Too much data, but little inter-changeability: a lesson learned from mining public data on tissue specificity of gene expression

BACKGROUND: The tissue expression pattern of a gene often provides an important clue to its potential role in a biological process. A vast amount of gene expression data have been and are being accumulated in public repository through different technology platforms. However, exploitations of these rich data sources remain limited in part due to issues of technology standardization. Our objective is to test the data comparability between SAGE and microarray technologies, through examining the expression pattern of genes under normal physiological states across variety of tissues. RESULTS: There are 42–54% of genes showing significant correlations in tissue expression patterns between SAGE and GeneChip, with 30–40% of genes whose expression patterns are positively correlated and 10–15% of genes whose expression patterns are negatively correlated at a statistically significant level (p = 0.05). Our analysis suggests that the discrepancy on the expression patterns derived from technology platforms is not likely from the heterogeneity of tissues used in these technologies, or other spurious correlations resulting from microarray probe design, abundance of genes, or gene function. The discrepancy can be partially explained by errors in the original assignment of SAGE tags to genes due to the evolution of sequence databases. In addition, sequence analysis has indicated that many SAGE tags and Affymetrix array probe sets are mapped to different splice variants or different sequence regions although they represent the same gene, which also contributes to the observed discrepancies between SAGE and array expression data. CONCLUSION: To our knowledge, this is the first report attempting to mine gene expression patterns across tissues using public data from different technology platforms. Unlike previous similar studies that only demonstrated the discrepancies between the two gene expression platforms, we carried out in-depth analysis to further investigate the cause for such discrepancies. Our study shows that the exploitation of rich public expression resource requires extensive knowledge about the technologies, and experiment. Informatic methodologies for better interoperability among platforms still remain a gap. One of the areas that can be improved practically is the accurate sequence mapping of SAGE tags and array probes to full-length genes. REVIEWERS: This article was reviewed by Dr. I. King Jordan, Dr. Joel Bader, and Dr. Arcady Mushegian

A deep learning method for foot-type classification using plantar pressure images

Background: Flat foot deformity is a prevalent and challenging condition often leading to various clinical complications. Accurate identification of abnormal foot types is essential for appropriate interventions.Method: A dataset consisting of 1573 plantar pressure images from 125 individuals was collected. The performance of the You Only Look Once v5 (YOLO-v5) model, improved YOLO-v5 model, and multi-label classification model was evaluated for foot type identification using the collected images. A new dataset was also collected to verify and compare the models.Results: The multi-label classification algorithm based on ResNet-50 outperformed other algorithms. The improved YOLO-v5 model with Squeeze-and-Excitation (SE), the improved YOLO-v5 model with Convolutional Block Attention Module (CBAM), and the multilabel classification model based on ResNet-50 achieved an accuracy of 0.652, 0.717, and 0.826, respectively, which is significantly higher than those obtained using the ordinary plantar-pressure system and the standard YOLO-v5 model.Conclusion: These results indicate that the proposed DL-based multilabel classification model based on ResNet-50 is superior in flat foot type detection and can be used to evaluate the clinical rehabilitation status of patients with abnormal foot types and various foot pathologies when more data on patients with various diseases are available for training

The Cytoplasmic Domain of MUC1 Induces Hyperplasia in the Mammary Gland and Correlates with Nuclear Accumulation of β-Catenin

MUC1 is an oncoprotein that is overexpressed in up to 90% of breast carcinomas. A previous in vitro study by our group demonstrated that the cytoplasmic domain of MUC1 (MUC1-CD), the minimal functional unit of MUC1, contributes to the malignant phenotype in cells by binding directly to β-catenin and protecting β-catenin from GSK3β-induced degradation. To understand the in vivo role of MUC1-CD in breast development, we generated a MUC1-CD transgenic mouse model under the control of the MMTV promoter in a C57BL/6J background, which is more resistant to breast tumor. We show that the expression of MUC1-CD in luminal epithelial cells of the mammary gland induced a hyperplasia phenotype characterized by the development of hyper-branching and extensive lobuloalveoli in transgenic mice. In addition to this hyperplasia, there was a marked increase in cellular proliferation in the mouse mammary gland. We further show that MUC1-CD induces nuclear localization of β-catenin, which is associated with a significant increase of β-catenin activity, as shown by the elevated expression of cyclin D1 and c-Myc in MMTV-MUC1-CD mice. Consistent with this finding, we observed that overexpression of MUC1-C is associated with β-catenin nuclear localization in tumor tissues and increased expression of Cyclin D1 and c-Myc in breast carcinoma specimens. Collectively, our data indicate a critical role for MUC1-CD in the development of mammary gland preneoplasia and tumorigenesis, suggesting MUC1-CD as a potential target for the diagnosis and chemoprevention of human breast cancer

Lithium-Ion Polymer Battery for 12-Voltage Applications: Experiment, Modelling, and Validation

Modelling, simulation, and validation of the 12-volt battery pack using a 20 Ah lithium–nickel–manganese–cobalt–oxide cell is presented in this paper. The cell characteristics influenced by thermal effects are also considered in the modelling. The parameters normalized directly from a single cell experiment are foundations of the model. This approach provides a systematic integration of actual cell monitoring with a module model that contains four cells connected in series. The validated battery module model then is utilized to form a high fidelity 80 Ah 12-volt battery pack with 14.4 V nominal voltage. The battery cell thermal effectiveness and battery module management system functions are constructed in the MATLAB/Simulink platform. The experimental tests are carried out in an industry-scale setup with cycler unit, temperature control chamber, and computer-controlled software for battery testing. As the 12-volt lithium-ion battery packs might be ready for mainstream adoption in automotive starting–lighting–ignition (SLI), stop–start engine idling elimination, and stationary energy storage applications, this paper investigates the influence of ambient temperature and charging/discharging currents on the battery performance in terms of discharging voltage and usable capacity. The proposed simulation model provides design guidelines for lithium-ion polymer batteries in electrified vehicles and stationary electric energy storage applications

Mainlobe Deceptive Jammer Suppression Based on Quadratic Phase Coding in FDA-MIMO Radar

In this paper, the problem of mainlobe deceptive jammer suppression is solved with the frequency diversity array-multiple-input multiple-output (FDA-MIMO) radar system. At the modeling stage, based on the FDA-MIMO radar, a quadratic phase code (QPC) is applied along the slow time dimension in the transmit array. In the receiver, after decoding and principal range compensation, the true and false targets that are generated in an identical angle, can be discriminated in the joint transmit-receive-Doppler frequency domain. Particularly, the false targets are equivalently moved from the mainlobe to the sidelobes in the transmit spatial frequency domain. Then, by performing the data-dependent transmit-receive-Doppler three-dimensional beamforming, the false targets are suppressed owing to Doppler and range mismatches. Moreover, by moving the jammers to nulls in the Doppler frequency domain, the capability in terms of the maximum number of suppressible jammers can be strengthened with an appropriate coding coefficient and frequency increment. Numerical results can certify the suppression capability of the QPC-FDA-MIMO radar

Preparation of nitrilotriacetic acid/Co2+-linked, silicalboron-coated magnetite nanoparticles for purification of 6 x histidine-tagged proteins

In this report, we describe the preparation of novel nitrilotri acetic acid/Co2+-linked, silica/boron-coated magnetite nanoparticles for purification of 6 x His-tagged proteins. The nanoparticles were approximately 200 nm in size and were stable against hydrochloric acid and had negligible non-specific binding for protein. Elimination of non-specific binding by nucleic acids was readily achieved by digestion of samples with DNase and RNase. The modified nanoparticles were used to purify two model proteins: one had a C-terminal 6 x His tag, and the other had an internal 6 x His tag. Both proteins were purified within one hour into single band purity on sodium dodecyl sulfate-polyacrylamide electrophoresis gel. (c) 2006 Published by Elsevier B.V

Suppression of Mainlobe Jammers with Quadratic Element Pulse Coding in MIMO Radar

The problem of suppressing mainlobe deceptive jammers, which spoof radar systems by generating multiple false targets, has attracted widespread attention. To tackle this problem, in this paper, the multiple-input multiple-output (MIMO) radar system was utilized by applying a quadratic element phase code (QEPC) to the transmitted pulses of different elements. In the receiver, by utilizing the spatial frequency and Doppler frequency offset generated after decoding, the jammers were equivalently distributed in the sidelobes of the joint Doppler-transmit-receive domain and were distinguishable from the true target. Then, further spatial frequency compensation and Doppler compensation were performed to align the true target to the zero point in the transmit spatial and Doppler domains. Moreover, by designing appropriate coding coefficients, the jammers were suppressed by data-independent Doppler-transmit-receive three-dimensional beamforming. However, the beamforming performance was sensitive to angular estimation mismatches, resulting in performance degradation of jammer suppression. To this end, a center-boundary null-broadening control (CBNBC) approach was used to broaden the nulls in the equivalent beampattern by generating multiple artificial jammers with preset powers around the nulls. Thus, the false targets (FTs) with deviations were sufficiently suppressed in the broadened notches. Numerical simulations and theoretical analysis demonstrated the performance of the developed jammer suppression method

Effects of State-of-Charge and Penetration Location on Variations in Temperature and Terminal Voltage of a Lithium-Ion Battery Cell during Penetration Tests

The nail penetration test has been widely adopted as a battery safety test for reproducing internal short-circuits. In this paper, the effects of cell initial State-of-Charge (SOC) and penetration location on variations in cell temperature and terminal voltage during penetration tests are investigated. Three different initial SOCs (10%, 50%, and 90%) and three different penetration locations (one is at the center of the cell, the other two are close to the edge of the cell) are used in the tests. Once the steel cone starts to penetrate the cell, the cell terminal voltage starts to drop due to the internal short-circuit. The penetration tests with higher initial cell SOCs have larger cell surface temperature increases during the tests. Also, the penetration location always has the highest temperature increment during all penetration tests, which means the heat source is always at the penetration location. The absolute temperature increment at the penetration location is always higher when the penetration is close to the edge of the cell, compared to when the penetration is at the center of the cell. The heat generated at the edges of the cell is more difficult to dissipate. Additionally, a battery cell internal short-circuit model with different penetration locations is built in ANSYS Fluent, based on the specifications and experimental data of the tested battery cells. The model is validated with an acceptable discrepancy range by using the experimental data. Simulated data shows that the temperature gradually reduces from penetration locations to their surroundings. The gradients of the temperature distributions are much larger closer to the penetration locations. Overall, this paper provides detailed information on the temperature and terminal voltage variations of a lithium-ion polymer battery cell with large capacity and high power under penetration tests. The presented information can be used for assessing the safety of the onboard battery pack of electric vehicles