Applied Science and Technology Research Institute

Transcription factors drive gene expression dysregulation in cancer. However, non-canonical oncogenic mechanisms of these factors are unclear. Utilizing function-centric proteomics to discover RNA-dependent protein-protein interactions, we uncovered an unexpected interaction between transcription factor oncogene SOX2 and methionine cycle enzyme AHCY. Immunofluorescence and CUT&RUN revealed that SOX2 expression sequesters AHCY to the chromatin. A candidate RNA-immunoprecipitation screen identified non-coding RNA RMST as a mediator of the SOX2 and AHCY interaction. The SOX2-AHCY interaction is reduced upon RMST knockdown. SOX2 expression sequesters RMST and AHCY in the nucleus, an activity dependent on the RNA-binding Arginine Rich Motif (ARM) domain of SOX2. Metabolite profiling revealed that SOX2 expression alters methionine cycle intermediates, particularly at the AHCY catalyzed step. Methylation precursor S-adenosylmethionine (SAM) production is also inhibited by SOX2. These metabolic changes are rescued with SOX2 ARM mutation. Whole genome bisulfite sequencing revealed that SOX2 expression induces DNA hypomethylation in cancer cells. DNA hypomethylation and its downstream DNA damage effect are rescued with SAM supplementation or SOX2 ARM mutation. These data suggest that SOX2 mis-expression in cancer sequesters AHCY, through an RMST adaptor, in the nucleus. This reduces the availability of cytoplasmic AHCY to participate in the methionine cycle, reprograming this metabolic process. As a result, SAM levels are reduced, causing DNA hypomethylation and downstream DNA damage. Our findings were validated in cancer patient biopsies. Strikingly, knockdown and pharmacological inhibition of AHCY targets SOX2-expressing cancer cells in culture and in vivo. This suggests that low SAM levels, induced by decreased cytoplasmic AHCY, sensitize SOX2-expressing cancer cells to AHCY inhibition. Overall, our results suggest that a transcription factor can coopt a non-coding RNA to perform non-canonical metabolic reprograming, creating a druggable metabolic dependency in transcription factor-driven cancer.

We introduce the concept of cyclic covers, which generalizes the classical notion of covers in strings. Given any string X , a factor W of X is called a cyclic cover if each position of X belongs to an occurrence of a cyclic shift of W in X . Two cyclic covers are distinct if one is not a cyclic shift of the other. The cyclic covers problem asks for all distinct cyclic covers of an input string X . We present an algorithm that solves the cyclic covers problem in O ( n log ⁡ n ) time, where n is the length of X . It is based on finding a well-structured set of standard occurrences of a constant number of factors of a cyclic cover candidate W , computing the regions of X covered by cyclic shifts of W , extending those factors, and taking the union of the results. • We introduce the cyclic cover problem. • Two cyclic covers are distinct if one is not a cyclic shift of the other. • The cyclic cover problem requires finding all distinct cyclic covers of X . • We show that for a string of length n, the cyclic cover problem can be solved in O ( n log ⁡ n ) time.

The field of two-dimensional (2D) materials has seen remarkable progress, driven by their exceptional electronic, optical, and mechanical properties. In addition to their intrinsic qualities, 2D materials are atomically thin and readily integrated with a wide range of devices, offering immense potential for next-generation on-chip technologies across various domains, beyond just electronics and optics. Central to the fabrication of 2D devices is the development of effective transfer methods, which are crucial for maintaining clean material interfaces and intact material properties. However, as some of the most fundamental techniques, transfer methods are incredibly diverse, making it challenging to navigate the various approaches. This review provides a comprehensive analysis of the state-of-the-art transfer methods, including a preliminary discussion on high-quality 2D material preparation, along with evaluation of the strengths and weaknesses of various transfer techniques. Furthermore, despite being a foundational field, there are still many significant tasks to be undertaken, with several bottlenecks awaiting breakthroughs. We also highlight emerging trends, such as reconfigurable transfer, all-transfer for chip manufacturing, and the application of transfer techniques to low-dimensional materials across broader research fields, such as chemistry, polariton, tribology, haptics, thermal transport, thermodynamic control, quantum science, neuromorphic computing, electrocatalysts, and energy, offering insights into future directions for 2D material integration.

Twistronics, harnessing interlayer rotation to tailor electronic states in van der Waals materials, has predominantly focused on small-angle regimes. Here, we unveil the pivotal role of intervalley Umklapp scattering in large-angle twisted bilayer graphene, which governs low-energy physics and drives unconventional band topology. By constructing symmetry-constrained effective k·p models for ±21.8°-twisted bilayers, we demonstrate how structural chirality imprints distinct electronic responses. The D6 configuration exhibits a gapped spectrum with chiral interlayer coupling, while the D3-symmetric stacking configuration displays semimetallic behavior. Crucially, chirality inversion creates topological domain-wall states, which manifest as counterpropagating pseudospin modes at interfaces between oppositely twisted regions. These states, absent in untwisted bilayers, emerge from a Jackiw-Rebbi-like mechanism tied to chirality reversal. Atomistic simulations confirm these topological states and demonstrate their robustness against symmetry-breaking perturbations. The interplay between twist-induced chirality and topology opens new pathways for engineering domain-wall states in twisted materials.

Calcium ion batteries (CIBs) are promising for energy storage with volumetric capacity and reduction potential comparable to lithium, while richer in earth abundance. However, sluggish cation transport and unstable cycling performance primarily due to anode surface passivation remain vital challenges for realizing high‐performance CIBs. Herein, two kinds of redox covalent organic frameworks (PT‐COFs and PQ‐COFs) with different‐density carbonyl groups are prepared as quasi‐solid‐state electrolytes (QSSEs) to address those challenges. In particular, PT‐COFs exhibit ionic conductivity of 0.46 and 5.05 mS cm−1 at room temperature and 80 °C, respectively, and Ca2+ transference number of 0.532. Due to the efficient ionic conduction and intrinsic stability of PT‐COFs structure, the prepared full calcium ion cell with PT‐COFs demonstrates the highest reversible specific capacity of 155.9 mAh g−1 at 0.15 A g−1 (1 C), and stable cycle performance (capacity retention over 74.6% at 1 A g−1 after 1000 cycles). This work shows the effectiveness of the redox COFs and their promising potential as SSE for the development of high‐performance CIBs.

In modern chip design, the placement of millions of circuit modules poses a significant challenge. However, macro placement can significantly enhance this process by enabling greater modularization of the chip design. In many cases, macros can occupy more than half of the die area, making their placement a critical factor in defining the die’s overall shape. Despite its importance, existing algorithms often either neglect this aspect or fail to address it adequately. This paper presents a novel macro placer that enhances reinforcement learning architectures to produce compact and shape-optimized placements. Our research demonstrates that strategically decoupling standard cells during macro placement can significantly reduce network complexity, enabling an efficient relinking solution. Additionally, we introduce a shape force mask into the reinforcement learning-based visual representation learning framework. The approach proposed in this paper enables the creation of compact and shape-optimized chip designs. Experimental results validate that our algorithm can produce more compact and regular macro placements while consistently maintaining other key performance metrics.

In the process of daily psychological counseling, it is found that more and more school-age children have a dislike for learning, and the rate is increasing year by year. They are unclear about the purpose of learning, lose interest in class, do not listen carefully, cannot complete homework independently, and even skip school. This case analyzes the mechanism of a 7-year-old primary school student's aversion to learning and analyzes and discusses this behavior.