世居及移居高原青少年最大有氧能力的特征

本文报道海拔３４１７ｍ和４２８０ｍ地区世居藏族和移居汉族青少年运动状态下心肺功能的对比研究。结果显示：３４１７ｍ和４２８０ｍ世居藏族的最大氧耗量、无氧阈值及最大心输出量都明显大于汉族,血氧饱和度（Ｓａｏ２）随运动负荷的增加而降低。海拔３４１７ｍ藏、汉族的△Ｓａｏ２分别为７．４６％和１０．０３％,４２８０ｍ处为８．５７％和１３．７５％,最大心率随海拔升高而下降。研究提示,藏族青少年有较高的最大有氧能力,反映了他们对低氧环境的适应优势。     

The phylogeny of the ant tribe Formicini (Hymenoptera: Formicidae) with the description of a new genus

Abstract. The holarctic ant tribe Formicini is revised, the new genus Bajcaridris described, and possible phylogenetic relationships are discussed. The subgenus Iberoformica is synonymized with Formica. A synopsis, diagnosis and keys to the genera are provided.     

Homozygosity for a missense mutation in the 67 kDa isoform of glutamate decarboxylase in a family with autosomal recessive spastic cerebral palsy: parallels with Stiff-Person Syndrome and other movement disorders

Background  

Cerebral palsy (CP) is an heterogeneous group of neurological disorders of movement and/or posture, with an estimated incidence of 1 in 1000 live births. Non-progressive forms of symmetrical, spastic CP have been identified, which show a Mendelian autosomal recessive pattern of inheritance. We recently described the mapping of a recessive spastic CP locus to a 5 cM chromosomal region located at 2q24-31.1, in rare consanguineous families.     

Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006; Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct (http://www.ebi.ac.uk/intact/; Aranda et al., 2010), The Arabidopsis Information Resource (TAIR; http://www.Arabidopsis.org/; Poole, 2007), and BioGRID (http://www.thebiogrid.org/; Breitkreutz et al., 2008). These databases curate experimentally established interactions available from the peer-reviewed literature (as opposed to predicted interactions, which will be discussed below). Each repository takes its own approach to the capture, storage, and representation of protein interaction data. TAIR focuses on Arabidopsis thaliana protein–protein interaction data exclusively; BioGRID currently focuses on the plant species Arabidopsis and rice (Oryza sativa), while IntAct attempts to capture protein interaction data from any plant species. Unlike the other repositories, IntAct follows a deep curation strategy that captures detailed experimental and biophysical details, such as binding regions and subcellular locations of interactions using controlled vocabularies (Aranda et al., 2010). While the majority of plant interaction data held by IntAct concern protein–protein interaction data in Arabidopsis, there is a small but growing content of interaction data relating to protein–DNA, protein–RNA, and protein–small molecule interactions, as well as interaction data from other plant species.Using the IntAct Arabidopsis data set as an example, we outline how the accumulating knowledge captured in these repositories can be used to further our understanding of the plant proteome. We compare the characteristics of predicted interactomes with the IntAct protein–protein interaction data set, which consists entirely of experimentally measured protein interactions, to gauge the predictive accuracy of these studies. Finally, we show how the IntAct data set can be used together with a recently developed Divide and Conquer k-Nearest Neighbors Method (DC-kNN; K. Lee et al., 2008) to predict the subcellular locations for most Arabidopsis proteins. This data set predicts high confidence subcellular locations for many unannotated Arabidopsis proteins and should act as a useful resource for future studies of protein function. Although this article focuses on the IntAct Arabidopsis protein–protein interaction data set, readers are also encouraged to explore the resources offered by our colleagues at TAIR and BioGRID.Each database employs its own system to report molecular interactions, as represented in the referenced source publications, and each avoids making judgments on interaction reliability or whether two participants in a complex have a direct interaction. Thus, the user should carefully filter these data sets for their specific purpose based on the full annotation of the data sets. In particular, the user should consider the experimental methods and independent observation of the same interaction in different publications when assessing the reliability and type of interaction of the proteins (e.g., direct or indirect). Confidence scoring schemes for interaction data are discussed widely in the literature (Yu and Finley, 2009).     

Sex differences in the rate of fatigue development and recovery

Background

Many musculoskeltal injuries in the workplace have been attributed to the repetitive loading of muscle and soft tissues. It is not disputed that muscular fatigue is a risk factor for musculoskeltal injury, however the disparity between gender with respect to muscular fatigability and rate of recovery is not well understood. Current health and safety guidelines do not account for sex differences in fatiguability and may be predisposing one gender to greater risk. The purpose of this study was to quantify the sex differences in fatigue development and recovery rate of lower and upper body musculature after repeated bouts of sustained isometric contractions.

Methods

Twenty-seven healthy males (n = 12) and females (n = 15) underwent bilateral localized fatigue of either the knee extensors (male: n = 8; female: n = 8), elbow flexors (male: n = 8; female: n = 10), or both muscle groups. The fatigue protocol consisted of ten 30-second sub-maximal isometric contractions. The changes in maximum voluntary contraction (MVC), electrically evoked twitches, and motor unit activation (MUA) were assessed along with the ability to control the sustained contractions (SLP) during the fatigue protocol using a mixed four-factor repeated measures ANOVA (gender × side × muscle × time) design with significance set at p < 0.05.

Results

There was a significant loss of MVC, MUA, and evoked twitch amplitude from pre- to post-fatigue in both the arms and legs. Males had greater relative loss of isometric force, a higher rate of fatigue development, and were less capable of maintaining the fatiguing contractions in the legs when compared to the females.

Conclusion

The nature of the induced fatigue was a combination of central and peripheral fatigue that did not fully recover over a 45-minute period. The results appear to reflect sex differences that are peripheral, and partially support the muscle mass hypothesis for explaining differences in muscular fatigue.

     

Regulation of the endosomal SNARE protein syntaxin 7 by colony-stimulating factor 1 in macrophages

Colony-stimulating factor 1 (CSF-1) is the main growth factor controlling the development of macrophages from myeloid progenitor cells. However, CSF-1 also regulates some of the key effector functions of macrophages (e.g., phagocytosis and cytokine secretion). The endosomal SNARE protein syntaxin 7 (Stx7) regulates vesicle trafficking events involved in phagocytosis and cytokine secretion. Therefore, we investigated the ability of CSF-1 to regulate Stx7. CSF-1 upregulated Stx7 expression in primary mouse macrophages; it also upregulated expression of its SNARE partners Vti1b and VAMP8 but not Stx8. Additionally, CSF-1 induced the rapid serine phosphorylation of Stx7 and enhanced its binding to Vti1b, Stx8, and VAMP8. Bioinformatics analysis and results from experiments with kinase inhibitors suggested the CSF-1-induced phosphorylation of Stx7 was mediated by protein kinase C and Akt in response to phosphatidylinositol 3-kinase activation. Based on mutagenesis studies, CSF-1 appeared to increase the binding of Stx7 to its SNARE partners by inducing the phosphorylation of serine residues in the Habc domain and/or “linker” region of Stx7. Thus, CSF-1 is a key regulator of Stx7 expression and function in macrophages. Furthermore, the effects of CSF-1 on Stx7 may provide a mechanism for the regulation of macrophage effector functions by CSF-1.