Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices

Accurate and up-to-date spatial agricultural information is essential for applications including agro-environmental assessment, crop management, and appropriate targeting of agricultural technologies. There is growing research interest in spatial analysis of agricultural ecosystems applying satellite remote sensing technologies. However, usability of information generated from many of remotely sensed data is often constrained by accuracy problems. This is of particular concern in mapping complex agro-ecosystems in countries where small farm holdings are dominated by diverse crop types. This study is a contribution to the ongoing efforts towards overcoming accuracy challenges faced in remote sensing of agricultural ecosystems. We applied time-series analysis of vegetation indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) derived from the Moderate Resolution Imaging Spectrometer (MODIS) sensor to detect seasonal patterns of irrigated and rainfed cropping patterns in five townships in the Central Dry Zone of Myanmar, which is an important agricultural region of the country has been poorly mapped with respect to cropping practices. To improve mapping accuracy and map legend completeness, we implemented a combination of (i) an iterative participatory approach to field data collection and classification, (ii) the identification of appropriate size and types of predictor variables (VIs), and (iii) evaluation of the suitability of three Machine Learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and C5.0 algorithms under varying training sample sizes. Through these procedures, we were able to progressively improve accuracy and achieve maximum overall accuracy of 95% When a small sized training dataset was used, accuracy achieved by RF was significantly higher compared to SVM and C5.0 (P < 0.01), but as sample size increased, accuracy differences among the three machine learning algorithms diminished. Accuracy achieved by use of NDVI was consistently better than that of EVI (P < 0.01). The maximum overall accuracy was achieved using RF and 8-days NDVI composites for three years of remote sensing data. In conclusion, our findings highlight the important role of participatory classification, especially in areas where cropping systems are highly diverse and differ over space and time. We also show that the choice of classifiers and size of predictor variables are essential and complementary to the participatory mapping approach in achieving desired accuracy of cropping pattern mapping in areas where other sources of spatial information are scarce

Direct detection and measurement of wall shear stress using a filamentous bio-nanoparticle

The wall shear stress (WSS) that a moving fluid exerts on a surface affects many processes including those relating to vascular function. WSS plays an important role in normal physiology (e.g. angiogenesis) and affects the microvasculature's primary function of molecular transport. Points of fluctuating WSS show abnormalities in a number of diseases; however, there is no established technique for measuring WSS directly in physiological systems. All current methods rely on estimates obtained from measured velocity gradients in bulk flow data. In this work, we report a nanosensor that can directly measure WSS in microfluidic chambers with sub-micron spatial resolution by using a specific type of virus, the bacteriophage M13, which has been fluorescently labeled and anchored to a surface. It is demonstrated that the nanosensor can be calibrated and adapted for biological tissue, revealing WSS in micro-domains of cells that cannot be calculated accurately from bulk flow measurements. This method lends itself to a platform applicable to many applications in biology and microfluidics

Reversible Integration of Microfluidic Devices with Microelectrode Arrays for Neurobiological Applications

The majority of current state-of-the-art microfluidic devices are fabricated via replica molding of the fluidic channels into PDMS elastomer and then permanently bonding it to a Pyrex surface using plasma oxidation. This method presents a number of problems associated with the bond strengths, versatility, applicability to alternative substrates, and practicality. Thus, the aim of this study was to investigate a more practical method of integrating microfluidics which is superior in terms of bond strengths, reversible, and applicable to a larger variety of substrates, including microfabricated devices. To achieve the above aims, a modular microfluidic system, capable of reversible microfluidic device integration, simultaneous surface patterning and multichannel fluidic perfusion, was built. To demonstrate the system’s potential, the ability to control the distribution of A549 cells inside a microfluidic channel was tested. Then, the system was integrated with a chemically patterned microelectrode array, and used it to culture primary, rat embryo spinal cord neurons in a dynamic fluidic environment. The results of this study showed that this system has the potential to be a cost effective and importantly, a practical means of integrating microfluidics. The system’s robustness and the ability to withstand extensive manual handling have the additional benefit of reducing the workload. It also has the potential to be easily integrated with alternative substrates such as stainless steel or gold without extensive chemical modifications. The results of this study are of significant relevance to research involving neurobiological applications, where primary cell cultures on microelectrode arrays require this type of flexible integrated solution

Targeted kinetic strategy for improving the thermal conductivity of epoxy composite containing percolating multi-layer graphene oxide chains

By adding 2 wt% multi-layer graphene oxide (MGO) to an epoxy resin, the thermal conductivity of the composite reached a maximum, 2.03 times that of the epoxy. The presence of 2 wt%MGO percolating chains leads to an unprecedentedly sharp rise in energy barrier at final curing stage, but an increased epoxy curing degree (αIR) is observed; however, this αIR difference nearly disappears after aging or thermal annealing. These results suggest that the steep concentration gradient of –OH, originated from the 2 wt%MGO percolating chains, exerts the vital driving force on the residual isolated/trapped epoxy to conquer barrier for epoxy-MGO reaction. A modified Shrinking Core Model customized for the special layered-structure of MGO sheet was proposed to understand the resistance variation during the intercalative epoxy-MGO reaction. It shows that the promoted intercalative crosslinking is highly desirable for further improving the thermal conductivity of the composite, but it meets with increased resistance. Guided by the kinetic studies, targeted optimization on the cure processing strategy was accordingly proposed to promote the intercalative crosslinking, a thermal conductivity, 2.96 times that of the epoxy, was got with only a small amount (30°C) increase of the post-heating temperature

Genomics-driven discovery of a biosynthetic gene cluster required for the synthesis of BII-Rafflesfungin from the fungus Phoma sp. F3723

10.1186/s12864-019-5762-6BMC Genomics20137