bluuebunny/crossref_metadata_2025_split huggingface dataset:
Potential of ultra-high-resolution photon-counting CT of bone metastases: initial experiences in breast cancer patients
Authors: Wehrse E. , Sawall S. , Klein L. , Glemser P. , Delorme S. , Schlemmer H.-P. , Kachelrieß M. , Uhrig M. , Ziener C. H. , Rotkopf L. T. 
Published: January 2021

AbstractConventional CT scanners use energy-integrating detectors (EIDs). Photon-counting detector (PCD) computed tomography (CT) utilizes a CT detector technology based on smaller detector pixels capable of counting single photons and in addition discriminating their energy. Goal of this study was to explore the potential of higher spatial resolution for imaging of bone metastases. Four female patients with histologically confirmed breast cancer and bone metastases were included between July and October 2019. All patients underwent conventional EID CT scans followed by a high resolution non-contrast experimental PCD CT scan. Ultra-high resolution (UHR) reconstruction kernels were used to reconstruct axial slices with voxel sizes of 0.3 mm × 0.3 mm (inplane) × 1 mm (z-direction). Four radiologists blinded for patient identity assessed the images and compared the quality to conventional CT using a qualitative Likert scale. In this case series, we present images of bone metastases in breast cancer patients using an experimental PCD CT scanner and ultra-high-resolution kernels. A tendency to both a smaller inter-reader variability in the structural assessment of lesion sizes and in the readers’ opinion to an improved visualization of lesion margins and content was observed. In conclusion, while further studies are warranted, PCD CT has a high potential for therapy monitoring in breast cancer.
MCM3 upregulation confers endocrine resistance in breast cancer and is a predictive marker of diminished tamoxifen benefit
Authors: Løkkegaard Sanne , Elias Daniel , Alves Carla L. , Bennetzen Martin V. , Lænkholm Anne-Vibeke , Bak Martin , Gjerstorff Morten F. , Johansen Lene E. , Vever Henriette , Bjerre Christina , Kirkegaard Tove , Nordenskjöld Bo , Fornander Tommy , Stål Olle , Lindström Linda S. , Esserman Laura J. , Lykkesfeldt Anne E. , Andersen Jens S. , Leth-Larsen Rikke , Ditzel Henrik J. 
Published: January 2021

AbstractResistance to endocrine therapy in estrogen receptor-positive (ER+) breast cancer is a major clinical problem with poorly understood mechanisms. There is an unmet need for prognostic and predictive biomarkers to allow appropriate therapeutic targeting. We evaluated the mechanism by which minichromosome maintenance protein 3 (MCM3) influences endocrine resistance and its predictive/prognostic potential in ER+ breast cancer. We discovered that ER+ breast cancer cells survive tamoxifen and letrozole treatments through upregulation of minichromosome maintenance proteins (MCMs), including MCM3, which are key molecules in the cell cycle and DNA replication. Lowering MCM3 expression in endocrine-resistant cells restored drug sensitivity and altered phosphorylation of cell cycle regulators, including p53(Ser315,33), CHK1(Ser317), and cdc25b(Ser323), suggesting that the interaction of MCM3 with cell cycle proteins is an important mechanism of overcoming replicative stress and anti-proliferative effects of endocrine treatments. Interestingly, the MCM3 levels did not affect the efficacy of growth inhibitory by CDK4/6 inhibitors. Evaluation of MCM3 levels in primary tumors from four independent cohorts of breast cancer patients receiving adjuvant tamoxifen mono-therapy or no adjuvant treatment, including the Stockholm tamoxifen (STO-3) trial, showed MCM3 to be an independent prognostic marker adding information beyond Ki67. In addition, MCM3 was shown to be a predictive marker of response to endocrine treatment. Our study reveals a coordinated signaling network centered around MCM3 that limits response to endocrine therapy in ER+ breast cancer and identifies MCM3 as a clinically useful prognostic and predictive biomarker that allows personalized treatment of ER+ breast cancer patients.
High-throughput reaction engineering to assess the oxidation stability of MAX phases
Authors: Sauceda D. , Singh P. , Falkowski A. R. , Chen Y. , Doung T. , Vazquez G. , Radovic M. , Arroyave R. 
Published: January 2021

AbstractThe resistance to oxidizing environments exhibited by some Mn+1AXn (MAX) phases stems from the formation of stable and protective oxide layers at high operating temperatures. The MAX phases are hexagonally arranged layered nitrides or carbides with general formula Mn+1AXn, n = 1, 2, 3, where M is early transition elements, A is A block elements, and X is C/N. Previous attempts to model and assess oxide phase stability in these systems has been limited in scope due to higher computational costs. To address the issue, we developed a machine-learning driven high-throughput framework for the fast assessment of phase stability and oxygen reactivity of 211 chemistry MAX phase M2AX. The proposed scheme combines a sure independence screening sparsifying operator-based machine-learning model in combination with grand-canonical linear programming to assess temperature-dependent Gibbs free energies, reaction products, and elemental chemical activity during the oxidation of MAX phases. The thermodynamic stability, and chemical activity of constituent elements of Ti2AlC with respect to oxygen were fully assessed to understand the high-temperature oxidation behavior. The predictions are in good agreement with oxidation experiments performed on Ti2AlC. We were also able to explain the metastability of Ti2SiC, which could not be synthesized experimentally due to higher stability of competing phases. For generality of the proposed approach, we discuss the oxidation mechanism of Cr2AlC. The insights of oxidation behavior will enable more efficient design and accelerated discovery of MAX phases with maintained performance in oxidizing environments at high temperatures.
Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
Authors: Lee Kookjin , Nam Sangjin , Ji Hyunjin , Choi Junhee , Jin Jun-Eon , Kim Yeonsu , Na Junhong , Ryu Min-Yeul , Cho Young-Hoon , Lee Hyebin , Lee Jaewoo , Joo Min-Kyu , Kim Gyu-Tae 
Published: January 2021

AbstractTwo-dimensional (2D) layered materials such as graphene, molybdenum disulfide (MoS2), tungsten disulfide (WSe2), and black phosphorus (BP) provide unique opportunities to identify the origin of current fluctuation, mainly arising from their large surface areas compared with those of their bulk counterparts. Among numerous material characterization techniques, nondestructive low-frequency (LF) noise measurement has received significant attention as an ideal tool to identify a dominant scattering origin such as imperfect crystallinity, phonon vibration, interlayer resistance, the Schottky barrier inhomogeneity, and traps and/or defects inside the materials and dielectrics. Despite the benefits of LF noise analysis, however, the large amount of time-resolved current data and the subsequent data fitting process required generally cause difficulty in interpreting LF noise data, thereby limiting its availability and feasibility, particularly for 2D layered van der Waals hetero-structures. Here, we present several model algorithms, which enables the classification of important device information such as the type of channel materials, gate dielectrics, contact metals, and the presence of chemical and electron beam doping using more than 100 LF noise data sets under 32 conditions. Furthermore, we provide insights about the device performance by quantifying the interface trap density and Coulomb scattering parameters. Consequently, the pre-processed 2D array of Mel-frequency cepstral coefficients, converted from the LF noise data of devices undergoing the test, leads to superior efficiency and accuracy compared with that of previous approaches.
A dopamine metabolite stabilizes neurotoxic amyloid-β oligomers
Authors: Cataldi Rodrigo , Chia Sean , Pisani Katarina , Ruggeri Francesco S. , Xu Catherine K. , Šneideris Tomas , Perni Michele , Sarwat Sunehera , Joshi Priyanka , Kumita Janet R. , Linse Sara , Habchi Johnny , Knowles Tuomas P. J. , Mannini Benedetta , Dobson Christopher M. , Vendruscolo Michele 
Published: January 2021

AbstractAberrant soluble oligomers formed by the amyloid-β peptide (Aβ) are major pathogenic agents in the onset and progression of Alzheimer’s disease. A variety of biomolecules can influence the formation of these oligomers in the brain, although their mechanisms of action are still largely unknown. Here, we studied the effects on Aβ aggregation of DOPAL, a reactive catecholaldehyde intermediate of dopamine metabolism. We found that DOPAL is able to stabilize Aβ oligomeric species, including dimers and trimers, that exert toxic effects on human neuroblastoma cells, in particular increasing cytosolic calcium levels and promoting the generation of reactive oxygen species. These results reveal an interplay between Aβ aggregation and key biochemical processes regulating cellular homeostasis in the brain.
Deep‐reinforcement learning for fair distributed dynamic spectrum access in priority buffered heterogeneous wireless networks
Authors: Barqi Janiar Siavash , Pourahmadi Vahid 
Published: January 2021

AbstractIn this paper, distributed heterogeneous wireless networks are studied and a spectrum access scheme that fairly allocates channels among users based on their request levels is investigated. To be closer to what happens in practice, users' request levels to have different priorities are also considered, i.e. some packets are more important for the user to be transmitted than the others. Due to the distributed nature of the problem and since users are not able to coordinate with each other before transmission, a scheme based on reinforcement learning is proposed in which the network state is estimated based on the previous successful transmissions of packets in the network. The reinforcement learning method's adaptive nature lets our method work in heterogeneous settings where there exist users with other medium access control protocols. The performance of the proposed scheme is evaluated in different network settings, and it is shown that it tries to implement a fair spectrum access policy (considering the packets' priorities) while maximising the total throughput of the network.
Compensation for the non‐uniformity correction of multi‐channel TDI CCD under variable gain
Authors: Li Yun‐Hui , Wang Xiao‐Dong , Wang Zhi 
Published: January 2021

AbstractThe compensation for non‐uniformity correction of multi‐channel time delay integration charge coupled device (TDI CCD) under variable gain is presented. The mathematical model of photoelectric conversion is established first, and, then, a two‐step non‐uniformity correction method for multi‐channel TDI CCD is proposed. Based on this method, a compensation algorithm for non‐uniformity correction under variable gain is elaborated in detail. The experimental results show that the proposed algorithm can always maintain the photo response non‐uniformity (PRNU) of the corrected images better than 1.14% when the programmable gain amplification (PGA) gain varies within the range of 1–3, compared to the conventional method of degrading to 2.91%. Meanwhile, a compression method for correction coefficients under multiple integral stages is proposed, which can reduce the storage space requirement by 50% at the cost of the PRNU being degraded by a maximum of 0.19% at all integral stages.
Effect of polarity‐reversal voltage on charge accumulation and carrier mobility in silicone rubber/silicon carbide composites
Authors: Du Boxue , Han Chong , Li Zhonglei , Han Chenlei , Li Jin , Xiao Meng , Yang Zhuoran 
Published: January 2021

AbstractPolarity‐reversal voltage in high‐voltage direct current transmission system would cause serious electric field distortion, accelerating charge injection. The effect of polarity reversal on charge transport characteristics in silicone rubber (SiR)/silicon carbide (SiC) composites with nonlinear conductivity at different temperatures is studied. The charge transport characteristics of silicone rubber/silicon carbide composites under unipolarity voltage and polarity‐reversal voltage at 30°C, 70°C and 90°C are obtained by the isothermal surface potential decay method, and the carrier mobility and trap characteristics of the composites are further calculated. Studies have shown that a large amount of hetero‐charge remains in silicone rubber materials under polarity‐reversal voltage, which in turn leads to intensive charge neutralisation and severe electric field distortion. The silicon carbide doped composites exhibit excellent performance in suppressing the accumulation of residual charges and weakening the charge neutralisation under polarity‐reversal voltage. In addition, high temperature enhances the conductivity of silicone rubber/silicon carbide composites and reduces the accumulation of residual charges, which restrains the distortion of the local electric field under polarity‐reversal voltage. This research shows that the application of non‐linear conductivity material is an effective measure to solve the problem of cable insulation under polarity‐reversal voltage.
Diversity-Oriented Synthesis of Novel Trihalomethyl-Containing Spirochromeno[3,4-a](thia)pyrrolizidines and Spirochromeno-[3,4-a]indolizidines by One-Pot, Three-Component [3+2]-Cyclo­addition Reaction
Authors: Korotaev Vladislav Y. , Kutyashev Igor B. , Sannikov Maxim S. , Kochnev Ivan A. , Barkov Alexey Y. , Zimnitskiy Nikolay S. , Sosnovskikh Vyacheslav Y. 
Published: January 2021

AbstractRegio- and stereoselective methods for the synthesis of 6′-trifluoro(trichloro)methyl substituted spiro[acenaphthylene-1,11′-chromeno[3,4-a](thia)pyrrolizidin]-2-ones and spiro[acenaphthylene-1,12′-chromeno[3,4-a]indolizidin]-2-ones have been developed based on the three-component reaction of 3-nitro-2-trifluoro(trichloro)methyl-2H-chromenes with azomethine ylides generated in situ from acenaphthenequinone and cyclic α-amino acids. The cycloaddition proceeds under mild conditions in ethanol or DMSO, and only endo-isomers of the products with cis-arrangement of nitro and trifluoromethyl groups are formed. The relative configuration of cycloadducts is reliably confirmed by X-ray diffraction analysis and by 2D NOESY spectroscopy.
Single-cell transcriptomic analysis reveals the immune landscape of lung in steroid-resistant asthma exacerbation
Authors: Wang Lingli , Netto Keilah G. , Zhou Lujia , Liu Xiaojie , Wang Ming , Zhang Guojun , Foster Paul S. , Li Fuguang , Yang Ming 
Published: January 2021

SignificanceAsthma exacerbation is not prevented by standard corticosteroid-based therapy and is the major burden in terms of morbidity, mortality, and health care costs associated with asthma. Etiology of disease exacerbation is highly heterogeneous. Using single-cell RNA deep sequencing, we undertook a large-scale, high-dimensional analysis of the immune landscape in lung following dexamethasone treatments in a mouse model of asthma exacerbation. We found multicellular signaling pathways closely associated with disease progression during the exacerbation. IL-13 produced by CD8+memory T cells, ILC2, and basophils is key a cytokine in driving the pathogenesis of asthma exacerbation in this model. These data provide important insights into how the immune landscape in lung during asthma exacerbation can shape the progress of disease.
Six-year follow-up study of residential displacement and health outcomes following the 2011 Japan Earthquake and Tsunami
Authors: Hikichi Hiroyuki , Aida Jun , Kondo Katsunori , Kawachi Ichiro 
Published: January 2021

Significance
The long-term health consequences of residential relocation following large-scale natural disasters remain poorly understood. We implemented a longitudinal study following the 2011 Earthquake and Tsunami in which survivors were relocated to temporary housing by different means: group relocation versus individual relocation. Group relocation was adversely associated with weight gain and depressive symptoms. An implication of our findings is that moving survivors into a highly dense environment conducive to social gatherings may paradoxically promote unhealthy changes in behavior (e.g., drinking and snacking). By contrast, we found that individual relocation was associated with deteriorated instrumental activities of daily living and cognitive function. The resettlement of survivors can have complex effects on physical and mental well-being, depending on the method of relocation.
Lessons being learned from the Covid-19 pandemic for radiological emergencies and vice versa: report from expert discussions
Authors: Martell Meritxell , Perko Tanja , Zeleznik Nadja , Molyneux-Hodgson Susan 
Published: January 2021

Abstract
In order to examine what lessons radiological emergency management may offer for the management of the Covid-19 pandemic, and vice versa, a series of three online webinars were conducted with leading experts, scholars and practitioners from a wide range of disciplines essential for emergency management and long-term risk governance. The first webinar debated the lessons we are learning from the Covid-19 pandemic for radiological risk communication, the second explored issues around longer-term outcomes of a crisis and how to balance these with short-term actions whilst the third focused on the key challenges of the ‘transition phase’, using lessons learned from the Chernobyl (1986) and Fukushima Daichii (2011) accidents. This paper reviews the discussions and provides valuable lessons for the radiation protection community. Results of the discussions indicated that: (a) non-radiological and non-epidemiological consequences of emergencies, for example psychological (mental health), societal and economic, should not be underestimated; (b) multidisciplinary expertise is imperative for communication efforts and for effective emergency management, including decision-making in the application of protective measures; (c) stakeholder engagement, including the involvement of the potentially affected population, should be encouraged from an early stage; and (d) trust is increased if policy-makers and the main science agencies show a unified voice.
Magneto-induced Mullins effect of anisotropic MREs under compression mode
Authors: Gan Ruyi , Qi Song , Zhao Yi , Fu Jie , Li Shixu , Li Yaping , Yu Miao 
Published: January 2021

Abstract
The filled elastomer materials exhibit obvious stress-softening phenomenon after initial stretching, which is called the Mullins effect. Since magnetorheological elastomers tend to work under a magnetic field, it will show a different magneto-induced Mullins effect of traditional materials. Therefore, in this paper, the effects of anisotropy, magnetic flux density, pre-strain and compression times on the magneto-induced Mullins effect were studied by rheometer. The results demonstrated that, under a fixed magnetic flux density, when the arrangement of magnetic particles is perpendicular to the magnetic field, the residual strain energy of the material is the smallest. This indicates that the magneto-induced Mullins effect is the weakest and the stability of the material is the best. Meanwhile, when the compression pre-strain is constant, the materials will show an obvious stress-hardening phenomenon under magnetic field. When the arrangement of magnetic particles is 30° to the magnetic field, the force between the particles is the smallest. This indicates that the magneto-induced Mullins effect is the weakest and the performance stability is the best. At the same time, this paper attempts to establish the corresponding model of the magneto-induced Mullins effect from the perspective of strain energy. The error between the fitting and the experimental results is less than 3%, which shows that the model can well explain the phenomena obtained in the experiment.
Direct comparison of ITS-90 and PLTS-2000 from 0.65 K to 1 K at LNE-CNAM
Authors: Pan Changzhao , Sparasci Fernando , Plimmer Mark , Risegari Lara , Daugas Jean-Michel , Rouille Gérard , Gao Bo , Pitre Laurent 
Published: January 2021

Abstract
In the temperature range between 0.65 K and 1 K, the international temperature scale of 1990 (ITS-90) is based on 3He vapour–pressure thermometers and overlaps with the provisional low temperature scale of 2000 (PLTS-2000) defined by the melting pressure of 3He. An indirect comparison at PTB revealed differences between the two scales of up to 1.5 mK at 0.65 K (Engert et al 2007 Metrologia 44 40–52). Stimulated by the PTB results, we have performed a direct comparison T
90–T
2000 from 0.65 K to 1 K at LNE-CNAM. To test repeatability, the experiment was conducted twice: in 2019 and 2020. We find differences T
90–T
2000 of 0.28 mK at 1 K, increasing to 1.58 mK at 0.65 K. The direct comparison, eliminates the uncertainty component due to the transfer resistance thermometer and its calibration. Except for a point near 1 K, the new results are in accordance with those obtained at PTB (differences of less than 0.22 mK), which makes it possible to improve the accuracy of the equation specified in ITS-90.
Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification
Authors: Liu Xiuling , Lv Linyang , Shen Yonglong , Xiong Peng , Yang Jianli , Liu Jing 
Published: January 2021

Abstract

Objective. Motor imagery (MI) electroencephalography (EEG) classification is regarded as a promising technology for brain–computer interface (BCI) systems, which help people to communicate with the outside world using neural activities. However, decoding human intent accurately is a challenging task because of its small signal-to-noise ratio and non-stationary characteristics. Methods that directly extract features from raw EEG signals ignores key frequency domain information. One of the challenges in MI classification tasks is finding a way to supplement the frequency domain information ignored by the raw EEG signal. Approach. In this study, we fuse different models using their complementary characteristics to develop a multiscale space-time-frequency feature-guided multitask learning convolutional neural network (CNN) architecture. The proposed method consists of four modules: the space-time feature-based representation module, time-frequency feature-based representation module, multimodal fused feature-guided generation module, and classification module. The proposed framework is based on multitask learning. The four modules are trained using three tasks simultaneously and jointly optimized. Results. The proposed method is evaluated using three public challenge datasets. Through quantitative analysis, we demonstrate that our proposed method outperforms most state-of-the-art machine learning and deep learning techniques for EEG classification, thereby demonstrating the robustness and effectiveness of our method. Moreover, the proposed method is employed to realize control of robot based on EEG signal, verifying its feasibility in real-time applications. Significance. To the best of our knowledge, a deep CNN architecture that fuses different input cases, which have complementary characteristics, has not been applied to BCI tasks. Because of the interaction of the three tasks in the multitask learning architecture, our method can improve the generalization and accuracy of subject-dependent and subject-independent methods with limited annotated data.