Notes

Mycoplasma pneumoniae-induced break outs along with mucositis: An incident statement.
With the aim of better understanding the enabling and limiting factors regarding staying or returning to work for cancer patients and to test specific measures of support, advisory and survey projects took place at the Robert Bosch Hospital in Stuttgart (RBK), the National Centre for Tumor Diseases in Heidelberg (NCT) as well as partly on behalf of and in cooperation with the Working Group Social Work in Oncology (ASO) of the German Cancer Society e. V. All projects have shown that the problems of career (re)integration faced by cancer patients in treatment, therapy and aftercare need to be taken more into account, as they play an essential role in securing the livelihood of and managing the stress experienced by the patient. Experience with the implementation of specialised counselling (at the NCT) and low-threshold, relationship-oriented support (at the RBK) makes it clear in which direction psychosocial support services, in the context of the clinic, can be expanded to better support cancer patients in their efforts to "stay in life".
As a consequence of the corona pandemic, universities nationwide had stopped classroom teaching by the start of the summer semester 2020. As part of the second lockdown, in many states schools and day care centers were also closed or reduced to a minimum. In this context the effect of room air filters has already been discussed multiple times; however, mobile devices for air filtration are currently not recommended by the German Federal Environment Agency. The following investigation shows the real effects of mobile air filters on aerosol concentrations when used in lecture theaters, canteens or school learning centers.

The effects of amobile air purifier (DEMA-airtech, Stuttgart, Germany) were measured in three large rooms (alecture theater, acompany canteen and a learning center of agrammar school). Aerosol and carbon dioxide concentrations were determined with devices from the company Palas (Karlsruhe, Germany).

All three scenarios showed arelevant and permanent decrease in aerosol concentrations through the use of air filters. The effect partly even surpassed the effectiveness of simple ventilation by opening the windows.

In addition to social distancing and wearing highly efficient face masks, the use air filters is recommended. This measure could enable classroom teaching to be resumed.
In addition to social distancing and wearing highly efficient face masks, the use air filters is recommended. This measure could enable classroom teaching to be resumed.The use of games in daily life, especially in education, has been in an incline during the COVID-2019 pandemic. Thus, game-based learning environments have caused an increase in the need of game contents, but generation of the game contents and levels is a time-consuming and costly process. Generated game contents and levels should be balanced, dense, aesthetic and reachable. Also, the time as well as the costs spent should be decreased. In order to overcome this problem, automatic and intelligent game content and level generation methods have emerged, and procedural content generation (PCG) is the most popular one of these methods. Artificial intelligence techniques are used for procedural game level generation instead of traditional methods. In this study, bidirectional long short-term memory (BiLSTM) and fuzzy analytic hierarchy process-genetic algorithm (FAHP-GA) methods were used to generate procedural game levels. This proposed hybrid system was used in a developed educational game as a case study to create game levels. The performance of the proposed study was compared to the other multi-criteria decision-making (MCDM) methods, and also further statistical analyses were investigated. The results showed that the BiLSTM-based FAHP-GA method can be used for procedural game level generation effectively.Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. Phorbol 12-myristate 13-acetate In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning-based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures, such as AlexNet and VGG16, with and without transfer learning. The complementarity between the handcrafted and non-handcrafted features was also evaluated with late fusion techniques. The datasets of EGC images used in the experiments, which are also contributions of this paper, are composed of three different chronic degenerative diseases Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. The experimental results, supported by statistical analysis, show that the proposed approach can distinguish healthy cells from the sick ones with a recognition rate of 89.30% (Rheumatoid Arthritis), 98.45% (Cancer), and 95.13% (Diabetes Mellitus), being achieved by combining classifiers obtained on both feature scenarios.Generative adversarial networks (GANs) are one of the most powerful generative models, but always require a large and balanced dataset to train. Traditional GANs are not applicable to generate minority-class images in a highly imbalanced dataset. Balancing GAN (BAGAN) is proposed to mitigate this problem, but it is unstable when images in different classes look similar, e.g., flowers and cells. In this work, we propose a supervised autoencoder with an intermediate embedding model to disperse the labeled latent vectors. With the enhanced autoencoder initialization, we also build an architecture of BAGAN with gradient penalty (BAGAN-GP). Our proposed model overcomes the unstable issue in original BAGAN and converges faster to high-quality generations. Our model achieves high performance on the imbalanced scale-down version of MNIST Fashion, CIFAR-10, and one small-scale medical image dataset. https//github.com/GH920/improved-bagan-gp.Electron spin relaxation times T1 and Tm of Tb3+ and Tm3+ in 11 waterethanol and of Tb3+ doped (2%) in crystalline La2(oxalate)3 decahydrate were measured between about 4.2 and 10 K. Both cations are non-Kramers ions and have J = 6 ground states. Echo-detected spectra are compared with CW spectra and with field-stepped direct-detected EPR spectra. Due to the strong temperature dependence of T1, measurements were not made above 10 K. Between about 4.2 and 6 K T1 is strongly concentration dependent between 1 and ~50 mM. T1 values at 4.2 K are in the μs range which is orders of magnitude faster than for 3d transition metals. Phase memory times, Tm, are less than 500 ns, which is short relative to values observed for 3d transition metals and organic radicals at 4 K. Tm is longer in the oxalate lattice which is attributed to the lower proton concentration in oxalate than in the organic solvent, which decreases nuclear spin diffusion. The rigidity of the crystalline lattice also may contribute to longer Tm.A clear understanding of community response to government decisions is crucial for policy makers and health officials during the COVID-19 pandemic. In this study, we document the determinants of implementation and compliance with stay-at-home orders in the USA, focusing on trust and social capital. Using cell phone data measuring changes in non-essential trips and average distance traveled, we find that mobility decreases significantly more in high-trust counties than in low-trust counties after the stay-at-home orders are implemented, with larger effects for more stringent orders. We also provide evidence that the estimated effect on post-order compliance is especially large for confidence in the press and governmental institutions, and relatively smaller for confidence in medicine and in science.Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. link2 Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The "official" approach adopted by public institutions to estimate the "excess mortality" during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. link3 In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in "ordinary" years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.
The online version contains supplementary material available at 10.1007/s00148-021-00857-y.
The online version contains supplementary material available at 10.1007/s00148-021-00857-y.Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.
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