bluuebunny/crossref_metadata_2025_split huggingface dataset:
Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation
Authors: Conte Luana , Amodeo Ilaria , De Nunzio Giorgio , Raffaeli Genny , Borzani Irene , Persico Nicola , Griggio Alice , Como Giuseppe , Cascio Donato , Colnaghi Mariarosa , Mosca Fabio , Cavallaro Giacomo 
Published: February 2024

AbstractPrenatal assessment of lung size and liver position is essential to stratify congenital diaphragmatic hernia (CDH) fetuses in risk categories, guiding counseling, and patient management. Manual segmentation on fetal MRI provides a quantitative estimation of total lung volume and liver herniation. However, it is time-consuming and operator-dependent. In this study, we utilized a publicly available deep learning (DL) segmentation system (nnU-Net) to automatically contour CDH-affected fetal lungs and liver on MRI sections. Concordance between automatic and manual segmentation was assessed by calculating the Jaccard coefficient. Pyradiomics standard features were then extracted from both manually and automatically segmented regions. The reproducibility of features between the two groups was evaluated through the Wilcoxon rank-sum test and intraclass correlation coefficients (ICCs). We finally tested the reliability of the automatic-segmentation approach by building a ML classifier system for the prediction of liver herniation based on support vector machines (SVM) and trained on shape features computed both in the manual and nnU-Net-segmented organs. We compared the area under the classifier receiver operating characteristic curve (AUC) in the two cases. Pyradiomics features calculated in the manual ROIs were partly reproducible by the same features calculated in nnU-Net segmented ROIs and, when used in the ML procedure, to predict liver herniation (both AUC around 0.85). Conclusion: Our results suggest that automatic MRI segmentation is feasible, with good reproducibility of pyradiomics features, and that a ML system for liver herniation prediction offers good reliability. Trial registration: https://clinicaltrials.gov/ct2/show/NCT04609163?term=NCT04609163&draw=2&rank=1; Clinical Trial Identification no. NCT04609163.

What is Known:• Magnetic resonance imaging (MRI) is crucial for prenatal congenital diaphragmatic hernia (CDH) assessment. It enables the quantification of the total lung volume and the extent of liver herniation, which are essential for stratifying the severity of CDH, guiding counseling, and patient management.• The manual segmentation of MRI scans is a time-consuming process that is heavily reliant upon the skill set of the operator.

What is New:• MRI lung and liver automatic segmentation using the deep learning nnU-Net system is feasible, with good Jaccard coefficient values and satisfactory reproducibility of pyradiomics features compared to manual results.• A feasible ML system for predicting liver herniation could improve prenatal assessments and CDH patient management.
Score prediction of anastomotic leak in colorectal surgery: a systematic review
Authors: Litchinko Alexis , Buchs Nicolas , Balaphas Alexandre , Toso Christian , Liot Emilie , Meurette Guillaume , Ris Frédéric , Meyer Jeremy 
Published: February 2024

Abstract
Objective
Predicting the risk of anastomotic leak (AL) is of importance when defining the optimal surgical strategy in colorectal surgery. Our objective was to perform a systematic review of existing scores in the field.

Methods
We followed the PRISMA checklist (S1 Checklist). Medline, Cochrane Central and Embase were searched for observational studies reporting on scores predicting AL after the creation of a colorectal anastomosis. Studies reporting only validation of existing scores and/or scores based on post-operative variables were excluded. PRISMA 2020 recommendations were followed. Qualitative analysis was performed.

Results
Eight hundred articles were identified. Seven hundred and ninety-one articles were excluded after title/abstract and full-text screening, leaving nine studies for analysis. Scores notably included the Colon Leakage Score, the modified Colon Leakage Score, the REAL score, www.anastomoticleak.com and the PROCOLE score. Four studies (44.4%) included more than 1.000 patients and one extracted data from existing studies (meta-analysis of risk factors). Scores included the following pre-operative variables: age (44.4%), sex (77.8%), ASA score (66.6%), BMI (33.3%), diabetes (22.2%), respiratory comorbidity (22.2%), cardiovascular comorbidity (11.1%), liver comorbidity (11.1%), weight loss (11.1%), smoking (33.3%), alcohol consumption (33.3%), steroid consumption (33.3%), neo-adjuvant treatment (44.9%), anticoagulation (11.1%), hematocrit concentration (22.2%), total proteins concentration (11.1%), white blood cell count (11.1%), albumin concentration (11.1%), distance from the anal verge (77.8%), number of hospital beds (11.1%), pre-operative bowel preparation (11.1%) and indication for surgery (11.1%). Scores included the following peri-operative variables: emergency surgery (22.2%), surgical approach (22.2%), duration of surgery (66.6%), blood loss/transfusion (55.6%), additional procedure (33.3%), operative complication (22.2%), wound contamination class (1.11%), mechanical anastomosis (1.11%) and experience of the surgeon (11.1%). Five studies (55.6%) reported the area under the curve (AUC) of the scores, and four (44.4%) included a validation set.

Conclusion
Existing scores are heterogeneous in the identification of pre-operative variables allowing predicting AL. A majority of scores was established from small cohorts of patients which, considering the low incidence of AL, might lead to miss potential predictors of AL. AUC is seldom reported. We recommend that new scores to predict the risk of AL in colorectal surgery to be based on large cohorts of patients, to include a validation set and to report the AUC.
Adaptive temperature scaling for Robust calibration of deep neural networks
Authors: Balanya Sergio A. , Maroñas Juan , Ramos Daniel 
Published: February 2024

AbstractIn this paper, we study the post-hoc calibration of modern neural networks, a problem that has drawn a lot of attention in recent years. Despite the plethora of calibration methods proposed, there is no consensus yet on the inherent complexity of the task and, while some authors claim that simple functions solve the problem, others suggest that more expressive models are needed to capture misscalibration. As a first approach, we focus on the task of confidence scaling, specifically on post-hoc methods that generalize Temperature Scaling, which we refer to as the Adaptive Temperature Scaling family. We begin by demonstrating that while complex models like neural networks provide an advantage when there is ample data, they fail in scenarios where it is limited, notably common in fields like medical diagnosis. We then show how under this ideal data conditions the more expressive methods learn a relationship between the entropy of a prediction and its level of overconfidence, and based on this observation, we propose Entropy-based Temperature Scaling, a simple method that scales the confidence of a prediction according to this relationship. Results show that our method obtains state-of-the-art performance and is robust against data scarcity. Moreover, our proposed model enables a deeper understanding of the calibration process by the interpretation of the entropy as a measure of uncertainty in the network outputs.
Parametric RSigELU: a new trainable activation function for deep learning
Authors: Kiliçarslan Serhat , Celik Mete 
Published: February 2024

AbstractActivation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. Thus, the development of activation functions which affect deep learning models’ performances is of great interest to researchers. In the literature, mostly, nonlinear activation functions are preferred since linear activation functions limit the learning performances of the deep learning models. Non-linear activation functions can be classified as fixed-parameter and trainable activation functions based on whether the activation function parameter is fixed (i.e., user-given) or modified during the training process of deep learning models. The parameters of the fixed-parameter activation functions should be specified before the deep learning model training process. However, it takes too much time to determine appropriate function parameter values and can cause the slow convergence of the deep learning model. In contrast, trainable activation functions whose parameters are updated in each iteration of deep learning models training process achieve faster and better convergence by obtaining the most suitable parameter values for the datasets and deep learning architectures. This study proposes parametric RSigELU (P+RSigELU) trainable activation functions, such as P+RSigELU Single (P+RSigELUS) and P+RSigELU Double (P+RSigELUD), to improve the performance of fixed-parameter activation function of RSigELU. The performances of the proposed trainable activation functions were evaluated on the benchmark datasets of MNIST, CIFAR-10, and CIFAR-100 datasets. Results show that the proposed activation functions outperforms PReLU, PELU, ALISA, P+FELU, PSigmoid, and GELU activation functions found in the literature. To access the codes of the activation function; https://github.com/serhatklc/P-RsigELU-Activation-Function.
Intradiscal vacuum phenomenon matches lumbar spine degeneration patterns in an ageing population
Authors: Cawley Derek T. , Simpkin Andrew , Abrahim Elizabeth , Doyle Thomas , Elsheikh Nada , Fallon John , Habash Mohammed , Phua Rou Jiing , Langille Jaimie , Matini Elvis , McNamee Conor , Mohamed Fayhaa , Gabhann Cliona Nic , Noorani Ali , Oh Jieun , O’Reilly Padraig , O’Sullivan David , Devitt Aiden 
Published: February 2024

Abstract
Purpose
Intra-Discal Vacuum phenomenon (IDVP) is well-recognised, yet poorly visualised and poorly understood radiological finding in disc degeneration, particularly with regard to its role in spinal alignment. CT analysis of the lumbar spine in an aging population aims to identify patterns associated with IDVP including lumbopelvic morphology and associated spinal diagnoses.

Methods
An analysis was performed of an over-60s population sample of 2020 unrelated abdominal CT scans, without acute spinal presentations. Spinal analysis included sagittal lumbopelvic reconstructions to assess for IDVP and pelvic incidence (PI). Subjects with degenerative pathologies, including previous vertebral fractures, auto-fusion, transitional vertebrae, and listhesis, were also selected out and analysed separately.

Results
The prevalence of lumbar spine IDVP was 50.3% (955/1898) and increased with age (125 exclusions). This increased in severity towards the lumbosacral junction (L1L2 8.3%, L2L3 10.9%, L3L4 11.5%, L4L5 23.9%, and L5S1 46.3%). A lower PI yielded a higher incidence of IDVP, particularly at L5S1 (p < 0.01). A total of 292 patients had IDVP with additional degenerative pathologies, which were more likely to occur at the level of isthmic spondylolisthesis, adjacent to a previous fracture or suprajacent to a lumbosacral transitional vertebra (p < 0.05).

Conclusions
This study identified the prevalence and severity of IDVP in an aging population. Sagittal patterns that influence the pattern of IVDP, such as pelvic incidence and degenerative pathologies, provide novel insights into the function of aging spines.
Impaired dual-task gait in Parkinson’s disease is associated with brain morphology changes
Authors: Krupička Radim , Malá Christiane , Neťuková Slávka , Hubená Tereza , Havlík Filip , Bezdicek Ondrej , Dušek Petr , Růžička Evžen 
Published: February 2024

AbstractIn Parkinson’s disease (PD), impaired gait and cognition affect daily activities, particularly in the more advanced stages of the disease. This study investigated the relationship between gait parameters, cognitive performance, and brain morphology in patients with early untreated PD. 64 drug-naive PD patients and 47 healthy controls (HC) participated in the study. Single- and dual-task gait (counting task) were examined using an expanded Timed Up & Go Test measured on a GaitRite walkway. Measurements included gait speed, stride length, and cadence. A brain morphometry analysis was performed on T1-weighted magnetic resonance (MR) images. In PD patients compared to HC, gait analysis revealed reduced speed (p < 0.001) and stride length (p < 0.001) in single-task gait, as well as greater dual-task cost (DTC) for speed (p = 0.007), stride length (p = 0.014) and cadence (p = 0.029). Based on the DTC measures in HC, PD patients were further divided into two subgroups with normal DTC (PD-nDTC) and abnormally increased DTC (PD-iDTC). For PD-nDTC, voxel-based morphometric correlation analysis revealed a positive correlation between a cluster in the left primary motor cortex and stride-length DTC (r = 0.57, p = 0.027). For PD-iDTC, a negative correlation was found between a cluster in the right lingual gyrus and the DTC for gait cadence (r=-0.35, pFWE = 0.018). No significant correlations were found in HC. The associations found between brain morphometry and gait performance with a concurrent cognitive task may represent the substrate for gait and cognitive impairment occurring since the early stages of PD.
Effect of realistic distributed loads on the bi-stable behavior of a pre-stressed aileron
Authors: Corsi Giovanni , Battista Francesco , Gualtieri Paolo , Vidoli Stefano 
Published: February 2024

AbstractWe study both experimentally and numerically the aeroelastic response of a pre-stressed curved aileron in a high Reynolds number flow undisturbed at infinity. The structure is designed to have a peculiar nonlinear behavior. Specifically, the aileron has only one stable equilibrium when the external forces are vanishing, but it is bistable when distributed aerodynamic loads are applied. Hence, for sufficiently high fluid velocities, another equilibrium branch is possible. We test a prototype of such an aileron in a wind tunnel. A sudden change (snap) of the shell configuration is observed when the fluid velocity exceeds a critical threshold: the snapped configuration is characterized by sensibly lower drag. However, when the velocity is reduced to zero, the structure recovers its initial shape. A similar nonlinear behavior can have important applications for drag-reduction strategies since the transition between a bluff body-like and a slender body-like behavior is controlled by the free-stream fluid velocity and does not require any external actuation.
Double the data rate in underwater acoustic communication using OFDM based on subcarrier power modulation
Authors: Alraie Hussam , Alahmad Raji , Ishii Kazuo 
Published: February 2024

AbstractUnderwater communication is one of the most important and difficult challenges facing researchers due to the high attenuation of the signal, communication with the surface because of the harsh medium of water, and data transmission performance degradation as a result of various effects. Underwater acoustic communication (UWA) has a low data rate, which describes the disadvantage of this type of communication. In addition, it has a low bandwidth range and high latency but has a long transmission range as an advantage. Multicarrier wireless transmission systems increase the data rate by sending the data using more than one carrier. We proposed a noncoherent orthogonal frequency division multiplexing (OFDM) method to increase the data rate in UWA communication systems. In addition, doubling the data rate in the OFDM using Subcarrier Power Modulation (OFDM-SPM) system can save half of the bandwidth. The MATLAB simulation program was used to implement the system in the underwater acoustic environment to increase its throughput. The proposed design uses Differential Phase Shift Keying (DPSK) with power control, and the data stream is transmitted through two-dimensional modulation schemes, the DPSK, and the power level of each subcarrier in the OFDM system with cyclic prefix (CP). The underwater channel was designed using a Rician fading multipath with a spreading loss formula as a function of distance and frequency. We designed an equalizer at the receiver side to recover the original signal as a function of three parameters which are: the channel effect as a rate between transmitting and receiving symbols, the Rician channel response, and the UWA spreading loss. OFDM-Subcarrier Power Modulation (OFDM-SPM) using the proposed equalizer performed better than the theoretical OFDM-SPM in the Rayleigh channel. The designed equalizer increased the performance of the OFDM-SPM system by 25% which helped to enhance the system’s throughput and doubled the data rate compared with the OFDM system, doubling the data rate using OFDM-SPM had been validated in laboratory experiments in the Time domain.
Nonlinear dimensionality reduction with q-Gaussian distribution
Authors: Abe Motoshi , Nomura Yuichiro , Kurita Takio 
Published: February 2024

AbstractIn recent years, the dimensionality reduction has become more important as the number of dimensions of data used in various tasks such as regression and classification has increased. As popular nonlinear dimensionality reduction methods, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) have been proposed. However, the former outputs only one low-dimensional space determined by the t-distribution and the latter is difficult to control the distribution of distance between each pair of samples in low-dimensional space. To tackle these issues, we propose novel t-SNE and UMAP extended by q-Gaussian distribution, called q-Gaussian-distributed stochastic neighbor embedding (q-SNE) and q-Gaussian-distributed uniform manifold approximation and projection (q-UMAP). The q-Gaussian distribution is a probability distribution derived by maximizing the tsallis entropy by escort distribution with mean and variance, and a generalized version of Gaussian distribution with a hyperparameter q. Since the shape of the q-Gaussian distribution can be tuned smoothly by the hyperparameter q, q-SNE and q-UMAP can in- tuitively derive different embedding spaces. To show the quality of the proposed method, we compared the visualization of the low-dimensional embedding space and the classification accuracy by k-NN in the low-dimensional space. Empirical results on MNIST, COIL-20, OliverttiFaces and FashionMNIST demonstrate that the q-SNE and q-UMAP can derive better embedding spaces than t-SNE and UMAP.
Comparison between the 0- and 30-s balloon dilation time in percutaneous transluminal angioplasty for restenosed arteriovenous fistula among hemodialysis patients: a multicenter, prospective, randomized trial (CARP study)
Authors: Saiki Tomoki , Sasaki Kensuke , Doi Shigehiro , Takahashi Akira , Osaki Yosuke , Ishiuchi Naoki , Maeoka Yujiro , Kawai Toru , Kawaoka Koichiro , Takahashi Shunsuke , Nagai Takuo , Irifuku Taisuke , Nakashima Ayumu , Masaki Takao 
Published: February 2024

Abstract
Background
This study aims to compare patency rates of the 0- and 30-s (sec) balloon dilation time in hemodialysis (HD) patients with restenosis after percutaneous transluminal angioplasty (PTA).

Methods
The patients who underwent PTA within 6 months for failed arteriovenous fistula at the forearm were randomly assigned the 0-s or 30-s dilation time group. Effect of dilation time on the 3- and 6-month patency rates after PTA was examined.

Results
Fifty patients were enrolled in this study. The 3-month patency rate in the 30-s dilation group was better than that in the 0-s dilation group (P = 0.0050), while the 6-month patency rates did not show a significant difference between the two groups (P = 0.28). Cox’s proportional hazard model revealed that 30-s of inflation time (hazard ratio 0.027; P = 0.0072), diameter of the proximal (hazard ratio 0.32; P = 0.031), and dilation pressure (hazard ratio 0.63; P = 0.014) were associated with better 3-month patency. Dilation pressure between previous and present PTA did not differ in the 0-s (P = 0.15) and 30-s dilation groups (P = 0.16). The 6-month patency rate of the present PTA in the 30-s dilation group was higher than that of the previous PTA (P = 0.015). The visual analog scale did not differ between the two groups (P = 0.51).

Conclusion
The presenting data suggest that 30-s dilation potentially results in a better 3-month patency rate than 0-s dilation in HD patients with restenosis after PTA.
Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways
Authors: Lim Tristan 
Published: February 2024

AbstractThe rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.