Calibration to maximize temporal radiometric repeatability of airborne hyperspectral imaging data.

Many studies provide insight into calibration of airborne remote sensing data but very few specifically address the issue of temporal radiometric repeatability. In this study, we acquired airborne hyperspectral optical sensing data from experimental objects (white Teflon and colored panels) during 52 flight missions on three separate days. Data sets were subjected to four radiometric calibration methods: no radiometric calibration (radiance data), empirical line method calibration based on white calibration boards (ELM calibration), and two atmospheric radiative transfer model calibrations: 1) radiometric calibration with irradiance data acquired with a drone-mounted down-welling sensor (ARTM), and 2) modeled sun parameters and weather variables in combination with irradiance data from drone-mounted down-welling sensor (ARTM+). Spectral bands from 900-970 nm were found to be associated with disproportionally lower temporal radiometric repeatability than spectral bands from 416-900 nm. ELM calibration was found to be highly sensitive to time of flight missions (which is directly linked to sun parameters and weather conditions). Both ARTM calibrations outperformed ELM calibration, especially ARTM2+. Importantly, ARTM+ calibration markedly attenuated loss of radiometric repeatability in spectral bands beyond 900 nm and therefore improved possible contributions of these spectral bands to classification functions. We conclude that a minimum of 5% radiometric error (radiometric repeatability<95%), and probably considerably more error, should be expected when airborne remote sensing data are acquired at multiple time points across days. Consequently, objects being classified should be in classes that are at least 5% different in terms of average optical traits for classification functions to perform with high degree of accuracy and consistency. This study provides strong support for the claim that airborne remote sensing studies should include repeated data acquisitions from same objects at multiple time points. Such temporal replication is essential for classification functions to capture variation and stochastic noise caused by imaging equipment, and abiotic and environmental variables.

Modeling and validation of oviposition by a polyphagous insect pest as a function of temperature and host plant species.

Modeling oviposition as a function of female insect age, temperature, and host plant suitability may provide valuable insight into insect population growth of polyphagous insect pests at a landscape level. In this study, we quantified oviposition by beet leafhoppers, Circulifer (= Neoaliturus) tenellus (Baker) (Hemiptera: Cicadellidae), on four common non-agricultural host plant species [Erodium cicutarium (L.) L'Hér. (Geraniaceae), Kochia scoparia (L.) Schrader (Amaranthaceae), Plantago ovata Forsskál (Plantaginaceae), and Salsola tragus L. (Amaranthaceae)] at two constant temperature conditions. Additionally, temperature-based oviposition models for each host plant species were validated, under semi-field and greenhouse conditions. We found that K. scoparia was the most suitable host plant, and optimal temperature for oviposition was estimated to be 30.6°C. Accordingly, beet leafhoppers appear to be well-adapted to high-temperature conditions, so increasing temperatures due to climate change may favor population growth in non-agricultural areas. Maximum total fecundity (Rm) was used as an indicator of relative suitability of host plants. S. tragus has been considered an important non-agricultural host plant, however, we found that S. tragus and E. cicutarium have lower Rm compared to K. scoparia and P. ovata. The combination of detailed experimental oviposition bioassays, modeling, and model validation is considered widely relevant and applicable to host plant assessments and modeling of population dynamics of other polyphagous insect pests.

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects.

BACKGROUND: Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. METHODS: As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. RESULTS: For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). CONCLUSION: We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

Insect vector manipulation by a plant virus and simulation modeling of its potential impact on crop infection.

There is widespread evidence of plant viruses manipulating behavior of their insect vectors as a strategy to maximize infection of plants. Often, plant viruses and their insect vectors have multiple potential host plant species, and these may not overlap entirely. Moreover, insect vectors may not prefer plant species to which plant viruses are well-adapted. In such cases, can plant viruses manipulate their insect vectors to preferentially feed and oviposit on plant species, which are suitable for viral propagation but less suitable for themselves? To address this question, we conducted dual- and no-choice feeding studies (number and duration of probing events) and oviposition studies with non-viruliferous and viruliferous [carrying beet curly top virus (BCTV)] beet leafhoppers [Circulifer tenellus (Baker)] on three plant species: barley (Hordeum vulgare L.), ribwort plantain (Plantago lanceolata L.), and tomato (Solanum lycopersicum L.). Barley is not a host of BCTV, whereas ribwort plantain and tomato are susceptible to BCTV infection and develop a symptomless infection and severe curly top symptoms, respectively. Ribwort plantain plants can be used to maintain beet leafhopper colonies for multiple generations (suitable), whereas tomato plants cannot be used to maintain beet leafhopper colonies (unsuitable). Based on dual- and no-choice experiments, we demonstrated that BCTV appears to manipulate probing preference and behavior by beet leafhoppers, whereas there was no significant difference in oviposition preference. Simulation modeling predicted that BCTV infection rates would to be higher in tomato fields with barley compared with ribwort plantain as a trap crop. Simulation model results supported the hypothesis that manipulation of probing preference and behavior may increase BCTV infection in tomato fields. Results presented were based on the BCTV-beet leafhopper pathosystem, but the approach taken (combination of experimental studies with complementary simulation modeling) is widely applicable and relevant to other insect-vectored plant pathogen systems involving multiple plant species.

Cell Attachment on Inside-Outside Surface and Cell Encapsulation in Wall of Microscopic Tubular Scaffolds for Vascular Tissue-Like Formation.

By using the microfluidic spinning technology we generated tiny hydrogel tubular scaffolds. Fibroblast (NIH/3T3) cell cultures were performed for seventeen days to demonstrate the potential of cell attachment on surfaces and encapsulation in the wall of he microscopic scaffolds for blood vessel-like structure forming. Over theculture period, the NIH/3T3 confluence reached around 80\%, and 100\% on the inside and outside scaffolds' surface respectively while cells proliferated and coalesced in cell group in the hydrogel wall. These results could further be applied to endothelial co-culturing for forming engineered blood vessel.

Predicting temporal shifts in the spring occurrence of overwintered Scotinophara lurida (Hemiptera: Pentatomidae) and rice phenology in Korea with climate change.

Climate change could shift the phenology of insects and plants and alter their linkage in space and time. We examined the synchrony of rice and its insect pest, Scotinophara lurida (Burmeister), under the representative concentration pathways (RCP) 8.5 climate change scenario by comparing the mean spring immigration time of overwintered S. lurida with the mean rice transplanting times in Korea. The immigration time of S. lurida was estimated using an overwintered adult flight model. The rice transplanting time of three cultivars (early, medium, and medium-late maturing) was estimated by forecasting the optimal cultivation period using leaf appearance and final leaf number models. A temperature increase significantly advanced the 99% immigration time of S. lurida from Julian day 192.1 in the 2000s to 178.4 in the 2050s and 163.1 in the 2090s. In contrast, rice transplanting time was significantly delayed in the early-maturing cultivar from day 141.2 in the 2000s to 166.7 in the 2050s and 190.6 in the 2090s, in the medium-maturing cultivar from day 130.6 in the 2000s to 156.6 in the 2050s and 184.7 in the 2090s, and in the medium-late maturing cultivar from day 128.5 in 2000s to 152.9 in the 2050s and 182.3 in the 2090s. These simulation results predict a significant future phenological asynchrony between S. lurida and rice in Korea.

The effect of melatonin on retinal ganglion cell survival in ischemic retina.

Our objective was to determine whether melatonin increases retinal ganglion cell (RGC) survival in ischemic mouse retina. Transient retinal ischemia was induced by an acute elevation of intraocular pressure in C57BL/6 mice. To evaluate the effect of melatonin on retinal ischemia, an equal amount of either melatonin or vehicle was intraperitoneally injected into the mice 1 hour before ischemia, at the time of ischemia, and 1 hour after ischemia. Hypoxia inducible factor 1α (HIF-1α) and glial fibrillary acidic protein (GFAP) expression were assessed 6, 12, and 24 hours after ischemia-reperfusion by Western blot. RGC survival was measured 2 weeks after ischemia-reperfusion. The expression of HIF-1α and GFAP peaked 24 hours after ischemia-reperfusion in ischemic retina. The treatment of ischemic retina with melatonin resulted in the inhibition of increased expression of HIF-1α and GFAP. RGC survival was greater in retinas treated with melatonin than in retinas treated with vehicle 2 weeks after ischemia-reperfusion. On the basis of our results, we suggest that melatonin treatment increased RGC survival in ischemic mouse retina. The neuroprotective effect of melatonin is mediated by the inhibition of HIF-1α stabilization and reduced activity of glial cells in ischemic mouse retina.