Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

Background: This study investigated the potential link between blood pressure variability (BPV) and the incidence of aortic stenosis (AS) using Korean National Health Insurance Service data from 2002 to 2019. Methods: We collected annual systolic blood pressure variability (SBPV) measurements consisting of three consecutive blood pressure readings each year over three years. The obtained SBPV data was divided into five quantiles, with the highest quintile representing a high fluctuation of blood pressure. Results: Analyzing 9,341,629 individuals with a mean age of 40.7 years, the study found 3981 new AS diagnoses during an average 8.66-year follow-up. Independent predictors for AS included higher blood pressure levels and elevated systolic blood pressure variability (SBPV). The hazard ratios (HR) for different SBPV quintiles compared to the reference (1st quintile) were as follows: 2nd quintile HR 1.09 (p = 0.18), 3rd quintile HR 1.13 (p = 0.04), 4th quintile HR 1.13 (p = 0.04), and 5th quintile HR 1.39 (p < 0.001). Conclusion: Our findings suggest that both hypertension and high fluctuations in SBP during consecutive visits are associated with an increased risk of incident AS. These results emphasize the importance of blood pressure management and stability in the prevention of AS.

BACKGROUND: The use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of computer-aided diagnosis systems, often based on annotated medical radiology images. However, the lack of large annotated image datasets remains a major obstacle, as the annotation process is time-consuming and costly. This study aims to overcome this challenge by proposing an automated method for annotating a large database of medical radiology images based on their semantic similarity.
RESULTS: An automated, unsupervised approach is used to create a large annotated dataset of medical radiology images originating from the Clinical Hospital Centre Rijeka, Croatia. The pipeline is built by data-mining three different types of medical data: images, DICOM metadata and narrative diagnoses. The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation.
CONCLUSIONS: The results indicate that fusing the embeddings of all three data sources together provides the best results for the task of unsupervised clustering of large-scale medical data and leads to the most concise clusters. Hence, this work marks the initial step towards building a much larger and more fine-grained annotated dataset of medical radiology images.

Bipolar psychometric scales data are widely used in psychologic healthcare. Adequate psychological profiling benefits patients and saves time and costs. Grant funding depends on the quality of psychotherapeutic measures. Bipolar Likert scales yield compositional data because any order of magnitude of agreement towards an item assertion implies a complementary order of magnitude of disagreement. Using an isometric log-ratio (ilr) transformation the bivariate information can be transformed towards the real valued interval scale yielding unbiased statistical results increasing the statistical power of the Pearson correlation significance test if the Central Limit Theorem (CLT) of statistics is satisfied. In practice, however, the applicability of the CLT depends on the number of summands (i.e., the number of items) and the variance of the data generating process (DGP) of the ilr transformed data. Via simulation we provide evidence that the ilr approach also works satisfactory if the CLT is violated. That is, the ilr approach is robust towards extremely large or infinite variances of the underlying DGP increasing the statistical power of the correlation test. The study generalizes former results pointing out the universality and reliability of the ilr approach in psychometric big data analysis affecting psychometric health economics, patient welfare, grant funding, economic decision making and profits.

UNASSIGNED: To describe the demographics and clinical profile of patients with glaucoma presenting during the novel coronavirus (COVID-19) lockdown and unlock phases in India.
UNASSIGNED: This retrospective hospital-based comparative study included patients presenting between March 25, 2017, and March 31, 2021. All patients who presented with glaucoma disorders were included as cases. The demographic and clinical data of these glaucoma patients were collected using an electronic medical record system.
UNASSIGNED: Overall, 34,419 patients (mean 47 per day) diagnosed with glaucoma diseases presented to the network and were included for analysis. The mean age of the patients was 54.16 ± 18.74 years and most were male (n=21,140; 61.42%) from the urban region (n=12,871;37.4%). On categorizing based on the timeline of the COVID-19 pandemic, most of the patients presented pre-COVID-19 (n=29,122; 84.61%), followed by a minority (n=175; 0.51%) during the lockdown and the rest (n=5,122; 14.88%) during unlock phase. An increasing number of patients with secondary glaucoma (n=82; 46.86%) and presenting from the local intra-city (n=82; 46.86%) was seen during the lockdown. There was a 6.6-fold increase in neovascular glaucoma and a 2.7-fold increase in lens induced glaucoma during the lockdown phase ((p<0.001) for both). There was a significant increase in subjects in 4th decade (p<0.03) and a decrease in subjects in 7th decade (p<0.008) during the lockdown period.
UNASSIGNED: The presentation of patients with glaucoma disorders to the hospital is evolving due to the COVID-19 pandemic. The footfalls of patients during the unlock regained to two-thirds of the pre COVID-19 level. During the lockdown, the older patients were less, there was an increase in younger patients and those with secondary glaucoma, and the majority presenting from within the city.

BACKGROUND: Linkage errors that occur according to linkage levels can adversely affect the accuracy and reliability of analysis results. This study aimed to identify the differences in results according to personally identifiable information linkage level, sample size, and analysis methods through empirical analysis.
METHODS: The difference between the results of linkage in directly identifiable information (DII) and indirectly identifiable information (III) linkage levels was set as III linkage based on name, date of birth, and sex and DII linkage based on resident registration number. The datasets linked at each level were named as databaseIII (DBIII) and databaseDII (DBDII), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed.
RESULTS: The linkage rates for DBDII and DBIII were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was \"none\" to \"very little.\" With respect to cervical cancer that had a relatively small sample size, analysis of DBIII resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DBIII vs. 1.80 [95% CI: 1.18-2.73] in DBDII). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DBIII vs. 1.92 [95% CI: 1.70-2.17] in DBDII for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DBIII vs. 2.05 [95% CI: 1.67-2.52] in DBDII for the regional stage).
CONCLUSIONS: To prevent distortion of the analyses results in health and medical research, it is important to check that the patient population and sample size by each factor of interest (FOI) are sufficient when different data are linked using DBDII. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.

OBJECTIVE: We describe new curriculum materials for engaging secondary school students in exploring the \"big data\" in the NIH All of Us Research Program\'s Public Data Browser and the co-design processes used to collaboratively develop the materials. We also describe the methods used to develop and validate assessment items for studying the efficacy of the materials for student learning as well as preliminary findings from these studies.
METHODS: Secondary-level biology teachers from across the United States participated in a 2.5-day Co-design Summer Institute. After learning about the All of Us Research Program and its Data Browser, they collaboratively developed learning objectives and initial ideas for learning experiences related to exploring the Data Browser and big data. The Genetic Science Learning Center team at the University of Utah further developed the educators\' ideas. Additional teachers and their students participated in classroom pilot studies to validate a 22-item instrument that assesses students\' knowledge. Educators completed surveys about the materials and their experiences.
RESULTS: The \"Exploring Big Data with the All of Us Data Browser\" curriculum module includes 3 data exploration guides that engage students in using the Data Browser, 3 related multimedia pieces, and teacher support materials. Pilot testing showed substantial growth in students\' understanding of key big data concepts and research applications.
CONCLUSIONS: Our co-design process provides a model for educator engagement. The new curriculum module serves as a model for introducing secondary students to big data and precision medicine research by exploring diverse real-world datasets.

OBJECTIVE: To estimate the population-specific reference intervals (RIs) for neonatal thyroid stimulating hormone (TSH) in Pakistani neonates, utilising the refineR algorithm.
METHODS: Observational study. Place and Duration of the Study: Department of Pathology and Laboratory Medicine, The Aga Khan University Hospital, Karachi, Pakistan, from 17th May to 30th November 2023.
METHODS: A data mining analysis was conducted on serum TSH results of neonates (≤1 month) over a period of six years, following approval from the Institutional Ethical Review Committee. Two subgroups were assessed based on the age as 0 - 5 days and 6 - 30 days. The refineR algorithm was implemented using refineR package (version 1.0.0), ensuring accurate analysis and insights.
RESULTS: A total of non-duplicate 82,299 neonatal serum TSH tests were retrieved, including 70,788 (88%) aged 0 - 5 days and 11,511 (12%) aged ranging from 6 - 30 days. The estimated RI was from 0.67 µIU/mL (90% CI 0.641 - 0.72) to 15.0 µIU/mL (90% CI 13.2 - 17.3) for the first age group and 0.65 µIU/mL (90% CI 0.6 - 0.84) to 8.6 µIU/mL (90% CI 8.05 - 9.71) for the second age group.
CONCLUSIONS: Reference intervals for neonatal serum TSH of the Pakistani population were estimated, considering the genetic differences of this demographic in comparison to the Western population. Results aligned with global literature, validating the refineR indirect approach\'s applicability.
BACKGROUND: Reference intervals, Neonatal, Thyroid stimulating hormone, RefineR algorithm, Big data, Pakistan.

To address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future.

慢性鼻窦炎（CRS）的病因及发病机制尚未完全明确。目前多组学和大数据以其数据的多样性、易处理性等优势，已被用于探索多种疾病的发病机制和生物标志物等，多种高级机器学习方法也有助于完善CRS的精准诊疗服务，并在CRS的研究中取得了一定进展。然而，多组学数据网络的建设以及其研究成果的临床转化等，仍是制约该技术进一步发展的主要问题。本文对多组学和大数据驱动的CRS研究现状及其面临的机遇与挑战进行了论述。.