Digitizing mass spectrometry data to explore the chemical diversity and distribution of marine cyanobacteria and algae.

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium

Development of a rangefinding method for determining the coordinates of targets by a network of radar stations in counter-battery warfare

The increase in the accuracy of determining the coordinates of targets is explained by the use of a network of counter-battery radar stations and the rangefinding method for determining the coordinates of targets. The main advantage of using the rangefinding method for determining the coordinates of targets in a network of counter-battery radar stations is to ensure the required accuracy in determining the coordinates of targets without using accurate measurement of angular coordinates. The minimum geometry of the system, which ensures the use of the rangefinding method for determining coordinates, is given. The method of determining the coordinates of targets by a network of counter-battery radar stations has been improved. In contrast to the known ones, information about the range to the target is additionally used in a spatially distributed network of radar stations for counter-battery combat. The boundaries of the working zones of the network of two and three counter-battery radar stations are calculated. The features of creating a continuous strip using the rangefinding method for determining the coordinates of the target are considered. Statistical modeling of the rangefinding method for determining the plane coordinates of the target has been carried out. It has been established that the use of the rangefinding method ensures the determination of the planar coordinates of the target in a sector of at least 120°. The targets are at a distance of direct radio visibility in relation to the counter-battery radar. The root-mean-square error in determining the target range in this case is no more than 50 m. It has been established that the creation of continuous bands of a low-altitude radar field at a certain height is possible by arranging radar stations in a line. In this case, the distance between the counter-battery radar stations should be no more than half the target detection range at this heigh

Методи сегментування зображень з безпілотних літальних апаратів на основі k-means та генетичного алгоритму

The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined. An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones, image segmentation by a k-means-based method that successfully works on images from space surveillance systems cannot be directly applied to image segmentation from unmanned aerial vehicles. Unlike known ones, image segmentation by a method based on a genetic algorithm that successfully works on images from space surveillance systems also cannot be directly applied to image segmentation from unmanned aerial vehicles. The quality of segmentation of images from unmanned aerial vehicles by methods based on k-means and a genetic algorithm was assessed. It was established that: – the average level of first-kind errors is 70 % and 51 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively; – average level of second-kind errors is 61 % and 43 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively. It was concluded that further research must be carried out to develop methods for segmenting images from unmanned aerial vehicles.Об’єктом дослідження є процес сегментування зображень з безпілотних літальних апаратів. Встановлено, що методи сегментування на основі k-means та генетичного алгоритму якісно працюють на зображеннях з космічних систем спостереження. Пропонується використання методів сегментування на основі k-means та генетичного алгоритму для сегментування зображень з безпілотних літальних апаратів. Визначені основні етапи методів сегментування зображень на основі k-means та генетичного алгоритму. Проведено експериментальне дослідження сегментування зображень з безпілотних літальних апаратів. На відміну від відомих, сегментування зображення методом на основі k-means, яке успішно працює на зображеннях з космічних систем спостереження, не може бути напряму застосовано до сегментування зображення з безпілотних літальних апаратів. На відміну від відомих, сегментування зображення методом на основі генетичного алгоритму, яке успішно працює на зображеннях з космічних систем спостереження, також не може бути напряму застосовано до сегментування зображення з безпілотних літальних апаратів. Проведено оцінювання якості сегментування зображень з безпілотних літальних апаратів методами на основі k-means та генетичного алгоритму. Встановлено, що: – середній рівень помилок І роду складає 70 % та 51 % при сегментуванні зображення з безпілотного літального апарату методами на основі k-means та генетичного алгоритму відповідно; – середній рівень помилок ІІ роду складає 61 % та 43 % при сегментуванні зображення з безпілотного літального апарату методами на основі k-means та генетичного алгоритму відповідно. Зроблено висновок про подовження подальших досліджень щодо розробки методів сегментування зображень з безпілотних літальних апараті

Latency Estimation Tool and Investigation of Neural Networks Inference on Mobile GPU

A lot of deep learning applications are desired to be run on mobile devices. Both accuracy and inference time are meaningful for a lot of them. While the number of FLOPs is usually used as a proxy for neural network latency, it may not be the best choice. In order to obtain a better approximation of latency, the research community uses lookup tables of all possible layers for the calculation of the inference on a mobile CPU. It requires only a small number of experiments. Unfortunately, on a mobile GPU, this method is not applicable in a straightforward way and shows low precision. In this work, we consider latency approximation on a mobile GPU as a data- and hardware-specific problem. Our main goal is to construct a convenient Latency Estimation Tool for Investigation (LETI) of neural network inference and building robust and accurate latency prediction models for each specific task. To achieve this goal, we make tools that provide a convenient way to conduct massive experiments on different target devices focusing on a mobile GPU. After evaluation of the dataset, one can train the regression model on experimental data and use it for future latency prediction and analysis. We experimentally demonstrate the applicability of such an approach on a subset of the popular NAS-Benchmark 101 dataset for two different mobile GPU

Selection, Characterization, and Application of ssDNA Aptamer against Furaneol

Furaneol is an aroma compound which occurs naturally in foods and is used as an artificial flavor. Detection of furaneol is required in food science and food processing industry. Capture- Systematic Evolution of Ligands by EXponential enrichment (SELEX) protocol was applied for the isolation of an aptamer binding to furaneol, a small volatile organic substance contributing to the flavor of various products. Thirteen cycles of selection were performed. The resulting DNA pool was cloned, using blunt-end cloning, and ninety-six plasmids were sequenced and analyzed. Eight oligonucleotides were selected as aptamer candidates and screened for the ability to bind to furaneol, using three different methods—magnetic-beads associated elution assay, SYBR Green I assay, and exonuclease protection assay. One of the candidates was further characterized as an aptamer. The apparent equilibrium constant was determined to be (1.1 ± 0.4) µM, by the fluorescent method. The reported aptamer was applied for development of the ion-sensitive field-effect transistor (ISFET)-based biosensor, for the analysis of furaneol, in the concentration range of 0.1⁻10 µM

Réflexion non-spéculaire d’un faisceau gaussien spatialement modulé en phase

International audienc

Exploiting plasmon resonances in a composite photonic crystal for polarization-sensitive dichroic filtering

International audienc

Polarization-selective defect mode amplification in a photonic crystal with intracavity 2D arrays of metallic nanoparticles

International audienc

Non-specular reflection of a narrow spatially phase-modulated Gaussian beam

International audienc

Управление смещением отраженного гауссова пучка от диэлектрической пластины при помощи фазовой модуляции (Controlling the displacement of a reflected Gaussian beam from a dielectric plate using phase modulation)

International audienc