Molecular Identification and Antifungal Properties of Four Thaumatin-like Proteins in Spruce (Picea likiangensis)

Thaumatin-like proteins (TLPs) are involved in the plant defense response against pathogens, and most of them exhibit antifungal activity. However, the role of TLPs in pathogen-induced defense responses in spruce is not fully understood. In this study, four TLP genes encoding thaumatin-like protein, designated as PlTLP1–4, were isolated and identified from Picea likiangensis needles. Sequence analysis showed that PlTLP1, PlTLP3, and PlTLP4 contained 16 conserved cysteine residues, while PlTLP2 had only 10 conserved cysteine residues. qPCR analysis showed that PlTLPs were expressed in all tissues tested, PlTLP1, PlTLP3, and PlTLP4 had the highest expression levels in young fruits, while PlTLP2 had the highest expression levels in roots. In addition, the expression levels of four PlTLPs were significantly upregulated during infection by Lophodermium piceae. Four recombinant PlTLPs expressed in Escherichia coli exhibited obvious β-1,3-glucanase activity. The antifungal activity assay showed that four recombinant PlTLPs had significant inhibitory effects on the mycelial growth of L. piceae, Fusarium proliferatum, Botrytis cinerea, and Roussoella doimaesalongensis. Microscopic observation revealed that the recombinant PlTLP1–4 induced the morphological changes of the mycelia of L. piceae, and the recombinant PlTLP2 and PlTLP3 induced the morphological changes of the mycelia of F. proliferatum and R. doimaesalongensis, while all the recombinant PlTLPs had no obvious negative effect on the morphology of B. cinerea mycelium. These results suggest that PlTLP genes may play an important role in the defense response of P. likiangensis against L. piceae invasion

Molecular Identification and Antifungal Properties of Four Thaumatin-like Proteins in Spruce (Picea likiangensis)

Thaumatin-like proteins (TLPs) are involved in the plant defense response against pathogens, and most of them exhibit antifungal activity. However, the role of TLPs in pathogen-induced defense responses in spruce is not fully understood. In this study, four TLP genes encoding thaumatin-like protein, designated as PlTLP1–4, were isolated and identified from Picea likiangensis needles. Sequence analysis showed that PlTLP1, PlTLP3, and PlTLP4 contained 16 conserved cysteine residues, while PlTLP2 had only 10 conserved cysteine residues. qPCR analysis showed that PlTLPs were expressed in all tissues tested, PlTLP1, PlTLP3, and PlTLP4 had the highest expression levels in young fruits, while PlTLP2 had the highest expression levels in roots. In addition, the expression levels of four PlTLPs were significantly upregulated during infection by Lophodermium piceae. Four recombinant PlTLPs expressed in Escherichia coli exhibited obvious β-1,3-glucanase activity. The antifungal activity assay showed that four recombinant PlTLPs had significant inhibitory effects on the mycelial growth of L. piceae, Fusarium proliferatum, Botrytis cinerea, and Roussoella doimaesalongensis. Microscopic observation revealed that the recombinant PlTLP1–4 induced the morphological changes of the mycelia of L. piceae, and the recombinant PlTLP2 and PlTLP3 induced the morphological changes of the mycelia of F. proliferatum and R. doimaesalongensis, while all the recombinant PlTLPs had no obvious negative effect on the morphology of B. cinerea mycelium. These results suggest that PlTLP genes may play an important role in the defense response of P. likiangensis against L. piceae invasion

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method

HPV Infection in Esophageal Squamous Cell Carcinoma and Its Relationship to the Prognosis of Patients in Northern China

Purpose. Human papillomavirus (HPV) as a risk factor for esophageal squamous cell carcinoma (ESCC) has previously been studied, but importance of HPV status in ESCC for prognosis is less clear. Methods. A total of 105 specimens with ESCC were tested by in situ hybridization for HPV 16/18 and immunohistochemistry for p16 expression. The 5-year overall survival (OS) and progression-free survival were calculated in relation to these markers and the Cox proportional hazards model was used to determine the hazard ratio (HR) of variables in univariate and multivariate analysis. Results. HPV was detected in 27.6% (29) of the 105 patients with ESCC, and all positive cases were HPV-16. Twenty-five (86.2%) of the 29 HPV-positive tumors were stained positive for p16. HPV infected patients had better 5-year rates of OS (65.9% versus 43.4% among patients with HPV-negative tumors; P = 0.002 by the log-rank test) and had a 63% reduction in the risk of death (adjusted HR = 0.37, 95% CI = 0.16 to 0.82, and P = 0.01). Conclusions. HPV infection may be one of many factors contributing to the development of ESCC and tumor HPV status is an independent prognostic factor for survival among patients with ESCC

Disruption of cortical integration during midazolam-induced light sedation: Effects of Midazolam-Induced Sedation on RSNs

This work examines the effect of midazolam‐induced light sedation on intrinsic functional connectivity of human brain, using a randomized, double‐blind, placebo‐controlled, cross‐over, within‐subject design. Fourteen healthy young subjects were enrolled and midazolam (0.03 mg/kg of the participant's body mass, to a maximum of 2.5 mg) or saline were administrated with an interval of one week. Resting‐state fMRI was conducted before and after administration for each subject. We focus on two types of networks: sensory related lower‐level functional networks and higher‐order functions related ones. Independent component analysis (ICA) was used to identify these resting‐state functional networks. We hypothesize that the sensory (visual, auditory, and sensorimotor) related networks will be intact under midazolam‐induced light sedation while the higher‐order (default mode, executive control, salience networks, etc.) networks will be functionally disconnected. It was found that the functional integrity of the lower‐level networks was maintained, while that of the higher‐level networks was significantly disrupted by light sedation. The within‐network connectivity of the two types of networks was differently affected in terms of direction and extent. These findings provide direct evidence that higher‐order cognitive functions including memory, attention, executive function, and language were impaired prior to lower‐level sensory responses during sedation. Our result also lends support to the information integration model of consciousness. Hum Brain Mapp 36:4247–4261, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc

Deep learning based customer preferences analysis in industry 4.0 environment

Customer preferences analysis and modelling using deep learning in edge computing environment are critical to enhance customer relationship management that focus on a dynamically changing market place. Existing forecasting methods work well with often seen and linear demand patterns but become less accurate with intermittent demands in the catering industry. In this paper, we introduce a throughput deep learning model for both short-term and long-term demands forecasting aimed at allowing catering businesses to be highly efficient and avoid wastage. Moreover, detailed data collected from a business online booking system in the past three years have been used to train and verify the proposed model. Meanwhile, we carefully analyzed the seasonal conditions as well as past local or national events (event analysis) that could have had critical impact on the sales. The results are compared with the best performing forecast methods Xgboost and autoregressive moving average model (ARMA), and they suggest that the proposed method significantly improves demand forecasting accuracy (up to 80%) for dishes demand along with reduction in associated costs and labor allocation