Radium content of carbonate shells

Radium determinations have been made for shells from Swedish waters, the Mediterranean Sea and the Pacific Ocean. Their Ra/Ca ratio has been estimated and compared to the Ra/Ca ratio in sea water. It was found that, in general, the Ra/Ca ratio is proportional to the Sr/Ca ratio found by Thompson and Chow...

A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control

COVID-19 outbreak prediction with machine learning

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models

Dynamic architecture of the Escherichia coli structural maintenance of chromosomes (SMC) complex, MukBE

Ubiquitous Structural Maintenance of Chromosomes (SMC) complexes use a proteinaceous ring-shaped architecture to organize and individualize chromosomes, thereby facilitating chromosome segregation. They utilize cycles of adenosine triphosphate (ATP) binding and hydrolysis to transport themselves rapidly with respect to DNA, a process requiring protein conformational changes and multiple DNA contact sites. By analysing changes in the architecture and stoichiometry of the Escherichia coli SMC complex, MukBEF, as a function of nucleotide binding to MukB and subsequent ATP hydrolysis, we demonstrate directly the formation of dimer of MukBEF dimer complexes, dependent on dimeric MukF kleisin. Using truncated and full length MukB, in combination with MukEF, we show that engagement of the MukB ATPase heads on nucleotide binding directs the formation of dimers of heads-engaged dimer complexes. Complex formation requires functional interactions between the C- and N-terminal domains of MukF with the MukB head and neck, respectively, and MukE, which organizes the complexes by stabilizing binding of MukB heads to MukF. In the absence of head engagement, a MukF dimer bound by MukE forms complexes containing only a dimer of MukB. Finally, we demonstrate that cells expressing MukBEF complexes in which MukF is monomeric are Muk−, with the complexes failing to associate with chromosomes

Dynamic architecture of the Escherichia coli structural maintenance of chromosomes (SMC) complex, MukBE

Ubiquitous Structural Maintenance of Chromosomes (SMC) complexes use a proteinaceous ring-shaped architecture to organize and individualize chromosomes, thereby facilitating chromosome segregation. They utilize cycles of adenosine triphosphate (ATP) binding and hydrolysis to transport themselves rapidly with respect to DNA, a process requiring protein conformational changes and multiple DNA contact sites. By analysing changes in the architecture and stoichiometry of the Escherichia coli SMC complex, MukBEF, as a function of nucleotide binding to MukB and subsequent ATP hydrolysis, we demonstrate directly the formation of dimer of MukBEF dimer complexes, dependent on dimeric MukF kleisin. Using truncated and full length MukB, in combination with MukEF, we show that engagement of the MukB ATPase heads on nucleotide binding directs the formation of dimers of heads-engaged dimer complexes. Complex formation requires functional interactions between the C- and N-terminal domains of MukF with the MukB head and neck, respectively, and MukE, which organizes the complexes by stabilizing binding of MukB heads to MukF. In the absence of head engagement, a MukF dimer bound by MukE forms complexes containing only a dimer of MukB. Finally, we demonstrate that cells expressing MukBEF complexes in which MukF is monomeric are Muk−, with the complexes failing to associate with chromosomes