bluuebunny/crossref_metadata_2025_split huggingface dataset:
An automated methodology for whole-body, multimodality tracking of individual cancer lesions
Authors: Santoro-Fernandes Victor , Huff Daniel T , Rivetti Luciano , Deatsch Alison , Schott Brayden , Perlman Scott B , Jeraj Robert 
Published: March 2024

Abstract

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities. Approach. This study introduces a lesion tracking methodology and benchmarked it using 23 68Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance. Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes). Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.
Hierarchical decomposed dual-domain deep learning for sparse-view CT reconstruction
Authors: Han Yoseob 
Published: March 2024

Abstract

Objective. X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the inadequate number of projection views, an analytic reconstruction method utilizing filtered backprojection results in severe streaking artifacts. Recently, deep learning (DL) strategies employing image-domain networks have demonstrated remarkable performance in eliminating the streaking artifact caused by analytic reconstruction methods with sparse projection views. Nevertheless, it is difficult to clarify the theoretical justification for applying DL to sparse view computed tomography (CT) reconstruction, and it has been understood as restoration by removing image artifacts, not reconstruction. Approach. By leveraging the theory of deep convolutional framelets (DCF) and the hierarchical decomposition of measurement, this research reveals the constraints of conventional image and projection-domain DL methodologies, subsequently, the research proposes a novel dual-domain DL framework utilizing hierarchical decomposed measurements. Specifically, the research elucidates how the performance of the projection-domain network can be enhanced through a low-rank property of DCF and a bowtie support of hierarchical decomposed measurement in the Fourier domain. Main results. This study demonstrated performance improvement of the proposed framework based on the low-rank property, resulting in superior reconstruction performance compared to conventional analytic and DL methods. Significance. By providing a theoretically justified DL approach for sparse-view CT reconstruction, this study not only offers a superior alternative to existing methods but also opens new avenues for research in medical imaging. It highlights the potential of dual-domain DL frameworks to achieve high-quality reconstructions with lower radiation doses, thereby advancing the field towards safer and more efficient diagnostic techniques. The code is available at https://github.com/hanyoseob/HDD-DL-for-SVCT.
Equivalent uniform aerobic dose in radiotherapy for hypoxic tumors
Authors: Chvetsov Alexei V , Muzi Mark 
Published: March 2024

Abstract

Objective. Equivalent uniform aerobic dose (EUAD) is proposed for comparison of integrated cell survival in tumors with different distributions of hypoxia and radiation dose. Approach. The EUAD assumes that for any non-uniform distributions of radiation dose and oxygen enhancement ratio (OER) within a tumor, there is a uniform distribution of radiation dose under hypothetical aerobic conditions with OER = 1 that produces equal integrated survival of clonogenic cells. This definition of EUAD has several advantages. First, the EUAD allows one to compare survival of clonogenic cells in tumors with intra-tumor and inter-tumor variation of radio sensitivity due to hypoxia because the cell survival is recomputed under the same benchmark oxygen level (OER = 1). Second, the EUAD for homogeneously oxygenated tumors is equal to the concept of equivalent uniform dose. Main results. We computed the EUAD using radiotherapy dose and the OER derived from the 18F-Fluoromisonidazole PET (18F-FMISO PET) images of hypoxia in patients with glioblastoma, the most common and aggressive type of primary malignant brain tumor. The 18F-FMISO PET images include a distribution of SUV (Standardized Uptake Value); therefore, the SUV is converted to partial oxygen pressure (pO2) and then to the OER. The prognostic value of EUAD in radiotherapy for hypoxic tumors is demonstrated using correlation between EUAD and overall survival (OS) in radiotherapy for glioblastoma. The correction to the EUAD for the absolute hypoxic volume that traceable to the tumor control probability improves the correlation with OS. Significance. While the analysis proposed in this research is based on the 18F-FMISO PET images for glioblastoma, the EUAD is a universal radiobiological concept and is not associated with any specific cancer or any specific PET or MRI biomarker of hypoxia. Therefore, this research can be generalized to other cancers, for example stage III lung cancer, and to other hypoxia biomarkers.
Dual-parameter stretchable, transferable mesh piezoresistive sensor for electronic skin detection of strain and temperature changes
Authors: Wang Chunhui , Xin Chaojie , Song Qihang , Wang Shuobang , Cheng Siyi , Shi Chao , Zhao Dongyuan , He Qingyuan , Zhang Jie , Chen Xiaoming 
Published: March 2024

Abstract
Wearable sensors integrating multiple functions have great potential in artificial intelligence and flexible electronics at this stage and can perceive various external stimuli with high sensitivity and accuracy, such as strain, stress, and temperature. However, because multiple parameters do affect each other and reduce the sensing performance, making a single device that can detect multiple functions simultaneously is a huge challenge. In this paper, a strain-temperature dual-parameter sensor is developed with a planar structure design and used poly(3,4-ethylenedioxythiophene): poly(styrenesulfonic acid) and multi-walled carbon nanotubes polymerization materials to prepare a micron-sized film. The influence of two-dimensional structures on sensing performance is explored through simulation, and a structure with large deformation is selected to improve the strain detection range. The sensor can detect static and dynamic strain signals, and can maintain good linearity and response speed below 100 ms within a large strain range of 20%. In addition, the sensor also exhibits good temperature detection capability, with a temperature sensitivity of 18.2 μV K−1 and the ability to detect static and dynamic temperature changes with long-term stability. Finally, the sensor is tested in some actual scenarios, reflecting that the sensor manufactured has the dual-detection ability, showing sensitive strain monitoring and temperature perception decoupled between the dual signals. The sensor is realized with circuit board acquisition and wireless communication, combining multi-channel applications. Our research provides a feasible method for constructing multi-parameter human-computer interaction sensors.
Conditionally integrable
PT
-symmetric hierarchies and near-Lax pairs of the KdV and KPII equations
Authors: Choudhury S Roy , Pecora Keri 
Published: March 2024

Abstract
Earlier work is generalized to obtain novel conditionally-integrable hierarchies of various

P
T

-symmetric, nonlinear partial differential equations. The Painlevé Test yields the possible integrable cases. These are labeled by the integer n, the order of the dominant pole in the Laurent expansion for the solution. For the

P
T

-symmetric Korteweg–de Vries (KdV) equation which has been considered earlier, further analysis using the extended homogeneous balance technique gives the near-Lax Pair. Next, a

P
T

-Symmetric hierarchy of (2 + 1) Kadomtsev-Petviashvili equations is considered. The Painlevé Test and Invariant Painlevé analysis in (2 + 1) dimensions yield special solutions and Bäcklund Transformations for the integrable cases.
Liquid level measurement using a single electrode capacitive sensor made of carbon nanotube-paper composite
Authors: Qian Zhongjie , Li Tianyi , Kim Shawn , Cheng Yu-Jen , Sakthivelpathi Vigneshwar , Chung Jae-Hyun 
Published: March 2024

Abstract
Liquid level measurements play a vital role in various fields, including environmental, industrial, and medical applications. While hydrostatic, optical, and ultrasonic sensors are commonly used for this purpose, capacitive sensors have also gained prominence. However, capacitive sensors have inherent limitations in terms of dynamic range and resolution. These sensors consist of a pair of electrodes with a gap, and the size of this gap directly affects the sensor’s dynamic range and resolution. Increasing the gap size enhances the dynamic range but compromises resolution. To overcome this challenge, a novel approach involving the investigation of a single-electrode capacitive sensor is presented. This sensor consists of using a carbon nanotube-paper composite (CPC), which offers unique advantages for measuring liquid levels with improved dynamic range and resolution. The sensing performance of the single-electrode sensor is evaluated in both conductive and non-conductive containers, ensuring its versatility and applicability in different scenarios. Furthermore, the study explores the implementation of a differential configuration for the single-electrode sensor. This configuration aims to enhance accuracy and stability, particularly in achieving femto-Farad level accuracy. By leveraging the potential of the single-electrode capacitive sensor, numerous applications such as liquid level sensing, immersible liquid level sensing, and rain sensing are demonstrated. This result holds potential for advancing liquid level measurement capabilities across various industries and opening up new opportunities for sensor applications.
Analysis of T-Orthogonal greedy algorithm in noisy measurement
Authors: Deep Sunder , Poumai Khole Timothy , Kaushik S K 
Published: March 2024

Abstract
We define the Tikhonov Orthogonal Greedy algorithm (T-OGA), a variant of the orthogonal greedy algorithm, to study the recovery of sparse signals from noisy measurements. We establish sufficient conditions for T-OGA to recover sparse signals via restricted isometry property (RIP) inherited from frames. We introduce various concepts such as match matrix, match vector, and residue vector to execute T-OGA. Various technical lemmas have been established to prove our main theorem. In the execution of T-OGA, we employ Tikhonov regularization with regularization parameter λ to solve the minimization problem for the N-sparse solution. In our main result, we proved that if a frame satisfies the RIP of order N + 1 with isometry constant τ and

(
τ
+
λ
)
<

1

3

N

, then T-OGA can recover every N-sparse signal in the atmost N iterations.
AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling
Authors: Lloyd David A , Alejandra Gonzalez-Gonzalez Maria , Romero-Ortega Mario I 
Published: March 2024

Abstract

Objective. Bioelectronic treatments targeting near-organ innervation have unprecedented clinical applications. Particularly in the spleen, the inhibition of the cholinergic inflammatory response by near-organ nerve stimulation has potential to replace pharmacological treatments in chronic and autoimmune diseases. A caveat is that the optimization of therapeutic stimulation parameters relies on in vivo experimentation, which becomes challenging due to the small nerve diameters (2

μ

m), complex anatomy, and mixed axon type composition of the autonomic nerves. Effective development of in silico models requires tools which allow for fast and efficient quantification of axonal composition of specific nerves. Current approaches to generate such information rely on manual image segmentation and quantification. Approach. We developed a combined image-segmentation and model-generation software called AxoDetect: a target- and format-agnostic computer vision algorithm which can segment myelin, endo/epineurium, and both myelinated and unmyelinated fibers from a nerve image without training. Main results. AxoDetect is over 10 times faster on average when compared with current automatic methods while maintaining flexibility through the use of tunable pixel threshold filters to detect different types of tissue. When compared to a distribution-based and a manually segmented model of the splenic nerve terminal branch 1, the model generated with AxoDetect had comparable threshold prediction and was able to accurately detect an increase in activation threshold caused by the addition of surrounding fat tissue to the modeled nerve. Significance. AxoDetect contributes to the acceleration of neuromodulation treatment development through faster model design and iteration without requiring training. Furthermore, the computer vision approach and tunable nature of the filters in our method allow for its use in a variety of histological applications. Our approach will impact not only the study of nerves but also the design of implantable neural interfaces to enhance bioelectronic therapeutic options.
Canopy cover and microtopography control precipitation-enhanced thaw of ecosystem-protected permafrost
Authors: Eklof Joel F , Jones Benjamin M , Dafflon Baptiste , Devoie Élise G , Ring Katie M , English Marie E , Waldrop Mark P , Neumann Rebecca B 
Published: March 2024

Abstract
Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015–2020) to 56 ± 12 cm yr−1 (2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.
Multifunctional beam modulation based on gratings combined with additive metasurfaces
Authors: Chen Cong , Gao Peng , Dai Yaowei , Tong Xiamin , Gao Jingyuan , Liu Hai 
Published: March 2024

Abstract
A design scheme combining grating and element additive metasurface is proposed in this paper. Multi-dimensional joint control of the phase, amplitude, angle and polarization and multi-functional integrated metasurface applications were realized by using the inherent characteristics of the light wave properties (polarization and wavelength). Firstly, suitable structural parameters are obtained based on the reflection response of the unit structure, and a 2-bit encoded metasurface array is constructed by utilizing the combinatorial operation between the unit structure. Combined with the polarization selectivity of the lower grating, the dual-function integration of asymmetric transmission (the contrast ratio is 98%) and abnormal reflections (operating bandwidth is 300 nm) was realized. Next, two new half-wave plates were obtained by addition and rotating the elemental structure, and the polarization conversion efficiency of 95%–97% was achieved thanks to the Fabry–Perot cavity formed by the upper metasurface and lower grating. Finally, encoded metasurface based on the geometric phase principle to achieve 300 nm bandwidth cross-polarized wave deflection characteristics with polarization conversion efficiency close to 100%. Our work combines grating structures with element additive metasurfaces, which solves the problem that element structure needs to be redesigned with the change of function, and also paves the way for the application of multifunctional integrated metasurfaces in optical sensing, optical imaging and other fields.
Exploring the photocatalytic degradation of organic textile dye using hydrothermally synthesized Ce-doped metal chalcogenide nanomaterial
Authors: Bhat Bilal Ahmad , Jadon Nimisha , Sharma Harendra K 
Published: March 2024

Abstract
The advancement of economical, facile and non-toxic approaches towards waste water treatment is a promising research area currently among scientific community. In this regard photocatalysis has specifically received significant attention due to its application in the eradication of toxic elements from wastewater. Hence, in the present study Ce-doped FeSe2 photocatalysts were prepared employing the simple, inexpensive and ecofriendly hydrothermal method. The prepared Ce-FeSe2 nanomaterial was subjected to different characterization techniques like x-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy-dispersive spectroscopy (EDS), Cyclic Voltammetry (CV) and UV–visible spectroscopy to explore the characteristic properties of the prepared nanomaterials. XRD results revealed good crystallinity and purity of all synthesized Ce-FeSe2 nanomaterials. Surface morphology and elemental composition of the prepared nanomaterials was determined by FE-SEM and EDS spectroscopy. UV-Visible spectroscopy was used to explore the optical characteristic of the prepared photocatalysts. The results of optical absorption spectrum confirmed that increasing dopant percentage shows an improvement towards optical properties. The photocatalytic ability of the prepared Ce-FeSe2 nanomaterials was examined against the potentially toxic industrial dyes like Rhoadmine B (RB). The photocatalytic results show that 95.21% of dye degradation was exhibited by 7% Ce- doped FeSe2 nanomaterial under UV-visible light illumination duration of 120 min. Findings of the photocatalytic degradation process fitted well with pseudo first order reaction kinetics for RB dye.
OA-GAN: organ-aware generative adversarial network for synthesizing contrast-enhanced medical images
Authors: Yang Yulin , Liu Jing , Zhan Gan , Chen Qingqing , Wang Fang , Li Yinhao , Kumar Jain Rahul , Lin Lanfen , Hu Hongjie , Chen Yen-Wei 
Published: March 2024

Abstract
Contrast-enhanced computed tomography (CE-CT) images are vital for clinical diagnosis of focal liver lesions (FLLs). However, the use of CE-CT images imposes a significant burden on patients due to the injection of contrast agents and extended shooting. Deep learning-based image synthesis models offer a promising solution that synthesizes CE-CT images from non-contrasted CT (NC-CT) images. Unlike natural images, medical image synthesis requires a specific focus on certain organs or localized regions to ensure accurate diagnosis. Determining how to effectively emphasize target organs poses a challenging issue in medical image synthesis. To solve this challenge, we present a novel CE-CT image synthesis model called, Organ-Aware Generative Adversarial Network (OA-GAN). The OA-GAN comprises an organ-aware (OA) network and a dual decoder-based generator. First, the OA network learns the most discriminative spatial features about the target organ (i.e. liver) by utilizing the ground truth organ mask as localization cues. Subsequently, NC-CT image and captured feature are fed into the dual decoder-based generator, which employs a local and global decoder network to simultaneously synthesize the organ and entire CECT image. Moreover, the semantic information extracted from the local decoder is transferred to the global decoder to facilitate better reconstruction of the organ in entire CE-CT image. The qualitative and quantitative evaluation on a CE-CT dataset demonstrates that the OA-GAN outperforms state-of-the-art approaches for synthesizing two types of CE-CT images such as arterial phase and portal venous phase. Additionally, subjective evaluations by expert radiologists and a deep learning-based FLLs classification also affirm that CE-CT images synthesized from the OA-GAN exhibit a remarkable resemblance to real CE-CT images.