Studies on the expression and biological functions of ZIC5 in hepatocellular carcinoma

Purpose: To study the expression of zinc finger protein of the cerebellum 5 (ZIC5) and its biological functions in hepatocellular carcinoma (HCC). Methods: Sixty-five patients undergoing HCC surgery were selected. Expression of ZIC5 in HCC and para-carcinoma tissue was examined by quantitative real time-polymerase chain reaction (qRT-PCR) and western blotting. The relationship between ZIC5 expression and clinicopathological features, postoperative survival rate, and prognosis of liver cancer patients was analyzed by t-test, Kaplan-Meier method, and Cox regression analysis, respectively. The effects of ZIC5 silencing on Huh-7 cell proliferation, migration, invasion, and apoptosis were assessed using Cell Counting Kit-8 (CCK-8), wound healing assay, Transwell assay, and flow cytometry, respectively. Results: ZIC5 expression in liver cancer tissue was significantly higher than in the para-carcinoma tissue and was significantly correlated with TNM stage and differentiation degree (p < 0.001). The overall survival rate of patients with high ZIC5 expression level was significantly lower than that of patients with low ZIC5 expression (p < 0.01). ZIC5 expression, TNM stage, and differentiation degree were independent prognostic factors. ZIC5 silencing significantly inhibited the proliferative, migratory, invasive, and anti-apoptotic capacity of Huh-7 cells (p < 0.01). Conclusion: ZIC5 is highly expressed in HCC, and this can promote liver cancer cell proliferation, migration, and invasion

Effects of nitrogen and phosphorus additions on nitrous oxide emission in a nitrogen-rich and two nitrogen-limited tropical forests

Nitrogen (N) deposition is generally considered to increase soil nitrous oxide (N2O) emission in N-rich forests. In many tropical forests, however, elevated N deposition has caused soil N enrichment and further phosphorus (P) deficiency, and the interaction of N and P to control soil N2O emission remains poorly understood, particularly in forests with different soil N status. In this study, we examined the effects of N and P additions on soil N2O emission in an N-rich old-growth forest and two N-limited younger forests (a mixed and a pine forest) in southern China to test the following hypotheses: (1) soil N2O emission is the highest in old-growth forest due to the N-rich soil; (2) N addition increases N2O emission more in the old-growth forest than in the two younger forests; (3) P addition decreases N2O emission more in the old-growth forest than in the two younger forests; and (4) P addition alleviates the stimulation of N2O emission by N addition. The following four treatments were established in each forest: Control, N addition (150 kg N ha(-1) yr(-1)), P addition (150 kg P ha(-1) yr(-1)), and NP addition (150 kg N ha(-1) yr(-1) plus 150 kg P ha(-1) yr(-1)). From February 2007 to October 2009, monthly quantification of soil N2O emission was performed using static chamber and gas chromatography techniques. Mean N2O emission was shown to be significantly higher in the old-growth forest (13.9 +/- 0.7 mu g N2O-N m(-2) h(-1)) than in the mixed (9.9 +/- 0.4 mu g N2O-N m(-2) h(-1)) or pine (10.8 +/- 0.5 mu g N2O-N m(-2) h(-1)) forests, with no significant difference between the latter two. N addition significantly increased N2O emission in the old-growth forest but not in the two younger forests. However, both P and NP addition had no significant effect on N2O emission in all three forests, suggesting that P addition alleviated the stimulation of N2O emission by N addition in the old-growth forest. Although P fertilization may alleviate the stimulated effects of atmospheric N deposition on N2O emission in N-rich forests, this effect may only occur under high N deposition and/or long-term P addition, and we suggest future investigations to definitively assess this management strategy and the importance of P in regulating N cycles from regional to global scales

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = (1 + e-15.2829x(RAGDDi-0.1944))-1 - (1 + e-11.6517x(RAGDDi-1.0267))-1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status

STUDY Of Electromyographic Patterns Of erector Spinae And Lower-limb Muscles during Different Modalities Of Gait In post-stroke Individuals

Stroke is one of the leading causes of motor disability in the world. New technologies have been developed to increase efficiency and reduce costs of rehabilitation of poststroke individuals. Objective: To compare electromyographic patterns related to muscle onset/offset, duration of activation and analysis of neuromuscular fatigue of erector spinae (ES) and lower-limb muscles during different modalities of gait in poststroke and healthy individuals. Methodology: The changes in the median frequency (MDF) was analyzed during isometric tasks and walking on a treadmill in healthy individuals (N = 10) to identify fatigue. Ten post-stroke and 30 healthy subjects participated of the second stage of the study, in which ES and three lower-limb muscles were analyzed during different gaits (walking on treadmill and ground, with and without arm swing, and using a walker), with the neuromuscular fatigue analyzed in stroke gait. Muscle analysis was also conducted with two post-stroke subjects while using the UFESs robotic walker. Results: For the healthy subjects, all the lower-limb muscles showed reduction in their MDF during walking on treadmill. Walking on treadmill had a stronger influence on the onset/offset muscles than the arm swing in the healthy individuals. For post-stroke subjects, their ES muscles presented a similar pattern to the healthy subjects, but the contralateral side had longer activation near the toe-off than the ipsilateral side in both gaits. All the observed changes in the activation for each phase indicated a longer duration of activation of the post-stroke subjects. Regarding neuromuscular fatigue, it was not possible to detect reduced MDF values for post-stroke individuals. The use of the UFESs robotic walker improved the symmetry of one post-stroke subject, and the symmetry of duration of activation in the swing phase for all muscles of the other subject. Conclusion: MDF changes were detected in non-strenuous exercises in healthy subjects. ES muscle activation is not influenced by arm swing in healthy individuals, with the same behavior in post-stroke individuals. As a finding of this research, we concluded that trunk muscles can be used in rehabilitation processes and also to control robotic devices for assistance or rehabilitation

Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52–0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48–0.65 and 0.39–0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.Comment: This paper has been accepted by IEEE TNNL

Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection