bluuebunny/crossref_metadata_2025_split huggingface dataset:
The involvement of RIPK4 in TNF-α-stimulated IL-6 and IL-8 production by melanoma cells
Authors: Madej Ewelina , Lisek Anna , Brożyna Anna A. , Cierniak Agnieszka , Wronski Norbert , Deptula Milena , Wardowska Anna , Wolnicka-Glubisz Agnieszka 
Published: April 2024

Abstract
Purpose
The receptor-interacting protein kinase (RIPK4) has an oncogenic function in melanoma, regulates NF-κB and Wnt/β-catenin pathways, and is sensitive to the BRAF inhibitors: vemurafenib and dabrafenib which lead to its decreased level. As its role in melanoma remains not fully understood, we examined the effects of its downregulation on the transcriptomic profile of melanoma.

Methods
Applying RNA-seq, we revealed global alterations in the transcriptome of WM266.4 cells with RIPK4 silencing. Functional partners of RIPK4 were evaluated using STRING and GeneMANIA databases. Cells with transient knockdown (via siRNA) and stable knockout (via CRISPR/Cas9) of RIPK4 were stimulated with TNF-α. The expression levels of selected proteins were assessed using Western blot, ELISA, and qPCR.

Results
Global analysis of gene expression changes indicates a complex role for RIPK4 in regulating adhesion, migration, proliferation, and inflammatory processes in melanoma cells. Our study highlights potential functional partners of RIPK4 such as BIRC3, TNF-α receptors, and MAP2K6. Data from RIPK4 knockout cells suggest a putative role for RIPK4 in modulating TNF-α-induced production of IL-8 and IL-6 through two distinct signaling pathways—BIRC3/NF-κB and p38/MAPK. Furthermore, increased serum TNF-α levels and the correlation of RIPK4 with NF-κB were revealed in melanoma patients.

Conclusion
These data reveal a complex role for RIPK4 in regulating the immune signaling network in melanoma cells and suggest that this kinase may represent an alternative target for melanoma-targeted adjuvant therapy.
Open versus robot-assisted retroperitoneal tumors resection involving inferior vena cava, abdominal aorta, and renal hilum: a comparative study
Authors: Sulaiman Manan , Ali Khan Akhtar , Chunguang Yang , Hashim Rubina , Luan Yang , Xiong Ze Zhong , Huang Hui , Wang Zhihua 
Published: April 2024

Abstract
Introduction
Surgery is currently the only effective treatment for retroperitoneal tumors that do not involve any specific organ. The use of robots for removing both benign and malignant retroperitoneal tumors is considered safe and feasible. However, there is insufficient evidence to determine whether robotic retroperitoneal tumor resection (RMBRs) is superior to open retroperitoneal malignant resection (OMBRs). This study compares the short-term outcomes of robotic excision of benign and malignant retroperitoneal tumors with open excision of the same-sized tumors.

Methods
The study compared demographics and outcomes of patients who underwent robotic resection (n = 54) vs open resection (n = 54) of retroperitoneal tumors between March 2018 and December 2022. A 1:1 matching analysis was conducted to ensure a fair comparison.

Results
The study found that RBMRs resulted in reduced operative time (OT), estimated blood loss (EBM), and postoperative hospital stay (PSH) when compared to OBMRs. Additionally, RBMRs reduced EBL, PHS, and OT for patients with malignant tumor involvement in major vessels. No significant differences were found in tumor size, blood transfusion rate, and morbidity rate between the RBMRs and OBMRs groups.

Conclusion
When comparing RMBRs to OMBRs, it was observed that RMBR was associated with lower (EBL), shorter postoperative hospital stays (PHS), and reduced operative time (OT) in a specific group of patients with both benign and malignant tumors.
Proto-tool use for food processing in wild Arabian babblers: matching processing methods, substrates and prey types
Authors: Ben Mocha Yitzchak , Frisoni Francesca , Keynan Oded , Griesser Michael 
Published: April 2024

Abstract
Cognition is a powerful adaptation, enabling animals to utilise resources that are unavailable without manipulation. Tool use and food processing are examples of using cognition to overcome the protective mechanisms of food resources. Here, we describe and examine the flexibility of proto-tool use (defined as the alteration of an object through object-substrate manipulation) for food processing in a cooperatively breeding bird, the Arabian babbler (Argya squamiceps). Field observations demonstrate that the birds transport different caterpillar species to different substrate types depending on the processing method needed to prepare the caterpillar for eating. Species with toxic setae (e.g. Casama innotata) are transported to be rubbed on rough substrates (e.g. sand) before consumption, while other species (e.g. Hyles livornica) are transported to be pounded against hard substrates until their inner organs are removed and only their external body part is consumed. These results are among the few to describe flexible proto-tool use for food processing in wild animals. They thereby contribute to the taxonomic mapping of proto-tool use and food processing in non-human species, which is a fundamental step to advance comparative studies on the evolution of these behaviours and their underlying cognitive mechanisms.
Duration, numerosity and length processing in healthy ageing and Parkinson’s disease
Authors: Romeo Z. , Dolfi S. , D’Amelio M. , Mioni G. 
Published: April 2024

AbstractPeople constantly process temporal, numerical, and length information in everyday activities and interactions with the environment. However, it is unclear whether quantity perception changes during ageing. Previous studies have provided heterogeneous results, sometimes showing an age-related effect on a particular quantity, and other times reporting no differences between young and elderly samples. However, three dimensions were never compared within the same study. Here, we conducted two experiments with the aim of investigating the processing of duration, numerosity and length in both healthy and pathological ageing. The experimental paradigm consisted of three bisection tasks in which participants were asked to judge whether the presented stimulus (i.e. a time interval, a group of dots, or a line) was more similar to the short/few or long/many standards. The first study recruited healthy young and elderly participants, while the second recruited healthy elderly participants and patients with Parkinson’s disease, a clinical condition commonly associated with temporal impairments. The results of both experiments showed that discrimination precision differed between domains in all groups, with higher precision in the numerosity task and lower sensitivity in judging duration. Furthermore, while discrimination abilities were affected in healthy elderly and, even more so, in Parkinson’s disease group, no domain-specific impairments emerged. According to our research, reduced discrimination precision might be explained by an alteration of a single system for all quantities or by an age-related general cognitive decline.
Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review
Authors: Hcini Ghazala , Jdey Imen , Dhahri Habib 
Published: April 2024

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides a comprehensive analysis of the use of deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), for the classification of AD using brain imaging data. While previous reviews have covered similar topics, this paper offers a unique perspective by providing a detailed comparison of CNN and ViT for AD classification, highlighting the strengths and limitations of each approach. Additionally, the review presents an updated and thorough analysis of the most recent studies in the field, including the latest advancements in CNN and ViT architectures, training methods, and performance evaluation metrics. Furthermore, the paper discusses the ethical considerations and challenges associated with the use of deep learning models for AD classification, such as the need for interpretability and the potential for bias. By addressing these issues, this review aims to provide valuable insights for future research and clinical applications, ultimately advancing the field of AD classification using deep learning techniques.
A deep learning-based approach for predicting in-flight estimated time of arrival
Authors: Silvestre Jorge , Martínez-Prieto Miguel A. , Bregon Anibal , Álvarez-Esteban Pedro C. 
Published: April 2024

AbstractPredictability is key for efficient and safe air traffic management. In particular, accurately estimating time of arrival for current passenger flights may help terminal controllers to plan ahead and optimize airport operations in terms of safety and resource allocation. While traditional physics-based simulations are still widely used, they are complex to model and often fail to include many factors affecting the progress of a flight. In this paper, we propose a deep learning approach based on LSTM that leverages the 4D trajectory of the flight and weather data at the destination airport, to accurately predict estimated time of arrival. We evaluate our model on flights arriving at Adolfo Suárez-Madrid Barajas airport (Spain), in the first three quarters of 2022, achieving a mean absolute error of 2.65 min over the entire flight and reporting competitive short- and long-term predictions at different spatial and temporal horizons.
SMARCA4-Deficient Undifferentiated Esophageal Carcinoma: A Clinical Case Series and Literature Review
Authors: Shweikeh Faris , Hong Gordon , Walter Jacob , Hoscheit Matthew , Lembo Anthony , Mouchli Mohamad , Lane Jason 
Published: April 2024

Abstract
Purpose
Undifferentiated carcinoma of the esophagus (UEC) is a rare malignancy. Deficiency in SMARCA genes, critical for chromatin regulation, has been observed in cases of UEC. Research in UEC is sparse, however, and we present a case series along with a comprehensive review of the literature.

Case Series
Case 1 is a 49-year-old female with abdominal pain and dysphagia and esophagogastroduodenoscopy (EGD) showing a friable mass at the gastroesophageal (GE) junction. Biopsies showed a poorly differentiated neoplasm and immunohistochemistry showed loss for SMARCA4. With metastatic disease, she agreed to undergo palliative chemotherapy and radiation, passing away at 4 months. Case 2 is an 88-year-old male with dysphagia, nausea, vomiting, and distal esophageal mass with biopsy showing a malignancy with loss of SMARCA4 expression. Due to extensive metastases, he was counseled on hospice care. Case 3 is a 53-year-old male with extensive alcohol and smoking history presenting with hematemesis, passing away shortly. Posthumous histopathology consistent with undifferentiated SMARCA4-deficient carcinoma of the esophagus. Results of the literature review indicate a predilection towards males (75.0%) and a variable age range (39–88 years). Majority (76.2%) reported with a distal esophagus location. Metastatic disease was common at initial presentation. Median survival was 2.60 months. Some were managed with chemotherapy and radiation.

Conclusions
Research in SMARCA-deficient UEC is very limited. It is more common in men, age is variable, and associated with Barret’s esophagus. Further research is necessary to better understand it and to establish treatment guidelines; however, it is clear that SMARCA4-deficient UEC carries a significantly poor prognosis.
A single-frame infrared small target detection method based on joint feature guidance
Authors: Xu Xiaoyu , Zhan Weida , Jiang Yichun , Zhu Depeng , Chen Yu , Guo Jinxin , Li Jin , Liu Yanyan 
Published: April 2024

AbstractSingle-frame infrared small target detection is affected by the low image resolution and small target size, and is prone to the problems of small target feature loss and positional offset during continuous downsampling; at the same time, the sparse features of the small targets do not correlate well with the global-local linkage of the background features. To solve the above problems, this paper proposes an efficient infrared small target detection method. First, this paper incorporates BlurPool in the feature extraction part, which reduces the loss and positional offset of small target features in the process of convolution and pooling. Second, this paper designs an interactive attention deep feature fusion module, which acquires the correlation information between the target and the background from a global perspective, and designs a compression mechanism based on deep a priori knowledge, which reduces the computational difficulty of the self-attention mechanism. Then, this paper designs the context local feature enhancement and fusion module, which uses deep semantic features to dynamically guide shallow local features to realize enhancement and fusion. Finally, this paper proposes an edge feature extraction module for shallow features, which utilizes the complete texture and location information in the shallow features to assist the network to initially locate the target position and edge shape. Numerous experiments show that the method in this paper significantly improves nIoU, F1-Measure and AUC on IRSTD-1k Datasets and NUAA-SIRST Datasets.
Innovative Solutions for High-Performance Silicon Anodes in Lithium-Ion Batteries: Overcoming Challenges and Real-World Applications
Authors: Khan Mustafa , Yan Suxia , Ali Mujahid , Mahmood Faisal , Zheng Yang , Li Guochun , Liu Junfeng , Song Xiaohui , Wang Yong 
Published: April 2024

AbstractSilicon (Si) has emerged as a potent anode material for lithium-ion batteries (LIBs), but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation, leading to material pulverization and capacity degradation. Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance, yet still grapples with issues like pulverization, unstable solid electrolyte interface (SEI) growth, and interparticle resistance. This review delves into innovative strategies for optimizing Si anodes’ electrochemical performance via structural engineering, focusing on the synthesis of Si/C composites, engineering multidimensional nanostructures, and applying non-carbonaceous coatings. Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li+ transport, thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency. We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss. Our review uniquely provides a detailed examination of these strategies in real-world applications, moving beyond theoretical discussions. It offers a critical analysis of these approaches in terms of performance enhancement, scalability, and commercial feasibility. In conclusion, this review presents a comprehensive view and a forward-looking perspective on designing robust, high-performance Si-based anodes the next generation of LIBs.
Characterization of geopolymer composites for 3D printing: a microstructure approach
Authors: Youssef Passant , El-Feky Muhammad S. , Ragab Ahmed M. , Serag Mohamed I. 
Published: April 2024

AbstractGiven the importance of geopolymer in terms of 3D printing performance and the scarcity of information on the subject, as well as previous research findings, the purpose of this study is to propose a design methodology for geopolymer concrete mix that can be used in the 3D printing process by studying the effect of fine aggregate size and type, binder type (either slag or metakaolin) and ratio (slag/metakaolin), and alkaline solution amount and ratio (NaOH/Na2SiO3). The study concluded that MK has lower fluidity than BFS under the same conditions that used the same amount of SP. However, it can be improved by adding slag. The best dosage was chosen, which is 50% MK and 50% BFS. Because of these proportions, the mixture is very dense, homogeneous and compact, which increases its microstructure analysis behavior.
Synthesis of silica-encapsulated myristic acid phase-change-assisted nanocapsules for thermal management applications
Authors: Ghufran Muhammad , Huitink David 
Published: April 2024

AbstractMyristic acid-based silica (MA/SiO2) nano-encapsulated phase change materials (NePCMs) were synthesized by the sol–gel process. Five different samples of the MA/SiO2 nanocapsules were prepared by varying the mass of the myristic acid. The nanocapsules were characterized by Fourier transform infrared spectrophotometer (FTIR), X-Ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM), energy dispersive X-ray (EDX), and dynamic light scattering (DLS). These characterization techniques confirmed the successful encapsulation of the myristic acid inside the silica shell. The maximum latent heat was found to be 114.46 J/g for the sample which was prepared with 25 g myristic acid and 20 ml tetraethyl orthosilicate precursor material. The highest encapsulation ratio of approximately 62% was obtained in the same sample and the mean size of the nanocapsules was 597 nm. The thermal stability of these nanocapsules was assessed using the thermogravimetric analyzer (TGA) and differential scanning calorimetry (DSC) results exhibited that the thermophysical properties remained consistent after 50 heating/cooling thermal cycles indicating excellent durability of the NePCMs.
Energy Harvesting Floor Tile Using Piezoelectric Patches for Low-Power Applications
Authors: Selim Kyrillos K. , Yehia Hossam M. , Saleeb Demyana A. 
Published: April 2024

Abstract
Purpose
One of the sustainable energy sources derived from kinetic energy is human footsteps. This research sought to find a substitute for conventional power sources to lessen dependence on them. As a result, a floor tile excited by human footsteps was demonstrated and presented to generate usable electrical power.

Methods
Piezoelectric patches, hot melt glue sticks, wood plates, and foam plates are just a few of the commercially available materials used in the suggested technique, making it suitable and practical. In addition to the components, uncomplicated circuits like a voltage multiplier and rectifier with a capacitance filter were employed for the electrical power capture. The proposed prototype has a length of 455 mm and a width of 405 mm.

Results
Two LEDs were effectively illuminated as an actual load using electrical energy collected from human footsteps. The maximum useful power that could be harvested successfully via the proposed floor tile (one tile) was 246 mW, with an approximate cost of $10.2.

Conclusions
Designing an array of footsteps-based energy harvesting tiles covering broad areas to maximize the harvested power could be considered as a future work. Moreover, the number of pedestrians variable can be also studied for the proposed design of this study in a real excitation environment such as a railway station, subway station, street, discotheque, and wedding festival hall.
Validation of the Greek Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a Mature Student Community-Based Sample
Authors: Petrogiannis Konstantinos , Sangeorzan Irina , Andriopoulou Panoraia 
Published: April 2024

AbstractThis paper reports the validation of the Greek Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a mature student community-based sample (n = 734). The WEMWBS was administered as part of a battery of questionnaires, and the survey data were analyzed employing rigorous advanced multivariate methods to determine its reliability and validity. The findings revealed excellent internal consistency, a unidimensional structure substantiated by exploratory and confirmatory factor analyses, and adequate convergent validity, confirming its validity as a cohesive metric for assessing mental well-being. The validated WEMWBS has the potential to be an instrument, for researchers, healthcare professionals, and other individuals involved in assessing the current condition of mental well-being in Greek-speaking populations.
From Research to Applications: What Can We Extract with Social Media Sensing?
Authors: Bozas Aris , Andreadis Stelios , Chatzakou Despoina , Symeonidis Spyridon , Theodosiadou Ourania , Kyriakidis Pantelis , Kokkalas Alexandros , Stathopoulos Evangelos A. , Diplaris Sotiris , Tsikrika Theodora , Gialampoukidis Ilias , Vrochidis Stefanos , Kompatsiaris Ioannis 
Published: April 2024

AbstractWith the constant growth of social media in our daily lives, a huge amount of information is generated online by multiple social networks. However, what can we actually extract with the science of social media sensing? It is a very challenging task to mine meaningful data out of this vast crowdsourcing volume, which also rapidly changes or ends up being misleading. The scope of this paper is to present different approaches that overcome these challenges and utilize social media information from various sources. This work illustrates applications that: improve the performance of architectural design; preserve the cultural heritage; enhance citizen security; provide early detection for disasters; and discover creeping crisis events. A large variety of analyses are presented, including, among other, disaster or crime event detection, user identity linkage, relevance classification, and community detection techniques. The evaluation of the presented methods is also given in this article, proving that they can be practical and valuable in many applications.
Explainable Anomaly Detection of Synthetic Medical IoT Traffic Using Machine Learning
Authors: Aversano Lerina , Bernardi Mario Luca , Cimitile Marta , Montano Debora , Pecori Riccardo , Veltri Luca 
Published: April 2024

AbstractIn the context of the Internet of Things (IoT), particularly within medical facilities, the detection and categorization of Internet traffic remain significant challenges. While conventional methods for IoT traffic analysis can be applied, obtaining suitable medical traffic data is challenging due to the stringent privacy constraints associated with the health domain. To address this, this study proposes a network traffic simulation approach using an open-source tool called IoT Flock, which supports both CoAP and MQTT protocols. The tool is used to create a synthetic dataset, to simulate IoT traffic originating from various smart devices in different hospital rooms. The study shows a complete anomaly detection analysis of IoT-Flock-generated traffic, both normal and malicious, by leveraging and comparing traditional machine learning techniques, deep learning models with multiple hidden layers, and explainable artificial intelligence techniques. The results are very promising. For the binary classification, for example, the obtained accuracy is close to $$100\%$$

100
%

in the case of the CoAP protocol. Good results are also obtained when the multinomial classification is performed, observing that CoAP packets are classified better than MQTT packets, even if the identification of the different MQTT packets reaches very high metrics for the most of the considered algorithms. Moreover, the obtained classification rules are also meaningful in the considered IoT context. The results indicate that IoT-Flock synthetic data can effectively be used to train and test machine and deep learning models for detecting abnormal IoT traffic in medical scenarios. This research attempts also to bridge the gap between IoT security and healthcare, providing useful insights into securing medical IoT networks in general.