bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Objective Optimization in Construction Project Management Based on NSGA-III: Pareto Front Development and Decision-Making
Authors: Zhan Zhengjie , Hu Yan , Xia Pan , Ding Junzhi
Published: July 2024

With the continuous improvement of construction management standards, thorough investigation into various management objectives becomes crucial. To address the current gaps in project management concerning time, cost, safety, and carbon emissions interrelationships, this study adopts the multi-objective optimization (MOP) theory and makes the following contributions: (1) proposes an innovative carbon emission model, which enhances the cost model by incorporating carbon cost factors, and establishes a MOP model covering time–cost–safety–carbon emissions; (2) validates the effectiveness of the proposed model and the feasibility of algorithms through empirical research and algorithm comparison analysis—the research demonstrates that NSGA-III exhibits significant advantages in solving MOP problems of this scale; (3) applies the entropy-weighted VIKOR method to objectively analyze the solution set obtained by NSGA-III for optimal solution selection. This study provides practical management tools for project managers and offers significant insights for researchers in construction management regarding multi-objective problem-solving and algorithm selection.
A Reconstruction Methodology of Dynamic Construction Site Activities in 3D Digital Twin Models Based on Camera Information
Authors: He Jingyao , Li Pengfei , An Xuehui , Wang Chengzhi
Published: July 2024

Digital twin technology significantly enhances construction site management efficiency; however, dynamically reconstructing site activities presents a considerable challenge. This study introduces a methodology that leverages camera data for the 3D reconstruction of construction site activities. The methodology was initiated using 3D scanning to meticulously reconstruct the construction scene and dynamic elements, forming a model base. It further integrates deep learning algorithms to precisely identify static and dynamic elements in obstructed environments. An enhanced semi-global block-matching algorithm was then applied to derive depth information from the imagery, facilitating accurate element localization. Finally, a near-real-time projection method was introduced that utilizes the spatial relationships among elements to dynamically incorporate models into a 3D base, enabling a multi-perspective view of site activities. Validated by simulated construction site experiments, this methodology showcased an impressive reconstruction accuracy reaching up to 95%, this underscores its significant potential in enhancing the efficiency of creating a dynamic digital twin model.
Vitacrystallography: Structural Biomarkers of Breast Cancer Obtained by X-ray Scattering
Authors: Denisov Sergey , Blinchevsky Benjamin , Friedman Jonathan , Gerbelli Barbara , Ajeer Ash , Adams Lois , Greenwood Charlene , Rogers Keith , Mourokh Lev , Lazarev Pavel
Published: July 2024

With breast cancer being one of the most widespread causes of death for women, there is an unmet need for its early detection. For this purpose, we propose a non-invasive approach based on X-ray scattering. We measured samples from 107 unique patients provided by the Breast Cancer Now Tissue Biobank, with the total dataset containing 2958 entries. Two different sample-to-detector distances, 2 and 16 cm, were used to access various structural biomarkers at distinct ranges of momentum transfer values. The biomarkers related to lipid metabolism are consistent with those of previous studies. Machine learning analysis based on the Random Forest Classifier demonstrates excellent performance metrics for cancer/non-cancer binary decisions. The best sensitivity and specificity values are 80% and 92%, respectively, for the sample-to-detector distance of 2 cm and 86% and 83% for the sample-to-detector distance of 16 cm.
Glioma-Stem-Cell-Derived Exosomes Remodeled Glioma-Associated Macrophage via NEAT1/miR-125a/STAT3 Pathway
Authors: Pan Tong , Xie Dong-Kun , Li Juan , Qiang Yu-Jie , Fan Song-Yuan , Wang Ting-Ting , Han Yuan-Yuan , Zang Jian , Yang Yang , Zhao Jun-Long , Li San-Zhong , Wu Shuang
Published: July 2024

Glioblastoma (GBM), as the most common primary brain tumor, usually results in an extremely poor prognosis, in which glioma stem cells (GSCs) and their immunosuppressive microenvironment prominently intervene in the resistance to radiotherapy and chemotherapy that directly leads to tumor recurrence and shortened survival time. The specific mechanism through which exosomes generated from GSCs support the creation of an immunosuppressive microenvironment remains unknown, while it is acknowledged to be engaged in intercellular communication and the regulation of the glioma immunosuppressive microenvironment. The elevated expression of LncRNA-NEAT1 was found in glioma cells after radiotherapy, chemotherapy, and DNA damage stimulation, and NEAT1 could promote the malignant biological activities of GSCs. Emerging evidence suggests that lncRNAs may reply to external stimuli or DNA damage by playing a role in modulating different aspects of tumor biology. Our study demonstrated a promotive role of the carried NEAT1 by GSC-derived exosomes in the polarization of M2-like macrophages. Further experiments demonstrated the mediative role of miR-125a and its target gene STAT3 in NEAT1-induced polarization of M2-like macrophages that promote glioma progression. Our findings elucidate the mechanism by which GSCs influence the polarization of M2-like macrophages through exosomes, which may contribute to the formation of immunosuppressive microenvironments. Taken together, our study reveals the miR-125a-STAT3 pathway through which exosomal NEAT1 from treatment-resistant GSCs contributes to M2-like macrophage polarization, indicating the potential of exosomal NEAT1 for treating glioma.
The Influence of Inflammatory and Nutritional Status on the Long-Term Outcomes in Advanced Stage Ovarian Cancer
Authors: Bacalbasa Nicolae , Petrea Sorin , Gaspar Bogdan , Pop Lucian , Varlas Valentin , Hasegan Adrian , Gorecki Gabriel , Martac Cristina , Stoian Marilena , Zgura Anca , Balescu Irina
Published: July 2024

Background: Despite improving surgical techniques and achieving more often complete debulking procedures, certain patients with advanced-stage ovarian cancer still have a very poor prognosis. The aim of the current paper is to investigate whether inflammatory and nutritional status can predict the long-term outcomes of ovarian cancer patients. Methods: A retrospective analysis of 57 cases diagnosed with advanced-stage ovarian cancer submitted to surgery as first intent therapy was carried out. In all cases, the preoperative status was determined by calculating the CRP/albumin ratio, as well as the Glasgow score, the modified Glasgow score and the prognostic nutritional index. Results: Patients presenting higher values of the CRP/albumin ratio, with a higher Glasgow score, modified Glasgow score and prognostic nutritional index (PNI), were more frequently associated with incomplete debulking surgery, a higher peritoneal carcinomatosis index and poorer overall survival (20 months versus 9 months for the CRP/albumin ratio p = 0.011, 42 versus 27 versus 12 months for the Glasgow score p = 0.042, 50 versus 19 versus 12 months for the modified Glasgow score, p = 0.001, and 54 months versus 21 months, p = 0.011 for the prognostic nutritional index). Conclusions: A strong relationship between the nutritional and inflammatory status in advanced-stage ovarian cancer seems to exist.
Point of Care Liquid Biopsy for Cancer Treatment—Early Experience from a Community Center
Authors: Nicholas Champica , Beharry Andrea , Bendzsak Anna M. , Bisson Kassandra R. , Dadson Keith , Dudani Shaan , Iafolla Marco , Irshad Kashif , Perdrizet Kirstin , Raskin William , Singh Raviya , Tsui David Chun Cheong , Wang Xin , Yeung Ching , Cheema Parneet K. , Sheffield Brandon S.
Published: July 2024

Liquid biopsy is rapidly becoming an indispensable tool in the oncologist’s arsenal; however, this technique remains elusive in a publicly funded healthcare system, and real-world evidence is needed to demonstrate utility and feasibility. Here, we describe the first experience of an in-house point of care liquid biopsy program at a Canadian community hospital. A retrospective review of consecutive cases that underwent plasma-based next-generation sequencing (NGS) was conducted. Liquid biopsy was initiated at the discretion of clinicians. Sequencing followed a point of care workflow using the Genexus™ integrated sequencer and the Oncomine precision assay, performed by histotechnologists. Results were reported by the attending pathologist. Eligible charts were reviewed for outcomes of interest, including the intent of the liquid biopsy, results of the liquid biopsy, and turnaround time from blood draw to results available. A total of 124 cases, with confirmed or suspected cancer, underwent liquid biopsy between January 2021 and November 2023. The median turnaround time for liquid biopsy results was 3 business days (range 1–12 days). The sensitivity of liquid biopsies was 71%, compared to tissue testing in cases with matched tissue results available for comparison. Common mutations included EGFR (29%), in 86 lung cancer patients, and PIK3CA (22%), identified in 13 breast cancer patients. Healthcare providers ordered liquid biopsies to inform diagnostic investigations and treatment decisions, and to determine progression or resistance mechanisms, as these reasons often overlapped. This study demonstrates that rapid in-house liquid biopsy using point of care methodology is feasible. The technique facilitates precision treatment and offers many additional advantages for cancer care.
The Multifaceted Role of Neutrophils in NSCLC in the Era of Immune Checkpoint Inhibitors
Authors: Miao Shucheng , Rodriguez Bertha Leticia , Gibbons Don L.
Published: July 2024

Lung cancer is the most common cause of cancer-related death in both males and females in the U.S. and non-small-cell lung cancer (NSCLC) accounts for 85%. Although the use of first- or second-line immune checkpoint inhibitors (ICIs) exhibits remarkable clinical benefits, resistance to ICIs develops over time and dampens the efficacy of ICIs in patients. Tumor-associated neutrophils (TANs) have an important role in modulating the tumor microenvironment (TME) and tumor immune response. The major challenge in the field is to characterize the TANs in NSCLC TME and understand the link between TAN-related immunosuppression with ICI treatment response. In this review, we summarize the current studies of neutrophil interaction with malignant cells, T-cells, and other components in the TME. Ongoing clinical trials are aimed at utilizing reagents that have putative effects on tumor-associated neutrophils, in combination with ICI. Elevated neutrophil populations and neutrophil-associated factors could be potential therapeutic targets to enhance anti-PD1 treatment in NSCLC.
Primary Meningeal Melanocytic Tumors of the Central Nervous System: A Review from the Ultra-Rare Brain Tumors Task Force of the European Network for Rare Cancers (EURACAN)
Authors: Pellerino Alessia , Verdijk Robert M. , Nichelli Lucia , Andratschke Nicolaus H. , Idbaih Ahmed , Goldbrunner Roland
Published: July 2024

Background: Primary meningeal melanocytic tumors are ultra-rare entities with distinct histological and molecular features compared with other melanocytic or pigmented lesions, such as brain and leptomeningeal metastases from metastatic melanoma. Methods: The European Network for Rare Cancers (EURACAN) Task Force on Ultra-Rare Brain Tumors (domain 10, subdomain 10) performed a literature review from January 1985 to December 2023 regarding the epidemiologic and clinical characteristics, histological and molecular features, radiological findings, and efficacy of local treatments (surgery and radiotherapy) and systemic treatments for these entities. Results: Molecular analysis can detect specific mutations, including GNAQ, GNA11, SF3B1, EIF1AX, BAP1, that are typically found in circumscribed primary meningeal melanocytic tumors and not in other melanocytic lesions, whereas NRAS and BRAF mutations are typical for diffuse primary meningeal melanocytic tumors. The neuroimaging of the whole neuroaxis suggests a melanocytic nature of a lesion, depicts its circumscribed or diffuse nature, but cannot predict the tumor’s aggressiveness. Gross-total resection is the first choice in the case of circumscribed meningeal melanocytoma and melanoma; conversely, meningeal biopsy may be reserved for patients with diffuse and multinodular leptomeningeal spread to achieve a definitive diagnosis. High-dose radiotherapy is rarely indicated in diffuse melanocytic tumors except as palliative treatment to alleviate symptoms. Last, a definitive advantage of a specific systemic treatment could not be concluded, as most of the data available derive from case reports or small cohorts. Conclusions: As primary meningeal melanocytic tumors are extremely rare, the correlations between the clinical characteristics, molecular profile, radiological findings at diagnosis and progression are weak, and poor evidence on the best therapeutic approach is available. There is a need to develop shared platforms and registries to capture more knowledge regarding these ultra-rare entities.
Associations of TRAF2 (rs867186), TAB2 (rs237025), IKBKB (rs13278372) Polymorphisms and TRAF2, TAB2, IKBKB Protein Levels with Clinical and Morphological Features of Pituitary Adenomas
Authors: Zaliunas Balys Remigijus , Gedvilaite-Vaicechauskiene Greta , Kriauciuniene Loresa , Tamasauskas Arimantas , Liutkeviciene Rasa
Published: July 2024

Aim: The aim of this study was to determine associations of TRAF2 (rs867186), TAB2 (rs237025), IKBKB (rs13278372) gene polymorphisms and TRAF2, TAB2, IKBKB protein levels with clinical and morphological features of pituitary adenomas (PAs). Methods: This case–control study included 459 individuals divided into two groups: a control group (n = 320) and a group of individuals with PAs (n = 139). DNA from peripheral blood leukocytes was isolated using salt precipitation and column method. Real-time PCR was used for TRAF2 (rs867186), TAB2 (rs237025), and IKBKB (rs13278372) SNP genotyping, and TRAF2, TAB2, IKBKB protein concentration measurements were performed by immunoenzymatic analysis tests using a commercial ELISA kit according to the manufacturer’s recommendations. The labeling index Ki-67 was determined by immunohistochemical analysis using a monoclonal antibody (clone SP6; Spring Bioscience Corporation). Statistical data analysis was performed using the programs "IMB SPSS Statistics 29.0". Results: We found significant differences in TRAF2 (rs867186) genotypes (AA, AG, GG) between groups: 79.1%, 17.3%, 3.6% vs. 55.3%, 20.9%, 23.8% (p < 0.001). The G allele was less frequent in the PA group than in controls (12.2% vs. 34.2%, p < 0.001). The AG and GG genotypes reduced PA occurrence by 1.74-fold and 9.43-fold, respectively, compared to AA (p < 0.001). In the dominant model, GG and AG genotypes reduced PA odds by 3.07-fold, while in the recessive model, the GG genotype reduced PA odds by 8.33-fold (p < 0.001). Each G allele decreased PA odds by 2.49-fold in the additive model (p < 0.001). Microadenomas had significant genotype differences compared to controls: 81.3%, 18.8%, 0.0% vs. 55.3%, 20.9%, 23.8% (p < 0.001), with the G allele being less frequent (9.4% vs. 34.2%, p < 0.001). In macroadenomas, genotype differences were 78%, 16.5%, 5.5% vs. 55.3%, 20.9%, 23.8% (p < 0.001), and the G allele was less common (13.7% vs. 34.2%, p < 0.001). The dominant model showed that GG and AG genotypes reduced microadenoma odds by 3.5-fold (p = 0.001), and each G allele reduced microadenoma odds by 3.1-fold (p < 0.001). For macroadenomas, the GG genotype reduced odds by 6.1-fold in the codominant model (p < 0.001) and by 2.9-fold in GG and AG genotypes combined compared to AA (p < 0.001). The recessive model indicated the GG genotype reduced macroadenoma odds by 5.3-fold (p < 0.001), and each G allele reduced odds by 2.2-fold in the additive model (p < 0.001). Conclusions: The TRAF2 (rs867186) G allele and GG genotype are significantly associated with reduced odds of pituitary adenomas, including both microadenomas and macroadenomas, compared to the AA genotype. These findings suggest a protective role of the G allele against the occurrence of these tumors.
Safe Space for Dialogue—A Practice for Connected Consciousness and Compassion
Authors: Briciu Bianca , Michel Sergio , Chavez Rosario
Published: July 2024

This paper analyzes Safe Space for Dialogue as a group practice for accessing connected consciousness through the safe expression of emotional experience, empathic listening, and compassionate witnessing. It highlights the importance of connected consciousness to overcome architectures of separation that breed fear, isolation, domination, and instrumentalization of relationships. Connected consciousness is an intersubjective, relational experience that makes possible connection, compassion, and empathic and generative dialogue. This article offers an outline of the main qualities of connected consciousness—safety, empathy and compassion, attunement, and resonance; it analyzes how the practice of Safe Space for Dialogue develops expanded awareness and compassion, supporting participants’ access to the connected consciousness.
Mechanism of DAPK1 for Regulating Cancer Stem Cells in Thyroid Cancer
Authors: You Mi-Hyeon
Published: July 2024

Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine (Ser/Thr) protein kinase and is characteristically downregulated in metastatic cancer. Several studies showed that DAPK1 is involved in both the early and late stages of cancer. DAPK1 downregulation is elaborately controlled by epigenetic, transcriptional, posttranscriptional, and posttranslational processes. DAPK1 is known to regulate not only cancer cells but also stromal cells. Recent studies showed that DAPK1 was involved not only in tumor suppression but also in epithelial-mesenchymal transition (EMT) and cancer stem cell (CSC) formation in colon and thyroid cancers. CSCs are major factors in determining cancer aggressiveness in cancer metastasis and treatment prognosis by influencing EMT. However, the molecular mechanism involved in the regulation of cancer cells by DAPK1 remains unclear. In particular, little is known about the existence of CSCs and how they are regulated in papillary thyroid carcinoma (PTC) among thyroid cancers. In this review, we describe the molecular mechanism of CSC regulation by DAPK1 in PTC progression.
Mechanobiology in Metabolic Dysfunction-Associated Steatotic Liver Disease and Obesity
Authors: Rudolph Emily L. , Chin LiKang
Published: July 2024

With the ongoing obesity epidemic, the prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) is expected to rise and necessitates a greater understanding of how the disease proceeds from benign excess lipid in hepatocytes to liver fibrosis and eventually to liver cancer. MASLD is caused, at least in part, by hepatocytes’ storage of free fatty acids (FAs) that dysfunctional adipocytes are no longer able to store, and therefore, MASLD is a disease that involves both the liver and adipose tissues. The disease progression is not only facilitated by biochemical signals, but also by mechanical cues such as the increase in stiffness often seen with fibrotic fatty livers. The change in stiffness and accumulation of excess lipid droplets impact the ability of a cell to mechanosense and mechanotranduce, which perpetuates the disease. A mechanosensitive protein that is largely unexplored and could serve as a potential therapeutic target is the intermediate filament vimentin. In this review, we briefly summarize the recent research on hepatocyte and adipocyte mechanobiology and provide a synopsis of studies on the varied, and sometimes contradictory, roles of vimentin. This review is intended to benefit and encourage future studies on hepatocyte and adipocyte mechanobiology in the context of MASLD and obesity.
Isolation of a Virulent Clostridium perfringens Strain from Elaphurus davidianus and Characterization by Whole-Genome Sequence Analysis
Authors: Zhang Zhao , Wang Xiao , Li Siyuan , Fu Yuhang , Li Yan , Nawaz Shah , Chen Jing , Yang Guoxiang , Li Jiakui , Shi Daoliang
Published: July 2024

Clostridium perfringens (C. perfringens) is an important veterinary pathogen and a noteworthy threat to human and animal health. Recently, there has been a significant rise in the number of moose fatalities caused by this rare, endemic species in China. Currently, there is an increasing trend in conducting whole-genome analysis of C. perfringens strains originating from pigs and chickens, whereas fewer studies have been undertaken on Elaphurus davidianus-originating strains at the whole-genome level. Our laboratory has identified and isolated five C. perfringens type A from affected Elaphurus davidianus. The current study identified the most potent strain of C. perfringens, which originated from Elaphurus davidianus, and sequenced its genome to reveal virulence genes and pathogenicity. Our findings show that strain CX1-4 exhibits the highest levels of phospholipase activity, hemolytic activity, and mouse toxicity compared to the other four isolated C. perfringens type A strains. The chromosome sequence length of the CX1-4 strain was found to be 3,355,389 bp by complete genome sequencing. The current study unveils the genomic characteristics of C. perfringens type A originating from Elaphurus davidianus. It provides a core foundation for further investigation regarding the prevention and treatment of such infectious diseases in Elaphurus davidianus.
Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer
Authors: Jeong Yeonuk , Chu Jinah , Kang Juwon , Baek Seungjun , Lee Jae-Hak , Jung Dong-Sub , Kim Won-Woo , Kim Yi-Rang , Kang Jihoon , Do In-Gu
Published: July 2024

Identifying the primary site of origin of metastatic cancer is vital for guiding treatment decisions, especially for patients with cancer of unknown primary (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and is responsible for a considerable number of cancer-related fatalities. Understanding its origin is crucial for effective management and potentially improving patient outcomes. This study introduces a machine learning framework, ONCOfind-AI, that leverages transcriptome-based gene set features to enhance the accuracy of predicting the origin of metastatic cancers. We demonstrate its potential to facilitate the integration of RNA sequencing and microarray data by using gene set scores for characterization of transcriptome profiles generated from different platforms. Integrating data from different platforms resulted in improved accuracy of machine learning models for predicting cancer origins. We validated our method using external data from clinical samples collected through the Kangbuk Samsung Medical Center and Gene Expression Omnibus. The external validation results demonstrate a top-1 accuracy ranging from 0.80 to 0.86, with a top-2 accuracy of 0.90. This study highlights that incorporating biological knowledge through curated gene sets can help to merge gene expression data from different platforms, thereby enhancing the compatibility needed to develop more effective machine learning prediction models.
Effect of Solid/Liquid and Eutectic Front Velocities on Microstructure Evolution in Al-20%Cu Alloys
Authors: Abdallah Alaaldeen , Roósz András , Rónaföldi Arnold , Veres Zsolt
Published: July 2024

During the solidification process, microstructures are affected by the experimental conditions, the thermophysical characteristics of the alloy, and the type of grain-refining particles. Unidirectional solidification experiments were performed in a vertical Bridgman-type furnace to investigate the effect of the solidification front velocity on the solidified microstructure of a non-refined and refined Al-20%Cu alloy. The samples were solidified by rapidly increasing the sample velocity (v) range from 0.02 mm/s to 0.2 mm/s while maintaining an almost constant temperature gradient (~5 K/mm). As a result, despite changes in the solid/liquid front velocity along the sample, the microstructure of the non-refined alloys remained columnar. In the refined alloy, the columnar structure changed into an equiaxed structure at two different front velocities.
Taxonomic and Functional Diversity of Reptiles in a Heterogeneous Landscape of Jalisco State, West-Central Mexico
Authors: Rosas-Espinoza Verónica Carolina , Rodríguez-Zaragoza Fabián Alejandro , Álvarez-Grzybowska Eliza , Peña-Joya Karen Elizabeth , Santiago-Pérez Ana Luisa , Godoy-González Arquímedes Alfredo , Huerta-Martínez Francisco Martín
Published: July 2024

Reptiles are threatened by anthropogenic landscape transformation, largely due to agriculture. However, ecosystems nowadays constitute a matrix of fragmented landscapes. We analyzed a heterogeneous landscape’s reptile taxonomic and functional diversity patterns across ten land cover/use types in Jalisco state, in west-central Mexico. At the alpha diversity level, we assessed the taxonomic diversity using q-order indices, and functional diversity with multidimensional indices (FRic, FDiv, and FEve) by land cover/use. We evaluated the differences in species composition among land cover/use types (beta diversity). We utilized multidimensional distance-based analyses (dbRDA) to evaluate the association between reptile diversity, habitat structure, and environmental variables. Species richness did not correlate with functional richness across land cover types overall, except for riparian habitat surrounded by crops (RH-C), which exhibited higher species richness and functional diversity. Secondary vegetation surrounded by temperate forest (SV-TF), riparian habitat surrounded by tropical dry forest (RH-TDF), and RH-C were the land cover/use types with the most functional groups. Herbaceous cover is crucial for preserving both reptile diversity facets in this landscape. These findings suggest that the availability of resources (e.g., riparian habitat and herbaceous cover) regardless of perturbation level could be more relevant for reptile diversity than the condition (tropical vs. temperate) due to the high plasticity and adaptation of the group. It is essential to recognize the ecological value of these habitats by adopting a holistic approach that values the intrinsic and ecological importance of reptile diversity.
Determination of the Course of Cyan Fluorescence of Pseudomonas aeruginosa with a Handheld Bacterial Imaging Device
Authors: Pham Emily , Reynolds-Reber Landrye , Navarro Stephany , Hamood Abdul , Jones-Donaldson Laura M. , Smith Allie Clinton
Published: July 2024

Chronic wound infections are of clinical concern as they often lead to high rates of mortality and morbidity. A point-of-care handheld bacterial fluorescence imaging has been designed to detect the auto-fluorescent characteristics of most clinically relevant species of bacteria. This device causes most species of bacteria to exhibit red fluorescence due to the production of exoproduct porphyrins. One of the most significant contributors to the pathogenicity of chronic wounds is the pathogen Pseudomonas aeruginosa, and interestingly, this organism exhibits an additional unique cyan fluorescence signature. There is an over 90% positive predictive value that, when a chronic wound exhibits cyan fluorescence with the bacterial fluorescence imaging device, the wound will harbor P. aeruginosa. This project seeks to understand what genetic factor(s) contribute to the cyan phenotype observed.
Purtscher Retinopathy Resulting from a Car Crash Accident—Multimodal Imaging Value
Authors: Rotuski Grzegorz , Bojarska Magdalena , Patyk Michał , Różycki Radosław , Gołębiewska Joanna
Published: July 2024

Purtscher retinopathy is a rare but severe sight-threatening eye condition that mostly occurs in middle-aged men after chest compression or head injury. In cases such as acute pancreatitis, connective tissue disorders, kidney failure or COVID-19 infection with similar ocular findings but no history of trauma, a diagnosis of Purtscher-like retinopathy is made. We present a case of a 72-year-old female with typical symptoms of Purtscher retinopathy in both eyes after a car crash accident. Although the pathophysiology of the disease is not fully understood, the main cause of Purtscher retinopathy seems to be an embolic occlusion of the precapillary arterioles which supply the superficial peripapillary capillaries. Activation of the C5a component of the complement predisposes the leukocytes to aggregation, which obstructs blood flow. The main symptom of Purtscher retinopathy is sudden, painless deterioration of vision which occurs up to 48 h after the injury. In most patients, the changes observed in the fundus of the eye resolve within several months, and visual acuity slowly improves, sometimes even returning to the state from before the injury. However, risk factors such as older age, high hyperopia, and late treatment implementation can make the prognosis less favorable.
Ultrasonographic Assessment of the Diaphragm
Authors: Itagaki Taiga , Akimoto Yusuke , Takashima Takuya , Oto Jun
Published: July 2024

Mechanical ventilation injures not only the lungs but also the diaphragm, resulting in dysfunction associated with poor outcomes. Diaphragm ultrasonography is a noninvasive, cost-effective, and reproducible diagnostic method used to monitor the condition and function of the diaphragm. With advances in ultrasound technology and the expansion of its clinical applications, diaphragm ultrasonography has become increasingly important as a tool to visualize and quantify diaphragmatic morphology and function across multiple medical specialties, including pulmonology, critical care, and rehabilitation medicine. This comprehensive review aims to provide an in-depth analysis of the role and limitations of ultrasonography in assessing the diaphragm, especially among critically ill patients. Furthermore, we discuss a recently published expert consensus and provide a perspective for the future.
A Comparison Study: Possible Bias in Troponin I Measurement Obtained with a Point of Care Testing and a Central Laboratory Analyzers Employing Different Biological Matrices and Anticoagulants
Authors: Sammartano Antonino , Buonocore Ruggero , Fiorini Roberto , Dieci Elisabetta , Di Franco Anna , Di Stasi Bruna , Tortorella Giovanni , Ippolito Luigi
Published: July 2024

Background: high-sensitive cardiac TroponinI (hs-cTnI) is widely used for diagnosis of acute coronary syndromes. The latest recommendation for hs-cTnI determination is the protocol 0–1 h finalized to improve the rule out accuracy of the test. A Point of Care Testing able to guarantee these performances could be very useful due to reducing the turnaround time and ruling out patients suspected of ACS, especially by using biological matrices that are not required for centrifuge. The aim of our work is to compare the results for hs-cTnI obtained using different biological matrices and anticoagulants, obtained between Atellica® VTLi hs-cTnI POCT and Access AccuTnI+3 DxI800 performances, in order to establish a possible bias derived directly from these pre-analytical conditions. Methods: Li-heparinized pool samples were primary employ for hs-cTnI with Atellica® VTLi as whole blood, then centrifuged and tested on Atellica® VTLi and DxI800. K3EDTA pool samples were centrifuged and measured on DxI800 too. A comparison of methods was performed according to CLSI_EP-09A2 protocol. Constant and proportional errors were investigated with Deming regression. Bias between methods was evaluated with the Bland Altman test. Results: comparing whole blood lithium heparin results obtained with Atellica versus lithium heparin and K3EDTA plasma tested on DxI 800, the Deming regression revealed a proportional error, whereas in both cases Bland Altman highlighted a minimal underestimation. A similar performance was revealed when considering plasma lithium heparin tested on Atellica versus lithium heparin and K3EDTA plasma obtained with DxI800, confirming the same underestimation. Considering values close to the cut off, no significant differences were found. Conclusions: in the laboratory, the estimation of the bias of two different analyzers is pivotal. Once more this is crucial when different biological matrices and anticoagulants are employed for the analysis. Our study demonstrates that no significant differences among the two matrices are present when comparing Atellica and DxI800 performances.
A Lightweight Neural Network for the Real-Time Dehazing of Tidal Flat UAV Images Using a Contrastive Learning Strategy
Authors: Yang Denghao , Zhu Zhiyu , Ge Huilin , Qiu Haiyang , Wang Hui , Xu Cheng
Published: July 2024

In the maritime environment, particularly within tidal flats, the frequent occurrence of sea fog significantly impairs the quality of images captured by unmanned aerial vehicles (UAVs). This degradation manifests as a loss of detail, diminished contrast, and altered color profiles, which directly impact the accuracy and effectiveness of the monitoring data and result in delays in the execution and response speed of monitoring tasks. Traditional physics-based dehazing algorithms have limitations in terms of detail recovery and color restoration, while neural network algorithms are limited in their real-time application on devices with constrained resources due to their model size. To address the above challenges, in the following study, an advanced dehazing algorithm specifically designed for images captured by UAVs over tidal flats is introduced. The algorithm integrates dense convolutional blocks to enhance feature propagation while significantly reducing the number of network parameters, thereby improving the timeliness of the dehazing process. Additionally, an attention mechanism is introduced to assign variable weights to individual channels and pixels, enhancing the network’s ability to perform detail processing. Furthermore, inspired by contrastive learning, the algorithm employs a hybrid loss function that combines mean squared error loss with contrastive regularization. This function plays a crucial role in enhancing the contrast and color saturation of the dehazed images. Our experimental results indicate that, compared to existing methods, the proposed algorithm has a model parameter size of only 0.005 M and a latency of 0.523 ms. When applied to the real tidal flat image dataset, the algorithm achieved a peak signal-to-noise ratio (PSNR) improvement of 2.75 and a mean squared error (MSE) reduction of 9.72. During qualitative analysis, the algorithm generated high-quality dehazing results, characterized by a natural enhancement in color saturation and contrast. These findings confirm that the algorithm performs exceptionally well in real-time fog removal from UAV-captured tidal flat images, enabling the effective and timely monitoring of these environments.
Gasoline Engine Misfire Fault Diagnosis Method Based on Improved YOLOv8
Authors: Li Zhichen , Qin Zhao , Luo Weiping , Ling Xiujun
Published: July 2024

In order to realize the online diagnosis and prediction of gasoline engine fire faults, this paper proposes an improved misfire fault detection algorithm model based on YOLOv8 for sound signals of gasoline engines. The improvement involves substituting a C2f module in the YOLOv8 backbone network by a BiFormer attention module and another C2f module substituted by a CBAM module that combines channel and spatial attention mechanisms which enhance the neural network’s capacity to extract the complex features. The normal and misfire sound signals of a gasoline engine are processed by wavelet transformation and converted to time–frequency images for the training, verification, and testing of convolutional neural network. The experimental results show that the precision of the improved YOLOv8 algorithm model is 99.71% for gasoline engine fire fault tests, which is 2 percentage points higher than for the YOLOv8 network model. The diagnosis time of each sound is less than 100 ms, making it suitable for developing IoT devices for gasoline engine misfire fault diagnosis and driverless vehicles.
MLBSNet: Mutual Learning and Boosting Segmentation Network for RGB-D Salient Object Detection
Authors: Xia Chenxing , Wang Jingjing , Ge Bing
Published: July 2024

RGB-D saliency object detection (SOD) primarily segments the most salient objects from a given scene by fusing RGB images and depth maps. Due to the inherent noise in the original depth map, fusion failures may occur, leading to performance bottlenecks. To address this issue, this paper proposes a mutual learning and boosting segmentation network (MLBSNet) for RGB-D saliency object detection, which consists of a deep optimization module (DOM), a semantic alignment module (SAM), a cross-modal integration (CMI) module, and a separate reconstruct decoder (SRD). Specifically, the deep optimization module aims to obtain optimal depth information by learning the similarity between the original and predicted depth maps. To eliminate the uncertainty of single-modal neighboring features and capture the complementary features of multiple modalities, a semantic alignment module and a cross-modal integration module are introduced. Finally, a separate reconstruct decoder based on a multi-source feature integration mechanism is constructed to overcome the accuracy loss caused by segmentation. Through comparative experiments, our method outperforms 13 existing methods on five RGB-D datasets and achieves excellent performance on four evaluation metrics.
Ship Detection in SAR Images Based on Steady CFAR Detector and Knowledge-Oriented GBDT Classifier
Authors: Sun Shuqi , Wang Junfeng
Published: July 2024

Ship detection is a significant issue in remote sensing based on Synthetic Aperture Radar (SAR). This paper combines the advantages of a steady constant false alarm rate (CFAR) detector and a knowledge-oriented Gradient Boosting Decision Tree (GBDT) classifier to achieve the location and the classification of ship candidates. The steady CFAR detector smooths the image by a moving-average filter and models the probability distribution of the smoothed clutter as a Gaussian distribution. The mean and the standard deviation of the Gaussian distribution are estimated according to the left half of the histogram to remove the effect of land, ships, and other targets. From the Gaussian distribution and a preset constant false alarm rate, a threshold is obtained to segment land, ships, and other targets from the clutter. Then, a series of morphological operations are introduced to eliminate land and extract ships and other targets, and an active contour algorithm is utilized to refine ships and other targets. Finally, ships are recognized from other targets by a knowledge-oriented GBDT classifier. Based on the brain-like ship-recognition process, we change the way of the decision-tree generation and achieve a higher classification performance than the original GBDT. The results on the AIRSARShip-1.0 dataset demonstrate that this scheme has a competitive performance against deep learning, especially in the detection of offshore ships.
An Improved Large Planar Point Cloud Registration Algorithm
Authors: Geng Haocheng , Song Ping , Zhang Wuyang
Published: July 2024

The traditional Iterative Closest Point (ICP) algorithm often suffers from low computational accuracy and efficiency in certain scenarios. It is highly sensitive to the initial pose, has a poor ability to resist interference, and frequently becomes trapped in local optima. Extracting feature points accurately from partially overlapping points with weak three-dimensional features, such as smooth planes or surfaces with low curvature, is challenging using only the traditional ICP algorithm for registration. This research introduces a “First Rough then Precise” registration strategy. Initially, the target position is extracted in complex environments using an improved clustering method, which simultaneously reduces the impact of environmental factors and noise on registration accuracy. Subsequently, an improved method for calculating normal vectors is applied to the Fast Point Feature Histogram (FPFH) to extract feature points, providing data for the Sample Consistency Initial Algorithm (SAC-IA). Lastly, an improved ICP algorithm, which has strong anti-interference capabilities for partially overlapping point clouds, is utilized to merge such point clouds. In the experimental section, we validate the feasibility and precision of the proposed algorithm by comparing its registration outcomes with those of various algorithms, using both standard point cloud dataset models and actual point clouds obtained from camera captures.
An Online Review Data-Driven Fuzzy Large-Scale Group Decision-Making Method Based on Dual Fine-Tuning
Authors: Yuan Xuechan , Xu Tingyu , He Shiqi , Zhang Chao
Published: July 2024

Large-scale group decision-making (LSGDM) involves aggregating the opinions of participating decision-makers into collective opinions and selecting optimal solutions, addressing challenges such as a large number of participants, significant scale, and a low consensus. In real-world scenarios of LSGDM, various challenges are often encountered due to factors such as fuzzy uncertainties in decision information, the large size of decision groups, and the diverse backgrounds of participants. This paper introduces a dual fine-tuning-based LSGDM method using an online review. Initially, the sentiment analysis is conducted on online review data, and the identified sentiment words are graded and quantified into a fuzzy data set to understand the emotional tendencies of the text. Then, the Louvain algorithm is used to cluster the decision-makers. Meanwhile, a method combining Euclidean distances with Wasserstein distances is introduced to accurately measure data similarities and improve clustering performances. During the consensus-reaching process (CRP), a two-stage approach is employed to adjust the scores: to begin with, by refining the scores of the decision representatives via minor-scale group adjustments to generate a score matrix. Then, by identifying the scores corresponding to the minimum consensus level in the matrix for adjustment. Subsequently, the final adjusted score matrix is integrated with the prospect–regret theory to derive the comprehensive brand scores and rankings. Ultimately, the practicality and efficiency of the proposed model are demonstrated using a case study focused on the purchase of solar lamps. In summary, not only does the model effectively extract the online review data and enhance decision efficiency via clustering, but the dual fine-tuning mechanism in the model to improve consensus attainment also reduces the number of adjustment rounds and avoids multiple cycles without achieving the consensus.
A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data
Authors: Yang Dan , Yang Shuya , Qu Junsuo , Wang Ke
Published: July 2024

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10−3, 3.03 × 10−3, and 30.66 × 10−3, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction.
Influence of Slope Aspect and Position on Xylem Formation Dynamics in Subtropical Chinese Fir Plantations
Authors: Huang Yingni , Li Qianlin , Bai Chunmei , Zhao Wendi , Rodríguez-Hernández Diego Ismael , Guo Xiali
Published: July 2024

Recent studies on the intra-annual dynamics of trees were mainly focused on mature trees in natural forests; however, less is known about how topography (e.g., slope’s position and aspect) shape the intra-annual xylem formation dynamics of young trees in plantation forests. We monitored intra-annual xylem formation dynamics of 6-year-old Chinese fir (Cunninghamia lanceolata (Lamb.)) trees across two different aspects (northeast and southwest) and three different positions (upper, middle, and lower) of the slope in a planted forest in subtropical China. We found that the intra-annual xylem formation of trees on the northeast slope aspect (176.98 ± 34.52 cells) was significantly higher than that on the southwest slope aspect (140.19 ± 36.07 cells) due to the higher growth rate (0.67 ± 0.11 cells/day vs. 0.53 ± 0.10 cells/day). In the middle slope position, xylem formation (187.89 ± 19.81 cells) was also significantly higher than that of the upper (147.35 ± 29.08 cells) and lower slope positions (140.51 ± 48.36 cells), resulting from higher growth rate rather than longer growing season length. Our study demonstrated that intra-annual xylem formation dynamics of Chinese fir were altered by different topographic features and therefore encourage the implementation of management strategies that consider different slope aspects and positions to maximize forest productivity.
Habitat Suitability Evaluation and Ecological Corridor Construction for Asian Elephants: The Case of Jiangcheng, a New Range for Elephants in Southwestern China
Authors: Zhang Lanzhong , Li Churui , Yue Cairong , Luo Hongbin , Li Xin , Yu Qiongfen , Li Jia , Shen Jian , Yang Song , Chen Fei
Published: July 2024

In recent years, the northward migration of elephant herds in China’s Yunnan Province has attracted unprecedented public attention to the conservation of Asian elephants, with habitat fragmentation and human disturbance thought to be key factors. In this study, we used Jiangcheng, a new distribution site for Asian elephants in southwest China, as an example, combined the available remote sensing and monitoring data with the MaxEnt3.4.1 model, to analyze the factors affecting the activities of Asian elephants under the conditions of human disturbance and habitat degradation. The Least Cumulative Resistance model was utilized to construct the potential ecological dispersal corridors, and the key corridors were identified through the gravity model to explore the ecological security pattern of the habitat of Asian elephants in Jiangcheng County. The results show that the habitat of Asian elephants in Jiangcheng County is fragmented, showing a north–south strip distribution, never moving to the northeast. The existing suitable habitat is located in the low-elevation area (<1500 m), which is close to water sources and roads, and there is no nature reserve in this area. The land is mainly occupied by scrub or grassland near mountainous forests, but part of it is also under cultivation, which leads to conflicts between humans and elephants occur frequently. There are 14 ecological source areas, which are mainly distributed in the two regions of Zhengdong and Kangping, and there are 92 ecological corridors, of which 3 are important corridors and 89 are general corridors. This study comprehensively analyzes the current status and connectivity of Asian elephant habitat in Jiangcheng County at the regional scale, which helps to optimize the pattern of suitable habitat, promotes the dispersal of Asian elephants and habitat connectivity, and provides realistic guidance and basic information for the conservation planning of isolated populations of this species and their habitats.