bluuebunny/crossref_metadata_2025_split huggingface dataset:
Behavioural determinants of adaptation of farmers to climate change in rice cultivation in the mountainous area of Vietnam
Authors: Ho-Xuan Huong , Hai Le Dinh , Huyen Nguyen Thi Thanh , Dung Bui Tri , Hang Pham Thi 
Published: June 2024

Abstract
Climate change adaptation is one of the essential solutions for sustainable agricultural development in the context of global change. Besides socio-economical drivers, a thorough understanding of the behavioural dimension of climate change adaptation will provide important information for policymakers in promoting adaptation activities and contributing to the improvement of people’s livelihoods. The purpose of this study is to explore the knowledge gaps in understanding behavioural factors that affect adaptive intention in rice cultivation in the mountainous area of Vietnam. This research was carried out through a sociological survey of 254 rice-farming households in Da Bac districts, Hoa Binh province. Based on applying the Extended Theory of Planned Behaviour (E-TPB) and the data analysis using the PLS-SEM model, the research pointed out that the perceived impact of climate change and policy support are the most important determinants that affect the adaptation intention in rice cultivation. The research also discovered that the Extended Theory of Planned Behaviour explained 56.8% of the change in adaptive intention, much more than 11.9% in comparison with the basic TPB theory. The research emphasized the crucial role of policy support and raising awareness of climate change for farmers to promote the implementation of adaptive measures.
Characteristics and distribution of greenhouse gas emissions from motorcycles in Hanoi, Vietnam
Authors: Tran Hien , Nghiem Trung Dung , Hoang Vu Khue Ngoc , Quoc Ho Bang 
Published: June 2024

Abstract
Motorcycles contribute to the notable emission of greenhouse gases (GHG) and air pollutants in urban areas. These vehicles are the most widely used means of road transportation in Vietnam. This study is the first to use the EMISENS model in combination with the IVE modelling system to calculate greenhouse gas emissions from motorcycles at tier 3 in IPCC guideline. There are more than 8700 motorcycles circulating on the roads in Hanoi per hour. With the characteristics of motorcycles compiled, the average age of motorcycles is around 11 years, and each vehicle travels an average of 16.4 km per day with an average start-up frequency of 3.46. The emission factors of greenhouse gases CO2 and CH4 when running (erunning) were 72.55 and 0.24 grams per km, respectively. The emission factors for CO2, CH4 and N2O during vehicle start-up (estart-up) were 6.96, 0.53 and 0.0009 grams per start-up, respectively. Inventory emission data from 2021-2023 reveal that that motorcycles in Hanoi emitted nearly 4.6 mil tons of CO2e per year. Emission distribution is concentrated in inner-city districts and some residential areas at the intersections with provincial roads, national highways, and industrial parks.
Integration of acoustic micromixing with cyclic olefin copolymer microfluidics for enhanced lab-on-a-chip applications in nanoscale liposome synthesis
Authors: Agha Abdulrahman , Abu-Nada Eiyad , Alazzam Anas 
Published: June 2024

Abstract
The integration of acoustic wave micromixing with microfluidic systems holds great potential for applications in biomedicine and lab-on-a-chip technologies. Polymers such as cyclic olefin copolymer (COC) are increasingly utilized in microfluidic applications due to its unique properties, low cost, and versatile fabrication methods, and incorporating them into acoustofluidics significantly expands their potential applications. In this work, for the first time, we demonstrated the integration of polymer microfluidics with acoustic micromixing utilizing oscillating sharp edge structures to homogenize flowing fluids. The sharp edge mixing platform was entirely composed of COC fabricated in a COC-hydrocarbon solvent swelling based microfabrication process. As an electrical signal is applied to a piezoelectric transducer bonded to the micromixer, the sharp edges start to oscillate generating vortices at its tip, mixing the fluids. A 2D numerical model was implemented to determine the optimum microchannel dimensions for experimental mixing assessment. The system was shown to successfully mix fluids at flow rates up to 150 µl h−1 and has a modest effect even at the highest tested flow rate of 600 µl h−1. The utility of the fabricated sharp edge micromixer was demonstrated by the synthesis of nanoscale liposomes.
Optothermal generation, steady-state trapping, and 3D manipulation of bubbles: an experimental and theoretical analysis of the Marangoni effect
Authors: Sarabia-Alonso Julio Aurelio , Ramos-García Rubén 
Published: June 2024

Abstract
Since Nobel Laureate Arthur Ashkin first introduced the trapping and manipulation of microparticles using light, numerous studies have explored this technique not only for dielectric/metallic particles but also for organic matter. This advancement has significantly expanded the landscape of non-contact and non-invasive micromanipulation at the nanometric scale. However, micromanipulation of particles with a refractive index smaller than the host medium, n
p
< n
m, proves challenging with Gaussian beams. To overcome this obstacle, a force known as thermocapillary, or the Marangoni force, has emerged as a straightforward trapping mechanism for bubbles in liquids. The Marangoni force results from the surface tension of bubbles, induced either thermally or chemically—by creating a temperature gradient or adding surfactants, respectively. The surface tension gradient on the liquid host induces tangential stress on the bubble wall, causing the bubble to move toward the region of lower surface tension, where it faces less opposing force. When the Marangoni force is generated by a laser beam’s temperature gradient, it becomes an exceptionally effective mechanism for the steady-state trapping and three-dimensional manipulation of bubbles, even with low optical power lasers. This force produces both longitudinal and transversal forces, resembling optical forces, creating a three-dimensional potential well capable of handling bubbles with radii of tens to hundreds of microns. This work provides guidance and demonstrates, both experimentally and theoretically, the step-by-step process of quasi-steady-state trapping and three-dimensional manipulation of bubbles through optothermal effects. The bubbles in question are tens of microns in size, significantly larger than those that optical tweezers can trap/manipulate. Furthermore, the study emphasizes the crucial role of the Marangoni force in this process, outlining its various advantages.
Effects of transparent conducting electrodes and hole transport layers on the performance of MAPbI3 solar cells fabricated on PET substrates
Authors: Bhandari Bishal , Bonner Justin C , Piper Robert T , Hsu Julia W P 
Published: June 2024

Abstract
This study investigates how the performance of perovskite solar cells (PSCs) made on polyethylene terephthalate (PET) substrates depends on transparent conducting electrodes (TCEs) and hole transport layers (HTLs). We fabricated PSCs using commercially available PET/TCEs and compared their performance with PSCs manufactured on Glass/indium tin oxide (ITO) substrates. Poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) with varying levels of acidity and NiO nanoparticles were used as HTLs. The current density-voltage characteristics of PSCs made on PET/TCEs were found to be significantly lower when highly acidic PEDOT:PSS was used as the HTL. However, this was not observed for PSCs made on Glass/ITO. To investigate the interaction between HTL and TCE, atomic force microscopy was carried out after dipping the TCEs in PEDOT:PSS solutions of different acidity. X-ray photoelectron spectroscopy measurements further revealed differences in the chemical composition between ITO film on PET vs. on glass. Our results indicate that the performance of PSCs depends both on the TCE substrates and HTLs, which can be explained by their chemical interaction.
Stuff and Structure
Authors: Strawson Galen 
Published: June 2024

AbstractChapter 7 considers the relation between stuff and structure. It rejects all-out ontic structural realism. It suggests that just as structural difference entails stuff difference, so stuff difference entails structural difference: no two concrete entities can be [i] identical under all possible true structural descriptions and [ii] different in their stuff. It assesses this claim within a given world or universe with a given dimensionality or Existenzraum (Chapter 4), and across different worlds or universes. As regards the existence of relations: deploying the general notion of dimensionality presented in Chapter 2, it argues that concrete being entails relationality: for [1] being entails quality, [2] quality entails (positive) quantity, [3] quantity entails dimensionality or Existenzraum, [4] dimensionality entails position* and difference of position*, [5] quantity entails actual occupation* of different positions*, [6] actual occupation* of different positions* entails (concrete) relationality. It concludes with a brief discussion of Frank Jackson’s views.
A cloud-based training module for efficient de novo transcriptome assembly using Nextflow and Google cloud
Authors: Seaman Ryan P , Campbell Ross , Doe Valena , Yosufzai Zelaikha , Graber Joel H 
Published: June 2024

Abstract
This study describes the development of a resource module that is part of a learning platform named “NIGMS Sandbox for Cloud-based Learning” (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications.
A customizable secure DIY web application for accessing, sharing, and browsing aggregate experimental results and metadata
Authors: Lee Jaewoo , Achuthan Mehita , Chen Lucas , Carmona-Mora Paulina 
Published: June 2024

Abstract

Summary
A problem spanning across many research fields is that processed data and research results are often scattered, which makes data access, analysis, extraction, and team sharing more challenging. We have developed a platform for researchers to easily manage tabular data with features like browsing, bookmarking, and linking to external open knowledge bases. The source code, originally designed for genomics research, is customizable for use by other fields or data, providing a no- to low-cost DIY system for research teams.

Availability and implementation
The source code of our DIY app is available on https://github.com/Carmona-MoraUCD/Human-Genomics-Browser. It can be downloaded and run by anyone with a web browser, Python3, and Node.js on their machine. The web application is licensed under the MIT license.
SpecEncoder: deep metric learning for accurate peptide identification in proteomics
Authors: Liu Kaiyuan , Tao Chenghua , Ye Yuzhen , Tang Haixu 
Published: June 2024

Abstract

Motivation
Tandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification.

Results
We evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%–2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%–15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%–12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder’s potential to enhance peptide identification for proteomic data analyses.

Availability and Implementation
The source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu.
Reference-free structural variant detection in microbiomes via long-read co-assembly graphs
Authors: Curry Kristen D , Yu Feiqiao Brian , Vance Summer E , Segarra Santiago , Bhaya Devaki , Chikhi Rayan , Rocha Eduardo P C , Treangen Todd J 
Published: June 2024

AbstractMotivation: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining.Results: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux.Availability and implementation: rhea is open source and available at: https://github.com/treangenlab/rhea.
Fast multiple sequence alignment via multi-armed bandits
Authors: Mazooji Kayvon , Shomorony Ilan 
Published: June 2024

Abstract

Summary
Multiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences.

Availability and implementation
The code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA.
Label-guided seed-chain-extend alignment on annotated De Bruijn graphs
Authors: Mustafa Harun , Karasikov Mikhail , Mansouri Ghiasi Nika , Rätsch Gunnar , Kahles André 
Published: June 2024

Abstract

Motivation
Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with sample IDs. Low-depth sequencing samples correspond to fragmented subgraphs, complicating finding the long contiguous walks required for alignment queries. Aligners that target single-labelled subgraphs reduce alignment lengths due to fragmentation, leading to low recall for long reads. While some (e.g. label-free) aligners partially overcome fragmentation by combining information from multiple samples, biologically irrelevant combinations in such approaches can inflate the search space or reduce accuracy.

Results
We introduce a new scoring model, ‘multi-label alignment’ (MLA), for annotated DBGs. MLA leverages two new operations: To promote biologically relevant sample combinations, ‘Label Change’ incorporates more informative global sample similarity into local scores. To improve connectivity, ‘Node Length Change’ dynamically adjusts the DBG node length during traversal. Our fast, approximate, yet accurate MLA implementation has two key steps: a single-label seed-chain-extend aligner (SCA) and a multi-label chainer (MLC). SCA uses a traditional scoring model adapting recent chaining improvements to assembly graphs and provides a curated pool of alignments. MLC extracts seed anchors from SCAs alignments, produces multi-label chains using MLA scoring, then finally forms multi-label alignments. We show via substantial improvements in taxonomic classification accuracy that MLA produces biologically relevant alignments, decreasing average weighted UniFrac errors by 63.1%–66.8% and covering 45.5%–47.4% (median) more long-read query characters than state-of-the-art aligners. MLAs runtimes are competitive with label-combining alignment and substantially faster than single-label alignment.

Availability and implementation
The data, scripts, and instructions for generating our results are available at https://github.com/ratschlab/mla.
Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data
Authors: Sashittal Palash , Chen Viola , Pasarkar Amey , Raphael Benjamin J 
Published: June 2024

Abstract

Motivation
Eukaryotic cells contain organelles called mitochondria that have their own genome. Most cells contain thousands of mitochondria which replicate, even in nondividing cells, by means of a relatively error-prone process resulting in somatic mutations in their genome. Because of the higher mutation rate compared to the nuclear genome, mitochondrial mutations have been used to track cellular lineage, particularly using single-cell sequencing that measures mitochondrial mutations in individual cells. However, existing methods to infer the cell lineage tree from mitochondrial mutations do not model “heteroplasmy,” which is the presence of multiple mitochondrial clones with distinct sets of mutations in an individual cell. Single-cell sequencing data thus provide a mixture of the mitochondrial clones in individual cells, with the ancestral relationships between these clones described by a mitochondrial clone tree. While deconvolution of somatic mutations from a mixture of evolutionarily related genomes has been extensively studied in the context of bulk sequencing of cancer tumor samples, the problem of mitochondrial deconvolution has the additional constraint that the mitochondrial clone tree must be concordant with the cell lineage tree.

Results
We formalize the problem of inferring a concordant pair of a mitochondrial clone tree and a cell lineage tree from single-cell sequencing data as the Nested Perfect Phylogeny Mixture (NPPM) problem. We derive a combinatorial characterization of the solutions to the NPPM problem, and formulate an algorithm, MERLIN, to solve this problem exactly using a mixed integer linear program. We show on simulated data that MERLIN outperforms existing methods that do not model mitochondrial heteroplasmy nor the concordance between the mitochondrial clone tree and the cell lineage tree. We use MERLIN to analyze single-cell whole-genome sequencing data of 5220 cells of a gastric cancer cell line and show that MERLIN infers a more biologically plausible cell lineage tree and mitochondrial clone tree compared to existing methods.

Availability and implementation
https://github.com/raphael-group/MERLIN.
Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions
Authors: Platt Daniel , Bose Aritra , Rhrissorrakrai Kahn , Levovitz Chaya , Parida Laxmi 
Published: June 2024

AbstractMotivationThe emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA).ResultsHere, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with C19, as well as modification of risk factor impact by hyperlipidemia (HL) on severe C19. RTDA found higher-order relationships between RAAS pathway and severe C19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, HL, chronic kidney failure, and disproportionately affecting Black individuals. RTDA combined with CuNA (cumulant-based network analysis) yielded a higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and HL, of patients with severe bouts of C19. Where single variable association tests with outcome can struggle, TDA’s higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as C19 work in concert.Availability and ImplementationCode for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/.Supplementary InformationSupplementary data are available at Bioinformatics online.