Open-access Mega-journals in Health and Life Sciences: What Every Researcher needs to know about this Publishing Model.

A mega-journal is a peer-reviewed scientific open-access journal designed to be much larger than a traditional classical journal. The low selectivity review criteria largely focused on the scientific soundness of the research methodology and ethical issues regardless of the importance and application of the results, the fast peer review, and a very broad scope usually covering a whole discipline, such as biomedicine or social science, are the major hallmarks. This publishing model was pioneered by PLOS One and was soon followed by other publishers. A few years ago, it was believed that the academic journal landscape would dominate by the mega-journals model, but a decline has been registered in the last few years. This editorial aimed at presenting the current state-of-the-art of the open-access mega-journals (OAMJs) in scientific publications.

Random access with a distributed Bitmap Join Index for Star Joins.

Indices improve the performance of relational databases, especially on queries that return a small portion of the data (i.e., low-selectivity queries). Star joins are particularly expensive operations that commonly rely on indices for improved performance at scale. The development and support of index-based solutions for Star Joins are still at very early stages. To address this gap, we propose a distributed Bitmap Join Index (dBJI) and a framework-agnostic strategy to solve join predicates in linear time. For empirical analysis, we used common Hadoop technologies (e.g., HBase and Spark) to show that dBJI significantly outperforms full scan approaches by a factor between 59% and 88% in queries with low selectivity from the Star Schema Benchmark (SSB). Thus, distributed indices may significantly enhance low-selectivity query performance even in very large databases.

Growth kinetics of Escherichia coli O157: H7 on the epicarp of fresh vegetables and fruits

ABSTRACT Despite the increasing reports on the incidence of fresh vegetables and fruits as a possible vehicle for human pathogens, there is currently limited knowledge on the growth potential of Escherichia coli O157:H7 on different plant substrates. This study analyzed the selective adhesion and growth of E. coli O157:H7 on chili habanero (Capsicum chinense L.), cucumber (Cucumis sativus), radish (Raphanus sativus), tomato (Lycopersicon esculentum), beet (Beta vulgaris subsp. vulgaris), and onion (Allium cepa L.) under laboratory conditions. The Gompertz parameters were used to determine the growth kinetics. Scanning electron microscopy was used to visualize the adhesion of E. coli O157:H7 on the epicarp of the samples. Predictive models were constructed to compare the growth of E. coli O157:H7 on the samples with different intrinsic factors and to demonstrate the low selectivity of the pathogen. No significant difference was observed in the lag-phase duration (LPD), generation time (GT), and exponential growth rate (EGR) of the pathogen adhered to the samples. The interaction between the microorganism and the substrate was less supportive to the growth of E. coli O157:H7 for onion, whereas for tomato and cucumber, the time for the microorganism to attain the maximum growth rate (M) was significantly longer than that recorded for other samples.

Growth kinetics of Escherichia coli O157:H7 on the epicarp of fresh vegetables and fruits.

Despite the increasing reports on the incidence of fresh vegetables and fruits as a possible vehicle for human pathogens, there is currently limited knowledge on the growth potential of Escherichia coli O157:H7 on different plant substrates. This study analyzed the selective adhesion and growth of E. coli O157:H7 on chili habanero (Capsicum chinense L.), cucumber (Cucumis sativus), radish (Raphanus sativus), tomato (Lycopersicon esculentum), beet (Beta vulgaris subsp. vulgaris), and onion (Allium cepa L.) under laboratory conditions. The Gompertz parameters were used to determine the growth kinetics. Scanning electron microscopy was used to visualize the adhesion of E. coli O157:H7 on the epicarp of the samples. Predictive models were constructed to compare the growth of E. coli O157:H7 on the samples with different intrinsic factors and to demonstrate the low selectivity of the pathogen. No significant difference was observed in the lag-phase duration (LPD), generation time (GT), and exponential growth rate (EGR) of the pathogen adhered to the samples. The interaction between the microorganism and the substrate was less supportive to the growth of E. coli O157:H7 for onion, whereas for tomato and cucumber, the time for the microorganism to attain the maximum growth rate (M) was significantly longer than that recorded for other samples.

Prodrug Strategies for Paclitaxel.

Paclitaxel is an anti-tumor agent with remarkable anti-tumor activity and wide clinical uses. However, it is also faced with various challenges especially for its poor water solubility and low selectivity for the target. To overcome these disadvantages of paclitaxel, approaches using small molecule modifications and macromolecule modifications have been developed by many research groups from all over the world. In this review, we discuss the different strategies especially prodrug strategies that are currently used to make paclitaxel more effective.