bluuebunny/crossref_metadata_2025_split huggingface dataset:
Building community resilience to extreme heat: Lessons learned from Spokane, WA community conversations
Authors: Reed A , Henning B , Cortes Espinosa M , McKinley H , Busch Isaksen T 
Published: September 2024

Abstract
Heat is the primary cause of weather-related mortality in the United States. The 2021 Northwest heat dome highlighted this susceptibility. In Washington State, 159 excess deaths were attributed to the 7-day period of unprecedented extreme heat between June 26th and July 2nd. This impact was felt even in some of the more acclimatized parts of the state, like Spokane County, where 19 heat-related deaths were reported. As climate change increases the frequency, duration, and intensity of extreme heat events, creating and sustaining heat-resilient communities has become an urgent public health priority. On 6 June 2023, Gonzaga University, in partnership with the University of Washington, hosted the Spokane Extreme Heat Risk Intervention Stakeholder Synthesis Symposium. The goals of the symposium were to debrief from recent heat events, identify extreme heat risk reduction interventions used in the region, and characterize remaining practice-relevant research priorities. The symposium convened 45 stakeholders including representatives from local and state agencies, academia, and community-based and Tribal organizations. Symposium participants engaged in small group discussions using the World Café MethodTM. Notes from each discussion were coded using a content analysis approach. Symposium participants identified strengths, barriers to heat resilience, and solutions to reduce risk throughout the Spokane community. We present these findings by practice topic, including risk communication, intervention, collaboration, policy, and research. Additionally, we utilize the socio-ecological model as a conceptual framework to illustrate the complex interplay of factors that govern an individual’s experience of, and ability to respond to, extreme heat events. Given extreme heat’s impact on global public health, the methods used to increase community resilience in Spokane, WA, USA could be used by other communities worldwide to increase their own heat-resilience.
Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies
Authors: Liu Xin , Pang Yu , Shan Junqi , Wang Yunfei , Zheng Yanhua , Xue Yuhang , Zhou Xuerong , Wang Wenjun , Sun Yanlai , Yan Xiaojing , Shi Jiantao , Wang Xiaoxue , Gu Hongcang , Zhang Fan 
Published: September 2024

Abstract
Deoxyribonucleic acid (DNA) methylation plays a key role in gene regulation and is critical for development and human disease. Techniques such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) allow DNA methylation analysis at the genome scale, with Illumina NovaSeq 6000 and MGI Tech DNBSEQ-T7 being popular due to their efficiency and affordability. However, detailed comparative studies of their performance are not available. In this study, we constructed 60 WGBS and RRBS libraries for two platforms using different types of clinical samples and generated approximately 2.8 terabases of sequencing data. We systematically compared quality control metrics, genomic coverage, CpG methylation levels, intra- and interplatform correlations, and performance in detecting differentially methylated positions. Our results revealed that the DNBSEQ platform exhibited better raw read quality, although base quality recalibration indicated potential overestimation of base quality. The DNBSEQ platform also showed lower sequencing depth and less coverage uniformity in GC-rich regions than did the NovaSeq platform and tended to enrich methylated regions. Overall, both platforms demonstrated robust intra- and interplatform reproducibility for RRBS and WGBS, with NovaSeq performing better for WGBS, highlighting the importance of considering these factors when selecting a platform for bisulfite sequencing.
Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA–gene–disease networks
Authors: Si Yuxuan , Huang Zihan , Fang Zhengqing , Yuan Zhouhang , Huang Zhengxing , Li Yingming , Wei Ying , Wu Fei , Yao Yu-Feng 
Published: September 2024

Abstract
Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA–disease associations (MDAs), miRNA–target interactions (MTIs), and disease–gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.
Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides
Authors: Wang Qian , Hu Xiaotong , Wei Zhiqiang , Lu Hao , Liu Hao 
Published: September 2024

AbstractUsing amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning’s strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\%$, thereby significantly accelerating the process of drug-like peptide generation.
BenchStab: a tool for automated querying of web-based stability predictors
Authors: Velecký Jan , Berezný Matej , Musil Milos , Damborsky Jiri , Bednar David , Mazurenko Stanislav 
Published: September 2024

Abstract

Summary
Protein design requires information about how mutations affect protein stability. Many web-based predictors are available for this purpose, yet comparing them or using them en masse is difficult. Here, we present BenchStab, a console tool/Python package for easy and quick execution of 19 predictors and result collection on a list of mutants. Moreover, the tool is easily extensible with additional predictors. We created an independent dataset derived from the FireProtDB and evaluated 24 different prediction methods.

Availability and implementation
BenchStab is an open-source Python package available at https://github.com/loschmidt/BenchStab with a detailed README and example usage at https://loschmidt.chemi.muni.cz/benchstab. The BenchStab dataset is available on Zenodo: https://zenodo.org/records/10637728
mgikit: demultiplexing toolkit for MGI fastq files
Authors: Al Bkhetan Ziad , Wang Sen 
Published: September 2024

Abstract

Summary
MGI sequencing is reported to be an inexpensive solution to obtain genomics information. There is a growing need for software and tools to analyse MGI’s outputs efficiently. mgikit is a tool collection to demultiplex MGI fastq data, reformat it effectively and produce visual quality reports. mgikit overcomes several limitations of the standard MGI demultiplexer. It is highly customizable to suit different kinds of datasets and is designed to achieve high performance and optimal memory utilization.

Availability and implementation
The tool and its documentation are available at: https://sagc-bioinformatics.github.io/mgikit/.
Dual-energy CT: Impact of detecting bone marrow oedema in occult trauma in the Emergency
Authors: Ahmad Muhammad Israr , Liu Lulu , Sheikh Adnan , Nicolaou Savvas 
Published: September 2024

Abstract
Dual-energy computed tomography (DECT) is an advanced imaging technique that acquires data using two distinct X-ray energy spectra, typically at 80 and 140 kVp, to differentiate materials based on their atomic number and electron density. This capability allows for the enhanced visualisation of various pathologies, including bone marrow oedema (BMO), by providing high-resolution images with notable energy spectral separation while maintaining radiation doses comparable to conventional CT. DECT’s ability to create colour-coded virtual non-calcium (VNCa) images has proven particularly valuable in detecting traumatic bone marrow lesions (BMLs) and subtle fractures, offering a reliable alternative or complement to MRI. DECT has emerged as a significant tool in the detection and characterisation of bone marrow pathologies, especially in traumatic injuries. Its ability to generate high-resolution images and distinguish between different tissue types makes it a valuable asset in clinical diagnostics. With its comparable diagnostic accuracy to MRI and the added advantage of reduced examination time and increased availability, DECT represents a promising advancement in the imaging of BMO and related conditions.
Complicating the Role of Relationships in Reducing Student Absenteeism
Authors: Stokes Kimberly L , Lenhoff Sarah Winchell , Singer Jeremy 
Published: September 2024

Abstract
A strong connection between schools and families is vital for student success and well-being. Previous studies on school–family relationships found that open communication between parties may aid in supporting students’ needs and ability to attend school. In this paper, authors analyze how leveraging relationships to gain an increased understanding of a family’s attendance barriers is more beneficial than relationships built on accountability. From interviews with 38 caregivers, 29 students, and 40 staff members in an urban school district with high rates of chronic absenteeism, authors found that efforts to build relationships with families focused on accountability and caregiver education about their children’s attendance and the importance of attending school. Families sometimes perceived these efforts as superficial and ineffective. Authors discuss how school districts and key school personnel, such as social workers, can intentionally use the power of relationships to identify student and family needs and connect them with resources.
Liver-targeted Angptl4 silencing by antisense oligonucleotide treatment attenuates hyperlipidaemia and atherosclerosis development in APOE*3-Leiden.CETP mice
Authors: Modder Melanie , het Panhuis Wietse In , Li Mohan , Afkir Salwa , Dorn Alexandra L , Pronk Amanda C M , Streefland Trea C M , Lalai Reshma A , Pierrou Stefan , Nilsson Stefan K , Olivecrona Gunilla , Kooijman Sander , Rensen Patrick C N , Schönke Milena 
Published: September 2024

Abstract

Aims
Angiopoietin-like 3 (ANGPTL3) and 4 (ANGPTL4) inhibit lipoprotein lipase to regulate tissue fatty acid (FA) uptake from triglyceride (TG)-rich lipoproteins such as very low density lipoproteins (VLDL). While pharmacological inhibition of ANGPTL3 is being evaluated as a lipid-lowering strategy, systemic ANGPTL4 inhibition is not pursued due to adverse effects. This study aims to compare the therapeutic potential of liver-specific Angptl3 and Angptl4 silencing to attenuate hyperlipidemia and atherosclerosis development in APOE*3-Leiden.CETP mice, a well-established humanized model for lipoprotein metabolism.

Methods and results
Mice were subcutaneously injected twice per week with saline or liver-targeted antisense oligonucleotides against Angptl3, Angptl4, both, or a scrambled oligonucleotide. Plasma lipid levels, VLDL clearance, and hepatic VLDL production were determined, and atherosclerosis development was assessed. For toxicological evaluation, cynomolgus monkeys were treated with three dosages of liver-targeted ANGPTL4-silencing oligonucleotides. Liver-targeted Angptl4 silencing reduced plasma TGs (−48%) and total cholesterol (−56%), explained by higher VLDL-derived FA uptake by brown adipose tissue and lower VLDL production by the liver. Accordingly, Angptl4 silencing reduced atherosclerotic lesion size (−86%) and improved lesion stability. Hepatic Angptl3 silencing similarly attenuated hyperlipidemia and atherosclerosis development. While Angptl3 and Angptl4 silencing lowered plasma TGs in the refed and fasted state, respectively, combined Angptl3/4 silencing lowered plasma TGs independent of the nutritional state. In cynomolgus monkeys, anti-ANGPTL4 ASO treatment was well tolerated without adverse effects.

Conclusion
Liver-targeted Angptl4 silencing potently attenuates hyperlipidemia and atherosclerosis development in APOE*3-Leiden.CETP mice, and liver-targeted ANGPTL4 silencing is well tolerated in non-human primates. These data warrant further clinical development of liver-targeted ANGPTL4 silencing.
How mentoring and a shift in mindset and practices can help navigate fast-paced science
Authors: Bercht Anna Lena , Kamm Ruth 
Published: September 2024

Abstract
In the competitive realm of academia, the pursuit of meaningful knowledge often collides with the imperative to publish quickly and frequently, sidelining patient investigation and deep understanding. This article aims to share critical insights into this issue, sparking further discussions about the challenges of fast-paced science and cultivating a more ethical scientific landscape, one that values exploration and ingenuity over rushed output and flashy headlines. The narrative traces the growing disillusionment of the first author with the prevailing publish-or-perish culture and her concerns about its impact on research quality and genuine collaboration. The article showcases how a mentoring programme for women postdoctoral researchers and network activities have aided her in navigating these challenges and carving out her career path. Her experiences are enriched by the perspectives of the second author, the coordinator of the mentoring programme. Through one-on-one mentoring, get-togethers, and workshops, the first author gained the inspiration, skills, and encouragement necessary to rekindle her commitment to science. The lesson learned is the importance of providing scientists with a guided, safe, and brave space for open dialogue about individual concerns and general working conditions in academia. Examples are provided that illustrate potential strategies and practices for challenging the status quo. By nurturing an environment that puts quality before quantity, academia can pave the way for transformative discoveries and profound understanding across all career levels.
“I Flip, Therefore I Am”: Smartphone Detoxing as a Practice of Sovereignty
Authors: Markussen Håvard Rustad 
Published: September 2024

Abstract
This article theorizes smartphone detoxing as a practice of sovereignty. The article begins by arguing that the smartphone enables the exercise of psychopolitical control, a new mode of neoliberal governmentality under which individuals are governed through the algorithmic modification of behavior. Against this background, smartphone detoxing can be seen as a practice of sovereignty in the sense that it aims to recover the subject's autonomy and rationality—qualities that are lost as humans become smartphone addicts. Ironically, however, smartphone detoxing is also a neoliberal practice in the sense that it plays on an ethos of self-care and cultivates market-oriented notions of freedom. As such, the article contends that the detoxing subject is a post-smartphone subject that performs political subjectivity by negotiating the tension between sovereignty and neoliberalism. To explore how smartphone detoxing performs political subjectivity in practice, the article analyses the testimonies of four influencers who share their detoxing journeys on YouTube. The analysis finds that detoxers recover sovereignty by rediscovering the Self, reconnecting with Others, and reclaiming time, and that they—through these very practices—also strive for the neoliberal virtues of wellness, authenticity and productivity.
Is Lycorma delicatula (Hemiptera: Fulgoridae) a blooming threat to citrus?
Authors: Molfini Marco , West Mari , Gómez-Marco Francesc , Torres Jorge Braz , Hoddle Mark 
Published: September 2024

Abstract
Examining the host range of emerging invasive insects is essential to assess their invasion potential and to anticipate the negative impacts of their spread. The ongoing North American invasion of spotted lanternfly (SLF) [Lycorma delicatula (White, 1845)] threatens agricultural, urban, and natural areas. The survival and development of SLF nymphs on Washington navel orange [Citrus sinensis (L.) Osbeck (Sapindales: Rutaceae)] trees were assessed in a quarantine facility. Results indicated that SLF nymphs can develop to at least the third instar by feeding exclusively on Washington navel orange. This finding suggests that, at least up to the third stage of nymphal development, Washington navel orange might be a suitable host for SLF, highlighting the possibility that this invasive pest represents an unrecognized threat to this globally important crop and possibly to other Citrus species.
Using Multi-Objective Optimization to build non-Random Forest
Authors: Klikowska Joanna , Woźniak Michał 
Published: September 2024

Abstract
The use of multi-objective optimization to build classifier ensembles is becoming increasingly popular. This approach optimizes more than one criterion simultaneously and returns a set of solutions. Thus the final solution can be more tailored to the user’s needs. The work proposes the MOONF method using one or two criteria depending on the method’s version. Optimization returns solutions as feature subspaces that are then used to train decision tree models. In this way, the ensemble is created non-randomly, unlike the popular Random Subspace approach (such as the Random Forest classifier). Experiments carried out on many imbalanced datasets compare the proposed methods with state-of-the-art methods and show the advantage of the MOONF method in the multi-objective version.
Performance evaluation of a low-throughput qPCR-based Legionella assay for utility as an onsite industrial water system monitoring method
Authors: Corrigan Alexsandra , Niemaseck Benjamin , Moore Mackenzie , McIlwaine Douglas , Duguay Jeremy 
Published: September 2024

Abstract

Legionella is a bacterial genus found in natural aquatic environments, as well as domestic and industrial water systems. Legionella presents potential human health risks when aerosolized and inhaled by at-risk individuals and is commonly monitored at locations with likelihood of proliferation and human exposure. Legionella monitoring is widely performed using culture-based testing, which faces limitations including turnaround time and interferences. Molecular biology methodologies, including quantitative polymerase chain reaction (qPCR), are being explored to supplement or replace culture-based testing because of faster turnaround and lower detection limits, allowing for more rapid water remediation measures. In this study, three methods were compared by testing industrial water samples: culture-based testing by a certified lab, high throughput qPCR testing (HT qPCR), and field deployable low throughput qPCR testing (LT qPCR). The qPCR test methods reported more positive results than culture testing, indicating improved sensitivity and specificity. The LT qPCR test is portable with quick turnaround times, and can be leveraged for environmental surveillance, process optimization, monitoring, and onsite case investigations. The LT qPCR test had high negative predictive value and would be a useful tool for negative screening of Legionella samples from high-risk environments and/or outbreak investigations to streamline samples for culture testing.

One-Sentence Summary
This study compared three test methods for Legionella to evaluate performance of a low throughput quantitative polymerase chain reaction (LT qPCR) test for Legionella that can be used onsite; the study found that the high throughput (HT) and LT qPCR tests used in this study gave more positive results than culture testing, and the results indicated a similar negative predictive value for the HT and LT qPCR tests, supporting that the LT qPCR method could be useful for negative screening of Legionella samples in industrial water systems onsite.