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Chronic kidney disease is considered an inflammatory state and a high fiber intake is associated with decreased inflammation in the general population. Here, we determined whether fiber intake is associated with decreased inflammation and mortality in chronic kidney disease, and whether kidney disease modifies the associations of fiber intake with inflammation and mortality. To do this, we analyzed data from 14,543 participants in the National Health and Nutrition Examination Survey III. The prevalence of chronic kidney disease (estimated glomerular filtration rate less than 60ml/min per 1.73m2) was 5.8%. For each 10-g/day increase in total fiber intake, the odds of elevated serum C-reactive protein levels were decreased by 11% and 38% in those without and with kidney disease, respectively. Dietary total fiber intake was not significantly associated with mortality in those without but was inversely related to mortality in those with kidney disease. The relationship of total fiber with inflammation and mortality differed significantly in those with and without kidney disease. Thus, high dietary total fiber intake is associated with lower risk of inflammation and mortality in kidney disease and these associations are stronger in magnitude in those with kidney disease. Interventional trials are needed to establish the effects of fiber intake on inflammation and mortality in kidney disease.

Insider threats remain a serious anxiety for organizations, government agencies, and businesses. Normally, the most hazardous cyber attacks are formed by trusted insiders and not by malicious outsiders. The malicious behaviors resulting from unplanned or planned mishandling of resources, data, networks, and systems of an organization constitute an insider threat. The unsupervised behavioral anomaly detection methods are mostly developed by the traditional machine learning methods for identifying unusual or anomalous variations in user behavior. The insider threat mainly originates from an individual inside the organization who is a current or former employee who has access to sensitive information about the organization. For achieving an improvement over traditional methods, the Stacked Convolutional Neural Network- Attentional Bi-directional Gated Recurrent Unit model is proposed in this paper to detect insider threats. The CNN-Attentional BiGRU model utilizes the user activity logs and user information for time-series classification. Using the log files, the temporal data representations, and weekly and daily numerical features from various sub-models of CNN are learned by the stacked generalization. Based on the chosen feature vectors, a model is trained on the CERT insider threat dataset. The stacked CNN is combined with the Attentional BiGRU model to incorporate more complex features of the user activity logs and user data during each convolution operation without raising network parameters. Thus the classification performance is improved with less complexity. The non-linear time control, chaos-based strategy, update rules, and opposite-based learning strategies are evaluated for generating the Modified-Equilibrium Optimization. The simulation outputs obtained by the model are 92.52% accuracy, 98% Precision, 95% Recall, and 96% F1-score. Thus, the proposed model has reached higher detection performance.

A large number of association rules often minimizes the reliability of data mining results; hence, a dimensionality reduction technique is crucial for data analysis. When analyzing massive datasets, existing models take more time to scan the entire database because they discover unnecessary items and transactions that are not necessary for data analysis. For this purpose, the Fuzzy Rough Set-based Horse Herd Optimization (FRS-HHO) algorithm is proposed to be integrated with the Map Reduce algorithm to minimize query retrieval time and improve performance. The HHO algorithm minimizes the number of unnecessary items and transactions with minimal support value from the dataset to maximize fitness based on multiple objectives such as support, confidence, interestingness, and lift to evaluate the quality of association rules. The feature value of each item in the population is obtained by a Map Reduce-based fitness function to generate optimal frequent itemsets with minimum time. The Horse Herd Optimization (HHO) is employed to solve the high-dimensional optimization problems. The proposed FRS-HHO approach takes less time to execute for dimensions and has a space complexity of 38% for a total of 10 k transactions. Also, the FRS-HHO approach offers a speedup rate of 17% and a 12% decrease in input–output communication cost when compared to other approaches. The proposed FRS-HHO model enhances performance in terms of execution time, space complexity, and speed.

Numerous public networks, namely Instagram, YouTube, Facebook, Twitter, etc., share their own feelings and idea as videotapes, posts, and pictures. In future research, adapting to such data and mining valuable information from it will be an undeniably troublesome errand. This paper proposes a novel audio–video–textual-based multimodal sentiment analysis approach. The proposed approach investigates the sentiments that are collected from the web recordings that utilize audio, video, and textual modalities for further extraction. A feature-level fusion technique is employed in fusing the extracted features from different modalities. Therefore, the extracted features are optimally chosen by using a novel oppositional grass bee optimization (OGBEE) algorithm to obtain the best optimal feature set. Here, 12 benchmark functions are developed to validate the numerical efficiency and the effectiveness of a novel OGBEE algorithm for various aspects. Moreover, our proposed approach utilizes multilayer perceptron-based neural network (MLP-NN) for sentiment classification. The experimental analysis reveals that the proposed approach provides better classification accuracy of about 95.2% with less computational time.

We introduce a new utilization of an Aerodynamic Lens Stack (ALS) for concentrating aerosols in the production of high energy (>200 keV) electrons through their interaction with intense( > 10 16 W/cm 2 ), ultra-short (30 fs) laser pulses. The lens was designed and simulated in COMSOL with various parameters such as inlet dimensions and backing pressures. Subsequently, the particle jet was analyzed using particle streak velocimetry (PSV). Following the characterization process, the jet was exposed to the laser, and the emission of electrons was investigated and described. Our results demonstrate the effectiveness of the lens in producing and focussing aerosols originating from liquid sources, underscoring its potential as a precise microtarget for laser interactions.

Design of effective algorithm for reliable and energy optimized secure routing protocol (SRP) for wireless sensor networks (WSNs) is a demanding design issue now. To handle this problem, we propose a trust and encryption-based SRP based on trust modelling with intrusion detection, elliptic curve cryptography (ECC), clustering, fuzzy rules and ant colony optimization (ACO)-oriented SRP for WSN routing. In this paper, an extended convolutional neural networks with Schrodinger equation and particle swarm optimization is proposed for developing and intrusion detection-based trust modelling. Moreover, a new node authentication scheme and an encryption-based secure routing protocol are also proposed in this work for increasing the security. This proposed secure protocol known as trust and ECC encryption-based ACO-SRP (TECC-ACO-SRP) performs authentication and trust analysis on the nodes using intrusion detection, and then, the data are communicated after data encryption using ECC encryption technique. This proposed system combines dominant set clustering with fuzzy rules to make clusters with similar type of nodes as members and then selects cluster heads (CHs) for every cluster. This SRP ensures improved security, reduced delay and energy usage with higher packet delivery ratio than other existing SRPs.

Wireless Sensor Network (WSN) communication encounters security vulnerabilities, particularly with network traffic being susceptible to attacks during routing. The effective use of Deep Learning (DL) methods has been demonstrated in developing Intrusion Detection Systems (IDSs) to manage security attacks in Wireless Sensor Networks (WSN). Consequently, the development of new IDS becomes imperative, with DL and optimization algorithms offering superior attack detection capabilities. To address this need, we propose one new IDS by integrating Fuzzy Temporal rules and Artificial Bee Colony (ABC) optimization algorithm with Convolutional Neural Network (CNN) optimized with (FT-ABC-CNN) to enhance the classifier performance. To assess its effectiveness, a comparative analysis was conducted between the newly proposed FT-ABC-CNN algorithm and other classification algorithms commonly employed in Intrusion Detection System design, such as CNN, Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). Experimental evaluations revealed that the FT-ABC-CNN algorithm surpassed these comparable classifiers in terms of accuracy enhancement and reduction in false positive rates.

The MLL complex promotes androgen receptor signaling and drives growth of castration resistant prostate cancer Resistance to androgen deprivation therapies and increased androgen receptor (AR) activity are major drivers of castration-resistant prostate cancer (CRPC). Although prior work has focused on targeting AR directly, co-activators of AR signaling, which may represent new therapeutic targets, are relatively underexplored. Here we demonstrate that the mixed-lineage leukemia protein (MLL) complex, a well-known driver of MLL fusion–positive leukemia, acts as a co-activator of AR signaling. AR directly interacts with the MLL complex via the menin–MLL subunit. Menin expression is higher in CRPC than in both hormone-naive prostate cancer and benign prostate tissue, and high menin expression correlates with poor overall survival of individuals diagnosed with prostate cancer. Treatment with a small-molecule inhibitor of menin–MLL interaction blocks AR signaling and inhibits the growth of castration-resistant tumors in vivo in mice. Taken together, this work identifies the MLL complex as a crucial co-activator of AR and a potential therapeutic target in advanced prostate cancer.

Laser plasma electron acceleration from the interaction of an intense femtosecond laser pulse with an isolated microparticle surrounded by a low-density gas is studied here. Experiments presented here show that optimized plasma tailoring by introducing a pre-pulse boosts parametric instabilities to produce MeV electron energies and generates electron temperatures as large as 200 keV with the total charge being as high as 350 fC/shot/sr, even at a laser intensity of a few times 10 16  Wcm −2 . Corroborated by particle-in-cell simulations, these measurements reveal that two plasmon decay in the vicinity of the microparticle is the main contributor to hot electron generation.

Aim The aim of this randomized clinical study was to compare the anticandidal efficacy of 0.20% sodium hypochlorite and Triphala churna in diabetic and nondiabetic complete denture wearers. Materials and Methods In this randomized clinical study, 40 participants were allocated into four subgroups (n = 10 each): diabetic participants using sodium hypochlorite (Group 1A), diabetic participants using Triphala churna (Group 1B), nondiabetic participants using sodium hypochlorite (Group 2A), and nondiabetic participants using Triphala churna (Group 2B). Biofilm samples were collected from the intaglio surface of maxillary dentures at baseline and after 21 days of daily immersion. Candida colony‐forming units (CFU/mL) were quantified using Sabouraud Dextrose Agar and confirmed using HiCrome Candida Differential Agar. Statistical analysis included paired and unpaired t‐tests and two‐way repeated measures ANOVA with Bonferroni post hoc tests. Results All subgroups demonstrated a significant reduction in CFU counts after 21 days (p  < 0.01). Two‐way repeated measures ANOVA revealed a significant effect of time (p  < 0.001), while no significant interaction was observed between cleanser type and diabetic status (p  > 0.05). Neither sodium hypochlorite nor Triphala churna showed superiority over the other. Conclusion Both 0.20% sodium hypochlorite and Triphala churna effectively reduced Candida colonization on complete dentures in diabetic and nondiabetic individuals. Triphala churna may serve as a safe and cost‐effective herbal alternative for routine denture hygiene, particularly for patients seeking nonchemical cleansing options.