bluuebunny/crossref_metadata_2025_split huggingface dataset:
Seasonal variability in the feeding ecology of an oceanic predator
Authors: Lovell Mitchell S. , Polito Michael J. , Schuster Josef A. , Shallow Emily E. , Janosik Alexis M. , Falterman Brett J. , Dance Michael A. 
Published: July 2024

AbstractComplementary approaches (stomach contents, DNA barcoding, and stable isotopes) were used to examine seasonal shifts in the feeding ecology of an oceanic predator, yellowfin tuna (Thunnus albacares, n = 577), in the northern Gulf of Mexico. DNA barcoding greatly enhanced dietary resolution and seasonally distinct prey assemblages were observed for both sub-adults and adults. In general, diet was characterized by ommastrephid squids and exocoetids in spring, juvenile fishes (i.e., carangids and scombrids) in summer, migratory coastal fishes during fall, and an increased consumption of planktonic prey (e.g., amphipods) in winter. Seasonal variability in bulk stable isotope values (δ13C, δ15N, and δ34S) was also observed, with low δ15N values and high δ34S values during late summer/early fall and high δ15N values (low δ34S) during late winter/early spring. Bayesian stable isotope mixing models corroborated seasonal diet shifts, highlighting the importance of oceanic nekton in spring/summer, coastal nekton during fall, and oceanic plankton during winter. Seasonal shifts in diet appeared to be influenced by prey reproductive cycles, habitat associations, and environmental conditions. Findings highlight the complex food web dynamics supporting an opportunistic oceanic predator and the importance of seasonal cycles in prey availability to predator resource utilization in open-ocean ecosystems.
Lower diastolic tension may be indicative of higher proarrhythmic propensity in failing human cardiomyocytes
Authors: Zhou Xin , Levesque Paul , Chaudhary Khuram , Davis Myrtle , Rodriguez Blanca 
Published: July 2024

AbstractChronic heart failure is one of the most common reasons for hospitalization. Current risk stratification is based on ejection fraction, whereas many arrhythmic events occur in patients with relatively preserved ejection fraction. We aim to investigate the mechanistic link between proarrhythmic abnormalities, reduced contractility and diastolic dysfunction in heart failure, using electromechanical modelling and simulations of human failing cardiomyocytes. We constructed, calibrated and validated populations of human electromechanical models of failing cardiomyocytes, that were able to reproduce the prolonged action potential, reduced contractility and diastolic dysfunction as observed in human data, as well as increased propensity to proarrhythmic incidents such as early afterdepolarization and beat-to-beat alternans. Our simulation data reveal that proarrhythmic incidents tend to occur in failing myocytes with lower diastolic tension, rather than with lower contractility, due to the relative preserved SERCA and sodium calcium exchanger current. These results support the inclusion of end-diastolic volume to be potentially beneficial in the risk stratifications of heart failure patients.
Efficacy of various amendments for immobilization of potentially toxic elements in wastewater contaminated soils
Authors: Manzoor Muhammad Zeeshan , Sarwar Ghulam , Alamery Salman , Ibrahim Muhammad , Sami Amtul , Ahmed Bilal , Ahsan Fariha , Gul Salma , Attia Kotb A. , Fiaz Sajid , Ullah Ikram 
Published: July 2024

AbstractFarmers are using municipal wastewater either treated or untreated for irrigation because of limited fresh water resources. Wastewater extensively used for irrigation purposes is enriched with many nutrients. The reuse of wastewater is imposing a negative impact on human health and the ecosystem. It is a need of the day to identify and assess issues of the reuse of wastewater. In the current experiment, impact of organic/inorganic amendments was studied to mitigate the toxic effects of pollutants present in wastewater. Soil was brought from the site having consistent use of wastewater and different treatments were applied as per plan. The experiment has 28 treatments with 04 replications. Nine different amendments were used at 3 varying levels. Incubation time of 30 days was given after the addition of all treatments. The results of the study showed the application of FYM @ 5.0% w/w soil reduced soil pH (7.44), EC (2.16 dS m−1), SAR (8.14), lead (8.48 mg kg−1), cadmium (1.14 mg kg−1), nickel (10.55 mg kg−1) and arsenic (2.03 mg kg−1) when compared with control and other treatments. Usage of compost and horse waste followed FYM. On the basis of this study, it is recommended that wastewater can be used for irrigation purpose after treating with FYM preferably and compost in general.
Harnessing the frontal aslant tract’s structure to assess its involvement in cognitive functions: new insights from 7-T diffusion imaging
Authors: Serrano-Sponton Lucas , Lange Felipa , Dauth Alice , Krenzlin Harald , Perez Ana , Januschek Elke , Schumann Sven , Jussen Daniel , Czabanka Marcus , Ringel Florian , Keric Naureen , Gonzalez-Escamilla Gabriel 
Published: July 2024

AbstractThe first therapeutical goal followed by neurooncological surgeons dealing with prefrontal gliomas is attempting supramarginal tumor resection preserving relevant neurological function. Therefore, advanced knowledge of the frontal aslant tract (FAT) functional neuroanatomy in high-order cognitive domains beyond language and speech processing would help refine neurosurgeries, predicting possible relevant cognitive adverse events and maximizing the surgical efficacy. To this aim we performed the recently developed correlational tractography analyses to evaluate the possible relationship between FAT’s microstructural properties and cognitive functions in 27 healthy subjects having ultra-high-field (7-Tesla) diffusion MRI. We independently assessed FAT segments innervating the dorsolateral prefrontal cortices (dlPFC-FAT) and the supplementary motor area (SMA-FAT). FAT microstructural robustness, measured by the tract’s quantitative anisotropy (QA), was associated with a better performance in episodic memory, visuospatial orientation, cognitive processing speed and fluid intelligence but not sustained selective attention tests. Overall, the percentual tract volume showing an association between QA-index and improved cognitive scores (pQACV) was higher in the SMA-FAT compared to the dlPFC-FAT segment. This effect was right-lateralized for verbal episodic memory and fluid intelligence and bilateralized for visuospatial orientation and cognitive processing speed. Our results provide novel evidence for a functional specialization of the FAT beyond the known in language and speech processing, particularly its involvement in several higher-order cognitive domains. In light of these findings, further research should be encouraged to focus on neurocognitive deficits and their impact on patient outcomes after FAT damage, especially in the context of glioma surgery.
A machine learning approach to predict in vivo skin growth
Authors: Nagle Matt , Conroy Broderick Hannah , Buganza Tepole Adrian , Fop Michael , Ní Annaidh Aisling 
Published: July 2024

AbstractSince their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Currently, however, the sole quantitative method to assess skin growth requires skin excision. Thus, in the context of patient outcomes, a machine learning method which uses non-invasive measurements to predict in vivo skin growth and other skin properties, holds significant value. In this study, the finite element method was used to simulate a typical skin expansion protocol and to perform various simulated wave propagation experiments during the first few days of expansion on 1,000 individual virtual subjects. An artificial neural network trained on this dataset was shown to be capable of predicting the future skin growth at 7 days (avg. $$R^2 = 0.9353$$

R
2

=
0.9353

) as well as the subject-specific shear modulus ($$R^2 = 0.9801$$

R
2

=
0.9801

), growth rate ($$R^2 = 0.8649$$

R
2

=
0.8649

), and natural pre-stretch ($$R^2 = 0.9783$$

R
2

=
0.9783

) with a very high degree of accuracy. The method presented here has implications for the real-time prediction of patient-specific skin expansion outcomes and could facilitate the development of patient-specific protocols.
Origami single-end capacitive sensing for continuous shape estimation of morphing structures
Authors: Ray Lala , Geißler Daniel , Zhou Bo , Lukowicz Paul , Greinke Berit 
Published: July 2024

AbstractIn this work, we propose a novel single-end morphing capacitive sensing method for shape tracking, FxC, by combining Folding origami structures and Capacitive sensing to detect the morphing structural motions using state-of-the-art sensing circuits and deep learning. It was observed through embedding areas of origami structures with conductive materials as single-end capacitive sensing patches, that the sensor signals change coherently with the motion of the structure. Different from other origami capacitors where the origami structures are used in adjusting the thickness of the dielectric layer of double-plate capacitors, FxC uses only a single conductive plate per channel, and the origami structure directly changes the geometry of the conductive plate. We examined the operation principle of morphing single-end capacitors through 3D geometry simulation combined with physics theoretical deduction, which deduced similar behavior as observed in experimentation. Then a software pipeline was developed to use the sensor signals to reconstruct the dynamic structural geometry through data-driven deep neural network regression of geometric primitives extracted from vision tracking. We created multiple folding patterns to validate our approach, based on folding patterns including Accordion, Chevron, Sunray and V-Fold patterns with different layouts of capacitive sensors using paper-based and textile-based materials. Experimentation results show that the geometry primitives predicted from the capacitive signals have a strong correlation with the visual ground truth with R-squared value of up to 95% and tracking error of 6.5 mm for patches. The simulation and machine learning constitute two-way information exchange between the sensing signals and structural geometry. By embedding part of the origami surface with morphing single-end capacitive sensors, FxC presents a unique solution that leverages both the mechanical properties of origami and sensing properties of capacitive sensing.
Epigenetic aging studies of pair bonding in prairie voles
Authors: Sailer Lindsay L. , Haghani Amin , Zoller Joseph A. , Li Caesar Z. , Ophir Alexander G. , Horvath Steve 
Published: July 2024

AbstractThe quality of romantic relationships can predict health consequences related to aging. DNA methylation-based biomarkers of aging accurately estimate chronological age. We developed several highly accurate epigenetic aging clocks, based on highly conserved mammalian CpGs, for the socially monogamous prairie vole (Microtus ochrogaster). In addition, our dual-species human-vole clock accurately measured relative age and illustrates high species conservation of epigenetic aging effects. Next, we assessed how pair bonding impacts epigenetic aging. We did not find evidence that pair-bonded voles exhibit accelerated or decelerated epigenetic aging effects in blood, ear, liver, or brain tissue. Our epigenome wide association study identified CpGs in five genes strongly associated with pair bonding: Foxp4, Phf2, Mms22l, Foxb1, and Eif1ad. Overall, we present accurate DNA methylation-based estimators of age for a species of great interest to researchers studying monogamy in animals. We did not find any evidence that sex-naive animals age differently from pair-bonded animals.
Early-stage recovery of lithium from spent batteries via CO2-assisted leaching optimized by response surface methodology
Authors: Milicevic Neumann Ksenija , Ans Muhammad , Friedrich Bernd 
Published: July 2024

Abstract
Recycling lithium (Li) from spent lithium-ion batteries (LIBs) due to the depletion of natural resources and potential toxicity is becoming a progressively favourable measure to realize green sustainability. Presently, the prevalent recycling technique relying on pyrometallurgy lacks the capability to extract lithium. Meanwhile, conventional hydrometallurgical processes frequently employ robust acidic solutions like sulfuric acid and precipitation agents such as sodium carbonate. Unfortunately, this approach tends to result in the extraction of lithium at the end of a lengthy process chain, leading to associated losses and creating challenges in managing complex waste. This study addresses a cost-effective and environmentally friendly early-stage lithium recovery from the thermally conditioned black mass. In this sense, a thermally conditioned black mass is subjected to the carbonization process in a water solution to transform the water-insoluble Li phase into soluble lithium bicarbonate (LiHCO3) and carbonate (Li2CO3) facilitating its selective separation from other elements. Response surface methodology (RSM)—a statistical tool integrated with central composite design (CCD) is employed to optimize the parameters for Li recovery. Temperature, solid–liquid (S/L) ratio, leaching time and CO2 flow rate are considered as variable factors in modelling the optimum recycling process. A quadratic regression model is developed for Li recovery and based on ANOVA analysis, (S/L) ratio, temperature and time are identified as statistically significant factors. Experimental results demonstrate a maximum leaching efficiency of lithium with optimized parameter set, achieving a recovery rate of 97.18% with a fit response of 93.54%.
An attention-based deep learning for acute lymphoblastic leukemia classification
Authors: Jawahar Malathy , Anbarasi L. Jani , Narayanan Sathiya , Gandomi Amir H. 
Published: July 2024

AbstractThe bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks’ feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.
On-site sensing for aflatoxicosis poisoning via ultraviolet excitable aptasensor based on fluorinated ethylene propylene strip: a promising forensic tool
Authors: Mazlan Nur-Fadhilah , Sage Edison Eukun , Mohamad Nur Syamimi , Mackeen Mukram Mohamed , Tan Ling Ling 
Published: July 2024

AbstractThe environmental contamination by extremophile Aspergillus species, i.e., Aflatoxin B1, is hardly controllable in Southeast Asia and Sub-Saharan Africa, which lack handling resources and controlled storage facilities. Acute aflatoxicosis poisoning from aflatoxin-prone dietary staples could cause acute hepatic necrosis, acute liver failure, and death. Here, as the cheaper, more straightforward, and facile on-site diagnostic kit is needed, we report an ultraviolet-excitable optical aptasensor based on a fluorinated ethylene propylene film strip. Molecular dynamics on the aptamer.AFB1 complex revealed that the AFB1 to the aptamer increases the overall structural stability, suggesting that the aptamer design is suitable for the intended application. Under various influencing factors, the proposed label-free strategy offers a fast 20-min on-site fabrication simplicity and 19-day shelf-life. The one-pot incubation provides an alternative to catalytic detection and exhibited 4 times reusability. The recovery of crude brown sugar, processed peanuts, and long-grain rice were 102.74 ± 0.41 (n = 3), 86.90 ± 3.38 (n = 3), and 98.50 ± 0.42 (n = 3), comparable to High-Performance Liquid Chromatography-Photodiode Array Detector results. This study is novel owing to the peculiar UV-active spectrum fingerprint and the convenient use of hydrophobic film strips that could promote breakthrough innovations and new frontiers for on-site/forensic detection of environmental pollutants.
Diffusion of nanoparticles in heterogeneous hydrogels as vitreous humour in vitro substitutes
Authors: Lorenzo Lopez Moira , Kearns Victoria R. , Curran Judith M. , Patterson Eann A. 
Published: July 2024

AbstractNanomedicine has the potential to increase the biostability of drugs to treat retinal diseases, improving their performance and decreasing the required number of intravitreal injections. However, accurate pharmacokinetic studies of these nanoparticle-drug conjugates, nanoparticle motion across the vitreous humour and interaction with the retinal cell layers still need to be investigated. Existing nanoparticle tracking techniques require fluorescent labels, which can impact cytotoxicity, nanoparticles’ motion, protein interactions, and cell internalization. In this study, a real-time label-free tracking technology, for single nanoparticles in an optical microscope based on the optical phenomena of caustics, was used to characterise the diffusion of nanoparticles in agar-hyaluronic acid hydrogels, previously validated as vitreous humour substitutes for in vitro models. The results demonstrated that the diffusion of nanoparticles through these hydrogels was heterogeneous, and that nanoparticle size had an important role in nanoparticle distribution across and within in vitro vitreous substitutes. These findings suggest that nanoparticle diameter is a critical parameter for designing novel therapeutics for retinal diseases. Moreover, nanoparticle charge did not affect nanoparticle diffusion or distribution in these synthetic hydrogels. The use of caustics in optical microscopy has been demonstrated to be a reproducible, inexpensive technique for screening novel therapeutics in eye in vitro models.
Gynecologic Cancer InterGroup CA125 response has a high negative predictive value for CHK1 inhibitor RECIST response in recurrent ovarian cancer
Authors: Ibanez Kristen R. , Donohue Duncan , Malys Tyler , Lee Jung-Min 
Published: July 2024

AbstractWe investigated the association of CA125 response with prognosis and RECIST response/progressive disease (PD) criteria in recurrent high grade serous ovarian cancer (HGSOC) patients treated with a cell cycle checkpoint kinase 1 inhibitor (CHK1i), prexasertib. 81 patients had measurable disease per RECISTv1.1, of which 72 and 70 were measurable by Gynecologic Cancer InterGroup (GCIG) CA125 response and PD criteria, respectively. Univariate and multivariate analyses showed that GCIG CA125 response (n = 32) is associated with improved progression-free survival (PFS) and overall survival (OS) compared to no GCIG CA125 response (n = 40) (median PFS 8.0 vs. 3.5 months [HR: 0.30, 95% CI: 0.18–0.51,p < 0.0001]; median OS 19.8 vs. 10.0 months [HR: 0.38, 95% CI: 0.23–0.64,p < 0.001]) independent of BRCA mutation status, platinum-sensitivity, previous PARP inhibitor therapy, ECOG performance status, and FIGO stage. Notably, GCIG CA125 response had a high negative predictive value (NPV: 93%, 95% CI: 80–98), but poor positive predictive value (PPV: 53%, 95% CI: 35–71) in predicting RECIST response. CA125 PD criteria also showed poor concordance with RECIST PD (PPV 56%, 95% CI: 40–71; NPV 33%, 95% CI: 17–54). Therefore, serum CA125 may be useful as a highly accessible prognostic and predictive biomarker to CHK1i therapy in recurrent HGSOC.
A network-based trans-omics approach for predicting synergistic drug combinations
Authors: Iida Midori , Kuniki Yurika , Yagi Kenta , Goda Mitsuhiro , Namba Satoko , Takeshita Jun-ichi , Sawada Ryusuke , Iwata Michio , Zamami Yoshito , Ishizawa Keisuke , Yamanishi Yoshihiro 
Published: July 2024

Abstract
Background
Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.

Methods
The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

Results
Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.

Conclusions
The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.