Thermally resistant nuclease in Serratia marcescens hinders PCR reactions and degrades PCR products.

Polymerase chain reaction (PCR) is an important tool for exogenous gene acquisition and recombinants identification. There exist two problems when using Serratia marcescens as a template for PCR amplification: amplified PCR products are rapidly degraded, and the results of PCR amplification are unstable. The aim of the present work was to elucidate the reasons for this. By mixing PCR products amplified from Escherichia coli DH5α with S. marcescens supernatant or pellet, we found that the DNA-degrading substance in S. marcescens is thermally resistant and present both intracellularly and extracellularly. We then determined that it is protein, and most likely S. marcescens nuclease, that degrades PCR products since the addition of SDS and EDTA can effectively inhibit or block the degradation of PCR products. By knocking out the S. marcescens nuclease encoding gene, nucA, we confirmed that the nuclease is responsible for the degradation of PCR products and the instability of PCR amplification. This work is the first to show that the S. marcescens nuclease is temporarily and partially inhibited by high temperatures during PCR and recovers rapidly at room temperature after PCR.

Interaction between the EPHB2 receptor and EFNB1 ligand drives gastric cancer invasion and metastasis via the Wnt/ß-catenin/FAK pathway.

The survival benefit for patients with gastric cancer (GC) is modest due to its high transfer potential. Targeted therapy for metastasis-related genes in GC may be a viable approach, however, inhibitors specifically targeting GC are limited. In this study, GC patient-derived xenografts (PDX) with metastatic burden were established via orthotopic transplantation. PCR-Array analysis of primary and metastatic tumors revealed EPH receptor B2 (EPHB2) as the most significantly upregulated gene. The interaction between the EPHB2 receptor and its cognate-specific EFNB1 ligands was high in GC and correlated with a poor prognosis. Fc-EFNB1 treatment increased the invasion and migration abilities of GC cells and induced a high EPHB2 expression. EPHB2 knockdown in GC cells completely abolished the ephrin ligand-induced effects on invasion and migration abilities. Signal transduction analysis revealed Wnt/ß-catenin and FAK as downstream signaling mediators potentially inducing the EPHB2 phenotype. In conclusion, the observed deregulation of EPHB2/EFNB1 expression in GC enhances the invasive phenotype, suggesting a potential role of EPHB2/EFNB1 compound in local tumor cell invasion and the formation of metastasis.

scDMV: a zero-one inflated beta mixture model for DNA methylation variability with scBS-seq data.

MOTIVATION: The utilization of single-cell bisulfite sequencing (scBS-seq) methods allows for precise analysis of DNA methylation patterns at the individual cell level, enabling the identification of rare populations, revealing cell-specific epigenetic changes, and improving differential methylation analysis. Nonetheless, the presence of sparse data and an overabundance of zeros and ones, attributed to limited sequencing depth and coverage, frequently results in reduced precision accuracy during the process of differential methylation detection using scBS-seq. Consequently, there is a pressing demand for an innovative differential methylation analysis approach that effectively tackles these data characteristics and enhances recognition accuracy. RESULTS: We propose a novel beta mixture approach called scDMV for analyzing methylation differences in single-cell bisulfite sequencing data, which effectively handles excess zeros and ones and accommodates low-input sequencing. Our extensive simulation studies demonstrate that the scDMV approach outperforms several alternative methods in terms of sensitivity, precision, and controlling the false positive rate. Moreover, in real data applications, we observe that scDMV exhibits higher precision and sensitivity in identifying differentially methylated regions, even with low-input samples. In addition, scDMV reveals important information for GO enrichment analysis with single-cell whole-genome sequencing data that are often overlooked by other methods. AVAILABILITY AND IMPLEMENTATION: The scDMV method, along with a comprehensive tutorial, can be accessed as an R package on the following GitHub repository: https://github.com/PLX-m/scDMV.

Multi-target angiogenesis inhibitor combined with PD-1 inhibitors may benefit advanced non-small cell lung cancer patients in late line after failure of EGFR-TKI therapy.

Treatments for NSCLC patients with EGFR-TKI resistance are limited. Given that immunotherapy and antiangiogenic agents may have synergistic antitumor effects, we aimed to analyze the effect of multi-target angiogenesis inhibitor anlotinib and immune checkpoint inhibitors (ICIs) combination therapy in NSCLC patients who failed EGFR-TKI. The medical records of lung adenocarcinoma (LUAD) patients with EGFR-TKI resistance were reviewed. After EGFR-TKI resistance, patients who simultaneously received anlotinib and ICIs were enrolled in the observation group, and those who received platinum-pemetrexed chemotherapy were included in the control group. A total of 80 LUAD patients were reviewed and allocated to the anlotinib and ICIs combination therapy (n = 38) and chemotherapy (n = 42) groups. A re-biopsy was performed in all patients in the observation group before the administration of anlotinib and ICIs. The median follow-up was 15.63 months (95% CI: 12.19-19.08). Combination therapy exhibited better PFS (median PFS: 4.33 months [95% CI: 2.62-6.05] vs 3.60 months [95% CI: 2.48-4.73], P = .005), and better OS (median OS: 14.17 months [95% CI: 10.17-18.17] vs 9.00 months [95% CI: 6.92-11.08], P = .029) than chemotherapy. Most patients (73.7%) received combination therapy as fourth and later lines of therapy, with a median PFS of 4.03 months (95% CI: 2.05-6.02) and a median OS of 13.80 months (95% CI: 8.25-19.36). The disease control rate was 92.1%. Four patients discontinued the combination therapy due to adverse events, but the other adverse reactions were manageable and reversible. The combination of anlotinib and PD-1 inhibitors is a promising regimen for the late-line treatment of LUAD patients with EGFR-TKI resistance.

LRRC8A Is a Promising Prognostic Biomarker and Therapeutic Target for Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor of the digestive system with increasing morbidity and mortality. The lack of sensitive and reliable biomarkers is one of the main reasons for the poor prognosis. Volume-regulated anion channels (VRAC), which are ubiquitously expressed in the vertebrate cell membrane, are composed of leucine-rich repeat-containing 8A (LRRC8A) and four other homologous family members (LRRC8B-E). VRAC heterogeneous complex is implicated in each of the six "hallmarks of cancer" and represents a novel therapeutic target for cancer. In this study, LRRC8A was speculated to be a promising prognostic biomarker and therapeutic target for PAAD based on a series of bioinformatics analyses. Additional cell experiments and immunohistochemical assays demonstrated that LRRC8A can affect the prognosis of PAAD and is correlated to cell proliferation, cell migration, drug resistance, and immune infiltration. Functional analysis indicated that LRRC8A influences the progression and prognosis of patients with PAAD by the regulation of CD8+ T cells immune infiltration. Taken together, these results can help in the design of new therapeutic drugs for patients with PAAD.

DNA- and RNA-Binding Proteins Linked Transcriptional Control and Alternative Splicing Together in a Two-Layer Regulatory Network System of Chronic Myeloid Leukemia.

DNA- and RNA-binding proteins (DRBPs) typically possess multiple functions to bind both DNA and RNA and regulate gene expression from more than one level. They are controllers for post-transcriptional processes, such as splicing, polyadenylation, transportation, translation, and degradation of RNA transcripts in eukaryotic organisms, as well as regulators on the transcriptional level. Although DRBPs are reported to play critical roles in various developmental processes and diseases, it is still unclear how they work with DNAs and RNAs simultaneously and regulate genes at the transcriptional and post-transcriptional levels. To investigate the functional mechanism of DRBPs, we collected data from a variety of databases and literature and identified 118 DRBPs, which function as both transcription factors (TFs) and splicing factors (SFs), thus called DRBP-SF. Extensive investigations were conducted on four DRBP-SFs that were highly expressed in chronic myeloid leukemia (CML), heterogeneous nuclear ribonucleoprotein K (HNRNPK), heterogeneous nuclear ribonucleoprotein L (HNRNPL), non-POU domain-containing octamer-binding protein (NONO), and TAR DNA-binding protein 43 (TARDBP). By integrating and analyzing ChIP-seq, CLIP-seq, RNA-seq, and shRNA-seq data in K562 using binding and expression target analysis and Statistical Utility for RBP Functions, we discovered a two-layer regulatory network system centered on these four DRBP-SFs and proposed three possible regulatory models where DRBP-SFs can connect transcriptional and alternative splicing regulatory networks cooperatively in CML. The exploration of the identified DRBP-SFs provides new ideas for studying DRBP and regulatory networks, holding promise for further mechanistic discoveries of the two-layer gene regulatory system that may play critical roles in the occurrence and development of CML.

CORN-Condition Orientated Regulatory Networks: bridging conditions to gene networks.

A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments.

Monoamine oxidase A drives neuroendocrine differentiation in prostate cancer.

Neuroendocrine transdifferentiation (NED) of prostate cancer (PCa) is the main cause of failure of androgen receptor inhibitor treatment. However, the molecular mechanisms underlying the development of NEPC, especially treatment-induced NEPC, remain unclear. Emerging evidence indicates that elevated monoamine oxidase A (MAOA) contribute to the proliferation, cell stemness, and bone metastasis in PCa. Here, we generated an enzalutamide-induced NED cell model to assess the role of MAOA during NED. Overall, MAOA expression was significantly increased upon Enz long-term exposure and was required for neuroendocrine marker expression. In particular, Enz was found to induce NED via the MAOA/mTOR/HIF-1α signaling axis. Further analyses revealed that the MAOA inhibitor clorgyline(CLG) may bring multiple benefits to CRPC patients, including better therapeutic effect and delays NED. These findings suggest that MAOA may be an important target for the development of anti-NED therapies, thereby providing a novel strategy for the combined application of CLG and AR inhibitors in the clinic.

Impact of Type 2 Diabetes Mellitus on the Prognosis of Non-Small Cell Lung Cancer.

OBJECTIVE: Type 2 diabetes mellitus (T2DM) is the most common metabolic disease and is characterized by sustained hyperglycemia. The impact of T2DM on the survival of lung cancer patients remains controversial. The aim of this study was to investigate the associations of type 2 diabetes with lung cancer mortality. METHODS: From January 2019 to January 2020, 228 patients with non-small cell lung cancer (NSCLC) staging earlier than IIIA were included. RESULTS: In our study, we found that the overall survival (OS) and progression-free survival (PFS) of lung cancer patients with diabetes was longer than non-diabetes group. Diagnosed T2DM was associated with the prognosis of lung cancer after adjusting for age and covariates. The association between T2DM and OS was influenced by age, stage of cancer and cancer treatment, as well as whether taking metformin was associated with the OS of lung cancer. However, with the adjustment for age and covariates, the relation trended to lose statistical significance. CONCLUSION: T2DM is an independent prognostic factor for patients with NSCLC staging before IIIA. The patients with both NSCLC and T2DM trended to having a longer OS, possibly due to metformin.

Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data.

Cell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost.

Reduction BACE1 expression via suppressing NF-κB mediated signaling by Tamibarotene in a mouse model of Alzheimer's disease.

This present study examined the effect of Tamibarotene (AM80) in APP/PS1 mice, a well-established AD mouse model. AM80 was intraperitoneal administered to 3-month-old APP/PS1 mice at a dose of 5 mg/kg/day for 16 weeks. The results clearly showed that AM80 could reduce amyloid-ß peptides through impact on APP processing and reduce microglia and astrocyte activation in APP/PS1 mice. The most notable finding in the present study was that inhibitory effect on BACE1 mediated by NF-κB pathway underlies the anti-inflammatory action of AM80. Moreover, AM80 could significantly decrease synaptic loss and enhance the expressions of Synapsin and Drebrin. Therefore, AM80 treatment may have the preclinical prevention of AD with new therapeutic strategies.

Integration of single-cell multi-omics for gene regulatory network inference.

The advancement of single-cell sequencing technology in recent years has provided an opportunity to reconstruct gene regulatory networks (GRNs) with the data from thousands of single cells in one sample. This uncovers regulatory interactions in cells and speeds up the discoveries of regulatory mechanisms in diseases and biological processes. Therefore, more methods have been proposed to reconstruct GRNs using single-cell sequencing data. In this review, we introduce technologies for sequencing single-cell genome, transcriptome, and epigenome. At the same time, we present an overview of current GRN reconstruction strategies utilizing different single-cell sequencing data. Bioinformatics tools were grouped by their input data type and mathematical principles for reader's convenience, and the fundamental mathematics inherent in each group will be discussed. Furthermore, the adaptabilities and limitations of these different methods will also be summarized and compared, with the hope to facilitate researchers recognizing the most suitable tools for them.

Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths.

Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x-loading weights (x-LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of "Fuji" and "Qinguan" apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for "Fuji" and "Qinguan" apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the "Fuji" apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x-LW for the "Qinguan" apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for "Fuji" and "Qinguan" apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both "Fuji" and "Qinguan" apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.

CrusTF: a comprehensive resource of transcriptomes for evolutionary and functional studies of crustacean transcription factors.

BACKGROUND: Crustacea, the second largest subphylum of Arthropoda, includes species of major ecological and economic importance, such as crabs, lobsters, crayfishes, shrimps, and barnacles. With the rapid development of crustacean aquaculture and biodiversity loss, understanding the gene regulatory mechanisms of growth, reproduction, and development of crustaceans is crucial to both aquaculture development and biodiversity conservation of this group of organisms. In these biological processes, transcription factors (TFs) play a vital role in regulating gene expression. However, crustacean transcription factors are still largely unknown, because the lack of complete genome sequences of most crustacean species hampers the studies on their transcriptional regulation on a system-wide scale. Thus, the current TF databases derived from genome sequences contain TF information for only a few crustacean species and are insufficient to elucidate the transcriptional diversity of such a large animal group. RESULTS: Our database CrusTF ( http://qinlab.sls.cuhk.edu.hk/CrusTF ) provides comprehensive information for evolutionary and functional studies on the crustacean transcriptional regulatory system. CrusTF fills the knowledge gap of transcriptional regulation in crustaceans by exploring publicly available and newly sequenced transcriptomes of 170 crustacean species and identifying 131,941 TFs within 63 TF families. CrusTF features three categories of information: sequence, function, and evolution of crustacean TFs. The database enables searching, browsing and downloading of crustacean TF sequences. CrusTF infers DNA binding motifs of crustacean TFs, thus facilitating the users to predict potential downstream TF targets. The database also presents evolutionary analyses of crustacean TFs, which improve our understanding of the evolution of transcriptional regulatory systems in crustaceans. CONCLUSIONS: Given the importance of TF information in evolutionary and functional studies on transcriptional regulatory systems of crustaceans, this database will constitute a key resource for the research community of crustacean biology and evolutionary biology. Moreover, CrusTF serves as a model for the construction of TF database derived from transcriptome data. A similar approach could be applied to other groups of organisms, for which transcriptomes are more readily available than genomes.

Analysis of Temperature and Humidity Field in a New Bulk Tobacco Curing Barn Based on CFD.

A new structure bulk tobacco curing barn was presented. To study the temperature and humidity field in the new structure tobacco curing barn, a 3D transient computational fluid dynamics (CFD) model was developed using porous medium, species transport, κ-ε turbulence and discrete phase models. The CFD results demonstrated that (1) the temperature and relative humidity predictions were validated by the experimental results, and comparison of simulation results with experimental data showed a fairly close agreement; (2) the temperature of the bottom and inlet area was higher than the top and outlet area, and water vapor concentrated on the top and outlet area in the barn; (3) tobacco loading density and thickness of tobacco leaves had an explicit effect on the temperature distributions in the barn.

A BCI-based Environmental Control System for Patients with Severe Spinal Cord Injuries.

This study proposes an event-related potential (ERP) BCI-based environmental control system that integrates household electrical appliances, a nursing bed, and an intelligent wheelchair to provide daily assistance to paralyzed patients with severe spinal cord injuries (SCIs). METHODS: An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving selfpaced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate. By contrast, when the user selects a function of the device (e.g., a TV channel), a synchronous mode is used to improve the accuracy and speed of BCI detection. Two experiments involving six SCI patients were conducted separately in a nursing bed and a wheelchair, and the patients were instructed to control the nursing bed, the wheelchair, and household electrical appliances (an electric light, an air conditioner, and a TV). RESULTS: The average false rate of BCI commands in the control state was 10.4%, whereas the average false operation ratio was 4.9% (a false BCI command might not necessarily result in a false operation according to our system design). During the idle state, there was an average of 0.97 false positives per minute, which did not result in any false operations. CONCLUSION: All SCI patients could use the proposed ERP BCIbased environmental control system satisfactorily. SIGNIFICANCE: The proposed ERP-based environmental control system could be used to assist patients with severe SCIs in their daily lives.

Role of Exchange Protein Directly Activated by Cyclic AMP Isoform 1 in Energy Homeostasis: Regulation of Leptin Expression and Secretion in White Adipose Tissue.

Epacs (exchange proteins directly activated by cyclic AMP [cAMP]) act as downstream effectors of cAMP and play important roles in energy balance and glucose homeostasis. While global deletion of Epac1 in mice leads to heightened leptin sensitivity in the hypothalamus and partial protection against high-fat diet (HFD)-induced obesity, the physiological functions of Epac1 in white adipose tissue (WAT) has not been explored. Here, we report that adipose tissue-specific Epac1 knockout (AEKO) mice are more prone to HFD-induced obesity, with increased food intake, reduced energy expenditure, and impaired glucose tolerance. Despite the fact that AEKO mice on HFD display increased body weight, these mice have decreased circulating leptin levels compared to their wild-type littermates. In vivo and in vitro analyses further reveal that suppression of Epac1 in WAT decreases leptin mRNA expression and secretion by inhibiting cAMP response element binding (CREB) protein and AKT phosphorylation, respectively. Taken together, our results demonstrate that Epac1 plays an important role in regulating energy balance and glucose homeostasis by promoting leptin expression and secretion in WAT.

[Detection of Late Blight Disease on Potato Leaves Using Hyperspectral Imaging Technique].

Hyperspectral imaging feature on potato leaves stressed by late blight was studied in the present paper. The experiment used 60 potato leaves. Among those 60 potato leaves, 48 leaves were vitro inoculated with pathogen of potato late blight, the rest 12 leaves were used as control samples. The leaves were observed for 7 continuous days before and after inoculated and samples including healthy and infested were acquired. Hyperspectral data of healthy and infected potato samples of different disease severity were obtained by the hyperspectral imaging system from 374 to 1,018 nm and then extract spectral data of region of interest (ROI) from those hyperspectral data by the ENVI software. In order to improve the signal-to-noise ratio, the spectral data were preprocessed using different pretreatment methods such as moving average smoothing, normalization, derivative, baseline etc. The least squares-support vector machine(LS-SVM) models were developed based on the raw and those preprocessed data. Among the nine models, the model that used the raw data and the data after the spectroscopic transformation performed best with the discrimination of 94.87%. It was demonstrated that it is realized to determine the potato late blight disease of different disease severity using hyperspectral imaging technique.

[Kinetic models for determination of yeast in fresh jujube using near infrared spectroscopy].

The objectives of this study were: (1) to optimize a near-infrared (NIR) spectroscopy model for fresh jujube stored at room temperature to predict the quality change (yeast growth), (2) to establish a kinetic model of yeast growth for fresh jujubes at room temperature according to NIR spectroscopy data and storage time, and (3) to predict the shelf life of fresh jujube at room temperature. The Lizao samples of fresh jujubes were adopted as the research object in the study. The NIR spectral data were achieved before yeast infection level measured. In order to optimize the NIR model, the pretreatment techniques such as Savitzky-Golay smoothing (S-G smoothing), multiplicative scatter correction (MSC), first derivative (1-Der) and second derivative (2-Der) were compared with the raw spectra by using a statistical software package (Unscrambler 9.8), and the regression coefficient (RC) method was used to choose the characteristic wavenumber. Multiple linear regression (MLR) was applied as NIR modeling method. According to the predicted yeast infection level using NIR model, the chemical kinetic models of spectral data and storage time at room temperature with zero-order and first-order reaction were established by using a statistical software package (SPSS 18). The shelf life could be predicted based on the chemical kinetic model. The results showed that the characteristic wave numbers of 10 300, 8 330, 6 900, 5 666, 5 150 and 4 060 cm(-1) in the whole near-infrared range with MSC technique could be chosen to model the quality deterioration of fresh jujube at room temperature. The NIR model that produced the best prediction had the form of B = 320.027 - 233.920(chi1) - 206.663(chi2) - 61.584(chi3) - 14.847(chi4) - 2.680(chi5) - 9.131(chi6), where B is yeast value (lg/cfu x g(-1)), chi1-chi6 are absorbance value of characteristic wavenumber. The correlation coefficient of calibration (R(c)) was 0.950, the root mean square error of calibration (RMSEC) was 2. 560, the correlation coefficient of prediction (R(p)) was 0.863, and the root mean square error of prediction (RMSEP) was 2.447. The zero-order reaction kinetic model performed better than the first-order model. The zero-order reaction kinetic model of yeast growth with storage time was predicted by B(t) = 171.395-124.445(chi1) - 109.945(chi2) - 32.763(chi3) - 7.899(chi4) - 1.426(chi5) - 4.857(chi6) + 0.045t with a correlation coefficient of 0.996. Based on the linear correlation between the NIR measurement and storage time, the shelf life of fresh jujube at room temperature was predicted to be 8 days for the yeast infection level less than 10 cfu x g(-1). The study showed that the NIR when combed with kinetic models could be used as a non-destructive, rapid method to detect the yeast growth in fresh jujube, and to predict the shelf life and ensure the quality and safety of fresh jujube.

Marine compound catunaregin inhibits angiogenesis through the modulation of phosphorylation of akt and eNOS in vivo and in vitro.

Angiogenesis is the formation of blood vessels from pre-existing vasculature. Excessive or uncontrolled angiogenesis is a major contributor to many pathological conditions whereas inhibition of aberrant angiogenesis is beneficial to patients with pathological angiogenesis. Catunaregin is a core of novel marine compound isolated from mangrove associate. The potential anti-angiogenesis of catunaregin was investigated in human umbilical vein endothelial cells (HUVECs) and zebrafish. HUVECs were treated with different concentrations of catunaregin in the presence or absence of VEGF. The angiogenic phenotypes including cell invasion cell migration and tube formation were evaluated following catunaregin treatment in HUVECs. The possible involvement of AKT, eNOS and ERK1/2 in catunaregin-induced anti-angiogenesis was explored using Western blotting. The anti-angiogenesis of catunaregin was further tested in the zebrafish embryo neovascularization and caudal fin regeneration assays. We found that catunaregin dose-dependently inhibited angiogenesis in both HUVECs and zebrafish embryo neovascularization and zebrafish caudal fin regeneration assays. In addition, catunaregin significantly decreased the phosphorylation of Akt and eNOS, but not the phosphorylation of ERK1/2. The present work demonstrates that catunaregin exerts the anti-angiogenic activity at least in part through the regulation of the Akt and eNOS signaling pathways.