Leukocyte Trafficking and Hemostasis in the Mouse Fetus in vivo: A Practical Guide

In vivo observations of blood cells and organ compartments within the fetal mammalian organism are difficult to obtain. This practical guide describes a mouse model for in vivo observation of the fetal yolk-sac and corporal microvasculature throughout murine gestation, including imaging of various organ compartments, microvascular injection procedures, different methods for staining of blood plasma, vessel wall and circulating cell subsets. Following anesthesia of pregnant mice, the maternal abdominal cavity is opened, the uterus horn exteriorized, and the fetus prepared for imaging while still connected to the placenta. Microinjection methods allow delivery of substances directly into the fetal circulation, while substances crossing the placenta can be easily administered via the maternal circulation. Small volume blood sample collection allows for further in vitro workup of obtained results. The model permits observation of leukocyte-endothelial interactions, hematopoietic niche localization, platelet function, endothelial permeability studies, and hemodynamic changes in the mouse fetus, using appropriate strains of fluorescent protein expressing reporter mice and various sophisticated intravital microscopy techniques. Our practical guide is of interest to basic physiologists, developmental biologists, cardiologists, and translational neonatologists and reaches out to scientists focusing on the origin and regulation of hematopoietic niches, thrombopoiesis and macrophage heterogeneity

Refining mutation variants in Cartesian genetic programming

In this work, we improve upon two frequently used mutation algorithms and therefore introduce three refined mutation strategies for Cartesian Genetic Programming. At first, we take the probabilistic concept of a mutation rate and split it into two mutation rates, one for active and inactive nodes respectively. Afterwards, the mutation method Single is taken and extended. Single mutates nodes until an active node is hit. Here, our extension mutates nodes until more than one but still predefined number n of active nodes are hit. At last, this concept is taken and a decay rate for n is introduced. Thus, we decrease the required number of active nodes hit per mutation step during CGP’s training process. We show empirically on different classification, regression and boolean regression benchmarks that all methods lead to better fitness values. This is then further supported by probabilistic comparison methods such as the Bayesian comparison of classifiers and the Mann-Whitney-U-Test. However, these improvements come with the cost of more mutation steps needed which in turn lengthens the training time. The third variant, in which n is decreased, does not differ from the second mutation strategy listed

Role of Platelets in Leukocyte Recruitment and Resolution of Inflammation

Platelets are most often recognized for their crucial role in the control of acute hemorrhage. However, current research has greatly expanded the appreciation of platelets beyond their contribution to primary hemostasis, indicating that platelets also actively participate in leukocyte recruitment and the regulation of the host defense in response to exogenous pathogens and sterile injury. Early recruitment of leukocytes, especially neutrophils, is the evolutionary stronghold of the innate immune response to successfully control exogenous infections. Platelets have been shown to physically interact with different leukocyte subsets during inflammatory processes. This interaction holds far-reaching implications for the leukocyte recruitment into peripheral tissues as well as the regulation of leukocyte cell autonomous functions, including the formation and liberation of neutrophil extracellular traps. These functions critically depend on the interaction of platelets with leukocytes. The host immune response and leukocyte recruitment must be tightly regulated to avoid excessive tissue and organ damage and to avoid chronification of inflammation. Thus, platelet-leukocyte interactions and the resulting leukocyte activation and recruitment also underlies tight regulation by several inherited feedback mechanisms to limit the extend of vascular inflammation and to protect the host from collateral damage caused by overshooting immune system activation. After the acute inflammatory phase has been overcome the host defense response must eventually be terminated to allow for resolution from inflammation and restoration of tissue and organ function. Besides their essential role for leukocyte recruitment and the initiation and propagation of vascular inflammation, platelets have lately also been implicated in the resolution process. Here, their contribution to phagocyte clearance, T cell recruitment and macrophage reprogramming is also of outmost importance. This review will focus on the role of platelets in leukocyte recruitment during the initiation of the host defense and we will also discuss the participation of platelets in the resolution process after acute inflammation

Equidistant Reorder operator for Cartesian Genetic Programming

The Reorder operator, an extension to Cartesian Genetic Programming (CGP), eliminates limitations of the classic CGP algorithm by shuffling the genome. One of those limitations is the positional bias, a phenomenon in which mostly genes at the start of the genome contribute to an output, while genes at the end rarely do. This can lead to worse fitness or more training iterations needed to find a solution. To combat this problem, the existing Reorder operator shuffles the genome without changing its phenotypical encoding. However, we argue that Reorder may not fully eliminate the positional bias but only weaken its effects. By introducing a novel operator we name Equidistant-Reorder, we try to fully avoid the positional bias. Instead of shuffling the genome, active nodes are reordered equidistantly in the genome. Via this operator, we can show empirically on four Boolean benchmarks that the number of iterations needed until a solution is found decreases; and fewer nodes are needed to e fficiently find a solution, which potentially saves CPU time with each iteration. At last, we visually analyse the distribution of active nodes in the genomes. A potential decrease of the negative effects of the positional bias can be derived with our extension

Weighted mutation of connections to mitigate search space limitations in Cartesian Genetic Programming

This work presents and evaluates a novel modification to existing mutation operators for Cartesian Genetic Programming (CGP). We discuss and highlight a so far unresearched limitation of how CGP explores its search space which is caused by certain nodes being inactive for long periods of time. Our new mutation operator is intended to avoid this by associating each node with a dynamically changing weight. When mutating a connection between nodes, those weights are then used to bias the probability distribution in favour of inactive nodes. This way, inactive nodes have a higher probability of becoming active again. We include our mutation operator into two variants of CGP and benchmark both versions on four Boolean learning tasks. We analyse the average numbers of iterations a node is inactive and show that our modification has the intended effect on node activity. The influence of our modification on the number of iterations until a solution is reached is ambiguous if the same number of nodes is used as in the baseline without our modification. However, our results show that our new mutation operator leads to fewer nodes being required for the same performance; this saves CPU time in each iteration

Towards understanding crossover for Cartesian Genetic Programming

Unlike in traditional Genetic Programming, Cartesian Genetic Programming (CGP) does not commonly feature a recombination/crossover operator, although recombination plays an important role in other evolutionary techniques, including Genetic Programming from which CGP originates. Instead, CGP mainly depends on mutation and selection operators in their evolutionary search. To this day, it is still unclear as to why CGP’s performance does not generally improve with the addition of crossover. In this work, we argue that CGP’s positional bias might be a reason for this phenomenon. This bias describes a skewed distribution of active and inactive nodes, which might lead to destructive behaviour of standard recombination operators. We provide a first assessment with preliminary results. No final conclusion to this hypothesis can be drawn yet, as more thorough evaluations must be done first. However, our first results show promising trends and may lay the foundationf or future work

Filter evolution using Cartesian genetic programming for time series anomaly detection

Industrial monitoring relies on reliable and resilient systems to cope with unforeseen and changing environmental factors. The identification of critical conditions calls for efficient feature selection and algorithm configuration for accurate classification. Since the design process depends on experts who define parameters and develop classification models, it remains a time-consuming and error-prone task. In this paper, we suggest an evolutionary learning approach to create filter pipelines for machine health and condition monitoring. We apply a method called Cartesian Genetic Programming (CGP) to explore the search space and tune parameters for time series segmentation problems. CGP is a nature-inspired algorithm where nodes are aligned in a two-dimensional grid. Since programs generated by CGP are compact and short, we deem this concept efficient for filter evolution and parameter tuning to create performant classifiers. For better use of resources, we introduce a dependency grap h to allow only valid combinations of filter operators during training. Furthermore, this novel concept is critically discussed from a efficiency and quality point of view as well as its effect on classifier accuracy on scarce data. Experimental results show promising results which - in combination with the novel concept - competes with state-of-the-art classifiers for problems of low and medium complexity. Finally, this paper poses research questions for future investigations and experimentation

Telling friend from foe in emergency vertigo and dizziness: does season and daytime of presentation help in the differential diagnosis?

Distinguishing between serious (e.g., stroke) and benign (e.g., benign paroxysmal positional vertigo, BPPV) disorders remains challenging in emergency consultations for vertigo and dizziness (VD). A number of clues from patient history and clinical examination, including several diagnostic index tests have been reported recently. The objective of the present study was to analyze frequency and distribution patterns of specific vestibular and non-vestibular diagnoses in an interdisciplinary university emergency room (ER), including data on daytime and season of presentation. A retrospective chart analysis of all patients seen in a one-year period was performed. In the ER 4.23% of all patients presented with VD (818 out of 19,345). The most frequent-specific diagnoses were BPPV (19.9%), stroke/transient ischemic attack (12.5%), acute unilateral vestibulopathy/vestibular neuritis (UVH; 8.3%), and functional VD (8.3%). Irrespective of the diagnosis, the majority of patients presented to the ER between 8 a.m. and 4 p.m. There are, however, seasonal differences. BPPV was most prevalent in December/January and rare in September. UVH was most often seen in October/November; absolute and relative numbers were lowest in August. Finally, functional/psychogenic VD was common in summer and autumn with highest numbers in September/October and lowest numbers in March. In summary, daytime of presentation did not distinguish between diagnoses as most patients presented during normal working hours. Seasonal presentation revealed interesting fluctuations. The UVH peak in autumn supports the viral origin of the condition (vestibular neuritis). The BPPV peak in winter might be related to reduced physical activity and low vitamin D. However, it is likely that multiple factors contribute to the fluctuations that have to be disentangled in further studies

Model-driven optimisation of monitoring system configurations for batch production

The increasing need to monitor asset health and the deployment of IoT devices have driven the adoption of non-desctructive testing methods in the industry sector. In fact, they constitute a key to production efficiency. However, engineers still struggle to meet requirements sufficiently due to the complexity and cross-dependency of system parameters. In addition, the design and configuration of industrial monitoring systems remains dependent on recurring issues: data collection, algorithm selection, model configuration and objective function modelling. In this paper, we shine a light on impact factors of machine vision and signal processing in industrial monitoring, from sensor configuration to model development. Since system design requires a deep understanding of the physical characteristics, we apply graph-based design languages to improve the decision and configuration process. Our model and architecture design method are adapted for processing image and signal data in highly sen sitive installations to increase transparency, shorten time-to-production and enable defect monitoring in environments with varying conditions. We explore the potential of model selection, pipeline generation and data quality assessment and discuss their impact on representative manufacturing processes