Real-Time Monitoring of the Yeast Intracellular State During Bioprocesses With a Toolbox of Biosensors

Industrial fermentation processes strive for high robustness to ensure optimal and consistent performance. Medium components, fermentation products, and physical perturbations may cause stress and lower performance. Cellular stress elicits a range of responses, whose extracellular manifestations have been extensively studied; whereas intracellular aspects remain poorly known due to lack of tools for real-time monitoring. Genetically encoded biosensors have emerged as promising tools and have been used to improve microbial productivity and tolerance toward industrially relevant stresses. Here, fluorescent biosensors able to sense the yeast intracellular environment (pH, ATP levels, oxidative stress, glycolytic flux, and ribosome production) were implemented into a versatile and easy-to-use toolbox. Marker-free and efficient genome integration at a conserved site on chromosome X of Saccharomyces cerevisiae strains and a commercial Saccharomyces boulardii strain was developed. Moreover, multiple biosensors were used to simultaneously monitor different intracellular parameters in a single cell. Even when combined together, the biosensors did not significantly affect key physiological parameters, such as specific growth rate and product yields. Activation and response of each biosensor and their interconnection were assessed using an advanced micro-cultivation system. Finally, the toolbox was used to screen cell behavior in a synthetic lignocellulosic hydrolysate that mimicked harsh industrial substrates, revealing differences in the oxidative stress response between laboratory (CEN.PK113-7D) and industrial (Ethanol Red) S. cerevisiae strains. In summary, the toolbox will allow both the exploration of yeast diversity and physiological responses in natural and complex industrial conditions, as well as the possibility to monitor production processes.

Survey on Smart Security Surveillance System

As an integral part of the safety and security many organizations, video rental has established its value and benefits many times by providing immediate management of property, people, the environment and property. This project operates in the form of the Embedded Real-Time Surveillance System Based Raspberry Pi SBC for internal detection that enhances monitoring technology to provide critical safety in our lives as well as consistent performance and alert operation. The proposed security solution depends on our integration of cameras and motion detectors into a web application. Raspberry Pi operates and controls motion detectors and video cameras for remote hearing and monitoring, streams streaming video and recording for future playback. This research focuses on the development of a detection system that detects strangers and responds quickly by taking and transferring images to wireless modules based on owners. This Raspberry Pi program based on Smart Surveillance System provides a remote location monitoring concept. The proposed solution provides a fully functional, efficient and easy-to-use global solution. This project will also introduce the concept of motion detection and tracking using image processing. This type of technology is very important when it comes to surveillance and security. The live video stream will be used to show how things can be found and tracked. The detection and tracking process will be based on the pixel threshold. Keyword: Internet Of Things (IOT), Raspberry pi, Picamera, PIR Sensor, Dropbox.

THEORETICAL AND METHODOLOGICAL ASPECTS OF PHYSICAL CULTURE AND SPORTS SPECIALISTS TO BE PROJECT COMPETENCE FORMATION

The pedagogical nature and subject matter of project competence of physical culture and sports specialists to be are defined in the article. The education process at modern higher educational institutions where the bases of project competence of physical culture and sports specialists to be are established is defined by active search of effective ways of optimization and modernization of the system of quality improving of students’ professional training. It is proved that the project activity is a set of actions of making and development of a project, planning of specific work and its consistent performance, so the project activity is a constructive and productive personal activity, targeted towards the solution of vital problem, achieving of ultimate results in process of goal determination, planning, development and implementation of a project.The integration of a set of scientific notions allowed to come to a conclusion that the formation of project competence of physical culture and sports specialists to be considered as a dynamic and integrated process aimed at consciousness of grounds, need for sound use of project activity in professional field which provides for getting of theoretical knowledge and understanding of project development and management; getting of ways and means of project activity; readiness and capacity to use new professional experience in practical task solution in the field of sports and health; physical culture and sports specialist’s to be consciousness of relevance of the level of own professional capabilities and skills, and the demands of project activity in the field of physical culture, sports and health protection.On the basis of the integration of literary sources complex it is established that the project competence of physical culture and sports specialists to be may be presented in the dynamic unity of the following components: motivational and axiological, cognitive, activity, reflective and evaluating. The content of functional components is modified under the conditions of formation of project competence of physical culture and sports specialists to be which is shown up in the following set of project competence functions: worldview, research, heuristic, integral, prognostic, reflective and evaluating, constructive and administrative. The formation of project competence in process of professional training of physical culture and sports specialists to be requires the development of specific project skills: problematization, project development skills, skills of theoretical and practical conscious activities on project implementation, skills of meaningful choice and use of computer-aided and other technologies in project technology, reflexion skills.

Improved immunoassay sensitivity and specificity using single-molecule colocalization

Enzyme-linked immunosorbent assays (ELISAs) are a cornerstone of modern molecular detection, but the technique still suffers some notable challenges. One of the biggest problems is discriminating true signal generated by target molecules versus non-specific background arising from the interaction of detection antibodies with the assay substrate or interferents in the sample matrix. Single-Molecule Colocalization Assay (SiMCA) overcomes this problem by employing total internal reflection fluorescence (TIRF) microscopy to quantify target proteins based on the colocalization of fluorescent signal from orthogonally labeled capture and detection antibodies. By specifically counting colocalized fluorescent signals, we can essentially eliminate the confounding effects of background produced by non-specific binding of detection antibodies. We further employed a normalization strategy to account for the heterogeneous distribution of the capture antibodies, greatly improving the reproducibility of our measurements. In a series of experiments with TNF-α, we show that SiMCA can achieve a three-fold lower limit of detection compared to conventional single-color assays using the same antibodies and exhibits consistent performance for assays performed in complex specimens such as chicken serum and human blood. Our results help define the pernicious effects of non-specific background in immunoassays and demonstrate the diagnostic gains that can be achieved by eliminating those effects.

Hybrid Sleep Stage Classification for Clinical Practices across Different Polysomnography Systems Using Frontal EEG

Automatic bio-signal processing and scoring have been a popular topic in recent years. This includes sleep stage classification, which is time-consuming when carried out by hand. Multiple sleep stage classification has been proposed in recent years. While effective, most of these processes are trained and validated against a singular set of data in uniformed pre-processing, whilst in a clinical environment, polysomnography (PSG) may come from different PSG systems that use different signal processing methods. In this study, we present a generalized sleep stage classification method that uses power spectra and entropy. To test its generality, we first trained our system using a uniform dataset and then validated it against another dataset with PSGs from different PSG systems. We found that the system achieved an accuracy of 0.80 and that it is highly consistent across most PSG records. A few samples of NREM3 sleep were classified poorly, and further inspection showed that these samples lost crucial NREM3 features due to aggressive filtering. This implies that the system’s effectiveness can be evaluated by human knowledge. Overall, our classification system shows consistent performance against PSG records that have been collected from different PSG systems, which gives it high potential in a clinical environment.

Hypergraph-Supervised Deep Subspace Clustering

Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays an essential role in regularizing the feature learning. However, the self-reconstruction adversely affects the discriminative feature learning of AE, thereby hampering the downstream subspace clustering. To address this issue, we propose a hypergraph-supervised reconstruction to replace the self-reconstruction. Specifically, instead of enforcing the decoder in the AE to merely reconstruct samples themselves, the hypergraph-supervised reconstruction encourages reconstructing samples according to their high-order neighborhood relations. By the back-propagation training, the hypergraph-supervised reconstruction cost enables the deep AE to capture the high-order structure information among samples, facilitating the discriminative feature learning and, thus, alleviating the adverse effect of the self-reconstruction cost. Compared to current DSC methods, relying on the self-reconstruction, our method has achieved consistent performance improvement on benchmark high-dimensional datasets.

A Multistate Comparison Study of COVID-19 Cases Among Accredited and Nonaccredited Nursing Homes

Widely acknowledged is the disproportionate number of COVID-19 cases among nursing home residents. This observational study examined the relationship between accreditation status and COVID-19 case rates in states where the numbers and proportions of Joint Commission accredited facilities made such comparisons possible (Illinois (IL), Florida (FL), and Massachusetts (MA)). COVID-19 data were accessed from the Centers for Medicare & Medicaid Services (CMS) Nursing Home Compare Public Use File, which included retrospective COVID-19 data submitted by nursing homes to the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network. The outcome variable was the total number of nursing home-identified COVID-19 cases from June 2020 to January 2021. Joint Commission accreditation status was the independent variable. Mediating factors included state, and county-level case rates. Increases in the county rate had a significant association with higher nursing home COVID-19 case rates ( p < .001). After adjusting for county case rates, no differences were observed in the mean group case rates for accredited and nonaccredited nursing homes. However, comparing predicted case rates to actual case rates revealed that accredited nursing homes were more closely aligned with their predicted rates. Performance of the nonaccredited nursing homes was more variable and had proportionally more outliers compared to accredited nursing homes. Community prevalence of COVID-19 is the strongest predictor of nursing home cases. While accreditation status did not have an impact on overall mean group performance, nonaccredited nursing homes had greater variation in performance and a higher proportion of negative outliers. Accreditation was associated with more consistent performance during the COVID-19 pandemic, despite being located in counties with a higher prevalence of COVID-19.

SEPARATION OF HEMICELLULOSES FROM AN INDUSTRIAL STREAM BY NANOFILTRATION AND ULTRAFILTRATION PROCESSES

The performance of nanofiltration (NF) and ultrafiltration (UF) membranes was studied for separating hemicelluloses from a highly alkaline industrial stream, containing 17-18 wt% sodium hydroxide, resulting from the viscose process. Initially, screening experiments were performed to select suitable membranes, which were then investigated on a pilot scale spiral module. Screening experiments showed that the UF membrane, with a nominal molecular weight cut-off (MWCO) value of 3 kDa, and the NF one, with a nominal MWCO value of 0.5 kDa, showed a similar range of filtration performance and a flux of 4.2 L/m2.h. Further, a retention efficiency of 50% was observed for the 5 kDa and the 10 kDa membranes, indicating absence of any significant proportion of hemicelluloses in this range of molecular weights. The effects of process conditions were studied to understand their correlation with membrane performance with respect to hemicelluloses retention and permeate flux. UF membranes were found to be more prone to performance deterioration over time and with the number of cycles of usage during the pilot scale study, whereas the NF membrane showed consistent performance. It was seen that feed dilution can improve the membrane performance with respect to sodium hydroxide recovery. Significant reduction in feed viscosity with dilution resulted in a 50% increase in flux after normalizing for concentration.

Accuracy estimation of two global burned area products at national scale

In this work we perform an initial assessment of the accuracy of two publicly available MODIS burned area products, MCD64A1 C6 and MODIS FireCCI51, at national scale in a Mediterranean region. The research focused on two fire seasons for the years 2016 and 2017 and comparison was performed against a higher resolution Sentinel-2 dataset. The specific objectives were to assess their capabilities in detection of fire events occurring primarily in forest and semi-natural lands and also to investigate their spatial uncertainties. The analysis combined monthly fire observations and accuracy metrics derived from error matrices. Satisfactory performance was achieved by the two products in detection of larger fires (> 100 ha) whereas their spatial performance exhibited good agreement with the reference data. MCD64A1 C6 exhibited a more consistent performance overall and the 250 m FireCCI51 product exhibited relatively higher sensitivity in detection of smaller (<100 ha) fires. Although additional work is required for a more rigorous assessment of the variability of these burned area products, our research has implications for their usability in fire-related applications at finer scales.

Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification

Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents.