We present Ishtar, a power-efficient RISC-V baseband system-on-chip (SoC) tailored for multi-standard integrated sensing and communications (ISAC) in low-altitude wireless networks (LAWNs). Ishtar integrates a hierarchical scheduling scheme and a system-level power-gating architecture that dynamically controls power domains to balance performance and energy efficiency. It supports dynamic task scheduling across heterogeneous protocols using a domain-specific, graph-based representation. Implemented in 40 nm technology and running at 300 MHz, Ishtar achieves better normalized efficiency than state-of-the-art SDR SoCs, delivering real-time multi-standard sniffing under stringent power and area constraints.

Wireless baseband processing (WBP) is a key element of wireless communications, with a series of signal processing modules to improve data throughput and counter channel fading. Conventional hardware solutions, such as digital signal processors (DSPs) and more recently, graphic processing units (GPUs), provide various degrees of parallelism, yet they both fail to take into account the cyclical and consecutive character of WBP. Furthermore, the large amount of data in WBPs cannot be processed quickly in symmetric multiprocessors (SMPs) due to the unpredictability of memory latency. To address this issue, we propose a hierarchical dataflow-driven architecture to accelerate WBP. A pack-and-ship approach is presented under a non-uniform memory access (NUMA) architecture to allow the subordinate tiles to operate in a bundled access and execute manner. We also propose a multi-level dataflow model and the related scheduling scheme to manage and allocate the heterogeneous hardware resources. Experiment results demonstrate that our prototype achieves 2× and 2.3× speedup in terms of normalized throughput and single-tile clock cycles compared with GPU and DSP counterparts in several critical WBP benchmarks. Additionally, a link-level throughput of 288 Mbps can be achieved with a 45-core configuration.

Vector processing is crucial for boosting processor performance and efficiency, particularly with data-parallel tasks. The RISC-V ”V” Vector Extension (RVV) enhances algorithm efficiency by supporting vector registers of dynamic sizes and their grouping. Nevertheless, for very long vectors, the static number of RVV vector registers and its power-of-two grouping can lead to performance restrictions. To counteract this limitation, this work introduces Zoozve, a RISC-V vector instruction extension that eliminates the need for strip-mining. Zoozve allows for flexible vector register length and count configurations to boost data computation parallelism. With a data-adaptive register allocation approach, Zoozve permits any register groupings and accurately aligns vector lengths, cutting down register overhead and alleviating performance declines from strip-mining. Additionally, the paper details Zoozve’s compiler and hardware implementations using LLVM and SystemVerilog. Initial results indicate Zoozve yields a minimum 10.10× reduction in dynamic instruction count for fast Fourier transform (FFT), with a mere 5.2% increase in overall silicon area.

Autonomous driving technologies are swiftly evolving, characterized by two main strategies: Single-Vehicle Autonomous Driving (SVAD) and Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD). SVAD depends entirely on the vehicle’s internal sensors and processing capabilities, whereas VICAD benefits from a synergistic network combining roadside infrastructure, connected vehicles, and cloud services to boost safety and efficiency. Nevertheless, VICAD encounters challenges with high-bandwidth data transmission and perception latency. To mitigate these concerns, we introduce an innovative intelligent roadside unit (I-RSU) platform integrating perception, computing, and communication into one cohesive system. The platform features dual neural processing units (NPUs) for the effective extraction of images and LiDAR features, alongside a C-V2X communication module, all realized on a Field-Programmable Gate Array (FPGA). This setup minimizes latency and expenses by enabling computation near the sensors and facilitating selective data transmission. Our system also supports multi-modal fusion, enhancing overall perception and safety. Through extensive real-world trials and simulations, our system demonstrates a substantial reduction in end-to-end latency, providing a scalable solution for VICAD scenarios.

With the evolution of wireless communication systems, there is a growing demand for high reliability and low latency in channel coding, particularly in 5G and beyond wireless systems used in applications such as autonomous driving and remote medical services. For the decoding of polar codes, the multi-bit successive cancellation list (MSCL) decoding technique was recently introduced to decrease the decoding latency by decoding several short inner codes in parallel, which preserves high reliability compared to the conventional successive cancellation list (SCL) decoding. However, as parallelism increases, the complexity of the decoding path sorting also increases significantly, which makes it resource-intensive for hardware implementation. To address this issue, this paper proposes a configurable critical-set-based multi-bit successive cancellation list (CS-MSCL) decoding algorithm, which first introduces critical sets to the MSCL decoding for the optimization of path pruning. Subsequently, an enhanced CS-MSCL algorithm is introduced for large list-size MSCL decoding, which can boost the error correction performance. Then, an area-efficient decoding architecture is introduced, which supports the cyclic redundancy check (CRC) and the CS-MSCL decoding compatible with the 5G standard. The proposed decoder is implemented in SMIC 40 nm CMOS technology with a parallelism degree of 8, which has a peak area efficiency of$4.64~\mathrm {Gbps/mm^{2}}$for list size 4 and$2.01~\mathrm {Gbps/mm^{2}}$for list size 8. Compared to state-of-the-art SCL-based decoders, the normalized area efficiency is improved by 7.16% and 17.54% for list sizes 4 and 8, respectively.

Wireless baseband processing (WBP) serves as an ideal scenario for utilizing vector processing, which excels in managing data-parallel operations due to its parallel structure. However, conventional vector architectures face certain constraints such as limited vector register sizes, reliance on power-of-two vector length (VL) multipliers, and vector permutation capabilities tied to specific architectures. To address these challenges, we have introduced an instruction set extension (ISE) based on RISC-V known as unlimited vector processing (UVP). This extension enhances both the flexibility and efficiency of vector computations. UVP employs a novel programming model that supports non-power-of-two register groupings (RGs) and hardware strip mining, thus enabling smooth handling of vectors of varying lengths while reducing the software strip-mining burden. Vector instructions are categorized into symmetric and asymmetric classes, complemented by specialized load/store strategies to optimize execution. Moreover, we present a hardware implementation of UVP featuring sophisticated hazard detection mechanisms, optimized pipelines for symmetric tasks such as fixed-point multiplication and division, and a robust permutation engine for effective asymmetric operations. Comprehensive evaluations demonstrate that UVP significantly enhances performance, achieving up to$3.0\times $and$2.1\times $speedups in matrix multiplication and fast Fourier transform (FFT) tasks, respectively, when measured against lane-based vector architectures. Our synthesized register transfer level (RTL) for a 16-lane configuration using SMIC 40-nm technology spans 0.94 mm2and achieves an area efficiency of 21.2 GOPS/mm2.

The advent of single-cell transcriptomics has revolutionized our ability to analyze cellular heterogeneity and dynamics at a fine resolution, yet covering the vast array of potential perturbations remains challenging due to biological variability. To address this, we propose scCADE, a novel computational approach utilizing contrastive learning and an attention mechanism to decouple gene expression signatures and predict cellular responses to perturbations. scCADE excels in predicting responses in cells to perturbations observed in other cells but not yet seen in the target cells. Through rigorous ablation studies and validation across three datasets involving drug and gene editing perturbations, scCADE consistently outperformed existing methods, underscoring its efficacy and potential to advance genomics and personalized medicine by accurately forecasting responses to novel perturbations.

FIR (Finite Impulse Response) filters are widely used in digital communication systems, digital image processing, and many other fields. A great deal of research has been done on the flexible configuration of FIR filters, particularly on the dynamic adjustment of coefficients and orders. Existing FIR filter structures can be configured to a higher-order filter for a lower-order use, leading to low hardware utilization. This paper presents a dynamically configurable architecture for FIR filters based on a novel architecture with systolic arrays and serial multiply accumulators (MACs). This design can be configured to a higher-order filter or to several independent lower-order filters, thus increasing utilization. We demonstrate a 2048-order FIR filter that can be configured to a minimum of 16 orders and a maximum of 128 channels using only 256 multipliers and adders.

MOTIVATION: Somatic mutation co-occurrence has been proven to have a profound effect on tumorigenesis. While some studies have been conducted on co-mutations, a centralized resource dedicated to co-mutations in cancer is still lacking. RESULTS: Using multi-omics data from over 30 000 subjects and 1747 cancer cell lines, we present the Cancer co-mutation database (CoMutDB), the most comprehensive resource devoted to describing cancer co-mutations and their characteristics. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the online database CoMutDB: http://www.innovebioinfo.com/Database/CoMutDB/Home.php.

MOTIVATION: As an important player in transcriptome regulation, microRNAs may effectively diffuse somatic mutation impacts to broad cellular processes and ultimately manifest disease and dictate prognosis. Previous studies that tried to correlate mutation with gene expression dysregulation neglected to adjust for the disparate multitudes of false positives associated with unequal sample sizes and uneven class balancing scenarios. RESULTS: To properly address this issue, we developed a statistical framework to rigorously assess the extent of mutation impact on microRNAs in relation to a permutation-based null distribution of a matching sample structure. Carrying out the framework in a pan-cancer study, we ascertained 9008 protein-coding genes with statistically significant mutation impacts on miRNAs. Of these, the collective miRNA expression for 83 genes showed significant prognostic power in nine cancer types. For example, in lower-grade glioma, 10 genes' mutations broadly impacted miRNAs, all of which showed prognostic value with the corresponding miRNA expression. Our framework was further validated with functional analysis and augmented with rich features including the ability to analyze miRNA isoforms; aggregative prognostic analysis; advanced annotations such as mutation type, regulator alteration, somatic motif, and disease association; and instructive visualization such as mutation OncoPrint, Ideogram, and interactive mRNA-miRNA network. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in MutMix, at http://innovebioinfo.com/Database/TmiEx/MutMix.php.

Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.

Diverse application scenarios in 5G and beyond wireless communication systems have introduced various requirements in code lengths and rates of channel codes. For the decoding of polar codes, especially the belief-propagation (BP) decoding, flexible configuration of codeword length is still not involved in current decoders. In this paper, a semi-folded decoding structure is proposed which can be reconfigured to support multiple codeword lengths. Up to 16 codes can be decoded in parallel and the utilization of processing units is no less than 87.5% for various codeword lengths. The peak throughput of 19.29 Gbps can be achieved by the proposed decoder in SMIC 55 nm CMOS technology.

Coronavirus disease 2019 pandemic is the most damaging pandemic in recent human history. Rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and variant strains is paramount for recovery from this pandemic. Conventional SARS-CoV-2 tests interrogate only limited regions of the whole SARS-CoV-2 genome, which are subjected to low specificity and miss the opportunity of detecting variant strains. In this work, we developed the first SARS-CoV-2 tiling array that captures the entire SARS-CoV-2 genome at single nucleotide resolution and offers the opportunity to detect point mutations. A thorough bioinformatics protocol of two base calling methods has been developed to accompany this array. To demonstrate the effectiveness of the tiling array, we genotyped all genomic positions of eight SARS-CoV-2 samples. Using high-throughput sequencing as the benchmark, we show that the tiling array had a genome-wide accuracy of at least 99.5%. From the tiling array analysis results, we identified the D614G mutation in the spike protein in four of the eight samples, suggesting the widespread distribution of this variant at the early stage of the outbreak in the United States. Two additional nonsynonymous mutations were identified in one sample in the nucleocapsid protein (P13L and S197L), which may complicate future vaccine development. With around $5 per array, supreme accuracy, and an ultrafast bioinformatics protocol, the SARS-CoV-2 tiling array makes an invaluable toolkit for combating current and future pandemics. Our SARS-CoV-2 tilting array is currently utilized by Molecular Vision, a CLIA-certified lab for SARS-CoV-2 diagnosis.

Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.

BACKGROUND: DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. RESULTS: In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ([Formula: see text]) and PDB186 ([Formula: see text]), respectively. CONCLUSION: Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.