Limosilactobacillus fermentum ING8, a Potential Multifunctional Non-Starter Strain with Relevant Technological Properties and Antimicrobial Activity

Lactic acid bacteria (LAB) have gained particular attention among different exopolysaccharide-producing microorganisms due to their safety status and effects on human health and food production. Exopolysaccharide-producing LAB play a crucial role in different ways, such as improving texture, mouthfeel, controlling viscosity, and for low-calorie food production. In this study, we isolated a mul-tifunctional strain with good exopolysaccharide production properties. Limosilactobacillus fermentum ING8 was isolated from an Indian traditional fermented milk (Dahi) and evaluated for its safety, enzymatic activity, NaCl resistance and temperature tolerance, milk coagulation, and storage stability. Finally, the complete genome of this strain was sequenced and subjected to safety in silico evaluation and genomic analysis. The results revealed that L. fermentum ING8 possesses relevant technological properties, such as exopolysaccharide production, antimicrobial activity, and galactose utilization. Besides, this strain showed very high stability to storage conditions at refrigeration temperature. In addition, the genomic analysis did not evidence any possible deleterious elements, such as acquired antibiotic resistance genes, virulence genes, or hemolysis-related genes. However, all structural genes related to the galactose operon and EPS production were detected. Therefore, L. fermentum ING8 can be considered a promising multifunctional bacterium to be proposed as non-starter in different types of dairy productions

Thermal resistance and high-performance microwave decontamination assessment of Bacillus endospores isolated from food-grade herbal extracts

Generally, endospore contamination can occur from different sources during product manufacturing in many industries and therefore lower its quality by affecting physicochemical properties and shelf-life. Bacterial endospores can germinate inside the product and produce several enzymes, which can cause several undesirable changes. This study assessed the spores thermal resistance and applied a microwave decontamination technique toward herbal extracts (Tilia tomentosa and Centella asiatica) containing ethanol or glycerol. Based on 16S rRNA analysis, the detected contaminant endospores belonged to different Bacillus species, namely B. subtilis, B. zhangzhouensis, and B. pumilus. The thermal resistance assessment using inoculated endospores in the actual products revealed B. pumilus T2 as the most resistant endospore to the heat treatments tested in both T. tomentosa and C. asiatica extracts. Finally, a high-performance microwave technique was used to decontaminate T. tomentosa extract against the mixture of Bacillus spores. Results from the microwave technique indicate that the increase of temperature from 100°C to 105°C not only decontaminated the product but also could dramatically decrease the effective thermal treatment time (10 times), which can benefit the product quality. The results provided in this study considerably contribute to improving an original decontamination method for products containing glycerol and ethanol with the most negligible effect on product quality

Diagnosing COVID-19 disease using an efficient CAD system

Todays, COVID-19 has caused much death and its spreading speed is increasing, regarding virus mutation. This outbreak warns diagnosing infected people is an important issue. So, in this research, a computer-aided diagnosis (CAD) system called COV-CAD is proposed for diagnosing COVID-19 disease. This COV-CAD system is created by a feature extractor, a classification method, and a content-based imaged retrieval (CBIR) system. The proposed feature extractor is created by using the modified AlexNet CNN. The first modification changes ReLU activation functions to LeakyReLU for increasing efficiency. The second change is converting a fully connected (FC) layer of AlexNet CNN with a new FC, which results in reducing learnable parameters and training time. Another FC layer with dimensions 1 × 64 is added at the end of the feature extractor as the feature vector. In the classification section, a new classification method is defined in which the majority voting technique is applied on outputs of CBIR, SVM, KNN, and Random Forest for final diagnosing. Furthermore, in retrieval section, the proposed method uses CBIR because of its ability to retrieve the most similar images to the image of a patient. Since this feature helps physicians to find the most similar cases, they could conduct further statistical evaluations on profiles of similar patients. The system has been evaluated by accuracy, sensitivity, specificity, F1-score, and mean average precision and its accuracy for CT and X-ray datasets is 93.20% and 99.38%, respectively. The results demonstrate that the proposed method is more efficient than other similar studies

A new method for image classification and image retrieval using convolutional neural networks

This article proposes a new method for image classification and image retrieval. The advantages of the proposed method are its high performance and requiring less memory compared to other methods. In order to extract image features, a Convolutional Neural Network (CNN), AlexNet, has been used. For image classification, we design a committee of four classifiers trained on graphics cards, narrowing the gap to human performance. For image retrieval, the similarity between extracted features from dataset images and features of the query image is calculated and the final results are visualized. Comprehensive experiments on Corel-1k, Corel-10k, Caltech-101 object and Scene-67 datasets have been investigated to find optimal parameters of the proposed method. The experiments demonstrate the high performance of the proposed method in comparison with the state-of-the-art in the field

A fast and yet efficient YOLOv3 for blood cell detection

These days, blood cell detection in microscopic images plays a vital role in cognition, the health of a patient. Since disease detection based on manual checking of blood cells is mostly time-consuming and full of errors, analysis of blood cells using object detectors can be considered as an effective tool. Hence, in this study, an object detector has been proposed which is used for detecting blood objects such as white blood cells, red blood cells, and platelets. This detector is called FED (Fast and Efficient YOLOv3) and it is a One-Stage detector, which is similar to YOLOv3, performs detection in three scales. For the purpose of increasing efficiency and flexibility, the proposed object detector utilizes the EfficientNet Convolutional Neural Network as the backbone effectiveness. Furthermore, the Dilated Convolution is indeed applied in order to increase receptive view of the backbone. In addition, the Depthwise Separable Convolution method is utilized to minimize the detector's parametersand the Distance Intersection over Union is further used for bounding box regression. Besides, for increasing the performance, the Swish activation function is employed. The experiments are run on the BCCD dataset that the average precision of platelets, red blood cells, and white blood cells become 90.25%, 80.41%, and 98.92%, respectively. The results of experiments and comparisons demonstrate that the proposed FED detector is more efficient than other existing studies for blood cell detection

Safety, functionality and genomic assessment of Pediococcus acidilactici strains isolated from traditional Persian fermented products with potential probiotic properties and hypocholesterolemic effect

Pediococcus acidilactici has a good reputation for its technological properties, particularly in the production of fermented sausages and has also been considered as a potential probiotic species. Since in recent years there is an increasing demand for probiotics of non-dairy origin, assessing bacterial species from non-dairy environments could be pretty advantageous. In this study, different lactic acid bacteria (LAB) were isolated from a traditional Persian food (Kashk Zard), and strains discrimination was carried out by RAPD-PCR. Subsequently, some strains were identified to the species level and evaluated for their safety and functionality as probiotics, including properties such as antimicrobial activity, resistance to simulated human gastrointestinal conditions, and cholesterol-lowering effects. The genome of P. acidilactici strain IRZ12B was sequenced and the in silico analysis revealed that this strain possesses interesting probiotic properties, such as cholesterol-lowering capability and antimicrobial activitiy. Furthermore, genome analysis confirmed the absence of transmissible antibiotic resistance genes, plasmids, and virulence factors inside the genome. The results reported in this study make P. acidilactici IRZ12B a promising potential probiotic strain to be considered for the production of novel non-dairy-based functional food