Search Results (3,131)

Microalgal biotechnology is gaining attention due to its potential to produce pigments, lipids, biofuels, and value-added products. However, challenges persist in terms of the economic viability of microalgal lipid production in photobioreactors due to slow growth rates, expensive media, complex downstream processing, limited product yields, and contamination risks. Recent studies suggest that co-cultivating microalgae with bacteria can enhance the profitability of microalgal bioprocesses. Immobilizing bacteria offers advantages such as protection against shear forces, the prevention of overgrowth, and continuous product secretion. Previous work has shown that biopolymeric immobilization of Paenibacillus polymyxa enhances 2,3-butanediol production. In this study, a novel co-fermentation process was developed by exploiting the chemical crosstalk between a freshwater microalga Scenedesmus obliquus, also known as Tetradesmus obliquus, and an immobilized plant-growth-promoting bacterium, Paenibacillus polymyxa. This co-cultivation resulted in increased metabolite production, with a 1.5-fold increase in the bacterial 2,3-butanediol concentration and a 3-fold increase in the microalgal growth rates compared to these values in free-cell co-cultivation. Moreover, the co-culture with the immobilized bacterium exhibited a 5-fold increase in the photosynthetic pigments and a 3-fold increase in the microalgal lipid concentration compared to these values in free-cell co-cultivation. A fixed bed photobioreactor was further constructed, and the co-cultivation bioprocess was implemented to improve the bacterial 2,3-butanediol and microalgal lipid production. In conclusion, this study provides conclusive evidence for the potential of co-cultivation and biopolymeric immobilization techniques to enhance 2,3-butanediol and lipid production. Full article

The disambiguation of word senses (Word Sense Disambiguation, WSD) plays a crucial role in various natural language processing (NLP) tasks, such as machine translation, sentiment analysis, and information retrieval. Due to the complex morphological structure and polysemy of the Korean language, the meaning of words can change depending on the context, making the WSD problem challenging. Since a single word can have multiple meanings, accurately distinguishing between them is essential for improving the performance of NLP models. Recently, large-scale pre-trained models like BERT and GPT, based on transfer learning, have shown promising results in addressing this issue. However, for languages with complex morphological structures, like Korean, the tokenization mismatch between pre-trained models and fine-tuning data prevents the rich contextual and lexical information learned by the pre-trained models from being fully utilized in downstream tasks. This paper proposes a novel method to address the tokenization mismatch issue during the fine-tuning of Korean WSD, leveraging BERT-based pre-trained models and the Sejong corpus, which has been annotated by language experts. Experimental results using various BERT-based pre-trained models and datasets from the Sejong corpus demonstrate that the proposed method improves performance by approximately 3–5% compared to existing approaches. Full article

In the domain of natural language processing (NLP), a primary challenge pertains to the process of Chinese tokenization, which remains challenging due to the lack of explicit word boundaries in written Chinese. The existing tokenization methods often treat each Chinese character as an indivisible unit, neglecting the finer semantic features embedded in the characters, such as radicals. To tackle this issue, we propose a novel token representation method that integrates radical-based features into the process. The proposed method extends the vocabulary to include both radicals and original character tokens, enabling a more granular understanding of Chinese text. We also conduct experiments on seven datasets covering multiple Chinese natural language processing tasks. The results show that our method significantly improves model performance on downstream tasks. Specifically, the accuracy of BERT on the BQ Croups dataset was enhanced to 86.95%, showing an improvement of 1.65% over the baseline. Additionally, the BERT-wwm performance demonstrated a 1.28% enhancement, suggesting that the incorporation of fine-grained radical features offers a more efficacious solution for Chinese tokenization and paves the way for future research in Chinese text processing. Full article

G protein-coupled receptors (GPCRs) represent a family of membrane proteins that regulate several cellular processes. Among the GPCRs, G protein-coupled receptor kinases (GRKs) regulate downstream signaling pathways and receptor desensitization. GRK2 has gained significant interest due to its cardiovascular physiology and pathological involvement. GRK2’s presence in cardiac tissue and its influence on cardiac function, β-adrenergic signaling, and myocardial remodeling underlies its involvement in cardiovascular diseases such as heart failure and ischemia. GRK2’s canonical role is receptor desensitization, but emerging evidence suggests its involvement in mitochondrial dynamics and bioenergetics, influencing processes such as oxidative phosphorylation, reactive oxygen species production, and apoptosis. Moreover, GRK2’s localization within mitochondria suggests a direct role in regulating mitochondrial health and function. Notably, while GRK2 inhibition seems to be a therapeutic approach to heart failure, its precise role in mitochondrial dynamics and pathology needs further investigation. This review explores the complex relationship between mitochondrial function and GRK2 and clarifies the implications for cardiovascular health. Cardiovascular medicine might greatly benefit from future studies that focus on understanding the processes behind GRK2–mitochondrial crosstalk to develop personalized therapies Full article

Extracellular vesicles (EVs) are lipid-bound vesicles secreted by cells, including exosomes, microvesicles, and apoptotic bodies. Proteomic analyses of EVs, particularly in relation to cancer, reveal specific biomarkers crucial for diagnosis and therapy. However, isolation techniques such as ultracentrifugation, size-exclusion chromatography, and ultrafiltration face challenges regarding purity, contamination, and yield. Contamination from other proteins complicates downstream processing, leading to difficulties in identifying biomarkers and interpreting results. Future research will focus on refining EV characterization for diagnostic and therapeutic applications, improving proteomics tools for greater accuracy, and exploring the use of EVs in drug delivery and regenerative medicine. In this review, we provide a bird’s eye view of various challenges, starting with EV isolation methods, yield, purity, and limitations in the proteome analysis of EVs for identifying protein targets. Full article

Seaweed is a resilient macrophytic plant thriving in intertidal zones. These are rapidly gaining attention due to their autotrophic nourishment, rapid growth, and minimal land requirement for cultivation. Seaweed is used in various food and non-food sectors, thus possessing immense potential as a valuable bioresource with high commercial value. However, utilizing seaweed as a bioresource comes with various challenges at processing levels, particularly at cost-effective downstream processing. Hence, this review highlights the advancement in seaweed biomass processing together with its application in food, nutraceuticals, pharmaceuticals, cosmetics, and non-food sectors. Additionally, the advancements in seaweed cultivation and the applications of seaweed in agriculture as a biostimulant, biofuel production, and packaging material are also reviewed. Finally, this review addresses the need for technology intensification, public awareness, and financial investment to enhance the commercialization and integration of seaweed-based products into the bioeconomy. The potential of seaweed to contribute to climate change mitigation and the circular economy is underscored, calling for further research and development to optimize its multifaceted applications. Full article

Sialic acids serve as crucial terminal sugars on glycoproteins or glycolipids present on cell surfaces. These sugars are involved in diverse physiological and pathological processes through their interactions with carbohydrate-binding proteins, facilitating cell–cell communication and influencing the outcomes of bacterial and viral infections. The role of hypersialylation in tumor growth and metastasis has been widely studied. Recent research has highlighted the significance of aberrant sialylation in enabling tumor cells to escape immune surveillance and sustain their malignant behavior. Acute lymphoblastic leukemia (ALL) is a heterogenous hematological malignancy that primarily affects children and is the second leading cause of mortality among individuals aged 1 to 14. ALL is characterized by the uncontrolled proliferation of immature lymphoid cells in the bone marrow, peripheral blood, and various organs. Sialic acid-binding immunoglobulin-like lectins (Siglecs) are cell surface proteins that can bind to sialic acids. Activation of Siglecs triggers downstream reactions, including induction of cell apoptosis. Siglec-7 and Siglec-9 have been reported to promote cancer progression by driving macrophage polarization, and their expressions on natural killer cells can inhibit tumor cell death. This comprehensive review aims to explore the sialylation mechanisms and their effects on ALL in children. Understanding the complex interplay between sialylation and ALL holds great potential for developing novel diagnostic tools and therapeutic interventions in managing this pediatric malignancy. Full article

The rolling system for stainless steel, particularly in the production of diamond plates, represents a complex industrial control scenario. The process requires precise load distribution to effectively manage pattern height, due to the high strength, hardness, and required dimensional accuracy of the material. This paper addresses the limitations of offline methods, which include heavy reliance on initial conditions, intricate parameter settings, susceptibility to local optima, and suboptimal performance under stringent constraints. A Multi-Objective Adaptive Rolling Iteration method that incorporates local constraints (MOARI-LC) is proposed. The MOARI-LC method simplifies the complex multi-dimensional nonlinear constrained optimization problem of load distribution, into a one-dimensional multi-stage optimization problem without explicit constraints. This simplification is achieved through a single variable cycle iteration involving reduction rate and rolling equipment selection. The rolling results of HBD-SUS304 show that the pattern height to thickness ratio obtained by MOARI-LC is 0.20–0.22, which is within a specific range of dimensional accuracy. It outperforms the other two existing methods, FCRA-NC and DCRA-GC, with results of 0.19~0.24 and 0.15~0.25, respectively. MOARI-LC has increased the qualification rate of test products by more than 25%, and it has also been applied to the other six industrial production experiments. The results show that MOARI-LC can control the absolute value of the rolling force prediction error of the downstream stands of the hot strip finishing rolls within 5%, and the absolute value of the finished stand within 3%. These results validate the scalability and accuracy of MOARI-LC. Full article

The hydraulic model serves as an effective tool for operational simulation, dispatch decision-making, and engineering planning in water distribution systems (WDSs). The increasing complexity of large-scale networks and the growing number of monitoring devices present both challenges and opportunities for the online calibration of WDSs in terms of efficiency and accuracy. To address these issues, this paper introduces a novel strategy, Flowmeter-Monitoring Path-Partitioning (FMPP), for nodal demand calibration of hydraulic models. FMPP partitions nodes based on the monitoring paths of flowmeters, which include all downstream nodes of a given flowmeter. Then, a system of equations is formulated from the mass and energy conservation, and an iterative optimization process is employed to calibrate the nodal demands. This method enables the partitioning of nodes to achieve the optimal granularity, enabling each flowmeter to be calibrated individually and also reducing the calibration parameters through node grouping. The performance of the proposed method has been validated through two comprehensive case studies, demonstrating its superiority to conventional calibration techniques in terms of accuracy, computational efficiency, and practical applicability in real-time nodal demand estimation. This approach meets the requirements for the real-time calibration of nodal demand in complex large-scale pipe networks. Full article

Purpose: The molecular mechanisms involved in bone metabolism abnormalities in individuals with type 2 diabetes mellitus (T2DM) are a prominent area of investigation within the life sciences field. Myostatin (MSTN), a member of the TGF-β superfamily, serves as a critical negative regulator of skeletal muscle growth and bone metabolism. Current research on the exercise-mediated regulation of MSTN expression predominantly focuses on its role in skeletal muscle. However, due to the intricate and multifaceted mechanical and biochemical interactions between muscle and bone, the precise mechanisms by which exercise modulates MSTN to enhance bone metabolic disorders in T2DM necessitate additional exploration. The objective of this review is to systematically synthesize and evaluate the role of MSTN in the development of bone metabolism disorders associated with T2DM and elucidate the underlying mechanisms influenced by exercise interventions, aiming to offer novel insights and theoretical recommendations for enhancing bone health through physical activity. Methods: Relevant articles in Chinese and English up to July 2024 were selected using specific search terms and databases (PubMed, CNKI, Web of Science); 147 studies were finally included after evaluation, and the reference lists were checked for other relevant research. Results: Myostatin’s heightened expression in the bone and skeletal muscle of individuals with T2DM can impede various pathways, such as PI3K/AKT/mTOR and Wnt/β-catenin, hindering osteoblast differentiation and bone mineralization. Additionally, it can stimulate osteoclast differentiation and bone resorption capacity by facilitating Smad2-dependent NFATc1 nuclear translocation and PI3K/AKT/AP-1-mediated pro-inflammatory factor expression pathways, thereby contributing to bone metabolism disorders. Physical exercise plays a crucial role in ameliorating bone metabolism abnormalities in individuals with T2DM. Exercise can activate pathways like Wnt/GSK-3β/β-catenin, thereby suppressing myostatin and downstream Smads, CCL20/CCR6, and Nox4 target gene expression, fostering bone formation, inhibiting bone resorption, and enhancing bone metabolism in T2DM. Conclusion: In the context of T2DM, MSTN has been shown to exacerbate bone metabolic disorders by inhibiting the differentiation of osteoblasts and the process of bone mineralization while simultaneously promoting the differentiation and activity of osteoclasts. Exercise interventions have demonstrated efficacy in downregulating MSTN expression, disrupting its downstream signaling pathways, and enhancing bone metabolism. Full article

As a national strategic development area, the Yellow River Basin (YRB) has seen progress in research on the synergy efficiency of pollution reduction and carbon reduction (SEPCR). However, there are still notable gaps. The theoretical framework for this area is lacking, leading to diverse and inconsistent conclusions. Additionally, difficulties in data collection and processing, along with incomplete and inconsistent data, negatively affect the accuracy of research findings. Current studies tend to focus on single aspects and lack a comprehensive and systematic analysis of the SEPCR across the entire basin. There is insufficient understanding of key network nodes, connections, and overall structural characteristics. A scientific assessment of its spatial correlation structure has far-reaching implications for the national battle against pollution and the realization of “dual carbon” goals. This study is based on panel data from 75 cities in the YRB from 2006 to 2022. It employs an ultra-efficiency SBM model to measure the SEPCR. Additionally, it utilizes a modified gravity model and social network analysis to explore the spatial network correlation structure in depth. Furthermore, the QAP model is used to clarify the mechanisms of various influencing factors. The research findings indicate that there is an imbalance in the spatial and temporal distribution of the SEPCR in the YRB. Although there is a fluctuating upward trend over time, significant internal spatial disparities exist. While the gaps between regions are gradually narrowing, there are still evident research disparities. Moreover, the spatial connectivity of the SEPCR in the YRB is gradually strengthening, with overall network connectivity also improving, yet there remains a considerable distance from an ideal state. The network density shows a decreasing trend from the downstream to the midstream and then to the upstream regions, with significant differences in spatial network centrality among these areas, particularly pronounced between the midstream and upstream regions. Differences in economic development levels, technological development levels, and industrial structure development levels promote the formation of spatial correlations in SEPCR, while disparities in energy utilization have a suppressive effect. Full article

Neurological disorders are defined by a deterioration or disruption of the nervous system’s structure and function. These diseases, which include multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and schizophrenia, are caused by intricate pathological processes that include excitotoxicity, neuroinflammation, oxidative stress, genetic mutations, and compromised neurotrophic signaling. Although current pharmaceutical treatments relieve symptoms, their long-term efficacy is limited due to adverse side effects and weak neuroprotective properties. However, when combined with other neuroprotective drugs or adjunct therapy, they may offer additional benefits and improve treatment outcomes. Phytochemicals have emerged as attractive therapeutic agents due to their ability to regulate essential neurotrophic pathways, especially the brain-derived neurotrophic factor (BDNF) signaling cascade. BDNF is an important target for neurodegenerative disease (ND) treatment since it regulates neuronal survival, synaptic plasticity, neurogenesis, and neuroprotection. This review emphasizes the molecular pathways through which various phytochemicals—such as flavonoids, terpenoids, alkaloids, and phenolic compounds—stimulate BDNF expression and modulate its downstream signaling pathways, including GSK-3β, MAPK/ERK, PI3K/Akt/mTOR, CREB, and Wnt/β-catenin. This paper also highlights how phytochemical combinations may interact to enhance BDNF activity, offering new therapeutic options for ND treatment. Despite their potential for neuroprotection, phytochemicals face challenges related to pharmacokinetics, blood–brain barrier (BBB) permeability, and absorption, highlighting the need for further research into combination therapies and improved formulations. Clinical assessment and mechanistic understanding of BDNF-targeted phytotherapy should be the main goals of future studies. The therapeutic efficacy of natural compounds in regulating neurotrophic signaling is highlighted in this review, providing a viable approach to the prevention and treatment of NDs. Full article

Crossflow membrane separation was used as a scalable downstream process for the up concentrate of low-molecular-weight peptides and for the removal of salt (NaCl) from Calanus finmarchicus hydrolysate. Membrane processes are increasingly used for various applications in both upstream and downstream processing. The C. finmarchicus hydrolysate was prepared by enzymatic hydrolysis, followed by crossflow separation. The stepwise membrane nanofiltration of hydrolysate contributed to a progressive reduction in salt in the hydrolysate. The salt concentration in the concentrates decreased by 34%, 53%, and 75%, highlighting the efficiency of the filtration process in separating NaCl from peptides. This gradual reduction in salt concentration suggests that the membrane effectively facilitated NaCl removal while retaining peptides. Briefly, 75% NaCl removal was achieved, with peptide recovery reaching 57% using an NFX membrane in crossflow filtration. Full article

Plant secondary metabolites are known to be valuable agents to hamper inflammation owing to their multiple mechanisms of action. This study investigates the molecular mechanisms underlying the anti-inflammatory effects of vicenin-2 in lipopolysaccharide (LPS)-stressed THP-1 cells. After ascertaining the safety of vicenin-2 in our in vitro model, we assessed the anti-inflammatory potential of this flavonoid. Indeed, it counteracted the increase of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6 levels, as well as the overexpression of both inducible nitric oxide synthase (iNOS) and cyclooxygenase (COX)-2 caused by the exposure of THP-1 cells to LPS. Acknowledged the role of SIRT1 in the inflammatory process, we focused our attention on this enzyme. Our results showed that LPS dramatically decreased the expression of SIRT1, whereas vicenin-2 restored the levels of this enzyme to those of unexposed cells. These effects were also observed in terms of acetylated p53, a SIRT1 substrate. Notably, we observed that vicenin-2 did not act as a direct activator of SIRT1. Therefore, we investigated the potential involvement of AMP-activated protein kinase (AMPK), an upstream activator of SIRT1. Of note, by blocking AMPK by dorsomorphin, the protective effects of vicenin-2 on SIRT1 expression and activity were lost, suggesting the engagement of this kinase. Consequently, the blockage of AMPK caused a downstream loss of the anti-inflammatory effect of vicenin-2, which was no longer able to decrease both the activation of nuclear factor (NF)-κB and the production of cytokines induced by LPS. Finally, docking simulation suggested that vicenin-2 might act as an activator of Ca²⁺/calmodulin-dependent protein kinase kinase β (CaMKKβ), one of the regulators of AMPK. Overall, our results suggest that the anti-inflammatory effects of vicenin-2 may be due to the interaction with the CaMKKβ-AMPK-SIRT1 axis. Full article

Reconstructing high-resolution hyperspectral images (HR-HSIs) by fusing low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) is a significant challenge in image processing. Traditional fusion methods focus on visual and statistical metrics, often neglecting the requirements of downstream tasks. To address this gap, we propose a novel three-stream fusion network, 3DCNet, designed to integrate spatial and spectral information from LR-HSIs and HR-MSIs. The framework includes two dedicated branches for extracting spatial and spectral features, alongside a hybrid spatial–spectral branch (HSSI). The spatial block (SpatB) and the spectral block (SpecB) are designed to extract spatial and spectral details. The training process employs the global loss, spatial edge loss, and spectral angle loss for fusion tasks, with an alternating training iteration strategy (ATIS) to enhance downstream classification by iteratively refining the fusion and classification networks. Fusion experiments on seven datasets demonstrate that 3DCNet outperforms existing methods in generating high-quality HR-HSIs. Superior performance in downstream classification tasks on four datasets proves the importance of the ATIS. Ablation studies validate the importance of each module and the ATIS process. The 3DCNet framework not only advances the fusion process by leveraging downstream knowledge but also sets a new benchmark for classification-oriented hyperspectral fusion. Full article