A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic Information

Biological research has revealed that the verbal semantic information in the brain cortex, as an additional source, participates in nonverbal semantic tasks, such as visual encoding. However, previous visual encoding models did not incorporate verbal semantic information, contradicting this biological finding. This paper proposes a multimodal visual information encoding network model based on stimulus images and associated textual information in response to this issue. Our visual information encoding network model takes stimulus images as input and leverages textual information generated by a text-image generation model as verbal semantic information. This approach injects new information into the visual encoding model. Subsequently, a Transformer network aligns image and text feature information, creating a multimodal feature space. A convolutional network then maps from this multimodal feature space to voxel space, constructing the multimodal visual information encoding network model. Experimental results demonstrate that the proposed multimodal visual information encoding network model outperforms previous models under the exact training cost. In voxel prediction of the left hemisphere of subject 1's brain, the performance improves by approximately 15.87%, while in the right hemisphere, the performance improves by about 4.6%. The multimodal visual encoding network model exhibits superior encoding performance. Additionally, ablation experiments indicate that our proposed model better simulates the brain's visual information processing

Global and Individualized Community Detection in Inhomogeneous Multilayer Networks

In network applications, it has become increasingly common to obtain datasets in the form of multiple networks observed on the same set of subjects, where each network is obtained in a related but different experiment condition or application scenario. Such datasets can be modeled by multilayer networks where each layer is a separate network itself while different layers are associated and share some common information. The present paper studies community detection in a stylized yet informative inhomogeneous multilayer network model. In our model, layers are generated by different stochastic block models, the community structures of which are (random) perturbations of a common global structure while the connecting probabilities in different layers are not related. Focusing on the symmetric two block case, we establish minimax rates for both \emph{global estimation} of the common structure and \emph{individualized estimation} of layer-wise community structures. Both minimax rates have sharp exponents. In addition, we provide an efficient algorithm that is simultaneously asymptotic minimax optimal for both estimation tasks under mild conditions. The optimal rates depend on the \emph{parity} of the number of most informative layers, a phenomenon that is caused by inhomogeneity across layers.Comment: Corrected a few typos. 96 pages (main manuscript: 27 pages, appendices: 69 pages), 5 figure

Mid-21st century ozone air quality and health burden in China under emissions scenarios and climate change

Despite modest emissions reductions of air pollutants in recent years, China still suffers from poor air quality, and the outlook for future air quality in China is uncertain. We explore the impact of two disparate 2050 emissions scenarios relative to 2015 in the context of a changing climate with the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 3 (GFDL-AM3) chemistry-climate model. We impose the same near-term climate change for both emission scenarios by setting global sea surface temperature (SST) and sea ice cover (SIC) to the average over 2010–2019 and 2046–2055, respectively, from a three-member ensemble of GFDL coupled climate model simulations under the RCP8.5 (Representative Concentration Pathway) scenario. By the 2050s, annual mean surface ozone increases throughout China by up to 8 ppbv from climate change alone (estimated by holding air pollutants at 2015 levels while setting SIC and SST to 2050 conditions in the model) and by 8–12 ppbv in a scenario in which emissions of ozone precursors nitrogen oxides (NO x ) and anthropogenic volatile organic compounds (VOCs) increase by ~10%. In a scenario in which NO x and anthropogenic VOC emissions decline by 60%, annual mean surface ozone over China decreases by 16–20 ppbv in the 2050s relative to the 2010s. The ozone increase from climate change alone results in an additional 62 000 premature deaths in China as compared to 330 000 fewer premature deaths by the 2050s under a strong emissions mitigation scenario. In springtime over Southwestern China in the 2050s, the model projects 9–12 ppbv enhancements to surface ozone from the stratosphere (diagnosed with a model tracer) and from international anthropogenic emissions (diagnosed by differencing AM3 simulations with the same emissions within China but higher versus lower emissions in the rest of the world). Our findings highlight the effectiveness of emissions controls in reducing the health burden in China due to air pollution, and also the potential for climate change and rising global emissions to offset, at least partially, some of the ozone decreases attained with regional emission reductions in China

One-Way Matching of Datasets with Low Rank Signals

We study one-way matching of a pair of datasets with low rank signals. Under a stylized model, we first derive information-theoretic limits of matching. We then show that linear assignment with projected data achieves fast rates of convergence and sometimes even minimax rate optimality for this task. The theoretical error bounds are corroborated by simulated examples. Furthermore, we illustrate practical use of the matching procedure on two single-cell data examples.Comment: 64 pages, 7 figure

A Mixed Visual Encoding Model Based on the Larger-Scale Receptive Field for Human Brain Activity

Research on visual encoding models for functional magnetic resonance imaging derived from deep neural networks, especially CNN (e.g., VGG16), has been developed. However, CNNs typically use smaller kernel sizes (e.g., 3 × 3) for feature extraction in visual encoding models. Although the receptive field size of CNN can be enlarged by increasing the network depth or subsampling, it is limited by the small size of the convolution kernel, leading to an insufficient receptive field size. In biological research, the size of the neuronal population receptive field of high-level visual encoding regions is usually three to four times that of low-level visual encoding regions. Thus, CNNs with a larger receptive field size align with the biological findings. The RepLKNet model directly expands the convolution kernel size to obtain a larger-scale receptive field. Therefore, this paper proposes a mixed model to replace CNN for feature extraction in visual encoding models. The proposed model mixes RepLKNet and VGG so that the mixed model has a receptive field of different sizes to extract more feature information from the image. The experimental results indicate that the mixed model achieves better encoding performance in multiple regions of the visual cortex than the traditional convolutional model. Also, a larger-scale receptive field should be considered in building visual encoding models so that the convolution network can play a more significant role in visual representations

Berbamine Suppresses the Progression of Bladder Cancer by Modulating the ROS/NF-κB Axis

Berbamine (BBM), one of the bioactive ingredients extracted from Berberis plants, has attracted intensive attention because of its significant antitumor activity against various malignancies. However, the exact role and potential molecular mechanism of berbamine in bladder cancer (BCa) remain unclear. In the present study, our results showed that berbamine inhibited cell viability, colony formation, and proliferation. Additionally, berbamine induced cell cycle arrest at S phase by a synergistic mechanism involving stimulation of P21 and P27 protein expression as well as downregulation of CyclinD, CyclinA2, and CDK2 protein expression. In addition to suppressing epithelial-mesenchymal transition (EMT), berbamine rearranged the cytoskeleton to inhibit cell metastasis. Mechanistically, the expression of P65, P-P65, and P-IκBα was decreased upon berbamine treatment, yet P65 overexpression abrogated the effects of berbamine on the proliferative and metastatic potential of BCa cells, which indicated that berbamine attenuated the malignant biological activities of BCa cells by inhibiting the NF-κB pathway. More importantly, berbamine increased the intracellular reactive oxygen species (ROS) level through the downregulation of antioxidative genes such as Nrf2, HO-1, SOD2, and GPX-1. Following ROS accumulation, the intrinsic apoptotic pathway was triggered by an increase in the ratio of Bax/Bcl-2. Furthermore, berbamine-mediated ROS accumulation negatively regulated the NF-κB pathway to a certain degree. Consistent with our in vitro results, berbamine successfully inhibited tumor growth and blocked the NF-κB pathway in our xenograft model. To summarize, our data demonstrated that berbamine exerts antitumor effects via the ROS/NF-κB signaling axis in bladder cancer, which provides a basis for further comprehensive study and presents a potential candidate for clinical treatment strategies against bladder cancer