Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.

Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation.

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach.

Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation.

Android has become the leading mobile ecosystem because of its accessibility and adaptability. It has also become the primary target of widespread malicious apps. This situation needs the immediate implementation of an effective malware detection system. In this study, an explainable malware detection system was proposed using transfer learning and malware visual features. For effective malware detection, our technique leverages both textual and visual features. First, a pre-trained model called the Bidirectional Encoder Representations from Transformers (BERT) model was designed to extract the trained textual features. Second, the malware-to-image conversion algorithm was proposed to transform the network byte streams into a visual representation. In addition, the FAST (Features from Accelerated Segment Test) extractor and BRIEF (Binary Robust Independent Elementary Features) descriptor were used to efficiently extract and mark important features. Third, the trained and texture features were combined and balanced using the Synthetic Minority Over-Sampling (SMOTE) method; then, the CNN network was used to mine the deep features. The balanced features were then input into the ensemble model for efficient malware classification and detection. The proposed method was analyzed extensively using two public datasets, CICMalDroid 2020 and CIC-InvesAndMal2019. To explain and validate the proposed methodology, an interpretable artificial intelligence (AI) experiment was conducted.

Disparities in Colorectal Cancer Screening Practices in a Midwest Urban Safety-Net Healthcare System.

AIMS: Although colorectal cancer screening (CRC) using stool-based test is well-studied, evidence on fecal immunochemical test (FIT) patterns in a safety-net healthcare system utilizing opportunistic screening is limited. We studied the FIT completion rates and adenoma detection rate (ADR) of positive FIT-colonoscopy (FIT-C) in an urban safety-net system. METHODS: We performed a retrospective cross-sectional chart review on individuals ≥ 50 years who underwent CRC screening using FIT or screening colonoscopy, 09/01/2017-08/30/2018. Demographic differences in FIT completion were studied; ADR of FIT-C was compared to that of screening colonoscopy. RESULTS: Among 13,427 individuals with FIT ordered, 7248 (54%) completed the stool test and 230 (48%) followed up a positive FIT with colonoscopy. Increasing age (OR 1.01, CI 1.01-1.02), non-Hispanic Blacks (OR 0.87, CI 0.80-0.95, p = 0.002), current smokers (OR 0.84, CI 0.77-0.92, p < 0.0001), those with Medicaid (OR 0.86, CI 0.77-0.96, p = 0.006), commercial insurance (OR 0.85, CI 0.78-0.94, p = 0.002), CCI score ≥ 3 (OR 0.82, CI 0.74-0.91, p < 0.0001), orders by family medicine providers (OR 0.87, CI 0.81-0.94, p < 0.0001) were associated with lower completion of stool test. Individuals from low median household income cities had lower follow-up of positive FIT, OR 0.43, CI 0.21-0.86, p = 0.017. ADR of FIT-C was higher than that of screening colonoscopy. CONCLUSION: Adherence to CRC screening is low in safety-net systems employing opportunistic screening. Understanding demographic differences may allow providers to formulate targeted strategies in high-risk vulnerable groups.

Histone deacetylase HDA710 controls salt tolerance by regulating ABA signaling in rice.

Plants have evolved numerous mechanisms that assist them in withstanding environmental stresses. Histone deacetylases (HDACs) play crucial roles in plant stress responses; however, their regulatory mechanisms remain poorly understood. Here, we explored the function of HDA710/OsHDAC2, a member of the HDAC RPD3/HDA1 family, in stress tolerance in rice (Oryza sativa). We established that HDA710 localizes to both the nucleus and cytoplasm and is involved in regulating the acetylation of histone H3 and H4, specifically targeting H4K5 and H4K16 under normal conditions. HDA710 transcript accumulation levels were strongly induced by abiotic stresses including drought and salinity, as well as by the phytohormones jasmonic acid (JA) and abscisic acid (ABA). hda710 knockout mutant plants showed enhanced salinity tolerance and reduced ABA sensitivity, whereas transgenic plants overexpressing HDA710 displayed the opposite phenotypes. Moreover, ABA- and salt-stress-responsive genes, such as OsLEA3, OsABI5, OsbZIP72, and OsNHX1, were upregulated in hda710 compared with wild-type plants. These expression differences corresponded with higher levels of histone H4 acetylation in gene promoter regions in hda710 compared with the wild type under ABA and salt-stress treatment. Collectively, these results suggest that HDA710 is involved in regulating ABA- and salt-stress-responsive genes by altering H4 acetylation levels in their promoters. This article is protected by copyright. All rights reserved.

Cerebral Oximetry Use For Cardiac Surgery.

BACKGROUND: Studies have shown maintaining good cerebral perfusion during Cardiac Surgeries is very important in terms of patient outcomes and reducing the hospital stay, which may have its financial and clinical implications. The aim of this review study was to determine the effectiveness of Cerebral Oximetry (Transcranial Near-Infrared Spectroscopy-NIRS to monitor cerebral oxygenation) for Cardiac Surgery and to propose a possible concluding remark about its potential applications, overall clinical value and whether to keep using it or not. METHODS: Medical database and archives including Pubmed, Embase, index medicus, index copernicus and Medline were searched. Different papers were looked upon and each had an argument, scientific evidence and background. Fifteen research papers were selected and brought under review after carefully consideration. RESULTS: The papers were carefully reviewed and findings were given in favour of not using NIRS technique for Cerebral Oximetry in Cardiac Surgery. CONCLUSIONS: This can rightly be concluded from this study that NIRS Cerebral Oximetry does not carry the clinical significance and relevance which was previously thought. The subject under observation needs further studies and research to evaluate the effectiveness of the Cerebral Oximetry Use for Cardiac Surgery.

Pancreatic Duct Stones.

pancreatitis appears to exist in the presence of such calculi upon radiology. Having said that, pancreatic ductal stone due to biliary causes (origin), in face of acute pancreatitis, is rare. To the best of our knowledge this was the first case of its kind presented to our hospital in recent past. A 25-year-old female presented to the emergency department of our hospital with an acute episode of pancreatitis. Computerized tomography (CT) scan, endoscopic retrograde cholangiopancreatography (ERCP) & magnetic resonance cholangiopancreatography (MRCP) concluded acute pancreatitis (AP) with dilated main pancreatic duct left side branches and intra ductal calculi. The findings were not suggestive of any chronic pancreatitis. Conservative treatment was given for the episodic attack of AP. After the episode resolved, an exploration and extraction of the pancreatic ductal calculus was performed successfully. The pancreatic duct stones were removed by lateral pancreaticojejunostomy (partington-rochelle procedure). The patient made a remarkable recovery after the procedure and was perfectly healthy and well-oriented in time and space at 4-months follow up. Acute pancreatitis is an inflammatory condition of pancreas, when, associated with pancreatic duct stones a lateral pancreaticojejunostomy is done, which, results in better outcomes decreasing the mortality and morbidity. Acute pancreatitis due to ductal calculi is rare for which extraction is safe after resolution of the episode of AP. Studies need to be carried out to look for the outcome and the effectiveness of the procedure, when, specifically and specially done for this condition.

Angiofibroma In A 50-Year-Old Patient.

Juvenile Angiofibroma (JNA) is a benign tumour that tends to bleed and occur in the nasopharynx with most cases occurring in pre-pubertal and adolescent males 10-20 years. We present the case of a 50-year-old male shopkeeper who consulted the ENT out patients' department (OPD) of Khyber Teaching Hospital (KTH) with the chief complaint of right sided nasal obstruction for the last 2.5 months which was associated with two episodes of epistaxis and diplopia which started 2 months back. He complained of right sided frontal and periorbital pain for the last 15 days. Past medical and surgical history was insignificant. Computerized Tomography (CT) scan without contrast and magnetic resonance imaging (MRI) showed finding consistent with a pedunculated tumour like growth. After baseline investigations, surgery was done and a Wilson's incision was given and the mass was excised and sent to the lab for histopathological report which showed angiofibroma. The age of the patient shows that this is a very rare case of angiofibroma. Dissection of such tumours is important as they have propensity to bleed. Excision along with biopsy is the method of choice. Proper surgical techniques and use of better medical technology are required to make and early diagnosis. Further studies/case reports around the world would assert our findings that a nasopharyngeal angiofibroma can also be found in middle aged men.

Eales Disease.

Eales disease is an eponym after a British ophthalmologist Henry Eales. The aetiology behind Eales disease is ill-understood and stands controversial. Various systemic diseases associated with peripheral retinal revascularization and Retinal vasculitis could imitate the proliferative and inflammatory phases of Eales' disease, respectively. We present a case of a 30 years old female patient with Eales disease and discuss the clinical features, treatment plan and its outcome in our patient. Tuberculosis appears to be the cause of Eales disease but the relation is yet to be established and clinically proven. Steroid therapy is usually the main stay of treatment with tapering doses of systemic corticosteroids. Other interventions are vitrectomy, photocogulation or cryotherapy.

Elucidating the inhibitory mechanism of Zika virus NS2B-NS3 protease with dipeptide inhibitors: Insights from molecular docking and molecular dynamics simulations.

Microcephaly, Guillain-Barré syndrome, and potential sexual transmission stand as prominent complications associated with Zika virus (ZIKV) infection. The absence of FDA-approved drugs or vaccines presents a substantial obstacle in combatting the virus. Furthermore, the inclusion of pregnancy in the pharmacological screening process complicates and extends the endeavor to ensure molecular safety and minimal toxicity. Given its pivotal role in viral assembly and maturation, the NS2B-NS3 viral protease emerges as a promising therapeutic target against ZIKV. In this context, a dipeptide inhibitor was specifically chosen as a control against 200 compounds for docking analysis. Subsequent molecular dynamics simulations extending over 200 ns were conducted to ascertain the stability of the docked complex and confirm the binding of the inhibitor at the protein's active site. The simulation outcomes exhibited conformity to acceptable thresholds, encompassing parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), ligand-protein interaction analysis, ligand characterization, and surface area analysis. Notably, analysis of ligand angles bolstered the identification of prospective ligands capable of inhibiting viral protein activity and impeding virus dissemination. In this study, the integration of molecular docking and dynamics simulations has pinpointed the dipeptide inhibitor as a potential candidate ligand against ZIKV protease, thereby offering promise for therapeutic intervention against the virus.

The HABD: Home of All Biological Databases Empowering Biological Research With Cutting-Edge Database Systems.

The emergence of computer technologies and computing power has led to the development of several database systems that provide standardized access to vast quantities of data, making it possible to collect, search, index, evaluate, and extract useful knowledge across various fields. The Home of All Biological Databases (HABD) has been established as a continually expanding platform that aims to store, organize, and distribute biological data in a searchable manner, removing all dead and non-accessible data. The platform meticulously categorizes data into various categories, such as COVID-19 Pandemic Database (CO-19PDB), Database relevant to Human Research (DBHR), Cancer Research Database (CRDB), Latest Database of Protein Research (LDBPR), Fungi Databases Collection (FDBC), and many other databases that are categorized based on biological phenomena. It currently provides a total of 22 databases, including 6 published, 5 submitted, and the remaining in various stages of development. These databases encompass a range of areas, including phytochemical-specific and plastic biodegradation databases. HABD is equipped with search engine optimization (SEO) analyzer and Neil Patel tools, which ensure excellent SEO and high-speed value. With timely updates, HABD aims to facilitate the processing and visualization of data for scientists, providing a one-stop-shop for all biological databases. Computer platforms, such as PhP, html, CSS, Java script and Biopython, are used to build all the databases. © 2024 Wiley Periodicals LLC.

A comprehensive review of wind power integration and energy storage technologies for modern grid frequency regulation.

Integrating wind power with energy storage technologies is crucial for frequency regulation in modern power systems, ensuring the reliable and cost-effective operation of power systems while promoting the widespread adoption of renewable energy sources. Power systems are changing rapidly, with increased renewable energy integration and evolving system architectures. These transformations bring forth challenges like low inertia and unpredictable behavior of generation and load components. As a result, frequency regulation (FR) becomes increasingly important to ensure grid stability. Energy Storage Systems (ESS) with their adaptable capabilities offer valuable solutions to enhance the adaptability and controllability of power systems, especially within wind farms. This research provides an updated analysis of critical frequency stability challenges, examines state-of-the-art control techniques, and investigates the barriers that hinder wind power integration. Moreover, it introduces emerging ESS technologies and explores their potential applications in supporting wind power integration. Furthermore, this paper offers suggestions and future research directions for scientists exploring the utilization of storage technologies in frequency regulation within power systems characterized by significant penetration of wind power.

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach.

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

CO-19 PDB 2.0: A Comprehensive COVID-19 Database with Global Auto-Alerts, Statistical Analysis, and Cancer Correlations.

Biological databases serve as critical basics for modern research, and amid the dynamic landscape of biology, the COVID-19 database has emerged as an indispensable resource. The global outbreak of Covid-19, commencing in December 2019, necessitates comprehensive databases to unravel the intricate connections between this novel virus and cancer. Despite existing databases, a crucial need persists for a centralized and accessible method to acquire precise information within the research community. The main aim of the work is to develop a database which has all the COVID-19-related data available in just one click with auto global notifications. This gap is addressed by the meticulously designed COVID-19 Pandemic Database (CO-19 PDB 2.0), positioned as a comprehensive resource for researchers navigating the complexities of COVID-19 and cancer. Between December 2019 and June 2024, the CO-19 PDB 2.0 systematically collected and organized 120 datasets into six distinct categories, each catering to specific functionalities. These categories encompass a chemical structure database, a digital image database, a visualization tool database, a genomic database, a social science database, and a literature database. Functionalities range from image analysis and gene sequence information to data visualization and updates on environmental events. CO-19 PDB 2.0 has the option to choose either the search page for the database or the autonotification page, providing a seamless retrieval of information. The dedicated page introduces six predefined charts, providing insights into crucial criteria such as the number of cases and deaths', country-wise distribution, 'new cases and recovery', and rates of death and recovery. The global impact of COVID-19 on cancer patients has led to extensive collaboration among research institutions, producing numerous articles and computational studies published in international journals. A key feature of this initiative is auto daily notifications for standardized information updates. Users can easily navigate based on different categories or use a direct search option. The study offers up-to-date COVID-19 datasets and global statistics on COVID-19 and cancer, highlighting the top 10 cancers diagnosed in the USA in 2022. Breast and prostate cancers are the most common, representing 30% and 26% of new cases, respectively. The initiative also ensures the removal or replacement of dead links, providing a valuable resource for researchers, healthcare professionals, and individuals. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://www.co-19pdb.habdsk.org/. Database URL: https://www.co-19pdb.habdsk.org/.

Correction: The Omega-3 Fatty Acid Eicosapentaenoic Acid Accelerates Disease Progression in a Model of Amyotrophic Lateral Sclerosis.

[This corrects the article DOI: 10.1371/journal.pone.0061626.].

A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions with Swish Activations.

Skin cancer remains one of the deadliest kinds of cancer, with a survival rate of about 18-20%. Early diagnosis and segmentation of the most lethal kind of cancer, melanoma, is a challenging and critical task. To diagnose medicinal conditions of melanoma lesions, different researchers proposed automatic and traditional approaches to accurately segment the lesions. However, visual similarity among lesions and intraclass differences are very high, which leads to low-performance accuracy. Furthermore, traditional segmentation algorithms often require human inputs and cannot be utilized in automated systems. To address all of these issues, we provide an improved segmentation model based on depthwise separable convolutions that act on each spatial dimension of the image to segment the lesions. The fundamental idea behind these convolutions is to divide the feature learning steps into two simpler parts that are spatial learning of features and a step for channel combination. Besides this, we employ parallel multidilated filters to encode multiple parallel features and broaden the view of filters with dilations. Moreover, for performance evaluation, the proposed approach is evaluated on three different datasets including DermIS, DermQuest, and ISIC2016. The finding indicates that the suggested segmentation model has achieved the Dice score of 97% for DermIS and DermQuest and 94.7% for the ISBI2016 dataset, respectively.

A molecular dynamics simulations analysis of repurposing drugs for COVID-19 using bioinformatics methods.

A number of multidisciplinary methods have piqued the interest of researchers as means to accelerate and lower the cost of medication creation. The goal of this research was to find target proteins and then select a lead drug against SARS-CoV-2. The three-dimensional structure is taken from the RCSB PDB using its specific PDB ID 6lu7. Virtual screening based on pharmacophores is performed using Molecular Operating Environment software. We looked for a potent inhibitor in the FDA-approved database. For docking, AutoDock Vina uses Pyrx. The compound-target protein binding interactions were tested using BIOVIA Discovery Studio. The stability of protein and inhibitor complexes in a physiological setting was investigated using Desmond's Molecular Dynamics Simulation (MD simulation). According to our findings, we repurpose the FDA-approved drugs ZINC000169677008 and ZINC000169289767, which inhibit the activity of the virus's main protease (6lu7). The scientific community will gain from this finding, which might create new medicine. The novel repurposed chemicals were promising inhibitors with increased efficacy and fewer side effects.Communicated by Ramaswamy H. Sarma.

A Scalable Federated Learning Approach for Collaborative Smart Healthcare Systems with Intermittent Clients using Medical Imaging.

The healthcare industry is one of the most vulnerable to cybercrime and privacy violations because health data is very sensitive and spread out in many places. Recent confidentiality trends and a rising number of infringements in different sectors make it crucial to implement new methods that protect data privacy while maintaining accuracy and sustainability. Moreover, the intermittent nature of remote clients with imbalanced datasets poses a significant obstacle for decentralized healthcare systems. Federated learning (FL) is a decentralized and privacy-protecting approach to deep learning and machine learning models. In this paper, we implement a scalable FL framework for interactive smart healthcare systems with intermittent clients using chest X-ray images. Remote hospitals may have imbalanced datasets with intermittent clients communicating with the FL global server. The data augmentation method is used to balance datasets for local model training. In practice, some clients may leave the training process while others join due to technical or connectivity issues. The proposed method is tested with five to eighteen clients and different testing data sizes to evaluate performance in various situations. The experiments show that the proposed FL approach produces competitive results when dealing with two distinct problems, such as intermittent clients and imbalanced data. These findings would encourage medical institutions to collaborate and use rich private data to quickly develop a powerful patient diagnostic model.

Community-Acquired Skin and Soft Tissue Infections: Epidemiology and Management in Patients Presenting to the Emergency Department of a Tertiary Care Hospital.

BACKGROUND: Skin and soft tissue infections are one of the most common diseases presenting to the emergency department (ED). There is no study available on the management of Community-Acquired Skin and Soft Tissue Infections (CA-SSTIs) in our population recently. This study aims to describe the frequency and distribution of CA-SSTIs as well as their medical and surgical management among patients presenting to our ED. METHODS: We conducted a descriptive cross-sectional study on patients presenting with CA-SSTIs to the ED of a tertiary care hospital in Peshawar, Pakistan. The primary objective was to estimate the frequency of common CA-SSTIs presenting to the ED and to assess the management of these infections in terms of diagnostic workup and treatment modalities used. The secondary objectives were to study the association of different baseline variables, diagnostic modalities, treatment modalities, and improvement with the surgical procedure performance for these infections. Descriptive statistics were obtained for quantitative variables like age. Frequencies and percentages were derived for categorical variables. The chi-square test was used to compare different CA-SSTIs in terms of categorical variables like diagnostic and treatment modalities. We divided the data into two groups based on the surgical procedure. A chi-square analysis was conducted to compare these two groups in terms of categorical variables. RESULTS: Out of the 241 patients, 51.9% were males and the mean age was 34.2 years. The most common CA-SSTIs were abscesses, infected ulcers, and cellulitis. Antibiotics were prescribed to 84.2% of patients. Amoxicillin + Clavulanate was the most frequently prescribed antibiotic. Out of the total, 128 (53.11%) patients received some type of surgical intervention. Surgical procedures were significantly associated with diabetes mellitus, heart disease, limitation of mobility, or recent antibiotic use. There was a significantly higher rate of prescription of any antibiotic and anti-methicillin-resistant Staphylococcus aureus (anti-MRSA) agents in the surgical procedure group. This group also saw a higher rate of oral antibiotics prescription, hospitalization, wound culture, and complete blood count. CONCLUSION: This study shows a higher frequency of purulent infections in our ED. Antibiotics were prescribed more frequently for all infections. Surgical procedures like incision and drainage were much lower even in purulent infections. Furthermore, beta-lactam antibiotics like Amoxicillin-Clavulanate were commonly prescribed. Linezolid was the only systemic anti-MRSA agent prescribed. We suggest physicians should prescribe antibiotics appropriate to the local antibiograms and the latest guidelines.