Recent Advancements in Receptor Layer Engineering for Applications in SPR-Based Immunodiagnostics

The rapid progress in the development of surface plasmon resonance-based immunosensing platforms offers wide application possibilities in medical diagnostics as a label-free alternative to enzyme immunoassays. The early diagnosis of diseases or metabolic changes through the detection of biomarkers in body fluids requires methods characterized by a very good sensitivity and selectivity. In the case of the SPR technique, as well as other surface-sensitive detection strategies, the quality of the transducer-immunoreceptor interphase is crucial for maintaining the analytical reliability of an assay. In this work, an overview of general approaches to the design of functional SPR-immunoassays is presented. It covers both immunosensors, the design of which utilizes well-known and often commercially available substrates, as well as the latest solutions developed in-house. Various approaches employing chemical and passive binding, affinity-based antibody immobilization, and the introduction of nanomaterial-based surfaces are discussed. The essence of their influence on the improvement of the main analytical parameters of a given immunosensor is explained. Particular attention is paid to solutions compatible with the latest trends in the development of label-free immunosensors, such as platforms dedicated to real-time monitoring in a quasi-continuous mode, the use of in situ-generated receptor layers (elimination of the regeneration step), and biosensors using recombinant and labelled protein receptors.

Reliability and Economic Evaluation of Offshore Wind Power DC Collection Systems

One key directive to realize the global transition towards net-zero emission goals is to integrate more renewable energy resources into the generation mix. Due to higher and more consistent wind speeds, offshore wind farms (OWFs) have the potential to generate more energy at a steadier rate than their onshore counterpart. However, at the collection system level, all the OWFs use alternating current (AC) technology at present. Nonetheless, with an increasing capacity of the single wind turbine (WT) and larger distances to the shore, the use of direct current (DC) technology at the collection system level is beneficial. To select a suitable DC collection system topology, this paper proposes a comprehensive analytical reliability evaluation method, based on the Universal Generating Function technique, together with associated economic factors. Four candidates DC collection system options were evaluated with different WT capacities for a 400 MW OWF. The availability indices such as Generation Ratio Availability and Expected Energy Not Supplied were used to assess their reliability levels. The results show that the radial topology with a single platform DC/DC converter is more reliable and economical than the other candidate options.

Применение оптических технологий для изучения и идентификации микроорганизмов (обзор)

The study and identification of microorganisms plays asignificant role in the diagnosis of infectious diseases, so an important and urgent task is to introduce new technologies aimed at improving research. Obtaining quantitative and qualitative information about bacteria with high speed, specificity, detection sensitivity, low cost and ease of analysis is particularly substantial. This review examines the use of modern diagnostic technologies that allow for the study of biological systems with a high degree of analytical reliability. Technologies of Raman spectroscopy, FTIR spectroscopy, and surface plasmon resonance used in modern biosensors have proven to be significant and promising research tools. The main advantages and disadvantages of the methods used, as well as the results of their practical use, are indicated. A significant interest induce modern approaches in studying of microorganisms with the use of genetic protein dye – GFP and with applaying nanomaterials – theranostics.

PRACTICAL ASPECTS OF OPERATING AND ANALYTICAL RELIABILITY ASSESSMENT OF FPGA-BASED I&C SYSTEMS

Operating reliability assessment of instrumentation and control systems (I&Cs) is always one of the most important activities, especially for critical domains such as nuclear power plants (NPPs). It is an important source of I&C reliability information preferable to lab testing data because it provides information on I&C reliability under real use conditions. That is the reason that now it is a common practice for companies to have an established process of collecting operating reliability data on a large variety of used components on regular basis, maintaining a database with failure information, total operation time, typical failure modes, etc. The intensive use of complicated components like field-programmable gate arrays (FPGAs) in I&C which appear in upgrades and newly-built nuclear power plants makes the task to develop and validate advanced operating reliability assessment methods that consider specific technology features very topical. Increased integration densities make the reliability of integrated circuits the most crucial point in modern NPP I&Cs. Moreover, FPGAs differ in some significant ways from other integrated circuits: they are shipped as blanks and are very dependent on the design configured into them. Furthermore, FPGA design could be changed during planned NPP outage for different reasons. Considering all possible failure modes of FPGA-based NPP instrumentation and control systems at the design stage is a quite challenging task. Therefore, operating reliability assessment is one of the most preferable ways to perform a comprehensive analysis of FPGA-based NPP I&Cs. Based on information in the literature and own experience, operational vs analytical reliability could be pretty far apart. For that reason, analytical reliability assessment using reliability block diagrams (RBD), failure modes, effects and diagnostics analysis (FMEDA), fault tree analysis (FTA), fault insertion testing (FIT), and other techniques and their combinations are important to meet requirements for such systems. The paper summarizes our experience in operating and analytical reliability assessment of FPGA based NPP I&Cs.

Segmentation of Suspicious Region using GAN Based CNN in Brain MR Images

Generative Adversarial networks (GANs) are algorithmic architectures that use dual neural networks, pitting one in obstruction to the other (therefore the “opposing”) with a intent to produce new, artificial times of evidences that can avoid for real proofs. They are used significantly in image group. In the scope of therapeutic imaging, creating precise technical impulsive shots which are dissimilar from the Adversarial exact ones, signify an inspiring and esteemed goal. The consequential artificial pics are probably to expand analytical reliability , permitting for data augmentation in computer-aided estimation in addition to medic trial. There are optimistic hard states in producing unreal multi-collection awareness Magnetic Resonance (MR) photos. The main trouble being low difference MR photos, dynamic steadiness in attention framework, and private-series volatility. In this paper, we realization on Generative Networks (GANs) for generating artificial multi-series attention Magnetic Resonance (MR) images. This comprises snags largely as a result of small dissimilarity MR pictures, durable correctness in Brain composition, and private-series inconsistency. This effort proposes a kind novel GAN founded deep learning mark that syndicates GAN group, augmentation, detection and gathering of suspicious regions. The proposed stroke is measured with the aid of pictures developed from BRATS (Multimodal Brain Tumour Image Segmentation Challenge) and dataset IXI in 2015. The usefulness of the future process is added and the outcomes are discussed limited the paper..