Super-multiplex vibrational imaging

The ability to visualize directly a large number of distinct molecular species inside cells is increasingly essential for understanding complex systems and processes. Even though existing methods have successfully been used to explore structure–function relationships in nervous systems, to profile RNA in situ, to reveal the heterogeneity of tumour microenvironments and to study dynamic macromolecular assembly, it remains challenging to image many species with high selectivity and sensitivity under biological conditions. For instance, fluorescence microscopy faces a ‘colour barrier’, owing to the intrinsically broad (about 1,500 inverse centimetres) and featureless nature of fluorescence spectra that limits the number of resolvable colours to two to five (or seven to nine if using complicated instrumentation and analysis). Spontaneous Raman microscopy probes vibrational transitions with much narrower resonances (peak width of about 10 inverse centimetres) and so does not suffer from this problem, but weak signals make many bio-imaging applications impossible. Although surface-enhanced Raman scattering offers high sensitivity and multiplicity, it cannot be readily used to image specific molecular targets quantitatively inside live cells. Here we use stimulated Raman scattering under electronic pre-resonance conditions to image target molecules inside living cells with very high vibrational selectivity and sensitivity (down to 250 nanomolar with a time constant of 1 millisecond). We create a palette of triple-bond-conjugated near-infrared dyes that each displays a single peak in the cell-silent Raman spectral window; when combined with available fluorescent probes, this palette provides 24 resolvable colours, with the potential for further expansion. Proof-of-principle experiments on neuronal co-cultures and brain tissues reveal cell-type-dependent heterogeneities in DNA and protein metabolism under physiological and pathological conditions, underscoring the potential of this 24-colour (super-multiplex) optical imaging approach for elucidating intricate interactions in complex biological systems

Impact of Opinions and Relationships Coevolving on Self-Organization of Opinion Clusters

In a social network, individual opinions and interpersonal relationships always interact and coevolve. This continuously leads to self-organization of opinion clusters in the whole network. In this article we study how the coevolution on the two kinds of complex networks and the self-organization of opinion clusters are differently affected by the dynamic parameters, the structural parameters and the propagating parameters. It is found that the two dynamic parameters are homogeneous bringing about the strong and weak relations, while the two structural parameters are heterogeneous having equivalent relations. Moreover, the impact of the propagating parameter has been found only above its threshold

Error-mitigated Quantum Approximate Optimization via Learning-based Adaptive Optimization

Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum computing is envisioned as a powerful tool offering potential computational advantages for solving some of these problems. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve certain combinatorial optimization problems by transforming a discrete optimization problem into a classical optimization problem over a continuous circuit parameter domain. QAOA objective landscape over the parameter variables is notorious for pervasive local minima and barren plateaus, and its viability in training significantly relies on the efficacy of the classical optimization algorithm. To enhance the performance of QAOA, we design double adaptive-region Bayesian optimization (DARBO), an adaptive classical optimizer for QAOA. Our experimental results demonstrate that the algorithm greatly outperforms conventional gradient-based and gradient-free optimizers in terms of speed, accuracy, and stability. We also address the issues of measurement efficiency and the suppression of quantum noise by successfully conducting the full optimization loop on the superconducting quantum processor. This work helps to unlock the full power of QAOA and paves the way toward achieving quantum advantage in practical classical tasks.Comment: Main text: 11 pages, 4 figures, SI: 5 pages, 5 figure

A new treatment for neurogenic inflammation caused by EV71 with CR2-targeted complement inhibitor

BACKGROUND: Enterovirus 71 (EV71), one of the most important neurotropic EVs, has caused death and long-term neurological sequelae in hundreds of thousands of young children in the Asia-Pacific region in the past decade. The neurological diseases are attributed to infection by EV71 inducing an extensive peripheral and central nervous system (CNS) inflammatory response with abnormal cytokine production and lymphocyte depletion induced by EV71 infection. In the absence of specific antiviral agents or vaccines, an effective immunosuppressive strategy would be valuable to alleviate the severity of the local inflammation induced by EV71 infection. PRESENTATION OF THE HYPOTHESIS: The complement system plays a pivotal role in the inflammatory response. Inappropriate or excessive activation of the complement system results in a severe inflammatory reaction or numerous pathological injuries. Previous studies have revealed that EV71 infection can induce complement activation and an inflammatory response of the CNS. CR2-targeted complement inhibition has been proved to be a potential therapeutic strategy for many diseases, such as influenza virus-induced lung tissue injury, postischemic cerebral injury and spinal cord injury. In this paper, a mouse model is proposed to test whether a recombinant fusion protein consisting of CR2 and a region of Crry (CR2-Crry) is able to specifically inhibit the local complement activation induced by EV71 infection, and to observe whether this treatment strategy can alleviate or even cure the neurogenic inflammation. TESTING THE HYPOTHESIS: CR2-Crry is expressed in CHO cells, and its biological activity is determined by complement inhibition assays. 7-day-old ICR mice are inoculated intracranially with EV71 to duplicate the neurological symptoms. The mice are then divided into two groups, in one of which the mice are treated with CR2-Crry targeted complement inhibitor, and in the other with phosphate-buffered saline. A group of mice deficient in complement C3, the breakdown products of which bind to CR2, are also infected with EV71 virus. The potential bioavailability and efficacy of the targeted complement inhibitor are evaluated by histology, immunofluorescence staining and radiolabeling. IMPLICATIONS OF THE HYPOTHESIS: CR2-Crry-mediated targeting complement inhibition will alleviate the local inflammation and provide an effective treatment for the severe neurological diseases associated with EV71 infection

ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution

Directed evolution is a versatile technique in protein engineering that mimics the process of natural selection by iteratively alternating between mutagenesis and screening in order to search for sequences that optimize a given property of interest, such as catalytic activity and binding affinity to a specified target. However, the space of possible proteins is too large to search exhaustively in the laboratory, and functional proteins are scarce in the vast sequence space. Machine learning (ML) approaches can accelerate directed evolution by learning to map protein sequences to functions without building a detailed model of the underlying physics, chemistry and biological pathways. Despite the great potentials held by these ML methods, they encounter severe challenges in identifying the most suitable sequences for a targeted function. These failures can be attributed to the common practice of adopting a high-dimensional feature representation for protein sequences and inefficient search methods. To address these issues, we propose an efficient, experimental design-oriented closed-loop optimization framework for protein directed evolution, termed ODBO, which employs a combination of novel low-dimensional protein encoding strategy and Bayesian optimization enhanced with search space prescreening via outlier detection. We further design an initial sample selection strategy to minimize the number of experimental samples for training ML models. We conduct and report four protein directed evolution experiments that substantiate the capability of the proposed framework for finding of the variants with properties of interest. We expect the ODBO framework to greatly reduce the experimental cost and time cost of directed evolution, and can be further generalized as a powerful tool for adaptive experimental design in a broader context.Comment: 27 pages, 13 figure

The transport properties of Kekul\'e-ordered graphene pp-nn junctions

The transport properties of electrons in graphene $p$-$n$ junction with uniform Kekul\'e lattice distortion have been studied using the tight-binding model and the Landauer-B\"uttiker formalism combined with the nonequilibrium Green's function method. In the Kekul\'e-ordered graphene, the original $K$ and $K^{\prime}$ valleys of the pristine graphene are folded together due to the $\sqrt{3} \times \sqrt{3}$ enlargement of the primitive cell. When the valley coupling breaks the chiral symmetry, special transport properties of Dirac electrons exist in the Kekul\'e lattice. In the O-shaped Kekul\'e graphene $p$-$n$ junction, Klein tunneling is suppressed, and only resonance tunneling occurs. In the Y-shaped Kekul\'e graphene $p$-$n$ junction, the transport of electrons is dominated by Klein tunneling. When the on-site energy modification is introduced into the Y-shaped Kekul\'e structure, both Klein tunneling and resonance tunneling occur, and the electron tunneling is enhanced. In the presence of a strong magnetic field, the conductance of O-shaped and on-site energy-modified Y-shaped Kekul\'e graphene $p$-$n$ junctions is non-zero due to the occurrence of resonance tunneling. It is also found that the disorder can enhance conductance, with conductance plateaus forming in the appropriate range of disorder strength. The ideal plateau value is found only in the Kekul\'e-Y system.Comment: 8 pages, 7 figure

Acidosis Decreases c-Myc Oncogene Expression in Human Lymphoma Cells: A Role for the Proton-Sensing G Protein-Coupled Receptor TDAG8

Acidosis is a biochemical hallmark of the tumor microenvironment. Here, we report that acute acidosis decreases c-Myc oncogene expression in U937 human lymphoma cells. The level of c-Myc transcripts, but not mRNA or protein stability, contributes to c-Myc protein reduction under acidosis. The pH-sensing receptor TDAG8 (GPR65) is involved in acidosis-induced c-Myc downregulation. TDAG8 is expressed in U937 lymphoma cells, and the overexpression or knockdown of TDAG8 further decreases or partially rescues c-Myc expression, respectively. Acidic pH alone is insufficient to reduce c-Myc expression, as it does not decrease c-Myc in H1299 lung cancer cells expressing very low levels of pH-sensing G protein-coupled receptors (GPCRs). Instead, c-Myc is slightly increased by acidosis in H1299 cells, but this increase is completely inhibited by ectopic overexpression of TDAG8. Interestingly, TDAG8 expression is decreased by more than 50% in human lymphoma samples in comparison to non-tumorous lymph nodes and spleens, suggesting a potential tumor suppressor function of TDAG8 in lymphoma. Collectively, our results identify a novel mechanism of c-Myc regulation by acidosis in the tumor microenvironment and indicate that modulation of TDAG8 and related pH-sensing receptor pathways may be exploited as a new approach to inhibit Myc expression

A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography

Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. They exhibit similar echocardiographic characteristics, often leading to diagnostic challenges. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between HCM and CA. The method begins by classifying 2D echocardiography data into five distinct echocardiographic views: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. It then extracts features of each view separately and combines five features for disease classification. A total of 212 patients diagnosed with HCM, and 30 patients diagnosed with CA, along with 200 individuals with normal cardiac function(Normal), were enrolled in this study from 2018 to 2022. This approach achieved a precision, recall of 0.905, and micro-F1 score of 0.904, demonstrating its effectiveness in accurately identifying HCM and CA using a multi-view analysis