Effect of data normalization on fuzzy clustering of DNA microarray data

BACKGROUND: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. RESULTS: In this study we applied the fuzzy partitional clustering method known as Fuzzy C-Means (FCM) to overcome the limitations of hard clustering. To identify the effect of data normalization, we used three normalization methods, the two common scale and location transformations and Lowess normalization methods, to normalize three microarray datasets and three simulated datasets. First we determined the optimal parameters for FCM clustering. We found that the optimal fuzzification parameter in the FCM analysis of a microarray dataset depended on the normalization method applied to the dataset during preprocessing. We additionally evaluated the effect of normalization of noisy datasets on the results obtained when hard clustering or FCM clustering was applied to those datasets. The effects of normalization were evaluated using both simulated datasets and microarray datasets. A comparative analysis showed that the clustering results depended on the normalization method used and the noisiness of the data. In particular, the selection of the fuzzification parameter value for the FCM method was sensitive to the normalization method used for datasets with large variations across samples. CONCLUSION: Lowess normalization is more robust for clustering of genes from general microarray data than the two common scale and location adjustment methods when samples have varying expression patterns or are noisy. In particular, the FCM method slightly outperformed the hard clustering methods when the expression patterns of genes overlapped and was advantageous in finding co-regulated genes. Thus, the FCM approach offers a convenient method for finding subsets of genes that are strongly associated to a given cluster

Optimal Quantum State Estimation with Use of the No-Signaling Principle

A simple derivation of the optimal state estimation of a quantum bit was obtained by using the no-signaling principle. In particular, the no-signaling principle determines a unique form of the guessing probability independently of figures of merit, such as the fidelity or information gain. This proves that the optimal estimation for a quantum bit can be achieved by the same measurement for almost all figures of merit.Comment: 3 pages, 1 figur

Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification

Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samples are collected from a variety of electronic stethoscopes, which could potentially introduce biases into the trained models. When a significant distribution shift occurs within the test dataset or in a practical scenario, it can substantially decrease the performance. To tackle this issue, we introduce cross-domain adaptation techniques, which transfer the knowledge from a source domain to a distinct target domain. In particular, by considering different stethoscope types as individual domains, we propose a novel stethoscope-guided supervised contrastive learning approach. This method can mitigate any domain-related disparities and thus enables the model to distinguish respiratory sounds of the recording variation of the stethoscope. The experimental results on the ICBHI dataset demonstrate that the proposed methods are effective in reducing the domain dependency and achieving the ICBHI Score of 61.71%, which is a significant improvement of 2.16% over the baseline.Comment: accepted to ICASSP 202

Wide and Dual-Band MIMO Antenna with Omnidirectional and Directional Radiation Patterns for Indoor Access Points

A wide-band multiple-input multiple-output (MIMO) antenna with dual-band (2.4 and 5 GHz) operation is proposed for premium indoor access points (IAPs). Typically, an omni-directional pattern is used for dipole antennas and a directional radiation pattern is used for patch antennas. In this paper, both antenna types were used to compare their performance with that of the proposed 2 × 2 MIMO antenna. We simulated and measured the performance of the MIMO antenna, including the isolation, envelope correlation coefficient (ECC), mean effective gain (MEG) for the IAPs, and the throughput, in order to determine its communication quality. The performance of the antennas was analyzed according to the ECC and MEG. The proposed antenna has sufficient performance and excellent characteristics, making it suitable for IAPs. We analyzed the communication performance of wireless networks using the throughput data of a typical office environment. The network throughput of an 802.11n device was used for the comparison and was conducted according to the antenna type. The results showed that the values of the ECC, MEG, and the throughput have unique characteristics in terms of their directivity, antenna gains, isolation, etc. This paper also discusses the communication performance of various aspects of MIMO in multipath situations

Exposure to sound vibrations lead to transcriptomic, proteomic and hormonal changes in Arabidopsis

Sound vibration (SV) is considered as an external mechanical force that modulates plant growth and development like other mechanical stimuli (e.g., wind, rain, touch and vibration). A number of previous and recent studies reported developmental responses in plants tailored against SV of varied frequencies. This strongly suggests the existence of sophisticated molecular mechanisms for SV perception and signal transduction. Despite this there exists a huge gap in our understanding regarding the SV-mediated molecular alterations, which is a prerequisite to gain insight into SV-mediated plant development. Herein, we investigated the global gene expression changes in Arabidopsis thaliana upon treatment with five different single frequencies of SV at constant amplitude for 1 h. As a next step, we also studied the SV-mediated proteomic changes in Arabidopsis. Data suggested that like other stimuli, SV also activated signature cellular events, for example, scavenging of reactive oxygen species (ROS), alteration of primary metabolism, and hormonal signaling. Phytohormonal analysis indicated that SV-mediated responses were, in part, modulated by specific alterations in phytohormone levels; especially salicylic acid (SA). Notably, several touch regulated genes were also up-regulated by SV treatment suggesting a possible molecular crosstalk among the two mechanical stimuli, sound and touch. Overall, these results provide a molecular basis to SV triggered global transcriptomic, proteomic and hormonal changes in plant