Content of Trace Metals in Medicinal Plants and their Extracts

The heavy metals (Fe, Cu, Zn and Mn) contents of selected plant species, grown in Southeast region of Serbia, that are traditionally used in alternative medicine were determined. Among the considered metals, iron content was the highest one and varied from 137.53 up to 423.32 mg/kg, while the contents of Cu, Zn and Mn were remarkably lower, and ranged from 8.91 to 62.20 mg/kg. In addition, an analysis of plants extracts showed a significant transfer of heavy metals during extraction procedure; therefore, the corresponding extraction coefficients reached values up to 88.8%. Those were especially high in the ethanol based extracts. Moreover, it is was established that such coefficients mostly depend on the solvent nature and also on the treated plant species. The obtained results impose that medicinal plants from Southeast region of Serbia due to rather low content of heavy metals are appropriate for preparation of teas and medicinal extracts

Continuous Neural Algorithmic Planners

Neural algorithmic reasoning studies the problem of learning algorithms with neural networks, especially using graph architectures. A recent proposal, XLVIN, reaps the benefits of using a graph neural network that simulates the value iteration algorithm in deep reinforcement learning agents. It allows model-free planning without access to privileged information about the environment, which is usually unavailable. However, XLVIN only supports discrete action spaces, and is hence nontrivially applicable to most tasks of real-world interest. We expand XLVIN to continuous action spaces by discretization, and evaluate several selective expansion policies to deal with the large planning graphs. Our proposal, CNAP, demonstrates how neural algorithmic reasoning can make a measurable impact in higher-dimensional continuous control settings, such as MuJoCo, bringing gains in low-data settings and outperforming model-free baselines

'Small but mighty': conditions for prototypicality claims within low-status merger partners

Mergers and acquisitions often exhibit asymmetric group structure and dynamics. Minority (low-status) merger partners frequently perceive themselves as less prototypical of the new organisation, potentially hindering corporate development. This research examines three variables – morality, indispensability, and merger patterns, which may influence prototypicality perceptions in minority merger partners. In respective online-based surveys, each of the variables is experimentally manipulated via scenarios and perceptions of relative ingroup prototypicality and indicators of merger support are measured as dependent variables. Ultimately, the aim of the studies is to give an insight into minority perceptions and suggest better practices for human resource management.info:eu-repo/semantics/publishedVersio

Endothelial cell apoptosis in brown adipose tissue of rats induced by hyperinsulinaemia: the possible role of TNF-α

The aim of the present study was to investigate whether hyperinsulinaemia, which frequently precedes insulin resistance syndrome (obesity, diabetes), induces apoptosis of endothelial cells (ECs) in brown adipose tissue (BAT) and causes BAT atrophy and also, to investigate the possible mechanisms underlying ECs death. In order to induce hyperinsuli-naemia, adult male rats of Wistar strain were treated with high dose of insulin (4 U/kg, intraperitonely) for one or three days. Examinations at ultrastructural level showed apoptotic changes of ECs, allowing us to point out that changes mainly but not exclusively, occur in nuclei. Besides different stages of condensation and alterations of the chromatin, nuclear fragmentation was also observed. Higher number of ECs apoptotic nuclei in the BAT of hyperinsulinaemic rats was also confirmed by propidium iodide staining. Immunohistochemical localization of tumor necrosis factor-alpha (TNF-α) revealed increased expression in ECs of BAT of hyperinsulinaemic animals, indicating its possible role in insulin-induced apoptotic changes. These results suggest that BAT atrophy in hyperinsulinaemia is a result of endothelial and adipocyte apoptosis combined, rather than any of functional components alone

Low temperature exposure induces browning of bone marrow stem cell derived adipocytes in vitro

Brown and beige adipocytes are characterised as expressing the unique mitochondrial uncoupling protein (UCP)1 for which the primary stimulus in vivo is cold exposure. The extent to which cold-induced UCP1 activation can also be achieved in vitro, and therefore perform a comparable cellular function, is unknown. We report an in vitro model to induce adipocyte browning using bone marrow (BM) derived mesenchymal stem cells (MSC), which relies on differentiation at 32 °C instead of 37 °C. The low temperature promoted browning in adipogenic cultures, with increased adipocyte differentiation and upregulation of adipogenic and thermogenic factors, especially UCP1. Cells exhibited enhanced uncoupled respiration and metabolic adaptation. Cold-exposed differentiated cells showed a marked translocation of leptin to adipocyte nuclei, suggesting a previously unknown role for leptin in the browning process. These results indicate that BM-MSC can be driven to forming beige-like adipocytes in vitro by exposure to a reduced temperature. This in vitro model will provide a powerful tool to elucidate the precise role of leptin and related hormones in hitherto functions in the browning process

Caffeine exposure induces browning features in adipose tissue in vitro and in vivo

Brown adipose tissue (BAT) is able to rapidly generate heat and metabolise macronutrients, such as glucose and lipids, through activation of mitochondrial uncoupling protein 1 (UCP1). Diet can modulate UCP1 function but the capacity of individual nutrients to promote the abundance and activity of UCP1 is not well established. Caffeine consumption has been associated with loss of body weight and increased energy expenditure, but whether it can activate UCP1 is unknown. This study examined the effect of caffeine on BAT thermogenesis in vitro and in vivo. Stem cell-derived adipocytes exposed to caffeine (1 mM) showed increased UCP1 protein abundance and cell metabolism with enhanced oxygen consumption and proton leak. These functional responses were associated with browning-like structural changes in mitochondrial and lipid droplet content. Caffeine also increased peroxisome proliferator-activated receptor gamma coactivator 1-alpha expression and mitochondrial biogenesis, together with a number of BAT selective and beige gene markers. In vivo, drinking coffee (but not water) stimulated the temperature of the supraclavicular region, which co-locates to the main region of BAT in adult humans, and is indicative of thermogenesis. Taken together, these results demonstrate that caffeine can promote BAT function at thermoneutrality and may have the potential to be used therapeutically in adult humans

DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation

Heterogeneous information network has been widely used to alleviate sparsity and cold start problems in recommender systems since it can model rich context information in user-item interactions. Graph neural network is able to encode this rich context information through propagation on the graph. However, existing heterogeneous graph neural networks neglect entanglement of the latent factors stemming from different aspects. Moreover, meta paths in existing approaches are simplified as connecting paths or side information between node pairs, overlooking the rich semantic information in the paths. In this paper, we propose a novel disentangled heterogeneous graph attention network DisenHAN for top-$N$ recommendation, which learns disentangled user/item representations from different aspects in a heterogeneous information network. In particular, we use meta relations to decompose high-order connectivity between node pairs and propose a disentangled embedding propagation layer which can iteratively identify the major aspect of meta relations. Our model aggregates corresponding aspect features from each meta relation for the target user/item. With different layers of embedding propagation, DisenHAN is able to explicitly capture the collaborative filtering effect semantically. Extensive experiments on three real-world datasets show that DisenHAN consistently outperforms state-of-the-art approaches. We further demonstrate the effectiveness and interpretability of the learned disentangled representations via insightful case studies and visualization.Comment: Accepted at CIKM202

Multivariate Relations Aggregation Learning in Social Networks

Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional Network) model in node classification tasks, and can significantly reduce time cost.Comment: 11 pages, 6 figure

LRIG1 and epidermal growth factor receptor in renal cell carcinoma: a quantitative RT–PCR and immunohistochemical analysis

In all, 31 renal cell carcinomas (RCCs) were examined for expression of the potential tumour suppressor LRIG1 (formerly Lig-1) and the epidermal growth factor receptor (EGFR). Eight matched samples of uninvolved kidney cortex were also evaluated. Gene expression was examined by quantitative real-time RT-PCR. In the eight matched sample pairs (uninvolved kidney cortex and tumour), protein expression was examined by immunohistochemistry. Conventional (clear cell) tumours showed an expected upregulation of EGFR. LRIG1 expression was generally downregulated in conventional and papillary RCC but not in chromophobic RCC. The ratio between EGFR and LRIG1 was more than 2.5-fold higher in the eight tumours compared with matched uninvolved kidney cortex and was at least two-fold higher than the mean normal ratio in 21 of 31 samples analysed. The observed downregulation of LRIG1 and increased EGFR/LRIG1 ratios are consistent with LRIG1 being a suppressor of oncogenesis in RCC by counteracting the tumour-promoting properties of EGFR. Further studies are justified to elucidate the explicit role of LRIG1 in the oncogenesis of RCC.</p