bluuebunny/crossref_metadata_2025_split huggingface dataset:
Orally consumed cannabinoids: the effect of carrier oil on acute tissue distribution in male C57BL/6 mice
Authors: Lust Cody A.C. , Hillyer Lyn M. , Pallister Mitchell , Wright Amanda J. , Rogers Michael A. , Rock Erin M. , Limebeer Cheryl L. , Parker Linda A. , Ma David W.L. 
Published: August 2024

Abstract

Background: Fundamental gaps in knowledge exist in understanding the tissue distribution of cannabinoids, cannabidiol (CBD) and tetrahydrocannabinol (THC), following oral ingestion. CBD and THC are lipid-soluble and oral bioavailability is increased when combined with long-chain fatty acid carrier oils prior to oral ingestion. Oils with eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) confer positive health benefits and have yet to be examined as a carrier oil for oral cannabinoid delivery thus, examination is warranted.
Methods: This study investigated the acute tissue distribution of cannabinoids in serum, adipose, brain, liver, heart, and muscle of male C57BL/6 mice at 1, 2, and 3 hours (H) post oral ingestion. Mice were gavaged with CBD (5 mg/kg) and THC (1 mg/kg) combined with either sesame (SES), mixed EPA/DHA (EPA/DHA), or DHA enriched (DHA) oil as a carrier. With assistance of the Analytical Facility for Bioactive Molecules (Toronto, Canada), tissue concentration of cannabinoids was quantified using liquid chromatography with tandem mass spectrometry.
Results: SES oil resulted in a significantly greater concentration of CBD and THC (p<0.05) across all tissues and times compared to n-3 PUFA oils. The ratio of EPA:DHA in the carrier oils modestly affected distribution of cannabinoids to tissues, notably, DHA oil resulted in a greater concentration of CBD in the brain. Heart tissue had the highest concentration of CBD at 1 and 2H post-oral gavage, and adipose tissue had the highest concentration at 3H which was consistent across all three carrier oils.
Conclusions: This study profiled the greatest number of tissues to-date for the acute distribution of CBD and THC following oral consumption with a lipid carrier in mice which demonstrated a non-uniform distribution to tissues over time. SES oil proved to be far more effective as a carrier oil at delivering orally consumed cannabinoids to tissues compared to two different n-3 PUFA containing oils. Further developing our fundamental understanding of cannabinoid distribution across tissues following their consumption from foods and pharmaceuticals are necessary to establish specific pharmacokinetic profiles to aid oral dosing strategies and maximize the bioactive potential of cannabinoids.
Reducing Neonatal Infection Rate Among the Neonates Born at Remera Rukoma Hospital: A Report of Quality Improvement Project
Authors: SEBERA Emile , Diane UWIMPAYE , Philemon NZEYIMANA , Leonille NIZEYEMALIYA , AHISHAKIYE Albertine , HAGENIMANA Celestin , TWAGIRUMUKIZA Emile 
Published: August 2024

Abstract

Background
Neonatal infection is one of the leading causes of neonatal mortality globally, significantly higher mortality rates are observed in Lower and middle-income countries, where Rwanda is located. Remera Rukoma District Hospital reported an increasing rate of neonatal infection necessitating assessment of the contributing causes and urgent intervention.
Methods
The project was conducted from January to June 2024. The Fishbone diagram was adopted to conduct a comprehensive root cause analysis. A multidisciplinary team of a quality improvement officer, clinicians, and different stakeholders identified contributing factors across multiple domains were categorized into healthcare practices, equipment, hospital environment, and patient factors. Interventions were implemented and monitored using monthly Plan-Do-Study-Act (PDSA) cycles to track progress and make necessary adjustments.
Results
There was a downward trend from a high rate of neonatal infection of 22.9% in January to 13% in June 2024. Despite not meeting the ambitious target of 10%, the results indicated a significant improvement. The systematic approach, involving targeted training of all hospital personnel, and fostering infection control measures, contributed to this progress.
Conclusion
The project demonstrated that a structured, data-driven approach to quality improvement can reduce neonatal infection rates. Key lessons include the effectiveness of comprehensive root cause analysis and the importance of continuous staff engagement and education. While the initial target was not met, the reduction achieved lays a foundation for continued efforts to improve neonatal outcomes at Remera Rukoma Hospital. Sustained commitment and further refinement of strategies are essential for ongoing progress in infection control and overall patient care. Lessons learned include the value of staff engagement, continuous education, and data-driven decision-making in infection control efforts.
Assessment of Biocompatibility of 16HBE14o- Human Bronchial Epithelial Cells in Alginate-Methylcellulose Bioinks Revealed Spheroid Formation
Authors: Wood Nathan , Qin Hongmin , Li Wanhe , Doria Esther 
Published: August 2024

Abstract

The biocompatibility of 16HBE14o- human bronchial epithelial cells in ionically cross-linked alginate-methylcellulose bioinks was assessed. This was accomplished by encapsulating 16HBE14o- cells in either a sodium alginate bioink or a bioink with sodium alginate and added methylcellulose in a 1:1 ratio. To differentiate the effects of methylcellulose from those of cross-linking on cell viability, two concentrations of calcium chloride cross-linker were used for both alginate only and alginate-methylcellulose bioinks. Using fluorescence microscopy, it was observed that bioinks with methylcellulose showed a small but significant reduced cell viability and a decreased presence of cell spheroids compared to their methylcellulose free alginate counterparts. However, alginate-methylcellulose bioinks still supported cell proliferation and appeared to be biocompatible. Additionally, the concentration of cross-linker seemed to impact cell viability. This study has implications for the use of methylcellulose as a viscosity tuner for both general 3D 16HBE14o- human epithelial cell culture and 3D bioprinting. The presence of spheroids suggests that alginate-methylcellulose bioinks could be useful in generating 3D 16HBE14o- human epithelial cell culture to address questions in cell biology, including signal transduction, metabolic activity, and cancer hallmarks.
lista-GEM: the genome-scale metabolic reconstruction of Lipomyces starkeyi
Authors: Almeida Eduardo , Ferreira Mauricio , Silveira Wendel 
Published: August 2024

Abstract

Oleaginous yeasts cultivation in low-cost substrates is an alternative for more sustainable production of lipids and oleochemicals. Lipomyces starkeyi accumulates high amounts of lipids from different carbon sources, such as glycerol, and glucose and xylose (lignocellulosic sugars). Systems metabolic engineering approaches can further enhance its capabilities for lipid production, but no genome-scale metabolic networks have been reconstructed and curated for L. starkeyi. Herein, we propose lista-GEM, a novel genome-scale metabolic model of L. starkeyi. We reconstructed the model using two high-quality models of oleaginous yeasts as templates and further curated the model to reflect the metabolism of L. starkeyi. We simulated phenotypes and predicted flux distributions in good accordance with experimental data. We also predicted targets to improve lipid production in glucose, xylose, and glycerol. The phase plane analysis indicated that the carbon availability affected lipid production more than oxygen availability. We found that the maximum lipid production in glucose and xylose required more oxygen than glycerol. Enzymes related to lipid synthesis in the endoplasmic reticulum were the main targets to improve lipid production: stearoyl-CoA desaturase, fatty-acyl-CoA synthase, diacylglycerol acyltransferase, and glycerol-3-phosphate acyltransferase. The glycolytic genes encoding pyruvate kinase, enolase, phosphoglycerate mutase, glyceraldehyde-3-phosphate dehydrogenase, and phosphoglycerate kinase were predicted as targets for overexpression. Pyruvate decarboxylase, acetaldehyde dehydrogenase, acetyl-CoA synthetase, adenylate kinase, inorganic diphosphatase, and triose-phosphate isomerase were predicted only when glycerol was the carbon source. Therefore, we demonstrated that lista-GEM provides multiple metabolic engineering targets to improve lipid production by L. starkeyi using carbon sources from agricultural and industrial wastes.
Brazilian Ocean Thermal Energy Park and Its Sites: Definitions and Feasibility of Thermal Energy Conversion in the Blue Amazon
Authors: de Souza Roberto Valente , Fernandes Elisa Helena Leão , de Azevedo José Luiz Lima , Corrêa Rafaela Martins , Passos Mariana dos Santos , Gomes Rodrigo Klim , Junior Joel Sena Sales 
Published: August 2024

Abstract

The search for energy alternatives is becoming more important in the strategic development of countries, as global energy demand accelerates. The thermal energy in the oceans, the highest exploitation potential, is renewable and has the ability to remove heat from the oceans and carbonic gas from the atmosphere. In this study, we used the temperature results from the numerical model HYCOM for resoucers evaluation of Ocean Thermal Energy Conversion (OTEC). Based on these data, daily, monthly, seasonal and annual averages were calculated, allowing to evaluate the thermal gradient variability in the Blue Amazon, region where Brazil has sovereign rights for exploration, conservation and management of living and nonliving natural resources. Three thermal energy sites were obtained the definition of their viability and operational of his conversion by teorical OTEC plant throughout the period of the average. The brazilian northern region stands out with the higher energy potential between the surface and a depth range of 600m ≤ z ≥ 1000m, from the results we indicate that the Brazilian Ocean Thermal Energy Park (OTEP) is technically feasible for the operations of OTEC. However, form the economic perspective, results show that only portions closest to the continental shelf, have the capacity to operate.
Dormancy Regulator Prunus mume DAM6 Promotes Ethylene-Mediated Leaf Senescence and Abscission
Authors: Hsiang Tzu-Fan , Chen Yue-Yu , Nakano Ryohei , Oikawa Akira , Matsuura Takakazu , Ikeda Yoko , Yamane Hisayo 
Published: August 2024

Abstract

Leaf senescence and abscission in autumn are critical phenological events in deciduous woody perennials. After leaf fall, dormant buds remain on deciduous woody perennials, which then enter a winter dormancy phase. Thus, leaf fall is widely believed to be linked to the onset of dormancy. In Rosaceae fruit trees, DORMANCY-ASSOCIATED MADS-box (DAM) transcription factors control bud dormancy. However, apart from their regulatory effects on bud dormancy, the biological functions of DAMs have not been thoroughly characterized. In this study, we revealed a novel DAM function influencing leaf senescence and abscission in autumn. In Prunus mume, PmDAM6 expression was gradually up-regulated in leaves during autumn toward leaf fall. Our comparative transcriptome analysis using two RNA-seq datasets for the leaves of transgenic plants overexpressing PmDAM6 and peach (Prunus persica) DAM6 (PpeDAM6) indicated Prunus DAM6 may up-regulate the expression of genes involved in ethylene biosynthesis and signaling as well as leaf abscission. Significant increases in 1-aminocyclopropane-1-carboxylate accumulation and ethylene emission in DEX-treated 35S:PmDAM6-GR leaves reflect the inductive effect of PmDAM6 on ethylene biosynthesis. Additionally, ethephon treatments promoted autumn leaf senescence and abscission in apple and P. mume, mirroring the changes due to PmDAM6 overexpression. Collectively, these findings suggest that PmDAM6 may induce ethylene emission from leaves, thereby promoting leaf senescence and abscission. This study clarified the effects of Prunus DAM6 on autumn leaf fall, which is associated with bud dormancy onset. Accordingly, in Rosaceae, DAMs may play multiple important roles affecting whole plant growth during the tree dormancy induction phase.
Variable and slow-paced neural dynamics in HVC underlie plastic song production in juvenile zebra finches
Authors: Bistere Linda , Wilczek Stefan , Vallentin Daniela 
Published: August 2024

Abstract

Zebra finches undergo a gradual refinement of their vocalizations, transitioning from variable juvenile songs to the stereotyped song of adulthood. To investigate the neural mechanisms underlying song crystallization, a critical phase in this developmental process, we conducted intracellular recordings in HVC, a premotor nucleus essential for song learning and production. We found that HVC projection neurons in juvenile zebra finches in the song crystallization phase exhibited more variable spiking patterns compared to the precise bursting observed in adult HVC projection neurons. Additionally, subthreshold membrane potential fluctuations in juvenile neurons were characterized by longer duration and larger amplitude excitatory postsynaptic potentials. These distinct temporal dynamics in HVC during song crystallization likely play a crucial role in the fine-tuning processes that shape the precise timing and structure of the mature zebra finch song.
Nonlinear Plane-Wave Expansion Method for Analyzing Dispersion Properties of Piezoelectric Metamaterial Lattices with Encapsulated Resonators
Authors: Shen Yichang , Lacarbonara Walter 
Published: August 2024

Abstract

Wave propagation and band gap behavior of square lattices encapsulating identical nonlinear resonators in each cell are investigated through an asymptotic treatment of the wave propagation equations. The nonlinear effects of the resonators, composed of suspended piezoelectric membranes with a central mass, are investigated through the introduction of a generalized nonlinear version derived from the plane-wave expansion (PWE) method. This method leads to nonlinear wave propagation equations and the analytical derivation of nonlinear dispersion functions using the method of multiple scales. Extensive numerical simulations verify the validity of the analytical solutions. This study demonstrates that the proposed nonlinear PWE method overcomes the limitations of the popular approach based on the enforcement of the Floquet-Bloch theorem coupled with the cell projection method. While the latter provides the dispersion curves of the fundamental propagation mode, the nonlinear PWE delivers the nonlinear dispersion curves of all modes, offering a broader perspective into the design process for semi-adaptively programmable metamaterials aimed at controlling wave propagation.
From Table to Image: Boosting Credit Risk Prediction via Transfer MLP-like Network on Structured Data
Authors: Li Yan , Wen Guihua , Liu Bo 
Published: August 2024

Abstract

At present, deep learning has limited application in the field of financial credit risk because deep learning is good at processing unstructured data such as images, voice, and text, while the credit risk field processes structured tabular data, which makes the existing deep learning methods not well adapted to financial structured data tasks. To this end, this paper proposes a new Table-to-Image Converted Transfer MLP-like network for financial credit risk prediction. First, our method attempts to represent structured data from a new perspective and proposes a data homology based table-to-image conversion method to convert the tabular financial credit risk prediction data into image-like financial data. Then, based on the Strip-MLP structure, a pretrained MLP-like network is proposed to be applied to the credit prediction of the converted image-like financial data. The model is pre-trained with a public financial dataset, and its pre-trained parameters are transferred to the private dataset of financial institutions with different feature numbers and feature contents through transfer learning. Experimental results show that for the task of financial credit risk prediction, the methods proposed in this paper have significantly improved the effect compared with the baseline algorithm.
The Feasibility of Constructing Rubber Concrete Pavement Reinforced with Recycled and Industrial Steel Fibers
Authors: Shadfar Arash , Nani Mehdi , Shirinabadi Reza , Hosseini Seyed Azim 
Published: August 2024

Abstract

Using tires in the construction sector can have a lot of advantages because they are constructed of materials that are high resistance, durable, high quality, and flexible. Unlike asphalt pavements, rigid concrete pavements are not as flexible, even though they have a respectable resistance to bending. Rubber concrete reinforced with fibers is created by utilizing tire waste from worn automobile tires, which contains steel and rubber fibers. Because of its special qualities, this kind of concrete is a popular choice for concrete pavement construction. This paper's goal is to examine how rubber concrete reinforced with industrial and tire-derived steel fibers behaves when used to create concrete pavements. In this paper, various-sized rubber pieces were used in place of some of the coarse and fine grains. It was assessed whether kind of steel fibers—manufacturer-produced steel fibers (MSF) and recycled steel fibers from used car tires (RTSF)—would work best for reinforcing rubberized concrete. Based on the results, adding rubber to some of the concrete's natural particles reduced the material's density and efficiency while increasing its air content. The modulus of elasticity, compressive strength, and bending strength all declined with increased use of rubber; however, the compressive strength of regular concrete rose by almost 30% when 40 \(\:\raisebox{1ex}{$Kg$}\!\left/\:\!\raisebox{-1ex}{${m}^{3}$}\right.\) of steel fibers were added. Additionally, bending strength decreased when steel fibers were added to rubber concrete reinforcement, whereas compressive strength and modulus of elasticity increased by 12.5 and 28.4 percent, respectively. Ultimately, it was determined that replacing 60% of the rubber pieces with regular concrete particles and adding 20 \(\:\raisebox{1ex}{$Kg$}\!\left/\:\!\raisebox{-1ex}{${m}^{3}$}\right.\) of industrial and 20 \(\:Kg\) of recycled fibers was the optimal combination to use when building flexible rubber concrete pavement reinforced with steel fibers.
Nutritional niches of endemic, facultatively anaerobic heterotrophs from an isolated Antarctic terrestrial hydrothermal refugium elucidated through metagenomics
Authors: Herbold Craig W. , Noell Stephen E. , Lee Charles K. , Vickers Chelsea J. , Stott Matthew B. , Eisen Jonathan A. , McDonald Ian R. , Cary S. Craig 
Published: August 2024

Abstract

Background
Tramway Ridge, a geothermal Antarctic Specially Protected Area (1) (elevation 3340 m) located near the summit of Mount Erebus, is home to a unique community composed of cosmopolitan surface-associated micro-organisms and abundant, poorly understood subsurface-associated microorganisms (2–5). Here, we use shotgun metagenomics to compare the functional capabilities of this community to those found elsewhere on Earth and to infer endemism and metabolic capabilities of abundant subsurface taxa.
Results
We found that the functional potential in this community is most similar to that found in terrestrial hydrothermal environments (hot springs, sediments) and that the two dominant organisms in the subsurface are primarily endemic. They were found to be facultative anaerobic heterotrophs that likely share a pool of nitrogenous organic compounds while specializing in different carbon compounds.
Conclusions
Metagenomic insights have provided a detailed understanding of the microbe-based ecosystem found in geothermally heated fumaroles at Tramway Ridge. This approach enabled us to compare Tramway Ridge with other microbial systems, identify endemic taxa and elucidate the key metabolic pathways that may enable specific organisms to dominate the ecosystem.
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data
Authors: Bakhtiari Mohammad , Bonn Stefan , Theis Fabian , Zolotareva Olga , Baumbach Jan 
Published: August 2024

Abstract

scRNA-seq data from clinical samples are prone to batch effects, while hospitals are hesitant to share their data for centralized analysis, including batch effect correction, due to the privacy sensitivity of human genomic data. We present FedscGen, a novel privacy-aware federated method based on the generative integration approach scGen. FedscGen presents two federated workflows for training and correction of batch effects with inclusion of new studies. We benchmark FedscGen and scGen using eight datasets and nine metrics to demonstrate competitive results. On the Human Pancreas dataset, for instance, the performance difference of all models is zero for NMI, GC, ILF1, ASW_C, and kBET while FedscGen outperforms by 0.03 in EBM. FedscGen opens a privacy-preserving path for single-cell RNAseq batch effect correction in particular in clinical multi-center studies. FedscGen is published as a FeatureCloud app to be used in real world federated collaboration (https://featurecloud.ai/app/fedscgen).
Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.
Authors: Mohan Anand , Anand RS 
Published: August 2024

Abstract

EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A novel method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG addresses the limitations of traditional methods by providing a more robust representation of imagined speech signals. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method.