Collagen is the most studied protein with a wide range of applications including pharmaceutical, biomedical, cosmetics, leather, and film industries due to its special characteristics that are high biocompatibility, good bioactivity, and weak antigenicity. Although collagen sources are abundant, the outbreak of varied diseases among land animals posed threat to its utilization in our daily life. Thus, a probe for an alternative source began, which in turn revealed the immense untapped marine sources, such as fish, jellyfish, and some marine Mammals. The present article deals with a brief description of collagen, its characteristics, marine sources, extraction, collagen peptides and their biological activities, potential use and application in various field.

Background: Depression is a common mental disease that mainly manifests as bad mood, decreased interest, pessimism, slow thinking, lack of initiative, poor diet and sleep. Patients with severe depression have suicidal tendencies. Exosomes are small vesicles released by the fusion of a multivesicular body and membranes, and they contain specific proteins, nucleic acids, and lipids related to the cells from which they originate. MicroRNAs (miRNAs) are 20-24 nt RNAs that can be packaged into exosomes and can play important regulatory roles. Astrocytes are the most abundant cell population in the central nervous system and have a close link to depression. Astrocyte activation could result in the release of inflammatory cytokines, including IL-1β, IL-6, and TNFα, which could promote the symptoms of depression. In previous research, our team confirmed that NK cells regulate depression in mice. Here, we propose that miRNA in the exosomes from NK cells performs this antidepressant function.Methods: Exosomes from NK cells were shown by in vivo and in vitro experiments to alleviate symptoms of chronic mild stress in mice and decrease pro-inflammatory cytokines release from astrocytes. The production of pro-inflammatory cytokines was assessed by ELISA. Microarray analysis was used to identify critical miRNAs. Luciferase reporter assays, qPCR, and other experiments were used to prove that exosomal miR-207 has an important role in alleviating the symptoms of stress in mice.Results: MiRNA-containing exosomes from NK cells could alleviate symptoms of chronic mild stress in mice. In vivo experiments showed that these exosomes decreased the levels of pro-inflammatory cytokines (IL-1β, IL-6, and TNFα) released by astrocytes. By microarray analysis of exosome miRNA profiles, miR-207 was found to be overexpressed in exosomes derived from unstressed mice. Experiments confirmed that miR-207 directly targets TLR4 interactor with leucine-rich repeats (Tril) and inhibits NF-κB signaling in astrocytes. MiR-207 could decrease the release of pro-inflammatory cytokines and inhibit expression of Tril in vitro. In vivo experiments revealed that exosomes with low miR-207 levels showed decreased antidepressant activity.(Continued on next page) Conclusion: Collectively, our findings revealed that exosomal miR-207 alleviated symptoms of depression in stressed mice by targeting Tril to inhibit NF-κB signaling in astrocytes.

Background and aim: Chronic low-grade inflammation is associated with various health outcomes, including cardiovascular diseases (CVDs) and cancers. Systemic immune inflammation index (SII) and system inflammation response index (SIRI) have lately been explored as novel prognostic markers for all-cause mortality and cardiovascular mortality. However, studies on prediction value in nationwide representative population are scarce, which limit their generalization. To bridge the knowledge gap, this study aims to prospectively assess the association of SII, SIRI with all-cause mortality and cardiovascular mortality in the National Health and Nutrition Examination Survey (NHANES). Methods: From 1999 to 2018, 42,875 adults who were free of pregnancy, CVDs (stroke, acute coronary syndrome), cancers, and had follow-up records and participated in the NHANES were included in this study. SII and SIRI were quantified by calculating the composite inflammation indicators from the blood routine. To explore the characteristics of the population in different SII or SIRI levels, we divided them according to the quartile of SII or SIRI. The associations between SII, SIRI, and all-cause mortality and cardiovascular mortality events were examined using a Cox regression model. To investigate whether there was a reliable relationship between these two indices and mortalities, we performed subgroup analysis based on sex and age. Results: A total of 42,875 eligible individuals were enrolled, with a mean age of 44 ± 18 years old. During the follow-up period of up to 20 years, 4250 deaths occurred, including 998 deaths from CVDs. Cox proportional hazards modeling showed that adults with SII levels of >655.56 had higher all-cause mortality (hazard ratio [HR], 1.29; 95% confidence interval [CI], 1.18–1.41) and cardiovascular mortality (HR, 1.33; 95% CI, 1.11–1.59) than those with SII levels of <335.36. Adults with SIRI levels of >1.43 had higher risk of all-cause (HR, 1.39; 95% CI, 1.26–1.52) and cardiovascular death (HR, 1.39; 95% CI, 1.14–1.68) than those with SIRI levels of <0.68. In general population older than 60 years, the elevation of SII or SIRI was associated with the risk of all-cause death. Conclusion: Two novel inflammatory composite indices, SII and SIRI, were closely associated with cardiovascular death and all-cause death, and more attention should be paid to systemic inflammation to provide better preventive strategies.

In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions.

a b s t r a c t a r t i c l e i n f oWe propose an automatic pest identification method suitable for large scale, long term monitoring for mobile or embedded devices in situ with less computational cost. A procedure of segmentation and image separation was devised to identify common greenhouse pests, whiteflies, aphid and thrips. Initially, the watershed algorithm was used to segment insects from the background (i.e., sticky trap) images. Color feature of the insects were subsequently extracted by Mahalanobis distance for identification of pest species. Accuracy and computational costs were evaluated across different image resolutions. The correlation of determination (R 2 ) between the proposed identification scheme and manual identification were high, showing 0.934 for whitefly, 0.925 for thrips, and 0.945 for aphids even with low resolution images. Comparing with the conventional methods, pests were efficiently identified with low computational cost. Optimal image resolution for species identification regarding long-term survey was discussed in practical aspect with less computational complexity.

Pathological angiogenesis is necessary for tumor development and metastasis. Tumor-derived extracellular vesicles (EVs) play an important role in mediating the crosstalk between cancer cells and vascular endothelial cells. To date, whether and how microRNAs (miRNAs) encapsulated in tumor-derived EVs affect angiogenesis in esophageal squamous cell carcinoma (ESCC) remains unclear. Here, we showed that miR-181b-5p, an angiogenesis-promoting miRNA of ESCC, can be transferred from ESCC cells to vascular endothelial cells via EVs. In addition, ESCC-derived EVs-miR-181b-5p dramatically induced angiogenesis by targeting PTEN and PHLPP2, and thereby facilitated tumor growth and metastasis. Moreover, miR-181b-5p was highly expressed in ESCC tissues and serum EVs. High miR-181b-5p expression level in ESCC patients was well predicted for poor overall survival. Our work suggests that intercellular crosstalk between tumor cells and vascular endothelial cells is mediated by tumor-derived EVs. miR-181b-5penriched EVs secreted from ESCC cells are involved in angiogenesis that control metastasis of ESCC, providing a potential diagnostic biomarker or drug target for ESCC patients.

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We propose an in situ detection method of multiple leaves with overlapping and occlusion in greenhouse conditions. Initially a multilayer perceptron (MLP) is used to classify partial boundary images of pepper leaves. After the partial leaf boundary detection, active shape models (ASMs) are subsequently built to employ the images of entire leaves based on a priori knowledge using landmark. Two deformable models were developed with pepper leaves:Boundary-ASM and MLP-ASM. Matching processes are carried out by deforming the trained leaf models to fit real leaf images collected in the greenhouse. MLP-ASM detected 76.7 and 87.8% of overlapping and occluded pepper leaves respectively, while Boundary-ASM showed detection rates of 63.4 and 76.7%. The detection rates by the conventional ASM were 23.3 and 29.3%. The leaf models trained with pepper leaves were further tested with leaves of paprika, in the same family but with more complex shapes (e.g., holes and rolling). Although the overall detection rates were somewhat lower than those for pepper, the rates for the occluded and overlapping leaves of paprika were still higher with MLP-ASM (ranging from 60.4 to 76.7%) and Boundary-ASM (ranging from 50.5 to 63.3%) than using the conventional active shape model (from 21.6 to 30.0%). The modified active shape models with the boundary classifier could be an efficient means for detecting multiple leaves in field conditions. ª 2013 IAgrE. Published by Elsevier Ltd. All rights reserved. IntroductionIn conventional farming, agricultural cultivation and pest management are conducted manually by farmers, causing human health problems and productivity loss. Recently, precision agriculture has been proposed to reduce the chemical stress to humans and agricultural products, relying on new technologies (Zhang, Wang, & Wang, 2002), such as computer vision and robotics. Proper judgement of leaf status is essential for effective management of crops including cultivation and crop protection. Automatic detection of individual leaves is a fundamental * Corresponding author. Tel.: þ82 51 5102261; fax: þ82 51 5812962.E-mail address: tschon@pusan.ac.kr (T.-S. Chon).Available online at www.sciencedirect.com journal homepa ge: www .e lsev ie r.com/locate/issn/153 75110 b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 3 e3 5