Identification of the Optimal Season and Spectral Regions for Shrub Cover Estimation in Grasslands.

Woody plant encroachment (WPE), the expansion of native and non-native trees and shrubs into grasslands, is a less studied factor that leads to declines in grassland ecosystem health. With the increasing application of remote sensing in grassland monitoring and measuring, it is still difficult to detect WPE at its early stages when its spectral signals are not strong enough. Even at late stages, woody species have strong vegetation characteristics that are commonly categorized as healthy ecosystems. We focus on how shrub encroachment can be detected through remote sensing by looking at the biophysical and spectral properties of the WPE grassland ecosystem, investigating the appropriate season and wavelengths that identify shrub cover, testing the spectral separability of different shrub cover groups and by revealing the lowest shrub cover that can be detected by remote sensing. Biophysical results indicate spring as the best season to distinguish shrubs in our study area. The earliest shrub encroachment can be identified most likely only when the cover reaches between 10% and 25%. A correlation between wavelength spectra and shrub cover indicated four regions that are statistically significant, which differ by season. Furthermore, spectral separability of shrubs increases with their cover; however, good separation is only possible for pure shrub pixels. From the five separability metrics used, Transformed divergence and Jeffries-Matusita distance have better interpretations. The spectral regions for pure shrub pixel separation are slightly different from those derived by correlation and can be explained by the influences from land cover mixtures along our study transect.

Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods.

Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall vegetation cover, ignoring variation in live and dead fractions. In the arid regions of the Canadian prairies, grass cover is typically a mixture of green and senescent plant material, and it is essential to monitor both green and senescent vegetation fractional cover. In this study, we designed and built a camera stand to acquire the close-range photographs of rangeland fractional vegetation cover. Photographs were processed by four approaches: SamplePoint software, object-based image analysis (OBIA), unsupervised and supervised classifications to estimate the fractional cover of green vegetation, senescent vegetation, and background substrate. These estimates were compared to in situ surveys. Our results showed that the SamplePoint software is an effective alternative to field measurements, while the unsupervised classification lacked accuracy and consistency. The Object-based image classification performed better than other image classification methods. Overall, SamplePoint and OBIA produced mean values equivalent to those produced by in situ assessment. These findings suggest an unbiased, consistent, and expedient alternative to in situ grassland vegetation fractional cover estimation, which provides a permanent image record.

Interannual variation in riparian vegetation cover and its relationship with river flow under a high level of human intervention: an example from the Yongding River Basin.

Riparian vegetation cover is significantly affected by a river's hydrological conditions. Especially in arid and semiarid areas, low flow will degrade riparian vegetation, and recent, intensive human activities in the Yongding River Basin have caused a sharp decrease in river flow. We analyzed interannual change in riparian vegetation, river flow effects, and land use on vegetation coverage using the 40 years (1977-2016) of remote sensing images and river flow, combined with 38 years (1980-2018) of land use data. The normalized difference vegetation index (NDVI) was used to determine vegetation cover in five different categories: extremely low, low, medium, high, and extremely high based on the pixel dichotomy model. The weighted average was calculated to obtain vegetation cover trends. We show that riparian vegetation cover from four rivers increased. Compared with 1977, in 2016, combined high and extremely high vegetation covers at the Dongyang, Yang, Sanggan, and Yongding Rivers increased by 20.3%, 26.7%, 50.0%, and 39.2%, respectively. High (R = -0.976, P < 0.01) and extremely high (R = -0.762, P < 0.05) vegetation covers are negatively correlated with flow in the Yongding River. The high vegetation cover of the Sanggan River riparian zone is negatively correlated with river flow (R = -0.683, P < 0.05). In the Dongyang and Sanggan Rivers, land use analysis in the riparian zone showed that change in cultivated land, grassland, and forest were significantly correlated with high and extremely high vegetation cover. The abundant cultivated land and restoration activities are likely responsible for the increase of riparian vegetation cover as river flows decline.

A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands.

Green (GV) and non-photosynthetic vegetation (NPV) cover are both important biophysical parameters for grassland research. The current methodology for cover estimation, including subjective visual estimation and digital image analysis, requires human intervention, lacks automation, batch processing capabilities and extraction accuracy. Therefore, this study proposed to develop a method to quantify both GV and standing dead matter (SDM) fraction cover from field-taken digital RGB images with semi-automated batch processing capabilities (i.e., written as a python script) for mixed grasslands with more complex background information including litter, moss, lichen, rocks and soil. The results show that the GV cover extracted by the method developed in this study is superior to that by subjective visual estimation based on the linear relation with normalized vegetation index (NDVI) calculated from field measured hyper-spectra (R2 = 0.846, p < 0.001 for GV cover estimated from RGB images; R2 = 0.711, p < 0.001 for subjective visual estimated GV cover). The results also show that the developed method has great potential to estimate SDM cover with limited effects of light colored understory components including litter, soil crust and bare soil. In addition, the results of this study indicate that subjective visual estimation tends to estimate higher cover for both GV and SDM compared to that estimated from RGB images.

Hourly photosynthetically active radiation estimation in Midwestern United States from artificial neural networks and conventional regressions models.

The relationship between hourly photosynthetically active radiation (PAR) and the global solar radiation (R s ) was analyzed from data gathered over 3 years at Bondville, IL, and Sioux Falls, SD, Midwestern USA. These data were used to determine temporal variability of the PAR fraction and its dependence on different sky conditions, which were defined by the clearness index. Meanwhile, models based on artificial neural networks (ANNs) were established for predicting hourly PAR. The performance of the proposed models was compared with four existing conventional regression models in terms of the normalized root mean square error (NRMSE), the coefficient of determination (r (2)), the mean percentage error (MPE), and the relative standard error (RSE). From the overall analysis, it shows that the ANN model can predict PAR accurately, especially for overcast sky and clear sky conditions. Meanwhile, the parameters related to water vapor do not improve the prediction result significantly.

Some insights on grassland health assessment based on remote sensing.

Grassland ecosystem is one of the largest ecosystems, which naturally occurs on all continents excluding Antarctica and provides both ecological and economic functions. The deterioration of natural grassland has been attracting many grassland researchers to monitor the grassland condition and dynamics for decades. Remote sensing techniques, which are advanced in dealing with the scale constraints of ecological research and provide temporal information, become a powerful approach of grassland ecosystem monitoring. So far, grassland health monitoring studies have mostly focused on different areas, for example, productivity evaluation, classification, vegetation dynamics, livestock carrying capacity, grazing intensity, natural disaster detecting, fire, climate change, coverage assessment and soil erosion. However, the grassland ecosystem is a complex system which is formed by soil, vegetation, wildlife and atmosphere. Thus, it is time to consider the grassland ecosystem as an entity synthetically and establish an integrated grassland health monitoring system to combine different aspects of the complex grassland ecosystem. In this review, current grassland health monitoring methods, including rangeland health assessment, ecosystem health assessment and grassland monitoring by remote sensing from different aspects, are discussed along with the future directions of grassland health assessment.

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Dry-season length affects the annual ecosystem carbon balance of a temperate semi-arid shrubland.

Semi-arid ecosystems have been shown to dominate over tropical forests in determining the trend and interannual variability of land carbon (C) sink. However, the magnitude and variability of ecosystem C balance remain largely uncertain for temperate semi-arid shrublands at the decadal scale. Using eddy-covariance and micro-meteorological measurements, we quantified the interannual variation in net ecosystem production (NEP) and its components, gross primary production (GPP) and ecosystem respiration (Reco, i.e., the sum of autotrophic and heterotrophic respiration), in a semi-arid shrubland of the Mu Us Desert, northern China during 2012-2022. This shrubland was an overall weak C sink over the 11 years (NEP = 12 ± 46 g C m-2 yr-1, mean ± SD). Annual NEP ranged from -66 to 77 g C m-2 yr-1, with the ecosystem frequently switching between being an annual C sink and a C source. GPP was twice as sensitive as Reco to prolonged dry seasons, leading to a close negative relationship between annual NEP and dry-season length (R2 = 0.80, P < 0.01). Annual GPP (R2 = 0.51, P = 0.01) and NEP (R2 = 0.58, P < 0.01) were positively correlated with annual rainfall. Negative annual NEP (the ecosystem being a C source) tended to occur when the dry season exceeded 50 d yr-1 or rainfall dropped below 280 mm yr-1. Increases in dry-season length strengthened the effects of low soil moisture relative to high vapor pressure deficit in constraining NEP. Both GPP and NEP were more closely correlated with C uptake amplitude (annual maximum daily values) than with C uptake period. These findings indicate that dry-season extension under climate change may reduce the long-term C sequestration in semi-arid shrublands. Plant species adapted to prolonged dry seasons should be used in ecosystem restoration in the studied area to enhance ecosystem functions.

Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework.

Harmful algal blooms (HABs) are severe ecological disasters threatening aquatic systems throughout the World, which necessitate scientific efforts in detecting and monitoring them. Compared with traditional in situ point observations, satellite remote sensing is considered as a promising technique for studying HABs due to its advantages of large-scale, real-time, and long-term monitoring. The present review summarizes the suitability of current satellite data sources and different algorithms for detecting HABs. It also discusses the spatial scale issue of HABs. Based on the major problems identified from previous literature, including the unsystematic understanding of HABs, the insufficient incorporation of satellite remote sensing, and a lack of multiple oceanographic explanations of the mechanisms causing HABs, this review also attempts to provide a comprehensive understanding of the complicated mechanism of HABs impacted by multiple oceanographic factors. A potential synthesized framework can be established by combining multiple accessible satellite remote sensing approaches including visual interpretation, spectra analysis, parameters retrieval and spatial-temporal pattern analysis. This framework aims to lead to a systematic and comprehensive monitoring of HABs based on satellite remote sensing from multiple oceanographic perspectives.

Mechanisms of functional connectivity: the case of free-ranging bison in a forest landscape.

Functional connectivity is a key determinant of animal distributions in heterogeneous landscapes. Patch connectivity depends on both patch preference and accessibility, but few studies have integrated habitat selection and movement analyses to gain a general understanding of functional connectivity. In this paper, we define functional connectivity by identifying which factors influence the choice of the patch that is visited next, the location from which animals leave the current patch, and the inter-patch trajectory. Our study provides tools to anticipate movement trajectories and, therefore, animal distribution in patchy landscapes. We followed 23 radio-collared bison across the meadow network of Prince Albert National Park between 2005 and 2008. Selection of the next meadow visited over available meadows was assessed by comparing their characteristics and land cover composition of the area separating them from the departure meadow. Additionally, we used 196 bison trails originating from 29 meadows to evaluate movement rules during inter-patch travels. Bison preferred to travel in deciduous rather than in conifer stands during summer and fall but displayed no preference during winter and spring. They also selected meadows offering higher plant biomass in winter than in other seasons. Throughout the year, meadow proximity was an important determinant of meadow selection. Inter-patch trajectory was influenced by directional persistence, as well as movement biases toward the next meadow and toward canopy gaps. Unlike the choices individuals made in selecting their next meadow, bison displayed no preference between forest stands during inter-meadow travel, indicating that functional connectivity involves hierarchical movement decisions. We showed that the behavioral determinants of functional connectivity varied over spatiotemporal scales. First, forest stand composition between meadows influenced the next target, but not the trajectory during inter-meadow travels. Second, meadow selection varied among seasons. Therefore, although structural connectivity may be immutable to these behaviorally induced changes in inter-patch movements, functional connectivity would adequately account for such modifications in animal spatial dynamics.

Phenotyping Flowering in Canola (Brassica napus L.) and Estimating Seed Yield Using an Unmanned Aerial Vehicle-Based Imagery.

Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and, therefore, seed yield. However, quantifying flowering based on traditional visual scales is subjective, time-consuming, and labor-consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica juncea variety, were grown near Saskatoon, SK, Canada from 2016 to 2018 and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination (R 2) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R 2 up to 0.42. These results indicate that NDYI-based flowering pixel numbers can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.

Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Understanding the factors associated with long-term reconstructed turbidity in Lake Diefenbaker from Landsat-imagery.

Turbidity affects a variety of aquatic ecosystem processes. Turbidity events are dominated by suspended sediment in many systems. High levels of suspended sediment in lakes can occur during periods of high inflows from turbid tributaries or suspension of sediment from lake beds. This study reconstructed historic turbidity levels using Landsat-imagery on Lake Diefenbaker (LD), a large river-reservoir constructed in the late 1960's on the naturally turbid South Saskatchewan River (SSR). We examined the factors that were associated with it. Reconstructed turbidity levels, from Landsat-images, were similar to actual turbidity. The SSR flow and wind speed explained 64%, 54% and 69% of the variability in estimated turbidity levels at the riverine zone, the transition zone and the entire reservoir, respectively. The decrease in estimated turbidity from June to October and down the length of the reservoir is likely associated with the decline in the SSR flow and the settling of suspended sediments. The relationship between estimated turbidity and wind speed may be associated with the re-suspension of bottom sediment at the upper reach of LD. Wind speed and direction were related to estimated turbidity at the lacustrine zone (r2adj = 0.19, P < 0.05), which may be attributed to the persistence of sediments. We observed high turbidity in 2002 that exceeded other estimates of turbidity. Since 2002 was preceded by a prolonged drought, the high estimate turbidity may be related to an increase in sediment loads from the SSR flow and an increase in shoreline erosion from a rise in LD's water level. Hence, extreme events (drought and flooding) are associated with high turbidity in LD. As the Canadian Prairies continues to undergo climate change, lakes located in this region are predicted to experience more frequent extreme events. These extreme events will cause further deterioration of water quality.

Contributions of climate change to the terrestrial carbon stock of the arid region of China: A multi-dataset analysis.

Dryland ecosystems have been threatened in recent decades by rapid climate change. However, the effects of climate change and rising CO2 levels on the terrestrial carbon stock of the arid region of China remain unclear. In this study, we used three climate reanalysis datasets to drive an arid ecosystem model (AEM), which we used to assess uncertainties in spatial climate datasets. All simulations suggest that the arid region of China acted as a carbon sink (0.20-0.34 Pg C) from 1980 to 2014. However, we found large uncertainties in the spatial pattern of carbon stocks during this period, especially in northern Xinjiang and western Inner Mongolia. These uncertainties are related to changes in precipitation. To reduce the uncertainty of carbon stock assessment results in the arid region of China, efforts should be implemented to improve the reliability of climate data in northern Xinjiang and western Inner Mongolia. Specifically, China's policy makers should pay close attention to climate change and ecosystem health in southwestern Xinjiang. According to our study, this area experienced significant decreases in precipitation and increases in temperature from 1980 to 2014. The severe ecosystem degradation that occurred will very likely continue into the future. In addition, the Climate Forecast System Reanalysis (CFSR) dataset may overestimate ecosystem carbon sinks as this dataset overestimates the increase in precipitation in the arid region of China. Therefore, it is advisable to be cautious when using the CFSR dataset in ecological studies in northern Eurasian dryland areas.

Significant tumor regression induced by microencapsulation of recombinant tumor cells secreting fusion protein.

Implantation of microencapsulated engineered cells secreting molecules with antineoplastic properties into tumors is a novel approach to cancer gene therapy. In this study, we constructed an engineered tumor cell line, VkCk/RM4-TNF-alpha, which secreted RM4/TNF-alpha fusion protein containing the chimeric antitumor antibody, F(ab')2 (RM4), recognizing the tumor antigen TAG72, as well as the TNF-alpha moiety. The engineered cells were encapsulated into microencapsules. The RM4/TNF-alpha fusion protein secreted by encapsulated VkCk/RM4-TNF-alpha cells could be diffused through the microencapsule membrane into the supernatant and exert a cytotoxic effect on L929 cells in vitro. The antigen-specific binding-reactivity of RM4/TNF-alpha for the TAG72 antigen was confirmed by immunohistochemical staining of rat LMCR tumor cells which expressed TAG72 antigen. Implantation of microencapsules containing VkCk/RM4-TNF-alpha cells into LMCR tumors in rats induced tumor regression as a result of tumor necrosis formation. Taken together, these data suggest that microencapsulation of recombinant tumor cells secreting antibody/cytokine fusion protein might be an alternative approach in the treatment of cancers.

Fusion hybrid of dendritic cells and engineered tumor cells expressing interleukin-12 induces type 1 immune responses against tumor.

AIMS AND BACKGROUND: Dendritic cell (DC)-tumor fusion hybrid vaccinees that facilitate antigen presentation represent a novel powerful strategy in cancer immunotherapy. Preclinical studies have demonstrated that IL-12 promotes specific antitumor immunity mediated by T cells in several types of tumors. In the present study, we investigated the antitumor immunity derived from vaccination of fusion hybrids between DCs and engineered J558/IL-12 myeloma cells secreting Th1 cytokine IL-12. METHODS: The expression vector pcDNA-IL-12 was generated and transfected into J558 myeloma cells and then bone marrow-derived DCs were fused with engineered J558/IL-12 cells. The antitumor immunity derived from vaccination of the fusion hybrid DC/J558/IL-12 was evaluated in vitro and in vivo. RESULTS: DC/J558/IL-12 cells secreted recombinant IL-12 (1.6 ng/mL), and inoculation of BALB/c mice with DC/J558/IL-12 hybrid induced a Th1 dominant immune response and resulted in tumor regression. Immunization of mice with engineered DC/J558/IL-12 hybrid elicited stronger J558 tumor-specific cytotoxic T lymphocyte (CTL) responses in vitro as well as more potent protective immunity against J558 tumor challenge in vivo than immunization with the mixture of DCs and J558/IL-12, J558/IL-12 and J558, respectively. Furthermore, the anti-tumor immunity mediated by DC/J558/IL-12 tumor cell vaccination in vivo appeared to be dependent on CD8+ CTL. CONCLUSIONS: These results demonstrate that the engineered fusion hybrid vaccines that combine Th1 cytokine gene-modified tumor cells with DCs may be an attractive strategy for cancer immunotherapy.

[Preliminary attempt at the speciation of 25-elements in the Chinese medicinal herbs].

OBJECTIVE: To make an attempt at the multi-element speciation in the Chinese medicinal herbs by determining the concentrations of 25 elements in different extraction solutions. METHOD: Firstly, five Chinese medicinal herbs (Buddleja officinalis, Dictamnus dasycarpus, Myristica fragrans, Albizia judibrissin and Inula japonica) from the same region of China were treated to obtain water-soluble phase, lipid-soluble phase and non-soluble phase by water extraction, organic solvent extraction and acid digestion, respectively. Secondly, Phytolacca acinosa, a Chinese medicinal herb collected from 9 regions of China, was extracted by 0% EtOH, 50% EtOH, 75% EtOH, 95% EtOH, respectively, referring the Chinese Pharmacopoeia. Finally, the concentrations of 25 elements, such as Be, Cr, Cu, Zn, Ge, Sr, Y, Mo, Cd, Tl, Pb and REEs, in the above three phases were determined by ICP-MS. RESULT: Under the optimal conditions, all the 25 elements could be determined with detection limits ranged from 0.003 to 0.71 ng x g(-1). The average recoveries of the elements in P. acinosa were 88% approximately 119%, with the relative standard deviations 1.7% approximately 13.3%. It was observed that the determined 25 elements distributed in all the water-soluble, lipid-soluble and non-soluble phases, indicating that the inorganic species, organicspecies, as well as the protein bound species were coexisted in the herbs. Big differences of the element extraction rates could be found by using different ethanol solutions. CONCLUSION: With the aid of the obtained results, we may increase the extraction of necessary elements while decrease that of the toxic elements from the herbs by choosing a suitable solvent during the drug production.

Comparison of laboratory and field remote sensing methods to measure forage quality.

Recent research in range ecology has emphasized the importance of forage quality as a key indicator of rangeland condition. However, we lack tools to evaluate forage quality at scales appropriate for management. Using canopy reflectance data to measure forage quality has been conducted at both laboratory and field levels separately, but little work has been conducted to evaluate these methods simultaneously. The objective of this study is to find a reliable way of assessing grassland quality through measuring forage chemistry with reflectance. We studied a mixed grass ecosystem in Grasslands National Park of Canada and surrounding pastures, located in southern Saskatchewan. Spectral reflectance was collected at both in-situ field level and in the laboratory. Vegetation samples were collected at each site, sorted into the green grass portion, and then sent to a chemical company for measuring forage quality variables, including protein, lignin, ash, moisture at 135 °C, Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), Total Digestible, Digestible Energy, Net Energy for Lactation, Net Energy for Maintenance, and Net Energy for Gain. Reflectance data were processed with the first derivative transformation and continuum removal method. Correlation analysis was conducted on spectral and forage quality variables. A regression model was further built to investigate the possibility of using canopy spectral measurements to predict the grassland quality. Results indicated that field level prediction of protein of mixed grass species was possible (r² = 0.63). However, the relationship between canopy reflectance and the other forage quality variables was not strong.

Increased susceptibility of tumorigenicity and decreased anti-tumor effect of DC vaccination in aged mice are potentially associated with increased number of NK1.1+CD3+ NKT cells.

AIM: It is established that aging leads to declines in immune function. However, the mechanisms underlying remain poorly understood. METHODS: In this study, we compared the tumoriginecity of MO4 (ovalbumin-transfected) tumor cells, the efficacy of dendritic cell (DC) vaccination and cytotoxic T lymphocytes (CTLs), and the number of NK1.1+CD3+ NKT cells between aged mice and young mice using T cell proliferation and cytotoxicity assays and flow cytometry. RESULTS: We showed that, in comparison to young mice, aged mice are 10-fold more susceptible to tumorigenicity of MO4 tumor cells. Aged mice immunized with bone marrow-derived DCs pulsed with ovalbumin (DCOVA) survived significantly shorter after challenge with MO4 tumor cells as compared to equally treated young mice. Furthermore, CTLs from aged mice immunized with DCOVA displayed 4-fold weaker cytotoxicity as compared to CTLs from immunized young mice. Interestingly, the number of NK1.1+CD3+ NKT cell significantly increase with aging (p < 0.05). Of particular importance, NK1.1+CD3+ NKT cells isolated from aged mice suppress the proliferation of T cells. CONCLUSIONS: Based on these data, we conclude that NK1.1+CD3+ NKT cells from aged mice mediate immunosuppression, and further suggest that increased number of NK1.1+CD3+ NKT cells in aged mice might, among others, diminish their immune function by mediation of immunosuppression.

A novel approach to tumor suppression using microencapsulated engineered J558/TNF-alpha cells.

AIM: Immunoisolation technology using microencapsulated nonautologous cells is a novel alternative approach to the treatment of cancer. The work was aimed on investigation of the effect of implantation of microencapsulates on tumor growth in vivo. METHODS: In this study, we constructed an engineered tumor cell line J558/TNF-alpha that secreted functional tumor necrosis factor-alpha (TNF-alpha) (2 ng/ml), and went on to encapsulate the engineered cells into microencapsules. RESULTS: Our data showed that the microencapsulates thus produced could release functional TNF-alpha (1.2 ng/ml), which then diffused through the microencapsule membrane into the supernatant, and produced a cytotoxic effect on L929 cells in vitro. Microencapsulated cells were intratumorally (i.t.) implanted into athymic nude mice bearing the human breast cancer MCF-7. The results showed that the i.t. implantation induced extensive tumor cell apoptosis and necrosis leading to significant tumor regression and slower tumor growth than in the control groups that were i.t. injected with microencapsulated J558 or PBS respectively (p < 0.05). CONCLUSION: This study provides further evidence that the microencapsulation of recombinant tumor cells secreting cytokines may be an alternative approach in treatment of cancer.