Effects of frying on the fatty acid composition in farmed and wild gilthead sea bream (Sparus aurata)

The effects of frying in soybean (FWSO) and olive oils (FWOO) on the fatty acid composition of farmed and wild gilthead sea bream Sparus aurata were evaluated. The fat content increased with both frying treatments. However, after FWOO the moisture content of the fish was reduced to a greater extent than that in fish FWSO. The concentration of saturated fatty acids (SFA) decreased significantly during both frying processes ( P  < 0.01). However, the concentration of monounsaturated fatty acids (MUFA) increased significantly in fish fried in olive oil ( P  < 0.01). The fried fish contained a higher level of n-6 polyunsaturated fatty acids (PUFA) and a lower level of n-3 PUFA compared to raw fish. The n-3/n-6 ratio decreased in wild fish FWSO and FWOO from 0.94 ± 0.08 to 0.15 ± 0.01 and 0.15 ± 0.02, respectively. In farmed bream, the ratios decreased from 2.51 ± 0.03 to 0.18 ± 0.03 and 0.36 ± 0.01, respectively. The concentration of trans fatty acids decreased significantly in both fish types after frying ( P  < 0.05). The frying process widely affected the EPA and DHA content, limiting the positive effects of n-3 PUFA.     

Arabic Named Entity Recognition: A BERT-BGRU Approach

Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models that are used to transfer knowledge learned from large-scale datasets to domain-specific tasks. Bidirectional Encoder Representation from Transformer (BERT) is a contextual language model that generates the semantic vectors dynamically according to the context of the words. BERT architecture relay on multi-head attention that allows it to capture global dependencies between words. In this paper, we propose a deep learning-based model by fine-tuning BERT model to recognize and classify Arabic named entities. The pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit (BGRU) and were fine-tuned using two annotated Arabic Named Entity Recognition (ANER) datasets. Experimental results demonstrate that the proposed model outperformed state-of-the-art ANER models achieving 92.28% and 90.68% F-measure values on the ANERCorp dataset and the merged ANERCorp and AQMAR dataset, respectively.     

Formulation and in vitro evaluation of a thermoreversible mucoadhesive nasal gel of itopride hydrochloride

Itopride hydrochloride (ITO HCl) is a prokinetic agent, used in the treatment of gastrointestinal motility disorders. The aim of the study was to develop stable mucoadhesive thermoreversible nasal gel to avoid first pass effect. ITO HCl was incorporated into the blends of thermoreversible polymers like poloxamer 407 and various mucoadhesive polymers in different concentrations to increase the contact of the formulations with nasal mucosa. The compatibility between the drug and the suggested polymers was studied by Fourier transform infrared and differential scanning calorimetry (DSC). The formulations were evaluated for clarity, pH, gelation temperature, mucoadhesive strength, gel strength, viscosity, and drug content. In addition, the in vitro drug release and the dissolution efficiency (DE)% were measured. The optimized formulations that showed the highest dissolution efficiency% (DE%) in saline phosphate buffer of pH 6.4 at 35?±?0.5?°C were chosen for stability testing at temperatures of 4?±?2 and 25?±?2?°C/60?±?5% RH. It was found that F1 and F17 that contain 18% w/v poloxamer 407 and 0.5% w/v of hydroxypropylmethyl cellulose K4M or methyl cellulose (MC), respectively, showed higher stability results as indicated by their higher t₉₀ values (days).     

Matching point features under small nonrigid motion

This paper describes a method for matching point features between images of objects that have undergone small nonrigid motion. Feature points are assumed to be available and, given a properly extracted set of feature points, a robust matching is established under the condition that the local nonrigid motion of each point is restricted to a circle of radius δ, where δ is not too large. This is in contrast to other techniques for point matching which assume either rigid motion or nonrigid motion of a known kind. The point matching problem is viewed in terms of weighted bipartite graph matching. In order to account for the possibility that the feature selector can be imprecise, we incorporate a greedy matching strategy with the weighted graph matching algorithm. Our algorithm is robust and insensitive to noise and missing features. The resulting matching can be used with image warping or other techniques for nonrigid motion analysis, image subtraction, etc. We present our experimental results on sequences of mammograms, images of a deformable clay object and satellite cloud images. In the first two cases we provide quantitative comparison with known ground truth.     

A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based cyberattack detection framework. The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions. The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations. To optimally train the proposed DAE, a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy. The efficacy of the suggested framework is evaluated via two standard and open-source datasets. The proposed DAE achieved the accuracies of 98.86%, and 98.26% for NSL-KDD, 99.32%, and 98.79% for the UNSW-NB15 dataset in binary class and multi-class scenarios. The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes. Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.     

Nanomedicine Strategies for Management of Drug Resistance in Lung Cancer

Lung cancer (LC) is one of the leading causes of cancer occurrence and mortality worldwide. Treatment of patients with advanced and metastatic LC presents a significant challenge, as malignant cells use different mechanisms to resist chemotherapy. Drug resistance (DR) is a complex process that occurs due to a variety of genetic and acquired factors. Identifying the mechanisms underlying DR in LC patients and possible therapeutic alternatives for more efficient therapy is a central goal of LC research. Advances in nanotechnology resulted in the development of targeted and multifunctional nanoscale drug constructs. The possible modulation of the components of nanomedicine, their surface functionalization, and the encapsulation of various active therapeutics provide promising tools to bypass crucial biological barriers. These attributes enhance the delivery of multiple therapeutic agents directly to the tumor microenvironment (TME), resulting in reversal of LC resistance to anticancer treatment. This review provides a broad framework for understanding the different molecular mechanisms of DR in lung cancer, presents novel nanomedicine therapeutics aimed at improving the efficacy of treatment of various forms of resistant LC; outlines current challenges in using nanotechnology for reversing DR; and discusses the future directions for the clinical application of nanomedicine in the management of LC resistance.     

The Insulin-like Growth Factor Signalling Pathway in Cardiac Development and Regeneration

Insulin and Insulin-like growth factors (IGFs) perform key roles during embryonic development, regulating processes of cell proliferation and survival. The IGF signalling pathway comprises two IGFs (IGF1, IGF2), two IGF receptors (IGFR1, IGFR2), and six IGF binding proteins (IGFBPs) that regulate IGF transport and availability. The IGF signalling pathway is essential for cardiac development. IGF2 is the primary mitogen inducing ventricular cardiomyocyte proliferation and morphogenesis of the compact myocardial wall. Conditional deletion of the Igf1r and the insulin receptor (Insr) genes in the myocardium results in decreased cardiomyocyte proliferation and ventricular wall hypoplasia. The significance of the IGF signalling pathway during embryonic development has led to consider it as a candidate for adult cardiac repair and regeneration. In fact, paracrine IGF2 plays a key role in the transient regenerative ability of the newborn mouse heart. We aimed to review the current knowledge about the role played by the IGF signalling pathway during cardiac development and also the clinical potential of recapitulating this developmental axis in regeneration of the adult heart.     

Highlights on the Role of KRAS Mutations in Reshaping the Microenvironment of Pancreatic Adenocarcinoma

The most frequent mutated oncogene family in the history of human cancer is the RAS gene family, including NRAS, HRAS, and, most importantly, KRAS. A hallmark of pancreatic cancer, recalcitrant cancer with a very low survival rate, is the prevalence of oncogenic mutations in the KRAS gene. Due to this fact, studying the function of KRAS and the impact of its mutations on the tumor microenvironment (TME) is a priority for understanding pancreatic cancer progression and designing novel therapeutic strategies for the treatment of the dismal disease. Despite some recent enlightening studies, there is still a wide gap in our knowledge regarding the impact of KRAS mutations on different components of the pancreatic TME. In this review, we will present an updated summary of mutant KRAS role in the initiation, progression, and modulation of the TME of pancreatic ductal adenocarcinoma (PDAC). This review will highlight the intriguing link between diabetes mellitus and PDAC, as well as vitamin D as an adjuvant effective therapy via TME modulation of PDAC. We will also discuss different ongoing clinical trials that use KRAS oncogene signaling network as therapeutic targets.     

Parameters affecting the synthesis of geranyl butyrate by esterase 30,000 from Mucor miehei

The factors affecting the synthesis of geranyl butyrate by esterase 30,000 of Mucor miehei were studied in a solvent-free system. The effects of substrate molar ratio, temperature, agitation speed, and initial addition of water were investigated. The equimolar ratio was most interesting for ester production in batch. There were no diffusion limitations, and the reaction could be realized at low agitation. The catalytic activity of the enzyme was irreversibly deactivated at 60°C, and the initial addition of water decreased the rate of conversion after 75 h of reaction. The enzyme activity increased with increased linear chainlength of the acid and was also affected by the alcohol structure. Esterase 30,000 gave the highest conversion of butyric acid with hexanol and terpenic alcohols (citronellol, nerol) and the lowest with the secondary alcohol (2-hexanol). Finally, five other industrial enzymatic preparations were investigated for their ability to synthesize geranyl butyrate and to hydrolyze olive oil. We observed, for the lipase from Rhizopus javanicua, that there is no relationship between hydrolytic and synthetic activities; this example shows that the hydrolytic lipase activity data cannot predict the capability of lipases in esterification reactions.