bluuebunny/crossref_metadata_2025_split huggingface dataset:
SGReg: segmentation guided 3D/2D rigid registration for orthogonal X-ray and CT images in spine surgery navigation
Authors: Mi Jia , Yin Wenhao , Zhao Lei , Chen Yangfan , Zhou Yujia , Feng Qianjin 
Published: May 2023

Abstract

Objective. One of the essential technologies in various image-guided spine surgeries is the rigid registration of 3D pre-operative CT and 2D intra-operative X-ray images. The 3D/2D registration is patterned as two essential tasks, that is, dimensional correspondence establishment and estimation of the 3D pose. 3D data is projected to 2D for dimensional correspondence by most of the existing methods, which makes pose parameters difficult to estimate caused by the loss of spatial information. This work aims to develop a reconstruction based 3D/2D registration method for spine surgery navigation. Approach. A novel segmentation-guided 3D/2D registration (SGReg) method for orthogonal X-ray and CT images was proposed based on reconstruction. SGReg consists of a bi-path segmentation network and an inter-path multi-scale pose estimation module. The X-ray segmentation path in the bi-path segmentation network reconstructs 3D spatial information from 2D orthogonal X-ray images to segmentation masks; meanwhile, the CT segmentation path predicts segmentation masks from 3D CT images, thereby bringing the 3D/2D data into dimensional correspondence. In the inter-path multi-scale pose estimation module, the features from the two segmentation paths are integrated, and the pose parameters are directly regressed under the guidance of the coordinate information. Main result. We evaluated SGReg using a public dataset CTSpine1k and compared the registration performance with other methods. SGReg achieved considerable improvement over other methods with great robustness. Significance We have proposed an end-to-end 3D/2D registration framework named SGReg. Based on the idea of reconstruction, SGReg performs a unified framework between dimensional correspondence establishment and direct pose estimation in 3D space, showing significant potential in spine surgery navigation.
Optimized Monte Carlo simulations for voxel-based internal dosimetry
Authors: Cordeiro Leanderson P , de Sá Lidia V , Kitamikado Rafael A , Sapienza Marcelo T , B Bonifacio Daniel A 
Published: May 2023

Abstract

Objective. The scientific community has considered internal dosimetry by the Monte Carlo method the gold standard. However, there is a trade-off between simulation processing time and the statistical quality of the results that makes it a challenge to obtain accurate absorbed dose values in some situations, such as dose estimation in organs affected by cross-irradiation or limited computing power. Variance reduction techniques are used to reduce computational processing time without impairing the statistical quality of the results, such as tracking energy cutoff, secondary particle production threshold, and parallelism of different types of emissions from radionuclides. Approach. In this work, GATE Monte Carlo code and its variance reduction techniques were evaluated to calculate S values of organs from the international commission on radiological protection (ICRP) report 110 male phantom for the lutetium-177, iodine-131, yttrium-90, and radium-223 radionuclides. The results are compared with the data from the OpenDose collaboration. Main results. A cutoff of 5 MeV for local electron deposition and 2.0 mm of secondary particle production range resulted in a computational efficiency increase of 7.9 and 1.05 times, respectively. Simulation of ICRP 107 spectra-based source proved to be about 5 times more efficient when compared to a decay simulation using G4RadioactiveDecay (Geant4-based radioactive decay processes). Track length estimator (TLE) and split exponential track length estimator (seTLE) techniques were used to calculate the absorbed dose of photon emissions, resulting in computational efficiency up to 29.4 and 62.5 times higher when compared to traditional simulations, respectively. In particular, the seTLE technique accelerates the simulation time by up to 1426 times, achieving a statistical uncertainty of 10% in volumes affected by cross-irradiation. Significance. The variance reduction techniques used in this work drastically reduced the simulation time and maintained the statistical quality of the calculated absorbed dose values, proving the feasibility of the use of the Monte Carlo method in internal dosimetry under challenging situations and making it viable for clinical routine or web applications.
Fermi arc surface state and topological switch in the gyromagnetic metamaterials
Authors: Li Mingzhu , Han Ning , Qi Lu , Lin Zhuhong , Cui Songya , Chen Guang , Peng Liang 
Published: May 2023

Abstract
A landmark feature of the Weyl system is that it possesses the Fermi arc surface states. In this work, we demonstrate that the Fermi arc surface states connect the vacuum state and the Weyl points of gyromagnetic metamaterials (GMs). The nonzero Chern numbers and Berry phases show the nontrivial topological property of the GMs in momentum space. Full-wave simulations demonstrate that the chiral surface waves on the boundary between the GMs and vacuum state can achieve robustness against sharp corners of step-type configurations. Remarkably, the topological switch can be realized by adopting the Fermi arc surface states between two different GMs. We theoretically prove that the physical mechanism of realizing topological switch is caused by different gap Chern numbers of the material system. Moreover, the direction of the topological switch can be operated by manipulating the gyromagnetic parameters of the GMs in the ‘button’ region. Our work may provide more flexibility for the flexible and robust topological devices.
A microstructural slip analysis of radiative thermophoretic flow of ternary nanofluid flowing through porous medium
Authors: Madhukesh J K , Ramesh G K , Shehzad S A , Rauf A , Omar M 
Published: May 2023

Abstract
Slip flows play a vital role in the microsystems, such as micro-valves, micro-pumps, hard disc drives, and nozzles. As a result, this study aims to examine the micro-structural slip behavior of ternary nanoliquid flow past a stretchable surface. Linear radiation is included in the law of energy, whereas the law of mass treats thermophoretic particle decomposition. The nonlinear partial differential system is reduced to an ordinary differential system by executing the appropriate transformations. Using a shooting approach with the help of Maple software, the flow, heat mass transportation, and micro-rotation profiles are estimated numerically. The effect of varying parameters on the physical quantities distributions is discussed via graphical demonstrations. The presence of microstructural slip reduces the velocity and micro-rotation profiles, whereas the nanoparticle concentration and temperature increase. The incrementing thermal radiation values augmented the temperature distribution. The concentration field narrows due to thermophoretic constraints.
Enhancement of UV detection properties of ZnO thin films via Ni doping
Authors: Aboud Ahmed A , Bukhari Zahid , Al-Ahmadi Ameenah N 
Published: May 2023

Abstract
In this work, pure and Ni-doped ZnO thin films have been deposited onto glass substrates using the spray pyrolysis technique. All films were deposited at constant deposition parameters but the Ni content was changed from 0 to 7 weight (wt) %. XRD results revealed the formation of a hexagonal ZnO phase whilst no other phases were detected. The crystallite size was determined using Scherrer’s equation and found to be 45.9 nm for the pure film. Scanning electron microscope images show the formation of irregular grains with a broad size distribution. The existence of Ni in the deposited films was confirmed using energy dispersive spectroscopy (EDX), where the Ni content in the film increases as the weight % increases in the starting solution. The optical band gap was determined and found to be 3.3 eV for the pure ZnO films, which was reduced with Ni doping. The performance of the deposited films for UV radiation has been examined for the 365 nm wavelength and at different applied potentials and constant power. The rise and decay times for doped films were observed to exhibit faster rise/recovery as compared to pure films. The minimum response time was found to be 0.09 s for Ni-7 wt% film and the minimum decay time is 0.07 s for Ni-1 wt%.
Patient-specific quality assurance failure prediction with deep tabular models
Authors: Levin R , Aravkin A Y , Kim M 
Published: May 2023

Abstract

Purpose. Patient-specific quality assurance (PSQA) failures in radiotherapy can cause a delay in patient care and increase the workload and stress of staff. We developed a tabular transformer model based directly on the multi-leaf collimator (MLC) leaf positions (without any feature engineering) to predict IMRT PSQA failure in advance. This neural model provides an end-to-end differentiable map from MLC leaf positions to the probability of PSQA plan failure, which could be useful for regularizing gradient-based leaf sequencing optimization algorithms and generating a plan that is more likely to pass PSQA. Method. We retrospectively collected DICOM RT PLAN files of 968 patient plans treated with volumetric arc therapy. We constructed a beam-level tabular dataset with 1873 beams as samples and MLC leaf positions as features. We trained an attention-based neural network FT-Transformer to predict the ArcCheck-based PSQA gamma pass rates. In addition to the regression task, we evaluated the model in the binary classification context predicting the pass or fail of PSQA. The performance was compared to the results of the two leading tree ensemble methods (CatBoost and XGBoost) and a non-learned method based on mean-MLC-gap. Results. The FT-Transformer model achieves 1.44% Mean Absolute Error (MAE) in the regression task of the gamma pass rate prediction and performs on par with XGBoost (1.53 % MAE) and CatBoost (1.40 % MAE). In the binary classification task of PSQA failure prediction, FT-Transformer achieves 0.85 ROC AUC (compared to the mean-MLC-gap complexity metric achieving 0.72 ROC AUC). Moreover, FT-Transformer, CatBoost, and XGBoost all achieve 80% true positive rate while keeping the false positive rate under 20%. Conclusions. We demonstrated that reliable PSQA failure predictors can be successfully developed based solely on MLC leaf positions. FT-Transformer offers an unprecedented benefit of providing an end-to-end differentiable map from MLC leaf positions to the probability of PSQA failure.
Mitigation of adversarial attacks on voter model dynamics by network heterogeneity
Authors: Chiyomaru Katsumi , Takemoto Kazuhiro 
Published: May 2023

Abstract
Voter model dynamics in complex networks are vulnerable to adversarial attacks. In particular, the voting outcome can be inverted by adding extremely small perturbations that are strategically generated in social networks, even when one opinion is dominant over the other. However, the mitigation of adversarial attacks on the voter model dynamics in complex networks has not been thoroughly investigated. Thus, we examined network structures that could mitigate adversarial attacks using model networks and real-world networks, considering that the network structure affects the voter model dynamics. Numerical simulations demonstrated that the heterogeneity of node degrees in the networks (degree heterogeneity) significantly mitigates adversarial attacks. In particular, for complex networks with a power-law degree distribution

P
(
k
)
∼

k

−
γ

, the mitigation effect is significant for

γ
⩽
3

. However, the mitigation effect of the degree heterogeneity was relatively weak for large and dense networks. The degree correlation and clustering in the networks exhibited almost no mitigation effect. The results enhance our understanding of how opinion dynamics and collective decision-making are distorted in social networks and may be useful for considering defense strategies against adversarial attacks.
CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis
Authors: Victor Ikechukwu Agughasi , S Murali 
Published: May 2023

Abstract
Automatic identification of salient features in large medical datasets, particularly in chest x-ray (CXR) images, is a crucial research area. Accurately detecting critical findings such as emphysema, pneumothorax, and chronic bronchitis can aid radiologists in prioritizing time-sensitive cases and screening for abnormalities. However, traditional deep neural network approaches often require bounding box annotations, which can be time-consuming and challenging to obtain. This study proposes an explainable ensemble learning approach, CX-Net, for lung segmentation and diagnosing lung disorders using CXR images. We compare four state-of-the-art convolutional neural network models, including feature pyramid network, U-Net, LinkNet, and a customized U-Net model with ImageNet feature extraction, data augmentation, and dropout regularizations. All models are trained on the Montgomery and VinDR-CXR datasets with and without segmented ground-truth masks. To achieve model explainability, we integrate SHapley Additive exPlanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) techniques, which enable a better understanding of the decision-making process and provide visual explanations of critical regions within the CXR images. By employing ensembling, our outlier-resistant CX-Net achieves superior performance in lung segmentation, with Jaccard overlap similarity of 0.992, Dice coefficients of 0.994, precision of 0.993, recall of 0.980, and accuracy of 0.976. The proposed approach demonstrates strong generalization capabilities on the VinDr-CXR dataset and is the first study to use these datasets for semantic lung segmentation with semi-supervised localization. In conclusion, this paper presents an explainable ensemble learning approach for lung segmentation and diagnosing lung disorders using CXR images. Extensive experimental results show that our method efficiently and accurately extracts regions of interest in CXR images from publicly available datasets, indicating its potential for integration into clinical decision support systems. Furthermore, incorporating SHAP and Grad-CAM techniques further enhances the interpretability and trustworthiness of the AI-driven diagnostic system.
Deep learning enhanced noise spectroscopy of a spin qubit environment
Authors: Martina Stefano , Hernández-Gómez Santiago , Gherardini Stefano , Caruso Filippo , Fabbri Nicole 
Published: May 2023

Abstract
The undesired interaction of a quantum system with its environment generally leads to a coherence decay of superposition states in time. A precise knowledge of the spectral content of the noise induced by the environment is crucial to protect qubit coherence and optimize its employment in quantum device applications. We experimentally show that the use of neural networks (NNs) can highly increase the accuracy of noise spectroscopy, by reconstructing the power spectral density that characterizes an ensemble of carbon impurities around a nitrogen-vacancy (NV) center in diamond. NNs are trained over spin coherence functions of the NV center subjected to different Carr–Purcell sequences, typically used for dynamical decoupling (DD). As a result, we determine that deep learning models can be more accurate than standard DD noise-spectroscopy techniques, by requiring at the same time a much smaller number of DD sequences.
Double-stranded RNA immunohistochemistry as a screening tool for viral encephalitis
Authors: Piantadosi Anne , Shariatzadeh Nima , Bombin Andrei , Arkun Knarik , Alexandrescu Sanda , Kleinschmidt-DeMasters B K , Solomon Isaac H 
Published: May 2023

Abstract

Objectives
Viral infections of the central nervous system can be challenging to diagnose because of the wide range of causative agents and nonspecific histologic features. We sought to determine whether detection of double-stranded RNA (dsRNA), produced during active RNA and DNA viral infections, could be used to select cases for metagenomic next-generation sequencing (mNGS) from formalin-fixed, paraffin-embedded brain tissue.

Methods
Eight commercially available anti-dsRNA antibodies were optimized for immunohistochemistry (IHC) and the top antibody tested in a series of cases with confirmed viral infections (n = 34) and cases with inflammatory brain lesions of unclear etiology (n = 62).

Results
Among known positives, anti-dsRNA IHC produced a strong cytoplasmic or nuclear staining pattern for Powassan virus, West Nile virus, rabies virus, JC polyoma virus, and adenovirus while failing to detect Eastern equine encephalitis virus, Jamestown Canyon virus, or any herpesvirus. All the unknown cases were negative by anti-dsRNA IHC, while mNGS detected rare viral reads (0.3-1.3 reads per million total reads) in 2 cases (3%), with only 1 having potential clinical significance.

Conclusions
Anti-dsRNA IHC can effectively identify a subset of clinically relevant viral infections but not all. The absence of staining should not exclude cases from mNGS if sufficient clinical and histologic suspicion exists.
Influence of Neighborhood Social and Natural Environment on Prostate Tumor Histology in a Cohort of Male Health Professionals
Authors: Iyer Hari S , Vaselkiv Jane B , Stopsack Konrad H , Roscoe Charlotte , DeVille Nicole V , Zhang Yiwen , Penney Kathryn L , Balk Steven P , Fiorentino Michelangelo , Hart Jaime E , James Peter , De Vivo Immaculata , Mucci Lorelei A , Laden Francine , Rebbeck Timothy R 
Published: May 2023

Abstract
Adverse neighborhood social and natural (green space) environments may contribute to the etiology of prostate cancer (CaP), but mechanisms are unclear. We examined associations between neighborhood environment and prostate intratumoral inflammation in 967 men diagnosed with CaP with available tissue samples from 1986–2009 in the Health Professionals Follow-up Study. Exposures were linked to work or residential addresses in 1988. We estimated indices of neighborhood socioeconomic status (nSES) and segregation (Index of Concentration at the Extremes (ICE)) using US Census tract–level data. Surrounding greenness was estimated using seasonal averaged Normalized Difference Vegetation Index (NDVI) data. Surgical tissue underwent pathological review for acute and chronic inflammation, corpora amylacea, and focal atrophic lesions. Adjusted odds ratios (aORs) for inflammation (ordinal) and focal atrophy (binary) were estimated using logistic regression. No associations were observed for acute or chronic inflammation. Each interquartile-range increase in NDVI within 1,230 m of the participant’s work or home address (aOR = 0.74, 95% confidence interval (CI): 0.59, 0.93), in ICE-income (aOR = 0.79, 95% CI: 0.61, 1.04), and in ICE-race/income (aOR = 0.79, 95% CI: 0.63, 0.99) was associated with lower odds of postatrophic hyperplasia. Interquartile-range increases in nSES (aOR = 0.76, 95% CI: 0.57, 1.02) and ICE-race/income (aOR = 0.73, 95% CI: 0.54, 0.99) were associated with lower odds of tumor corpora amylacea. Histopathological inflammatory features of prostate tumors may be influenced by neighborhood.
Self-Measured Blood Pressure Telemonitoring Programs: A Pragmatic How-to Guide
Authors: McGrath Debra , Meador Margaret , Wall Hilary K , Padwal Raj S 
Published: May 2023

Abstract
Self-measured blood pressure (SMBP) telemonitoring is the process of securely storing and tele-transmitting reliably measured, patient self-performed blood pressure (BP) measurements to healthcare teams, while ensuring that these data are viewable and clinically actionable for the purposes of improving hypertension diagnosis and management. SMBP telemonitoring is a vital component of an overall hypertension control strategy. Herein, we present a pragmatic guide for implementing SMBP in clinical practice and provide a comprehensive list of resources to assist with implementation. Initial steps include defining program goals and scope, selecting the target population, staffing, choosing appropriate (clinically validated) BP devices with proper cuff sizes, and selecting a telemonitoring platform. Adherence to recommended data transmission, security, and data privacy requirements is essential. Clinical workflow implementation involves patient enrollment and training, review of telemonitored data, and initiating or titrating medications in a protocolized fashion based upon this information. Utilizing a team-based care structure is preferred and calculation of average BP for hypertension diagnosis and management is important to align with clinical best practice recommendations. Many stakeholders in the United States are engaged in overcoming challenges to SMBP program adoption. Major barriers include affordability, clinician and program reimbursement, availability of technological elements, challenges with interoperability, and time/workload constraints. Nevertheless, it is anticipated that uptake of SMBP telemonitoring, still at a nascent stage in many parts of the world, will continue to grow, propagated by increased clinician familiarity, broader platform availability, improvements in interoperability, and reductions in costs that occur with scale, competition, and technological innovation.
Access to inclusive and culturally sensitive mental healthcare in Black, Indigenous, and People of Color pharmacy students and residents
Authors: Thakkar Esha , Liu Ina , Hosea Kalynn , Katz Shana , Marks Katie , Hall Sarah , Liu Cat , Harris Suzanne C 
Published: May 2023

Abstract

Purpose
Disparities in accessing culturally sensitive mental healthcare exist and may be exacerbated in pharmacy trainees. The purpose of this study was to identify barriers to culturally sensitive mental healthcare and how to improve access for racially and ethnically minoritized pharmacy students and residents.

Methods
This institutional review board–exempt study included in-person and virtual focus groups. Eligible participants were first-, second-, third-, and fourth-year doctor of pharmacy (PharmD) students and pharmacy residents completing a postgraduate year 1 or 2 program who identified as Black, Indigenous, and People of Color (BIPOC). Barriers to care, identity’s influence on seeking care, and areas in which the training programs are doing well or areas for improvement were assessed. Responses were transcribed and analyzed using an open coding system by 2 reviewers, followed by discussion as a team to reach consensus.

Results
This study enrolled 8 first-year, 5 second-year, 7 third-year, and 2 fourth-year PharmD students and 4 residents (N = 26). Barriers to care included time, access to resources, and internal and external stigma. Identity barriers included cultural and family stigma and lack of representation in therapists with regard to race, ethnicity, and gender. Areas going well included supportive faculty and paid time off, while areas for improvement included wellness days, reduced workload, and increased diversity within the workforce.

Conclusion
This study is the first to identify barriers to culturally sensitive mental healthcare in pharmacy trainees who identify as BIPOC while providing insight on how to increase culturally sensitive mental healthcare resources.