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Immune infiltration of tumors is closely associated with clinical outcome in renal cell carcinoma (RCC). Tumor‐infiltrating immune cells (TIICs) regulate cancer progression and are appealing therapeutic targets. The purpose of this study was to determine the composition of TIICs in RCC and further reveal the independent prognostic values of TIICs. CIBERSORT, an established algorithm, was applied to estimate the proportions of 22 immune cell types based on gene expression profiles of 891 tumors. Cox regression was used to evaluate the association of TIICs and immune checkpoint modulators with overall survival (OS). We found that CD8+ T cells were associated with prolonged OS (hazard ratio [HR] = 0.09, 95% confidence interval [CI].01‐.53; P = 0.03) in chromophobe carcinoma (KICH). A higher proportion of regulatory T cells was associated with a worse outcome (HR = 1.59, 95% CI 1.23‐.06; P < 0.01) in renal clear cell carcinoma (KIRC). In renal papillary cell carcinoma (KIRP), M1 macrophages were associated with a favorable outcome (HR = .43, 95% CI .25‐.72; P < 0.01), while M2 macrophages indicated a worse outcome (HR = 2.55, 95% CI 1.45‐4.47; P < 0.01). Moreover, the immunomodulator molecules CTLA4 and LAG3 were associated with a poor prognosis in KIRC, and IDO1 and PD‐L2 were associated with a poor prognosis in KIRP. This study indicates TIICs are important determinants of prognosis in RCC meanwhile reveals potential targets and biomarkers for immunotherapy development. We described the immune landscape in detail, revealing the distinct immune infiltration patterns of different subtypes and stages of RCC. We further revealed relationships between TIIC and molecular subtypes, tumor stages, recurrent genomic alterations and survival in RCC. Our work advances the understanding of immune response meanwhile reveals potential targets and biomarkers for immunotherapy development.

Two-dimensional network-structured carbon nanoscale building blocks, going beyond graphene, are of fundamental importance, and creating such structures and developing their applications have broad implications in environment, electronics and energy. Here, we report a facile route, based on electro-spraying/netting, to self-assemble two-dimensional carbon nanostructured networks on a large scale. Manipulation of the dynamic ejection, deformation and assembly of charged droplets by control of Taylor cone instability and micro-electric field, enables the creation of networks with characteristics combining nanoscale diameters of one-dimensional carbon nanotube and lateral infinity of two-dimensional graphene. The macro-sized (meter-level) carbon nanostructured networks show extraordinary nanostructural properties, remarkable flexibility (soft polymeric mechanics having hard inorganic matrix), nanoscale-level conductivity, and outstanding performances in distinctly different areas like filters, separators, absorbents, and wearable electrodes, supercapacitors and cells. This work should make possible the innovative design of high-performance, multi-functional carbon nanomaterials for various applications. Creating two-dimensional carbon nanoscale building blocks is of fundamental importance, but is extremely challenging. Here, the authors report a facile electro-spraying/netting technique for self-assembly of two-dimensional carbon nanostructured networks with superflexibility and multi-functionality.

Incorporating passive heating structures into personal thermal management technologies could effectively mitigate the escalating energy crisis. However, current passive heating materials struggle to balance thickness and insulating capability, resulting in compromised comfort, space efficiency, and limited thermoregulatory performance. Here, a dual air-gelation strategy, is developed to directly synthesize ultrathin and self-sustainable heating metafabric with 3D dual-network structure during electrospinning. Controlling the interactions among polymer, solvent, and water enables the microphase separation of charged jets, while adjusting the distribution of carbon black nanoparticles within charged fluids to form fibrous networks composed of interlaced aerogel micro/nanofibers with heat storage capabilities. With a low thickness of 0.18 mm, the integrated metafabric exhibits exceptional thermal insulation performance (15.8 mW m −1 K −1 ), superhydrophobicity, enhanced mechanical properties, and high breathability while maintaining self-sustainable radiative heating ability (long-lasting warming of 8.8 °C). This strategy provides rich possibilities to develop advanced fibrous materials for smart textiles and thermal management. Aerogels have limited application in textiles due to their fragility and poor processability. Here, authors synthesize an ultrathin aerogel-structured micro/nanofiber metafabric for self-sustained heating by developing a dual air-gelation strategy.

Solid polymer electrolytes with large-scale processability and interfacial compatibility are promising candidates for solid-state lithium metal batteries. Among various systems, poly(vinylidene fluoride)-based polymer electrolytes with residual solvent are appealing for room-temperature battery operations. However, their porous structure and limited ionic conductivity hinder practical application. Herein, we propose a phase regulation strategy to disrupt the symmetry of poly(vinylidene fluoride) chains and obtain the dense composite electrolyte through the incorporation of MoSe 2 sheets. The electrolyte with high dielectric constant can optimize the solvation structures to achieve high ionic conductivity and low activation energy. The in-situ reactions between MoSe 2 and Li metal generate Li 2 Se fast conductor in solid electrolyte interphase, which improves the Coulombic efficiency and interfacial kinetics. The solid-state Li||Li cells achieve robust cycling at 1 mA cm −2 , and the Li||LiNi 0.8 Co 0.1 Mn 0.1 O 2 full cells show practical performance at high rate (3C), high loading (2.6 mAh cm −2 ) and in pouch cell. Polymer electrolytes based on poly(vinylidene fluoride) with residual solvents are appealing for room-temperature battery operations. Here, the authors present a phase regulation approach to achieve a dense electrolyte and enhance ionic conductivity through the incorporation of MoSe 2 sheets.

Aqueous Zn-halogen batteries, valued for high safety, large capacity, and low cost, suffer from the polyhalide shuttle effect and chaotic zinc electrodeposition, reducing energy efficiency and lifespan. Here we show a cation-driven positive electrode phase transition to suppress the shuttle effect and achieve uniform zinc electrodeposition, along with an anion kinetic enhancement strategy to improve energy efficiency and lifespan. Taking tetramethylammonium halide (TMAX, X = F, Cl, Br) as a subject, TMA + promotes oriented zinc (101) deposition on the negative electrode through electrostatic shielding, significantly extending cycling life. Concurrently, it captures I 3 – on the positive electrode, forming a stable solid-phase interhalide complex that enhances coulombic efficiency. Compared to I 3 – and TMAI 3 , X – anions lower the Gibbs free energy differences of I –  → I 2 X – and I 2 X –  → TMAI 2 X, accelerating I – /I 2 X – /TMAI 2 X conversions and improving voltage efficiency. In TMAF-modified electrolytes, zinc interhalide complex batteries achieve a high energy efficiency of 95.2% at 0.2 A g –1 with good reversibility, showing only 0.1% capacity decay per cycle over 1000 cycles. At 1 A g –1 , they show a low decay rate of 0.1‰ per cycle across 10,000 cycles. This study provides insights into enhancing energy efficiency and long-term stability for sustainable energy storage. Aqueous Zn-halogen batteries suffer from efficiency loss due to polyhalide shuttling and chaotic Zn plating. Here, authors demonstrate a cation-anion synergy strategy that suppresses shuttling and directs uniform Zn deposition, achieving 95.2% high energy efficiency.

There is an increasing demand worldwide on advanced two-dimensional (2D) nanofibrous networks with applications ranging from environmental protection and electrical devices to bioengineering. Design of such nanoarchitectured materials has been considered a long-standing challenge. Herein, we report a direct electronetting technology for the fabrication of self-assembled 2D nanoarchitectured networks (nano-nets) from various materials. Tailoring of the precursor solution and of the microelectric field allows charged droplets, which are ejected from a Taylor cone, to levitate, deform and phase separate before they self-assemble a 2D nanofibre network architecture. The fabricated nano-nets show mechanical robustness and benefit from nanostructural properties such as enhanced surface wettability, high transparency, separation and improved air filtration properties. Calcination of the nano-nets results in the formation of carbon nano-nets with electric conductivity and titanium dioxide nano-nets with bioprotective properties. Nanofibrous networks are applied in environmental protection and electrical or bioengineering and experience a great demand, but the design of these functional materials is challenging. Here the authors demonstrate an electronetting technology for fabricating self-assembled nanonets from various materials.

Traffic noise pollution has posed a huge burden to the global economy, ecological environment and human health. However, most present traffic noise reduction materials suffer from a narrow absorbing band, large weight and poor temperature resistance. Here, we demonstrate a facile strategy to create flexible ceramic nanofibrous sponges (FCNSs) with hierarchically entangled graphene networks, which integrate unique hierarchical structures of opened cells, closed-cell walls and entangled networks. Under the precondition of independent of chemical crosslinking, high enhancement in buckling and compression performances of FCNSs is achieved by forming hierarchically entangled structures in all three-dimensional space. Moreover, the FCNSs show enhanced broadband noise absorption performance (noise reduction coefficient of 0.56 in 63–6300 Hz) and lightweight feature (9.3 mg cm –3 ), together with robust temperature-invariant stability from –100 to 500 °C. This strategy paves the way for the design of advanced fibrous materials for highly efficient noise absorption. Noise pollution has been a burden to the global economy, environment, and human health. Here the authors demonstrate a facile route to produce flexible ceramic nanofibrous sponges with hierarchically entangled graphene networks and its excellent noise absorption properties at elevated temperatures.

The development history and principle of interwell tracer testing and single-well tracer testing were investigated, and the application conditions and application advantages of different tracer testing were analyzed through the analysis of two types of tracer testing technologies and test results in the production process of SMi oilfield with medium porosity and low permeability, so as to provide reference for the study of tracer development in the same type of oilfield. Tracer testing technology can master the connection between injection well and production well, effectively judge the connection relationship between Wells, and provide a basis for well pattern adjustment; The seepage velocity of injected fluid in the formation can be directly observed, and the dominant injection direction can be determined, which provides reference for solving the plane contradiction. It can judge whether there is fracture system or high permeability strip, which provides ideas for high precision numerical simulation to avoid risks. It can master the remaining oil situation of single well and judge the washing efficiency of chemical agents effectively on the basis of saving investment.

Achieving surface design and control of biomaterial scaffolds with nanometer- or micrometer-scaled functional films is critical to mimic the unique features of native extracellular matrices, which has significant technological implications for tissue engineering including cell-seeded scaffolds, microbioreactors, cell assembly, tissue regeneration, etc. Compared with other techniques available for surface design, layer-by-layer (LbL) self-assembly technology has attracted extensive attention because of its integrated features of simplicity, versatility, and nanoscale control. Here we present a brief overview of current state-of-the-art research related to the LbL self-assembly technique and its assembled biomaterials as scaffolds for tissue engineering. An overview of the LbL self-assembly technique, with a focus on issues associated with distinct routes and driving forces of self-assembly, is described briefly. Then, we highlight the controllable fabrication, properties, and applications of LbL self-assembly biomaterials in the forms of multilayer nanofilms, scaffold nanocoatings, and three-dimensional scaffolds to systematically demonstrate advances in LbL self-assembly in the field of tissue engineering. LbL self-assembly not only provides advances for molecular deposition but also opens avenues for the design and development of innovative biomaterials for tissue engineering.

Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising. Using relations of surrounding pixels can effectively resolve this problem. Inspired by that, we propose a robust deformed denoising CNN (RDDCNN) in this paper. The proposed RDDCNN contains three blocks: a deformable block (DB), an enhanced block (EB) and a residual block (RB). The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture, according to relations of surrounding pixels. The EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers, batch normalisation (BN) and ReLU, which can enhance the learning ability of the proposed RDDCNN. To address long‐term dependency problem, the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean image. Besides, we implement a blind denoising model. Experimental results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative analysis. Codes can be obtained at https://github.com/hellloxiaotian/RDDCNN.