BETTER OUTCOME OF HIGH-DOSE CEFTAZIDIME IN HEMATO – ONCOLOGICAL PATIENTS WITH INFECTIONS CAUSED BY EXTENSIVELY DRUG-RESISTANT PSEUDOMONAS AERUGINOSA

Background: P. aeruginosa sepsis in immunocompromised patients is serious complication of cancer treatment, especially in case of XDR pathogen. The purpose of the study is to evaluate the efficacy of high-dose ceftazidime in treatment of XDR P. aeruginosa infection and to compare it with the conventionally treated cohort in hemato-oncological patients. Methods: We identified 27 patients with XDR P. aeruginosa infection during the 2008-2018 period, 16 patients served as a conventionally treated cohort with antipseudomonal beta-lactam antibiotic in standard dose (cohort A), and 11 patients were treated with high-dose ceftazidime (cohort B).  Most of the patients were neutropenic and under active treatment for their cancer in both cohorts. Results: Mortality and related mortality were statistically significantly better for cohort B compared to cohort A,  it was  18.2% and 9.1% for cohort B and 68.8% and 68.8% for cohort A, respectively. More patients in cohort A needed mechanical ventilation and renal replacement therapy, 75% and 50% for cohort A and 27.3% and 9.9% for cohort B, respectively.  It corresponded well with the worst SOFA in cohort A in comparison to cohort B, 16 versus 7 respectively. Reversible neurotoxicity was seen only in two patients in cohort B. Conclusion: Ceftazidime in high doses is a very potent ATB for the treatment of XDR P. aeruginosa infections in neutropenic cancer with acceptable toxicity

Embryo Ecology: Developmental Synchrony and Asynchrony in the Embryonic Development of Wild Annual Fish Populations

Embryo–environment interactions are of paramount importance during the development of all organisms, and impacts during this period can echo far into later stages of ontogeny. African annual fish of the genus Nothobranchius live in temporary pools and their eggs survive the dry season in the dry bottom substrate of the pools by entering a facultative developmental arrest termed diapause. Uniquely among animals, the embryos (encased in eggs) may enter diapause at three different developmental stages. Such a system allows for the potential to employ different regulation mechanisms for each diapause. We sampled multiple Nothobranchius embryo banks across the progressing season, species, and populations. We present important baseline field data and examine the role of environmental regulation in the embryonic development of this unique system. We describe the course of embryo development in the wild and find it to be very different from the typical development under laboratory conditions. Development across the embryo banks was synchronized within and across the sampled populations with all embryos entering diapause I during the rainy season and diapause II during the dry season. Asynchrony occurred at transient phases of the habitat, during the process of habitat desiccation, and at the end of the dry season. Our findings reveal the significance of environmental conditions in the serial character of the annual fish diapauses

Towards On-Board Hyperspectral Satellite Image Segmentation: Understanding Robustness of Deep Learning through Simulating Acquisition Conditions

Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are the pivotal steps in virtually all applications, hence developing new techniques for these tasks is a vital research area. Here, deep learning has established the current state of the art. However, deploying large-capacity deep models on-board an Earth observation satellite poses additional technological challenges concerned with their memory footprints, energy consumption requirements, and robustness against varying-quality image data, with the last problem being under-researched. In this paper, we tackle this issue, and propose a set of simulation scenarios that reflect a range of atmospheric conditions and noise contamination that may ultimately happen on-board an imaging satellite. We verify their impact on the generalization capabilities of spectral and spectral-spatial convolutional neural networks for hyperspectral image segmentation. Our experimental analysis, coupled with various visualizations, sheds more light on the robustness of the deep models and indicate that specific noise distributions can significantly deteriorate their performance. Additionally, we show that simulating atmospheric conditions is key to obtaining the learners that generalize well over image data acquired in different imaging settings