Levels of cyclin B in THP-1 cells incubated in hibernating and non-hibernating bullfrog plasma, Lithobates catesbeianus

Hibernation is a process that occurs in nature where organisms undergo dormancy for long periods of time in order to improve their survival during extreme environmental conditions. During this time, organisms undergo physiological changes such as reduction in core body temperature and metabolic rate, as well as cells being impeded from going into mitosis. Alvarado et al. (2015) discovered that genomic DNA methylation is dynamic across torpor-arousal bouts during winter hibernation in thirteen lined ground squirrels (Ictidomys tridecemlineatus), indicating that physiological changes during hibernation are the results of more than simply cold temperatures slowing down metabolism and that there are cellular mechanisms that are responsible for such physiological changes. A study by Robbins (2017) found that THP-1 cells incubated in hibernating bullfrog plasma (Lithobates catesbeianus) stop undergoing cellular division, with THP-1 cells being restricted to the G2 stage of the cell cycle. This suggests that there may be one or more substances in hibernating bullfrog plasma that prevent THP-1 cells from progressing in cell division. In this study, I explored the effects of cyclin B levels on cellular division and its potential impact on confining THP-1 cells in the G2 phase, while maintained in hibernating bullfrog plasma. My results indicate that the levels of cyclin B in hibernating and non-hibernating bullfrog plasma do not vary, suggesting that the expression of cyclin B may not be responsible for confining THP-1 cells to the G2 stage of the cell cycle

Lower cerebrospinal fluid/plasma fibroblast growth factor 21 (FGF21) ratios and placental FGF21 production in gestational diabetes

Objectives: Circulating Fibroblast Growth Factor 21 (FGF21) levels are increased in insulin resistant states such as obesity, type 2 diabetes mellitus and gestational diabetes mellitus (GDM). In addition, GDM is associated with serious maternal and fetal complications. We sought to study human cerebrospinal fluid (CSF) and corresponding circulating FGF21 levels in women with gestational diabetes mellitus (GDM) and in age and BMI matched control subjects. We also assessed FGF21 secretion from GDM and control human placental explants. Design: CSF and corresponding plasma FGF21 levels of 24 women were measured by ELISA [12 GDM (age: 26–47 years, BMI: 24.3–36.3 kg/m2) and 12 controls (age: 22–40 years, BMI: 30.1–37.0 kg/m2)]. FGF21 levels in conditioned media were secretion from GDM and control human placental explants were also measured by ELISA. Results: Glucose, HOMA-IR and circulating NEFA levels were significantly higher in women with GDM compared to control subjects. Plasma FGF21 levels were significantly higher in women with GDM compared to control subjects [234.3 (150.2–352.7) vs. 115.5 (60.5–188.7) pg/ml; P<0.05]. However, there was no significant difference in CSF FGF21 levels in women with GDM compared to control subjects. Interestingly, CSF/Plasma FGF21 ratio was significantly lower in women with GDM compared to control subjects [0.4 (0.3–0.6) vs. 0.8 (0.5–1.6); P<0.05]. FGF21 secretion into conditioned media was significantly lower in human placental explants from women with GDM compared to control subjects (P<0.05). Conclusions: The central actions of FGF21 in GDM subjects maybe pivotal in the pathogenesis of insulin resistance in GDM subjects. The significance of FGF21 produced by the placenta remains uncharted and maybe crucial in our understanding of the patho-physiology of GDM and its associated maternal and fetal complications. Future research should seek to elucidate these points

Energy Informatics - Current and Future Research Directions

Due to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The arti- cle attempts to characterize this young and dynamic field of research by de- scribing current EI research topics and methods and provides an outlook of how the field might evolve in the fu- ture. It is shown that two general re- search questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage infor- mation and communication technol- ogy (ICT) to (1) improve energy effi- ciency, and (2) to integrate decentral- ized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the re- spective research questions are broken down into specific research projects and how EI researchers have made con- tributions based on their individual academic background

Insulin-like growth factor levels in cord blood, birth weight and breast cancer risk

Breast cancer incidence and birth weight are higher among Caucasian than Asian women, and birth size has been positively associated with breast cancer risk. Pregnancy hormone levels, however, have been generally lower in Caucasian than Asian women. We studied components of the insulin-like growth factor (IGF) system in cord blood from 92 singleton babies born in Boston, USA, and 110 born in Shanghai, China, in 1994–1995. Cord blood IGF-1 was significantly higher among Caucasian compared with Chinese babies (P<10−6). The opposite was noted for IGF-2 (P∼10−4). IGF-1 was significantly positively associated with birth weight and birth length in Boston, but not Shanghai. In contrast, stronger positive, though statistically non-significant, associations of IGF-2 with birth size were only evident in Shanghai. The associations of birth weight and birth length were positive and significant in taller women (for IGF-1 in Boston P∼0.003 and 0.03, respectively; for IGF-2 in Shanghai P∼0.05 and ∼0.04, respectively), among whom maternal anthropometry does not exercise strong constraints in foetal growth. The documentation of higher cord blood levels of IGF-1, a principal growth hormone that does not cross the placenta, among Caucasian than in Asian newborns is concordant with breast cancer incidence in these populations

Relationship between dairy product intake during pregnancy and neonatal and maternal outcomes among Portuguese women

The role played by dairy product intake during pregnancy on neonatal outcomes has raised interest in the last few years. However, studies on this association remain scarce. Thus, the aim of this study was to determine the association between dairy product consumption during pregnancy and neonatal and maternal outcomes.info:eu-repo/semantics/publishedVersio

Deep Learning for Encrypted Traffic Classification and Unknown Data Detection

Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from them. Therefore, in this work, we exploit the probability distribution of a DNN output layer to filter the data from applications that are not considered during the model training (i.e., unknown data). The proposed framework uses a time window-based approach to divide the traffic flow of activity into segments so that in-app activities can be identified just by observing only a fraction of the activity-related traffic. Our tests have shown that the DNN-based framework has demonstrated an accuracy of 90% or above in identifying previously trained in-app activities and an average accuracy of 79% in identifying previously untrained in-app activity traffic as unknown data when this framework is employed

Effects of glucose and fatty acids on human trophoblasts in primary culture

Background: Diabetes in pregnancy is a major cause of maternal and neonatal complications, which also places the new born at risk of developing metabolic syndrome in adult life. Dyslipidaemia often accompanies hyperglycaemia in diabetic pregnancy. Hyperglycaemia has been shown to enhance the toxic effects of fatty acids in other tissues, a process termed glucolipotoxicity. Glucolipotoxicity therefore could contribute to the adverse outcomes of diabetic pregnancy, particularly if it were to occur at the feto-placental interphase. The aim of this study was to characterise the effects of elevated glucose, elevated fatty acids and their interactions on human trophoblast metabolism, function and morphology. Experimental approach: Human trophoblasts were isolated from term, normal placentae and established in culture over 16 h prior to experiments. Assessments of trophoblasts were carried out following their short and long-term exposure to various concentrations of glucose and fatty acids. Metabolic studies were carried out using radio-labeled tracers to determine the rates of glucose utilization, fatty acid oxidation and fatty acid esterification. Morphological changes examined were formation of lipid droplets, cell aggregation and syncytialisation. Cell viability, proliferation and apoptosis were assessed. Secretory function was assessed by measuring the levels of hormones and cytokines secreted into the culture media. Results: Glucose utilisation via glycolysis was near maximal at a low glucose concentration of 4 mM and maximal at 8 mM glucose. Esterification of fatty acids into triacylglycerols and diacylglycerols increased with increasing fatty acid levels without showing any evidence of plateau. Furthermore, pre-incubation of trophoblasts in fatty acids for 24 h caused up-regulation of fatty acid esterification and down-regulation of fatty acid oxidation and lipolysis processes. Trophoblasts avidly formed lipid droplets when exposed to fatty acids, however, their accumulation appeared to be less in cells cultured at 10 mM compared to 1 mM glucose. Fatty acids promoted trophoblast aggregation and syncytia formation and enhanced the secretion of TNF-alpha and IL-1beta. Elevated glucose did not alter trophoblast metabolism and function significantly except to increase glycogen accumulation as seen by electron microscopy. Glucose, fatty acids or their combination did not affect cell viability, apoptosis or hormone secretory function. Conclusions: The finding that fatty acids enhance their own storage into lipid droplets is indicative of up-regulation of a buffering mechanism to protect the fetus from excess lipid. The novel findings that fatty acids promote syncytialisation and cytokine production may be indicative of harmful effects of dyslipidaemia via effects on placental structure and inflammation. It was remarkable how little effect glucose had on trophoblasts compared to the effects of fatty acids. Glucose did not alter fatty acid partitioning as was predicted by the glucolipotoxicity hypothesis. Dyslipidaemia, therefore, may have greater pathogenic significance than hyperglycaemia at the level of trophoblast in diabetic pregnancy

User activity detection using encrypted in-app data

The advancements in the Internet technology and computer networks have led to an increased importance of network traffic classification. Significant amount of attention to network traffic classification has been given from both industry and academia. Network traffic classification has many possibilities to solve personal, business, Internet service provider and government network problems such as anomaly detection, quality of service control, application performance, capacity planning, traffic engineering, trend analysis, interception and intrusion detection. There are different methods to perform network traffic classification. However, it is not always reliable to apply traditional port-based and payload-based methods because many current applications have started to use dynamic port allocation and payload encryption. Recent research initiatives have put significant attention on applying machine learning techniques. To enhance and maintain privacy and security encryption technologies are applied in different levels of the communication process. However, this research shows the possibility to perform network traffic classification even in encrypted domain and infer information of mobile users. Side-channel information such as frame length, inter arrival time, direction (outgoing / incoming) of packets which may leak from encrypted traffic flows are used to perform the traffic classification. The research presented in this thesis focuses on identifying user actions performed on mobile applications. A user’s online activities performed on mobile apps are sensitive and contain private information. Rather than identifying coarse-grained activities such as browsing, downloading, uploading etc., identifying fine-grained user activities such as posting a photo on Facebook, posting a video on Facebook, posting a long text on Facebook, posting a short text on Facebook etc., provides more valuable information for an analyst to recognise the users where confidential information is retained. To achieve robustness of the classifier, the proposed solution is designed to identify user activities even by observing only a subset of an activity’s traffic. Even though this level of fine-grained analysis is challenging to perform in encrypted domain, from a subset of network traffic, using only side channel data, the classification performances showed the proposed classifiers have overcome this challenge successfully. There are a wide variety of mobile applications available for the users. It is not practical to train a model for all the available apps and for every user activity that can be performed using these apps. Therefore, to make the classifier adapt to new environments and data streams with little or no training, it is designed to handle traffic analysis in the presence of noise generated by unknown traffic. The probability distribution of the classifier’s output layer is exploited to filter the data from applications that have not been considered during the model training. In this way the classifier avoids labelling unknown samples with one of the known classes and thereby reduce the misclassification rate. This research explores and applies different machine learning methods to perform network traffic classification, such as Random Forest, Bayes Net, J48, Deep Neural Network (DNN) and Convolutional Neural Network (CNN). Different formats of input are provided to these classifiers. For classical machine learning algorithms and DNN, statistical features generated from side channel data are provided. For the CNN, images are generated from the traffic flows and input to the model. In this research, all the proposed machine learning classifiers achieved an accuracy greater than 90% in identifying fine-grained user activities even by observing encrypted network traffic segments of 0.2 seconds and removing noise traffic generated by unknown user activities with an average accuracy of 88%.</p

CNN for User Activity Detection Using Encrypted In-App Mobile Data

In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity&rsquo;s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previously trained (known) and previously untrained (unknown) in-app activities as well as the known in-app activity type. The proposed method extracts and selects salient features for encrypted traffic classification. This is the first-known approach proposing to filter unknown traffic with an average accuracy of 88%. Once the unknown traffic is filtered, the classification accuracy of our model would be 92%