On-chip integrated metasystem for spin-dependent multi-channel color holography.

On-chip integrated metasurface driven by in-plane guided waves is of great interests in various light-field manipulation applications such as colorful augmented reality and holographic display. However, it remains a challenge to design colorful multichannel holography by a single on-chip metasurface. Here we present metasurfaces integrated on top of a guided-wave photonic slab that achieves multi-channel colorful holographic light display. An end-to-end scheme is used to inverse design the metasurface for projecting off-chip preset multiple patterns. Particular examples are presented for customized patterns that were encoded into the metasurface with a single-cell meta-atom, working simultaneously at RGB color channels and for several different diffractive distances, with polarization dependence. Holographic images are generated at 18 independent channels with such a single-cell metasurface. The proposed design scheme is easy to implement, and the resulting device is viable for fabrication, promising plenty of applications in nanophotonics.

Visual-Inertial RGB-D SLAM with Encoder Integration of ORB Triangulation and Depth Measurement Uncertainties.

In recent years, the accuracy of visual SLAM (Simultaneous Localization and Mapping) technology has seen significant improvements, making it a prominent area of research. However, within the current RGB-D SLAM systems, the estimation of 3D positions of feature points primarily relies on direct measurements from RGB-D depth cameras, which inherently contain measurement errors. Moreover, the potential of triangulation-based estimation for ORB (Oriented FAST and Rotated BRIEF) feature points remains underutilized. To address the singularity of measurement data, this paper proposes the integration of the ORB features, triangulation uncertainty estimation and depth measurements uncertainty estimation, for 3D positions of feature points. This integration is achieved using a CI (Covariance Intersection) filter, referred to as the CI-TEDM (Triangulation Estimates and Depth Measurements) method. Vision-based SLAM systems face significant challenges, particularly in environments, such as long straight corridors, weakly textured scenes, or during rapid motion, where tracking failures are common. To enhance the stability of visual SLAM, this paper introduces an improved CI-TEDM method by incorporating wheel encoder data. The mathematical model of the encoder is proposed, and detailed derivations of the encoder pre-integration model and error model are provided. Building on these improvements, we propose a novel tightly coupled visual-inertial RGB-D SLAM with encoder integration of ORB triangulation and depth measurement uncertainties. Validation on open-source datasets and real-world environments demonstrates that the proposed improvements significantly enhance the robustness of real-time state estimation and localization accuracy for intelligent vehicles in challenging environments.

[Baduanjin improves sleep quality in patients with type 2 diabetes possibly via regulating Bmal1 gene].

The incidence of diabetes mellitus is increasing, and the sleep quality of patients with diabetes mellitus is often affected. Baduanjin may act on biological rhythm of the body, skeletal muscle glucose metabolism, skeletal muscle fibers and suprachiasmatic nucleus (SCN) by regulating the expression of Bmal1 gene, thus regulating the blood glucose level and circadian rhythm of patients with type 2 diabetes mellitus (T2DM) and improving their physiological functions. This article reviews the regulatory effect and mechanism of Baduanjin on Bmal1 gene expression in diabetes patients, and discusses the possibility of Baduanjin to improve the sleep quality of T2DM patients by regulating Bmal1 gene expression. This review can provide a new field for the clinical application of traditional Chinese Qigong Baduanjin, and provide a new scientific basis for exercise therapy of diabetes.

Genes that escape from X-chromosome inactivation and sexual dimorphism of systemic lupus erythematosus.

X chromosome inactivation can balance the effects of the two X chromosomes in females, and emerging evidence indicates that numerous genes on the inactivated X chromosome have the potential to evade inactivation. The mechanisms of escape include modification of DNA, RNA, histone, epitope, and various regulatory proteins, as well as the spatial structure of chromatin. The study of X chromosome inactivation escape has paved the way for investigating sex dimorphism in human diseases, particularly autoimmune diseases. It has been demonstrated that the presence of TLR7, CD40L, IRAK-1, CXCR3, and CXorf21 significantly contributes to the prevalence of SLE (systemic lupus erythematosus) in females. This article mainly reviews the molecular mechanisms underlying these genes that escape from X-chromosome inactivation and sexual dimorphism of systemic lupus erythematosus. Therefore, elucidating the molecular mechanisms underlying sexual dimorphism in SLE is not only crucial for diagnosing and treating the disease, but also holds theoretical significance in comprehensively understanding the development and regulatory mechanisms of the human immune system.

The foundational framework of tumors: Gametogenesis, p53, and cancer.

The completion-of-tumor hypothesis involved in the dynamic interplay between the initiating oncogenic event and progression is essential to better recognize the foundational framework of tumors. Here we review and extend the gametogenesis-related hypothesis of tumors, because high embryonic/germ cell traits are common in tumors. The century-old gametogenesis-related hypothesis of tumors postulated that tumors arise from displaced/activated trophoblasts, displaced (lost) germ cells, and the reprogramming/reactivation of gametogenic program in somatic cells. Early primordial germ cells (PGCs), embryonic stem (ES) cells, embryonic germ cells (EGCs), and pre-implantation embryos at the stage from two-cell stage to blastocysts originating from fertilization or parthenogenesis have the potential to develop teratomas/teratocarcinomas. In addition, the teratomas/teratocarcinomas/germ cells occur in gonads and extra-gonads. Undoubtedly, the findings provide strong support for the hypothesis. However, it was thought that these tumor types were an exception rather than verification. In fact, there are extensive similarities between somatic tumor types and embryonic/germ cell development, such as antigens, migration, invasion, and immune escape. It was documented that embryonic/germ cell genes play crucial roles in tumor behaviors, e.g. tumor initiation and metastasis. Of note, embryonic/germ cell-like tumor cells at different developmental stages including PGC and oocyte to the early embryo-like stage were identified in diverse tumor types by our group. These embryonic/germ cell-like cancer cells resemble the natural embryonic/germ cells in morphology, gene expression, the capability of teratoma formation, and the ability to undergo the process of oocyte maturation and parthenogenesis. These embryonic/germ cell-like cancer cells are derived from somatic cells and contribute to tumor formation, metastasis, and drug resistance, establishing asexual meiotic embryonic life cycle. p53 inhibits the reactivation of embryonic/germ cell state in somatic cells and oocyte-like cell maturation. Based on earlier and our recent studies, we propose a novel model to complete the gametogenesis-related hypothesis of tumors, which can be applied to certain somatic tumors. That is, tumors tend to establish a somatic asexual meiotic embryonic cycle through the activation of somatic female gametogenesis and parthenogenesis in somatic tumor cells during the tumor progression, thus passing on corresponding embryonic/germ cell traits leading to the malignant behaviors and enhancing the cells' independence. This concept may be instrumental to better understand the nature and evolution of tumors. We rationalize that targeting the key events of somatic pregnancy is likely a better therapeutic strategy for cancer treatment than directly targeting cell mitotic proliferation, especially for those tumors with p53 inactivation.

Single-cell transcriptomic analyses reveal cellular and molecular patterns of rubber tree response to early powdery mildew infection.

As a perennial woody plant, the rubber tree (Hevea brasiliensis) must adapt to various environmental challenges through gene expression in multiple cell types. It is still unclear how genes in this species are expressed at the cellular level and the precise mechanisms by which cells respond transcriptionally to environmental stimuli, especially in the case of pathogen infection. Here, we characterized the transcriptomes in Hevea leaves during early powdery mildew infection using single-cell RNA sequencing. We identified 10 cell types and constructed the first single-cell atlas of Hevea leaves. Distinct gene expression patterns of the cell clusters were observed under powdery mildew infection, which was especially significant in the epidermal cells. Most of the genes involved in host-pathogen interactions in epidermal cells exhibited a pattern of dramatically increased expression with increasing pseudotime. Interestingly, we found that the HbCNL2 gene, encoding a nucleotide-binding leucine-rich repeat protein, positively modulated the defence of rubber leaves against powdery mildew. Overexpression of the HbCNL2 gene triggered a typical cell death phenotype in tobacco leaves and a higher level of reactive oxygen species in the protoplasts of Hevea leaves. The HbCNL2 protein was located in the cytomembrane and nucleus, and its leucine-rich repeat domain interacted with the histidine kinase-like ATPase domain of the molecular chaperone HbHSP90 in the nucleus. Collectively, our results provide the first observation of the cellular and molecular responses of Hevea leaves to biotrophic pathogen infection and can guide the identification of disease-resistance genes in this important tree species.

Inverse design of a light nanorouter for a spatially multiplexed optical filter.

It is attractive to use an optical nanorouter by artificial nanostructures to substitute the traditional Bayer filter for an image array sensor, which, however, poses great challenges in balancing the design strategy and the ease of fabrication. Here, we implement and compare two inverse design schemes for rapid optimization of RGGB Bayer-type optical nanorouter. One is based on the multiple Mie scattering theory and the adjoint gradient that is applicable to arrays of nanospheres with varying sizes, and the other is based on the rigorous coupled wave analysis and the genetic algorithm. In both cases, we study layered nanostructures that can be efficiently modeled respectively which greatly accelerates the inverse design. It is shown that the color-dependent peak collection efficiencies of nanorouters designed in the two methods for red, green, and blue wavelengths reach 37%, 44%, and 45% and 52%, 50%, and 66%, respectively. We further demonstrate color nanorouters that provide light focusing to four quadrants working in both the visible and infrared bands, which promises multispectral imaging applications.

Co/Zn Bimetallic Catalysis Enables Enantioselective Alkynylation of Isatins and α-Ketoesters Using Terminal Alkynes.

We present herein a novel cobalt/zinc bimetallic catalysis system for the realization of an efficient and enantioselective alkynylation of isatins and α-ketoesters using terminal alkynes. By using a simple procedure with all commercially available catalysts, including cobalt bromide, a chiral bisphosphine ligand, and zinc triflate, a range of synthetically useful tertiary propargylic alcohols are accessed with high yields and good enantioselectivities. Control reactions showed that this catalytic system proceeds through activation of the terminal alkyne by zinc triflate and a base, transmetalation from zinc to cobalt, and then the enantio-determining alkynylation of ketones.

Unified Architecture Adaptation for Compressed Domain Semantic Inference.

Advances in both lossy image compression and semantic content understanding have been greatly fueled by deep learning techniques, yet these two tasks have been developed separately for the past decades. In this work, we address the problem of directly executing semantic inference from quantized latent features in the deep compressed domain without pixel reconstruction. Although different methods have been proposed for this problem setting, they either are restrictive to a specific architecture, or are sub-optimal in terms of compressed domain task accuracy. In contrast, we propose a lightweight, plug-and-play solution which is generally compliant with popular learned image coders and deep vision models, making it attractive to vast applications. Our method adapts prevalent pixel domain neural models that are deployed for various vision tasks to directly accept quantized latent features (other than pixels). We further suggest training the compressed domain model by transferring knowledge from its corresponding pixel domain counterpart. Experiments show that our method is compliant with popular learned image coders and vision task models. Under fair comparison, our approach outperforms a baseline method by a) more than 3% top-1 accuracy for compressed domain classification, and b) more than 7% mIoU for compressed domain semantic segmentation, at various data rates.

Rapid Point-of-Care Assay by SERS Detection of SARS-CoV-2 Virus and Its Variants.

Addressing the spread of coronavirus disease 2019 (COVID-19) has highlighted the need for rapid, accurate, and low-cost diagnostic methods that detect specific antigens for SARS-CoV-2 infection. Tests for COVID-19 are based on reverse transcription PCR (RT-PCR), which requires laboratory services and is time-consuming. Here, by targeting the SARS-CoV-2 spike protein, we present a point-of-care SERS detection platform that specifically detects SARS-CoV-2 antigen in one step by captureing substrates and detection probes based on aptamer-specific recognition. Using the pseudovirus, without any pretreatment, the SARS-CoV-2 virus and its variants were detected by a handheld Raman spectrometer within 5 min. The limit of detection (LoD) for the pseudovirus was 124 TU µL-1 (18 fM spike protein), with a linear range of 250-10,000 TU µL-1. Moreover, this assay can specifically recognize the SARS-CoV-2 antigen without cross reacting with specific antigens of other coronaviruses or influenza A. Therefore, the platform has great potential for application in rapid point-of-care diagnostic assays for SARS-CoV-2.

DNF: diffractive neural field for lensless microscopic imaging.

Lensless imaging has emerged as a robust means for the observation of microscopic scenes, enabling vast applications like whole-slide imaging, wave-front detection and microfluidic on-chip imaging. Such system captures diffractive measurements in a compact optical setup without the use of optical lens, and then typically applies phase retrieval algorithms to recover the complex field of target object. However existing techniques still suffer from unsatisfactory performance with noticeable reconstruction artifacts especially when the imaging parameter is not well calibrated. Here we propose a novel unsupervised Diffractive Neural Field (DNF) method to accurately characterize the imaging physical process to best reconstruct desired complex field of the target object through very limited measurement snapshots by jointly optimizing the imaging parameter and implicit mapping between spatial coordinates and complex field. Both simulations and experiments reveal the superior performance of proposed method, having > 6 dB PSNR (Peak Signal-to-Noise Ratio) gains on synthetic data quantitatively, and clear qualitative improvement on real-world samples. The proposed DNF also promises attractive prospects in practical applications because of its ultra lightweight complexity (e.g., 50× model size reduction) and plug-to-play advantage (e.g., random measurements with a coarse parameter estimation).

Activation of embryonic/germ cell-like axis links poor outcomes of gliomas.

BACKGROUND: It is unclear which core events drive the malignant progression of gliomas. Earlier studies have revealed that the embryonic stem (ES) cell/early PGC state is associated with tumourigenicity. This study was designed to investigate the role of ES/PGC state in poor outcomes of gliomas. METHODS: Crispr-Cas9 technology, RT-PCR and animal experiments were used to investigate whether PGC-like cell formation play crucial roles in the tumorigenicity of human glioma cells. Bioinformatic analysis was used to address the link between ES/PGC developmental axis and glioma overall outcomes. RESULTS: Here, our findings showed that germ cell-like cells were present in human gliomas and cultured glioma cells and that the formation of germ cell-like cells was essential for glioma tumours. Bioinformatic analysis showed that the mRNA levels of genes related to embryonic/germ cell development could be detected in most gliomas. Our findings showed that the activation of genes related to reprogramming or the germ cell-like state alone seemed to be insufficient to lead to a malignant prognosis, whereas increased mRNA levels of genes related to the activation of the embryonic/germ cell-like cycle (somatic PGC-EGC-like cycle and somatic parthenogenetic embryo-like cycle) were positively correlated with malignant prognoses and poor clinical outcomes of gliomas. Genes related to the embryonic/germ cell cycle alone or in combination with the WHO grade or 1p19q codeletion status could be used to subdivide gliomas with distinct clinical behaviours. CONCLUSION: Together, our findings indicated that a crucial role of germ cell-like cell formation in glioma initiation as well as activation of genes related with the parthenogenetic embryo-like cycle and PGC-EGC-like cycle link to the malignant prognosis and poor outcomes of gliomas, which might provide a novel way to better understand the nature of and develop targeted therapies for gliomas as well as important markers for predicting clinical outcomes in gliomas.

Current status and outlook of advances in exosome isolation.

Exosomes are extracellular vesicles with a diameter ranging from 30 to 150 nm, which are an important medium for intercellular communication and are closely related to the progression of certain diseases. Therefore, exosomes are considered promising biomarkers for the diagnosis of specific diseases, and thereby, treatments based on exosomes are being widely examined. For exosome-related research, a rapid, simple, high-purity, and recovery isolation method is the primary prerequisite for exosomal large-scale application in medical practice. Although there are no standardized methods for exosome separation and analysis, various techniques have been established to explore their biochemical and physicochemical properties. In this review, we analyzed the progress in exosomal isolation strategies and proposed our views on the development prospects of various exosomal isolation techniques.

Dynamical clustering interrupts motility-induced phase separation in chiral active Brownian particles.

One of the most intriguing phenomena in active matter has been the gas-liquid-like motility-induced phase separation (MIPS) observed in repulsive active particles. However, experimentally, no particle can be a perfect sphere, and the asymmetric shape, mass distribution, or catalysis coating can induce an active torque on the particle, which makes it a chiral active particle. Here, using computer simulations and dynamic mean-field theory, we demonstrate that the large enough torque of circle active Brownian particles in two dimensions generates a dynamical clustering state interrupting the conventional MIPS. Multiple clusters arise from the combination of the conventional MIPS cohesion, and the circulating current caused disintegration. The nonvanishing current in non-equilibrium steady states microscopically originates from the motility "relieved" by automatic rotation, which breaks the detailed balance at the continuum level. This suggests that no equilibrium-like phase separation theory can be constructed for chiral active colloids even with tiny active torque, in which no visible collective motion exists. This mechanism also sheds light on the understanding of dynamic clusters observed in a variety of active matter systems.

Grape Seed Proanthocyanidins Exert a Neuroprotective Effect by Regulating Microglial M1/M2 Polarisation in Rats with Spinal Cord Injury.

Spinal cord injury (SCI) is a highly disabling disorder for which few effective treatments are available. Grape seed proanthocyanidins (GSPs) are polyphenolic compounds with various biological activities. In our preliminary experiment, GSP promoted functional recovery in rats with SCI, but the mechanism remains unclear. Therefore, we explored the protective effects of GSP on SCI and its possible underlying mechanisms. We found that GSP promoted locomotor recovery, reduced neuronal apoptosis, increased neuronal preservation, and regulated microglial polarisation in vivo. We also performed in vitro studies to verify the effects of GSP on neuronal protection and microglial polarisation and their potential mechanisms. We found that GSP regulated microglial polarisation and inhibited apoptosis in PC12 cells induced by M1-BV2 cells through the Toll-like receptor 4- (TLR4-) mediated nuclear factor kappa B (NF-κB) and phosphatidylinositol 3-kinase/serine threonine kinase (PI3K/AKT) signaling pathways. This suggests that GSP regulates microglial polarisation and prevents neuronal apoptosis, possibly by the TLR4-mediated NF-κB and PI3K/AKT signaling pathways.

Clinical Features and Outcomes of Pulmonary Artery Sarcoma.

OBJECTIVES: A retrospective cohort study was designed to describe the clinical features and outcomes of pulmonary artery sarcoma (PAS). METHODS: Twenty-two (22) consecutive patients diagnosed with PAS by pathological examination were enrolled and followed up until they died or until January 2020. The medical records were retrospectively reviewed to evaluate the clinical characteristics, image findings, and outcomes. RESULTS: 1) Twenty-one (21, 95.5%) patients were firstly misdiagnosed. Dyspnoea was the most common presenting symptom (19 of 22, 86.4%). 2) Filling defects in the right pulmonary artery were seen in 17 patients (77.3%) with computed tomography pulmonary angiography or magnetic resonance pulmonary angiography. Among those patients, 14 underwent positron emission tomography-computed tomography detection and 13 (92.9%) were found to have increased uptake value in the pulmonary artery. 3) The median survival (from diagnosis to death or January 2020) of the total series was 11.6 months (range, 0.7-68.5 months). The estimated cumulative survival rates at 1, 2, and 3 years were 52.6%, 32.8%, and 19.7%, respectively. Patients who received surgery and/or chemo-radiotherapy treatment had a better survival rate compared with patients without treatment (the estimated cumulative survival rates at 1, 2, and 3 years were 60.3%, 39.1%, and 29.3%, respectively, vs 33.3%, 16.6%, and 0, accordingly) and better survival time (median survival 17.02 vs 3.16 months, respectively) (p=0.025). CONCLUSIONS: Pulmonary artery sarcoma is easily misdiagnosed, as the symptoms and routine image detection are nonspecific. Positron emission tomography-computed tomography may be helpful in diagnosis. Surgery and/or chemo-radiotherapy offer a chance for better outcomes.

Data-driven coarse-grained modeling of non-equilibrium systems.

Modeling a high-dimensional Hamiltonian system in reduced dimensions with respect to coarse-grained (CG) variables can greatly reduce computational cost and enable efficient bottom-up prediction of main features of the system for many applications. However, it usually experiences significantly altered dynamics due to loss of degrees of freedom upon coarse-graining. To establish CG models that can faithfully preserve dynamics, previous efforts mainly focused on equilibrium systems. In contrast, various soft matter systems are known to be out of equilibrium. Therefore, the present work concerns non-equilibrium systems and enables accurate and efficient CG modeling that preserves non-equilibrium dynamics and is generally applicable to any non-equilibrium process and any observable of interest. To this end, the dynamic equation of a CG variable is built in the form of the non-stationary generalized Langevin equation (nsGLE), where the two-time memory kernel is determined from the data of the auto-correlation function of the observable of interest. By embedding the nsGLE in an extended dynamics framework, the nsGLE can be solved efficiently to predict the non-equilibrium dynamics of the CG variable. To prove and exploit the equivalence of the nsGLE and extended dynamics, the memory kernel is parameterized in a two-time exponential expansion. A data-driven hybrid optimization process is proposed for the parameterization, which integrates the differential-evolution method with the Levenberg-Marquardt algorithm to efficiently tackle a non-convex and high-dimensional optimization problem.

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics.

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Proliferation of Bifidobacterium L80 under different proportions of milk protein hydrolysate.

The intestinal microecological environment is critical to an infant's growth. For those infants consuming milk power, it is very important to improve the intestinal microecological environment to promote the healthy growth of infants. In this paper, Milk protein hydrolysate (MPH), consisting of different proportions of proteins and small molecule peptides (5:5, 4:6, 3:7, 2:8, 1:9) were added to infant formula powder (IFP). The effects of MFP-enriched IFP addition on proliferation and metabolism of Bifidobacterium L80 were studied. Compared with MPH-free IFP, MFP-enriched IFP with 1:9 of proteins to small molecule peptides significantly enhanced the proliferation of Bifidobacterium L80, resulting in higher cell density, greater viable counts and higher titratable acidity. MFP-enriched IFP increased the content of seven organic acids and H2O2 in the system, and improved the antibacterial activity to E. coli BL21. This study suggested that MPH could be an effective addition to infant formula powder to promote the growth of Bifidobacterium, so to improve the intestinal health of infants.

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved.

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system. The CG modeling is built upon the framework of the generalized Langevin equation (GLE). The key is to determine each term in the GLE by directly linking it to atomistic data. In particular, we propose a two-stage Gaussian process-based Bayesian optimization method to infer the non-Markovian memory kernel from the data of the velocity autocorrelation function (VACF). Considering that the long-time behaviors of the VACF and memory kernel for polymer solutions can exhibit hydrodynamic scaling (algebraic decay with time), we further develop an active learning method to determine the emergence of hydrodynamic scaling, which can accelerate the inference process of the memory kernel. The proposed methods do not rely on how the mean force or CG potential in the GLE is constructed. Thus, we also compare two methods for constructing the CG potential: a deep learning method and the iterative Boltzmann inversion method. With the memory kernel and CG potential determined, the GLE is mapped onto an extended Markovian process to circumvent the expensive cost of directly solving the GLE. The accuracy and computational efficiency of the proposed CG modeling are assessed in a model star-polymer solution system at three representative concentrations. By comparing with the reference atomistic simulation results, we demonstrate that the proposed CG modeling can robustly and accurately reproduce the dynamic and structural properties of polymers in solution.