Development of a compact and low-cost weather station for renewable energy applications

This paper describes the development of a weather station integrating several sensors which allows the measurement and data storage of the following environmental parameters: solar irradiance, temperature, humidity, wind speed, and wind direction. The collected data is later transferred to a mobile device, where it is stored in a database and processed in order to be visualized and analyzed by the user. For such purpose, a dedicated mobile app was developed and presented along the paper. The weather station also integrates small solar photovoltaic modules of three different technologies: polycrystalline, monocrystalline and amorphous silicon. Based on that, the weather station also collects information that may be employed to help the user in determining the most suitable solar photovoltaic technology for installation in a particular location. The developed system uses a Bluetooth Low Energy (BLE) wireless network to transfer the data to the mobile device when the user approaches the weather station. The system operation was validated through experimental tests that encompass all the main developed features, from the data acquisition in the weather station, to the visualization in the mobile device.- (undefined

Salt Stress Induced Variation in DNA Methylation Pattern and Its Influence on Gene Expression in Contrasting Rice Genotypes

BACKGROUND: Salinity is a major environmental factor limiting productivity of crop plants including rice in which wide range of natural variability exists. Although recent evidences implicate epigenetic mechanisms for modulating the gene expression in plants under environmental stresses, epigenetic changes and their functional consequences under salinity stress in rice are underexplored. DNA methylation is one of the epigenetic mechanisms regulating gene expression in plant's responses to environmental stresses. Better understanding of epigenetic regulation of plant growth and response to environmental stresses may create novel heritable variation for crop improvement. METHODOLOGY/PRINCIPAL FINDINGS: Methylation sensitive amplification polymorphism (MSAP) technique was used to assess the effect of salt stress on extent and patterns of DNA methylation in four genotypes of rice differing in the degree of salinity tolerance. Overall, the amount of DNA methylation was more in shoot compared to root and the contribution of fully methylated loci was always more than hemi-methylated loci. Sequencing of ten randomly selected MSAP fragments indicated gene-body specific DNA methylation of retrotransposons, stress responsive genes, and chromatin modification genes, distributed on different rice chromosomes. Bisulphite sequencing and quantitative RT-PCR analysis of selected MSAP loci showed that cytosine methylation changes under salinity as well as gene expression varied with genotypes and tissue types irrespective of the level of salinity tolerance of rice genotypes. CONCLUSIONS/SIGNIFICANCE: The gene body methylation may have an important role in regulating gene expression in organ and genotype specific manner under salinity stress. Association between salt tolerance and methylation changes observed in some cases suggested that many methylation changes are not "directed". The natural genetic variation for salt tolerance observed in rice germplasm may be independent of the extent and pattern of DNA methylation which may have been induced by abiotic stress followed by accumulation through the natural selection process

Genomic-Assisted Enhancement in Stress Tolerance for Productivity Improvement in Sorghum

Sorghum [Sorghum bicolor (L.) Moench], the fifth most important cereal crop in the world after wheat, rice, maize, and barley, is a multipurpose crop widely grown for food, feed, fodder, forage, and fuel, vital to the food security of many of the world’s poorest people living in fragile agroecological zones. Globally, sorghum is grown on ~42 million hectares area in ~100 countries of Africa, Asia, Oceania, and the Americas. Sorghum grain is used mostly as food (~55%), in the form of flat breads and porridges in Asia and Africa, and as feed (~33%) in the Americas. Stover of sorghum is an increasingly important source of dry season fodder for livestock, especially in South Asia. In India, area under sorghum cultivation has been drastically come down to less than one third in the last six decades but with a limited reduction in total production suggesting the high-yield potential of this crop. Sorghum productivity is far lower compared to its genetic potential owing to a limited exploitation of genetic and genomic resources developed in the recent past. Sorghum production is challenged by various abiotic and biotic stresses leading to a significant reduction in yield. Advances in modern genetics and genomics resources and tools could potentially help to further strengthen sorghum production by accelerating the rate of genetic gains and expediting the breeding cycle to develop cultivars with enhanced yield stability under stress. This chapter reviews the advances made in generating the genetic and genomics resources in sorghum and their interventions in improving the yield stability under abiotic and biotic stresses to improve the productivity of this climate-smart cereal

SARS-CoV-2 Omicron-B.1.1.529 leads to widespread escape from neutralizing antibody responses

On 24th November 2021, the sequence of a new SARS-CoV-2 viral isolate Omicron-B.1.1.529 was announced, containing far more mutations in Spike (S) than previously reported variants. Neutralization titers of Omicron by sera from vaccinees and convalescent subjects infected with early pandemic Alpha, Beta, Gamma, or Delta are substantially reduced, or the sera failed to neutralize. Titers against Omicron are boosted by third vaccine doses and are high in both vaccinated individuals and those infected by Delta. Mutations in Omicron knock out or substantially reduce neutralization by most of the large panel of potent monoclonal antibodies and antibodies under commercial development. Omicron S has structural changes from earlier viruses and uses mutations that confer tight binding to ACE2 to unleash evolution driven by immune escape. This leads to a large number of mutations in the ACE2 binding site and rebalances receptor affinity to that of earlier pandemic viruses

A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis

Recent developments in hyperspectral sensors have made it possible to acquire hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures. Despite these advantages, HSI suffers from many challenges also, like higher spatial variability of spectral signatures, the Hughes effect due to higher dimensionality, and a limited number of labeled training samples compared to the dimensions of the spectral space. Superpixels can be a potentially effective tool in tackling these challenges. Superpixel segmentation is a process of segmenting the spatial image into several semantic subregions with similar characteristic features. Such grouping by similarity can significantly ease the subsequent processing steps. Because of this, superpixels have been successfully applied to various fields of HSI processing such as classification, spectral unmixing, dimensionality reduction, band selection, active learning (AL), denoising, and anomaly detection. This article focuses on classification, presenting a detailed survey of superpixel segmentation approaches for the classification of HSI. The superpixel creation algorithm framework and postprocessing frameworks for superpixels in HSI are also analyzed. Also, a brief description of various application areas of superpixels is provided. An experimental analysis of existing superpixel segmentation approaches is also provided in this article, supported by quantitative results on standard benchmark datasets. The challenges and future research directions for the implementation of superpixel algorithms are also discussed

Error removal by energy scaling from hyperspectral images for performance improvement of spectral classifiers

In the remote sensing community, HyperSpectral (HS) images (HSI) are becoming increasingly popular as the advancement of technology and the consequent reduction of cost make them financially more accessible. The reason for their success is the higher capability they can offer, with respect to multispectral data, to discriminate classes that are spectrally similar. A series of pre-processing steps such as geometric, radiometric and atmospheric corrections are carried out on raw data captured by HS sensors. The processed data is then passed to the data analyst, whose work generally relies on the assumption that the received HS data is 'clean,' as all possible corrections have already been implemented; however, corrections are hardly perfect and residual disturbances can still bias the quality of results. At this stage, however, all corrections based on ancillary information have already been made and the possibilities for 'exogenous' correction of data are exhausted. More could be possibly done by sourcing additional information from the data itself. In this paper, we propose a simple yet effective additional step for error suppression through energy scaling, termed 'Error Removal by Energy Scaling' (ERES). In classification problems, the absolute value of wavelength lambda is often overlooked, except for, e.g. removal of strong absorption bands; yet the lambda value can actually further support the classification process if their physical meaning is tapped. The proposed ERES method is indeed a non-linear scaling method, derived from physical phenomena linked with radiation extinction properties. In ERES, each band is associated with an energy level, that is inversely related to its own wavelength. The associated nonlinear energy information in HSI, neglected in most classification strategies, prevents optimal separation of class-specific spectral signatures, that are generated by the physics of wave-matter interaction. This is especially true for linear classifiers such as Support Vector Machines (SVM). Removing this physics-linked information makes data more suitable to be classified with physics-unaware classification strategies, typically used in down stream remotely sensed data processing. The relevance of the issue, and the benefits of ERES, are discussed and validated in this work over three different datasets, using accuracy improvements on the popular Spectral Angle Mapper and SVM classifiers as a means to gauge the effectiveness of the correction strategy. Results clearly reveal the positive impact of applying ERES to the data before proceeding to classification