Single-step electrochemical functionalization of double-walled carbon nanotube (DWCNT) membranes and the demonstration of ionic rectification

Carbon nanotube (CNT) membranes allow the mimicking of natural ion channels for applications in drug delivery and chemical separation. Double-walled carbon nanotube membranes were simply functionalized with dye in a single step instead of the previous two-step functionalization. Non-faradic electrochemical impedance spectra indicated that the functionalized gatekeeper by single-step modification can be actuated to mimic the protein channel under bias. This functional chemistry was proven by a highly efficient ion rectification, wherein the highest experimental rectification factor of ferricyanide was up to 14.4. One-step functionalization by electrooxidation of amine provides a simple and promising functionalization chemistry for the application of CNT membranes

Realization of Two-Dimensional Spin-orbit Coupling for Bose-Einstein Condensates

Cold atoms with laser-induced spin-orbit (SO) interactions provide intriguing new platforms to explore novel quantum physics beyond natural conditions of solids. Recent experiments demonstrated the one-dimensional (1D) SO coupling for boson and fermion gases. However, realization of 2D SO interaction, a much more important task, remains very challenging. Here we propose and experimentally realize, for the first time, 2D SO coupling and topological band with $^{87}$Rb degenerate gas through a minimal optical Raman lattice scheme, without relying on phase locking or fine tuning of optical potentials. A controllable crossover between 2D and 1D SO couplings is studied, and the SO effects and nontrivial band topology are observed by measuring the atomic cloud distribution and spin texture in the momentum space. Our realization of 2D SO coupling with advantages of small heating and topological stability opens a broad avenue in cold atoms to study exotic quantum phases, including the highly-sought-after topological superfluid phases.Comment: 27 pages, 5 figure

Curative effect of immediate reconstruction after neoadjuvant chemotherapy for breast cancer: a systematic review and meta-analysis

BackgroundThe safety of mastectomy (MT) with immediate reconstruction (IR) in breast cancer patients who have completed neoadjuvant chemotherapy (NAC) is not apparent. This meta-analysis aims to systematically evaluate the differences in surgical complications and postoperative survival rates between MT with IR (MT+IR) and MT alone in post-NAC breast cancer patients.MethodsThe PubMed, Embase, Cochrane Library, WanFang Data, and CNKI databases were systematically searched, and cohort studies of post-NAC breast cancer patients with MT+IR or MT surgery were collected from databases inception to May 25, 2023. Two researchers independently executed literature screening, data extraction, and bias risk assessment, and meta-analysis was performed using Revman 5.3 software.ResultsA total of 12 studies involving 7378 cases who have accepted NAC were collected for this study. The results showed that compared with the MT group, the relative risk of surgical complications in the MT+IR group was increased by 44%, with no statistical significant [RR=1.44, 95% CI (0.99, 2.09), P=0.06]. While among study subgroups with a median follow-up of less than one year, more surgical complications occurred in the MT+IR group by 23% [RR=1.23, 95% CI (1.00, 1.52), P=0.05]. There was no significant differences in overall survival, disease-free survival, local relapse-free survival, and distant metastasis-free survival between the two groups.ConclusionsCompared with the MT, MT+IR does not affect the postoperative survival rate in post-NAC breast cancer patients, accompanied by a mild increase in short-term surgical complications, but no significant difference in long-term complications.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42023421150

Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction

Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules. However, this approach is constrained by higher memory expenses and limited representation capabilities. In this paper, we introduce a novel dynamic grid optimization method for high-fidelity 3D surface reconstruction that incorporates both RGB and depth observations. Rather than treating each voxel equally, we optimize the process by dynamically modifying the grid and assigning more finer-scale voxels to regions with higher complexity, allowing us to capture more intricate details. Furthermore, we develop a scheme to quantify the dynamic subdivision of voxel grid during optimization without requiring any priors. The proposed approach is able to generate high-quality 3D reconstructions with fine details on both synthetic and real-world data, while maintaining computational efficiency, which is substantially faster than the baseline method NeuralRGBD.Comment: For the project, see https://yanqingan.github.io

An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation

Self-interacting proteins (SIPs), whose more than two identities can interact with each other, play significant roles in the understanding of cellular process and cell functions. Although a number of experimental methods have been designed to detect the SIPs, they remain to be extremely time-consuming, expensive, and challenging even nowadays. Therefore, there is an urgent need to develop the computational methods for predicting SIPs. In this study, we propose a deep forest based predictor for accurate prediction of SIPs using protein sequence information. More specifically, a novel feature representation method, which integrate position-specific scoring matrix (PSSM) with wavelet transform, is introduced. To evaluate the performance of the proposed method, cross-validation tests are performed on two widely used benchmark datasets. The experimental results show that the proposed model achieved high accuracies of 95.43 and 93.65% on human and yeast datasets, respectively. The AUC value for evaluating the performance of the proposed method was also reported. The AUC value for yeast and human datasets are 0.9203 and 0.9586, respectively. To further show the advantage of the proposed method, it is compared with several existing methods. The results demonstrate that the proposed model is better than other SIPs prediction methods. This work can offer an effective architecture to biologists in detecting new SIPs

Molecular cloning of dihydroflavonol 4-reductase gene from grape berry and preparation of an anti-DFR polyclonal antibody

Dihydroflavonol 4-reductase (DFR, EC 1.1.1.219) is a key enzyme of the flavonoid pathway, which synthesizes numerous secondary metabolites to determine the quality of grape berry and wine. The full-length dfr cDNA with 1014 bp was cloned from grape berry, and then introduced into an expressed plasmid pET-30a (+) vector at the EcoR I and Xho I restriction sites. With induction of the isopropyl-β-D-thiogalactoside (IPTG), the pET-dfr was highly expressed in Escherichia coli BL21 (DE3) pLysS cells. A fusion protein with the His-Tag was purified through Ni-NTA His Bind Resin and then used as the antigen to immunize a New Zealand rabbit. The resulting antiserum was further purified precipitated by 50 % saturated ammonium sulfate and DEAE-Sepharose FF chromatography to obtain the immunoglobulin G (IgG) fraction. The resulting polyclonal antibody was found capable of immuno-recognizing the DFR of the crude protein extracts from grape berry. This work undoubtedly provides the possibility for further studies on biological regulation of DFR activity in grape berry.