Rotationally Invariant Circuits: Universality with the Exchange Interaction and Two Ancilla Qubits.

Universality of local unitary transformations is one of the cornerstones of quantum computing with many applications and implications that go beyond this field. However, it has recently been shown that this universality does not hold in the presence of continuous symmetries: generic symmetric unitaries on a composite system cannot be implemented, even approximately, using local symmetric unitaries on the subsystems. In this Letter, we show that, despite these constraints, any SU(2) rotationally invariant unitary can be realized with the Heisenberg exchange interaction, which is 2-local and rotationally invariant, provided that the system interacts with a pair of ancilla qubits. We also show that a single ancilla is not enough to achieve universality. Furthermore, we study qubit circuits formed from k-local rotationally invariant unitaries and fully characterize the constraints imposed by locality on the realizable unitaries. We also find an interpretation of these constraints in terms of the average energy of states with a fixed angular momentum.

A method for predicting drugs that can boost the efficacy of immune checkpoint blockade.

Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.

Spatiotemporal Utilization of Latent Heat in Erythritol-based Phase Change Materials as Solar Thermal Fuels.

Solar thermal fuels (STFs) have been particularly concerned as sustainable future energy due to their impressive ability to store solar energy in chemical bonds and controllably release thermal energy. However, currently studied STFs mainly focus on molecule-based materials with high photochemical activity, toxicity, and compromised features, which greatly restricts their applications in practical scenarios of solar energy utilization. Herein, we present a novel erythritol-based composite phase change material (PCM) as a new type of STFs with an outstanding capability to store solar energy as latent heat in its stable supercooling state and release thermal energy as needed. This composite PCM with stored thermal energy can be maintained stably at room temperature and subsequently release latent heat as high as 224.9 J/g during the crystallization process triggered by thermal stimuli. Remarkably, solar energy can be converted into latent heat stored in the composite PCM over months. Through mechanical stimulations, the released latent heat can increase the temperature of the composite up to 91 °C. This work presents a new concept of using spatiotemporal storage and release of latent heat in PCMs for solar energy utilization, making it a potential candidate as STFs for developing future clean energy techniques.

Elasticity-controlled jamming criticality in soft composite solids.

Soft composite solids are made of inclusions dispersed within soft matrices. They are ubiquitous in nature and form the basis of many biological tissues. In the field of materials science, synthetic soft composites are promising candidates for building various engineering devices due to their highly programmable features. However, when the volume fraction of the inclusions increases, predicting the mechanical properties of these materials poses a significant challenge for the classical theories of composite mechanics. The difficulty arises from the inherently disordered, multi-scale interactions between the inclusions and the matrix. To address this challenge, we systematically investigated the mechanics of densely filled soft elastomers containing stiff microspheres. We experimentally demonstrate how the strain-stiffening response of the soft composites is governed by the critical scalings in the vicinity of a shear-jamming transition of the included particles. The proposed criticality framework quantitatively connects the overall mechanics of a soft composite with the elasticity of the matrix and the particles, and captures the diverse mechanical responses observed across a wide range of material parameters. The findings uncover a novel design paradigm of composite mechanics that relies on engineering the jamming properties of the embedded inclusions.

scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.

DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.

Predicting air pollutant emissions of the foundry industry: Based on the electricity big data.

Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R2 = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (-17.09-24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry.

Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain.

Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1 technologies to generate 301,626 methylomes and 176,003 chromatin conformation-methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular-spatial and regulatory genome diversity of the mouse brain.

Brain-wide correspondence of neuronal epigenomics and distant projections.

Single-cell analyses parse the brain's billions of neurons into thousands of 'cell-type' clusters residing in different brain structures1. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq2 to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription-factor-binding motifs, and neurotransmitter use.

Conserved and divergent gene regulatory programs of the mammalian neocortex.

Divergence of cis-regulatory elements drives species-specific traits1, but how this manifests in the evolution of the neocortex at the molecular and cellular level remains unclear. Here we investigated the gene regulatory programs in the primary motor cortex of human, macaque, marmoset and mouse using single-cell multiomics assays, generating gene expression, chromatin accessibility, DNA methylome and chromosomal conformation profiles from a total of over 200,000 cells. From these data, we show evidence that divergence of transcription factor expression corresponds to species-specific epigenome landscapes. We find that conserved and divergent gene regulatory features are reflected in the evolution of the three-dimensional genome. Transposable elements contribute to nearly 80% of the human-specific candidate cis-regulatory elements in cortical cells. Through machine learning, we develop sequence-based predictors of candidate cis-regulatory elements in different species and demonstrate that the genomic regulatory syntax is highly preserved from rodents to primates. Finally, we show that epigenetic conservation combined with sequence similarity helps to uncover functional cis-regulatory elements and enhances our ability to interpret genetic variants contributing to neurological disease and traits.

Single-cell analysis of chromatin accessibility in the adult mouse brain.

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1-4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs-specifically, those identified from a subset of cortical excitatory neurons-are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.

SIX1 amplification modulates stemness and tumorigenesis in breast cancer.

BACKGROUND: Sine oculis homeobox homolog 1 (SIX1) is a transcription factor that has recently been identified as a crucial regulator of embryonic development and tumorigenesis. SIX1 is upregulated in different types of tumors, including breast cancer. However, the role and mechanism of SIX1 upregulation in breast cancer carcinogenesis remains uncertain. METHODS: In this study, we utilized various databases such as UALCAN, TCGA, STRING, and Kaplan-Meier Plotter to investigate the mRNA expression, prognosis, transcriptional profile changes, signal pathway rewiring, and interaction with cancer stem cells of SIX1 in breast cancer. We also conducted both in vitro and in vivo experiments to validate its positive regulation effect on breast cancer stem cells. RESULTS: Our findings demonstrated that the expression of SIX1 varies among different subtypes of breast cancer and that it upregulates breast cancer grading and lymph node metastasis. Besides, SIX1 participates in the rewiring of several cancer signaling pathways, including estrogen, WNT, MAPK, and other pathways, and interacts with cancer stem cells. SIX1 showed a significant positive correlation with breast cancer stem cell markers such as ALDH1A1, EPCAM, ITGB1, and SOX2. Moreover, our in vitro and in vivo experiments confirmed that SIX1 can promote the increase in the proportion of stem cells and tumor progression. CONCLUSIONS: Altogether, our results suggest that SIX1 plays an essential regulatory role in breast cancer's occurrence, and its amplification can be utilized as a diagnostic and prognostic predictor. The interaction between SIX1 and cancer stem cells may play a critical role in regulating breast cancer's initiation and metastasis.

Electromagnetic protection strategy using adaptive energy selective mechanism.

Electromagnetic spaces face growing threats from both naturally occurring and artificial electromagnetic pulses; however, the current protection methodologies are still far from practical needs. To address this issue, we propose an electromagnetic protection strategy that makes use of an adaptive energy selective mechanism. This strategy, carried out using electromagnetic metamaterials, provides in-band protection to electronic equipment with a high tolerance threshold and fast response. We propose several approaches to further enhance the protective performance of electromagnetic metamaterials. These include reconfigurable designs based on digital circuits and deep learning algorithms, as well as the adoption of nanoscale field-controlled devices made of two-dimensional (2D) phase transition materials and nanoelectromechanical systems. Our study can not only lead to a comprehensive protection system with superior compatibility, but also offer reliable support for maintaining electromagnetic spatial security.

The IFN-γ-CXCL9/CXCL10-CXCR3 axis in vitiligo: Pathological mechanism and treatment.

Vitiligo is a disease featuring distinct white patches that result from melanocyte destruction. The overall pathogenesis of vitiligo remains to be elucidated. Nevertheless, considerable research indicates that adaptive immune activation plays a key role in this process. Specifically, the interferon-gamma (IFN-γ), C-X-C motif chemokine ligands (CXCL9/10), and C-X-C motif chemokine receptor (CXCR3) signaling axis, collectively referred to as IFN-γ-CXCL9/10-CXCR3 or ICC axis, has emerged as a key mediator responsible for the recruitment of autoimmune CXCR3+ CD8+ T cells. These cells serve as executioners of melanocytes by promoting their detachment and apoptosis. Moreover, IFN-γ is generated by activated T cells to create a positive feedback loop, exacerbating the autoimmune response. This review not only delves into the mechanistic insights of the ICC axis but also explores the significant immunological effects of associated cytokines and their receptors. Additionally, the review provides a thorough comparison of existing and emerging treatment options that target the ICC axis for managing vitiligo. This review aims to foster further advancements in basic research within related fields and facilitate a deeper understanding of alternative treatment strategies targeting different elements of the axis.

An Enzymatic Strategy for the Selective Methylation of High-Value-Added Tetrahydroprotoberberine Alkaloids.

Tetrahydroprotoberberines (THPBs) are plant-specific alkaloids with significant medicinal value. They are present in trace amounts in plants and are difficult to chemically synthesize due to stereoselectivity and an unfavorable environment. In this study, a selective methylation strategy was developed for the biocatalysis of seven high-value-added THPB compounds using 4'-O-methyltransferase (Cj4'OMT), norcoclaurine 6-O-methyltransferase (Cj6OMT), and (S)-scoulerine 9-O-methyltransferase (SiSOMT and PsSOMT) in engineered E. coli. The methyltransferases Cj4'OMT, Cj6OMT, PsSOMT, and SiSOMT were expressed heterologously in E. coli. Compound 1 (10-methoxy-2,3,9-tetrahydroxyberbine) was synthesized using the recombinant E. coli strain Cj4'OMT and the substrate 2,3,9,10-tetrahydroxyberbine. Compound 2 (9-methoxy-2,3,10-tetrahydroxyberbine) was produced in the recombinant Escherichia coli (E. coli) strain PsSOMT, and compounds 2 and 3 (discretamine) were produced in the recombinant E. coli strain SiSOMT. Compounds 4 (9,10-methoxy-2,3-tetrahydroxyberbine) and 5 (corypalmine) were obtained by co-culturing the recombinant strains Cj4'OMT and SiSOMT with substrate. Compounds 6 (scoulerine) and 7 (isoscoulerine) were produced by co-culturing the substrate with the recombinant strains Cj4'OMT and Cj6OMT. To increase the yield of novel compound 2, the flask culture conditions of the engineered SiSOMT strain were optimized, resulting in the production of 165.74 mg/L of this compound. This study thus presents an enzymatic approach to the synthesis of high-value-added THPBs with minimum environmental wastage.

MECE: a method for enhancing the catalytic efficiency of glycoside hydrolase based on deep neural networks and molecular evolution.

The demand for high efficiency glycoside hydrolases (GHs) is on the rise due to their various industrial applications. However, improving the catalytic efficiency of an enzyme remains a challenge. This investigation showcases the capability of a deep neural network and method for enhancing the catalytic efficiency (MECE) platform to predict mutations that improve catalytic activity in GHs. The MECE platform includes DeepGH, a deep learning model that is able to identify GH families and functional residues. This model was developed utilizing 119 GH family protein sequences obtained from the Carbohydrate-Active enZYmes (CAZy) database. After undergoing ten-fold cross-validation, the DeepGH models exhibited a predictive accuracy of 96.73%. The utilization of gradient-weighted class activation mapping (Grad-CAM) was used to aid us in comprehending the classification features, which in turn facilitated the creation of enzyme mutants. As a result, the MECE platform was validated with the development of CHIS1754-MUT7, a mutant that boasts seven amino acid substitutions. The kcat/Km of CHIS1754-MUT7 was found to be 23.53 times greater than that of the wild type CHIS1754. Due to its high computational efficiency and low experimental cost, this method offers significant advantages and presents a novel approach for the intelligent design of enzyme catalytic efficiency. As a result, it holds great promise for a wide range of applications.

N6 -methyladenosine-modified circSTX6 promotes hepatocellular carcinoma progression by regulating the HNRNPD/ATF3 axis and encoding a 144 amino acid polypeptide.

BACKGROUND: Circular RNAs (circRNAs) play a significant role in the initiation and progression of various cancers, including hepatocellular carcinoma (HCC). Circular syntaxin 6 (circSTX6, also known as hsa_circ_0007905) has been identified as a microRNA (miRNA) sponge in pancreatic adenocarcinoma. However, its full range of functions in terms of protein scaffold and translation remain largely unexplored in the context of HCC. METHODS: The expression of circSTX6 and its encoded protein was examined in HCC tumour tissues. N6 -methyladenosine (m6 A) on circSTX6 was verified and quantified by methylated RNA immunoprecipitation (Me-RIP), RIP and dual luciferase reporter assays. The biological functions of circSTX6 and its encoded protein in HCC were clarified by in vitro and in vivo experiments. Mechanistically, the interaction between circSTX6 and heterogeneous nuclear ribonucleoprotein D (HNRNPD) was investigated by RNA pull-down, RIP and fluorescence in situ hybridization (FISH)/IF. The regulatory effects of circSTX6 and HNRNPD on activating transcription factor 3 (ATF3) mRNA were determined by mRNA stability and RIP assays. Furthermore, the presence of circSTX6-encoded protein was verified by mass spectrometry. RESULTS: CircSTX6 and its encoded 144 amino acid polypeptide, circSTX6-144aa, were highly expressed in HCC tumour tissues and served as independent risk factors for overall survival in HCC patients. The expression of circSTX6 was regulated by METTL14 in an m6 A-dependent manner. Functionally, circSTX6 accelerated HCC proliferation and tumourigenicity and reinforced tumour metastasis in vitro and in vivo. Mechanistically, circSTX6 acted as a sponge for HNRNPD protein, facilitating its binding to ATF3 mRNA, consequently promoting ATF3 mRNA decay. Meanwhile, circSTX6-144aa promoted HCC proliferation, migration and invasion independent of circSTX6 itself. CONCLUSION: Collectively, our study reveals that m6 A-modified circSTX6 drives malignancy in HCC through the HNRNPD/ATF3 axis, while its encoded circSTX6-144aa contributes to HCC progression independent of circSTX6. CirSTX6 and its encoded protein hold promise as potential biomarkers and therapeutic targets in HCC.

Erratum: Quantum Criticality of Antiferromagnetism and Superconductivity with Relativity [Phys. Rev. Lett. 128, 117202 (2022)].

This corrects the article DOI: 10.1103/PhysRevLett.128.117202.

Single-cell DNA methylation and 3D genome architecture in the human brain.

Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.

The beneficial potential of protein hydrolysates as prebiotic for probiotics and its biological activity: a review.

Probiotics are not only a food supplement, but they have shown great potential in their nutritional, health and therapeutic effects. To maximize the beneficial effects of probiotics, it is commonly achieved by adding prebiotics. Prebiotics primarily comprise indigestible carbohydrates, specific peptides, proteins, and lipids, with oligosaccharides being the most extensively studied prebiotics. However, these rapidly fermenting oligosaccharides have many drawbacks and can cause diarrhea and flatulence in the body. Hence, the exploration of new prebiotic is of great interest. Besides oligosaccharides, protein hydrolysates have been demonstrated to enhance the expression of beneficial properties of probiotics. Consequently, this paper outlines the mechanism underlying the action of protein hydrolysates on probiotics, as well as the advantageous impacts of proteins hydrolysates derived from various food sources on probiotics. In addition, this paper also reviews the currently reported biological activities of protein hydrolysates. The aim is a theoretical basis for the development and implementation of novel prebiotics.