bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
Authors: Duan Ganglong , Zhang Shaoyang , Shang Yanying , Kong Weiwei
Published: April 2024

Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients, and in severe cases, can result in life-threatening conditions such as bone cancer. In this paper, a hybrid model HRD (Human-Resnet50-Densenet121) based on deep learning and human participation is proposed to efficiently identify disease features by classifying X-ray images. Feasibility testing of the model was conducted using the MURA dataset, with metrics such as accuracy, recall rate, F1-score, ROC curve, Cohen’s kappa, and AUC values employed for evaluation. Experimental results indicate that, in terms of model accuracy, the hybrid model constructed through a combination strategy surpassed the accuracy of any individual model by more than 4%. The model achieved a peak accuracy of 88.81%, a maximum recall rate of 94%, and the highest F1-score value of 87%, all surpassing those of any single model. The hybrid model demonstrates excellent generalization performance and classification accuracy.
Mechanical Faults Analysis in Switched Reluctance Motor
Authors: Lorencki Jakub , Radkowski Stanisław
Published: April 2024

The switched reluctance motor (SRM) is an electric motor that can function effectively in challenging operating conditions thanks to its sturdy construction and resilience to external factors. Despite somewhat weaker parameters in terms of energy and power density compared to other types of electric motors, the SRM is recommended for applications such as the military, mining, industry, and other locations where the reliability of vehicle drive is essential. Therefore, monitoring the motor’s operating state and identifying the fault’s condition while it is still in the beginning phase is crucial. The paper presents SRM diagnostic methods and the authors’ research on the test stand. The examined faults were dynamic eccentricity and imbalance. Experiments were performed for various rotational speeds and loads. The analysis of the results consisted of the interpretation of the current and acceleration spectra acquired from proper sensors. The spectra bands are compared in terms of their amplitudes and frequency values. These results show the nonlinear characteristics of the motor’s operation, and interpretation of these results allows for estimating the impact of a fault parameter on a motor’s performance.
Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers
Authors: Tripathi Shailesh , Bachmann Nadine , Brunner Manuel , Jodlbauer Herbert
Published: April 2024

This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.
Comparing Several P300-Based Visuo-Auditory Brain-Computer Interfaces for a Completely Locked-in ALS Patient: A Longitudinal Case Study
Authors: Bettencourt Rute , Castelo-Branco Miguel , Gonçalves Edna , Nunes Urbano J. , Pires Gabriel
Published: April 2024

In a completely locked-in state (CLIS), often resulting from traumatic brain injury or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), patients lose voluntary muscle control, including eye movement, making communication impossible. Brain-computer interfaces (BCIs) offer hope for restoring communication, but achieving reliable communication with these patients remains a challenge. This study details the design, testing, and comparison of nine visuo-auditory P300-based BCIs (combining different visual and auditory stimuli and different visual layouts) with a CLIS patient over ten months. The aim was to evaluate the impact of these stimuli in achieving effective communication. While some interfaces showed promising progress, achieving up to 90% online accuracy in one session, replicating this success in subsequent sessions proved challenging, with the average online accuracy across all sessions being 56.4 ± 15.2%. The intertrial variability in EEG signals and the low discrimination between target and non-target events were the main challenge. Moreover, the lack of communication with the patient made BCI design a challenging blind trial-and-error process. Despite the inconsistency of the results, it was possible to infer that the combination of visual and auditory stimuli had a positive impact, and that there was an improvement over time.
Data-Driven Prediction of Severe Convection at Deutscher Wetterdienst (DWD): A Brief Overview of Recent Developments
Authors: Müller Richard , Barleben Axel
Published: April 2024

Thunderstorms endanger life and infrastructure. The accurate and precise prediction of thunderstorms is therefore helpful to enable protection measures and to reduce the risks. This manuscript presents the latest developments to improve thunderstorm forecasting in the first few hours. This includes the description and discussion of a new Julia-based method (JuliaTSnow) for the temporal extrapolation of thunderstorms and the blending of this method with the numerical weather prediction model (NWP) ICON. The combination of ICON and JuliaTSnow attempts to overcome the limitations associated with the pure extrapolation of observations with atmospheric motion vectors (AMVs) and thus increase the prediction horizon. For the blending, the operational ICON-D2 is used, but also the experimental ICON-RUC, which is implemented with a faster data assimilation update cycle. The blended products are evaluated against lightning data. The critical success index (CSI) for the blended RUC product is higher for all forecast time steps. This is mainly due to the higher resolution of the AMVs (prediction hours 0–2) and the rapid update cycle of ICON-RUC (prediction hours 2–6). The results demonstrate the potential of the rapid update cycle to improve the short-term forecasts of thunderstorms. Moreover, the transition between AMV-driven nowcasting to NWP is much smoother in the blended RUC product, which points to the advantages of fast data assimilation for seamless predictions. The CSI is well above the critical value of 0.5 for the 0–2 h forecasts. Values below 0.5 mean that the number of hits (correct informations) is lower than the number of failures, which results from the missed cells plus false alarms. The product is then no longer useful in forecasting thunderstorms with a spatial accuracy of 0.3 degrees. Unfortunately, with RUC, the CSI also drops below 0.5 when the last forecast is more than 3 h away from the last data assimilation, indicating the lack of model physics to accurately predict thunderstorms. This lack is simply a result of chaos theory. Within this context, the role of NWP in comparison with artificial intelligence (AI) is discussed, and it is concluded that AI could replace physical short-term forecasts in the near future.
Effects of Cannabidiol, ∆9-Tetrahydrocannabinol, and WIN 55-212-22 on the Viability of Canine and Human Non-Hodgkin Lymphoma Cell Lines
Authors: Omer Saba , Pathak Suhrud , Mansour Mahmoud , Nadar Rishi , Bowen Dylan , Dhanasekaran Muralikrishnan , Pondugula Satyanarayana R. , Boothe Dawn
Published: April 2024

In our previous study, we demonstrated the impact of overexpression of CB1 and CB2 cannabinoid receptors and the inhibitory effect of endocannabinoids (2-arachidonoylglycerol (2-AG) and Anandamide (AEA)) on canine (Canis lupus familiaris) and human (Homo sapiens) non-Hodgkin lymphoma (NHL) cell lines’ viability compared to cells treated with a vehicle. The purpose of this study was to demonstrate the anti-cancer effects of the phytocannabinoids, cannabidiol (CBD) and ∆9-tetrahydrocannabinol (THC), and the synthetic cannabinoid WIN 55-212-22 (WIN) in canine and human lymphoma cell lines and to compare their inhibitory effect to that of endocannabinoids. We used malignant canine B-cell lymphoma (BCL) (1771 and CLB-L1) and T-cell lymphoma (TCL) (CL-1) cell lines, and human BCL cell line (RAMOS). Our cell viability assay results demonstrated, compared to the controls, a biphasic effect (concentration range from 0.5 μM to 50 μM) with a significant reduction in cancer viability for both phytocannabinoids and the synthetic cannabinoid. However, the decrease in cell viability in the TCL CL-1 line was limited to CBD. The results of the biochemical analysis using the 1771 BCL cell line revealed a significant increase in markers of oxidative stress, inflammation, and apoptosis, and a decrease in markers of mitochondrial function in cells treated with the exogenous cannabinoids compared to the control. Based on the IC50 values, CBD was the most potent phytocannabinoid in reducing lymphoma cell viability in 1771, Ramos, and CL-1. Previously, we demonstrated the endocannabinoid AEA to be more potent than 2-AG. Our study suggests that future studies should use CBD and AEA for further cannabinoid testing as they might reduce tumor burden in malignant NHL of canines and humans.
A Pioneer Tool to Reduce Restrictive Practices toward People with Intellectual and Developmental Disabilities
Authors: Sánchez-Gómez Victoria , Verdugo Miguel Ángel , Crespo Manuela , San Román Amalia
Published: April 2024

Reducing restrictive practices toward individuals with intellectual and developmental disabilities is a globally recognized imperative and human rights priority. This paper presents a novel tool called LibRe for assessing and reducing restrictive practices. This tool involved an instrumental multistage design and collaboration between professionals, individuals with disabilities, family members, and experts from different fields. It addresses diverse restrictive practices in five key domains: physical or mechanical, chemical or pharmacological, structural, relational, and practices related to contexts and supports. It addresses practices that are pertinent to the Spanish context and that existing tools have not covered. Embedded as a step within an organizational approach, LibRe fosters organizational transformation and provides resources to achieve outcomes within reduction plans for restrictive practices. In total, 156 teams comprising 585 professionals, 64 people with disabilities, and 44 family members responded to the tool. In terms of evidence for internal structure validity, the oblique five-factor model exhibited an adequate fit through confirmatory factor analysis, along with satisfactory reliability indices, according to ordinal alpha and omega. Users positively appraised the tool’s usefulness and identified its strengths and challenges. Although further research is needed, preliminary evidence frames LibRe as a useful resource for practice and research.
A New Module for the Evaluation of Bridges Based on Visual Inspection through a Digital Application Linked to an Up-to-Date Database of Damage Catalog for Colombia
Authors: Muñoz-Diaz Edgar E. , Vargas-Luna Andrés , Nuñez-Moreno Federico , Florez Carlos F. , Alvarado Yezid A. , Ruiz Daniel M. , Mora Álvaro , Correal Juan F.
Published: April 2024

Road structures undergo a series of chemical and physical processes once they are put into service. This phenomenon results from the action of the load and the influence of the environment, which causes their progressive deterioration. In order to mitigate the risk of progressive deterioration and guarantee their stability and durability, various maintenance tasks are required, including visual inspections. The Intelligent Bridge Management System of Colombia (SIGP) includes visual inspection as one of its modules. The system has been designed based on state-of-the-art criteria and national experience with relevant damages and bridge collapses. This paper presents the visual inspection methodology, which includes several stages such as a classification scale, condition index, evaluation areas, damage catalog, and evaluation criteria. In addition, a digital application has been developed to facilitate real-time data collection during field inspections using mobile devices, which can be uploaded directly to the system database hosted in the cloud. The results from the inspection of bridges of different typologies and years of construction are presented, as well as general inspection results from 150 bridges in Colombia. The relevance, comprehensiveness, and accuracy of the inspection are supported by a damage catalog, which allows the identification of intervention needs and reduces the bias of the collected data.
Enhancing Drone Operator Competency within the Construction Industry: Assessing Training Needs and Roadmap for Skill Development
Authors: Nwaogu Janet Mayowa , Yang Yang , Chan Albert P. C. , Wang Xiaowei
Published: April 2024

Industry 5.0 is expected to solve the issue of job insecurity and reluctance to adopt emerging technologies in Industry 4.0 through increased workforce participation. To achieve that, proactive training should be afforded to enable the workforce to co-work with new technologies. Drones are an emerging technology increasingly adopted in construction, which has enormous data collection and safety implications when operational skills are inadequate. Yet, current drone training programs appear to be generic, and their ability to equip operators for flying tasks is questioned. This study aims to answer this question by assessing the adequacy of existing drone training programs and proposing training needs and strategies for drone operators in the construction industry. Data collected using semi-structured interviews from 22 purposively selected respondents in Hong Kong and desk research of websites were subjected to inductive content and thematic analysis using MAXQDA Analytics Pro 2022 software and narrative review. It was deduced that drone training courses in Hong Kong were mostly generic (UAS Pilot Training—Level 1, FPV operation). Interviewees considered existing drone training/training courses as inadequate for four reasons, including “lack of context-fitting considerations”, “incompetence of drone operators”, and “lack of demand/interest”. Regarding the need for specialized drone training courses, two barriers and two training ecosystem themes emerged, with the high cost of specialized courses being a potential barrier to adoption since they could hamper enrolment. The training ecosystem themes were consistent with the “the training is inadequate” theme. This study proposes two drone operation training strategies: “competence-based training” and a “train-the-trainer” model. Drone training courses or programs under each training strategy should include (i) training content such as “safety training” and off-GPS training and (ii) knowledge and skill maintenance measures such as mandatory continuous professional development and retraining techniques. The proposed training strategies will equip operators to work efficiently and safely with drones. The study offers valuable references for training organizations and government authorities.
Influence of Specimen Size on the Compressive Strength of Wood
Authors: Zhao Chuan , Liu Degui , Zhang Chuntao , Li Yanyan , Wang Yuhao
Published: April 2024

This study aimed to discuss the influence of specimen sizes on the compressive strength parameters of wood, specifically focusing on their compression strength, elastic modulus, and Poisson’s ratio. Therefore, three different-sized specimens (20 mm × 20 mm × 30 mm, 40 mm × 40 mm × 60 mm, 60 mm × 90 mm × 90 mm) were manufactured and tested in the longitudinal, radial, and tangential directions, following the standard testing method for acquiring the compressive strength of wood. Subsequently, based on the experimental results, compressive parameters, failure mechanisms, load–displacement curves, and stress–strain relationships were systematically analyzed for the three different-sized specimens. Meanwhile, the influence of specimen size on the compressive strength parameters of wood was also evaluated through finite element numerical simulations, utilizing the obtained mechanical parameters. The results revealed a significant correlation between compressive strength and specimen size, indicating a decrease in compressive strength with an increasing specimen size. Conversely, the elastic modulus and Poisson’s ratio exhibited less sensitivity to specimen size changes. Notably, the compressive strength parameters derived from small-sized specimens (20 mm × 20 mm × 30 mm) exhibited a lack of rationality, while those obtained from medium-sized (40 mm × 40 mm × 60 mm), and large-sized specimens (60 mm × 90 mm × 90 mm) demonstrated greater reliability, providing precise results in finite element numerical simulations.
A Numerical Investigation of the Influence of Humid Environments on the Thermal Performance of a Phase Change Thermal Storage Cooling System in Buildings
Authors: Gao Xiangkui , Sheng Qing , Li Na
Published: April 2024

Phase change thermal energy storage (PCTES) technology has garnered significant attention in addressing thermal management challenges in building HVAC systems. However, the cooling performance of PCTES systems in humid scenarios remains unexplored, which is crucial in subtropical regions, high-humidity underground areas, and densely populated spaces. Taking the mine refuge chamber (MRC) as an example, this study focuses on a passive temperature and humidity control system by employing cold storage phase change plates (PCPs) for 96 h. First, an improved and simplified full-scale numerical model including PCPs and MRC parts is established. Then, the model is validated through the experimental results and solved using a numerical method. Finally, the influence of various factors within the system is investigated and an optimization method involving batch operation is proposed. The results indicate that (1) within 40 h, the use of cold storage PCPs leads to an indoor temperature reduction of 4.8 °C and a 7% decrease in relative humidity; (2) the PCPs show asynchronous states in sensible and latent heat transfer rates; (3) for every 50 additional PCPs, the average indoor temperature increases by 0.6 °C and the relative humidity decreases by 1.5%; (4) implementing batch operation of PCPs ensures that the indoor Heat Index drops by 10 °C, which is vital for human survival. The findings will play a crucial role in the global expansion and application (including geographical and functional aspects) of phase change thermal storage technology.
A Science Mapping Approach-Based Review of Construction Workers’ Safety-Related Behavior
Authors: Feng Jing , Gao Xin , Li Hujun , Liu Baijian , Tang Xiaoying
Published: April 2024

Promoting safe behaviors among construction workers and mitigating unsafe behaviors is an effective approach to enhancing safety performance in the construction industry. Although progress has been made, the research themes concerning construction workers’ safety-related behaviors (CWSRB) and the detailed progress of each theme remain unclear due to differences in review perspectives and conceptual scopes. This study utilized CiteSpace software (V6.2R3 version) to conduct an analysis of co-authorship networks, co-word networks, and co-citations on 563 published articles in this field from 2013 to 2023. This study’s outcomes highlight several key insights: (1) journals such as Safety Science play a pivotal role in the domain; (2) institutions such as the City University of Hong Kong and Hong Kong Polytechnic University, along with prolific authors like Li, are major contributors to the field; (3) the focus of research has evolved from early organizational factors towards a more diverse range of topics, with deep learning emerging as a significant current research hotspot; (4) this study has identified high-cited literature and 11 primary clusters within the field. Current research focuses on five areas: safety-related behavior concepts, influencing factors and consequences, formation mechanisms, interventions, and applications of new technologies. Establishing clear classification criteria for unsafe behaviors, comprehensively understanding the formation mechanisms of safety-related behaviors, evaluating the effectiveness of intervention strategies, and exploring the practical applications of new technologies are future research directions. This study provides researchers with a holistic view of the present state of research and potential avenues for future exploration, thereby deepening the knowledge and comprehension of stakeholders within this domain.
Presence of CD44v9-Expressing Cancer Stem Cells in Circulating Tumor Cells and Effects of Carcinoembryonic Antigen Levels on the Prognosis of Colorectal Cancer
Authors: Sawai Katsuji , Goi Takanori , Kimura Youhei , Koneri Kenji
Published: April 2024

Circulating tumor cells (CTCs) are cancer cells released from the primary tumor into the bloodstream, and contain cancer stem cells that influence tumor survival, recurrence, and metastasis. Here, we investigated CD44v9 expression in CTCs and impact of preoperative carcinoembryonic antigen (CEA) levels on colorectal cancer (CRC) prognosis. We analyzed the expression of CD44v9 mRNA in CTCs using reverse transcription-polymerase chain reaction and preoperative CEA levels in blood samples obtained from 300 patients with CRC. Subsequently, we evaluated the association of CD44v9 expression and CEA levels with clinicopathological factors. CD44v9 mRNA was expressed in 31.3% of the patients, and was significantly associated with liver metastasis. Patients with positive CD44v9 expression had a lower 5-year survival rate (62.3%) than those with negative CD44v9 expression (82.8%, p < 0.001). Cox regression analysis identified CD44v9 expression and high CEA levels (≥5 ng/mL) as poor prognostic factors, while negative CD44v9 expression and low CEA levels (<5 ng/mL) were associated with favorable prognosis (hazard ratio = 0.285, p = 0.006). These results suggest that a combination of CD44v9 mRNA expression in CTCs and serum CEA levels could serve as a valuable prognostic marker for CRC, potentially enhancing the accuracy of prognosis predictions.
Discovery of Novel Potential Prognostic Markers and Targeted Therapy to Overcome Chemotherapy Resistance in an Advanced-Stage Wilms Tumor
Authors: Choochuen Pongsakorn , Nokchan Natakorn , Khongcharoen Natthapon , Laochareonsuk Wison , Surachat Komwit , Chotsampancharoen Thirachit , Sila Thanit , Sangkhathat Surasak
Published: April 2024

Wilms tumor (WT), the most prevalent type of renal cancer in children, exhibits overall survival rates exceeding 90%. However, chemotherapy resistance, which occurs in approximately 10% of WT cases, is a major challenge for the treatment of WT, particularly for advanced-stage patients. In this study, we aimed to discover potential mutation markers and drug targets associated with chemotherapy resistance in advanced-stage WT. We performed exome sequencing to detect somatic mutations and molecular targets in 43 WT samples, comprising 26 advanced-stage WTs, of which 7 cases were chemotherapy-resistant. Our analysis revealed four genes (ALPK2, C16orf96, PRKDC, and SVIL) that correlated with chemotherapy resistance and reduced disease-free survival in advanced-stage WT. Additionally, we identified driver mutations in 55 genes within the chemotherapy-resistant group, including 14 druggable cancer driver genes. Based on the mutation profiles of the resistant WT samples, we propose potential therapeutic strategies involving platinum-based agents, PARP inhibitors, and antibiotic/antineoplastic agents. Our findings provide insights into the genetic landscape of WT and offer potential avenues for targeted treatment, particularly for patients with chemotherapy resistance.
Coagulation Protease-Driven Cancer Immune Evasion: Potential Targets for Cancer Immunotherapy
Authors: Paul Subhojit , Mukherjee Tanmoy , Das Kaushik
Published: April 2024

Blood coagulation and cancer are intrinsically connected, hypercoagulation-associated thrombotic complications are commonly observed in certain types of cancer, often leading to decreased survival in cancer patients. Apart from the common role in coagulation, coagulation proteases often trigger intracellular signaling in various cancers via the activation of a G protein-coupled receptor superfamily protease: protease-activated receptors (PARs). Although the role of PARs is well-established in the development and progression of certain types of cancer, their impact on cancer immune response is only just emerging. The present review highlights how coagulation protease-driven PAR signaling plays a key role in modulating innate and adaptive immune responses. This is followed by a detailed discussion on the contribution of coagulation protease-induced signaling in cancer immune evasion, thereby supporting the growth and development of certain tumors. A special section of the review demonstrates the role of coagulation proteases, thrombin, factor VIIa, and factor Xa in cancer immune evasion. Targeting coagulation protease-induced signaling might be a potential therapeutic strategy to boost the immune surveillance mechanism of a host fighting against cancer, thereby augmenting the clinical consequences of targeted immunotherapeutic regimens.
Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study
Authors: Santoro Miriam , Zybin Vladislav , Coada Camelia Alexandra , Mantovani Giulia , Paolani Giulia , Di Stanislao Marco , Modolon Cecilia , Di Costanzo Stella , Lebovici Andrei , Ravegnini Gloria , De Leo Antonio , Tesei Marco , Pasquini Pietro , Lovato Luigi , Morganti Alessio Giuseppe , Pantaleo Maria Abbondanza , De Iaco Pierandrea , Strigari Lidia , Perrone Anna Myriam
Published: April 2024

Background: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Methods: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. Results: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62–0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65–0.85). Conclusions: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.
The Genomic, Transcriptomic, and Immunologic Landscape of HRAS Mutations in Solid Tumors
Authors: Kareff Samuel A. , Trabolsi Asaad , Krause Harris B. , Samec Timothy , Elliott Andrew , Rodriguez Estelamari , Olazagasti Coral , Watson Dionysios C. , Bustos Matias A. , Hoon Dave S. B. , Graff Stephanie L. , Antonarakis Emmanuel S. , Goel Sanjay , Sledge George , Lopes Gilberto
Published: April 2024

Tipifarnib is the only targeted therapy breakthrough for HRAS-mutant (HRASmt) recurrent or metastatic head and neck squamous cell carcinoma (HNSCC). The molecular profiles of HRASmt cancers are difficult to explore given the low frequency of HRASmt. This study aims to understand the molecular co-alterations, immune profiles, and clinical outcomes of 524 HRASmt solid tumors including urothelial carcinoma (UC), breast cancer (BC), non-small-cell lung cancer (NSCLC), melanoma, and HNSCC. HRASmt was most common in UC (3.0%), followed by HNSCC (2.82%), melanoma (1.05%), BC (0.45%), and NSCLC (0.44%). HRASmt was absent in Her2+ BC regardless of hormone receptor status. HRASmt was more frequently associated with squamous compared to non-squamous NSCLC (60% vs. 40% in HRASwt, p = 0.002). The tumor microenvironment (TME) of HRASmt demonstrated increased M1 macrophages in triple-negative BC (TNBC), HNSCC, squamous NSCLC, and UC; increased M2 macrophages in TNBC; and increased CD8+ T-cells in HNSCC (all p < 0.05). Finally, HRASmt was associated with shorter overall survival in HNSCC (HR: 1.564, CI: 1.16–2.11, p = 0.003) but not in the other cancer types examined. In conclusion, this study provides new insights into the unique molecular profiles of HRASmt tumors that may help to identify new targets and guide future clinical trial design.
Direct Conversion of Ethanol to Propylene over Zn-Modified HBeta Zeolite: Influence of Zinc Precursors
Authors: Bai Ting , Li Xiaohui , Ding Liang , Wang Jin , Xiao Yong-Shan , Cao Bin
Published: April 2024

A series of Zn-modified HBeta (Zn/HBeta) catalysts were prepared via the wetness impregnation method with different zinc precursors such as ZnSO4·7H2O, ZnCl2, C4H6O4Zn·2H2O and Zn(NO3)2·6H2O, and their catalytic performance in the conversion of ethanol to propylene reaction was evaluated. Results indicate that the amount and strength distribution of the acid sites of the Zn/HBeta catalysts were easily tuned by employing different types of zinc precursors. More importantly, when the zinc species were introduced to the HBeta, the propylene yield was significantly enhanced, whereas the yields of ethylene and C2–C4 alkanes were remarkably suppressed. For the catalyst prepared by using the ZnCl2 precursor, a higher propylene yield of up to 43.4% for Zn/HBeta-C was achieved as a result of the moderate amount and strength distribution of acid sites. The average coking rate of the used Zn/HBeta catalysts strongly depended on the amount of total acid sites, especially the strong acid sites, i.e., the higher the amount of total acid sites of the catalyst, the greater the average coking rate. For the catalyst prepared by using the ZnSO4·7H2O precursor, Zn/HBeta-S exhibited a better stability even after depositing more coke, which was due to the higher amount of strong acid sites.
Stably Improving the Catalytic Activity of Oxygen Evolution Reactions via Two-Dimensional Graphene Oxide-Incorporated NiFe-Layered Double Hydroxides
Authors: Chen Ling , Lu Yue , Duanmu Manman , Zhao Xin , Song Shenglu , Duan Liyue , Ma Zhipeng , Song Ailing , Shao Guangjie
Published: April 2024

NiFe-layered double hydroxides (NiFe-LDH) have been reported to possess exceptional oxygen evolution reaction (OER) activity. However, maintaining the stability of high activity over a long time remains a critical challenge that needs to be addressed for their practical application. Here, we report a custom-sized deep recombination of 2D graphene oxide with NiFe-LDH (NiFe-LDH/GO/NF) through a simple electrodeposition method that improves OER activity and achieves excellent stability. The excellent performance of the catalyst mainly comes from the three-phase interface and electron transport channel dredged by the three-dimensional structure constructed by the deep composite, which can not only significantly reduce its charge and electron transfer resistance, improving the material conductivity, but it also effectively increases the specific surface area, inhibits aggregation, and exposes rich active sites. In addition, GO with good conductivity not only supports NiFe-LDH well but also increases the heterogeneous interface, putting the NiFe-LDH/GO composites in close contact with Ni foam and increasing the electrocatalytic stability of the NiFe-LDH/GO/NF. The experimental results show that the overpotential of NiFe-LDH/20,000GO/NF is only 295 mV at a current density of 100 mA cm−2; the Tafel slope is 52 mV dec−1, and the charge transfer resistance (Rct) is only 0.601 Ω in 1 M KOH. This indicates that GO has excellent potential to assist in constructing geometric and electronic structures of NiFe-LDH in long-term applications.
Electrochemical Sensor for the Evaluation of Doxorubicin from Novel Pharmaceutical Formulations and Serum
Authors: Pusta Alexandra , Tertis Mihaela , Bura Irina , Bogdan Diana , Suciu Maria , Mirel Simona , Cristea Cecilia
Published: April 2024

This study focuses on addressing the challenges associated with doxorubicin (DOX), an anthracycline chemotherapeutic widely used in cancer treatment. Despite its efficacy, DOX is linked to severe side effects that limit its clinical applications. Novel pharmaceutical formulations aim to mitigate these issues, providing better safety profiles. The development of these formulations requires analytical methods that can accurately and quickly quantify DOX. A cost-effective and portable electrochemical sensor for DOX detection was developed utilizing in-house printed carbon electrodes decorated with gold nanoparticles. DOX was detected using differential pulse voltammetry. The sensor demonstrated an accurate quantification of DOX from novel pharmaceutical formulations and serum, presenting a dynamic range of 1 to 500 μg/mL and a low detection limit of 0.3 μg/mL. The method, successfully applied to characterize DOX-loaded nanosomes, offers a valuable alternative in the early stages of formulation development, reducing costs and saving time, while maintaining accuracy.
Excitation-Wavelength- and Time-Dependent Fluorescent Ink Based on RGB Building Blocks for Advanced Anti-Counterfeiting
Authors: Lin Chengxin , Kang Chenxi , He Xinxin , Yi Jiaci , Zhu Qi
Published: April 2024

What is reported here is an advanced anti-counterfeiting ink whose luminous effect changes over time and at different excitation wavelengths. Unlike traditional anti-counterfeit fluorescent materials, the phosphors used here exhibit multicolor emissions under multiple excitation modes. In this work, the most important building blocks are three classic phosphors with primary colors, red (Ca2YNbO6:0.4Eu3+), green (SrAl2O4:0.01Eu2+, 0.02Dy3+) and blue (CaAl2O4:0.012Eu2+, 0.06Nd3+, 0.036Gd3+), which were synthesized using the high-temperature solid-state method. The phosphors formed homogeneous solid solutions and were uniformly distributed throughout the mixture. A homogeneous transparent luminescent ink was obtained by blending the multi-mode phosphors with transparent screen-printing ink, resulting in multi-mode luminescence by simply varying the proportions of the red (R), green (G) and blue (B) phosphors. Thanks to this simple process, an advanced anti-counterfeiting ink with low production costs was achieved. Anti-counterfeiting logos of a “Giraffe” and “Steam Train” were printed using the transparent fluorescent ink onto black cardstock, exhibiting the characteristic of dynamic luminescence dependent on the duration and excitation wavelength. The anti-counterfeiting effect of the patterns suggests that the fluorescent ink is worth developing and is reliable in its application.
Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases
Authors: Kapoor Neal D. , Groot Olivier Q. , Buckless Colleen G. , Twining Peter K. , Bongers Michiel E. R. , Janssen Stein J. , Schwab Joseph H. , Torriani Martin , Bredella Miriam A.
Published: April 2024

The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this, 31% (61) of patients had postoperative complications within 30 days, and 16% (31) of patients underwent reoperation. Lower muscle CSA was associated with increased postoperative complications within 30 days (OR [95% CI] = 0.99 [0.98–0.99], p = 0.03). Through multivariate analysis, it was found that lower muscle CSA was also associated with an increased postoperative complication rate after controlling for the albumin, ASIA score, previous systemic therapy, and thoracic metastases (OR [95% CI] = 0.99 [0.98–0.99], p = 0.047). LOS and reoperations were not associated with any body composition measures. Low muscle mass may serve as a biomarker for the prediction of complications in patients with spinal metastases. The routine assessment of muscle mass on opportunistic CTs may help to predict outcomes in these patients.
Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024
Authors: Carriero Alessandro , Groenhoff Léon , Vologina Elizaveta , Basile Paola , Albera Marco
Published: April 2024

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
Hyperacute Radiation Pneumonitis after Severe irAE
Authors: Chou Yang , Chuang Wei-Kai
Published: April 2024

A 54-year-old woman presented to an outpatient clinic with a recurrence of triple-negative breast cancer and multiple bone metastases. The patient had a large mass lesion of 10 cm on the sternum. She received the immune checkpoint inhibitors pembrolizumab and taxane. Initially, the patient responded excellently to treatment, but stopped pembrolizumab for grade IV skin toxicity with multiple ulcerative wounds over the bilateral leg and trunk. The lesions abated following administration of antibiotics and oral prednisolone for two months. After that, she was referred to the radiation oncology department for further treatment. She received radiotherapy for the sternum mass but stopped radiation at 42Gy/21 fractions for severe dyspnea and fever. Blood sampling found leukocytosis with neutrophil predominance. Chest radiography showed bilateral lung infiltration. Pulmonary CT scan yielded bilateral lung patchy consolidation compatible with radiation isodose-line. Bronchial lavage showed positive Pneumocystis jiroveci PCR. Dyspnea improved after titrating methylprednisolone within two days. The patient recovered well with TMP-SMX and glucocorticoids after the initiation of therapy.
The Intellectual Evolution of Educational Leadership Research: A Combined Bibliometric and Thematic Analysis Using SciMAT
Authors: Karakose Turgut , Leithwood Kenneth , Tülübaş Tijen
Published: April 2024

This study aims to describe the century-long trajectory of educational leadership research (ELR), including changes over time in its main and subsidiary themes, as well as its most influential authors, papers, and journals. The study combines the bibliometric performance and science mapping analysis of 7282 articles retrieved from the Scopus and WoS databases. SciMAT software (version 1.1.04) was used to analyze changes over four sequential time periods and to exhibit the thematic evolution of the field—Period 1 (1907 to 2004), Period 2 (2005 to 2012), Period 3 (2013 to 2019), and Period 4 (2020–2023). Research during Period 1 focused on principals and included efforts to distinguish between their administrative functions and forms of ‘strong’ leadership contributing to school improvement. Period 2 included research aimed at understanding what strong principal leadership entailed, including the development and testing of more coherent models of such leadership. While instructional and transformational leadership models were prominent during Periods 1 and 2, Period 3 research invested heavily in conceptions of leadership distribution. Early research about ‘social justice leadership’ appeared during this period and eventually flourished during Period 4. While principals were an active focus through all Periods, the leadership of others gradually dominated ELR and accounted for the broader leadership theme found in all four periods. The results point to the evolutionary nature of ELR development, which eventually produced a relatively robust knowledge base. Experiences with the COVID-19 pandemic suggest that crises such as this might prompt more revolutionary orientations in the ELR field.
Leveraging Neighbor Attention Initialization (NAI) for Efficient Training of Pretrained LLMs
Authors: Tan Qiao , Zhang Jingjing
Published: April 2024

In the realm of pretrained language models (PLMs), the exponential increase in computational resources and time required for training as model sizes expand presents a significant challenge. This paper proposes an innovative approach named neighbor attention initialization (NAI) to expedite the training process of larger PLMs by leveraging smaller PLMs through parameter initialization. Our methodology hinges on the hypothesis that smaller PLMs, having already learned fundamental language structures and patterns, can provide a robust foundational knowledge base for larger models, which is called function preserving. Specifically, we present a comprehensive framework detailing the process of transferring learned features on transformer-based language models mainly using the neighbor attention head and neighbor layer. We conducted experiments in GPT-2 and demonstrated that our method yields considerable savings in training costs compared to standard approaches, including learning from scratch and bert2BERT, indicating a notable improvement in training efficiency for large PLMs.
A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
Authors: Zhao Feng , Cheng Ruimin , Li Chunhai , Su Zhaoyu , Liang Guoling , Yang Changsong
Published: April 2024

Blockchain technology provides a reliable information access environment for the Internet of Vehicles, but the high latency and complex computing consensus mechanism in blockchain make it difficult to port to onboard devices. Recently, there are many methods to reduce the time cost of consensus by optimizing node grouping or reducing redundant calculations, but this would lower the security level of the blockchain. To address these issues and reduce the adverse effects of frequently changing channel quality on consensus results, a consensus mechanism based on vehicle comprehensive state factors for nodes selection (PoMS) is proposed. Firstly, the vehicle nodes utilize the machine learning model to predict local driving parameters and broadcast the predicted results to the other nodes. Secondly, each node uses interactive data to calculate the state values, and the leader comprehensively evaluates the nodes participating in the consensus and selects the nodes as relays. Finally, we also adopted a double-layer blockchain structure to accelerate the selection process of relay nodes. In order to verify the performance of the proposed consensus algorithm, we conducted tests on transmission time and communication quality. The experimental results show that compared to traditional consensus mechanisms, the algorithm proposed in this paper can reduce time overhead by an average of 12.7% and maintain a good transmission rates under a certain number of malicious nodes.
Lightweight UAV Object-Detection Method Based on Efficient Multidimensional Global Feature Adaptive Fusion and Knowledge Distillation
Authors: Sun Jian , Gao Hongwei , Yan Zhiwen , Qi Xiangjing , Yu Jiahui , Ju Zhaojie
Published: April 2024

Unmanned aerial vehicles (UAVs) equipped with remote-sensing object-detection devices are increasingly employed across diverse domains. However, the detection of small, densely-packed objects against complex backgrounds and at various scales presents a formidable challenge to conventional detection algorithms, exacerbated by the computational constraints of UAV-embedded systems that necessitate a delicate balance between detection speed and accuracy. To address these issues, this paper proposes the Efficient Multidimensional Global Feature Adaptive Fusion Network (MGFAFNET), an innovative detection method for UAV platforms. The novelties of our approach are threefold: Firstly, we introduce the Dual-Branch Multidimensional Aggregation Backbone Network (DBMA), an efficient architectural innovation that captures multidimensional global spatial interactions, significantly enhancing feature distinguishability for complex and occluded targets. Simultaneously, it reduces the computational burden typically associated with processing high-resolution imagery. Secondly, we construct the Dynamic Spatial Perception Feature Fusion Network (DSPF), which is tailored specifically to accommodate the notable scale variances encountered during UAV operation. By implementing a multi-layer dynamic spatial fusion coupled with feature-refinement modules, the network adeptly minimizes informational redundancy, leading to more efficient feature representation. Finally, our novel Localized Compensation Dual-Mask Distillation (LCDD) strategy is devised to adeptly translate the rich local and global features from the higher-capacity teacher network to the more resource-constrained student network, capturing both low-level spatial details and high-level semantic cues with unprecedented efficacy. The practicability and superior performance of our MGFAFNET are corroborated by a dedicated UAV detection platform, showcasing remarkable improvements over state-of-the-art object-detection methods, as demonstrated by rigorous evaluations conducted using the VisDrone2021 benchmark and a meticulously assembled proprietary dataset.