Ru(II)-Catalyzed Divergent C-H Alkynylation Cascade with Bifunctional α-Alcohol Haloalkynes.

Native functionality directed the C-H activation cascade to enable rapid construction of molecular complexity, featuring step-economy and synthetic efficiency. Herein, by exploiting bifunctional α-alcohol haloalkynes, we developed Ru(II)-catalyzed carboxylic acid, amine, and amide assisted divergent C-H alkynylation and annulation cascade, affording polyfunctional heterocycles. Significantly, a bilateral aryl C-H polycyclization cascade of azobenzenes was achieved using the versatile haloalkynes.

Source-Free Domain Adaptation With Domain Generalized Pretraining for Face Anti-Spoofing.

Source-free domain adaptation (SFDA) shows the potential to improve the generalizability of deep learning-based face anti-spoofing (FAS) while preserving the privacy and security of sensitive human faces. However, existing SFDA methods are significantly degraded without accessing source data due to the inability to mitigate domain and identity bias in FAS. In this paper, we propose a novel Source-free Domain Adaptation framework for FAS (SDA-FAS) that systematically addresses the challenges of source model pre-training, source knowledge adaptation, and target data exploration under the source-free setting. Specifically, we develop a generalized method for source model pre-training that leverages a causality-inspired PatchMix data augmentation to diminish domain bias and designs the patch-wise contrastive loss to alleviate identity bias. For source knowledge adaptation, we propose a contrastive domain alignment module to align conditional distribution across domains with a theoretical equivalence to adaptation based on source data. Furthermore, target data exploration is achieved via self-supervised learning with patch shuffle augmentation to identify unseen attack types, which is ignored in existing SFDA methods. To our best knowledge, this paper provides the first full-stack privacy-preserving framework to address the generalization problem in FAS. Extensive experiments on nineteen cross-dataset scenarios show our framework considerably outperforms state-of-the-art methods.

A prognostic model based on tumor microenvironment and immune cell in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is the second leading cause of cancer-related death. Immunotherapy is one of the new options for cancer treatment. This study aimed to develop an immune-related signature associated with CRC. METHODS: We performed differential analysis to screen out the differentially expressed genes (DEGs) of The Cancer Genome Atlas-Colorectal Cancer (TCGA-CRC) datasets. Weighted gene co-expression network analysis (WGCNA) was performed to obtain the key module genes associated with differential immune cells. The candidate genes were obtained through overlapping key DEGs and key module genes. The univariate and multivariate Cox regression analyses were adopted to build a CRC prognostic signature. We further conducted immune feature estimation and chemotherapy analysis between two risk subgroups. Finally, we verified the expression of immune-related prognostic genes at the transcriptional level. RESULTS: A total of 61 candidate genes were obtained by overlapping key DEGs and key module genes associated with differential immune cells. Then, an immune-related prognostic signature was built based on the three prognostic genes (HAMP, ADAM8, and CD1B). The independent prognostic analysis suggested that age, stage, and RiskScore could be used as independent prognostic factors. Further, we found significantly higher expression of three prognostic genes in the CRC group compared with the normal group. Finally, real-time polymerase chain reaction verified the expression of three genes in patients with CRC. CONCLUSION: The prognostic signature comprising HAMP, ADAM8, and CD1B based on immune cells was established, providing a theoretical basis and reference value for the research of CRC.

Pathologic Complete Response After Gastric Artery Chemoembolization Combined With Tislelizumab for Neoadjuvant Therapy of Locally Advanced Gastric Cancer: A Case Report.

Gastric cancer is the most common type of gastrointestinal cancer in China which about 80% of patients are locally advanced or advanced when diagnosed. Surgery along brings high recurrence rate for locally advanced gastric cancer (LAGC), and neoadjuvant therapies are needed. The use of programmed cell death-1 (PD-1)/programmed death-ligand 1 inhibitor nowadays improved the disease-free survival for LAGC, however, only <35% of patients achieved pathologic complete response (pCR) after neoadjuvant therapy nowadays. Therefore, new regimens are needed to be investigated. Gastric artery chemoembolization is applied to metastasis gastric cancer and researches showed interventional therapy can enhance the antitumor effect of PD-1 inhibitor. Here, for the first time, we combined gastric artery chemoembolization with tislelizumab (a PD-1 inhibitor) for neoadjuvant therapy of a patient with LAGC. The patient achieved pCR after a D2 resection and tumor regression grade score was 1. After surgery, the patient received tislelizumab 200 mg per 3 weeks, and showed no sign of recurrence after 6 months of follow-up. The study indicated the use of tislelizumab and gastric artery chemoembolization for neoadjuvant therapy may bring a better pCR rate and prognosis of LAGC.

Energy-Efficient ReS2-Based Optoelectronic Synapse for 3D Object Reconstruction and Recognition.

The neuromorphic vision system (NVS) equipped with optoelectronic synapses integrates perception, storage, and processing and is expected to address the issues of traditional machine vision. However, owing to their lack of stereo vision, existing NVSs focus on 2D image processing, which makes it difficult to solve problems such as spatial cognition errors and low-precision interpretation. Consequently, inspired by the human visual system, an NVS with stereo vision is developed to achieve 3D object recognition, depending on the prepared ReS2 optoelectronic synapse with 12.12 fJ ultralow power consumption. This device exhibits excellent optical synaptic plasticity derived from the persistent photoconductivity effect. As the cornerstone for 3D vision, color planar information is successfully discriminated and stored in situ at the sensor end, benefiting from its wavelength-dependent plasticity in the visible region. Importantly, the dependence of the channel conductance on the target distance is experimentally revealed, implying that the structure information on the object can be directly captured and stored by the synapse. The 3D image of the object is successfully reconstructed via fusion of its planar and depth images. Therefore, the proposed 3D-NVS based on ReS2 synapses for 3D objects achieves a recognition accuracy of 97.0%, which is much higher than that for 2D objects (32.6%), demonstrating its strong ability to prevent 2D-photo spoofing in applications such as face payment, entrance guard systems, and others.

A Bioinspired Retinomorphic Device for Spontaneous Chromatic Adaptation.

Chromatic adaptation refers to the sensing and preprocessing of the spectral composition of incident light on the retina, and it is important for color-image recognition. It is challenging to apply sensing, memory, and processing functions to color images via the same physical process using the complementary metal-oxide-semiconductor technology because of redundant data detection, complicated signal conversion processes, and the requirement for additional memory modules. Inspired by the highly efficient chromatic adaptation of the human retina, a 2D oxygen-mediated platinum diselenide (PtSe2 ) device is presented to simultaneously apply sensing, memory, and processing functions to color images. The device exhibits a wavelength-dependent bipolar photoresponse and the linear pulse-number dependence of photoconductivity, which is dominated by the photon-mediated physical adsorption and desorption of oxygen molecules on bilayer PtSe2 . The proposed retinomorphic device shows superior image classification accuracy (over 90%) compared to an independent pseudocolor channel (less than 75%). Hence, it is promising for developing artificial vision perception systems with reduced architectural complexity.

Pure voltage-driven spintronic neuron based on stochastic magnetization switching behaviour.

Voltage-driven stochastic magnetization switching in a nanomagnet has attracted more attention recently with its superiority in achieving energy-efficient artificial neuron. Here, a novel pure voltage-driven scheme with ∼27.66 aJ energy dissipation is proposed, which could rotate magnetization vector randomly using only a pair of electrodes covered on the multiferroic nanomagnet. Results show that the probability of 180° magnetization switching is examined as a sigmoid-like function of the voltage pulse width and magnitude, which can be utilized as the activation function of designed neuron. Considering the size errors of designed neuron in fabrication, it's found that reasonable thickness and width variations cause little effect on recognition accuracy for MNIST hand-written dataset. In other words, the designed pure voltage-driven spintronic neuron could tolerate size errors. These results open a new way toward the realization of artificial neural network with low power consumption and high reliability.

Synergistic effect of aluminum diethylphosphinate/sodium stearate modified vermiculite on flame retardant and smoke suppression properties of amino coatings.

Various inorganic fillers are proved to be desirable synergists to improve the fire resistance of fire-retardant coatings. Herein, a functional filler (ANE) with flame retardant property was prepared by intercalating aluminum diethylphosphinate into microwave expanded vermiculite and grafting sodium stearate on its surface. The structure of ANE was fully characterized by FTIR, XRD, XPS and SEM analyses. Then ANE was applied to melamine modified urea-formaldehyde resin to produce fire-retardant coatings. The fire resistance test, TGA and cone calorimeter test demonstrate that ANE imparts great heat insulation, thermal stability, and flame retardancy to the coatings. Moreover, the introduction of ANE exhibits an excellent synergistic effect on reducing the heat release and smoke emission of the coatings. Specifically, with the addition of 3 wt% ANE, the heat release rate and smoke density grade of the coatings are decreased by 25.24% and 60.32%, respectively, compared to that without ANE. The excellent flame retardancy and smoke suppression performances of the coatings are mainly attributed to the formation of more cross-linking structures in the carbon layers, resulting in a more stable and compact char structure. In addition, the good hydrophobicity of ANE coatings can ensure the durability of flame retardancy.

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

BACKGROUND: Single-cell RNA sequencing (scRNAseq) data always involves various unwanted variables, which would be able to mask the true signal to identify cell-types. More efficient way of dealing with this issue is to extract low dimension information from high dimensional gene expression data to represent cell-type structure. In the past two years, several powerful matrix factorization tools were developed for scRNAseq data, such as NMF, ZIFA, pCMF and ZINB-WaVE. But the existing approaches either are unable to directly model the raw count of scRNAseq data or are really time-consuming when handling a large number of cells (e.g. n>500). RESULTS: In this paper, we developed a fast and efficient count-based matrix factorization method (single-cell negative binomial matrix factorization, scNBMF) based on the TensorFlow framework to infer the low dimensional structure of cell types. To make our method scalable, we conducted a series of experiments on three public scRNAseq data sets, brain, embryonic stem, and pancreatic islet. The experimental results show that scNBMF is more powerful to detect cell types and 10 - 100 folds faster than the scRNAseq bespoke tools. CONCLUSIONS: In this paper, we proposed a fast and efficient count-based matrix factorization method, scNBMF, which is more powerful for detecting cell type purposes. A series of experiments were performed on three public scRNAseq data sets. The results show that scNBMF is a more powerful tool in large-scale scRNAseq data analysis. scNBMF was implemented in R and Python, and the source code are freely available at https://github.com/sqsun .