Discovering Dysfunction of Multiple MicroRNAs Cooperation in Disease by a Conserved MicroRNA Co-Expression Network

MicroRNAs, a new class of key regulators of gene expression, have been shown to be involved in diverse biological processes and linked to many human diseases. To elucidate miRNA function from a global perspective, we constructed a conserved miRNA co-expression network by integrating multiple human and mouse miRNA expression data. We found that these conserved co-expressed miRNA pairs tend to reside in close genomic proximity, belong to common families, share common transcription factors, and regulate common biological processes by targeting common components of those processes based on miRNA targets and miRNA knockout/transfection expression data, suggesting their strong functional associations. We also identified several co-expressed miRNA sub-networks. Our analysis reveals that many miRNAs in the same sub-network are associated with the same diseases. By mapping known disease miRNAs to the network, we identified three cancer-related miRNA sub-networks. Functional analyses based on targets and miRNA knockout/transfection data consistently show that these sub-networks are significantly involved in cancer-related biological processes, such as apoptosis and cell cycle. Our results imply that multiple co-expressed miRNAs can cooperatively regulate a given biological process by targeting common components of that process, and the pathogenesis of disease may be associated with the abnormality of multiple functionally cooperative miRNAs rather than individual miRNAs. In addition, many of these co-expression relationships provide strong evidence for the involvement of new miRNAs in important biological processes, such as apoptosis, differentiation and cell cycle, indicating their potential disease links

A 0.00426 mm2 77.6-dB Dynamic Range VCO-Based CTDSM for Multi-Channel Neural Recording

Driven by needs in neuroscientific research, future neural interface technologies demand integrated circuits that can record a large number of channels of neural signals in parallel while maintaining a miniaturized physical form factor. Using conventional methods, it is challenging to reduce circuit area while maintaining the high dynamic range, low noise, and low power consumption required in the neural application. This paper proposes to address this challenge using a VCO-based continuous-time delta-sigma modulator (CTDSM) circuit, which can record and digitize neural signals directly without the need for front-end instrumentation amplifiers and anti-aliasing filters, which are limited by the abovementioned circuit-area performance tradeoff. Thanks to the multi-level quantization and intrinsic mismatch-shaping capabilities of the VCO-based approach, the proposed first-order CTDSM can achieve comparable electrical performance to a higher-order CTDSM while offering further area and power reductions. We prototyped the circuit in a 22-channel test chip and demonstrate, based on the chip measurement results, that the proposed modulator occupies an area of 0.00426 mm2 while achieving input-referred noise levels of 6.26 and 3.54 µVrms in the action potential (AP) and local field potential (LFP) bands, respectively. With a 77.6 dB wide-dynamic range, the noise and total harmonic distortion meet the requirements of a neural interface with up to 149 mVpp input AC amplitude or up to ±68 mV DC offsets. We also validated the feasibility of the circuit for multi-channel recording applications by examining the impact of cross-channel VCO oscillation interferences on the circuit noise performance. The experimental results demonstrate the proposed architecture is an excellent candidate to implement future multi-channel neural-recording interfaces

A 0.00426 mm² 77.6-dB Dynamic Range VCO-Based CTDSM for Multi-Channel Neural Recording

Driven by needs in neuroscientific research, future neural interface technologies demand integrated circuits that can record a large number of channels of neural signals in parallel while maintaining a miniaturized physical form factor. Using conventional methods, it is challenging to reduce circuit area while maintaining the high dynamic range, low noise, and low power consumption required in the neural application. This paper proposes to address this challenge using a VCO-based continuous-time delta-sigma modulator (CTDSM) circuit, which can record and digitize neural signals directly without the need for front-end instrumentation amplifiers and anti-aliasing filters, which are limited by the abovementioned circuit-area performance tradeoff. Thanks to the multi-level quantization and intrinsic mismatch-shaping capabilities of the VCO-based approach, the proposed first-order CTDSM can achieve comparable electrical performance to a higher-order CTDSM while offering further area and power reductions. We prototyped the circuit in a 22-channel test chip and demonstrate, based on the chip measurement results, that the proposed modulator occupies an area of 0.00426 mm2 while achieving input-referred noise levels of 6.26 and 3.54 µVrms in the action potential (AP) and local field potential (LFP) bands, respectively. With a 77.6 dB wide-dynamic range, the noise and total harmonic distortion meet the requirements of a neural interface with up to 149 mVpp input AC amplitude or up to ±68 mV DC offsets. We also validated the feasibility of the circuit for multi-channel recording applications by examining the impact of cross-channel VCO oscillation interferences on the circuit noise performance. The experimental results demonstrate the proposed architecture is an excellent candidate to implement future multi-channel neural-recording interfaces

Trade based on alliance chain in energy from distributed photovoltaic grids

With the rapid development of distributed photovoltaic grids, more and more users join the power sales side, and the traditional power grid operation mode is no longer applicable. This paper analyzes the characteristics of the distributed photovoltaic grid under overload conditions, and further summarizes the problems that the distributed photovoltaic grid will face under these conditions. To solve these problems, the alliance chain technology was introduced into the distributed photovoltaic grid. At the same time, this paper establishes a photovoltaic pricing strategy that considers power transmission loss. Finally, the feasibility of the theory is verified by constructing a virtual model

Identification of Prognostic Gene Signatures by Developing a scRNA-Seq-Based Integration Approach to Predict Recurrence and Chemotherapy Benefit in Stage II–III Colorectal Cancer

Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II–III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chemotherapy benefits in drug-treated validation datasets. Additionally, they also performed well in patients with CMS4 subtype. Subsequent analysis of drug sensitivity showed that expressions of these two signatures were significantly associated with the sensitivities of CRC cell lines to multiple drugs. In summary, we proposed a novel prognostic biomarker identification approach, which could be used to identify novel prognostic markers for stage II–III CRC patients who will undergo adjuvant chemotherapy and facilitate their further personalized treatments

Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future

Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future