A novel formula for predicting the ultimate compressive strength of the cylindrically curved plates

The present study aims to develop an empirical formula to predict the ultimate compressive strength of unstiffened cylindrically curved plates. Drawing from an extensive analysis of 400 unique curved plate scenarios under longitudinal compression, we investigated critical parameters: the flank angle (denoted as ɵ), plate aspect ratio (denoted as a/b), and plate slenderness ratio (denoted as β). The ANSYS Nonlinear Finite Element Method (NLFEM) was employed to assess each scenario, considering the average level of initial imperfections (denoted as 0.1β2t) and configurations of one-bay and one-span. It is important to note that the models were designed without accounting for the effects of residual stresses. The simulation data generated from this analysis served as the foundation for developing our empirical formula. The proposed formula strongly agreed with the numerical simulations and experimental test results. This research provides structural engineers with a reliable predictive tool, aiding in more accurate predictions of the ultimate limit state (ULS) of curved plates during early design phases

An integrative approach for exploring the nature of fibroepithelial neoplasms.

BACKGROUND: Malignant phyllodes tumour (MPT) is a rare breast malignancy with epithelial and mesenchymal features. Currently, there are no appropriate research models or effective targeted therapeutic approaches for MPT. METHODS: We collected fresh frozen tissues from nine patients with MPT and performed whole-exome and RNA sequencing. Additionally, we established patient-derived xenograft (PDX) models from patients with MPT and tested the efficacy of targeting dysregulated pathways in MPT using the PDX model from one MPT. RESULTS: MPT has unique molecular characteristics when compared to breast cancers of epithelial origin and can be classified into two groups. The PDX model derived from one patient with MPT showed that the mouse epithelial component increased during tumour growth. Moreover, targeted inhibition of platelet-derived growth factor receptor (PDGFR) and phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) by imatinib mesylate and PKI-587 showed in vivo tumour suppression effects. CONCLUSIONS: This study revealed the molecular profiles of MPT that can lead to molecular classification and potential targeted therapy, and suggested that the MPT PDX model can be a useful tool for studying the pathogenesis of fibroepithelial neoplasms and for preclinical drug screening to find new therapeutic strategies for MPT

Evaluating the Effectiveness of Machine Learning in Storage Systems: Are We Taking the Right Direction?

The rapid advancements in technology have led to a significant increase in the amount of data being generated and stored. This has created a greater demand for storage systems that are efficient and reliable, especially in critical sectors like finance and healthcare. However, traditional storage solutions are struggling to keep up with the growing data volumes and the need for real-time data processing. To tackle these challenges, machine learning techniques have emerged as a promising approach to improve storage systems. Storage vendors have widely adopted these techniques to enhance their offerings. However, as we evaluate the various ways machine learning can be applied to storage, we've noticed a gap in how effectively it aligns with business objectives. In this presentation, we aim to address this gap by suggesting additional approaches that can complement existing solutions in a non-disruptive manner. Our talk focuses on three key areas. First, we delve into the philosophical aspect of machine learning interpretability. We ponder whether it is truly necessary to have a complete understanding of how black box models make decisions. Second, we explore the importance of counterfactual reasoning and what-if scenarios, particularly in risk-sensitive systems like storage security. We discuss how leveraging causal inference can provide a more comprehensive and informed perspective, leading to better decision-making processes. Finally, we introduce the concept of algorithm-agnostic uncertainty quantification using the conformal prediction framework. This framework acts as a wrapper around any machine learning model and can quantify the reliability of individual predictions, a feature that is currently lacking in most models. Throughout the presentation, we showcase the effectiveness of these focus areas through real-life storage use cases such as data security, intelligent tiering, anomaly detection, and predictive maintenance. Our goal is to empower the audience to apply machine learning techniques in their own industry settings. We provide practical insights and guidance that will enable industry professionals to navigate the ever-changing landscape of storage systems, contributing to a better and more progressive storage community

Machine Learning in Chaos-Based Encryption: Theory, Implementations, and Applications

Chaos-based encryption is a promising approach to secure communication due to its complexity and unpredictability. However, various challenges lie in the design and implementation of efficient, low-power, attack-resistant chaos-based encryption schemes with high encryption and decryption rates. In addition, Machine learning (ML) has emerged as a promising tool for enhancing the growing security and efficiency concerns and maximizing the potential of emerging computing platforms across diverse domains. With the rapid advancements in technology and the increasing complexity of computing systems, ML offers a unique approach to addressing security challenges and optimizing performance. This paper presents a comprehensive study on the application of ML techniques to secure chaotic communication for wearable devices, with an emphasis on chaos-based encryption. The theoretical foundations of ML for secure chaotic communication are discussed, including the use of ML algorithms for signal synchronization, noise reduction, and encryption. Various ML algorithms, such as deep neural networks, support vector machines, decision trees, and ensemble learning methods, are explored for designing chaos-based encryption algorithms. This paper places a greater emphasis on methodological aspects, metrics, and performance evaluation of machine learning algorithms. In addition, the paper presents an in-depth investigation into state-of-the-art ML-assisted defense and attacks on chaos-based encryption schemes, covering their theoretical foundations and practical implementations. Furthermore, a review of the potential advantages and limitations associated with the utilization of ML techniques in secure communication systems and encryption is provided. The study extends to exploring the diverse range of applications that can benefit from ML-assisted encryption, such as secure communication in the Internet of Things (IoTs), cloud computing, and wireless networks. Overall, we provide insights into the applications of ML for secure chaotic communication in wearable devices, its challenges, and opportunities, offering a foundation for further research and development and facilitating advancements in the field of secure chaotic communication

Molecular characterization of human respiratory syncytial virus in Seoul, South Korea, during 10 consecutive years, 2010-2019.

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections and hospitalization in infants and young children. Here, we analyzed the genetic diversity of RSV using partial G gene sequences in 84 RSV-A and 78 RSV- B positive samples collected in Seoul, South Korea, for 10 consecutive years, from 2010 to 2019. Our phylogenetic analysis revealed that RSV-A strains were classified into either the ON1 (80.9%) or NA1 (19.0%) genotypes. On the other hand, RSV-B strains demonstrated diversified clusters within the BA genotype. Notably, some sequences designated as BA-SE, BA-SE1, and BA-DIS did not cluster with previously identified BA genotypes in the phylogenetic trees. Despite this, they did not meet the criteria for the assignment of a new genotype based on recent classification methods. Selection pressure analysis identified three positive selection sites (amino acid positions 273, 274, and 298) in RSV-A, and one possible positive selection site (amino acid position 296) in RSV-B, respectively. The mean evolutionary rates of Korean RSV-A from 1999 to 2019 and RSV-B strains from 1991 and 2019 were estimated at 3.51 × 10-3 nucleotides (nt) substitutions/site/year and 3.32 × 10-3 nt substitutions/site/year, respectively. The population dynamics in the Bayesian skyline plot revealed fluctuations corresponding to the emergence of dominant strains, including a switch of the dominant genotype from NA1 to ON1. Our study on time-scaled cumulative evolutionary analysis contributes to a better understanding of RSV epidemiology at the local level in South Korea

Molecular characterization of human respiratory syncytial virus in Seoul, South Korea, during 10 consecutive years, 2010–2019

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections and hospitalization in infants and young children. Here, we analyzed the genetic diversity of RSV using partial G gene sequences in 84 RSV-A and 78 RSV- B positive samples collected in Seoul, South Korea, for 10 consecutive years, from 2010 to 2019. Our phylogenetic analysis revealed that RSV-A strains were classified into either the ON1 (80.9%) or NA1 (19.0%) genotypes. On the other hand, RSV-B strains demonstrated diversified clusters within the BA genotype. Notably, some sequences designated as BA-SE, BA-SE1, and BA-DIS did not cluster with previously identified BA genotypes in the phylogenetic trees. Despite this, they did not meet the criteria for the assignment of a new genotype based on recent classification methods. Selection pressure analysis identified three positive selection sites (amino acid positions 273, 274, and 298) in RSV-A, and one possible positive selection site (amino acid position 296) in RSV-B, respectively. The mean evolutionary rates of Korean RSV-A from 1999 to 2019 and RSV-B strains from 1991 and 2019 were estimated at 3.51 × 10−3 nucleotides (nt) substitutions/site/year and 3.32 × 10−3 nt substitutions/site/year, respectively. The population dynamics in the Bayesian skyline plot revealed fluctuations corresponding to the emergence of dominant strains, including a switch of the dominant genotype from NA1 to ON1. Our study on time-scaled cumulative evolutionary analysis contributes to a better understanding of RSV epidemiology at the local level in South Korea

MicroRNA Expression Profiles in Gastric Carcinogenesis

Abstract Intestinal-type gastric carcinoma exhibits a multistep carcinogenic sequence from adenoma to carcinoma with a gradual increase in genomic alterations. But the roles of microRNAs (miRNA) in this multistage cascade are not fully explored. To identify differentially expressed miRNA (DEM) during early gastric carcinogenesis, we performed miRNA microarray profiling with 24 gastric cancers and precursor lesions (7 early gastric cancer [EGC], 3 adenomas with high-grade dysplasia, 4 adenomas with low-grade dysplasia, and 10 adjacent normal tissues). Alterations in the expression of 132 miRNA were detected; these were categorized into three groups based on their expression patterns. Of these, 42 miRNAs were aberrantly expressed in EGC. Five miRNA (miR-26a, miR-375, miR-574-3p, miR-145, and miR-15b) showed decreased expression since adenoma. Expression of two miRNA, miR-200C and miR-29a, was down-regulated in EGCs compared to normal mucosa or adenomas. Six miRNA (miR-601, miR-107, miR-18a, miR-370, miR-300, and miR-96) showed increased expression in gastric cancer compared to normal or adenoma samples. Five representative miRNAs were further validated with RT-qPCR in independent 77 samples. Taken together, these results suggest that the dysregulated miRNA show alterations at the early stages of gastric tumorigenesis and may be used as a candidate biomarker

Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells

Although RNA-guided genome editing via the CRISPR-Cas9 system is now widely used in biomedical research, genome-wide target specificities of Cas9 nucleases remain controversial. Here we present Digenome-seq, in vitro Cas9-digested whole-genome sequencing, to profile genome-wide Cas9 off-target effects in human cells. This in vitro digest yields sequence reads with the same 5' ends at cleavage sites that can be computationally identified. We validated off-target sites at which insertions or deletions were induced with frequencies below 0.1%, near the detection limit of targeted deep sequencing. We also showed that Cas9 nucleases can be highly specific, inducing off-target mutations at merely several, rather than thousands of, sites in the entire genome and that Cas9 off-target effects can be avoided by replacing 'promiscuous' single guide RNAs (sgRNAs) with modified sgRNAs. Digenome-seq is a robust, sensitive, unbiased and cost-effective method for profiling genome-wide off-target effects of programmable nucleases including Cas9.

Digenome-seq: Genome-wide profiling of CRISPR-Cas9 off-target effects in human cells

Although RNA-guided genome editing via the CRISPR-Cas9 system is now widely used in biomedical research, genome-wide target specificities of Cas9 nucleases remain controversial. Here we present Digenome-seq, in vitro Cas9-digested whole-genome sequencing, to profile genome-wide Cas9 off-target effects in human cells. This in vitro digest yields sequence reads with the same 5â €2 ends at cleavage sites that can be computationally identified. We validated off-target sites at which insertions or deletions were induced with frequencies below 0.1%, near the detection limit of targeted deep sequencing. We also showed that Cas9 nucleases can be highly specific, inducing off-target mutations at merely several, rather than thousands of, sites in the entire genome and that Cas9 off-target effects can be avoided by replacing 'promiscuous' single guide RNAs (sgRNAs) with modified sgRNAs. Digenome-seq is a robust, sensitive, unbiased and cost-effective method for profiling genome-wide off-target effects of programmable nucleases including Cas912692751sciescopu