Electrospun Nanofibrous Membranes Accelerate Biofilm Formation and Probiotic Enrichment: Enhanced Tolerances to pH and Antibiotics.

Biofilms are the oldest, most successful, and most widely distributed form of microorganism life on earth, existing even in extreme environments. Presently, probiotics in biofilm phenotype are thought as the most advanced fourth-generation probiotics. However, high-efficiency and large-scale biofilm enrichment in an artificial way is difficult. Here, fibrous membranes as probiotic biofilm-enriching materials are studied. Electrospun cellulose acetate nanofibrous membranes with nano-sized fibers show outstanding superiority over fibrous membranes with micron-sized fibers in Lactobacillus paracasei biofilm enrichment. The special 3D structure of electrospun nanofibrous membranes makes other facilitating biofilm formation factors insignificant. With a suitable scaffold/culture medium ratio, nearly 100% of L. paracasei cells exist as biofilm phenotype on the membrane from the very beginning, not planktonic state. L. paracasei biofilms possess a potential for long-term survival and high tolerances toward strong acidic and alkali conditions and antibiotics. RNA sequencing results explain why L. paracasei biofilms possess high tolerances toward harsh environments as compared to planktonic L. paracasei. Electrospun nanofibrous membranes can serve as powerful biofilm-enriching scaffolds for probiotics and other valuable microbes.

Effect of miR-140-5p on the regulation of proliferation and apoptosis in NSCLC and its underlying mechanism.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer accounting for ~80% of lung cancer cases. According to novel research, numerous microRNAs (miRs) have been suggested to function as important regulators of cancer. In addition, the expression of miR-140-5p is decreased in patients with NSCLC. Therefore, it is important to further elucidate the role of miR-140-5p in NSCLC. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used in order to investigate the expression of miR-140-5p in NSCLC tissues and matched normal tissues and to determine miR-140-5p levels following transfection with mimics into A549 lung cancer cells. Targetscan software was used to predict the oncogene target of miR-140-5p. This analysis revealed that YES proto-oncogene 1 (YES1) includes a target site for miR-140-5p binding. The results revealed that YES1 is a potential target gene of miR-140-5p, and this was further confirmed by the results of luciferase reporter assays, which demonstrated that miR-140-5p directly targeted the predicted binding site in the 3'-untranslated region of YES1. Cell Counting Kit-8 (CCK-8) and flow cytometry assays were performed to determine the levels of cell viability and apoptosis. Western blot assays was performed to investigate the expression levels of YES1 and proteins associated with apoptosis in A549 cells following transfection. The results revealed that miR-140-5p expression was significantly downregulated in NSCLC tissues compared with matched normal tissues. The expression of miR-140-5p was significantly increased following transfection with miR-140-5p mimics. The results of CCK-8 and flow cytometry assays indicated that miR-140-5p inhibited proliferation and induced apoptosis of tumor cells. Western blot analysis and RT-qPCR revealed that YES1 and B-cell lymphoma 2 (Bcl-2) mRNA and protein expression levels were markedly decreased in A549 cells, while Bcl-2 associated X (Bax) and caspase-3 expression levels increased significantly following transfection with miR-140-5p mimics compared with the negative control group. In conclusion, miR-140-5p may induce apoptosis in A549 cells by targeting YES1 and regulating the expression of apoptosis-associated proteins Bcl-2, Bax and caspase-3.

Alleviate Similar Object in Visual Tracking via Online Learning Interference-Target Spatial Structure.

Correlation Filter (CF) based trackers have demonstrated superior performance to many complex scenes in smart and autonomous systems, but similar object interference is still a challenge. When the target is occluded by a similar object, they not only have similar appearance feature but also are in same surrounding context. Existing CF tracking models only consider the target's appearance information and its surrounding context, and have insufficient discrimination to address the problem. We propose an approach that integrates interference-target spatial structure (ITSS) constraints into existing CF model to alleviate similar object interference. Our approach manages a dynamic graph of ITSS online, and jointly learns the target appearance model, similar object appearance model and the spatial structure between them to improve the discrimination between the target and a similar object. Experimental results on large benchmark datasets OTB-2013 and OTB-2015 show that the proposed approach achieves state-of-the-art performance.

A Reliable and Real-Time Tracking Method with Color Distribution.

Occlusion is a challenging problem in visual tracking. Therefore, in recent years, many trackers have been explored to solve this problem, but most of them cannot track the target in real time because of the heavy computational cost. A spatio-temporal context (STC) tracker was proposed to accelerate the task by calculating context information in the Fourier domain, alleviating the performance in handling occlusion. In this paper, we take advantage of the high efficiency of the STC tracker and employ salient prior model information based on color distribution to improve the robustness. Furthermore, we exploit a scale pyramid for accurate scale estimation. In particular, a new high-confidence update strategy and a re-searching mechanism are used to avoid the model corruption and handle occlusion. Extensive experimental results demonstrate our algorithm outperforms several state-of-the-art algorithms on the OTB2015 dataset.

An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration.

In this paper, we propose a novel automatic multi-target registration framework for non-planar infrared-visible videos. Previous approaches usually analyzed multiple targets together and then estimated a global homography for the whole scene, however, these cannot achieve precise multi-target registration when the scenes are non-planar. Our framework is devoted to solving the problem using feature matching and multi-target tracking. The key idea is to analyze and register each target independently. We present a fast and robust feature matching strategy, where only the features on the corresponding foreground pairs are matched. Besides, new reservoirs based on the Gaussian criterion are created for all targets, and a multi-target tracking method is adopted to determine the relationships between the reservoirs and foreground blobs. With the matches in the corresponding reservoir, the homography of each target is computed according to its moving state. We tested our framework on both public near-planar and non-planar datasets. The results demonstrate that the proposed framework outperforms the state-of-the-art global registration method and the manual global registration matrix in all tested datasets.