Search Results (497)

We describe five new isolates of two Gambierdiscus species from Bahía de La Paz in the southern Gulf of California. Batch cultures of Gambierdiscus were established for morphological characterization using light microscopy (LM) and scanning electron microscopy (SEM). Pigment and amino acid profiles were also analyzed using high-performance liquid chromatography (HPLC-UV and HPLC-DAD). Finally, toxicity (CTX-like and MTX-like activity) was evaluated using the Artemia salina assay (ARTOX), mouse assay (MBA), marine fish assay (MFA), and fluorescent receptor binding assay (fRBA). These strains were identified as Gambierdiscus cf. caribaeus and Gambierdiscus cf. carpenteri. Toxicity for CTX-like and MTX-like activity was confirmed in all evaluated clones. Seven pigments were detected, with chlorophyll a, pyridine, Chl2, and diadinoxanthin being particularly noteworthy. For the first time, a screening of the amino acid profile of Gambierdiscus from the Pacific Ocean was conducted, which showed 14 amino acids for all strains except histidine, which was only present in G. cf. caribeaus. We report the presence of Gambierdiscus and Fukuyoa species in the Mexican Pacific, where ciguatera fish poisoning (CFP) cases have occurred. Full article

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements. Full article

This study presents a soft-computing-based method for determining polymer pipelines’ creep function parameters (CFPs) and pressure wave speeds (PWSs) through transient flow analysis. To this end, first, a numerical model for transient flow in polymer pipes was developed in the time domain. Then, by considering a pipeline with a specific geometry, 2000 transient flow signals were generated for different CFPs and PWSs. The amplitudes obtained by transforming the time-domain pressure signals to the frequency domain using the fast Fourier transform algorithm are the inputs for an artificial neural network model. The results showed that the proposed approach accurately estimated the creep function of the polymer pipes. Full article

Developing effective and long-lasting electrocatalysts for oxygen evolution reaction (OER) is critical for increasing sustainable hydrogen production. This paper describes the production and characterization of CoFeP nanoparticles (CFP NPs) as high-performance electrocatalysts for OER. The CFP NPs were produced using a simple hydrothermal technique followed by phosphorization, yielding an amorphous/crystalline composite structure with improved electrochemical characteristics. Our results reveal that CFP NPs have a surprisingly low overpotential of 284 mV at a current density of 100 mA cm⁻², greatly exceeding the precursor CoFe oxide/hydroxide (CFO NPs) and the commercial RuO₂ catalyst. Furthermore, CFP NPs demonstrate exceptional stability, retaining a constant performance after 70 h of continuous operation. Post-OER characterization analysis revealed transformations in the catalyst, including the formation of cobalt–iron oxides/oxyhydroxides. Despite these changes, CFP NPs showed superior long-term stability compared to native metal oxides/oxyhydroxides, likely due to enhanced surface roughness and increased active sites. This study proposes a viable strategy for designing low-cost, non-precious metal-based OER catalysts, which will help advance sustainable energy technology. Full article

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions, has never been more urgent. This review paper explores the crucial role of digital technologies (i.e., data automation (DA) and decision support systems (DSSs)) in enhancing decision making and achieving a ZERONET initiative (decarbonation efforts) within the realms of solid waste management (SWM), wastewater treatment (WWT), and contaminated soil remediation (CSR). Specifically, the paper provides (a) an overview of the carbon footprint (CFP) in relation to environmental management (EM) and the role of DA and DSS in decarbonization; (b) case studies in areas of SWM, WWT, and CSR in relation to the use of (i) digital technology; ((ii) life cycle assessment (LCA)-based DSS; and (iii) multi-criteria decision analysis (MCDA)-based DSS; and (c) optimal contractual delivery method-based DSS case studies in EM practices. This review concludes that the adoption of DA and DSSs in SWM, WWT, and CSR holds significant potential for enhancing decision making and decarbonizing EM processes. By optimizing operations, enhancing resource efficiency, and integrating renewable energy sources, smart EM technologies can contribute to a reduction in GHG emissions and the promotion of sustainable EM practices. As the demand for more effective and eco-friendly solutions grows, the role of DA and DSSs will become increasingly pivotal in achieving global decarbonization goals. Full article

Quantifying the formation and decomposition of amyloid is a crucial issue in the development of new drugs and therapies for treating amyloidosis. The current technologies for grasping amyloid formation and decomposition include fluorescence analysis using thioflavin-T, secondary structure analysis using circular dichroism, and image analysis using atomic force microscopy or transmission electron microscopy. These technologies typically require spectroscopic devices or expensive nanoscale imaging equipment and involve lengthy analysis, which limits the rapid screening of amyloid-degrading drugs. In this study, we introduce a technology for rapidly assessing amyloid decomposition using capillary flow-based paper (CFP). Amyloid solutions exhibit gel-like physical properties due to insoluble denatured polymers, resulting in a shorter flow distance on CFP compared to pure water. Experimental conditions were established to consistently control the flow distance based on a hen-egg-white lysozyme amyloid solution. It was confirmed that as amyloid is decomposed by trypsin, the flow distance increases on the CFP. Our method is highly useful for detecting changes in the gel properties of amyloid solutions within a minute, and we anticipate its use in the rapid, large-scale screening of anti-amyloid agents in the future. Full article

The literature has reached a consensus that ISO standardization enhances the Environmental, Social, and Governance (ESG) performance of companies, which in turn has a positive effect on corporate financial performance (CFP). There is less understanding in terms of the effect of different certifications and underlying mechanisms between the effect of the ISO certification on the CFP. The purpose of this paper is to investigate the impact of different ISO certifications on the CFP of Turkish companies listed on the Istanbul Stock Exchange (BIST). Based on audited financial statements of a population of 148 manufacturing companies listed during 2010–2022 and using the generalized method of moments (GMM) technique, this study shows that the number of ISO certifications has a positive impact on return on asset (ROA) and Tobin’s Q, however, no direct effect on operational efficient and R&D intensity. While there is no effect of the occupational health and safety management systems certification on ROA and Tobin’s Q, the analysis brought forward that ROA seems to be positively affected by the standards referring to environmental, energy, quality, and information security management systems certification while Tobin’s Q is positively affected by the last two certifications. Full article

The Cell Formation Problem (CFP) is a widely studied issue that aims to group machines effectively based on criteria such as productivity, lower costs, and greater efficiency. In recent years, more characteristics were summarized relating to this problem. This paper provides a bibliographic examination of methodologies addressing the CFP in cellular manufacturing, focusing on novel approaches such as alternative routes and machine reliability. The articles were obtained from Scopus and Web of Science and filtered using the PRISMA methodology. Classification based on objective functions, constraints, and methodologies facilitated informative visualizations for analysis. Findings indicate a focus on capital utilization optimization, with cost reduction via intercellular moves minimization as the primary objective. Common constraints include limits on the number of machines per cell, restricting machines to a single cell and singular production routes per part. The genetic algorithm predominates as a non-exact solution approach, while the “$\epsilon$-constraint” method is commonly used. This study offers insights into contemporary trends in solving the CFP with alternative routings and machine reliability, aiding researchers and professionals in the field to improve the quality of their investigations. Full article

How rural land transfer affects the growth of non-agricultural income and the changes in its sources are important research topics. This study uses the micro-data from the China Family Panel Studies (CFPS) spanning from 2014 to 2020 and empirically analyzes the impact of rural land transfer on the growth of non-agricultural income, based on a multi-dimensional decomposition of rural household income structure. This study found that (1) land transfer has a significant promoting effect on the growth of non-agricultural income. Transferring out land is conducive to increasing wage income and transfer income, while transferring in land compensates for the decrease in operating income by achieving a higher operating income, ultimately leading to an increase in total income. (2) The effect of land transfer on the growth of non-agricultural income is higher in the Eastern region than in the Central and Western regions. The higher the education level of family members, the greater the income-increasing effect of land transfer on farmers. (3) Mechanism analysis shows that land transfer increases farmers’ opportunities for migrant work and improves farmers’ operational efficiency, which are the main channels for the growth in non-agricultural income. This study demonstrates that land circulation will promote farmers’ income growth and prosperity through rental income, share cooperation and dividends, labor transfer and wage income, industrial chain extension and value-added income, and policy support and subsidies. Full article

Background: This study aimed to evaluate the potential of human–machine interaction (HMI) in a deep learning software for discerning the malignancy of choroidal melanocytic lesions based on fundus photographs. Methods: The study enrolled individuals diagnosed with a choroidal melanocytic lesion at a tertiary clinic between 2011 and 2023, resulting in a cohort of 762 eligible cases. A deep learning-based assistant integrated into the software underwent training using a dataset comprising 762 color fundus photographs (CFPs) of choroidal lesions captured by various fundus cameras. The dataset was categorized into benign nevi, untreated choroidal melanomas, and irradiated choroidal melanomas. The reference standard for evaluation was established by retinal specialists using multimodal imaging. Trinary and binary models were trained, and their classification performance was evaluated on a test set consisting of 100 independent images. The discriminative performance of deep learning models was evaluated based on accuracy, recall, and specificity. Results: The final accuracy rates on the independent test set for multi-class and binary (benign vs. malignant) classification were 84.8% and 90.9%, respectively. Recall and specificity ranged from 0.85 to 0.90 and 0.91 to 0.92, respectively. The mean area under the curve (AUC) values were 0.96 and 0.99, respectively. Optimal discriminative performance was observed in binary classification with the incorporation of a single imaging modality, achieving an accuracy of 95.8%. Conclusions: The deep learning models demonstrated commendable performance in distinguishing the malignancy of choroidal lesions. The software exhibits promise for resource-efficient and cost-effective pre-stratification. Full article

Research on Chinese parenting styles using representative samples is limited, particularly during COVID-19, with most studies focusing on individual factors while neglecting regional influences. This study examines the impact of provincial income inequality, measured by the Gini coefficient, on parenting styles and how these effects vary across subgroups. Using data from the China Family Panel Studies (CFPS) 2020, encompassing 3768 children aged 7–16 years from 25 regions, we employed a multinomial logistic regression model to analyze the predictability of provincial income inequality on parenting styles identified through latent class analysis. Three parenting styles emerged during the first year of COVID-19: authoritarian (48.2%), autonomy granting (27.7%), and average-level undifferentiated (24.1%). A higher Gini coefficient related to a greater likelihood of parents adopting authoritarian or autonomy-granting parenting styles over average-level undifferentiated parenting. Subgroup analyses revealed a higher likelihood of adopting autonomy-granting and authoritarian parenting for male children compared to female. Mothers with lower education levels and parents in rural areas tend to favor authoritarian parenting in response to higher income inequality. This trend was less evident among more educated mothers and parents living in urban areas. These findings suggest that parenting styles in China are influenced by complex and region-specific factors. Full article

Alongside the economic system reforms and the rapid development of the Chinese economy, wealth gap in China is widening, gradually evolving into an important issue that threatens the sustainable development of China. To comprehensively understand the evolution of wealth disparity in China and the underlying reasons behind its changes, we use CHIP and CFPS datasets to construct time series data of China’s wealth inequality. Based on that, we explore the main reasons of the wealth change through asset decomposition, urban–rural decomposition, and regional decomposition, which is followed by the analysis of the possible role of policies in this process. Our findings reveal a two-stage evolution of wealth inequality in China: from 1995 to 2010, there was a rapid escalation in wealth disparity; after 2010, the rate of increase in China’s wealth disparity was gradually mitigated, yet persisted at a heightened level. Net housing assets, urban–rural disparity, and regional disparity have been pivotal in this evolution. In recent years, financial assets have demonstrated significant substitutability for housing assets, progressively supplanting housing assets as the principal driver of wealth inequality in China. We scrutinize the evolution of it in conjunction with China’s real estate, land, and capital market policies, finding these policies to have played critical roles in shaping the trajectory of inequality evolution. Full article

The pivotal role of digital infrastructure as hardware support for fostering economic efficiency in the digital economy is widely acknowledged. However, it begs the question, can the development of digital infrastructure also advance social equity, particularly concerning horizontal equity, as exemplified by the quality of non-farm employment among rural laborers, which serves as a barometer for the fairness and inclusivity of the social opportunity landscape? This article delves into the ramifications of digital infrastructure development on the quality of non-farm employment for rural laborers. Initially, it conducts a theoretical exploration of the impact and mechanisms of digital infrastructure construction on non-farm employment quality within rural labor sectors, drawing upon the Todaro model framework and existing scholarly discourse. Subsequently, by integrating data on digital infrastructure construction at the prefecture-level city level with four periods of the China Family Panel Studies (CFPS) data spanning 2014 to 2020, employing various endogenous treatment methods including two-way fixed effects, sensitivity analysis, and instrumental variable techniques, it empirically tests and analyzes the internal mechanisms. The findings reveal that digital infrastructure construction plays a beneficial role in enhancing the quality of non-farm employment for rural laborers, encompassing both subjective perceptions and objective circumstances of non-farm work. Notably, it is observed that digital infrastructure construction significantly fosters improvements in the quality of informal employment among rural laborers, with notable disparities across gender and skill levels. This discovery exerts a positive influence on advancing the sustainable development of the labor market. Specifically, female rural laborers necessitate higher skill proficiency and educational attainment to attain commensurate benefits as their male counterparts. Moreover, caution is warranted regarding the potential for digital infrastructure construction to exacerbate existing power differentials and widen socioeconomic disparities through the perpetuation of the digital divide. Full article

Transcriptomes and proteomes can be normalized with a handful of RNAs or proteins (or their peptides), such as GAPDH, β-actin, RPBMS, and/or GAP43. Even with hundreds of standards, normalization cannot be achieved across different molecular mass ranges for small molecules, such as lipids and metabolites, due to the non-linearity of mass by charge ratio for even the smallest part of the spectrum. We define the amount (or range of amounts) of metabolites and/or lipids per a defined amount of a protein, consistently identified in all samples of a multiple-model organism comparison, as the normative level of that metabolite or lipid. The defined protein amount (or range) is a normalized value for one cohort of complete samples for which intrasample relative protein quantification is available. For example, the amount of citrate (a metabolite) per µg of aconitate hydratase (normalized protein amount) identified in the proteome is the normative level of citrate with aconitase. We define normativity as the amount of metabolites (or amount range) detected when compared to normalized protein levels. We use axon regeneration as an example to illustrate the need for advanced approaches to the normalization of proteins. Comparison across different pharmacologically induced axon regeneration mouse models entails the comparison of axon regeneration, studied at different time points in several models designed using different agents. For the normalization of the proteins across different pharmacologically induced models, we perform peptide doping (fixed amounts of known peptides) in each sample to normalize the proteome across the samples. We develop Regen V peptides, divided into Regen III (SEB, LLO, CFP) and II (HH4B, A1315), for pre- and post-extraction comparisons, performed with the addition of defined, digested peptides (bovine serum albumin tryptic digest) for protein abundance normalization beyond commercial labeled relative quantification (for example, 18-plex tandem mass tags). We also illustrate the concept of normativity by using this normalization technique on regenerative metabolome/lipidome profiles. As normalized protein amounts are different in different biological states (control versus axon regeneration), normative metabolite or lipid amounts are expected to be different for specific biological states. These concepts and standardization approaches are important for the integration of different datasets across different models of axon regeneration. Full article

With the rapid advancements in data analysis and the increasing complexity of high-dimensional datasets, traditional dimensionality reduction techniques like Local Linear Embedding (LLE) often face challenges in maintaining accuracy and efficiency. This research aims to overcome the limitations of LLE, specifically its reliance on the nearest neighbor concept, its inability to distinguish differences among manifold points, and its underutilization of data discrimination information. To address these issues, we propose an advanced LLE algorithm that integrates decision tree-based neighbor recognition with Gaussian kernel density estimation. Decision trees accurately determine neighboring relationships, which are then optimized using Gaussian kernel density estimation to better reflect the distribution of sample points on the manifold. The algorithm also incorporates data discrimination information to enhance classification accuracy and efficiency. Evaluations in facial recognition tasks using SVM classifiers demonstrate significant improvements. Integrating decision trees (LLE-DT) yielded accuracy gains, with LFW at 98.75%, CFP 96.10%, and Olivetti 92.18%. Gaussian density estimation (LLE-GDE) achieved further enhancements, especially in LFW (99.13%), with CFP at 96.85%, and Olivetti at 91.82%. Combining both methods (LLE-DT-GDE) led to substantial improvements: LFW 99.61%, CFP 97.23%, and Olivetti 93.56%, highlighting the synergy between decision trees and Gaussian estimation. This advanced LLE algorithm effectively addresses the limitations of traditional approaches, showing promising results in complex data processing tasks such as facial recognition. These findings suggest its potential for broader applications in fields requiring robust data analysis and classification. Full article