Inhibition of TREM-1 and Dectin-1 Alleviates the Severity of Fungal Keratitis by Modulating Innate Immune Responses

Purpose

To explore the possibility that inhibiting triggering receptor expressed on myeloid cells-1 (TREM-1) and Dendritic cell-associated C-type lectin-1(Dectin-1) could modulate the innate immune response and alleviate the severity of corneal fungal keratitis.

Method

TREM-1 and Dectin-1 expression was detected in fungus-infected human corneal specimens by real-time PCR. C57BL/6 (B6) mice were injected with Aspergillus fumigatus and divided into 4 groups that received subconjunctival injections of PBS and IgG as a control (group I), mTREM-1/IgG fusion protein (group II), the soluble β-glucan antagonist laminarin (group III), or mTREM-1/Fc and laminarin (group IV). Corneal virulence was evaluated based on clinical scores. TREM-1 and Dectin-1 mRNA levels were assayed using real-time PCR. The distribution patterns of TREM-1, Dectin-1 and cellular infiltrates in fungus-infected corneas were examined by immunohistochemistry. Moreover, changes in T Helper Type1 (Th1)-/ T Helper Type1 (Th2)- type cytokines and proinflammatory cytokines were measured.

Results

The expression of TREM-1 and Dectin-1 increased significantly and correlated positively with the progression of fungal keratitis. Most infiltrated cells were neutrophils and secondarily macrophages in infected cornea. The clinical scores decreased after interfering with TREM-1 and Dectin-1 expression in infected mouse corneas. Levels of Th1-type cytokines including interleukin-12 (IL-12), IL-18 and interferon-γ (IFN-γ) were decreased in the cornea, while the levels of Th2-type cytokines, including IL-4, IL-5 and IL-10, showed obvious increases.

Conclusion

TREM-1 and Dectin-1 function concurrently in the corneal innate immune response by regulating inflammatory cytokine expression in fungal keratitis. Inhibition of TREM-1 and Dectin-1 can alleviate the severity of corneal damage by downregulating the excessive inflammatory response.     

Urban and Rural Differences of Acute Cardiovascular Disease Events: A Study from the Population-Based Real-Time Surveillance System in Zhejiang,China in 2012

Zhejiang province, China, has implemented a population based, real-time surveillance system that tracks acute cardiovascular diseases (CVDs) events since 2001. This study aimed to describe the system and report CVD incidence, mortality and case-fatality between urban and rural areas in Zhejiang in 2012. The surveillance system employs a stratified random sampling method covering all permanent residents of 30 counties/districts in Zhejiang. Acute CVD events such as coronary heart disease (CHD) and stroke were defined, registered and reviewed based on the adapted MONICA (Monitoring Trends and Determinants in Cardiovascular Disease) definitions. Data were collected from health facilities, vital registries, supplementary surveys, and additional investigations, and were checked for data quality before input in the system. We calculated the rates and compared them by gender, age and region. In 2012, the incidence, mortality and case-fatality of total acute CVD events were 367.0 (CHD 59.1, stroke 307.9), 127.1 (CHD 43.3, stroke 83.8) per 100,000 and 34.6% (CHD 73.2%, stroke 27.2%), respectively. Compared with rural areas, urban areas reported higher incidence and mortality but lower case-fatality rates for CHD (P<0.001), while lower incidence but higher mortality and case-fatality rates for stroke (P<0.001). We found significant differences on CHD and stroke epidemics between urban and rural areas in Zhejiang. Special attentions need to be given to stroke control, especially in rural areas.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

链霉菌底盘细胞的开发现状及其应用

天然产物及其衍生物在现代医疗中扮演着举足轻重的角色,其生物活性多样性以及化学结构的丰富性是新药研发的源泉和动力。利用纯化学方法合成天然产物在技术和成本上有很大的困难,加上许多天然产物的原始产生菌具有培养条件苛刻、产量低下等缺点,而且大量基因簇在原始菌株中是沉默的,这使得利用合成生物学思想来指导天然产物生物合成基因簇的异源表达具有重大意义。作为抗生素、抗肿瘤活性物质、免疫抑制剂等次级代谢产物主要来源的放线菌一直是研究者们关注的焦点,特别是随着基因测序技术的飞速发展,人们发现链霉菌基因组中包含着极为丰富的天然产物生物合成基因簇资源。这意味着开发链霉菌底盘细胞作为异源表达宿主有其得天独厚的优势。本综述从底盘细胞开发的意义入手,重点阐述链霉菌底盘细胞构建的策略及现状,随后通过实例阐述了各种底盘链霉菌的实际应用。     

A taxonomic revision of <Emphasis Type="Italic">Bursera</Emphasis> subgen. <Emphasis Type="Italic">Bursera</Emphasis> in the Greater Antilles and the Bahamas,including a new species from Cuba

A revision of Bursera in the Greater Antilles and the Bahamas has confirmed that Commiphora does not occur in the region and that all but one species in the region belong to Bursera subgen. Bursera. Here we describe a new species, Bursera yaterensis; B. nashii is synonymized with B. glauca and B. ovata is synonymized with B. trinitensis; and we return five species from Commiphora to Bursera. A dichotomous key is provided using mostly vegetative characters due to the frequent lack of adequate reproductive material and the relative uniformity of most floral and fruit characters.     

Scale-dependent bi-trophic interactions in a semi-arid savanna: how herbivores eliminate benefits of nutrient patchiness to plants

The scale of resource heterogeneity may influence how resources are locally partitioned between co-existing large and small organisms such as trees and grasses in savannas. Scale-related plant responses may, in turn, influence herbivore use of the vegetation. To examine these scale-dependent bi-trophic interactions, we varied fertilizer [(nitrogen (N)/phosphorus (P)/potassium (K)] applications to patches to create different scales of nutrient patchiness (patch size 2 × 2 m, 10 × 10 m, or whole-plot 50 × 50 m) in a large field experiment in intact African savanna. Within-patch fertilizer concentration and the total fertilizer load per plot were independently varied. We found that fertilization increased the leaf N and P concentrations of trees and grasses, resulting in elevated utilization by browsers and grazers. Herbivory off-take was particularly considerable at higher nutrient concentrations. Scale-dependent effects were weak. The net effect of fertilization and herbivory was that plants in fertilized areas tended to grow less and develop smaller rather than larger standing biomass compared to plants growing in areas that remained unfertilized. When all of these effects were considered together at the community (plot) level, herbivory completely eliminated the positive effects of fertilization on the plant community. While this was true for all scales of fertilization, grasses tended to profit more from coarse-grained fertilization and trees from fine-grained fertilization. We conclude that in herbivore-dominated communities, such as the African savanna, nutrient patchiness results in the herbivore community profiting rather more than the plant community, irrespective of the scale of patchiness. At the community level, the allometric scaling theory’s prediction of plant—and probably also animal—production does not hold or may even be reversed as a result of complex bi-trophic interactions.