Octree-based hierarchical sampling optimization for the volumetric super-resolution of scientific data

When introducing physics-constrained deep learning solutions to the volumetric super-resolution of scientific data, the training is challenging to converge and always time-consuming. We propose a new hierarchical sampling method based on octree to solve these difficulties. In our approach, scientific data is preprocessed before training, and a hierarchical octree-based data structure is built to guide sampling on the latent context grid. Each leaf node in the octree corresponds to an indivisible subblock of the volumetric data. The dimensions of the subblocks are different, making the number of sample points in each randomly cropped training data block to be adaptive. We reconstruct the octree at intervals according to loss distribution to perform the multi-stage training. With the Rayleigh-B\'enard convection problem, we deploy our method to state-of-the-art models. We constructed adequate experiments to evaluate the training performance and model accuracy of our method. Experiments indicate that our sampling optimization improves the convergence performance of physics-constrained deep learning super-resolution solutions. Furthermore, the sample points and training time are significantly reduced with no drop in model accuracy. We also test our method in training tasks of other deep neural networks, and the results show our sampling optimization has extensive effectiveness and applicability. The code is publicly available at https://github.com/xinjiewang/octree-based_sampling

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance

Ailanthone targets p23 to overcome MDV3100 resistance in castration-resistant prostate cancer

Androgen receptor (AR) antagonist MDV3100 is the first therapeutic approach in treating castration-resistant prostate cancer (CRPC), but tumours frequently become drug resistant via multiple mechanisms including AR amplification and mutation. Here we identify the small molecule Ailanthone (AIL) as a potent inhibitor of both full-length AR (AR-FL) and constitutively active truncated AR splice variants (AR-Vs). AIL binds to the co-chaperone protein p23 and prevents AR's interaction with HSP90, thus resulting in the disruption of the AR-chaperone complex followed by ubiquitin/proteasome-mediated degradation of AR as well as other p23 clients including AKT and Cdk4, and downregulates AR and its target genes in PCa cell lines and orthotopic animal tumours. In addition, AIL blocks tumour growth and metastasis of CRPC. Finally, AIL possesses favourable drug-like properties such as good bioavailability, high solubility, lack of CYP inhibition and low hepatotoxicity. In general, AIL is a potential candidate for the treatment of CRPC

Rectifying interphases for preventing Li dendrite propagation in solid-state electrolytes

Solid-state electrolytes have emerged as the grail for safe and energy-dense Li metal batteries but still face significant challenges of Li dendrite propagation and interfacial incompatibility. In this work, an interface engineering approach is applied to introduce an electronic rectifying interphase between the solid-state electrolyte and Li metal anode. The rectifying behaviour restrains electron infiltration into the electrolyte, resulting in effective dendrite reduction. This interphase consists of a p-Si/n-TiO2 junction and an external Al layer, created using a multi-step sputter deposition technique on the surface of garnet pellets. The electronic rectifying behaviour is investigated via the asymmetric I-V responses of on-chip devices and further confirmed via the one-order of magnitude lower current response by electronic conductivity measurements on the pellets. The Al layer contributes to interface compatibility, which is verified from the lithiophilic surface and reduced interfacial impedance. Electrochemical measurements via Li symmetric cells show a significantly improved lifetime from dozens of hours to over two months. The reduction of the Li dendrite propagation behaviour is observed through 3D reconstructed morphologies of the solid-state electrolyte by X-ray computed tomography

Photosensitisation of inkjet printed graphene with stable all-inorganic perovskite nanocrystals

All-inorganic perovskite nanocrystals (NCs) with enhanced environmental stability are of particular interest for optoelectronic applications. Here we report on the formulation of CsPbX3 (X is Br or I) inks for inkjet deposition and utilise these NCs as photosensitive layers in graphene photodetectors, including those based on single layer graphene (SLG) as well as inkjet-printed graphene (iGr) devices. The performance of these photodetectors strongly depends on the device structure, geometry and the fabrication process. We achieve a high photoresponsivity, R > 106 A W−1 in the visible wavelength range and a spectral response controlled by the halide content of the perovskite NC ink. By utilising perovskite NCs, iGr and gold nanoparticle inks, we demonstrate a fully inkjet-printed photodetector with R ≈ 20 A W−1, which is the highest value reported to date for this type of device. The performance of the perovskite/graphene photodetectors is explained by transfer of photo-generated charge carriers from the perovskite NCs into graphene and charge transport through the iGr network. The perovskite ink developed here enabled realisation of stable and sensitive graphene-based photon detectors. Compatibility of inkjet deposition with conventional Si-technologies and with flexible substrates combined with high degree of design freedom provided by inkjet deposition offers opportunities for partially and fully printed optoelectronic devices for applications ranging from electronics to environmental sciences

Dual inhibition of AKT‐mTOR and AR signaling by targeting HDAC3 in PTEN‐ or SPOP‐mutated prostate cancer

Abstract AKT‐mTOR and androgen receptor (AR) signaling pathways are aberrantly activated in prostate cancer due to frequent PTEN deletions or SPOP mutations. A clinical barrier is that targeting one of them often activates the other. Here, we demonstrate that HDAC3 augments AKT phosphorylation in prostate cancer cells and its overexpression correlates with AKT phosphorylation in patient samples. HDAC3 facilitates lysine‐63‐chain polyubiquitination and phosphorylation of AKT, and this effect is mediated by AKT deacetylation at lysine 14 and 20 residues and HDAC3 interaction with the scaffold protein APPL1. Conditional homozygous deletion of Hdac3 suppresses prostate tumorigenesis and progression by concomitant blockade of AKT and AR signaling in the Pten knockout mouse model. Pharmacological inhibition of HDAC3 using a selective HDAC3 inhibitor RGFP966 inhibits growth of both PTEN‐deficient and SPOP‐mutated prostate cancer cells in culture, patient‐derived organoids and xenografts in mice. Our study identifies HDAC3 as a common upstream activator of AKT and AR signaling and reveals that dual inhibition of AKT and AR pathways is achievable by single‐agent targeting of HDAC3 in prostate cancer