bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intravoxel incoherent motion magnetic resonance imaging reconstruction from highly under-sampled diffusion-weighted PROPELLER acquisition data via physics-informed residual feedback unrolled network
Authors: Wang Jiechao , Geng Wenhua , Wu Jian , Kang Taishan , Wu Zhigang , Lin Jianzhong , Yang Yu , Cai Congbo , Cai Shuhui 
Published: August 2023

Abstract

Objective. The acquisition of diffusion-weighted images for intravoxel incoherent motion (IVIM) imaging is time consuming. This work aims to accelerate the scan through a highly under-sampling diffusion-weighted turbo spin echo PROPELLER (DW-TSE-PROPELLER) scheme and to develop a reconstruction method for accurate IVIM parameter mapping from the under-sampled data. Approach
. The proposed under-sampling DW-TSE-PROPELLER scheme for IVIM imaging is that a few blades per b-value are acquired and rotated along the b-value dimension to cover high-frequency information. A physics-informed residual feedback unrolled network (PIRFU-Net) is proposed to directly estimate distortion-free and artifact-free IVIM parametric maps (i.e., the perfusion-free diffusion coefficient D and the perfusion fraction f) from highly under-sampled DW-TSE-PROPELLER data. PIRFU-Net used an unrolled convolution network to explore data redundancy in the k-q space to remove under-sampling artifacts. An empirical IVIM physical constraint was incorporated into the network to ensure that the signal evolution curves along the b-value follow a bi-exponential decay. The residual between the realistic and estimated measurements was fed into the network to refine the parametric maps. Meanwhile, the use of synthetic training data eliminated the need for genuine DW-TSE-PROPELLER data. Main results
. The experimental results show that the DW-TSE-PROPELLER acquisition was six times faster than full k-space coverage PROPELLER acquisition and within a clinically acceptable time. Compared with the state-of-the-art methods, the distortion-free D and f maps estimated by PIRFU-Net were more accurate and had better-preserved tissue boundaries on a simulated human brain and realistic phantom/rat brain/human brain data. Significance
. Our proposed method greatly accelerates IVIM imaging. It is capable of directly and simultaneously reconstructing distortion-free, artifact-free, and accurate D and f maps from six-fold under-sampled DW-TSE-PROPELLER data.
An improved dual-domain network for metal artifact reduction in CT images using aggregated contextual transformations
Authors: Tang Hui , Jiang Sudong , Lin Yubing , Li Yu , Bao Xudong 
Published: August 2023

Abstract

Objective. Metal artifact reduction (MAR) remains a challenging task due to the difficulty of removing artifacts while preserving anatomical details of the tissue. Although current dual-domain networks have shown promising performance in MAR, they heavily rely on the image domain, which can be too smooth and lose important information in the metal-affected area. To address this problem, we propose an improved dual domain network framework. Approach. We enhance sinogram completion performance by utilizing an aggregated contextual transformations network in the sinogram domain. Furthermore, we utilize a prior-projection-based linearized correction method to obtain images with beam-hardening artifacts removed, which are incorporated into the input of the image post-processing network to assist in training the image domain network. Finally, we train the sinogram domain network and the image domain network separately to their respective convergences. Main results. In experiments conducted on a simulated dataset, our method achieves the best average RMSE of 25.1, SSIM of 0.973, and PSNR of 42.1, respectively. Significance. The proposed method is capable of preserving tissue structures near metallic objects while eliminating metal artifacts from the reconstructed images. Related codes will be released at https://github.com/Corinna-China/AOTDudoNet
DAWTran: dynamic adaptive windowing transformer network for pneumothorax segmentation with implicit feature alignment
Authors: Liang Pengchen , Chen Jianguo , Yao Lei , Yu Yanfang , Liang Kaiyi , Chang Qing 
Published: August 2023

Abstract

Objective. This study aims to address the significant challenges posed by pneumothorax segmentation in computed tomography images due to the resemblance between pneumothorax regions and gas-containing structures such as the trachea and bronchus. Approach. We introduce a novel dynamic adaptive windowing transformer (DAWTran) network incorporating implicit feature alignment for precise pneumothorax segmentation. The DAWTran network consists of an encoder module, which employs a DAWTran, and a decoder module. We have proposed a unique dynamic adaptive windowing strategy that enables multi-head self-attention to effectively capture multi-scale information. The decoder module incorporates an implicit feature alignment function to minimize information deviation. Moreover, we utilize a hybrid loss function to address the imbalance between positive and negative samples. Main results. Our experimental results demonstrate that the DAWTran network significantly improves the segmentation performance. Specifically, it achieves a higher dice similarity coefficient (DSC) of 91.35% (a larger DSC value implies better performance), showing an increase of 2.21% compared to the TransUNet method. Meanwhile, it significantly reduces the Hausdorff distance (HD) to 8.06 mm (a smaller HD value implies better performance), reflecting a reduction of 29.92% in comparison to the TransUNet method. Incorporating the dynamic adaptive windowing (DAW) mechanism has proven to enhance DAWTran’s performance, leading to a 4.53% increase in DSC and a 15.85% reduction in HD as compared to SwinUnet. The application of the implicit feature alignment (IFA) further improves the segmentation accuracy, increasing the DSC by an additional 0.11% and reducing the HD by another 10.01% compared to the model only employing DAW. Significance. These results highlight the potential of the DAWTran network for accurate pneumothorax segmentation in clinical applications, suggesting that it could be an invaluable tool in improving the precision and effectiveness of diagnosis and treatment in related healthcare scenarios. The improved segmentation performance with the inclusion of DAW and IFA validates the effectiveness of our proposed model and its components.
Analytical modeling of architecture dependent atypical scaling trends in metal–Hf0.5Zr0.5O2–metal-SiO2–Si negative capacitance transistors
Authors: Semwal Sandeep , Kranti Abhinav 
Published: August 2023

Abstract
In order to better understand the possible improvement through the incorporation of a ferroelectric (FE) layer in the gate stack of the nanoscale transistor, this work develops analytical expressions to assess the scalability of cylindrical (CYL) nanowire and planar double gate (DG) metal–FE–metal–insulator–semiconductor (MFMIS) negative capacitance (NC) transistors. While predicting a sub-60 mV dec−1 subthreshold swing and a negative drain induced barrier lowering (DIBL), the results indicate that at lower FE thickness, the performance of the NC field effect transistor (NCFET) is primarily governed by the electrostatic integrity of the baseline transistor, i.e. the CYL architecture outperforms planar DG NCFET. However, for relatively thicker T
FE, the performance of an MFMIS NCFET is strongly governed by the FE coupling, which indicates the comparable performance of DG and CYL MFMIS NCFETs. The formalism, while predicting atypical trends, showcases a pragmatic design criterion for achieving a sub-60 mV dec−1 subthreshold swing and DIBL-free characteristics in MFMIS NC transistors.
Can gravitational vacuum condensate stars be a dark energy source?
Authors: Avelino P.P. 
Published: August 2023

Abstract
Gravitational vacuum condensate stars, also known as gravastars, have been proposed as an alternative to black holes. Their interior contains a perfect fluid with an equation of state akin to that of a cosmological constant. For this reason, they have recently been considered as a possible astrophysical source of dark energy. In this work we argue that gravitational vacuum condensate stars cannot be the source of dark energy and highlight that a direct coupling of their mass to the dynamics of the Universe would lead to an additional velocity dependent acceleration, damping their motion with respect to the cosmological frame. We briefly discuss the potential impact of this additional acceleration in the context of a recent proposal that the observed mass growth of compact objects at the core of elliptical galaxies might result from such a cosmological coupling.
Search for the lepton flavor violating decay μ
+ → e
+
e
-
e
+ with the Mu3e experiment at PSI
Authors: Martín Pérez C. 
Published: August 2023

Abstract
Mu3e is an experiment under construction at the Paul Scherrer Institute (Switzerland) aiming to find or exclude the charged lepton flavor violating decay μ
+ → e
+
e
-
e
+ at branching fractions above 10-16. A first running phase using an existing beamline targets a single event sensitivity of 2 · 10-15, while the ultimate sensitivity is reached in a second phase with a new high intensity muon beamline. Achieving such sensitivity requires a high muon rate along with a detector of large kinematic acceptance able to reconstruct the low momenta of the decay positrons and electrons with minimal material budget. The Mu3e experiment is mounted with ultra thin tracking detectors based on high-voltage monolithic active pixel sensors, combined with scintillating fibers and tiles for precise timing. This paper presents an overview of the experimental requirements, the technical design, and the construction readiness of Mu3e.
Optimization of moderator materials by NSGA II based on macroscopic cross-sections: applications in accelerator neutron sources
Authors: Ge Yulin , Zhong Yao , Yuan Nan , Sun Yanbing , Yang Zhen , Ma Wei , Zou Liping , Murata Isao , Lu Liang 
Published: August 2023

Abstract
In recent years, genetic algorithms have been applied in
nuclear technology design, which have been shown to produce
optimized results more efficiently than traditional enumeration
methods. This advancement in optimization techniques is particularly
useful in the field of nuclear technology design, where complexity
is high and decision-making time is critical. It can be used to
optimize moderator materials for ANS to find composite materials
that provide high neutron beam quality. At present, the direct
combination of Monte Carlo method and genetic algorithm requires a
lot of computing resources and time. And the weights of different
optimization objectives are controversial. Thus, we propose a
two-step method based on NSGA II, which uses macroscopic section as
the intermediate parameters for optimization. It can greatly reduce
the time of genetic algorithm optimization. The method is applied to
the PAFA project of Sun Yat-sen University, the computational speed
has been increased by 50 times based on a 50-generation
optimization. And the results of the genetic algorithm show that the
neutron beam obtained by using composite materials as moderator is
30.8% better than that obtained by using only MgF2 as
moderator. The two-step genetic algorithm optimization has shown its
great potential in the optimization problem of moderator materials.
Data-driven plasma modelling: surrogate collisional radiative models of fluorocarbon plasmas from deep generative autoencoders
Authors: Daly G A , Fieldsend J E , Hassall G , Tabor G R 
Published: August 2023

Abstract
We have developed a deep generative model that can produce accurate optical emission spectra and colour images of an ICP plasma using only the applied coil power, electrode power, pressure and gas flows as inputs—essentially an empirical surrogate collisional radiative model. An autoencoder was trained on a dataset of 812 500 image/spectra pairs in argon, oxygen, Ar/O2, CF4/O2 and SF6/O2 plasmas in an industrial plasma etch tool, taken across the entire operating space of the tool. The autoencoder learns to encode the input data into a compressed latent representation and then decode it back to a reconstruction of the data. We learn to map the plasma tool’s inputs to the latent space and use the decoder to create a generative model. The model is very fast, taking just over 10 s to generate 10 000 measurements on a single GPU. This type of model can become a building block for a wide range of experiments and simulations. To aid this, we have released the underlying dataset of 812 500 image/spectra pairs used to train the model, the trained models and the model code for the community to accelerate the development and use of this exciting area of deep learning. Anyone can try the model, for free, on Google Colab.
A Psychosocial Model of COVID-19 Vaccination: Antecedent and Concurrent Effects of Demographics, Traits, Political Beliefs, Vaccine Intention, Information Sources, Mandates, and Flu Vaccine History
Authors: Godfrey Olivia , Bogg Tim , Milad Elizabeth 
Published: August 2023

Abstract

Background
To date, research investigating psychosocial correlates of COVID-19 vaccination has been cross-sectional, parochial, and/or reliant upon non-stratified international samples, resulting in difficulty in clarifying the contributions of various vaccination-related influences.

Purpose
The present study tested a novel integration of prospective and concurrent associations of demographic and dispositional tendencies, intervening illness and preventive beliefs, vaccine intention, illness experiences, and concurrent contextual vaccine-related influences with subsequent COVID-19 vaccination.

Methods
The preregistered study used a stratified online U.S. sample (N = 500), with assessments aligned to (a) “15 days to slow the spread” in March 2020, (b) vaccine authorization and major case/mortality surge during December 2020 and January 2021, and (c) the period following full vaccine approval in August 2021 during the third major/case mortality surge during September and October 2021.

Results
Path modeling showed the absence of children in the household and greater education were prospective predictors of vaccination. Trait openness and less conservative political beliefs showed indirect prospective associations with vaccination via stronger intermediating vaccine intention. Contextual vaccine-related influences of vaccine-related information sources, employer mandates, and flu vaccine history also showed direct associations with vaccination. In contrast to expectations, lower conscientiousness showed a direct prospective association with vaccination.

Conclusions
Controlling for interrelations among study variables, the results of the integrative psychosocial model clarified the unique contributions and pathways from antecedent characteristics to vaccination while accounting for vaccine-related contextual influences, providing further direction for refining the timing and content of public health messaging for vaccination.
Deep multi-view contrastive learning for cancer subtype identification
Authors: Chen Wenlan , Wang Hong , Liang Cheng 
Published: August 2023

Abstract
Cancer heterogeneity has posed great challenges in exploring precise therapeutic strategies for cancer treatment. The identification of cancer subtypes aims to detect patients with distinct molecular profiles and thus could provide new clues on effective clinical therapies. While great efforts have been made, it remains challenging to develop powerful computational methods that can efficiently integrate multi-omics datasets for the task. In this paper, we propose a novel self-supervised learning model called Deep Multi-view Contrastive Learning (DMCL) for cancer subtype identification. Specifically, by incorporating the reconstruction loss, contrastive loss and clustering loss into a unified framework, our model simultaneously encodes the sample discriminative information into the extracted feature representations and well preserves the sample cluster structures in the embedded space. Moreover, DMCL is an end-to-end framework where the cancer subtypes could be directly obtained from the model outputs. We compare DMCL with eight alternatives ranging from classic cancer subtype identification methods to recently developed state-of-the-art systems on 10 widely used cancer multi-omics datasets as well as an integrated dataset, and the experimental results validate the superior performance of our method. We further conduct a case study on liver cancer and the analysis results indicate that different subtypes might have different responses to the selected chemotherapeutic drugs.
CoCoNat: a novel method based on deep learning for coiled-coil prediction
Authors: Madeo Giovanni , Savojardo Castrense , Manfredi Matteo , Martelli Pier Luigi , Casadio Rita 
Published: August 2023

Abstract

Motivation
Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state.

Results
In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states.

Availability and implementation
CoCoNat web server is available at https://coconat.biocomp.unibo.it. Standalone version is available on GitHub at https://github.com/BolognaBiocomp/coconat.
Kernel interpolation generalizes poorly
Authors: Li Yicheng , Zhang Haobo , Lin Qian 
Published: August 2023

Summary
One of the most interesting problems in the recent renaissance of the studies in kernel regression might be whether kernel interpolation can generalize well, since it may help us understand the ‘benign overfitting phenomenon’ reported in the literature on deep networks. In this paper, under mild conditions, we show that, for any ε&gt;0, the generalization error of kernel interpolation is lower bounded by Ω(n−ε). In other words, the kernel interpolation generalizes poorly for a large class of kernels. As a direct corollary, we can show that overfitted wide neural networks defined on the sphere generalize poorly.
ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology
Authors: Lewandowski Miłosz , Łukowicz Paweł , Świetlik Dariusz , Barańska-Rybak Wioletta 
Published: August 2023

Abstract

Background
The global use of artificial intelligence (AI) has the potential to revolutionize the healthcare industry. Despite the fact that AI is becoming more popular, there is still a lack of evidence on its use in dermatology.

Objectives
To determine the capacity of ChatGPT-3.5 and ChatGPT-4 to support dermatology knowledge and clinical decision-making in medical practice.

Methods
Three Specialty Certificate Examination in Dermatology tests, in English and Polish, consisting of 120 single-best-answer, multiple-choice questions each, were used to assess the performance of ChatGPT-3.5 and ChatGPT-4.

Results
ChatGPT-4 exceeded the 60% pass rate in every performed test, with a minimum of 80% and 70% correct answers for the English and Polish versions, respectively. ChatGPT-4 performed significantly better on each exam (P &lt; 0.01), regardless of language, compared with ChatGPT-3.5. Furthermore, ChatGPT-4 answered clinical picture-type questions with an average accuracy of 93.0% and 84.2% for questions in English and Polish, respectively. The difference between the tests in Polish and English were not significant; however, ChatGPT-3.5 and ChatGPT-4 performed better overall in English than in Polish by an average of 8 percentage points for each test. Incorrect ChatGPT answers were highly correlated with a lower difficulty index, denoting questions of higher difficulty in most of the tests (P &lt; 0.05).

Conclusions
The dermatology knowledge level of ChatGPT was high, and ChatGPT-4 performed significantly better than ChatGPT-3.5. Although the use of ChatGPT will not replace a doctor’s final decision, physicians should support the development of AI in dermatology to raise the standards of medical care.
Gene expression analyses of TAS1R taste receptors relevant to the treatment of cardiometabolic disease
Authors: Stavrou Mariah R , So Sean Souchiart , Finch Angela M , Ballouz Sara , Smith Nicola J 
Published: August 2023

Abstract
The sweet taste receptor (STR) is a G protein-coupled receptor (GPCR) responsible for mediating cellular responses to sweet stimuli. Early evidence suggests that elements of the STR signaling system are present beyond the tongue in metabolically active tissues, where it may act as an extraoral glucose sensor. This study aimed to delineate expression of the STR in extraoral tissues using publicly available RNA-sequencing repositories. Gene expression data was mined for all genes implicated in the structure and function of the STR, and control genes including highly expressed metabolic genes in relevant tissues, other GPCRs and effector G proteins with physiological roles in metabolism, and other GPCRs with expression exclusively outside the metabolic tissues. Since the physiological role of the STR in extraoral tissues is likely related to glucose sensing, expression was then examined in diseases related to glucose-sensing impairment such as type 2 diabetes. An aggregate co-expression network was then generated to precisely determine co-expression patterns among the STR genes in these tissues. We found that STR gene expression was negligible in human pancreatic and adipose tissues, and low in intestinal tissue. Genes encoding the STR did not show significant co-expression or connectivity with other functional genes in these tissues. In addition, STR expression was higher in mouse pancreatic and adipose tissues, and equivalent to human in intestinal tissue. Our results suggest that STR expression in mice is not representative of expression in humans, and the receptor is unlikely to be a promising extraoral target in human cardiometabolic disease.