizon approach.
Reference genome and annotation updates lead to contradictory prognostic predictions in gene expression signatures: a case study of resected stage I lung adenocarcinoma
Authors: Zhang Zheyang , Zhang Sainan , Li Xin , Zhao Zhangxiang , Chen Changjing , Zhang Juxuan , Li Mengyue , Wei Zixin , Jiang Wenbin , Pan Bo , Li Ying , Liu Yixin , Cao Yingyue , Zhao Wenyuan , Gu Yunyan , Yu Yan , Meng Qingwei , Qi Lishuang 
Published: May 2020

Abstract
RNA-sequencing enables accurate and low-cost transcriptome-wide detection. However, expression estimates vary as reference genomes and gene annotations are updated, confounding existing expression-based prognostic signatures. Herein, prognostic 9-gene pair signature (GPS) was applied to 197 patients with stage I lung adenocarcinoma derived from previous and latest data from The Cancer Genome Atlas (TCGA) processed with different reference genomes and annotations. For 9-GPS, 6.6% of patients exhibited discordant risk classifications between the two TCGA versions. Similar results were observed for other prognostic signatures, including IRGPI, 15-gene and ORACLE. We found that conflicting annotations for gene length and overlap were the major cause of their discordant risk classification. Therefore, we constructed a prognostic 40-GPS based on stable genes across GENCODE v20-v30 and validated it using public data of 471 stage I samples (log-rank P &lt; 0.0010). Risk classification was still stable in RNA-sequencing data processed with the newest GENCODE v32 versus GENCODE v20–v30. Specifically, 40-GPS could predict survival for 30 stage I samples with formalin-fixed paraffin-embedded tissues (log-rank P = 0.0177). In conclusion, this method overcomes the vulnerability of existing prognostic signatures due to reference genome and annotation updates. 40-GPS may offer individualized clinical applications due to its prognostic accuracy and classification stability.
Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations
Authors: Guo Dan , Föll Melanie Christine , Volkmann Veronika , Enderle-Ammour Kathrin , Bronsert Peter , Schilling Oliver , Vitek Olga 
Published: May 2020

Abstract

Motivation
Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance.

Results
To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states.

Availability and implementation
The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.
MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function
Authors: Shen Zeyang , Hoeksema Marten A , Ouyang Zhengyu , Benner Christopher , Glass Christopher K 
Published: May 2020

Abstract

Motivation
Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which motifs are prone to impact transcriptional regulation if mutated. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects.

Results
We present MAGGIE (Motif Alteration Genome-wide to Globally Investigate Elements), a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutations of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared with the state-of-the-art motif analysis approaches. We use MAGGIE to gain novel insights into the divergent functions of distinct NF-κB factors in pro-inflammatory macrophages, revealing the association of p65–p50 co-binding with transcriptional activation and the association of p50 binding lacking p65 with transcriptional repression.

Availability and implementation
The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie. The accession number for the NF-κB ChIP-seq data generated for this study is Gene Expression Omnibus: GSE144070.

Supplementary information
Supplementary data are available at Bioinformatics online.
IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data
Authors: Rahman Mohammad Arifur , Rangwala Huzefa 
Published: May 2020

Abstract

Motivation
The human body hosts more microbial organisms than human cells. Analysis of this microbial diversity provides key insight into the role played by these microorganisms on human health. Metagenomics is the collective DNA sequencing of coexisting microbial organisms in an environmental sample or a host. This has several applications in precision medicine, agriculture, environmental science and forensics. State-of-the-art predictive models for phenotype predictions from metagenomic data rely on alignments, assembly, extensive pruning, taxonomic profiling and reference sequence databases. These processes are time consuming and they do not consider novel microbial sequences when aligned with the reference genome, limiting the potential of whole metagenomics. We formulate the problem of predicting human disease from whole-metagenomic data using Multiple Instance Learning (MIL), a popular supervised learning paradigm. Our proposed alignment-free approach provides higher accuracy in prediction by harnessing the capability of deep convolutional neural network (CNN) within a MIL framework and provides interpretability via neural attention mechanism.

Results
The MIL formulation combined with the hierarchical feature extraction capability of deep-CNN provides significantly better predictive performance compared to popular existing approaches. The attention mechanism allows for the identification of groups of sequences that are likely to be correlated to diseases providing the much-needed interpretation. Our proposed approach does not rely on alignment, assembly and reference sequence databases; making it fast and scalable for large-scale metagenomic data. We evaluate our method on well-known large-scale metagenomic studies and show that our proposed approach outperforms comparative state-of-the-art methods for disease prediction.

Availability and implementation
https://github.com/mrahma23/IDMIL.
Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study
Authors: Seifert Stephan , Gundlach Sven , Junge Olaf , Szymczak Silke 
Published: May 2020

AbstractMotivationHigh-throughput technologies allow comprehensive characterization of individuals on many molecular levels. However, training computational models to predict disease status based on omics data is challenging. A promising solution is the integration of external knowledge about structural and functional relationships into the modeling process. We compared four published random forest-based approaches using two simulation studies and nine experimental datasets.ResultsThe self-sufficient prediction error approach should be applied when large numbers of relevant pathways are expected. The competing methods hunting and learner of functional enrichment should be used when low numbers of relevant pathways are expected or the most strongly associated pathways are of interest. The hybrid approach synthetic features is not recommended because of its high false discovery rate.Availability and implementationAn R package providing functions for data analysis and simulation is available at GitHub (https://github.com/szymczak-lab/PathwayGuidedRF). An accompanying R data package (https://github.com/szymczak-lab/DataPathwayGuidedRF) stores the processed and quality controlled experimental datasets downloaded from Gene Expression Omnibus (GEO).Supplementary informationSupplementary data are available at Bioinformatics online.
Marx, Say’s Law and Commodity Money
Authors: Trigg Andrew B 
Published: May 2020

Abstract
Under Marx’s critique of Say’s Law, as originally devised by Say and James Mill, money hoarding leads to a shortfall in aggregate demand. This paper responds to a Post Keynesian argument that hoarding does not restrict aggregate demand since for Marx money consists of a produced commodity, and hoarding is just one form of commodity demand. Drawing on Marx’s monetary writings, a new monetary equilibrium is suggested in which produced gold is used to replace wear and tear in circulation. An alternative critique of Say’s Law is thus proposed as a contribution to understanding the complexity of Marx’s monetary foundations.
A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function
Authors: Holliday Gemma L , Brown Shoshana D , Mischel David , Polacco Benjamin J , Babbitt Patricia C 
Published: May 2020

Abstract
Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how’ these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).
Dynamics of the Stool Virome in Very Early-Onset Inflammatory Bowel Disease
Authors: Liang Guanxiang , Conrad Maire A , Kelsen Judith R , Kessler Lyanna R , Breton Jessica , Albenberg Lindsey G , Marakos Sarah , Galgano Alissa , Devas Nina , Erlichman Jessi , Zhang Huanjia , Mattei Lisa , Bittinger Kyle , Baldassano Robert N , Bushman Frederic D 
Published: May 2020

Abstract

Background and Aims
Dysbiosis of the gut microbiota is a well-known correlate of the pathogenesis of inflammatory bowel disease [IBD]. However, few studies have examined the microbiome in very early-onset [VEO] IBD, which is defined as onset of IBD before 6 years of age. Here we focus on the viral portion of the microbiome—the virome—to assess possible viral associations with disease processes, reasoning that any viruses potentially associated with IBD might grow more robustly in younger subjects, and so be more detectable.

Methods
Virus-like particles [VLPs] were purified from stool samples collected from patients with VEO-IBD [n = 54] and healthy controls [n = 23], and characterized by DNA and RNA sequencing and VLP particle counts.

Results
The total number of VLPs was not significantly different between VEO-IBD and healthy controls. For bacterial viruses, the VEO-IBD subjects were found to have a higher ratio of Caudovirales vs to Microviridae compared to healthy controls. An increase in Caudovirales was also associated with immunosuppressive therapy. For viruses infecting human cells, Anelloviridae showed higher prevalence in VEO-IBD compared to healthy controls. Within the VEO-IBD group, higher levels of Anelloviridae DNA were also positively associated with immunosuppressive treatment. To search for new viruses, short sequences enriched in VEO-IBD samples were identified, and some could be validated in an independent cohort, although none was clearly viral; this provides sequence tags to interrogate in future studies.

Conclusions
These data thus document perturbations to normal viral populations associated with VEO-IBD, and provide a biomarker—Anelloviridae DNA levels—potentially useful for reporting the effectiveness of immunosuppression.
Hierarchical Effects of Tamarix aphylla Afforestation in a Sand Dune Environment on Vegetation Structure and Plant Diversity
Authors: Katz Ofir , Stavi Ilan 
Published: May 2020

AbstractOne method of controlling dune encroachment is afforestation, which may result in biodiversity loss because of habitat change and adverse effects of trees on ecosystem functioning. We carried out a study on the effects of planting discrete areas with Tamarix aphylla (L.) Karsten trees, over 50 years ago, in a semiarid dunefield in the northern Negev, Israel. We surveyed the vegetation and sampled litter and soil in five microhabitats formed by this afforestation scheme. Afforestation had spatially and functionally hierarchical effects on vegetation and plant diversity. The strongest effect was associated with land-use change, showing reduced species richness (by 30–50 percent) and litter production following afforestation. The second strongest effect was associated with salt accumulation in T. aphylla leaves and litter, which increases soil salinity under T. aphylla canopies by 4–5, forming “salinity islands,” and leading to over 30 percent decrease in plant cover and aboveground biomass. The assumed effect of trees in blocking solar irradiance and wind was observed only outside canopies and had a weak impact on plant cover. Therefore, afforestation can increase plant diversity at the regional scale, but at the local (microhabitat) scale it has an overall adverse effect on measured ecosystem functions.
Association of Epigenetic Age and p16
INK4a With Markers of T-Cell Composition in a Healthy Cohort
Authors: Burd Christin E , Peng Juan , Laskowski Bryon F , Hollyfield Jennifer L , Zhang Suohui , Fadda Paolo , Yu Lianbo , Andridge Rebecca R , Kiecolt-Glaser Janice K 
Published: May 2020

Abstract
How the measurement of aging biomarkers in peripheral blood T-lymphocytes (PBTLs) is influenced by cell composition is unclear. Here, we collected peripheral blood and isolated CD3+ PBTLs from 117 healthy couples between the ages of 21 and 72. Each sample was profiled for Horvath epigenetic clock (DNAm), p16INK4a expression, cytomegalovirus (CMV) seropositivity and 74 mRNA markers of PBTL subtype, differentiation, immune checkpoints, and cytokine production. Correlations between individual aging biomarkers (DNAm or p16INK4a) and PBTL mRNAs were corrected for chronological age, sex, and couple. DNAm measurements correlated with CMV seropositivity as well as PBTL mRNAs indicative of effector function (CD8A, EOMES, TBX21, GZMB), poor proliferative capacity (KLRG1, CD57), differentiation (CD45RO, CD45RA), and immune checkpoints (PDCD1, TIGIT, LAG3, CD160, CD244). In contrast, only three PBTL mRNAs, CD28, CD244, and p14ARF, showed a significant association with p16INK4a. p16INK4a expression also showed a weaker association with immunosenescent PBTL subsets than DNAm in flow cytometry analyses. These data suggest that PBTL composition has a greater influence on DNAm than p16INK4a and link accelerated epigenetic aging to immunosenescent phenotypes.
A Metabolite Composite Score Attenuated a Substantial Portion of the Higher Mortality Risk Associated With Frailty Among Community-Dwelling Older Adults
Authors: Marron Megan M , Harris Tamara B , Boudreau Robert M , Clish Clary B , Moore Steven C , Murphy Rachel A , Murthy Venkatesh L , Sanders Jason L , Shah Ravi V , Tseng George C , Wendell Stacy G , Zmuda Joseph M , Newman Anne B 
Published: May 2020

Abstract

Background
Frailty is more prevalent among black versus white older Americans. We previously identified 37 metabolites associated with the vigor to frailty spectrum using the Scale of Aging Vigor in Epidemiology (SAVE) among older black men from the Health, Aging, and Body Composition (Health ABC) study. Here, we sought to develop a metabolite composite score based on the 37 SAVE-associated metabolites and determine whether the composite score predicts mortality and whether it attenuates the association between frailty and mortality among older black men.

Methods
Plasma metabolites were measured using liquid chromatography–mass spectrometry. Most of the 37 metabolites were organic acids/derivatives or lipids. Metabolites were ranked into tertiles: tertiles associated with more vigorous SAVE scores were scored 0, mid-tertiles were scored 1, and tertiles associated with frailer SAVE scores were scored 2. Composite scores were the sum of metabolite tertile scores. We examined mortality associations using Cox regression. Percent attenuation estimated the extent to which metabolites attenuated the association between frailty and mortality.

Results
One standard deviation frailer SAVE was associated with 30% higher mortality, adjusting for age and site (p = .0002); this association was attenuated by 56% after additionally adjusting for the metabolite composite score. In this model, one standard deviation higher metabolite composite score was associated with 46% higher mortality (p &lt; .0001). Metabolite composite scores also predicted mortality (p = .045) in a validation sample of 120 older adults (40% men, 90% white).

Conclusion
These metabolites may provide a deeper characterization of the higher mortality that is associated with frailty among older adults.