Unique Function Research Articles

Electroanalytical application of phenol-functionalized reduced graphene oxide produced using gallic acid in a single step

This study introduces a simple one-step approach for synthesizing phenol-functionalized reduced graphene oxide for electroanalytical purposes, just using gallic acid (GA) and graphene oxide (GO). GA serves as both a reductant for GO and a stabilizing, functionalizing agent, introducing phenolic groups to the nanomaterial. This functionalization imparts remarkable attributes to the nanomaterial, allowing complete dispersion in aqueous solutions and tuning of its electrochemical performance, making it very convenient for electrode modification. The resulting nanomaterials (GA-rGO) underwent characterization through UV–visible, Fourier-transform infrared (FTIR), and Raman spectroscopies, as well as scanning electron microscopy (SEM). Glassy carbon electrodes modified with aqueous dispersions of these nanomaterials (GA-rGO/GCE) were employed in voltammetric experiments to evaluate the optimal dispersion composition for electroanalytical application.These GA-rGO/GCE displayed high electrocatalytic activity for the electrochemical oxidation of relevant clinical and environmental analytes. The unique functionalization of GA-rGO facilitated the selective accumulation of dopamine (DA) and uric acid (UA) on the electrode surface, even in the presence of significant amounts of ascorbic acid (AA) in mixtures. Under the specified conditions, voltammetric currents display linear increments over the concentration ranges of 3.0 x 10-7 M to 2.0 x 10-5 M for DA and 7.0 x 10-6 M to 1.0 x 10-4 M for UA. The sensor demonstrated a low detection limit of 0.090 and 2.1 μM for DA and UA, respectively. The reliability of the electroanalytical performance of the proposed sensor in real samples was demonstrated by the quantification of DA in medications, as well as DA and UA in human urine samples, yielding recovery values between 82 % and 105 %, with relative standard deviation below 11 %.

The Role of Farmers in Developing the Existence of Madrasah Diniyah Takmiliyah Wustho Al-Munawaroh

Community participation, both material and moral, is very important in efforts to develop the Diniyah takmiliyah Wustho madrasah. This research aims to analyze the role of the farming community in the development of Madrasah Diniyah Takmiliyah wustho in Timbuan Hamlet, Tambakmenjangan Village, Sarirejo District, Lamongan Regency. The subjects of this research are people who work as farmers in the hamlet who are in the development and existence of activities at Madrasah Diniyah Takmiliyah Wustho. This research method is a qualitative approach. Data collection techniques use participant observation, in-depth interviews, and documentation. The research results show that the farming community has an important role in the development of Madrasah Diniyah Takmiliyah Wustho. This is proven by the high enthusiasm of the Timbuan Hamlet farming community to contribute to the development and existence of activities at the madrasah. This enthusiasm is based on several factors, namely the opinion that the Diniyah takmiliyah wustho madrasah is an educational institution with complete knowledge because it teaches general knowledge and religious knowledge, the integration of learning Islamic religious knowledge into life, the view that rural communities still prioritize morals over knowledge, and the approach madrasas through regular monthly meetings. very affecting. From this it can be concluded that community relations in the world of education is a unique management function and supports the development and maintenance of community members, both communication and between organizations

Predicting evolutionary targets and parameters of gene deletion from expression data.

Gene deletion is traditionally thought of as a nonadaptive process that removes functional redundancy from genomes, such that it generally receives less attention than duplication in evolutionary turnover studies. Yet, mounting evidence suggests that deletion may promote adaptation via the "less-is-more" evolutionary hypothesis, as it often targets genes harboring unique sequences, expression profiles, and molecular functions. Hence, predicting the relative prevalence of redundant and unique functions among genes targeted by deletion, as well as the parameters underlying their evolution, can shed light on the role of gene deletion in adaptation. Here, we present CLOUDe, a suite of machine learning methods for predicting evolutionary targets of gene deletion events from expression data. Specifically, CLOUDe models expression evolution as an Ornstein-Uhlenbeck process, and uses multi-layer neural network, extreme gradient boosting, random forest, and support vector machine architectures to predict whether deleted genes are "redundant" or "unique", as well as several parameters underlying their evolution. We show that CLOUDe boasts high power and accuracy in differentiating between classes, and high accuracy and precision in estimating evolutionary parameters, with optimal performance achieved by its neural network architecture. Application of CLOUDe to empirical data from Drosophila suggests that deletion primarily targets genes with unique functions, with further analysis showing these functions to be enriched for protein deubiquitination. Thus, CLOUDe represents a key advance in learning about the role of gene deletion in functional evolution and adaptation. CLOUDe is freely available on GitHub (https://github.com/anddssan/CLOUDe).

SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.

SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64%, 73.36%, 0.48% and 74.00%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.

Shock Wave Detection for In-Process Depth Measurement in Laser Ablation Using a Photonic Nanojet

Three-dimensional micro- and submicrometer-scale structures exhibit unique functions that cannot be obtained with bulk materials. To create such three-dimensional microstructures with high precision and efficiency, we proposed laser ablation using a photonic nanojet. A photonic nanojet is an optical beam with both a small beam diameter and a large depth of focus, which is obtained by irradiating a dielectric microsphere using a laser beam. In this study, we proposed an in-process depth measurement method to improve the machining accuracy of laser ablation using a photonic nanojet. We focused on the propagation characteristics of the shock waves generated during laser ablation. Shock waves were generated at the deepest point of the machining area and reached the microspheres as the pressure decayed, showing that different machining depths exerted different pressures on the microspheres. The microspheres were displaced by the pressure of the shock wave, and the amount of displacement depended on the pressure. Therefore, microspheres can be used as probes for shock wave detection, and the machining depth can be determined by measuring the displacement of microspheres during photonic nanojet machining. In this study, the displacement of a microsphere was measured simultaneously during photonic nanojet machining using a confocal optical system. From the obtained microsphere vibration data, the effect of the shock wave pressure was extracted, and the displacement of the microsphere due to the shock wave was obtained. When the hole depth varied from 155 to 1121 nm, the displacement of the microspheres varied from 0.58 to 0.03 µm. The experimental results show that the displacement of the microspheres vibrated by the shock wave decreased as the machining depth increased. This was due to an increase in the shock wave propagation distance and a decrease in the pressure of the shock wave as the machining depth increased. In conclusion, in-process depth measurements are possible in laser ablation using a photonic nanojet with a microsphere as a probe to detect shock waves.

Pickering multiphase materials using plant‐based cellulosic micro/nanoparticles

AbstractPickering multiphase systems stabilized by solid particles have recently attracted increasing attention due to their excellent stability. Among various solid stabilizers, natural and renewable cellulosic micro/nanoparticles that are derived from agricultural and forestry sources have become promising candidates for Pickering stabilization due to their unique morphological features and tunable surface properties. In this review, recent progress on forming and stabilizing Pickering multiphase systems using cellulosic colloidal particles is summarized, including the physicochemical factors affecting their assembly at the interfaces and the preparation methods suitable for producing Pickering emulsions. In addition, relevant application prospects of corresponding Pickering multiphase materials are outlined. Finally, current challenges and future perspectives of such renewable Pickering multiphase systems are presented. This review aims to encourage the utilization of cellulosic micro/nanoparticles as key components in the development of Pickering systems, leading to enhanced performance and unique functionalities.

Type2 soft biclustering framework for Alzheimer microarray

Microarray technology is a powerful tool that enables simultaneous analysis of the expression level of a large number of genes for different samples. Reliable information on gene expression level is much needed in the health system as it is widely used to predict, diagnose, and treat human diseases (e.g., Alzheimer's). For the analysis of the microarray dataset, biclustering is known to be a highly capable approach, however some characteristics of the dataset including high dimensionality, noise, uncertainty, and complex biological processes need to be handled properly. Concerning these characteristics, the current paper proposes a novel two-stage biclustering framework based on soft clustering and a metaheuristic technique. The integration of the two stages ensures a reliable search process to find similar expression patterns concerning gene expression characteristics. The proposed framework employs fuzzy and possibilistic clustering along with Type2-Fuzzy Sets theory to handle high-level uncertainty, noise, and outliers in microarray datasets. Considering the NP-hard nature of the biclustering method, the proposed framework incorporates the Genetic Algorithm with a unique chromosome representation, fitness function, and modification mechanisms. Real microarray datasets of Alzheimer's Disease have been used to evaluate the proposed framework. The comparative analysis of different versions of our proposed framework and some well-known biclustering methods demonstrates that the proposed framework is superior in terms of some indices including the mean squared residual and variance indices. The final results are further evaluated using the defined fitness function, which indicates the better performance of our possibilistic-based biclustering methods.

Mitochondrial Esterase Activity Measured at the Single Organelle Level by Nano-flow Cytometry.

Monitoring mitochondrial esterase activity is crucial not only for investigating mitochondrial metabolism but also for assessing the effectiveness of mitochondrial-targeting prodrugs. However, accurately detecting esterase activity within mitochondria poses challenges due to its ubiquitous presence in cells and the uncontrolled localization of fluorogenic probes. To overcome this hurdle and reveal variations among different mitochondria, we isolated mitochondria and preserved their activity and functionality in a buffered environment. Subsequently, we utilized a laboratory-built nano-flow cytometer in conjunction with an esterase-responsive calcein-AM fluorescent probe to measure the esterase activity of individual mitochondria. This approach enabled us to investigate the influence of temperature, pH, metal ions, and various compounds on the mitochondrial esterase activity without any interference from other cellular constituents. Interestingly, we observed a decline in the mitochondrial esterase activity following the administration of mitochondrial respiratory chain inhibitors. Furthermore, we found that mitochondrial esterase activity was notably higher in the presence of a high concentration of ATP compared to that of ADP and AMP. Additionally, we noticed a correlation between elevated levels of complex IV and increased mitochondrial esterase activity. These findings suggest a functional connection between the mitochondrial respiratory chain and mitochondrial esterase activity. Moreover, we detected an upsurge in mitochondrial esterase activity during the early stages of apoptosis, while cellular esterase activity decreased. This highlights the significance of analyzing enzyme activity within specific organelle subregions. In summary, the integration of a nano-flow cytometer and fluorescent dyes introduces a novel method for quantifying mitochondrial enzyme activity with the potential to uncover the alterations and unique functions of other mitochondrial enzymes.

The ubiquitin ligases Cbl and Cbl-b regulate macrophage growth by controlling CSF-1R import into macropinosomes.

The ubiquitination of transmembrane receptors regulates endocytosis, intracellular traffic, and signal transduction. Bone marrow-derived macrophages from myeloid Cbl-/- and Cbl-b-/- double knockout (DKO) mice display sustained proliferation mirroring the myeloproliferative disease that these mice succumb to. Here, we found that the ubiquitin ligases Cbl and Cbl-b have overlapping functions for controlling the endocytosis and intracellular traffic of the CSF-1R. DKO macrophages displayed complete loss of ubiquitination of the CSF-1R whereas partial ubiquitination was observed for either single Cbl-/- or Cbl-b-/- macrophages. Unlike wild type, DKO macrophages were immortal and displayed slower CSF-1R internalization, elevated AKT signaling, and a failure to transport the CSF-1R into the lumen of nascent macropinosomes, leaving its cytoplasmic region available for signaling. CSF-1R degradation depended upon lysosomal vATPase activity in both WT and DKO macrophages, with this degradation confined to macropinosomes in WT but occurring in distributed/tubular lysosomes in DKO cells. RNA-sequencing comparison of Cbl-/-, Cbl-b-/- and DKO macrophages indicated that while the overall macrophage transcriptional program remained intact, DKO macrophages had alterations in gene expression associated with growth factor signaling, cell cycle, inflammation and senescence. Cbl-b-/- had minimal effect on the transcriptional program whereas Cbl-/- led to more alternations but only DKO macrophages demonstrated substantial changes in the transcriptome, suggesting overlapping but unique functions for the two Cbl-family members. Thus, Cbl/Cbl-b-mediated ubiquitination of CSF-1R regulates its endocytic fate, constrains inflammatory gene expression, and regulates signaling for macrophage proliferation.

Polymer ionogels and their application in flexible ionic devices

AbstractPolymer ionogel (PIG) is a new type of flexible, stretchable, and ion‐conductive material, which generally consists of two components (polymer matrix materials and ionic liquids/deep eutectic solvents). More and more attention has been received owing to its excellent properties, such as nonvolatility, good ionic conductivity, excellent thermal stability, high electrochemical stability, and transparency. In this review, the latest research and developments of PIGs are comprehensively reviewed according to different polymer matrices. Particularly, the development of novel structural designs, preparation methods, basic properties, and their advantages are respectively summarized. Furthermore, the typical applications of PIGs in flexible ionic skin, flexible electrochromic devices, flexible actuators, and flexible power supplies are reviewed. The novel working mechanism, device structure design strategies, and the unique functions of the PIG‐based flexible ionic devices are briefly introduced. Finally, the perspectives on the current challenges and future directions of PIGs and their application are discussed.

Chicken Interleukin-5 is Expressed in Splenic Lymphocytes and Affects Antigen-Specific Antibody Production.

Vaccination is important for reducing disease incidence in the poultry industry. To enhance immunity and vaccine efficacy, chicken cytokines associated with antibody production must be identified. In this study, we focused on interleukin-5 (IL-5), involved in antibody production in mice, measuring its expression and effects on antibody production. Concanavalin A-stimulated splenocytes were used for RT-PCR to clone IL5 cDNAs. Recombinant IL-5 was prepared from the clone and administered to chickens with antigen via the ocular-topical route twice every alternate week. IL-5 enhanced antigen-specific IgY and inhibited antigen-specific serum IgA production in serum. Our findings suggest that IL-5 plays an important role in chicken antibody production, with possible unique functions.

Eliminative Oximation of O-Glycans from Mucins.

The large variety and high concentration of O-glycans are characteristic properties of mucins and play a crucial role in their unique functions. Analyzing the O-glycans of mucins is essential for investigating the functions of mucins. Eliminative oximation is an aqueous reaction that can be used to obtain O-glycan oximes from mucins. Using diazabicyclo undec-7ene (DBU) as a base, an organic superbase that can be removed with an organic solvent during solid-phase extraction, and adding hydroxylamine to the reaction mixture in advance, the O-glycans released from the mucin are immediately converted to the corresponding glycan oximes. The glycan oxime can then be fluorescently labeled with a fluorescent labeling reagent and 2-picoline borane via reductive amination. O-glycans that have been fluorescently labeled can be analyzed using conventional HPLC techniques.

CD-BFT: Canonical Decomposition-Based Belief Functions Transformation in Possibility Theory.

Based on subjective possibilistic semantics, an agent's subjective probability mass function is dominated by a qualitative Possibility Mass Function (PossMF), which can also be transformed into a unique consonant mass function. However, the existing transformation method cannot maintain the consistency of combination rules, i.e., fusing PossMFs and consonant mass functions with same information content, respectively, the results no longer maintain the reversible transformation. To address the above issue, a novel belief functions transformation is proposed, which can be interpreted based on both Smets' canonical decomposition and Pichon's canonical decomposition. The proposed method is validated based on consistency of combination rules, the least commitment principle, and its application in the fusion of information. In addition, based on the two canonical decompositions, we extend the transformation to possibilistic belief structure, and offer a new perspective of relationship between possibilistic information and evidential information.

Game-changing insights on vertebral skeletal stem cells in bone metastasis and therapeutic horizons.

Greenblatt and his team have unveiled vertebral skeletal stem cells (vSSCs) as a critical player in the landscape of bone metastasis. This commentary delves into the transformative discoveries surrounding vSSCs, emphasizing their distinct role in bone metastasis compared to other stem cell lineages. We illuminate the unique properties and functions of vSSCs, which may account for the elevated susceptibility of vertebral bones to metastatic invasion. Furthermore, we explore the exciting therapeutic horizons opened by this newfound understanding. These include potential interventions targeting vSSCs, modulation of associated signaling pathways, and broader implications for the treatment and management of bone metastasis. By shedding light on these game-changing insights, we hope to pave the way for novel strategies that could revolutionize the prognosis and treatment landscape for cancer patients with metastatic bone disease.

Exploring Circular RNA Profile and Expression in Extracellular Vesicles.

Extracellular vesicles (EVs) are small vesicles secreted by various cell types and are enriched in multiple body fluids. EVs containing RNA have the potential to modulate biological processes and are being investigated for their diagnostic and therapeutic applications. Circular RNAs (circRNAs), generated through back-splicing of exons, are enriched in EVs. Given their unique characteristics and diverse functions, EV-circRNAs are important players in disease pathology. This chapter describes a workflow for investigating the expression profile of EV-circRNAs, which includes EVs separation, library preparation, and bioinformatics analysis. This workflow can aid the investigation of EV-circRNAs and their potential role in disease pathology.

Advancements and applications of upconversion nanoparticles in wound dressings.

Wound healing is a complex process that requires effective management to prevent infections and promote efficient tissue regeneration. In recent years, upconversion nanoparticles (UCNPs) have emerged as promising materials for wound dressing applications due to their unique optical properties and potential therapeutic functionalities. These nanoparticles possess enhanced antibacterial properties when functionalized with antibacterial agents, helping to prevent infections, a common complication in wound healing. They can serve as carriers for controlled drug delivery, enabling targeted release of therapeutic agents to the wound site, allowing for tailored treatment and optimal healing conditions. These nanoparticles possess the ability to convert near-infrared (NIR) light into the visible and/or ultraviolet (UV) regions, making them suitable for therapeutic (photothermal therapy and photodynamic therapy) and diagnostic applications. In the context of wound healing, these nanoparticles can be combined with other materials such as hydrogels, fibers, metal-organic frameworks (MOFs), graphene oxide, etc., to enhance the healing process and prevent the growth of microbial infections. Notably, UCNPs can act as sensors for real-time monitoring of the wound healing progress, providing valuable feedback to healthcare professionals. Despite their potential, the use of UCNPs in wound dressing applications faces several challenges. Ensuring the stability and biocompatibility of UCNPs under physiological conditions is crucial for their effective integration into dressings. Comprehensive safety and efficacy evaluations are necessary to understand potential risks and optimize UCNP-based dressings. Scalability and cost-effectiveness of UCNP synthesis and manufacturing processes are important considerations for practical applications. In addition, efficient incorporation of UCNPs into dressings, achieving uniform distribution, poses an important challenge that needs to be addressed. Future research should prioritize addressing concerns regarding stability and biocompatibility, efficient integration into dressings, rigorous safety evaluation, scalability, and cost-effectiveness. The purpose of this review is to critically evaluate the advantages, challenges, and key properties of UCNPs in wound dressing applications to provide insights into their potential as innovative solutions for enhancing wound healing outcomes. We have provided a detailed description of various types of smart wound dressings, focusing on the synthesis and biomedical applications of UCNPs, specifically their utilization in different types of wound dressings.

A Novel Feature Engineering Method Based on Latent Representation Learning for Radiomics: Application in NSCLC Subtype Classification.

Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features. In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p<0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance. Our research shows that latent representation learning is a more effective feature engineering method, which has the potential to be used as a general technology in a wide range of radiomics researches.

Highly oriented hydrogels for tissue regeneration: design strategies, cellular mechanisms, and biomedical applications.

Many human tissues exhibit a highly oriented architecture that confers them with distinct mechanical properties, enabling adaptation to diverse and challenging environments. Hydrogels, with their water-rich "soft and wet" structure, have emerged as promising biomimetic materials in tissue engineering for repairing and replacing damaged tissues and organs. Highly oriented hydrogels can especially emulate the structural orientation found in human tissue, exhibiting unique physiological functions and properties absent in traditional homogeneous isotropic hydrogels. The design and preparation of highly oriented hydrogels involve strategies like including hydrogels with highly oriented nanofillers, polymer-chain networks, void channels, and microfabricated structures. Understanding the specific mechanism of action of how these highly oriented hydrogels affect cell behavior and their biological applications for repairing highly oriented tissues such as the cornea, skin, skeletal muscle, tendon, ligament, cartilage, bone, blood vessels, heart, etc., requires further exploration and generalization. Therefore, this review aims to fill that gap by focusing on the design strategy of highly oriented hydrogels and their application in the field of tissue engineering. Furthermore, we provide a detailed discussion on the application of highly oriented hydrogels in various tissues and organs and the mechanisms through which highly oriented structures influence cell behavior.

THE EXECUTION OF THE MULTILEVEL FAST MULTIPOLE ALGORITHM USING FOURIER ANALYSIS TO SOLVE PLANE WAVE SCATTERING FROM PERFECT ELECTRIC CONDUCTOR SURFACES

The multilevel fast multipole algorithm (MLFMA) is an algorithm employed to speed up the matrix-vector multiplication required to solve equations using an iterative method. In this algorithm, the Green's function is approximated by an expression that separates the receiver point from the source point. Each problem has a unique Green's function, which means derivations are required to get the approximation. As a result, although the algorithm is the same for all problems, each problem requires a new code. To solve these issues, the MLFMA is executed by approximating the Green's function using a Fourier series. The proposed approach is to finding the approximation is straightforward. In addition, the code will always be the same regardless of the problem. In this study, electromagnetic wave scattering from two- and three-dimensional perfect electric conductor surfaces was solved using the proposed approach, the conventional approach, and the method of moments. The results revealed that the suggested method is precise and faster compared to the method of moments and slightly slower than the MLFMA for a large number of unknowns.

Transient intermediate in the formation of an amorphous metal-organic framework.

Amorphous metal-organic frameworks are rarely formed via direct synthesis. Our limited understanding of their atomic assembly in solution prevents full exploitation of their unique structural complexity. Here, we use in situ synchrotron X-ray absorption spectroscopy with sub-second time resolution to probe the formation of the amorphous Fe-BTC framework. Using a combination of spectral fingerprinting, linear combination analysis, and principal component analysis coupled with kinetic analyses, we reveal a multi-stage formation mechanism that, crucially, proceeds via the generation of a transient intermediate species.