Search Results (133)

Complex wastewater matrices hinder the efficacy of conventional treatment methods due to the presence of various inorganic and organic pollutants, along with their intricate interactions. Leveraging the synergy between algae and bacteria, algal–bacterial symbiosis (ABS) systems offering an evolutionary and highly effective approach. The ABS system demonstrates 10–30% higher removal efficiency than conventional biological/physicochemical methods under identical conditions, especially at low C/N ratios. Recent advances in biology techniques and big data analytics have deepened our understanding of the synergistic mechanisms involved. Despite the system’s considerable promise, challenges persist concerning complex pollution scenarios and scaling it for industrial applications, particularly regarding system design, environmental adaptability, and stable operation. In this review, we explore the current forms and operational modes of ABS systems, discussing relevant mechanisms in various wastewater treatment contexts. Furthermore, we examine the advantages and limitations of ABS systems in treating complex wastewater matrices, highlighting challenges and proposing future directions. Full article

Background: Multiple myeloma (MM) is a heterogeneous plasma cell malignancy with non-specific symptoms and disease heterogeneity at clinical and biological levels. This non-specific set of symptoms, including bone pain, anemia, renal failure, hypercalcemia, and neuropathy, can mislead diagnosis as chronic or benign conditions, resulting in a delay in diagnosis. Timely identification is paramount to prevent organ damage and reduce morbidity. Methods: In this review, we present an overview of recent literature concerning the factors leading to the delayed diagnosis of MM and the impact of delayed diagnosis. This includes factors relevant to physicians and systems, diagnostic processes, primary healthcare services, and laboratory and imaging data access and interpretation. Other emerging technologies to diagnose NCIs include AI-based decision support systems and biomarker-focused strategies. Findings: Delayed diagnosis can lead to presentation at advanced disease stages associated with life-threatening complications and shorter progression-free survival. Patients are often seen by many physicians before they are referred to hematology. Understanding of clinical red flags for MM in primary care is inadequate. Our findings indicate that limited access to diagnostic tests, inconsistent follow-up of MGUS/SMM patients, and a lack of interdepartmental coordination delay the diagnostic process. Conclusions: Multimodal tools for early diagnosis of MM. Educational campaigns to raise awareness of the disease, algorithms dedicated to routine care and novel technologies, including AI and big data analytics, and new biomarkers may serve this purpose, as well as genomic approaches to the premalignant MGUS stage. Full article

In recent decades, access to powerful computational resources has brought about a major transformation in science, with supercomputers drawing significant attention from academia, industry, and governments. Among these resources, high-performance computing (HPC) has emerged as one of the most critical processing infrastructures, providing a suitable platform for evaluating and implementing novel technologies. In this context, the development of emerging computing technologies has opened up new horizons in information processing and the delivery of computing services. In this regard, this paper systematically reviews and classifies emerging HPC-related computing technologies, including quantum computing, nanocomputing, in-memory architectures, neuromorphic systems, serverless paradigms, adiabatic technology, and biological solutions. Within the scope of this research, 142 studies which were mostly published between 2018 and 2025 are analyzed, and relevant hardware solutions, domain-specific programming languages, frameworks, development tools, and simulation platforms are examined. The primary objective of this study is to identify the software and hardware dimensions of these technologies and analyze their roles in improving the performance, scalability, and efficiency of HPC systems. To this end, in addition to a literature review, statistical analysis methods are employed to assess the practical applicability and impact of these technologies across various domains, including scientific simulation, artificial intelligence, big data analytics, and cloud computing. The findings of this study indicate that emerging HPC-related computing technologies can serve as complements or alternatives to classical computing architectures, driving substantial transformations in the design, implementation, and operation of high-performance computing infrastructures. This article concludes by identifying existing challenges and future research directions in this rapidly evolving field. Full article

Big data approaches are rapidly expanding across many fields of science and are seeing increasing application, yet the use of big data in research related to invasive species lags. Big data can play a key role in predicting, detecting, preventing, controlling, and eradicating biological invasions. Here, we assess terms in the literature related to big data, biological invasions, and agriculture and review sources of big data, including museum records, crowdsourcing observations, natural history collections, and DNA-based information. These sources can be combined with environmental data to build models, predict the origins of invasive species, and develop control methods. To harness the power of data for agricultural biological invasions, several action areas are recommended to streamline processes and improve data sources. Full article

Continuous monitoring of glucose levels is important for diabetes management and prevention. While traditional glucose monitoring methods are often invasive and expensive, recent approaches using machine learning (ML) models have explored non-invasive alternatives—but many still depend on manually logged food intake and activity, which is burdensome and impractical for everyday use. In this study, we propose a novel approach that eliminates the need for manual input by utilizing only passively collected, automatically recorded multi-modal data from non-invasive wearable sensors. This enables practical and continuous glucose prediction in real-world, free-living environments. We used the BIG IDEAs Lab Glycemic Variability and Wearable Device Data (BIGIDEAs) dataset, which includes approximately 26,000 CGM readings, simultaneous ly collected wearable data, and demographic information. A total of 236 features encompassing physiological, behavioral, circadian, and demographic factors were constructed. Feature selection was conducted using random-forest-based importance analysis to select the most relevant features for model training. We evaluated the effectiveness of various ML regression techniques, including linear regression, ridge regression, random forest regression, and XGBoost regression, in terms of prediction and clinical accuracy. Biological sex, circadian rhythm, behavioral features, and tonic features of electrodermal activity (EDA) emerged as key predictors of glucose levels. Tree-based models outperformed linear models in both prediction and clinical accuracy. The XGBoost (XR) model performed best, achieving an R-squared of 0.73, an RMSE of 11.9 mg/dL, an NRMSE of 0.52 mg/dL, a MARD of 7.1%, and 99.4% of predictions falling within Zones A and B of the Clarke Error Grid. This study demonstrates the potential of combining feature engineering and tree-based ML regression techniques for continuous glucose monitoring using wearable sensors. Full article

An important area of interest for therapeutic development is the IL-1 superfamily, a critical group of immune regulators with profound implications in a variety of disorders. This study clarifies the evolutionary patterns of IL-1 family members by thoroughly analyzing more than 400 animal species, demonstrating their ancient roots that extend back to the earliest vertebrates. Important results show that, although IL-1 ligands expanded significantly over the evolution of mammals, their corresponding receptors remained remarkably structurally conserved. Identifying both lineage-specific adaptations and evolutionarily conserved residues provides vital information for treatment design. These findings point to the possibility of two different therapeutic strategies: addressing species-specific variants may allow for more targeted interventions, whereas focusing on conserved motifs may result in broad-acting treatments. The study also identified less well-known species as useful models for comprehending early immune systems. In addition to advancing our knowledge of the function of the IL-1 family in autoimmune, inflammatory, and carcinogenic illnesses, this research lays the groundwork for the development of more potent targeted therapeutics by creating an evolutionary framework for the IL-1 family. Full article

The term “big data analytics (BDA)” defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems. Full article

Background: The RSPO gene family encodes secreted glycoproteins that are rich in cysteine, which generally serve as activators of the Wnt signaling pathway in animals. Four types of this family have been identified in a few model species. However, the evolution of the family remains unclear. Methods: In this study, we identified a total of 1496 RSPO homologs through an extensive survey of the RSPO genes in 430 animals. Gene family clustering and phylogenetic analysis identified four major subtypes of the family (RSPO1–RSPO4) and clarified their distribution of copy number in different species. Results and Conclusions: Members of the RSPO4 subfamily that were closest to ancestral forms existed in both Deuterostomes and Protostomates, and we speculate that representatives of this subfamily already existed in Urbilatera, the last common ancestor of Deuterostomes. Particularly, in some RSPO3 subtypes of Actinopterygii (ray-finned fishes), an FU repeated motif with three conserved cysteines was identified. Further conservative analysis of amino acids and alignment of tertiary protein structure revealed the potential functional sites for each subgroup. The results provide insight into the phylogenetic relationships and evolutionary patterns of conserved motifs of RSPO family genes in animal kingdoms, which will guide further studies on the biological functions of RSPO in other non-model species. Full article

On behalf of the Canadian Association of Psychosocial Oncology, we are pleased to present the Abstracts from the 2025 Annual Conference, titled “Responding to the Human Experience of Cancer and Caring for the Soul: Building on 40 years of global leadership in psychosocial oncology”. The 40th Annual CAPO Conference was held in Toronto from 23 April 2025 to 25 April 2025. In an era marked by the rapid advancement of biologically focused precision medicine, it is imperative to redirect our attention towards the human experience of illness and the soul of medicine. Biomedicine has conceptualized illness in ways that have proved profoundly productive from a curative and biological point of view. But it cannot—and it does not pretend to—illuminate the experience of living with it. (Hurwitz 2009). This conference aims to delve into the intricate interplay between cutting-edge biomedical technologies inclusive of artificial intelligence and big data and the deeply personal narratives of individuals navigating illness. By shifting the focus from mere disease pathology to encompassing the holistic human experience, we aspire to foster a more compassionate and patient-centered approach to healthcare with psychosocial support at the core of humanistic care that can improve survival and well-being in all aspects of a whole-person approach to illness. Through interdisciplinary dialogue and introspection, we endeavor to illuminate the profound connection between mind, body, and spirit in the practice of medicine, reaffirming the timeless significance of empathy, understanding, and human connection in healing and psychosocial aspects of care as fundamental to living well with cancer. This conference brought together key stakeholders including multidisciplinary professionals from nursing, psychology, psychiatry, social work, spiritual care, nutrition, medicine, rehabilitation medicine, occupational health and radiation therapy for both adult and pediatric populations. Participants included clinicians, researchers, educators in cancer care, community-based organizations and patient representatives. Patients, caregivers and family members presented abstracts that speak to their role in managing cancer experiences and care. Over two hundred (200) abstracts were submitted for presentation as symposia, 20-minute oral presentations, 10-minute oral presentations, 90-minute workshops and poster presentations. We congratulate all the presenters on their research work and contribution. Full article

Angelica biserrata (Shan et Yuan) Yuan et Shan (A. biserrata) roots, a widely distributed medicinal crop with intraspecific diversity, exhibits significant variability in coumarin content across habitats. This study integrated metabolomics and transcriptomics to dissect the spatial heterogeneity in metabolite profiles and gene expression, revealing the mechanisms driving coumarin biosynthesis divergence. By synthesizing climate-related big data with machine learning and Bayesian-optimized deep learning models, we identified key environmental drivers and predicted optimal cultivation conditions. The key findings were as follows: (1) differential regions most strongly influenced coumarin; (2) upstream genes (such as PAL-1, PAL-2, BGLU44, etc.) modulated downstream coumarin metabolites; (3) elevation (Elev) and warmest quarter temperature (Bio10) dominated coumarin variation, whereas May solar radiation (Srad5) and precipitation seasonality (Bio15) controlled transcriptomic reprogramming; (4) the optimized environment for bioactive compounds included mean annual temperature (Bio1) = 9.99 °C, annual precipitation (Bio12) = 1493 mm, Elev = 1728 m, cumulative solar radiation = 152,643 kJ·m⁻²·day⁻¹, and soil organic carbon = 11,883 g·kg⁻¹. This study aimed to clarify the biological characteristics and differential regulatory mechanisms of A. biserrata roots in different habitats, establish a theoretical framework for understanding the molecular mechanisms controlling metabolic changes under various habitats, and contribute to elucidating the formation of active constituents while facilitating their effective utilization. Full article

Background: Dihydroflavonol 4-reductase (DFR) is a key enzyme in the flavonoid biosynthetic pathway that regulates anthocyanin and proanthocyanidin accumulation in plants. Although DFR genes have been studied in various species, their origin of the DFR gene family, its distribution across the plant kingdom, and the reasons behind the emergence of different DFR subtypes Methods: This study performed a whole-genome analysis of DFR genes in 237 plant species, including algae, mosses, ferns, gymnosperms, and angiosperms, integrating phylogeny, conserved motifs, duplication mechanisms, positive selection, and expression pattern analyses. Results: These results indicate that the DFR gene family originated from the common ancestor of extant ferns and seed plants, and the emergence of asparagine (Asn)-type and aspartic (Asp)-type DFRs is associated with gymnosperms. Notably, we report for the first time the presence of Asn-type, Asp-type, and arginine (Arg)-type DFRs in some species, which breaks the previous notion that Arg-type DFRs are exclusive to ferns. Tandem duplication is considered the primary driving force behind the expansion of the DFR family and is associated with the formation of different DFR subtypes. Furthermore, Asn-type DFRs were highly expressed during the early stages of seed development, suggesting their important role in seed development. Conclusions: Overall, this study revealed the dynamic evolutionary trajectory of the DFR gene family in plants, providing a theoretical foundation for future research on DFR genes. Full article

MycelialNet is a novel deep neural network (DNN) architecture inspired by natural mycelial networks. Mycelia, the vegetative part of fungi, form extensive underground networks that, in a very efficient way, connect biological entities, transport nutrients and signals, and dynamically adapt to environmental conditions. Drawing inspiration from these properties, MycelialNet integrates dynamic connectivity, self-optimization, and resilience into its artificial structure. This paper explores how mycelial-inspired neural networks can enhance big data analysis, particularly in mineralogy, petrology, and other Earth disciplines, where exploration and exploitation must be efficiently balanced during the process of data mining. We validate our approach by applying MycelialNet to synthetic data first, and then to a large petrological database of volcanic rock samples, demonstrating its superior feature extraction, clustering, and classification capabilities with respect to other conventional machine learning methods. Full article

This research aims to investigate the roles of ontology and Semantic Web Technologies (SWT) in modern knowledge representation and data management. By analyzing a dataset of 10,037 academic articles from Web of Science (WoS) published in the last 6 years (2019–2024) across several fields, such as computer science, engineering, and telecommunications, our research identifies important trends in the use of ontologies and semantic frameworks. Through bibliometric and semantic analyses, Natural Language Processing (NLP), and topic modeling using Latent Dirichlet Allocation (LDA) and BERT-clustering approach, we map the evolution of semantic technologies, revealing core research themes such as ontology engineering, knowledge graphs, and linked data. Furthermore, we address existing research gaps, including challenges in the semantic web, dynamic ontology updates, and scalability in Big Data environments. By synthesizing insights from the literature, our research provides an overview of the current state of semantic web research and its prospects. With a 0.75 coherence score and perplexity = 48, the topic modeling analysis identifies three distinct thematic clusters: (1) Ontology-Driven Knowledge Representation and Intelligent Systems, which focuses on the use of ontologies for AI integration, machine interpretability, and structured knowledge representation; (2) Bioinformatics, Gene Expression and Biological Data Analysis, highlighting the role of ontologies and semantic frameworks in biomedical research, particularly in gene expression, protein interactions and biological network modeling; and (3) Advanced Bioinformatics, Systems Biology and Ethical-Legal Implications, addressing the intersection of biological data sciences with ethical, legal and regulatory challenges in emerging technologies. The clusters derived from BERT embeddings and clustering show thematic overlap with the LDA-derived topics but with some notable differences in emphasis and granularity. Our contributions extend beyond theoretical discussions, offering practical implications for enhancing data accessibility, semantic search, and automated knowledge discovery. Full article

Due to advances in big data technology, deep learning, and knowledge engineering, biological sequence visualization has been extensively explored. In the post-genome era, biological sequence visualization enables the visual representation of both structured and unstructured biological sequence data. However, a universal visualization method for all types of sequences has not been reported. Biological sequence data are rapidly expanding exponentially and the acquisition, extraction, fusion, and inference of knowledge from biological sequences are critical supporting technologies for visualization research. These areas are important and require in-depth exploration. This paper elaborates on a comprehensive overview of visualization methods for DNA sequences from four different perspectives—two-dimensional, three-dimensional, four-dimensional, and dynamic visualization approaches—and discusses the strengths and limitations of each method in detail. Furthermore, this paper proposes two potential future research directions for biological sequence visualization in response to the challenges of inefficient graphical feature extraction and knowledge association network generation in existing methods. The first direction is the construction of knowledge graphs for biological sequence big data, and the second direction is the cross-modal visualization of biological sequences using machine learning methods. This review is anticipated to provide valuable insights and contributions to computational biology, bioinformatics, genomic computing, genetic breeding, evolutionary analysis, and other related disciplines in the fields of biology, medicine, chemistry, statistics, and computing. It has an important reference value in biological sequence recommendation systems and knowledge question answering systems. Full article

The rapid expansion of big data has transformed research practices across disciplines, yet disparities exist in its adoption among U.S. institutions of higher education. This study examines trends in NSF-funded big data-driven research across research domains, institutional classifications, and directorates. Using a quantitative approach and natural language processing (NLP) techniques, we analyzed NSF awards from 2006 to 2022, focusing on seven NSF research areas: Biological Sciences, Computer and Information Science and Engineering, Engineering, Geosciences, Mathematical and Physical Sciences, Social, Behavioral and Economic Sciences, and STEM Education (formally known as Education and Human Resources). Findings indicate a significant increase in big data-related awards over time, with CISE (Computer and Information Science and Engineering) leading in funding. Machine learning and artificial intelligence are dominant themes across all institutions’ classifications. Results show that R1 and non-minority-serving institutions receive the majority of big data-driven research funding, though HBCUs have seen recent growth due to national diversity initiatives. Topic modeling reveals key subdomains such as cybersecurity and bioinformatics benefiting from big data, while areas like Biological Sciences and Social Sciences engage less with these methods. These findings suggest the need for broader support and funding to foster equitable adoption of big data methods across institutions and disciplines. Full article