Notes

[Effects of no-tillage seeding using crop stubbles upon seed-shedding beginning as well as generate associated with spring whole wheat throughout Hexi Retreat Irrigated Location, Northwest China].
Dissolved inorganic carbon (DIC) assimilation is essential to the reef-building capacity of crustose coralline algae (CCA). Little is known, however, about the DIC uptake strategies and their potential plasticity under ongoing ocean acidification (OA) and warming. The persistence of CCA lineages throughout historical oscillations of pCO2 and temperature suggests that evolutionary history may play a role in selecting for adaptive traits. We evaluated the effects of pCO2 and temperature on the plasticity of DIC uptake strategies and associated energetic consequences in reef-building CCA from different evolutionary lineages. We simulated past, present, moderate (IPCC RCP 6.0) and high pCO2 (RCP 8.5) and present and high (RCP 8.5) temperature conditions and quantified stable carbon isotope fractionation (13ε), organic carbon content, growth and photochemical efficiency. All investigated CCA species possess CO2-concentrating mechanisms (CCMs) and assimilate CO2 via diffusion to varying degrees. Under OA and warming, CCA either increased or maintained CCM capacity, which was associated with overall neutral effects on metabolic performance. More basal taxa, Sporolithales and Hapalidiales, had greater capacity for diffusive CO2 use than Corallinales. We suggest that CCMs are an adaptation that supports a robust carbon physiology and are likely responsible for the endurance of CCA in historically changing oceans.p53 is implicated in several cellular pathways such as induction of cell-cycle arrest, differentiation, senescence, and apoptosis. p53 is activated by a broad range of stress signals, including viral infections. While some viruses activate p53, others induce its inactivation, and occasionally p53 is differentially modulated during the replicative cycle. During calicivirus infections, apoptosis is required for virus exit and spread into the host; yet, the role of p53 during infection is unknown. By confocal microscopy, we found that p53 associates with FCV VP1, the protease-polymerase NS6/7, and the dsRNA. This interaction was further confirmed by proximity ligation assays, suggesting that p53 participates in the FCV replication. Knocked-down of p53 expression in CrFK cells before infection, resulted in a strong reduction of the non-structural protein levels and a decrease of the viral progeny production. These results indicate that p53 is associated with the viral replication complex and is required for an efficient FCV replication.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emergent RNA virus that spread around the planet in about 4 months. The consequences of this rapid dispersion are under investigation. click here In this work, we analyzed thousands of genomes and protein sequences from Africa, America, Asia, Europe, and Oceania. We provide statistically significant evidence that SARS-CoV-2 phylogeny is spatially structured. Remarkably, the virus phylogeographic patterns were correlated with ancestral amino acidic substitutions, suggesting that such mutations emerged along colonization events. We hypothesize that geographic structuring is the result of founder effects occurring as a consequence of, and local evolution occurring after, long-distance dispersion. Based on previous studies, the possibility that this could significantly affect the virus biology is not remote.In this paper, we propose a generalized wrapper-based feature selection, called GeFeS, which is based on a parallel new intelligent genetic algorithm (GA). The proposed GeFeS works properly under different numerical dataset dimensions and sizes, carefully tries to avoid overfitting and significantly enhances classification accuracy. To make the GA more accurate, robust and intelligent, we have proposed a new operator for features weighting, improved the mutation and crossover operators, and integrated nested cross-validation into the GA process to properly validate the learning model. The k-nearest neighbor (kNN) classifier is utilized to evaluate the goodness of selected features. We have evaluated the efficiency of GeFeS on various datasets selected from the UCI machine learning repository. The performance is compared with state-of-the-art classification and feature selection methods. The results demonstrate that GeFeS can significantly generalize the proposed multi-population intelligent genetic algorithm under different sizes of two-class and multi-class datasets. We have achieved the average classification accuracy of 95.83%, 97.62%, 99.02%, 98.51%, and 94.28% while reducing the number of features from 56 to 28, 34 to 18, 279 to 135, 30 to 16, and 19 to 9 under lung cancer, dermatology, arrhythmia, WDBC, and hepatitis, respectively.
In medical diagnostics, breast ultrasound is an inexpensive and flexible imaging modality. The segmentation of breast ultrasounds to identify tumour regions is a challenging and complex task. The major problems of effective tumour identification are speckle noise, artefacts and low contrast. The gold standard for segmentation is manual processing; however, manual segmentation is a cumbersome task. To address this problem, the automatic multiscale superpixel method for the segmentation of breast ultrasounds is proposed.

The original breast ultrasound image was transformed into multiscaled images, and then, the multiscaled images were preprocessed. Next, a boundary efficient superpixel decomposition of the multiscaled images was created. Finally, the tumour region was generated by the boundary graph cut segmentation method. The proposed method was evaluated with 120 images from the Thammassat University Hospital database. The dataset consists of 30 malignant, 30 benign tumors, 60 fibroadenoma, and 60 cyst images. Popular metrics, such as the accuracy, sensitivity, specificity, Dice index, Jaccard index and Hausdorff distance, were used for the evaluation.

The results indicate that the proposed method achieves segmentation accuracy of 97.3% for benign tumors, 94.2% for malignant, 96.4% for cysts and 96.7% for fibroadenomas. The results validate that the proposed model outperforms selected state-of-the-art segmentation methods.

The proposed method outperforms selected state-of-the-art segmentation methods with an average segmentation accuracy of 94%.
The proposed method outperforms selected state-of-the-art segmentation methods with an average segmentation accuracy of 94%.
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