Spatial clustering of array CGH features in combination with hierarchical multiple testing

We propose a new approach for clustering DNA features using array CGH data from multiple tumor samples. We distinguish data-collapsing: joining contiguous DNA clones or probes with extremely similar data into regions, from clustering: joining contiguous, correlated regions based on a maximum likelihood principle. The model-based clustering algorithm accounts for the apparent spatial patterns in the data. We evaluate the randomness of the clustering result by a cluster stability score in combination with cross-validation. Moreover, we argue that the clustering really captures spatial genomic dependency by showing that coincidental clustering of independent regions is very unlikely. Using the region and cluster information, we combine testing of these for association with a clinical variable in an hierarchical multiple testing approach. This allows for interpreting the significance of both regions and clusters while controlling the Family-Wise Error Rate simultaneously. We prove that in the context of permutation tests and permutation-invariant clusters it is allowed to perform clustering and testing on the same data set. Our procedures are illustrated on two cancer data sets

Collaboration Management System between the Device based on Machine Socialization

The basis of IoT is in the interconnection and communication between different devices to achieve common goals through internet. These devices are interconnected through a network which enables communication within these devices without any direct human intervention. But with such great potential, this technology reached a road-block due to incompatibility within various manufacturers of the same type of device and proprietary standards. I started this project with this problem in mind and I have created a brand and platform independent machine socialization device manager system. In this paper, to overcome the above mentioned problem, I have utilized micro controllers to connect to various existing device to solve the problem and propose a device to device communication with collaboration management. This technology is not restricted to usage in only the new network module enabled smart devices but also this can be used to operate the existing old (not smart) home appliances. Machine socialization was made possible with the use of XML, (an internet standard schema language) which we have used to gather device, task and relationship information of all the devices to show schema information

Observation of photon-pair generation in the normal group-velocity-dispersion regime with slight detuning from the pump wavelength

A fiber-based photon-pair source in the telecom C-band is suitable for quantum information science including quantum communications. Spontaneous four-wave mixing effects are known to create photon pairs that are slightly detuned from the pump wavelength only in the anomalous group-velocity-dispersion (GVD) regime. Here, we achieve high-quality photon-pair generation slightly detuned from the pump wavelength in the normal GVD regime through a dispersion shifted fiber, for the first time. The photon pairs in C-band exhibit strong temporal correlation with each other and excellent heralded anti-bunching property. This photon-pair generation scheme can be exploited as telecom-band quantum light sources for quantum information applications.11Ysciescopu

Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning

While many FSCIL studies have been undertaken, achieving satisfactory performance, especially during incremental sessions, has remained challenging. One prominent challenge is that the encoder, trained with an ample base session training set, often underperforms in incremental sessions. In this study, we introduce a novel training framework for FSCIL, capitalizing on the generalizability of the Contrastive Language-Image Pre-training (CLIP) model to unseen classes. We achieve this by formulating image-object-specific (IOS) classifiers for the input images. Here, an IOS classifier refers to one that targets specific attributes (like wings or wheels) of class objects rather than the image's background. To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session. From an FSCIL standpoint, our framework is structured to retain previous knowledge and swiftly adapt to new sessions without forgetting or overfitting. This considers the updatability of modules in each session and some tricks empirically found for fast convergence. Our approach consistently demonstrates superior performance compared to state-of-the-art methods across the miniImageNet, CIFAR100, and CUB200 datasets. Further, we provide additional experiments to validate our learned model's ability to achieve IOS classifiers. We also conduct ablation studies to analyze the impact of each module within the architecture.Comment: 8 pages, 4 figures, 4 table

A pan-cancer analysis of driver gene mutations, DNA methylation and gene expressions reveals that chromatin remodeling is a major mechanism inducing global changes in cancer epigenomes.

BACKGROUND: Recent large-scale cancer sequencing studies have discovered many novel cancer driver genes (CDGs) in human cancers. Some studies also suggest that CDG mutations contribute to cancer-associated epigenomic and transcriptomic alterations across many cancer types. Here we aim to improve our understanding of the connections between CDG mutations and altered cancer cell epigenomes and transcriptomes on pan-cancer level and how these connections contribute to the known association between epigenome and transcriptome. METHOD: Using multi-omics data including somatic mutation, DNA methylation, and gene expression data of 20 cancer types from The Cancer Genome Atlas (TCGA) project, we conducted a pan-cancer analysis to identify CDGs, when mutated, have strong associations with genome-wide methylation or expression changes across cancer types, which we refer as methylation driver genes (MDGs) or expression driver genes (EDGs), respectively. RESULTS: We identified 32 MDGs, among which, eight are known chromatin modification or remodeling genes. Many of the remaining 24 MDGs are connected to chromatin regulators through either regulating their transcription or physically interacting with them as potential co-factors. We identified 29 EDGs, 26 of which are also MDGs. Further investigation on target genes\u27 promoters methylation and expression alteration patterns of these 26 overlapping driver genes shows that hyper-methylation of target genes\u27 promoters are significantly associated with down-regulation of the same target genes and hypo-methylation of target genes\u27 promoters are significantly associated with up-regulation of the same target genes. CONCLUSION: This finding suggests a pivotal role for genetically driven changes in chromatin remodeling in shaping DNA methylation and gene expression patterns during tumor development