Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology

Essential oil/alginate microcapsules; obtaining and applying

Introduction: Nowadays, plant extracts are highly applied in food industries either as sources of bioactive components or as an alternative to artificial additives. Therefore, food manufacturers are focused on innovative products, which can satisfy consumers' requirements. Objectives: This study investigates the encapsulation of Origanum majorana, Achillea millefolium, Foeniculum vulgare, Juniperus communis and Anethum graveolens EOs in alginate capsules as a means of controlling the fast release of volatile constituents. Materials and Methods: The EOs were obtained via steam distillation. Sodium alginate was chosen as a carrier because of its biodegradable and biocompatible properties. The paper describes the simple dripping technique used for the preparation of the alginate microcapsules with EO cores, and a possible application of the microcapsules as a natural flavor additive. Results: Sensorial properties of the final product were subjectively analyzed and described. The changes of the taste and the flavour of candies in comparison with the control sample were significant. Nevertheless, the strong herbal odour was found as "uncommon in confectionary but pleasant. Conclusion: It has been investigated, that the sodium alginate encapsulated EOs have to be added as a final step of a recipe to save its antimicrobial and antioxidant potential. Further assays need to be performed to investigate the recipe, which includes the EO alginate microcapsules in order to get a high-quality final product that can be used for commercial purposes

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology

Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends

Biodegradation is a plausible route toward sustainable management of the millions of tons of plastic waste that have accumulated in terrestrial and marine environments. However, the global diversity of plastic-degrading enzymes remains poorly understood. Taking advantage of global environmental DNA sampling projects, here we constructed hidden Markov models from experimentally verified enzymes and mined ocean and soil metagenomes to assess the global potential of microorganisms to degrade plastics. By controlling for false positives using gut microbiome data, we compiled a catalogue of over 30,000 nonredundant enzyme homologues with the potential to degrade 10 different plastic types. While differences between the ocean and soil microbiomes likely reflect the base compositions of these environments, we find that ocean enzyme abundance increases with depth as a response to plastic pollution and not merely taxonomic composition. By obtaining further pollution measurements, we observed that the abundance of the uncovered enzymes in both ocean and soil habitats significantly correlates with marine and country-specific plastic pollution trends. Our study thus uncovers the earth microbiome\u27s potential to degrade plastics, providing evidence of a measurable effect of plastic pollution on the global microbial ecology as well as a useful resource for further applied research. IMPORTANCE Utilization of synthetic biology approaches to enhance current plastic degradation processes is of crucial importance, as natural plastic degradation processes are very slow. For instance, the predicted lifetime of a polyethylene terephthalate (PET) bottle under ambient conditions ranges from 16 to 48 years. Moreover, although there is still unexplored diversity in microbial communities, synergistic degradation of plastics by microorganisms holds great potential to revolutionize the management of global plastic waste. To this end, the methods and data on novel plastic-degrading enzymes presented here can help researchers by (i) providing further information about the taxonomic diversity of such enzymes as well as understanding of the mechanisms and steps involved in the biological breakdown of plastics, (ii) pointing toward the areas with increased availability of novel enzymes, and (iii) giving a basis for further application in industrial plastic waste biodegradation. Importantly, our findings provide evidence of a measurable effect of plastic pollution on the global microbial ecology

Characterization, Antioxidant and Antibacterial Activity of Essential Oils and Their Encapsulation into Biodegradable Material Followed by Freeze Drying

The study assessed the antimicrobial and antioxidant activities of commonly used and commercially available essential oils as an alternative to synthetic preservatives. The plant sources were as follows: lavender (Lavandula angustifolia), tea tree (Melaleuca alternifolia), bergamot (Citrus bergamia) and peppermint (Mentha piperita). The antioxidant activity of essential oils was tested by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) methods. The microdilution broth susceptibility assay revealed that lavender and bergamot essential oils were more efficient in inhibiting the bacterial growth than other tested oils, with the minimum inhibitory concentration of 5 mu g/mL. This study also reports the successful implementation of an electrostatic extrusion technique for encapsulating essential oils into alginate beads, which enables the essential oils to maintain their free radical scavenging ability over time

Antimicrobial Activity of Essential Oils from Plants against Selected Microorganisms

Background:       it is known that plants have many potential benefits for human health. The purpose of this study was to investigate the antimicrobial properties of several commonly used herbs against specific microorganisms responsible for disease and food spoilage. Essential oils of yarrow, fennel, juniper and marjoram were evaluated, with respect to their efficacy at controlling the growth and survival of several common bacterial and fungal microorganisms. Methods:     The agar diffusion test was used to test the essential oils efficacy at inhibiting microbial growth. The following microorganisms were tested with varied results: Escherichia coli, Aspergillus niger, Enterococcus faecalis, Salmonella typhimurium, Salmonella anatum, Staphylococcus aureus, Candida albicans, Penicillium glaucum and Bacillus cereus. Results:      Marjoram showed the highest inhibition with a zone up to 14 mm. Juniper and fennel essential oils were also highly inhibitory to Candida albicans and the other tested bacteria and fungi. Conclusion:       In conclusion, some of the oils were highly effective at inhibiting the studied microorganisms. This knowledge may be useful for further examining the efficacy of pathogenic prevention and food preservation by studied essential oils

Розробка методу побудови раціонального маршруту автомобільних вантажних перевезень на основі модифікованого мурашиного алгоритму

The method of rational routing based on the modified ant algorithm with introduction of a quality function that characterizes the qualitative state of roads was proposed. Comprehensive assessment of potential routes takes into account practical throughput, actual state of the route sections and the vehicle traffic intensity.The function of quality of the transportation process was formed. Formalization of the roadbed condition on selected sections of the route was proposed to be carried out with the use of a fuzzy set apparatus for describing the membership function. The roadway condition was estimated on the basis of empirical data and was reduced to the appropriate coefficients that characterize unsatisfactory, partially satisfactory and satisfactory road conditions. Description of practical throughput in individual sections of the designed routes was formalized. The probability of occurrence of vehicles of diverse types in the travel line was taken into consideration when determining theoretical throughput of road sections with a subsequent reducing to corresponding expert coefficients.Introduction of additional parameters through the quality function into the model of ant algorithm makes it possible to improve its efficiency and expand possibilities for taking into account additional conditions of transportation, such as the road relief, existing service infrastructure, appearance of emergency road situations, climatic conditions, etc. The proposed approach may be useful in solving the synthesis problem since it will enable prompt taking into consideration complex and varying actual conditions of the transportation process.Comparison of effectiveness of the classical and modified ant algorithm was carried out on the example of transportation routing from the point of departure to the point of destination on an example of a road network between Odesa and Dnipro. Implementation of the classical and modified algorithms proved effectiveness of the proposed approach and made it possible to determine the route that avoids road sections with unsatisfactory road conditions.The proposed modified algorithm makes it possible to focus on not only the distance indicators but also on qualitative characteristics of the road. Calculations using the modified algorithm in the MATLAB programming environment have allowed us to determine the most rational route.The results obtained in the study can later be used in the decision support systems for management in the process of rational routing. The proposed methodological approach can be useful in solving the synthesis problem since it will enable consideration of complicated and changing conditions of practical realization, in particular, in a real-time mode.Предложен метод построения рационального маршрута на основе модификации муравьиного алгоритма с введением функции качества, характеризующей реальное состояние дорог. Комплексная оценка потенциальных маршрутов учитывает практическую пропускную способность, реальное состояние участков маршрута и интенсивность движения транспортных средств. Проведено сравнение результатов расчетов на основе классического и модифицированного муравьиных алгоритмовЗапропоновано метод побудови раціонального маршруту на основі модифікації мурашиного алгоритму з введенням функції якості, що характеризує реальний стан доріг. Комплексна оцінка потенційних маршрутів враховує практичну пропускну спроможність, реальний стан ділянок маршруту та інтенсивність руху транспортних засобів. Проведено порівняння результатів розрахунків на основі класичного й модифікованого мурашиних алгоритмі