bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inorganic–Organic Interpenetrating Network Hydrogels as Tissue‐Integrating Luminescent Implants: Physicochemical Characterization and Preclinical Evaluation
Authors: Unruh Rachel M. , Bornhoeft Lindsey R. , Nichols Scott P. , Wisniewski Natalie A. , McShane Michael J. 
Published: November 2021

AbstractSensors capable of accurate, continuous monitoring of biochemistry are crucial to the realization of personalized medicine on a large scale. Great strides have been made to enhance tissue compatibility of long‐term in vivo biosensors using biomaterials strategies such as tissue‐integrating hydrogels. However, the low level of oxygen in tissue presents a challenge for implanted devices, especially when the biosensing function relies on oxygen as a measure—either as a primary analyte or as an indirect marker to transduce levels of other biomolecules. This work presents a method of fabricating inorganic–organic interpenetrating network (IPN) hydrogels to optimize the oxygen transport through injectable biosensors. Capitalizing on the synergy between the two networks, various physicochemical properties (e.g., swelling, glass transition temperature, and mechanical properties) are shown to be independently adjustable while maintaining a 250% increase in oxygen permeability relative to poly(2‐hydroxyethyl methacrylate) controls. Finally, these gels, when functionalized with a Pd(II) benzoporphyrin phosphor, track tissue oxygen in real time for 76 days as subcutaneous implants in a porcine model while promoting tissue ingrowth and minimizing fibrosis around the implant. These findings support IPN networks for fine‐tuned design of implantable biomaterials in personalized medicine and other biomedical applications.
Dynamic boundary‐insensitive loss for magnetic resonance medical image segmentation
Authors: Qiu Mingyan , Zhang Chenxi , Song Zhijian 
Published: November 2021

AbstractPurposeA deep learning method has achieved great success in MR medical image segmentation. One challenge in applying deep learning segmentation models to clinical practice is their poor generalization mainly due to limited labeled training samples, inter‐site heterogeneity of different datasets, and ambiguous boundary definition, etc. The objective of this work is to develop a dynamic boundary‐insensitive (DBI) loss to address this poor generalization caused by an uncertain boundary.MethodsThe DBI loss is designed to assign higher penalties to misclassified voxels farther from the boundaries in each training iteration to reduce the sensitivity of the segmentation model to the uncertain boundary. The weighting factor of the DBI loss can be adjusted adaptively without any manual setting and adjustment. Extensive experiments were conducted to verify the performance of our DBI loss and its variant, DiceDBI, on four heterogeneous prostate MRI datasets for prostate zonal segmentation and whole prostate segmentation.ResultsExperimental results show that our DBI loss, when combined with Dice loss, outperforms all competing loss functions in dice similarity coefficient (DSC) and improves the segmentation performance across all datasets consistently, especially on unseen datasets and when segmenting small or narrow targets.ConclusionsThe proposed DiceDBI loss will be valuable for enhancement of the generalization performance of the segmentation model.
Computational simulation of 1H NMR profiles of complex biofluid analyte mixtures at differential operating frequencies: Applications to low‐field benchtop spectra
Authors: Edgar Mark , Kuhn Stefan , Page Georgina , Grootveld Martin 
Published: November 2021

AbstractEstimations of accurate and reliable NMR chemical shift values, coupling patterns and constants within a reasonable timeframe remain significantly challenging, and the unavailability of reliable software strategies for the prediction of low‐field (e.g., 60 MHz) spectra from those acquired at higher operating frequencies hampers their direct comparison. Hence, this study explored the applications of accessible software options for predicting these parameters in the 1H NMR profiles of analytes as a function of magnetic field strength; this was performed for individual analytes and also for complex biofluid matrices featured in metabolomics investigations. For this purpose, results from the very first successful experimental acquisition and simulation of the 1H NMR profiles of intact human salivary supernatant samples on a 60 MHz benchtop spectrometer were evaluated. Using salivary metabolite concentrations determined at 400 MHz, it was demonstrated that simulation of the low‐field spectra of five biomolecules with the most prominent 1H resonances detectable allowed multiple component fits to be applied to experimental spectra. Hence, these salivary 1H NMR profiles could be successfully predicted throughout the 45–600 MHz operating frequency range. With the exception of propionate resonance multiplets, which revealed more complex coupling patterns at low field and required more astute computational and fitting options, valuable quantitative metabolomics data on salivary acetate, formate, methanol and glycine could be attained from low‐field spectrometres. These studies are both timely and pertinent in view of the recent advancement of low‐field benchtop NMR facilities for diagnostically significant biomarker tracking in biofluids. Experiments performed with added ammonium chloride to facilitate the release of salivary metabolites from biopolymer binding sites provided evidence that a small but nevertheless significant proportion of propionate, but not lactate, was bound to such sites, an observation of much relevance to biomolecule quantification in salivary metabolomics investigations.
Sufficient optimality conditions for the optimal control problem
Authors: Djendel Khelifa , Zhou Zhongcheng 
Published: November 2021

AbstractThe main purpose of this paper is to establish the sufficient optimality conditions for the optimal controls under some convexity assumptions. For the Bolza problem, under concavity of the Hamiltonian and convexity of the cost functional, the extreme control must be optimal control. However, for the Mayer problem, the cost functional can be relaxed to quasi‐convex and pseudo‐convex. Finally, some examples illustrate these theoretical results and some counter examples show that the convexity assumptions of these results cannot be further weakened in some sense.
Developing a variables modified chain sampling plan with Taguchi capability index
Authors: Wang Zih‐Huei , Wu Chien‐Wei , Lin Wei‐Ren 
Published: November 2021

AbstractAcceptance sampling plan has been a practical tool in quality management, which provides both the producer and the consumer a general rule for sentencing lots. In recent years, a new strategy, chain sampling plan, considering disposition of historical lots for sentencing the current lot has been discussed. However, reviewing results of preceding lots only happens when the quality of current lot is on the undefined region, which may not be completely utilized. Therefore, in this study, we develop a variables modified‐ChSP (VMChSP) for variable inspection based on the Taguchi capability index to improve above situation. Optimization model of the proposed plan is formulated to solve plan parameters by minimizing the required sample size. Plan parameters under various allowable risks and quality levels are also provided for practical purpose. Finally, further analysis and comparisons between the proposed plan and conventional plans are provided and discussed in this research.
State preparation and evolution in quantum computing: A perspective from Hamiltonian moments
Authors: Aulicino Joseph C. , Keen Trevor , Peng Bo 
Published: November 2021

AbstractQuantum algorithms on noisy intermediate‐scale quantum (NISQ) devices are expected to soon simulate quantum systems that are classically intractable. However, the non‐negligible gate error present on NISQ devices impedes the implementation of many purely quantum algorithms, necessitating the use of hybrid quantum‐classical algorithms. One such hybrid quantum‐classical algorithm, is based upon quantum computed Hamiltonian moments , with respect to quantum state . In this tutorial review, we will give a brief review of these quantum algorithms with focuses on the typical ways of computing Hamiltonian moments using quantum hardware and improving the accuracy of the estimated state energies based on the quantum computed moments. We also present a computation of the Hamiltonian moments of a four‐site Heisenberg model and compute the energy and magnetization of the model utilizing the imaginary time evolution on current IBM‐Q hardware. Finally, we discuss some possible developments and applications of Hamiltonian moment methods.
Effect of swine manure on soil health properties: A systematic review
Authors: Yost Jenifer L. , Schmidt Amy M. , Koelsch Rick , Schott Linda R. 
Published: November 2021

AbstractA comprehensive review of research data on the effects of swine manure on soil health properties that is applicable to agricultural producers is lacking. The objectives of this review were (a) to synthesize literature describing effects of swine manure on soil properties that affect “soil health” and (b) to identify knowledge gaps and research needs to further our understanding of this topic. We reviewed papers from two databases and extracted soil property and swine manure data based on a set of criteria. For a paper to be included, (a) the studies were replicated field experiments, (b) manure was the only differing factor between or among treatments, and (c) data means of swine manure amended treatments and controls were included. In total, 40 papers met the criteria and were included. Based on this review, swine manure has the potential to significantly increase soil organic carbon. Soil organic carbon, soil organic matter, and microbial biomass carbon increased when swine manure was applied to crop fields, whereas bulk density tended to decrease. To conclude, previous literature reviews failed to account for differences in methodologies between individual research studies and whether research is applicable to producers utilizing swine manure as amendments to improve soil health. Future research studies should measure and report a standard set of metrics for both soil and manure properties and should investigate the effects of application frequency on short‐ and long‐term soil health properties.
Handheld Microfluidic Filtration Platform Enables Rapid, Low‐Cost, and Robust Self‐Testing of SARS‐CoV‐2 Virus
Authors: Xu Jiang , Suo Wenhao , Goulev Youlian , Sun Lei , Kerr Liam , Paulsson Johan , Zhang Yan , Lao Taotao 
Published: November 2021

AbstractHere, a novel microfluidic test kit combining ultrahigh throughput hydrodynamic filtration and sandwich immunoassay is reported. Specifically, nano and microbeads coated with two different, noncompetitive antibodies, are used to capture the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) nucleocapsid (N) proteins simultaneously, forming larger complexes. Microfluidic filtration discards free nanobeads but retains antigen‐bridged complexes in the observation zone, where a display of red color indicates the presence of antigen in the sample. This testing platform exhibits high throughput separation (<30 s) and enrichment of antigen that exceeds the traditional lateral flow assays or microfluidic assays, with a low limit of detection (LoD) < 100 copies mL‐1. In two rounds of clinical trials conducted in December 2020 and August 2021, the assays demonstrate high sensitivities of 95.4% and 100%, respectively, which proves this microfluidic test kit is capable of detecting SARS‐CoV‐2 virus variants evolved over significant periods of time. Furthermore, the mass‐produced chip can be fabricated at a cost of $0.98/test and the robust design allows the chip to be reused for over 50 times. All of these features make the microfluidic test kit particularly suitable for areas with inadequate medical infrastructure and a shortage of laboratory resources.
Fast modeling of lightweight glubam frame structures based on connection test information
Authors: Wang Rui , Li Zhi , Xiao Yan 
Published: November 2021

SummaryThe main lateral load‐carrying components of lightweight glued laminated bamboo (glubam) structures are shear walls. It is essential to understand lightweight glubam shear walls' performances to assess lightweight glubam structures' overall performance. A simplified numerical model was developed to predict the nonlinear response of lightweight glubam shear walls and structures. Based on the assumption that (1) the panel‐frame connectors govern the lateral resistance of the lightweight frame shear walls due to the high in‐plane shear stiffness and strength of the sheathing panel, (2) the out‐of‐plane bulking of the sheathing panel is avoided through the frame and the connections between them, and (3) the resistance capacity of the frame is negligible, the numerical model was developed based on the connection test information. Glubam nail connection tests were conducted considering different nail types and loading directions. The corresponding simplified hysteric wall model is composed of two kinds of input parameters. First are the parameters determining the wall's envelope curve under monotonic loading conditions, which regulate the hysteresis rules. The parameters related to the envelope curve are obtained through the modeling results of the nonlinear static analysis of the shear wall, whereas those related to the hysteresis rules are estimated through the connection test information. The influences of different nails, wall sizes, and openings on the walls' behavior are evaluated based on this simplified model. Based on the numerical simulation results of shear walls, the seismic performance of a glubam house was assessed.
How stereotypes and relationships influence women and underrepresented minority students' fit in engineering
Authors: Campbell‐Montalvo Rebecca , Kersaint Gladis , Smith Chrystal A. S. , Puccia Ellen , Skvoretz John , Wao Hesborn , Martin Julie P. , MacDonald George , Lee Reginald 
Published: November 2021

AbstractWomen and underrepresented minority (URM) undergraduates declare and complete science, technology, engineering, and mathematics majors at different rates in comparison to majority groups. Explanations of these differences have long been deficit oriented, focusing on aptitude or similar characteristics, but more recent work focuses on institutional contexts, such as academic climate and feelings of belonging (fit). This study examines the experiences of women and URM students in engineering undergraduate programs, focusing on how they fit, experiential factors affecting fit, and how fit is mitigated by social relationships from their networks and organizations in which they participate (i.e., social capital). Thematic analysis of 55 women and URM interviewee responses shows that students who fit well were those with majority characteristics, including race (i.e., White, White‐passing) and gender (i.e., men, masculine appearance), and those in groups well represented in their programs numerically (i.e., men, Asian). In contrast, women and Black students encountered threats to their fit due to stereotyping from bias and differential treatment from others (i.e., being excluded from group work). However, students received advice from their social networks (i.e., family, professors) in which they were warned to expect discrimination, or through organizations in which they participated (i.e., National Association of Black Engineers) where their sense of community was expanded. The advice and resources provided through this network‐based and participatory social capital mitigated fit for women and Black students, albeit in different ways, helping to preserve their feelings of belonging and promote their persistence in engineering. We offer suggestions to enact university policies to increase access to social capital with homophilious alters and educational opportunities for majority groups.
Application of Q‐Learning in Routing of Software‐Defined Wireless Mesh Network
Authors: Leevangtou Khamxay , Ochiai Hideya , Aswakul Chaodit 
Published: November 2021

AbstractA wireless mesh network uses an ad‐hoc routing module for communication between nodes by referring to the addresses assigned to the nodes according to the rules kept in the routing table. If links fail, then the necessary network routing information must be propagated via the relevant remaining reachable nodes. The weaknesses of this conventional wireless mesh network are its difficulty in terms of management and configuration adjustments due to the complex structure of the network. To cope with such difficulty, we propose the use of a machine learning technique to implement dynamic routing to deal with failures in the wireless mesh network. The aim is to improve network routing via a reinforcement‐learning framework. Specifically, the Q‐learning algorithm has been applied. The investigation results are reported for network scenarios under conditions of link failures, and the algorithm's parameter sensitivity analysis is given. In addition, the proposed algorithm is compared with the Dijkstra algorithm with either no or immediate link cost updates. The resultant end‐to‐end route latency is reported from the simulated conditions as parameterized by the real per‐hop latency measurement from the actual outdoor testbed implementation. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Licochalcone A inhibits cell growth through the downregulation of the Hippo pathway via PES1 in cholangiocarcinoma cells
Authors: Xu Ke‐dong , Miao Yi , Li Pan , Li Ping‐ping , Liu Jie , Li Juan , Cao Fang 
Published: November 2021

AbstractOverexpression or activation of Yes‐associated protein (YAP) is common in cancer cells. Thus, targeting YAP may be a strategy for cancer therapy. Licochalcone A (LicA) is a primary active compound of licorice root and is known to have medicinal effects, such as antioxidant, antibacterial, antiviral, and anticancer effects. However, the anticancer pharmacological mechanism of LicA has not been investigated in cholangiocarcinoma. In this study, we investigated the antiproliferative effect of LicA and the underlying molecular mechanism in HCCC‐9810 and RBE human cholangiocarcinoma cells. Our experiments indicated that LicA suppressed the growth of cholangiocarcinoma cells through inactivation of the Hippo pathway. Pescadillo ribosomal biogenesis factor 1 (PES1) was notably upregulated and related to carcinogenesis. We also found that LicA suppressed the expression and nuclear localization of PES1, which was associated with the inhibition of YAP expression and transcriptional activity.
Orbánistan and the Anti-gender Rhetoric in Hungary
Authors: Fodor Eva 
Published: November 2021

AbstractThis chapter introduces Hungary’s anti-liberal political rule and its gender regime. It traces policy changes in Hungary since 2010, discusses the legacies of the state socialist gender regimes and the formation of a new, anti-liberal one. I introduce the term “carefare” and discuss how the concept of “gender” has been deployed by Hungarian politicians to legitimate an increase in women’s unpaid care burden and their lack of attention to gender inequality in the labor market. I end the chapter with a description of my research methods and provide an outline for the rest of the book.
On an exponential representation of the gravitational S-matrix
Authors: Damgaard Poul H. , Planté Ludovic , Vanhove Pierre 
Published: November 2021

Abstract
An exponential representation of the S-matrix provides a natural framework for understanding the semi-classical limit of scattering amplitudes. While sharing some similarities with the eikonal formalism it differs from it in details. Computationally, rules are simple because pieces that must be subtracted are given by combinations of unitarity cuts. Analyzing classical gravitational scattering to third Post-Minkowskian order in both maximal supergravity and Einstein gravity we find agreement with other approaches, including the contributions from radiation reaction terms. The kinematical relation for the two-body problem in isotropic coordinates follows immediately from this procedure, again with the inclusion of radiation reaction pieces up to third Post-Minkowskian order.
Wt1 haploinsufficiency induces browning of epididymal fat and alleviates metabolic dysfunction in mice on high-fat diet
Authors: Kirschner Karin M. , Foryst-Ludwig Anna , Gohlke Sabrina , Li Chen , Flores Roberto E. , Kintscher Ulrich , Schupp Michael , Schulz Tim J. , Scholz Holger 
Published: November 2021

Abstract
Aims/hypothesis
Despite a similar fat storing function, visceral (intra-abdominal) white adipose tissue (WAT) is detrimental, whereas subcutaneous WAT is considered to protect against metabolic disease. Recent findings indicate that thermogenic genes, expressed in brown adipose tissue (BAT), can be induced primarily in subcutaneous WAT. Here, we investigate the hypothesis that the Wilms tumour gene product (WT1), which is expressed in intra-abdominal WAT but not in subcutaneous WAT and BAT, suppresses a thermogenic program in white fat cells.

Methods
Heterozygous Wt1 knockout mice and their wild-type littermates were examined in terms of thermogenic and adipocyte-selective gene expression. Glucose tolerance and hepatic lipid accumulation in these mice were assessed under normal chow and high-fat diet conditions. Pre-adipocytes isolated from the stromal vascular fraction of BAT were transduced with Wt1-expressing retrovirus, induced to differentiate and analysed for the expression of thermogenic and adipocyte-selective genes.

Results
Expression of the thermogenic genes Cpt1b and Tmem26 was enhanced and transcript levels of Ucp1 were on average more than tenfold higher in epididymal WAT of heterozygous Wt1 knockout mice compared with wild-type mice. Wt1 heterozygosity reduced epididymal WAT mass, improved whole-body glucose tolerance and alleviated severe hepatic steatosis upon diet-induced obesity in mice. Retroviral expression of WT1 in brown pre-adipocytes, which lack endogenous WT1, reduced mRNA levels of Ucp1, Ppargc1a, Cidea, Prdm16 and Cpt1b upon in vitro differentiation by 60–90%. WT1 knockdown in epididymal pre-adipocytes significantly lowered Aldh1a1 and Zfp423 transcripts, two key suppressors of the thermogenic program. Conversely, Aldh1a1 and Zfp423 mRNA levels were increased approximately five- and threefold, respectively, by retroviral expression of WT1 in brown pre-adipocytes.

Conclusion/interpretation
WT1 functions as a white adipocyte determination factor in epididymal WAT by suppressing thermogenic genes. Reducing Wt1 expression in this and other intra-abdominal fat depots may represent a novel treatment strategy in metabolic disease.

Graphical abstract
Fast approximation by periodic kernel-based lattice-point interpolation with application in uncertainty quantification
Authors: Kaarnioja Vesa , Kazashi Yoshihito , Kuo Frances Y. , Nobile Fabio , Sloan Ian H. 
Published: November 2021

AbstractThis paper deals with the kernel-based approximation of a multivariate periodic function by interpolation at the points of an integration lattice—a setting that, as pointed out by Zeng et al. (Monte Carlo and Quasi-Monte Carlo Methods 2004, Springer, New York, 2006) and Zeng et al. (Constr. Approx. 30: 529–555, 2009), allows fast evaluation by fast Fourier transform, so avoiding the need for a linear solver. The main contribution of the paper is the application to the approximation problem for uncertainty quantification of elliptic partial differential equations, with the diffusion coefficient given by a random field that is periodic in the stochastic variables, in the model proposed recently by Kaarnioja et al. (SIAM J Numer Anal 58(2): 1068–1091, 2020). The paper gives a full error analysis, and full details of the construction of lattices needed to ensure a good (but inevitably not optimal) rate of convergence and an error bound independent of dimension. Numerical experiments support the theory.