bluuebunny/crossref_metadata_2025_split huggingface dataset:
A dataset of a proxy variable for the poultry trade flows in China
Authors: Kong Lingcai , Zhou Xuan , Duan Mengwei , Yang Xinyi , Zhou Caifeng , Liao Yilan 
Published: November 2022

AbstractUnderstanding the intercity poultry trading network is crucial for assessing the risk of avian influenza prevalence. Unfortunately, the poultry trading network in China has rarely been described. Here, with a modified radiation model, we obtain values for a proxy variable for poultry trade flows among 318 prefecture-level cities in China in 2015 utilizing the product capacity and demand quantity of poultry of the cities. The results are validated by comparing the proxy variable values with the trade volumes investigated in the literature, and it is found that the modified radiation model can accurately predict the main poultry trade flows among cities. This is the first dataset on China’s poultry trade pattern, and it can be used to analyze the production and consumption structure of poultry in prefecture-level cities within China. The dataset can be a tool for avian influenza epidemic risk assessment as well as a basis to develop prevention and control measures during an epidemic.
Co-axial acoustic-based optical coherence vibrometry probe for the quantification of resonance frequency modes in ocular tissue
Authors: McAuley Ryan , Nolan A. , Curatolo A. , Alexandrov S. , Zvietcovich F. , Varea Bejar A. , Marcos S. , Leahy M. , Birkenfeld J. S. 
Published: November 2022

AbstractWe present a co-axial acoustic-based optical coherence vibrometry probe (CoA-OCV) for vibro-acoustic resonance quantification in biological tissues. Sample vibrations were stimulated via a loudspeaker, and pre-compensation was used to calibrate the acoustic spectrum. Sample vibrations were measured via phase-sensitive swept-source optical coherence tomography (OCT). Resonance frequencies of corneal phantoms were measured at varying intraocular pressures (IOP), and dependencies on Young´s Modulus (E), phantom thickness and IOP were observed. Cycling IOP revealed hysteresis. For E = 0.3 MPa, resonance frequencies increased with IOP at a rate of 3.9, 3.7 and 3.5 Hz/mmHg for varied thicknesses and 1.7, 2.5 and 2.8 Hz/mmHg for E = 0.16 MPa. Resonance frequencies increased with thickness at a rate of 0.25 Hz/µm for E = 0.3 MPa, and 0.40 Hz/µm for E = 0.16 MPa. E showed the most predominant impact in the shift of the resonance frequencies. Full width at half maximum (FWHM) of the resonance modes increased with increasing thickness and decreased with increasing E. Only thickness and E contributed to the variance of FWHM. In rabbit corneas, resonance frequencies of 360–460 Hz were observed. The results of the current study demonstrate the feasibility of CoA-OCV for use in future OCT-V studies.
Self-reported sleep quality and mental health mediate the relationship between chronic diseases and suicidal ideation among Chinese medical students
Authors: Gui Zhen , Sun Long , Zhou Chengchao 
Published: November 2022

AbstractHigh prevalence and strong associations of chronic disease, poor sleep quality, poor mental health, and suicidal ideation (SI) have been reported worldwide. However, the underlying mechanism remains unexamined. The participants were 2646 Chinese medical college students with an average age of 20.13 years. Pittsburgh Sleep Quality Index, the Kessler Psychological Distress Scale, and SI was evaluated. The lifetime SI, past 12-month SI, and chronic disease prevalence rates were 10.0%, 8.4%, and 4.6%, respectively. The results of logistic regression analysis in this study found that chronic disease, self-reported poor sleep quality, poor mental health, gender and scholarship were associated with lifetime SI. Similar results were also found for 12-month SI with an exception of region. This result indicated that the effects of chronic diseases on the SI were mediated by self-reported sleep quality and mental health. Physical diseases, sleep-related concerns, and mental health issues need to be addressed through a multidisciplinary team approach and various delivery systems to prevent SI among medical college students.
Identifying plastics with photoluminescence spectroscopy and machine learning
Authors: Lotter Benjamin , Konde Srumika , Nguyen Johnny , Grau Michael , Koch Martin , Lenz Peter 
Published: November 2022

AbstractA quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data characterizing amount and composition of plastics has to be based on two crucial components: (i) An experimental approach that is simple enough to be accessible worldwide and sensible enough to capture the diversity of plastics; (ii) An analysis pipeline that is able to extract the relevant parameters from the vast amount of experimental data. In this study, we demonstrate that such an approach could be realized by a combination of photoluminescence spectroscopy and a machine learning-based theoretical analysis. We show that appropriate combinations of classifiers with dimensional reduction algorithms are able to identify specific material properties from the spectroscopic data. The best combination is based on an unsupervised learning technique making our approach robust to alternations of the input data.
Functional connectivity response to acute pain assessed by fNIRS is associated with BDNF genotype in fibromyalgia: an exploratory study
Authors: de Oliveira Franco Álvaro , de Oliveira Venturini Guilherme , da Silveira Alves Camila Fernanda , Alves Rael Lopes , Vicuña Paul , Ramalho Leticia , Tomedi Rafaela , Bruck Samara Machado , Torres Iraci L. S. , Fregni Felipe , Caumo Wolnei 
Published: November 2022

AbstractFibromyalgia is a heterogenous primary pain syndrome whose severity has been associated with descending pain modulatory system (DPMS) function and functional connectivity (FC) between pain processing areas. The brain-derived neurotrophic factor (BDNF) Val66Met single nucleotide polymorphism has been linked to vulnerability to chronic pain. In this cross-sectional imaging genetics study, we investigated fibromyalgia, the relationship between BDNF Val66Met heterozygous genotypes (Val/Met), and the functional connectivity (FC) response pattern to acute pain stimulus in the motor (MC) and prefrontal (PFC) cortex assessed by near-infrared spectroscopy (fNIRS) before and after a cold pressor test utilizing water (0–1 °C). Also, we assessed the relationship between this genotype with the DPMS function and quality of life. We included 42 women (Val/Val = 30; Val/Met = 12) with fibromyalgia, ages 18–65. The MANCOVA comparing Val/Met to Val/Val genotypes showed higher ΔFC between left(l)-PFC—l-MC (β = 0.357, p = 0.048), l-PFC—right(r)-PFC (β = 0.249, p = 0.012), l-PFC—r-MC (β = 0.226, p = 0.022), and l-MC—r-PFC (β = 0.260, p = 0.016). Val/Met genotypes showed higher efficiency of the DPMS and lower disability due to pain. Here we show that fibromyalgia patients carrying the Val/Met BDNF genotype presented an increased ΔFC across MC and PFC in response to acute pain associated with differences in acute pain perception and fibromyalgia symptoms.
Simulated global warming affects endophytic bacterial and fungal communities of Antarctic pearlwort leaves and some bacterial isolates support plant growth at low temperatures
Authors: Perazzolli Michele , Vicelli Bianca , Antonielli Livio , Longa Claudia M. O. , Bozza Elisa , Bertini Laura , Caruso Carla , Pertot Ilaria 
Published: November 2022

AbstractAntarctica is one of the most stressful environments for plant life and the Antarctic pearlwort (Colobanthus quitensis) is adapted to the hostile conditions. Plant-associated microorganisms can contribute to plant survival in cold environments, but scarce information is available on the taxonomic structure and functional roles ofC. quitensis-associated microbial communities. This study aimed at evaluating the possible impacts of climate warming on the taxonomic structure ofC. quitensisendophytes and at investigating the contribution of culturable bacterial endophytes to plant growth at low temperatures. The culture-independent analysis revealed changes in the taxonomic structure of bacterial and fungal communities according to plant growth conditions, such as the collection site and the presence of open-top chambers (OTCs), which can simulate global warming. Plants grown inside OTCs showed lower microbial richness and higher relative abundances of biomarker bacterial genera (Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium,Aeromicrobium,Aureimonas,Hymenobacter,Novosphingobium,Pedobacter,PseudomonasandSphingomonas) and fungal genera (Alternaria,Cistella,andVishniacozyma) compared to plants collected from open areas (OA), as a possible response to global warming simulated by OTCs. Culturable psychrotolerant bacteria ofC. quitensiswere able to endophytically colonize tomato seedlings and promote shoot growth at low temperatures, suggesting their potential contribution to plant tolerance to cold conditions.
Quantification of receptor binding from response data obtained at different receptor levels: a simple individual sigmoid fitting and a unified SABRE approach
Authors: Buchwald Peter 
Published: November 2022

AbstractMethods that allow quantification of receptor binding (occupancy) by measuring response (effect) data only are of interest as they can be used to allow characterization of binding properties (e.g., dissociation constant, Kd) without having to perform explicit ligand binding experiments that require different setups (e.g., use of labeled ligands). However, since response depends not just on the binding affinity-determined receptor occupancy, but also on receptor activation, which is affected by ligand efficacy (plus constitutive activity, if present), and downstream pathway amplification, this requires the acquisition and fitting of multiple concentration-response data. Here, two alternative methods, which both are straightforward to implement using nonlinear regression software, are described to fit such multiple responses measured at different receptor levels that can be obtained, for example, by partial irreversible receptor inactivation (i.e., Furchgott method) or different expression levels. One is a simple method via straightforward fitting of each response with sigmoid functions and estimation of Kd from the obtained Emax and EC50 values as Kd = (Emax·EC′50 − E′max·EC50)/(Emax − E′max). This is less error-prone than the original Furchgott method of double-reciprocal fit and simpler than alternatives that require concentration interpolations, thus, should allow more widespread use of this so-far underutilized approach to estimate binding properties. Relative efficacies can then be compared using Emax·Kd/EC50 values. The other is a complex method that uses the SABRE receptor model to obtain a unified fit of the multiple concentration-response curves with a single set of parameters that include binding affinity Kd, efficacy ε, amplification γ, and Hill coefficient n. Illustrations with simulated and experimental data are presented including with activity data of three muscarinic agonists measured in rabbit myocardium.
Temporal and functional interrelationships between bacterioplankton communities and the development of a toxigenic Microcystis bloom in a lowland European reservoir
Authors: Mankiewicz-Boczek Joanna , Font-Nájera Arnoldo 
Published: November 2022

AbstractThe cyanobacteria-associated microbiome is constantly reshaped by bloom development. However, the synergistic-antagonistic nature of the relationships betweenMicrocystisand its microbiome still remains unclear. Therefore, temporal changes of bacterioplankton communities and their functional potential through different developing stages of aMicrocystistoxigenic bloom were investigated, considering bacterioplankton assemblages as particle-attached (PAB) and free-living (FLB) bacteria. 16S rRNA sequencing revealed that PAB were represented by Proteobacteria and Cyanobacteria, while FLB by Proteobacteria and Actinobacteria. Network and ordination analyses indicated that PAB inter-relationships were more complex—numerous connections between taxa with stronger correlations, than FLB—rather influenced by physico-chemical parameters. PAB in pre-summer was diverse with Proteobacteria containing potential taxa involved in nitrogen-transforming processes. In mid-summer, PAB presented a mix-bloom dominated bySnowella,Aphanizomenon, andMicrocystis, which were succeeded by toxigenicMicrocystisin post-summer. Both periods were associated to potential taxa with parasitic/predatory lifestyles against cyanobacteria. In post-summer, Sutterellaceae were recognized as poor water quality indicators, and their strong association withMicrocystiscould have represented an increased threat for that period.Microcystiswas a major factor significantly reducing PAB diversity and evenness, suggesting that it negatively influenced bacterioplankton assemblages, probably also altering the overall community functional potential.
Safety evaluations of a synthetic antimicrobial peptide administered intravenously in rats and dogs
Authors: Cresti Laura , Falciani Chiara , Cappello Giovanni , Brunetti Jlenia , Vailati Silvia , Melloni Elsa , Bracci Luisa , Pini Alessandro 
Published: November 2022

AbstractThe antimicrobial peptide SET-M33 is under study for the development of a new antibiotic against major Gram-negative pathogens. Here we report the toxicological evaluation of SET-M33 administered intravenously to rats and dogs. Dose range finding experiments determined the doses to use in toxicokinetic evaluation, clinical biochemistry analysis, necroscopy and in neurological and respiratory measurements. Clinical laboratory investigations in dogs and rats showed a dose-related increase in creatinine and urea levels, indicating that the kidneys are the target organ. This was also confirmed by necroscopy studies of animal tissues, where signs of degeneration and regeneration were found in kidney when SET-M33 was administered at the highest doses in the two animal species. Neurological toxicity measurements by the Irwin method and respiratory function evaluation in rats did not reveal any toxic effect even at the highest dose. Finally, repeated administration of SET-M33 by short infusion in dogs revealed a no-observed-adverse-effect-level of 0.5 mg/kg/day.
Leptin alleviates endoplasmic reticulum stress induced by cerebral ischemia/reperfusion injury via the PI3K/Akt signaling pathway
Authors: Zhang Yan , Cheng Daobin , Jie Chunxiao , Liu Tao , Huang Shixiong , Hu Shijun 
Published: November 2022

Abstract
Background: Cerebral ischemic/reperfusion injury (CIRI) is a key factor for the prognosis of ischemic stroke (IS), the leading disease in terms of global disability and fatality rates. Recent studies have shown that endoplasmic reticulum stress (ERS) may be a target against CIRI and that leptin, a peptide hormone, has neuroprotective activity to mitigate CIRI.
Methods: An in vitro CIRI model was induced in primary cortical neurons by oxygen-glucose deprivation and reoxygenation (OGD/R) after pretreatment with LY294002 (10 µmol/L) and/or leptin (0.4 mg/L), and cell viability, neuronal morphology and endoplasmic reticulum (ER) dysfunction were evaluated. An in vivo CIRI model was established in rats by middle cerebral artery occlusion and reperfusion (MCAO/R) after the injection of LY294002 (10 μmol/L) and/or leptin (1 mg/kg), and neurological function, infarct volume, cerebral pathological changes, the expression of ERS-related proteins and cell apoptosis were examined.
Results: In vitro, leptin treatment improved the cell survival rate, ameliorated neuronal pathological morphology and alleviated OGD/R-induced ERS. In vivo, administration of leptin significantly reduced the infarct volume, neurological deficit scores and neuronal apoptosis as well as pathological alterations. In addition, leptin suppressed MCAO/R-induced ERS and may decrease apoptosis by inhibiting ERS-related death and caspase 3 activation. It also regulated expression of the antiapoptotic protein Bcl-2 and the proapoptotic protein Bax in the cortex. Furthermore, the inhibitory effect of leptin on ERS was significantly decreased by the effective phosphatidylinositol 3-kinase (PI3K) inhibitor LY294002.
Conclusions: These results confirm that ERS plays an important role in CIRI and that leptin can inhibit the activation of ERS through the PI3K/Akt pathway, thereby alleviating CIRI. These findings provide novel therapeutic targets for IS.
CMOS voltage‐controlled oscillator using inductive dual‐balance source degeneration
Authors: Chang Yu‐Hsin , Liang Jyun‐Fu 
Published: November 2022

AbstractThis study reports a voltage‐controlled oscillator (VCO) implemented in a 0.18‐μm CMOS process. By utilizing an inductive dual‐balance source degeneration (IDSD) technique with a current‐reused structure, the proposed VCO improves the phase noise and the power dissipation. The measured output frequency operates at 2.91–3.07 GHz by adjusting a control voltage from 0 to 1.1 V. The measured phase noise of the carrier of 2.94 GHz is −117.44 and −138.34 dBc/Hz at 1‐ and 10‐MHz offsets, respectively. The proposed VCO core consumes 2.38 mW from a supply voltage of 1.1 V. The figures‐of‐merits (FOMs) of the proposed VCO at 1‐ and 10‐MHz offsets are −183.04 dBc/Hz and −183.94 dBc/Hz, respectively.
A combined BP‐MF algorithm for wideband off‐grid DOA estimation with DP prior
Authors: Li Mengge , Lu Xinhua , Li Ji , Guan Shanwen 
Published: November 2022

AbstractDirection‐of‐arrival (DOA) estimation based on sparse Bayesian learning (SBL) framework has attracted extensive attention. The accuracy of on‐grid DOA estimation is restricted by the prescribed grid, while off‐grid approaches resolves the problem of grid mismatch partly. This letter deals with off‐grid DOA estimation problem of wideband signals. A factor graph is designed to describe the problem with Dirichlet process (DP) prior, which clusters the sparse structure of wideband signals. The authors employ the combined belief propagation‐mean field (BP‐MF) rule on the factor graph and lead to a message passing algorithm. Simulation results verify the superiority of the proposed combined BP‐MF algorithm for off‐grid DOA estimation compared to other state‐of‐art methods.
Optimization of stator ventilation structure of high‐speed railway traction motor based on the genetic algorithm
Authors: Cao Junci , Yan Hua , Li Wenlong , Li Dong , Wang Yu 
Published: November 2022

AbstractAiming at the problems of high power density and difficult heat dissipation of high‐speed train traction motors, this study takes a 600 kW asynchronous traction motor as a research object and optimises the motor cooling structure through the fluid‐structure coupled heat transfer analysis and genetic algorithm. Based on the principles of fluid dynamics and heat transfer, the motor's fluid flow and heat transfer laws are calculated and analysed, and the calculation accuracy is verified by the experimental data. Based on exploring the influence of the stator ventilation aperture and the number on motor fluid and temperature, aiming to reduce the maximum temperature of each motor component, a bivariate multi‐objective mathematical model of stator ventilation aperture and number is established, and the optimal solution is found with the help of the genetic algorithm. Finally, the optimisation effect is verified by comparing the motor temperature before and after optimisation.
Driver identification through vehicular CAN bus data: An ensemble deep learning approach
Authors: Hu Hongyu , Liu Jiarui , Chen Guoying , Zhao Yuting , Men Yuzhuo , Wang Pin 
Published: November 2022

AbstractDriver identification using in‐vehicle data is receiving considerable attention in the field of intelligent transportation owing to the advances in deep learning (DL). In order to improve accuracy and robustness of identification, this paper proposes an ensemble deep learning framework that integrates a modified one‐dimensional convolutional neural network (M 1‐D CNN) and bidirectional long short‐term memory (BLSTM) to improve the performance and robustness of driver identification using information extracted from vehicular CAN‐bus signals. The M 1‐D CNN architecture is developed by adopting inception blocks, residual connection, and global average pooling to obtain optimal deep‐feature representations of local time series. The BLSTM is used to learn the bidirectional long‐term temporal dependencies. Results of extensive experiments using real driving data show that the proposed ensemble DL model can improve the accuracy and robustness of driver identification. Furthermore, four data augmentation methods, namely up‐sampling, adding noise, data reversal, and random drifting, are used to expand the original training data to improve the performance of the ensemble method. Especially, few‐shot learning is performed using the four data augmentation methods, and it shows excellent potential for driver identification with limited data.
A combination of novel hybrid deep learning model and quantile regression for short‐term deterministic and probabilistic PV maximum power forecasting
Authors: Tuyen Nguyen Duc , Thanh Nguyen Trong , Huu Vu Xuan Son , Fujita Goro 
Published: November 2022

AbstractRecently, the increasing penetration of renewable energy resources in power system, especially photovoltaic (PV) systems, has caused severe technical issues due to its randomness and dependency on primary sources. Therefore, precise output power forecast is vital for both system operators and PV system owners to improve grid stability and generation quality, respectively. This article proposes a novel hybrid deep learning model named EDSACL comprising four components, namely, convolutional neural network (CNN), long short‐term memory (LSTM), self‐attention mechanism (SAm), and residual learning (RL) strategy for short‐term PV maximum power prediction with forecast horizon of 1, 2, and 4 h. This proposed model initially extracts spatial‐temporal features of input data based on CNN and LSTM before using SAm to achieve hidden features of LSTM layers. RL strategy is employed to maintain the information flow entering the network. Besides, interval prediction is also implemented based on a combination of the EDSACL model and quantile regression with different probabilities. The forecast accuracy of the proposed model is validated based on two real‐world data sets. The predicted results show the superiority of the proposed model over numerous predictive models; in particular, mean absolute percentage error values of the proposed hybrid model are under 6% in all case studies.
Direct‐Drive wave energy conversion with linear generator: A review of research status and challenges
Authors: Zhang Jing , Yu Haitao , Chen Minshuo 
Published: November 2022

AbstractOcean wave energy is sustainable and renewable energy, wave energy conversion (WEC) effectively solves the disadvantage of traditional power system with fossil energy. WEC system is the power generation process that converts wave kinetic energy of undulation and swing into electric energy, and direct‐drive WEC system (D‐DWEC) converts wave energy into electric power directly by linear generator to improve the efficiency. At present, the research of WEC system has the characteristic of interdisciplinary, multi‐system integration, engineering application, and commercial operation. This paper summarizes the types, principle, and classification of WEC system, mainly focusing on the aspects of the kinds and characteristics of linear generators used in D‐DWEC system, power post‐processing, and power control methods. At the end of the paper, giving the development trend and challenges of D‐DWEC technology, the research of ocean complementary energy generation platform with offshore wind, solar and ocean wave energy is analysed. It shows that the platform has advantages owing to stable power output, efficient utilization, ocean saving‐space, and power generation cost reduction.