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Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The growth period of catch crops in Germany is often marked by dense cloud cover, which limits land surface monitoring through optical remote sensing. In such conditions, synthetic aperture radar (SAR) emerges as a viable option. Despite the known advantages of SAR, the understanding of temporal behavior of radar parameters in relation to catch crops remains largely unexplored. Hence, in this study, we exploited the dense time series of Sentinel-1 data within the Copernicus Space Component to study the temporal characteristics of catch crops over a test site in the center of Germany. Radar parameters such as VV, VH, VH/VV backscatter, dpRVI (dual-pol Radar Vegetation Index) and VV coherence were extracted, and temporal profiles were interpreted for catch crops and preceding main crops along with in situ, temperature, and precipitation data. Additionally, we examined the temporal profiles of winter main crops (winter oilseed rape and winter cereals), that are grown parallel to the catch crop growing cycle. Based on the analyzed temporal patterns, we defined 22 descriptive features from VV, VH, VH/VV and dpRVI, which are specific to catch crop identification. Then, we conducted a Kruskal–Wallis test on the extracted parameters, both crop-wise and group-wise, to assess the significance of statistical differences among different catch crop groups. Our results reveal that there exists a unique temporal pattern for catch crops compared to main crops, and each of these extracted parameters possess a different sensitivity to catch crops. Parameters VV and VH are sensitive to phenological stages and crop structure. On the other hand, VH/VV and dpRVI were found to be highly sensitive to crop biomass. Coherence can be used to detect the sowing and harvest events. The preceding main crop analysis reveals that winter wheat and winter barley are the two dominant main crops grown before catch crops. Moreover, winter main crops (winter oilseed rape, winter cereals) cultivated during the catch crop cycle can be distinguished by exploiting the observed sowing window differences. The extracted descriptive features provide information about sowing, harvest, vigor, biomass, and early/late die-off nature specific to catch crop types. In the Kruskal–Wallis test, the observed high H-statistic and low p-value in several predictors indicates significant variability at 0.001 level. Furthermore, Dunn’s post hoc test among catch crop group pairs highlights the substantial differences between cold-sensitive and legume groups (p < 0.001).

Coastal erosion is a persistent problem occurring along the Shoreline of India. Several factors that influence the severity of erosion are community infrastructures along the coast, natural disasters, and several man-made activities. There is an increased demand for computing precise information regarding past and present trends and rates of changes in the shoreline. This research investigates the shoreline changes of the Vishakhapatnam district in Andhra Pradesh Coast, as well as finds the quantity of the erosion and accretion rate. Vishakhapatnam having a coastal length of 135 km used for the study, length of study area categorized by fours zones based on the district boundary Zone 1 - Bheemunipatnam, Zone 2 - Vishakhapatnam, Zone 3 - Anakapalli, and Zone 4 - Yellamanchili. The changes in Shoreline were computed with the help of multitemporal satellite images (Landsat 7 and Landsat 4 and 5) for the past 45 years' period, i.e., from 1974 to 2020 using Digital Shoreline Analysis System (DSAS). The rate of shoreline change was accessed using Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), and Linear Regression Rate (LRR). Based on the findings the coastal maps are prepared to estimate the geological changes and shifting of shoreline position. This detailed study is found useful for exploring erosion and accretion processes in the region. About 135 km of coastline was found to be accreting in nature with an average rate of +1.13 m/yr followed by erosion of the shoreline - 0.15 m/yr. The highest and lowest SCE of about 63.4 m/yr and 75.25 m/yr were recorded in Vishakhapatnam Coast. In addition, the Multi-output Adaptive Neuro Fuzzy Inference System (MANFIS) technique is used to predict EPR, NSM, SCE, and LRR for monitoring the shoreline changes, and it could be used for further planning and development and also for disaster management authority in the decision-making process in the study area.

Operation Approach on a new class of topological spaces with -open subsets was called a -open set. In this article, we examined some basic properties of open sets such as closure, , limit points, derived sets, neighbourhood, interior, exterior, boundary, frontier and saturated set of already existing -open sets in topological spaces.

Cas12i is a genome editing platform with compact size that fits in AAV vector with short 43-mer gRNA, absence of tracrRNA, ability to process pre-crRNA, and high specificity. Here the authors present an unbiased mutational scanning approach to engineer Cas12i, which shows low activity in mammalian cells, and identify single substitutions that significantly improve indel activity.

PurposeThis study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging.MethodsDifferent training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting.ResultsGuiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime.ConclusionUsing simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

Purpose This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. Methods Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. Results Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime. Conclusion Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

Macrolides and ketolides antibiotics selectively interfere with the translation of a specific subset of proteins. Here the authors show how the macrolide erythromycin and the ketolide telithromycin interplay with the nascent polypeptide chain to arrest translation.

Due to its wide range of applications, the impact of multimedia in the real world has shown stupendous growth. Texts, images, audio, and video are the different forms of multimedia which are utilized by humans in various applications such as education and surveillance applications. A wide range of research has been carried out, and here in this paper, we propose an object racking with the aid of rough set theory in combination with the eminent soft computing technique evolutionary programming. Initially, the input video is segregated into frames, then the frames that belong to particular shots are identified through the shot segmentation process, and after that the object to be tracked is identified manually. Subsequently, the shape and texture feature is extracted, and then the rough set theory is applied. This is done to identify the presence of object in the frames. Consequently, genetic algorithm (GA) is utilized for the object monitoring process to mark the object with variant color. As a result, the selected object is tracked in an effective manner.

Eucalyptus camaldulensis and E. tereticornis are closely related species commonly cultivated for pulp wood in many tropical countries including India. Understanding the genetic structure and linkage disequilibrium (LD) existing in these species is essential for the improvement of industrially important traits. Our goal was to evaluate the use of simple sequence repeat (SSR) loci for species discrimination, population structure and LD analysis in these species. Investigations were carried out with the most common alleles in 93 accessions belonging to these two species using 62 SSR markers through cross amplification. The polymorphic information content (PIC) ranged from 0.44 to 0.93 and 0.36 to 0.93 in E. camaldulensis and E. tereticornis respectively. A clear delineation between the two species was evident based on the analysis of population structure and species-specific alleles. Significant genotypic LD was found in E. camaldulensis, wherein out of 135 significant pairs, 17 pairs showed r(2)≥0.1. Similarly, in E. tereticornis, out of 136 significant pairs, 18 pairs showed r(2)≥0.1. The extent of LD decayed rapidly showing the significance of association analyses in eucalypts with higher resolution markers. The availability of whole genome sequence for E. grandis and the synteny and co-linearity in the genome of eucalypts, will allow genome-wide genotyping using microsatellites or single nucleotide polymorphims.