bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experienced financial toxicity among long-term cancer survivors: results from a national cross-sectional survey
Authors: Klok Jente M. , Duijts Saskia F. A. , Engelen Vivian , Masselink Roel , Dingemans Anne-Marie C. , Aerts Joachim G. J. V. , Lingsma Hester F. , van Klaveren David 
Published: September 2024

Abstract
Purpose
Financial toxicity, the subjective distress caused by objective financial burden, significantly impacts cancer survivors. Yet, enduring effects on survivors remain unclear. Therefore, we investigated the experienced objective financial burden and subjective financial distress in long-term cancer survivors.

Methods
A cross-sectional nationwide online survey of adult cancer survivors ≥ 5y after diagnosis were analyzed. Objective financial burden was measured via extra expenses and income loss, while subjective financial distress covered psychological well-being, coping and support-seeking behavior, and financial concerns. Groups were compared (i.e., having cancer vs. former patients) by t-tests and chi-squared tests. Financial toxicity was visualized with Sankey plots and sunburst diagrams.

Results
4,675 respondents completed the survey, of whom 2,391 (51%) were ≥ 5y after their cancer diagnosis. Among them, 75% experienced income loss and/or extra expenses after diagnosis. One-third of the previously employed respondents relied on work disability benefits. Further, ‘being unable to make ends meet’ increased from 2% before diagnosis to 13% ≥ 5y after diagnosis (p < .001). Additionally, 58% reported negative psychological impacts of financial toxicity, and 47% worried about their financial future.

Conclusions
Cancer survivors often face income loss and additional expenses, leading to ongoing financial difficulties that affect their psychological well-being. Despite this significant impact, there is a lack of guidance and support to help them manage these financial challenges. These findings highlight the need for healthcare professionals to recognize and address the financial challenges.

Implications for Cancer Survivors
This study underscores the widespread financial challenges cancer survivors encounter, emphasizing the need for ongoing financial support and comprehensive assessments of their physical and psychological well-being.
PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms
Authors: Hosseini Seyyed Ali , Hajianfar Ghasem , Ghaffarian Pardis , Seyfi Milad , Hosseini Elahe , Aval Atlas Haddadi , Servaes Stijn , Hanaoka Mauro , Rosa-Neto Pedro , Chawla Sanjeev , Zaidi Habib , Ay Mohammad Reza 
Published: September 2024

AbstractThe current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.
Fuzzy deep visual panic detection
Authors: Lazzez Onsa , Hamdani Tarek M. , Chabchoub Habib , Ouhada Khmaies , Alimi Adel M. 
Published: September 2024

Abstract
Deep visual data analysis from social network has become an increasingly important area of research. In fact, this form of assessment makes it viable to recognize new information on social users which incorporates emotions. In order to recognize users’ emotions and other latent attributes, most of the existing approaches have used textual data and have obtained accurate results. However, little attention has been paid to visual data that have become increasingly popular in recent years.This work describes how to develop a conceptual representation model for social network analysis and social emotion analysis based on the integration of fuzzy logic and ontological modeling. The primary aim is to create an ontology that can extract new information about a social user’s mood, which can be classified as panic, no-panic, or neutral. Fuzzy logic is necessary to deal with subjective data, as users share imprecise and vague data on their social profiles. Fuzzy logic has been observed as a successful method to capture the expression of emotions due to the fuzzy nature of emotions and the ambiguous definitions of emotion words. The proposed work investigate the role of fuzzy logic in social network analysis. This study simulate a fuzzy deep system integrated with ontology for classifying social visual data (shared images) into panic, no-panic or neutral classes in order to determine the social users’ stress intensity. The Social distancing and the huge amount of shared data in Tunisia were calculated to demonstrate this classification. The experiments performed in this paper aim to create not only a novel annotated visual database named visual panic database, but also a new semantic model for modeling users’ profiles, in social network, based on the combination between ontology and deep learning techniques. In this context, we will work in the future on the combination between a user’s visual and textual data in order to improve the performance of this recognition. The proposed fuzzy system reflected the viral proliferation in stressed users and achieved an accuracy of 87%.
Similarities and differences in Swiss general practices with and without nurse practitioners
Authors: Schlunegger Margarithe Charlotte , Palm Rebecca , Zumstein-Shaha Maya 
Published: September 2024

Abstract
Background
New care models are needed for the increasing elderly, chronically ill, and multimorbid population, especially in primary healthcare. Rural regions are particularly affected where bottlenecks in provision of care and a shortage of health professionals are emerging. To address these challenges, nurse practitioners have been implemented in Swiss general practices to improve primary healthcare of chronically ill people.

Aim
We aimed to explore Swiss primary healthcare delivery for chronically ill people in general practices with and without nurse practitioners and identify similarities and differences in these general practices.

Methods
We conducted multiple case studies. Data were collected using interviews, observations, patient records, and questionnaires. We described the similarities and differences by drawing on Wagner’s Chronic Care Model and applied deductive thematic cross-case analysis. We included five general practices located in rural areas in Switzerland, with nurse practitioners in two general practices. A total of 34 patients and 26 health professionals were included. The Enhancing the QUAlity and Transparency Of health Research (EQUATOR) reporting standards for organizational case studies were used.

Results
Similarities were that chronically ill patients valued to be perceived as a whole person; guidelines for multimorbidity and reimbursement of relevant services such as coordination were limited. Differences in general practices with nurse practitioners were unclear role allocation, criteria for patient referral were missing and interprofessional teams were in an extended process of change. Nurse practitioners provided a bridge between the general practices and local home healthcare organizations, thereby, strengthening interface management and obtaining a more comprehensive nursing perspective.

Conclusion
This study highlights that new models of care can better respond to patients’ needs as additional nursing skills are available. Interprofessional teams undergo substantial changes in the new care model requiring a revision of existing structures.
Linear Time Train Contraction Minor Labeling for Railway Line Capacity Analysis
Authors: Zhong Qinglun , Xu Ruihua 
Published: September 2024

AbstractWhen no more than one train is feasibly contained in the separation headway times of two other trains, a triangular gap problem-based method is used to compute the consumed capacity in linear time. This is a strong condition limiting its applicability, while real-world operations often feature mixed traffic of various speeds, creating more complex structures beyond the characterization of a triangular gap. In this paper, we attempt to investigate the multi-train gap scenario and provide a general solution to the railway timetable structure and capacity analysis problem. Given a timetable, we use an incidence graph, the so-called train contraction minor, for representing the consecutive train operations and show that a longest path for spanning the compressed timetable is any path in a graph induced by some topological subsequence of trains that connects the predefined vertices in the train contraction minor. By vector-valued vertex labeling of this minor, we acquire an efficient algorithm that computes the consumed capacity of the timetable. Our algorithm runs in O(mn) time, where m and n are the number of trains and stations, respectively, and is free from the limitation and outperforms the near-linear time policy iteration implementation in the max-plus system of train operations. A toy example and a real-world case study demonstrate the effectiveness and computational performance of the proposed method. The proposed method based on train contraction minor contributes to the railway operations community by promoting the efficient computation of railway line capacity into linear time.
Parents’ learning in family literacy: a mixed-methods evaluation
Authors: Clymer Carol , Lee Jungeun , McLean Elisabeth L. , Prins Esther 
Published: September 2024

AbstractIn the USA, comprehensive family literacy programs integrate adult education and parent education, interactive parent-child literacy activities, and early childhood education or school for children. Although parents’ learning is central to family literacy, research overwhelmingly focuses on children’s outcomes or positions parents as conduits of children’s learning. Thus, we know little about changes in parents’ language and literacy capabilities, self-concepts, social support systems, or other benefits. This study reports findings from a multi-year, mixed-methods evaluation of five family literacy programs in Philadelphia, Pennsylvania. Participants were primarily immigrant mothers. Qualitative data, along with statistically significant quantitative data from a pre-post survey (n = 139), demonstrate learning in four domains: educational, personal, social, and parenting. Specifically, parents developed literacy and language skills; enjoyed reading more and spent more time reading alone and with children; were more involved in everyday literacy practices; increased their self-confidence and self-esteem; provided support for each other, developed friendships, and built a sense of community; and increased support for and involvement in their children’s development, literacy, and education. These results build on prior research on parental outcomes and illustrate the value of using multi-faceted, holistic measures to examine how parents benefit from family literacy.
Metric geometry of spaces of persistence diagrams
Authors: Che Mauricio , Galaz-García Fernando , Guijarro Luis , Membrillo Solis Ingrid Amaranta 
Published: September 2024

AbstractPersistence diagrams are objects that play a central role in topological data analysis. In the present article, we investigate the local and global geometric properties of spaces of persistence diagrams. In order to do this, we construct a family of functors $${\mathcal {D}}_p$$

D
p

, $$1\le p \le \infty $$

1
≤
p
≤
∞

, that assign, to each metric pair (X, A), a pointed metric space $${\mathcal {D}}_p(X,A)$$

D
p

(
X
,
A
)

. Moreover, we show that $${\mathcal {D}}_{\infty }$$

D
∞

is sequentially continuous with respect to the Gromov–Hausdorff convergence of metric pairs, and we prove that $${\mathcal {D}}_p$$

D
p

preserves several useful metric properties, such as completeness and separability, for $$p \in [1,\infty )$$

p
∈
[
1
,
∞
)

, and geodesicity and non-negative curvature in the sense of Alexandrov, for $$p=2$$

p
=
2

. For the latter case, we describe the metric of the space of directions at the empty diagram. We also show that the Fréchet mean set of a Borel probability measure on $${\mathcal {D}}_p(X,A)$$

D
p

(
X
,
A
)

, $$1\le p \le \infty $$

1
≤
p
≤
∞

, with finite second moment and compact support is non-empty. As an application of our geometric framework, we prove that the space of Euclidean persistence diagrams, $${\mathcal {D}}_{{p}}({\mathbb {R}}^{2n},\Delta _n)$$

D
p

(

R

2
n

,

Δ
n

)

, $$1\le n$$

1
≤
n

and $$1\le p<\infty $$

1
≤
p
<
∞

, has infinite covering, Hausdorff, asymptotic, Assouad, and Assouad–Nagata dimensions.
Toward a Better Understanding of Who Is Likely to Be Susceptible to the Effects of Rumination on Obsessive–Compulsive Symptoms: An Explorative Analysis
Authors: Wahl Karina , Kollárik Martin , Heinzel Carlotta V. , Koch Stefan , Voderholzer Ulrich , Lieb Roselind 
Published: September 2024

AbstractWe previously found that rumination maintains obsessive–compulsive (OC) symptoms. Our goal was to explore the moderating roles of three characteristics in the immediate and intermediate effects of rumination on OC symptoms: trait rumination, severity of comorbid depressive symptoms, and the tendency to misinterpret the occurrence of unwanted intrusive thoughts as meaningful. We reanalyzed our previous study’s data and explored in a sample of 145 individuals diagnosed with obsessive–compulsive disorder (OCD) whether any of the three characteristics moderated the observed immediate and intermediate effects of rumination on OC symptoms. Only the tendency to misinterpret unwanted intrusive thoughts moderated the immediate and intermediate effects of rumination on OC symptoms. If this result is confirmed in future studies, individuals with OCD and a high tendency to misinterpret unwanted intrusive thoughts might benefit particularly from supplemental interventions targeting the reduction of excessive rumination.
Dynamic clustering based on Minkowski similarity for web services aggregation
Authors: Ayfan Suad Kamil , Al-Shammary Dhiah , Mahdi Ahmed M. , Sufi Fahim 
Published: September 2024

AbstractThis research, introduces a new dynamic clustering method offering a new approach utilizing Minkowski Distance methods for calculating similarity of xml messages to effectively compress and aggregate them. The increase in Web services utilization has led to bottlenecks and congestion on network links with limited bandwidth. Furthermore, Simple Object Access Protocol (SOAP) is an eXtensible Markup Language (XML) based messaging system often utilized on the internet. It leads to interoperability by facilitating connection both users and their service providers across various platforms. The large amount and huge size of the SOAP messages being exchanged lead to congestion and bottlenecks. Aggregation tools for SOAP messages can effectively decrease the significant amount that traffic generated. This has shown a notable enhancement in performance. Enhancements can be made by using similarity methods. These techniques group together multiple SOAP messages that share a significant level of similarity. Present techniques utilizing grouping for aggregating XML messages have demonstrated efficiency and compression ratio limitations. Practically, the proposed model groups messages into clusters based on minimum distance, supporting Huffman (variable-length) and (fixed-length) encoding compressing for aggregating multiple compressed XML web messages into a single compact message. Generally, the suggested model’s performance has been evaluated through a comparison with K-Means, Principle Component Analysis (PCA) with K-Means, Hilbert, and fractal self-similarity clustering models. Minkowski distance clustering model has shown excellent performance, especially in all message sizes like small, medium, large, V.large. Technically, the model achieved superior average Compression Ratio and it has outperformed all other models.
Enhancing Soil Carbon Sequestration with C-Rich Carrier Materials from Spent Mushroom Substrate and Composted Wheat Straw: Implications for Smart Fertilizer Design
Authors: Calabi-Floody Marcela , Medina Jorge , Richardson Alan E. , Guarda-Reyes María Javiera , Villegas-Pizarro Pamela , Zirotti-Lecaros Isidora , Mora-Pérez Lizeth , Aponte Humberto , Knicker Heike , Rumpel Cornelia , de la Luz Mora María 
Published: September 2024

AbstractThis study aimed to evaluate the potential of two carbon (C)-rich carrier materials derived from agricultural residues, spent mushroom substrate (SMS) and composted wheat straw (CWS) for enhancing soil carbon stabilization in an Andisol, with a view towards their future application in smart fertilizer design. We investigated how their contrasting nitrogen contents and application rates affect soil organic carbon dynamics and contribute to sustainable soil management and climate change mitigation. Soil incubations were conducted over 365 days, during which C mineralization, enzymatic activities, and C and N sequestration were assessed. SMS addition at both low and high rates (0.5% and 1% soil C increase, respectively) enhanced soil organic carbon (SOC) stabilization, increasing mean residence times (40.6 and 48.8 years) and half-life times (28.1 and 33.8 years) of the stable C pool compared to unamended soil (35.9 and 24.9 years). High-rate CWS application (1% soil C increase) promoted native SOC decomposition, increasing C losses (5.8%) and reducing C sequestration potential (96%). However, low-rate CWS application (0.5% soil C increase) showed promise, increasing mean residence time (46.8 years) and half-life time (32.4 years) of the stable C pool. Spearman correlations revealed positive associations between electrical conductivity, total N, humification indices, and C stabilization parameters, highlighting the importance of nutrient availability and humification potential for C stabilization. Incorporating C-rich carrier materials with balanced nutrient content, such as SMS, can enhance soil C stabilization and support climate-smart agriculture goals. Low-rate CWS application also shows potential as an alternative C-rich carrier material. However, careful consideration of application rates and material properties is crucial to avoid adverse effects on native SOC mineralization.
Dealing with Overparenting: Developmental Outcomes in Emerging Adults Exposed to Overprotection and Overcontrol
Authors: Urone Chiara , Verdi Chiara , Iacono Chiara Lo , Miano Paola 
Published: September 2024

AbstractOverparenting is defined as a parenting style characterized by overprotection, overcontrol, and an excess of involvement in the life of one’s offspring. These types of parenting practices turn out to be particularly intrusive during the period of emerging adults in which the need for autonomy is the element that most characterizes this stage of development. The current review aimed at summarizing studies that considered the repercussions of overparenting’s dysfunctional behaviors on offspring during the developmental phase of emerging adulthood and was carried out using the new Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol. Articles were retrieved from Scopus and Web of Science databases. After having initially considered 832 papers, 31 studies were deemed eligible to be included in this review. The analysis of these studies showed that individuals subjected to overparenting are prone to developing various internalizing and externalizing symptoms. Internalizing symptoms are more prevalent and are often associated with conditions such as depression, social anxiety, and generalized anxiety. Pivotal recommendations have been suggested to reduce the impact of overparenting practices on the well-being of young adults. These include the development of coping strategies and the development of trust, resilience, and open communication within the supportive context of clinical intervention.
DSCI: a database of synthetic biology components for innate immunity and cell engineering decision-making processes
Authors: Zhang Chenqiu , Chen Tianjian , Li Zhiyu , Lu Qing , Luo Xiaotong , Cai Sihui , Zhou Jie , Ren Jian , Cui Jun 
Published: September 2024

AbstractAlthough significant progress of clinical strategy has been made in gene editing and cell engineering in immunotherapy, it is now apparent that design and modification in terms of complex signaling pathways and motifs on medical synthetic biology are still full of challenges. Innate immunity, the first line of host defense against pathogens, is critical for anti-pathogens immune response as well as regulating durable and protective T cell-mediated anti-tumor responses. Here, we introduce DSCI (Database of Synthetic Biology Components for Innate Immunity,
https://dsci.renlab.cn/), a web-accessible and integrative database that provides better insights and strategies for innate immune signaling circuit design in biosynthesis. Users can interactively navigate comprehensive and carefully curated components resources that presented as visualized signaling motifs that participate in innate immunity. The current release of DSCI incorporates 1240 independent components and more than 4000 specific entries contextually annotated from public literature with experimental verification. The data integrated into DSCI includes the components of pathways, relationships between regulators, signal motifs based on regulatory cascades, and loop graphs, all of which have been comprehensively annotated to help guide modifications to gene circuits. With the support of DSCI, users can easily obtain guidance of gene circuits construction to make decision of cell engineering based on innate immunity. DSCI not only provides comprehensive and specialized resource on the biological components of innate immune synthesis, but also serves as a useful tool to offer modification or generation strategies for medical synthetic biology.