Gene regulation varies across different cell types and developmental stages, leading to distinct cellular roles across cellular populations. Investigating cell type-specific gene coexpression is therefore crucial for understanding gene functions and disease pathology. However, reconstructing gene coexpression networks from single-cell transcriptome data is challenging due to artifacts, noise, and data sparsity. Here, we present an efficient method for inference of gene coexpression networks via variance decomposition analysis (GCNVDA) to explore the underlying gene regulatory mechanisms from single-cell transcriptome data. Our model incorporates multiple sources of variability, including a random effect term $G$ to capture gene-level variance and a random effect term $E$ to account for residual errors. We applied GCNVDA to three real-world single-cell datasets, demonstrating that our method outperforms existing state-of-the-art algorithms in both sensitivity and specificity for identifying tissue- or state-specific gene regulations. Furthermore, GCNVDA facilitates the discovery of functional modules that play critical roles in key biological processes such as embryonic development. These findings provide new insights into cell-specific regulatory mechanisms and have the potential to significantly advance research in developmental biology and disease pathology.

With the evolution of wireless sensing technology, WiFi-based human identification has demonstrated tremendous potential in human-computer interaction and home security. However, most existing research operates in controlled environments, overlooking the complexities of real-world scenarios, such as signal fading, signal interference and uncertainty, and the diversity of application requirements. This article presents a comprehensive analysis of a series of representative research articles on WiFi-based human identification and summarizes five major challenges in achieving high-precision identification in complex application scenarios. Non-line-of-sight (NLOS) user sensing, coexistence user sensing, dynamic group user sensing, cross-domain user sensing, and multi-task sensing. Additionally, this article proposes a series of current solutions, including improving the signal-to-noise ratio (SNR) of NLOS sensing from the perspective of communication signals and sensing models, addressing coexistence sensing issues, designing adaptable tasks for dynamic user groups, leveraging techniques like adversarial learning and transfer learning for cross-domain problems, and employing modular deep learning architectures. By providing a comprehensive overview of WiFi-based human identification, this survey not only offers insights into current research but also charts a roadmap for future investigations. It is anticipated that this survey will stimulate innovative research endeavors and foster the expansion of wireless sensing technology across diverse application domains.

SUMMARY: Single-cell multi-omics technologies provide a unique platform for characterizing cell states and reconstructing developmental process by simultaneously quantifying and integrating molecular signatures across various modalities, including genome, transcriptome, epigenome, and other omics layers. However, there is still an urgent unmet need for novel computational tools in this nascent field, which are critical for both effective and efficient interrogation of functionality across different omics modalities. Scbean represents a user-friendly Python library, designed to seamlessly incorporate a diverse array of models for the examination of single-cell data, encompassing both paired and unpaired multi-omics data. The library offers uniform and straightforward interfaces for tasks, such as dimensionality reduction, batch effect elimination, cell label transfer from well-annotated scRNA-seq data to scATAC-seq data, and the identification of spatially variable genes. Moreover, Scbean's models are engineered to harness the computational power of GPU acceleration through Tensorflow, rendering them capable of effortlessly handling datasets comprising millions of cells. AVAILABILITY AND IMPLEMENTATION: Scbean is released on the Python Package Index (PyPI) (https://pypi.org/project/scbean/) and GitHub (https://github.com/jhu99/scbean) under the MIT license. The documentation and example code can be found at https://scbean.readthedocs.io/en/latest/.

Many IoT applications require users to share their devices’ location, and enhanced privacy-protection means sharing location anonymously, unlinkably and without relying on any administrators. But under such protection, it is difficult to trust shared location data, which may be from unregistered devices or from the same one’s multiple logins or from the cloned device ID, even be generated by an attacker without any devices! Such untrusted location sharing cheats system, misleads users, even attacks system. To the best of our knowledge, such problems have not been solved in a decentralized system. To solve them in one scheme, we put forward the first decentralized accumulator for device registration and construct the first practical decentralized anonymous authentication for device login. When logging in, the device provides a special knowledge proof, which integrates zero-knowledge (for privacy) with knowledge-leakage (for identifying abnormal behaviors) designing for blockchain (for decentralization). Therefore, in our system, only registered IoT devices can upload location data and their logins are anonymous and unlinkable, while login exceeding${K}$times in a system period or cloning ID to login concurrently can be identified and tracked without any trusted centers. In addition, we provide the security proofs and the application examples of the proposed scheme. And the efficiency analysis and experimental data show that the performance of our scheme can meet the needs of real-world location sharing on IoT.

MOTIVATION: Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations. RESULTS: Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by leveraging variational inference and deep learning. We perform VIMCCA and compare it with 10 existing state-of-the-art algorithms on four paired multi-modal datasets sequenced by different protocols. Results demonstrate that VIMCCA facilitates integrating various types of joint-modality data, thus leading to more reliable and accurate downstream analysis. VIMCCA improves our ability to identify novel or rare cell subtypes compared to existing widely used methods. Besides, it can also facilitate inferring cell lineage based on joint-modality profiles. AVAILABILITY AND IMPLEMENTATION: The VIMCCA algorithm has been implemented in our toolkit package scbean (≥0.5.0), and its code has been archived at https://github.com/jhu99/scbean under MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the deepening of research, how to construct a fully homomorphic signcryption scheme based on standard assumptions is a problem that we need to solve. For this question, recently, Jin et al. proposed a leveled fully homomorphic signcryption scheme from standard lattices. However, when verifying, it is supposed to unsigncrypt first as they utilize sign‐then‐encrypt method. This leads to users being unable to verify the authenticity of the data first, which resulting in the waste of resources. This raises another question of how to construct an fully homomorphic signcryption (FHSC) scheme with public verifiability. To solve this problem, we propose a leveled fully homomorphic signcryption scheme that can be publicly verified and show its completeness, IND‐CPA security, and strong unforgeability.

Compact E-cash achieves an efficient system by withdrawing 2n coins within O(1) operations and storing the coins in O(n) bits. For preventing a double-spender from cheating again, it is necessary to trace his e-coins. So full-tracing in compact E-cash system means tracing double-spender and tracing his coins. However, the efficiency problem caused by coin-tracing without TTP (trusted third party) has not been solved. For solving this problem, we introduce a non-standard construction into zero-knowledge proof of payment protocol, which leaks coin information when double-spending but is proven to be perfect zero-knowledge to verifier when spending a coin only once. Therefore, it achieves tracing dishonest users' coins and preserving the anonymity of honest users. Comparing with the existing most efficient method of coin-tracing without TTP, we improve computational complexity from O(k) to O(1) with less storage space. In addition, to improve efficiency and practicality further, batch-spending (spending any number of coins in one operation) and compact-spending (spending all coins in one operation) had been proposed. Based on the non-standard zero-knowledge proof, our scheme provides more efficient batch/compact-spending. Moreover, we also make a comparison with Bitcoin and Bitcoin Lightning Network, which have attracted considerable attention.

In a periodic K-times anonymous authentication system, user can anonymously show credential at most K times in one time period. In the next time period, user can automatically get another K-times authentication permission. If a user tries to show credential beyond K times in one time period, anyone can identify the dishonest user (the violator). But identifying violators is not enough for some systems, where it is also desirable to revoke violators' credentials for preventing them from abusing the anonymous property again. However, the problem of revoking credential without trusted third party has not been solved efficiently and practically. To solve it, we present an efficient scheme with efficient revocation of violator's credential. In fact, our method also solves an interesting problem-leaking information in a statistic zero-knowledge way, so our solution to the revocation problem outperforms all prior solutions. For achieving it, we use the special zero-knowledge proof with special information leak for revoking the violator's credential, but it can still be proven to be perfect statistic zero knowledge for guaranteeing the honest user's anonymity. Comparing with existing schemes, our scheme is efficient, and moreover, our method of revoking violator's credential is more practical with the least additional costs.