Circulating tumor DNA (ctDNA) holds promise as a biomarker for predicting clinical responses to therapy in solid tumors, and multiple ctDNA assays are in development. However, the heterogeneity in ctDNA levels prior to treatment (baseline) across different cancer types and stages and across ctDNA assays has not been widely studied. Friends of Cancer Research formed a collaboration across multiple commercial ctDNA assay developers to assess baseline ctDNA levels across five cancer types in early- and late-stage disease. This retrospective study included eight commercial ctDNA assay developers providing summary-level de-identified data for patients with non-small cell lung cancer (NSCLC), bladder, breast, prostate, and head and neck squamous cell carcinoma following a common analysis protocol. Baseline ctDNA levels across late-stage cancer types were similarly detected, highlighting the potential use of ctDNA as a biomarker in these cancer types. Variability was observed in ctDNA levels across assays in early-stage NSCLC, indicative of the contribution of assay analytical performance and methodology on variability. We identified key data elements, including assay characteristics and clinicopathological metadata, that need to be standardized for future meta-analyses across multiple assays. This work facilitates evidence generation opportunities to support the use of ctDNA as a biomarker for clinical response. Full article

In recent decades, information security has become increasingly valued, including many aspects of privacy protection, copyright protection, and digital forensics. Therefore, many data hiding schemes have been proposed and applied to various carriers such as text, images, audio, and videos. Vector Quantization (VQ) compression is a well-known method for compressing images. In previous research, most methods related to VQ compressed images have focused on hiding information in index tables, while only a few of the latest studies have explored embedding data in codebooks. We propose a data hiding scheme for VQ codebooks. With our approach, a sender XORs most of the pixel values in a codebook and then applies a threshold to control data embedding. The auxiliary information generated during this process is embedded alongside secret data in the index reordering phase. Upon receiving the stego codebook and the reordered index table, the recipient can extract the data and reconstruct the VQ-compressed image using the reverse process. Experimental results demonstrate that our scheme significantly improves embedding capacity compared to the most recent codebook-based methods. Specifically, we observe an improvement rate of 223.66% in a small codebook of size 64 and an improvement rate of 85.19% in a codebook of size 1024. Full article

To provide a theoretical basis for the application of ALA in pear production, the effects of exogenous 5-aminolevulinic acid (ALA) treatment on leaf photosynthetic gas exchange parameters, chlorophyll fast fluorescence properties, and relative expression of the related genes were investigated using pear (Pyrus pyrifolia Nakai cv. ‘Whasan’) as a material in the study. The results show that exogenous ALA treatment improved the photosynthetic gas exchange parameters of pear leaves, upregulated the expression of multiple key genes which are related to ALA biosynthesis, metabolism, and transformation into chlorophylls. GUS staining in tobacco leaves showed that exogenous ALA activated the promoter activity of PypHEMA and PypCHLH genes, implying that the synthesis of endogenous ALA and chlorophylls was promoted by exogenous ALA. Furthermore, ALA promoted the expression of the genes encoding photosystem II (PSII) reaction center proteins, such as core protein D1, inner light-harvesting pigment proteins CP43 and CP47, and cytochrome b559. This led to increased PSII reaction center activity. In addition, ALA alleviated the donor side oxygen-evolving complex inhibition and reduced the closure rate on the receptor side, allowing for increased photochemical electron transfer and reduced heat dissipation while improving the photosynthetic performance index PI_abs and PI_total. The findings of this study contribute to a better understanding of ALA’s promotion of plant photosynthetic efficiency, providing valuable insights for further research and potential applications in pear production. Full article

Sunstone is a member of the feldspar group. Natural sunstones from Oregon exhibit unique optical effects and hold significant market value. However, since 2008, there has been a persistent issue of diffused red feldspars masquerading as natural sunstones in the market, severely undermining consumer confidence in purchasing natural sunstones. Fluorescence characteristics under 305–335 nm ultraviolet excitation are considered an effective method for distinguishing copper-diffused red feldspars from natural sunstones. In this paper, through detailed analysis and testing of ten market-acquired red and green feldspar samples, including UV-vis spectra, microscopic characteristics, fluorescence spectra, and chemical compositions, we validate the efficacy of fluorescence characteristics in identifying copper-diffused feldspars. The results verify the widespread prevalence of copper diffusion treatment in market-acquired red and green feldspars, shedding light on their treatment history and providing valuable insights for jewelry consumers. This research not only enhances our understanding of sunstone treatments but also strengthens the reliability and applicability of fluorescence spectroscopy in gemstone identification, offering promising prospects for its broader adoption in the jewelry market. Full article

Phytophthora sojae is a type of pathogenic oomycete that causes Phytophthora root stem rot (PRSR), which can seriously affect the soybean yield and quality. To subvert immunity, P. sojae secretes a large quantity of effectors. However, the molecular mechanisms regulated by most P. sojae effectors, and their host targets remain unexplored. Previous studies have shown that the expression of PsAvh113, an effector secreted by Phytophthora sojae, enhances viral RNA accumulations and symptoms in Nicotiana benthamiana via VIVE assay. In this study, we analyzed RNA-sequencing data based on disease symptoms in N. benthamiana leaves that were either mocked or infiltrated with PVX carrying the empty vector (EV) and PsAvh113. We identified 1769 differentially expressed genes (DEGs) dependent on PsAvh113. Using stricter criteria screening and Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis of DEGs, we found that 38 genes were closely enriched in response to PsAvh113 expression. We selected three genes of N. benthamiana (NbNAC86, NbMyb4, and NbERF114) and found their transcriptional levels significantly upregulated in N. benthamiana infected with PVX carrying PsAvh113. Furthermore, individual silencing of these three genes promoted P. capsici infection, while their overexpression increased resistance to P. capsici in N. benthamiana. Our results show that PsAvh113 interacts with transcription factors NbMyb4 and NbERF114 in vivo. Collectively, these data may help us understand the pathogenic mechanism of effectors and manage PRSR in soybeans. Full article

Due to the increasing development of highway reconstruction and expansion projects in China, many large-span highway tunnels are being constructed near existing highway tunnels. Tunneling underneath will inevitably cause variation in the surrounding rock displacement and may even lead to collapse. In this study, based on an analysis of extensive field monitoring data from the Gucheng tunnel, the variation law for the surrounding rock full-displacement and the influence of the tunnel-face spatial effect in a large-span mudstone tunnel are analyzed. The change in the full displacement experienced the following sequence: slow pre-displacement growth → rapid increase → slow increase → gradual stability. The displacement released by the excavation of the tunnel construction accounts for 40~60% of the total displacement, and the closer to the excavation contour, the more obvious the displacement release. The final convergence value of vertical displacement is obtained by hyperbolic function regression prediction analysis. Based on this value, Lee and Hoek equations are used for parameter analysis and field-data fitting. It is concluded that the larger the proportion of the early displacement of the surrounding rock before construction to the total displacement, the smaller the influence of the tunnel-face spatial effect on the surrounding rock. The numerical simulation results are compared with actual monitoring results, and good agreement is observed. The larger the burial depth of the tunnel, the smaller the influence range in the tunnel-face spatial effect, and the more concentrated the displacement release. The variation law and the influential range for the surrounding rock full-displacement described in this paper can provide a reference for predicting and controlling the deformation during the construction of future large-span mudstone tunnels. Full article

Drug candidates must undergo thermal evaluation as early as possible in the preclinical phase of drug development because undesirable changes in their structure and physicochemical properties may result in decreased pharmacological activity or enhanced toxicity. Hence, the detailed evaluation of nitrogen-rich heterocyclic esters as potential drug candidates, i.e., imidazolidinoannelated triazinylformic acid ethyl esters 1–3 (where R₁ = 4–CH₃ or 4–OCH₃ or 4–Cl, and R₂ = –COOC₂H₅) and imidazolidinoannelated triazinylacetic acid methyl esters 4–6 (where R₁ = 4–CH₃ or 4–OCH₃ or 4–Cl, and R₂ = –CH₂COOCH₃)—in terms of their melting points, melting enthalpy values, thermal stabilities, pyrolysis, and oxidative decomposition course—has been carried out, using the simultaneous thermal analysis methods (TG/DTG/DSC) coupled with spectroscopic techniques (FTIR and QMS). It was found that the melting process (documented as one sharp peak related to the solid–liquid phase transition) of the investigated esters proceeded without their thermal decomposition. It was confirmed that the melting points of the tested compounds increased in relation to R₁ and R₂ as follows: 2 (R₁ = 4–OCH₃; R₂ = –COOC₂H₅) < 6 (R₁ = 4–Cl; R₂ = –CH₂COOCH₃) < 5 (R₁ = 4–OCH₃; R₂ = –CH₂COOCH₃) < 3 (R₁ = 4–Cl; R₂ = –COOC₂H₅) < 1 (R₁ = 4–CH₃; R₂ = –COOC₂H₅) < 4 (R₁ = 4–CH₃; R₂ = –CH₂COOCH₃). All polynitrogenated heterocyclic esters proved to be thermally stable up to 250 °C in inert and oxidising conditions, although 1–3 were characterised by higher thermal stability compared to 4–6. The results confirmed that both the pyrolysis and the oxidative decomposition of heterocyclic ethyl formates/methyl acetates with para-substitutions at the phenyl moiety proceed according to the radical mechanism. In inert conditions, the pyrolysis process of the studied molecules occurred with the homolytic breaking of the C–C, C–N, and C–O bonds. This led to the emission of alcohol (ethanol in the case of 1–3 or methanol in the case of 4–6), NH₃, HCN, HNCO, aldehydes, CO₂, CH₄, HCl, aromatics, and H₂O. In turn, in the presence of air, cleavage of the C–C, C–N, and C–O bonds connected with some oxidation and combustion processes took place. This led to the emission of the corresponding alcohol depending on the analysed class of heterocyclic esters, NH₃, HCN, HNCO, aldehydes, N₂, NO/NO₂, CO, CO₂, HCl, aromatics, and H₂O. Additionally, after some biological tests, it was proven that all nitrogen-rich heterocyclic esters—as potential drug candidates—are safe for erythrocytes, and some of them are able to protect red blood cells from oxidative stress-induced damage. Full article

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model’s efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology. Full article

While there has been recent attention in the scientific community on the ethical and welfare implications of different dog training methods, less research has investigated what methods and training tools United States dog guardians use, where they obtain information about dog training, and the reasons they give for their choices of method. We conducted two surveys with nearly 800 Arizona State University undergraduate students to gain a more realistic look into how dog guardians in the United States train their dogs and where they are receiving their training information. Only 5% of respondents reported utilizing a trainer when they had concerns regarding their dog’s behavior; 60% would ask a friend or family member or seek advice online. Few reported taking their dog to any training classes; 70% reported either training the dog themselves or not implementing any formal training. When asked general questions, most respondents reported using rewards-based methods but, when asked about specific problem behaviors, 57% of respondents noted that they would use auditory or physical corrections. Respondents who trained with rewards-based methods reported that these methods of training were more effective significantly more frequently than those who trained with mixed methods reported that those methods were most effective (Fisher’s Exact Test, p < 0.01). Full article

Traumatic experiences can have long-lasting negative effects on individuals, organizations, and societies. If trauma is not addressed, it can create unsafe cultures with constant arousal, untrusting relationships, and the use of coercive measures. Trauma-informed care (TIC) can play a central role in mitigating these negative consequences, but it is unknown how and in which way(s) TIC should be implemented. Our objective was to conduct a scoping review that systematically explored and mapped research conducted in this area and to identify existing knowledge about the implementation of TIC. The search was conducted on the CINAHL, Cochrane, Embase, ERIC, Medline, PsycINFO, and Web of Science databases, and more than 3000 empirical papers, published between 2000 and 2022, were identified. Following further screening, we included 157 papers in our review, which were mainly from the USA, Australia, New Zealand, and Canada, focusing on study settings, methodologies, and definitions of TIC, as well as the types of interventions and measures used. This review shows that TIC is a complex and multifaceted framework, with no overarching structure or clear theoretical underpinnings that can guide practical implementations. TIC has been defined and adapted in varied ways across different settings and populations, making it difficult to synthesize knowledge. A higher level of agreement on how to operationalize and implement TIC in international research could be important in order to better examine its impact and broaden the approach. Full article

This paper presents a novel wideband circularly polarized millimeter-wave (mmWave) hemispherical dielectric resonator antenna (HDRA). Two distinct configurations of alumina dielectric resonator antennas (DRAs) are investigated, each featuring a different coating: the first configuration incorporates a polyimide layer, while the second involves a perforated alumina. Both configurations demonstrate promising characteristics, including impedance and axial ratio (AR) bandwidths of 58% and 17.7%, respectively, alongside a maximum gain of 10 dBic at 28 GHz. Leveraging additive manufacturing technology, the HDRA with the perforated coating layer is fabricated, simplifying assembly and eliminating potential air gaps between layers, thereby enhancing the overall performance. This innovative approach yields a circularly polarized (CP) HDRA suitable for Beyond 5G (B5G) communication systems. Agreement between measurements and simulations validates the efficacy of the proposed design, affirming its potential in practical applications. Full article

Mesoscale eddies are common in global oceans, playing crucial roles in ocean dynamics, ocean circulation, and heat transport, and their vertical structures can affect the water layers from tens to thousands of meters. In this study, we integrated sea surface height and sea surface temperature data into deep learning methods to study the mesoscale eddy subsurface temperature structure and to explore the relationship between sea surface data and eddy subsurface layers. In this study, we introduce Dual_EddyNet, a deep learning algorithm designed to invert the subsurface temperature structure of mesoscale eddies. Using this algorithm, we explore the impact of the sea surface height and sea surface temperature on the subsurface temperature structure inversion of mesoscale eddies. Furthermore, we compare different data fusion strategies, namely single-stream neural networks and dual-stream neural networks, to validate the effectiveness of the dual-stream model. To capture the interrelations among surface data and integrate feature information across various dimensions, we introduce the Triplet Attention Mechanism. The experimental results demonstrate that the proposed Dual_EddyNet performs well in reconstructing the three-dimensional structure of mesoscale eddies in the South China Sea (within a depth of 1000 m), with an inversion accuracy of 91.44% for cyclonic eddies and 95.25% for anticyclonic eddies. This algorithm provides a new method for inverting the subsurface temperatures of mesoscale eddies, and can not only be directly deployed in systems, embedded in ship moving platforms, etc., but can also provide a data reference for assimilations and numerical simulations, demonstrating its rich application potential. Full article

Background: The biomechanical analysis of spine and postural misalignments is important for surgical and non-surgical treatment of spinal pain. We investigated the examiner reliability of sagittal cervical alignment variables compared to the reliability and concurrent validity of computer vision algorithms used in the PostureRay^® software 2024. Methods: A retrospective database of 254 lateral cervical radiographs of patients between the ages of 11 and 86 is studied. The radiographs include clearly visualized C1–C7 vertebrae that were evaluated by a human using the software. To evaluate examiner reliability and the concurrent validity of the trained CNN performance, two blinded trials of radiographic digitization were performed by an extensively trained expert user (US) clinician with a two-week interval between trials. Then, the same clinician used the trained CNN twice to reproduce the same measures within a 2-week interval on the same 254 radiographs. Measured variables included segmental angles as relative rotation angles (RRA) C1–C7, Cobb angles C2–C7, relative segmental translations (RT) C1–C7, anterior translation C2–C7, and absolute rotation angle (ARA) C2–C7. Data were remotely extracted from the examiner’s PostureRay^® system for data collection and sorted based on gender and stratification of degenerative changes. Reliability was assessed via intra-class correlations (ICC), root mean squared error (RMSE), and R² values. Results: In comparing repeated measures of the CNN network to itself, perfect reliability was found for the ICC (1.0), RMSE (0), and R² (1). The reliability of the trained expert US was in the excellent range for all variables, where 12/18 variables had ICCs ≥ 0.9 and 6/18 variables were 0.84 ≤ ICCs ≤ 0.89. Similarly, for the expert US, all R² values were in the excellent range (R² ≥ 0.7), and all RMSEs were small, being 0.42 ≤ RMSEs ≤ 3.27. Construct validity between the expert US and the CNN network was found to be in the excellent range with 18/18 ICCs in the excellent range (ICCs ≥ 0.8), 16/18 R² values in the strong to excellent range (R² ≥ 0.7), and 2/18 in the good to moderate range (R² RT C6/C7 = 0.57 and R² Cobb C6/C7 = 0.64. The RMSEs for expert US vs. the CNN network were small, being 0.37 ≤ RMSEs ≤ 2.89. Conclusions: A comparison of repeated measures within the computer vision CNN network and expert human found exceptional reliability and excellent construct validity when comparing the computer vision to the human observer. Full article

Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending hospital stays and escalating healthcare costs. Given that this disorder is frequently underestimated in vulnerable populations, there is an urgent need for enhanced diagnostic and therapeutic strategies. Traditional diagnostic tools such as the videofluoroscopic swallowing study (VFSS) and flexible endoscopic evaluation of swallowing (FEES) require interpretation by clinical experts and may lead to complications. In contrast, non-invasive sensors offer a more comfortable and convenient approach for assessing swallowing function. This review systematically examines recent advancements in non-invasive swallowing function detection devices, focusing on the validation of the device designs and their implementation in clinical practice. Moreover, this review discusses the swallowing process and the associated biomechanics, providing a theoretical foundation for the technologies discussed. It is hoped that this comprehensive overview will facilitate a paradigm shift in swallowing assessments, steering the development of technologies towards more accessible and accurate diagnostic tools, thereby improving patient care and treatment outcomes. Full article

Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO₂ sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized. Full article

The importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection. Full article

This paper introduces a genetic algorithm designed to optimize the sizing of a hybrid solar–wind microgrid connected to the main electric grid in Chile, serving a simulated town of 2000 houses. The goal is to promote sustainable development by using renewable energy sources (RES) to supply a small village. The model, considering local meteorological conditions, aggregated load, and Chilean electrical regulations, establishes the optimal number of photovoltaic modules and wind turbine generators and allows for the monitoring of the microgrid’s operation, whose operating strategy is proposed herein. Adhering to Chilean regulations, a maximum exporting power of 9 MW is analyzed, with no restrictions on importing power, which is needed when the renewable resources are not enough to meet the demand. The optimization algorithm, aimed at sizing the RES supply, identified an optimal solution composed of 5 photovoltaic modules of 500 Wp each (2.5 kWp in total) and 123 wind turbines of 100 kW each (total of 12,300 kW), meeting around 85% of the demand through renewable generation. Due to time mismatches between generation and load patterns, the remaining energy was imported. The project’s net present value is approximately EUR 25 M, with a levelized cost of energy at 37 EUR/MWh. A comparison with HOMER grid simulations validates the efficacy of the developed model. Full article

Tuberous sclerosis complex (TSC) presents with renal cysts and benign tumors, which eventually lead to kidney failure. The factors promoting kidney cyst formation in TSC are poorly understood. Inactivation of carbonic anhydrase 2 (Car2) significantly reduced, whereas, deletion of Foxi1 completely abrogated the cyst burden in Tsc1 KO mice. In these studies, we contrasted the ontogeny of cyst burden in Tsc1/Car2 dKO mice vs. Tsc1/Foxi1 dKO mice. Compared to Tsc1 KO, the Tsc1/Car2 dKO mice showed few small cysts at 47 days of age. However, by 110 days, the kidneys showed frequent and large cysts with overwhelming numbers of A-intercalated cells in their linings. The magnitude of cyst burden in Tsc1/Car2 dKO mice correlated with the expression levels of Foxi1 and was proportional to mTORC1 activation. This is in stark contrast to Tsc1/Foxi1 dKO mice, which showed a remarkable absence of kidney cysts at both 47 and 110 days of age. RNA-seq data pointed to profound upregulation of Foxi1 and kidney-collecting duct-specific H⁺-ATPase subunits in 110-day-old Tsc1/Car2 dKO mice. We conclude that Car2 inactivation temporarily decreases the kidney cyst burden in Tsc1 KO mice but the cysts increase with advancing age, along with enhanced Foxi1 expression. Full article

An incremental deep deterministic policy gradient (IDDPG) algorithm is devised for the trajectory tracking control of a four-wing variable sweep (FWVS) aircraft with uncertainty. The IDDPG algorithm employs the line-of-sight (LOS) method for path tracking, formulates a reward function based on position and attitude errors, and integrates long short-term memory (LSTM) units into IDDPG algorithm to enhance its adaptability to environmental changes during flight. Finally, environmental disturbance factors are introduced in simulation to validate the designed controller’s ability to track climbing trajectories of morphing aircraft in the presence of uncertainty. Full article

Object detection plays a crucial role in autonomous driving assistance systems. It requires high accuracy for prediction, a small size for deployment on mobile devices, and real-time inference speed to ensure safety. In this paper, we present a compact and efficient algorithm called YOLOX with United Attention Head (UAH-YOLOX) for detection in autonomous driving scenarios. By replacing the backbone network with GhostNet for feature extraction, the model reduces the number of parameters and computational complexity. By adding a united attention head before the YOLO head, the model effectively detects the scale, position, and contour features of targets. In particular, an attention module called Spatial Self-Attention is designed to extract spatial location information, demonstrating great potential in detection. In our network, the IOU Loss (Intersection of Union) has been replaced with CIOU Loss (Complete Intersection of Union). Further experiments demonstrate the effectiveness of our proposed methods on the BDD100k dataset and the Caltech Pedestrian dataset. UAH-YOLOX achieves state-of-the-art results by improving the detection accuracy of the BDD100k dataset by 1.70% and increasing processing speed by 3.37 frames per second (FPS). Visualization provides specific examples in various scenarios. Full article

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies. Full article

Herpesvirus entry mediator (HVEM) is a molecular switch that can modulate immune responses against cancer. The significance of HVEM as an immune checkpoint target and a potential prognostic biomarker in malignancies is still controversial. This study aims to determine whether HVEM is an immune checkpoint target with inhibitory effects on anti-tumor CD4⁺ T cell responses in vitro and whether HVEM gene expression is dysregulated in patients with acute lymphocytic leukemia (ALL). HVEM gene expression in tumor cell lines and peripheral blood mononuclear cells (PBMCs) from ALL patients and healthy controls was measured using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Tumor cells were left untreated (control) or were treated with an HVEM blocker before co-culturing with CD4⁺ T cells in vitro in a carboxyfluorescein succinimidyl ester (CFSE)-dependent proliferation assay. HVEM expression was upregulated in the chronic myelogenous leukemia cell line (K562) (FC = 376.3, p = 0.086) compared with normal embryonic kidney cells (Hek293). CD4⁺ T cell proliferation was significantly increased in the HVEM blocker-treated K562 cells (p = 0.0033). Significant HVEM differences were detected in ALL PBMCs compared with the controls, and these were associated with newly diagnosed ALL (p = 0.0011) and relapsed/refractory (p = 0.0051) B cell ALL (p = 0.0039) patients. A significant differentiation between malignant ALL and the controls was observed in a receiver operating characteristic (ROC) curve analysis with AUC = 0.78 ± 0.092 (p = 0.014). These results indicate that HVEM is an inhibitory molecule that may serve as a target for immunotherapy and a potential ALL biomarker. Full article