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. First, a saliency detection module is designed to separate the foreground regions from the background regions for images. Then a dual-branch module is introduced to implement color transfer for images. Finally, a brightness optimization operation is designed during the fusion of foreground and background regions for color transfer. Experimental results show that the proposed method can implement the color transfer for images while keeping the color consistency well. Compared with other existing studies, the proposed method can obtain significant performance improvement. The source code and pre-trained models are available at https://github.com/PlanktonQAQ/SCTNet.

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. Quercetin-3-methyl ether is a natural compound existed in various plants with diverse biological activities like antioxidant and antiaging. Here, we reported that Quercetin-3-methyl ether inhibits colorectal cancer cell viability, and promotes apoptosis in a dose-dependent and time-dependent manner. Intriguingly, the polyamine levels, including spermidine and spermine, in colorectal cancer cells were reduced upon treatment of Quercetin-3-methyl ether. This is likely resulted from the downregulation of SMOX, a key enzyme in polyamine metabolism that catalyzes the oxidation of spermine to spermidine. These findings suggest Quercetin-3-methyl ether decreases cellular polyamine level by suppressing SMOX expression, thereby inducing colorectal cancer cell apoptosis. Our results also reveal a correlation between the anti-tumor activity of Quercetin-3-methyl ether and the polyamine metabolism modulation, which may provide new insights into a better understanding of the pharmacological activity of Quercetin-3-methyl ether and how it reprograms cellular polyamine metabolism.

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. Included studies were assessed for quality and risk of bias using The Quality Assessment of Diagnostic Accuracy-2 tool. The meta-analysis of the included studies was performed using Stata 17.0 and Review Manager version 5.4. Findings: Eleven studies were included in this meta-analysis involving 1259 serum samples from 947 patients with sepsis. Our results showed that the serum NSE levels of patients with SAE were higher than those of the non-encephalopathy sepsis group (mean deviation, MD,12.39[95% CI 8.27-16.50, Z = 5.9, p < 0.00001]), and the serum NSE levels of patients with sepsis who died were higher than those of survivors (MD,4.17[95% CI 2.66-5.68, Z = 5.41, p < 0.00001]). Conclusion: Elevated serum NSE levels in patients with sepsis are associated with the early diagnosis of SAE and mortality; therefore, serum NSE probably is a valid biomarker for the early diagnosis and prognosis of patients with SAE. Systematic review registration: This study was registered in PROSPERO, CRD42023433111.

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. This paper aims to examine the role of perceived familiarity with COVID-19 vaccine misinformation on various message perceptions, including perceived accuracy, agreement, perceived message effectiveness, and determinants of vaccination, including vaccine attitude and vaccination intention. Furthermore, it explores the impact of misinformation evidence (statistical vs. narrative) on the magnitude of the effects of perceived familiarity. To investigate these factors, a between-subjects experimental study was conducted, employing a 2 (Familiarity: strong vs. weak) × 3 (Evidence type: statistical, narrative, and both evidence) + 1 (Control: a message about drinking water) design. The results revealed that perceived familiarity with COVID-19 vaccine misinformation significantly predicted perceived accuracy, which was found to be negatively correlated with vaccine attitudes and vaccination intentions. Moreover, statistical evidence presented in misinformation was perceived as more persuasive in perceived message effectiveness, compared to narrative and mixed evidence. Interestingly, the effects of perceived familiarity were not contingent on the type of evidence used in COVID-19 vaccine misinformation. These findings emphasize the importance of avoiding the repetition of misinformation, reducing the processing fluency associated with misinformation correction, and educating individuals on how to critically evaluate statistical evidence when encountering (mis)information.

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 cues for foraging and predator avoidance, exhibiting distinctive local body oscillations, such as body lifting, thrusting, and swaying. These inherent features pose significant challenges for realistic bee simulations in practical animation applications. In this paper, we present a bio-inspired model for bee simulations capable of replicating both macro collective behavior and local body dynamics of bees. Our approach utilizes a visually-driven system to simulate a bee's local body dynamics, incorporating obstacle perception and body rolling control for effective collision avoidance. Moreover, we develop an oscillation rule that captures the dynamics of the bee's local bodies, drawing on insights from biological research. Our model extends beyond simulating individual bees' dynamics; it can also represent bee swarms by integrating a fluid-based field with the bees' innate noise and zigzag motions. To fine-tune our model, we utilize pre-collected honeybee flight data. Through extensive simulations and comparative experiments, we demonstrate that our model can efficiently generate realistic low-aligned and inherently noisy bee swarms.

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. Therefore, we propose a Global-Interactive Non-local (GINL) module, leveraging global interaction between multi-scale key information to improve the robustness of feature representations. To solve the interference of smoke-like objects, a Pyramid High-level Semantic Aggregation (PHSA) module is designed, where the learned high-level category semantics from classification aids model by providing additional guidance to correct the wrong information in segmentation representations at the image level and alleviate the inter-class similarity problem. Besides, we further propose a novel loss function, termed Smoke-aware loss (SAL), by assigning different weights to different objects contingent on their importance. We evaluate our SAGINN on extensive synthetic and real data to verify its generalization ability. Experimental results show that SAGINN achieves 83% average mIoU on the three testing datasets (83.33%, 82.72% and 82.94%) of SYN70K with an accuracy improvement of about 0.5%, 0.002 mMse and 0.805 Fß on SMOKE5K, which can obtain more accurate location and finer boundaries of smoke, achieving satisfactory results on smoke-like objects.

Latest advances and clinical application prospects of resveratrol therapy for neurocognitive disorders.

Neurocognitive disorders, such as Alzheimer's disease, vascular dementia, and postoperative cognitive dysfunction, are non-psychiatric brain syndromes in which a significant decline in cognitive function causes great trauma to the mental status of the patient. The lack of effective treatments for neurocognitive disorders imposes a considerable burden on society, including a substantial economic impact. Over the past few decades, the identification of resveratrol, a natural plant compound, has provided researchers with an opportunity to formulate novel strategies for the treatment of neurocognitive disorders. This is because resveratrol effectively protects the brain of those with neurocognitive disorders by targeting some mechanisms such as inflammation and oxidative stress. This article reviews the status of recent research investigating the use of resveratrol for the treatment of different neurocognitive disorders. By examining the possible mechanisms of action of resveratrol and the shared mechanisms of different neurocognitive disorders, treatments for neurocognitive disorders may be further clarified.

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. The spinal cord's poor regenerative capacity makes repairing damaged and lost neurons a critical treatment step. Reprogramming-based neuronal transdifferentiation has recently shown great potential in repair and plasticity, as it can convert mature somatic cells into functional neurons for spinal cord injury repair in vitro and in vivo, effectively halting the progression of spinal cord injury and promoting functional improvement. However, the mechanisms of the neuronal transdifferentiation and the induced neuronal subtypes are not yet well understood. This review analyzes the mechanisms of resident cellular transdifferentiation based on a review of the relevant recent literature, describes different molecular approaches to obtain different neuronal subtypes, discusses the current challenges and improvement methods, and provides new ideas for exploring therapeutic approaches for spinal cord injury.

Towards 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. In this work, we aim to create a more transparent deep learning framework for IAA by introducing explainable semantic features. To achieve this, we propose Tag-based Content Descriptors (TCDs), where each value in a TCD describes the relevance of an image to a human-readable tag that refers to a specific type of image content. This allows us to build IAA models from explicit descriptions of image contents. We first propose the explicit matching process to produce TCDs that adopt predefined tags to describe image contents. We show that a simple MLP-based IAA model with TCDs only based on predefined tags can achieve an SRCC of 0.767, which is comparable to most state-of-the-art methods. However, predefined tags may not be sufficient to describe all possible image contents that the model may encounter. Therefore, we further propose the implicit matching process to describe image contents that cannot be described by predefined tags. By integrating components obtained from the implicit matching process into TCDs, the IAA model further achieves an SRCC of 0.817, which significantly outperforms existing IAA methods. Both the explicit matching process and the implicit matching process are realized by the proposed TCD generator. To evaluate the performance of the proposed TCD generator in matching images with predefined tags, we also labeled 5101 images with photography-related tags to form a validation set. And experimental results show that the proposed TCD generator can meaningfully assign photography-related tags to images.

Study of Spatio-Temporal Modeling in Video Quality Assessment.

Video quality assessment (VQA) has received remarkable attention recently. Most of the popular VQA models employ recurrent neural networks (RNNs) to capture the temporal quality variation of videos. However, each long-term video sequence is commonly labeled with a single quality score, with which RNNs might not be able to learn long-term quality variation well: What's the real role of RNNs in learning the visual quality of videos? Does it learn spatio-temporal representation as expected or just aggregating spatial features redundantly? In this study, we conduct a comprehensive study by training a family of VQA models with carefully designed frame sampling strategies and spatio-temporal fusion methods. Our extensive experiments on four publicly available in- the-wild video quality datasets lead to two main findings. First, the plausible spatio-temporal modeling module (i. e., RNNs) does not facilitate quality-aware spatio-temporal feature learning. Second, sparsely sampled video frames are capable of obtaining the competitive performance against using all video frames as the input. In other words, spatial features play a vital role in capturing video quality variation for VQA. To our best knowledge, this is the first work to explore the issue of spatio-temporal modeling in VQA.

Measuring Perceptual Color Differences of Smartphone Photographs.

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this article, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.

One kind of challenging tetrapeptide biomimetic chromatographic resin for antibody separation.

Short peptide biomimetic chromatography technology as a developing protein separation technology has huge potential for antibody purification. In this study, four tetrapeptide ligands (Ac-FYKH, Ac-YEHF, Ac-YFLH and Ac-FYHI) with high potential binding ability to antibody were selected for the optimal ligand to antibody purification. The results showed that Ac-YEHF-4FF resin had higher binding capacity and selectivity for hIgG among the four resins. And at pH 7.0 and 0.3 ml/min, the highest Q10%-hIgG of Ac-YEHF-4FF resin was 26.2 mg/ml resin while its Q10%-BSA was just 2.2 mg/ml resin. Further, Ac-YEHF-4FF resin was used to purify protein mixtures. By binding at pH 7.0 and being eluted at pH 5.0 and pH 4.0, Ac-YEHF-4FF resin was well used to separate hIgG from BSA containing feedstock, HSA containing feedstock and human serum with the purity and yield both more than 95 %. And the screened resin could also separate mAb from CHO cell culture supernatant with purity 94.3 % and yield 97.5 %. The adsorption and separation results of Ac-YEHF-4FF resin indicated that the goal of getting the efficacy of critical residues from protein A to biomimetic its structure and function could be achieved, which had great significance to the establishment and improvement of tetrapeptide biomimetic chromatography, and also provided a new method for the field of antibody separation and purification.

Effect of Kvass on Improving Functional Dyspepsia in Rats.

Functional dyspepsia (FD) is a common digestive system disease, and probiotics in the treatment of FD have a good curative effect. Patients with gastrointestinal diseases often show a poor response to traditional drug treatments and suffer from adverse reactions. Kvass can be used as a functional drink without side effects to improve the symptoms of FD patients. The results showed that compared with those of the model group, the body weight and food intake of the treatment group were significantly increased (P < 0.05), and the gastric residual rate of the treatment group was significantly decreased (P < 0.05); the amount of pepsin in the treatment group was significantly higher than that in the model group (P < 0.05); a high dose of Kvass could increase the contents of ghrelin, motilin (MTL), and gastrin (GAS) in the plasma and decrease the contents of vasoactive intestinal peptide (VIP) in the plasma; the contents of ghrelin, MTL, and GAS in the gastric antrum were also increased in the high-dose group. Kvass beverage can significantly improve the gastrointestinal function of rats, which may be because it can improve the contents of ghrelin, MTL, GAS, and VIP in both the serum and gastric antrum by regulating the expression of short-chain fatty acids in the colon.

Visual Cluster Grounding for Image Captioning.

Attention mechanisms have been extensively adopted in vision and language tasks such as image captioning. It encourages a captioning model to dynamically ground appropriate image regions when generating words or phrases, and it is critical to alleviate the problems of object hallucinations and language bias. However, current studies show that the grounding accuracy of existing captioners is still far from satisfactory. Recently, much effort is devoted to improving the grounding accuracy by linking the words to the full content of objects in images. However, due to the noisy grounding annotations and large variations of object appearance, such strict word-object alignment regularization may not be optimal for improving captioning performance. In this paper, to improve the performance of both grounding and captioning, we propose a novel grounding model which implicitly links the words to the evidence in the image. The proposed model encourages the captioner to dynamically focus on informative regions of the objects, which could be either discriminative parts or full object content. With slacked constraints, the proposed captioning model can capture correct linguistic characteristics and visual relevance, and then generate more grounded image captions. In addition, we propose a novel quantitative metric for evaluating the correctness of the soft attention mechanism by considering the overall contribution of all object proposals when generating certain words. The proposed grounding model can be seamlessly plugged into most attention-based architectures without introducing inference complexity. We conduct extensive experiments on Flickr30k (Young et al., 2014) and MS COCO datasets (Lin et al., 2014), demonstrating that the proposed method consistently improves image captioning in both grounding and captioning. Besides, the proposed attention evaluation metric shows better consistency with the captioning performance.

Electrostatic pull-in application in flexible devices: A review.

The electrostatic pull-in effect is a common phenomenon and a key parameter in the design of microscale and nanoscale devices. Flexible electronic devices based on the pull-in effect have attracted increasing attention due to their unique ductility. This review summarizes nanoelectromechanical switches made by flexible materials and classifies and discusses their applications in, among others, radio frequency systems, microfluidic systems, and electrostatic discharge protection. It is supposed to give researchers a more comprehensive understanding of the pull-in phenomenon and the development of its applications. Also, the review is meant to provide a reference for engineers to design and optimize devices.

Subjective and Objective Quality of Experience of Free Viewpoint Videos.

Free viewpoint videos (FVVs) provide immersive experiences for end-users, and they have been applied in many applications, such as movies, sports, and TV shows. However, the development of quantifying the quality of experience (QoE) of FVVs is still relatively slow due to the high costs of data collection and limited public databases. In this paper, we conduct a comprehensive study on FVV QoE. First, we construct the largest, to the best of our knowledge, FVV QoE database called Youku-FVV from two complex real scenarios, i. e., entertainment and sports. Specifically, Youku-FVV originates from the videos captured by dozens of real cameras arranged annularly. We use these videos to generate virtual viewpoints, which make up FVVs together with real views. In constructing the FVV QoE database, we consider both internal and external influencing factors of QoE, which correspond to FVV generation and playback, respectively. Besides, we make an initial attempt to train an efficient no reference FVV QoE prediction model using this database, where several sparse frame sampling strategies are validated. And we demonstrate the feasibility of striving for the balance between effectiveness and efficiency of FVV QoE prediction. The proposed FVV QoE database and source codes are publicly available at https://github.com/QTJiebin/FVV_QoE.

VCRNet: Visual Compensation Restoration Network for No-Reference Image Quality Assessment.

Guided by the free-energy principle, generative adversarial networks (GAN)-based no-reference image quality assessment (NR-IQA) methods have improved the image quality prediction accuracy. However, the GAN cannot well handle the restoration task for the free-energy principle-guided NR-IQA methods, especially for the severely destroyed images, which results in that the quality reconstruction relationship between the distorted image and its restored image cannot be accurately built. To address this problem, a visual compensation restoration network (VCRNet)-based NR-IQA method is proposed, which uses a non-adversarial model to efficiently handle the distorted image restoration task. The proposed VCRNet consists of a visual restoration network and a quality estimation network. To accurately build the quality reconstruction relationship between the distorted image and its restored image, a visual compensation module, an optimized asymmetric residual block, and an error map-based mixed loss function, are proposed for increasing the restoration capability of the visual restoration network. For further addressing the NR-IQA problem of severely destroyed images, the multi-level restoration features which are obtained from the visual restoration network are used for the image quality estimation. To prove the effectiveness of the proposed VCRNet, seven representative IQA databases are used, and experimental results show that the proposed VCRNet achieves the state-of-the-art image quality prediction accuracy. The implementation of the proposed VCRNet has been released at https://github.com/NUIST-Videocoding/VCRNet.

Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos.

Omnidirectional images (also referred to as static 360 ° panoramas) impose viewing conditions much different from those of regular 2D images. How do humans perceive image distortions in immersive virtual reality (VR) environments is an important problem which receives less attention. We argue that, apart from the distorted panorama itself, two types of VR viewing conditions are crucial in determining the viewing behaviors of users and the perceived quality of the panorama: the starting point and the exploration time. We first carry out a psychophysical experiment to investigate the interplay among the VR viewing conditions, the user viewing behaviors, and the perceived quality of 360 ° images. Then, we provide a thorough analysis of the collected human data, leading to several interesting findings. Moreover, we propose a computational framework for objective quality assessment of 360 ° images, embodying viewing conditions and behaviors in a delightful way. Specifically, we first transform an omnidirectional image to several video representations using different user viewing behaviors under different viewing conditions. We then leverage advanced 2D full-reference video quality models to compute the perceived quality. We construct a set of specific quality measures within the proposed framework, and demonstrate their promises on three VR quality databases.

Analysis of the Impact of Medical Features and Risk Prediction of Acute Kidney Injury for Critical Patients Using Temporal Electronic Health Record Data With Attention-Based Neural Network.

Acute kidney injury (AKI) is one of the most severe consequences of kidney injury, and it will also cause or aggravate the complications by the fast decline of kidney excretory function. Accurate AKI prediction, including the AKI case, AKI stage, and AKI onset time interval, can provide adequate support for effective interventions. Besides, discovering how the medical features affect the AKI result may also provide supporting information for disease treatment. An attention-based temporal neural network approach was employed in this study for AKI prediction and for the analysis of the impact of medical features from temporal electronic health record (EHR) data of patients before AKI diagnosis. We used the publicly available dataset provided by the Medical Information Mart for Intensive Care (MIMIC) for model training, validation, and testing, and then the model was applied in clinical practice. The improvement of AKI case prediction is around 5% AUC (area under the receiver operating characteristic curve), and the AUC value of AKI stage prediction on AKI stage 3 is over 82%. We also analyzed the data by two steps: the associations between the medical features and the AKI case (positive or inverse) and the extent of the impact of medical features on AKI prediction result. It shows that features, such as lactate, glucose, creatinine, blood urea nitrogen (BUN), prothrombin time (PT), and partial thromboplastin time (PTT), are positively associated with the AKI case, while there are inverse associations between the AKI case and features such as platelet, hemoglobin, hematocrit, urine, and international normalized ratio (INR). The laboratory test features such as urine, glucose, creatinine, sodium, and blood urea nitrogen and the medication features such as nonsteroidal anti-inflammatory drugs, agents acting on the renin-angiotensin system, and lipid-lowering medication were detected to have higher weights than other features in the proposed model, which may imply that these features have a great impact on the AKI case.