bluuebunny/crossref_metadata_2025_split huggingface dataset:
GEOMETRICAL AND MEASURE-THEORETIC STRUCTURES OF MAPS WITH A MOSTLY EXPANDING CENTER
Authors: Yang Jiagang 
Published: July 2021

AbstractIn this article we study physical measures for
$\operatorname {C}^{1+\alpha }$
partially hyperbolic diffeomorphisms with a mostly expanding center. We show that every diffeomorphism with a mostly expanding center direction exhibits a geometrical-combinatorial structure, which we call skeleton, that determines the number, basins and supports of the physical measures. Furthermore, the skeleton allows us to describe how physical measures bifurcate as the diffeomorphism changes under
$C^1$
topology.Moreover, for each diffeomorphism with a mostly expanding center, there exists a
$C^1$
neighbourhood, such that diffeomorphism among a
$C^1$
residual subset of this neighbourhood admits finitely many physical measures, whose basins have full volume.We also show that the physical measures for diffeomorphisms with a mostly expanding center satisfy exponential decay of correlation for any Hölder observes. In particular, we prove that every
$C^2$
, partially hyperbolic, accessible diffeomorphism with 1-dimensional center and nonvanishing center exponent has exponential decay of correlations for Hölder functions.
Currency regimes and external competitiveness: the role of institutions, trade agreements and monetary frameworks
Authors: Aman Zunaira , Mallick Sushanta , Nemlioglu Ilayda 
Published: July 2021

AbstractThe literature lacks consensus on the role of currency regimes in explaining external competitiveness. Countries not only differ in terms of currency regimes, but can also have different institutional arrangements, namely trade agreements and inflation targeting (IT) frameworks in addition to the overall quality of governance. Hence, using the real effective exchange rate and by covering 35 developing countries over the period 1975–2014, we investigate the role of currency regimes in explaining the degree of misalignment while considering institutional factors. First, we find that intermediate regimes limit the currency misalignment with greater financial openness (FO). Second, non-reciprocal preferential trade agreements improve price competitiveness, whereas free trade and reciprocal ones can only be beneficial with a higher degree of FO. Third, misalignments in fixed regimes decline in the presence of stronger institutions or in countries with an IT type of monetary policy framework. The above results remain robust to alternative specifications.
Determining Zygosity in Infant Twins – Revisiting the Questionnaire Approach
Authors: Hardiansyah Irzam , Hamrefors Linnea , Siqueiros Monica , Falck-Ytter Terje , Tammimies Kristiina 
Published: July 2021

AbstractAccurate zygosity determination is a fundamental step in twin research. Although DNA-based testing is the gold standard for determining zygosity, collecting biological samples is not feasible in all research settings or all families. Previous work has demonstrated the feasibility of zygosity estimation based on questionnaire (physical similarity) data in older twins, but the extent to which this is also a reliable approach in infancy is less well established. Here, we report the accuracy of different questionnaire-based zygosity determination approaches (traditional and machine learning) in 5.5 month-old twins. The participant cohort comprised 284 infant twin pairs (128 dizygotic and 156 monozygotic) who participated in the Babytwins Study Sweden (BATSS). Manual scoring based on an established technique validated in older twins accurately predicted 90.49% of the zygosities with a sensitivity of 91.65% and specificity of 89.06%. The machine learning approach improved the prediction accuracy to 93.10%, with a sensitivity of 91.30% and specificity of 94.29%. Additionally, we quantified the systematic impact of zygosity misclassification on estimates of genetic and environmental influences using simulation-based sensitivity analysis on a separate data set to show the implication of our machine learning accuracy gain. In conclusion, our study demonstrates the feasibility of determining zygosity in very young infant twins using a questionnaire with four items and builds a scalable machine learning model with better metrics, thus a viable alternative to DNA tests in large-scale infant twin studies.
Integrated management of living mulches for weed control: A review
Authors: Bhaskar Vinay , Westbrook Anna S. , Bellinder Robin R. , DiTommaso Antonio 
Published: July 2021

AbstractLiving mulches are cover crops grown simultaneously with and in close proximity to cash crops. Advantages of living mulches over dead cover crops may include increased weed suppression, reduced erosion and leaching, better soil health, and greater resource-use efficiency. Advantages of living mulches over synthetic mulches may include enhanced agroecosystem biodiversity and suitability for a wider range of cropping systems. A major disadvantage of this practice is the potential for competition between living mulches and cash crops. The intensity and outcome of mulch-crop competition depend on agroecosystem management as well as climate and other factors. In this review, we consider the management of living mulches for weed control in field and vegetable cropping systems of temperate environments. More than 50 yr of research have demonstrated that mechanical or chemical suppression of a living mulch can limit mulch-crop competition without killing the mulch and thereby losing its benefits. Such tactics can also contribute to weed suppression. Mechanical and chemical regulation should be combined with cultural practices that give the main crop a competitive advantage over the living mulch, which, in turn, outcompetes the weeds. Promising approaches include crop and mulch cultivar selection; changes to planting time, density, and planting pattern; and changes to fertilization or irrigation regimes. A systems approach to living mulch management, including an increased emphasis on the interactions between management methods, may increase the benefits and lower the risks associated with this practice.
Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling
Authors: Xu Zexuan , Di Vittorio Alan , Zhang Jie , Rhoades Alan , Xin Xiaoge , Xu Hongmei , Xiao Chan 
Published: July 2021

AbstractClimate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.
Interplay between interleukin-6 signaling and the vascular endothelium in cytokine storms
Authors: Kang Sujin , Kishimoto Tadamitsu 
Published: July 2021

AbstractInterleukin-6 (IL-6) plays a crucial role in host defense against infection and tissue injuries and is a bioindicator of multiple distinct types of cytokine storms. In this review, we present the current understanding of the diverse roles of IL-6, its receptors, and its signaling during acute severe systemic inflammation. IL-6 directly affects vascular endothelial cells, which produce several types of cytokines and chemokines and activate the coagulation cascade. Endothelial cell dysregulation, characterized by abnormal coagulation and vascular leakage, is a common complication in cytokine storms. Emerging evidence indicates that a humanized anti-IL-6 receptor antibody, tocilizumab, can effectively block IL-6 signaling and has beneficial effects in rheumatoid arthritis, juvenile systemic idiopathic arthritis, and Castleman’s disease. Recent work has also demonstrated the beneficial effect of tocilizumab in chimeric antigen receptor T-cell therapy-induced cytokine storms as well as coronavirus disease 2019 (COVID-19). Here, we highlight the distinct contributions of IL-6 signaling to the pathogenesis of several types of cytokine storms and discuss potential therapeutic strategies for the management of cytokine storms, including those associated with sepsis and COVID-19.
Inflammation and tumor progression: signaling pathways and targeted intervention
Authors: Zhao Huakan , Wu Lei , Yan Guifang , Chen Yu , Zhou Mingyue , Wu Yongzhong , Li Yongsheng 
Published: July 2021

AbstractCancer development and its response to therapy are regulated by inflammation, which either promotes or suppresses tumor progression, potentially displaying opposing effects on therapeutic outcomes. Chronic inflammation facilitates tumor progression and treatment resistance, whereas induction of acute inflammatory reactions often stimulates the maturation of dendritic cells (DCs) and antigen presentation, leading to anti-tumor immune responses. In addition, multiple signaling pathways, such as nuclear factor kappa B (NF-kB), Janus kinase/signal transducers and activators of transcription (JAK-STAT), toll-like receptor (TLR) pathways, cGAS/STING, and mitogen-activated protein kinase (MAPK); inflammatory factors, including cytokines (e.g., interleukin (IL), interferon (IFN), and tumor necrosis factor (TNF)-α), chemokines (e.g., C-C motif chemokine ligands (CCLs) and C-X-C motif chemokine ligands (CXCLs)), growth factors (e.g., vascular endothelial growth factor (VEGF), transforming growth factor (TGF)-β), and inflammasome; as well as inflammatory metabolites including prostaglandins, leukotrienes, thromboxane, and specialized proresolving mediators (SPM), have been identified as pivotal regulators of the initiation and resolution of inflammation. Nowadays, local irradiation, recombinant cytokines, neutralizing antibodies, small-molecule inhibitors, DC vaccines, oncolytic viruses, TLR agonists, and SPM have been developed to specifically modulate inflammation in cancer therapy, with some of these factors already undergoing clinical trials. Herein, we discuss the initiation and resolution of inflammation, the crosstalk between tumor development and inflammatory processes. We also highlight potential targets for harnessing inflammation in the treatment of cancer.
Circulating HMGB1 is elevated in veterans with Gulf War Illness and triggers the persistent pro-inflammatory microglia phenotype in male C57Bl/6J mice
Authors: Garza-Lombó Carla , Thang Morrent , Greve Hendrik J. , Mumaw Christen L. , Messenger Evan J. , Ahmed Chandrama , Quinn Emily , Sullivan Kimberly , Block Michelle L. 
Published: July 2021

AbstractGulf War Illness (GWI) is a chronic, multi-symptom peripheral and CNS condition with persistent microglial dysregulation, but the mechanisms driving the continuous neuroimmune pathology are poorly understood. The alarmin HMGB1 is an autocrine and paracrine pro-inflammatory signal, but the role of circulating HMGB1 in persistent neuroinflammation and GWI remains largely unknown. Using the LPS model of the persistent microglial pro-inflammatory response, male C57Bl/6J mice injected with LPS (5 mg/kg IP) exhibited persistent changes in microglia morphology and elevated pro-inflammatory markers in the hippocampus, cortex, and midbrain 7 days after LPS injection, while the peripheral immune response had resolved. Ex vivo serum analysis revealed an augmented pro-inflammatory response to LPS when microglia cells were cultured with the 7-day LPS serum, indicating the presence of bioactive circulating factors that prime the microglial pro-inflammatory response. Elevated circulating HMGB1 levels were identified in the mouse serum 7 days after LPS administration and in the serum of veterans with GWI. Tail vein injection of rHMGB1 in male C57Bl/6 J mice elevated TNFα mRNA levels in the liver, hippocampus, and cortex, demonstrating HMGB1-induced peripheral and CNS effects. Microglia isolated at 7 days after LPS injection revealed a unique transcriptional profile of 17 genes when compared to the acute 3 H LPS response, 6 of which were also upregulated in the midbrain by rHMGB1, highlighting a distinct signature of the persistent pro-inflammatory microglia phenotype. These findings indicate that circulating HMGB1 is elevated in GWI, regulates the microglial neuroimmune response, and drives chronic neuroinflammation that persists long after the initial instigating peripheral stimulus.
Clinical course of epilepsy and white matter abnormality linked to a novel DYRK1A variant
Authors: Okazaki Tetsuya , Yamada Hiroyuki , Matsuura Kaori , Kasagi Noriko , Miyake Noriko , Matsumoto Naomichi , Adachi Kaori , Nanba Eiji , Maegaki Yoshihiro 
Published: July 2021

AbstractEpilepsy and white matter abnormality have been reported in DYRK1A-related intellectual disability syndrome; however, the clinical course has yet to be elucidated. Here, we report the clinical course of an 18-year-old male with a novel heterozygous DYRK1A variant (NM_001396.4: c.957C>G, p.Tyr319*); based on previous reports, epilepsy with this syndrome tends to be well controlled. Follow-up MRIs of the patient’s lesion revealed slightly reduced signal intensity compared to the first image.
Blastocyst formation, embryo transfer and breed comparison in the first reported large scale cloning of camels
Authors: Olsson P. O. , Tinson A. H. , Al Shamsi N. , Kuhad K. S. , Singh R. , Son Y. B. , Jeong Y. , Jeong Y. W. , Cai L. , Sakaguchi K. , Kim S. , Choi E. J. , Yu X. , Hwang W. S. 
Published: July 2021

AbstractCloning, through somatic cell nuclear transfer (SCNT), has the potential for a large expansion of genetically favorable traits in a population in a relatively short term. In the present study we aimed to produce multiple cloned camels from racing, show and dairy exemplars. We compared several parameters including oocyte source, donor cell and breed differences, transfer methods, embryo formation and pregnancy rates and maintenance following SCNT. We successfully achieved 47 pregnancies, 28 births and 19 cloned offspring who are at present healthy and have developed normally. Here we report cloned camels from surgical embryo transfer and correlate blastocyst formation rates with the ability to achieve pregnancies. We found no difference in the parameters affecting production of clones by camel breed, and show clear differences on oocyte source in cloning outcomes. Taken together we demonstrate that large scale cloning of camels is possible and that further improvements can be achieved.
In-silico investigation towards the non-invasive optical detection of blood lactate
Authors: Chatterjee Subhasri , Budidha Karthik , Qassem Meha , Kyriacou Panicos A. 
Published: July 2021

AbstractThis paper uses Monte Carlo simulations to investigate the interaction of short-wave infrared (SWIR) light with vascular tissue as a step toward the development of a non-invasive optical sensor for measuring blood lactate in humans. The primary focus of this work was to determine the optimal source-detector separation, penetration depth of light at SWIR wavelengths in tissue, and the optimal light power required for reliable detection of lactate. The investigation also focused on determining the non-linear variations in absorbance of lactate at a few select SWIR wavelengths. SWIR photons only penetrated 1.3 mm and did not travel beyond the hypodermal fat layer. The maximum output power was only 2.51% of the input power, demonstrating the need for a highly sensitive detection system. Simulations optimized a source-detector separation of 1 mm at 1684 nm for accurate measurement of lactate in blood.
Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
Authors: Gong Julia , Holsinger F. Christopher , Noel Julia E. , Mitani Sohei , Jopling Jeff , Bedi Nikita , Koh Yoon Woo , Orloff Lisa A. , Cernea Claudio R. , Yeung Serena 
Published: July 2021

AbstractSurgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.
Multilevel information fusion for cryptographic substitution box construction based on inevitable random noise in medical imaging
Authors: Khan Muhammad Fahad , Saleem Khalid , Alshara Mohammed Ali , Bashir Shariq 
Published: July 2021

AbstractBlock cipher has been a standout amongst the most reliable option by which data security is accomplished. Block cipher strength against various attacks relies on substitution boxes. In literature, extensively algebraic structures, and chaotic systems-based techniques are available to design the cryptographic substitution boxes. Although, algebraic and chaotic systems-based approaches have favorable characteristics for the design of substitution boxes, but on the other side researchers have also pointed weaknesses in these approaches. First-time multilevel information fusion is introduced to construct the substitution boxes, having four layers; Multi Sources, Multi Features, Nonlinear Multi Features Whitening and Substitution Boxes Construction. Our proposed design does not hold the weakness of algebraic structures and chaotic systems because our novel s-box construction relies on the strength of true random numbers. In our proposed method true random numbers are generated from the inevitable random noise of medical imaging. The proposed design passes all the substitution box security evaluation criteria including Nonlinearity, Bit Independence Criterion (BIC), Strict Avalanche Criterion (SAC), Differential Approximation Probability (DP), Linear Approximation Probability (LP), and statistical tests, including resistance to Differential Attack, Correlation Analysis, 2D, 3D histogram analysis. The outcomes of the evaluation criteria validate that the proposed substitution boxes are effective for block ciphers; furthermore, the proposed substitution boxes attain better cryptographic strength as compared to very recent state-of-the-art techniques.
In-vitro, in-vivo, and in-silico assessment of radical scavenging and cytotoxic activities of Oliveria decumbens essential oil and its main components
Authors: Jamali Tahereh , Kavoosi Gholamreza , Jamali Yousef , Mortezazadeh Saeed , Ardestani Susan K. 
Published: July 2021

AbstractWe aimed to explore and compare new insights on the pharmacological potential ofOliveria decumbenceessential oil (OEO) and its main components highlighting their antioxidant activity in-vitro, in-vivo, and in-silico and also cytotoxic effects of OEO against A549 lung cancer cells. At first, based on GC–MS analysis, thymol, carvacrol,p-cymene, and γ-terpinene were introduced as basic ingredients of OEO and their in-vitro antioxidant capacity was considered by standard methods. Collectively, OEO exhibited strong antioxidant properties even more than its components. In LPS-stimulated macrophages treated with OEO, the reduction of ROS (Reactive-oxygen-species) and NO (nitric-oxide) and down-regulation of iNOS (inducible nitric-oxide-synthase) and NOX (NADPH-oxidase) mRNA expression was observed and compared with that of OEO components. According to the results, OEO, thymol, and carvacrol exhibited the highest radical scavenging potency compared top-cymene, and γ-terpinene. In-silico Molecular-Docking and Molecular-Dynamics simulation indicated that thymol and carvacrol but nop-cymene and γ-terpinene may establish coordinative bonds in iNOS active site and thereby inhibit iNOS. However, they did not show any evidence for NOX inhibition. In the following, MTT assay showed that OEO induces cytotoxicity in A549 cancer cells despite having a limited effect on L929 normal cells. Apoptotic death and its dependence on caspase-3 activity and Bax/Bcl2 ratio in OEO-treated cells were established by fluorescence microscopy, flow cytometry, colorimetric assay, and western blot analysis. Additionally, flow cytometry studies demonstrated increased levels of ROS in OEO-treated cells. Therefore, OEO, despite showing antioxidant properties, induces apoptosis in cancer cells by increasing ROS levels. Collectively, our results provided new insight into the usage of OEO and main components, thymol, and carvacrol, into the development of novel antioxidant and anti-cancer agents.
Computing the orientational-average of diffusion-weighted MRI signals: a comparison of different techniques
Authors: Afzali Maryam , Knutsson Hans , Özarslan Evren , Jones Derek K. 
Published: July 2021

AbstractNumerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or ‘shell’), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres. To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its ‘isotropic part’. Here, these different methods are simulated and compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61 orientations per shell), and isotropically-distributed sampling vectors, all averaging methods give comparable results, (MAP-MRI-based estimates give slightly higher accuracy, albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give significantly higher accuracy compared with the other methods. We also apply these approaches to in vivo data where the results are broadly consistent with our simulations. A statistical analysis of the simulated data shows that the orientationally-averaged signals at each b-value are largely Gaussian distributed.