PerkinElmer (Germany)

This study aimed to determine what childbearing women want when using virtual reality as an intrapartum pain management method. Researchers performed a qualitative exploratory study using content analysis. Two focus groups were organized including pregnant women anticipating a vaginal birth and women who recently had given birth, no longer than 6 months ago. The focus groups included a 30-minute virtual reality demo. In total, 10 women participated. Five themes emerged: (1) “try, test, and explore”: the need to receive information and to get acquainted with virtual reality during the antenatal period; (2) “variety and diversity in physical and digital options”: the preference for a variety in virtual content and view virtual reality as a complementary method for intrapartum pain management; (3) “distraction versus focus”: virtual reality as a method to distract from pain, from the clinical context or to help them focus; (4) “comfort both physical and digital”: measures to ensure a comfortable physical and virtual experience; and (5) “birthing partner”: the potential need to include partners. This study is an essential step informing the development, implementation, and research of labor-specific virtual reality and informing antenatal health care providers when offering women virtual reality as intrapartum pain management.

This study aimed to determine what childbearing women want when using virtual reality as an intrapartum pain management method. Researchers performed a qualitative exploratory study using content analysis. Two focus groups were organized including pregnant women anticipating a vaginal birth and women who recently had given birth, no longer than 6 months ago. The focus groups included a 30-minute virtual reality demo. In total, 10 women participated. Five themes emerged: (1) "try, test and explore": the need to receive information and to get acquainted with virtual reality during the antenatal period; (2) "variety and diversity in physical and digital options": the preference for a variety in virtual content and view virtual reality as a complementary method to methods for intrapartum pain management; (3) "distraction versus focus": virtual reality as a method to distract from pain, from the clinical context or to help them focus; (4) "comfort both physical and digital": measures to ensure a comfortable physical and virtual experience; and (5) "birthing partner": the potential need to include partners. This study is an essential step informing the development, implementation, and research of labor-specific virtual reality and informing antenatal healthcare providers when offering women virtual reality as intrapartum pain management.

Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While deep learning models have given encouraging results on this problem, the most powerful approaches have not yet been tested for attacking it. Here, we review and evaluate state-of-the-art very deep convolutional neural network architectures and training strategies for segmenting nuclei from brightfield cell images. We tested U-Net as a baseline model; considered U-Net++, Tiramisu, and DeepLabv3+ as latest instances of advanced families of segmentation models; and propose PPU-Net, a novel light-weight alternative. The deeper architectures outperformed standard U-Net and results from previous studies on the challenging brightfield images, with balanced pixel-wise accuracies of up to 86%. PPU-Net achieved this performance with 20-fold fewer parameters than the comparably accurate methods. All models perform better on larger nuclei and in sparser images. We further confirmed that in the absence of plentiful training data, augmentation and pretraining on other data improve performance. In particular, using only 16 images with data augmentation is enough to achieve a pixel-wise F1 score that is within 5% of the one achieved with a full data set for all models. The remaining segmentation errors are mainly due to missed nuclei in dense regions, overlapping cells, and imaging artifacts, indicating the major outstanding challenges.

The prevalence of food allergy has increased over the last decades and consequently the food labeling policies have improved over the time in different countries to regulate allergen presence in foods. In particular, Reg 1169 in EU mandates the labelling of 14 allergens whenever intentionally added to foods, but the inadvertent contamination by allergens still remains an uncovered topic. In order to warn consumers on the risk of cross-contamination occurring in certain categories of foods, a precautionary allergen labelling system has been put in place by food industries on a voluntary basis. In order to reduce the overuse of precautionary allergen labelling (PAL), reference doses and action limits have been proposed by the Voluntary Incidental Trace Allergen Labelling VITAL project representing a guide in this jeopardizing scenario. Development of sensitive and reliable mass spectrometry methods are therefore of paramount importance in this regard to check the contamination levels in foods. In this paper we describe the development of a time-managed multiple reaction monitoring (MRM) method based on a triple quadrupole platform for milk and egg quantification in processed food. The method was in house validated and allowed to achieve levels of proteins lower than 0.2 mg of total milk and egg proteins, respectively, in cookies, challenging the doses recommended by VITAL. The method was finally applied to cookies labeled as milk and egg-free. This method could represent, in perspective, a promising tool to be implemented along the food chain to detect even tiny amounts of allergens contaminating food commodities.

The tritiation of natural photoffinity ligands chlorpromazine and cytochalasin B is described.

The development of new drugs is long-winded and expensive. The first step consists in the search for active pharmaceutical ingredients that are suitable for the treatment of previously difficult to treat diseases. This problem requires optimized mathematical methods—it is not solvable by pure biotechnological developments.

Abstract In this paper a method for segmentation of fluorescent cellular images is introduced, which uses geodesic active contours. After segmentation of the nuclei by a thresholding algorithm, the appropriate whole cells are detected by inflation of the nuclei masks and gradient information. Because the implicit level set formulation provides topological changes and the topology of the nuclei and the whole cells is equal, a topology–preserving modification is necessary. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

A simple and stable NO/sub 2/ gas sensor device with rapid response/recovery time and low operating temperature has been developed using polycrystalline tellurium. The effect of thickness of the sample, thermal treatment and temperature to sensitivity against NO/sub 2/ is presented for the first time. The sensitivity strongly increases with thickness of the film decreasing, especially at thickness less than 100 nm. Thermal treatment of the films (at temperatures greater than 100/spl deg/C) and increasing of operating temperature above 80/spl deg/C result in decrease of sensitivity to NO/sub 2/, while it strongly improves the response and recovery times. By conditions of best compromise between the main sensing parameters, achieved at operating temperature 50/spl deg/C, there are no noticeable drifts of baseline or of gas induced current. The sensing mechanism is explained by interaction of adsorbed species with lone-pair electrons, which form the upper part of the valence band.