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In this article, we propose a novel bilayer low-rankness measure and two models based on it to recover a low-rank (LR) tensor. The global low rankness of underlying tensor is first encoded by LR matrix factorizations (MFs) to the all-mode matricizations, which can exploit multiorientational spectral low rankness. Presumably, the factor matrices of all-mode decomposition are LR, since local low-rankness property exists in within-mode correlation. In the decomposed subspace, to describe the refined local LR structures of factor/subspace, a new low-rankness insight of subspace: a double nuclear norm scheme is designed to explore the so-called second-layer low rankness. By simultaneously representing the bilayer low rankness of the all modes of the underlying tensor, the proposed methods aim to model multiorientational correlations for arbitrary N -way ( N ≥ 3 ) tensors. A block successive upper-bound minimization (BSUM) algorithm is designed to solve the optimization problem. Subsequence convergence of our algorithms can be established, and the iterates generated by our algorithms converge to the coordinatewise minimizers in some mild conditions. Experiments on several types of public datasets show that our algorithm can recover a variety of LR tensors from significantly fewer samples than its counterparts.

African swine fever virus (ASFV) has spread through many countries and regions worldwide, causing significant losses. Timely detection of ASFV-infected pigs is crucial for disease control. In this study, we assessed the performance of two pen-side tests: a portable real-time PCR (qPCR) test for detecting viral genomic DNA and a lateral flow immunoassay (LFIA) for detecting viral antigens. To determine the time from infection to the earliest detection, 10 ASFV-seronegative pigs were inoculated intramuscularly with 10 4.0 hemadsorption dose 50 of a highly virulent ASFV strain. Whole blood and oral swab samples were alternately collected from each group of five pigs daily until all succumbed to the infection. Samples were promptly subjected to the two pen-side tests upon collection, and a subset was transported to a veterinary diagnostic laboratory for analysis using a reference qPCR assay. Viral genomic DNA was consistently detected by the reference qPCR assay in all blood samples from 2 days postinfection (dpi), preceding the onset of clinical signs, and in oral swabs from 4 dpi onwards. The portable qPCR test demonstrated comparable performance to the reference qPCR assay for both whole blood and oral swab samples. The LFIA exhibited 100% specificity when testing with whole blood samples but showed reduced sensitivity, particularly for blood samples collected early or late after infection. The antigen test did not perform well with oral swabs., (© 2024. The Author(s).)

Today, environmental sustainability is one of the most critical issue. Hence, the food service industry is actively seeking ways to minimize its ecological footprint. One solution to address this issue is the adoption of reusable foodware in the food service industry. This approach requires a careful process for the collection and thorough cleaning of the foodware, ensuring it can be safely reused. However, reusable foodware might be damaged during the collection process, which can pose food safety hazards for customers. Additionally, there are cases where the cleaning process might not effectively remove all contaminants and therefore cannot be reused after the washing process. To ensure consumer safety, a manual inspection is typically conducted after the cleaning process. However, this step is labor-intensive and prone to human error, particularly as workers' attention may decrease over extended periods. Consequently, the adoption of precise and automated methods for detecting defects and contaminants is becoming crucial, not only to ensure safety but also to achieve scalability and enhance cost-efficiency in the pursuit of environmental sustainability. In our research, we explore various data augmentation strategies and the application of knowledge transfer from various samples of reusable food containers. This method only requires few images from a clean sample to teach the network about normal patterns, and to detect defects by identifying irregular details that do not exist in normal samples. This allows us to rapidly deploy the detection system even with a limited number of collected samples. Experimental results demonstrate the effectiveness of our approach in detecting both contamination and cracks on food containers., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: TRUONG MINH ANH reports financial support was provided by Europees Fonds voor Regionale Ontwikkeling. HIEP LUONG reports financial support was provided by Europees Fonds voor Regionale Ontwikkeling. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

African swine fever virus (ASFV) is circulating in many swine-producing countries, causing significant economic losses. It is observed that pigs experimentally vaccinated with a live-attenuated virus (LAV) but not a killed virus (KV) vaccine develop solid homologous protective immunity. The objective of this study was to comparatively analyze antibody profiles between pigs vaccinated with an LAV vaccine and those vaccinated with a KV vaccine to identify potential markers of vaccine-induced protection. Thirty ASFV seronegative pigs were divided into three groups: Group 1 received a single dose of an experimental LAV, Group 2 received two doses of an experimental KV vaccine, and Group 3 was kept as a non-vaccinated (NV) control. At 42 days post-vaccination, all pigs were challenged with the parental virulent ASFV strain and monitored for 21 days. All pigs vaccinated with the LAV vaccine survived the challenge. In contrast, eight pigs from the KV group and seven pigs from the NV group died within 14 days post-challenge. Serum samples collected on 41 days post-vaccination were analyzed for their reactivity against a panel of 29 viral structural proteins. The sera of pigs from the LAV group exhibited a strong antibody reactivity against various viral structural proteins, while the sera of pigs in the KV group only displayed weak antibody reactivity against the inner envelope (p32, p54, p12). There was a negative correlation between the intensity of antibody reactivity against five ASFV antigens, namely p12, p14, p15, p32, and pD205R, and the viral DNA titers in the blood of animals after the challenge infection. Thus, antibody reactivities against these five antigens warrant further evaluation as potential indicators of vaccine-induced protection.

Antibody profile of pigs naturally infected with a virulent African swine fever virus (ASFV) strain under field conditions was studied. Twenty-three serum samples were collected from pigs surviving a natural ASFV infection: 17 samples from finishing pigs (∼7 months old) and 6 samples from sows (between 12 and 36 months old). Additionally, 24 serum samples were collected from ASFV-naïve pigs to serve as negative controls. All sera from ASFV-surviving pigs tested positive while all sera from control pigs tested negative by two different commercial ELISA kits. Antibody reactivity of each serum sample was simultaneously measured against six selected ASFV antigens including p12, p32, p54, pp62, C-type lectin and CD2v. All ASFV-surviving pigs had antibody against p32, p54 and pp62 while 91.3% surviving pigs had antibody against p12. Only small portions of ASFV-surviving pigs exhibited antibodies against C-type lectin (34.8%) and CD2v (26.1%). While antibodies against p12, p32, p54 and pp62 were similarly detected in both finishing pigs and sows, antibodies against C-type lectin and CD2v were mainly detected in sows but not in finishing pigs. These results suggest a differential humoral immune response to ASFV infection in sows and finishing pigs. Further studies are needed to better understand the nature of immune responses to ASFV infection in different pig populations., (Copyright © 2021. Published by Elsevier B.V.)

Luciferase-immunoprecipitation system (LIPS), a liquid phase immunoassay, was used to evaluate antibody responses directed against the structural proteins of PRRSV in pigs that were experimentally infected with virulent PRRSV strains. First, the viral N protein was used as a model antigen to validate the assay. The LIPS results were highly comparable to that of the commercial IDEXX PRRS X3 ELISA. Subsequently, the assay was applied to simultaneously measure antibody reactivity against all eight structural proteins of PRRSV. The highest immunoreactivities were detected against GP3, M, and N proteins while the lowest reactivity was detected against ORF5a protein. Comparative analysis of the kinetics of antibody appearance revealed that antibodies specific to N protein appeared earlier than antibodies against GP3. Finally, the assay was applied to measure immunoreactivities of clinical serum samples against N and GP3. The diagnostic sensitivity of the LIPS with N protein was superior to that of the LIPS with GP3. Collectively, the results provide additional information about the host antibody response to PRRSV infection.

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets.

Broccoli sprouts are an excellent source of health-promoting phytochemicals such as vitamins, glucosinolates, and phenolics. The study aimed to investigate in vitro antioxidant, antiproliferative, apoptotic, and antibacterial activities of broccoli sprouts. Five-day-old sprouts extracted by 70% ethanol showed significant antioxidant activities, analyzed to be 68.8 μmol Trolox equivalent (TE)/g dry weight by 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulphonic (ABTS) assay, 91% scavenging by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, 1.81 absorbance by reducing power assay, and high phenolic contents by high-performance liquid chromatography (HPLC). Thereafter, sprout extract indicated considerable antiproliferative activities towards A549 (lung carcinoma cells), HepG2 (hepatocellular carcinoma cells), and Caco-2 (colorectal adenocarcinoma cells) using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, with IC 50 values of 0.117, 0.168 and 0.189 mg/mL for 48 h, respectively. Furthermore, flow cytometry confirmed that Caco-2 cells underwent apoptosis by an increase of cell percentage in subG1 phase to 31.3%, and a loss of mitochondrial membrane potential to 19.3% after 48 h of treatment. Afterward, the extract exhibited notable antibacterial capacities against Bacillus subtilis and Salmonella Typhimurium with minimum inhibition concentration (MIC) values of 0.39 and 0.78 mg/mL, appropriately, along with abilities against Staphylococcus aureus and Escherichia coli with an MIC value of 1.56 mg/mL. Thus, broccoli sprouts were confirmed as a potential food source for consumers' selection and functional food industry.

In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods., (© 2018 The Authors Journal of Microscopy © 2018 Royal Microscopical Society.)

The goal of modern microscopy is to acquire high-quality image based data sets. A typical microscopy workflow is set up in order to address a specific biological question and involves different steps. The first step is to precisely define the biological question, in order to properly come to an experimental design for sample preparation and image acquisition. A better object representation allows biological users to draw more reliable scientific conclusions. Image restoration can manipulate the acquired data in an effort to reduce the impact of artifacts (spurious results) due to physical and technical limitations, resulting in a better representation of the object of interest. However, precise usage of these algorithms is necessary so as to avoid further artifacts that might influence the data analysis and bias the conclusions. It is essential to understand image acquisition, and how it introduces artifacts and degradations in the acquired data, so that their effects on subsequent analysis can be minimized. This paper provides an overview of the fundamental artifacts and degradations that affect many micrographs. We describe why artifacts appear, in what sense they impact overall image quality, and how to mitigate them by first improving the acquisition parameters and then applying proper image restoration techniques.