The hepatoprotective effect of sodium butyrate on hepatic inflammatory injury mediated by the NLRP3 inflammatory pathway in subchronic fluoride-exposed mice.

BACKGROUND: Excessive subchronic fluoride exposure can cause severe damage to detoxification organs, including the liver. Sodium butyrate has anti-inflammatory, antitumor, antioxidant and immunomodulatory properties. However, relatively few studies have investigated the effects of sodium butyrate on liver injury caused by subchronic fluoride exposure. The purpose of this research was to investigate the effect and mechanism of sodium butyrate on fluoride-induced hepatic inflammatory injury via the expression of nod-like receptor protein 3 (NLRP3). METHODS: Mice were subjected to randomization into four groups, control group (C), fluorosis group (F), sodium butyrate alone group (S), and treatment group (Y). The mice in groups F and F + S drank 100 mg/L sodium fluoride-containing distilled water freely every day. After fluoride exposure lasted for 3 months, the mice in group S and F + S were gavaged with sodium butyrate daily at a concentration of 1000 mg/kg. Following the treatment regimen, liver specimens were collected for analysis. The mRNA and protein expression levels of inflammatory factors and NLRP3 and its downstream gene were measured by RT-qPCR and western blotting. RESULTS: The histological hematoxylin and eosin (H&E) staining of liver showed that the subchronic fluoride-exposed group were chronic inflammation. The liver of treatment group were less vacuolar degeneration and inflammatory infiltration. The results of the biochemical assay showed that the subchronic fluoride-exposed group were liver injury. In addition, the detection of oxidative stress indicators showed that chronic subchronic fluoride exposure could lead to an increase in the level of oxidative stress in the liver, and the treatment alleviated this increase. RT-qPCR results showed that compared with those in the control group, the mRNA levels of the inflammatory factors TNF-α, IL-6 and IL-1ß, the NLRP3 inflammasome and its downstream factors NLRP3, caspase-1, gasdermin D (GSDMD) and IL-18 increased in the liver tissue of mice in the subchronic fluoride-exposed group. Sodium butyrate released inflammatory factors during subchronic fluoride exposure and inhibited the protein expression of activated NLRP3 to a certain extent. CONCLUSIONS: Sodium butyrate may play a protective role by antagonizing the production of activated inflammasomes and their downstream inflammatory factors in the livers of subchronic fluoride-exposed mice.

S-nitrosoglutathione reductase alleviates morphine analgesic tolerance by restricting PKCα S-nitrosation.

Morphine, a typical opiate, is widely used for controlling pain but can lead to various side effects with long-term use, including addiction, analgesic tolerance, and hyperalgesia. At present, however, the mechanisms underlying the development of morphine analgesic tolerance are not fully understood. This tolerance is influenced by various opioid receptor and kinase protein modifications, such as phosphorylation and ubiquitination. Here, we established a murine morphine tolerance model to investigate whether and how S-nitrosoglutathione reductase (GSNOR) is involved in morphine tolerance. Repeated administration of morphine resulted in the down-regulation of GSNOR, which increased excessive total protein S-nitrosation in the prefrontal cortex. Knockout or chemical inhibition of GSNOR promoted the development of morphine analgesic tolerance and neuron-specific overexpression of GSNOR alleviated morphine analgesic tolerance. Mechanistically, GSNOR deficiency enhanced S-nitrosation of cellular protein kinase alpha (PKCα) at the Cys78 and Cys132 sites, leading to inhibition of PKCα kinase activity, which ultimately promoted the development of morphine analgesic tolerance. Our study highlighted the significant role of GSNOR as a key regulator of PKCα S-nitrosation and its involvement in morphine analgesic tolerance, thus providing a potential therapeutic target for morphine tolerance.

LIM homeobox 8 reduced apoptosis and promoted periodontal tissue regeneration function of dental pulp stem cells.

Stem cell-mediated tissue regeneration is a promising strategy for repairing tissue defects and functional reconstruction in periodontitis, a common disease that leads to the loss of alveolar bone and teeth. However, stem cell apoptosis, widely observed during tissue regeneration, impairs its efficiency. Therefore, the regulation of stem cell apoptosis is critical for improving regeneration efficiency. The LIM homeobox 8 gene LHX8, belongs to the LIM homeobox family, which was involved in tooth morphogenesis. Here, we found that LHX8 was significantly expressed in dental pulp. LHX8 knockdown significantly increased dental pulp mesenchymal stem cells (DPSCs) apoptosis, as confirmed by RT-PCR, western blotting, flow cytometry, and transmission electron microscopy. Additionally, LHX8 overexpression inhibited apoptosis and enhanced the osteo/odontogenic differentiation potential of hDPSCs in vitro. Furthermore, LHX8-overexpression could enhance the periodontal tissue regeneration efficiency of hDPSCs in mice with periodontitis. In conclusion, the present study indicates that LHX8 inhibits stem cell apoptosis and promotes functional tissue formation in stem cell-based tissue regeneration engineering, suggesting a new therapeutic target to increase the efficacy of periodontal tissue regeneration.

AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoising, semantic segmentation, and radiomics evaluation.

BACKGROUND AND OBJECTIVE: The metabolic syndrome induced by obesity is closely associated with cardiovascular disease, and the prevalence is increasing globally, year by year. Obesity is a risk marker for detecting this disease. However, current research on computer-aided detection of adipose distribution is hampered by the lack of open-source large abdominal adipose datasets. METHODS: In this study, a benchmark Abdominal Adipose Tissue CT Image Dataset (AATCT-IDS) containing 300 subjects is prepared and published. AATCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. RESULTS: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATCT-IDS reveals three adipose distributions in the subject population. CONCLUSION: AATCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/AATTCT-IDS/23807256.

Noise characterization of an ultra-stable laser for optical clocks.

We report on the development and performance evaluation of an ultra-stable laser for an 27Al+ optical clock. After a series of noise suppressions, especially the vibrational and temperature fluctuation noise, the 30 cm long cavity stabilized laser obtains a frequency instability of 1.3 × 10-16 @1 s. This result is predicted by noise summation and confirmed by the three-cornered hat method. The 27Al+ optical clock transition is also used to characterize the laser frequency noise, and consistent results are yielded. This is the first reported instance of using single ion optical clocks to measure the frequency noise of ultra-stable lasers, as far as we know. With the implementation of the ultra-stable clock laser, an ultra-narrow linewidth clock transition of 2.8 Hz is obtained.

GSNOR negatively regulates the NLRP3 inflammasome via S-nitrosation of MAPK14.

Hyperactivation of the NLRP3 inflammasome has been implicated in the pathogenesis of numerous diseases. However, the precise molecular mechanisms that modulate the transcriptional regulation of NLRP3 remain largely unknown. In this study, we demonstrated that S-nitrosoglutathione reductase (GSNOR) deficiency in macrophages leads to significant increases in the Nlrp3 and Il-1ß expression levels and interleukin-1ß (IL-1ß) secretion in response to NLRP3 inflammasome stimulation. Furthermore, in vivo experiments utilizing Gsnor-/- mice revealed increased disease severity in both lipopolysaccharide (LPS)-induced septic shock and dextran sodium sulfate (DSS)-induced colitis models. Additionally, we showed that both LPS-induced septic shock and DSS-induced colitis were ameliorated in Gsnor-/- Nlrp3-/- double-knockout (DKO) mice. Mechanistically, GSNOR deficiency increases the S-nitrosation of mitogen-activated protein kinase 14 (MAPK14) at the Cys211 residue and augments MAPK14 kinase activity, thereby promoting Nlrp3 and Il-1ß transcription and stimulating NLRP3 inflammasome activity. Our findings suggested that GSNOR is a regulator of the NLRP3 inflammasome and that reducing the level of S-nitrosylated MAPK14 may constitute an effective strategy for alleviating diseases associated with NLRP3-mediated inflammation.

Integrated analysis of ceRNA-miRNA changes in paraquat-induced pulmonary epithelial-mesenchymal transition via high-throughput sequencing.

Recent studies have shown that epithelial-mesenchymal transition (EMT) plays an important role in paraquat (PQ)-induced tissue fibrosis, which is the main cause of death in patients with PQ poisoning. However, no effective treatment for pulmonary interstitial fibrosis caused by PQ poisoning exists. It is of great significance for us to find new therapeutic targets through bioinformatics in PQ-induced EMT. We conducted transcriptome sequencing to determine the expression profiles of 1210 messenger RNAs (mRNAs), 558 long noncoding RNAs, 28 microRNAs (miRNAs), including 18 known-miRNAs, 10 novel-miRNAs and 154 circular RNAs in the PQ-exposed EMT group mice. Using gene ontology and Kyoto Encyclopaedia of Genes and Genomes analyses, we identified the pathways associated with signal transduction, cancers, endocrine systems and immune systems were involved in PQ-induced EMT. Furthermore, we constructed long noncoding RNA-miRNA-mRNA interrelated networks and found that upregulated genes included Il22ra2, Mdm4, Slc35e2 and Angptl4, and downregulated genes included RGS2, Gabpb2, Acvr1, Prkd3, Sp100, Tlr12, Syt15 and Camk2d. Thirteen new potential competitive endogenous RNA targets were also identified for further treatment of PQ-induced pulmonary tissue fibrosis. Through further study of the pathway and networks, we may identify new molecular targets in PQ-induced pulmonary EMT.

PHE-SICH-CT-IDS: A benchmark CT image dataset for evaluation semantic segmentation, object detection and radiomic feature extraction of perihematomal edema in spontaneous intracerebral hemorrhage.

BACKGROUND AND OBJECTIVE: Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets. METHODS: This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods. RESULTS: This study conducts numerous experiments using classical machine learning and deep learning methods, demonstrating the differences in various segmentation and detection methods on the PHE-SICH-CT-IDS. The highest precision achieved in semantic segmentation is 76.31%, while object detection attains a maximum precision of 97.62%. The experimental results on radiomic feature extraction and analysis prove the suitability of PHE-SICH-CT-IDS for evaluating image features and highlight the predictive value of these features for the prognosis of SICH patients. CONCLUSION: To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937.

ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions.

BACKGROUND: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. METHODS: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. RESULTS: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. CONCLUSION: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: https://figshare.com/articles/dataset/ECPC-IDS/23808258.

The inhibitory potential of green manure return on the germination and seedling growth of Eleusine indica L.

Trifolium repens L. (white clover) and Lolium perenne L. (ryegrass) are green manures widely used in conservation tillage systems worldwide. Eleusine indica L. (goosegrass) is a globally recognized noxious weed. Herein, we investigated the effects of aqueous extracts, decomposed liquids, and different straw-to-soil ratios on the germination and growth of goosegrass. The results showed that high concentrations (≥ 30%) of aqueous extracts or decomposed liquids of both green manures significantly inhibited germination-related parameters of goosegrass. The strongest inhibitory effect was observed for the 7-day decomposition treatment, and white clover's inhibitory effect was greater than ryegrass's. A pot experiment showed that non-photochemical quenching, catalase, and peroxidase activity levels of goosegrass leaves were significantly increased. At the same time, the net photosynthetic rate significantly decreased. Seedling growth was inhibited when the straw-to-soil ratio was greater than 3:100. The ryegrass treatments inhibited goosegrass seedlings more than the white clover treatments. This study demonstrated the inhibitory potential of white clover and ryegrass straw return on seed germination and seedling growth of goosegrass. The study has also helped to identify weed-resistant substances in these green manures so that their weed-control properties can be used more effectively and herbicide usage can be reduced.

Image completion algorithm of anthurium spathes based on multi-scale feature learning.

Machine vision has been used to grade the potted anthurium plant in large-scale production recently. Images are taken to measure the number and size of anthurium spathes. However, due to the limitation of the shooting angle, the occlusion problem reduces the accuracy of measurement. It is necessary to segment the overlapping spathes and repair the incomplete ones. The traditional image completion model has good performance on missing small areas, but it is not satisfactory for missing large areas. In this article, a multi-scale fusion Recurrent Feature Reasoning (RFR) network was proposed to repair the spathe images. Unlike the traditional RFR, a multi-layer component was used in the feature reasoning module. This network can combine multi-scale features to complete the learning task and obtain more details of the spathe, which makes the network more advantageous in image completion when missing large areas of spathes. In this study, a comparison experiment between this network and the widely used image completion network was performed, and the results showed that this network performed well in all types of image completion, especially with large-area incomplete images.

A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae.

Introduction: Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures. Methods: This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison. Results: The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. Discussion: These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.

Function of Innate Lymphoid Cells in Periodontal Tissue Homeostasis: A Narrative Review.

Periodontitis is an irreversible inflammatory response that occurs in periodontal tissues. Given the size and diversity of natural flora in the oral mucosa, host immunity must strike a balance between pathogen identification and a complicated system of tolerance. The innate immune system, which includes innate lymphoid cells (ILCs), certainly plays a crucial role in regulating this homeostasis because pathogens are quickly recognized and responded to. ILCs are a recently discovered category of tissue-resident lymphocytes that lack adaptive antigen receptors. ILCs are found in both lymphoid and non-lymphoid organs and are particularly prevalent at mucosal barrier surfaces, where they control inflammatory response and homeostasis. Recent studies have shown that ILCs are important players in periodontitis; however, the mechanisms that govern the innate immune response in periodontitis still require further investigation. This review focuses on the intricate crosstalk between ILCs and the microenvironment in periodontal tissue homeostasis, with the purpose of regulating or improving immune responses in periodontitis prevention and therapy.

EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks.

Background and purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of colorectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 4,456 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients. EBHI-Seg is publicly available at: https://figshare.com/articles/dataset/EBHI-SEG/21540159/1.

Weed suppression and antioxidant activity of Astragalus sinicus L. decomposition leachates.

Astragalus sinicus L. (milk vetch), a versatile plant that has a soil-enriching effect as green manure, is widely planted in the temperate zone of China. In previous experiments, milk vetch incorporated into the soil as green manure showed potential for goosegrass control. However, "what exactly happens at the chemical level?" and "what are the compounds that are potentially responsible for the phytotoxic effects observed during those previous assays?" In a recent study, in vitro phytotoxicity bioassays and chemical analyses of milk vetch decomposition leachates were carried out to explore the relationship between the temporal phytotoxic effects and the dynamics of chemical composition. For that, milk vetch decomposition leachates with a decay time of 12 h, 9 days, 12 days, 15 days, and 18 days were analyzed for organic compounds by liquid chromatography. The main results were as follows: (1) three compounds with goosegrass suppression potential produced during the decomposed process, i.e., 4-ethylphenol, N-acrylimorpholine, and allyl isothiocyanate. 2-Hydroxyethyl acrylate was present in the 12-h decomposition leachates but was at its highest concentration of 127.1 µg ml-1 at 15 days. (2) The cultures were configured according to the four concentrations of goosegrass-resistant active substances measured in the 15-day decomposition leachate and, as with the 15-day decomposition leachate, the mixture cultures inhibited 100% of goosegrass germination at the high concentrations (≥ 30%), which suggests that these substances have goosegrass suppression potential. (3) The high total phenolic content (302.8-532.3 mg L-1), the total flavonoid content (8.4-72.1 mg L-1), and the reducing activity of the decomposition leachates for different decay times may explain why the incorporation of milk vetch into the soil did not lead to peroxidation of goosegrass in the previous study. (4) Finally, the changes in acid fraction and total content (1.9-4.2 mg ml-1) for different decay times explain the variations in pH of the decomposition leachates, which, when discussed in conjunction with previous studies, may lead to changes in soil nutrient effectiveness and consequently affect crop growth. This study can provide a reference for green weed control research.

[Clinical application of percutaneous spinal endoscopy debridement and lavage in sacroiliac joint tuberculosis].

OBJECTIVE: To investigate the feasibility and clinical efficacy of percutaneous spinal endoscopic debridement and lavage for the treatment of sacroiliac joint tuberculosis. METHODS: The clinical data of 7 patients with sacroiliac joint tuberculosis treated with percutaneous spinal endoscopic debridement and lavage from January 2007 to April 2009 were retrospectively analyzed. There were 2 males and 5 females, aged from 29 to 69 years old, 4 cases on the right side of the lesion and 3 cases on the left side. The course ranged from 8 to 144 months. According to Kim classification, 5 cases were type Ⅲ and 2 cases were type Ⅳ. All patients were treated with HRZE quadruple anti tuberculosis drugs for 2 to 6 weeks beforeoperation. VAS, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and Oswestry Disability Index (ODI) were compared before and after surgery. RESULTS: All the operations were successful, the intraoperative blood loss was less than 50 ml on average, and no complications such as hematoma and infection were occurred. The follow-up time of 7 cases ranged from 18 to 40 months. Pain relief was obvious, and there was a statistical result in the VAS, ESR, and ODI of the patients at 1, 3, 6, 12, 18 months after operation compared with before treatment. At the final follow-up, the patient's clinical, imaging and laboratory examinations showed that the infection had disappeared, and the patient returned to normal life and work. CONCLUSION: Percutaneous spine endoscopic debridement and lavage has less tissue trauma, targeted operation, definite curative effects, and quick postoperative recovery, which has explored new options for minimally invasive treatment of sacroiliac joint tuberculosis.

IdentPMP: identification of moonlighting proteins in plants using sequence-based learning models.

BACKGROUND: A moonlighting protein refers to a protein that can perform two or more functions. Since the current moonlighting protein prediction tools mainly focus on the proteins in animals and microorganisms, and there are differences in the cells and proteins between animals and plants, these may cause the existing tools to predict plant moonlighting proteins inaccurately. Hence, the availability of a benchmark data set and a prediction tool specific for plant moonlighting protein are necessary. METHODS: This study used some protein feature classes from the data set constructed in house to develop a web-based prediction tool. In the beginning, we built a data set about plant protein and reduced redundant sequences. We then performed feature selection, feature normalization and feature dimensionality reduction on the training data. Next, machine learning methods for preliminary modeling were used to select feature classes that performed best in plant moonlighting protein prediction. This selected feature was incorporated into the final plant protein prediction tool. After that, we compared five machine learning methods and used grid searching to optimize parameters, and the most suitable method was chosen as the final model. RESULTS: The prediction results indicated that the eXtreme Gradient Boosting (XGBoost) performed best, which was used as the algorithm to construct the prediction tool, called IdentPMP (Identification of Plant Moonlighting Proteins). The results of the independent test set shows that the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUC) of IdentPMP is 0.43 and 0.68, which are 19.44% (0.43 vs. 0.36) and 13.33% (0.68 vs. 0.60) higher than state-of-the-art non-plant specific methods, respectively. This further demonstrated that a benchmark data set and a plant-specific prediction tool was required for plant moonlighting protein studies. Finally, we implemented the tool into a web version, and users can use it freely through the URL: http://identpmp.aielab.net/.

TeaAS: a comprehensive database for alternative splicing in tea plants (Camellia sinensis).

Alternative splicing (AS) increases the diversity of transcripts and proteins through the selection of different splice sites and plays an important role in the growth, development and stress tolerance of plants. With the release of the reference genome of the tea plant (Camellia sinensis) and the development of transcriptome sequencing, researchers have reported the existence of AS in tea plants. However, there is a lack of a platform, centered on different RNA-seq datasets, that provides comprehensive information on AS.To facilitate access to information on AS and reveal the molecular function of AS in tea plants, we established the first comprehensive AS database for tea plants (TeaAS, http://www.teaas.cn/index.php ). In this study, 3.96 Tb reads from 66 different RNA-seq datasets were collected to identify AS events. TeaAS supports four methods of retrieval of AS information based on gene ID, gene name, annotation (non-redundant/Kyoto encyclopedia of genes and genomes/gene ontology annotation or chromosomal location) and RNA-seq data. It integrates data pertaining to genome annotation, type of AS event, transcript sequence, and isoforms expression levels from 66 RNA-seq datasets. The AS events resulting from different environmental conditions and that occurring in varied tissue types, and the expression levels of specific transcripts can be clearly identified through this online database. Moreover, it also provides two useful tools, Basic Local Alignment Search Tool and Generic Genome Browser, for sequence alignment and visualization of gene structure.The features of the TeaAS database make it a comprehensive AS bioinformatics platform for researchers, as well as a reference for studying AS events in woody crops. It could also be helpful for revealing the novel biological functions of AS in gene regulation in tea plants.

IL-32 Promotes the Radiosensitivity of Esophageal Squamous Cell Carcinoma Cell through STAT3 Pathway.

OBJECTIVE: This study is set out to determine the relationship between IL-32 and radiosensitivity of esophageal squamous cell carcinoma (ESCC). METHODS: Western blot was adopted for measuring IL-32 expression in Eca-109 and TE-10 cells. Eca-109 and TE-10 cells with interference or overexpression of IL-32 were treated with the presence or absence of X-ray irradiation. Then, the use of CCK8 assay was to detect proliferation ability, and effects of IL-32 expression on radiosensitivity of ESCC were tested by colony formation assay. The cell apoptosis was detected using flow cytometry. STAT3 and p-STAT expression, and apoptotic protein Bax were detected by western blot. RESULTS: Colony formation assay and CCK8 assay showed that compared with the NC group without treatment, the growth of the ESCC cells, that is Eca-109 and TE-10, was significantly inhibited in the OE+IR group with highly expressed IL-32 and irradiation. In flow cytometry analysis, in Eca-109 and TE-10 cells, highly expressed IL-32 combined with irradiation significantly increased apoptosis compared with the control group. Highly expressed IL-32 has a synergistic effect with irradiation, inhibiting STAT3 and p-STAT3 expression and increasing apoptotic protein Bax expression. CONCLUSION: IL-32 can improve the radiosensitivity of ESCC cells by inhibiting the STAT3 pathway. Therefore, IL-32 can be used as a new therapeutic target to provide a new attempt for radiotherapy of ESCC.

Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea.

Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinous-rice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The nonlinear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea.