MiR-541-3p suppresses gastric cancer via negative regulation of HSF1

Purpose: To explore the effects of miR-541-3P on the expression of heat shock transcription factor 1 (HSF1) in gastric cancer cells (GC).Methods: The MicroRNA Target Prediction Database was used to predict whether miR-541-3p interacts with HSF1. Interaction was assessed by dual-luciferase reporter assays. Furthermore, miR-541-3p mRNA levels in GC cell lines were determined by qRT-PCR. Human GC cell lines MKN45 and NCI-N87 were transfected with miR-541-3p mimic. Cell apoptosis, proliferation, invasion, and migration were evaluated using flow cytometry, apoptosis assays, Edu assays, CCK-8 assays, and transwell assays, respectively. Caspase-3, Bcl-2, and cleaved caspase-3 expression levels were determined by western blot.Results: Expression of miR-541-3p was significantly down-regulated in GC cells. Functionally, miR-541-3p mimic inhibited GC cell proliferation, migration, and invasion and induced apoptosis in vitro (p <0.01). Mechanistically, miR-541-3p interacted with HSF1 and inhibited its expression. Overexpression of HSF1 counteracted the effects of miR-541-3p mimic in GC cells.Conclusion: These results indicate that miR-541-3p suppresses the development of GC by targeting HSF1 and thus, is a possible strategy for for the management of GC

Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs

Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Experimental results demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs

Combinational antitumor strategies of exosomes as drug carriers: Mini review

Cancer therapies have made tremendous progress in the last decade, but monotherapy still has apparent limitations and lacks therapeutic efficacy. Thus, the simultaneous administration of multiple drugs has been widely explored and has shown better outcomes. Exosomes, deriving from almost all living cells, are natural nanocarriers designed to deliver drugs to tumor sites. Therefore, combinational antitumor therapies based on exosomes, such as engineered exosomes and different combinations of chemotherapeutic agents, therapeutic nucleic acids, photosensitizers, immunotherapy and phytochemicals, have considerable prospects and potential for clinical translation. Here, we summarize current strategies of cancer combination therapy in exosomes and propose opportunities and challenges in the future

One Small Step for an Inflaton, One Giant Leap for Inflation: a novel non-Gaussian tail and primordial black holes

We report a novel prediction from single-field inflation that even a tiny step in the inflaton potential can change our perception of primordial non-Gaussianities of the curvature perturbation. Our analysis focuses on the tail of probability distribution generated by an upward step transition between two stages of slow-roll evolution. The nontrivial background dynamics with off-attractor behavior is identified. By using a non-perturbative $\delta N$ analysis, we explicitly show that a highly non-Gaussian tail can be generated by a tiny upward step, even when the conventional nonlinearity parameters $f_{NL}$, $g_{NL}$, etc. remain small. With this example, we demonstrate for the first time the sensitive dependence of non-perturbative effects on the tail of probability distribution. Our scenario has an inconceivable application to primordial black holes by either significantly boosting their abundance or completely forbidding their appearance.Comment: 7 pages, 4 figure

DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility

Relationships between structure and antioxidant capacity and activity of glycosylated flavonols

The antioxidant capacity (AC) and antioxidant activity (AA) of three flavonols (FLV), aglycones and their glycosylated derivatives were evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays in various solvents. Findings confirmed that the glycosylation at the 3-position (3-glycosylation) always decreased the AC under most conditions due to substitution of the 3-position hydroxyl group and glycoside disruption in the molecular planarity. The 7-glycosylated derivatives did not have the above effects, thus generally exhibited ACs similar to their aglycones. Glycosylation decreased the AA of kaempferol and isorhamnetin for both assays in methanol, 3-glycosylation inhibited quercetin AA in the ABTS assay. In the DPPH assay, the AA of 3-glycosylated quercetin was significantly higher than quercetin. Using LC–MS/MS analysis, we found that quercetin and quercetin-7-glucoside underwent dimerization during the antioxidant reaction, potentially leading to a decline in AAs. However, 3-glycoside substitution may have hindered dimer formation, thereby allowing the FLVs to retain strong free radical scavenging abilities.National Key Research and Development Program of China | Ref. 2019YFC160670

Two-stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge

In ICASSP 2023 speech signal improvement challenge, we developed a dual-stage neural model which improves speech signal quality induced by different distortions in a stage-wise divide-and-conquer fashion. Specifically, in the first stage, the speech improvement network focuses on recovering the missing components of the spectrum, while in the second stage, our model aims to further suppress noise, reverberation, and artifacts introduced by the first-stage model. Achieving 0.446 in the final score and 0.517 in the P.835 score, our system ranks 4th in the non-real-time track.Comment: Accepted by ICASSP 202

Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in which each client holds a locally heterogeneous dataset; if the datasets can be globally aggregated, they jointly exhibit a long-tailed distribution. Under such a setting, existing federated optimization and/or centralized long-tailed learning methods hardly apply due to challenges in (a) characterizing the global long-tailed distribution under privacy constraints and (b) adjusting the local learning strategy to cope with the head-tail imbalance. In response, we propose a method termed $\texttt{Fed-GraB}$, comprised of a Self-adjusting Gradient Balancer (SGB) module that re-weights clients' gradients in a closed-loop manner, based on the feedback of global long-tailed distribution evaluated by a Direct Prior Analyzer (DPA) module. Using $\texttt{Fed-GraB}$, clients can effectively alleviate the distribution drift caused by data heterogeneity during the model training process and obtain a global model with better performance on the minority classes while maintaining the performance of the majority classes. Extensive experiments demonstrate that $\texttt{Fed-GraB}$ achieves state-of-the-art performance on representative datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist.Comment: Accepted by NeurIPS 202