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Children of mothers with gestational diabetes have greater risk of developing hypertension but little is known about the mechanisms by which this occurs. The objective of this study was to test the hypothesis that high maternal concentrations of leptin during pregnancy, which are present in mothers with gestational diabetes and/or obesity, alter blood pressure, vascular structure and vascular function in offspring. Wildtype (WT) offspring of hyperleptinemic, normoglycemic, Leprdb/+ dams were compared to genotype matched offspring of WT-control dams. Vascular function was assessed in male offspring at 6, and at 31 weeks of age after half the offspring had been fed a high fat, high sucrose diet (HFD) for 6 weeks. Blood pressure was increased by HFD but not affected by maternal hyperleptinemia. On a standard diet, offspring of hyperleptinemic dams had outwardly remodeled mesenteric arteries and an enhanced vasodilatory response to insulin. In offspring of WT but not Leprdb/+ dams, HFD induced vessel hypertrophy and enhanced vasodilatory responses to acetylcholine, while HFD reduced insulin responsiveness in offspring of hyperleptinemic dams. Offspring of hyperleptinemic dams had stiffer arteries regardless of diet. Therefore, while maternal hyperleptinemia was largely beneficial to offspring vascular health under a standard diet, it had detrimental effects in offspring fed HFD. These results suggest that circulating maternal leptin concentrations may interact with other factors in the pre- and post -natal environments to contribute to altered vascular function in offspring of diabetic pregnancies.

Vascular endothelial growth factor-A (VEGF) is produced by most cancer cells as multiple isoforms, which display distinct biological activities. VEGF plays an undisputed role in tumour growth, vascularisation and metastasis; nevertheless the functions of individual isoforms in these processes remain poorly understood. We investigated the effects of three main murine isoforms (VEGF188, 164 and 120) on tumour cell behaviour, using a panel of fibrosarcoma cells we developed that express them individually under endogenous promoter control. Fibrosarcomas expressing only VEGF188 (fs188) or wild type controls (fswt) were typically mesenchymal, formed ruffles and displayed strong matrix-binding activity. VEGF164- and VEGF120-producing cells (fs164 and fs120 respectively) were less typically mesenchymal, lacked ruffles but formed abundant cell-cell contacts. On 3D collagen, fs188 cells remained mesenchymal while fs164 and fs120 cells adopted rounded/amoeboid and a mix of rounded and elongated morphologies respectively. Consistent with their mesenchymal characteristics, fs188 cells migrated significantly faster than fs164 or fs120 cells on 2D surfaces while contractility inhibitors accelerated fs164 and fs120 cell migration. VEGF164/VEGF120 expression correlated with faster proliferation rates and lower levels of spontaneous apoptosis than VEGF188 expression. Nevertheless, VEGF188 was associated with constitutively active/phosphorylated AKT, ERK1/2 and Stat3 proteins. Differences in proliferation rates and apoptosis could be explained by defective signalling downstream of pAKT to FOXO and GSK3 in fs188 and fswt cells, which also correlated with p27/p21 cyclin-dependent kinase inhibitor over-expression. All cells expressed tyrosine kinase VEGF receptors, but these were not active/activatable suggesting that inherent differences between the cell lines are governed by endogenous VEGF isoform expression through complex interactions that are independent of tyrosine kinase receptor activation. VEGF isoforms are emerging as potential biomarkers for anti-VEGF therapies. Our results reveal novel roles of individual isoforms associated with cancer growth and metastasis and highlight the importance of understanding their diverse actions.

As we begin to understand the signals that drive chemotaxis in vivo, it is becoming clear that there is a complex interplay of chemotactic factors, which changes over time as the inflammatory response evolves. New animal models such as transgenic lines of zebrafish, which are near transparent and where the neutrophils express a green fluorescent protein, have the potential to greatly increase our understanding of the chemotactic process under conditions of wounding and infection from video microscopy data. Measurement of the chemoattractants over space (and their evolution over time) is a key objective for understanding the signals driving neutrophil chemotaxis. However, it is not possible to measure and visualise the most important contributors to in vivo chemotaxis, and in fact the understanding of the main contributors at any particular time is incomplete. The key insight that we make in this investigation is that the neutrophils themselves are sensing the underlying field that is driving their action and we can use the observations of neutrophil movement to infer the hidden net chemoattractant field by use of a novel computational framework. We apply the methodology to multiple in vivo neutrophil recruitment data sets to demonstrate this new technique and find that the method provides consistent estimates of the chemoattractant field across the majority of experiments. The framework that we derive represents an important new methodology for cell biologists investigating the signalling processes driving cell chemotaxis, which we label the neutrophils eye-view of the chemoattractant field.

Abstract Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition.

Staining of histological slides with Hematoxylin and Eosin is widely used in clinical and laboratory settings as these dyes reveal nuclear structures as well as cytoplasm and collagen. For cancer diagnosis, these slides are used to recognize tissues and morphological changes. Tissue semantic segmentation is therefore important and at the same time a challenging and time-consuming task. This paper describes a UNet-like deep learning architecture called DRD-UNet, which adds a novel processing block called DRD (Dilation, Residual, and Dense block) to a UNet architecture. DRD is formed by the combination of dilated convolutions (D), residual connections (R), and dense layers (D). DRD-UNet was applied to the multi-class (tumor, stroma, inflammatory, necrosis, and other) semantic segmentation of histological images from breast cancer samples stained with Hematoxylin and Eosin. The histological images were released through the Breast Cancer Semantic Segmentation (BCSS) Challenge. DRD-UNet outperformed the original UNet architecture and 15 other UNet-based architectures on the segmentation of 12,930 image patches extracted from regions of interest that ranged in size between $1036 \times 1222$ to $6813 \times 7360$ pixels. DRD-UNet obtained the best performance as measured with Jaccard similarity index, Dice coefficient, in a per-class comparison and accuracy for overall segmentation.