Biosynthesis of glycosidase inhibitors on wheat bread wastes hydrolysate medium by Streptomyces sp. 170

ArticleThe aim of the present study is to investigate the potential effect of bread hydrolysate as a novel nutrient medium for cultivating Streptomyces sp. 170 (S.170). Moreover, it evaluates the productivity and inhibitory activity of pancreatic α-amylase inhibitors (PAAI). Bread hydrolysate medium (BHM) and corn starch hydrolysate medium (CHM) prepared with αamylase enzyme concentrations (1.5 and 2.5 units g -1 bread) and (1.5 units g -1 corn starch), respectively were utilized in the study. The Seherde-Blair and modified Akulova methods were applied to evaluate the carbohydrates concentration and the inhibitory activity of the media respectively. Results of bread and corn media were compared to each other. Furthermore, the activity of PAAI synthesized by S.170 was compared to other Streptomyces species. The results showed a significant difference (P < 0.05) between the total simple sugars (glucose + maltose) concentration produced in CHM (27.5%) and BHM prepared with α-amylase 1.5 units (45.1%). Besides, BHM produced by α-amylase 2.5 units demonstrated the maximum total concentration of simple sugars (49.9%). In addition, 48 h of S.170 incubation were quite enough to exhibit the highest inhibitory activity (2,632 IU mL-1 ) in BHM prepared with α-amylase 2.5 units. The analysis demonstrated a non-significant difference in the inhibitory activity of PAAI in CMH (1,300 IU mL-1 ) and BMH with α-amylase 1.5 units (1,111 IU mL-1 ). Also, compared to other Streptomyces species, S.170 conferred highly active PAAI. In conclusion, BHM showed its efficiency to a great extent in the cultivation of S.170 and production of PAAI with a notable high activity

ENZYMATIC HYDROLYSIS OF BREWING WASTE DIETARY FIBRES FOR CONFECTIONERIES PRODUCTION

In this paper, the effect of enzyme doze on the breakdown of brewing waste dietary fibres has been studied. An increase in the amount of an enzyme induced was found to a decrease in the percentage of fiber from 10% to 7%, as well as an increase in mass concentration of glucose in the supernatant solution from 18 to 72.Работа выполнена при финансовой поддержке Благотворительного фонда Потанина

Транслятор с возможностью статического анализа безопасности информационных потоков для простого языка программирования

We consider while-language programs with variables of two security types: low and high. Security static analysis of information flows of such programs identifies insecure information flows which can cause leaks. Semantic rules of such an analysis which was proposed in [6] assign security types for expressions, operators and compositions of operators. We use these rules to propose an algorithm of security static analysis to discover a security type of the program under consideration. If such a type can be assigned, information flows of the program are secure; otherwise, it contains insecure information flows. We have used flex and bison [5] tools to implement a translator for a while-language into the MMIX computer [2] instruction sequence.Рассматриваются программы, написанные на while-языке, с переменными двух типов безопасности: секретными и открытыми. Статический анализ безопасности информационных потоков программ идентифицирует небезопасные информационные потоки, через которые могут произойти утечки информации. При таком анализе по правилам, предложенным в [6], определяются типы конфиденциальности выражений, операторов и композиции операторов.На основе этих правил был разработан алгоритм статического анализа безопасности программ, который заключается в попытке их типизирования. Если в результате программа типизируема, то она является безопасной с точки зрения информационных потоков; если же программе нельзя присвоить тип безопасности, то в ней содержатся небезопасные информационные потоки, и, следовательно, она является небезопасной.С помощью средств генерации лексических и синтаксических анализаторов flex и bison [5] был разработан транслятор программ, написанных на while-языке, в код машины MMIX [2]

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Virtual Partner Interaction (VPI): Exploring Novel Behaviors via Coordination Dynamics

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI—Virtual Partner Interaction—a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications