Quality and flavor development of solid-state fermented surimi with Actinomucor elegans: A perspective on the impacts of carbon and nitrogen sources.

The influence of four carbon and nitrogen substrates on the quality and flavor of a novel surimi-based product fermented with Actinomucor elegans (A. elegans) was investigated, with a focus on carbon and nitrogen catabolite repression. The results showed that the substrate significantly affected mycelial growth, enzyme activities, and the metabolites of A. elegans. Although glucose significantly promoted A. elegans growth by 116.69%, it decreased enzyme secretion by 69.79% for α-amylase and 59.80% for protease, most likely by triggering the carbon catabolite repression pathway. Starch, soy protein, and wheat gluten substantially affected the textural properties of the fermented surimi. Furthermore, wheat gluten significantly promoted the protease activity (102.70%) and increased protein degradation during surimi fermentation. The fishy odor of surimi was alleviated through fermentation, and a correlation between the volatile compounds and A. elegans metabolism was observed. These results explore fermentation substrates in filamentous fungi metabolism from a catabolite repression perspective.

The effect of high altitude on ephedrine content and metabolic variations in two species of Ephedra.

Ephedra is an important plant in Chinese medicine; however, there are few reports on two species of Ephedra which are distributed at high altitudes from 3000 to 5200 meters. We collected a total of 84 individuals representing five Ephedra gerardiana and nine Ephedra saxatilis populations respectively located from 3158 to 5200 meters altitude, and determined the relative content of 213 metabolites using UHPLC-MS/MS (Ultra-High-Performance Liquid Chromatography-tandem mass spectrometry). 37 Chemical compositions were annotated using the KEGG (Kyoto Encyclopaedia of Genes and Genomes) database. From the top five significant enrichments in metabolic KEGG pathway analysis, we found a total of 166 compounds belonging to phenylpropanoids, 123 flavonoids, 67 metabolites carried by ABC transporters, and 61 in purine metabolism. We identified the top 8 altitude-related compounds in two species. Ephedrine and pseudoephedrine were found to be associated with altitude in both E. saxatilis and E. gerardiana. To verify which environmental factors influenced the metabolic content, the soil moisture and temperature of each population site were collected, and quantitative analysis of ephedrine and pseudoephedrine was performed using UHPLC-MS (Ultra-High-Performance liquid chromatography-tandem mass spectrometry). After detection, soil moisture ranged from 0.074 to 0.177 mm3/mm3, and temperature ranged from 9.7°C to 23.9°C. The content of ephedrine ranged from (0.84 ± 0.49)% to (2.01 ± 0.41)% in E. saxatilis, which was positively correlated with soil moisture; the content of pseudoephedrine ranged from (0.72 ± 0.45)% to (1.11 ± 0.57)% and was negatively correlated with soil moisture. In contrast to these results, in E. gerardiana, the content of ephedrine and pseudoephedrine was negatively correlated with soil moisture. Furthermore, the trends of alkaloid contents in two kinds of Ephedra were similar when the temperature was lower than 17°C even if the sum was various. With the increase in soil moisture and temperature, the total alkaloid content of E. saxatilis was higher than that of E. gerardiana. When the soil moisture was lower, the alkaloid content of the two Ephedra species was higher. These results provide useful data for the future separation of new compounds, and for seed homogeneous growth to determine artificial breeding of Ephedra located at high altitudes.

A unified method to revoke the private data of patients in intelligent healthcare with audit to forget.

Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model training. In order to secure patients' right to be forgotten, we proposed a solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing an approach called knowledge purification. To implement our solution, we developed an audit to forget software (AFS), which is able to evaluate and revoke patients' private data from pre-trained deep learning models. Here, we show the usability of AFS and its application potential in real-world intelligent healthcare to enhance privacy protection and data revocation rights.

CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides.

NcRNA-encoded small peptides (ncPEPs) have recently emerged as promising targets and biomarkers for cancer immunotherapy. Therefore, identifying cancer-associated ncPEPs is crucial for cancer research. In this work, we propose CoraL, a novel supervised contrastive meta-learning framework for predicting cancer-associated ncPEPs. Specifically, the proposed meta-learning strategy enables our model to learn meta-knowledge from different types of peptides and train a promising predictive model even with few labeled samples. The results show that our model is capable of making high-confidence predictions on unseen cancer biomarkers with only five samples, potentially accelerating the discovery of novel cancer biomarkers for immunotherapy. Moreover, our approach remarkably outperforms existing deep learning models on 15 cancer-associated ncPEPs datasets, demonstrating its effectiveness and robustness. Interestingly, our model exhibits outstanding performance when extended for the identification of short open reading frames derived from ncPEPs, demonstrating the strong prediction ability of CoraL at the transcriptome level. Importantly, our feature interpretation analysis discovers unique sequential patterns as the fingerprint for each cancer-associated ncPEPs, revealing the relationship among certain cancer biomarkers that are validated by relevant literature and motif comparison. Overall, we expect CoraL to be a useful tool to decipher the pathogenesis of cancer and provide valuable information for cancer research. The dataset and source code of our proposed method can be found at https://github.com/Johnsunnn/CoraL.

Comparison of phenolic composition and sensory quality among pear beverages made using Saccharomyces cerevisiae and Torulaspora delbrueckii.

The effects of Saccharomyces cerevisiae and Torulaspora delbrueckii on phenolic composition and sensory quality were characterized in the production of alcoholic beverages from selected pear cultivars with diverse biochemical characteristics. The fermentation process generally affected the phenolic composition by increasing the contents of hydroxycinnamic acids and flavan-3-ols and reducing the levels of hydroxybenzoic acids, procyanidins, and flavonols. Although the phenolic compositions and sensory properties of pear beverages depended primarily on pear cultivar selection, the applied yeast strains also played important roles in beverage quality. Fermentation with T. delbrueckii resulted in higher caffeoylquinic acid and quercetin-3-O-glucoside contents, higher rated intensities of 'cooked pear' and 'floral' odors and a sweeter taste than fermentation with S. cerevisiae. Moreover, higher concentrations of hydroxybenzoic acids, hydroxycinnamic acids, and flavonols correlated closely with astringency perception. Applying T. delbrueckii strains and breeding novel pear cultivars are important approaches to produce fermented beverages of high quality.

Chemical Composition of Juices Made from Cultivars and Breeding Selections of European Pear (Pyrus communis L.).

The phenolic profiles and other major metabolites in juices made from fruits of 17 cultivars and selections of European pears were investigated using UHPLC-DAD-ESI-QTOF-MS and GC-FID, respectively. A total of 39 phenolic compounds were detected, including hydroxybenzoic acids, hydroxycinnamic acids, flavan-3-ols, procyanidins, flavonols, and arbutin. Among these compounds, 5-O-caffeoylquinic acid was the most predominant, accounting for 14-39% of total quantified phenolic contents (TPA) determined in this study. The variations were mainly cultivar dependent. The genetic background effect on the chemical compositions is complex, and breeding selections from the same parental cultivars varied dramatically in chemical compositions. Putative perry pears contained more 4-O-caffeoylquinic acid, 5-O-caffeoylquinic acid, caffeoyl N-trytophan, caffeoylshikimic acid, coumaroylquinic acid isomer, syringic acid hexoside, procyanidin dimer B2, (+)-catechin, and malic acid, whereas putative dessert pears had higher esters, alcohols, and aldehydes. The results will be helpful in providing industry with phytochemical compositional information, assisting pear selections in commercial utilization.

Enzymatic hydrolysates of soy protein promote the physicochemical stability of mulberry anthocyanin extracts in food processing.

Soy protein papain hydrolysate (SPAH) and soy protein pepsin hydrolysate (SPEH) were used as protective agents for mulberry anthocyanin extracts (MAEs) to inhibit its color fading and enhance the anthocyanin stability at pH 6.3. Both SPAH and SPEH showed a significant protective effect on total anthocyanins in MAEs solutions. 1.0 mg/mL of SPEH presented the best protective effect on MAEs by increasing its half-life from 1.8 to 5.7 days. SPAH/SPEH-cyaniding-3-O-glucoside (C3G) interactions were investigated at pH 6.3 by fluorescence, Fourier-transform infrared spectroscopy (FT-IR), and Circular Dichroism (CD). Their association was mainly driven by hydrophobic interactions, and SPEH showed a higher binding affinity for C3G than SPAH, with a KA value of 2.62 × 105 M-1 at 300 K. The second structures of SPAH and SPEH were altered by C3G, with a decrease in the ß-sheets and an increase in the turns and random coils.

Phenolic compound profiles in Finnish apple (Malus × domestica Borkh.) juices and ciders fermented with Saccharomyces cerevisiae and Schizosaccharomyces pombe strains.

The phenolic compounds in juices and ciders made with Saccharomyces cerevisiae or Schizosaccharomyces pombe from eleven Finnish apple cultivars were analyzed using liquid chromatographic and mass spectrometric methods combined with multivariate data analysis. In general, the ciders contained less phenolic compounds than corresponding apple juices. In the studied apple juices and ciders, hydroxycinnamic acids were the most predominant, accounting for around 80% of total phenolic compounds. Apple juices contained more flavonol glycosides and dihydrochalcones whereas cider processing resulted in increased amount of free hydroxycinnamic acids. The contents of individual phenolic compounds were more dependent on the apple cultivars than the yeast species. Certain cultivars contained remarkably higher contents of dihydrochalcones and hydroxycinnamic acids when comparing with other cultivars. Ciders made using S. pombe remained higher contents of procyanidins and (+)-catechin while S. cerevisiae ciders contained higher individual hydroxycinnamic acids, such as 5-O-caffeoylquinic acid, 4-O-caffeoylquinic acid, 3-O-p-coumaroylquinic acid, and 4-O-p-coumaroylquinic acid.

Accelerating bioactive peptide discovery via mutual information-based meta-learning.

Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting in poor generalizability. Furthermore, there is no generic computational framework to predict the bioactivities of different peptides. Thus, a natural question is whether we can use limited samples to build an effective predictive model for different kinds of peptides. To address this question, we propose Mutual Information Maximization Meta-Learning (MIMML), a novel meta-learning-based predictive model for bioactive peptide discovery. Using few samples from various functional peptides, MIMML can sufficiently learn the discriminative information amongst various functions and characterize functional differences. Experimental results show excellent performance of MIMML though using far fewer training samples as compared to the state-of-the-art methods. We also decipher the latent relationships among different kinds of functions to understand what meta-model learned to improve a specific task. In summary, this study is a pioneering work in the field of functional peptide mining and provides the first-of-its-kind solution for few-sample learning problems in biological sequence analysis, accelerating the new functional peptide discovery. The source codes and datasets are available on https://github.com/TearsWaiting/MIMML.

iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization.

MOTIVATION: DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identification of DNA methylation sites is critical for better understanding and revealing their functional mechanisms. To date, several machine learning and deep learning methods have been developed for the prediction of different DNA methylation types. However, they still highly rely on manual features, which can largely limit the high-latent information extraction. Moreover, most of them are designed for one specific DNA methylation type, and therefore cannot predict multiple methylation sites in multiple species simultaneously. In this study, we propose iDNA-ABT, an advanced deep learning model that utilizes adaptive embedding based on Bidirectional Encoder Representations from Transformers (BERT) together with transductive information maximization (TIM). RESULTS: Benchmark results show that our proposed iDNA-ABT can automatically and adaptively learn the distinguishing features of biological sequences from multiple species, and thus perform significantly better than the state-of-the-art methods in predicting three different DNA methylation types. In addition, TIM loss is proven to be effective in dichotomous tasks via the comparison experiment. Furthermore, we verify that our features have strong adaptability and robustness to different species through comparison of adaptive embedding and six handcrafted feature encodings. Importantly, our model shows great generalization ability in different species, demonstrating that our model can adaptively capture the cross-species differences and improve the predictive performance. For the convenient use of our method, we further established an online webserver as the implementation of the proposed iDNA-ABT. AVAILABILITY AND IMPLEMENTATION: Our proposed iDNA-ABT and data are freely accessible via http://server.wei-group.net/iDNA_ABT and our source codes are available for downloading in the GitHub repository (https://github.com/YUYING07/iDNA_ABT). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides.

MOTIVATION: Anticancer peptides (ACPs) have recently emerged as effective anticancer drugs in cancer therapy. Machine learning-based predictors have been developed to identify ACPs and achieve satisfactory performance. However, existing methods suffer from experience-based feature engineering, which not only restricts the representation ability of the models to a certain extent but also lacks adaptivity for different data, limiting the further improvement of the predictive performance and impacting the robustness of the predictive models. To alleviate the above problems, we propose a novel deep-learning-based predictor named ACPred-LAF, in which we propose a novel multisense and multiscaled embedding algorithm to automatically learn and extract context sequential characteristics of ACPs. RESULTS: Through the feature comparative analysis, we demonstrate that our learnable and self-adaptive embedding features are better than hand-crafted features in capturing discriminative information, which can effectively benefit the performance improvement for ACP prediction. In addition, benchmarking comparison results demonstrate that our ACPred-LAF outperforms the state-of-the-art methods both on existing benchmark datasets and our newly constructed dataset. Furthermore, we also prove and validate the robustness of the model via the data interference experiment. To avoid potential evaluation bias, here, we construct a new ACP benchmark dataset named ACP-Mixed by integrating existing datasets. We expect our newly constructed dataset to be a golden standard benchmark dataset in this field. To facilitate the use of our model, we develop a web server as the implementation of ACPred-LAF. AVAILABILITY AND IMPLEMENTATION: Our proposed ACPred-LAF, newly constructed benchmark dataset ACP-Mixed are open source collaborative initiatives available in the GitHub repository (https://github.com/TearsWaiting/ACPred-LAF). Besides, a webserver as the implementation of ACPred-LAF that can be accessed via: http://server.malab.cn/ACPred-LAF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture.

The prediction of peptide secondary structures is fundamentally important to reveal the functional mechanisms of peptides with potential applications as therapeutic molecules. In this study, we propose a multi-view deep learning method named Peptide Secondary Structure Prediction based on Multi-View Information, Restriction and Transfer learning (PSSP-MVIRT) for peptide secondary structure prediction. To sufficiently exploit discriminative information, we introduce a multi-view fusion strategy to integrate different information from multiple perspectives, including sequential information, evolutionary information and hidden state information, respectively, and generate a unified feature space. Moreover, we construct a hybrid network architecture of Convolutional Neural Network and Bi-directional Gated Recurrent Unit to extract global and local features of peptides. Furthermore, we utilize transfer learning to effectively alleviate the lack of training samples (peptides with experimentally validated structures). Comparative results on independent tests demonstrate that our proposed method significantly outperforms state-of-the-art methods. In particular, our method exhibits better performance at the segment level, suggesting the strong ability of our model in capturing local discriminative information. The case study also shows that our PSSP-MVIRT achieves promising and robust performance in the prediction of new peptide secondary structures. Importantly, we establish a webserver to implement the proposed method, which is currently accessible via http://server.malab.cn/PSSP-MVIRT. We expect it can be a useful tool for the researchers of interest, facilitating the wide use of our method.

Effect of Saccharomyces cerevisiae and Schizosaccharomyces pombe strains on chemical composition and sensory quality of ciders made from Finnish apple cultivars.

Composition of volatile compounds and concentrations of sugars and organic acids were studied in apple ciders produced with Saccharomyces cerevisiae and Schizosaccharomyces pombe yeasts using eleven different Finnish apple cultivars. Moreover, sensory quality of selected ciders was studied using check-all-that-apply test with untrained panelists. Seventy-seven volatile compounds were detected in the samples using HS-SPME-GC-MS. In general, the ciders had higher concentrations of higher alcohols, aldehydes, and acetals whereas the juices contained higher contents of C6-alcohols. Simultaneously, fermentation using S. pombe resulted in lower concentrations of malic acid, ethyl pentanoate, ethyl hexanoate, and volatile acids and higher concentrations of residual sugars compared to S. cerevisiae. Ciders made using S. cerevisiae were characterized as 'alcoholic' and 'yeasty' while S. pombe ciders were more frequently described as 'sweet', 'honey-like', and less rated as sour. Besides the strong effect by the yeasts, apple cultivars had significant effects on the compositional and sensorial properties of apple ciders.

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework.

Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication process. Detection of ORIs' distribution in genome scale is one of key steps to in-depth understanding their regulation mechanisms. In this study, we presented a novel machine learning-based approach called Stack-ORI encompassing 10 cell-specific prediction models for identifying ORIs from four different eukaryotic species (Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana). For each cell-specific model, we employed 12 feature encoding schemes that cover nucleic acid composition, position-specific and physicochemical properties information. The optimal feature set was identified from each encoding individually and developed their respective baseline models using the eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. Extensive experimental results show that Stack-ORI achieves significantly better performance as compared with their baseline models on both training and independent datasets. Interestingly, Stack-ORI consistently outperforms existing predictor in all cell-specific models, not only on training but also on independent test. Moreover, our novel approach provides necessary interpretations that help understanding model success by leveraging the powerful SHapley Additive exPlanation algorithm, thus underlining the most important feature encoding schemes significant for predicting cell-specific ORIs.

Effect of preheated milk proteins and bioactive compounds on the stability of cyanidin-3-O-glucoside.

Native and preheated whey protein isolates (WPI) and casein (at 55 °C-90 °C) were used as protective carriers. Three bioactive compounds, including (-)-Epigallocatechin-3-gallte (EGCG), gallic acid, and vitamin C, were added to enhance the stability of cyanidin-3-O-glucoside (C3G). Under acidic (pH 3.6) and neutral (pH 6.3) conditions, both native and preheated milk proteins showed significant protective effect on C3G. WPI preheated at 85 °C presented the best protective effect on C3G under neutral condition by reducing its thermal, oxidation, and photo degradation rates 25.0%, 38.0%, and 41.1%, respectively. The addition of vitamin C into the protein-anthocyanin solutions accelerated the color loss of C3G, whereas EGCG and gallic acid improved its thermal stability. Among the bioactive compounds, gallic acid provided the most significant protective effect on C3G by further decreasing the thermal degradation rate of C3G 44.6% as a result of the formation of 85 °C preheated WPI-gallic acid-C3G complexes.

Effects of Latitude and Weather Conditions on Proanthocyanidins in Blackcurrant (Ribes nigrum) of Finnish Commercial Cultivars.

Blackcurrants of three Finnish commercial cultivars 'Mortti', 'Ola', and 'Melalahti' cultivated in southern and northern Finland were compared on the basis of the content and composition of proanthocyanidins (PAs). Seventeen B-type PA oligomers (degree of polymerization 2-5 and 7) were detected by hydrophilic interaction liquid chromatography and electrospray ionization mass spectrometry. Total PAs, dimers, trimers, and tetramers were quantified. Among the three cultivars, 'Ola' had the highest contents of both total PAs and PA oligomers. 'Melalahti' was separated from both 'Mortti' and 'Ola' by PA profiles in the partial least-squares discriminant analysis model. All three cultivars revealed distinct responses to latitude and weather conditions. The content of total PAs showed a positive correlation to latitude in 'Ola' and 'Melalahti'. Among the meteorological variables, high temperature and radiation correlated negatively with total PAs, while only specific variables showed a correlation with PA oligomers.

Effect of preheat treatment of milk proteins on their interactions with cyanidin-3-O-glucoside.

In this study, the binding of cyanidin-3-O-glucoside (C3G) to preheated milk proteins ß-lactoglobulin (ß-Lg) and ß-casein (ß-CN) at 55-90 °C under pH 3.6 and pH 6.3 was investigated using multi-spectral techniques. Fluorescence quenching spectroscopy data showed C3G quenched milk proteins' fluorescence strongly. Thermodynamic analysis revealed that C3G bound to ß-Lg mainly through hydrogen bonding and hydrophobic interactions, and that their binding affinity increased gradually with increasing preheating temperature at pH 6.3, whereas it decreased at pH 3.6. Hydrogen bonding and van der Waals forces played the major roles in the interaction of ß-CN with C3G, their affinity decreasing with increasing preheating temperature at both pH values. The combination of C3G and preheated ß-Lg at 85 °C had the strongest binding affinity, with a KA of 14.10 (±0.33) × 105 M-1 (pH 6.3, 298 K). Preheating of milk proteins did not change their major forces with C3G. Fourier transform infrared spectra (FT-IR) results showed that C3G binding altered the secondary structures of ß-Lg and ß-CN by reducing the proportion of α-helix and ß-sheet structures and increasing the proportion of random coil and turn structures. The structural changes of preheated ß-Lg upon C3G binding were more pronounced than that of native ß-Lg, while there was little difference between preheated and native ß-CN in their structural changes upon C3G binding. These results will be helpful in better understanding the relevance of native and preheated milk protein-C3G interactions to the stability of C3G, and in promoting its application in the food industry as a natural pigment.

The genus Scaphidium Olivier in East China (Coleoptera, Staphylinidae, Scaphidiinae).

A review of 21 species of Scaphidium Olivier from East China is presented, including 6 new species: S. jinmingi sp. n. (Zhejiang, Anhui, Chongqing), S. crypticum sp. n. (Zhejiang, Fujian, Jiangxi, Guangxi), S. varifasciatum sp. n. (Zhejiang, An'hui), S. robustum sp. n. (Fujian, Guizhou, Chongqing, Guangxi, Yunnan), S. connexum sp. n. (Zhejiang, Fujian, Guangxi), and S. bayibini sp. n. (An'hui). New province records for S. comes Löbl, S. grande Gestro, S. sauteri Miwa & Mitono, S. formosanum Pic, S. carinense Achard, S. sinense Pic, S. delatouchei Achard, S. biwenxuani He, Tang & Li, S. klapperichi Pic, S. stigmatinotum Löbl, S. wuyongxiangi He, Tang & Li, and S. direptum Tang & Li as well as some biological notes are reported. Habitus and diagnostic characters of all species are photographed and a key to Scaphidium species of East China is provided.

Chemical composition and antimicrobial activity of the essential oil from the edible aromatic plant Aristolochia delavayi.

The essential oil obtained by hydrodistillation from the aerial parts of Aristolochia delavayi Franch. (Aristolochiaceae), a unique edible aromatic plant consumed by the Nakhi (Naxi) people in Yunnan, China, was investigated using GC/MS analysis. In total, 95 components, representing more than 95% of the oil composition, were identified, and the main constituents found were (E)-dec-2-enal (52.0%), (E)-dodec-2-enal (6.8%), dodecanal (3.35%), heptanal (2.88%), and decanal (2.63%). The essential oil showed strong inhibitory activity (96% reduction) of the production of bacterial volatile sulfide compounds (VSC) by Klebsiella pneumoniae, an effect that was comparable with that of the reference compound citral (91% reduction). Moreover, the antimicrobial activity of the essential oil and the isolated major compound against eight bacterial and six fungal strains were evaluated. The essential oil showed significant antibacterial activity against Providencia stuartii and Escherichia coli, with minimal inhibitory concentrations (MIC) ranging from 3.9 to 62.5 µg/ml. The oil also showed strong inhibitory activity against the fungal strains Trichophyton ajelloi, Trichophyton terrestre, Candida glabrata, Candida guilliermondii, and Cryptococcus neoformans, with MIC values ranging from 3.9 to 31.25 µg/ml, while (E)-dec-2-enal presented a lower antifungal activity than the essential oil.