bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flow-signal correlation in seal whisker array sensing
Authors: Liu Geng , Jiang Weili , Zheng Xudong , Xue Qian 
Published: November 2021

Abstract
Phocid seals detect and track artificial or biogenic hydrodynamic trails based on mechanical signals of their whisker arrays. In this paper, we investigated the correlations between flow structures and whisker array signals using a simplified numerical model of fluid–structure interaction (FSI). Three-dimensional (3D) wakes of moving paddles in three different shapes (rectangular plate, undulated plate, and circular cylinder) were simulated using an in-house immersed-boundary-method-based computational fluid dynamics solver. One-way FSI was then simulated to obtain the dynamic behavior and root signal of each whisker in the two whisker arrays on a seal head in each wake. The position, geometry, and material properties of each whisker were modeled based on the measurements reported in literatures. The correlations between the wake structures and whisker array signals were analyzed. It was found that the patterns of the signals on the whisker arrays can reflect the strength, timing, and moving trajectories of the jets induced by the vortices in the wakes. Specifically, the rectangular plate generates the strongest starting vortex ring as well as the strongest jets, while the undulated plate generates the weakest ones. These flow features are fully reflected by the largest whisker signal magnitude in the rectangular plate sensing and the smallest one in the undulated plate sensing. Moreover, the timing of the signal initiation and the maximum signal agree well with the timing of the jet reaching the arrays and the maximum flow speed, respectively. The correlation coefficient between the moving trajectories of the jet and the movement of the high signal level region in the array was found to be higher than 0.9 in the rectangular plate case. The results provide a physical insight into the mechanisms of seal whisker flow sensing.
Monitoring decellularization via absorbance spectroscopy during the derivation of extracellular matrix scaffolds
Authors: Mora-Navarro Camilo , Garcia Mario E , Sarker Prottasha , Ozpinar Emily W , Enders Jeffrey R , Khan Saad , Branski Ryan C , Freytes Donald O 
Published: November 2021

Abstract
Extracellular matrix (ECM) is a complex structure composed of bioactive molecules representative of the local tissue microenvironment. Decellularized ECM biomaterials harness these biomolecules for regenerative medicine applications. One potential therapeutic application is the use of vocal fold (VF) specific ECM to restore the VFs after injury. ECM scaffolds are derived through a process of decellularization, which aims to remove unwanted immunogenic biomolecules (e.g. DNA) while preserving the composition of the ECM. The effectiveness of the decellularization is typically assessed at the end by quantifying ECM attributes such as final dsDNA content. However, batch-to-batch variability in ECM manufacturing remains a significant challenge for the standardization, cost-effectiveness, and scale-up process. The limited number of tools available for in-process control heavily restricts the uncovering of the correlations between decellularization process parameters and ECM attributes. In this study, we developed a technique applicable to both the classical batch method and semi-continuous decellularization systems to trace the decellularization of two laryngeal tissues in real-time. We hypothesize that monitoring the bioreactor’s effluent absorbance at 260 nm as a function of time will provide a representative DNA release profile from the tissue and thus allow for process optimization. The DNA release profiles were obtained for laryngeal tissues and were successfully used to optimize the derivation of VF lamina propria-ECM (auVF-ECM) hydrogels. This hydrogel had comparable rheological properties to commonly used biomaterials to treat VF injuries. Also, the auVF-ECM hydrogel promoted the down-regulation of CCR7 by THP-1 macrophages upon lipopolysaccharide stimulation in vitro suggesting some anti-inflammatory properties. The results show that absorbance profiles are a good representation of DNA removal during the decellularization process thus providing an important tool to optimize future protocols.
Virtual nitrogen and phosphorus flow associated with interprovincial crop trade and its effect on grey water stress in China
Authors: Ren Dandan , Liu Wenfeng , Yang Hong , Zhuo La , Tong Yindong , Liu Yilin , Yang Yonghui , Zhou Lingfeng 
Published: November 2021

Abstract
The grey water footprint (GWF) is defined as freshwater requirements for diluting pollutants in receiving water bodies. It is widely used to measure the impact of pollutant loads on water resources. GWF can be transferred from one area to another through trade. Although pollution flow has previously been investigated at the national level, there has been no explicit study on the extent to which crop trade affects GWF across regions and the associated changes in grey water stress (GWS). This study analyzes pollution flow associated with interprovincial crop trade based on nitrogen (N) and phosphorus (P) loss intensity of three major crops, namely, maize, rice and wheat, which is simulated by a grid-based crop model for the period 2008–2012, and evaluates the spatial patterns of GWS across China. The results indicate that the integrated national GWF for N and P was 1271 billion m3 yr−1, with maize, rice, and wheat contributing 39%, 37%, and 24%, respectively. Through interprovincial crop trade, southern China outsourced substantial N and P losses to the north, leading to a 30% GWS increase in northern China and 66% GWS mitigation in southern China. Specifically, Jilin, Henan, and Heilongjiang Provinces in the northern China showed increases in GWS by 161%, 114%, and 55%, respectively, while Fujian, Shanghai, and Zhejiang in the south had GWS reductions of 83%, 85%, and 80%, respectively. It was found that the interprovincial crop trade led to reduced national GWF and GWS. Insights into GWF and GWS can form the basis for policy developments on N and P pollution mitigation across regions in China.
Financing climate change mitigation in agriculture: assessment of investment cases
Authors: Khatri-Chhetri Arun , Sapkota Tek B , Sander Bjoern O , Arango Jacobo , Nelson Katherine M , Wilkes Andreas 
Published: November 2021

Abstract
More than one-quarter of the world’s greenhouse gas emissions come from agriculture, forestry, and land-use change. As with other sectors of the economy, agriculture should also contribute to meeting countries’ emission reduction targets. Transformation of agriculture to low-carbon food systems requires much larger investments in low emission development options from global climate finance, domestic budgets, and the private sector. Innovative financing mechanisms and instruments that integrate climate finance, agriculture development budgets, and private sector investment can improve and increase farmers’ and other value chain actors’ access to finance while delivering environmental, economic, and social benefits. Investment cases assessed in this study provide rich information to design and implement mitigation options in agriculture through unlocking additional sources of public and private capital, strengthening the links between financial institutions, farmers, and agribusiness, and coordination of actions across multiple stakeholders. These investment cases expand support for existing agricultural best practices, integrate forestry and agricultural actions to avoid land-use change, and support the transition to market-based solutions.
Statistics and optimization of random pan stacking
Authors: Talbot J , Antoine C 
Published: November 2021

Abstract
We consider a minimal model of random pan stacking. A single pan consists of a V-shaped object characterized by its internal angle α. The stack is constructed by piling up N pans with different angles in a given, random order. The set of pans is generated by sampling from various kinds of distributions of the pan angles: discrete or continuous, uniform or optimized. For large N the mean height depends principally on the average of the distance between the bases of two consecutive pans, and the effective compaction of the stack, compared with the unstacked pans, is 2 log 2/π. We also obtain the discrete and continuous distributions that maximize the mean stack height. With only two types of pans, the maximum occurs for equal probabilities, while when many types of pans are available, the optimum distribution strongly favours those with the most acute and the most obtuse angles. With a continuous distribution of angles, while one never finds two identical pans, the behaviour is similar to a system with a large number of discrete angles.
Non-abelian anyons and some cousins of the Arad–Herzog conjecture
Authors: Buican Matthew , Li Linfeng , Radhakrishnan Rajath 
Published: November 2021

Abstract
Long ago, Arad and Herzog (AH) conjectured that, in finite simple groups, the product of two conjugacy classes of length greater than one is never a single conjugacy class. We discuss implications of this conjecture for non-abelian anyons in 2 + 1-dimensional discrete gauge theories. Thinking in this way also suggests closely related statements about finite simple groups and their associated discrete gauge theories. We prove these statements and provide some physical intuition for their validity. Finally, we explain that the lack of certain dualities in theories with non-abelian finite simple gauge groups provides a non-trivial check of the AH conjecture.
Clinical implications of concentration of alveolar nitric oxide in asthmatic and non-asthmatic subacute cough
Authors: Zeng Guan-Sheng , Chen Hui , Chen Li-Chang , Wu Ling-Ling , Yu Hua-Peng 
Published: November 2021

Abstract
Asthma is an important cause of subacute cough. The concentration of alveolar nitric oxide (CANO) is a sensitive inflammatory indicator in peripheral airways, and it has received much less attention than the fraction of exhaled nitric oxide (FeNO50). The main objective of this study was to explore the correlation between CANO and clinical parameters in asthmatic and non-asthmatic subacute cough, which might promote understanding of the clinical utility of CANO in these special patient populations. 155 patients with subacute cough were included consecutively, of which 25 were diagnosed as asthmatic. Data for demographic characteristics, FeNO50, CANO, baseline spirometry, bronchial provocation test (or bronchodilation test) and response dose ratio (RDR) were collected. Differences between the asthmatic and non-asthmatic groups were analyzed. Spearman’s correlation coefficient (ρ) was used to evaluate the correlation between FeNO50, CANO and other clinical parameters. In patients with subacute cough, baseline CANO values did not differ between asthmatic and non-asthmatic patients (4.4(1.3, 11.4) versus 4.0(2.1, 6.8) ppb, P > 0.05). Besides, CANO exhibited a stronger association with pulmonary function parameters when compared with FeNO50. For asthmatic subacute cough, CANO was inversely correlated with FEV1/FVC (ρ = −0.69, P < 0.01) and small airway parameters including MEF25 (ρ = −0.47, P < 0.05) and MMEF (ρ = −0.45, P < 0.05). For non-asthmatic subacute cough, CANO was inversely correlated with MEF25 (ρ = −0.19, P < 0.05) and RDR (ρ = −0.21, P < 0.05). In subacute cough, asthmatic and non-asthmatic patients had similar values of baseline CANO. In both asthmatic and non-asthmatic subacute cough, CANO exhibited a stronger association with pulmonary function parameters when compared with FeNO50. A low CANO value in non-asthmatic subacute cough corresponded to a higher value of RDR, which implied a stronger tendency towards airway responsiveness.
Noninvasive real-time breath test for controlling hormonal background of the human body: detection of serotonin and melatonin with quantum point-contact sensors
Authors: Kamarchuk Lyudmila , Pospelov Alexander , Harbuz Dmytro , Belan Victor , Volkova Yuliya , Tkachenko Anna , Kamarchuk Gennadii 
Published: November 2021

Abstract
Significant progress in development of noninvasive diagnostic tools based on breath analysis can be expected if one employs a real-time detection method based on finding a spectral breath profile which would contain some energy characteristics of the analyzed gas mixture. Using the fundamental energy parameters of a quantum system, it is possible to determine with a high accuracy its quantitative and qualitative composition. Among the most efficient tools to measure energy characteristics of quantum systems are sensors based on Yanson point contacts. This paper reports the results of serotonin and melatonin detection as an example of testing the human hormonal background with point-contact sensors, which have already demonstrated their high efficiency in detecting carcinogenic strains of Helicobacter pylori and selective detection of complex gas mixtures. When comparing the values of serotonin and melatonin with the characteristic parameters of the spectral profile of the exhaled breath of each patient, high correlation dependences of the concentration of serotonin and melatonin with a number of characteristic parameters of the response curve of the point-contact sensor were found. The performed correlation analysis was complemented with the regression analysis. As a result, empiric regression relations were proposed to realize in practice the new non-invasive breath test for evaluation of the human hormonal background. Registration of the patient’s breath profile using point-contact sensors makes it possible to easily monitor the dynamics of changes in the human hormonal background and perform a quantitative evaluation of serotonin and melatonin levels in the human body in real time without invasive interventions (blood collection) and expensive equipment or reagents.
Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization
Authors: Stephens Hunter , Wu Q Jackie , Wu Qiuwen 
Published: November 2021

Abstract
Deep learning algorithms for radiation therapy treatment planning automation require large patient datasets and complex architectures that often take hundreds of hours to train. Some of these algorithms require constant dose updating (such as with reinforcement learning) and may take days. When these algorithms rely on commerical treatment planning systems to perform dose calculations, the data pipeline becomes the bottleneck of the entire algorithm’s efficiency. Further, uniformly accurate distributions are not always needed for the training and approximations can be introduced to speed up the process without affecting the outcome. These approximations not only speed up the calculation process, but allow for custom algorithms to be written specifically for the purposes of use in AI/ML applications where the dose and fluence must be calculated a multitude of times for a multitude of different situations. Here we present and investigate the effect of introducing matrix sparsity through kernel truncation on the dose calculation for the purposes of fluence optimzation within these AI/ML algorithms. The basis for this algorithm relies on voxel discrimination in which numerous voxels are pruned from the computationally expensive part of the calculation. This results in a significant reduction in computation time and storage. Comparing our dose calculation against calculations in both a water phantom and patient anatomy in Eclipse without heterogenity corrections produced gamma index passing rates around 99% for individual and composite beams with uniform fluence and around 98% for beams with a modulated fluence. The resulting sparsity introduces a reduction in computational time and space proportional to the square of the sparsity tolerance with a potential decrease in cost greater than 10 times that of a dense calculation allowing not only for faster caluclations but for calculations that a dense algorithm could not perform on the same system.
Response of the low-pressure hot-filament discharge plasma to a positively biased auxiliary disk electrode
Authors: CHOUDHARY Mangilal , SREEJITH Poyyeri Kunnath 
Published: November 2021

Abstract
In a steady-state plasma, the loss rate of plasma particles to the chamber wall and surfaces in contact with plasma is balanced by the ionization rate of background neutrals in the hot-filament discharges. The balance between the loss rate and ionization rate of plasma particles (electrons and ions) maintains quasi-neutrality of the bulk plasma. In the presence of an external perturbation, it tries to retain its quasi-neutrality condition. In this work, we studied how the properties of bulk plasma are affected by an external DC potential perturbation. An auxiliary biased metal disk electrode was used to introduce a potential perturbation to the plasma medium. A single Langmuir probe and an emissive probe, placed in the line of the discharge axis, were used for the characterization of the bulk plasma. It is observed that only positive bias to the auxiliary metal disk increases the plasma potential, electron temperature, and plasma density but these plasma parameters remain unaltered when the disk is biased with a negative potential with respect to plasma potential. The observed plasma parameters for two different-sized, positively as well as negatively biased, metal disks are compared and found inconsistent with the existing theoretical model at large positive bias voltages. The role of the primary energetic electrons population in determining the plasma parameters is discussed. The experimentally observed results are qualitatively explained on the basis of electrostatic confinement arising due to the loss of electrons to a biased metal disk electrode.
Bandwidth control of loop type frequency selective surfaces using dual elements in various arrangements
Authors: Littman Nickolas , O’Keefe Steven G , Galehdar Amir , Espinosa Hugo G , Thiel David V 
Published: November 2021

Abstract
Frequency selective surfaces (FSSs) have applications across multiple disciplines due to their unique electromagnetic properties. This paper investigates the use of both rounded square loops, and simple loop type dual elements arranged in unique patterns, to control the transmission and reflection bandwidth and resonant frequencies over KU and K frequency bands supported by equivalent circuit models. The FSSs were fabricated using laser engraving to create conductive loop type elements on a thin, flexible and optically transparent Mylar substrate (relative permittivity of 2.7 and thickness of 65 μm). The frequency response of the surfaces are controlled through the element self-inductance and capacitive coupling with neighbouring elements. This work shows that different arrangements result in the formation of multiple distinct resonances. The theoretical and experimental results were in good agreement where rounded squares and dual element arrays were employed to create broadband and multiband band-stop FSSs. A polarization sensitive surface exhibited stop-bands at 12 and 16 GHz in transverse electric polarization and a stop-band at 14.4 GHz in transverse magnetic polarization. This technique can be applied to any periodic array through careful selection of the individual elements in the array, as well as their arrangement.
Path-optimized nonadiabatic geometric quantum computation on superconducting qubits
Authors: Ding Cheng-Yun , Ji Li-Na , Chen Tao , Xue Zheng-Yuan 
Published: November 2021

Abstract
Quantum computation based on nonadiabatic geometric phases has attracted a broad range of interests, due to its fast manipulation and inherent noise resistance. However, it is limited to some special evolution paths, and the gate-times are typically longer than conventional dynamical gates, resulting in weakening of robustness and more infidelities of the implemented geometric gates. Here, we propose a path-optimized scheme for geometric quantum computation (GQC) on superconducting transmon qubits, where high-fidelity and robust universal nonadiabatic geometric gates can be implemented, based on conventional experimental setups. Specifically, we find that, by selecting appropriate evolution paths, the constructed geometric gates can be superior to their corresponding dynamical ones under different local errors. Numerical simulations show that the fidelities for single-qubit geometric phase, π/8 and Hadamard gates can be obtained as 99.93%, 99.95% and 99.95%, respectively. Remarkably, the fidelity for two-qubit control-phase gate can be as high as 99.87%. Therefore, our scheme provides a new perspective for GQC, making it more promising in the application of large-scale fault-tolerant quantum computation.
Attention-based quantum tomography
Authors: Cha Peter , Ginsparg Paul , Wu Felix , Carrasquilla Juan , McMahon Peter L , Kim Eun-Ah 
Published: November 2021

Abstract
With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Vaswani et al (2017 NIPS) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.
preciseTAD: a transfer learning framework for 3D domain boundary prediction at base-pair resolution
Authors: Stilianoudakis Spiro C , Marshall Maggie A , Dozmorov Mikhail G 
Published: November 2021

Abstract

Motivation
Chromosome conformation capture technologies (Hi-C) revealed extensive DNA folding into discrete 3D domains, such as Topologically Associating Domains and chromatin loops. The correct binding of CTCF and cohesin at domain boundaries is integral in maintaining the proper structure and function of these 3D domains. 3D domains have been mapped at the resolutions of 1 kilobase and above. However, it has not been possible to define their boundaries at the resolution of boundary-forming proteins.

Results
To predict domain boundaries at base-pair resolution, we developed preciseTAD, an optimized transfer learning framework trained on high-resolution genome annotation data. In contrast to current TAD/loop callers, preciseTAD-predicted boundaries are strongly supported by experimental evidence. Importantly, this approach can accurately delineate boundaries in cells without Hi-C data. preciseTAD provides a powerful framework to improve our understanding of how genomic regulators are shaping the 3D structure of the genome at base-pair resolution.

Availability and implementation
preciseTAD is an R/Bioconductor package available at https://bioconductor.org/packages/preciseTAD/.

Supplementary information
Supplementary data are available at Bioinformatics online.