Search Results (41)

This study aimed to ferment protein-rich amaranth flour with different strains of lactic acid bacteria (LAB) and to analyse the fermented dough’s functional properties. The fermented dough analysis was conducted using titrimetric, spectrophotometric, and chromatographic methods. The antioxidant activity of the fermented doughs was evaluated using the DPPH (2,2-Diphenyl-1-picrylhydrazyl) and ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) methods, finding ABTS radical scavenging values ranging from 26.00 ± 1.05% to 58.92 ± 6.05%, while the DPPH values ranged from 21.29 ± 0.83% to 28.24 ± 5.48%. By RP-HPLC (Reversed Phase-High Performance Liquid Chromatography) characterisation, several phenolic acids and flavonoids were identified and quantified. Among these compounds, epigallocatechin was the most abundant, with the highest concentration recorded at 7789.88 ± 17.0 ng/µL in the control sample. This was followed by a 6942.47 ± 5.632 ng/µL concentration in the dough fermented with Lacticaseibacillus rhamnosus MIUG BL38 strain and 4983.16 ± 7.29 ng/µL in the dough fermented with Lactiplantibacillus pentosus MIUG BL24 strain. These two LAB strains (Lc. rhamnosus MIUG BL38 and Lp. pentosus MIUG BL24), with probiotic properties previously demonstrated, were selected based on their acidification potential, antioxidant activity, and bioactivity for future optimisation studies. Lactic acid fermentation significantly enhances bioactive characteristics of the amaranth flour, enabling the design of diverse gluten-free products with increased functional properties based on the attributes induced by the prebiotic, probiotic and postbiotic contents (tribiotics). Full article

Ship collisions pose a significant threat to maritime safety, especially in congested precautionary areas with high vessel traffic density. Traditional collision risk assessment methods, such as distance to closest point of approach (DCPA) and time to closest point of approach (TCPA), often overlook environmental uncertainties and variations in human response. To address these limitations, this study proposes a novel approach for collision risk assessment using automatic identification system (AIS) data. AIS data from vessels in precautionary areas are resampled to synchronize their temporal frameworks, enabling the systematic identification of ship encounters. Each encounter is analyzed by evaluating critical parameters, including the minimum ship encounter distance (MSED), relative azimuth angles, and trajectories, within a customized ship domain model that incorporates vessel characteristics such as ship length and course. Key metrics, such as intrusion depth and time, are calculated based on vessels’ entry and exit points during each encounter. A set of collision risk indices, which integrates both intrusion depth and time, is introduced, with particular emphasis on intrusion depth due to its heightened sensitivity to proximity danger and constrained maneuvering space. An extensive analysis of vessel interactions in the precautionary area establishes a holistic collision risk index. A case study using AIS data from Ningbo–Zhoushan Port, involving a dataset of 1000 ship encounters, demonstrates the effectiveness of the proposed method. Specifically, the holistic collision risk in the No.2 precautionary area is 0.456, while the No.3 precautionary area shows a risk value of 0.443. These results confirm the effectiveness and feasibility of the proposed method for evaluating and classifying collision risks, offering a more precise and reliable framework for collision risk assessment in complex navigational environments. Full article

Non-O1, non-O139 Vibrio cholerae (NOVC) extraintestinal infections are rare, but recently, several clinical incidents have been reported worldwide. Toxigenic V. cholerae is a well-known etiological agent of cholera, responsible for acute dehydrating watery diarrhea. Outbreaks occur in an epidemic seasonal pattern, particularly in countries with poverty and poor sanitation. Strains of NOVC are usually not involved in causing the epidemic or pandemic outbreaks seen with potential strains of V. cholerae serogroup O1 and O139. However, they can still cause severe sporadic cases of intestinal as well as extraintestinal infections. In this study, we investigated a case of extraintestinal infections associated with the NOVC serogroup isolated from a deep closed wound abscess. The isolate was screened for the presence of three major virulence genes, toxR, ctxA, and tcpA. The strain tested positive for the toxR gene encoding the regulatory protein and cholera toxin (ctx) gene and tested negative for the toxin-coregulated pilus (TCP) gene, which is essential for the colonization of the human intestine, causing the severe diarrheal disease cholera. To the best of our knowledge, this is the first case of extraintestinal infection caused by toxigenic Vibrio cholerae non-O1/non-O139 in a hospitalized patient in Saudi Arabia. Full article

T cells play diverse roles in cancer immunology, acting as tumor suppressors, cytotoxic effectors, enhancers of cytotoxic T lymphocyte responses and immune suppressors; providing memory and surveillance; modulating the tumor microenvironment (TME); or activating innate immune cells. However, cancer cells can disrupt T cell function, leading to T cell exhaustion and a weakened immune response against the tumor. The expression of exhausted T cell (Tex) markers plays a pivotal role in shaping the immune landscape of multiple cancers. Our aim was to systematically investigate the role of known T cell exhaustion (Tex) markers across multiple cancers while exploring their molecular interactions, mutation profiles, and potential implications for immunotherapy. The mRNA expression profile of six Tex markers, LAG-3, PDCD1, TIGIT, HAVCR2, CXCL13, and LAYN was investigated in pan-cancer. Utilizing data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), The Cancer Proteome Atlas (TCPA), and other repositories, we characterized the differential expression of the Tex markers, their association with the patients’ survival outcome, and their mutation profile in multiple cancers. Additionally, we analyzed the effects on cancer-related pathways and immune infiltration within the TME, offering valuable insights into mechanisms of cancer immune evasion and progression. Finally, the correlation between their expression and sensitivity to multiple anti-cancer drugs was investigated extensively. Differential expression of all six markers was significantly associated with KIRC and poor prognosis in several cancers. They also played a potential activating role in apoptosis, EMT, and hormone ER pathways, as well as a potential inhibitory role in the DNA damage response and RTK oncogenic pathways. Infiltration of different immune cells was also found to be associated with the expression of the Tex-related genes in most cancer types. These findings underline that the reviving of exhausted T cells can be used to enhance the efficacy of immunotherapy in cancer patients. Full article

Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. The model consists of multiple parallel temporal correlation perceptual attention (TCPA) modules, enabling us to extract features at different timescales and identify dependencies among features across various timescales. Our simulations show that MSTC-PANet significantly improves the detection of channel occupancy at low signal-to-noise ratios (SNR), particularly in untrained scenarios with lower SNR conditions and modulation uncertainties. The analysis of the ROC curve indicates that at an SNR of -20 dB, the proposed MSTC-PANet achieves a detection rate of 98% with a false alarm rate of 10%. Furthermore, MSTC-PANet, which has been trained using digital modulation techniques, also demonstrates applicability to analog modulation. Full article

Intelligent ships are a key focus for the future development of maritime transportation, relying on efficient decision-making and autonomous control within complex environments. To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. The study specifically targets the extraction and modeling of critical events, including variations in ship speed, course changes, vessel encounters, and port entries and exits. To evaluate the urgency of encounters, mathematical algorithms are applied to the Distance to Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA) metrics. Furthermore, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to identify suitable docking berths. Additionally, multi-source meteorological data are integrated with ship dynamic data, providing a more comprehensive representation of the maritime environment. The resulting knowledge system effectively combines ship attributes, navigational status, event relationships, and environmental factors, thereby offering a robust framework for supporting intelligent ship operations. Full article

In multi-vessel collision avoidance decision-making, the collision between merchant and fishing vessels is a significant challenge. This paper proposes a fuzzy fusion method for making avoidance decisions under the influence of the navigation environment. First, C-means clustering was used to collect and analyze Automatic Identification System (AIS) data from fishing vessels. On this basis, the environment collision risk was determined using fuzzy reasoning. Second, the basic collision risk is obtained by calculating the DCPA and TCPA, and the integrated Collision Risk Index (CRI) is concluded by fuzzy logic through basic collision risk and the environment collision risk. The similar cases are extracted from the fuzzy case database, and collision avoidance decisions for merchant vessels are formulated following fuzzy adjustments. Finally, to validate the method, data from Chengshantou coastal waters is employed for verification. The results show that it can provide theoretical guidance and practical value for merchant vessels in making collision avoidance decisions. Full article

Thyroid carcinoma (THCA) ranks among the most prevalent cancers globally. Integrating advanced genomic and proteomic analyses to construct a protein-based prognostic model promises to identify effective biomarkers and explore new therapeutic avenues. In this study, proteomic data from The Cancer Proteomics Atlas (TCPA) and clinical data from The Cancer Genome Atlas (TCGA) were utilized. Using Kaplan–Meier, Cox regression, and LASSO penalized Cox analyses, we developed a prognostic risk model comprising 13 proteins (S100A4, PAI1, IGFBP2, RICTOR, B7-H3, COLLAGENVI, PAR, SNAIL, FAK, Connexin-43, Rheb, EVI1, and P90RSK_pT359S363). The protein prognostic model was validated as an independent predictor of survival time in THCA patients, based on risk curves, survival analysis, receiver operating characteristic curves and independent prognostic analysis. Additionally, we explored the immune cell infiltration and tumor mutational burden (TMB) related to these features. Notably, our study proved a novel approach for predicting treatment responses in THCA patients, including those undergoing chemotherapy and targeted therapy. Full article

The accurate timing for collision avoidance actions is crucial for preventing maritime collisions. Traditional methods often rely on collision risk assessments, using quantitative indicators like the Distance to the Closest Point of Approach (DCPA) and the Time to the Closest Point of Approach (TCPA). Ship Officers on Watch (OOWs) are required to execute avoidance maneuvers once these indicators reach or exceed preset safety thresholds. However, the effectiveness of these indicators is limited by uncertainties in the maritime environment and the human behaviors of OOWs. To address these limitations, this study introduces a machine learning method to learn collision avoidance behavior from empirical data of ship collision avoidance, particularly in cross-encounter situations. The research utilizes Automatic Identification System (AIS) data from the open waters around Ningbo Zhoushan Port. After data preprocessing and applying spatio-temporal constraints, this study identifies ship trajectory pairs in crossing scenarios and calculates their relative motion parameters. The Douglas–Peucker algorithm is used to identify the timing of ship collision avoidance actions and a collision avoidance decision dataset is constructed. The Random Forest algorithm was then used to analyze the factors affecting the timing of collision avoidance, and six key factors were identified: the distance, relative speed, relative bearing, DCPA, TCPA, and the ratio of the lengths of the giving-way and stand-on ships. These factors serve as inputs for the XGBoost algorithm model, which is enhanced with Particle Swarm Optimization (PSO), and thus constructing a ship collision avoidance decision model. In addition, considering the inherent errors in any model and the dynamic nature of the ship collision avoidance process, an action time window for collision avoidance is introduced, which provides a more flexible time range for ships to make timely collision avoidance responses based on actual conditions and the specific encounter environment. This model provides OOWs with accurate timing for taking collision avoidance decisions. Case studies have validated the practicality and effectiveness of this model, offering new theoretical foundations and practical guidance for maritime collision avoidance. Full article

SET-domain containing 2 (SETD2) is a histone methyltransferase and an epigenetic modifier with oncogenic functionality. In the current study, we investigated the potential prognostic role of SETD2 in prostate cancer. A cohort of 202 patients’ samples was assembled on tissue microarrays (TMAs) containing incidental, advanced, and castrate-resistant CRPCa cases. Our data showed significant elevated SETD2 expression in advanced and castrate-resistant disease (CRPCa) compared to incidental cases (2.53 ± 0.58 and 2.21 ± 0.63 vs. 1.9 ± 0.68; p < 0.001, respectively). Interestingly, the mean intensity of SETD2 expression in deceased vs. alive patients was also significantly different (2.31 ± 0.66 vs. 2 ± 0.68; p = 0.003, respectively). Overall, high SETD2 expression was found to be considered high risk and was significantly associated with poor prognosis and worse overall survival (OS) (HR 1.80; 95% CI: 1.28–2.53, p = 0.001) and lower cause specific survival (CSS) (HR 3.14; 95% CI: 1.94–5.08, p < 0.0001). Moreover, combining high-intensity SETD2 with PTEN loss resulted in lower OS (HR 2.12; 95% CI: 1.22–3.69, p = 0.008) and unfavorable CSS (HR 3.74; 95% CI: 1.67–8.34, p = 0.001). Additionally, high SETD2 intensity with ERG positive expression showed worse prognosis for both OS (HR 1.99, 95% CI 0.87–4.59; p = 0.015) and CSS (HR 2.14, 95% CI 0.98–4.68, p = 0.058). We also investigated the protein expression database TCPA, and our results showed that high SETD2 expression is associated with a poor prognosis. Finally, we performed TCGA PRAD gene set enrichment analysis (GSEA) data for SETD2 overexpression, and our data revealed a potential association with pathways involved in tumor progression such as the AMPK signaling pathway, the cAMP signaling pathway, and the PI3K-Akt signaling pathway, which are potentially associated with tumor progression, chemoresistance, and a poor prognosis. Full article

While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: “phone scams”, “cybersecurity”, “identity theft”, and “business data breach”. By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches. Full article

This study aimed to determine the incidence and virulence factor profiling of Vibrio species from hospital wastewater (HWW) and community wastewater effluents. Wastewater samples from selected sites were collected, processed, and analysed presumptively by the culture dependent methods and molecular techniques. A total of 270 isolates were confirmed as Vibrio genus delineating into V. cholerae (27%), V. parahaemolyticus (9.1%), V. vulnificus (4.1%), and V. fluvialis (3%). The remainder (>50%) may account for other Vibrio species not identified in the study. The four Vibrio species were isolated from secondary hospital wastewater effluent (SHWE), while V. cholerae was the sole specie isolated from Limbede community wastewater effluent (LCWE) and none of the four Vibrio species was recovered from tertiary hospital wastewater effluent (THWE). However, several virulence genes were identified among V. cholerae isolates from SHWE: ToxR (88%), hylA (81%), tcpA (64%), VPI (58%), ctx (44%), and ompU (34%). Virulence genes factors among V. cholerae isolates from LCWE were: ToxR (78%), ctx (67%), tcpA (44%), and hylA (44%). Two different genes (vfh and hupO) were identified in all confirmed V. fluvialis isolates. Among V. vulnificus, vcgA (50%) and vcgB (67%) were detected. In V. parahaemolyticus, tdh (56%) and tlh (100%) were also identified. This finding reveals that the studied aquatic niches pose serious potential health risk with Vibrio species harbouring virulence signatures. The distribution of virulence genes is valuable for ecological site quality, as well as epidemiological marker in the control and management of diseases caused by Vibrio species. Regular monitoring of HWW and communal wastewater effluent would allow relevant establishments to forecast, detect, and mitigate any public health threats in advance. Full article

Ship collisions are a major maritime accident; various systems have been proposed to prevent them. Through investigating and analyzing the causes of maritime accidents, it has been established that ship collisions can either caused by delaying actions or not taking the sufficient actions to avoid them. Recognizing the limitations in providing quantitative numerical values for avoiding ship collisions, this study aimed to use Bayesian regularized artificial neural networks (BRANNs) to suggest the proper time and sufficient actions required for ship collision avoidance consistent with the Convention on the International Regulations for Preventing Collisions at Sea. We prepared the data by calculating the proper times and sufficient actions based on precedent research and used them to train, validate, and assess the BRANNs. Subsequently, an artificial neural network controller was designed and proposed. The data of the proposed neural network controller were verified via simulation, validating the controller. This study is limited in cases such as overtaking a ship in front. However, it is expected that this controller can be improved by establishing the criteria for an appropriate overtaking distance after further examining the closest point of approach (CPA) and time to the CPA (TCPA) for overtaking a ship in front and using the method presented herein. Full article

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies. Full article

Four pathogenic Vibrio species were isolated from three diseased black rockfish Sebastes schlegeli in Yantai, Shandong Province, China. The strains were identified based on physiological and biochemical characteristics and 16S rDNA sequencing and named SF-2, SF-3, SF-5, and SF-6, respectively. SF-2 was Vibrio scophthalmi, SF-3 was V. harveyi, SF-5 was V. alginolyticus, and SF-6 was V. parahaemolyticus. This is the first time that V. scophthalmi was isolated from black rockfish. The present research shows that V. scophthalmi is a potential pathogen. Detection of virulence genes using polymerase chain reaction showed that SF-3, SF-5, and SF-6 carried FlaB; SF-5 and SF-6 carried TcpA; and SF-2, SF-5, and SF-6 carried ToxS. Tdh, Trh, Tlh, ToxR, and Zot were not detected. SF-3, SF-5, and SF-6 all had protease, gelatinase, lipase, and lecithinase. They were all intermediately sensitive to erythromycin, whereas SF-2, SF-5, and SF-6 were sensitive to spectinomycin, and SF-3 was sensitive to cotrimoxazole and chloramphenicol. They were resistant to most antibiotics and multidrug resistance was obvious. Full article