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Bacteria of the genus Pseudomonas are ubiquitous on Earth due to their great metabolic versatility and adaptation to fluctuating environments and different hosts. Some groups are important animal/human and plant pathogens, whereas others are studied for their biotechnological applications, including bioremediation, biological control of phytopathogens and plant growth promotion. Notably, their adaptability is mediated by various signal transduction systems, with the post-transcriptional Gac-Rsm cascade playing a key role. This pervasive Pseudomonas pathway controls major transitions at the population level, such as motile/sessile lifestyle, primary/secondary metabolism or replicative/infective behaviour. A hallmark of the Gac-Rsm cascade is the participation of small, regulatory, non-coding RNAs of the Rsm clan. These RNAs are synthetised in response to cell-density-dependent autoinducer signals channelled through the GacS/GacA two-component system, and they counteract, by molecular mimicry, the translational control that RNA-binding proteins of the RsmA family exert over hundreds of mRNAs. Rsm RNAs have been investigated in a few Pseudomonas model species, evidencing the presence of a variable number and families of genes depending on the taxonomic clade. However, the global picture of the distribution of these riboregulators at the genus level was unknown until now. We have undertaken a comprehensive survey and annotation of the vast array of gene sequences encoding members of the Rsm RNA clan in 245 complete genomes that cover 28 phylogenomic clades across the entire genus. The properties of the different families of rsm genes, their phylogenetic radiation, as well as the features of their promoters and adjacent regions, are discussed. The novel insights presented in our manuscript will significantly boost research on the biology of these prevalent RNAs in understudied species of the genus Pseudomonas and closely related genera.

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The development of efficient methods for dereplication has been critical in the re-emergence of the research in natural products as a source of drug leads. Current dereplication workflows rapidly identify already known bioactive secondary metabolites in the early stages of any drug discovery screening campaign based on natural extracts or enriched fractions. Two main factors have driven the evolution of natural products dereplication over the last decades. First, the availability of both commercial and public large databases of natural products containing the key annotations against which the biological and chemical data derived from the studied sample are searched for. Second, the considerable improvement achieved in analytical technologies (including instrumentation and software tools) employed to obtain robust and precise chemical information (particularly spectroscopic signatures) on the compounds present in the bioactive natural product samples. This chapter describes the main methods of dereplication, which rely on the combined use of large natural product databases and spectral libraries, alongside the information obtained from chromatographic, UV-Vis, MS, and NMR spectroscopic analyses of the samples of interest.

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The potential zones of gold mineralization were identified in this study using aeromagnetic and aero-radiometric methods. The Nigerian Geological Survey Agency (NGSA) provided half-degree airborne magnetic and radiometric datasets covering the southern part of Kebbi State. Magnetic data were subjected to first vertical derivative (1VD), total gradient amplitude (TGA), total horizontal derivative (THD), source edge detection (SED), center for exploration targeting (CET), Euler deconvolution (ED) and source parameter imaging (SPI) to identify favourable structures to gold mineralization. Aero-radiometric data delineation of the region of hydrothermal alteration zones through the K/eTh ratio, K_deviation, F_parameters, and Ternary image analyses were successful. The results of the magnetic data techniques revealed the regions of major structures/or lineaments with gold mineralization attributes, trending in the NE to SW directions and the SE to NE parts of the study area. The depth to the structures of the magnetic source hosting gold mineralization was less than 5 m using algorithm ED and SPI techniques. Normalized radiometric data showed the area of anomalously high and moderate hydrothermal altered zones. The region's designation as a gold field is supported by major fault lines observed on the 1VD, a sequence of bristle fractures from the CET analysis, and high values of K_deviation and F_parameter, all of which are were visible on the Ternary images. The integrated results revealed zones of major structures and hydrothermal regions of gold fields at Agwara, Western Magama, Rijau, Fakai, Bukkuyum, and Borgu in the SE of the study area.

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Milk casein is regarded as source to release potential sleep-enhancing peptides. Although various casein hydrolysates exhibited sleep-enhancing activity, the underlying reason remains unclear. This study firstly revealed the structural features of potential sleep-enhancing peptides from casein hydrolysates analyzed through peptidomics and multivariate analysis. Additionally, a random forest model and a potential Tyr-based peptide library were established, and then those peptides were quantified to facilitate rapidly-screening. Our findings indicated that YP-, YI/L, and YQ-type peptides with 4-10 amino acids contributed more to higher sleep-enhancing activity of casein hydrolysates, due to their crucial structural features and abundant numbers. Furthermore, three novel strong sleep-enhancing peptides, YQKFPQY, YPFPGPIPN, and YIPIQY were screened, and their activities were validated in vivo. Molecular docking results elucidated the importance of the YP/I/L/Q- structure at the N-terminus of casein peptides in forming crucial hydrogen bond and π-alkyl interactions with His-102 and Asn-60, respectively in the GABAA receptor for activation.

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In recent years, the isolation, purification, structural characterization of plant polysaccharides from natural resources have arrested widespread attention. Aralia elata (Miq.) Seem (A. elata) belongs to the Aralia genus of the Araliaceae family, which is one of the most popular edible mountain vegetables in East Asia. A. elata has been widely distributed in China, particularly in Liaoning, Jilin, and Heilongjiang provinces in northeast China, in which it has been used as a traditional herbal medicine for thousands of years to treat various diseases, such as hepatitis and rheumatoid arthritis. A. elata polysaccharides (AEPs) are one of the major active ingredients of A. elata, the monosaccharide composition of which consist primarily of Gal, Glc, Man, Ara, and Rha, with molecular weights ranging from 1.56 × 104 Da to 1.12 × 105 Da. AEPs have attracted worldwide attention owing to their various biological activities, including antioxidant activity, antitumor activity and hepatoprotection. The present review aims to comprehensively summarize the research advances on the polysaccharides isolated from A. elata, including the extraction, separation, physical-chemical properties, structural characteristics, and bioactivities over the past few decades. This review would establish a solid foundation for further development and application in the field of AEPs.

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This article, from the perspective of structural features, focuses on in-car user interface icons and explores the impact of different icon structural features on visual search efficiency. Initially, we categorised the icons into four groups based on structural features: individual structure icons (ISI), enclosed structure icons (ESI), horizontal structure icons (HSI) and vertical structure icons (VSI). Subsequently, we conducted a visual search experiment with structure as the sole variable, recording participants' behaviours and eye-tracking data. Finally, data analysis was conducted using methods including analysis of variance and logistic regression. The results indicate that differences in icon structural features significantly affect visual search efficiency, showcasing significant intergroup differences. HSI exhibit the highest visual search efficiency, while ESI show the lowest efficiency. ISI have shorter response times but the lowest matching accuracy. VSI only perform better than ESI. These findings hold significant implications for optimising icon design and enhancing visual search efficiency.

Visual search efficiency of icons is crucial for human-computer interaction. We investigated how the structural features of icons influence visual search efficiency. Horizontal icons are most effective, enclosed icons the least. Individual icons are quick but less accurate. Vertical icons outperform enclosed ones. Structural features should be considered in design.

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Weed resistance is a critical issue in crop production. Among the known herbicides, 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors are crucial for addressing weed resistance. HPPD inhibitors constitute a pivotal aspect of contemporary crop protection strategies. The advantages of these herbicides are their broad weed spectrum, flexible application, and excellent compatibility with other herbicides. They also exhibit satisfactory crop selectivity and low toxicity and are environmentally friendly. An increasing number of new HPPD inhibitors have been designed by combining computer-aided drug design with conventional design approaches. Herein, the molecular design and structural features of innovative HPPD inhibitors are reviewed to guide the development of new HPPD inhibitors possessing an enhanced biological efficacy.

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Eucommia ulmoides Oliv. (tu-chung), as a famous nature medical and edible plant, has the effect of tonifying liver and kidney, strengthening the function of the muscles and bones, and miscarriage prevention. Accumulating evidence has demonstrated that the polysaccharides from Eucommia ulmoides Oliv. (EUPs) are a kind of vital and representative biologically active macromolecules and have various health-promoting biological activities in vivo and in vitro, such as antioxidant activity, immunomodulatory activity, hypolipidemic and hypoglycemic activities, anti-inflammatory activities, anti-tumor activity, and among others. The review aims to comprehensively and systematically collate the recent research progress on extraction and purification methods, structural characteristics, biological activities, mechanism of action, structural modification, and toxicity of EUPs to support their therapeutic potential and health-care functions. New valuable insights for future research with EUPs were also proposed in the areas of structural characterization and pharmacological activities to promoting the development of therapeutic agents and functional foods.

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Porphyra haitanensis (P. haitanensis), an important food source for coastal residents in China, has a long history of medicinal and edible value. P. haitanensis polysaccharides are some of the main active ingredients in P. haitanensis. It is worth noting that P. haitanensis polysaccharides have a surprising and satisfactory biological activity, which explains the various benefits of P. haitanensis to human health, such as anti-oxidation, immune regulation, anti-allergy, and anticancer properties. Hence, a systematic review aimed at comprehensively summarizing the recent research advances in P. haitanensis polysaccharides is necessary for promoting their better understanding. In this review, we systematically and comprehensively summarize the research progress on the extraction, purification, structural characterization, modification, and biological activity of P. haitanensis polysaccharides and address the shortcomings of the published research and suggest area of focus for future research, providing a new reference for the exploitation of polysaccharides from P. haitanensis in the fields of medicine and functional foods.

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Sterigmatocystins and aflatoxins are a group of mycotoxins mainly isolated from fungi of the genera Aspergillus. Since the discovery of sterigmatocystins in 1954 and aflatoxins in 1961, many scholars have conducted a series of studies on their structural identification, synthesis and biological activities. Studies have shown that sterigmatocystins and aflatoxins have a wide range of biological activities such as antitumour, antibacterial, anti-inflammatory, antiplasmodial, etc. The sterigmatocystins and aflatoxins had been shown to be hepatotoxic and nephrotoxic in animals. This review attempts to give a comprehensive summary of progress on the chemical structural features, synthesis, and bioactivity of sterigmatocystins and aflatoxins reported from 1954 to April 2024. A total of 72 sterigmatocystins and 20 aflatoxins are presented in this review. This paper reviews the chemical diversity and potential activity and toxicity of sterigmatocystins and aflatoxins, enhances the understanding of sterigmatocystins and aflatoxins that adversely affect humans and animals, and provides ideas for their prevention, research and development.

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Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

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Semen Coicis (S. Coicis) has been regarded as a valuable source of traditional herbal medicine in China for thousands of years. S. Coicis polysaccharides (SCPs) are one of the most important bioactive ingredients of S. Coicis, which have attracted worldwide attention, because of their great marketing potential and development prospects. Hot water extraction is currently the most commonly used method to isolate SCPs. The structural characteristics of SCPs have been extensively investigated through various advanced modern analytical techniques to dissect the structure-activity relationships. SCPs are mainly composed of diverse monosaccharides, from which Rha and Ara are the most prevalent glycosyl groups. In addition, the structures of SCPs are found to be closely related to their multiple biological activities, including antioxidant activity, immunomodulatory function, antitumor activity, hypoglycemic effect, intestinal microbiota regulatory activity, anti-inflammatory activity, among others. In view of this, this review aimed to provide systematic and current information on the isolation, structural characteristics, and bioactivities of SCPs to support their future applications as therapeutic agents and functional foods.

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Background and Objectives: As the gaming industry experiences exponential growth, concerns about gaming disorder (GD) also grow. It is crucial to understand the structural features of games that can interact with individual characteristics of gamers to promote GD. This research consolidates the views of an international body of panelists to create an assessment tool for gauging the addictive potential of distinct games. Methods: Utilizing the iterative and structured Delphi method, an international panel of researchers, clinicians, and people with lived experience were recruited to offer a multifaceted viewpoint on the addictive risk associated with specific structural elements in games. Two rounds of surveys facilitated consensus. Results: The panel initially included 40 members-ten from research, eight from clinical settings, and 22 with lived experiences. The second round included 27 panelists-seven from research, eight from clinical settings, and 11 with lived experiences. The study identified 25 structural features that contribute to potentially addictive gaming patterns. Discussion and Conclusions: Consensus was found for 25 features, which were distilled into a 23-item evaluation tool. The Saini-Hodgins Addiction Risk Potential of Games Scale (SHARP-G) consists of five overarching categories: 'Social,' 'Gambling-Like Features,' 'Personal Investment,' 'Accessibility,' and 'World Design.' SHARP-G yields a total score indicating level of addiction risk. A case study applying the scale to three games of differing perceived risk levels demonstrated that that score corresponded to game risk as expected. While the SHARP-G scale requires further validation, it provides significant promise for evaluating gaming experiences and products.

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Due to the high content of impurities such as proteins in tamarind seed polysaccharide (TSP), they must be separated and purified before it can be used. TSP can disperse in cold water, but a solution can only be obtained by heating the mixture. Therefore, it is important to understand the dispersion and dissolution process of TSP at different temperatures to expand the application of TSP. In this study, pasting behavior and rheological properties as a function of temperature were characterized in comparison with potato starch (PS), and their relationship with TSP molecular features and microstructure was revealed. Pasting behavior showed that TSP had higher peak viscosity and stronger thermal stability than PS. Rheological properties exhibited that G' and G'' of TSP gradually increased with the increase in temperature, without exhibiting typical starch gelatinization behavior. The crystalline or amorphous structure of TSP and starch was disrupted under different temperature treatment conditions. The SEM results show that TSP particles directly transformed into fragments with the temperature increase, while PS granules first expanded and then broken down into fragments. Therefore, TSP and PS underwent different dispersion mechanisms during the dissolution process: As the temperature gradually increased, TSP possibly underwent a straightforward dispersion and was then dissolved in aqueous solution, while PS granules initially expanded, followed by disintegration and dispersion.

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Platycodon grandiflorum (P. grandiflorum) has long been used as a food and traditional herbal medicine. As a food, P. grandiflorum is often transformed into pickles for consumption, and as a traditional Chinese medicine, P. grandiflorum clears the lung, nourishes the pharynx, dispels phlegm, and discharges pus. Polysaccharides are among the main active components of P. grandiflorum. Recent literature has described the preparation, identification, and pharmacological activity of these polysaccharides. Studies have shown that these polysaccharides exhibit a variety of significant biological effects in vitro and in vivo, such as immune stimulation and antioxidant, anti-liver injury, anti-apoptosis and antitumour effects. However, there is no systematic summary of the related research articles on P. grandiflorum polysaccharide, which undoubtedly brings some difficulties to the future research. The purpose of this review is to comprehensively describe research progress on the extraction, purification, structural characterization, modification, and biological activity of P. grandiflorum polysaccharides. The shortcomings of recent research are summarized, further research on their biological activity is proposed to provide new reference value for the application of P. grandiflorum polysaccharides in drugs and health products in the future.

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T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-ß regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-ß sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-ß sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

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Protein lysine methylation is a particular type of post translational modification that plays an important role in both histone and non-histone function regulation in proteins. Deregulation caused by lysine methyltransferases has been identified as the cause of several diseases including cancer as well as both mental and developmental disorders. Identifying lysine methylation sites is a critical step in both early diagnosis and drug design. This study proposes a new Machine Learning method called CNN-Meth for predicting lysine methylation sites using a convolutional neural network (CNN). Our model is trained using evolutionary, structural, and physicochemical-based presentation along with binary encoding. Unlike previous studies, instead of extracting handcrafted features, we use CNN to automatically extract features from different presentations of amino acids to avoid information loss. Automated feature extraction from these representations of amino acids as well as CNN as a classifier have never been used for this problem. Our results demonstrate that CNN-Meth can significantly outperform previous methods for predicting methylation sites. It achieves 96.0%, 85.1%, 96.4%, and 0.65 in terms of Accuracy, Sensitivity, Specificity, and Matthew's Correlation Coefficient (MCC), respectively. CNN-Meth and its source code are publicly available at https://github.com/MLBC-lab/CNN-Meth.

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In recent years, Flammulina velutipes (F. velutipes) has attracted consequential attention in various research fields due to its rich composition of proteins, vitamins, amino acids, polysaccharides, and polyphenols. F. velutipes polysaccharides (FVPs) are considered as key bioactive components of F. velutipes, demonstrating multiple physiological activities, including immunomodulatory, anti-inflammatory, and antibacterial properties. Moreover, they offer health benefits such as antioxidant and anti-aging properties, which have exceptionally valuable clinical applications. Polysaccharides derived from different sources exhibit a wide range of biomedical functions and distinct biological activities. The varied biological functions of polysaccharides, coupled with their extensive application in functional foods and clinical applications, have prompted a heightened focus on polysaccharide research. Additionally, the extraction, deproteinization, and purification of FVPs are fundamental to investigate the structure and biological activities of polysaccharides. Therefore, this review provides a comprehensive and systematic overview of the extraction, deproteinization, purification, characterization, and structural elucidation of FVPs. Furthermore, the biological activities and mechanisms of FVPs have been further explored through in vivo and in vitro experiments. This review aims to provide a theoretical foundation and guide future research and development of FVPs.

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Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.

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In the beginning was the word. But there were no words for N-glycans, at least, no simple words. Next to chemical formulas, the IUPAC code can be regarded as the best, most reliable and yet immediately comprehensible annotation of oligosaccharide structures of any type from any source. When it comes to N-glycans, the venerable IUPAC code has, however, been widely supplanted by highly simplified terms for N-glycans that count the number of antennae or certain components such as galactoses, sialic acids and fucoses and give only limited room for exact structure description. The highly illustrative - and fortunately now standardized - cartoon depictions gained much ground during the last years. By their very nature, cartoons can neither be written nor spoken. The underlying machine codes (e.g., GlycoCT, WURCS) are definitely not intended for direct use in human communication. So, one might feel the need for a simple, yet intelligible and precise system for alphanumeric descriptions of the hundreds and thousands of N-glycan structures. Here, we present a system that describes N-glycans by defining their terminal elements. To minimize redundancy and length of terms, the common elements of N-glycans are taken as granted. The preset reading order facilitates definition of positional isomers. The combination with elements of the condensed IUPAC code allows to describe even rather complex structural elements. Thus, this "proglycan" coding could be the missing link between drawn structures and software-oriented representations of N-glycan structures. On top, it may greatly facilitate keyboard-based mining for glycan substructures in glycan repositories.