Dynamic attentional bias for pictorial and textual food cues in the visual search paradigm.

Previous studies have found that individuals have an attentional bias for food cues, which may be related to the energy level or the type of stimulus (e.g., pictorial or textual food cues) of the food cues. However, the available evidence is inconsistent, and there is no consensus about how the type of stimulus and food energy modulate food-related attentional bias. Searching for food is one of the most important daily behaviors. In this study, a modified visual search paradigm was used to explore the attentional bias for food cues, and eye movements were recorded. Food cues consisted of both food words and food pictures with different energy levels (i.e., high- and low-calorie foods). The results showed that there was an attentional avoidance in the early stage but a later-stage attentional approach for all food cues in the pictorial condition. This was especially true for high-calorie food pictures. Participants showed a later-stage conflicting attentional bias for foods with different energy levels in the textual condition. They showed an attentional approach to high-calorie food words but an attentional avoidance of low-calorie food words. These data show that food-related attentional bias varied along with different time courses, which was also modulated by the type of stimulus and food energy. These findings regarding dynamic attentional bias could be explained using the Goal Conflict Model of eating behavior.

H. pylori infection induces CXCL8 expression and promotes gastric cancer progress through downregulating KLF4.

Tumour-derived CXCL8 facilitates the movement of myeloid-derived suppressor cells, which are able to restrain antitumour immune responses to the tumour microenvironment. Kruppel-like factor 4 (KLF4) is a potential tumour suppressor in gastric cancer (GC). However, knowledge regarding correlations between KLF4 and CXCL8 in GC is limited. We use cellular and molecular biological methods to assess whether these two factors interact in GC. Expression CXCL8 and KLF4 was altered in human GC tissues compared to normal gastric tissues in opposite ways. Additionally, cytotoxin-associated gene A protein (CagA) gene transduction or Helicobacter pylori (H. pylori) infection upregulated CXCL8 expression. Knockdown of KLF4 expression increased CXCL8 protein and RNA expression, whereas its overexpression had the opposite effect. CXCL8-mediated enhancement of GC cell migration and proliferation was reversed by upregulation of KLF4 expression. Further mechanistic research revealed that KLF4 binds the CXCL8 promoter, suppressing CXCL8 transcription. Moreover, CXCL8 stimulation reduced KLF4 protein expression and promoted GC cell proliferation and migration, eventually promoting neoplasm growth in vivo. Together, our findings demonstrate that CagA promotes CXCL8 and inhibits KLF4. CXCL8 is a decisive downstream target gene of KLF4, and KLF4 negatively regulates CXCL8 in GC. Furthermore, CXCL8's negative regulation of KLF4 in vivo and in vitro, indicates that CagA may downregulate KLF4 by inducing CXCL8 expression, low expression of KLF4 further promotes that of CXCL8, forming a vicious circle in GC. Targeted KLF4 activation might improve the immunosuppressive microenvironment through direct negative regulation of CXCL8, providing a new potential target to strengthen the efficacy of immunotherapy in GC patients.

Structured-Light 3D Imaging Based on Vector Iterative Fourier Transform Algorithm.

Quasi-continuous-phase metasurfaces overcome the side effects imposed by high-order diffraction on imaging and can impart optical parameters such as amplitude, phase, polarization, and frequency to incident light at sub-wavelength scales with high efficiency. Structured-light three-dimensional (3D) imaging is a hot topic in the field of 3D imaging because of its advantages of low computation cost, high imaging accuracy, fast imaging speed, and cost-effectiveness. Structured-light 3D imaging requires uniform diffractive optical elements (DOEs), which could be realized by quasi-continuous-phase metasurfaces. In this paper, we design a quasi-continuous-phase metasurface beam splitter through a vector iterative Fourier transform algorithm and utilize this device to realize structured-light 3D imaging of a target object with subsequent target reconstruction. A structured-light 3D imaging system is then experimentally implemented by combining the fabricated quasi-continuous-phase metasurface illuminated by the vertical-cavity surface-emitting laser and a binocular recognition system, which eventually provides a new technological path for the 3D imaging field.

BNET: Batch Normalization With Enhanced Linear Transformation.

Batch normalization (BN) is a fundamental unit in modern deep neural networks. However, BN and its variants focus on normalization statistics but neglect the recovery step that uses linear transformation to improve the capacity of fitting complex data distributions. In this paper, we demonstrate that the recovery step can be improved by aggregating the neighborhood of each neuron rather than just considering a single neuron. Specifically, we propose a simple yet effective method named batch normalization with enhanced linear transformation (BNET) to embed spatial contextual information and improve representation ability. BNET can be easily implemented using the depth-wise convolution and seamlessly transplanted into existing architectures with BN. To our best knowledge, BNET is the first attempt to enhance the recovery step for BN. Furthermore, BN is interpreted as a special case of BNET from both spatial and spectral views. Experimental results demonstrate that BNET achieves consistent performance gains based on various backbones in a wide range of visual tasks. Moreover, BNET can accelerate the convergence of network training and enhance spatial information by assigning important neurons with large weights accordingly.

A Security Awareness and Protection System for 5G Smart Healthcare Based on Zero-Trust Architecture.

The key features of 5G network (i.e., high bandwidth, low latency, and high concurrency) along with the capability of supporting big data platforms with high mobility make it valuable in coping with emerging medical needs, such as COVID-19 and future healthcare challenges. However, enforcing the security aspect of a 5G-based smart healthcare system that hosts critical data and services is becoming more urgent and critical. Passive security mechanisms (e.g., data encryption and isolation) used in legacy medical platforms cannot provide sufficient protection for a healthcare system that is deployed in a distributed manner and fail to meet the need for data/service sharing across "cloud-edge-terminal" in the 5G era. In this article, we propose a security awareness and protection system that leverages zero-trust architecture for a 5G-based smart medical platform. Driven by the four key dimensions of 5G smart healthcare including "subject" (i.e., users, terminals, and applications), "object" (i.e., data, platforms, and services), "behavior," and "environment," our system constructs trustable dynamic access control models and achieves real-time network security situational awareness, continuous identity authentication, analysis of access behavior, and fine-grained access control. The proposed security system is implemented and tested thoroughly at industrial-grade, which proves that it satisfies the needs of active defense and end-to-end security enforcement of data, users, and services involved in a 5G-based smart medical system.