Scientific Community Research Articles

Effect of Mo Content on the Structural, Mechanical, and Tribological Properties of New Zr-Nb-Mo Alloys Obtained by Combining Powder Metallurgy and Vacuum Arc Melting Methods.

Considering the high demand for innovative solutions in medicine, a major increase in interest in biomaterials research has been noticed, with the most significant advancements in metals and their alloys. Titanium-based alloys are one of the most recognised in the scientific community but do not represent the only way to achieve optimal results. Zirconium alloys for medical applications are a novelty with significant research potential based on their outstanding properties, which may be of value for medicine. The aim of the present study was to obtain new biomedical Zr-Nb-Mo alloys with varying ratios of their respective elements-Zr and Mo-using combined powder metallurgy (PM) and arc melting (VAM) methods. The obtained samples underwent microstructure analysis using an optical microscope (OM) and a scanning electron microscope (SEM). The study of element distribution was conducted with energy dispersive spectroscopy (EDS), whereas the phase composition was determined using X-ray diffraction (XRD). Mechanical properties were examined with a Micro Combi Tester MCT3, whereas tribological properties were assessed with a TRN Tribometer, and Ringer's solution was used as a lubricant. Additionally, the wear tracks of the studied samples were observed using the SEM. The research results indicated that increased Mo content conduced to microstructure refinement and homogeneity. Furthermore, the higher content of this element contributed to the growth of the HVIT, HIT, and EIT parameters, together with the improvement in the tribological performance of the alloys. XRD analysis revealed that the obtained samples were multiphase, and raising the Mo addition promoted the formation of new phases, including a ternary phase-Zr0.9Nb0.66Mo1.44 (Fd3¯m). The chemical composition study showed uneven distribution of niobium and areas of uneven mutual distribution of zirconium and molybdenum.

Automated Method for Intracranial Aneurysm Classification Using Deep Learning.

Intracranial aneurysm (IA) is now a common term closely associated with subarachnoid hemorrhage. IA is the bulging of a blood vessel caused by a weakening of its wall. This bulge can rupture and, in most cases, cause internal bleeding. In most cases, internal bleeding leads to death or other fatal consequences. Therefore, the development of an automated system for detecting IA is needed to help physicians make more accurate diagnoses. For this reason, we have focused on this problem. In this paper, we propose a 2D Convolutional Neural Network (CNN) based on a network commonly used for data classification in medicine. In addition to our proposed network, we also tested ResNet 50, ResNet 101 and ResNet 152 on a publicly available dataset. In this case, ResNet 152 achieved better results than our proposed network, but our network was significantly smaller and the classifications took significantly less time. Our proposed network achieved an overall accuracy of 98%. This result was achieved on a dataset consisting of 611 images. In addition to the mentioned networks, we also experimented with the VGG network, but it was not suitable for this type of data and achieved only 20%. We compare the results in this work with neural networks that have been verified by the scientific community, and we believe that the results obtained by us can help in the creation of an automated system for the detection of IA.

The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review

Marine pollution, a major disturbance to the sustainable use of oceans, is becoming more prevalent around the world. Multidimensional and sustainable ocean governance have become increasingly focused on managing, reducing, and eliminating marine pollution. Artificial intelligence has been used more and more in recent years to monitor and control marine pollution. This systematic literature review, encompassing studies from the Web of Science and Scopus databases, delineates the extensive role of artificial intelligence in marine pollution management, revealing a significant surge in research and application. This review aims to provide information and a better understanding of the application of artificial intelligence in marine pollution. In marine pollution, 57% of AI applications are used for monitoring, 24% for management, and 19% for prediction. Three areas are emphasized: (1) detecting and responding to oil pollution, (2) monitoring water quality and its practical application, and (3) monitoring and identifying plastic pollution. Each area benefits from the unique capabilities of artificial intelligence. If the scientific community continues to explore and refine these technologies, the convergence of artificial intelligence and marine pollution may yield more sophisticated solutions for environmental conservation. Although artificial intelligence offers powerful tools for the treatment of marine pollution, it does have some limitations. Future research recommendations include (1) transferring experimental outcomes to industrial applications in a broader sense; (2) highlighting the cost-effective advantages of AI in marine pollution control; and (3) promoting the use of AI in the legislation and policy-making about controlling marine pollution.

An analysis comparing the performance of lead and tin halides organic Perovskite Solar Cells and numerical simulation with SCAPS

The scientific community focused on finding environmentally safe alternative metal halides to address the instability and toxicity associated with lead perovskite materials. These alternatives are supposed to replace lead halides without altering the distinctive properties of lead perovskite materials. Additionally, they should contribute to producing longer-lasting and stable perovskite in solar cells. This research presents a practical investigation of using tin- and lead-based composites in perovskite iodide solar cells with Titanium dioxide (TiO2) and Copper oxide (CuO) as electron transport materials (ETM) and hole transport materials (HTM), respectively. The performance parameters of the perovskite solar cells (PSCs) are compared with those of lead-based and lead-free PSCs with the same charge transport layers of ETM and HTM, as well as compare the practical results with the software results of Solar Cell Capacitance Simulation (SCAPS) package. In this study, the performance of PSCs with an absorption layer ((CH3NH3PbI3)t/(CH3NH3SnI3)1-t) was examined. The thickness of the perovskite layer is represented by (t), which can take the values(t = 1, 0.5, or 0) μm. The results of the laboratory study were compared to the performance of PSCs based on numerical analysis of SCAPS modeling. PSCs were prepared with a configuration structure (FTO/compact TiO2/(CH3NH3PbI3)t/(CH3NH3SnI3)1-t/CuO/Cu-electrode). A tetragonal phase was obtained for perovskite materials CH3NH3PbI3 and CH3NH3SnI3 with a direct optical energy gap estimated at (1.55 and 1.35) eV, respectively. The l-V curve of the prepared PSCs was studied, and the performance of the solar cells was examined under a light intensity of 100 mW/cm2. The power conversion efficiency, PCE, of the prepared solar cells, was (PCE = 2.9, 3.11, or 0.5) at (t = 1, 0.5, or 0)μm, respectively. A comparative analysis was also performed between the empirical performance of the manufactured PSCs and the theoretical results acquired by the SCAPS package software.

Wine quality implications of the treatment of oak wood with plasma activated water (PAW): A preliminary study

Microbiological and chemical contamination of oak barrels poses significant challenges in the wine ageing process. This study explores the Plasma Activated Water (PAW) as an innovative probable solution for cleaning oak wood of various origins. Although data demonstrated reductions in the culturable microbial populations of three wine spoilage microorganisms from the tested wood (American, French and Spanish with medium and plus toastings), this initial study was focused on physicochemical and sensorial parameters of wines. Importantly, wines aged with PAW-treated wood showed physicochemical parameters like those of traditional oak barrel ageing, maintaining colour and avoiding oxidation. Sensory analysis revealed high quality red wines with harmonious balance of fruity and spicy notes, comparable to those treated with sulphur, without significant differences between PAW generation methods. The results of this study highlight PAW as a promising and sustainable method that might be tested in the wine industry under industrial conditions. PAW could offer an effective approach to disinfecting oak wood, ensuring consistent wine quality without compromising sensory or physicochemical attributes. This research provides a valuable preliminary contribution to the scientific community and the wine industry, paving the way for the adoption of PAW as a cutting-edge technology in wine production.

Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization.

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their representation and study in the form of graphs or networks is also an area of research which many scholars are focused on. However, the large size of such graph datasets poses challenges in data mining and visualization. For example, knowledge discovery from the Bio-Mouse-Gene dataset, which has over 43 thousand nodes and 14.5 million edges, is a non-trivial job. In this regard, summarizing the large graphs provided is a useful alternative. Graph summarization aims to provide the efficient analysis of such complex and large-sized data; hence, it is a beneficial approach. During summarization, all the nodes that have similar structural properties are merged together. In doing so, traditional methods often overlook the importance of personalizing the summary, which would be helpful in highlighting certain targeted nodes. Personalized or context-specific scenarios require a more tailored approach for accurately capturing distinct patterns and trends. Hence, the concept of personalized graph summarization aims to acquire a concise depiction of the graph, emphasizing connections that are closer in proximity to a specific set of given target nodes. In this paper, we present a faster algorithm for the personalized graph summarization (PGS) problem, named IPGS; this has been designed to facilitate enhanced and effective data mining and visualization of datasets from various domains, including biosensors. Our objective is to obtain a similar compression ratio as the one provided by the state-of-the-art PGS algorithm, but in a faster manner. To achieve this, we improve the execution time of the current state-of-the-art approach by using weighted, locality-sensitive hashing, through experiments on eight large publicly available datasets. The experiments demonstrate the effectiveness and scalability of IPGS while providing a similar compression ratio to the state-of-the-art approach. In this way, our research contributes to the study and analysis of sensory datasets through the perspective of graph summarization. We have also presented a detailed study on the Bio-Mouse-Gene dataset, which was conducted to investigate the effectiveness of graph summarization in the domain of biosensors.

On What Topics Does the Scientific Community Cooperate in Defense Economics? A Science Mapping of the First 30 Years of the Journal Defence and Peace Economics

ABSTRACT The academic journal Defence and Peace Economics, which celebrated its pearl jubilee in 2024, is acknowledged as the leading journal in defense economics, bringing together a scientific community of authors from a diverse array of countries. We conduct a bibliometric study using science mapping to analyze the evolution of author networks and research themes within the journal from 1994 to 2023. Analyzing 1069 articles, our findings reveal a growing internationalization of the journal, alongside an increasing diversification of topics. However, we observe a decrease in the rate of international co-authorship during the most recent period of our analysis, i.e. 2019-2023. The core of the publications remains focused on military spending and its interconnection with economic growth. Moreover, our analysis points to themes related to technology, innovation, and R&D in the defense industry as research opportunities.

Natural compounds-based nanomedicines for cancer treatment: Future directions and challenges.

Several efforts have been extensively accomplished for the amelioration of the cancer treatments using different types of new drugs and less invasives therapies in comparison with the traditional therapeutic modalities, which are widely associated with numerous drawbacks, such as drug resistance, non-selectivity and high costs, restraining their clinical response. The application of natural compounds for the prevention and treatment of different cancer cells has attracted significant attention from the pharmaceuticals and scientific communities over the past decades. Although the use of nanotechnology in cancer therapy is still in the preliminary stages, the application of nanotherapeutics has demonstrated to decrease the various limitations related to the use of natural compounds, such as physical/chemical instability, poor aqueous solubility, and low bioavailability. Despite the nanotechnology has emerged as a promise to improve the bioavailability of the natural compounds, there are still limited clinical trials performed for their application with various challenges required for the pre-clinical and clinical trials, such as production at an industrial level, assurance of nanotherapeutics long-term stability, physiological barriers and safety and regulatory issues. This review highlights the most recent advances in the nanocarriers for natural compounds secreted from plants, bacteria, fungi, and marine organisms, as well as their role on cell signaling pathways for anticancer treatments. Additionally, the clinical status and the main challenges regarding the natural compounds loaded in nanocarriers for clinical applications were also discussed.

COVID-19 ethics: unique aspects and a review as of early 2024.

COVID-19 presents a variety of ethical challenges in a set of arenas, arenas not always considered in past pandemics. These challenges include issues related to autonomy, distributive ethics, and the establishment of policies of equity and justice. Methods are a literature review based on regular editing of an online textbook during the COVID-19 outbreak and a literature review using key ethical terms. Patients are confronted with new issues related to autonomy. Providers need to expand their concepts of ethical issues to include decisions based on proportionality and public health ethics. The public health sector needs to assess the beneficence of alternative modes of disease control. The research community needs to redefine the concept of informed consent in emergent conditions. All elements of the medical spectrum-physicians, scientists, and the community-at-large including the pharmaceutical industry-need to consider the multifaceted methods for preventing future pandemics. This will require giving particular emphasis to public health funding and ending the documented discrimination that exists in the provision of proven therapies. The developing world is especially at risk for most of the ethical issues, especially those related to equity and justice. The ethical issues associated with the COVID-19 outbreak are not unique but provide a diverse set of issues that apply to patients, providers, social groups, and investigators. The further study of such issues can help with preventing future outbreaks.

Quality Control in Human Adipose-Derived Stromal/Stem Cells and Tissue Engineering Fat Models for Aging Studies.

Adipose tissue is recognized not only as an endocrine organ but also as a reservoir for adipose-derived stromal/stem cells (ASCs). ASCs have stimulated the interest of both the scientific and medical communities due to their therapeutic potential and applications in tissue engineering and regenerative medicine. ASCs are leveraged for their multipotency and their paracrine function. ASC behavior is highly variable and donor dependent. Donor age, body mass index, disease status, sex, and ethnicity can lead to differential overall function and quality. The impact of donor age and passage on ASC behavior has been well documented, impacting cell proliferation and differentiation potential and thus must be taken into careful consideration when conducting in vitro studies. Pooling of ASCs from different donors reduces heterogeneity among individual donors and produces ASCs with a consistent differentiation and paracrine profiles, an advantage for studies in biological aging. This chapter provides a detailed overview for studies related to quality control for ASC pools considering biological and chronological aging in ASCs. There are hallmarks of biological aging and specific assays associated with the evaluation of each hallmark. Nevertheless, here we present the assays that provide a standardized characterization and qualification of donor pools for their regenerative potential, considering chronological and biological age of the pool. The assays included in this chapter are considered quality control standards to evaluate cell proliferation, differentiation, colony-forming units, and cellular senescence from different donor age and cell passage cohorts.

Impact of particle size and associated minerals on rare earth desorption and incorporation mechanisms in a South American ion-adsorption clay

This research delves into the intricate nexus of particle size, mineralogical composition, surface attributes, elemental mapping, and rare earth element (REE) adsorption mechanisms within an ion-adsorption clay sample from South America. The investigation entails the fractionation of the ion-adsorption clay into three size categories: S1 (< 0.25 mm), S2 (0.25–0.5 mm), and S3 (0.5–2 mm). Each fraction undergoes meticulous characterization to unveil its elemental composition, mineralogical composition, surface area, morphological characteristics, elemental mapping, and the mechanisms governing REE incorporation. The results indicate that S1 has 31% physiosorbed, 8% chemisorbed, and 61% mineralized REEs, while S2 has 40% physiosorbed, 5% chemisorbed, and 55% mineralized REEs, and S3 has 24% physiosorbed, 5% chemisorbed, and 71% mineralized REEs. The physisorbed REEs are attributed to the presence of kaolinite, conducive to mostly physisorption. In terms of grain size and REE content/type relationship, the results show that REE content decreases with increasing grain size; however, there is not a clear trend in terms of REE occurrence modes with grain size. Heavy rare earth elements (HREEs) are discernibly favored in adsorption over light rare earth elements (LREEs). This preference is underpinned by the weathering processes that led to the formation of ion-adsorption clay, which facilitated the transport and accumulation of HREEs. Notably, the ion-adsorption clay encompasses a substantial content of mineralized REEs, necessitating more demanding extraction methodologies, such as acid baking followed by water leaching if complete extraction of all REEs is desired. Among the desorbable REEs, physisorption dominates, encompassing over 80% of the total. Chemisorbed REEs exhibit versatility in association with various minerals, encompassing kaolinite, quartz, and goethite. In essence, this study unveils the intricate interplay between particle dimensions, mineralogical constitution, surface attributes, and REE adsorption modes within this ion-adsorption clay sample. The ion-adsorption clay in this study contains a significant portion of mineralized REEs that cannot be extracted using the mild conditions typically employed for the desorption process. Additionally, the REE concentration in this ion-adsorption clay is notably higher than the average found in clay deposits worldwide, reaching levels comparable to those of regolith deposits in China, which are a major global source of REEs. This remarkable concentration of REEs, along with the unique modes of their occurrence in this deposit, presents a significant interest to the scientific community.

Smart Chemometric‑Assisted Spectrophotometric Approaches for Simultaneous Quantification of Tertiary Combination Recommended for COVID-19 Supportive Treatments with Their Greenness Assessment.

There is an increasing interest of scientific community in developing innovative methodologies for their analysis needs within green analytical chemistry framework. UV spectrophotometry is one of the most promising eco-friendly methods, which is integrated with advanced chemometric tools to enhance the selectivity of the analysis of complex mixtures with severe overlapped signals. Simultaneous determination of a triple-combination of pseudoephedrine hydrochloride (PSE), carbinoxamine maleate (CRX), and paracetamol (PAR) using an artificial intelligence system and multivariate calibration methods. This combination is recently recommended for COVID-19 home-treated patients as part of a symptomatic treatment. Namely, the suggested models are: Artificial Neural Networks, Partial Least Squares, and Principal Component Regression. The proposed algorithms were optimized and developed with the aid of a five-level, three-factor experimental design. The investigated methods were applied over the concentration range of 100-180 μg/mL, 18-16 μg/mL, and 4-12 μg/mL for PSE, CRX, and PAR, respectively. The models validation results demonstrated excellent recoveries (around 98 to 102%), signaling the approaches outstanding resolution capacity for the cited compounds in the presence of common excipients. The outcomes of the studied methods were statistically compared to the official approaches, and no significant difference was found. The suggested models were efficiently employed to determine the selected drugs in their combined tablets without any initial separation steps. The impact of these methods on the environment was evaluated via greenness tools, namely; National Environmental Method Index, Raynie and Driver's green assessment method, analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric. Green chemometric quality assessment of PSE, CRX, and PAR in their pure and pharmaceutical dosage forms. The established approaches are innovative, sustainable, smart, fast, selective, and cost-effective. These models are potential green nominees for routine analysis of the investigated mixture in quality control laboratories.

CORAL-Catamaran for Underwater Exploration: Development of a Multipurpose Unmanned Surface Vessel for Environmental Studies.

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats.

Statistical data transformation in agrarian sciences for variance analysis: a systematic review

In statistical analyses, a common practice for enhancing the validity of variance analysis is the application of data transformation to convert measurements into a different mathematical scale. This technique was first employed in 1898 by Edgeworth and remains relevant in current scientific publications despite the proliferation of more modern and advanced techniques that obviate the need for certain assumptions. Data transformations, when appropriately used, can make the model error terms approximate a normal distribution. It is also possible to use the technique to correct the heterogeneity of variances or to render an additive model, ensuring the validity of the analysis of variances. Given that this technique can be hastily applied, potentially leading to erroneous or invalid results, we conducted a systematic literature review of studies in the field of agrarian sciences that utilized data transformations for the validation of analysis of variances. The aim was to check the transformations employed by the scientific community, the motivation behind their use, and to identify possible errors and inconsistencies in applying the technique in publications. In this study, we identified shortcomings and misconceptions associated with using this method, and we observed incomplete and inadequate utilization of the technique in 94.28 % of the analysed sample, resulting in misguided and erroneous conclusions in scientific research outcomes.

Exploring Multifaceted Roles of Nitroreductases in Bacterial, Animal and Plant Biosystems and their Detection using Fluorescent Probes

AbstractNitroreductases are the enzymes that can convert nitroaromatic substances including explosives like TNT to their corresponding nitrite, hydroxylamine or amino derivatives with reduced NADH or NADPH as an electron donor. A wide range of nitroreductases are known to present in bacterial genomes that are explored for their potential in the reduction of nitroaromatic compounds and they are used in converting prodrugs to toxic hydroxylamine derivatives causing tumor cell killing. Likewise, in cancer cells, the low oxygen condition called hypoxia is a characteristic of solid tumors and during hypoxia nitroreductase gene expression occurs. Moreover, due to the ability of nitroreductases to reduce nitroaromatic compounds, phytoremediation potential of plants was inspected by expressing bacterial nitroreductase genes in plants through genetic engineering. Due to its diversity and widespread applications in different biosystems, its detection has gained momentum among the scientific community. Designing fluorescent probes for detecting nitroreductases has emerged as a beneficial tool compared to conventional monitoring approaches due to their high sensitivity, selectivity, and real‐time monitoring ability. This review aims to provide an understanding of the activity and the status of nitroreductases in bacteria, plants and animal cells as well as the significance of fluorescent imaging‐related studies by employing recently designed probes.

A Smart Visual Sensor for Smoke Detection Based on Deep Neural Networks.

The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge, data available for training deep neural networks is limited and not fully representative of real environments. Within this context, in this paper we propose a new method for smoke detection based on the combination of motion and appearance analysis with a modern convolutional neural network (CNN). Moreover, we propose a new dataset, called the MIVIA Smoke Detection Dataset (MIVIA-SDD), publicly available for research purposes; it consists of 129 videos covering about 28 h of recordings. The proposed hybrid method, trained and evaluated on the proposed dataset, demonstrated to be very effective by achieving a 94% smoke recognition rate and, at the same time, a substantially lower false positive rate if compared with fully deep learning-based approaches (14% vs. 100%). Therefore, the proposed combination of motion and appearance analysis with deep learning CNNs can be further investigated to improve the precision of fire detection approaches.

Deep learning for quantifying spatial patterning and formation process of early differentiated human-induced pluripotent stem cells with micropattern images.

Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, we propose a deep learning method to quantify spatial patterning of the germ layers in the early differentiation stage of hiPSCs using micropattern images. We propose decoding and encoding U-net structures learning labelled Hoechst (DNA-stained) hiPSC regions with corresponding Hoechst and bright-field micropattern images to segment hiPSCs on Hoechst or bright-field images. We also propose a U-net structure to extract extraembryonic regions on a micropattern, and an algorithm to compares intensities of the fluorescence images staining respective germ-layer cells and extract their regions. The proposed method thus can quantify the pluripotency of a hiPSC line with spatial patterning including cell numbers, areas and distributions of germ-layer and extraembryonic cells on a micropattern, and reveal the formation process of hiPSCs and germ layers in the early differentiation stage by segmenting live-cell bright-field images. In our assay, the cell-number accuracy achieved 86% and 85%, and the cell region accuracy 89% and 81% for segmenting Hoechst and bright-field micropattern images, respectively. Applications to micropattern images of multiple hiPSC lines, micropattern sizes, groups of markers, living and fixed cells show the proposed method can be expected to be a useful protocol and tool to quantify pluripotency of a new hiPSC line before providing it to the scientific community.

Parallel ensemble of a randomization-based online sequential neural network for classification problems using a frequency criterion

Randomization-based neural networks have gained wide acceptance in the scientific community owing to the simplicity of their algorithm and generalization capabilities. Random vector functional link (RVFL) networks and their variants are a class of randomization-based neural networks. RVFL networks have shown promising results in classification, regression, and clustering problems. For real-world applications, learning algorithms that can train with new samples over previous results are necessary because of to the constant generation of problems related to large-scale datasets. Various online sequential algorithms, commonly involving an initial learning phase followed by a sequential learning phase, have been proposed to address this issue. This paper presents a training algorithm based on multiple online sequential random vector functional link (OS-RVFL) networks for large-scale databases using a shared memory architecture. The training dataset is distributed among p OS-RVFL networks, which are trained in parallel using p threads. Subsequently, the test dataset samples are classified using each trained OS-RVFL network. Finally, a frequency criterion is applied to the results obtained from each OS-RVFL network to determine the final classification. Additionally, an equation was derived to reasonably predict the total training time of the proposed algorithm based on the learning time in the initial phase and the time scaling factor compared to the sequential learning phase. The results demonstrate a drastic reduction in training time because of data distribution and an improvement in accuracy because of the adoption of the frequency criterion.

Advancing research on Blastocystis through a One Health approach

Blastocystis is the most prevalent intestinal eukaryotic microorganism with significant impacts on both human and animal health. Despite extensive research, its pathogenicity remains controversial. The COST Action CA21105, "Blastocystis under One Health" (OneHealthBlastocystis), aims to bridge gaps in our understanding by fostering a multidisciplinary network. This initiative focuses on developing standardised diagnostic methodologies, establishing a comprehensive subtype and microbiome databank, and promoting capacity building through education and collaboration. The Action is structured into five working groups, each targeting specific aspects of Blastocystis research, including epidemiology, diagnostics, 'omics technologies, in vivo and in vitro investigations, and data dissemination. By integrating advances across medical, veterinary, public, and environmental health, this initiative seeks to harmonise diagnostics, improve public health policies, and foster innovative research, ultimately enhancing our understanding of Blastocystis and its role in health and disease. This collaborative effort is expected to lead to significant advancements and practical applications, benefiting the scientific community and public health.

Spatial landscape of malignant pleural and peritoneal mesothelioma tumor immune microenvironment.

Immunotherapies have demonstrated limited clinical efficacy in malignant mesothelioma treatment. We conducted multiplex immunofluorescence (mIF) analyses on tissue microarrays (n=3) from malignant peritoneal (MPeM, n=25) and pleural (MPM, n=88) mesothelioma patients. Our study aimed to elucidate spatial distributions of key immune cell populations and their association with LAG3, BAP1, NF2, and MTAP, with MTAP serving as a CDKN2A/B surrogate marker. Additionally, we examined the relationship between the spatial distribution of major immune cell types with MM patient prognosis and clinical characteristics. We observed a higher degree of interaction between immune cells and tumor cells in MPM compared to MPeM. Notably, within MPM tumors, we detected a significantly increased interaction between tumor cells and CD8+ T cells in tumors with low BAP1 expression compared to those with high BAP1 expression. To support the broader research community, we have developed The Human Spatial Atlas of Malignant Mesothelioma (https://mesotheliomaspatialatlas.streamlit.app/), containing mIF and hematoxylin and eosin (H&E) images.