bluuebunny/crossref_metadata_2025_split huggingface dataset:
The optimal cut-off value of postoperative day three C-reactive protein to predict for major complications in colorectal cancer patients
Authors: Smit Coco , Janssen-Heijnen Maryska L. , van Osch Frits , Rops Jonas , Gielen Anke H. C. , van Heinsbergen Maarten , Melenhorst Jarno , Konsten Joop L. M. 
Published: February 2025

Abstract

Purpose
To identify an optimal postoperative day 3 (POD3) C-reactive protein (CRP) cut-off for predicting major complications in colorectal cancer (CRC) patients. Secondary objectives included identifying patient and surgical factors associated with POD3 CRP levels and assessing the accuracy of the cut-off across subgroups.

Methods
A retrospective cohort study of 1536 CRC patients who underwent an oncological resection was conducted. The predictive accuracy of POD3 CRP for major complications was tested using Receiver Operating Characteristics curves. The CRP cut-off was tested across subgroups. Multivariable logistic regression analyses was performed to evaluate the predictive value of the POD3 CRP cut-off, while also determining whether patient and surgical characteristics independently predicted major complications.

Results
An optimal cut-off of 114 mg/L was identified, with a sensitivity of 0.80 and specificity of 0.59 and an Area Under the Curve for POD3 CRP of 0.78. Sensitivity remained consistently high across all subgroups, whereas specificity exhibited variability, with a notable decrease observed in the subgroups; aged 66–69, obese, ASA III and open surgery. After adjusting for patient and surgery characteristics, a POD3 CRP level above 114 mg/L was associated with a significant 5.29-fold increase in the odds for developing major complications.

Conclusions
A POD3 CRP cut-off of 114 mg/L is an effective predictor of major complications following CRC surgery, supporting safe early discharge. The cut-off remains a reliable predictor, even after adjusting for patient and surgery factors.
Genetic variants and phenotypic data curated for the CAGI6 intellectual disability panel challenge
Authors: Aspromonte Maria Cristina , Del Conte Alessio , Polli Roberta , Baldo Demetrio , Benedicenti Francesco , Bettella Elisa , Bigoni Stefania , Boni Stefania , Ciaccio Claudia , D’Arrigo Stefano , Donati Ilaria , Granocchio Elisa , Mammi Isabella , Milani Donatella , Negrin Susanna , Nosadini Margherita , Soli Fiorenza , Stanzial Franco , Turolla Licia , Piovesan Damiano , Tosatto Silvio C. E. , Murgia Alessandra , Leonardi Emanuela 
Published: February 2025

Abstract
Neurodevelopmental disorders (NDDs) are common conditions including clinically diverse and genetically heterogeneous diseases, such as intellectual disability, autism spectrum disorders, and epilepsy. The intricate genetic underpinnings of NDDs pose a formidable challenge, given their multifaceted genetic architecture and heterogeneous clinical presentations. This work delves into the intricate interplay between genetic variants and phenotypic manifestations in neurodevelopmental disorders, presenting a dataset curated for the Critical Assessment of Genome Interpretation (CAGI6) ID Panel Challenge. The CAGI6 competition serves as a platform for evaluating the efficacy of computational methods in predicting phenotypic outcomes from genetic data. In this study, a targeted gene panel sequencing has been used to investigate the genetic causes of NDDs in a cohort of 415 paediatric patients. We identified 60 pathogenic and 49 likely pathogenic variants in 102 individuals that accounted for 25% of NDD cases in the cohort. The most mutated genes were ANKRD11, MECP2, ARID1B, ASH1L, CHD8, KDM5C, MED12 and PTCHD1 The majority of pathogenic variants were de novo, with some inherited from mildly affected parents. Loss-of-function variants were the most common type of pathogenic variant. In silico analysis tools were used to assess the potential impact of variants on splicing and structural/functional effects of missense variants. The study highlights the challenges in variant interpretation especially in cases with atypical phenotypic manifestations. Overall, this study provides valuable insights into the genetic causes of NDDs and emphasises the importance of understanding the underlying genetic factors for accurate diagnosis, and intervention development in neurodevelopmental conditions.
Quantification of indocyanine green fluorescence angiography in colorectal surgery: a systematic review of the literature
Authors: McEntee Philip D. , Singaravelu Ashokkumar , McCarrick Cathleen A. , Murphy Edward , Boland Patrick A. , Cahill Ronan A. 
Published: February 2025

Abstract

Background
Indocyanine green fluorescence angiography (ICGFA) during colorectal surgery associates with reduced post-operative anastomotic complication rates. Because its interpretation is subjective, quantification has been proposed to address inter-user variability. This study reviews the published literature regarding ICGFA quantification during colorectal surgery with a focus on impactful clinical deployment.

Methods
A systematic review was performed of English language publications regarding clinical studies of ICGFA quantification in colorectal surgery in PubMed, Scopus, Web of Science and Cochrane Library on 29th August 2024, updated to 18th November 2024, following PRISMA guidelines. Newcastle Ottawa scale (NOS) was used to assess quality.

Results
A total of 1428 studies were screened with 22 studies (1469 patients) selected. There was significant heterogeneity of ICGFA methodology, quantification methods and parameter selection and only three studies were NOS “high” quality. Extracorporeal application was most common. Four studies (154 patients) conducted real-time ICGFA analyses (others were post hoc) and four utilised artificial intelligence methods. Eleven studies only included patients undergoing left-sided resection (six focusing specifically on rectal resections). Only one study employed the quantification method to guide intra-operative decision-making regarding colonic transection. Twenty-six different perfusion parameters were assessed, with time from injection to visible fluorescence and maximum intensity the most commonly (but not only) correlated parameters regarding anastomotic complication (n = 18). Other grounding correlates were tissue oxygenation (n = 3, two with hyperspectral imagery), metabolites (n = 2) and surgeon interpretation (n = 5).

Conclusion
Quantification of the ICGFA signal for colorectal surgery is feasible but has so far seen limited academic advancement beyond feasibility.
A lightweight CNN model for UAV-based image classification
Authors: Deng Xinjie , Shi Michael , Khan Burhan , Choo Yit Hong , Ghaffar Fazal , Lim Chee Peng 
Published: February 2025

Abstract
For many unmanned aerial vehicle (UAV)-based applications, especially those that need to operate with resource-limited edge networked devices in real-time, it is crucial to have a lightweight computing model for data processing and analysis. In this study, we focus on UAV-based forest fire imagery detection using a lightweight convolution neural network (CNN). The task is challenging owing to complex image backgrounds and insufficient training samples. Specifically, we enhance the MobileNetV2 model with an attention mechanism for UAV-based image classification. The proposed model first employs a transfer learning strategy that leverages the pre-trained weights from ImageNet to expedite learning. Then, the model incorporates randomly initialised weights and dropout mechanisms to mitigate over-fitting during training. In addition, an ensemble framework with a majority voting scheme is adopted to improve the classification performance. A case study on forest fire scenes classification with benchmark and real-world images is demonstrated. The results on a publicly available UAV-based image data set reveal the competitiveness of our proposed model as compared with those from existing methods. In addition, based on a set of self-collected images with complex backgrounds, the proposed model illustrates its generalisation capability to undertake forest fire classification tasks with aerial images.
How vulnerable are the nesting sites of loggerhead turtles in Cabo Verde?
Authors: Sousa-Guedes Diana , Marco Adolfo , Neves Elton , Medina Maria , Taxonera Albert , Fairweather Kirsten , Queiruga Alberto , Veiga Jairson , Patino-Martinez Juan , Alírio João , Bessa Filipa , Sillero Neftalí 
Published: February 2025

Abstract
Marine turtles’ nesting grounds face imminent threats from urbanisation, climate change, and pollution. In this study, we estimated the vulnerability of loggerhead turtles (Caretta caretta) nesting beaches in Cabo Verde, one of the largest rookeries globally. We surveyed 61 sandy beach segments (~ 100 m) with a drone and modelled nest density by incorporating topographical and oceanographic variables. Then, we digitised all beaches across the islands, divided them into smaller sections (digitised segments), and projected the model onto this layer. Each digitised beach segment was evaluated considering their exposure to five risk factors: (a) marine litter density, (b) inundation trends, (c) land surface temperature trends, (d) light pollution trends, and (e) tourism pressure. Our analysis revealed that 48% of digitised segments are exposed to at least one threat. São Vicente, Santiago, and Sal are the most affected islands, mainly from marine debris and light pollution, while Santo Antão and Maio are the least impacted. Sal showed the highest overlap between vulnerable areas and high-density sites, highlighting its priority for conservation, though less affected islands should also be protected. We found that marine debris correlates positively with nest density, while light pollution negatively affects it. Our study emphasises the need for conservation efforts to ensure the sustainability of nesting sites amid escalating global changes.
Rapid Biodegradation Assessment of Biomass Plastics Using Closed Recirculating Aquaculture Systems: A Novel Approach for Environmental Sustainability
Authors: Kotcharoen Wilasinee , Wada Naoki , Kakikawa Makiko , Takiguchi Noboru , Takahashi Yoshinori , Takahashi Kenji , Takeuchi Yutaka 
Published: February 2025

Abstract
The increasing plastic production causes serious problems in the marine environment, and the main source of plastic waste comes from the fishing and aquaculture industries. Although there have been various efforts to develop aquaculture equipment with marine biodegradable plastics, an urgent need is to develop an assay to evaluate their biodegradation in aquaculture environments. This study focused on evaluating the biodegradation of biomass plastic in recirculating aquaculture systems (RAS) that mimic freshwater, brackish water, and saltwater aquacultures. The methods used to assess biomass plastic biodegradability included changes in physical properties, weight loss, biochemical oxygen demand, and microbial community investigation using poly(butylene succinate-co-adipate) (PBSA) as a model. Scanning electron microscopy studies indicated the erosion on the biomass plastic surface from 1 to 2 days in the RAS tank (salinity, 0–0.5%) harboring Nile tilapia (Oreochromis niloticus). 4′,6-Diamidino-2-phenylindole fluorescence microscopy confirmed the presence of the microorganisms on the PBSA surface. The microorganisms in RAS tanks degraded 11.6% of 1 g/L PBSA in 7 days, demonstrating their biodegradation potential. 16S rRNA gene sequencing showed that Pseudomonas plays a major role as an early decomposer in the biodegradation process within 24 h. A multifaceted analytical method that provides sufficient evidence was developed to show that the erosion on the PBSA surface in RAS tanks results from biodegradation. The ability of RAS to control various aquatic environments (pH, salinity, temperature, and bacterial density) makes it suitable for testing the marine biodegradability of biomass plastics for use in aquaculture and fishery industries.
Impact of genetic mutations on prognosis and chemotherapy efficacy in advanced appendiceal carcinoma: insights from the nationwide Japanese comprehensive genomic profiling test database
Authors: Taniguchi Sakura Hiraide , Takahashi Masanobu , Chiu Shih-Wei , Komine Keigo , Wakayama Shonosuke , Numakura Ryunosuke , Yoshida Yuya , Kasahara Yuki , Ouchi Kota , Imai Hiroo , Saijo Ken , Shirota Hidekazu , Ishioka Chikashi 
Published: February 2025

Abstract

Background
Appendiceal carcinoma (AC) is a rare malignancy and has distinct genomic features, but their impact on prognosis and chemotherapy efficacy requires further investigation.

Methods
This retrospective study analyzed patients with advanced AC from the Japanese nationwide comprehensive genomic profiling test database, the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database, focusing on genetic alterations and their associations with clinical outcomes.

Results
Of the 314 patients, the histological types Queryincluded adenocarcinoma (Ad) (51.9%), mucinous adenocarcinoma (MAd) (30.3%), goblet cell adenocarcinoma (12.4%), and signet-ring cell adenocarcinoma (5.4%). The most common mutations were KRAS (52.5%), TP53 (49.4%), SMAD4 (18.8%), and GNAS (17.2%). KRAS mutations were most frequent in MAd (68.4%) and Ad (58.9%), whereas TP53 mutations were mostly prevalent in Ad (62.6%). We classified patients into molecular subtypes based on the presence of mutations and analyzed differences in overall survival (OS) by molecular subtype. Patients with TP53-mutant (mut) dominant tumors (all TP53-mut) and KRAS-mut focused tumors (TP53-wild-type (wt)/GNAS-wt/KRAS-mut/any SMAD4) showed a poorer median OS compared with those with GNAS-mut focused tumors (TP53-wt/GNAS-mut/any KRAS /any SMAD4) (median 47.4 and 37.5 months vs. not reached; p = 0.01 and p = 0.01, respectively). TP53 mutation was associated with poor time to treatment failure and OS with the oxaliplatin-based regimen for first-line chemotherapy.

Conclusions
This study suggested that the genetic mutations influenced the prognosis and chemotherapy efficacy in AC.
$C^{\infty }$ Well-Posedness of Higher Order Hyperbolic Pseudo-Differential Equations with Multiplicities
Authors: Garetto Claudia , Sabitbek Bolys 
Published: February 2025

Abstract
In this paper, we study higher order hyperbolic pseudo-differential equations with variable multiplicities. We work in arbitrary space dimension and we assume that the principal part is time-dependent only. We identify sufficient conditions on the roots and the lower order terms (Levi conditions) under which the corresponding Cauchy problem is

$C^{\infty }$

C
∞

well-posed. This is achieved via transformation into a first order system, reduction into upper-triangular form and application of suitable Fourier integral operator methods previously developed for hyperbolic non-diagonalisable systems. We also discuss how our result compares with the literature on second and third order hyperbolic equations.
A statistical categorization-based curriculum learning approach for multi-task classification of images
Authors: Veranyurt Ozan , Sakar C. Okan 
Published: February 2025

Abstract
Image classification and the detection of features within images remain significant challenges in computer vision. Several approaches, including serial task models and multi-output models, have been explored to address these challenges. This study focuses on multitasking attention mechanisms, which enable simultaneous categorization of data and tasks. By applying a statistical framework, the proposed method enhances the efficiency and accuracy of image classification and feature detection, with a focus on handling multiple tasks concurrently. To enhance the robustness of the model, a data-driven approach based on curriculum learning was proposed. The experiments were conducted using two distinct datasets. The first dataset involves forensic examinations, specifically identifying firearms and their calibers from firing pin marks. The proposed model achieved an accuracy of 95% in brand detection and 98% in caliber detection on this dataset. In the second part of the experiments, the animals with attributes 2 (AwA2) dataset, where state-of-the-art models have previously been applied, was used. The proposed model reduced classification errors by 1 to 10% compared to traditional convolutional neural network (CNN) architectures. The experimental results from both the forensic and public datasets demonstrate that the proposed model effectively handles multitask classification tasks, validating its applicability across diverse domains.
Antidiabetic potential of vanadium complexes combined with olive leaf extracts: a viable approach to reduce metal toxicity
Authors: Sanna Daniele , Fadda Angela , Casula Milena , Palomba Grazia , Sini Maria Cristina , Colombino Maria , Rozzo Carla , Palmieri Giuseppe , Gallo Carmela , Carbone Dalila , Siracusa Laura , Pulvirenti Luana , Ugone Valeria 
Published: February 2025

Abstract
Vanadium compounds are known for their antidiabetic properties due to their ability to interfere with numerous mechanisms that lead to the decrease of blood glucose levels. Although some of these compounds have reached clinical trials and have the advantage of being orally administrable, no vanadium-containing drugs are currently available on the market, primarily due to the high doses required, which can lead intestinal and renal problems in case of long-term treatments. In this study, plant extracts obtained from olive leaves (Olea europaea L.) were combined with vanadium complexes with established antidiabetic activity with the aim of reducing their metal toxicity and, at the same time, amplifying their hypoglycemic action. The extracts were characterized by chromatographic and spectroscopic methods showing a composition rich in polyphenols and a high antioxidant activity. Formulations containing a vanadium complex (bis(maltolato)oxidovanadium(IV), BMOV, or bis(picolinato)oxidovanadium(IV), BPOV) mixed with different amount of olive leaves extract were tested in vitro to evaluate intestinal toxicity and hypoglycemic activity. The results demonstrated that the plant extracts are generally non-toxic toward human colon fibroblast in the whole range of tested concentrations and some of them are particularly effective in reducing the toxicity of the two vanadium compounds. Further in vitro tests conducted on differentiated human adipocyte cell lines revealed a significant increase in glucose uptake following treatment with the mixed formulations, compared to the effect of the individual components, indicating a synergistic effect. Immunocytochemical assays suggested that the translocation of GLUT4 transporter can be involved in the mechanism of action.
What Predicts Changes in the Work Situation of Recently Diagnosed People with Multiple Sclerosis and do these Predictors also Apply to Healthy People?
Authors: Saddal Shalina R. D. , van der Hiele Karin , Motazedi Ehsan , van Egmond Elianne E. A. , Visser Leo H. , Waskowiak Pauline T. , van der Kruit Amber , van Dam Maureen , Schoonheim Menno M. , de Groot Vincent , Hulst Hanneke E. , Schaafsma Frederieke G. 
Published: February 2025

Abstract

Purpose
To study associations of baseline predictors with work difficulties and number of working hours after one year in recently diagnosed people with multiple sclerosis (PwMS). Furthermore, to analyze whether these predictors are generic, i.e., apply to healthy people as well, or are disease specific.

Methods
TEMPRANO and MS@Work data were combined resulting in a dataset of 135 recently diagnosed PwMS (≤ 1 year) and 172 healthy people. We analyzed the associations of baseline predictors that fit within the international classification of functioning and health (ICF) framework using a mixed-effect negative-binomial model with log link for work difficulties and number of working hours after one year, and fitted each model using a fully Bayesian approach.

Results
Slower information processing speed was a disease-specific predictor for more work difficulties after one year (posterior mean ratio (PMR) = 0.82, 95% confidence interval (CI) = [0.69, 0.97]). Higher conscientiousness was a generic predictor for more working hours after one year (PMR = 1.02, 95%CI = [1.01, 1.03] for PwMS and PMR = 1.01, 95% CI = [1.00, 1.02] for healthy people). Being male (PMR = 0.75, 95% CI = [0.58, 0.96]), being younger (PMR = 0.99, 95% CI = [0.98, 1.00]), higher information processing speed (PMR = 1.08, 95% CI = [1.04, 1.13]), better learning and memory (PMR = 1.09, 95% CI = [1.03, 1.15]), and mentally burdensome job tasks (PMR = 1.36, 95% CI = [1.22, 1.52]) were disease-specific predictors for more working hours after one year.

Conclusion
For recently diagnosed PwMS, disease-specific predictors that fit within the ICF framework associate with perceived work difficulties and working hours after one year.
Simulating and validating virus machines
Authors: Orellana-Martín David , Ramírez-de-Arellano Antonio , Pérez-Jiménez Mario J. 
Published: February 2025

Abstract
Virus machines are computing devices inspired by the transmission and replication of viruses. This model of computation has been proved to be as powerful as Turing machines, while using very simple semantics: instructions can open channels to let viruses travel between different hosts. The basic model is sequential, in the sense that only one instruction can be executed in each time step. This behaviour is, in principle, easy to follow by using pen and paper, but it can become harder when the model is big enough, as it happens with other models of computation. This paper introduces a base software for virus machines that simulates their behaviour and has an easy approach for both researchers and developers. Besides, apart from the simulator, the software has two other main purposes: on the one hand, a experimental validator has been introduced to help the researcher with both the design and the formal verification of such devices; on the other hand, it has included a tool to create a LaTeX graphic of a virus machine with the usual visuals.
Numerical Hopf normal form for delay-differential equations
Authors: Kalmár-Nagy Tamás , Horváth Dávid András 
Published: February 2025

Abstract
A data-driven algorithm to approximate the Poincaré–Lyapunov constant has been developed to overcome the tedious calculations involved in the center manifold reduction for delay-differential equations. By using a single numerical solution at the critical value of the bifurcation parameter, the flow on the center manifold is recovered by a least-squares fit. This planar system is then used to compute the Poincaré–Lyapunov constant. The algorithm is tested for the delayed Liénard equation, the analytic center manifold reduction forms the basis of the comparison. A performance metric for the method is defined and computed. Numerical results demonstrate that our method works well for identifying the Poincaré–Lyapunov constant for delay-differential equations.