Interest in CRISPR technology, an instrumental component of prokaryotic adaptive immunity which enables prokaryotes to detect any foreign DNA and then destroy it, has gained popularity among members of the scientific community. This is due to CRISPR’s remarkable gene editing and cleaving abilities. While the application of CRISPR in human genome editing and diagnosis needs to be researched more fully, and any potential side effects or ambiguities resolved, CRISPR has already shown its capacity in an astonishing variety of applications related to genome editing and genetic engineering. One of its most currently relevant applications is in diagnosis of infectious and non-infectious diseases. Since its initial discovery, 6 types and 22 subtypes of CRISPR systems have been discovered and explored. Diagnostic CRISPR systems are most often derived from types II, V, and VI. Different types of CRISPR-Cas systems which have been identified in different microorganisms can target DNA (e.g. Cas9 and Cas12 enzymes) or RNA (e.g. Cas13 enzyme). Viral, bacterial, and non-infectious diseases such as cancer can all be diagnosed using the cleavage activity of CRISPR enzymes from the aforementioned types. Diagnostic tests using Cas12 and Cas13 enzymes have already been developed for detection of the emerging SARS-CoV-2 virus. Additionally, CRISPR diagnostic tests can be performed using simple reagents and paper-based lateral flow assays, which can potentially reduce laboratory and patient costs significantly. In this review, the classification of CRISPR-Cas systems as well as the basis of the CRISPR/Cas mechanisms of action will be presented. The application of these systems in medical diagnostics with emphasis on the diagnosis of COVID-19 will be discussed.

Microcystis spp., are Gram-negative, oxygenic, photosynthetic prokaryotes which use solar energy to convert carbon dioxide (CO2) and minerals into organic compounds and biomass. Eutrophication, rising CO2 concentrations and global warming are increasing Microcystis blooms globally. Due to its high availability and protein content, Microcystis biomass has been suggested as a protein source for animal feeds. This would reduce dependency on soybean and other agricultural crops and could make use of "waste" biomass when Microcystis scums and blooms are harvested. Besides proteins, Microcystis contain further nutrients including lipids, carbohydrates, vitamins and minerals. However, Microcystis produce cyanobacterial toxins, including microcystins (MCs) and other bioactive metabolites, which present health hazards. In this review, challenges of using Microcystis blooms in feeds are identified. First, nutritional and toxicological (nutri-toxicogical) data, including toxicity of Microcystis to mollusks, crustaceans, fish, amphibians, mammals and birds, is reviewed. Inclusion of Microcystis in diets caused greater mortality, lesser growth, cachexia, histopathological changes and oxidative stress in liver, kidney, gill, intestine and spleen of several fish species. Estimated daily intake (EDI) of MCs in muscle of fish fed Microcystis might exceed the provisional tolerable daily intake (TDI) for humans, 0.04 μg/kg body mass (bm)/day, as established by the World Health Organization (WHO), and is thus not safe. Muscle of fish fed M. aeruginosa is of low nutritional value and exhibits poor palatability/taste. Microcystis also causes hepatotoxicity, reproductive toxicity, cardiotoxicity, neurotoxicity and immunotoxicity to mollusks, crustaceans, amphibians, mammals and birds. Microbial pathogens can also occur in blooms of Microcystis. Thus, cyanotoxins/xenobiotics/pathogens in Microcystis biomass should be removed/degraded/inactivated sufficiently to assure safety for use of the biomass as a primary/main/supplemental ingredient in animal feed. As an ameliorative measure, antidotes/detoxicants can be used to avoid/reduce the toxic effects. Before using Microcystis in feed ingredients/supplements, further screening for health protection and cost control is required.

A residual neural network was adapted and applied to the Physionet/Computing data in Cardiology Challenge 2020 to detect 24 different classes of cardiac abnormalities from 12-lead. Additive Gaussian noise, signal shifting, and the classification of signal sections of different lengths were applied to prevent the network from overfitting and facilitating generalization. Due to the use of a global pooling layer after the feature extractor, the network is independent of the signal's length. On the hidden test set of the challenge, the model achieved a validation score of 0.656 and a full test score of 0.27, placing us 15th out of 41 officially ranked teams (Team name: UC_Lab_Kn). These results show the potential of deep neural networks for application to raw data and a complex multi-class multi-label classification problem, even if the training data is from diverse datasets and of differing lengths.

Constructed wetlands (CWs) are one of the main countermeasures to reduce diffuse phosphorus (P) losses, but there is still a lack of systematic guidance accounting for spatially variable effects of hydraulic and P load on P retention. We present a three-step modelling approach for determining suitable placement of CWs in four different size groups (0.1–1.0 ha), based on incoming hydraulic and P load. The modelled hypothetical CW area was only 17% of that previously estimated and area of efficient CWs is even lower. The mean area-specific P retention increased with CW size. However, the spatial variation in retention was large for all size groups and largest (6–155 kg ha−1 year−1) for the smallest CWs due to highly variable incoming P loads, showing the possible benefits of targeted placement of CWs. The presented modelling approach has also flexibility to include and account for possible future changes in land cover and management.

During testing of parallel systems, which allow asynchronous communication, test flakiness is sometimes avoided by explicitly inserting delays in test code. The choice of delay approach can be a trade-off between short-term gain and long-term robustness. In this work, we present an approach for automatic detection and classification of delay insertions, with the goal of identifying those that could be made more robust. The approach has been implemented using an open-source compiler tooling framework and validated using test code from the telecom industry.

Among advanced manufacturing techniques for Fiber-Reinforced Polymer-matrix Composites (FRPCs) which are critical for aerospace, marine, automotive, and energy industries, Frontal Polymerization (FP) has been recently proposed to save orders of magnitude time and energy. However, the cure kinetics of the matrix phase, usually a thermosetting polymer, brings difculty to the design and control of the process. Here, we develop a deep learning model, ChemNet, to solve an inverse problem in predicting and optimizing the cure kinetics parameters of the thermosetting FRPCs for a desired fabrication strategy. ChemNet consists of a fully connected FeedForward 9-layer deep neural network trained on one million examples, and predicts activation energy and reaction enthalpy given the front characteristics such as speed and maximum temperature. ChemNet provides highly accurate predictions measured by the mean square error (MSE) and by the maximum absolute error metrics. The MSE of ChemNet, on the train set and test set attain the values of 1E-4 and 2E-4, respectively.

The diploid Celina/QTee® (‘Colorée de Juillet’ × ‘Williams’), one of the most promising pear cultivars developed by the Norwegian breeding program Graminor, was launched in 2010. In Norway, the flowering is medium to late, while the fruits ripen in the beginning of September. The fruits are attractive with an intense red blush (50%) on a green background. Although, ‘Celina’ is cultivated in the most climatically suitable regions for fruit cultivation, present in Norway, unfavorable environmental conditions for pear pollination can have a very negative effect on fruit set and consequent yield. The aim of this study was to determine the S-alleles of ‘Celina’, as well as its frequently used pollinizers, and, through paternity testing of ‘Celina’ seeds, give a recommendation regarding the most important pollinizers of this pear cultivar. In order to accomplish this, ‘Celina’ and its potential pollinizers were all S-genotyped. After harvest, seeds collected from ‘Celina’ fruit in 2017 and 2018 were genotyped using eleven microsatellite markers. Genomic DNA was also extracted from leaf material collected from ‘Celina’, as well as from five pear cultivars used as pollinizers in the three examined orchards, and analyzed using the same marker set. Subsequently a simple sequence repeat (SSR) database was constructed and used for gene assignment analyses with the aim of quantifying pollen donor contribution from individual pollinizers. The obtained results indicate that ‘Anna’, the only examined pollinizer that was fully cross-compatible with ‘Celina’, together with ‘Fritjof’, the genotype which had the highest flowering overlap with ‘Celina’, proved to be the most successful pollinizers across all seasons and orchards. Although both cultivars were ubiquitous in the examined orchards, either as planted trees or as branches introduced during the flowering period, they were the most abundant pollinizers in only one orchard each. It is therefore possible to conclude that pollinizer abundance has a secondary significance in pollinizer success within investigated ‘Celina’ orchards.