Low-complexity adaptive multipath interference and channel noise mitigation scheme based on optimized detection for bandwidth-limited IM/DD systems.

In this Letter, we propose a low-complexity adaptive multipath interference (MPI) and channel noise mitigation (AMCM) scheme in the receiver digital signal processing (DSP) for bandwidth-limited intensity modulation and direct detection (IM/DD) transmission systems. Following channel equalization, MPI and channel noise are distributed in the low- and high-frequency parts, respectively, and exhibit the characteristics of bandstop filtering. The proposed AMCM is designed based on optimized detection, which incorporates an adaptive bandpass filter (BPF) and a log-maximum a posteriori estimation with a lookup table-based fixed number of surviving states (LUT-based FS-MAP) decoder. The adaptive BPF is capable of mitigating the MPI and channel noise based on spectral distribution. Moreover, the LUT-based FS-MAP decoder can eliminate intersymbol interference (ISI) introduced by the BPF. The proposed AMCM is implemented in an O-band 56-Gbaud IM/DD optical 4-level pulse amplitude modulation (PAM-4) system with a 10.7-GHz bandwidth over a 10-km standard single-mode fiber with different linewidths. The results demonstrate that the proposed AMCM scheme can enhance signal-to-interference ratio (SIR) tolerance to 11 dB with only three real-valued multiplications per symbol, achieving a 7% hard-decision forward error correction threshold. To the best of our knowledge, for the first time, this represents the inaugural instance of optimized detection being employed for MPI mitigation.

Optimized equalization based on MAP detection for C-band bandwidth-limited IM/DD systems.

Entangled dynamic and deterministic inter-symbol interferences (ISIs) induced by complicated channel impairments, limit the transmission capacity of intensity modulation and direct detection (IM/DD) systems. This Letter proposes a colored noise-suppressed channel shortening filter (CNS-CSF)-enabled maximum a posteriori (MAP) estimation (CNS-CSF-MAP) scheme to disentangle and mitigate deterministic and dynamic ISIs, where the CNS-CSF is deployed to perform the optimized dynamic ISI equalization with equalization-enhanced noise suppression, and the subsequent MAP algorithm is used to eliminate the residual deterministic ISI. The performance of the CNS-CSF-MAP scheme is evaluated and demonstrated in a C-band 61-Gb/s 100-km optical on-off keying (OOK) IM/DD system. The experimental results show that the proposed CNS-CSF-MAP scheme reaches the 20% and 7% forward error correction (FEC) thresholds at received optical powers (ROPs) of -6.6 dBm and -4 dBm, achieving 0.5- and 1.5-dB gains over a conventional post-filter-enabled MAP (PF-MAP) scheme.

Low-complexity clustered polynomial nonlinear filter-based electrical dispersion pre-compensation scheme in IM/DD transmission systems.

A polynomial nonlinear filter (PNLF)-based electrical dispersion pre-compensation (pre-EDC) scheme assisted with Gerchberg-Saxton (GS) algorithm is proposed to compensate the chromatic dispersion (CD) for intensity-modulation and direct-detection (IM/DD) optical transmission systems, where PNLF is utilized to fit the nonlinear transfer function of the iterative GS algorithm-based pre-EDC scheme to realize a low-complexity non-iterative CD pre-compensation. The capability of PNLF to fit the nonlinear iterative process enables the PNLF-based pre-EDC scheme to compensate for CD-induced linear distortions and address CD-induced nonlinear distortions, which are typically captured through iterative approaches. Additionally, to further reduce the computational complexity, we also introduce the k-means clustering algorithm to eliminate the weight redundancy and propose a lower-complexity clustered PNLF-based pre-EDC scheme. Simulation results show that PNLF-based and clustered PNLF-based pre-EDC schemes save 76.0% and 97.5% complexity with only 0.3 dB receiver sensitivity penalty at 20% forward error correction (FEC) threshold, compared with GS-based pre-EDC scheme in C-band 56 GBaud 80-km on-off keying (OOK) system. Furthermore, the effectiveness of PNLF-based and clustered PNLF-based pre-EDC schemes is also evaluated through the experimental demonstration. Experimental results show that under C-band 32 GBaud 80-km OOK system, bit error ratio (BER) satisfying 20% FEC threshold is achieved by applying PNLF-based and clustered PNLF-based pre-EDC schemes, which save 78.3% and 94.2% complexity with only 0.4 dB receiver sensitivity penalty compared with GS-based pre-EDC scheme, respectively. The research results indicate that the (clustered) PNLF-based pre-EDC scheme has the great application potential for CD compensation in high-performance and low-cost IM/DD optical transmission systems.

Low-complexity optimized detection with cluster assisting for C-band 64-Gb/s OOK transmission over 100-km SSMF.

In this paper, a low-complexity optimized detection scheme consisting of a post filter with weight sharing (PF-WS) and cluster-assisted log-maximum a posteriori estimation (CA-Log-MAP) is proposed. Besides, a modified equal-width discrete (MEWD) clustering algorithm is proposed to eliminate the training process during clustering. After channel equalization, optimized detection schemes improve performance by suppressing the in-band noise raised by the equalizers. The proposed optimized detection scheme was experimentally performed in a C-band 64-Gb/s on-off keying (OOK) transmission system over 100-km standard single-mode fiber (SSMF) transmission. Compared with the optimized detection scheme with the lowest complexity, the proposed method saves 69.23% required number of real-valued multiplications per symbol (RNRM) at 7% hard-decision forward error correction (HD-FEC). In addition, when the detection performance reaches saturation, the proposed CA-Log-MAP with MEWD saves 82.93% RNRM. Compared with the classic k-means clustering algorithm, the proposed MEWD has the same performance without a training process. To the best of our knowledge, this is the first time clustering algorithms have been applied to optimize decision schemes.

DLUT: Decoupled Learning-Based Unsupervised Tracker.

Unsupervised learning has shown immense potential in object tracking, where accurate classification and regression are crucial for unsupervised trackers. However, the classification and regression branches of most unsupervised trackers calculate object similarities by sharing cross-correlation modules. This leads to high coupling between different branches, thus hindering the network performance. To address the above issue, we propose a Decoupled Learning-based Unsupervised Tracker (DLUT). Specifically, we separate the training pipelines of different branches to unlock their inherent learning potential so that different branches can fully explore the focused feature regions of interest. Furthermore, we design independent adaptive decoupling-correlation modules according to the characteristics of each branch to obtain more discriminative and easily locatable feature response maps. Finally, to suppress the noise interference brought by unsupervised pseudo-label training and highlight the foreground object, we propose a novel suppression-ranking-based unsupervised training strategy. Extensive experiments demonstrate that our DLUT outperforms state-of-the-art unsupervised trackers.

Characterization and Application of Guar Gum/Polyvinyl Alcohol-Based Food Packaging Films Containing Betacyanins from Pokeweed (Phytolacca acinosa Roxb.) Berries and Silver Nanoparticles.

Food packaging films were prepared by using guar gum/polyvinyl alcohol (GP) as the film matrix, 2% Ag nanoparticles (AgNPs) as reinforcing filler and antimicrobial agent, and 1%, 2% and 3% pokeweed betacyanins (PB) as the colorant and antioxidant agent. The structures and color-changing, barrier, mechanical, thermal and antioxidant/antibacterial properties of different films were measured. The results show that the PB were pH-sensitive pigments with pink, purple and yellow colors at pH 3-8, pH 9-11 and pH 12, respectively. PB improved the compatibility of guar gum and polyvinyl alcohol through hydrogen bonds. The films with PB showed a color-changing capacity under ammonia vapor and good color stability in chilled storage. AgNPs and PB elevated the barrier capacity of GP film to light, water vapor and oxygen gas. Meanwhile, AgNPs and PB improved the stiffness, thermal stability and antioxidant/antibacterial activity of GP film. The film with AgNPs and 3% PB showed the highest barrier capacity, stiffness, thermal stability and antioxidant/antimicrobial activity. In shrimp spoilage test, the films with AgNPs and 2% and 3% PB indicated shrimp freshness through film color changes. The results reveal the potential use of the prepared films in active and smart packaging.

Rat Hepatocytes Mitigate Cadmium Toxicity by Forming Annular Gap Junctions and Degrading Them via Endosome-Lysosome Pathway.

Gap junction protein connexin 43 (Cx43) plays a critical role in gap junction communication in rat hepatocytes. However, those located between hepatocytes are easily internalized following exposure to poisons. Herein, we investigated the potential of buffalo rat liver 3A (BRL 3A) cells to generate annular gap junctions (AGJs) proficient at alleviating cadmium (Cd) cytotoxic injury through degradation via an endosome-lysosome pathway. Our results showed that Cd-induced damage of liver microtubules promoted Cx43 internalization and increased Cx43 phosphorylation at Ser373 site. Furthermore, we established that Cd induced AGJs generation in BRL 3A cells, and AGJs were subsequently degraded through the endosome-lysosome pathway. Overall, our results suggested that Cx43 internalization and the generation of AGJs were cellular protective mechanisms to alleviate Cd toxicity in rat hepatocytes.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.

BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions. RESULTS: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA-protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA-protein networks of Mus musculus successfully. CONCLUSIONS: In general, our proposed method EDLMFC improved the accuracy of ncRNA-protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC .

Neuromorphic Vision Based Contact-Level Classification in Robotic Grasping Applications.

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.

A near-infrared fluorescent probe for the ratiometric detection and living cell imaging of ß-galactosidase.

ß-Galactosidase (ß-gal) has captured the attention of biologists, chemists, and medical researchers as an important biomarker for cell senescence and primary ovarian cancer. Therefore, many fluorescent probes with visible light emission have been developed for the detection and imaging of ß-gal in living cells. However, near-infrared (NIR) ratiometric probes are more suitable for bioimaging because near-infrared light can effectively avoid the interference of autofluorescence and the ratiometric approach can improve sensitivity and accuracy of the detection. In this work, we designed an NIR ratiometric probe (TMG) for the highly sensitive detection of ß-gal. Using a spontaneous degradation mechanism based on the ICT effect, the change in ratio (F650/F580) exhibited a prominent ß-gal-dependent performance and proved a strong linear response to the activity of ß-gal at an enzyme concentration between 0 and 200 U L-1, with a limit of detection as low as 0.86 U L-1, and the response speed is much faster than the same type of probes previously reported. The probe also revealed an excellent biocompatibility and a large Stokes shift. Moreover, fluorescence microscopy imaging experiments confirmed that this probe could be successfully used for the detection of endogenous ß-gal in living cells. Graphical abstract.

The antipsychotics sulpiride induces fatty liver in rats via phosphorylation of insulin receptor substrate-1 at Serine 307-mediated adipose tissue insulin resistance.

Cumulative evidence has suggested that many antipsychotics cause metabolic abnormalities. Adipose tissue insulin resistance (Adipo-IR) contributes to the development and progress of metabolic abnormalities including fatty liver by inducing excessive free fatty acid release from adipose tissue. Sulpiride is an old antipsychotic still frequently used in many developing countries. However, its adverse metabolic effects remain poorly understood. Here, chronic administration of sulpiride (80 mg/kg, subcutaneously, once daily for 6 weeks) elevated fasting insulin concentration and the index of the homeostasis model assessment of insulin resistance in rats. More importantly, sulpiride increased hepatic triglyceride accumulation and Oil Red O-stained area, indicating the induction of fatty liver by sulpiride. Sulpiride also increased plasma non-esterified fatty acid concentrations at the baseline and during an oral glucose tolerance test, the Adipo-IR index, and adipocyte size. Adipose gene expression profile revealed that sulpiride decreased mRNA and protein expression of insulin receptor substrate (IRS)-1, but not IRS-2. Furthermore, sulpiride increased phosphorylation of both Ser307 in IRS-1 and Ser473 in Akt at baseline. Co-treatment with bromocriptine (a dopamine D2 receptor agonist) attenuated sulpiride-induced hyperprolactinemia, but it was without effect on insulin resistance and fatty liver. Therefore, the present results suggest that sulpiride induces fatty liver in rats via phosphorylation of IRS-1 at Ser307-mediated adipose tissue insulin resistance, in which dopamine D2 receptor is possibly not involved. Our findings may provide new insights into the mechanisms underlying the steatotic effect of the old antipsychotic.

Transcriptomic Analyses in Zebrafish Cancer Models for Global Gene Expression and Pathway Discovery.

The past decade has witnessed a remarkable advancement of the zebrafish model in cancer research. With the rapid development of genomic tools, it is increasingly feasible to perform genome-wide analyses to identify changes associated with cancer in a wide array of model organisms. These genomic tools, particularly transcriptomic analyses using DNA microarray and RNA sequencing platforms, have now become widely used in zebrafish cancer models to uncover novel biology and common molecular pathways underlying hepatocellular carcinoma, intrahepatic cholangiocarcinoma, melanoma, embryonal rhabdomyosarcoma (ERMS), T cell acute lymphoblastic leukemia (T-ALL), Ewing's sarcoma and glioma. An important finding from these studies is the high similarity and conservation of molecular pathways that underlie cancer in complementary zebrafish models and their human counterparts. Finally, these transcriptomic tools have also proven effective in the development and the validation of specific assays for chemical compound screening. In the future, other genomic tools, such as epigenetic, proteomic and metabolomic tools will likely be incorporated into zebrafish cancer studies, further refining our understanding of cancer.

Transcriptomic analysis of a transgenic zebrafish hepatocellular carcinoma model reveals a prominent role of immune responses in tumour progression and regression.

Using our previously established xmrk transgenic zebrafish, hepatocellular carcinoma (HCC) was generated by induced expression of xmrk, which encoded a hyperactive epidermal growth factor receptor (EGFR) homolog, and regressed by suppression of xmrk expression. To investigate molecular changes in liver tumour progression and regression, RNA-Seq was performed for induced HCC and early and late stages of liver tissues during tumour regression. We found that Xmrk-induced zebrafish HCC shared strong molecular characteristics with a human HCC subtype (S2), which shows activated Myc signalling, upregulated phosphor-S6 and epithelial cell adhesion molecule. In the HCC stage, there were enhanced proteasome, antigen processing and presentation, aminosugars metabolisms, p53 and cell cycle pathways. During tumour regression, the transcriptomic profile showed a reversed trend of molecular changes compared with human HCC progression. Interestingly, distinct immune responses in tumour progression and regression were observed, including increased major histocompatibility complex class I (MHCI) at the HCC stage, enriched immune cell trafficking signals and inflammation in early regression and enhanced MHCII in late regression. Both neutrophils and macrophages were enriched during tumour progression and regression; however, the distribution of neutrophils and macrophages in HCC was relatively uniform, whereas both types of immune cells were regionally clustered during tumour regression, especially with dominant blood vessel association of macrophage in late regression, suggesting differential functions of these immune cells in tumour progression and regression. As tumour regression in our model resembles the targeted inhibition of EGFR in cancer therapy, our observations may provide molecular insights into the targeted inhibition and highlight the importance of immune response in tumour regression.

Discovery of an independent poor-prognosis subtype associated with tertiary lymphoid structures in breast cancer.

Introduction: Tertiary lymphoid structures (TLSs) are ectopic lymphoid formations that arise in non-lymphoid tissues due to chronic inflammation. The pivotal function of TLSs in regulating tumor invasion and metastasis has been established across several cancers, such as lung cancer, liver cancer, and melanoma, with a positive correlation between increased TLS presence and improved prognosis. Nevertheless, the current research about the clinical significance of TLSs in breast cancer remains limited. Methods: In our investigation, we discovered TLS-critical genes that may impact the prognosis of breast cancer patients, and categorized breast cancer into three distinct subtypes based on critical gene expression profiles, each exhibiting substantial differences in prognosis (p = 0.0046, log-rank test), with Cluster 1 having the best prognosis, followed by Cluster 2, and Cluster 3 having the worst prognosis. We explored the impact of the heterogeneity of these subtypes on patient prognosis, the differences in the molecular mechanism, and their responses to drug therapy and immunotherapy. In addition, we designed a machine learning-based classification model, unveiling highly consistent prognostic distinctions in several externally independent cohorts. Results: A notable marker gene CXCL13 was identified in Cluster 3, potentially pivotal in enhancing patient prognosis. At the single-cell resolution, we delved into the adverse prognosis of Cluster 3, observing an enhanced interaction between fibroblasts, myeloid cells, and basal cells, influencing patient prognosis. Furthermore, we identified several significantly upregulated genes (CD46, JAG1, IL6, and IL6R) that may positively correlate with cancer cells' survival and invasive capabilities in this subtype. Discussion: Our study is a robust foundation for precision medicine and personalized therapy, presenting a novel perspective for the contemporary classification of breast cancer.

scAnnoX: an R package integrating multiple public tools for single-cell annotation.

Background: Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. Methods: This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. Results: The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment.

Event-based cameras are commonly leveraged to mitigate issues such as motion blur, low dynamic range, and limited time sampling, which plague conventional cameras. However, a lack of dedicated event-based datasets for benchmarking segmentation algorithms, especially those offering critical depth information for occluded scenes, has been observed. In response, this paper introduces a novel Event-based Segmentation Dataset (ESD), a high-quality event 3D spatial-temporal dataset designed for indoor object segmentation within cluttered environments. ESD encompasses 145 sequences featuring 14,166 manually annotated RGB frames, along with a substantial event count of 21.88 million and 20.80 million events from two stereo-configured event-based cameras. Notably, this densely annotated 3D spatial-temporal event-based segmentation benchmark for tabletop objects represents a pioneering initiative, providing event-wise depth, and annotated instance labels, in addition to corresponding RGBD frames. By releasing ESD, our aim is to offer the research community a challenging segmentation benchmark of exceptional quality.

Protective effect of honokiol on cadmium-induced liver injury in chickens.

Cadmium (Cd), a highly toxic heavy metal in the environment, poses a significant threat to livestock and poultry farming. Honokiol (HNK), a Chinese herbal extract with potent antioxidant activity, acts through oxidative damage and inflammation. Cd induces oxidative stress and causes liver damage in animals. However, whether HNK can alleviate Cd-induced liver injury in chickens and its mechanism remains unclear. In this study, the 48 chickens were randomly allocated into 4 groups, control group, Cd group (70 mg/kg Cd), HNK group (200 mg/kg HNK) and Cd + HNK group (70 mg/kg Cd+200 mg/kg HNK). Results showed that HNK improved the Cd induced reduction in chicken body weight, liver weight, and liver coefficient. HNK recovered the Cd induced liver damaged through increased serum liver biochemical indexes, impaired liver oxidase activity and the disordered the expression level of antioxidant genes. HNK alleviated Cd induced pathological and ultrastructure damage of liver tissue and liver cell that leads apoptosis. HNK decreased Cd contents in the liver, Cd induced disturbances in the levels of trace elements such as iron, copper, zinc, manganese, and selenium. HNK attenuated the damage to the gap junction structure of chicken liver cells caused by Cd and reduced the impairment of oxidase activity and the expression level of antioxidant genes induced by Cd. In conclusion, HNK presents essential preventive measures and a novel pharmacological potential therapy against Cd induced liver injury. Our experiments show that HNK can be used as a new green feed additive in the poultry industry, which provides a theoretical basis for HNK to deal with the pollution caused by Cd in the poultry industry.

Development and application of intelligent packaging films based on guar gum, polyvinyl alcohol and hyacinth bean (Lablab purpureus (L.) sweet) anthocyanins.

The pH-response color-changeable films were prepared by adding different contents (1 %, 2 % and 3 %) of hyacinth bean anthocyanins (HBA) into guar gum/polyvinyl alcohol blend matrix. The structural characterization and optical, barrier, mechanical, thermal, antioxidant and color-changeable properties of the films were determined. The films were applied to monitor the freshness of chilled shrimp and pork. Results showed that HBA were pH-dependent color-changeable pigments that endowed the films with purple color. 2 % and 3 % of HBA improved the uniformity and compactness of the films by forming hydrogen bonds with film matrix. The barrier ability of the films against UV-vis light, water vapor and oxygen was significantly elevated by 2 % and 3 % of HBA. The mechanical, thermal and antioxidant properties of the films were improved by HBA. The films containing HBA were much sensitive to pH variation and ammonia vapor, presenting obvious color changes (purple→green→yellow-green). The films containing HBA showed good color stability when stored at 4 °C for 30 days. Moreover, the film containing 2 % HBA showed color changes (purple→green) when the chilled shrimp and pork decayed. The results suggested that the film containing 2 % HBA was suitable to monitor the freshness of meat products in intelligent packaging field.

Preparation, Characterization, and Application of pH-Response Color-Changeable Films Based on Pullulan, Cooked Amaranth (Amaranthus tricolor L.) Juice, and Bergamot Essential Oil.

Pullulan-based smart packaging films were prepared by mixing cooked amaranth juice and bergamot essential oil. The impact of cooked amaranth juice and bergamot essential oil on the color-changeability, structural characterization, and barrier, antioxidant, mechanical and thermal properties of pullulan-based films was determined. Results showed the cooked amaranth juice contained pH-response color-changing betacyanins. The pullulan films containing cooked amaranth juice were color-changeable in pH 9-12 buffers and in ammonia vapor. The color-changeable property of betacyanins in cooked amaranth juice was unaffected by bergamot essential oils. The inner structure of pullulan films was greatly affected by cooked amaranth juice, forming big and ordered humps in film cross-sections. The crystallinity of pullulan films was improved by the combined addition of cooked amaranth juice and bergamot essential oil. Among the films, the pullulan film containing cooked amaranth juice and 6% bergamot essential oil showed the highest UV-vis light barrier property, antioxidant activity, and tensile strength; while the pullulan film containing cooked amaranth juice and 4% bergamot essential oil showed the highest oxygen barrier property and thermal stability. Moreover, the pullulan films containing cooked amaranth juice were able to monitor the freshness of shrimp by presenting color changes from reddish purple to dark red.

Effects of sulfate on the photosynthetic physiology characteristics of Hydrocotyle vulgaris under zinc stress.

The effects of sulfate on the zinc (Zn) bioaccumulation characteristics and photophysiological mechanisms of the ornamental plant Hydrocotyle vulgaris were explored using a hydroponic culture under three Zn concentrations (300, 500 and 700mgL-1 ) with (400µmolL-1 ) or without the addition of sulfate. Results showed that: (1) tissue Zn concentrations and total Zn contents increased with increasing hydroponic culture Zn concentrations; and sulfate addition decreased Zn uptake and translocation from roots to shoots; (2) Zn exposure decreased photosynthetic pigment synthesis, while sulfate changed this phenomenon, especially for chlorophyll a under 300mgL-1 Zn treatment; (3) Zn exposure decreased photosynthetic function, while sulfate had positive effects, especially on the photosynthetic rate (Pn ) and stomatal conductance (Gs ); and (4) chlorophyll fluorescence parameters related to light energy capture, transfer and assimilation were generally downregulated under Zn stress, while sulfate had a positive effect on these processes. Furthermore, compared to photosynthetic pigment synthesis and photosynthesis, chlorophyll fluorescence was more responsive, especially under 300mgL-1 Zn treatment with sulfate addition. In general, Zn stress affected photophysiological processes at different levels, while sulfate decreased Zn uptake, translocation, and bioaccumulation and showed a positive function in alleviating Zn stress, ultimately resulting in plant growth promotion. All of these results provide a theoretical reference for combining H. vulgaris with sulfate application in the bioremediation of Zn-contaminated environments at the photophysiological level.