Comprehensive Validation Research Articles

Applying Metagenomic Analysis Using Nanopore Sequencer (MinION) for Precision Medicine in Bacterial Keratoconjunctivitis: Comprehensive Validation of Molecular Biological and Conventional Examinations.

Smear microscopic examination and culture of the corneal scrapings are the gold standards for the diagnosis of bacterial keratoconjunctivitis. High-sensitivity molecular biological examinations of the ocular surface specimens are used clinically. However, the results require careful interpretation to avoid the unintentional detection of indigenous bacteria. Results of conventional and state-of-the-art examinations require clinical verification for specificity and sensitivity. In this study, smear microscopic examination, culture, and nanopore sequencing using the MinION of ocular surface specimens from eight clinically diagnosed bacterial keratoconjunctivitis cases were performed and compared. Seven of the eight cases (87.5%) were smear positive and five (62.5%) were culture positive. The former showed the same genus in >60% of the classified reads as one specific bacterium inferred from the smear microscopy when sequenced by the MinION. In two of the three culture-negative cases, the smear-positive images were highly reminiscent of the species comprising most of the MinION sequences. Four of the five culture-positive cases were consistent with the most prevalent bacteria in the sequencing results. Probable contamination among specimens processed on the same day were observed. In conclusion, the microscopic examination of the corneal scraping specimens may be more sensitive and specific than the culture examination. Additionally, although metagenomic analysis using the MinION contributes to more precise medication for bacterial keratoconjunctivitis, contamination can affect the results.

Influence of Interpolation Scheme on the Accuracy of Overset Method for Computing Rudder-Propeller Interaction

Abstract The overset method and associated interpolation schemes are usually thoroughly verified only on synthetic or academic test cases for which conclusions might not directly translate to real engineering problems. In the present work, an overset grid method is used to simulate a rudder-propeller flow, for which a comprehensive verification and validation study is performed. Three overset-related interpolation schemes (first order inverse distance, second order nearest cell gradient and third order least squares) are tested to quantify and qualify numerical errors on integral quantities, mass imbalance, flow features and rudder pressure distributions. The performance overhead is also measured to help make accuracy versus performance balance decisions. Rigorous solution verification is performed to estimate time and space Discretization, iterative and statistical uncertainties. Validation of the propeller-rudder flow against experimental data is also done. The results show that, while the choice of interpolation scheme has minimal impact on time-averaged integral quantities (like propeller and rudder forces), they do influence the smoothness of the time signals, with the first order scheme resulting in large intensity high-frequency temporal oscillations. Lower order interpolation methods also produce more interpolation artifacts in fringe cells, which are then convected downstream. Mass imbalance is also affected by the interpolation scheme, with higher order schemes such as the third order least squares approach resulting in an order of magnitude lower flux errors. The limitations of first order schemes do not, however, result in significant lower computational overhead, with the second order nearest cell gradient being even cheaper than the inverse distance scheme in the tested implementation. Lastly, validation shows promising results with rudder forces within 10% of the experiments.

Validation and verification of RANS solvers for TUDa-GLR-OpenStage transonic axial compressor

This paper presents a comprehensive validation and verification study of turbomachinery Reynolds-averaged Navier-Stokes flow solvers on the transonic axial compressor TUDa-GLR-OpenStage. Two commercial solvers namely Ansys CFX and Numeca FineTurbo are adopted to provide the benchmark solutions, which can be used for verification of other RANS solvers in the future. Based on these solvers, five sets of grids, two advection schemes (i.e., central difference and second-order upwind), four turbulence models (i.e., SA, SA-RC, SST and EARSM) and two rotor-stator interface models (i.e., mixing plane and sliding plane) are investigated to quantify their effects on predicting the performance and the flow field of the compressor stage. Results show that the choices of grid density and turbulence model are most sensitive to the prediction, leading to 5% and 7% variation in compressor performance characteristics, respectively. Regarding the choice of grid density, a method to estimate the grid discretization error is demonstrated, which is transferrable to other cases. Regarding the choice of turbulence model, the EARSM model is found overall most accurate among the investigated models, and the limitations and deficiencies of the rest models are discussed in detail based on the analysis of the mean flow fields and the eddy viscosity fields. The grids and the major CFD results presented in this work are open-accessed to the community for further research. The results and discussions presented in this paper provide a useful reference for future practices of RANS simulations for compressors.

Multi-scale modelling of evolving plastic anisotropy during Al-alloy sheet forming

Accurate modelling of local evolving texture and yield surface evolution in complex strain path are of crucial importance to the accurate sheet metal forming simulations. This work proposed a novel multi-scale computational scheme, in which the full-field crystal plasticity (CP) modelling is mapped and real-time updated into each integration point following the local strain path in the finite element (FE) model. By the real-time close-loop feedback and update between the FE model and CP model, the evolving texture and its induced anisotropic plasticity evolution are considered utilizing on-the-fly virtual tests in the full deformation field. To carefully verify the developed computational framework, a comprehensive validation including plastic anisotropy characterization, cyclic hardening tests and deep drawing tests is conducted in an AA6016-T4 aluminium sheet with strong cube texture using ESAFORM Benchmark 2021. By comparison with experimental results and the conventional approaches with constant yield surface for the forming case, the newly developed method is thoroughly validated, showing that the significant texture evolution is observed in large plastic deformation and various strain paths lead to different texture evolution. The local evolving texture induced plastic anisotropy evolution has a considerable effect on the prediction accuracy of the deep drawing simulations, such as earing profile, wall thickness and loading history. Additionally, this method does not increase too much computation cost, enabling cost-effectively simulation of complex forming processes.

Screening of trypsin inhibitors in Cotinus coggygria Scop. extract using at-line nanofractionation coupled with semi-preparative reverse-phase liquid chromatography

In this study, an at-line nanofractionation (ANF) platform was successfully fabricated in parallel with mass spectrometry and trypsin inhibitory bioactivity assessment for rapid screening of trypsin inhibitors (TIs) from natural products for the first time. After systematic optimization, the ANF platform was applied to screen and identify TIs in the extract of a traditional Chinese herb, i.e., Cotinus coggygria Scop. The semi-preparative reverse-phase liquid chromatography was used subsequently to further simplify and enrich the insufficiently separated components. After comprehensive evaluation and validation, the ANF platform successfully identified 12 compounds as potential TIs, including 8 flavonoids and 2 organic acids. Additionally, a comparison study was conducted using two other ligand fishing approaches, i.e., capillary monolithic and magnetic beads-based trypsin-immobilized enzyme microreactors, which successfully identified 8 identical flavonoids as TIs. Importantly, the molecular docking study showed the molecular interactions between enzymes and inhibitors, thus strongly supporting the experimental results. Overall, this work has fully demonstrated the feasibility of the established ANF platform for screening TIs from Cotinus coggygria Scop., and proved its great prospects for screening bioactive components from natural products.

Mapping annual 10 m rapeseed extent using multisource data in the Yangtze River Economic Belt of China (2017–2021) on Google Earth Engine

Knowledge on the distribution of rapeseed enables accurate yield forecasting and supports agricultural planning to assist global security in edible oil supply. Large-scale remotely sensed maps of rapeseed nevertheless are non-existent in China because of landscape fragmentation, cloudy weather conditions, and phenological variations. Existing rapeseed mapping methods based on spectral, phenological, and machine learning algorithms are challenging to accurately and seamlessly extract the planting distribution of rapeseed over a large spatial region. In view of this, the annual extent of rapeseed in the Yangtze River Economic Belt (YREB) of China from 2017 to 2021 was mapped. Multisource data including Sentinel-1/2, Google Earth very-high-resolution (GE-VHR) images, and Global Land Data Assimilation System (GLDAS) data were used to develop a systematic methodology. First, the ground reference dataset was collected from fieldwork and all available GE-VHR images to construct a rapeseed point collection (RPC). Second, RPC, Sentinel-1, and GLDAS data were used to estimate the annual rapeseed peak-flowering date maps (RPDMs) based on a random forest regression model. Third, a hybrid mapping strategy was implemented on Google Earth Engine leveraging the dense Sentinel-1/2 data to generate the annual seamless 10 m rapeseed extent maps (REMs). Accuracy assessment illustrates the reliability of the RPDM with an R2 of 0.86 (RMSE = 5.32 days). Comparisons with other rapeseed mapping methods and comprehensive validation attest to the high precision of the REMs, with an average F1 score of 0.93. Moreover, the rapeseed cultivation area decreased over the five study years. Rapeseed fields in the YREB are highly fragmented, averaging 18 per ha, but trend analysis suggests a potential for crop intensification. This study provides an essential reference and information service for rapeseed production and agricultural resource survey in China.

Commissioning of Helium Ion Therapy and the First Patient Treatment With Active Beam Delivery

Helium ions offer intermediate physical and biological properties to the clinically used protons and carbon ions. This work presents the commissioning of the first clinical treatment planning system (TPS) for helium ion therapy with active beam delivery to prepare the first patients' treatment at the Heidelberg Ion-Beam Therapy Center (HIT). Through collaboration between RaySearch Laboratories and HIT, absorbed and relative biological effectiveness (RBE)-weighted calculation methods were integrated for helium ion beam therapy with raster-scanned delivery in the TPS RayStation. At HIT, a modified microdosimetric kinetic biological model was chosen as reference biological model. TPS absorbed dose predictions were compared against measurements with several devices, using phantoms of different complexities, from homogeneous to heterogeneous anthropomorphic phantoms. RBE and RBE-weighted dose predictions of the TPS were verified against calculations with an independent RBE-weighted dose engine. The patient-specific quality assurance of the first treatment at HIT using helium ion beam with raster-scanned delivery is presented considering standard patient-specific measurements in a water phantom and 2 independent dose calculations with a Monte Carlo or an analytical-based engine. TPS predictions were consistent with dosimetric measurements and independent dose engines computations for absorbed and RBE-weighted doses. The mean difference between dose measurements to the TPS calculation was 0.2% for spread-out Bragg peaks in water. Verification of the first patient treatment TPS predictions against independent engines for both absorbed and RBE-weighted doses presents differences within 2% in the target and with a maximum deviation of 3.5% in the investigated critical regions of interest. Helium ion beam therapy has been successfully commissioned and introduced into clinical use. Through comprehensive validation of the absorbed and RBE-weighted dose predictions of the RayStation TPS, the first clinical TPS for helium ion therapy using raster-scanned delivery was employed to plan the first helium patient treatment at HIT.

Validation of the International Consultation on Incontinence Questionnaire-Pediatric Lower Urinary Tract Symptoms (ICIQ-CLUTS) for Spanish-speaking children

The International Consultation on Incontinence Questionnaire-Pediatric Lower Urinary Tract Symptoms (ICIQ-CLUTS) is a 12-item self-administered tool to screen lower urinary tract symptoms (LUTS) in children. The aim of this study is to translate and validate the ICIQ-CLUTS into Spanish (ICIQ-CLUTS-Sp) and to study its psychometric properties. The cross-cultural adaptation of the ICIQ-CLUTS was performed following international recommendations. The psychometric analysis of the ICIQ-CLUTS-Sp was carried out to determine the reliability, validity, and diagnostic accuracy in a sample of 155 children and parents who completed the Spanish version ICIQ-CLUTS. The reliability indicators for the ICIQ-CLUTS-Sp were excellent (Cronbach’s alpha was > 0.8 and ICC > 0.9 both for children’s and parents’ versions). There was a high Pearson r > 0.6 and a high agreement level between children’s and parents’ answers (ICC > 0.6), except in 4 items. For parents, the standard error of measurement (SEm) was 0.41, and the minimal detectable change (MDC) was 1.14 points. In children, these results were 0.42 and 1.16 points, respectively. Cut-off points greater than 15 points in the parent version or 16 points in the children version have the highest sensitivity and specificity for detecting LUTS. Conclusion: The Spanish version of the ICIQ-CLUTS questionnaire is a valid, reliable, and diagnostically accurate instrument to identify cases of children with LUTS. Therefore, it can be used to screen for lower urinary tract symptoms in Spanish speaking children and/or parents, as well as to monitor the effects of interventions.What is Known:• Lower urinary tract symptoms in children should be assessed multimodally using minimally invasive diagnostic procedures. One way to do this is to use the questionnaire to differentiate these cases in paediatric patients.• A cross-cultural adaptation of the ICIQ-CLUTS questionnaire to Spanish has not yet been done.What is New:• Based on a comprehensive validation methodology, this study highlights that the ICIQ-CLUTSSp questionnaire has good psychometric properties.

Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia

Childhood B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by recurrent genetic abnormalities that drive risk-directed treatment strategies. Using current techniques, accurate detection of such aberrations can be challenging, due to the rapidly expanding list of key genetic abnormalities. Whole genome sequencing (WGS) has the potential to improve genetic testing, but requires comprehensive validation. We performed WGS on 210 childhood B-ALL samples annotated with clinical and genetic data. We devised a molecular classification system to subtype these patients based on identification of key genetic changes in tumour-normal and tumour-only analyses. This approach detected 294 subtype-defining genetic abnormalities in 96% (202/210) patients. Novel genetic variants, including fusions involving genes in the MAP kinase pathway, were identified. WGS results were concordant with standard-of-care methods and whole transcriptome sequencing (WTS). We expanded the catalogue of genetic profiles that reliably classify PAX5alt and ETV6::RUNX1-like subtypes. Our novel bioinformatic pipeline improved detection of DUX4 rearrangements (DUX4-r): a good-risk B-ALL subtype with high survival rates. Overall, we have validated that WGS provides a standalone, reliable genetic test to detect all subtype-defining genetic abnormalities in B-ALL, accurately classifying patients for the risk-directed treatment stratification, while simultaneously performing as a research tool to identify novel disease biomarkers.

Ring Repeating Unit: An Upgraded Structure Representation of Linear Condensation Polymers for Property Prediction.

Unique structure representation of polymers plays a crucial role in developing models for polymer property prediction and polymer design by data-centric approaches. Currently, monomer and repeating unit (RU) approximations are widely used to represent polymer structures for generating feature descriptors in the modeling of quantitative structure-property relationships (QSPR). However, such conventional structure representations may not uniquely approximate heterochain polymers due to the diversity of monomer combinations and the potential multi-RUs. In this study, the so-called ring repeating unit (RRU) method that can uniquely represent polymers with a broad range of structure diversity is proposed for the first time. As a proof of concept, an RRU-based QSPR model was developed to predict the associated glass transition temperature (Tg) of polyimides (PIs) with deterministic values. Comprehensive model validations including external, internal, and Y-random validations were performed. Also, an RU-based QSPR model developed based on the same large database of 1321 PIs provides nonunique prediction results, which further prove the necessity of RRU-based structure representation. Promising results obtained by the application of the RRU-based model confirm that the as-developed RRU method provides an effective representation that accurately captures the sequence of repeat units and thus realizes reliable polymer property prediction by data-driven approaches.

Whole Animal Feeding FLat (WAFFL): a complete and comprehensive validation of a novel, high-throughput fly experimentation system.

Non-mammalian model organisms have been essential for our understanding of the mechanisms that control development, disease, and physiology, but they are underutilized in pharmacological and toxicological phenotypic screening assays due to their low throughput in comparison with cell-based screens. To increase the utility of using Drosophila melanogaster in screening, we designed the Whole Animal Feeding FLat (WAFFL), a novel, flexible, and complete system for feeding, monitoring, and assaying flies in a high-throughput format. Our 3D printed system is compatible with inexpensive and readily available, commercial 96-well plate consumables and equipment. Experimenters can change the diet at will during the experiment and video record for behavior analysis, enabling precise dosing, measurement of feeding, and analysis of behavior in a 96-well plate format.

A comprehensive validation framework addressing utility parameter validation for application in small and medium enterprises (SMEs):A case study in pharmaceutical industry

Purpose: The purpose of this paper is to provide a validation framework to guide future validation practitioners towards completing utility validation projects in small and medium enterprise successfully and in shorter duration compared to current practice. Design/methodology/approach: Literature review was carried out on various existing validation frameworks to analyse the positive and negative elements of the frameworks. The identified positive key elements from this analysis were incorporated to develop a comprehensive validation framework that can be used by future validation practitioners to validate utility system in manufacturing companies. Findings: The case study results proved the capability and effectiveness of the new framework as the validation of the reverse osmosis water system was completed successfully with proper documentation and within shorter period compared to previous validation projects in the case study company. Research limitations/implications: The proposed validation framework is well suited for use in small and medium enterprise companies. Further studies should be carried out to ascertain the effect of the framework. Originality/value: This paper provides a comprehensive validation framework that can guide future validation practitioners towards completing validation projects in small and medium enterprise companies successfully and in shorter period.

When Correlation Is Not Enough: Validating Populism Scores from Supervised Machine-Learning Models

AbstractDespite the ongoing success of populist parties in many parts of the world, we lack comprehensive information about parties’ level of populism over time. A recent contribution to Political Analysis by Di Cocco and Monechi (DCM) suggests that this research gap can be closed by predicting parties’ populism scores from their election manifestos using supervised machine learning. In this paper, we provide a detailed discussion of the suggested approach. Building on recent debates about the validation of machine-learning models, we argue that the validity checks provided in DCM’s paper are insufficient. We conduct a series of additional validity checks and empirically demonstrate that the approach is not suitable for deriving populism scores from texts. We conclude that measuring populism over time and between countries remains an immense challenge for empirical research. More generally, our paper illustrates the importance of more comprehensive validations of supervised machine-learning models.

A new quasi-dynamic method for the prediction of tendon tension of TLP platform

The assessment of tendon mechanics for the tension leg platform (TLP) is crucial to the safety of the whole TLP system. The tendon design includes nonlinear mechanical characteristics such as set-down and various dynamic effects such as slow drift and springing. In this study, a new quasi-dynamic method dedicated to the dynamic tension of tendon and global motion of TLP is established by employing two primary assumptions, i.e. the separation of the hull’s horizontal and vertical response, and the separation of low-frequency, wave-frequency, and high-frequency excitations due to waves. An analytical tendon model is proposed, and the simultaneous time history of tendon tension can be obtained through the post-treatment after carefully handling the hull’s various dynamic responses. A remarkable efficiency is achieved since the time-consuming convolution integral, finite element model of the tendon, etc., are avoided. A comprehensive validation regarding the TLP system, global motion, and tendon tension is conducted by the comparisons with model test measurement and fully coupled analysis using OrcaFlex. An excellent agreement is achieved no matter the cycles and phases in the time series or the extremes in the statistics. The study has proven the quasi-dynamic method a reliable approach with satisfactory accuracy and superb efficiency, which should be a useful method in the design of the TLP platform.

Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management

The continuous increase in vehicle ownership has caused overall energy consumption to increase rapidly. Developing new energy vehicle technologies and improving energy utilization efficiency are significant in saving energy. Plug-in hybrid electric vehicles (PHEVs) present a practical solution to the arising energy shortage concerns. However, existing battery technologies restrict PHEV application as the most popular lithium-ion battery has a relatively high capital cost and degradation during service time. This paper studies the application of a new type of lithium‑sulfur (LiS) battery with bilateral solid electrolyte interphases in the PHEV. Compared with metals such as cobalt and nickel used in conventional lithium-ion batteries, sulfur utilized in LiS is cheaper and easier to manufacture. The high energy density of the new LiS battery also provides a longer range for PHEVs. In this paper, a PHEV propulsion system model is introduced, which includes vehicle dynamics, engine, electric motor, and LiS battery models. Dynamic programming is formulated as a benchmark energy management strategy to reduce energy consumption. Besides the offline global optimal benchmark from dynamic programming, the real-time performance of the LiS battery is evaluated by Q-learning and rule-based strategies. For a more comprehensive validation, both light-duty vehicles and heavy-duty vehicles are considered. Compared with lithium-ion batteries, the new LiS battery reduces the fuel consumption by up to 14.63 % and battery degradation by up to 82.37 %.

Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration

Background: Underwater images, in general, suffer from low contrast and high color distortions due to the non-uniform attenuation of the light as it propagates through the water. In addition, the degree of attenuation varies with the wavelength, resulting in the asymmetric traversing of colors. Despite the prolific works for underwater image restoration (UIR) using deep learning, the above asymmetricity has not been addressed in the respective network engineering. Contributions: As the first novelty, this article shows that attributing the right receptive field size ( context ) based on the traversing range of the color channel may lead to a substantial performance gain for the task of UIR. Further, it is important to suppress the irrelevant multi-contextual features and increase the representational power of the model. Therefore, as a second novelty, we have incorporated an attentive skip mechanism to adaptively refine the learned multi-contextual features. The proposed framework, called Deep WaveNet , is optimized using the traditional pixel-wise and feature-based cost functions. An extensive set of experiments have been carried out to show the efficacy of the proposed scheme over existing best-published literature on benchmark datasets. More importantly, we have demonstrated a comprehensive validation of enhanced images across various high-level vision tasks, e.g., underwater image semantic segmentation and diver’s 2D pose estimation. A sample video to exhibit our real-world performance is available at https://tinyurl.com/yzcrup9n . Also, we have open-sourced our framework at https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration .

Design of Smart Health Monitoring System for Disease Detection

The most valuable asset is one's health. However, there isn't much free time for today's people to keep an eye on their health. A health monitoring system that tracks users automatically is necessary to alert users to their health status. Rapid advancement in internet and technology provides improvement in the health system. In the conventional method, people are required to visit their doctors on a regular basis for health checkups. The use of technology in the health monitoring system can save valuable time by having the health status automation. Additionally, the cloud, which revolutionized data transformation, contributes to the development of a better and more dependable health monitoring system. One of the most important technology-based systems has been the healthcare monitoring systems, humans struggle with the problem of premature mortality because people with various ailments don't receive prompt medical care. The main objective was to create a trustworthy smart health monitoring system so that medical professionals could keep an eye on their patients, whether they were in a hospital or at home, using the proposed integrated healthcare system to ensure better and more dependable care. A prototype of health monitoring system is created, and it will have a number of cutting-edge features that will revolutionize the healthcare sector, with less reliance on human labor and greater efficiency in tasks like temperature monitoring, ECG-analysis, ambient temperature and humidity supervising, oxygen level recording, and BPM monitoring, this system will work autonomously. Body temperature, BPM, oxygen, temperature, and humidity sensors are included in the system to help maintain optimal health. The system's accuracy, reliability, sensitivity, usability, and scalability will be tested through comprehensive testing and validation in actual healthcare settings. This enables proactive and individualized health management. The results are anticipated to develop healthcare technologies and improve the standard of healthcare services.

Dodecanoic acid & palmitic acid disarms rifampicin resistance by putatively targeting mycobacterial efflux pump Rv1218c.

Drug-resistant tuberculosis (TB) jeopardizes the treatment process with poor outcomes. Efflux pumps (EPs) belonging to the ABC transporter family in Mycobacterium tuberculosis confer resistance to rifampicin (RMP) besides genetic mutations thus serving as a target for a potential adjunct therapeutic inhibitory molecule. Rv1218c is one such pump that was previously reported to be active in multidrug-resistant TB clinical isolates. In this study, the inhibition potential of Rv1218c-EP was tested on 8 molecules that were shortlisted by in silico methods. These molecules were subjected to the minimum inhibitory concentration (MIC) determination, checkerboard drug combination assay, ethidium bromide-DNA binding assay, and in vitro and ex vivo cytotoxicity assay. Based on the outcome of the study, two molecules dodecanoic acid (DA) and palmitic acid (PA) were found to be potential enough to decrease the MIC of RMP by 8 to 1000 folds against multidrug-resistant clinical isolates and Rv1218c expressing recombinant Mycobacterium smegmatis. These molecules were also found to reduce the time taken by RMP to kill these drug-resistant Mycobacteria to 48 h, unlike control isolates that survived more than 240 h of RMP exposure. The functional concentration of both molecules was non-toxic to the epithelial and blood mononuclear cells. With further comprehensive scientific validation, PA and DA could be recommended as adjunct therapeutic molecules with first-line anti-TB drugs to treat drug-resistant TB.

A Comprehensive Analysis of the Prognostic, Immunological and Diagnostic Role of CCNF in Pan-cancer.

Background: Cyclin F (CCNF) represents a pivotal constituent within the family of cell cycle proteins, which also belongs to the F-box protein family and acts as a critical regulatory factor in cell cycle transition. Its heightened expression has been consistently identified across various cancer types, including breast, pancreatic, and colorectal cancer. Nonetheless, a comprehensive exploration of CCNF's involvement in pan-cancer remains lacking. Methods: This study collected transcriptomic data and clinical information from several databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and BioGPS detabase. Employing bioinformatics methods, we investigated the potential oncogenic role of CCNF, utilizing various databases such as cBioPortal, Human Protein Atlas (HPA), TIMER2, UALCAN, GEPIA, GSCALite, and CTD detabase. These analyses focused on exploring CCNF expression, prognosis, gene mutations, immune cell infiltration, DNA methylation levels, and targeted chemical drugs across different tumor types. Additionally, we obtained CCNF-related genes from GeneMANIA and GEPIA databases and conducted GO and KEGG enrichment analyses to gain deeper insights into the biological processes associated with CCNF. Furthermore, we validated the differential expression of CCNF in normal human breast cancer and breast cancer cell lines using experimental verification. Results: CCNF exhibited upregulation in the majority of cancer types, demonstrating early diagnostic potential in 15 cancers and prognostic implications for adverse outcomes across numerous malignancies. Furthermore, CCNF was found to be linked with markers of the tumor immune microenvironment in various cancers. Additionally, CCNF expression influenced genetic alterations in pan-cancer. Enrichment analysis revealed that CCNF primarily participates in crucial biological pathways such as the cell cycle, p53 signaling pathway, and cellular senescence pathways. RT-qpcr and WB assays further confirmed that CCNF expression was higher in human cancer cell lines than in normal cell lines. Conclusion: The underlying role and mechanism of CCNF in pan-cancer were elucidated through comprehensive bioinformatics analysis and experimental validation. CCNF holds promise as an invaluable early detection indicator and tumor biomarker, offering potential targets for tumor treatment and prevention.

Optimal Fuzzy Logic Enabled Intrusion Detection for Secure IoT-Cloud燛nvironment

Recently, Internet of Things (IoT) devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives. Despite the benefits of IoT devices, security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security operation. Consequently, IoT devices are vulnerable to different kinds of attacks. A single attack on networking system/device could result in considerable data to data security and privacy. But the emergence of artificial intelligence (AI) techniques can be exploited for attack detection and classification in the IoT environment. In this view, this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system (MFSFL-IDS) in the IoT environment. The presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT environment. To achieve this, the MFSFL-IDS model employs data pre-processing to transform the data into useful format. Besides, henry gas solubility optimization (HGSO) algorithm is applied as a feature selection approach to derive useful feature vectors. Moreover, adaptive neuro fuzzy inference system (ANFIS) technique was utilized for the recognition and classification of intrusions in the network. Finally, binary bat algorithm (BBA) is exploited for adjusting parameters involved in the ANFIS model. A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct aspects. The experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recent approaches with maximum accuracy of 99.80%.