bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climate warming drives a temperate-zone lizard to its upper thermal limits, restricting activity, and increasing energetic costs
Authors: Doucette Lisa I. , Duncan Richard P. , Osborne William S. , Evans Murray , Georges Arthur , Gruber Bernd , Sarre Stephen D. 
Published: June 2023

AbstractLizards are considered vulnerable to climate change because many operate near their thermal maxima. Exposure to higher temperatures could reduce activity of these animals by forcing them to shelter in thermal refugia for prolonged periods to avoid exceeding lethal limits. While rising temperatures should reduce activity in tropical species, the situation is less clear for temperate-zone species where activity can be constrained by both low and high temperatures. Here, we measure the effects of natural variation in environmental temperatures on activity in a temperate grassland lizard and show that it is operating near its upper thermal limit in summer even when sheltering in thermal refuges. As air temperatures increased above 32 °C, lizard activity declined markedly as individuals sought refuge in cool microhabitats while still incurring substantial metabolic costs. We estimate that warming over the last two decades has required these lizards to increase their energy intake up to 40% to offset metabolic losses caused by rising temperatures. Our results show that recent increases in temperature are sufficient to exceed the thermal and metabolic limits of temperate-zone grassland lizards. Extended periods of high temperatures could place natural populations of ectotherms under significantly increased environmental stress and contribute to population declines and extinction.
Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images
Authors: El-Den Niveen Nasr , Naglah Ahmed , Elsharkawy Mohamed , Ghazal Mohammed , Alghamdi Norah Saleh , Sandhu Harpal , Mahdi Hani , El-Baz Ayman 
Published: June 2023

AbstractAge-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient’s condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively.
Waning cellular immune responses and predictive factors in maintaining cellular immunity against SARS-CoV-2 six months after BNT162b2 mRNA vaccination
Authors: Ishii Takashi , Hamada Kensuke , Jubishi Daisuke , Hashimoto Hideki , Okamoto Koh , Hisasue Naoko , Sunohara Mitsuhiro , Saito Minako , Shinohara Takayuki , Yamashita Marie , Wakimoto Yuji , Otani Amato , Ikeda Mahoko , Harada Sohei , Okugawa Shu , Moriya Kyoji , Yanagimoto Shintaro 
Published: June 2023

AbstractSeveral clinical trials have shown that the humoral response produced by anti-spike antibodies elicited by coronavirus disease 2019 (COVID-19) vaccines gradually declines. The kinetics, durability and influence of epidemiological and clinical factors on cellular immunity have not been fully elucidated. We analyzed cellular immune responses elicited by BNT162b2 mRNA vaccines in 321 health care workers using whole blood interferon-gamma (IFN-γ) release assays. IFN-γ, induced by CD4 + and CD8 + T cells stimulated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike epitopes (Ag2), levels were highest at 3 weeks after the second vaccination (6 W) and decreased by 37.4% at 3 months (4 M) and 60.0% at 6 months (7 M), the decline of which seemed slower than that of anti-spike antibody levels. Multiple regression analysis revealed that the levels of IFN-γ induced by Ag2 at 7 M were significantly correlated with age, dyslipidemia, focal adverse reactions to full vaccination, lymphocyte and monocyte counts in whole blood, Ag2 levels before the second vaccination, and Ag2 levels at 6 W. We clarified the dynamics and predictive factors for the long-lasting effects of cellular immune responses. The results emphasize the need for a booster vaccine from the perspective of SARS-CoV-2 vaccine-elicited cellular immunity.
A collection of cannabinoid-related negative findings from autaptic hippocampal neurons
Authors: Straiker Alex , Dvorakova Michaela , Bosquez-Berger Taryn , Blahos Jaroslav , Mackie Ken 
Published: June 2023

AbstractAutaptic hippocampal neurons are an architecturally simple model of neurotransmission that express several forms of cannabinoid signaling. Over the past twenty years this model has proven valuable for studies ranging from enzymatic control of endocannabinoid production and breakdown, to CB1 receptor structure/function, to CB2 signaling, understanding ‘spice’ (synthetic cannabinoid) pharmacology, and more. However, while studying cannabinoid signaling in these neurons, we have occasionally encountered what one might call ‘interesting negatives’, valid and informative findings in the context of our experimental design that, given the nature of scientific publishing, may not otherwise find their way into the scientific literature. In autaptic hippocampal neurons we have found that: (1) The fatty acid binding protein (FABP) blocker SBFI-26 does not alter CB1-mediated neuroplasticity. (2) 1-AG signals poorly relative to 2-AG in autaptic neurons. (3) Indomethacin is not a CB1 PAM in autaptic neurons. (4) The CB1-associated protein SGIP1a is not necessary for CB1 desensitization. We are presenting these negative or perplexing findings in the hope that they will prove beneficial to other laboratories and elicit fruitful discussions regarding their relevance and significance.
A generalizable deep learning regression model for automated glaucoma screening from fundus images
Authors: Hemelings Ruben , Elen Bart , Schuster Alexander K. , Blaschko Matthew B. , Barbosa-Breda João , Hujanen Pekko , Junglas Annika , Nickels Stefan , White Andrew , Pfeiffer Norbert , Mitchell Paul , De Boever Patrick , Tuulonen Anja , Stalmans Ingeborg 
Published: June 2023

AbstractA plethora of classification models for the detection of glaucoma from fundus images have been proposed in recent years. Often trained with data from a single glaucoma clinic, they report impressive performance on internal test sets, but tend to struggle in generalizing to external sets. This performance drop can be attributed to data shifts in glaucoma prevalence, fundus camera, and the definition of glaucoma ground truth. In this study, we confirm that a previously described regression network for glaucoma referral (G-RISK) obtains excellent results in a variety of challenging settings. Thirteen different data sources of labeled fundus images were utilized. The data sources include two large population cohorts (Australian Blue Mountains Eye Study, BMES and German Gutenberg Health Study, GHS) and 11 publicly available datasets (AIROGS, ORIGA, REFUGE1, LAG, ODIR, REFUGE2, GAMMA, RIM-ONEr3, RIM-ONE DL, ACRIMA, PAPILA). To minimize data shifts in input data, a standardized image processing strategy was developed to obtain 30° disc-centered images from the original data. A total of 149,455 images were included for model testing. Area under the receiver operating characteristic curve (AUC) for BMES and GHS population cohorts were at 0.976 [95% CI: 0.967–0.986] and 0.984 [95% CI: 0.980–0.991] on participant level, respectively. At a fixed specificity of 95%, sensitivities were at 87.3% and 90.3%, respectively, surpassing the minimum criteria of 85% sensitivity recommended by Prevent Blindness America. AUC values on the eleven publicly available data sets ranged from 0.854 to 0.988. These results confirm the excellent generalizability of a glaucoma risk regression model trained with homogeneous data from a single tertiary referral center. Further validation using prospective cohort studies is warranted.
From fossils to mind
Authors: de Sousa Alexandra A. , Beaudet Amélie , Calvey Tanya , Bardo Ameline , Benoit Julien , Charvet Christine J. , Dehay Colette , Gómez-Robles Aida , Gunz Philipp , Heuer Katja , van den Heuvel Martijn P. , Hurst Shawn , Lauters Pascaline , Reed Denné , Salagnon Mathilde , Sherwood Chet C. , Ströckens Felix , Tawane Mirriam , Todorov Orlin S. , Toro Roberto , Wei Yongbin 
Published: June 2023

AbstractFossil endocasts record features of brains from the past: size, shape, vasculature, and gyrification. These data, alongside experimental and comparative evidence, are needed to resolve questions about brain energetics, cognitive specializations, and developmental plasticity. Through the application of interdisciplinary techniques to the fossil record, paleoneurology has been leading major innovations. Neuroimaging is shedding light on fossil brain organization and behaviors. Inferences about the development and physiology of the brains of extinct species can be experimentally investigated through brain organoids and transgenic models based on ancient DNA. Phylogenetic comparative methods integrate data across species and associate genotypes to phenotypes, and brains to behaviors. Meanwhile, fossil and archeological discoveries continuously contribute new knowledge. Through cooperation, the scientific community can accelerate knowledge acquisition. Sharing digitized museum collections improves the availability of rare fossils and artifacts. Comparative neuroanatomical data are available through online databases, along with tools for their measurement and analysis. In the context of these advances, the paleoneurological record provides ample opportunity for future research. Biomedical and ecological sciences can benefit from paleoneurology’s approach to understanding the mind as well as its novel research pipelines that establish connections between neuroanatomy, genes and behavior.
Social and asocial learning in zebrafish are encoded by a shared brain network that is differentially modulated by local activation
Authors: Pinho Júlia S. , Cunliffe Vincent , Kareklas Kyriacos , Petri Giovanni , Oliveira Rui F. 
Published: June 2023

AbstractGroup living animals use social and asocial cues to predict the presence of reward or punishment in the environment through associative learning. The degree to which social and asocial learning share the same mechanisms is still a matter of debate. We have used a classical conditioning paradigm in zebrafish, in which a social (fish image) or an asocial (circle image) conditioned stimulus (CS) have been paired with an unconditioned stimulus (US=food), and we have used the expression of the immediate early genec-fosto map the neural circuits associated with each learning type. Our results show that the learning performance is similar to social and asocial CSs. However, the brain regions activated in each learning type are distinct and a community analysis of brain network data reveals segregated functional submodules, which seem to be associated with different cognitive functions involved in the learning tasks. These results suggest that, despite localized differences in brain activity between social and asocial learning, they share a common learning module and social learning also recruits a specific social stimulus integration module. Therefore, our results support the occurrence of a common general-purpose learning module, that is differentially modulated by localized activation in social and asocial learning.
Multi-proxy validation of glacial-interglacial rainfall variations in southwest Sulawesi
Authors: Kimbrough Alena K. , Gagan Michael K. , Dunbar Gavin B. , Hantoro Wahyoe S. , Shen Chuan-Chou , Hu Hsun-Ming , Cheng Hai , Edwards R. Lawrence , Rifai Hamdi , Suwargadi Bambang W. 
Published: June 2023

AbstractSpeleothem δ18O is widely used as a proxy for rainfall amount in the tropics on glacial-interglacial to interannual scales. However, uncertainties in the interpretation of this renowned proxy pose a vexing problem in tropical paleoclimatology. Here, we present paired multi-proxy geochemical measurements for stalagmites from southwest Sulawesi, Indonesia, and confirm changes in rainfall amount across ice age terminations. Collectively, the stalagmites span two glacial-interglacial transitions from ~380,000 to 330,000 and 230,000 to 170,000 years ago. Mg/Ca in the slow-growing stalagmites is affected by water moving through the karst and prior calcite precipitation, making it a good proxy for changes in local rainfall. When paired, Mg/Ca and δ18O corroborate prominent shifts from drier glacials to wetter interglacials in the core of the Australasian monsoon domain. These shifts in rainfall occur 4,000-7,000 years later than glacial-interglacial increases in global temperature and the associated response of Sulawesi vegetation, determined by speleothem δ13C.
A computational program for automated surgical planning of fenestrated endovascular repair
Authors: Dillon Tom M. , Liang Patric , Schermerhorn Marc L. , Roche Ellen T. 
Published: June 2023

AbstractAn Abdominal Aortic Aneurysm (AAA) is a dilation of the aorta at the level of the abdomen. To reduce the risk of rupture, an endograft is often implanted inside the aneurysm to decrease pressure on the aneurysm sac. To maintain blood flow to major abdominal vessels, a fenestrated endograft can be used, whereby physicians modify commercial endografts by creating fenestrations based on preoperative computed tomography imaging. The manual process of aligning patient-specific visceral anatomy onto endografts can be tedious and subject to human error. Here we developed a computational program, ‘FenFit’, for automated fitting of fenestrations onto commercially available endografts. A pilot clinical study was conducted to evaluate the efficiency of FenFit compared to physician manual planning, showing FenFit can reduce planning time by 62-fold on average. Our program has potential to improve clinical outcomes by providing a user interface that is expeditious and far less susceptible to human error.
Energy efficient perching and takeoff of a miniature rotorcraft
Authors: Hsiao Yi-Hsuan , Bai Songnan , Zhou Yongsen , Jia Huaiyuan , Ding Runze , Chen Yufeng , Wang Zuankai , Chirarattananon Pakpong 
Published: June 2023

AbstractThe flight time of aircraft rapidly decreases with smaller scales because the lift-to-drag ratio decreases when scaling down. Aerial-surface locomotion, or perching is one energy efficient solution to prolong the fight time by maintaining the drone at a high vantage point. Current perching strategies require additional components to ensure robots firmly attach to the surfaces, and able to detach afterwards, resulting in increased power consumption owing to the added weight. Here, we report a 32-g rotorcraft with the ability to repeatedly perch and take off from overhangs and walls on different wet and dry substances. A propelling thrust is used to support the robot to keep rotorcraft balance against the surface. Integrating with the mussel-inspired wet adhesives, the rotorcraft dispenses the additional components required for attachment and taking off. The final rotorcraft is 32.15 g, only 1.09 g heavier than the original prototype, but shows a 50% and 85% reduction in power consumption when perching on ceilings and walls respectively. The saved power leads to a fourfold increase in the total mission time.
Intermediate observer based simultaneous estimation of system states and multiple faults
Authors: Shi Jiantao , Chen Xiang , Chen Chuang , Shen Mouquan 
Published: June 2023

AbstractIn this paper, an intermediate observer is proposed for simultaneously estimating the system state, actuator fault and sensor fault. The necessary and sufficient condition is given with respect to the existence of the designed observer. The observer gain matrix is guaranteed through solving Riccati inequalities. The estimation errors of the system state, actuator fault and sensor fault are proved to be globally uniformly stable with the prescribed performance index. Finally, a simulation study is given to verify the effectiveness of the proposed method.
Comparison of reliability and economic feasibility for the three configurations of multiple battery charging systems
Authors: Park Sung‐Jun , Kwak Yun‐Gi , Song Sung‐Geun , Kang Feel‐soon 
Published: June 2023

AbstractMultiple battery charging systems can be configured with various combinations of ac‐dc and dc‐dc converters. This paper reviews the reliability and economy of three representative configurations of multi‐battery charging systems. Existing PSA (part stress analysis) can analyse the failure rate considering the environmental factors for the type of parts, the number of parts, the connection state of parts and the voltage and current stress of parts. However, since it does not reflect the system's operating characteristics, the Fault‐tree analysis (FTA) method based on the fault tree is used. Still, it cannot be analysed considering the operational risk of the converter according to various load conditions. Therefore, in this study, by defining a failure according to the minimum and maximum load of the multiple battery charging systems and designing a separate fault tree, the converter operation characteristics were considered by reflecting the number of battery charges, the characteristics according to SOC (state of charge) and the redundancy effect that appears when cyclic charging is applied. Through the new enhanced FTA method, the multiple battery charging systems (Type 1) to which sequential charging is applied are quantitatively proven advantageous in terms of reliability and economy.
Flashover voltage and external insulation design for EMUs’ insulators at high altitude with non‐uniform distributed pollution
Authors: Sun Jixing , Hu Kaixuan , Fan Yongzhi , Liu Jiyong , Yang Sile , Guo Xugang , Zhang Kun 
Published: June 2023

AbstractPollution usually accumulates on the surface of the insulator rapidly on high‐speed electric multiple units (EMUs) under operation condition, and the flashover can occur frequently, especially when the EMUs is running on a high‐altitude line. To solve the problem, this paper proposes the calculation method of insulator withstand voltage under the condition of non‐uniform fouling distribution of high‐speed airflow based on the characteristics of high altitude of Lanzhou‐Xin Line in China, and gives the design and test verification of insulator support insulator for 3500‐km altitude. The design and test verification of the supporting insulator of the rolling stock applicable to 3500 km altitude are presented. The result shows that the insulator should be reliable operation on 4430‐km high altitude area if the insulator structure height is 430 mm, the creep distance is 1250 mm. The anti‐pollution performance is fine in high‐speed wind if the radiuses of the large and small shed of the insulator are 196 and 145 mm, respectively, and the shed spacing is 45 mm. The 50‐Hz withstand voltage of the developed insulator is 131 kV and the light impulse voltage is 228 kV which is suitable for lines running on 3500 m. This research provides a theoretical basis and data to support the safety of high‐speed rolling stock in high‐altitude areas.
Estimation of drone intention using trajectory frequency defined in radar's measurement phase planes
Authors: Yun Joongsup , Anderson David , Fioranelli Francesco 
Published: June 2023

AbstractThis paper presents a radar‐based algorithm for autonomous estimation of drone intention. The algorithm is based on radar's kinematic measurements, providing fast and robust intention estimation for multiple targets. The core idea of the proposed algorithm is to build intention‐specific features for each intention in advance and use them in actual drone surveillance situations. To effectively depict the trajectory characteristics of various intentions, the trajectory frequency is computed on multiple phase planes through Monte Carlo Simulations. Finally, a naive Bayes classifier is applied to integrate the trajectory likelihood in different phases and ultimately compute the intention likelihood of all intentions concerned. Numerical simulations for the three candidate intentions of image acquisition, smuggling, and kamikaze attack demonstrated the performance of the presented method. The simulation results show that one can estimate the true intention of a specific drone by comparing the value of each intention likelihood.
An effective electrocardiogram segments denoising method combined with ensemble empirical mode decomposition, empirical mode decomposition, and wavelet packet
Authors: Yue Yaru , Chen Chengdong , Wu Xiaoyuan , Zhou Xiaoguang 
Published: June 2023

AbstractElectrocardiogram (ECG) is the most extensively applied diagnostic approach for heart diseases. However, an ECG signal is a weak bioelectrical signal and is easily disturbed by baseline wander, powerline interference, and muscle artefacts, which make detection of heart diseases more difficult. Therefore, it is very important to denoise the contaminated ECG signal in practical application. In this article, an effective ECG segments denoising method combining the ensemble empirical mode decomposition (EEMD), empirical mode decomposition (EMD), and wavelet packet (WP) is designed. The ECG signal is decomposed using the EEMD for the first time, and then the highest frequency component is decomposed by the EMD for the second time, and the high frequency components obtained from the second time are decomposed and reconstructed by the WP for the third time. Finally, the processed signal components are fused to obtain the denoised ECG signal. Furthermore, the signal‐to‐noise ratio (SNR), mean square error (MSE), root mean square error (RMSE), and normalised cross correlation coefficient (R) are used to evaluate the noise reduction algorithm. The mean SNR, MSE, RMSE, and R are 5.7427, 0.0071, 0.0551, and 0.9050 in the China Physiological Signal Challenge 2018 dataset. Compared with others denoising methods, the experimental results not only exhibit that the SNR of the ECG signal is effectively improved, but also show that the details of the ECG signal are fully retained, laying a solid foundation for the automatic detection of ECG segments.