High-efficient refolding and purification of recombinant human interleukin-2 from inclusion bodies.

Human interleukin-2 (hIL-2) serves as a crucial cytokine in the treatment of cancer and autoimmune disorders. Nevertheless, the advancement of research and clinical applications involving this cytokine has been hindered by the constraints associated with the production of recombinant human interleukin-2 (rhIL-2). This study presents a scalable and robust purification protocol for rhIL-2 derived from inclusion bodies (IBs) in Escherichia coli.

Optimizing Inorganic CsCuSbCl/CsTiI Dual-Absorber Solar Cells: SCAPS-1D Simulations and Machine Learning.

Perovskite solar cells (PSCs) have emerged as a promising contender in photovoltaics, owing to their rapidly advancing power conversion efficiencies (PCEs) and compatibility with low-temperature solution processing techniques. Single-junction architectures reveal inherent limitations imposed by the Shockley-Queisser (SQ) limit, motivating adoption of a dual-absorber structure comprising CsCuSbCl (CCSC) and CsTiI (CTI)-lead-free perovskite derivatives valued for environmental benignity and intrinsic stability. Comprehensive theoretical screening of 26 electron/hole transport layer (ETL/HTL) candidates identified SrTiO (STO) and CuSCN as optimal charge transport materials, producing an initial simulated PCE of 16.

Analyzing the potential targets and mechanisms of liver damage induced by acetyl tributyl citrate plasticizer using network toxicology, molecular docking and experiments.

Background: Acetyl tributyl citrate (ATBC) may have adverse effects on liver health; however, the underlying mechanisms and pathophysiology remain unclear. The objective of this study was to elucidate the complex effects of ATBC on the liver and to determine the underlying molecular mechanisms by which environmental pollutants affect the disease process.

Methods: We used network toxicology and molecular docking techniques to analyze potential targets and mechanisms of liver injury caused by ATBC plasticizer.

SRENet: Saliency-Based Lighting Enhancement Network.

Lighting enhancement is a classical topic in low-level image processing. Existing studies mainly focus on global illumination optimization while overlooking local semantic objects, and this limits the performance of exposure compensation. In this paper, we introduce SRENet, a novel lighting enhancement network guided by saliency information.

Perceptual Quality Assessment of 360° Images Based on Generative Scanpath Representation.

Despite substantial efforts dedicated to the design of heuristic models for omnidirectional (i.e., 360°) image quality assessment (OIQA), a conspicuous gap remains due to the lack of consideration for the diversity of viewing behaviors that leads to the varying perceptual quality of 360° images.

Learning Guided Implicit Depth Function With Scale-Aware Feature Fusion.

Recently, the single image super-resolution based on implicit image function is a hot topic, which learns a universal model for arbitrary upsampling scales. By contrast, color-guided depth map super-resolution is less explored based on implicit function learning. The related research faces three questions.

A Perceptually Optimized and Self-Calibrated Tone Mapping Operator.

With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized. In Stage one, motivated by the physiology of the early stages of the human visual system, we first decompose an HDR image into a normalized Laplacian pyramid.

Spinal cord injury-derived exosomes exacerbate damage: miR-155-5p mediates inflammatory responses.

Spinal cord injury (SCI) is a critical event characterized by intricate pathogenic mechanisms. Although recent studies have highlighted tissue exosomes as key mediators of inflammatory responses in diverse organs and tissues, their role in SCI has yet to be determined. In this study, we investigated the role and mechanisms of spinal cord tissue exosomes in the inflammatory response following SCI.

Receptor-based bioassay for functional IL-2 quantification.

IL-2 is a potent cytokine that promotes multiple immune cells proliferation and activation. Accordingly, IL-2 based immunotherapies are emerging to treat cancers or AIDS by enhancing T cell growth and function. Besides, IL-2 is indispensable in in vitro cultivation of immune cells which is a critical step of CAR-T or CAR-NK immunotherapies.

Webly Supervised Fine-Grained Classification by Integrally Tackling Noises and Subtle Differences.

Webly-supervised fine-grained visual classification (WSL-FGVC) aims to learn similar sub-classes from cheap web images, which suffers from two major issues: label noises in web images and subtle differences among fine-grained classes. However, existing methods for WSL-FGVC only focus on suppressing noise at image-level, but neglect to mine cues at pixel-level to distinguish the subtle differences among fine-grained classes. In this paper, we propose a bag-level top-down attention framework, which could tackle label noises and mine subtle cues simultaneously and integrally.

Diffusion-Based Facial Aesthetics Enhancement With 3D Structure Guidance.

Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness by adjusting the structure and appearance of a facial image while preserving its identity as much as possible. Most existing methods adopted deep feature-based or score-based guidance for generation models to conduct FAE. Although these methods achieved promising results, they potentially produced excessively beautified results with lower identity consistency or insufficiently improved facial attractiveness.

Microglia-derived CXCL2 induced neuronal ferroptosis via CXCR2/Jun axis in sepsis-associated encephalopathy.

Background: Neuronal ferroptosis is a characteristic pathological change of sepsis-associated encephalopathy (SAE), which can be induced by activated microglia. CXCL2 is mainly secreted by inflammatory cells (neutrophil and microglia) and involved in neuronal damage. However, the specific mechanism behind microglia-neuron crosstalk in SAE remains unclear.

Visual Quality Assessment of Composite Images: A Compression-Oriented Database and Measurement.

Composite images (CIs) have experienced unprecedented growth, especially with the prosperity of a large number of generative AI technologies. They are usually created by combining multiple visual elements from different sources to form a single cohesive composition, which have an increasing impact on a variety of vision applications. However, transmission of CIs can degrade their visual quality, especially undergoing lossy compression to reduce bandwidth and storage.

Omnidirectional Image Quality Captioning: A Large-Scale Database and a New Model.

The fast growing application of omnidirectional images calls for effective approaches for omnidirectional image quality assessment (OIQA). Existing OIQA methods have been developed and tested on homogeneously distorted omnidirectional images, but it is hard to transfer their success directly to the heterogeneously distorted omnidirectional images. In this paper, we conduct the largest study so far on OIQA, where we establish a large-scale database called OIQ-10K containing 10,000 omnidirectional images with both homogeneous and heterogeneous distortions.

Image Super-Resolution via Efficient Transformer Embedding Frequency Decomposition With Restart.

Recently, transformer-based backbones show superior performance over the convolutional counterparts in computer vision. Due to quadratic complexity with respect to the token number in global attention, local attention is always adopted in low-level image processing with linear complexity. However, the limited receptive field is harmful to the performance.

Role of serum neuron-specific enolase levels in the early diagnosis and prognosis of sepsis-associated encephalopathy: a systematic review and meta-analysis.

Background: Sepsis-associated encephalopathy (SAE) is one of the most ubiquitous complications of sepsis and is characterized by cognitive impairment, poor prognosis, and a lack of uniform clinical diagnostic criteria. Therefore, this study investigated the early diagnostic and prognostic value of serum neuron-specific enolase (NSE) in SAE.

Methods: This systematic review and meta-analysis systematically searched for clinical trials with serum NSE information in patients with sepsis in the PubMed, Web of Science, Embase, and Cochrane databases from their inception to April 10, 2023.

Natural Product Quercetin-3-methyl ether Promotes Colorectal Cancer Cell Apoptosis by Downregulating Intracellular Polyamine Signaling.

Dysregulation of cellular metabolism is a key marker of cancer, and it is suggested that metabolism should be considered as a targeted weakness of colorectal cancer. Increased polyamine metabolism is a common metabolic change in tumors. Thus, targeting polyamine metabolism for anticancer therapy, particularly polyamine blockade therapy, has gradually become a hot topic.

Saliency Guided Deep Neural Network for Color Transfer With Light Optimization.

Color transfer aims to change the color information of the target image according to the reference one. Many studies propose color transfer methods by analysis of color distribution and semantic relevance, which do not take the perceptual characteristics for visual quality into consideration. In this study, we propose a novel color transfer method based on the saliency information with brightness optimization.

Why Do People Believe in Vaccine Misinformation? The Roles of Perceived Familiarity and Evidence Type.

The proliferation of health misinformation poses a significant threat to public health, making it increasingly important to understand why misinformation is accepted. The illusory truth effect, which refers to the increased believability of a message due to repeated exposure, has been widely studied. However, there is limited research on this effect in the context of COVID-19 vaccine misinformation.

A Bio-Inspired Model for Bee Simulations.

As eusocial creatures, bees display unique macro collective behavior and local body dynamics that hold potential applications in various fields, such as computer animation, robotics, and social behavior. Unlike birds and fish, bees fly in a low-aligned zigzag pattern. Additionally, bees rely on visual signals for foraging and predator avoidance, exhibiting distinctive local body oscillations, such as body lifting, thrusting, and swaying.

Smoke-Aware Global-Interactive Non-Local Network for Smoke Semantic Segmentation.

Compared with other objects, smoke semantic segmentation (SSS) is more difficult and challenging due to some special characteristics of smoke, such as non-rigid, translucency, variable mode and so on. To achieve accurate positioning of smoke in real complex scenes and promote the development of intelligent fire detection, we propose a Smoke-Aware Global-Interactive Non-local Network (SAGINN) for SSS, which harness the power of both convolution and transformer to capture local and global information simultaneously. Non-local is a powerful means for modeling long-range context dependencies, however, friendliness to single-scale low-resolution features limits its potential to produce high-quality representations.

A cutting-edge strategy for spinal cord injury treatment: resident cellular transdifferentiation.

Spinal cord injury causes varying degrees of motor and sensory function loss. However, there are no effective treatments for spinal cord repair following an injury. Moreover, significant preclinical advances in bioengineering and regenerative medicine have not yet been translated into effective clinical therapies.

Toward Transparent Deep Image Aesthetics Assessment With Tag-Based Content Descriptors.

Deep learning approaches for Image Aesthetics Assessment (IAA) have shown promising results in recent years, but the internal mechanisms of these models remain unclear. Previous studies have demonstrated that image aesthetics can be predicted using semantic features, such as pre-trained object classification features. However, these semantic features are learned implicitly, and therefore, previous works have not elucidated what the semantic features are representing.