Molecular cloning and characterization of DjRac1, a novel small G protein gene from planarian Dugesia japonica.

Rac proteins are classified as a subfamily of the Rho family of small G proteins. They are important molecular switches which act as key signal transducers regulating a wide variety of processes in the cell. DjRac1, a novel Rac gene from planarian Dugesia japonica was cloned by RACE method and characterized. This cDNA contains 851 bp with a putative open reading frame of 190 amino acids. It has a predicted molecular mass of 21.12 kDa and an isoelectric point of 8.42. Whole-mount in situ hybridization and relative quantitative real-time PCR were used to study the spatial and temporal expression pattern of DjRac1 from 1 to 7 days in the regenerating planarians. Results showed that the expression of DjRac1 was concentrated in the blastema and the transcription level of DjRac1 was significantly upregulated after amputation within three days, suggesting DjRac1 might participate in the process of regeneration in planarian.

Simultaneous Detection of Carnosine and Anserine by UHPLC-MS/MS and Its Application on Biomarker Analysis for Differentiation of Meat and Bone Meal.

A novel ultra-high performance liquid chromatography (UHPLC) procedure, coupled with tandem mass spectrometry (MS/MS), was established for the analysis of anserine (ANS) and carnosine (CAR) in meat and bone meal (MBM) (bovine, ovine, porcine, and poultry origins). The pretreatment strategies were optimized for four types of MBM samples prior to UHPLC-MS/MS analysis. This method allowed determining CAR and ANS in short analysis time (18 min per sample). The limits of detection (LODs) and limits of quantification (LOQs) of two analytes in four types of MBM samples were in the ranges of 0.41⁻3.07 ng/g and 0.83⁻5.71 ng/g, respectively. The recovery rates spiked with low, intermediate, and high levels of two analytes in four types of MBM samples were 48.53⁻98.93%, 60.12⁻98.94%, and 67.90⁻98.92%, respectively. Acceptable inter-day reproducibility (RSD < 12.63%) supported the application of this proposed method for determining CAR and ANS in MBM samples. Overall, this rapid, effective, and robust method was successfully applied for quantitative detection of CAR and ANS in MBM samples. Furthermore, The CAR/ANS ratio was found to be in the decreasing order: porcine > bovine > ovine > poultry MBM. This proposed methodology was novelly applied to identify the biomarker (CAR/ANS ratio) for species-specific identification of MBM.

Molecular cloning and characterization of a novel Y-box gene from planarian Dugesia japonica.

Y-box binding protein (YB protein) is an ancient conserved multifunctional DNA/RNA-binding protein. A novel YB protein DjY2 gene from planarian Dugesia japonica was cloned by RACE method and characterized. This cDNA contains 689 bp with a putative open reading frame of 197 amino acids. It has a predicted molecular mass of 22.14 kDa and an isoelectric point of 9.67. Whole-mount in situ hybridization and relative quantitative real-time PCR were used to study the spatial and temporal expression pattern of DjY2 in the process of planarian regeneration. Results showed that DjY2 was expressed in many parts of the body in intact planarian, but the expression level was low in head and pharynx. The transcripts of DjY2 was significantly increased both at the head parts and the tail parts after amputation, especially at the site of cutting. The spatial expression gradually recovered to the state of intact planarian with the time of regeneration. Our results indicated that DjY2 might participate in the process of regeneration in planarian.

Insight into Rapid DNA-Specific Identification of Animal Origin Based on FTIR Analysis: A Case Study.

In this study, a methodology has been proposed to identify the origin of animal DNA, employing high throughput extension accessory Fourier transform infrared (HT-FTIR) spectroscopy coupled with chemometrics. Important discriminatory characteristics were identified in the FTIR spectral peaks of 51 standard DNA samples (25 from bovine and 26 from fish origins), including 1710, 1659, 1608, 1531, 1404, 1375, 1248, 1091, 1060, and 966 cm-1. In particular, the bands at 1708 and 1668 cm-1 were higher in fish DNA than in bovine DNA, while the reverse was true for the band at 1530 cm-1 was shown the opposite result. It was also found that the PO2- Vas/Vs ratio (1238/1094 cm-1) was significantly higher (p < 0.05) in bovine DNA than in fish DNA. These discriminatory characteristics were further revealed to be closely related to the base content and base sequences of different samples. Multivariate analyses, such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were conducted, and both the sensitivity and specificity values of PLS-DA model were one. This methodology has been further validated by 20 meat tissue samples (4 from bovine, 5 from ovine, 5 from porcine, and 6 from fish origins), and these were successfully differentiated. This case study demonstrated that FTIR spectroscopy coupled with PLS-DA discriminant model could provide a rapid, sensitive, and reliable approach for the identification of DNA of animal origin. This methodology could be widely applied in food, feed, forensic science, and archaeology studies.

Generalizing to Out-of-Sample Degradations via Model Reprogramming.

Existing image restoration models are typically designed for specific tasks and struggle to generalize to out-of-sample degradations not encountered during training. While zero-shot methods can address this limitation by fine-tuning model parameters on testing samples, their effectiveness relies on predefined natural priors and physical models of specific degradations. Nevertheless, determining out-of-sample degradations faced in real-world scenarios is always impractical. As a result, it is more desirable to train restoration models with inherent generalization ability. To this end, this work introduces the Out-of-Sample Restoration (OSR) task, which aims to develop restoration models capable of handling out-of-sample degradations. An intuitive solution involves pre-translating out-of-sample degradations to known degradations of restoration models. However, directly translating them in the image space could lead to complex image translation issues. To address this issue, we propose a model reprogramming framework, which translates out-of-sample degradations by quantum mechanic and wave functions. Specifically, input images are decoupled as wave functions of amplitude and phase terms. The translation of out-of-sample degradation is performed by adapting the phase term. Meanwhile, the image content is maintained and enhanced in the amplitude term. By taking these two terms as inputs, restoration models are able to handle out-of-sample degradations without fine-tuning. Through extensive experiments across multiple evaluation cases, we demonstrate the effectiveness and flexibility of our proposed framework. Our codes are available at https://github.com/ddghjikle/Out-of-sample-restoration.

Effects of deep frying and baking on the quality attributes, water distribution, and flavor characteristics of duck jerky.

Introduction: The nutritional value of duck meat is well acknowledged due to its low cholesterol and high protein content. Nevertheless, the impacts of deep-frying and baking on its quality characteristics are not extensively documented in literature. Methods: The objective of this study is to examine the effects of deep-frying, pre-boilingdeep-frying, baking, and pre-boiling-baking on the quality attributes, water distribution, microstructure, and flavor characteristics of duck jerky. Results and discussion: The findings revealed that the deep-frying group had better quality attributes than the baking, pre-boiling-deep-frying, and pre-boiling-baking groups. The deepfried duck jerky had a higher a* value (4.25) and a lower b* value (5.87), with a more appropriate texture profile, and had the highest comprehensive impression score (5.84). Moreover, the drying rate was faster, and the intensity of the free water and oil signal was significantly elevated in the deep-frying group. The microstructure results indicated that the muscle fibers in the deep-frying group were closely packed, whereas those in the baking group were relatively loose. Furthermore, the GC-IMS test revealed that the deep-fried duck jerky had a wider range of volatile flavor compounds, including 11 unique compounds that were only found in this particular product.

Camouflaged Object Segmentation Based on Matching-Recognition-Refinement Network.

In the biosphere, camouflaged objects take the advantage of visional wholeness by keeping the color and texture of the objects highly consistent with the background, thereby confusing the visual mechanism of other creatures and achieving a concealed effect. This is also the main reason why the task of camouflaged object detection is challenging. In this article, we break the visual wholeness and see through the camouflage from the perspective of matching the appropriate field of view. We propose a matching-recognition-refinement network (MRR-Net), which consists of two key modules, i.e., the visual field matching and recognition module (VFMRM) and the stepwise refinement module (SWRM). In the VFMRM, various feature receptive fields are used to match candidate areas of camouflaged objects of different sizes and shapes and adaptively activate and recognize the approximate area of the real camouflaged object. The SWRM then uses the features extracted by the backbone to gradually refine the camouflaged region obtained by VFMRM, thus yielding the complete camouflaged object. In addition, a more efficient deep supervision method is exploited, making the features from the backbone input into the SWRM more critical and not redundant. Extensive experimental results demonstrate that our MRR-Net runs in real-time (82.6 frames/s) and significantly outperforms 30 state-of-the-art models on three challenging datasets under three standard metrics. Furthermore, MRR-Net is applied to four downstream tasks of camouflaged object segmentation (COS), and the results validate its practical application value. Our code is publicly available at: https://github.com/XinyuYanTJU/MRR-Net.

Advances in the Preparation, Stability, Metabolism, and Physiological Roles of Anthocyanins: A Review.

Anthocyanins are natural flavonoid polyphenolic compounds widely found in fruits and vegetables. They exhibit antioxidant properties and prophylactic effects in the immune and cardiovascular systems, confer protection against cancer, and contribute to the prevention of cardiovascular diseases. Thus, their incorporation into functional foods, pharmaceuticals, supplements, and cosmetic formulations aims at promoting human well-being. This review comprehensively outlined the structural attributes of anthocyanins, expanding upon diverse methodologies employed for their extraction and production. Additionally, the stability, metabolic pathways, and manifold physiological functions of anthocyanins were discussed. However, their constrained fat solubility, susceptibility to instability, and restricted bioavailability collectively curtail their applicability and therapeutic efficacy. Consequently, a multidimensional approach was imperative, necessitating the exploration of innovative pathways to surmount these limitations, thereby amplifying the utilitarian significance of anthocyanins and furnishing pivotal support for their continual advancement and broader application.

Path-enhanced graph convolutional networks for node classification without features.

Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of GNNs, very few methods are proposed to resolve the issue. In this work, we mainly focus on improving the performance of graph convolutional networks (GCNs) on the graphs without node features. In order to resolve the issue, we propose a method called t-hopGCN to describe t-hop neighbors by the shortest path between two nodes, then the adjacency matrix of t-hop neighbors as features to perform node classification. Experimental results show that t-hopGCN can significantly improve the performance of node classification in the graphs without node features. More importantly, adding the adjacency matrix of t-hop neighbors can improve the performance of existing popular GNNs on node classification.

Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling.

It is a challenging task to fool an image classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among existing black-box attacks, transfer-based methods tend to overfit the substitute model on parameter settings. Decision-based methods have low query efficiency due to fixed sampling and greedy search strategy. To alleviate the above problems, we present a new framework for query-efficient black-box adversarial attack by bridging transfer-based and decision-based attacks. We reveal the relationship between current noise and variance of sampling, the monotonicity of noise compression, and the influence of transition function on the decision-based attack. Guided by the new framework, we propose a black-box adversarial attack named Customized Iteration and Sampling Attack (CISA). CISA estimates the distance from nearby decision boundary to set the stepsize, and uses a dual-direction iterative trajectory to find the intermediate adversarial example. Based on the intermediate adversarial example, CISA conducts customized sampling according to the noise sensitivity of each pixel to further compress noise, and relaxes the state transition function to achieve higher query efficiency. Extensive experiments demonstrate CISA's advantage in query efficiency of black-box adversarial attacks.

Melanin/melanin-like nanoparticles: As a naturally active platform for imaging-guided disease therapy.

The development of biocompatible and efficient nanoplatforms that combine diagnostic and therapeutic functions is of great importance for precise disease treatment. Melanin, an endogenous biopolymer present in living organisms, has attracted increasing attention as a versatile bioinspired functional platform owing to its unique physicochemical properties (e.g., high biocompatibility, strong chelation of metal ions, broadband light absorption, high drug binding properties) and inherent antioxidant, photoprotective, anti-inflammatory, and anti-tumor effects. In this review, the fundamental physicochemical properties and preparation methods of natural melanin and melanin-like nanoparticles were outlined. A systematical description of the recent progress of melanin and melanin-like nanoparticles in single, dual-, and tri-multimodal imaging-guided the visual administration and treatment of osteoarthritis, acute liver injury, acute kidney injury, acute lung injury, brain injury, periodontitis, iron overload, etc. Was then given. Finally, it concluded with a reasoned discussion of current challenges toward clinical translation and future striving directions. Therefore, this comprehensive review provides insight into the current status of melanin and melanin-like nanoparticles research and is expected to optimize the design of novel melanin-based therapeutic platforms and further clinical translation.

Zein-yeast carboxymethyl glucan particles formed by anti-solvent precipitation for encapsulating resveratrol.

In the work, zein-yeast carboxymethyl glucan (ZY) particles were fabricated by a novel ultrasonic assisted anti-solvent precipitation (ASP) method, which was a good delivery system for resveratrol. The particle size and zeta-potential of ZY samples were detected by Zetasizer Pro analyzer, they gradually increased as the mass ratio of zein and yeast carboxymethyl glucan (YCG) changed from 10:1 to 10:5. The intermolecular interactions were investigated by zeta-potentiometric analyzer, Fourier transform infrared spectroscopy and fluorescence spectroscopy. Electrostatic interaction, hydrogen bonding and hydrophobic effects between zein and YCG molecules were identified as the main driving forces in the formation of ZY particles. The optimized ZY (10:3) binary particles were used as delivery system for encapsulating and protecting resveratrol. They had high encapsulation efficiency (85.4 %) and loading capacity (6.1 %), and increased the retention rate of resveratrol by 2.10 and 1.21 folds after exposure to light and heat conditions, effectively protect resveratrol against light and thermal degradation. These particles also delayed the release of resveratrol in simulated gastrointestinal digestion, which might improve its oral bioavailability. In conclusion, ZY binary particles could be regarded as a useful and promising delivery vehicle, which might contribute to the application of hydrophobic bioactive ingredients in functional foods.

Research Advances in Plant Protein-Based Products: Protein Sources, Processing Technology, and Food Applications.

Plant proteins are high-quality dietary components of food products. With the growing interest in sustainable and healthy food alternatives, plant proteins have gained significant attention as viable substitutes for animal-based proteins. Understanding the diversity of protein sources derived from plants, novel processing technology, and multiple applications is crucial for developing nutritious and sustainable plant protein-based products. This Review summarizes the natural sources of traditional and emerging plant proteins. The classifications, processing technologies, and applications of plant protein-based products in the food industry are explicitly elucidated. Moreover, the advantages and disadvantages of plant protein-based food products are revealed. Strategies such as protein fortification and complementation to overcome these shortcomings are critically discussed. We also demonstrate several issues that need to be addressed in future development.

Insight into the roles of microalgae on simultaneous nitrification and denitrification in microalgal-bacterial sequencing batch reactors: Nitrogen removal, extracellular polymeric substances, and microbial communities.

This study explored the influence and mechanism of microalgae on simultaneous nitrification and denitrification (SND) in microalgal-bacterial sequencing batch reactors (MB-SBR). It particularly focused on nitrogen transformation in extracellular polymeric substances (EPS) and functional groups associated with nitrogen removal. The results showed that MB-SBR achieved more optimal performance than control, with an SND efficiency of 68.01% and total nitrogen removal efficiency of 66.74%. Further analyses revealed that microalgae changed compositions and properties of EPS by increasing EPS contents and improving transfer, conversion, and storage capacity of nitrogen in EPS. Microbial community analysis demonstrated that microalgae promoted the enrichment of functional groups and genes related to SND and introduced diverse nitrogen removal pathways. Moreover, co-occurrence network analysis elucidated the interactions between communities of bacteria and microalgae and the promotion of SND by microalgae as keystone connectors in the MB-SBR. This study provides insights into the roles of microalgae for enhanced SND.

Improvement of physicochemical stability and digestive properties of quercetagetin using zein-chondroitin sulfate particles prepared by antisolvent co-precipitation.

Zein-quercetagetin-chondroitin sulfate (Zein-Que-CS) composite nanoparticles with different compositions were successfully fabricated using a novel antisolvent co-precipitation method. The mean particle diameter (97.5 to 219.4 nm), negative surface potential (-29.9 to -51.1 mV), and turbidity (265 to 370 NTU) of suspensions of Zein-Que nanoparticles increased after the addition of CS. Electrostatic attraction, hydrogen bonding, and hydrophobic attraction were the main driving forces for the formation of the composite nanoparticles. The encapsulation efficiency and loading capacity of the quercetagetin within the Zein-Que-CS (100:10:30) nanoparticles were 91.6 % and 6.1 %, respectively. The photostability and thermal stability of the encapsulated quercetagetin were 3.4- and 3.2- fold higher than that of the free form. The nanoparticles had good resistance to sedimentation and exhibited slow-release properties under simulated gastrointestinal conditions. The Zein-Que-CS nanoparticles developed in this study may therefore be useful for the encapsulation, protection, and delivery of quercetagetin.

Investigation of a novel smart and active packaging materials: Nanoparticle-filled carrageenan-based composite films.

In this study, curcumin, zein, epigallocatechin gallate (EGCG) and carrageenan were used to fabricate curcumin-zein-EGCG-carrageenan (CZEC) layer-by-layer nanoparticles. These nanoparticles were embedded within carrageenan films to form composite films with enhanced antioxidant activity and smart responses. Light scattering, micro-electrophoresis, FTIR, and SEM were used to characterize the size, charge, interactions, and morphology of CZEC nanoparticles. The optical, mechanical, morphological, spectroscopic, thermal, and other functional attributes of the films were evaluated. The CZEC nanoparticles were uniformly dispersed within the carrageenan matrices, and improved their UV barrier (2.4-11.1 A mm-1), mechanical (7.09 %-9.35 %), and thermal resistance properties. The films exhibited a color change, from yellow to red, in response to an increase in pH (2.0-12.0) or ammonia concentration (8.0 mM). The films also displayed relatively high DPPH (79.46 %) and ABTS (73.34 %) free radical scavenging activities. Finally, the composite film exhibited the ability of monitoring and extending the freshness of packaged fish.

Multiple headspace solid-phase microextraction using a new fiber for avoiding matrix interferences in the quantitative determination of ethyl carbamate in pickles.

Multiple headspace solid-phase microextraction (HS-SPME) using a novel fiber coated with anilino-methyl triethoxy silicane-methacrylic acid/terminated silicone oil has been introduced as a useful pretreatment technique coupled to gas chromatography-flame ionization detector for the detection of ethyl carbamate in pickles. Anilino-methyl triethoxy silicane and methacrylic acid are put into use simultaneously with the aim to increase the hydrogen interaction strength between ethyl carbamate and the coating. In addition, the new fiber exhibits high thermal stability, good reproducibility, and long lifetime. Extraction temperature, extraction time, amount of desiccant, and amount of sample were well optimized to guarantee the suitability of multiple HS-SPME. Significant matrix interference was observed among various types of pickles and the multiple HS-SPME procedure was proved to be effective in avoiding the matrix effect by a complete recovery of the analyte. The method showed satisfactory linearity (0.1-100 mg kg(-1)), precision (4.25%, n = 5), and detection limit (0.038 mg kg(-1)). The accuracy of the method was evaluated by comparison with standard addition method and the results were statistically equivalent. The study indicates that the multiple HS-SPME procedure is simple, convenient, accurate, and low-cost, and most of all, can be used for quantitative analysis in complex matrix without matrix effect.

Instance-Invariant Domain Adaptive Object Detection Via Progressive Disentanglement.

Most state-of-the-art methods of object detection suffer from poor generalization ability when the training and test data are from different domains. To address this problem, previous methods mainly explore to align distribution between source and target domains, which may neglect the impact of the domain-specific information existing in the aligned features. Besides, when transferring detection ability across different domains, it is important to extract the instance-level features that are domain-invariant. To this end, we explore to extract instance-invariant features by disentangling the domain-invariant features from the domain-specific features. Particularly, a progressive disentangled mechanism is proposed to decompose domain-invariant and domain-specific features, which consists of a base disentangled layer and a progressive disentangled layer. Then, with the help of Region Proposal Network (RPN), the instance-invariant features are extracted based on the output of the progressive disentangled layer. Finally, to enhance the disentangled ability, we design a detached optimization to train our model in an end-to-end fashion. Experimental results on four domain-shift scenes show our method is separately 2.3, 3.6, 4.0, and 2.0 percent higher than the baseline method. Meanwhile, visualization analysis demonstrates that our model owns well disentangled ability.

Production, Characterization, Delivery, and Cholesterol-Lowering Mechanism of Phytosterols: A Review.

Phytosterols are natural plant-based bioactive compounds that can lower blood cholesterol levels and help prevent cardiovascular diseases. Consequently, they are being utilized in functional foods, supplements, and pharmaceutical products designed to improve human health. This paper summarizes different approaches to isolate, purify, and characterize phytosterols. It also discusses the hypolipidemic mechanisms of phytosterols and their impact on cholesterol transportation. Phytosterols have a low water-solubility, poor chemical stability, and limited bioavailability, which limits their utilization and efficacy in functional foods. Strategies are therefore being developed to overcome these shortcomings. Colloidal delivery systems, such as emulsions, oleogels, liposomes, and nanoparticles, have been shown to be effective at improving the water-dispersibility, stability, and bioavailability of phytosterols. These delivery systems can be used to incorporate phytosterols into a broader range of cholesterol-lowering functional foods and beverages. We also discuses several issues that need to be addressed before these phytosterol delivery systems can find widespread commercial utilization.

Action Keypoint Network for Efficient Video Recognition.

Reducing redundancy is crucial for improving the efficiency of video recognition models. An effective approach is to select informative content from the holistic video, yielding a popular family of dynamic video recognition methods. However, existing dynamic methods focus on either temporal or spatial selection independently while neglecting a reality that the redundancies are usually spatial and temporal, simultaneously. Moreover, their selected content is usually cropped with fixed shapes (e.g., temporally-cropped frames, spatially-cropped patches), while the realistic distribution of informative content can be much more diverse. With these two insights, this paper proposes to integrate temporal and spatial selection into an Action Keypoint Network (AK-Net). From different frames and positions, AK-Net selects some informative points scattered in arbitrary-shaped regions as a set of "action keypoints" and then transforms the video recognition into point cloud classification. More concretely, AK-Net has two steps, i.e., the keypoint selection and the point cloud classification. First, it inputs the video into a baseline network and outputs a feature map from an intermediate layer. We view each pixel on this feature map as a spatial-temporal point and select some informative keypoints using self-attention. Second, AK-Net devises a ranking criterion to arrange the keypoints into an ordered 1D sequence. Since the video is represented with a 1D sequence after the specified layer, AK-Net transforms the subsequent layers into a point cloud classification sub-net by compacting the original 2D convolutional kernels into 1D kernels. Consequentially, AK-Net brings two-fold benefits for efficiency: The keypoint selection step collects informative content within arbitrary shapes and increases the efficiency for modeling spatial-temporal dependencies, while the point cloud classification step further reduces the computational cost by compacting the convolutional kernels. Experimental results show that AK-Net can consistently improve the efficiency and performance of baseline methods on several video recognition benchmarks.