bluuebunny/crossref_metadata_2025_split huggingface dataset:
Context-aware dynamic filtering network for confocal laser endomicroscopy image denoising
Authors: Zhou Jingjun , Dong Xiangjiang , Liu Qian 
Published: August 2023

Abstract

Objective. As an emerging diagnosis technology for gastrointestinal diseases, confocal laser endomicroscopy (CLE) is limited by the physical structure of the fiber bundle, leading to the inevitable production of various forms of noise during the imaging process. However, existing denoising methods based on hand-crafted features inefficiently deal with realistic noise in CLE images. To alleviate this challenge, we proposed context-aware kernel estimation and multi-scale dynamic fusion modules to remove realistic noise in CLE images, including multiplicative and additive white noise. Approach. Specifically, a realistic noise statistics model with random noise specific to CLE data is constructed and further used to develop a self-supervised denoised model without the participation of clean images. Secondly, context-aware kernel estimation, which improves the representation of features by similar learnable region weights, addresses the problem of the non-uniform distribution of noises in CLE images and proposes a lightweight denoised model (CLENet). Thirdly, we have developed a multi-scale dynamic fusion module that decouples and recalibrates features, providing a precise and contextually enriched representation of features. Finally, we integrated two developed modules into a U-shaped backbone to build an efficient denoising network named U-CLENet. Main Results. Both proposed methods achieve comparable or better performance with low computational complexity on two gastrointestinal disease CLE image datasets using the same training benchmark. Significance. The proposed approaches improve the visual quality of unclear CLE images for various stages of tumor development, helping to reduce the rate of misdiagnosis in clinical decision-making and achieve computer graphics-assisted diagnosis.
Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography
Authors: Zhang Jun , Zhang Gege , Chen Yi , Li Kang , Zhao Fengjun , Yi Huangjian , Su Linzhi , Cao 曹 Xin 欣 
Published: August 2023

Abstract

Objective. Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem. Approach. To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well as in vivo experiments. Main results. The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution. Siginificance. These findings indicate that the SCAD-GML method has the potential to advance the application of FMT in in vivo biological research.
A soft tactile sensing array with high sensitivity based on MWCNTs-PDMS composite of hierarchically porous structure
Authors: Yu Jianping , Yao Shengjie , Lin Huan , Zhang Yuanxiang , Jiang Xiaoliang , Ghaffar Abdul , Li Xin , Yao Zhehe 
Published: August 2023

Abstract
The advancement of wearable tactile sensors that involves with high sensitivity under ultra-low pressures is crucial for varieties of human-machine interactive applications, like smart phones, healthcare monitoring, and electronic skins. Here in this paper, a soft capacitive tactile sensing array is introduced based on hierarchically porous multi-walled carbon nanotubes (MWCNTs)-polydimethylsiloxane composite, which leads to sensitivity improvement attributing to a synergistic effect of the hierarchically porous elastomer and conductive MWCNTs supplements. The proposed device exhibits superior pressure-sensing performances, with high sensitivity (3.58 kPa−1) under small mechanical stimuli (<80 Pa), broad measuring range (0–265 kPa), fast response time (<45 ms), good repeatability, minimum limit of detection (<10 Pa), as well as low-hysteresis, allowing efficient sensing of pressure from all types of sources, from vulnerable signals such as human breathing, artery and venous pulses, and soft human finger touch to possible brutal variations such as sudden change of object weight or prompt collide. Moreover, extensive body attached experiments confirm that the soft tactile sensing array is fully human compatible and capable for a variety of human-machine interfaces and health monitoring applications.
State carving in a chirally-coupled atom-nanophotonic cavity
Authors: Hiew W S , Jen H H 
Published: August 2023

Abstract
Coherent quantum control of multiqubit systems represents one of the challenging tasks in quantum science and quantum technology. Here we theoretically investigate the reflectivity spectrum in an atom-nanophotonic cavity with collective nonreciprocal couplings. In the strong-coupling regime with a high cooperativity, we theoretically predict distinct on-resonance spectral dips owing to destructive interferences of chiral couplings. Due to the well-separated multiple dips in the spectrum, a contrasted reflectivity suggests a new control knob over the desired entangled state preparation in the basis of coupled and uncoupled states from the atoms’ internal hyperfine ground states. We propose to utilize such atom-nanophotonic cavity to quantum engineer the atomic internal states via photon-mediated dipole–dipole interactions in the coupled state and the chirality of decay channels, where the atomic Bell state and W states for arbitrary number of atoms can be tailored and heralded by state carving in the single-photon reflection spectrum. Our results pave the way toward quantum engineering of multiqubit states and offer new opportunities for coherent and scalable multipartite entanglement transport in atoms coupled to nanophotonic devices.
Dual-mode switchable metasurface for multi-type OAM vortex beam generation and dual-band perfect absorption in terahertz band
Authors: Zhou Zihao , Qi Yunping , Zhang Baohe , Wen Yujiao , Wang Li , Wang Xiangxian 
Published: August 2023

Abstract
As metasurface technology is developing rapidly in the past decades, multi-operating mode and tunability are evolving into hot spots in its development. In this paper, we present a dual-operating mode metasurface consisting of vanadium dioxide (VO2). At room temperature (25 °C), it operates as a reflection mode. Eight metasurface unit cells with different reflection phases are designed, which can achieve 2π phase coverage in the frequency band of 0.4 THz-0.5 THz. Furthermore, by bringing encoded convolution and superposition theorems into the design of metasurface arrays, vertically incident circularly polarized (CP) waves can be transformed into single-beam, multi-beam, deflected and superimposed orbital angular momentum (OAM) vortex beams, respectively. On the other hand, at high temperature (68 °C), it operates as a dual-band terahertz absorber. It achieves near-perfect absorption at 1.71 THz and 1.87 THz with 99.9% and 98.9%, and also has polarization insensitivity. Therefore, the metasurface designed in this paper has promising applications in future terahertz communications, high-resolution imaging, and electromagnetic stealth.
Exquisite control of electronic and spintronic properties on highly porous Covalent Organic Frameworks (COFs): transition metal intercalation in bilayers
Authors: Maldonado-Lopez Daniel , Mendoza Cortes Jose L 
Published: August 2023

Abstract
Two-dimensional covalent organic frameworks (2D COFs) are crystalline organic porous materials stacked in a layered fashion. In general, these materials have excellent structural tunability, which can be achieved through the various tools of organic synthesis. Their layered and porous nature makes them attractive candidates for electronics, optoelectronics, and catalysis. However, their application is still limited due to relatively poor π-delocalization and practical applications require controlling and tuning their electronic structure. In this paper, using hybrid density functional theory, we computationally explore a novel 2D COF architecture, consisting of only two crystalline atomic layers made of benzene, boroxine, and triazine rings. We study the intercalation of first-row transition metals in the bilayer to enhance and fine-tune their electronic and magnetic behavior. This resulted in the development of one pristine bilayer, 63 intercalated bilayers, and one trilayer 2D COF. We found that the concentration and position of transition metals in the structure can drastically change the 2D COFs’ electronic, magnetic, and spintronic features. Based on their spin-polarized electronic properties, these transition metal-intercalated 2D COFs have potential applications as water splitting catalysts, direct semiconductors in the visible range, half metals, half semiconductors, and bipolar magnetic semiconductors.
An optimization framework for targeted spinal cord stimulation
Authors: Mirzakhalili Ehsan , Rogers Evan R , Lempka Scott F 
Published: August 2023

Abstract

Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s). Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs). Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs. Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.
The mechanism of the global vertical force reduction in disruptions mitigated by massive material injection
Authors: Schwarz N. , Artola F.J. , Vannini F. , Hoelzl M. , Bernert M. , Bock A. , Driessen T. , Dunne M. , Giannone L. , Heinrich P. , de Marné P. , Papp G. , Pautasso G. , Gerasimov S. , Upgrade Team the ASDEX , Contributors JET ,   Team the JOREK
Published: August 2023

Abstract
Disruptions lead to a rapid loss of thermal and magnetic energy and can cause large heat loads, mechanical forces, and the excitation of a beam of relativistic runaway electrons. The operation of tokamaks at high energy and plasma current requires the use of a mitigation system to limit such detrimental effects. Mitigation techniques rely mainly on the injection of a large amount of impurities to radiate the majority of the thermal and magnetic energies. Heat loads and electro-magnetic (EM) forces as well as their toroidal asymmetries can be greatly reduced by such measures. In this paper, a theory is lined out to explain the reduction of the global vertical force based on large toroidal halo currents that keep the current centroid stationary in the midplane. As a consequence, the vertical current moment, which is linked to the EM-force, is reduced. The theory is backed up by experimental observations in shattered pellet injection mitigated vertical displacement event experiments in ASDEX Upgrade (AUG) and JET as well as by 2D simulations with the extended MHD code JOREK. Scans in the boundary heat flux are carried out to estimate the correct scrape-off layer temperature and the influence of the fraction of conducted energy. Finally, predictive simulations for ITER confirm the reduction of the vertical force by the injection of impurities.
A weakly supervised framework for high-resolution crop yield forecasts
Authors: Paudel Dilli , Marcos Diego , de Wit Allard , Boogaard Hendrik , Athanasiadis Ioannis N 
Published: August 2023

Abstract
Predictor inputs and labels (e.g. yield data) for crop yield forecasting are not always available at the same spatial resolution. Common statistical and machine learning methods require inputs and labels at the same resolution. Therefore, they cannot produce high resolution (HR) yield forecasts in the absence of HR yield data. We propose a weakly supervised (WS) deep learning framework that uses HR inputs and low resolution (LR) labels (crop areas and yields) to produce HR forecasts. The forecasting model was calibrated by aggregating HR forecasts and comparing with LR crop area and yield statistics. The framework was evaluated by disaggregating yields from parent statistical regions to sub-regions for five countries and two crops in Europe. Similarly, corn yields were disaggregated from counties to 10 km grids in the US. The performance of WS models was compared with naive disaggregation (ND) models, which assigned LR forecasts for a region or county to all HR sub-units, and strongly supervised models trained with HR yield labels. In Europe, all models (ND, WS and strongly supervised) were statistically similar, mainly due to the effect of yield trend. In the US, the WS models performed even better than the strongly supervised models. Based on Kendall’s rank correlation coefficient, the WS model forecasts captured significant amounts of HR yield variability. Combining information from WS with Trend model (using LR yield trend) and WS No Trend model (not using yield trend) provided good estimates of yields as well as spatial variability among sub-regions or grids. High resolution crop yield forecasts are useful to policymakers and other stakeholders for local analysis and monitoring. Our weakly supervised framework produces such forecasts even in the absence of high resolution yield data.
Conditioning Boltzmann generators for rare event sampling
Authors: Falkner Sebastian , Coretti Alessandro , Romano Salvatore , Geissler Phillip L , Dellago Christoph 
Published: August 2023

AbstractUnderstanding the dynamics of complex molecular processes is often linked to the study of infrequent transitions between long-lived stable states. The standard approach to the sampling of such rare events is to generate an ensemble of transition paths using a random walk in trajectory space. This, however, comes with the drawback of strong correlations between subsequently sampled paths and with an intrinsic difficulty in parallelizing the sampling process. We propose a transition path sampling scheme based on neural-network generated configurations. These are obtained employing normalizing flows, a neural network class able to generate statistically independent samples from a given distribution. With this approach, not only are correlations between visited paths removed, but the sampling process becomes easily parallelizable. Moreover, by conditioning the normalizing flow, the sampling of configurations can be steered towards regions of interest. We show that this approach enables the resolution of both the thermodynamics and kinetics of the transition region for systems that can be sampled using exact-likelihood generative models.
Pain in the Context of Virtual Neuropsychological Assessment of Older Adults
Authors: Patrick Karlee S , Gunstad John , Spitznagel Mary B 
Published: August 2023

Abstract

Objective
Pain and cognitive impairment are prevalent and often co-occur in older adults. Because pain may negatively affect cognitive test performance, identification of pain in the context of neuropsychological evaluation is important. However, pain detection based on self-report presents challenges, and pain is often under-detected in this population. Alternative methods (e.g., video-based automatic coding of facial biomarkers of pain) may facilitate pain identification and thus enhance interpretation of neuropsychological evaluation results.

Method
The current study examined pain in the context of virtual neuropsychological assessment in 111 community-dwelling older adults, first seeking to validate the use of software developed to automatically code biomarkers of pain. Measures of pain, including self-report of acute and chronic pain and automatic coding of pain, were compared while participants completed neuropsychological testing.

Results
Self-reported pain was negatively associated with poorer performance on a measure of executive function (both acute and chronic pain) and a global cognitive screening measure (acute pain only). However, self-reported acute and chronic pain did not correlate significantly with most neuropsychological tests. Automatic coding of pain did not predict self-report of pain or performance on neuropsychological tests beyond the influence of demographic factors and psychological symptoms.

Conclusions
Though results were largely not significant, correlations warrant further exploration of the influence of pain on neuropsychological test performance in this context to ensure that pain does not influence test performance in individuals with higher levels of pain and in other samples.
Tension Reduction With Force Modulating Tissue Bridges Reduces Wounds in Breast Surgery
Authors: Wall Holly C , Halani Sameer H , Mosieri Chizoba , Daniel Charles , Gallo Lucas , Eaves Felmont F , Coroneos Christopher J 
Published: August 2023

Abstract

Background
Tension on healing wounds increases the risk of dehiscence and poor or pathologic scar formation. Force modulating tissue bridges (FMTBs) represent a new class of wound closure and support devices designed to offload tension on healing wounds to improve wound healing and scar outcomes.

Objectives
The study was undertaken to assess the efficacy of FMTBs to reduce the risk of wound healing complications in elective breast surgery.

Methods
One hundred twenty-two consecutive patients undergoing bilateral aesthetic breast surgery underwent intraoperative placement of FMTBs on the vertical limb closure site. A matched case–control cohort of 121 consecutive patients was established for comparison. Wounds were considered significant if larger than 3 mm in diameter. The primary outcome of breast wounds &gt;3 mm was reported with a relative risk, and all outcomes were framed with number needed to treat.

Results
The control and intervention cohorts had similar demographics, comorbidities, type of operation, and incision pattern utilized. Within the FMTB group, 96.7% (n = 118) patients completed treatment per protocol. Significant wounds occurred in 1.7% (n = 2) of patients in the tissue bridge vs 15.2% (n = 19) in controls on a per patient/per protocol basis (89% reduction, P &lt; .001). Statistically significant improvements were maintained on sensitivity analyses with intention to treat, even when minor wounds were included. There were no complications noted related to FMTBs.

Conclusions
FMTBs are safe and highly effective at reducing the risk of wound formation in elective breast surgery. Results are consistent with sensitivity analyses based on clinical and methodological factors. Further research will assess long-term scar outcomes.

Level of Evidence: 4
GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs
Authors: Deng Yuzhong , Tang Jianxiong , Zhang Jiyang , Zou Jianxiao , Zhu Que , Fan Shicai 
Published: August 2023

Abstract

Motivation
Single-cell DNA methylation sequencing can assay DNA methylation at single-cell resolution. However, incomplete coverage compromises related downstream analyses, outlining the importance of imputation techniques. With a rising number of cell samples in recent large datasets, scalable and efficient imputation models are critical to addressing the sparsity for genome-wide analyses.

Results
We proposed a novel graph-based deep learning approach to impute methylation matrices based on locus-aware neighboring subgraphs with locus-aware encoding orienting on one cell type. Merely using the CpGs methylation matrix, the obtained GraphCpG outperforms previous methods on datasets containing more than hundreds of cells and achieves competitive performance on smaller datasets, with subgraphs of predicted sites visualized by retrievable bipartite graphs. Besides better imputation performance with increasing cell number, it significantly reduces computation time and demonstrates improvement in downstream analysis.

Availability and implementation
The source code is freely available at https://github.com/yuzhong-deng/graphcpg.git.