bluuebunny/crossref_metadata_2025_split huggingface dataset:
Super-resolved time–frequency measurements of coupled phonon dynamics in a 2D quantum material
Authors: Gentry Christian , Liao Chen-Ting , You Wenjing , Ryan Sinéad A. , Varner Baldwin Akin , Shi Xun , Guan Meng-Xue , Gray Thomas , Temple Doyle , Meng Sheng , Raschke Markus , Rossnagel Kai , Kapteyn Henry C. , Murnane Margaret M. , Cating-Subramanian Emma 
Published: November 2022

AbstractMethods to probe and understand the dynamic response of materials following impulsive excitation are important for many fields, from materials and energy sciences to chemical and neuroscience. To design more efficient nano, energy, and quantum devices, new methods are needed to uncover the dominant excitations and reaction pathways. In this work, we implement a newly-developed superlet transform—a super-resolution time-frequency analytical method—to analyze and extract phonon dynamics in a laser-excited two-dimensional (2D) quantum material. This quasi-2D system, 1T-TaSe2, supports both equilibrium and metastable light-induced charge density wave (CDW) phases mediated by strongly coupled phonons. We compare the effectiveness of the superlet transform to standard time-frequency techniques. We find that the superlet transform is superior in both time and frequency resolution, and use it to observe and validate novel physics. In particular, we show fluence-dependent changes in the coupled dynamics of three phonon modes that are similar in frequency, including the CDW amplitude mode, that clearly demonstrate a change in the dominant charge-phonon couplings. More interestingly, the frequencies of the three phonon modes, including the strongly-coupled CDW amplitude mode, remain time- and fluence-independent, which is unusual compared to previously investigated materials. Our study opens a new avenue for capturing the coherent evolution and couplings of strongly-coupled materials and quantum systems.
Classification of rice leaf blast severity using hyperspectral imaging
Authors: Zhang Guosheng , Xu Tongyu , Tian Youwen , Feng Shuai , Zhao Dongxue , Guo Zhonghui 
Published: November 2022

AbstractRice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.
Model-based exploration of hypokalemia in dairy cows
Authors: Plöntzke Julia , Berg Mascha , Ehrig Rainald , Leonhard-Marek Sabine , Müller Kerstin Elisabeth , Röblitz Susanna 
Published: November 2022

AbstractHypokalemia in dairy cows, which is characterized by too low serum potassium levels, is a severe mineral disorder that can be life threatening. In this paper, we explore different originating conditions of hypokalemia—reduced potassium intake, increased excretion, acid-base disturbances, and increased insulin—by using a dynamic mathematical model for potassium balance in non-lactating and lactating cows. The simulations confirm observations described in literature. They illustrate, for example, that changes in dietary intake or excretion highly effect intracellular potassium levels, whereas extracellular levels vary only slightly. Simulations also show that the higher the potassium content in the diet, the more potassium is excreted with urine. Application of the mathematical model assists in experimental planning and therefore contributes to the 3R strategy: reduction, refinement and replacement of animal experiments.
Formulation, characterization of glucosamine loaded transfersomes and in vivo evaluation using papain induced arthritis model
Authors: Rasheed Muhammad Shahzad , Ansari Sameen Fatima , Shahzadi Iram 
Published: November 2022

AbstractThe aim of current study was to develop the transdermal transfersomes of glucosamine for better drug delivery. Stretch ability and plasticity of transfersomes membranes mitigate the risk of vesicle rupture in the skin and allows the drug carrying transfersome to pass through the epidermis following the natural water gradient. Transdermal delivery of Glucosamine has an advantage over oral route, having greater local concentration and fewer systemic effects. Thin Film Rotary method was use to prepare transfersomes, and characterization was carried out physio-chemically using electron microscopic studies, zeta potential evaluation, entrapment efficiency studies. To add on in the stability, development of a secondary topical vehicle using Carbopol 940 was carried out to enhance the shelf life of transfersomes. Furthermore, in vivo studies on rabbits were also carried out using the papain induced arthritis model to support the effectiveness of treatment. The radiology studies of knee joint of rabbits proved the effectiveness of glucosamine loaded transfersomes in healing the osteoarthritis with the blood plasma analysis remain unaltered. In vitro characterization showed the successful development of nano-deformable entities with good entrapment efficiency but with little stability, therefore modified into a gel. In a nut shell this modified new dosage from can be best alternative to other conventional options that owe lot of demerits.
Effectiveness of ultrasound-guided fascia hydrorelease on the coracohumeral ligament in patients with global limitation of the shoulder range of motion: a pilot study
Authors: Kimura Hiroaki , Suda Masei , Kobayashi Tadashi , Suzuki Shigeki , Fukui Sho , Obata Hideaki 
Published: November 2022

AbstractWe conducted a prospective single-arm interventional study of the treatment efficacy of ultrasound-guided fascia hydrorelease (US-FHR) on the coracohumeral ligament (CHL) of patients with global limitation of shoulder range of motion (ROM) without local inflammation. The primary outcome was the change in passive ROM (pROM) of external rotation (ER) after first US-FHR. Secondary outcomes included the change in pROM of other directions from baseline, the pain visual analogue scale (pVAS) at the timepoints after each procedure (first, second US-FHR and rehabilitation) as well as the change in the Shoulder Pain and Disability Index (SPADI) from the first to the second visit. Eleven patients underwent US-FHR. The pROM of ER after the 1st US-FHR changed by a median of 7.1° (p < 0.01). There was a statistically significant improvement in the pROM of flexion, extension, abduction, external rotation, and internal rotation from baseline to each timepoints. The pVAS at rest showed no significant improvement, although the pVAS at maximal ER showed a trend towards improvement. The SPADI score decreased by a median of 13.4 (p < 0.01). No adverse events were observed. US-FHR on the CHL with or without rehabilitation might be an effective, less invasive treatment for patients with global limitation of shoulder ROM.
Fractal interwoven resonator based penta-band metamaterial absorbers for THz sensing and imaging
Authors: Ozpinar Hurrem , Aksimsek Sinan 
Published: November 2022

AbstractThis paper presents a unique penta-band metamaterial absorber platform for terahertz imaging systems.The proposed fractal metamaterial absorber (FMMA) consists of fractal triangle section metasurfaces. By combining fractal resonators posing different operation skills in the same unit cell, the absorber shows multiband spectral response. The proposed unit cell structure operates at five near perfect absorption modes corresponding to the frequency bands of 1.1 THz, 3.4 THz, 4.9 THz, 5.9 THz, and 7.8 THz, respectively. Based on the fractal metamaterial absorber array, we also propose a sensing pixel design for bimaterial cantilever array sensing systems. The single pixel assembles 4x6 fractal resonator array and $$SiO_2$$

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-Al bimaterial microcantilevers. The sensing region of the FMMA pixel can bend the bimaterial cantilevers effectively at multiple modes, enhancing the imaging capacity. The effective medium theory is executed to visualize the constitute parameters during the absorption and reveal the origin of the rising modes. The absorption mechanism is also discussed based on the surface current distributions and electric field profiles. The numerical outcomes prove that the proposed fractal metamaterial unit cell is a promising candidate as an absorbing platform for THz band sensing and imaging applications. The derived iterative formula used in the fractal design procedure is explained for further investigations of microelectromechanical systems (MEMS) compatible compact absorber arrays.
Uncertainty-aware mixed-variable machine learning for materials design
Authors: Zhang Hengrui , Chen Wei , Iyer Akshay , Apley Daniel W. , Chen Wei 
Published: November 2022

AbstractData-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.
Sirt3 deficiency induced down regulation of insulin degrading enzyme in comorbid Alzheimer’s disease with metabolic syndrome
Authors: Tyagi Alpna , Musa Musa , Labeikovsky Wladimir , Pugazhenthi Subbiah 
Published: November 2022

AbstractSIRT3 deacetylates mitochondrial proteins, thereby enhancing their function. We have previously demonstrated that Sirt3 gene deletion leads to brain mitochondrial dysfunction and neuroinflammation. We also reported that silencing of Sirt3 gene in APP/PS1 mice results in exacerbation of insulin resistance, neuroinflammation and β amyloid plaque deposition. To further understand how metabolic syndrome and amyloid pathology interact, we performed RNA-seq analysis of the brain samples of APP/PS1/Sirt3-/- mice. Gene expression patterns were modulated in metabolic and inflammatory pathways by Sirt3 gene deletion, amyloid pathology, and the combination. Following Sirt3 gene deletion, a key finding was the decreased expression of insulin-degrading enzyme (IDE), an enzyme that regulates the levels of insulin and Aβ peptides. Western diet feeding of Sirt3-/- and APP/PS1 mice resulted in decrease of IDE protein, parallel to Sirt3 downregulation. Conversely, activation of SIRT3 by nicotinamide riboside in vivo and in vitro resulted in IDE upregulation. SIRT3 activation in vivo also increased the levels of neprilysin, another Aβ degrading enzyme and decreased the levels of BACE1 which generates Aβ peptide suggesting SIRT3’s role in amyloid plaque reduction. Our findings provide a plausible mechanism linking metabolic syndrome and amyloid pathology. SIRT3 may be a potential therapeutic target to treat AD.
Soil weathering dynamics and erosion in a dry oceanic area of the southern hemisphere (Otago, New Zealand)
Authors: Raab Gerald , Egli Markus , Norton Kevin P. , Martin Adam P. , Ketterer Michael E. , Tikhomirov Dmitry , Wanner Rahel , Scarciglia Fabio 
Published: November 2022

AbstractLandscape evolution is driven by tectonics, climate and surface denudation. In New Zealand, tectonics and steep climatic gradients cause a dynamic landscape with intense chemical weathering, rapid soil formation, and high soil losses. In this study, soil, and elemental redistribution along two adjacent hillslopes in East Otago, New Zealand, having different landscape settings (ridge versus valley) are compared to identify soil weathering and erosion dynamics. Fallout radionuclides (239+240Pu) show that over the last ~ 60 years, average soil erosion rates in the valley (~ 260 [t km−2 year−1]) are low compared to the ridge (~ 990 [t km−2 year−1]). The ridge yields up to 26% lower soil weathering intensity than the topographical-protected valley. The lowest soil weathering intensity is found at both hilltop positions, where tors (residual rocks) are present and partially disintegrate. The soil weathering intensity increases with distance from tors, suggesting that tors rejuvenate the chemical weathering signature at the hilltop positions with fresh material. The inversed and decreasing weathering degree with all soil depth indicates that the fresh mineral contribution must be higher at the soil surface than at the bedrock weathering front. Higher erosion rates at the exposed ridge may be partially attributed to wind, consistent with rock abrasion of tors, and low local river sediment yields (56 [t km−2 year−1]). Thus, the East Otago spatial patterns of soil chemistry and erosion are governed by tor degradation and topographic exposure.
Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography
Authors: Hsu Kang , Yuh Da-Yo , Lin Shao-Chieh , Lyu Pin-Sian , Pan Guan-Xin , Zhuang Yi-Chun , Chang Chia-Ching , Peng Hsu-Hsia , Lee Tung-Yang , Juan Cheng-Hsuan , Juan Cheng-En , Liu Yi-Jui , Juan Chun-Jung 
Published: November 2022

AbstractDeep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal–Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.
The significance of right ear auditory processing to balance
Authors: Putter-Katz Hanna , Horev Niza , Yaakobi Erez , Been Ella 
Published: November 2022

AbstractAlthough the association between balance and hearing thresholds at different frequencies in the right/left ear is crucial, it has received scant empirical attention. Balance is widely ignored when evaluating hearing in adults. This study examined the relative contribution of left versus right ear hearing at different frequencies to balance, and the mediating role of suprathreshold speech perception on age-balance associations. Pure tone hearing thresholds (500–4000 Hz), suprathreshold speech perception, balance, and risk of falling were evaluated in 295 adults. The results indicate that the right ear contributes more to balance than the left ear. This might imply dominance of the left hemisphere in processing hearing cues for balance. Frequencies within the speech range (500/1000/2000 Hz) were correlated with balance and mediated the interaction between age and balance. These results should be considered when tailoring hearing and balance rehabilitation programs.
Hot weather hazard analysis over India
Authors: Srivastava Akhil , Mohapatra M. , Kumar Naresh 
Published: November 2022

AbstractHeat waves are often termed as the silent killer and have become even more important as recent studies suggest that the heat wave have become second most devastating extreme weather events in terms of human deaths and losses. It is also been largely realised by scientific community that it is not just the high temperatures which are responsible for the gruesome effect of heat waves but several other meteorological parameters play a vital role in aggravating the impact and causing much more damages. In view of the above the attention of scientific community, weather forecasters as well as disaster managers has shifted to also take into account the different meteorological parameters like maximum and minimum temperatures, relative humidity, wind speed, duration/spell of heat waves and its intensity which are aggravating the impact of heat stress. In this background, this study is undertaken as an attempt to quantify the effect of different meteorological parameters on heat wave on different regions of India for different summer months (March, April, May and June). In this study the impact of individual meteorological parameter as well their cumulative effect is studied based on data of 30 years (1981–2010) for 300 stations. The effect of different meteorological parameters is identified for different months for different regions of the country. Also the cumulative scores are calculated for different regions considering different meteorological parameters, as a first initiative to perform heat hazard analysis and zonation over the entire country. This could serve as initial step for planning mitigation and adaptation strategies throughout the country. These scores as thresholds for different regions may be also useful for operational forecaster’s for early impact based warning services as well as for the disaster managers, for taking effective and timely actions.
Factors underlying bird community assembly in anthropogenic habitats depend on the biome
Authors: Vaccaro Anahí S. , Filloy Julieta 
Published: November 2022

AbstractProduction activities drive the replacement of original habitats with artificial ones, leading to new bird assemblages. In this study, we assessed if anthropogenic habitats acted as environmental filters causing functional redundancy or as promoters of functional divergence, depending on the biome. We also investigated if functional patterns derived from phylogenetic convergence or clustering. For this purpose, we computed the standardized effect sizes (SES) for avian functional and phylogenetic diversity using null models and compared the SES values among tree plantations, urban settlements (US), cattle pastures (CP), crop fields (CF) and natural habitats from two biomes: grassland and forest. We used generalized least squares models to test if functional and phylogenetic SES indicated functional redundancy or divergence, and phylogenetic convergence or clustering. We found functional redundancy in grassland and functional divergence in forest associated with environmental filtering and competitive exclusion, respectively. In grassland, functional structure was not associated with a clear phylogenetic pattern, while in forest functional divergence was caused by evolutionary convergence in CF and CP and conservation in US. The prevalences of functional redundancy and functional divergence patterns and their associated predominant mechanism of community assembly were found to depend on the biome and the regional species pool.
Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks
Authors: Ito Koichi , Kanemitsu Shunsuke , Kimura Ryusuke , Omori Ryosuke 
Published: November 2022

AbstractHuman behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population’s behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.
Metacognitive deficits are associated with lower sensitivity to preference reversals in nicotine dependence
Authors: Soutschek Alexander , Bulley Adam , Wittekind Charlotte E. 
Published: November 2022

AbstractDeficits in impulse control belong to the core profile of nicotine dependence. Smokers might thus benefit from voluntarily self-restricting their access to the immediate temptation of nicotine products (precommitment) in order to avoid impulse control failures. However, little is known about how smokers’ willingness to engage in voluntary self-restrictions is determined by metacognitive insight into their general preferences for immediate over delayed rewards. Here, with a series of monetary intertemporal choice tasks, we provide empirical evidence for reduced metacognitive accuracy in smokers relative to non-smokers and show that smokers overestimate the subjective value of delayed rewards relative to their revealed preferences. In line with the metacognitive deficits, smokers were also less sensitive to the risk of preference reversals when deciding whether or not to restrict their access to short-term financial rewards. Taken together, the current findings suggest that deficits not only in impulse control but also in metacognition may hamper smokers’ resistance to immediate rewards and capacity to pursue long-term goals.