SCALING EVOLUTIONARY PROGRAMMING WITH THE USE OF APACHE SPARK

Organizations across the globe gather more and more data, encouraged by easy-to-use and cheap cloud storage services. Large datasets require new approaches to analysis and processing, which include methods based on machine learning. In particular, symbolic regression can provide many useful insights. Unfortunately, due to high resource requirements, use of this method for large-scale dataset analysis might be unfeasible. In this paper, we analyze a bottleneck in the open-source implementation of this method we call hubert. We identify that the evaluation of individuals is the most costly operation. As a solution to this problem, we propose a new evaluation service based on the Apache Spark framework, which attempts to speed up computations by executing them in a distributed manner on a cluster of machines. We analyze the performance of the service by comparing the evaluation execution time of a number of samples with the use of both implementations. Finally, we draw conclusions and outline plans for further research

Crisis Induced Innovation - the case of Artic Healthcare: How can a crisis be a driving force for innovation?

Through this case study we examine the phenomenon of Crisis Induced Innovation, with the purpose of exploring and further understanding the true nature of an emergency and how the driving forces across the market dimensions function. We approached this topic with some level of caution, as it is our first instinct to view a crisis as something inherently negative. The topic of our research covers the Covid-19 pandemic as a driver for change across the many different market dimensions, which we have elected to explore using theories within the fields of socio-technical dynamics, emergency frames, innovation management, and rhetorical situation. The overreaching objective that has been driving our academic focus is to understand how a firm may capitalize on radical changes in market conditions and make an adaptive move last beyond the crisis that caused the radical changes. Important notions of our research have been to understand how the dynamic of acutely heightened demand would affect the company in questions as the market returns to normalcy. Our analytical approach towards this phenomenon were based on the notion of Crisis Induced Innovation being the result of complex causal relationships that can be traced across market dimensions through the utilization of relevant framework. Our predictions to start was that Artic Healthcare, the company that served as a case study example for this purpose, started their operations in the beginning of the pandemic, and elected to utilize the rapid changes to speed up innovation processes. Among our findings is that Crisis Induced Innovation, as a dynamic effect, is subjected to be affected by its surroundings the same way it affects others. As we employed the MLP with a focus on temporal dynamics, it became obvious that time was an essential element of how the phenomenon evolves. However, we were not able to fully study these temporal dynamics and gain an understanding of how far these pressure points can give an effect, as the case in question reached a point in which progression stopped due to lack of funding for the innovation case we studied. Despite this setback, we have been able to analyse how the phenomenon affects the other dimensions through the employment of the multi-level perspective, we have examined temporal dynamics through the employment of innovation management theories that evaluates process speeds, and we have explored the complex nature of the phenomenon by evaluating it from multiple frames simultaneously, which is presented in our discussion

Crisis Induced Innovation- the case of Artic Healthcare: How can a crisis be a driving force for innovation?

Through this case study we examine the phenomenon of Crisis Induced Innovation, with the purpose of exploring and further understanding the true nature of an emergency and how the driving forces across the market dimensions function. We approached this topic with some level of caution, as it is our first instinct to view a crisis as something inherently negative. The topic of our research covers the Covid-19 pandemic as a driver for change across the many different market dimensions, which we have elected to explore using theories within the fields of socio-technical dynamics, emergency frames, innovation management, and rhetorical situation. The overreaching objective that has been driving our academic focus is to understand how a firm may capitalize on radical changes in market conditions and make an adaptive move last beyond the crisis that caused the radical changes. Important notions of our research have been to understand how the dynamic of acutely heightened demand would affect the company in questions as the market returns to normalcy. Our analytical approach towards this phenomenon were based on the notion of Crisis Induced Innovation being the result of complex causal relationships that can be traced across market dimensions through the utilization of relevant framework. Our predictions to start was that Artic Healthcare, the company that served as a case study example for this purpose, started their operations in the beginning of the pandemic, and elected to utilize the rapid changes to speed up innovation processes. Among our findings is that Crisis Induced Innovation, as a dynamic effect, is subjected to be affected by its surroundings the same way it affects others. As we employed the MLP with a focus on temporal dynamics, it became obvious that time was an essential element of how the phenomenon evolves. However, we were not able to fully study these temporal dynamics and gain an understanding of how far these pressure points can give an effect, as the case in question reached a point in which progression stopped due to lack of funding for the innovation case we studied. Despite this setback, we have been able to analyse how the phenomenon affects the other dimensions through the employment of the multi-level perspective, we have examined temporal dynamics through the employment of innovation management theories that evaluates process speeds, and we have explored the complex nature of the phenomenon by evaluating it from multiple frames simultaneously, which is presented in our discussion

Evolution-by-Coevolution of Neural Networks for Audio Classification

Neural networks are increasingly used in recognition problems, including static and moving images, sounds, etc. Unfortunately, the selection of optimal neural network architecture for a specific recognition problem is a difficult task, which often has an experimental nature. In this paper we present the use of evolutionary algorithms to obtain optimal architectures of neural networks used for audio sample classification. We extend the Pytorch DNN Evolution tool implementing co-evolutionary algorithms which create groups of neural networks that solve a given problem with a certain accuracy, with the support for problems in which training data consists of audio samples. In this paper we use the co-evolutionary approach to solve a sample sound classification problem. We describe how the sound data was prepared for processing with the use of the Mel Frequency Cepstral Coefficients (MFCC). Next we present the results of experiments conducted with the AudioMnist dataset. The obtained neural network architectures, whose classification accuracy is comparable to the classification accuracy attained by the AlexNet neural network, and their implications are discussed

TOWARDS AUTONOMIC SEMANTIC-BASED MANAGEMENT OF DISTRIBUTED APPLICATIONS

In this paper we present our approach to the management of distributed systems basedon semantic description of available resources. We use ontologies for a semantic descriptionof the monitored system and other aspects of monitoring and management (such as metrics)and introduce a feedback loop on underlying infrastructure. Such an approach allows toautomate monitoring and the ease the work of administrator. We introduce concepts behinda novel automatic management system, SAMM, developed within our research. We discussthe core mechanisms used in the system – the estimation of future measurements, approachto knowledge gathering, and the process of decision making. Then we provide some detailson the architecture and implementation of SAMM

Colon cancer cell-derived 12(S)-HETE induces the retraction of cancer-associated fibroblast via MLC2, RHO/ROCK and Ca2+ signalling

Retraction of mesenchymal stromal cells supports the invasion of colorectal cancer cells (CRC) into the adjacent compartment. CRC-secreted 12(S)-HETE enhances the retraction of cancer-associated fibroblasts (CAFs) and therefore, 12(S)-HETE may enforce invasivity of CRC. Understanding the mechanisms of metastatic CRC is crucial for successful intervention. Therefore, we studied pro-invasive contributions of stromal cells in physiologically relevant three-dimensional in vitro assays consisting of CRC spheroids, CAFs, extracellular matrix and endothelial cells, as well as in reductionist models. In order to elucidate how CAFs support CRC invasion, tumour spheroid-induced CAF retraction and free intracellular Ca2+ levels were measured and pharmacological-or siRNA-based inhibition of selected signalling cascades was performed. CRC spheroids caused the retraction of CAFs, generating entry gates in the adjacent surrogate stroma. The responsible trigger factor 12(S)-HETE provoked a signal, which was transduced by PLC, IP3, free intracellular Ca2+, Ca(2+)calmodulin-kinase-II, RHO/ROCK and MYLK which led to the activation of myosin light chain 2, and subsequent CAF mobility. RHO activity was observed downstream as well as upstream of Ca2+ release. Thus, Ca2+ signalling served as central signal amplifier. Treatment with the FDA-approved drugs carbamazepine, cinnarizine, nifedipine and bepridil HCl, which reportedly interfere with cellular calcium availability, inhibited CAF-retraction. The elucidation of signalling pathways and identification of approved inhibitory drugs warrant development of intervention strategies targeting tumour-stroma interaction

Gene and protein expression of glucose transporter 1 and glucose transporter 3 in human laryngeal cancer—the relationship with regulatory hypoxia-inducible factor-1α expression, tumor invasiveness, and patient prognosis

Increased glucose uptake mediated by glucose transporters and reliance on glycolysis are common features of malignant cells. Hypoxia-inducible factor-1α supports the adaptation of hypoxic cells by inducing genes related to glucose metabolism. The contribution of glucose transporter (GLUT) and hypoxia-inducible factor-1α (HIF-1α) activity to tumor behavior and their prognostic value in head and neck cancers remains unclear. The aim of this study was to examine the predictive value of GLUT1, GLUT3, and HIF-1α messenger RNA (mRNA)/protein expression as markers of tumor aggressiveness and prognosis in laryngeal cancer. The level of hypoxia/metabolic marker genes was determined in 106 squamous cell laryngeal cancer (SCC) and 73 noncancerous matched mucosa (NCM) controls using quantitative realtime PCR. The related protein levels were analyzed by Western blot. Positive expression of SLC2A1, SLC2A3, and HIF-1α genes was noted in 83.9, 82.1, and 71.7 % of SCC specimens and in 34.4, 59.4, and 62.5 % of laryngeal cancer samples. Higher levels of mRNA/protein for GLUT1 and HIF-1α were noted in SCC compared to NCM (p<0.05). SLC2A1 was found to have a positive relationship with grade, tumor front grading (TFG) score, and depth and mode of invasion (p<0.05). SLC2A3 was related to grade and invasion type (p<0.05). There were also relationships of HIF-1α with pTNM, TFG scale, invasion depth and mode, tumor recurrences, and overall survival (p<0.05). In addition, more advanced tumors were found to be more likely to demonstrate positive expression of these proteins. In conclusion, the hypoxia/metabolic markers studied could be used as molecular markers of tumor invasiveness in laryngeal cancer.This work was supported, in part, by the statutory fund of the Department of Cytobiochemistry, University of Łódź, Poland (506/811), and by grant fromtheNational Science Council, Poland (N403 043 32/2326)

Calcium-stimulated calcitonin - The “new standard” in the diagnosis of thyroid C-cell disease - clinically relevant gender-specific cut-off levels for an “old test”

Introduction: Pentagastrin (Pg) stimulated calcitonin (sCT) was used to enhance accuracy in medullary thyroid cancer (MTC) diagnosis. As it is now unavailable, calcium (Ca) has been recommended as an alternative. The aim of this study was to define gender-specific cut-off values to predict MTC in patients with elevated basal CT (bCT) following Pg-sCT and Ca-sCT stimulation and to compare the time courses of CT release during stimulation. Materials and methods: The stimulation tests were applied in 62 consecutive patients with thyroid nodules. Basal calcitonin was measured by chemiluminescent immunometric assay. All patients underwent thyroidectomy and bilateral central neck dissection. C-cell pathology was confirmed by histological and immunohistochemical evaluation. Results: In 39 (0.63) patients MTC was documented while isolated C-cell hyperplasia (CCH) was identified in 23 (0.37) patients. Medullary thyroid cancer was predicted in males with bCT values > 43 pg/mL or sCT concentrations > 470 pg/mL (Pg-sCT) or > 1500 pg/mL (Ca-sCT), and in females with bCT concentrations > 23 pg/mL or sCT concentrations > 200 pg/mL (Pg-sCT) or > 780 pg/mL (Ca-sCT), respectively. Pg-sCT correctly predicted MTC in 16 (0.66) compared to 13 (0.54) after Ca-sCT in males and in 12 (0.80) compared to 11 (0.73) in females; without statistical significance. In patients with CCH or low tumor burden, there was a tendency of faster CT release after Ca stimulation (CT peak after 3min in > 60%) compared to patients with advanced MTC (CT peak after 3min in < 10%). Conclusions: Using gender-specific cut-off values, Ca could replace Pg to predict MTC with similar diagnostic power