Macrophage depletion disrupts immune balance and energy homeostasis.

Increased macrophage infiltration in tissues including white adipose tissue and skeletal muscle has been recognized as a pro-inflammatory factor that impairs insulin sensitivity in obesity. However, the relationship between tissue macrophages and energy metabolism under non-obese physiological conditions is not clear. To study a homeostatic role of macrophages in energy homeostasis, we depleted tissue macrophages in adult mice through conditional expression of diphtheria toxin (DT) receptor and DT-induced apoptosis. Macrophage depletion robustly reduced body fat mass due to reduced energy intake. These phenotypes were reversed after macrophage recovery. As a potential mechanism, severe hypothalamic and systemic inflammation was induced by neutrophil (NE) infiltration in the absence of macrophages. In addition, macrophage depletion dramatically increased circulating granulocyte colony-stimulating factor (G-CSF) which is indispensable for NE production and tissue infiltration. Our in vitro study further revealed that macrophages directly suppress G-CSF gene expression. Therefore, our study indicates that macrophages may play a critical role in integrating immune balance and energy homeostasis under physiological conditions

Mining range associations for classification and characterization

In this paper, we propose a method that is able to derive rules involving range associations from numerical attributes, and to use such rules to build comprehensible classification and characterization (data summary) models. Our approach follows the classification association rule mining paradigm, where rules are generated in a way similar to association rule mining, but search is guided by rule consequents. This allows many credible rules, not just some dominant rules, to be mined from the data to build models. In so doing, we propose several sub-range analysis and rule formation heuristics to deal with numerical attributes. Our experiments show that our method is able to derive range-based rules that offer both accurate classification and comprehensible characterization for numerical data

Exploiting contextual information in attacking set-generalized transactions

Transactions are records that contain a set of items about individuals. For example, items browsed by a customer when shopping online form a transaction. Today, many activities are carried out on the Internet, resulting in a large amount of transaction data being collected. Such data are often shared and analyzed to improve business and services, but they also contain private information about individuals that must be protected. Techniques have been proposed to sanitize transaction data before their release, and set-based generalization is one such method. In this article, we study how well set-based generalization can protect transactions. We propose methods to attack set-generalized transactions by exploiting contextual information that is available within the released data. Our results show that set-based generalization may not provide adequate protection for transactions, and up to 70% of the items added into the transactions during generalization to obfuscate original data can be detected by our methods with a precision over 80%

Adiponectin Deficiency Impairs Maternal Metabolic Adaptation to Pregnancy in Mice.

Hypoadiponectinemia has been widely observed in patients with gestational diabetes mellitus (GDM). To investigate the causal role of hypoadiponectinemia in GDM, adiponectin gene knockout (Adipoq-/- ) and wild-type (WT) mice were crossed to produce pregnant mouse models with or without adiponectin deficiency. Adenoviral vector-mediated in vivo transduction was used to reconstitute adiponectin during late pregnancy. Results showed that Adipoq-/- dams developed glucose intolerance and hyperlipidemia in late pregnancy. Increased fetal body weight was detected in Adipoq-/- dams. Adiponectin reconstitution abolished these metabolic defects in Adipoq-/- dams. Hepatic glucose and triglyceride production rates of Adipoq-/- dams were significantly higher than those of WT dams. Robustly enhanced lipolysis was found in gonadal fat of Adipoq-/- dams. Interestingly, similar levels of insulin-induced glucose disposal and insulin signaling in metabolically active tissues in Adipoq-/- and WT dams indicated that maternal adiponectin deficiency does not reduce insulin sensitivity. However, remarkably decreased serum insulin concentrations were observed in Adipoq-/- dams. Furthermore, β-cell mass, but not glucose-stimulated insulin release, in Adipoq-/- dams was significantly reduced compared with WT dams. Together, these results demonstrate that adiponectin plays an important role in controlling maternal metabolic adaptation to pregnancy

The platelet-derived growth factor receptor alpha promoter-directed expression of cre recombinase in mouse placenta.

BackgroundNumerous pathologies of pregnancy originate from placental dysfunction. It is essential to understand the functions of key genes in the placenta in order to discern the etiology of placental pathologies. A paucity of animal models that allow conditional and inducible expression of a target gene in the placenta is a major limitation for studying placental development and function.ResultsTo study the platelet-derived growth factor receptor alpha (PDGFRα)-directed and tamoxifen-induced Cre recombinase expression in the placenta, PDGFRα-CreER mice were crossed with mT/mG dual-fluorescent reporter mice. The expression of endogenous membrane-localized enhanced green fluorescent protein (mEGFP) and/or dTomato in the placenta was examined to identify PDGFRα promoter-directed Cre expression. Pregnant PDGFRα-CreER;mT/mG mice were treated with tamoxifen at various gestational ages. Upon tamoxifen treatment, reporter protein mEGFP was observed in the junctional zone (JZ) and chorionic plate (CP). Furthermore, a single dose of tamoxifen was sufficient to induce the recombination.ConclusionsPDGFRα-CreER expression is restricted to the JZ and CP of mouse placentas. PDGFRα-CreER mice provide a useful tool to conditionally knock out or overexpress a target gene in these regions of the mouse placenta

Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO2 emission changes:A case study for Shanghai (China)

Although investment and R&D activities can exert significant effects on energy-related industrial CO2 emissions (EICE), related factors have not been fairly uncovered in the existing index decomposition studies. This paper extends the previous logarithmic mean Divisia index (LMDI) decomposition model by introducing three novel factors (R&D intensity, investment intensity, and R&D efficiency). The extended model not only considers the conventional drivers of EICE, but also reflects the microeconomic effects of investment and R&D behaviors on EICE. Furthermore, taking Shanghai as an example, which is the economic center and leading CO2 emitter in China, we use the extended model to decompose and explain EICE changes. Also, we incorporate renewable energy sources into the proposed model to carry out an alternative decomposition analysis at Shanghais entire industrial level. The results show that among conventional (macroeconomic) factors, expanding output scale is mainly responsible for the increase in EICE, and industrial structure adjustment is the most significant factor in mitigating EICE. Regardless of renewable energy sources, the emission-reduction effect of energy intensity focused on by the Chinese government is less than the expected due to the rebound effect, but the introduction of renewable energy sources intensifies its mitigating effect, partly resulting from the transmission from the abating effect of industrial structure adjustment. The effect of energy structure is the weakest. Although all the three novel factors exert significant effects on EICE, they are more sensitive to policy interventions than conventional factors. R&D intensity presents an obvious mitigating effect, while investment intensity and R&D efficiency display an overall promotion effect with some volatility. The introduction of renewable energy sources intensifies the promotion effect of R&D efficiency as a result of the "green paradox" effect. Finally, we propose that CO2 mitigation efforts should be made by considering both macroeconomic and microeconomic factors in order to achieve a desirable emission-reduction effect

High-fat feeding reprograms maternal energy metabolism and induces long-term postpartum obesity in mice.

BackgroundExcessive gestational weight gain (EGWG) closely associates with postpartum obesity. However, the causal role of EGWG in postpartum obesity has not been experimentally verified. The objective of this study was to determine whether and how EGWG causes long-term postpartum obesity.MethodsC57BL/6 mice were fed with high-fat diet during gestation (HFFDG) or control chow, then their body composition and energy metabolism were monitored after delivery.ResultsWe found that HFFDG significantly increased gestational weight gain. After delivery, adiposity of HFFDG-treated mice (Preg-HF) quickly recovered to the levels of controls. However, 3 months after parturition, Preg-HF mice started to gain significantly more body fat even with regular chow. The increase of body fat of Preg-HF mice was progressive with aging and by 9 months after delivery had increased 2-fold above the levels of controls. The expansion of white adipose tissue (WAT) of Preg-HF mice was manifested by hyperplasia in visceral fat and hypertrophy in subcutaneous fat. Preg-HF mice developed low energy expenditure and UCP1 expression in interscapular brown adipose tissue (iBAT) in later life. Although blood estrogen concentrations were similar between Preg-HF and control mice, a significant decrease in estrogen receptor α (ERα) expression and hypermethylation of the ERα promoter was detected in the fat of Preg-HF mice 9 months after delivery. Interestingly, hypermethylation of ERα promoter and low ERα expression were only detected in adipocyte progenitor cells in both iBAT and WAT of Preg-HF mice at the end of gestation.ConclusionsThese results demonstrate that HFFDG causes long-term postpartum obesity independent of early postpartum fat retention. This study also suggests that HFFDG adversely programs long-term postpartum energy metabolism by epigenetically reducing estrogen signaling in both BAT and WAT

Impact of Water Scarcity on the Fenhe River Basin and Mitigation Strategies

This study produced a drought map for the Fenhe River basin covering the period from 150 BC to 2012 using regional historical drought records. Based on meteorological and hydrological features, the characteristics and causes of water scarcity in the Fenhe River basin were examined, along with their impact on the national economy and ecological environment. The effects of water scarcity in the basin on the national economy were determined from agricultural, industrial, and domestic perspectives. The impact on aquatic ecosystems was ascertained through an evolution trend analysis of surface water systems, including rivers, wetlands, and slope ecosystems, and subterranean water systems, including groundwater and karst springs. As a result of these analyses, strategies are presented for coping with water scarcity in this basin, including engineering countermeasures, such as the construction of a water network in Shanxi, and the non-engineering approach of groundwater resource preservation. These comprehensive coping strategies are proposed with the aim of assisting the prevention and control of water scarcity in the arid and semi-arid areas of China

A strategic roadmap for BM change for the video-games industry

The global video games industry has experienced and exponential growth in terms of socioeconomic impact during the last 50 years. Surprisingly, little academic interest is directed towards the industry, particularly in the context of BM Change. As a technologically intensive creative industry, developing studios and publishers experience substantial internal and external forces to identify, and sustain, their competitive advantage. To achieve that, managers are called to systematically explore and exploit, alternative BMs that are compatible with the company’s strategy. We build on empirical analysis of the video-games industry to construct a Toolkit that i) will help practitioners and academics to describe the industrial ecosystem of BMs more accurately, and ii) use it a strategic roadmap for managers to navigate through alternatives for entrepreneurial and growth purposes

Modelling Confidence for Quality of Service Assessment in Cloud Computing

The ability to assess the quality of a service (QoS) is important to the emerging cloud computing paradigm. When many cloud service providers exist offering many functionally identical services, the prospective users of these services will wish to use one that offers the best quality. Many techniques and tools have been proposed to assess QoS, and the ability to deal with uncertainty surrounding the QoS verdicts given by any such techniques or tools is essential. In this paper, we present a probabilistic model to quantify confidence in QoS assessment. More specifically, we take the number of QoS data items used in assessment and the variation of data in the dataset into account in our measure of assessment reliability. Our experiments show that our confidence model can help consumers to select services based on their requirements effectively