Profiling Tools Research Articles

Advancements and characteristics of scholar profiling research in China

This study analyzes the development and characteristics of scholar profiling research in China from 2016 to 2022, utilizing content analysis of 38 core journal articles. Scholar profiling, adapted from user profiling in big data, involves extracting and analyzing academic data to build models that describe scholars' characteristics and behaviors. Our findings highlight the use of diverse data sources, including commercial databases and social platforms, and emphasize the importance of data preprocessing and modeling techniques. While applied research dominates, the field faces challenges such as the lack of standardized data collection and depth in model construction. The study underscores the need for robust profiling tools and broader application scenarios to enhance the effectiveness of scholar profiling systems.

The NOMAD mini-apps: A suite of kernels from ab initio electronic structure codes enabling co-design in high-performance computing.

This article introduces a suite of mini-applications (mini-apps) designed to optimise computational kernels in ab initio electronic structure codes. The suite is developed from flagship applications participating in the NOMAD Center of Excellence, such as the ELPA eigensolver library and the GW implementations of the exciting, Abinit, and FHI-aims codes. The mini-apps were identified by targeting functions that significantly contribute to the total execution time in the parent applications. This strategic selection allows for concentrated optimisation efforts. The suite is designed for easy deployment on various High-Performance Computing (HPC) systems, supported by an integrated CMake build system for straightforward compilation and execution. The aim is to harness the capabilities of emerging (post)exascale systems, which necessitate concurrent hardware and software development - a concept known as co-design. The mini-app suite serves as a tool for profiling and benchmarking, providing insights that can guide both software optimisation and hardware design. Ultimately, these developments will enable more accurate and efficient simulations of novel materials, leveraging the full potential of exascale computing in material science research.

Moats & comets - A methodology to explore for submerged prehistory at sea floor erosional features formed at artificial reefs, Atlantic continental shelf, GeorgiaUSA.

Our primary objective is to demonstrate a method that uses marine processes, principally seafloor scour, to broadly expose prehistoric landforms and associated localities. Too often, the loss of prehistoric strata to scour processes such as ravinement during broad-scale, continental shelf transgressions, are decried without the recognition of the potential for erosion at the local scale can expose those same strata for systematic study. The analysis of sea floor erosional features formed around artificial reefs by turbulent flows in the Georgia Bight have used interferometric side-scan sonar (SSS), Remotely Operated Vehicles (ROVs), ultra-high-definition videography (UHD), and sedimentological, palynological, paleontological, archaeological analytical techniques. These techniques were integrated within a geoarchaeological methodology focused on exploring the Continental Shelf for preserved evidence of prehistoric landforms, flora and fauna once thereon. A stepwise protocol was developed to first gather interferometric side-scan sonar data to be then processed by commercial software packages such as HYPACK Hydrographic and HYSWEEP together with HYSCAN and SONAR Wiz software. The sonar mosaics were imported to ArcGIS/QGIS software to characterize scour features/targets around artificial reefs that had exposed paleo-land surfaces previously obscured by the modern palimpsest sand sheet. Analysis of scours and depressions - “moats and comets” - adjacent to reef structures were evaluated using the profile tool within the HYPACK-HYSWEEP editor package. Symbology for the bathymetric raster layers was edited to provide a clear contrast and measurement at scour features. Measured GIS images were then classified as to scour extent and depth with respect to prevailing current directions responsible for producing turbulence. A ranking of the artificial reefs by factors such as scour depth and extent was used to guide subsequent investigation by ROVs. Sediment cores and grab samples were collected, and those samples underwent particle size analysis (PSA), macro-and micro-botanical analyses. All data – geophysical, GIS, video and geological – were collated and synthesized to produce an assessment of the potential of sea floor scour features to expose previously buried geomorphological/geological, paleontological and/or archaeological features of interest.

Haplotype-resolved genome assembly and implementation of VitExpress, an open interactive transcriptomic platform for grapevine

Haplotype-resolved genome assemblies were produced for Chasselas and Ugni Blanc, two heterozygous Vitis vinifera cultivars by combining high-fidelity long-read sequencing and high-throughput chromosome conformation capture (Hi-C). The telomere-to-telomere full coverage of the chromosomes allowed us to assemble separately the two haplo-genomes of both cultivars and revealed structural variations between the two haplotypes of a given cultivar. The deletions/insertions, inversions, translocations, and duplications provide insight into the evolutionary history and parental relationship among grape varieties. Integration of de novo single long-read sequencing of full-length transcript isoforms (Iso-Seq) yielded a highly improved genome annotation. Given its higher contiguity, and the robustness of the IsoSeq-based annotation, the Chasselas assembly meets the standard to become the annotated reference genome for V. vinifera. Building on these resources, we developed VitExpress, an open interactive transcriptomic platform, that provides a genome browser and integrated web tools for expression profiling, and a set of statistical tools (StatTools) for the identification of highly correlated genes. Implementation of the correlation finder tool for MybA1, a major regulator of the anthocyanin pathway, identified candidate genes associated with anthocyanin metabolism, whose expression patterns were experimentally validated as discriminating between black and white grapes. These resources and innovative tools for mining genome-related data are anticipated to foster advances in several areas of grapevine research.

Exosomal Serum Biomarkers as Predictors for Laryngeal Carcinoma.

The lack of screening methods for LSCC is a critical issue, as treatment options and the treatment outcome greatly depend on the stage of LSCC at initial diagnosis. Therefore, the objective of this study was to identify potential exosomal serum biomarkers that can diagnose LSCC and distinguish between early- and late-stage disease. A multiplexed proteomic array was used to identify differentially expressed proteins in exosomes isolated from the serum samples of LSCC patients compared to the control group (septorhinoplasty, SRP). The most promising proteins for diagnosis and differentiation were calculated using biostatistical methods and were validated by immunohistochemistry (IHC), Western blots (WB), and ELISA. Exosomal insulin-like growth factor binding protein 7 (IGFBP7) and Annexin A1 (ANXA1) were the most promising exosomal biomarkers for distinguishing between control and LSCC patients and also between different stages of LSCC (fold change up to 15.9, p < 0.001 for all). The identified proteins represent potentially novel non-invasive biomarkers. However, these results need to be validated in larger cohorts with a long-term follow-up. Exosomal biomarkers show a superior signal-to-noise ratio compared to whole serum and may therefore be an important tool for non-invasive biomarker profiling for laryngeal carcinoma in the future.

Two-Dimensional Thin-Layer Chromatography as an Accessible, Low-Cost Tool for Lipid-Class Profile Screening

The interest in lipid composition profiling is significantly increasing as research reveals the immense importance of lipids in medicine, plant science, food and agriculture. However, lipidomic analysis requires high-end specialty equipment. We used two-dimensional thin-layer chromatography (2D-TLC) as a readily available, low-cost tool for basic lipidomic profiling of lipid classes in algal samples in the models Chlamydomonas reinhardtii, Auxenochlorella protothecoides, and Euglena gracilis, validating lipid class identification using an LC-MS/MS analysis. Algal lipid extracts were separated on a 2D-TLC plate, and TLC analysis was followed by scraping individual TLC spots off the plate, and a subsequent liquid chromatography separation and tandem mass spectrometry (LC-MS/MS) analysis. For comparison, crude lipid extracts were also injected directly to the LC-MS/MS system. Lipid class annotation was achieved by a combination of accurate mass, retention time information, neutral loss and fragment ion analysis by MS2Analyzer, and by matching spectra to LipidBlast MS/MS library. Overall, we were able to identify 15 lipid classes, and to adequately profile the lipid classes in all three organisms. This TLC method is thus suggested as an accessible tool for lipid class profiling of algal, plant, and food lipids, alike, when a rapid and simple analysis is required, e.g., for screening purposes.

Efficient enzyme-free method to assess the development and maturation of the innate and adaptive immune systems in the mouse colon

Researchers who aim to globally analyze the gastrointestinal immune system via flow cytometry have many protocol options to choose from, with specifics generally tied to gut wall layers of interest. To get a clearer idea of the approach we should use on full-thickness colon samples from mice, we first undertook a systematic comparison of three tissue dissociation techniques: two based on enzymatic cocktails and the other one based on manual crushing. Using flow cytometry panels of general markers of lymphoid and myeloid cells, we found that the presence of cell-surface markers and relative cell population frequencies were more stable with the mechanical method. Both enzymatic approaches were associated with a marked decrease of several cell-surface markers. Using mechanical dissociation, we then developed two minimally overlapping panels, consisting of a total of 26 antibodies, for serial profiling of lymphoid and myeloid lineages from the mouse colon in greater detail. Here, we highlight how we accurately delineate these populations by manual gating, as well as the reproducibility of our panels on mouse spleen and whole blood. As a proof-of-principle of the usefulness of our general approach, we also report segment- and life stage-specific patterns of immune cell profiles in the colon. Overall, our data indicate that mechanical dissociation is more suitable and efficient than enzymatic methods for recovering immune cells from all colon layers at once. Additionally, our panels will provide researchers with a relatively simple tool for detailed immune cell profiling in the murine gastrointestinal tract, regardless of life stage or experimental conditions.

Is transanal irrigation the best treatment possibility for low anterior resection syndrome? A multicenter, randomized clinical trial: study protocol.

Up to 50% of patients who undergo rectal resection suffer from various and partly severe functional problems, despite the preservation of the anal sphincter. These complaints are defined as low anterior resection syndrome (LARS). So far, there are no randomized clinical trials regarding the most effective treatment for LARS. Our aim is to evaluate whether transanal irrigation improves bowel function and quality of life in patients following low anterior resection compared to best supportive care. Patients who have undergone low anterior resection will be approached for this study. On patient's visit, complaints regarding the defecation as well as any deterioration in their overall quality of life will be assessed using questionnaires such as the Low Anterior Resection Syndromes score, Wexner score, European Organization for Research and Treatment of Cancer (EORTC) Quality of Life (QOL) CR-29, and Measure Yourself Medical Outcome Profile tool. Few additional target questions will be also asked, such as "Would you recommend the treatment to anybody; did you expect the improvement following the treatment; etc." Questionnaires and scales will be filled on follow-up visits every 3 months for 1 year. This multicenter, randomized controlled trial will lead to a better understanding of LARS treatment. Moreover, it will be a hypothesis-generating study and will inform areas needing future prospective studies. ClinicalTrials.gov, identifier (NCT05920681).

Liquid Biopsies as Non-Invasive Tools for Mutation Profiling in Multiple Myeloma: Application Potential, Challenges, and Opportunities.

Over the last decades, the survival of multiple myeloma (MM) patients has considerably improved. However, despite the availability of new treatments, most patients still relapse and become therapy-resistant at some point in the disease evolution. The mutation profile has an impact on MM patients' outcome, while typically evolving over time. Because of the patchy bone marrow (BM) infiltration pattern, the analysis of a single bone marrow sample can lead to an underestimation of the known genetic heterogeneity in MM. As a result, interest is shifting towards blood-derived liquid biopsies, which allow for a more comprehensive and non-invasive genetic interrogation without the discomfort of repeated BM aspirations. In this review, we compare the application potential for mutation profiling in MM of circulating-tumor-cell-derived DNA, cell-free DNA and extracellular-vesicle-derived DNA, while also addressing the challenges associated with their use.

Circulating tumour DNA analysis and new uses of anti-EGFR monoclonal antibodies in patients with metastatic colorectal cancer

Currently, tumour tissue biopsy to determine RAS/BRAF gene alterations, assess microsatellite instability status, and determine HER‑2/neu gene amplification/hyperexpression is the gold standard of diagnosis and allows the selection of optimal molecularly targeted therapy when considering treatment strategies for patients with metastatic colorectal cancer. However, biopsy does not fully reflect the existing intratumoural heterogeneity and clonal evolution of tumour cells, which can often be the cause of therapeutic failures. In recent years, liquid biopsy has attracted increasing attention as an additional and potentially alternative non-invasive tool for molecular tumour profiling. Assessment of circulating tumour DNA allows changes in the genetic status of the tumour to be monitored and the «burden» of disease to be measured dynamically in real time. Advances in liquid biopsy technology have led to promising new strategies for the management of patients with metastatic colorectal cancer in late-line therapy. The standard drug arsenal in this group of patients is limited to either repeat administration of previously effective therapy or regorafenib and the combination of trifluridine/tipiracil with bevacizumab, which are characterized by limited clinical activity. However, thanks to the discovery of the NeoRAS wild-type phenomenon and the rechallenge strategy of anti-EGFR monoclonal antibodies based on the study of clonal selection and evolution of tumour cells, the administration of epidermal growth factor inhibitors in a molecularly selected by liquid biopsy population is accompanied by good tolerability and efficacy. Numerous clinical studies are ongoing to further understand the mechanisms of tumour resistance and to develop new evidence-based treatment approaches in order to realise the concept of personalised medicine.

Angry Citizens and Black Belt Employees: Cascading classifications of and around a predictive algorithm

Over past decades, predictive algorithms have been used extensively as profiling tools in the private sector, but today they are also increasingly entering public sector domains. This article builds on an ethnographic study of the development of a predictive algorithm in a debt collecting public sector organization. The algorithm was designed to profile citizens on the basis of their calculated ‘readiness to pay’ their debt and to guide employees’ case handling according to ‘type’ of citizen. The article examines how the classification of citizens produced by the algorithm was mediated by different visualizations and by organizational actors who superimposed new and different classifications (moral and emotional) onto those provided by the algorithm. The article draws on the concepts of nominal and ordinal classification to identify how intended non-hierarchical classification glides into new hierarchical valuations of both citizens and employees. Classifications were ‘cascading’ – a concept the article develops to account for how classification of and around the algorithm multiplied and had organizational ripple effects. Based on empirical insights, the study advocates an agnostic approach to how algorithmic predictions impact work, organizations, and the situation of profiled individuals. It emphasizes a dynamic and and unstable relationship between algorithms and organizational practices.

DEVELOPMENT OF ACADEMIC ADVISING INFORMATION SYSTEM APPLICATIONS AT MAKASSAR STATE UNIVERSITY USING THE ANDROID PLATFORM

Advising is the process where students communicate about issues that may arise during their study period. However, it is often hindered by the busy schedules and different timetables between students and their academic advisors. Therefore, a system has been developed to facilitate the advising process using the Android platform. This research adopts the Research and Development (R&amp;D) methodology, specifically using the waterfall development model. Data collection techniques include interviews and questionnaires. The application is tested based on the ISO/IEC 25010 quality standards, covering four aspects: functional suitability, performance efficiency, usability, and portability. The functional suitability testing involved two expert professors, and 24 features were tested, indicating that the application is "Suitable" for use. Portability testing on the application was conducted by observing the output on three Android versions: Nougat, Red Velvet Cake, and Tiramisu. The results showed that the application can run on all three Android versions. Performance efficiency testing utilized the Android Profiler tool to assess CPU, memory, and network usage while running the application. The findings revealed that CPU usage was less than 50%, memory allocation was 122.2 MB, and network usage was less than 48 KB/s. Usability testing conducted by students resulted in a test score of 81.6%, categorized as "Very Good" feasibility.

Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in Drosophila mushroom body contributing to olfactory learning.

O-GlcNAcylation is a dynamic post-translational modification that diversifies the proteome. Its dysregulation is associated with neurological disorders that impair cognitive function, and yet identification of phenotype-relevant candidate substrates in a brain-region specific manner remains unfeasible. By combining an O-GlcNAc binding activity derived from Clostridium perfringens OGA (CpOGA) with TurboID proximity labeling in Drosophila, we developed an O-GlcNAcylation profiling tool that translates O-GlcNAc modification into biotin conjugation for tissue-specific candidate substrates enrichment. We mapped the O-GlcNAc interactome in major brain regions of Drosophila and found that components of the translational machinery, particularly ribosomal subunits, were abundantly O-GlcNAcylated in the mushroom body of Drosophila brain. Hypo-O-GlcNAcylation induced by ectopic expression of active CpOGA in the mushroom body decreased local translational activity, leading to olfactory learning deficits that could be rescued by dMyc overexpression-induced increase of protein synthesis. Our study provides a useful tool for future dissection of tissue-specific functions of O-GlcNAcylation in Drosophila, and suggests a possibility that O-GlcNAcylation impacts cognitive function via regulating regional translational activity in the brain.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

PurposeTo evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients categorized as intermediate risk based on classic clinicopathological variables and eligible for chemotherapy.MethodsIn a case series comprising 234 invasive ER-positive/HER2-negative tumors, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved. The digitized HE slides were analysed by Stratipath Breast.ResultsOur findings showed that the Stratipath Breast analysis identified 49.6% of the clinically intermediate tumors as low risk and 50.4% as high risk. The Prosigna assay classified 32.5%, 47.0% and 20.5% tumors as low, intermediate and high risk, respectively. Among Prosigna intermediate-risk tumors, 47.3% were stratified as Stratipath low risk and 52.7% as high risk. In addition, 89.7% of Stratipath low-risk cases were classified as Prosigna low/intermediate risk. The overall agreement between the two tests for low-risk and high-risk groups (N = 124) was 71.0%, with a Cohen’s kappa of 0.42. For both risk profiling tests, grade and Ki67 differed significantly between risk groups.ConclusionThe results from this clinical evaluation of image-based risk stratification shows a considerable agreement to an established gene expression assay in routine breast pathology.

Stressed young adults: an emerging issue in Catholic youth organisations

The aim of this study is to learn about the demographic profile, information and communication technologies (ICT) tools, and internet platforms used by Catholic young adults in youth organisations under San Antonio de Padua Parish in the Province of Laguna, Philippines, during the COVID-19 pandemic. It made use of both descriptive and exploratory research that employed online interviews through ICT tools and online media platforms. Twenty (20) young Catholic adults in youth organisations were interviewed from the church list of youth organisations. The results showed that most participants were working students and belonged to lower-income families. They experienced school and work stress, demanded work efficiency and time pressure on school requirements, internet connectivity issues, and limited support from peers and family members. As explored, the causes of stress made them feel mentally and physically exhausted, and they felt the burden of multi-tasking as students, workers, and church members. The research also suggests an innovative implication for stress management and mitigation schemes through ICT tools and proposes the creation of an online education website by any Catholic Organisation to monitor young church members for Online stress mitigation sessions and stress management satisfaction.

Investigation of Common Pathways and Putative Biomarker Candidates of Colorectal Cancer and Insomnia by Using Integrative In-Silico Approaches.

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortalities across the globe. Accumulating evidence shows that individuals having sleep disorders such as insomnia are at high risk of developing CRC, yet the association of sleep disorders with CRC risk is still unclear. Here, we investigated the potential molecular connections between CRC and insomnia using integrative in silico approaches. This study aims to explore the potential molecular connections between CRC and insomnia utilizing integrative in-silico methodologies. Gene expression microarray datasets for CRC and insomnia samples were retrieved from the NCBI-GEO database and analyzed using R. Functional enrichment analysis of common differentially expressed genes (DEGs) was performed by the g: Profiler tool. Cytoscape software was used to construct a protein-protein interaction network and hub gene identification. Expression profiles of hub genes in TCGA datasets were also determined, and predicted miRNAs targeting hub genes were analyzed by miRNA target prediction tools. Our results revealed a total of 113 shared DEGs between the CRC and insomnia datasets. Six genes (HSP8A, GAPDH, HSP90AA1, EEF1G, RPS6, and RPLP0), which were also differently expressed in TCGA datasets, were prioritized as hub genes and were found to be enriched in pathways related to protein synthesis. hsa-miR-324-3p, hsa-miR-769-3p, and hsa-miR-16-5p were identified as promising miRNA biomarkers for two diseases. Our in-silico analysis provides promising evidence of the molecular link between CRC and insomnia and highlights multiple potential molecular biomarkers and pathways. Validation of the results by wet lab work can be utilized for novel translational and precision medicine applications to alleviate the public health burden of CRC.

IJCM_388A: Sleep disturbances and associated factors among undergraduate medical students in a tertiary health care center in central India: A cross sectional study

Background: Within the rigorous environment of medical education, the well-being of students is a critical concern. Sleep disturbances, a prevalent yet understudied issue, may pose significant challenges to the academics and mental health of medical students. Objective: To assess the sleep disturbances and associated factors among undergraduate medical students in a tertiary health care center. Methodology: A cross-sectional study was conducted among 256 undergraduate medical students in a tertiary health care center from September to November 2023. Study was conducted using self-administered questionnaire consisting of sociodemographic profile and standardized tool Pittsburgh Sleep Quality Index. Descriptive and analytical statistics were used. Results: Out of 256 students,161(62.9%) were suffering from sleep disturbances.72% of them had less than 1 hour time gap between screen time and their regular sleep. There was a significant association between sleep disturbances and repeated worrying about lots of tests, illness during past month, felt isolated and feeling lost something in life. Conclusion: This study showed that majority of undergraduate medical students suffer from sleep disturbances. It becomes evident that fostering a supportive environment and promoting healthy sleep practices are indispensable for the overall wellbeing and success of medical students.

Shotgun Lipidomic Profiling of Sebum Lipids via Photocatalyzed Paternò-Büchi Reaction and Ion Mobility-Mass Spectrometry.

Sebum lipids are composed of nonpolar lipids, and they pose challenges for mass spectrometry-based analysis due to low ionization efficiency and the existence of numerous isomers and isobars. To address these challenges, we have developed ethyl 2-oxo-2-(pyridine-3-yacetate as a charge-tagging Paternò-Büchi reagent and Michler's ketone as a highly efficient photocatalyst, achieving ∼90% conversion for C═C derivatization under 440 nm LED irradiation. This derivatization, when coupled with electrospray ionization-tandem mass spectrometry, boosts the detection of sebum lipids and pinpoints C═C location in a chain-specific fashion. Identification and quantitation of isomers are readily achieved for wax esters, a class of underexplored sebum lipids, which have C═C bonds distributed in fatty alcohol and fatty acyl chains. A shotgun analysis workflow has been developed by pairing the offline PB derivatization with cyclic ion mobility spectrometry-mass spectrometry. Besides the dominant n-10 C═C location in unsaturated wax esters, profiling of low abundance isomers, including the rarely reported n-7 and n-13 locations, is greatly enhanced due to separations of C═C diagnostic ions by ion mobility. Over 900 distinct lipid structures from human sebum lipid extract have been profiled at the chain-specific C═C level, including wax esters (500), glycerolipids (393), and cholesterol esters (22), far more exceeding previous reports. Overall, we have developed a fast and comprehensive lipidomic profiling tool for sebum samples, a type of noninvasive biofluids holding potential for the discovery of disease markers in distal organs.

Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization

The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily on well-constructed datasets. This paper addresses the critical DDoS dataset creation and evaluation domain, focusing on the cloud network. After conducting an in-depth analysis of 16 publicly available datasets, this research identifies 15 shortcomings across various dimensions, emphasizing the need for a new approach to dataset creation. Building upon this understanding, this paper introduces a new public DDoS dataset named BCCC-cPacket-Cloud-DDoS-2024. This dataset is meticulously crafted, addressing challenges identified in previous datasets through a cloud infrastructure featuring over eight benign user activities and 17 DDoS attack scenarios. Also, a Benign User Profiler (BUP) tool has been designed and developed to generate benign user network traffic based on a normal user behavior profile. We manually label the dataset and extract over 300 features from the network and transport layers of the traffic flows using NTLFlowLyzer. The experimental phase involves identifying an optimal feature set using three distinct algorithms: ANOVA, information gain, and extra tree. Finally, this paper proposes a multi-layered DDoS detection model and evaluates its performance using the generated dataset to cover the main issues of the traditional approaches.