Appraisal of the national biosafety management agency (NBMA) Act 2015

Since the advent and subsequent advancements in the field of biotechnology, there have been diverse reactions from stakeholders and experts in this rapidly growing field as to the advantages and adverse effects of products from biotechnology on the consumers, economy and the ecosystem as a whole. In the Nigerian context, this article examines the National Biosafety Management Agency Act 2015 in a bid to determine its adequacy or otherwise in regulating Genetically Modified Organisms (GMOs) and biotechnology generally, knowing fully well that technology keeps advancing and laws should be drafted in such a way as to accommodate scientific developments and advancements.Keywords: National Biosafety Management Agency Act 2015, Technology, Law, Nigeri

An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required

Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing

Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (Rrs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr−1 for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions

Feasting on Gruel

Singleton\u27s Southern-fried skills take a Novel tur

Gas Chromatography-Mass Spectrometry and Single Nucleotide Polymorphism-Genotype-By-Sequencing Analyses Reveal the Bean Chemical Profiles and Relatedness of Coffea canephora Genotypes in Nigeria.

The flavor and health benefits of coffee (Coffea spp.) are derived from the metabolites that accumulate in the mature bean. However, the chemical profiles of many C. canephora genotypes remain unknown, even as the production of these coffee types increases globally. Therefore, we used Gas Chromatography-Mass Spectrophotometry to determine the chemical composition of C. canephora genotypes in Nigeria-those conserved in germplasm repositories and those cultivated by farmers. GC-MS revealed 340 metabolites in the ripe beans, with 66 metabolites differing (p-value < 0.05) across the represented group. Univariate and multivariate approaches showed that the 'Niaouli' genotypes could be clearly distinguished from 'Kouillou' and 'Java' genotypes, while there was almost no distinction between 'Kouillou' and 'Java,'. Varietal genotyping based on bean metabolite profiling was synchronous with that based on genome-wide Single Nucleotide Polymorphism analysis. Across genotypes, the sucrose-to-caffeine ratio was low, a characteristic indicative of low cup quality. The sucrose-to-caffeine ratio was also highly correlated, indicative of common mechanisms regulating the accumulation of these compounds. Nevertheless, this strong correlative link was broken within the 'Niaouli' group, as caffeine and sucrose content were highly variable among these genotypes. These 'Niaouli' genotypes could therefore serve as useful germplasm for starting a Nigerian C. canephora quality improvement breeding program