We present a RNA deep sequencing (RNAseq) analysis of a comparison of the transcriptome responses to infection of zebrafish larvae with Staphylococcus epidermidis and Mycobacterium marinum bacteria. We show how our developed GeneTiles software can improve RNAseq analysis approaches by more confidently identifying a large set of markers upon infection with these bacteria. For analysis of RNAseq data currently, software programs such as Bowtie2 and Samtools are indispensable. However, these programs that are designed for a LINUX environment require some dedicated programming skills and have no options for visualisation of the resulting mapped sequence reads. Especially with large data sets, this makes the analysis time consuming and difficult for non-expert users. We have applied the GeneTiles software to the analysis of previously published and newly obtained RNAseq datasets of our zebrafish infection model, and we have shown the applicability of this approach also to published RNAseq datasets of other organisms by comparing our data with a published mammalian infection study. In addition, we have implemented the DEXSeq module in the GeneTiles software to identify genes, such as glucagon A, that are differentially spliced under infection conditions. In the analysis of our RNAseq data, this has led to the possibility to improve the size of data sets that could be efficiently compared without using problem-dedicated programs, leading to a quick identification of marker sets. Therefore, this approach will also be highly useful for transcriptome analyses of other organisms for which well-characterised genomes are available.Electronic supplementary materialThe online version of this article (doi:10.1007/s00251-014-0820-3) contains supplementary material, which is available to authorized users.

One of the most popular techniques in zebrafish research is microinjection. This is a rapid and efficient way to genetically manipulate early developing embryos, and to introduce microbes, chemical compounds, nanoparticles or tracers at larval stages. Here we demonstrate the development of a machine learning software that allows for microinjection at a trained target site in zebrafish eggs at unprecedented speed. The software is based on the open-source deep-learning library Inception v3. In a first step, the software distinguishes wells containing embryos at one-cell stage from wells to be skipped with an accuracy of 93%. A second step was developed to pinpoint the injection site. Deep learning allows to predict this location on average within 42 μm to manually annotated sites. Using a Graphics Processing Unit (GPU), both steps together take less than 100 milliseconds. We first tested our system by injecting a morpholino into the middle of the yolk and found that the automated injection efficiency is as efficient as manual injection (~ 80%). Next, we tested both CRISPR/Cas9 and DNA construct injections into the zygote and obtained a comparable efficiency to that of an experienced experimentalist. Combined with a higher throughput, this results in a higher yield. Hence, the automated injection of CRISPR/Cas9 will allow high-throughput applications to knock out and knock in relevant genes to study their mechanisms or pathways of interest in diverse areas of biomedical research.

This paper presents an algorithm that enables an extension of standard 3d capacitance extraction to take into account the effects of small dimensional variations of interconnects by calculating the corresponding capacitance sensitivities. By using an adjoint technique, capacitances and their sensitivities w.r.t. multiple geometric parameters can be obtained with one-time 3d extraction using the Boundary Element Method (BEM).

Bioconcentration factors (BCFs) are determined by fish flow-through tests performed according to Organisation for Economic Co-operation and Development test guideline 305. These are time-consuming and expensive and use a large number of animals. An alternative test design using the freshwater amphipod Hyalella azteca for bioconcentration studies has been recently developed and demonstrated a high potential. For bioconcentration studies using H. azteca, male amphipods are preferred compared with female organisms. Manual sexing of male adult amphipods is, however, timeconsuming and requires care and skill. A new fully automatic sorting and dispensing machine for H. azteca based on image analysis has recently been developed by the company Life Science Methods. Nevertheless, an anesthesia step is necessary prior to the automatic selection. In the present study, we show that a single-pulse of 90 min of tricaine at the concentration of 1 g/L can be used and is recommended to select H. azteca males manually or automatically using the sorting machine. In the second part, we demonstrate that the machine has the ability to select, sort, and disperse the males of a culture batch of H. azteca as efficiently as manual procedures. In the last part of the study, BCFs of two organic substances were evaluated using the H. azteca bioconcentration test (HYBIT) protocol, with an anesthetizing step and robotic selection compared with manual selection without an anesthetizing step. The different BCF values obtained were in accordance with those indicated in the literature and showed that an anesthetizing step had no effect on the BCF values. Therefore, these data validated the interest in this sorting machine for selecting males to perform bioconcentrations studies with H. azteca. Environ Toxicol Chem 2023;42:1075-1084.

33One of the most popular techniques in zebrafish research is microinjection, as it is a 34 rapid and efficient way to genetically manipulate early developing embryos, and to 35 introduce microbes or tracers at larval stages. 36Here we demonstrate the development of a machine learning software that allows for