Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology.

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information.

Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.

Twenty years of nmrshiftdb2: A case study of an open database for analytical chemistry.

In October 2003, 20 years ago, the open-source and open-content database NMRshiftDB was announced. Since then, the database, renamed as nmrshiftdb2 later, has been continuously available and is one of the longer-running projects in the field of open data in chemistry. After 20 years, we evaluate the success of the project and present lessons learnt for similar projects.

Shotgun proteomics to characterize wastewater proteins.

Classically, the characterization of wastewater components has been restricted to the measurement of indirect parameters (chemical and biological oxygen demand, total nitrogen) and small molecules of interest in epidemiology or for environmental control. Despite the fact that metaproteomics has provided important knowledge about the microbial communities in these waters, practically nothing is known about other non-microbial proteins transported in the wastewater. The method described here has allowed us to perform a large-scale characterization of the wastewater proteome. Wastewater protein profiles have shown to be very different in different collection sites probably reflecting their human population and industrial activities. We believe that wastewater proteomics is opening the doors to the discovery of new environmental and health biomarkers and the development of new, more effective monitoring devices for issues like monitorization of population health, pest control, or control of industry discharges. The method developed is relatively simple and combines procedures for the separation of the soluble and particulate fractions of wastewater and their concentration, and conventional shotgun proteomics using high-resolution mass spectrometry for protein identification. •Unprecedented method for wastewater proteome characterization.•Proteins as new potential biomarkers for sewage chemical-information mining, wastewater epidemiology and environmental monitoring.•Wastewater protein profiles reflect human and industrial activities.

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information.

Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.

Terahertz Constant Velocity Flying Spot for 3D Tomographic Imaging.

This work reports on a terahertz tomography technique using constant velocity flying spot scanning as illumination. This technique is essentially based on the combination of a hyperspectral thermoconverter and an infrared camera used as a sensor, a source of terahertz radiation held on a translation scanner, and a vial of hydroalcoholic gel used as a sample and mounted on a rotating stage for the measurement of its absorbance at several angular positions. From the projections made in 2.5 h and expressed in terms of sinograms, the 3D volume of the absorption coefficient of the vial is reconstructed by a back-projection method based on the inverse Radon transform. This result confirms that this technique is usable on samples of complex and nonaxisymmetric shapes; moreover, it allows 3D qualitative chemical information with a possible phase separation in the terahertz spectral range to be obtained in heterogeneous and complex semitransparent media.

4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies.

N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach.

A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals.

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

FAIR chemical structures in the Journal of Cheminformatics.

The ability to access chemical information openly is an essential part of many scientific disciplines. The Journal of Cheminformatics is leading the way for rigorous, open cheminformatics in many ways, but there remains room for improvement in primary areas. This letter discusses how both authors and the journal alike can help increase the FAIRness (Findability, Accessibility, Interoperability, Reusability) of the chemical structural information in the journal. A proposed chemical structure template can serve as an interoperable Additional File format (already accessible), made more findable by linking the DOI of this data file to the article DOI metadata, supporting further reuse.

Expanding the Chemical Information Science gateway.

As chemical information evolves, impacting many chemistry areas, effective ways to disseminate results by the scientific community are also changing. Thus, publication schemes adapt to meet the needs of researchers across disciplines to share high-quality data, information, and knowledge. Since 2015, the F1000Research Chemical Information Science (CIS) gateway has offered an open and unique model to disseminate science at the interface of chemoinformatics, bioinformatics, and several other informatic-related disciplines. In response to the evolution of chemical information science, the F1000Research CIS gateway has incorporated new members to the advisory board. It is also reinforcing and expanding the gateway areas with a particular focus on machine learning and metabolomics. The range of available article types, availability of data, exposure within complementary multidisciplinary F1000Research gateways, and indexing in major bibliographic databases increases the visibility of all contributions. As part of progressing open science in this field, we look forward to your high-quality contributions to the CIS gateway.

A systematic strategy for uncovering quality marker of Asari Radix et Rhizoma on alleviating inflammation based chemometrics analysis of components.

Asari Radix et Rhizoma (Asarum), a traditional Chinese medicine (TCM), has been applied in clinical generally. However, due to the lack of valid methods for Asarum quality control, inhomogenous quality and therapy issues have become severe with each passing day. In this study, we aimed to establish a comprehensive multi-system to explore the quality control markers underlying pharmaceutical effects based on chemometrics analysis on the total ingredients of Asarum. In brief, DNA barcoding technology was used to screen out the unadulterated herbs in the 15 batches Asarum collected from different origins. Then, the chemical profiles of volatile/nonvolatile components of 10 batches Asarum with definite resource were obtained by HPLC Q-TOF/MS and GC/MS. Combination with chemometrics methods, 14 characteristic ingredients and 4 qualitative and quantitative markers were figured out preliminarily. Moreover, correlation analysis between the characteristic ingredients and the cytokines integrating the virtual targets prediction of network pharmacology, 3 potential bioactive substance were ascertained. In conclusion, l-asarinin, 2-Methoxy-4-vinylphenol and safrole were considered as the potent candidates for quality control markers based on the comprehensive understanding for therapeutic effects and the chemical information of Asarum, which provided a novel perspective of the development for the quality control of TCM.

NMReDATA: Tools and applications.

The nuclear magnetic resonance extracted data (NMReDATA) format has been proposed as a way to store, exchange, and disseminate nuclear magnetic resonance (NMR) data and physical and chemical metadata of chemical compounds. In this paper, we report on analytical workflows that take advantage of the uniform and standardized NMReDATA format. We also give access to a repository of sample data, which can serve for validating software packages that encode or decode files in NMReDATA format.

Hyperspectral imaging in combination with data fusion for rapid evaluation of tilapia fillet freshness.

The potential of two different hyperspectral imaging systems (visible near infrared spectroscopy (Vis-NIR) and NIR) was investigated to determine TVB-N contents in tilapia fillets during cold storage. With Vis-NIR and NIR data, calibration models were established between the average spectra of tilapia fillets in the hyperspectral image and their corresponding TVB-N contents and optimized with various variable selection and data fusion methods. Superior models were obtained with variable selection methods based on low-level fusion data when compared with the corresponding methods based on single data blocks. Mid-level fusion data achieved the best model based on CARS, in comparison with all others. Finally, the respective optimized models of single Vis-NIR and NIR data were employed to visualize TVB-N contents distribution in tilapia fillets. In general, the results showed the great feasibility of hyperspectral imaging in combination with data fusion analysis in the nondestructive evaluation of tilapia fillet freshness.

Acute warming in winter eliminates chemical alarm responses in threatened Qinling lenok Brachymystax lenok tsinlingensis.

Knowledge of how temperature influences animal behavior is critical to understanding and predicting impacts of changing climate on individual species and biotic interactions. However, the effects of climate change, especially winter warming in freshwater systems, on fish behaviors and the use of chemical information have been largely unexplored. Qinling lenok Brachymystax lenok tsinlingensis, an endangered salmonid species endemic to the Qinling Mountain Range, China, is currently experiencing population decline and is a potential biological indicator of warming winter climate effects on freshwater fishes due to its temperature sensitivity and required habitat of small, cold-water streams. Our objective was to determine if transient winter warming (increases of ~4 °C) consistent with seasonal maxima in line with near-future climate projections will affect antipredator responses to damage-released chemical alarm cues in B. lenok tsinlingensis. Wild fish were collected during winter and held in captivity under food deprivation for four days, during which half were acclimated to a warmer temperature (6 °C) while the other half were maintained at ambient levels (2 °C). Individual acclimated fish were then exposed to injections of either conspecific alarm cues to simulate elevated predation risk or stream water as a control treatment. Focal fish demonstrated responses consistent with antipredator behaviors to alarm cues at ambient temperature, but no significant behavioral responses to alarm cues were found relative to controls at the warmer temperature. These results support our hypothesis that winter warming will negatively influence antipredator responses and indicate that projected warmer temperature patterns in winter may have significant impacts on chemically mediated predator-prey interactions in cold-water streams.

The ecological consequences of herbivore-induced plant responses on plant-pollinator interactions.

Plant induced responses to herbivory have long been found to function as plant direct and indirect defenses and to be major drivers of herbivore community and population dynamics. While induced defenses are generally understood as cost-saving strategies that allow plants to allocate valuable resources into defense expression, it recently became clear that, in particular, induced metabolic changes can come with significant ecological costs. In particular, interactions with mutualist pollinators can be significantly compromised by herbivore-induced changes in floral morphology and metabolism. We review recent findings on the evidence for ecological conflict between defending against herbivores and attracting pollinators while using similar modes of information transfer (e.g. visual, olfactory, tactile). Specifically, we discuss plant traits and mechanisms through which plants mediate interactions between antagonists and mutualist and present functional hypotheses for how plants can overcome the resulting conflicts.

Wastewater-based epidemiology to assess human exposure to personal care and household products - A review of biomarkers, analytical methods, and applications.

Humans are nowadays exposed to numerous chemicals in our day-to-day life, including parabens, UV filters, phosphorous flame retardants/plasticizers, bisphenols, phthalates and alternative plasticizers, which can have different adverse effects to human health. Estimating human's exposure to these potentially harmful substances is, therefore, of paramount importance. Human biomonitoring (HBM) is the existing approach to assess exposure to environmental contaminants, which relies on the analysis of specific human biomarkers (parent compounds and/or their metabolic products) in biological matrices from individuals. The main drawback is its implementation, which involves complex cohort studies. A novel approach, wastewater-based epidemiology (WBE), involves estimating exposure from the analysis of biomarkers in sewage (a pooled urine and feces sample of an entire population). One of the key challenges of WBE is the selection of biomarkers which are specific to human metabolism, excreted in sufficient amounts, and stable in sewage. So far, literature data on potential biomarkers for estimating exposure to these chemicals are scattered over numerous pharmacokinetic and HBM studies. Hence, this review provides a list of potential biomarkers of exposure to more than 30 widely used chemicals and report on their urinary excretion rates. Furthermore, the potential and challenges of WBE in this particular field is discussed through the review of pioneer WBE studies, which for the first time explored applicability of this novel approach to assess human exposure to environmental contaminants. In the future, WBE could be potentially applied as an "early warning system", which could promptly identify communities with the highest exposure to environmental contaminants.

Repositioning the Chemical Information Science Gateway.

The Chemical Information Science Gateway (CISG) of F1000Research was originally conceptualized as a forum for high-quality publications in chemical information science (CIS) including chemoinformatics. Adding a publication venue with open access and open peer review to the CIS field was a prime motivation for the introduction of CISG, aiming to support open science in this area. Herein, the CISG concept is revisited and the development of the gateway over the past four years is reviewed. In addition, opportunities are discussed to better position CISG within the publication spectrum of F1000Research and further increase its visibility and attractiveness for scientific contributions.

Free Accessible Databases as a Source of Information about Food Components and Other Compounds with Anticancer Activity⁻Brief Review.

Diet is considered to be a significant factor in cancer prevention and therapy. Many food components reveal anticancer activity. The increasing number of experiments concerning the anticancer potential of chemical compounds, including food components, is a challenge for data searching. Specialized databases provide an opportunity to overcome this problem. Data concerning the anticancer activity of chemical compounds may be found in general databases of chemical compounds and databases of drugs, including specialized resources concerning anticancer compounds, databases of food components, and databases of individual groups of compounds, such as polyphenols or peptides. This brief review summarizes the state of knowledge of chemical databases containing information concerning natural anticancer compounds (e.g., from food). Additionally, the information about text- and structure-based search options and links between particular internet resources is provided in this paper. Examples of the application of databases in food and nutrition sciences are also presented with special attention to compounds that are interesting from the point of view of dietary cancer prevention. Simple examples of potential database search possibilities are also discussed.

Annotation of Peptide Structures Using SMILES and Other Chemical Codes-Practical Solutions.

Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures-amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science "in silico" may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.