bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on characterization technology of porosity and fractal dimension of micro-arc oxidation coating
Authors: Wang Xiao-Fei , Zhang Tao , Li Ya-Li , Liu Wei-Wei , Chen Yu-Di , Wang Jia-Qi , Li Shuang 
Published: July 2023

Abstract
The porosity of the micro-arc oxidation coating of pure titanium was assessed through the utilization of Mercury intrusion porosimetry (MIP). The porosity values were subsequently adjusted by accounting for the proportion of coating volume, resulting in a more precise determination of the coating’s porosity. Additionally, the fractal dimension of the pore structure was determined through the application of a linear regression equation utilizing the Mercury intrusion data. The findings indicate that the observed numerical dispersion from image metohd is substantial, with a range of 34.9% between the minimum and maximum values. This suggests that the precision and validity of the porosity outcomes derived from this approach are inadequate; The MIP is capable of determining both the porosity, average pore size and pore size distribution of the sample, while also mitigating the impact of the matrix through test result correction, thereby yielding precise porosity values. Additionally, the results of the verification experiment demonstrate a positive correlation between the porosity alteration of the MIP and oxidation duration, thereby affirming the reliability of the test outcomes; Through the analysis of pressure, pore size, cumulative Mercury intrude volume, and incremental Mercury intrude volume data obtained from MIP, the fractal dimension of the coating’s pore structure then be determined. Research showed that as the oxidation time increases, the pore fractal dimension of the MAO coating gradually expands within a narrow range, from 2.57 and finally stabilized at 2.77, indicating a gradual increase in pore structure complexity.
Representation of single neuron dynamics using 1-D and 2-D Discrete dynamical systems
Authors: Zeki Mustafa , Kapçak Sinan 
Published: July 2023

Abstract
Biological neurons are typically modeled using the Hodgkin-Huxley formalism, which requires significant computational power to simulate. However, since realistic neural network models require thousands of synaptically coupled neurons, a faster approach is needed. Discrete dynamical systems are promising alternatives to continuous models, as they can simulate neuron activity in far fewer steps. Many existing discrete models are based on Poincaré-map-like approaches, which trace periodic activity at a cross section of the cycle. However, this approach is limited to periodic solutions. Biological neurons have many key properties beyond periodicity, such as the minimum applied current required for a resting cell to generate an action potential. To address these properties, we propose a discrete dynamical system model of a biological neuron that incorporates the threshold dynamics of the Hodgkin-Huxley model, the logarithmic relationship between applied current and frequency, modifications to relaxation oscillators, and spike-frequency adaptation in response to modulatory hyperpolarizing currents. It is important to note that several critical parameters are transferred from the continuous model to our proposed discrete dynamical system. These parameters include the membrane capacitance, leak conductance, and maximum conductance values for sodium and potassium ion channels, which are essential for accurately simulating the behavior of biological neurons. By incorporating these parameters into our model, we can ensure that it closely approximates the continuous model’s behavior, while also offering a more computationally efficient alternative for simulating neural networks.
Robust detection of marine life with label-free image feature learning and probability calibration
Authors: Schanz Tobias , Ove Möller Klas , Rühl Saskia , Greenberg David S 
Published: July 2023

Abstract
Advances in in situ marine life imaging have significantly increased the size and quality of available datasets, but automatic image analysis has not kept pace. Machine learning has shown promise for image processing, but its effectiveness is limited by several open challenges: the requirement for large expert-labeled training datasets, disagreement among experts, under-representation of various species and unreliable or overconfident predictions. To overcome these obstacles for automated underwater imaging, we combine and test recent developments in deep classifier networks and self-supervised feature learning. We use unlabeled images for pretraining deep neural networks to extract task-relevant image features, allowing learning algorithms to cope with scarcity in expert labels, and carefully evaluate performance in subsequent label-based tasks. Performance on rare classes is improved by applying data rebalancing together with a Bayesian correction to avoid biasing inferred in situ class frequencies. A divergence-based loss allows training on multiple, conflicting labels for the same image, leading to better estimates of uncertainty which we quantify with a novel accuracy measure. Together, these techniques can reduce the required label counts ∼100-fold while maintaining the accuracy of standard supervised training, shorten training time, cope with expert disagreement and reduce overconfidence.
Tunneling magnetoresistance materials and devices for neuromorphic computing
Authors: Yao Yuxuan , Cheng Houyi , Zhang Boyu , Yin Jialiang , Zhu Daoqian , Cai Wenlong , Li Sai , Zhao Weisheng 
Published: July 2023

Abstract
Artificial intelligence has become indispensable in modern life, but its energy consumption has become a significant concern due to its huge storage and computational demands. Artificial intelligence algorithms are mainly based on deep learning algorithms, relying on the backpropagation of convolutional neural networks or binary neural networks. While these algorithms aim to simulate the learning process of the human brain, their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption. The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power. Tunneling magnetoresistance (TMR)-based devices, namely magnetic tunnel junctions (MTJs), have great advantages in simulating the behavior of biological synapses and neurons. This is not only because MTJs can simulate biological behavior such as spike-timing dependence plasticity and leaky integrate-fire, but also because MTJs have intrinsic stochastic and oscillatory properties. These characteristics improve MTJs’ bio-fidelity and reduce their power consumption. MTJs also possess advantages such as ultrafast dynamics and non-volatile properties, making them widely utilized in the field of neuromorphic computing in recent years. We conducted a comprehensive review of the development history and underlying principles of TMR, including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence. We also explored various writing methods of MTJs and their potential applications. Furthermore, we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing. TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing, particularly in the development of spiking neural networks. Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future advances in the era of the internet of things.
Expanding the potential of biosensors: a review on organic field effect transistor (OFET) and organic electrochemical transistor (OECT) biosensors
Authors: Niu Yue , Qin Ze , Zhang Ying , Chen Chao , Liu Sha , Chen Hu 
Published: July 2023

Abstract
Organic electronics have gained significant attention in the field of biosensors owing to their immense potential for economical, lightweight, and adaptable sensing devices. This review explores the potential of organic electronics-based biosensors as a revolutionary technology for biosensing applications. The focus is on two types of organic biosensors: organic field effect transistor (OFET) and organic electrochemical transistor (OECT) biosensors. OFET biosensors have found extensive application in glucose, DNA, enzyme, ion, and gas sensing applications, but suffer from limitations related to low sensitivity and selectivity. On the other hand, OECT biosensors have shown superior performance in sensitivity, selectivity, and signal-to-noise ratio, owing to their unique mechanism of operation, which involves the modulation of electrolyte concentration to regulate the conductivity of the active layer. Recent advancements in OECT biosensors have demonstrated their potential for biomedical and environmental sensing, including the detection of neurotransmitters, bacteria, and heavy metals. Overall, the future directions of OFET and OECT biosensors involve overcoming these challenges and developing advanced devices with improved sensitivity, selectivity, reproducibility, and stability. The potential applications span diverse fields including human health, food analysis, and environment monitoring. Continued research and development in organic biosensors hold great promise for significant advancements in sensing technology, opening up new possibilities for biomedical and environmental applications.
Review of regulating Zn2+ solvation structures in aqueous zinc-ion batteries
Authors: Zhang Wanyao , Chen Yufang , Gao Hongjing , Xie Wei , Gao Peng , Zheng Chunman , Xiao Peitao 
Published: July 2023

Abstract
Aqueous zinc-ion batteries, due to their high power density, intrinsic safety, low cost, and environmental benign, have attracted tremendous attentions recently. However, their application is severely plagued by the inferior energy density and short cycling life, which was mainly ascribed to zinc dendrites, and interfacial side reactions, narrow potential window induced by water decomposition, all of which are highly related with the Zn2+ solvation structures in the aqueous electrolytes. Therefore, in this review, we comprehensively summarized the recent development of strategies of regulating Zn2+ solvation structures, specially, the effect of zinc salts, nonaqueous co-solvents, and functional additives on the Zn2+ solvation structures and the corresponding electrochemical performance of aqueous zinc-ion batteries. Moreover, future perspectives focused on the challenges and possible solutions for design and commercialization of aqueous electrolytes with unique solvation structures are provided.
Instrumental Variable Analysis of Racial Discrimination and Blood Pressure in a Sample of Young Adults
Authors: Lawrence Jourdyn A , Kawachi Ichiro , White Kellee , Bassett Mary T , Williams David R 
Published: July 2023

Abstract
Racial inequities in blood pressure levels have been extensively documented. Experiences of racial discrimination could explain some of this disparity, although findings from previous studies have been inconsistent. To address limitations of prior literature, including measurement error, we implemented instrumental variable analysis to assess the relationship between racial discrimination in institutional settings and blood pressure. Using data from 3,876 Black and White adults with an average age of 32 years from examination 4 (1992–1993) of the Coronary Artery Risk Development in Young Adults Study, our primary analysis examined the relationship between self-reported experiences of racial discrimination in institutional settings and blood pressure using reflectance meter measurement of skin color as an instrument. Findings suggested that an increase in experiences of racial discrimination was associated with higher systolic and diastolic blood pressure (β = 2.23 mm Hg (95% confidence interval: 1.85, 2.61) and β = 1.31 (95% confidence interval: 1.00, 1.62), respectively). Our instrumental variable estimates suggest that experiences of racial discrimination within institutional settings contribute to racial inequities in elevated blood pressure and cardiovascular disease outcomes in a relatively young cohort of adults and may yield clinically relevant differences in cardiovascular health over the life course.
Regulation of synthesis of polyamines by progesterone, estradiol, and their receptors in uteri of cyclic ewes
Authors: Newton Makenzie G , Stenhouse Claire , Halloran Katherine M , Sah Nirvay , Moses Robyn M , He Wenliang , Wu Guoyao , Bazer Fuller W 
Published: July 2023

AbstractProgesterone (P4), estradiol (E2), and expression of their receptors (PGR and ESR1, respectively) by cells of the uterus regulate reproductive performance of mammals through effects on secretion and transport of nutrients into the uterine lumen. This study investigated the effect of changes in P4, E2, PGR, and ESR1 on expression of enzymes for the synthesis and secretion of polyamines. Suffolk ewes (n = 13) were synchronized to estrus (Day 0) and then, on either Day 1 (early metestrus), Day 9 (early diestrus), or Day 14 (late diestrus) of the estrous cycle, maternal blood samples were collected, and ewes were euthanized before obtaining uterine samples and uterine flushings. Endometrial expression of MAT2B and SMS mRNAs increased in late diestrus (P &lt; 0.05). Expression of ODC1 and SMOX mRNAs decreased from early metestrus to early diestrus, and expression of ASL mRNA was lower in late diestrus than in early metestrus (P &lt; 0.05). Immunoreactive PAOX, SAT1, and SMS proteins were localized to uterine luminal, superficial glandular, and glandular epithelia, stromal cells, myometrium, and blood vessels. Concentrations of spermidine and spermine in maternal plasma decreased from early metestrus to early diestrus and decreased further in late diestrus (P &lt; 0.05). The abundances of spermidine and spermine in uterine flushings were less in late diestrus than early metestrus (P &lt; 0.05). These results indicate that synthesis and secretion of polyamines are affected by P4 and E2, as well as the expression of PGR and ESR1 in the endometria of cyclic ewes.
Changes in prevalence and sociodemographic correlates of tobacco and nicotine use in Finland during the COVID-19 pandemic
Authors: Peña Sebastián , Ilmarinen Katja , Kestilä Laura , Ruokolainen Otto , Ollila Hanna , Parikka Suvi , Karvonen Sakari 
Published: July 2023

AbstractBackgroundThe impact of the coronavirus disease 2019 (COVID-19) pandemic on tobacco and nicotine use remains debated. We examined whether the prevalence of tobacco and nicotine use and nicotine-replacement therapy (NRT) changed during the COVID-19 pandemic and whether changes differed by sociodemographic groups.MethodsRepeated cross-sectional study of three national surveys in Finland (2018, 2019 and 2020; n = 58 526 adults aged 20 and over). Outcomes were daily and occasional smoking, smokeless tobacco (snus) use, e-cigarettes use, total tobacco or nicotine use and NRT use. We examined changes for each outcome by sex, age, educational tertiles, marital status, mother tongue and social participation.ResultsDaily smoking decreased among males by 1.15 percentage points (pp) [95% confidence interval (CI) −2.10 to −0.20] between 2018 and 2020 and 0.86 pp among females (95% CI −1.58 to −0.15). Daily snus use remained stable in both sexes. Daily e-cigarette use was below 1% and remained stable. We found weak evidence of a reduction in total tobacco or nicotine use between 2018 and 2020 (males −1.18 pp, 95% CI −2.68 to 0.32 and females −0.8 pp, 95% CI −1.81 to 0.22). NRT use remained stable. Snus and NRT use decreased among 60- to 74-year-olds but remained stable in other age groups. We did not find evidence of interactions by subgroup for other outcomes.ConclusionsDaily smoking decreased in Finland between 2018 and 2020, but other forms of tobacco use did not experience a reduction. The COVID-19 pandemic does not seem to have altered the sustained reduction of smoking in Finland, although substantial sociodemographic differences persist.
Improved accuracy of plane-wave electromagnetic modelling by application of the total and scattered field decomposition and perfectly matched layers
Authors: Buntin L M , Kalscheuer T , Kreiss G , Ren Z 
Published: July 2023

SUMMARY
In 2-D magnetotelluric modelling, the standard application of Dirichlet boundary conditions (BC) may severely diminish the solution accuracy, because the unknown scattered part of the electromagnetic field is erroneously reflected at the domain boundary. Therefore, we adapt the total and scattered field decomposition (TSFD) to geophysical modelling, enabling the application of fully absorbing boundary methods, here perfectly matched layers (PML), to the scattered field. Our novel TSFD divides the modelling domain into two regions. In the total-field region containing the area of interest, the solution is computed for the total field. In the scattered-field region containing the boundaries, the solution is obtained for the scattered field, which is fully absorbed by PML at the boundaries. The plane-wave source is excited at the TSFD interface between both regions. Thus, boundary reflections are eradicated leading to superior solution accuracy, and boundaries can be placed closer to the receivers, shrinking the computational problem. Especially for challenging models with strong lateral changes, the solution accuracy of the TSFD is superior to that of the standard Dirichlet approach. Owing to the linearity of Maxwell’s equations, the inaccuracy introduced to the electric and magnetic fields by using Dirichlet BC can be expected to partly cancel out in the magnetotelluric transfer functions, for example the impedance tensor. In this work, we quantify this cancellation effect. The inaccuracy is less than typical measurement errors in the vast majority of apparent resistivity and phase data, even, when the primary fields are strongly inaccurate.
Isolation and application of pH- and salt-resistant Bacillus strains to reduce ammonia emission from pig manure during the storage period
Authors: Shen Weishou , Dai Haiyang , Gu Shenyi , Guo Fanjing , Li Tianling , Rajasekar Adharsh 
Published: July 2023

Abstract

Aim
Ammonia released during the storage period from pig manure causes severe air pollution and odor issues, ultimately leading to nitrogen loss in the manure. In this study, we investigated the application of 13 Bacillus spp. strains isolated from paddy soil and their potential to minimize reactive nitrogen loss during pig manure storage at 28°C and initial moisture content at 76.45%.

Methods and results
We selected five strains of Bacillus spp. named H3-1, H4-10, H5-5, H5-9, and Y3-28, capable of reducing ammonia emissions by 23.58%, 24.65%, 25.58%, 25.36%, and 26.82% in pig manure over 60 days compared to control. We further tested their ability on various pH, salinity, and ammonium-nitrogen concentrations for future field applications. Our investigation revealed that certain bacteria could survive and grow at pH 6, 8, and 10; 4, 8, and 10% salinity and up to 8 g l−1 of ammonium-nitrogen concentration.

Conclusions
The results from our study show that saline and ammonium–nitrogen tolerant Bacillus strains isolated from soil can potentially reduce ammonia emissions in pig manure, even at high moisture content during their storage period.
Lipopolysaccharide promotes apoptosis and oxidative injury of porcine small intestinal epithelial cells by down-regulating the expression of glutamine transporter ASCT2
Authors: Tang Xiaopeng , Xiong Kangning , Liu Jia , Li Meijun 
Published: July 2023

Abstract
The present study aimed to investigate the effects of lipopolysaccharide (LPS) stimulation on oxidative damage, apoptosis, and glutamine (Gln) transporter Alanine-Serine-Cysteine transporter 2 (ASCT2) expression in porcine small intestinal epithelial cells (IPEC-J2), and preliminarily elucidated the relationship between ASCT2 expression level and oxidative damage and apoptosis of IPEC-J2 cells. IPEC-J2 cells were treated without (control group, CON, N = 6) or with 1 μg/mL LPS (LPS group, LPS, N = 6). Cell viability, lactate dehydrogenase (LDH) content, malonaldehyde (MDA), anti-oxidant enzymes (superoxide dismutase [SOD], catalase [CAT], glutathione peroxidase [GSH-Px], and total anti-oxidant capacity [T-AOC]), apoptosis of IPEC-J2 cells, the expression of Caspase3, the expression of ASCT2 mRNA and ASCT2 protein was detected. The results showed that LPS stimulation of IPEC-J2 cells significantly reduced the cell viability, and anti-oxidant enzymes activity (SOD, CAT, and GSH-Px), and significantly increased LDH and MDA release. Flow cytometry results showed that LPS stimulation significantly increased the late apoptosis rate and the total apoptosis rate of IPEC-J2 cells. The immunofluorescence results showed that the fluorescence intensity of LPS stimulated IPEC-J2 cells was significantly enhanced. LPS stimulation significantly decreased the mRNA and protein expression of ASCT2 in IPEC-J2 cells. The correlation analysis showed that ASCT2 expression was negatively correlated with apoptosis, and positively correlated with the anti-oxidant capacity of IPEC-J2 cells. According to the results of this study, it can be preliminarily concluded that LPS promotes the apoptosis and oxidative injury of IPEC-J2 cells by down-regulating the expression of ASCT2.
Hydrogen bonds connecting the N-terminal region and the DE loop stabilize the monomeric structure of transthyretin
Authors: Inada Yuki , Ono Yuichiro , Okazaki Kyo , Yamashita Takuma , Kawaguchi Tomoyuki , Kawano Shingo , Kobashigawa Yoshihiro , Shinya Shoko , Kojima Chojiro , Shuto Tsuyoshi , Kai Hirofumi , Morioka Hiroshi , Sato Takashi 
Published: July 2023

Abstract
Transthyretin (TTR) is a homo-tetrameric serum protein associated with sporadic and hereditary systemic amyloidosis. TTR amyloid formation proceeds by the dissociation of the TTR tetramer and the subsequent partial unfolding of the TTR monomer into an aggregation-prone conformation. Although TTR kinetic stabilizers suppress tetramer dissociation, a strategy for stabilizing monomers has not yet been developed. Here, we show that an N-terminal C10S mutation increases the thermodynamic stability of the TTR monomer by forming new hydrogen bond networks through the side chain hydroxyl group of Ser10. Nuclear magnetic resonance spectrometry and molecular dynamics simulation revealed that the Ser10 hydroxyl group forms hydrogen bonds with the main chain amide group of either Gly57 or Thr59 on the DE loop. These hydrogen bonds prevent the dissociation of edge strands in the DAGH and CBEF β-sheets during the unfolding of the TTR monomer by stabilizing the interaction between β-strands A and D and the quasi-helical structure in the DE loop. We propose that introducing hydrogen bonds to connect the N-terminal region to the DE loop reduces the amyloidogenic potential of TTR by stabilizing the monomer.