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S. aureus, particularly methicillin-resistant S. aureus, has been recognized as a main cause of bovine mastitis and food poisoning. This study investigated the prevalence, antibiotic resistance, and phage-based biocontrol of S. aureus and methicillin-resistant S. aureus isolated from raw milk of cows with subclinical mastitis. The results showed that the prevalence of S. aureus and methicillin-resistant S. aureus was 12% (48/400) and 1.5% (6/400), respectively. The S. aureus isolates were highly resistant to penicillin (72.92%), erythromycin (43.75%), and tetracycline (39.58%). Out of 48 S. aureus isolates, 6 were identified as methicillin-resistant strains. Among them, one isolate was found to harbor the sea gene. A total of 5 phages were recovered from 50 pork and 50 chicken meat samples, 1 from pork and 4 from chicken meat samples. Phage PSA2 capable of lysing all 6 methicillin-resistant isolates was selected for characterization. The use of phage PSA2 completely inactivated methicillin-resistant S. aureus SA33 in raw milk at both 24 °C and 4 °C, indicating its potential as a promising antibacterial agent in controlling methicillin-resistant S. aureus in raw milk and treating bovine mastitis.

E. coli is an important zoonotic pathogen capable of causing foodborne illness and bovine mastitis. Bacteriophages have been increasingly considered a promising tool to control unwanted bacteria. The aim of this study is to determine the antibiotic resistance profile of E. coli isolated from raw milk and the efficacy of phage in controlling multidrug-resistant E. coli in raw milk. Antibiotic susceptibility testing showed the highest resistance rates of E. coli isolates to co-trime (27.34%) and ampicillin (27.34%), followed by streptomycin (25.18%), tetracycline (23.02%), and the lowest resistance rates to ciprofloxacin, gentamycin, and ceftazidime, all at a rate of 2.16%. All isolates were susceptible to meropenem. Of the 139 E. coli isolates, 57 (41.01%) were resistant to at least one antibiotic, and 35 (25.18%) were classified as MDR strains. Molecular characterization indicated that 5 (3.6%) out of the 139 isolates were STEC strains carrying stx1 gene. Seven (5.04%) isolates were phenotypically identified as ESBLEC, and four isolates (2.88%) were resistant to colistin. The results of the genotypic test revealed that four out of seven ESBLEC strains carried both blaTEM and blaCTX-M-1, two harbored blaTEM, and one possessed blaCTX-M-1, while mcr-1 was detected in all four colistin-resistant E. coli isolates. In particular, one isolated E. coli strain (EM148) was determined to be a multidrug-resistant strain simultaneously carrying blaTEM, blaCTX-M-1, and mcr-1. A total of eight phages were successfully recovered from raw milk. The application of phage PEM3 significantly reduced viable counts of multidrug-resistant host EM148 in raw milk by at least 2.31 log CFU/mL at both 24 °C and 4 °C.

Shiga toxin-producing Escherichia coli (STEC) is one of the most important foodborne pathogens, and the rise of antibiotic resistance to it is a significant threat to global public health. The purpose of this study is to investigate the prevalence, molecular characterization, and antibiotic resistance of STEC isolated from raw meat in Vietnam. The findings in this study showed that the prevalence of STEC in raw beef, pork, and chicken meat was 9.72% (7/72), 5.56% (4/72), and 1.39% (1/72), respectively. The STEC isolates were highly resistant to ampicillin (91.67%) and tetracycline (91.67%), followed by trimethoprim/sulfamethoxazole (83.33%), streptomycin (75%), and florfenicol (66.67%). The incidence of STEC virulence-associated genes, including stx1, stx2, eae, and ehxA, was 8.33% (1/12), 91.67% (11/12), 33.33% (4/12), and 58.33% (7/12), respectively. STEC serogroups O157, O26, and O111 were detected in 3 out of 12 STEC isolates. Two isolates were found to be ESBL producers carrying the blaCTX-M-55 gene, and three isolates were colistin-resistant strains harboring the mcr-1 gene. Notably, a STEC O111 isolate from chicken meat harbored both the blaCTX-M-55 and mcr-1 genes.

Colistin-resistant Escherichia coli (COE) has been recently recognized as a serious threat to animal and human health. This study aimed to determine the prevalence and antibiotic resistance profile of COE isolated from raw beef and cow feces in Vietnam. Our results showed that 16% (16/100) and 32% (32/100) of raw beef and cow feces samples were positive for COE, respectively. A total of 48 COE strains were isolated, with 16 originating from raw beef and 32 from cow feces samples. The antibiotic susceptibility test revealed that the COE isolates were highly resistant to ampicillin, tetracycline, florfenicol, trimethoprim/sulfamethoxazole, streptomycin, and nalidixic acid, with resistance rates ranging from 66.67% to 87.5%. In addition, 87.5% of the isolates were identified to be multidrug-resistant strains. Further molecular characterization indicated that all COE isolates carried the mcr-1 gene, with 16 of them also harboring blaCTX-M-55 genes. Taken together, the findings in this study demonstrate that raw beef and cow feces are important sources of COE, which can be potentially transmitted to humans through the food chain.

Clostridium perfringens is one of the most important zoonotic pathogens as it can cause food poisoning in humans and necrotic enteritis in both animals and humans. Meat, especially pork and chicken meat, is considered the main vehicle for the transmission of C. perfringens from animals to humans. The purpose of this study was to determine the prevalence, toxinotype, and antimicrobial resistance profile of C. perfringens isolated from pork and chicken meat sold in Vietnam. The isolation results showed that 15/50 (30%) of pork samples and 8/50 (16%) of chicken meat samples were contaminated with C. perfringens. The isolates exhibited their highest resistance rate to tetracycline (21/23; 91.30%) and clindamycin (10/23; 43.48%). On the contrary, their lowest resistance rates were observed in response to imipenem (2/23; 8.70%) and cefoxitin (1/23; 4.35%). In particular, 34.78% (8/23) of C. perfringens isolates were identified to be multidrug-resistant strains. The results of toxin genotyping indicated that all isolates were positive for the cpa gene and belonged to type A.

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.

Plastic waste poses a significant threat to the environment, impacting both aquatic ecosystems and human health. This study aimed to quantify the leakage of plastic waste from urban and rural areas into the Tam Giang–Cau Hai lagoon system area in Vietnam. The research involved conducting surveys and sampling plastic waste in wards and communes surrounding the Tam Giang–Cau Hai lagoon system, as well as utilizing a waste flow diagram to calculate the amount of plastic waste leakage into the environment. The findings of the study reveal that the annual plastic leakage in this study area is approximately 479 tons. The majority of this waste enters the water body system, accounting for 74.1% of the total leakage, followed by land areas at 23.4% and land burning at 2.5%. Among the sources contributing to the wastewater flow in the area, households and markets were found to be the two primary contributors. Household waste accounted for 70.4% (2806 tons year−1) of the total, while the market sources accounted for 16.9% (675 tons year−1). This study marks the inaugural effort to assess the extent of plastic waste released from Hue City into the Tam Giang–Cau Hai lagoon system. It plays a pivotal role in examining the makeup, source of plastic waste and path of plastic waste leakage.

The Neurovascular Unit (NVU), composed of glia (astrocytes, oligodendrocytes, microglia), neurons, pericytes and endothelial cells, is a dynamic interface ensuring the physiological functioning of the central nervous system (CNS), which gets affected and contributes to the pathology of several neurodegenerative diseases. Neuroinflammation is a common feature of neurodegenerative diseases and is primarily related to the activation state of perivascular microglia and astrocytes, which constitute two of its major cellular components. Our studies focus on monitoring in real time the morphological changes of perivascular astrocytes and microglia, as well as their dynamic interactions with the brain vasculature, under physiological conditions and following systemic neuroinflammation triggering both microgliosis and astrogliosis. To this end, we performed 2-photon laser scanning microscopy (2P-LSM) for intravital imaging of the cortex of transgenic mice visualizing the dynamics of microglia and astroglia following neuroinflammation induced by systemic administration of the endotoxin lipopolysaccharide (LPS). Our results indicate that following neuroinflammation the endfeet of activated perivascular astrocytes lose their close proximity and physiological cross-talk with vasculature, an event that most possibly contributes to a loss of blood–brain barrier (BBB) integrity. At the same time, microglial cells become activated and exhibit a higher extent of physical contact with the blood vessels. These dynamic responses of perivascular astrocytes and microglia are peaking at 4 days following LPS administration; however, they still persist at a lower level at 8 days after LPS injection, revealing incomplete reversal of inflammation affecting the glial properties and interactions within the NVU.

Identifying and ensuring the inactivation of the African Swine Fever virus in deadstock is a gap in the swine industry’s knowledge and response capabilities. The results of our study demonstrate that ASFv in deadstock was inactivated using static aerated composting as the carcass disposal method. Replicated compost piles with whole market hogs and two different carbon sources were constructed. In-situ bags containing ASFv-infected spleen tissue were placed alongside each of the carcasses and throughout the pile. The bags were extracted at days 0, 1, 3, 7, 14, 28, 56, and 144 for ASFv detection and isolation. Real-time PCR results showed that DNA of ASFv was detected in all samples tested on day 28. The virus concentration identified through virus isolation was found to be below the detection limit by day 3 in rice hulls and by day 7 in sawdust. Given the slope of the decay, near-zero concentration with 99.9% confidence occurred at 5.0 days in rice hulls and at 6.4 days in sawdust. Additionally, the result of virus isolation also showed that the virus in bone marrow samples collected at 28 days was inactivated.

African swine fever (ASF) is a highly contagious swine disease with high mortality. In many countries, culling pigs infected and exposed to the ASF virus is mandatory to control the disease, which poses a real challenge in the disposal of large numbers of carcasses during ASF outbreaks. Shallow burial with carbon (SBC) Thanks ew mortality disposal method developed from deep burial and composting. The present study investigates the effectiveness of SBC in disposing of ASF virus-infected pigs. The real-time PCR results showed that DNA of the ASF virus was still detected in bone marrow samples on day 56, while the virus isolation test revealed that the infectious ASF virus was destroyed in both spleen and bone marrow samples on day 5. Interestingly, decomposition was found to occur rapidly in these shallow burial pits. On day 144, only large bones were found in the burial pit. In general, the results of this study indicated that SBC is a potential method for the disposal of ASF-infected carcasses; however, further studies are needed to provide more scientific evidence for the efficacy of SBC in different environment conditions.

To maintain and improve an amateur athlete’s fitness throughout training and to achieve peak performance in sports events, good nutrition and physical activity (general and training specifically) must be considered as important factors. In our context, the terminology “amateur athletes” represents those who want to practice sports to protect their health from sickness and diseases and improve their ability to join amateur athlete events (e.g., marathons). Unlike professional athletes with personal trainer support, amateur athletes mostly rely on their experience and feeling. Hence, amateur athletes need another way to be supported in monitoring and recommending more efficient execution of their activities. One of the solutions to (self-)coaching amateur athletes is collecting lifelog data (i.e., daily data captured from different sources around a person) to understand how daily nutrition and physical activities can impact their exercise outcomes. Unfortunately, not all factors of the lifelog data can contribute to understanding the mutual impact of nutrition, physical activities, and exercise frequency on improving endurance, stamina, and weight loss. Hence, there is no guarantee that analyzing all data collected from people can produce good insights towards having a good model to predict what the outcome will be. Besides, analyzing a rich and complicated dataset can consume vast resources (e.g., computational complexity, hardware, bandwidth), and this therefore does not suit deployment on IoT or personal devices. To meet this challenge, we propose a new method to (i) discover the optimal lifelog data that significantly reflect the relation between nutrition and physical activities and training performance and (ii) construct an adaptive model that can predict the performance for both large-scale and individual groups. Our suggested method produces positive results with low MAE and MSE metrics when tested on large-scale and individual datasets and also discovers exciting patterns and correlations among data factors.

The explosive growth of the social media community has increased many kinds of misinformation and is attracting tremendous attention from the research community. One of the most prevalent ways of misleading news is cheapfakes. Cheapfakes utilize non-AI techniques such as unaltered images with false context news to create false news, which makes it easy and “cheap” to create and leads to an abundant amount in the social media community. Moreover, the development of deep learning also opens and invents many domains relevant to news such as fake news detection, rumour detection, fact-checking, and verification of claimed images. Nevertheless, despite the impact on and harmfulness of cheapfakes for the social community and the real world, there is little research on detecting cheapfakes in the computer science domain. It is challenging to detect misused/false/out-of-context pairs of images and captions, even with human effort, because of the complex correlation between the attached image and the veracity of the caption content. Existing research focuses mostly on training and evaluating on given dataset, which makes the proposal limited in terms of categories, semantics and situations based on the characteristics of the dataset. In this paper, to address these issues, we aimed to leverage textual semantics understanding from the large corpus and integrated with different combinations of text-image matching and image captioning methods via ANN/Transformer boosting schema to classify a triple of (image, caption1, caption2) into OOC (out-of-context) and NOOC (no out-of-context) labels. We customized these combinations according to various exceptional cases that we observed during data analysis. We evaluate our approach using the dataset and evaluation metrics provided by the COSMOS baseline. Compared to other methods, including the baseline, our method achieves the highest Accuracy, Recall, and F1 scores.

The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.

The optical properties of indium phosphide (InP) quantum dots (QDs) are significantly influenced by their surface native oxides, which are generally removed by treating InP cores with hydrofluoric acid (HF). Besides the harmful health effects of HF, its etching may cause over-etching or QD size broadening, and surface oxidation can also reoccur rapidly. In the present study, a safer bifunctional metal oleate treatment was developed to simultaneously remove the surface oxide layer and passivate the surface defects for aminophosphine-based InP QDs. Compared to conventional HF etching, the bifunctional metal oleate was able to more efficiently remove the surface oxide of InP cores and effectively preserve the oxide-free surface, leading to a 20% narrower photoluminescence (PL) bandwidth after growing a ZnSe/ZnS shell. The metal oleate treatment is thus considered a greener and safer post-synthetic method to remove InP surface oxide and provide additional passivation to improve the optical properties of aminophosphine-based InP QDs, which could have potential in industrial mass production.

Dental caries has been considered the heaviest worldwide oral health burden affecting a significant proportion of the population. To prevent dental caries, an appropriate and accurate early detection method is demanded. This proof-of-concept study aims to develop a two-stage computational system that can detect early occlusal caries from smartphone color images of unrestored extracted teeth according to modified International Caries Detection and Assessment System (ICDAS) criteria (3 classes: Code 0; Code 1–2; Code 3–6): in the first stage, carious lesion areas were identified and extracted from sound tooth regions. Then, five characteristic features of these areas were intendedly selected and calculated to be inputted into the classification stage, where five classifiers (Support Vector Machine, Random Forests, K-Nearest Neighbors, Gradient Boosted Tree, Logistic Regression) were evaluated to determine the best one among them. On a set of 587 smartphone images of extracted teeth, our system achieved accuracy, sensitivity, and specificity that were 87.39%, 89.88%, and 68.86% in the detection stage when compared to modified visual and image-based ICDAS criteria. For the classification stage, the Support Vector Machine model was recorded as the best model with accuracy, sensitivity, and specificity at 88.76%, 92.31%, and 85.21%. As the first step in developing the technology, our present findings confirm the feasibility of using smartphone color images to employ Artificial Intelligence algorithms in caries detection. To improve the performance of the proposed system, there is a need for further development in both in vitro and in vivo modeling. Besides that, an applicable system for accurately taking intra-oral images that can capture entire dental arches including the occlusal surfaces of premolars and molars also needs to be developed.