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Abstract As an entomopathogenic nematode (EPN), Steinernema hermaphroditum parasitizes insect hosts and harbors symbiotic Xenorhabdus griffinae bacteria. In contrast to other Steinernematids, S. hermaphroditum has hermaphroditic genetics, offering the experimental scope found in Caenorhabditis elegans. To enable study of S. hermaphroditum, we have assembled and analyzed its reference genome. This genome assembly has five chromosomal scaffolds and 83 unassigned scaffolds totaling 90.7 Mb, with 19,426 protein-coding genes having a BUSCO completeness of 88.0%. Its autosomes show higher densities of strongly conserved genes in their centers, as in C. elegans, but repetitive elements are evenly distributed along all chromosomes, rather than with higher arm densities as in C. elegans. Either when comparing protein motif frequencies between nematode species or when analyzing gene family expansions during nematode evolution, we observed two categories of genes preferentially associated with the origin of Steinernema or S. hermaphroditum: orthologs of venom genes in S. carpocapsae or S. feltiae; and some types of chemosensory G protein-coupled receptors, despite the tendency of parasitic nematodes to have reduced numbers of chemosensory genes. Three-quarters of venom orthologs occurred in gene clusters, with the larger clusters comprising functionally diverse gene groups rather than paralogous repeats of a single venom gene. While assembling S. hermaphroditum, we coassembled bacterial genomes, finding sequence data for not only the known symbiont, X. griffinae, but also for eight other bacterial genera. All eight genera have previously been observed to be associated with Steinernema species or the EPN Heterorhabditis, and may constitute a second bacterial circle of EPNs. 

The surge of covid-19-positive cases and mortality among different communities in the state of Louisiana are concerning. It has affected us in different ways: psychologically, physically (mobility restriction), socially, and economically. It is a global catastrophe and all of us are dealing with multiple challenges due to this. As of 9th April 2023, there are almost 1.6 million covid-19 cases and 18,984 people lost their lives in the state of Louisiana. This pandemic created tremendous pressure in healthcare with an unexpected surge in the demand (more than existing production capability). According to our data, there were 3,022 covid patients hospitalized on 08/17/2021, and there were 571 covid-positive patients on the ventilator on 04/04/2020 on a single day. Louisiana has about 33% black population which is about half of white population of 63.0%. However, the covid infection rate was almost 20.0% higher in the black population compared to the white population. Here, we present a demographic chart, the infection rate, and death by region and race in different communities in Louisiana. 

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results. 

As an entomopathogenic nematode (EPN), Steinernema hermaphroditum parasitizes insect hosts and harbors symbiotic Xenorhabdus griffinae bacteria. In contrast to other Steinernematids, S. hermaphroditum has hermaphroditic genetics, offering the experimental scope found in Caenorhabditis elegans. To enable biological analysis of S. hermaphroditum, we have assembled and analyzed its reference genome. This genome assembly has five chromosomal scaffolds and 83 unassigned scaffolds totaling 90.7 Mb, with 19,426 protein-coding genes having a BUSCO completeness of 88.0%. Its autosomes show higher densities of strongly conserved genes in their centers, as in C. elegans, but repetitive elements are evenly distributed along all chromosomes, rather than with higher arm densities as in C. elegans. Either when comparing protein motif frequencies between nematode species or when analyzing gene family expansions during nematode evolution, we observed two categories of genes preferentially associated with the origin of Steinernema or S. hermaphroditum: orthologs of venom genes in S. carpocapsae or S. feltiae; and some types of chemosensory G protein-coupled receptors, despite the tendency of parasitic nematodes to have reduced numbers of chemosensory genes. Three-quarters of venom orthologs occurred in gene clusters, with the larger clusters comprising functionally diverse pathogenicity islands rather than paralogous repeats of a single venom gene. While assembling the genome of S. hermaphroditum, we coassembled bacterial genomes, finding sequence data for not only the known symbiont, X. griffinae, but also for eight other bacterial genera. All eight genera have previously been observed to be associated with Steinernema species or the EPN Heterorhabditis, and may constitute a “second bacterial circle” of EPNs. The genome assemblies of S. hermaphroditum and its associated bacteria will enable use of these organisms as a model system for both entomopathogenicity and symbiosis. 

The ubiquity of the Internet plays a pivotal role in connecting individuals and facilitating easy access to various essential services. As of 2022, the International Telecommunication Union (ITU) reports that approximately 5.3 billion people are connected to the internet, underscoring its widespread coverage and indispensability in our daily lives. This expansive coverage enables a myriad of services, including communication, e-banking, e-commerce, online social security access, medical reporting, education, entertainment, weather information, traffic monitoring, online surveys, and more. However, this open platform also exposes vulnerabilities to malicious users who actively seek to exploit weaknesses in the virtual domain, aiming to gain credentials, financial benefits, or reveal critical information through the use of malware. This constant threat poses a serious challenge in safeguarding sensitive information in cyberspace. To address this challenge, we propose the use of ensemble and deep neural network (DNN) based machine learning (ML) techniques to detect malicious intent packets before they can infiltrate or compromise systems and applications. Attackers employ various tactics to evade existing security systems, such as antivirus or intrusion detection systems, necessitating a robust defense mechanism. Our approach involves implementing an ensemble, a collection of diverse classifiers capable of capturing different attack patterns and better generalizing from highly relevant features, thus enhancing protection against a variety of attacks compared to a single classifier. Given the highly unbalanced dataset, the ensemble classifier effectively addresses this condition, and oversampling is also employed to minimize bias toward the majority class. To prevent overfitting, we utilize Random Forest (RF) and the dropout technique in the DNN. Furthermore, we introduce a DNN to assess its ability to recognize complex attack patterns and variations compared to the ensemble approach. Various metrics, such as classification accuracy, precision, recall, F1-score, confusion matrix are utilized to measure the performance of our proposed system, with the aim of outperforming current state-of-the-art intrusion detection systems. 

Lithium metal is considered as an ideal anode for high-energy density storage systems with dendrites being a major issue for lifetime and safety. A gadolinium additive is found to be suppressing dendrite growth resulting higher performance retention. 

Abstract Constructing an artificial solid electrolyte interphase (SEI) on lithium metal electrodes is a promising approach to address the rampant growth of dangerous lithium morphologies (dendritic and dead Li⁰) and low Coulombic efficiency that plague development of lithium metal batteries, but how Li⁺transport behavior in the SEI is coupled with mechanical properties remains unknown. We demonstrate here a facile and scalable solution-processed approach to form a Li₃N-rich SEI with a phase-pure crystalline structure that minimizes the diffusion energy barrier of Li⁺across the SEI. Compared with a polycrystalline Li₃N SEI obtained from conventional practice, the phase-pure/single crystalline Li₃N-rich SEI constitutes an interphase of high mechanical strength and low Li⁺diffusion barrier. We elucidate the correlation among Li⁺transference number, diffusion behavior, concentration gradient, and the stability of the lithium metal electrode by integrating phase field simulations with experiments. We demonstrate improved reversibility and charge/discharge cycling behaviors for both symmetric cells and full lithium-metal batteries constructed with this Li₃N-rich SEI. These studies may cast new insight into the design and engineering of an ideal artificial SEI for stable and high-performance lithium metal batteries. 

At present, machine learning (ML) algorithms are essential components in designing the sophisticated intrusion detection system (IDS). They are building-blocks to enhance cyber threat detection and help in classification at host-level and network-level in a short period. The increasing global connectivity and advancements of network technologies have added unprecedented challenges and opportunities to network security. Malicious attacks impose a huge security threat and warrant scalable solutions to thwart large-scale attacks. These activities encourage researchers to address these imminent threats by analyzing a large volume of the dataset to tackle all possible ranges of attack. In this proposed method, we calculated the fitness value of each feature from the population by using a genetic algorithm (GA) and selected them according to the fitness value. The fitness values are presented in hierarchical order to show the effectiveness of problem decomposition. We implemented Support Vector Machine (SVM) to verify the consistency of the system outcome. The well-known NSL-knowledge discovery in databases (KDD) was used to measure the performance of the system. From the experiments, we achieved a notable classification accuracies using a SVM of the current state of the art intrusion detection.