Author: Latest Results

Abstract

Background

The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account.

Results

In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1 mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value =  \(1.5* {10}^{-29}\) ) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error < 5°. Finally, the method was tested with images of field-grown cereal crops and Beta functions were approximated from the calculated LADs. The mean CCC between reconstructed LAD and calculated Beta function was 0.66. According to Cohen, this indicates a high correlation.

Conclusion

This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions.

Abstract

Wildlife conservation in Andean countries is a global priority because of the high levels of biodiversity and endemism. Historically, these countries have had limited resources to monitor wildlife (e.g., through genetic tools) and establish conservation programs. Focusing on the study and emblematic use of a few charismatic species has been a strategic approach to direct efforts for conservation and development planning. Consequently, the Andean bear is a flagship and umbrella species for highly biodiverse Andean countries like Ecuador. The few studies exploring the population genetics of this species have concluded that it has low genetic diversity and few units for conservation as populations appear to be well connected. However, these results might be attributed to ascertainment bias as studies have been performed with heterologous molecular markers. Here, using both mtDNA sequences and species-specific microsatellite markers, we show that Andean bears in Ecuador have population structure. Additionally, we found through the study of three Ecuadorian populations that the species might have a higher genetic diversity than we previously thought. These results could support the revision of research priorities, conservation, and planning strategies to improve connectivity for this species which occurs in crucial biodiversity hotspots.

Abstract

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth’s land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria’s best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU’s Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between − 30 and 68.2, which were much better than the other products. The findings establish STS and CCD’s ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.

Abstract

The application of microfluidic devices as next-generation cell and tissue culture systems has increased impressively in the last decades. With that, a plethora of materials as well as fabrication methods for these devices have emerged. Here, we describe the rapid prototyping of microfluidic devices, using micromilling and vapour-assisted thermal bonding of polymethyl methacrylate (PMMA), to create a spheroid-on-a-chip culture system. Surface roughness of the micromilled structures was assessed using scanning electron microscopy (SEM) and atomic force microscopy (AFM), showing that the fabrication procedure can impact the surface quality of micromilled substrates with milling tracks that can be readily observed in micromilled channels. A roughness of approximately 153 nm was created. Chloroform vapour-assisted bonding was used for simultaneous surface smoothing and bonding. A 30-s treatment with chloroform-vapour was able to reduce the surface roughness and smooth it to approximately 39 nm roughness. Subsequent bonding of multilayer PMMA-based microfluidic chips created a durable assembly, as shown by tensile testing. MDA-MB-231 breast cancer cells were cultured as multicellular tumour spheroids in the device and their characteristics evaluated using immunofluorescence staining. Spheroids could be successfully maintained for at least three weeks. They consisted of a characteristic hypoxic core, along with expression of the quiescence marker, p27^kip1. This core was surrounded by a ring of Ki67-positive, proliferative cells. Overall, the method described represents a versatile approach to generate microfluidic devices compatible with biological applications.

Abstract

Technoscientific transformations in molecular genomics have begun to influence knowledge production in education. Interdisciplinary scientific consortia are seeking to identify ‘genetic influences’ on ‘educationally relevant’ traits, behaviors, and outcomes. This article examines the emerging ‘knowledge infrastructure’ of educational genomics, attending to the assembly and choreography of organizational associations, epistemic architecture, and technoscientific apparatuses implicated in the generation of genomic understandings from masses of bioinformation. As an infrastructure of datafied knowledge production, educational genomics is embedded in data-centered epistemologies and practices which recast educational problems in terms of molecular genetic associations—insights about which are deemed discoverable from digital bioinformation and potentially open to genetically informed interventions in policy and practice. While scientists claim to be ‘opening the black box of the genome’ and its association with educational outcomes, we open the black box of educational genomics itself as a source of emerging scientific authority. Data-intensive educational genomics does not straightforwardly ‘discover’ the biological bases of educationally relevant behaviors and outcomes. Rather, this knowledge infrastructure is also an experimental ‘ontological infrastructure’ supporting particular ways of knowing, understanding, explaining, and intervening in education, and recasting the human subjects of education as being surveyable and predictable through the algorithmic processing of bioinformation.