Evaluation of Nonparametric Machine-Learning Algorithms for an Optimal Crop Classification Using Big Data Reduction Strategy

International audienceAccurate crop classification can support analyses of food security, environmental, and climate changes. Most of the current research studies have focused on applying available algorithms to classify dominant crops on the landscape using one source of remotely sensed data due to geoprocessing constraints (e.g., big data access, availability, and processing power). In this research, we compared four classification algorithms, including the support vector machine (SVM), random forest (RF), regression tree (CART), and backpropagation network (BPN), to select a robust and efficient classification algorithm able to classify accurately many crop types. We used multiple sources of satellite images such as Sentinel-1 (S1) and Sentinel-2 (S2) and developed a new cropping classification method for a study site in the Bekaa valley, Lebanon, fully implemented on Google Earth Engine Platform, which minimized those geoprocessing constraints. The algorithm selection was based on their popularity, availability, simplicity, similarity, and diversity. In addition, we adopted different strategies that included changing the number of crops. The first strategy is to reduce the number of collected S2 images thereafter S1; the second strategy is to use S2 images separately and then combining S2 and S1. This study results proved that the RF is the most robust algorithm for crop classification, showing the highest overall accuracy (OA) (95.4%) and a kappa index of 0.94, followed by BPN, SVM, and CART, respectively. The performance of these algorithms based on major crop types such as wheat or potato showed that CART is the highest with OA (98%) followed by RF, SVM, and BPN, respectively. Nevertheless, CART fails to classify other minor crop types. We concluded that RF is the best algorithm for classifying different crop types in the study area, using multiple remote sensing data sources

تحديد نماذج المتسلسلات الزمنية الدورية ذاتية الانحدار متوسطات المتحركة باستخدام R

Periodic autoregressive moving average PARMA process extend the classical autoregressive moving average ARMA process by allowing the parameters to vary with seasons. Model identification is the identification of a possible model based on an available realization, i.e., determining the type of the model with appropriate orders. The Periodic Autocorrelation Function (PeACF) and the Periodic Partial Autocorrelation Function (PePACF) serve as useful indicators of the correlation or of the dependence between the values of the series so that they play an important role in model identification. The identification is based on the cut-off property of the Periodic Autocorrelation Function (PeACF). We derive an explicit expression for the asymptotic variance of the sample PeACF to be used in establishing its bands. Therefore, we will get in this study a new structure of the periodic autocorrelation function which depends directly to the variance that will derived to be used in establishing its bands for the PMA process over the cut-off region and we have studied the theoretical side and we will apply some simulated examples with R which agrees well with the theoretical results.تحديد نماذج المتسلسلات الزمنية الدورية ذاتية الانحدار متوسطات المتحركة باستخدام

G protein-receptor kinases 5/6 are the key regulators of G protein-coupled receptor 35-arrestin interactions

Human G protein–coupled receptor 35 is regulated by agonist-mediated phosphorylation of a set of five phospho-acceptor amino acids within its C-terminal tail. Alteration of both Ser300 and Ser303 to alanine in the GPR35a isoform greatly reduces the ability of receptor agonists to promote interactions with arrestin adapter proteins. Here, we have integrated the use of cell lines genome edited to lack expression of combinations of G protein receptor kinases (GRKs), selective small molecule inhibitors of subsets of these kinases, and antisera able to specifically identify either human GPR35a or mouse GPR35 only when Ser300 and Ser303 (orce; the equivalent residues in mouse GPR35) have become phosphorylated to demonstrate that GRK5 and GRK6 cause agonist-dependent phosphorylation of these residues. Extensions of these studies demonstrated the importance of the GRK5/6-mediated phosphorylation of these amino acids for agonist-induced internalization of the receptor. Homology and predictive modeling of the interaction of human GPR35 with GRKs showed that the N terminus of GRK5 is likely to dock in the same methionine pocket on the intracellular face of GPR35 as the C terminus of the α5 helix of Gα13 and, that while this is also the case for GRK6, GRK2 and GRK3 are unable to do so effectively. These studies provide unique and wide-ranging insights into modes of regulation of GPR35, a receptor that is currently attracting considerable interest as a novel therapeutic target in diseases including ulcerative colitis

An optimisation model for regional integrated solid waste management I. Model formulation

Increased environmental concerns and the emphasis on material and energy recovery are gradually changing the orientation of MSW management and planning. In this context, the application of optimisation techniques have been introduced to design the least cost solid waste management systems, considering the variety of management processes. This study presents a model that was developed and applied to serve as a solid waste decision support system for MSW management taking into account both socio-economic and environmental considerations. The model accounts for solid waste generation rates, composition, collection, treatment, disposal as well as potential environmental impacts of various MSW management techniques. The model follows a linear programming formulation with the framework of dynamic optimisation. The model can serve as a tool to evaluate various MSW management alternatives and obtain the optimal combination of technologies for the handling, treatment and disposal of MSW in an economic and environmentally sustainable way. The sensitivity of various waste management policies will be also addressed. The work is presented in a series of two papers: (I) model formulation, and (II) model application and sensitivity analysi

Determinants of COVID-19 Vaccine Acceptance among Dental Professionals: A Multi-Country Survey

Purpose: This study sought to investigate the acceptance rate and associated factors of COVID-19 vaccines among dentists and dental students in seven countries. Material and Methods: A structured questionnaire prepared and guided by the report of the SAGE Working Group on Vaccine Hesitancy was distributed among groups of dentists and dental students in seven countries across four continents. Results: A total of 1527 subjects (850 dentists and 677 dental students) participated in this survey. Although 72.5% of the respondents reported their intention to accept COVID-19 vaccines (dentists: 74.4%, dental students: 70.2%), there was a significant difference in agreement between dentists/dental students across countries; generally, respondents in upper-middle-, and high-income countries (UM-HICs) showed significantly higher acceptance rates compared to those in low- and lower-middle income countries (L-LMICs). Potential predictors of higher vaccine acceptance included being a dentist, being free of comorbidity, being well-informed about COVID-19 vaccines, having better knowledge about COVID-19 complications, having anxiety about COVID-19 infection, having no concerns about the side effects of the produced vaccines and being a resident of an UM-HIC. Conclusion: The results of our survey indicate a relatively good acceptance rate of COVID-19 among the surveyed dentists and dental students. However, dentists and dental students in L-LMICs showed significantly lower vaccine acceptance rates and trust in COVID-19 vaccines compared to their counterparts in UM-HICs. Our results provide important information to policymakers, highlighting the need for implementation of country-specific vaccine promotion strategies, with special focus on L-LMICs

WDR5 supports an N-myc transcriptional complex that drives a protumorigenic gene expression signature in neuroblastoma

MYCN gene amplification in neuroblastoma drives a gene expression program that correlates strongly with aggressive disease. Mechanistically, trimethylation of histone H3 lysine 4 (H3K4) at target gene promoters is a strict prerequisite for this transcriptional program to be enacted. WDR5 is a histone H3K4 presenter that has been found to have an essential role in H3K4 trimethylation. For this reason, in this study, we investigated the relationship between WDR5-mediated H3K4 trimethylation and N-Myc transcriptional programs in neuroblastoma cells. N-Myc upregulated WDR5 expression in neuroblastoma cells. Gene expression analysis revealed that WDR5 target genes included those with MYC-binding elements at promoters such as MDM2. We showed that WDR5 could form a protein complex at the MDM2 promoter with N-Myc, but not p53, leading to histone H3K4 trimethylation and activation of MDM2 transcription. RNAi-mediated attenuation of WDR5 upregulated expression of wild-type but not mutant p53, an effect associated with growth inhibition and apoptosis. Similarly, a small-molecule antagonist of WDR5 reduced N-Myc/WDR5 complex formation, N-Myc target gene expression, and cell growth in neuroblastoma cells. In MYCN-transgenic mice, WDR5 was overexpressed in precancerous ganglion and neuroblastoma cells compared with normal ganglion cells. Clinically, elevated levels of WDR5 in neuroblastoma specimens were an independent predictor of poor overall survival. Overall, our results identify WDR5 as a key cofactor for N-Myc-regulated transcriptional activation and tumorigenesis and as a novel therapeutic target for MYCN-amplified neuroblastomas