Statistical optimization of culture medium for production of exopolysaccharide from endophytic fungus Bionectria ochroleuca and its antitumor effect in vitro

Endophytic fungi have been recognized as possible useful sources of bioactive metabolites. However, exopolysaccharide (EPS) production from endophytic fungi and its antitumor activity have been less explored. In the present study, endophtic fungus Bionectria ochroleuca M21 was exploited for the production of EPS in submerged culture. Among tested medium components, glucose, yeast extract, MgSO4 and Tween80 were found to be effective and significant on EPS production. Response surface methodology (RSM) was employed to optimize medium composition. The results showed that the significant factors were glucose, yeast extract and Tween80. The optimal medium was observed at the composition of glucose 55.7 g/L, yeast extract 6.04 g/L, MgSO4 0.25g/L and Tween80 0.1 % (v/v). Using the optimized medium, EPS production was achieve at 2.65 ± 0.16 g/L after 4 days fermentation in a 5L bioreactor. Examination of cytotoxicity showed that the EPS from B. ochroleuca M21 did not have cytotoxic activity on human liver HL-7702 cells at concentration 0.025–1.6 mg/mL. In contrast, the EPS exhibited antiproliferative activities against cell lines of liver cancer (HepG2), gastric cancer (SGC-7901) and colon cancer (HT29) in a dose- and time-dependent manner in the concentration ranges of 0.1-0.45 mg/mL

A Missing Key to Understand the Electrical Resonance and the Mechanical Property of Neurons: a Channel-Membrane Interaction Mechanism

The recent study of the interaction between the fatty acyl tails of lipids and the K+ channel establishes the connection between flexoelectricity and the ion channel's dynamics, named Channel-Membrane Interaction (CMI), that may solve the electrical resonance in neurons

Optimization of 4-( N -Cycloamino)phenylquinazolines as a Novel Class of Tubulin-Polymerization Inhibitors Targeting the Colchicine Site

The 6-methoxy-1,2,3,4-tetrahydroquinoline moiety in prior leads 2-chloro- and 2-methyl-4-(6-methoxy-3,4-dihydroquinolin-1(2H)-yl)quinazoline (1a and 1b) was modified to produce 4-(N-cycloamino)quinazolines (4a–c and 5a–m). The new compounds were evaluated in cytotoxicity and tubulin inhibition assays, resulting in the discovery of new tubulin-polymerization inhibitors. 7-Methoxy-4-(2-methylquinazolin-4-yl)-3,4-dihydroquinoxalin- 2(1H)-one (5f), the most potent compound, exhibited high in vitro cytotoxic activity (GI50 1.9–3.2 nM), significant potency against tubulin assembly (IC50 0.77 μM), and substantial inhibition of colchicine binding (99% at 5 μM). In mechanism studies, 5f caused cell arrest in G2/M phase, disrupted microtubule formation, and competed mostly at the colchicine site on tubulin. Compound 5f and N-methylated analogue 5g were evaluated in nude mouse MCF7 xenograft models to validate their antitumor activity. Compound 5g displayed significant in vivo activity (tumor inhibitory rate 51%) at a dose of 4 mg/kg without obvious toxicity, whereas 5f unexpectedly resulted in toxicity and death at the same dose

Effect of EVA and DCP addition on injection moldability and tensile properties of recycled PE from disposable drip tapes

The effects of ethylene-vinyl acetate copolymer (EVA) and dicumyl peroxide (DCP) addition on injection moldability and tensile properties of recycled polyethylene (PE) from disposable drip tapes were studied. DCP showed an adverse impact on the injection moldability of recycled PE materials, while EVA had positive effects on it. Tensile properties of the reproduced PE increased with the increasing amount of EVA up to 10 wt.% and decreased beyond it. The DCP addition reduced the elastic properties of the reproduced PE, while it enhanced the tensile strength. Overall, the co-addition of 6 wt.% EVA + 0.4 wt.% DCP could improve the tensile strength of the reproduced PE materials

Direct observation and structural investigation of galactomannans by transmission electron microscope

313-316Galactomannans isolated from the seeds of Ceratonia siliqua, Itelicise tetragonoloba and Sophora japonica have been studied directly by transmission electron microscope and the fine structures of these galactomannans investigated. Although all the three types of galactomannans are linear, they can be distinguished from one another easily under transmission electron microscope. The results indicate that the fine structures of galactomannans vary with their origin. Generally, galactomannan from guar is non block-type, their α--galactose side-chains are disposed basically regular; but not unifonnly. Unlike guar gum, the galactomannans from sophora bean gum and locust bean gum have block-type dispositions which are composed of alternately distributed smooth zone and hair zone but the smooth zone of locust bean gum is longer than that sophora bean gum

Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering

The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method

Hemispheric Asymmetry of Functional Brain Networks under Different Emotions Using EEG Data

Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants watched different emotional videos. According to the videos’ emotional categories, the data were divided into four categories: high arousal high valence (HAHV), low arousal high valence (LAHV), low arousal low valence (LALV) and high arousal low valence (HALV). The phase lag index as a connectivity index was calculated in theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz) and gamma (31–45 Hz) bands. Hemispheric networks were constructed for each trial, and graph theory was applied to quantify the hemispheric networks’ topological properties. Statistical analyses showed significant topological differences in the gamma band. The left hemispheric network showed significantly higher clustering coefficient (Cp), global efficiency (Eg) and local efficiency (Eloc) and lower characteristic path length (Lp) under HAHV emotion. The right hemispheric network showed significantly higher Cp and Eloc and lower Lp under HALV emotion. The results showed that the left hemisphere was dominant for HAHV emotion, while the right hemisphere was dominant for HALV emotion. The research revealed the relationship between emotion and hemispheric asymmetry from the perspective of brain networks

Double-Camera Fusion System for Animal-Position Awareness in Farming Pens

In livestock breeding, continuous and objective monitoring of animals is manually unfeasible due to the large scale of breeding and expensive labour. Computer vision technology can generate accurate and real-time individual animal or animal group information from video surveillance. However, the frequent occlusion between animals and changes in appearance features caused by varying lighting conditions makes single-camera systems less attractive. We propose a double-camera system and image registration algorithms to spatially fuse the information from different viewpoints to solve these issues. This paper presents a deformable learning-based registration framework, where the input image pairs are initially linearly pre-registered. Then, an unsupervised convolutional neural network is employed to fit the mapping from one view to another, using a large number of unlabelled samples for training. The learned parameters are then used in a semi-supervised network and fine-tuned with a small number of manually annotated landmarks. The actual pixel displacement error is introduced as a complement to an image similarity measure. The performance of the proposed fine-tuned method is evaluated on real farming datasets and demonstrates significant improvement in lowering the registration errors than commonly used feature-based and intensity-based methods. This approach also reduces the registration time of an unseen image pair to less than 0.5 s. The proposed method provides a high-quality reference processing step for improving subsequent tasks such as multi-object tracking and behaviour recognition of animals for further analysis

Biomarker-Targeted Therapies in Non–Small Cell Lung Cancer: Current Status and Perspectives

Non-small-cell lung cancer (NSCLC) is one of the most common malignancies and the leading causes of cancer-related death worldwide. Despite many therapeutic advances in the past decade, NSCLC remains an incurable disease for the majority of patients. Molecular targeted therapies and immunotherapies have significantly improved the prognosis of NSCLC. However, the vast majority of advanced NSCLC develop resistance to current therapies and eventually progress. In this review, we discuss current and potential therapies for NSCLC, focusing on targeted therapies and immunotherapies. We highlight the future role of metabolic therapies and combination therapies in NSCLC

Double-Camera Fusion System for Animal-Position Awareness in Farming Pens

In livestock breeding, continuous and objective monitoring of animals is manually unfeasible due to the large scale of breeding and expensive labour. Computer vision technology can generate accurate and real-time individual animal or animal group information from video surveillance. However, the frequent occlusion between animals and changes in appearance features caused by varying lighting conditions makes single-camera systems less attractive. We propose a double-camera system and image registration algorithms to spatially fuse the information from different viewpoints to solve these issues. This paper presents a deformable learning-based registration framework, where the input image pairs are initially linearly pre-registered. Then, an unsupervised convolutional neural network is employed to fit the mapping from one view to another, using a large number of unlabelled samples for training. The learned parameters are then used in a semi-supervised network and fine-tuned with a small number of manually annotated landmarks. The actual pixel displacement error is introduced as a complement to an image similarity measure. The performance of the proposed fine-tuned method is evaluated on real farming datasets and demonstrates significant improvement in lowering the registration errors than commonly used feature-based and intensity-based methods. This approach also reduces the registration time of an unseen image pair to less than 0.5 s. The proposed method provides a high-quality reference processing step for improving subsequent tasks such as multi-object tracking and behaviour recognition of animals for further analysis