People With Human Immunodeficiency Virus Receiving Suppressive Antiretroviral Therapy Show Typical Antibody Durability After Dual Coronavirus Disease 2019 Vaccination and Strong Third Dose Responses.

BACKGROUND: Longer-term humoral responses to 2-dose coronavirus disease 2019 (COVID-19) vaccines remain incompletely characterized in people living with human immunodeficiency virus (HIV) (PLWH), as do initial responses to a third dose. METHODS: We measured antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain, angiotensin-converting enzyme 2 (ACE2) displacement, and viral neutralization against wild-type and Omicron strains up to 6 months after 2-dose vaccination, and 1 month after the third dose, in 99 PLWH receiving suppressive antiretroviral therapy and 152 controls. RESULTS: Although humoral responses naturally decline after 2-dose vaccination, we found no evidence of lower antibody concentrations or faster rates of antibody decline in PLWH compared with controls after accounting for sociodemographic, health, and vaccine-related factors. We also found no evidence of poorer viral neutralization in PLWH after 2 doses, nor evidence that a low nadir CD4+ T-cell count compromised responses. Post-third-dose humoral responses substantially exceeded post-second-dose levels, though Omicron-specific responses were consistently weaker than responses against wild-type virus. Nevertheless, post-third-dose responses in PLWH were comparable to or higher than controls. An mRNA-1273 third dose was the strongest consistent correlate of higher post-third-dose responses. CONCLUSION: PLWH receiving suppressive antiretroviral therapy mount strong antibody responses after 2- and 3-dose COVID-19 vaccination. Results underscore the immune benefits of third doses in light of Omicron.

Role of Staphylococcus aureus Formate Metabolism during Prosthetic Joint Infection.

Biofilms are bacterial communities characterized by antibiotic tolerance. Staphylococcus aureus is a leading cause of biofilm infections on medical devices, including prosthetic joints, which represent a significant health care burden. The major leukocyte infiltrate associated with S. aureus prosthetic joint infection (PJI) is granulocytic myeloid-derived suppressor cells (G-MDSCs), which produce IL-10 to promote biofilm persistence by inhibiting monocyte and macrophage proinflammatory activity. To determine how S. aureus biofilm responds to G-MDSCs and macrophages, biofilms were cocultured with either leukocyte population followed by RNA sequencing. Several genes involved in fermentative pathways were significantly upregulated in S. aureus biofilm following G-MDSC coculture, including formate acetyltransferase (pflB), which catalyzes the conversion of pyruvate and coenzyme-A into formate and acetyl-CoA. A S. aureus pflB mutant (ΔpflB) did not exhibit growth defects in vitro. However, ΔpflB formed taller and more diffuse biofilm compared to the wild-type strain as revealed by confocal microscopy. In a mouse model of PJI, the bacterial burden was significantly reduced with ΔpflB during later stages of infection, which coincided with decreased G-MDSC influx and increased neutrophil recruitment, and ΔpflB was more susceptible to macrophage killing. Although formate was significantly reduced in the soft tissue surrounding the joint of ΔpflB-infected mice levels were increased in the femur, suggesting that host-derived formate may also influence bacterial survival. This was supported by the finding that a ΔpflBΔfdh strain defective in formate production and catabolism displayed a similar phenotype to ΔpflB. These results revealed that S. aureus formate metabolism is important for promoting biofilm persistence.

Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring.

Parkinson's disease is a neurodegenerative disorder impacting patients' movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population's movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson's disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.

Cutibacterium acnes Central Nervous System Catheter Infection Induces Long-Term Changes in the Cerebrospinal Fluid Proteome.

Cutibacterium acnes is the third most common cause of cerebrospinal fluid (CSF) shunt infection and is likely underdiagnosed due to the difficulty in culturing this pathogen. Shunt infections lead to grave neurologic morbidity for patients especially when there is a delay in diagnosis. Currently, the gold standard for identifying CSF shunt infections is microbiologic culture. However, C. acnes infection often results in falsely negative cultures; therefore, new diagnostic methods are needed. To investigate potential CSF biomarkers of C. acnes CSF shunt infection we adapted a rat model of CSF catheter infection to C. acnes. We found elevated levels of interleukin-1ß (IL-1ß), IL-6, chemokine ligand 2, and IL-10 in the CSF and brain tissues of animals implanted with C. acnes-infected catheters compared to sterile controls at day 1 postinfection. This coincided with modest increases in neutrophils in the CSF and, to a greater extent, in the brain tissues of animals with C. acnes infection, which closely mirrors the clinical findings in patients with C. acnes shunt infection. Mass spectrometry revealed that the CSF proteome is altered during C. acnes shunt infection and changes over the course of disease, typified at day 1 postinfection by an acute-phase and pathogen neutralization response evolving to a response consistent with wound resolution at day 28 compared to a sterile catheter placement. Collectively, these results demonstrate that it is possible to distinguish C. acnes infection from sterile postoperative inflammation and that CSF proteins could be useful in a diagnostic strategy for this pathogen that is difficult to diagnose.

Brain Strategy Algorithm for Multiple Object Tracking Based on Merging Semantic Attributes and Appearance Features.

The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied from a cognitive perspective and imitated to build a novel algorithm. Two of these strategies gave our algorithm novel and outstanding results, rescuing saccades and stimulus attributes. First, rescue saccades were imitated by detecting the occlusion state in each frame, representing the critical situation that the human brain saccades toward. Then, stimulus attributes were mimicked by using semantic attributes to reidentify the person in these occlusion states. Our algorithm favourably performs on the MOT17 dataset compared to state-of-the-art trackers. In addition, we created a new dataset of 40,000 images, 190,000 annotations and 4 classes to train the detection model to detect occlusion and semantic attributes. The experimental results demonstrate that our new dataset achieves an outstanding performance on the scaled YOLOv4 detection model by achieving a 0.89 mAP 0.5.

Assessing Laparoscopic Surgical Skills Using Similarity Network Models: A Pilot Study.

Background: Medical devices are becoming more complex, and doctors need to learn quickly how to use new medical tools. However, it is challenging to objectively assess the fundamental laparoscopic surgical skill level and determine skill readiness for advancement. There is a lack of objective models to compare performance between medical trainees and experienced doctors. Methods: This article discusses the use of similarity network models for individual tasks and a combination of tasks to show the level of similarity between residents and medical students while performing each task and their overall laparoscopic surgical skill level using a medical device (eg laparoscopic instruments). When a medical student is connected to most residents, that student is competent to the next training level. Performance of sixteen participants (5 residents and 11 students) while performing 3 tasks in 3 different training schedules is used in this study. Results: The promising result shows the general positive progression of students over 4 training sessions. Our results also indicate that students with different training schedules have different performance levels. Students' progress in performing a task is quicker if the training sessions are held more closely compared to when the training sessions are far apart in time. Conclusions: This study provides a graph-based framework for evaluating new learners' performance on medical devices and their readiness for advancement. This similarity network method could be used to classify students' performance using similarity thresholds, facilitating decision-making related to training and progression through curricula.

Uncovering and characterizing splice variants associated with survival in lung cancer patients.

Splice variants have been shown to play an important role in tumor initiation and progression and can serve as novel cancer biomarkers. However, the clinical importance of individual splice variants and the mechanisms by which they can perturb cellular functions are still poorly understood. To address these issues, we developed an efficient and robust computational method to: (1) identify splice variants that are associated with patient survival in a statistically significant manner; and (2) predict rewired protein-protein interactions that may result from altered patterns of expression of such variants. We applied our method to the lung adenocarcinoma dataset from TCGA and identified splice variants that are significantly associated with patient survival and can alter protein-protein interactions. Among these variants, several are implicated in DNA repair through homologous recombination. To computationally validate our findings, we characterized the mutational signatures in patients, grouped by low and high expression of a splice variant associated with patient survival and involved in DNA repair. The results of the mutational signature analysis are in agreement with the molecular mechanism suggested by our method. To the best of our knowledge, this is the first attempt to build a computational approach to systematically identify splice variants associated with patient survival that can also generate experimentally testable, mechanistic hypotheses. Code for identifying survival-significant splice variants using the Null Empirically Estimated P-value method can be found at https://github.com/thecodingdoc/neep. Code for construction of Multi-Granularity Graphs to discover potential rewired protein interactions can be found at https://github.com/scwest/SINBAD.

Identification of Potential Cerebrospinal Fluid Biomarkers To Discriminate between Infection and Sterile Inflammation in a Rat Model of Staphylococcus epidermidis Catheter Infection.

Staphylococcus epidermidis cerebrospinal fluid (CSF) shunt infection is a common complication of hydrocephalus treatment, creating grave neurological consequences for patients, especially when diagnosis is delayed. The current method of diagnosis relies on microbiological culture; however, awaiting culture results may cause treatment delays, or culture may fail to identify infection altogether, so newer methods are needed. To investigate potential CSF biomarkers of S. epidermidis shunt infection, we developed a rat model allowing for serial CSF sampling. We found elevated levels of interleukin-10 (IL-10), IL-1ß, chemokine ligand 2 (CCL2), and CCL3 in the CSF of animals implanted with S. epidermidis-infected catheters compared to sterile controls at day 1 postinfection. Along with increased chemokine and cytokine expression early in infection, neutrophil influx was significantly increased in the CSF of animals with infected catheters, suggesting that coupling leukocyte counts with inflammatory mediators may differentiate infection from sterile inflammation. Mass spectrometry analysis revealed that the CSF proteome in sterile animals was similar to that in infected animals at day 1; however, by day 5 postinfection, there was an increase in the number of differently expressed proteins in the CSF of infected compared to sterile groups. The expansion of the proteome at day 5 postinfection was interesting, as bacterial burdens began to decline by this point, yet the CSF proteome data indicated that the host response remained active, especially with regard to the complement cascade. Collectively, these results provide potential biomarkers to distinguish S. epidermidis infection from sterile postoperative inflammation.

A novel graph theoretical approach for modeling microbiomes and inferring microbial ecological relationships.

BACKGROUND: Microbiomes play vital roles in shaping environments and stabilize them based on their compositions and inter-species relationships among its species. Variations in microbial properties have been reported to have significant impact on their host environment. For example, variants in gut microbiomes have been reported to be associated with several chronic conditions, such as inflammatory disease and irritable bowel syndrome. However, how microbial bacteria contribute to pathogenesis still remains unclear and major research questions in this domain remain unanswered. METHODS: We propose a split graph model to represent the composition and interactions of a given microbiome. We used metagenomes from Korean populations in this study. The dataset consists of three different types of samples, viz. mucosal tissue and stool from Crohn's disease patients and stool from healthy individuals. We use the split graph model to analyze the impact of microbial compositions on various host phenotypes. Utilizing the graph model, we have developed a pipeline that integrates genomic information and pathway analysis to characterize both critical informative components of inter-bacterial correlations and associations between bacterial taxa and various metabolic pathways. RESULTS: The obtained results highlight the importance of the microbial communities and their inter-relationships and show how these microbial structures are correlated with Crohn's disease. We show that there are significant positive associations between detected taxonomic biomarkers as well as multiple functional modules in the split graph of mucosal tissue samples from CD patients. Bacteria Moraxellaceae and Pseudomonadaceae were detected as taxonomic biomarkers in CD groups. Higher abundance of these bacteria have been reported in previous study and several metabolic pathways associated with these bacteria were characterized in CD samples. CONCLUSIONS: The proposed pipeline provides a new way to approach the analysis of complex microbiomes. The results obtained from this study show great potential in unraveling mechansims in complex biological systems to understand how various components in such complex environments are associated with critical biological functions.

Heterogeneity of Ly6G+ Ly6C+ Myeloid-Derived Suppressor Cell Infiltrates during Staphylococcus aureus Biofilm Infection.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature monocytes and granulocytes. While neutrophils (polymorphonuclear leukocytes [PMNs]) are classically identified as highly differentiated cells specialized for antimicrobial defense, our laboratory has reported minor contributions of PMNs to the immune response during Staphylococcusaureus biofilm infection. However, these two cell types can be difficult to differentiate because of shared surface marker expression. Here we describe a more refined approach to distinguish MDSCs from PMNs utilizing the integrin receptor CD11b combined with conventional Ly6G and Ly6C expression. This approach separated the Ly6G+ Ly6C+ population that we previously identified in a mouse model of S. aureus orthopedic implant infection into two subsets, namely, CD11bhigh Ly6G+ Ly6C+ MDSCs and CD11blow Ly6G+ Ly6C+ PMNs, which was confirmed by characteristic nuclear morphology using cytospins. CD11bhigh Ly6G+ Ly6C+ MDSCs suppressed T cell proliferation throughout the 28-day infection period, whereas CD11blow Ly6G+ Ly6C+ PMNs had no effect early (day 3 postinfection), although this population acquired suppressive activity at later stages of biofilm development. To further highlight the distinctions between biofilm-associated MDSCs and PMNs versus monocytes, transcriptional profiles were compared by transcriptome sequencing (RNA-Seq). A total of 6,466 genes were significantly differentially expressed in MDSCs versus monocytes, whereas only 297 genes were significantly different between MDSCs and PMNs. A number of genes implicated in cell cycle regulation were identified, and in vivo ethynyldeoxyuridine (EdU) labeling revealed that approximately 50% of MDSCs proliferated locally at the site of S. aureus biofilm infection. Based on their similar transcriptomic profiles to those of PMNs, biofilm-associated MDSCs are of a granulocytic lineage and can be classified as granulocytic MDSCs (G-MDSCs).

A Technique for Utilizing Ankylosed Teeth for Anchorage.

We present a novel method of reinforcing anchorage by utilizing ankylosed teeth. This technique provides simple solutions in otherwise challenging and complex cases.

Graph mining for next generation sequencing: leveraging the assembly graph for biological insights.

BACKGROUND: The assembly of Next Generation Sequencing (NGS) reads remains a challenging task. This is especially true for the assembly of metagenomics data that originate from environmental samples potentially containing hundreds to thousands of unique species. The principle objective of current assembly tools is to assemble NGS reads into contiguous stretches of sequence called contigs while maximizing for both accuracy and contig length. The end goal of this process is to produce longer contigs with the major focus being on assembly only. Sequence read assembly is an aggregative process, during which read overlap relationship information is lost as reads are merged into longer sequences or contigs. The assembly graph is information rich and capable of capturing the genomic architecture of an input read data set. We have developed a novel hybrid graph in which nodes represent sequence regions at different levels of granularity. This model, utilized in the assembly and analysis pipeline Focus, presents a concise yet feature rich view of a given input data set, allowing for the extraction of biologically relevant graph structures for graph mining purposes. RESULTS: Focus was used to create hybrid graphs to model metagenomics data sets obtained from the gut microbiomes of five individuals with Crohn's disease and eight healthy individuals. Repetitive and mobile genetic elements are found to be associated with hybrid graph structure. Using graph mining techniques, a comparative study of the Crohn's disease and healthy data sets was conducted with focus on antibiotics resistance genes associated with transposase genes. Results demonstrated significant differences in the phylogenetic distribution of categories of antibiotics resistance genes in the healthy and diseased patients. Focus was also evaluated as a pure assembly tool and produced excellent results when compared against the Meta-velvet, Omega, and UD-IDBA assemblers. CONCLUSIONS: Mining the hybrid graph can reveal biological phenomena captured by its structure. We demonstrate the advantages of considering assembly graphs as data-mining support in addition to their role as frameworks for assembly.

An alternative approach to debonding lingual brackets.

This clinical pearl describes a technique of debonding the lingual brackets with minimum discomfort to the patient. It also reduces the risk of swallowing or aspirating the brackets and decreases the risk of enamel damage.

Towards Internet QoS provisioning based on generic distributed QoS adaptive routing engine.

Increasing efficiency and quality demands of modern Internet technologies drive today's network engineers to seek to provide quality of service (QoS). Internet QoS provisioning gives rise to several challenging issues. This paper introduces a generic distributed QoS adaptive routing engine (DQARE) architecture based on OSPFxQoS. The innovation of the proposed work in this paper is its undependability on the used QoS architectures and, moreover, splitting of the control strategy from data forwarding mechanisms, so we guarantee a set of absolute stable mechanisms on top of which Internet QoS can be built. DQARE architecture is furnished with three relevant traffic control schemes, namely, service differentiation, QoS routing, and traffic engineering. The main objective of this paper is to (i) provide a general configuration guideline for service differentiation, (ii) formalize the theoretical properties of different QoS routing algorithms and then introduce a QoS routing algorithm (QOPRA) based on dynamic programming technique, and (iii) propose QoS multipath forwarding (QMPF) model for paths diversity exploitation. NS2-based simulations proved the DQARE superiority in terms of delay, packet delivery ratio, throughput, and control overhead. Moreover, extensive simulations are used to compare the proposed QOPRA algorithm and QMPF model with their counterparts in the literature.

Exploring the Impact of Hand Dominance on Laparoscopic Surgical Skills Development Using Network Models.

BACKGROUND: Laparoscopic surgery demands high precision and skill, necessitating effective training protocols that account for factors such as hand dominance. This study investigates the impact of hand dominance on the acquisition and proficiency of laparoscopic surgical skills, utilizing a novel assessment method that combines Network Models and electromyography (EMG) data. METHODS: Eighteen participants, comprising both medical and non-medical students, engaged in laparoscopic simulation tasks, including peg transfer and wire loop tasks. Performance was assessed using Network Models to analyze EMG data, capturing muscle activity and learning progression. The NASA Task Load Index (TLX) was employed to evaluate subjective task demands and workload perceptions. RESULTS: Our analysis revealed significant differences in learning progression and skill proficiency between dominant and non-dominant hands, suggesting the need for tailored training approaches. Network Models effectively identified patterns of skill acquisition, while NASA-TLX scores correlated with participants' performance and learning progression, highlighting the importance of considering both objective and subjective measures in surgical training. CONCLUSIONS: The study underscores the importance of hand dominance in laparoscopic surgical training and suggests that personalized training protocols could enhance surgical precision, efficiency, and patient outcomes. By leveraging advanced analytical techniques, including Network Models and EMG data analysis, this research contributes to optimizing clinical training methodologies, potentially revolutionizing surgical education and improving patient care.

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads.

BACKGROUND: Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly. RESULTS: Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies. CONCLUSIONS: Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies.

A base composition analysis of natural patterns for the preprocessing of metagenome sequences.

BACKGROUND: On the pretext that sequence reads and contigs often exhibit the same kinds of base usage that is also observed in the sequences from which they are derived, we offer a base composition analysis tool. Our tool uses these natural patterns to determine relatedness across sequence data. We introduce spectrum sets (sets of motifs) which are permutations of bacterial restriction sites and the base composition analysis framework to measure their proportional content in sequence data. We suggest that this framework will increase the efficiency during the pre-processing stages of metagenome sequencing and assembly projects. RESULTS: Our method is able to differentiate organisms and their reads or contigs. The framework shows how to successfully determine the relatedness between these reads or contigs by comparison of base composition. In particular, we show that two types of organismal-sequence data are fundamentally different by analyzing their spectrum set motif proportions (coverage). By the application of one of the four possible spectrum sets, encompassing all known restriction sites, we provide the evidence to claim that each set has a different ability to differentiate sequence data. Furthermore, we show that the spectrum set selection having relevance to one organism, but not to the others of the data set, will greatly improve performance of sequence differentiation even if the fragment size of the read, contig or sequence is not lengthy. CONCLUSIONS: We show the proof of concept of our method by its application to ten trials of two or three freshly selected sequence fragments (reads and contigs) for each experiment across the six organisms of our set. Here we describe a novel and computationally effective pre-processing step for metagenome sequencing and assembly tasks. Furthermore, our base composition method has applications in phylogeny where it can be used to infer evolutionary distances between organisms based on the notion that related organisms often have much conserved code.

Bacteria commonly associated with central nervous system catheter infections elicit distinct CSF proteome signatures.

Background: Cerebrospinal fluid (CSF) shunt infection is a common and devastating complication of the treatment of hydrocephalus. Timely and accurate diagnosis is essential as these infections can lead to long-term neurologic consequences including seizures, decreased intelligence quotient (IQ) and impaired school performance in children. Currently the diagnosis of shunt infection relies on bacterial culture; however, culture is not always accurate since these infections are frequently caused by bacteria capable of forming biofilms, such as Staphylococcus epidermidis, Cutibacterium acnes, and Pseudomonas aeruginosa resulting in few planktonic bacteria detectable in the CSF. Therefore, there is a critical need to identify a new rapid, and accurate method for diagnosis of CSF shunt infection with broad bacterial species coverage to improve the long-term outcomes of children suffering from these infections. Methods: To investigate potential biomarkers that would discriminate S. epidermidis, C. acnes and P. aeruginosa central nervous system (CNS) catheter infection we leveraged our previously published rat model of CNS catheter infection to perform serial CSF sampling to characterize the CSF proteome during these infections compared to sterile catheter placement. Results: P. aeruginosa infection demonstrated a far greater number of differentially expressed proteins when compared to S. epidermidis and C. acnes infection and sterile catheters, and these changes persisted throughout the 56-day time course. S. epidermidis demonstrated an intermediate number of differentially expressed proteins, primarily at early time points that dissipated over the course of infection. C. acnes induced the least amount of change in the CSF proteome when compared to the other pathogens. Conclusions: Despite the differences in the CSF proteome with each organism compared to sterile injury, several proteins were common across all bacterial species, especially at day 5 post-infection, which are candidate diagnostic biomarkers.

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy.

The outbreak of Coronavirus (COVID-19) has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this study, a new COVID-19 diagnosis strategy called Feature Correlated Naïve Bayes (FCNB) has been introduced. The FCNB consists of four phases, which are; Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP). The FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people by using the Genetic Algorithm as a wrapper method. The FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called Master Features (MFs) in which each MF contains a set of dependent features. The MFWP assigns a weight value to each MF by using a new weight calculation method. The FCNBP is used to classify patients based on the weighted Naïve Bayes algorithm with many modifications as the correlation between features. The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves the maximum (99%) detection accuracy.

Heat recovery steam generator (HRSG) three-element drum level control utilizing Fractional order PID and fuzzy controllers.

Shrink and swell is a phenomenon that causes transient variability in water level once boiler load variation occurs. The leading cause of the swell effect is the steam demand changes and the actual arrangement of steam generating tubes in the boiler. Steam bubbles beneath HRSG drum water make the level control very difficult, particularly with significant disturbances in the input heat to HRSG. Plant shutdown may occur in some situations, and combined cycle plant efficiency is diminished. The recently applied control methods in industry are single-element and three-element control with PID controllers, but these methods are not well suited for substantial load changes. The main aim of this paper is to investigate the shrink and swell phenomenon inside HRSG power plants. In addition to the existing PID loops, two different standalone controllers, namely, the FOPID controller and fuzzy controller, are implemented with the HRSG model. Besides, Artificial Bee Colony (ABC) algorithm is used to tune FOPID efficiently. Based on overshoot, rise time, ISE, IAE, ITAE as performance measures, the comparison has been held between the three controllers. Simulations show that how the ABC optimization algorithm is efficient with PID, FOPID. It turns out that the proposed method is capable of improving system responses compared to the conventional optimal controller.