Effect of domestic cooking methods on total phenolic content,antioxidant activity and sensory characteristics of Hericium erinaceus

This study evaluated the effect of different cooking methods (microwaving, boiling, steaming and stir-frying) on the phenolic acids and antioxidant activity in Hericium erinaceus (HE). The total phenolic content (TPC) of HE decreased after cooking and was in the order of dried (control) > stir-fried > microwaved > steamed > boiled. An increase in antioxidant activity was found in all cooked HE, albeit statistically insignificant (P > 0.05). Gallic acid was reported to be the dominant phenolic acid found in HE extracts in the range of 3.03–49.41 μg g⁻¹ dry weight. Low negative correlations were observed between 2,2-diphenyl-1-picrylhydrazyl (DPPH) and TPC as well as DPPH and gallic acid profile. Sensory data revealed that stir-fried HE received the most favourable response in colour, texture, aroma and flavour attributes. Overall, this study suggested that the method of stir-frying could be used to prepare HE for better retention of gallic acid and antioxidant activity with acceptable sensory characteristics.     

Optimization of Signal Peptide via Site-Directed Mutagenesis for Enhanced Secretion of Heterologous Proteins in Lactococcus lactis

Secretion efficiency of heterologous proteins in the Generally Regarded As Safe (GRAS) Lactococcus lactis is often reported to be insufficiently low due to limitations such as poor targeting and translocation by the signal peptide or degradation by the host proteases. In this study, the secretion efficiency in the host was enhanced through the utilization of a heterologous signal peptide (SP) SPK1 of Pediococcus pentosaceus. The SPK1 was subjected to site-directed mutations targeting its tripartite N-, H-, and C-domains, and the effect on secretion efficiency as compared to the wild-type SPK1 and native lactococcal USP45 was determined on a reporter nuclease (NUC) of Staphylococcus aureus. A Fluorescence Resonance Energy Transfer (FRET) analysis indicated that four out of eight SPK1 variants successfully enhanced the secretion of NUC, with the best mutant, SPKM19, showing elevated secretion efficiency up to 88% (or by 1.4-fold) and an improved secretion activity yield of 0.292 ± 0.122 U/mL (or by 1.7-fold) compared to the wild-type SPK1. Modifications of the SPK1 at the cleavage site C-domain region had successfully augmented the secretion efficiency. Meanwhile, mutations in the H-domain region had resulted in a detrimental effect on the NUC secretion. The development of heterologous SPs with better efficacy than the USP45 has been demonstrated in this study for enhanced secretion of heterologous production and mucosal delivery applications in the lactococcal host.     

Biophysical Evaluation of Water-Soluble Curcumin Encapsulated in β-Cyclodextrins on Colorectal Cancer Cells

Curcumin (CUR), a curcuminoid originating from turmeric root, possesses diverse pharmacological applications, including potent anticancer properties. However, the use of this efficacious agent in cancer therapy has been limited due to low water solubility and poor bioavailability. To overcome these problems, a drug delivery system was established as an excipient allowing improved dispersion in aqueous media coupled with enhanced in vitro anticancer effects. Different analyses such as UV–vis spectroscopy, differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), solubility and dissolution assays were determined to monitor the successful encapsulation of CUR within the inner cavity of a β-cyclodextrin (β-CD) complex. The results indicated that water solubility was improved by 205.75-fold compared to pure CUR. Based on cytotoxicity data obtained from MTT assays, the inclusion complex exhibited a greater decrease in cancer cell viability compared to pure CUR. Moreover, cancer cell migration rates were decreased by 75.5% and 38.92%, invasion rates were decreased by 37.7% and 35.7%, while apoptosis rates were increased by 26.3% and 14.2%, and both caused caspase 3 activation toward colorectal cancer cells (SW480 and HCT116 cells). This efficacious formulation that enables improved aqueous dispersion is potentially useful and can be extended for various chemotherapeutic applications. Preliminary toxicity evaluation also indicated that its composition can be safely used in humans for cancer therapy.     

Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After pre-processing, we generate the annotations of the dataset which is required for model training. Then, introduce DenseNet-65 at the feature extraction level of Faster-RCNN to compute the representative set of key points. Finally, the Faster-RCNN localizes and classifies the input sample into five classes. Rigorous experiments performed on a Kaggle dataset comprising of 88,704 images show that the introduced methodology outperforms with an accuracy of 97.2%. We have compared our technique with state-of-the-art approaches to show its robustness in term of DR localization and classification. Additionally, we performed cross-dataset validation on the Kaggle and APTOS datasets and achieved remarkable results on both training and testing phases.     

Numerical modelling of deep rolling influence over crankshaft bending and correlation with fatigue behaviour

The requirements for mechanical reliability of automotive crankshafts are continuously increasing, thus pushing the demand for an optimized processing. Nonetheless, the manufacturing‐induced residual stresses at critical sites for fatigue enhancement are not clarified in the state‐of‐the‐art on the topic. In particular, there is a lack of information on the effect of final manufacturing stages to improve the component life endurance, such as deep rolling, in the overall stress state while the component is under operational loads. This study deepens the validation of a finite element deep rolling model under development with the aid of an in‐house developed crankshaft resonance fatigue test rig. The stress state obtained from the deep rolling simulation was input as a predefined stress field for the simulation of operational conditions experimented at the test rig. Test results produced cracks at the fillet radii of the cast iron crankshafts as anticipated. Overlapping the fractography with the simulation's final stress field yielded interesting correlation with the crack morphology. This contributed with a strong indication of the model correctness. Moreover, it can be further implemented to indicate whether the process parameters such as roller force and angle are fully optimized for each particular crankshaft application.     

A decision tree framework for shot classification of field sports videos

The Journal of Supercomputing - Automated approaches to analyze sports video content have been heavily explored in the last few decades to develop more informative and effective solutions for...     

IoT Wireless Intrusion Detection and Network Traffic Analysis

Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the router. Almost every Internet of Things (IoT) devices such as smartphones, drones, and cameras use wireless technology (Infrared, Bluetooth, IrDA, IEEE 802.11, etc.) to establish multiple inter-device connections simultaneously. With the flexibility of the wireless network, one can set up numerous ad-hoc networks on-demand, connecting hundreds to thousands of users, increasing productivity and profitability significantly. However, the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very alarming. Perpetrators can launch attacks since there is no first line of defense in an ad hoc network setup besides the standard IEEE802.11 WPA2 authentication. One feasible countermeasure is to deploy intrusion detection systems at the edge of these ad hoc networks (Network-based IDS) or at the node level (Host-based IDS). The challenge here is that there is no readily available benchmark data available for IoT network traffic. Creating this benchmark data is very tedious as IoT can work on multiple platforms and networks, and crafting and labelling such dataset is very labor-intensive. This research aims to study the characteristics of existing datasets available such as KDD-Cup and NSL-KDD, and their suitability for wireless IDS implementation. We hypothesize that network features are parametrically different depending on the types of network and assigning weight dynamically to these features can potentially improve the subsequent threat classifications. This paper analyses packet and flow features for the data packet captured on a wireless network rather than a wired network. Combining domain heuristcs and early classification results, the paper had identified 19 header fields exclusive to wireless network that contain high information gain to be used as ML features in Wireless IDS.     

Embedding neural networks for semantic association in content based image retrieval

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

     

An efficient U-Net framework for lung nodule detection using densely connected dilated convolutions

The Journal of Supercomputing - Remote health monitoring is an important aspect especially for remote locations where standard medical facilities are not available. Smart cities use a similar...