bluuebunny/crossref_metadata_2025_split huggingface dataset:
Language impairments in seropositive and seronegative autoimmune encephalitis
Authors: Griffith Sarah P. , Wesselingh Robb , D’Aprano Fiore , Seery Nabil , Rushen Tiffany , Kyndt Chris , Long Brian , Seneviratne Udaya , Kalincik Tomas , Buzzard Katherine , Butzkueven Helmut , O’Brien Terence J. , Alpitsis Rubina , Malpas Charles B. , Monif Mastura ,   on behalf of Australian Autoimmune Encephalitis Consortium
Published: February 2024

Abstract
Background and objective
Autoimmune encephalitis (AE) is a rare neuroinflammatory disease affecting the central nervous system. To examine language functions in patients with different subsets of AE consisting of seropositive and seronegative groups.

Methods
Fifty-two patients were recruited from neurology departments in Melbourne, Australia, who met clinical criteria for possible AE. Language tests include the Naming Test from the Sydney Language Battery (SydBat), the semantic fluency trial from the Controlled Oral Word Association Test (COWAT), and the Vocabulary and Similarities subtests of the Weschler Abbreviated Scale of Intelligence–Second Edition. The results were standardised with normative data.

Results
The mean age of our cohort was 52.5 years old, with the average time from hospital admission to recruitment being 38.41 months. At an aggregate level, none of the mean language test z-scores were below normative data. At the patient level, impairment rates were 18.37% for COWAT (animals), 28.57% for SydBat (naming), 4.65% for Similarities, and 4.55% for Vocabulary. Chi-squared goodness of fit tests indicated that observed performances were significantly below expected performances for the SydBat (naming) test (p < 0.0001) and COWAT (animals) (p = 0.004).

Discussion
While, on average, language functions were within normal limits in patients with AE, but a subgroup exhibited lower performance in semantic fluency and visual confrontation naming, with impairment rates below expected norms. To advance understanding of language in chronic AE patients, exploring the impact of seizure burden, antiseizure medication use, and the relationship of language functions with other cognitive functions is crucial.
Mechanical characterization and torsional buckling of pediatric cardiovascular materials
Authors: Donmazov Samir , Piskin Senol , Gölcez Tansu , Kul Demet , Arnaz Ahmet , Pekkan Kerem 
Published: February 2024

AbstractIn complex cardiovascular surgical reconstructions, conduit materials that avoid possible large-scale structural deformations should be considered. A fundamental mode of mechanical complication is torsional buckling which occurs at the anastomosis site due to the mechanical instability, leading surgical conduit/patch surface deformation. The objective of this study is to investigate the torsional buckling behavior of commonly used materials and to develop a practical method for estimating the critical buckling rotation angle under physiological intramural vessel pressures. For this task, mechanical tests of four clinically approved materials, expanded polytetrafluoroethylene (ePTFE), Dacron, porcine and bovine pericardia, commonly used in pediatric cardiovascular surgeries, are conducted (n = 6). Torsional buckling initiation tests with n = 4 for the baseline case (L = 7.5 cm) and n = 3 for the validation of ePTFE (L = 15 cm) and Dacron (L = 15 cm and L = 25 cm) for each are also conducted at low venous pressures. A practical predictive formulation for the buckling potential is proposed using experimental observations and available theory. The relationship between the critical buckling rotation angle and the lumen pressure is determined by balancing the circumferential component of the compressive principal stress with the shear stress generated by the modified critical buckling torque, where the modified critical buckling torque depends linearly on the lumen pressure. While the proposed technique successfully predicted the critical rotation angle values lying within two standard deviations of the mean in the baseline case for all four materials at all lumen pressures, it could reliably predict the critical buckling rotation angles for ePTFE and Dacron samples of length 15 cm with maximum relative errors of 31% and 38%, respectively, in the validation phase. However, the validation of the performance of the technique demonstrated lower accuracy for Dacron samples of length 25 cm at higher pressure levels of 12 mmHg and 15 mmHg. Applicable to all surgical materials, this formulation enables surgeons to assess the torsional buckling potential of vascular conduits noninvasively. Bovine pericardium has been found to exhibit the highest stability, while Dacron (the lowest) and porcine pericardium have been identified as the least stable with the (unitless) torsional buckling resistance constants, 43,800, 12,300 and 14,000, respectively. There was no significant difference between ePTFE and Dacron, and between porcine and bovine pericardia. However, both porcine and bovine pericardia were found to be statistically different from ePTFE and Dacron individually (p < 0.0001). ePTFE exhibited highly nonlinear behavior across the entire strain range [0, 0.1] (or 10% elongation). The significant differences among the surgical materials reported here require special care in conduit construction and anastomosis design.
Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging
Authors: Rytky Santeri J. O. , Tiulpin Aleksei , Finnilä Mikko A. J. , Karhula Sakari S. , Sipola Annina , Kurttila Väinö , Valkealahti Maarit , Lehenkari Petri , Joukainen Antti , Kröger Heikki , Korhonen Rami K. , Saarakkala Simo , Niinimäki Jaakko 
Published: February 2024

Abstract
Purpose
Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data.

Methods
Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images.

Results
The SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available.

Conclusion
Model training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.
A survey of smart dustbin systems using the IoT and deep learning
Authors: Arthur Menaka Pushpa , Shoba S. , Pandey Aru 
Published: February 2024

AbstractWith massive population growth and a shift in the urban culture in smart cities, the constant generation of waste continues to create unsanitary living conditions for city dwellers. Overflowing solid waste in the garbage and the rapid generation of non-degradable solid waste produce a slew of infectious illnesses that proliferate throughout the ecosystem. Conventional solid waste management systems have proved to be increasingly harmful in densely populated areas like smart cities. Also, such systems require real-time manual monitoring of garbage, high labor costs, and constant maintenance. Monitoring waste management on a timely basis and reducing labor costs is scarcely possible, realistically, for a municipal corporation. A Smart Dustbin System (SDS) is proposed that is to be implemented in densely populated urban areas to ensure hygiene. This paper undertakes a comprehensive analysis of the application of smart dustbin systems, following an extensive literature review and a discussion of recent research that is expected to help improve waste management systems. A current SDS used in real-time is implemented with the most recent advances from deep learning, computer vision, and the Internet of Things. The smart dustbin system used in day-to-day life minimizes the overloading of bins, lowers labor costs, and saves energy and time. It also helps keep cities clean, lowering the risk of disease transmission. The primary users of the SDS are universities, malls, and high-rise buildings. The evolution of the SDS over the years with various features and technologies is well analyzed. The datasets used for Smart Waste Management and benchmark garbage image datasets are presented under AI perception. The results of the existing works are compared to highlight the potential limitations of these works.
Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples
Authors: Liu Zhe , Liu Fang , Petinrin Olutomilayo Olayemi , Wang Fuzhou , Zhang Yu , Wong Ka-Chun 
Published: February 2024

AbstractStomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.
The Myanmar radical tradition: revolution, reaction, and the changing imperial world order
Authors: Aung Geoffrey Rathgeb , Campbell Stephen 
Published: February 2024

AbstractThis article historicizes and conceptualizes the Myanmar radical tradition: a tradition of thought and practice that has animated radical politics across Myanmar’s twentieth and twenty-first centuries. From anti-colonial struggle to decolonization, and from communist insurgency to left feminism, ethnic rebellion, and today’s revolutionary upsurge following the 2021 coup d’état, this radical tradition is best understood not as something bounded or solitary. Rather, it names a productive conjoining of radical thought and practice from within Myanmar, as well as from other times and places, beginning in the imperial world order of the early twentieth century. Revisiting scholarship on transatlantic and transpacific radicalisms, we argue that attention to imperialism offers important insights into Myanmar’s modern history and contemporary dynamics, including the Myanmar radical tradition. Yet, the Myanmar radical tradition—heterogeneous and internally conflictual, a site of historical dispute—also sheds light on the changing imperial world order, which we show has a fundamentally reactive, counter-revolutionary quality. Today’s late imperialism, we argue, can be seen as a retaliatory response to the long arc of decolonization, a story within which Myanmar’s contemporary revolutionary struggle renders the Myanmar radical tradition very much a living tradition.
Proposing and testing a model relating students’ graph selection and graph reasoning for dynamic situations
Authors: Johnson Heather Lynn , Donovan Courtney , Knurek Robert , Whitmore Kristin A. , Bechtold Livvia 
Published: February 2024

AbstractUsing a mixed methods approach, we explore a relationship between students’ graph reasoning and graph selection via a fully online assessment. Our population includes 673 students enrolled in college algebra, an introductory undergraduate mathematics course, across four U.S. postsecondary institutions. The assessment is accessible on computers, tablets, and mobile phones. There are six items; for each, students are to view a video animation of a dynamic situation (e.g., a toy car moving along a square track), declare their understanding of the situation, select a Cartesian graph to represent a relationship between given attributes in the situation, and enter text to explain their graph choice. To theorize students’ graph reasoning, we draw on Thompson’s theory of quantitative reasoning, which explains students’ conceptions of attributes as being possible to measure. To code students’ written responses, we appeal to Johnson and colleagues’ graph reasoning framework, which distinguishes students’ quantitative reasoning about one or more attributes capable of varying (Covariation, Variation) from students’ reasoning about observable elements in a situation (Motion, Iconic). Quantitizing those qualitative codes, we examine connections between the latent variables of students’ graph reasoning and graph selection. Using structural equation modeling, we report a significant finding: Students’ graph reasoning explains 40% of the variance in their graph selection (standardized regression weight is 0.64, p < 0.001). Furthermore, our results demonstrate that students’ quantitative forms of graph reasoning (i.e., variational and covariational reasoning) influence the accuracy of their graph selection.
‘Go away and make a big thing of yourself somewhere else’: precarious mobilities and the uses of international capital in Irish academia
Authors: Courtois Aline , O’Keefe Theresa 
Published: February 2024

AbstractThe article interrogates the ‘mobility imperative’ and its impact on precarious academics. Drawing on 40 biographic interviews with academics with experience of long-term precarity in Irish higher education, and using a Bourdieusian framework, we identify the specific conditions, uses and impacts of international mobility for these workers. This method offers a unique retrospective advantage for an analysis of the utility of international capital for a cohort of workers typically excluded from studies of international mobility. Among the specific obstacles we identify which are faced by precarious academics in the accumulation and conversion of international capital are the lack of or compromised initial social capital; the dubious value of international capital in Irish academia, especially when associated with precarity; and the difficulty for workers to construct acceptable career scripts when both precarity and mobility have led them off-script. We suggest that the ability to accumulate and convert usable forms of international capital while working abroad is in part predetermined by prior struggles in the national field.
About the Optimal FPE for Non-linear 1d-SDE with Gaussian Noise: The Pitfall of the Perturbative Approach
Authors: Bianucci Marco , Bologna Mauro , Mannella Riccardo 
Published: February 2024

AbstractThis paper deals with the problem of finding the Fokker Planck Equation (FPE) for the single-time probability density function (PDF) that optimally approximates the single-time PDF of a 1-D Stochastic Differential Equation (SDE) with Gaussian correlated noise. In this context, we tackle two main tasks. First, we consider the case of weak noise and in this framework we give a formal ground to the effective correction, introduced elsewhere (Bianucci and Mannella in J Phys Commun 4(10):105019, 2020, https://doi.org/10.1088/2399-6528/abc54e), to the Best Fokker Planck Equation (a standard “Born-Oppenheimer” result), also covering the more general cases of multiplicative SDE. Second, we consider the FPE obtained by using the Local Linearization Approach (LLA), and we show that a generalized cumulant approach allows an understanding of why the LLA FPE performs so well, even for noises with long (but finite) time scales and large intensities.
LeCaSiM: Learning Causal Structure via Inverse of M-Matrices with Adjustable Coefficients
Authors: Cai Qingsong , Zhang Yongchang 
Published: February 2024

AbstractThe objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings.
Large-Scale Clustering on 100 M-Scale Datasets Using a Single T4 GPU via Recall KNN and Subgraph Segmentation
Authors: Liu Junjie , Jiang Rongxin , Liu Xuesong , Zhou Fan , Chen Yaowu , Shen Chen 
Published: February 2024

AbstractDespite the promising progress that has been made, large-scale clustering tasks still face various challenges: (i) high time and space complexity in K-nearest neighbors (KNN), which is often overlooked by most methods, and (ii) low recall rate caused by simply splitting the dataset. In this paper, we propose a novel framework for large-scale clustering tasks named large-scale clustering via recall KNN and subgraph segmentation (LS-RKSS) to perform faster clustering with guaranteed clustering performance, which embraces the ability of handling large-scale data up to 100 million using a single T4 GPU with less than 10% of the running time. We propose recall KNN (RKNN) and subgraph segmentation (SS) to effectively address the primary challenges in large-scale clustering tasks. Firstly, the recall KNN is proposed to perform efficient similarity search among dense vectors with lower time and space complexity compared to traditional exact search methods of KNN. Then, the subgraph segmentation is proposed to split the whole dataset into multiple subgraphs based on the recall KNN. Given the recall rate of RKNN based on traditional exact search methods, it is theoretically proved that dividing the dataset into multiple subgraphs using recall KNN and subgraph segmentation is a more reasonable and effective approach. Finally, clusters are generated independently on each subgraph, and the final clustering result is obtained by combining the results of all subgraphs. Extensive experiments demonstrate that LS-RKSS outperforms previous large-scale clustering methods in both effectiveness and efficiency.
Efficient parametric family of fourth-order Jacobian-free iterative vectorial schemes
Authors: Cordero Alicia , Rojas-Hiciano Renso V. , Torregrosa Juan R. , Triguero-Navarro Paula 
Published: February 2024

AbstractIn this work, a multiparametric family of iterative vectorial fourth-order methods free of Jacobian matrices is proposed. A convergence analysis of this family is carried out as well as a study of its efficiency. Several numerical experiments are made in order to compare the behaviour of the proposed family with other competitive methods of the literature.
Two Forms of Functional Reductionism in Physics
Authors: Lorenzetti Lorenzo 
Published: February 2024

AbstractFunctional reductionism characterises inter-theoretic reduction as the recovery of the upper-level behaviour described by the reduced theory in terms of the lower-level reducing theory. For instance, finding a statistical mechanical realiser that plays the functional role of thermodynamic entropy allows to establish a reductive link between thermodynamics and statistical mechanics. This view constitutes a unique approach to reduction that enjoys a number of positive features, but has received limited attention in the philosophy of science. This paper aims to clarify the meaning of functional reductionism in science, with a focus on physics, to define both its place with respect to other approaches to reduction and its connection to ontology. To do so, we develop and explore two alternative versions of functional reductionism, called Syntactic Functional Reductionism and Semantic Functional Reductionism, that expand and improve the basic functional reductionist approach along different lines, and make clear how the approach works in practice. The former elaborates on David Lewis’ account, is connected with the syntactic view of theories, employs a logical characterisation of functional roles, and is embedded within Nagelian reductionism. The latter adopts a semantic approach to theories, spells out functional roles mainly in terms of mathematical roles within the models, and is expressed in terms of the related structuralist approach to reduction. The development of these frameworks has the final goal of advancing functional reductionism, making it a fully developed account of reduction in science.