BackgroundIn crops, inflorescence complexity and the shape and size of the seed are among the most important characters that influence yield. For example, rice panicles vary considerably in the number and order of branches, elongation of the axis, and the shape and size of the seed. Manual low-throughput phenotyping methods are time consuming, and the results are unreliable. However, high-throughput image analysis of the qualitative and quantitative traits of rice panicles is essential for understanding the diversity of the panicle as well as for breeding programs.ResultsThis paper presents P-TRAP software (Panicle TRAit Phenotyping), a free open source application for high-throughput measurements of panicle architecture and seed-related traits. The software is written in Java and can be used with different platforms (the user-friendly Graphical User Interface (GUI) uses Netbeans Platform 7.3). The application offers three main tools: a tool for the analysis of panicle structure, a spikelet/grain counting tool, and a tool for the analysis of seed shape. The three tools can be used independently or simultaneously for analysis of the same image. Results are then reported in the Extensible Markup Language (XML) and Comma Separated Values (CSV) file formats. Images of rice panicles were used to evaluate the efficiency and robustness of the software. Compared to data obtained by manual processing, P-TRAP produced reliable results in a much shorter time. In addition, manual processing is not repeatable because dry panicles are vulnerable to damage. The software is very useful, practical and collects much more data than human operators.ConclusionsP-TRAP is a new open source software that automatically recognizes the structure of a panicle and the seeds on the panicle in numeric images. The software processes and quantifies several traits related to panicle structure, detects and counts the grains, and measures their shape parameters. In short, P-TRAP offers both efficient results and a user-friendly environment for experiments. The experimental results showed very good accuracy compared to field operator, expert verification and well-known academic methods.

Network management tools are usually inherited from one generation to another. This was successful since these tools have been kept in check and updated regularly to fit new networking goals and service requirements. Unfortunately, new networking services will render this approach obsolete and handcrafting new tools or upgrading the current ones may lead to complicated systems that will be difficult to maintain and improve. Fortunately, recent advances in AI have provided new promising tools that can help solving many network management problems. Following this interesting trend, the current article presents LEASCH, a deep reinforcement learning model able to solve the radio resource scheduling problem in the MAC layer of 5G networks. LEASCH is developed and trained in a sand-box and then deployed in a 5G network. It has been evaluated under different numerology settings. The experimental results show that it is both numerology-agnostic and efficient when compared to conventional baseline methods in many key performance indicators. INDEX TERMS 5G; MAC; deep reinforcement learning; scheduling; resource management.

BackgroundStaphylococcus aureus is both human commensal and an important human pathogen, responsible for community-acquired and nosocomial infections ranging from superficial wound infections to invasive infections, such as osteomyelitis, bacteremia and endocarditis, pneumonia or toxin shock syndrome with a mortality rate up to 40%. S. aureus reveals a high genetic polymorphism and detecting the genotypes is extremely useful to manage and prevent possible outbreaks and to understand the route of infection. One of current and expanded typing method is based on the X region of the spa gene composed of a succession of repeats of 21 to 27 bp. More than 10000 types are known. Extracting the repeats is impossible by hand and needs a dedicated software. Unfortunately the only software on the market is a commercial program from Ridom.FindingsThis article presents DNAGear, a free and open source software with a user friendly interface written all in Java on top of NetBeans Platform to perform spa typing, detecting new repeats and new spa types and synchronizing automatically the files with the open access database. The installation is easy and the application is platform independent. In fact, the SPA identification is a formal regular expression matching problem and the results are 100% exact. As the program is using Java embedded modules written over string manipulation of well established algorithms, the exactitude of the solution is perfectly established.ConclusionsDNAGear is able to identify the types of the S. aureus sequences and detect both new types and repeats. Comparing to manual processing, which is time consuming and error prone, this application saves a lot of time and effort and gives very reliable results. Additionally, the users do not need to prepare the forward-reverse sequences manually, or even by using additional tools. They can simply create them in DNAGear and perform the typing task. In short, researchers who do not have commercial software will benefit a lot from this application.

Switch-controller assignment is an essential task in multi-controller software-defined networking. Static assignments are not practical because network dynamics are complex and difficult to predetermine. Since network load varies both in space and time, the mapping of switches to controllers should be adaptive to sudden changes in the network. To that end, switch migration plays an important role in maintaining dynamic switch-controller mapping. Migrating switches from overloaded to underloaded controllers brings flexibility and adaptability to the network but, at the same time, deciding which switches should be migrated to which controllers, while maintaining a balanced load in the network, is a challenging task. This work presents a heuristic approach with solution shaking to solve the switch migration problem. Shift and swap moves are incorporated within a search scheme. Every move is evaluated by how much benefit it will give to both the immigration and outmigration controllers. The experimental results show that the proposed approach is able to outweigh the state-of-art approaches, and improve the load balancing results up to ≈ 14% in some scenarios when compared to the most recent approach. In addition, the results show that the proposed work is more robust to controller failure than the state-of-art methods.

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