scQTLbase: an integrated human single-cell eQTL database.

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with diseases and traits. However, the functional interpretation of these variants remains challenging. Expression quantitative trait loci (eQTLs) have been widely used to identify mutations linked to disease, yet they explain only 20-50% of disease-related variants. Single-cell eQTLs (sc-eQTLs) studies provide an immense opportunity to identify new disease risk genes with expanded eQTL scales and transcriptional regulation at a much finer resolution. However, there is no comprehensive database dedicated to single-cell eQTLs that users can use to search, analyse and visualize them. Therefore, we developed the scQTLbase (http://bioinfo.szbl.ac.cn/scQTLbase), the first integrated human sc-eQTLs portal, featuring 304 datasets spanning 57 cell types and 95 cell states. It contains ∼16 million SNPs significantly associated with cell-type/state gene expression and ∼0.69 million disease-associated sc-eQTLs from 3 333 traits/diseases. In addition, scQTLbase offers sc-eQTL search, gene expression visualization in UMAP plots, a genome browser, and colocalization visualization based on the GWAS dataset of interest. scQTLbase provides a one-stop portal for sc-eQTLs that will significantly advance the discovery of disease susceptibility genes.

xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs.

Genome-wide association studies (GWAS) have identified thousands of disease-associated non-coding variants, posing urgent needs for functional interpretation. Molecular Quantitative Trait Loci (xQTLs) such as eQTLs serve as an essential intermediate link between these non-coding variants and disease phenotypes and have been widely used to discover disease-risk genes from many population-scale studies. However, mining and analyzing the xQTLs data presents several significant bioinformatics challenges, particularly when it comes to integration with GWAS data. Here, we developed xQTLbiolinks as the first comprehensive and scalable tool for bulk and single-cell xQTLs data retrieval, quality control and pre-processing from public repositories and our integrated resource. In addition, xQTLbiolinks provided a robust colocalization module through integration with GWAS summary statistics. The result generated by xQTLbiolinks can be flexibly visualized or stored in standard R objects that can easily be integrated with other R packages and custom pipelines. We applied xQTLbiolinks to cancer GWAS summary statistics as case studies and demonstrated its robust utility and reproducibility. xQTLbiolinks will profoundly accelerate the interpretation of disease-associated variants, thus promoting a better understanding of disease etiologies. xQTLbiolinks is available at https://github.com/lilab-bioinfo/xQTLbiolinks.

ipaQTL-atlas: an atlas of intronic polyadenylation quantitative trait loci across human tissues.

Functional interpretation of disease-associated non-coding variants remains a significant challenge in the post-GWAS era. Our recent study has identified 3'UTR alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) and connects APA events with QTLs as a major driver of human traits and diseases. Besides 3'UTR, APA events can also occur in intron regions, and increasing evidence has connected intronic polyadenylation with disease risk. However, systematic investigation of the roles of intronic polyadenylation in human diseases remained challenging due to the lack of a comprehensive database across a variety of human tissues. Here, we developed ipaQTL-atlas (http://bioinfo.szbl.ac.cn/ipaQTL) as the first comprehensive portal for intronic polyadenylation. The ipaQTL-atlas is based on the analysis of 15 170 RNA-seq data from 838 individuals across 49 Genotype-Tissue Expression (GTEx v8) tissues and contains ∼0.98 million SNPs associated with intronic APA events. It provides an interface for ipaQTLs search, genome browser, boxplots, and data download, as well as the visualization of GWAS and ipaQTL colocalization results. ipaQTL-atlas provides a one-stop portal to access intronic polyadenylation information and could significantly advance the discovery of APA-associated disease susceptibility genes.

CRISPR-iPAS: a novel dCAS13-based method for alternative polyadenylation interference.

Alternative polyadenylation (APA) plays an important role in gene regulation. With the recent application of novel sequencing technology in APA profiling, an ever-increasing number of APA genes/sites have been identified. However, the phenotypic relevance of most of these APA isoforms remains elusive, which is largely due to the lack of a convenient genetics tool for APA interference. To address this issue, herein, an efficient method is developed based on the CRISPR-dCas13 system, termed as CRISPR-iPAS. Out of eight different dCas13 proteins, Porphyromonas gulae (Pgu) dCas13b, is identified as the most effective one in blocking the usage of the polyadenylation site (PAS). With guide RNAs targeting at core regulatory elements, dPguCas13b enabled APA regulation of endogenous genes with different APA types, including tandem 3'UTR, alternative terminal exon, as well as intronic PAS. Finally, we demonstrated that the proposed APA perturbation tool could be used to investigate the functional relevance of APA isoforms.

Genome-Wide Identification of the TGA Gene Family and Expression Analysis under Drought Stress in Brassica napus L.

TGA transcription factors belong to Group D of the bZIP transcription factors family and play vital roles in the stress response of plants. Brassica napus is an oil crop with rich economic value. However, a systematic analysis of TGA gene family members in B. napus has not yet been reported. In this study, we identified 39 full-length TGA genes in B. napus, renamed TGA1~TGA39. Thirty-nine BnTGA genes were distributed on 18 chromosomes, mainly located in the nucleus, and differences were observed in their 3D structures. Phylogenetic analysis showed that 39 BnTGA genes could be divided into five groups. The BnTGA genes in the same group had similar structure and motif compositions, and all the BnTGA genes had the same conserved bZIP and DOG1 domains. Phylogenetic and synteny analysis showed that the BnTGA genes had a close genetic relationship with the TGA genes of the Brassica juncea, and BnTGA11 and BnTGA29 may play an important role in evolution. In addition, qRT-PCR revealed that three genes (BnTGA14/17/23) showed significant changes in eight experimental materials after drought treatment. Meanwhile, it can be inferred from the results of drought treatment on different varieties of rapeseed that the stress tolerance of parental rapeseed can be transmitted to the offspring through hybridization. In short, these findings have promoted the understanding of the B. napus TGA gene family and will contribute to future research aimed at B. napus resistant breeding.

Enhanced Terahertz Fingerprint Sensing Mechanism Study of Tiny Molecules Based on Tunable Spoof Surface Plasmon Polaritons on Composite Periodic Groove Structures.

Highly sensitive detection of enhanced terahertz (THz) fingerprint absorption spectrum of trace-amount tiny molecules is essential for biosensing. THz surface plasmon resonance (SPR) sensors based on Otto prism-coupled attenuated total reflection (OPC-ATR) configuration have been recognized as a promising technology in biomedical detection applications. However, THz-SPR sensors based on the traditional OPC-ATR configuration have long been associated with low sensitivity, poor tunability, low refractive index resolution, large sample consumption, and lack of fingerprint analysis. Here, we propose an enhanced tunable high-sensitivity and trace-amount THz-SPR biosensor based on a composite periodic groove structure (CPGS). The elaborate geometric design of the spoof surface plasmon polaritons (SSPPs) metasurface increases the number of electromagnetic hot spots on the surface of the CPGS, improves the near-field enhancement effect of SSPPs, and enhances the interaction between THz wave and the sample. The results show that the sensitivity (S), figure of merit (FOM) and Q-factor (Q) can be increased to 6.55 THz/RIU, 4234.06 1/RIU and 629.28, respectively, when the refractive index range of the sample to measure is between 1 and 1.05 with the resolution 1.54×10-5 RIU. Moreover, by making use of the high structural tunability of CPGS, the best sensitivity (SPR frequency shift) can be obtained when the resonant frequency of the metamaterial approaches the biological molecule oscillation. These advantages make CPGS a strong candidate for the high-sensitivity detection of trace-amount biochemical samples.

Periodic Heterogeneous Surface with Superhydrophilic/Superhydrophobic Stripes Processed by a Picosecond Laser.

Heterogeneous surface with superhydrophilic/superhydrophobic stripes (HS-s/sS) has great practical significance, which can be used in fuel cell water management, condensation heat transfer enhancement, underwater drag reduction. Herein, a fast and simple method for uniform HS-s/sS on several mesh materials, including copper, stainless steel, and nickel, is achieved by using picosecond (ps) laser line-by-line scanning. Note that the scanning period between the lines is kept constant during processing, the HS-s/sS is formed by self-organized, while the similar structure cannot be processed on solid metal surfaces using the same parameters. The processing parameters, including scanning speed, defocus amount (DA), scanning period, and single pulse energy are systematically investigated to optimize HS-s/sS fabrication. It is found that the period of processed stripe on the mesh material is ∼1 mm, which is much larger than the scanning period. Interestingly, the as-prepared mesh surface show superhydrophobicity in the convex striped surface and superhydrophilicity in concave striped parts. The scanning electron microscopy results show that the structures on convex stripe are mainly composed of disordered hill-like structures, while the structures on the concave stripe mainly consist of periodic nanostripe structures. Moreover, the proportion of oxygen on the convex stripe is obviously higher than that on the concave stripe. The underlying mechanism of the HS-s/sS formation can be attributed to the interference between surface phonon polaritons (SPP) and the incident picosecond laser, as well as surface shock wave caused by the picosecond laser. We believe that such functional surfaces will be promising candidates for controlling liquid motion and fluid diversion processes.

Pan-tissue analysis of allelic alternative polyadenylation suggests widespread functional regulation.

Alternative polyadenylation (APA) is a major layer of gene regulation. However, it has recently been argued that most APA represents molecular noise. To clarify their functional relevance and evolution, we quantified allele-specific APA patterns in multiple tissues from an F1 hybrid mouse. We found a clearly negative correlation between gene expression and APA diversity for the 2,866 genes (24.9%) with a dominant polyadenylation site (PAS) usage above or equal to 90%, suggesting that their other PASs represent molecular errors. Among the remaining genes with multiple PASs, 3,971 genes (34.5%) express two or more isoforms with potentially functional importance. Interestingly, the genes with potentially functional minor PASs specific to neuronal tissues often express two APA isoforms with distinct subcellular localizations. Furthermore, our analysis of cis-APA divergence shows its pattern across tissues is distinct from that of gene expression. Finally, we demonstrate that the relative usage of alternative PASs is not only affected by their cis-regulatory elements, but also by potential coupling between transcriptional and APA regulation as well as competition kinetics between alternative sites.

Enhancing Performance of Reservoir Computing System Based on Coupled MEMS Resonators.

Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.

Modeling the Piezoelectric Cantilever Resonator with Different Width Layers.

The piezoelectric cantilever resonator is used widely in many fields because of its perfect design, easy-to-control process, easy integration with the integrated circuit. The tip displacement and resonance frequency are two important characters of the piezoelectric cantilever resonator and many models are used to characterize them. However, these models are only suitable for the piezoelectric cantilever with the same width layers. To accurately characterize the piezoelectric cantilever resonators with different width layers, a novel model is proposed for predicting the tip displacement and resonance frequency. The results show that the model is in good agreement with the finite element method (FEM) simulation and experiment measurements, the tip displacement error is no more than 6%, the errors of the first, second, and third-order resonance frequency between theoretical values and measured results are 1.63%, 1.18%, and 0.51%, respectively. Finally, a discussion of the tip displacement of the piezoelectric cantilever resonator when the second layer is null, electrode, or silicon oxide (SiO2) is presented, and the utility of the model as a design tool for specifying the tip displacement and resonance frequency is demonstrated. Furthermore, this model can also be extended to characterize the piezoelectric cantilever with n-layer film or piezoelectric doubly clamped beam.

Viaxl: A Solution of a Low-Cost Real-Time Visual Accelerometer Based on Laser Speckle Optical Flow Detection.

Non-contact and non-destructive acceleration measurement is receiving considerable attention due to their low cost, flexibility, and simplicity of implementation, as well as their excellent performance in some emerging applications such as medical electronics applications, vibration monitoring, and some other special scenarios. In this paper, a visual accelerometer system based on laser speckle optical flow detection named Viaxl is proposed. Compared with the conventional non-contact acceleration measurement method based on a laser system, Viaxl has moderate and stable performance with the advantages of low cost and simplicity of implementation. Experiment results demonstrate that Viaxl, which consists of a commercial camera and a low-cost laser pointer, can achieve real-time, non-contact acceleration measurement, and confirm the basic system performance of Viaxl: a measurement nonlinearity better than 1.3%, up to 31 dB signal-to-noise ratio, and 1150 Hz theoretic bandwidth; this demonstrates the huge potential of Viaxl in a wide range of applications, and provides a new possible technical method for non-contact acceleration detection.

WDSPdb: a database for WD40-repeat proteins.

WD40-repeat proteins, as one of the largest protein families, often serve as platforms to assemble functional complexes through the hotspot residues on their domain surfaces, and thus play vital roles in many biological processes. Consequently, it is highly required for researchers who study WD40 proteins and protein-protein interactions to obtain structural information of WD40 domains. Systematic identification of WD40-repeat proteins, including prediction of their secondary structures, tertiary structures and potential hotspot residues responsible for protein-protein interactions, may constitute a valuable resource upon this request. To achieve this goal, we developed a specialized database WDSPdb (http://wu.scbb.pkusz.edu.cn/wdsp/) to provide these details of WD40-repeat proteins based on our recently published method WDSP. The WDSPdb contains 63,211 WD40-repeat proteins identified from 3383 species, including most well-known model organisms. To better serve the community, we implemented a user-friendly interactive web interface to browse, search and download the secondary structures, 3D structure models and potential hotspot residues provided by WDSPdb.

MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge.

Reservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as memristors, and electrochemical reactions, among which the micro-electro-mechanical systems (MEMS) is supposed to be the closest to sensing and computing integration. While previous MEMS RCs have demonstrated their potential as reservoirs, the amplitude modulation mode was found to be inadequate for computing directly upon sensing. To achieve this objective, this paper introduces a novel MEMS reservoir computing system based on stiffness modulation, where natural signals directly influence the system stiffness as input. Under this innovative concept, information can be processed locally without the need for advanced data collection and pre-processing. We present an integrated RC system characterized by small volume and low power consumption, eliminating complicated setups in traditional MEMS RC for data discretization and transduction. Both simulation and experiment were conducted on our accelerometer. We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator. Consequently, our MEMS RC is capable of both classification and forecasting, surpassing the capabilities of our previous non-delay-based architecture. Our method successfully processed word classification, with a 99.8% accuracy, and chaos forecasting, with a 0.0305 normalized mean square error (NMSE), demonstrating its adaptability for multi-scene data processing. This work is essential as it presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing. Our approach has initiated edge computing, enabling emergent applications in MEMS for local computations.

Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches.

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.

Pan-cancer transcriptome analysis reveals widespread regulation through alternative tandem transcription initiation.

Abnormal transcription initiation from alternative first exon has been reported to promote tumorigenesis. However, the prevalence and impact of gene expression regulation mediated by alternative tandem transcription initiation were mostly unknown in cancer. Here, we developed a robust computational method to analyze alternative tandem transcription start site (TSS) usage from standard RNA sequencing data. Applying this method to pan-cancer RNA sequencing datasets, we observed widespread dysregulation of tandem TSS usage in tumors, many of which were independent of changes in overall expression level or alternative first exon usage. We showed that the dynamics of tandem TSS usage was associated with epigenomic modulation. We found that significant 5' untranslated region shortening of gene TIMM13 contributed to increased protein production, and up-regulation of TIMM13 by CRISPR-mediated transcriptional activation promoted proliferation and migration of lung cancer cells. Our findings suggest that dysregulated tandem TSS usage represents an addtional layer of cancer-associated transcriptome alterations.

A Modulation Method for Tunnel Magnetoresistance Current Sensors Noise Suppression.

To mitigate the impact of low-frequency noise from the tunnel magnetoresistance (TMR) current sensor and ambient stray magnetic fields on weak current detection accuracy, we propose a high-resolution modulation-demodulation test method. This method modulates and demodulates the measurement signal, shifting low-frequency noise to the high-frequency band for effective filtering, thereby isolating the target signal from the noise. In this study, we developed a Simulink model for the TMR current sensor modulation-demodulation test method. Practical time-domain and frequency-domain tests of the developed high-resolution modulation-demodulation method revealed that the TMR current sensor exhibits a nonlinearity as low as 0.045%, an enhanced signal-to-noise ratio (SNR) of 77 dB, and a heightened resolution of 100 nA. The findings indicate that this modulation-demodulation test method effectively reduces the impact of low-frequency noise on TMR current sensors and can be extended to other types of resistive devices.

A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study.

Alternative polyadenylation plays an important role in cancer initiation and progression; however, current transcriptome-wide association studies mostly ignore alternative polyadenylation when identifying putative cancer susceptibility genes. Here, we perform a pan-cancer 3' untranslated region alternative polyadenylation transcriptome-wide association analysis by integrating 55 well-powered (n > 50,000) genome-wide association studies datasets across 22 major cancer types with alternative polyadenylation quantification from 23,955 RNA sequencing samples across 7,574 individuals. We find that genetic variants associated with alternative polyadenylation are co-localized with 28.57% of cancer loci and contribute a significant portion of cancer heritability. We further identify 642 significant cancer susceptibility genes predicted to modulate cancer risk via alternative polyadenylation, 62.46% of which have been overlooked by traditional expression- and splicing- studies. As proof of principle validation, we show that alternative alleles facilitate 3' untranslated region lengthening of CRLS1 gene leading to increased protein abundance and promoted proliferation of breast cancer cells. Together, our study highlights the significant role of alternative polyadenylation in discovering new cancer susceptibility genes and provides a strong foundational framework for enhancing our understanding of the etiology underlying human cancers.

Analysis of the Thermally Induced Packaging Effects on the Frequency Drift of Micro-Electromechanical System Resonant Accelerometer.

Due to the working principle of MEMS resonant accelerometers, their thermally induced frequency drift is an inevitable practical issue for their extensive application. This paper is focused on reducing the thermally induced packaging effects on the frequency drift. A leadless ceramic chip carrier package with a stress-buffering layer was proposed for a MEMS resonant accelerometer, and the influences of packaging structure parameters on the frequency drift were investigated through finite element simulations and verified experimentally. Because of the thermal mismatch between dissimilar materials, the thermo-mechanical stress within the resonant beam leads to a change in the effective stiffness and causes the frequency drift to decrease linearly with increasing temperature. Furthermore, our investigations reveal that increasing the stress-buffering layer thickness and reducing the solder layer thickness can significantly minimize the thermo-mechanical stress within the resonant beam. As the neutral plane approaches the horizontal symmetry plane of the resonant beam when optimizing the packaging structure, the effects of the compressive and tensile stresses on the effective stiffness of the resonant beam will cancel each other out, which can dramatically reduce the frequency drift. These findings provide guidelines for packaging design through which to improve the temperature stability of MEMS resonant accelerometers.

Input-Output-Improved Reservoir Computing Based on Duffing Resonator Processing Dynamic Temperature Compensation for MEMS Resonant Accelerometer.

An MEMS resonant accelerometer is a temperature-sensitive device because temperature change affects the intrinsic resonant frequency of the inner silicon beam. Most classic temperature compensation methods, such as algorithm modeling and structure design, have large errors under rapid temperature changing due to the hysteresis of the temperature response of the accelerometer. To address this issue, we propose a novel reservoir computing (RC) structure based on a nonlinear silicon resonator, which is specifically improved for predicting dynamic information that is referred to as the input-output-improved reservoir computing (IOI-RC) algorithm. It combines the polynomial fitting with the RC on the input data mapping ensuring that the system always resides in the rich nonlinear state. Meanwhile, the output layer is also optimized by vector concatenation operation for higher memory capacity. Therefore, the new system has better performance in dynamic temperature compensation. In addition, the method is real-time, with easy hardware implementation that can be integrated with MEMS sensors. The experiment's result showed a 93% improvement in IOI-RC compared to raw data in a temperature range of -20-60 °C. The study confirmed the feasibility of RC in realizing dynamic temperature compensation precisely, which provides a potential real-time online temperature compensation method and a sensor system with edge computing.

A Scale Factor Calibration Method for MEMS Resonant Accelerometers Based on Virtual Accelerations.

This paper presents a scale factor calibration method based on virtual accelerations generated by electrostatic force. This method uses a series of voltage signals to simulate the inertial forces caused by the acceleration input, rather than frequent and laborious calibrations with high-precision instruments. The error transfer model of this method is systematically analyzed, and the geometrical parameters of this novel micromachined resonant accelerometer (MRA) are optimized. The experimental results demonstrate that, referring to the traditional earth's gravitational field tumble calibration method, the error of the scale factor calibration is 0.46% within ±1 g by using our method. Moreover, the scale factor is compensated by virtual accelerations. After compensation, the maximum temperature drift of the scale factor decreases from 2.46 Hz/g to 1.02 Hz/g, with a temperature range from 40 °C to 80 °C.