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Abstract Versatile printing of polymers, metals, and composites always calls for simple, economic approaches. Here we present an approach to three-dimensional (3D) printing of polymeric, metallic, and composite materials at room conditions, based on the polymeric vapor-induced phase separation (VIPS) process. During VIPS 3D printing (VIPS-3DP), a dissolved polymer-based ink is deposited in an environment where nebulized non-solvent is present, inducing the low-volatility solvent to be extracted from the filament in a controllable manner due to its higher chemical affinity with the non-solvent used. The polymeric phase is hardened in situ as a result of the induced phase separation process. The low volatility of the solvent enables its reclamation after the printing process, significantly reducing its environmental footprint. We first demonstrate the use of VIPS-3DP for polymer printing, showcasing its potential in printing intricate structures. We further extend VIPS-3DP to the deposition of polymer-based metallic inks or composite powder-laden polymeric inks, which become metallic parts or composites after a thermal cycle is applied. Furthermore, spatially tunable porous structures and functionally graded parts are printed by using the printing path to set the inter-filament porosity as well as an inorganic space-holder as an intra-filament porogen.

Abstract The genetic basis of human facial variation and craniofacial birth defects remains poorly understood. Distant-acting transcriptional enhancers control the fine-tuned spatiotemporal expression of genes during critical stages of craniofacial development. However, a lack of accurate maps of the genomic locations and cell type-resolved activities of craniofacial enhancers prevents their systematic exploration in human genetics studies. Here, we combine histone modification, chromatin accessibility, and gene expression profiling of human craniofacial development with single-cell analyses of the developing mouse face to define the regulatory landscape of facial development at tissue- and single cell-resolution. We provide temporal activity profiles for 14,000 human developmental craniofacial enhancers. We find that 56% of human craniofacial enhancers share chromatin accessibility in the mouse and we provide cell population- and embryonic stage-resolved predictions of their in vivo activity. Taken together, our data provide an expansive resource for genetic and developmental studies of human craniofacial development.

For simple-input and simple-output (SISO) discrete-time nonlinear systems, an observer-based event-triggered model-free adaptive sliding mode predictive control technique (EMFASPC) is put forth in this study. The estimate of pseudo partial derivatives (PPD) and the transmission of I/O data are both carried out periodically at the time of event triggering to conserve network resources. A unified framework of event-triggered modelfree adaptive control with an adaptive observer and an event-triggered PPD estimation method is constructed based on the equivalent data model after compact format dynamic linearization (CFDL). The controller part adopts integral sliding mode control (SMC) combined with a rolling optimization idea of model predictive control (MPC) to predict the expected trajectory of the sliding mode state and generate the optimal control input. According to the relationship among the system tracking error, current measurement data, and the previous trigger time output, the event trigger condition is set to determine the next event trigger time, which reduces the unnecessary transmission on the premise of system stability. The stability performance of the closed-loop system is analyzed by the Lyapunov method. Finally, numerical simulation and the shell-and-tube heat exchanger control system simulation are carried out to verify that the proposed algorithm has good robustness and tracking accuracy under the limited bandwidth and computing resources.

Abstract Background Pervasive usage of alternative promoters leads to the deregulation of gene expression in carcinogenesis and may drive the emergence of new genes in spermatogenesis. However, little is known regarding the mechanisms underpinning the activation of alternative promoters. Results Here we describe how alternative cancer-testis-specific transcription is activated. We show that intergenic and intronic CTCF binding sites, which are transcriptionally inert in normal somatic cells, could be epigenetically reprogrammed into active de novo promoters in germ and cancer cells. BORIS/CTCFL, the testis-specific paralog of the ubiquitously expressed CTCF, triggers the epigenetic reprogramming of CTCF sites into units of active transcription. BORIS binding initiates the recruitment of the chromatin remodeling factor, SRCAP, followed by the replacement of H2A histone with H2A.Z, resulting in a more relaxed chromatin state in the nucleosomes flanking the CTCF binding sites. The relaxation of chromatin around CTCF binding sites facilitates the recruitment of multiple additional transcription factors, thereby activating transcription from a given binding site. We demonstrate that the epigenetically reprogrammed CTCF binding sites can drive the expression of cancer-testis genes, long noncoding RNAs, retro-pseudogenes, and dormant transposable elements. Conclusions Thus, BORIS functions as a transcription factor that epigenetically reprograms clustered CTCF binding sites into transcriptional start sites, promoting transcription from alternative promoters in both germ cells and cancer cells.

Abstract The interphase genome is dynamically organized in the nucleus and decorated with chromatin-associated RNA (caRNA). It remains unclear whether the genome architecture modulates the spatial distribution of caRNA and vice versa. Here, we generate a resource of genome-wide RNA-DNA and DNA-DNA contact maps in human cells. These maps reveal the chromosomal domains demarcated by locally transcribed RNA, hereafter termed RNA-defined chromosomal domains. Further, the spreading of caRNA is constrained by the boundaries of topologically associating domains (TADs), demonstrating the role of the 3D genome structure in modulating the spatial distribution of RNA. Conversely, stopping transcription or acute depletion of RNA induces thousands of chromatin loops genome-wide. Activation or suppression of the transcription of specific genes suppresses or creates chromatin loops straddling these genes. Deletion of a specific caRNA-producing genomic sequence promotes chromatin loops that straddle the interchromosomal target sequences of this caRNA. These data suggest a feedback loop where the 3D genome modulates the spatial distribution of RNA, which in turn affects the dynamic 3D genome organization.

This paper studies the quantized output data observer based data-driven model-free adaptive control(qMFAC) for discrete-time nonlinear systems with unknown structures and network transmission constraints. First, an adaptive observer based on quantized output data is generated with the use of a logarithmic quantizer, and a pseudo-biased derivative(PPD) estimation scheme based on the output quantized data observer is proposed. By dynamic linearization(DL) techniques, a incomplete equivalent data model containing quantized output data are built. Then, the observer output is used to develop a data-driven model-free adaptive control strategy that only makes use of quantified output and input. With the Lyapunov function and sector boundary approaches, the bounded tracking performance of the proposed qMFAC is strictly theoretical analyzed, and the effectiveness of qMFAC is verified through numerical simulation and simulation experiments of the shell and tube heat exchanger control system.

Abstract Multiplexed DNA fluorescence in situ hybridization (FISH) imaging technologies have been developed to map the folding of chromatin fibers at tens of nanometers and up to several kilobases in resolution in single cells. However, computational methods to reliably identify chromatin loops from such imaging datasets are still lacking. Here we present a Single-Nucleus Analysis Pipeline for multiplexed DNA FISH (SnapFISH), to process the multiplexed DNA FISH data and identify chromatin loops. SnapFISH can identify known chromatin loops from mouse embryonic stem cells with high sensitivity and accuracy. In addition, SnapFISH obtains comparable results of chromatin loops across datasets generated from diverse imaging technologies. SnapFISH is freely available at https://github.com/HuMingLab/SnapFISH .

Abstract Topologically associating domain (TAD) boundaries partition the genome into distinct regulatory territories. Anecdotal evidence suggests that their disruption may interfere with normal gene expression and cause disease phenotypes1–3, but the overall extent to which this occurs remains unknown. Here we demonstrate that targeted deletions of TAD boundaries cause a range of disruptions to normal in vivo genome function and organismal development. We used CRISPR genome editing in mice to individually delete eight TAD boundaries (11–80 kb in size) from the genome. All deletions examined resulted in detectable molecular or organismal phenotypes, which included altered chromatin interactions or gene expression, reduced viability, and anatomical phenotypes. We observed changes in local 3D chromatin architecture in 7 of 8 (88%) cases, including the merging of TADs and altered contact frequencies within TADs adjacent to the deleted boundary. For 5 of 8 (63%) loci examined, boundary deletions were associated with increased embryonic lethality or other developmental phenotypes. For example, a TAD boundary deletion near Smad3/Smad6 caused complete embryonic lethality, while a deletion near Tbx5/Lhx5 resulted in a severe lung malformation. Our findings demonstrate the importance of TAD boundary sequences for in vivo genome function and reinforce the critical need to carefully consider the potential pathogenicity of noncoding deletions affecting TAD boundaries in clinical genetics screening.

Space–time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. When working in the heterogeneous environment, the number of training samples that satisfy independent and identically distributed (IID) conditions is insufficient, making it difficult to ensure the estimation accuracy of the clutter plus noise covariance matrix for traditional STAP methods. Sparse recovery–based STAP (SR–STAP) methods have received widespread attention in the past few years. The accurate estimation of the clutter plus noise covariance matrix can be achieved using only a few training samples. The iterative adaptive approach (IAA) can quickly and accurately estimate the power spectrum, but applying this method directly to the STAP method cannot produce good performance. In this paper, a fast IAA–based SR–STAP method is proposed. Based on the weighted l1 problem, the IAA spectrum is used as a weighted term to obtain a good approximation. In order to obtain an analytical solution, we use the weighted l2 norm to approximate the weighted l1 norm without loss of performance. Compared with the IAA–STAP method, the proposed method is more robust to errors. Moreover, the proposed method has a fast computational speed. The effectiveness of the proposed method is demonstrated by simulations.

Lung cancer is the leading cause of cancer death in the United States and worldwide, and a major source of cancer health disparities. Lung cancer cell lines provide key in vitro models for molecular studies of lung cancer development and progression, and for pre-clinical drug testing. To ensure health equity, it is imperative that cell lines representing different lung cancer histological types, carrying different cancer driver genes, and representing different genders, races, and ethnicities should be available. This is particularly relevant for cell lines from Black men, who experience the highest lung cancer mortality in the United States. Here, we undertook a review of the available lung cancer cell lines and their racial and ethnic origin. We noted a marked imbalance in the availability of cell lines from different races and ethnicities. Cell lines from Black patients were strongly underrepresented, and we identified no cell lines from Hispanic/Latin(x) (H/L), American Indian/American Native (AI/AN), or Native Hawaiian or other Pacific Islander (NHOPI) patients. The majority of cell lines were derived from White and Asian patients. Also missing are cell lines representing the cells-of-origin of the major lung cancer histological types, which can be used to model lung cancer development and to study the effects of environmental exposures on lung tissues. To our knowledge, the few available immortalized alveolar epithelial cell lines are all derived from White subjects, and the race and ethnicity of a handful of cell lines derived from bronchial epithelial cells are unknown. The lack of an appropriately diverse collection of lung cancer cell lines and lung cancer cell-of-origin lines severely limits racially and ethnically inclusive lung cancer research. It impedes the ability to develop inclusive models, screen comprehensively for effective compounds, pre-clinically test new drugs, and optimize precision medicine. It thereby hinders the development of therapies that can increase the survival of minority and underserved patients. The noted lack of cell lines from underrepresented groups should constitute a call to action to establish additional cell lines and ensure adequate representation of all population groups in this critical pre-clinical research resource.

This paper proposes a new observer-based model-free adaptive sliding mode predictive control method (MFASPC) for discrete-time nonlinear systems. This scheme first equates the discrete-time nonlinear system to a linear form using a data-driven compact form dynamic linearization (CFDL) technique, establishes a data model consisting of only the pseudo partial derivatives (PPD), input data and output data, designs adaptive observers to achieve the estimation of the unknown PPD. The controller design part uses integral sliding mode control (SMC) to ensure the system’s robustness. In contrast, with its constraint characteristics, the model predictive control (MPC) replaces the traditional switching control of SMC. The closed-loop control quantities are obtained by solving a rolling optimization problem in the finite time domain to provide dynamic optimal control action. The theoretical derivation of the Lyapunov function is used to demonstrate the system’s stability. In order to verify the effectiveness of the proposed algorithm, numerical simulations and Photovoltaic power generation system simulation experiments are conducted, respectively, and the results show that the proposed control algorithm has a very reliable tracking capability and control accuracy.

Abstract Sparse recovery algorithms have been applied to the Space‐time adaptive processing for reducing the requirement of samples over the past 15 years. However, many Sparse recovery algorithms are not robust and need accurate user parameters. Conventional sparse Bayesian learning (SBL) algorithms are insensitivity to user parameters but converge slowly. To remedy the limitation, two iterative reweighted algorithms are proposed based on SBL. In order to minimise the SBL penalty function, we construct its upper‐bounding surrogate function via the concave conjugate function and apply iterative reweighted algorithms to minimise the surrogate function. Theoretical analysis and numerical experiments all exhibit great performance of the proposed algorithms.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

The rapid and disorderly expansion of urban construction land has exacerbated the contradiction between land use and low-carbon development. In this paper, we use the spatial autocorrelation model and coupling model to analyze the spatial characteristics of the coupled coordination degree of land transfer and carbon emissions in 291 cities in China. The multi-scale geographically weighted regression (MGWR) model is used to explore the spatial heterogeneity of the influence of socioeconomic factors on their coupled coordination degree. The results show that: from 2005 to 2015, the scale of land transfer and carbon emissions has been increasing quantitatively and spatially showing a shift from the southeast coast to the central and western regions. In 2005, 2010, and 2015, the global Moran’s I of the coupled coordination degree are 0.3045, 0.3725, and 0.3388, respectively, indicating that the coupled coordination degree between land transfer and carbon emissions has a significant positive spatial autocorrelation. The MGWR model indicates that the influence of socioeconomic factors on the coupling coordination degree has significant spatial heterogeneity at different time nodes. In 2005 and 2015, the coefficients of the NGR on the coupling coordination of land transfer and carbon emissions have obvious stratification characteristics, with the coefficients decreasing from northeast to southwest. In 2010, the high coefficient (0.924∼0.989) of GPC is mainly distributed in the central region. The coefficient of the PD ranges from 0.464 to 0.918, but the difference of influence degree between the southeast coast and the northwest is obvious. This study may provide new clues for sustainable urban development and carbon reduction.

Enhancers for endodermal organs are primed at the chromatin level prior to lineage induction by FOXA pioneer transcription factors; how pervasive this is, is not well known. Here the authors show that only a small subset of organ-specific enhancers are bound and primed by FOXA prior to lineage induction, whereas the majority do not undergo chromatin priming and engage FOXA upon lineage induction.

Abstract To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target‐free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non‐homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi‐channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm.

Metastatic breast cancer is the second most common cancer related death amongst women in the United States. The common locations for metastases—lymph nodes, bone, lung, liver, brain – are well reported. Rarely, breast cancer metastasizes to the bladder. We present a case of primary hormone positive, HER2 unamplified breast cancer, thirteen years in remission, with triple negative metastases to the bladder. Urologists should consider breast metastases in patients with even a remote history of breast cancer who present with gross hematuria to avoid delays in treatment.

Aims: The use of 3D-printed titanium implant (DT) can effectively guide bone regeneration. DT triggers a continuous host immune reaction, including macrophage type 1 polarization, that resists osseointegration. Interleukin 4 (IL4) is a specific cytokine modulating osteogenic capability that switches macrophage polarization type 1 to type 2, and this switch favours bone regeneration. Methods: IL4 at concentrations of 0, 30, and 100 ng/ml was used at day 3 to create a biomimetic environment for bone marrow mesenchymal stromal cell (BMMSC) osteogenesis and macrophage polarization on the DT. The osteogenic and immune responses of BMMSCs and macrophages were evaluated respectively. Results: DT plus 30 ng/ml of IL4 (DT + 30 IL4) from day 3 to day 7 significantly (p < 0.01) enhanced macrophage type 2 polarization and BMMSC osteogenesis compared with the other groups. Local injection of IL4 enhanced new bone formation surrounding the DT. Conclusion: DT + 30 IL4 may switch macrophage polarization at the appropriate timepoints to promote bone regeneration.

The gap between in vitro and in vivo assays has inspired biomimetic model development. Tissue engineered models that attempt to mimic the complexity of microvascular networks have emerged as tools for investigating cell-cell and cell-environment interactions that may be not easily viewed in vivo. A key challenge in model development, however, is determining how to recreate the multi-cell/system functional complexity of a real network environment that integrates endothelial cells, smooth muscle cells, vascular pericytes, lymphatics, nerves, fluid flow, extracellular matrix, and inflammatory cells. The objective of this mini-review is to overview the recent evolution of popular biomimetic modeling approaches for investigating microvascular dynamics. A specific focus will highlight the engineering design requirements needed to match physiological function and the potential for top-down tissue culture methods that maintain complexity. Overall, examples of physiological validation, basic science discoveries, and therapeutic evaluation studies will emphasize the value of tissue culture models and biomimetic model development approaches that fill the gap between in vitro and in vivo assays and guide how vascular biologists and physiologists might think about the microcirculation.

Abstract Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 917 surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98 (95% confidence interval (CI): 0.97–1.00), 0.98 (95% CI: 0.96–1.00) and 0.97 (95% CI: 0.96–0.98), respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion.

Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by geometry and the problem of polarization. Sparse-recovery-based STAP (SR-STAP) methods have garnered significant attention in the past few decades because they only require a small number of training samples. Sparse Bayesian Learning (SBL) methods have seen increasing amounts of development due to its robust, self-regularizing nature and because it is not sensitive to user parameters, but it converges slowly. In this paper, a novel fast SBL (NFSBL) method is put forward to increase the rate of convergence. To minimize the SBL penalty function, the proposed method introduces the conjugate function to construct a surrogate function. Additional solution sparsity will be achieved through iteratively minimizing the surrogate function. Then, from the proposed method, we could obtain a more accurate clutter plus noise covariance matrix. Numerical simulation results express that this method could acquire better performance of STAP and improvement in convergence and computational complexity for a conformal array.

Single cell analysis of transposase-accessible chromatin is deepening our understanding on the origins of cellular diversity, yet methods are limited by data sparsity. Here, the authors introduce SnapATAC, a pipeline to resolve cellular heterogeneity and reveal candidate regulatory elements across different cell populations.

CTCF mediates DNA–DNA interactions by forming dimers that directly bind to both sides of the DNA. Here the authors reveal that a CTCF known associated zinc finger, ZNF143, mediates gene transcription through CTCF–DNA binding to maintain murine hematopoietic stem and progenitor cell integrity.

Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller.

Summary: Single-cell technologies measure unique cellular signatures but are typically limited to a single modality. Computational approaches allow the fusion of diverse single-cell data types, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells, we devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types and assessed the effectiveness of computational data fusion methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility, and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling the prediction of cell types that are associated with diseases.

AIM: To investigate the expression and significance of stromal cell-derived factor(SDF-1)and its receptor CXC chemokine receptor 4(CXCR4)in patients with pterygium of different ages.METHODS: From January 2018 to October 2018, surgical specimens of 60 patients with primary pterygium and 60 eyes(including 30 eyes younger than 50 years old and 30 eyes older than 50 years old)were collected from the No.474 Hospital of Chinese PLA, at the same time collect age matched strabismus diorthosis and normal conjunctiva tissues of 30 patients with retinal detachment repairs(including 15 eyes younger than 50 years, older than 50 years of age 15 eyes).The expression and localization of SDF-1/CXCR4 in pterygium tissue specimens were detected by HE staining and immunohistochemistry, and the relationship between the expression of SDF-1/CXCR4 and the clinical characteristics of patients was analyzed. The mean optical density of SDF-1 /CXCR4 was measured by IPP 6.0 software.RESULTS: SDF-1/CXCR4 showed slightly positive or no positive expression in normal conjunctival basal cells, but positive expression in both full-layer conjunctival epithelial cells and vascular endothelial cells in pterygium, with significant difference in expression level, and more obvious expression in basal cells, showing obvious polarity. The expression levels of SDF-1 and CXCR4 in pterygium tissues were higher than those in normal conjunctival tissues, and the difference was statistically significant(PPCONCLUSION: SDF-1 and CXCR4 expression is up-regulated in pterygium, suggesting that SDF-1 and CXCR4 participates in the formation of pterygium and inhibits the SDF-1/CXCR4 signaling pathway, which may inhibit the occurrence of pterygium, and may also become a drug therapeutic target for pterygium, and become a new research direction. The higher expression of CXCR4 in young pterygium patients suggests that individualized drug administration may be realized in the future to reduce the waste of medical resources.

Heart failure has a heterogeneous etiology and the genetic underpinnings are not well understood. Here, Arvanitis et al. perform GWAS meta-analysis including 10,976 heart failure cases and 437,573 controls, identify new loci near ABO and ACTN2 and show that deletion of a ACTN2 enhancer leads to reduced ACTN2 expression in differentiating cardiomyocytes.

Background: Recent studies indicate that bladder cancer is among the top 10 most common cancers in the world (Saginala et al. 2022). Bladder cancer frequently reoccurs, and prognostic judgments may vary among clinicians. As a favorable prognosis may help to inform less aggressive treatment plans, classification of histopathology slides is essential for the accurate prognosis and effective treatment of bladder cancer patients. Developing automated and accurate histopathology image analysis methods can help pathologists determine the prognosis of patients with bladder cancer. Materials and methods: In this study, we introduced Bladder4Net, a deep learning pipeline, to classify whole-slide histopathology images of bladder cancer into two classes: low-risk (combination of PUNLMP and low-grade tumors) and high-risk (combination of high-grade and invasive tumors). This pipeline consists of four convolutional neural network (CNN)-based classifiers to address the difficulties of identifying PUNLMP and invasive classes. We evaluated our pipeline on 182 independent whole-slide images from the New Hampshire Bladder Cancer Study (NHBCS) (Karagas et al., 1998; Sverrisson et al., 2014; Sverrisson et al., 2014) collected from 1994 to 2004 and 378 external digitized slides from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/tcga). Results: The weighted average F1-score of our approach was 0.91 (95% confidence interval (CI): 0.86–0.94) on the NHBCS dataset and 0.99 (95% CI: 0.97–1.00) on the TCGA dataset. Additionally, we computed Kaplan–Meier survival curves for patients who were predicted as high risk versus those predicted as low risk. For the NHBCS test set, patients predicted as high risk had worse overall survival than those predicted as low risk, with a log-rank p-value of 0.004. Conclusions: If validated through prospective trials, our model could be used in clinical settings to improve patient care.

Methyl jasmonate (MeJA) is a volatile substance derived from jasmonic acid (JA), and it responds to interbiotic and abiotic stresses by participating in interplant communication. Despite its function in interplant communication, the specific role of MeJA in insect defense responses is poorly understood. In this study, we found that carboxylesterase (CarE) activities, glutathione-S-transferase (GSTs) activities, and cytochrome mono-oxygenases (P450s) content increased more after the feeding of diets containing xanthotoxin, while larvae exposed to MeJA fumigation also showed higher enzyme activity in a dose-dependent manner: lower and medium concentrations of MeJA induced higher detoxification enzyme activities than higher concentrations of MeJA. Moreover, MeJA improved the growth of larvae fed on the control diet without toxins and diets with lower concentrations of xanthotoxin (0.05%); however, MeJA could not protect the larvae against higher concentrations of xanthotoxin (0.1%, 0.2%). In summary, we demonstrated that MeJA is effective at inducing S. litura defense response, but the enhanced detoxifying ability could not overcome the strong toxins.

Space-time adaptive processing (STAP) is a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-dependent clutter, non-uniform spatial steering vector, and polarization sensitivity. In this paper, a knowledge aided STAP method based on sparse learning via iterative minimization (SLIM) combined with Laplace distribution is proposed to improve the STAP performance for a conformal array. The proposed method can avoid selecting the user parameter. the proposed method constructs a dictionary matrix that is composed of the space-time steering vector by using the prior knowledge of the range cell under test (CUT) distributed in clutter ridge. Then, the estimated sparse parameters and noise power can be used to calculate a relatively accurate clutter plus noise covariance matrix (CNCM). This method could achieve superior performance of clutter suppression for a conformal array. Simulation results demonstrate the effectiveness of this method.

Abstract Background The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. Results To address these questions, we perform Hi-C on lymphoblastoid cell lines from 20 individuals. We identify thousands of regions across the genome where 3D chromatin conformation varies between individuals and find that this variation is often accompanied by variation in gene expression, histone modifications, and transcription factor binding. Moreover, we find that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality, and density of local cis contacts. We map hundreds of quantitative trait loci associated with 3D chromatin features and find evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. Conclusion Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized.

Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.

Risk loci for type 2 diabetes (T2D) reside in pancreatic islet enhancers. Here, the authors generate high-resolution maps of islet chromatin conformation using Hi-C which they combine with ATAC-seq and ChIP-seq data to annotate candidate target genes of enhancers and validate IGF2BP2 activity in mouse islets.

Structural variants (SVs) in human genomes contribute diversity and diseases. Here, the authors use a multi-platform strategy to generate haplotype-resolved SVs for three human parent–child trios.

The ecological environment is important for the survival and development of human beings, and objective and accurate monitoring of changes in the ecological environment has received extensive attention. Based on the normalized difference vegetation index (NDVI), wetness (WET), normalized differential build-up and bare soil index (NDBSI), and land surface temperature (LST), the principal component analysis method is used to construct a comprehensive index to evaluate the ecological environment’s quality. The R package “Relainpo” is used to estimate the relative importance and contribution rate of NDVI, WET, NDBSI, and LST to the remote sensing ecological index (RSEI). The optimal parameter geographic detector (OPGD) model is used to quantitatively analyze the influencing factors, degree of influence, and interaction of the RSEI. The results show that from 2001 to 2020, the area with a poor grade quality of the RSEI in Guangzhou decreased from 719.2413 km2 to 660.4146 km2, while the area with an excellent quality grade of the RSEI increased from 1778.8311 km2 to 1978.9390 km2. The NDVI (40%) and WET (35%) contributed significantly to the RSEI, while LST and NDBSI contributed less to the RSEI. The results of single factor analysis revealed that soil type have the greatest impact on the RSEI with a coefficient (Q) of 0.1360, followed by a temperature with a coefficient (Q) of 0.1341. The interaction effect of two factors is greater than that of a single factor on the RSEI, and the interaction effect of different factors on the RSEI is significant, but the degree of influence is not consistent. This research may provide new clues for the stabilization and improvement of ecological environmental quality.

Non-coding variation in complex human disease has been well established by genome-wide association studies, and is thought to involve regulatory elements, such as enhancers, whose variation affects the expression of the gene responsible for the disease. The regulatory elements often lie far from the gene they regulate, or within introns of genes differing from the regulated gene, making it difficult to identify the gene whose function is affected by a given enhancer variation. Enhancers are connected to their target gene promoters via long-range physical interactions (loops). In our study, we re-mapped, onto the human genome, more than 10,000 enhancers connected to promoters via long-range interactions, that we had previously identified in mouse brain-derived neural stem cells by RNApolII-ChIA-PET analysis, coupled to ChIP-seq mapping of DNA/chromatin regions carrying epigenetic enhancer marks. These interactions are thought to be functionally relevant. We discovered, in the human genome, thousands of DNA regions syntenic with the interacting mouse DNA regions (enhancers and connected promoters). We further annotated these human regions regarding their overlap with sequence variants (single nucleotide polymorphisms, SNPs; copy number variants, CNVs), that were previously associated with neurodevelopmental disease in humans. We document various cases in which the genetic variant, associated in humans to neurodevelopmental disease, affects an enhancer involved in long-range interactions: SNPs, previously identified by genome-wide association studies to be associated with schizophrenia, bipolar disorder, and intelligence, are located within our human syntenic enhancers, and alter transcription factor recognition sites. Similarly, CNVs associated to autism spectrum disease and other neurodevelopmental disorders overlap with our human syntenic enhancers. Some of these enhancers are connected (in mice) to homologs of genes already associated to the human disease, strengthening the hypothesis that the gene is indeed involved in the disease. Other enhancers are connected to genes not previously associated with the disease, pointing to their possible pathogenetic involvement. Our observations provide a resource for further exploration of neural disease, in parallel with the now widespread genome-wide identification of DNA variants in patients with neural disease.

In order to avoid the influence of the test system itself on the autonomous navigation and performance test accuracy of unmanned surface vehicles (USVs), a test method for the obstacle-avoidance performance of USVs based on mobile-buoy–shore multisource-sensing-data fusion is proposed. In this method, a mobile-buoy-integrated test system is designed (that is, the test instrument is installed on the mobile buoy). The buoy is both the carrier of the test instrument and the obstacle. The software and hardware functions of the test system are realized in modules, and the obstacle-avoidance monitoring function of the USV is realized by the trajectory-tracking method of buoy perception preprocessing and shore adaptive weighted fusion. Then, on the basis of the mobile-buoy–shore sensing-data-fusion method, performance tests and a quantitative evaluation of the obstacle perception, static-obstacle avoidance, and dynamic-obstacle avoidance of the USV were carried out. The results show that: (1) the tested USV can accurately identify the distance between buoys; (2) the three static-obstacle-avoidance performance scores of the single obstacle, continuous obstacle, and inflection-point obstacle are 74.81, 77.14, and 47.61, respectively, and the quantitative evaluation score of the static-obstacle-avoidance comprehensive performance is 66.4; (3) the obstacle-avoidance-performance scores of overtaking, encounter, and cross encounter are about 53.92, 36.51, and 6.48, respectively, and the quantitative evaluation score of the comprehensive performance of the dynamic-obstacle avoidance is 72.36. The above quantitative evaluation results show that the system can: participate in track intervention and obstacle-avoidance monitoring as an obstacle; give the static- and dynamic-obstacle-avoidance quantitative evaluation results in a predetermined way, which verifies the feasibility and effectiveness of the obstacle-avoidance-performance test system of the USV on the basis of mobile-buoy–shore multisource-sensing fusion; and be used for the testing and evaluation of the obstacle-avoidance performance of USVs.

The canonical Wnt pathway transcriptional co-activator β-catenin regulates self-renewal and differentiation of mammalian nephron progenitor cells (NPCs). We modulated β-catenin levels in NPC cultures using the GSK3 inhibitor CHIR99021 (CHIR) to examine opposing developmental actions of β-catenin. Low CHIR-mediated maintenance and expansion of NPCs are independent of direct engagement of TCF/LEF/β-catenin transcriptional complexes at low CHIR-dependent cell-cycle targets. In contrast, in high CHIR, TCF7/LEF1/β-catenin complexes replaced TCF7L1/TCF7L2 binding on enhancers of differentiation-promoting target genes. Chromosome confirmation studies showed pre-established promoter–enhancer connections to these target genes in NPCs. High CHIR-associated de novo looping was observed in positive transcriptional feedback regulation to the canonical Wnt pathway. Thus, β-catenin’s direct transcriptional role is restricted to the induction of NPCs, where rising β-catenin levels switch inhibitory TCF7L1/TCF7L2 complexes to activating LEF1/TCF7 complexes at primed gene targets poised for rapid initiation of a nephrogenic program.

Genetic variants associated with type 2 diabetes (T2D) risk affect gene regulation in metabolically relevant tissues, such as pancreatic islets. Here, we investigated contributions of regulatory programs active during pancreatic development to T2D risk. Generation of chromatin maps from developmental precursors throughout pancreatic differentiation of human embryonic stem cells (hESCs) identifies enrichment of T2D variants in pancreatic progenitor-specific stretch enhancers that are not active in islets. Genes associated with progenitor-specific stretch enhancers are predicted to regulate developmental processes, most notably tissue morphogenesis. Through gene editing in hESCs, we demonstrate that progenitor-specific enhancers harboring T2D-associated variants regulate cell polarity genes LAMA1 and CRB2. Knockdown of lama1 or crb2 in zebrafish embryos causes a defect in pancreas morphogenesis and impairs islet cell development. Together, our findings reveal that a subset of T2D risk variants specifically affects pancreatic developmental programs, suggesting that dysregulation of developmental processes can predispose to T2D.

The G/T transversion rs35705950, located approximately 3 kb upstream of the MUC5B start site, is the cardinal risk factor for idiopathic pulmonary fibrosis (IPF). Here, we investigate the function and chromatin structure of this –3 kb region and provide evidence that it functions as a classically defined enhancer subject to epigenetic programming. We use nascent transcript analysis to show that RNA polymerase II loads within 10 bp of the G/T transversion site, definitively establishing enhancer function for the region. By integrating Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) analysis of fresh and cultured human airway epithelial cells with nuclease sensitivity data, we demonstrate that this region is in accessible chromatin that affects the expression of MUC5B. Through applying paired single-nucleus RNA- and ATAC-seq to frozen tissue from IPF lungs, we extend these findings directly to disease, with results indicating that epigenetic programming of the –3 kb enhancer in IPF occurs in both MUC5B-expressing and nonexpressing lineages. In aggregate, our results indicate that the MUC5B-associated variant rs35705950 resides within an enhancer that is subject to epigenetic remodeling and contributes to pathologic misexpression in IPF.

We report a case of esophageal cancer with solitary metastasis to the testicle in a 71-year-old man. The tumor was picked up on physical exam following new onset complaints of pain and swelling. While most testicular masses in older men are due to lymphoma, this case highlights the need to consider metastatic disease as a source of new symptoms in patients with a recent cancer diagnosis.

Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient to guarantee the performance in the heterogeneous clutter environment. Currently, most sparse recovery/representation (SR) techniques to reduce the requirement of training samples still suffer from high computational complexities. To remedy this problem, a fast group sparse Bayesian learning approach is proposed. Instead of employing all the dictionary atoms, the proposed algorithm identifies the support space of the data and then employs the support space in the sparse Bayesian learning (SBL) algorithm. Moreover, to extend the modified hierarchical model, which can only apply to real-valued signals, the real and imaginary components of the complex-valued signals are treated as two independent real-valued variables. The efficiency of the proposed algorithm is demonstrated both with the simulated and measured data.

Interpretation of multiparametric magnetic resonance imaging (mpMRI) for prostate cancer diagnosis and staging can be challenging and, in some cases, benign prostate disease can mimic locally advanced malignancy. We present the case of a 57 year-old male with biopsy-proven Gleason 3 + 4 prostate cancer and a preoperative mpMRI showing extraprostatic extension who was later found to have infiltrating malakoplakia on final surgical pathology. This case highlights the importance of recognizing that malakoplakia of the prostate can present as a PI-RADS 5 lesion with extracapsular extension on mpMRI. Such cases can result in wide-excision, non-nerve sparing radical prostatectomies that may be unwarranted.

In response to the urgent need for quantitative evaluation of the obstacle avoidance performance of an unmanned ship, a quantitative evaluation model is established to evaluate quantitatively the obstacle perception performance, static obstacle avoidance performance and dynamic obstacle avoidance performance of an unmanned ship. The base data for calculating are derived from the shore-based database; the evaluation factor layer is evaluated by the cost function method. Based on the established evaluation model, the quantitative evaluation score of the obstacle perception performance of the unmanned ship is obtained by data analysis for the 50 to 100 m buoy and 100 m island obstacle perception test. The quantitative evaluation score of the static obstacle avoidance performance obtained by testing the performance of a single obstacle, continuous obstacle and inflection obstacle is 68.8 points. For buoys as dynamic obstacles, the dynamic obstacle avoidance performance quantitative evaluation score of 64.13 points is obtained by testing the performance of obstacle avoidance in chasing, facing and crossing encounters. The analysis of the test data saved to the database verify the rationality of the quantitative evaluation model, which can provide reference for the quantitative evaluation and improvement of the unmanned ship’s obstacle avoidance performance.

Summary: Liver cancer has become the second most deadly malignant disease, with no efficient targeted or immune therapeutic agents available yet. While dissecting the roles of cytoplasmic signaling molecules in hepatocarcinogenesis using an inducible mouse gene targeting system, Mx1-cre, we identified a potent liver tumor-inhibitory effect of synthetic double-stranded RNA (dsRNA), polyinosinic-polycytidylic acid (pIC), an inducer of the Mx1-cre system. Injection of pIC at the pre-cancer stage robustly suppressed liver tumorigenesis either induced by chemical carcinogens or by Pten loss and associated hepatosteatosis. The immunostimulatory dsRNA inhibited liver cancer initiation, apparently by boosting multiple anti-tumor activities of innate immunity, including induction of immunoregulatory cytokines, activation of NK cells and dendritic cells, and reprogramming of macrophage polarization. This study paves the way for the development of preventive and early interfering strategies for liver cancer to reduce the rapidly increasing incidences of liver cancer in an ever-growing population with chronic liver disorders. : Lee et al. report an inhibitory effect of pIC on liver cancer initiation through activating NK cells, reprogramming M1 and M2 macrophages, promoting injured hepatocyte death, and suppressing tumor cell genesis and expansion. Keywords: liver cancer, dsRNA, macrophage polarization, innate immune system, immunotherapy, preventive therapy, liver tumor-initiating cells

AIM: To evaluate the application and effect of bandage contact lens in pterygium excision combined with conjunctival transplantation(CAT).METHODS: In a prospective, randomized, controlled clinical study, 110 patients(110 eyes)diagnosed with primary pterygium were collected in PLA NO.474 Hospital from January 2015 to January 2016. The 110 patients enrolled in the study in turn, and divided into two groups by the odd and even number. The odd number divided into bandage contact lens group(CAT + bandage contact lens, n=55), while the even number divided into control group(CAT, n=55). Visual analog scale(VAS)and corneal irritation were evaluated on 1, 3 and 7d after operation. Cornea fluorescent staining testing was carried out on 3d after operation. Following-up all the patients with 1a at least observed the recurrence of pterygium. RESULTS:The score of VAS in bandage contact lens group less than that in control group on 1d(4.13±2.06 vs 5.80±1.93, t=4.391, Pvs 3.65±1.65, t=5.422, Pt=1.295, P=0.198)after operation. The corneal irritation in bandage contact lens group less than that in control group on 1d and 3d after operation(PP=0.052). Cornea fluorescent staining testing area in bandage contact lens group was less than that in control group on 7d after operation(0.33±0.37mm2 vs 2.73±2.21mm2, t=7.921, PP=1.000). CONCLUSION:Bandage contact lens could significantly release pain and corneal irritation, promote the healing of the corneal epitheliums in the defected area, and increase the postoperative comfort level in patients after operation.

AIM: To explore the possibility of human umbilical cord mesenchymal stem cells (hUCMSCs), human umbilical vein endothelial cells (hUVECs), human dental pulp stem cells (hDPSCs) and human periodontal ligament stem cells (hPDLSCs) serving as feeder cells in co-culture systems for the cultivation of limbal stem cells. METHODS: Different feeder layers were cultured in Dulbecco’s modified Eagle’s medium (DMEM)/F12 and were treated with mitomycin C. Rabbits limbal stem cells (LSCs) were co-cultured on hUCMSCs, hUVECs, hDPSCs, hPDLSCs and NIH-3T3, and then comparative analysis were made between each group to see their respective colony-forming efficiency (CFE) assay and immunofluorescence (IPO13,CK3/12). RESULTS: The efficiency of the four type cells in supporting the LSCs morphology and its cellular differentiation was similar to that of NIH-3T3 fibroblasts as demonstrated by the immunostaining properties analysis, with each group exhibiting a similar strong expression pattern of IPO13, but lacking CK3 and CK12 expression in terms of immunostaining. But hUCMSCs, hDPSCs and hPDLSCs feeder layers were superior in promoting colony formation potential of cells when compared to hUVECs and feeder-cell-free culture. CONCLUSION: hUCMSCs, hDPSCs and hPDLSCs can be a suitable alternative to conventional mouse NIH-3T3 feeder cells, so that risk of zoonotic infection can be diminished.

Summary: Technological improvements enable single-cell epigenetic analyses of organ development. We reasoned that high-resolution single-cell chromatin accessibility mapping would provide needed insight into the epigenetic reprogramming and transcriptional regulators involved in normal mammary gland development. Here, we provide a single-cell resource of chromatin accessibility for murine mammary development from the peak of fetal mammary stem cell (fMaSC) functional activity in late embryogenesis to the differentiation of adult basal and luminal cells. We find that the chromatin landscape within individual cells predicts both gene accessibility and transcription factor activity. The ability of single-cell chromatin profiling to separate E18 fetal mammary cells into clusters exhibiting basal-like and luminal-like chromatin features is noteworthy. Such distinctions were not evident in analyses of droplet-based single-cell transcriptomic data. We present a web application as a scientific resource for facilitating future analyses of the gene regulatory networks involved in mammary development. : The ability to deconstruct complex tissues into their constituent cell states and identify molecular mechanisms involved in cell differentiation is enabling deeper understanding of normal development and disease. Chung et al. use snATAC-seq to agnostically determine the chromatin states correlated with cell-state changes during embryonic and postnatal mammary development. Keywords: single cell, snATAC-seq, ATAC-seq, chromatin profiling, mammary gland, mammary gland development, transcription factor dynamics, lineage relationships, pseudotime ordering, differentiation trajectory