Ubiquitin ligase NEDD4 promotes the proliferation of hepatocellular carcinoma cells through targeting PCDH17 protein for ubiquitination and degradation.

Neural precursor cell expressed developmentally downregulated 4 (NEDD4), an E3 ubiquitin ligase, is commonly upregulated in human hepatocellular carcinoma (HCC) and functions as an oncogenic factor in the progression of HCC, but the molecular mechanism needs be further explored. In this study, we found that NEDD4 could facilitate the proliferation of HCC cells, which was associated with regulating the ERK signaling. Further investigation showed that protocadherin 17 (PCDH17) was a potential substrate of NEDD4, and restoration of PCDH17 could block the facilitation of ERK signaling and HCC cells proliferation induced by NEDD4 overexpression. Whereafter, we confirmed that NEDD4 interacted with PCDH17 and promoted the Lys33-linked polyubiquitination and degradation of it via the proteasome pathway. Finally, NEDD4 protein level was found to be inversely correlated with that of PCDH17 in human HCC tissues. In conclusion, these results suggest that NEDD4 acts as an E3 ubiquitin ligase for PCDH17 ubiquitination and degradation thereby promoting the proliferation of HCC cells through regulating the ERK signaling, which may provide novel evidence for NEDD4 to be a promising therapeutic target for HCC.

Anion Exchange of Metal Particles on Carbon-Based Skeletons for Promoting Dielectric Equilibrium and High-Efficiency Electromagnetic Wave Absorption.

The combination of carbon materials and magnetic elements is considered as an effective strategy to obtain high-performance electromagnetic wave (EMW) absorption materials. However, using nanoscale regulation to the optimization of composite material dielectric properties and enhanced magnetic loss properties is facing significant challenges. Here, the dielectric constant and magnetic loss capability of the carbon skeleton loaded with Cr compound particles are further tuned to enhance the EMW absorption performance. After 700 °C thermal resuscitation of the Cr3-polyvinyl pyrrolidone composite material, the chromium compound is represented as a needle-shaped structure of nanoparticles, which is fixed on the carbon skeleton derived from the polymer. The size-optimized CrN@PC composites are obtained after the substitution of more electronegative nitrogen elements using an anion-exchange strategy. The minimum reflection loss value of the composite is -105.9 dB at a CrN particle size of 5 nm, and the effective absorption bandwidth is 7.68 GHz (complete Ku-band coverage) at 3.0 mm. This work overcomes the limitations of impedance matching imbalance and magnetic loss deficiency in carbon-based materials through size tuning, and opens a new way to obtain carbon-based composites with ultra-high attenuation capability.

Statistical Physics of Unsupervised Learning with Prior Knowledge in Neural Networks.

Integrating sensory inputs with prior beliefs from past experiences in unsupervised learning is a common and fundamental characteristic of brain or artificial neural computation. However, a quantitative role of prior knowledge in unsupervised learning remains unclear, prohibiting a scientific understanding of unsupervised learning. Here, we propose a statistical physics model of unsupervised learning with prior knowledge, revealing that the sensory inputs drive a series of continuous phase transitions related to spontaneous intrinsic-symmetry breaking. The intrinsic symmetry includes both reverse symmetry and permutation symmetry, commonly observed in most artificial neural networks. Compared to the prior-free scenario, the prior reduces more strongly the minimal data size triggering the reverse-symmetry breaking transition, and moreover, the prior merges, rather than separates, permutation-symmetry breaking phases. We claim that the prior can be learned from data samples, which in physics corresponds to a two-parameter Nishimori constraint. This Letter thus reveals mechanisms about the influence of the prior on unsupervised learning.

Multifunctional Ti3C2Tx MXene-Based Composite Coatings with Superhydrophobic Anti-icing and Photothermal Deicing Properties.

Although advances in industrial products have brought convenience to our lives, severe weather has increased the safety risks to industrial facilities. Considerable efforts have been made to develop high-performance superhydrophobic anti-icing coatings. Nevertheless, designing a functional coating with both anti-icing properties and self-deicing remains a major challenge. Here, we propose a design strategy to exploit a photothermal superhydrophobic multifunctional coating with excellent anti-icing and deicing properties based on MXene by high-temperature sintering and layer-by-layer coating. Specifically, poly(tetrafluoroethylene) (PTFE) particles provide low surface energy and binding effects. Room-temperature-vulcanized silicone rubber (RTV) enhances the dispersion of the composite particles and the adhesion of the functional coating to a glass substrate. Furthermore, the functional coatings constructed with MXene exhibit outstanding photothermal effects, imparting excellent superhydrophobicity (CA = 160.18°, SA = 1.8°) and efficient photothermal conversion (equilibrium temperature of 109.3 °C). An anti-icing/deicing test is simulated to confirm their efficient anti-icing/deicing performance in practical applications. Overall, the functional coatings designed in this work can be applied in real industrial facilities.

Associative memory model with arbitrary Hebbian length.

Conversion of temporal to spatial correlations in the cortex is one of the most intriguing functions in the brain. The learning at synapses triggering the correlation conversion can take place in a wide integration window, whose influence on the correlation conversion remains elusive. Here we propose a generalized associative memory model of pattern sequences, in which pattern separations within an arbitrary Hebbian length are learned. The model can be analytically solved, and predicts that a small Hebbian length can already significantly enhance the correlation conversion, i.e., the stimulus-induced attractor can be highly correlated with a significant number of patterns in the stored sequence, thereby facilitating state transitions in the neural representation space. Moreover, an anti-Hebbian component is able to reshape the energy landscape of memories, akin to the memory regulation function during sleep. Our work thus establishes the fundamental connection between associative memory, Hebbian length, and correlation conversion in the brain.

Eigenvalue spectrum of neural networks with arbitrary Hebbian length.

Associative memory is a fundamental function in the brain. Here, we generalize the standard associative memory model to include long-range Hebbian interactions at the learning stage, corresponding to a large synaptic integration window. In our model, the Hebbian length can be arbitrarily large. The spectral density of the coupling matrix is derived using the replica method, which is also shown to be consistent with the results obtained by applying the free probability method. The maximal eigenvalue is then obtained by an iterative equation, related to the paramagnetic to spin glass transition in the model. Altogether, this work establishes the connection between the associative memory with arbitrary Hebbian length and the asymptotic eigen-spectrum of the neural-coupling matrix.

Design and synthesis of NiCo/Co4S3@C hybrid material with tunable and efficient electromagnetic absorption.

Producing electromagnetic wave absorbers with high absorption capacity and wide absorption bandwidth is still a challenge task, which limits its application in human life. In this study, a novel carbon-coated NiCo alloy/Co4S3 hybrid material was fabricated by a simple strategy. By adjusting the annealing temperature, the electromagnetic wave absorption property of NiCo/Co4S3@C hybrid material can be further improved. Herein, the NiCo/Co4S3@C hybrid material obtained at 750 °C exhibits satisfactory electromagnetic wave absorption performance. The minimum reflection loss value of -56.96 dB is obtained at 10.32 GHz, and the matching thickness is only 2.71 mm. When the thickness of the absorber is in the range of 1.8-5.0 mm, the effective absorption bandwidth is controlled between 4.64 and 18 GHz. The satisfactory properties of the hybrid materials are attributed to the space charge polarization caused by heterogeneous NiCo alloys, interfacial polarization between components and good impedance matching characteristics. The results show that NiCo/Co4S3@C hybrid material is a potential electromagnetic wave absorber.

Synthesis of a hierarchical carbon fiber@cobalt ferrite@manganese dioxide composite and its application as a microwave absorber.

In this study, a novel hierarchical carbon fiber@cobalt ferrite@manganese dioxide (CF@CoFe2O4@MnO2) composite was facilely prepared via a sol-gel method and hydrothermal reaction. The morphology, structure, chemical and element composition, crystal form, elemental binding energy, magnetic behavior and microwave absorbing performance of the composite were carefully investigated. According to its hysteresis loops, the composite exhibits a typical soft magnetic behavior, with a M s value of 30.2 emu g-1. Besides, the as-synthesized CF@CoFe2O4@MnO2 composite exhibits superior microwave absorption performance mainly due to reasonable electromagnetic matching, and its minimum reflection loss value can reach -34 dB with a sample thickness of just 1.5 mm. The composite can be regarded as an ideal microwave absorber.

Preparation and Characterization of Epoxy Resin Filled with Ti3C2Tx MXene Nanosheets with Excellent Electric Conductivity.

MXene represents new kinds of two-dimensional material transition metal carbides and/or carbonitrides, which have attracted much attention in various applications including electrochemical storage devices, catalysts, and polymer composite. Here, we report a facile method to synthesize Ti3C2Tx MXene nanosheets and prepare a novel electrically conductive adhesive based on epoxy resin filled with Ti3C2Tx MXene nanosheets by solution blending. The structure, morphology, and performance of Ti3C2Tx MXene nanosheets and epoxy/Ti3C2Tx MXene nanosheets composite were investigated. The results show that Ti3C2Tx MXene possesses nanosheet structure. Ti3C2Tx MXene nanosheets were homogeneously dispersed in epoxy resin. Electrical conductivity and mechanical properties measurements reveal that the epoxy/Ti3C2Tx MXene nanosheet composite exhibited both good electrical conductivity (4.52 × 10-4 S/m) and favorable mechanical properties (tensile strength of 66.2 MPa and impact strength of 24.2 kJ/m2) when the content of Ti3C2Tx MXene nanosheets is 1.2 wt %. Thus, Ti3C2Tx MXene is a promising filler for electrically conductive adhesive with high electric conductivity and high mechanical performance.

Potential Applications and Antifungal Activities of Engineered Nanomaterials against Gray Mold Disease Agent Botrytis cinerea on Rose Petals.

Nanoparticles (NPs) have great potential for use in the fields of biomedicine, building materials, and environmental protection because of their antibacterial properties. However, there are few reports regarding the antifungal activities of NPs on plants. In this study, we evaluated the antifungal roles of NPs against Botrytis cinerea, which is a notorious worldwide fungal pathogen. Three common carbon nanomaterials, multi-walled carbon nanotubes, fullerene, and reduced graphene oxide, and three commercial metal oxidant NPs, copper oxide (CuO) NPs, ferric oxide (Fe2O3) NPs, and titanium oxides (TiO2) NPs, were independently added to water-agar plates at 50 and 200-mg/L concentrations. Detached rose petals were inoculated with spores of B. cinerea and co-cultured with each of the six nanomaterials. The sizes of the lesions on infected rose petals were measured at 72 h after inoculation, and the growth of fungi on the rose petals was observed by scanning electron microscopy. The six NPs inhibited the growth of B. cinerea, but different concentrations had different effects: 50 mg/L of fullerene and CuO NPs showed the strongest antifungal properties among the treatments, while 200 mg/L of CuO and Fe2O3 showed no significant antifungal activities. Thus, NPs may have antifungal activities that prevent B. cinerea infections in plants, and they could be used as antifungal agents during the growth and post-harvesting of roses and other flowers.

Iron Oxide Nanoparticles as a Potential Iron Fertilizer for Peanut (Arachis hypogaea).

Nanomaterials are used in practically every aspect of modern life, including agriculture. The aim of this study was to evaluate the effectiveness of iron oxide nanoparticles (Fe2O3 NPs) as a fertilizer to replace traditional Fe fertilizers, which have various shortcomings. The effects of the Fe2O3 NPs and a chelated-Fe fertilizer (ethylenediaminetetraacetic acid-Fe; EDTA-Fe) fertilizer on the growth and development of peanut (Arachis hypogaea), a crop that is very sensitive to Fe deficiency, were studied in a pot experiment. The results showed that Fe2O3 NPs increased root length, plant height, biomass, and SPAD values of peanut plants. The Fe2O3 NPs promoted the growth of peanut by regulating phytohormone contents and antioxidant enzyme activity. The Fe contents in peanut plants with Fe2O3 NPs and EDTA-Fe treatments were higher than the control group. We used energy dispersive X-ray spectroscopy (EDS) to quantitatively analyze Fe in the soil. Peanut is usually cultivated in sandy soil, which is readily leached of fertilizers. However, the Fe2O3 NPs adsorbed onto sandy soil and improved the availability of Fe to the plants. Together, these results show that Fe2O3 NPs can replace traditional Fe fertilizers in the cultivation of peanut plants. To the best of our knowledge, this is the first research on the Fe2O3 NPs as the iron fertilizer.