bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning-based diagnosis of disease activity in patients with Graves’ orbitopathy using orbital SPECT/CT
Authors: Yao Ni , Li Longxi , Gao Zhengyuan , Zhao Chen , Li Yanting , Han Chuang , Nan Jiaofen , Zhu Zelin , Xiao Yi , Zhu Fubao , Zhao Min , Zhou Weihua 
Published: July 2023

Abstract
Purpose
Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO.

Materials and methods
GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM).

Results
The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p < 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82.

Conclusion
The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.
Anti-Xa level monitoring of low-molecular-weight heparin during intermittent venovenous hemofiltration
Authors: Xu Lengnan , Sun Ying , Wang Songlan , Li Chuanbao , Mao Yonghui 
Published: July 2023

AbstractLow-molecular-weight heparin (LMWH) is an anticoagulant used to prevent clotting during blood purification treatments. This study aimed to evaluate the clinical use of the anti-factor Xa level (anti-Xa) for monitoring LMWH anticoagulant levels during intermittent venovenous hemofiltration (IVVHF). This prospective observational study enrolled patients who required IVVHF for renal failure in Beijing Hospital between May 2019 and February 2021. The LMWH anticoagulation was assessed by the coagulation grade of the filter and line. One hundred and ten participants were included. There were 90 patients with a filter and line coagulation grade of ≤ 1 and 20 patients with grade > 1. The anti-Xa level of 0.2 IU/mL was a critical value. The multivariable logistic regression analysis showed that anti-Xa level > 0.2 IU/mL (odd ratio [OR] = 2.263; 95% CI: 1.290–4.871, P = 0.034) and cardiovascular disease (OR = 10.028; 95% CI: 1.204–83.488; P = 0.033) were independently associated with the coagulation grade of the filter and line. Anti-Xa level could monitor LMWH anticoagulation during IVVHF.
Preventive health care in blood cancer survivors: results from the ABC study
Authors: Baum Julia , Lax Hildegard , Lehmann Nils , Merkel-Jens Anja , Beelen Dietrich W. , Jöckel Karl-Heinz , Dührsen Ulrich 
Published: July 2023

Abstract
Background
Blood cancer survivors are at increased risk for second primary malignancies, cardiovascular diseases, and infections. Little is known about preventive care in blood cancer survivors.

Methods
Our questionnaire-based study included blood cancer patients diagnosed at the University Hospital of Essen before 2010, with a ≥ 3-year interval from the last intense treatment. One section of the retrospective study covered preventive care (cancer screening, cardiovascular screening, vaccination).

Results
Preventive care was delivered by a general practitioner for 1100 of 1504 responding survivors (73.1%), by an oncologist for 125 (8.3%), by a general practitioner together with an oncologist for 156 (10.4%), and by other disciplines for 123 (8.2%). Cancer screening was more consistently performed by general practitioners than by oncologists. The converse was true for vaccination, with particularly high vaccination rates in allogeneic transplant recipients. Cardiovascular screening did not differ between care providers. Cancer and cardiovascular screening rates in survivors eligible for statutory prevention programs were higher than in the general population (skin cancer screening 71.1%; fecal occult blood testing 70.4%; colonoscopy 64.6%; clinical breast examination 92.1%; mammography 86.8%; cervical smear 86.0%; digital rectal examination 61.9%; blood pressure test 69.4%; urine glucose test 54.4%; blood lipid test 76.7%; information about overweight 71.0%). The Streptococcus pneumoniae vaccination rate was higher (37.0%) and the influenza vaccination rate was lower (57.0%) than in the general population.

Conclusions
Utilization of preventive care is high among German blood cancer survivors. To ensure widespread delivery and avoid redundancy, communication between oncologists and preventive care providers is essential.
‘It’s communication between people who are going through the same thing’: experiences of informal interactions in hospital cancer treatment settings
Authors: P. Grant Matthew , A. M. Philip Jennifer , Deliens Luc , Komesaroff Paul A. 
Published: July 2023

Abstract
Purpose
In hospital settings, patients, visitors, and staff engage in many interactions outside formal clinical encounters. Whilst many of these may be inconsequential, others contribute significantly to how patients and their carers experience cancer and its treatment. This article aims to explore the experiences and significance of interactions that occur outside formal clinical encounters in hospital cancer treatment settings.

Methods
Semi-structured interviews were conducted with cancer patients, carers, and staff recruited from two hospital sites and cancer support groups. Hermeneutic phenomenology informed lines of questioning and data analysis.

Results
Thirty-one people participated in the study: 18 cancer patients, four carers, and nine staff members. The experiences of informal interactions were grouped into three themes: connecting, making sense, and enacting care. The participants described how these encounters allowed connection with others in the hospital spaces, facilitating a sense of belonging, normality, and self-worth. Through these interactions, individuals participated in making sense of their experiences, to better anticipate the decisions and challenges that might lie ahead. By connecting with other individuals, they cared for others and felt cared for themselves, and were able to learn from, teach, and support each other.

Conclusions
Outside the confines of the clinical discourses participants negotiate terms of engagement, sharing of information, expertise, and their own personal stories that they may employ to contribute to the individuals around them. These interactions occur within a loose and evolving framework of social interactions, an ‘informal community’, in which cancer patients, carers, and staff members play active and meaningful roles.
Compact anisotropic stable hypersurfaces with free boundary in convex solid cones
Authors: Rosales César 
Published: July 2023

AbstractWe consider a convex solid cone $$\mathcal {C}\subset \mathbb {R}^{n+1}$$

C
⊂

R

n
+
1

with vertex at the origin and boundary $$\partial \mathcal {C}$$

∂
C

smooth away from 0. Our main result shows that a compact two-sided hypersurface $$\Sigma $$
Σ
immersed in $$\mathcal {C}$$
C
with free boundary in $$\partial \mathcal {C}\setminus \{0\}$$

∂
C
\
{
0
}

and minimizing, up to second order, an anisotropic area functional under a volume constraint is contained in a Wulff-shape. The technique of proof also works for a non-smooth convex cone $$\mathcal {C}$$
C
provided the boundary of $$\Sigma $$
Σ
is away from the singular set of $$\partial \mathcal {C}$$

∂
C

.
Mathematical problems in and out of school: The impact of considering mathematical operations and reality on real-life solutions
Authors: Wisenöcker Andrea S. , Binder Sarah , Holzer Manuel , Valentic Anna , Wally Celina , Große Cornelia S. 
Published: July 2023

AbstractIn this study, effects of asking participants to make different types of considerations when solving a realistic word problem were investigated. A two-factorial experiment with the factors “addressing realistic considerations” (with vs. without) and “addressing mathematical operations” (with vs. without) was conducted. It was assumed that reality-based considerations would lead to reality-based problem-solving strategies, thus fostering real-life solutions, while considering mathematical operations would lead to problem-solving strategies usually promoted in school, which were expected to impair realistic solutions. Analyses are based on N = 165 participants. The results showed that being asked to make reality-based considerations did not significantly affect realistic solutions (F(1, 161) = 2.43, p = 0.121, ηp2 = 0.015), while being asked to consider appropriate mathematical operations significantly impaired realistic solutions (F(1, 161) = 8.54, p = 0.004, ηp2 = 0.050). These findings suggest that inducing typical school problem-solving strategies may be detrimental when it comes to solving mathematical problems in a realistic way.
Association of serum cortisol and cortisone levels and risk of recurrence after endocrine treatment in breast cancer
Authors: Wang Feng , Giskeødegård Guro F. , Skarra Sissel , Engstrøm Monica J. , Hagen Lars , Geisler Jürgen , Mikkola Tomi S. , Tikkanen Matti J. , Debik Julia , Reidunsdatter Randi J. , Bathen Tone F. 
Published: July 2023

AbstractMetabolic reprogramming in breast cancer involves changes in steroid hormone synthesis and metabolism. Alterations in estrogen levels in both breast tissue and blood may influence carcinogenesis, breast cancer growth, and response to therapy. Our aim was to examine whether serum steroid hormone concentrations could predict the risk of recurrence and treatment-related fatigue in patients with breast cancer. This study included 66 postmenopausal patients with estrogen receptor-positive breast cancer who underwent surgery, radiotherapy, and adjuvant endocrine treatment. Serum samples were collected at six different time points [before the start of radiotherapy (as baseline), immediately after radiotherapy, and then 3, 6, 12 months, and 7–12 years after radiotherapy]. Serum concentrations of eight steroid hormones (cortisol, cortisone, 17α-hydroxyprogesterone, 17β-estradiol, estrone, androstenedione, testosterone, and progesterone) were measured using a liquid chromatography–tandem mass spectrometry-based method. Breast cancer recurrence was defined as clinically proven relapse/metastatic breast cancer or breast cancer-related death. Fatigue was assessed with the QLQ-C30 questionnaire. Serum steroid hormone concentrations measured before and immediately after radiotherapy differed between relapse and relapse-free patients [(accuracy 68.1%, p  = 0.02, and 63.2%, p  = 0.03, respectively, partial least squares discriminant analysis (PLS-DA)]. Baseline cortisol levels were lower in patients who relapsed than in those who did not (p < 0.05). The Kaplan–Meier analysis showed that patients with high baseline concentrations of cortisol (≥ median) had a significantly lower risk of breast cancer recurrence than patients with low cortisol levels (<median) (p  = 0.02). During follow-up, there was a decrease in cortisol and cortisone concentrations in relapse-free patients, whereas these steroid hormones increased in patients who relapsed. In addition, steroid hormone concentrations immediately after radiotherapy were associated with treatment-related fatigue (accuracy of 62.7%, p  = 0.03, PLS-DA). However, baseline steroid hormone levels did not predict fatigue at 1 year or at 7–12 years. In conclusion, breast cancer patients with low baseline cortisol levels were more likely to experience recurrence. During follow-up, cortisol and cortisone levels decreased in relapse-free patients but increased in patients with recurrence. Thus, cortisol and cortisone may act as potential biomarkers indicating individual risk of recurrence.
Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions
Authors: Mao Zhiping , Meng Xuhui 
Published: July 2023

AbstractWe consider solving the forward and inverse partial differential equations (PDEs) which have sharp solutions with physics-informed neural networks (PINNs) in this work. In particular, to better capture the sharpness of the solution, we propose the adaptive sampling methods (ASMs) based on the residual and the gradient of the solution. We first present a residual only-based ASM denoted by ASM I. In this approach, we first train the neural network using a small number of residual points and divide the computational domain into a certain number of sub-domains, then we add new residual points in the sub-domain which has the largest mean absolute value of the residual, and those points which have the largest absolute values of the residual in this sub-domain as new residual points. We further develop a second type of ASM (denoted by ASM II) based on both the residual and the gradient of the solution due to the fact that only the residual may not be able to efficiently capture the sharpness of the solution. The procedure of ASM II is almost the same as that of ASM I, and we add new residual points which have not only large residuals but also large gradients. To demonstrate the effectiveness of the present methods, we use both ASM I and ASM II to solve a number of PDEs, including the Burger equation, the compressible Euler equation, the Poisson equation over an L-shape domain as well as the high-dimensional Poisson equation. It has been shown from the numerical results that the sharp solutions can be well approximated by using either ASM I or ASM II, and both methods deliver much more accurate solutions than the original PINNs with the same number of residual points. Moreover, the ASM II algorithm has better performance in terms of accuracy, efficiency, and stability compared with the ASM I algorithm. This means that the gradient of the solution improves the stability and efficiency of the adaptive sampling procedure as well as the accuracy of the solution. Furthermore, we also employ the similar adaptive sampling technique for the data points of boundary conditions (BCs) if the sharpness of the solution is near the boundary. The result of the L-shape Poisson problem indicates that the present method can significantly improve the efficiency, stability, and accuracy.
Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures
Authors: You H. Q. , Xu X. , Yu Y. , Silling S. , D’Elia M. , Foster J. 
Published: July 2023

AbstractMolecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.
Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems
Authors: Meng Xuhui 
Published: July 2023

AbstractPhysics-informed deep learning has recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINNs) and deep operator networks (DeepONets) are two such models. The former encodes the physical laws via the automatic differentiation, while the latter learns the hidden physics from data. Generally, the noisy and limited observational data as well as the over-parameterization in neural networks (NNs) result in uncertainty in predictions from deep learning models. In paper “MENG, X., YANG, L., MAO, Z., FERRANDIS, J. D., and KARNIADAKIS, G. E. Learning functional priors and posteriors from data and physics. Journal of Computational Physics, 457, 111073 (2022)”, a Bayesian framework based on the generative adversarial networks (GANs) has been proposed as a unified model to quantify uncertainties in predictions of PINNs as well as DeepONets. Specifically, the proposed approach in “MENG, X., YANG, L., MAO, Z., FERRANDIS, J. D., and KARNIADAKIS, G. E. Learning functional priors and posteriors from data and physics. Journal of Computational Physics, 457, 111073 (2022)” has two stages: (i) prior learning, and (ii) posterior estimation. At the first stage, the GANs are utilized to learn a functional prior either from a prescribed function distribution, e.g., the Gaussian process, or from historical data and available physics. At the second stage, the Hamiltonian Monte Carlo (HMC) method is utilized to estimate the posterior in the latent space of GANs. However, the vanilla HMC does not support the mini-batch training, which limits its applications in problems with big data. In the present work, we propose to use the normalizing flow (NF) models in the context of variational inference (VI), which naturally enables the mini-batch training, as the alternative to HMC for posterior estimation in the latent space of GANs. A series of numerical experiments, including a nonlinear differential equation problem and a 100-dimensional (100D) Darcy problem, are conducted to demonstrate that the NFs with full-/mini-batch training are able to achieve similar accuracy as the “gold rule” HMC. Moreover, the mini-batch training of NF makes it a promising tool for quantifying uncertainty in solving the high-dimensional partial differential equation (PDE) problems with big data.
Carcinoma of unknown primary (CUP): an update for histopathologists
Authors: Beauchamp Katie , Moran Bruce , O’Brien Timothy , Brennan Donal , Crown John , Sheahan Kieran , Cotter Maura Bríd 
Published: July 2023

AbstractCarcinoma of unknown primary (CUP) is a heterogeneous group of metastatic cancers in which the site of origin is not identifiable. These carcinomas have a poor outcome due to their late presentation with metastatic disease, difficulty in identifying the origin and delay in treatment. The aim of the pathologist is to broadly classify and subtype the cancer and, where possible, to confirm the likely primary site as this information best predicts patient outcome and guides treatment. In this review, we provide histopathologists with diagnostic practice points which contribute to identifying the primary origin in such cases. We present the current clinical evaluation and management from the point of view of the oncologist. We discuss the role of the pathologist in the diagnostic pathway including the control of pre-analytical conditions, assessment of sample adequacy, diagnosis of cancer including diagnostic pitfalls, and evaluation of prognostic and predictive markers. An integrated diagnostic report is ideal in cases of CUP, with results discussed at a forum such as a molecular tumour board and matched with targeted treatment. This highly specialized evolving area ultimately leads to personalized oncology and potentially improved outcomes for patients.
Prediction Versus Explanation in Educational Psychology: a Cross-Theoretical Approach to Using Teacher Behaviour to Predict Student Engagement in Physical Education
Authors: Noetel Michael , Parker Philip , Dicke Theresa , Beauchamp Mark R. , Ntoumanis Nikos , Hulteen Ryan M. , Diezmann Carmel , Yeung Alexander , Ahmadi Asghar , Vasconcellos Diego , Mahoney John , Datta Poulomee , Doidge Scott , Lonsdale Chris 
Published: July 2023

AbstractEducational psychology usually focuses on explaining phenomena. As a result, researchers seldom explore how well their models predict the outcomes they care about using best-practice approaches to predictive statistics. In this paper, we focus less on explanation and more on prediction, showing how both are important for advancing the field. We apply predictive models to the role of teachers on student engagement, i.e. the thoughts, attitudes, and behaviours, that translate motivation into progress. We integrate the suggestions from four prominent motivational theories (self-determination theory, achievement goal theory, growth mindset theory, and transformational leadership theory), and aim to identify those most critical behaviours for predicting changes in students’ engagement in physical education. Students (N = 1324 all from year 7, 52% girls) from 17 low socio-economic status schools rated their teacher’s demonstration of 71 behaviours in the middle of the school year. We also assessed students’ engagement at the beginning and end of the year. We trained elastic-net regression models on 70% of the data and then assessed their predictive validity on the held-out data (30%). The models showed that teacher behaviours predicted 4.39% of the variance in students’ change in engagement. Some behaviours that were most consistently associated with a positive change in engagement were being good role models (β = 0.046), taking interest in students’ lives outside of class (β = 0.033), and allowing students to make choices (β = 0.029). The influential behaviours did not neatly fit within any single motivational theory. These findings support arguments for integrating different theoretical approaches, and suggest practitioners may want to consider multiple theories when designing interventions. More generally, we argue that researchers in educational psychology should more frequently test how well their models not just explain, but predict the outcomes they care about.