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Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a deep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view. We demonstrate the feasibility of the approach with upper-abdomen, lung, and head-and-neck computed tomography scans from three patients. Volumetric reconstruction via deep learning could be useful in image-guided interventional procedures such as radiation therapy and needle biopsy, and might help simplify the hardware of tomographic imaging systems. A deep-learning model trained to map 2D projection views of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view.

Polycrystalline La2−xBixNiMnO6 (x = 0.2, 0.5, 1.0) samples were synthesized via a high-temperature and high-pressure method, with their structural, magnetic, and electrical properties systematically characterized. X-ray diffraction (XRD) confirms a monoclinic double perovskite structure (space group P21/n) for all samples, while Bi3+ induces a lattice volume expansion trend inferred from XRD peak shifts due to its larger ionic radius than La3+. Magnetically, all exhibit ferromagnetism and soft magnetic features, with magnetization decreasing as Bi content increases. The x = 0.2 and 0.5 samples show two distinct Curie temperatures, both decreasing with Bi substitution, whereas the higher Curie temperature vanishes in the x = 1.0 sample, likely due to Bi-induced structural changes. Electrically, all display semiconducting behavior (resistivity: x = 0.5 > x = 0.2 > x = 1.0) and negative magnetoresistance (MR) at 200 K, peaking at 12% (x = 0.5) and 7.5% (x = 1.0). For the x = 1.0 sample, negative magnetoresistance strengthens with decreasing temperature (130–200 K), with magnetoresistance-field (MR-H) curves showing herringbone and arc shapes.

Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques. Existing genomic data analysis methods tend to not take full advantage of underlying biological characteristics. Here, the authors leverage the inherent interactions of scRNA-seq data and develop a cartography strategy to contrive the data into a spatially configured genomap for accurate deep pattern discovery.

The development of the mammalian cerebral cortex depends on careful orchestration of proliferation, maturation, and migration events, ultimately giving rise to a wide variety of neuronal and non-neuronal cell types. To better understand cellular and molecular processes that unfold during late corticogenesis, we perform single-cell RNA-seq on the mouse cerebral cortex at a progenitor driven phase (embryonic day 14.5) and at birth—after neurons from all six cortical layers are born. We identify numerous classes of neurons, progenitors, and glia, their proliferative, migratory, and activation states, and their relatedness within and across age. Using the cell-type-specific expression patterns of genes mutated in neurological and psychiatric diseases, we identify putative disease subtypes that associate with clinical phenotypes. Our study reveals the cellular template of a complex neurodevelopmental process, and provides a window into the cellular origins of brain diseases. The authors perform single-cell RNA-seq of the mouse neocortex at an embryonic time point and at birth, and identify new and known cell types, and cell relatedness within and across age. These data serve as a resource to understand brain development and the cellular origins of brain diseases.

Osteoarthritis (OA) is the most common joint disease worldwide and a leading cause of disability. Characterized by degradation of articular cartilage, synovial inflammation, and changes in periarticular and subchondral bone, OA can negatively impact an individual’s physical and mental well-being. Recent studies have reported several critical signaling pathways as key regulators and activators of cellular and molecular processes during OA development. Wnt signaling is one such pathway whose signaling molecules and regulators were shown to be abnormally activated or suppressed. As such, agonists and antagonists of those molecules are potential candidates for OA treatment. Notably, a recent phase I clinical trial (NCT02095548) demonstrated the potential of SM04690, a small-molecule inhibitor of the Wnt signaling pathway, as a disease-modifying oseoarthritis drug (DMOAD). This review summarizes the role and mechanism of Wnt signaling and related molecules in regulating OA progression, with a view to accelerating the translation of such evidence into the development of strategies for OA treatment, particularly with respect to potential applications of molecules targeting the Wnt signaling pathway.

In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance. This Review discusses the use of deep generative models, federated learning and transformer models to address challenges in the deployment of machine learning for healthcare.

As dye demand continues to rapidly increase in the food, pharmaceutical, cosmetic, paper, textile, and leather industries, an industrialization increase is occurring. Meanwhile, the degradation and removal of azo dyes have raised broad concern regarding the hazards posed by these dyes to the ecological environment and human health. Physicochemical treatments have been applied but are hindered by high energy and economic costs, high sludge production, and chemicals handling. Comparatively, the bioremediation technique is an eco-friendly, removal-efficient, and cost-competitive method to resolve the problem. This paper provides scientific and technical information about recent advances in the biodegradation of azo dyes. It expands the biodegradation efficiency, characteristics, and mechanisms of various microorganisms containing bacteria, fungi, microalgae, and microbial consortia, which have been reported to biodegrade azo dyes. In addition, information about physicochemical factors affecting dye biodegradation has been compiled. Furthermore, this paper also sketches the recent development and characteristics of advanced bioreactors.

In recent years, circular RNAs (circRNAs), a new star of non-coding RNA, have been emerged as vital regulators and gained much attention for involvement of initiation and progression of diverse kinds of human diseases, especially cancer. However, regulatory role, clinical significance and underlying mechanisms of circRNAs in triple-negative breast cancer (TNBC) still remain largely unknown. Here, the expression profile of circRNAs in 4 pairs of TNBC tissues and adjacent non-tumor tissues was analyzed by RNA-sequencing. Quantitative real-time PCR and in situ hybridization were used to determine the level and prognostic values of circAGFG1 in two TNBC cohorts. Then, functional experiments in vitro and in vivo were performed to investigate the effects of circAGFG1 on tumor growth and metastasis in TNBC. Mechanistically, fluorescent in situ hybridization, dual luciferase reporter assay, RNA pull-down and RNA immunoprecipitation experiments were performed to confirm the interaction between circAGFG1 and miR-195-5p in TNBC. We found that circAGFG1 was evidently up-regulated in TNBC, and its level was correlated with clinical stage, pathological grade and poor prognosis of patients with TNBC. The results indicated that circAGFG1 could promote TNBC cell proliferation, mobility and invasion as well as tumorigenesis and metastasis in vivo. Mechanistic analysis showed that circAGFG1 may act as a ceRNA (competing endogenous RNA) of miR-195-5p to relieve the repressive effect of miR-195-5p on its target cyclin E1 (CCNE1). Our findings suggest that circAGFG1 promotes TNBC progression through circAGFG1/miR-195-5p/CCNE1 axis and it may serve as a new diagnostic marker or target for treatment of TNBC patients.

Background Trees such as Populus are planted extensively for reforestation and afforestation. However, their successful establishment greatly depends upon ambient environmental conditions and their relative resistance to abiotic and biotic stresses. Polyphenol oxidase (PPO) is a ubiquitous metalloproteinase in plants, which plays crucial roles in mediating plant resistance against biotic and abiotic stresses. Although the whole genome sequence of Populus trichocarpa has long been published, little is known about the PPO genes in Populus , especially those related to drought stress, mechanical damage, and insect feeding. Additionally, there is a paucity of information regarding hormonal responses at the whole genome level. Results A genome-wide analysis of the poplar PPO family was performed in the present study, and 18 PtrPPO genes were identified. Bioinformatics and qRT-PCR were then used to analyze the gene structure, phylogeny, chromosomal localization, gene replication, cis -elements, and expression patterns of PtrPPOs . Sequence analysis revealed that two-thirds of the PtrPPO genes lacked intronic sequences. Phylogenetic analysis showed that all PPO genes were categorized into 11 groups, and woody plants harbored many PPO genes. Eighteen PtrPPO genes were disproportionally localized on 19 chromosomes, and 3 pairs of segmented replication genes and 4 tandem repeat genomes were detected in poplars. Cis -acting element analysis identified numerous growth and developmental elements, secondary metabolism processes, and stress-related elements in the promoters of different PPO members. Furthermore, PtrPPO genes were expressed preferentially in the tissues and fruits of young plants. In addition, the expression of some PtrPPOs could be significantly induced by polyethylene glycol, abscisic acid, and methyl jasmonate, thereby revealing their potential role in regulating the stress response. Currently, we identified potential upstream TFs of PtrPPOs using bioinformatics. Conclusions Comprehensive analysis is helpful for selecting candidate PPO genes for follow-up studies on biological function, and progress in understanding the molecular genetic basis of stress resistance in forest trees might lead to the development of genetic resources.

High-performance quantum memory for quantized states of light is a prerequisite building block of quantum information technology. Despite great progresses of optical quantum memories based on interactions of light and atoms, physical features of these memories still cannot satisfy requirements for applications in practical quantum information systems, since all of them suffer from trade-off between memory efficiency and excess noise. Here, we report a high-performance cavity-enhanced electromagnetically-induced-transparency memory with warm atomic cell in which a scheme of optimizing the spatial and temporal modes based on the time-reversal approach is applied. The memory efficiency up to 67 ± 1% is directly measured and a noise level close to quantum noise limit is simultaneously reached. It has been experimentally demonstrated that the average fidelities for a set of input coherent states with different phases and amplitudes within a Gaussian distribution have exceeded the classical benchmark fidelities. Thus the realized quantum memory platform has been capable of preserving quantized optical states, and is ready to be applied in quantum information systems, such as distributed quantum logic gates and quantum-enhanced atomic magnetometry. Quantum memories usually suffer from a trade-off between efficiency and excess noise. Here, by exploiting the time-reversal approach for improving modes matching, the authors show a warm-atomic-cell-based cavity-enhanced memory with 67% efficiency and noise level close to quantum noise limit.