bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach
Authors: Stocker Daniel , Hectors Stefanie , Marinelli Brett , Carbonell Guillermo , Bane Octavia , Hulkower Miriam , Kennedy Paul , Ma Weiping , Lewis Sara , Kim Edward , Wang Pei , Taouli Bachir 
Published: October 2024

Abstract
Purpose
To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

Methods
This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6 months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6 months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6 months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests.

Results
A total 168 HCCs (mean size 2.9 ± 1.7 cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001).

Conclusion
The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.
Teaching Strategies in Interventional Radiology: A Narrative Review of the Literature
Authors: Srinivasan Divya , Kilic Yakup , Weston-Petrides Gina K. , Patel Rakesh , Yazdabadi Anosha , Asadi Hamed , Cazzato Roberto Luigi , Shaygi Behnam 
Published: October 2024

Abstract

Introduction
Interventional radiology (IR) is a rapidly developing speciality where innovation—especially in teaching practices—is vital. With workforce and capacity shortages, synthesis of classical educational theories and novel strategies utilising virtual reality (VR) and artificial intelligence (AI) provide opportunities to make teaching as efficient and effective as possible. The aim of this review is to examine the literature on different approaches in IR teaching and learning in undergraduates and postgraduates.

Methods
Literature was reviewed using a comprehensive search strategy with relevant keywords. Articles were limited to 2013–2023. Databases searched included MEDLINE, Embase, British Education Index and ERIC, in addition to a manual review of references.

Results
Of the 2903 unique abstracts reviewed by the authors, 43 were relevant to the purpose of this study. The major pedagogical approaches identified were categorised into the following—traditional master-apprentice mentoring, virtual reality/simulation, physical models, and remote teaching. VR simulations enable practise free from the limits of time and risk to patients, as well as potential for standardised formal curricula. AI has the capability to enhance training simulations and assessment of trainees. With recent events necessitating innovation in online remote teaching, programs that are accessible whilst arguably imparting just as much clinical knowledge as in-person education have now been developed.

Conclusion
Mentoring has conventionally been the standard for radiology teaching, however there are now several alternative pedagogical approaches available to the IR community. A combination of the most effective ideas within each is the optimal method by which IR should be taught.
Tuning multispectral fluorescence quantum dot–based identification of short-length amyloid β peptides by applying Cu(II) ions
Authors: Głowacz Klaudia , Tokarska Weronika , Olechowska Anita , Wezynfeld Nina E. , Ciosek-Skibińska Patrycja 
Published: October 2024

AbstractCurrently available methods for detecting amyloid β (Aβ) derivatives are mainly dedicated to determining the long forms Aβ1-42 and Aβ1-40. At the same time, the number of physiologically occurring Aβ analogs is much higher, including those truncated at the N- and C-termini. Their identification using standard methods is challenging due to the structural similarity of various Aβ analogs, but could highly benefit from both biomarkers discovery and pathophysiological studies of Alzheimer’s disease. Therefore a “chemical tongue” sensing strategy was employed for the detection of seven Aβ peptide derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. The proposed sensing system is based on competitive interactions between quantum dots, Cu(II) ions, and Aβ peptides, providing unique fluorescence fingerprints useful for the identification of analytes. After carefully evaluating the Aβ sample preparation protocol, perfect determination of all studied Aβ peptides was achieved using partial least square–discriminant analysis (PLS-DA). The developed PLS-DA models are characterized by excellent accuracy, sensitivity, precision, and specificity of analyte determination, emphasizing the potential of the proposed sensing strategy.
Graphical abstract
Challenges of Continuous Wave EPR of Broad Signals—The Ferritin Case
Authors: Otsuka Fabio Seiji , García Otaduy Maria Concepción , Nascimento Otaciro Rangel , Garrido Salmon Carlos Ernesto , Huber Martina 
Published: October 2024

AbstractThe study of continuous wave (cw) electron paramagnetic resonance (EPR) spectra still poses a challenge for very broad signals, especially when the spectrum extends over a large part of the accessible field range. The difficulties derive from instrumental challenges, because of insufficient modulation depth and the need to apply measurement conditions that enhance cavity background. The biggest problem, however, is how to define a baseline such that spectral distortions are minimized. Conventional methods rely on a suitable choice of points outside the range of the signal of interest to perform a polynomial interpolation. These methods are effective in most cases where the signal of interest comprises only a narrow range of magnetic field (narrow features). In this study, a novel method of baseline correction for broad signals is proposed and compared to conventional methods. It takes into account that there are only few anchor points for the baseline. The method is applied to the signal of the iron-storage protein ferritin. The ferritin signal is a broad band that extends from zero to 0.8 T. An approach is developed by which this broad signal is analyzed reliably. The method is also extended to the case where the broad signal is superimposed on narrow signals and enables to extract the parameters of both types of signals in a fitting pipeline.
Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR
Authors: Turnbull Robert , Mannix Evelyn 
Published: October 2024

AbstractThe capacity to isolate and recognize individual characters from facsimile images of papyrus manuscripts yields rich opportunities for digital analysis. For this reason the ‘ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri’ was held as part of the 17th International Conference on Document Analysis and Recognition. This paper discusses our submission to the competition. We used an ensemble of YOLOv8 models to detect and classify individual characters and employed two different approaches for refining the character predictions, including a transformer based DeiT approach and a ResNet-50 model trained on a large corpus of unlabeled data using SimCLR, a self-supervised learning method. Our submission won the recognition challenge with a mAP of 42.2%, and was runner-up in the detection challenge with a mean average precision of 51.4%. At a more relaxed intersection over union threshold of 0.5, we achieved the highest precision and recall for both detection and classification. The results demonstrate the potential of these techniques for automated character recognition on historical manuscripts. We ran the prediction pipeline on more than 4500 images from the Oxyrhynchus Papyri to illustrate the utility of our approach and release the results publicly in multiple formats.
Are seronegative patients with rheumatoid arthritis and clinically suspect arthralgia properly represented in randomized clinical trials?
Authors: D’Onofrio Bernardo , Selmi Carlo , Gremese Elisa 
Published: October 2024

Abstract
Rheumatoid arthritis (RA) is a chronic immuno-inflammatory disease whose outcomes can vary greatly from one patient to another. One of the main prognostic factors is the presence of serum autoantibodies, such as rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPA). Indeed, when seropositive, patients with RA are at higher risk of radiographic progression, disability, and increased mortality. Moreover, while the introduction of the 2010 American College of Rheumatology/European Alliance of Associations for Rheumatology (ACR/EULAR) classification criteria has allowed for an earlier diagnosis, studies on large early arthritis cohorts have also shown that these criteria are less capable of identifying seronegative patients, who are therefore at a higher risk of being diagnosed and treated late. In light of these, the major randomized controlled trials have mostly enrolled patients with autoantibody-positive disease. However, in recent years, it became evident that the two serotypes of RA differ significantly from many points of view. Alongside this, a greater understanding of the disease pathogenesis, particularly the presence of antibodies in patients’ serum even before the onset of arthritis, has generated significant interest in exploring whether the disease could be prevented by treating patients in the pre-arthritis phases. Once again, emerging trials predominantly enroll subjects positive for RA autoantibodies, potentially overlooking seronegative individuals with arthralgia-at-risk.
Contemporary gaps in research on digital divide in education: a literature review
Authors: Mirazchiyski Plamen Vladkov 
Published: October 2024

AbstractWhile computer technology permeates all spheres of life in modern society, the outcomes of using technology are different for different groups. The “digital divide” is probably one of the most undefined terms in educational research. One of the most pressing challenges is the definition of digital divide itself. Many of the contemporary research papers still focus on the gaps in access/possession of technology while this issue is not as relevant as it used to be. Most contemporary research papers simply do not even provide a definition of the construct they investigate. Those who target higher levels of the digital divide do not define the strand they focus on or use different interchangeable terms to name it. This article reviews contemporary literature critically and clarifies basic terms related to the digital divide with a specific focus on computer and information literacy. It also provides a definition of the digital divide that shifts focus from the traditional understanding of its levels and focuses on computer and information literacy as its core, as this construct encompasses all “new literacies” and is crucial for functioning and achieving outcomes in the information society.
Cost-effectiveness analysis of the digital fall preventive intervention Safe Step among community-dwelling older people aged 70 and older
Authors: Bajraktari Saranda , Sandlund Marlene , Pettersson Beatrice , Rosendahl Erik , Zingmark Magnus 
Published: October 2024

AbstractFalls are the most common cause of injury in older people, with consequences for the individual and society. With an increasing population of older people, falls and related costs are expected to increase. It is crucial to identify scalable and cost-effective interventions and subsequently reduce fall-related costs. The aim was to evaluate the cost-effectiveness of the Safe Step digital fall preventive exercise intervention over a period of 12 years and, in addition, to evaluate the impact of increased recruitment cost and decreased intervention effect. The intervention was evaluated in an observational study in a municipality context targeting community-dwelling older people of age 70 + . A Markov model with five states was used to model the cost-effectiveness of the Safe Step intervention and evaluate quality-adjusted life years (QALYs) and fall-related costs from a societal perspective. By using data from a meta-analysis as basis for the estimated intervention effect, the Safe Step intervention was compared with a no-intervention alternative. The results showed that the Safe Step intervention dominated no intervention. In the sensitivity analysis with the most conservative estimate of intervention effect, the ICER was €7 616 per QALY gained. Hence, Safe Step showed to be a cost-saving fall preventive intervention in older people at risk of falling and potentially cost-effective even with a low estimated intervention effect. Future studies on efficacy of fall preventive digital interventions will contribute in precising effect estimates and enhance the validity of these cost-effectiveness results.
Partial Algebras and Implications of (Weak) Matrix Properties
Authors: Hoefnagel Michael , Jacqmin Pierre-Alain 
Published: October 2024

AbstractMatrix properties are a type of property of categories which includes the ones of being Mal’tsev, arithmetical, majority, unital, strongly unital, and subtractive. Recently, an algorithm has been developed to determine implications $$\textrm{M}\Rightarrow _{\textrm{lex}_*}\textrm{N}$$

M

⇒

lex

∗

N

between them. We show here that this algorithm reduces to constructing a partial term corresponding to $$\textrm{N}$$
N
from a partial term corresponding to $$\textrm{M}$$
M
. Moreover, we prove that this is further equivalent to the corresponding implication between the weak versions of these properties, i.e., the one where only strong monomorphisms are considered instead of all monomorphisms.
Applications of regenerated bacterial cellulose: a review
Authors: Rosson Lucas , Tan Boon , Best Wayne , Byrne Nolene 
Published: October 2024

AbstractWhilst synthetic polymers have changed the world in many important ways, the negative impacts associated with these materials are becoming apparent in waste accumulation and microplastic pollution due to lack of biodegradability. Society has become aware of the need to replace or substitute environmentally persistent synthetic polymers, and cellulose has received a large amount of attention in this respect. The mechanical properties of cellulose, its renewable nature and biodegradability are advantageous properties. Drawbacks exist for the use of plant cellulose (PC), including the water footprint of cotton, deforestation associated with wood/dissolving pulp, and the extensive processing required to refine plants and wood into pure cellulose. Bacterial cellulose (BC), also known as microbial cellulose, is gaining momentum in both academic and industry settings as a potential solution to the many drawbacks of plant-based cellulose. Compared to PC, BC has high purity, crystallinity and degree of polymerisation, and can be manufactured from waste in a way that yields more cellulose per hectare, per annum, and requires less intense chemical processing. Native bacterial cellulose can be formed and shaped to an extent and is found in a variety of commercial products. However, dissolving and regenerating bacterial cellulose is a potential avenue to broaden the applications available to this material. The aim of this study is to review the applications which utilize regenerated bacterial cellulose, with a focus on the dissolution/regeneration methods used and discussing the associated limitations and future outlook.
In Defense of Anti-Haecceitism: Chance and Counterfactuals
Authors: Bigaj Tomasz 
Published: October 2024

AbstractBoris Kment formulates two challenges to anti-haecceitism: one based on the concept of chance, and the other involving counterfactuals. In this paper I refute the chance-based argument by appropriately reformulating its premises in a non-individualistic language. Regarding the argument from counterfactuals I consider three strategies of defense. The strategy that is ultimately accepted as most promising is a revamped approach based on the distinction between possible worlds and possibilities. I counter Kment’s claim that the introduction of fine-grained possibilities within possible worlds is haecceitism in disguise. My conclusion is that anti-haecceitism survives the criticism, and is an overall more attractive position than its competitor.
SLERP-TVDRK (STVDRK) Methods for Ordinary Differential Equations on Spheres
Authors: Leung Shingyu , Chau Wai Ming , Lee Young Kyu 
Published: October 2024

AbstractWe mimic the conventional explicit Total Variation Diminishing Runge–Kutta (TVDRK) schemes and propose a class of numerical integrators to solve differential equations on a unit sphere. Our approach utilizes the exponential map inherent to the sphere and employs spherical linear interpolation (SLERP). These modified schemes, named SLERP-TVDRK methods or STVDRK, offer improved accuracy compared to typical projective RK methods. Furthermore, they eliminate the need for any projection and provide a straightforward implementation. While we have successfully constructed STVDRK schemes only up to third-order accuracy, we explain the challenges in deriving STVDRK-r for $$r\ge 4$$

r
≥
4

. To showcase the effectiveness of our approach, we will demonstrate its application in solving the eikonal equation on the unit sphere and simulating p-harmonic flows using our proposed method.
Modelling and evaluating restricted ESNs on single- and multi-timescale problems
Authors: Wringe Chester , Stepney Susan , Trefzer Martin A. 
Published: October 2024

AbstractReservoir Computing is a computing model ideal for performing computation on varied physical substrates. However, these physical reservoirs can be difficult to scale up. We propose joining various reservoirs together as an approach to solving this problem, simulating physical reservoirs with Echo State Networks (ESNs). We investigate various methods of combining ESNs to form larger reservoirs, including a method that we dubRestricted ESNs. We provide a notation for describing Restricted ESNs, and use it to benchmark a standard ESN against restricted ones. We investigate two methods to keep the weight matrix density consistent when comparing a Restricted ESN to a standard one, which we calloverall consistencyandpatch consistency. We benchmark restricted ESNs on NARMA10 and the sunspot prediction benchmark, and find that restricted ESNs perform similarly to standard ones. We present some application scenarios in which restricted ESNs may offer advantages over standard ESNs. We then test restricted ESNs on a version of the multi-timescale Multiple Superimposed Sines tasks, in order to establish a baseline performance that can be improved upon in further work. We conclude that we can scale up reservoir performance by linking small homogeneous subreservoirs together without significant loss in performance over a single large reservoir, justifying future work on using heterogeneous subreservoirs for greater flexibility.
Hermeneutical Injustice Through Defective Concept Possession
Authors: Deans Danni 
Published: October 2024

AbstractThis paper identifies and analyses a novel species of hermeneutical epistemic injustice (HI). Fricker’s traditional account analyses HI in terms of a collective conceptual gap. (Epistemic injustice: power and the ethics of knowing, Oxford University Press, Oxford, 2007). Building on this, Simion’s analysis (in: Bondy P, Carter JA (eds) Well-founded belief: new essays on the epistemic basing relation. Routledge, New York, 2019) suggests the phenomenon is broader, and thus more ubiquitous: specifically, that agents who have been hermeneutically marginalised can be susceptible to HI through failing to ground their social experience in conceptual resources that are already available. This paper advances the literature further and presents a novel strand of the phenomena, where agents both have, and base, their experience on available concepts but are still subject to HI. I argue that this is an important species of HI that should be investigated and accounted for, and I suggest that in virtue of being hermeneutically marginalised, often agents only have available to them defective or oppressive concepts. I further provide a case study of what this can look like online and new concerns social media presents for this kind of HI. CW: Mentions of transphobia, racism, sexism, rape.