Low-dimensional piezoelectric and quantum piezotronics are two important branches of low-dimensional materials, playing a significant role in the advancement of low-dimensional devices, circuits, and systems. Here, we firstly propose a solid-phase sintering and vapor-liquid-solid growth (SS-VLS-like) method of preparing a quantum-sized oxide material, i.e., black phosphorus (BP)@MgO quantum dot (QD) crystal with a strong piezoelectric response. Quantum-sized MgO was obtained by Mg slowly released from MgB₂ within the confinement of a nanoflake BP matrix. Since the slow release of Mg only grows nanometer-sized MgO to hinder the further growth of MgO, we added a heterostructure matrix constraint: nanoflake BP. With the BP as the matrix confinement, MgO QDs embedded in the BP@MgO QD crystals were formed. These crystals have a layered two-dimensional (2D) structure with a thickness of 11 nm and are stable in the air. In addition, piezoresponse force microscopy (PFM) images show that they have extremely strong polarity. The strong polarity can also be proved by polarization reversal and a simple pressure sensor.

Although most microwave absorbing materials (MAMs) have good absorption ability above 8 GHz, they perform poorly in the low-frequency range (1–8 GHz). Metal–organic frameworks (MOFs) derived carbon-based composites have been highly sought after in electromagnetic materials and functional devices, due to their high specific area, high porosity, high thermal stability, low reflection loss, and adjustable composition. In this review, we first introduce the three loss types of MAMs and argue that composite materials are effective ways to achieve broadband absorption. Secondly, the absorbing properties of traditional materials and MOF materials in the literature are compared, followed by a discussion of the promising strategies for designing MAMs with broadband absorption in low frequencies based on the recent progress. Finally, the main problems, fabrication methods, and applications are discussed for their future prospects.

Understanding the molecular causes of complex diseases remains one of the most pressing challenges in biomedicine. Despite large-scale genome-wide association studies mapping thousands of risk loci, identifying which genetic variants truly drive disease remains difficult. Traditional statistical genetics has laid a strong foundation for variant discovery, but it often struggles to capture nonlinear interactions and cannot fully integrate the breadth of the interconnected multi-omics data. In recent years, deep learning approaches have shown promise in bridging these gaps: modelling high-order genetic interactions, uncovering latent biological structure, and enabling multi-layered data integration. However, most current deep learning models for genomics remain exploratory in nature, and issues such as susceptibility to overfitting, difficulties in interpretability, and the general lack of standardized evaluation frameworks have limited their widespread adoption for genomics research. In this review, we explore how traditional statistical and deep learning methods can be applied to uncover causal mechanisms in complex disease. We critically compare these two frameworks for their advantages and limitations in detecting genetic associations and prioritizing causal associations. Towards the end, we propose a future direction centred around hybrid models that blend the scalability of deep learning with the inferential power of statistical genetics. Our goal is to guide researchers in developing next-generation computational tools to uncover the molecular basis of complex diseases and accelerate the translation of genetic findings into effective treatments.

Understanding the molecular causes of complex diseases remains one of the most pressing challenges in biomedicine. Despite large-scale genome-wide association studies (GWAS) mapping thousands of risk loci, identifying which genetic variants truly drive disease remains difficult. Traditional statistical genetics has laid a strong foundation for variant discovery, but it often struggles to capture non-linear interactions and cannot fully integrate the breadth of the interconnected multi-omics data. In recent years, deep learning approaches have shown promise in bridging these gaps: modeling high-order genetic interactions, uncovering latent biological structure, and enabling multi-layered data integration. However, issues like overfitting, lack of interpretability, and limited statistical rigor have slowed their adoption in causal inference and therapeutic target discovery. In this review, we explore how traditional statistical and deep learning methods can be applied to uncover causal mechanisms in complex disease. We critically examine the strengths and blind spots of traditional GWAS, post-GWAS fine-mapping, and Mendelian randomization, and contrast them with emerging deep learning frameworks for epistasis detection and multi-omics integration. Finally, we propose a future direction centered around hybrid models that blend the scalability of deep learning with the inferential power of statistical genetics. Our goal is to guide researchers in developing next-generation computational tools to uncover the molecular basis of complex diseases and accelerate the translation of genetic findings into effective treatments.