Project description:Conventional Au nanomaterial synthesis typically necessitates the involvement of extensive surfactants and reducing agents, leading to a certain amount of chemical waste and biological toxicity. In this study, we innovatively employed ultra-small graphene oxide as a reducing agent and surfactant for the in situ generation of small Au nanoparticles under ultraviolet irradiation (UV) at ambient conditions. After ultra-small GO-Au seeds were successfully synthesized, we fabricated small star-like Au nanoparticles on the surface of GO, in which GO effectively prevented Austar from aggregation. To further use GO-Austar for cancer PTT therapy, through the modification of reduced human serum albumin-folic acid conjugate (rHSA-FA) and loading IR780, the final probe GO-Austar@rHSA-FA@IR780 was prepared. The prepared probe showed excellent biocompatibility and superb phototoxicity towards MGC-803 cells in vitro. In vivo, the final probe dramatically increased tumor temperature up to 58.6 °C after 5 minutes of irradiation by an 808 nm laser, significantly inhibiting tumor growth and nearly eradicating subcutaneous tumors in mice. This research provides a novel and simple method for the synthesis of GO-Au nanocomposites, showcasing significant potential in biological applications.
Project description:Computational analyses of functions of gene sets obtained in microarray analyses or by topical database searches are increasingly important in biology. To understand their functions, the sets are usually mapped to Gene Ontology knowledge bases by means of over-representation analysis (ORA). Its result represents the specific knowledge of the functionality of the gene set. However, the specific ontology typically consists of many terms and relationships, hindering the understanding of the 'main story'. We developed a methodology to identify a comprehensibly small number of GO terms as "headlines" of the specific ontology allowing to understand all central aspects of the roles of the involved genes. The Functional Abstraction method finds a set of headlines that is specific enough to cover all details of a specific ontology and is abstract enough for human comprehension. This method exceeds the classical approaches at ORA abstraction and by focusing on information rather than decorrelation of GO terms, it directly targets human comprehension. Functional abstraction provides, with a maximum of certainty, information value, coverage and conciseness, a representation of the biological functions in a gene set plays a role. This is the necessary means to interpret complex Gene Ontology results thus strengthening the role of functional genomics in biomarker and drug discovery.
Project description:While alkyl radicals have been well demonstrated to undergo both 1,5- and 1,6-hydrogen atom abstraction (HAA) reactions, 1,4-HAA is typically a challenging process both entropically and enthalpically. Consequently, chemical transformations based on 1,4-HAA have been scarcely developed. Guided by the general mechanistic principles of metalloradical catalysis (MRC), 1,4-HAA has been successfully incorporated as a key step, followed by 4-exo-tet radical substitution (RS), for the development of a new catalytic radical process that enables asymmetric 1,4-C-H alkylation of diazoketones for stereoselective construction of cyclobutanone structures. The key to success is the optimization of the Co(II)-based metalloradical catalyst through judicious modulation of D2-symmetric chiral amidoporphyrin ligand to adopt proper steric, electronic, and chiral environments that can utilize a network of noncovalent attractive interactions for effective activation of the substrate and subsequent radical intermediates. Supported by an optimal chiral ligand, the Co(II)-based metalloradical system, which operates under mild conditions, is capable of 1,4-C-H alkylation of α-aryldiazoketones with varied electronic and steric properties to construct chiral α,β-disubstituted cyclobutanones in good to high yields with high diastereoselectivities and enantioselectivities, generating dinitrogen as the only byproduct. Combined computational and experimental studies have shed light on the mechanistic details of the new catalytic radical process, including the revelation of facile 1,4-HAA and 4-exo-tet-RS steps. The resulting enantioenriched α,β-disubstituted cyclobutanones, as showcased with several enantiospecific transformations to other types of cyclic structures, may find useful applications in stereoselective organic synthesis.
Project description:Envelope insulation is a relevant technical solution to cut energy consumption and reduce environmental impacts in buildings. Insulation Cork Boards (ICB) are a natural thermal insulation material whose production promotes the recycling of agricultural waste. The aim of this paper is to determine and evaluate the environmental impacts of the production, use, and end-of-life processing of ICB. A "cradle-to-cradle" environmental Life Cycle Assessment (LCA) was performed according to International LCA standards and the European standards on the environmental evaluation of buildings. These results were based on site-specific data and resulted from a consistent methodology, fully described in the paper for each life cycle stage: Cork oak tree growth, ICB production, and end-of-life processing-modeling of the carbon flows (i.e., uptakes and emissions), including sensitivity analysis of this procedure; at the production stage-the modeling of energy processes and a sensitivity analysis of the allocation procedures; during building operation-the expected service life of ICB; an analysis concerning the need to consider the thermal diffusivity of ICB in the comparison of the performance of insulation materials. This paper presents the up-to-date "cradle-to-cradle" environmental performance of ICB for the environmental categories and life-cycle stages defined in European standards.
Project description:Robust data privacy regulations hinder the exchange of healthcare data among institutions, crucial for global insights and developing generalised clinical models. Federated learning (FL) is ideal for training global models using datasets from different institutions without compromising privacy. However, disparities in electronic healthcare records (EHRs) lead to inconsistencies in ML-ready data views, making FL challenging without extensive preprocessing and information loss. These differences arise from variations in services, care standards, and record-keeping practices. This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledge abstraction and filtering mechanism maps raw input representations to a unified, semantically rich shared space for effective global model training. Experiments on three healthcare datasets demonstrate the framework's effectiveness in overcoming data view heterogeneity and facilitating information sharing in a federated setup.
Project description:The development of antimicrobial materials with sustained drug release performance is of great importance. Graphene oxide (GO) is considered to be an ideal drug carrier. In this study, tetracycline hydrochloride (TC) was loaded onto polyethyleneimine-functionalized GO (PG) to fabricate TC/PG nanocomposites. The success of the fabrication was confirmed by zeta potential, TEM, FTIR, and Raman analyses. The TC/PG nanocomposites showed a controlled and sustained drug release behavior, and a pseudo second order kinetic model was employed to illustrate the release mechanism. The antibacterial activity was studied using the disk diffusion method against Escherichia coli and Staphylococcus aureus. The TC/PG nanocomposites exhibited great bacterial inhibition performance. The results indicate that the fabricated TC/PG nanocomposites with effective antibacterial activity have great potential in antibacterial applications.
Project description:The software-defined networking (SDN) paradigm relies on the decoupling of the control plane and data plane. Northbound interfaces enable the implementation of network services through logical centralised control. Suitable northbound interfaces and application-oriented abstractions are the core of the SDN ecosystem. This article presents an architecture to represent the network as a graph. The purpose of this architecture is to implement an abstraction of the SDN controller at the application plane. We abstract all network elements using a graph model, with the attributes of the elements as the attributes of the graph. This virtualized logical abstraction layer, which is not limited by the physical network, enables network administrators to schedule network resources directly in a global view. The feasibility of the presented graph abstraction was verified through experiments in topological display, dynamic route, access control, and data persistence. The performance of the shortest path in the graph-based abstraction layer and graph database proves the necessity of the graph abstraction layer. Empirical evidence demonstrates that the graph-based abstraction layer can facilitate network slicing, maintain a dependable depiction of the real network, streamline network administration and network application development, and provide a sophisticated abstraction that is easily understandable to network administrators.
Project description:SummaryKnowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results.Availability and implementationAn instance of the ROBOKOP UI for exploration of the ROBOKOP Knowledge Graph can be found at http://robokop.renci.org. The ROBOKOP Knowledge Graph can be accessed at http://robokopkg.renci.org. Code and instructions for building and deploying ROBOKOP are available under the MIT open software license from https://github.com/NCATS-Gamma/robokop.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:MotivationExperimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming, and cannot cope with the exponential growth of data generated by high throughput sequencing methods. Hence, researchers need reliable computational systems to help fill the gap with automatic function prediction. The results of the last Critical Assessment of Function Annotation challenge revealed that GO terms prediction remains a very challenging task. Recent developments on deep learning are significantly breaking out the frontiers leading to new knowledge in protein research thanks to the integration of data from multiple sources. However, deep models hitherto developed for functional prediction are mainly focused on sequence data and have not achieved breakthrough performances yet.ResultsWe propose DeeProtGO, a novel deep learning model for predicting GO annotations by integrating protein knowledge. DeeProtGO was trained for solving 18 different prediction problems, defined by the three GO sub-ontologies, the type of proteins, and the taxonomic kingdom. Our experiments reported higher prediction quality when more protein knowledge is integrated. We also benchmarked DeeProtGO against state-of-the-art methods on public datasets, and showed it can effectively improve the prediction of GO annotations.AvailabilityDeeProtGO and a case of use are available at https://github.com/gamerino/DeeProtGO.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:ObjectivesTo develop and validate an instrument about nutritional knowledge and feeding practices to be used in children aged 7-11 years, based on the latest Brazilian Food Guide.MethodsReview on the subject; instrument creation; content validity with two groups of judges: first, nutritionists and, after adjustments, a multidisciplinary group (content validity index [CVI]); FACE validity; reproducibility analysis (intraclass correlation coefficient [ICC], level of agreement, and kappa [k]); internal consistency analysis (Cronbach's alpha[α]) and construct validity (Kaiser-Meyer-Olkin and exploratory factorial analysis). The sample was calculated, considering at least ten subjects for each question of the questionnaire.ResultsThere was a final sample of 453 children (53.6% girls), with a mean age of 9.45 years (SD = 1.44). The content validity showed a CVI ≥ 0.80 for relevance in 62.3% of the items for nutritionists' group and 100% of the items for the multidisciplinary group, clarity (49.4%, 91.8%), and pertinence (58.8%, 98.4%), respectively. The test-retest showed a level of agreement of 84.3% and k = 0.740 for the Knowledge dimension and ICC = 0.754 for the Food Practices dimension. The internal consistency showed α = 0.589 for the Knowledge dimension and α = 0.618 for the Food Practices dimension. For the construct validity, Kaiser-Meyer-Olkin was 0.724 and exploratory factorial analysis showed a variance of 47.01 with varimax rotation and defined five factors for the Practices Dimension.ConclusionThe Food Knowledge and Practices Questionnaire (Questionário de Conhecimento e Práticas Alimentares [QCPA]) instrument showed validity and reliability to assess nutritional knowledge and food practices in children aged 7-11 years.