Honey bee colony collapse disorder is possibly caused by a dietary pyrethrum deficiency

Industrialized farming relies on bee keepers transporting hives to the vicinity of large areas of mono-crops for crop pollination. Hives are typically moved multiple times per growing season to satisfy the pollination need. A phenomenon wherein colonies of honey bees collapse in large numbers has been threatening crops in North America. Honey bees are hosts to at least two pathogenic mites; Varroa destructor and Acarapis woodi (a tracheal mite). Pyrethrums are a group of flowering plants which include Chrysanthemum coccineum, Chrysanthemum cinerariifolium, Chrysanthemum marschallii, and related species. These plants produce potent insecticides, also named pyrethrums, which are powerful mite toxins. We believe that a honey bee dietary deficiency of pyrethrums and other micro-nutrients from pyrethrum producing plants allows parasitic mites to either kill the honey bees directly or reduce honey bee resistance to other pathogens. Intermittent feeding of honey bees on pyrethrum producing plants might reverse or prevent colony collapse disorder.     

蜂群的分蜂行为及其影响因素

分蜂行为是蜂群重要的生物学特性,是一种特殊方式的繁殖。概述了3型蜂的发育、蜂群分蜂前的准备、分蜂的过程及影响蜂群分蜂的因素等蜜蜂生物学知识。还简要分析了环境污染对分蜂群的影响。     

Risk stratification of spontaneous bacterial peritonitis in cirrhosis with ascites based on classification and regression tree analysis

Risk stratification for spontaneous bacterial peritonitis (SBP) in patients with cirrhosis and ascites helps guide care. Existing prediction models, such as end-stage liver disease (MELD) score, are accurate but controversial in clinical practice. We developed and validated a practical user-friendly bedside tool for SBP risk stratification of patients with cirrhosis and ascites. Using classification and regression tree (CART) analysis, a model was developed for prediction of SBP in cirrhosis with ascites. The CART model was derived on data collected from 676 patients admitted from January 2007 to December 2009 retrospectively, and then was prospectively tested in another independent 198 inpatients between January 2010 and December 2010. The accuracy of CART model was evaluated using the area under the receiver operating characteristic curve. The performance of the model was further validated by comparing its predictive accuracy with that of the MELD score. Furthermore, the model was used to stratify SBP among patients with MELD scores under 15. CART analysis identified four variables for prediction of SBP: creatinine, total bilirubin, prothrombin time and white blood cell count, and three risk groups: low (2.0%), intermediate (27.5–33.3%) and high (60.6–86.4%) risk. The accuracy of CART model (0.881) exceeded that of MELD (0.791). Subjects in the intermediate risk and high risk groups had 22.21-fold (95% confident interval (CI), 9.98–49.45) and 173.50-fold (95% CI, 77.68–634.33) increased risk of SBP, respectively, comparing with the low risk group. Similar results were found when this risk stratification was applied to the validation cohort. Cirrhotic patients with ascites at low, intermediate, and high risk for SBP can be easily identified using CART model, which provides clinicians with a validated, practical bedside tool for SBP risk stratification.     

Lack of evidence for an association between Iridovirus and colony collapse disorder

Colony collapse disorder (CCD) is characterized by the unexplained losses of large numbers of adult worker bees (Apis mellifera) from apparently healthy colonies. Although infections, toxins, and other stressors have been associated with the onset of CCD, the pathogenesis of this disorder remains obscure. Recently, a proteomics study implicated a double-stranded DNA virus, invertebrate iridescent virus (Family Iridoviridae) along with a microsporidium (Nosema sp.) as the cause of CCD. We tested the validity of this relationship using two independent methods: (i) we surveyed healthy and CCD colonies from the United States and Israel for the presence of members of the Iridovirus genus and (ii) we reanalyzed metagenomics data previously generated from RNA pools of CCD colonies for the presence of Iridovirus-like sequences. Neither analysis revealed any evidence to suggest the presence of an Iridovirus in healthy or CCD colonies.     

多元回归树与双向指示种分析在群落分类中的应用比较

采用两种群落分类方法——以环境梯度为分类依据的多元回归树(MRT)和多年来广泛应用的双向指示种分析(TWINSPAN), 对吕梁山南段森林群落进行了数量分类, 同时依据植物群落分类和命名原则, 对分类后的各群系进行命名, 并用吻合系数比较两种分类结果的吻合程度, 分析MRT的优劣势, 为以后选择合适的植物群落数量分类方法提供参考。结果表明: (1) MRT将41个森林样方分为4个群系, 分别为侧柏(Platycladus orientalis)群系、青榨槭(Acer davidii)群系、辽东栎(Quercus wutaishanica)群系、柿(Diospyros kaki)群系, 群系类型与TWINSPAN的分类结果相同; (2)根据群系的样方组成, 两种分类结果的吻合度较高, 吻合系数达80.5%; (3)与TWINSPAN相比, MRT同时以物种和环境信息为依据, 对有过渡性质的样方划分更为可靠。因此, 单纯从植被分类的角度来看, 尽管TWINSPAN的分类结果更客观, 但当TWINSPAN分类遇到困难时, 如在划分大样地连续样方或具有过渡性质样方时, MRT更有优势。     

“Entombed Pollen”: A new condition in honey bee colonies associated with increased risk of colony mortality

Here we describe a new phenomenon, entombed pollen, which is highly associated with increased colony mortality. Entombed pollen is sunken, capped cells amidst “normal”, uncapped cells of stored pollen, and some of the pollen contained within these cells is brick red in color. There appears to be a lack of microbial agents in the pollen, and larvae and adult bees do not have an increased rate of mortality when they are fed diets supplemented with entombed pollen in vitro, suggesting that the pollen itself is not directly responsible for increased colony mortality. However, the increased incidence of entombed pollen in reused wax comb suggests that there is a transmittable factor common to the phenomenon and colony mortality. In addition, there were elevated pesticide levels, notably of the fungicide chlorothalonil, in entombed pollen. Additional studies are needed to determine if there is a causal relationship between entombed pollen, chemical residues, and colony mortality.     

广义模型及分类回归树在物种分布模拟中的应用与比较

比较3个应用较广的模拟物种地理分布模型:广义线性模型(GLM)、广义加法模型(GAM)与分类回归树(CART)对中国树种地理分布模拟的优劣,以提出更为合适的模拟物种地理分布模型,并用于预测气候变化对物种地理分布的影响。3个模型对中国15种树种地理分布的模拟研究表明:除对油松、辽东栎分布的模拟精度稍差外,对其余树种分布的模拟精度均较高,其中以GAM模型最好。结合地理信息系统(GIS),比较分析了这3个模型对青冈、木荷、红松和油松4种树种的地理分布模拟效果,结果亦表明:这3个模型均能很好模拟青冈和木荷的地理分布,而GLM模型对红松分布的模拟结果不太理想,3个模型对油松分布的模拟结果均不甚理想,其中以GLM模型最差。基于3个模型对未来气候变化下青冈与蒙古栎地理分布的预测表明:GLM模型与GAM模型对青冈分布的预测结果较为接近,青冈在未来气候变化情景下向西和向北扩展,而CART模型预测青冈在未来气候变化情景下除有向西、向北扩展趋势外,广东和广西南部的青冈分布区将消失;3个模型均预测蒙古栎在未来气候变化情景下向西扩展,扩展面积的大小为:模型的模拟面积>模型>模型。     

Pathway analysis using random forests classification and regression

MOTIVATION: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. RESULTS: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. AVAILABILITY: Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.     

Heritable risk factors associated with language impairments

There is a strong genetic contribution to children's language and literacy impairments. The aim of this study was to determine which aspects of the phenotype are familial by comparing 34 parents of probands with language/literacy impairments and 33 parents of typically developing probands. The parents responded to questionnaires regarding previous history for language/reading impairment and participated in psychometric testing. The psychometric test battery consisted of tests assessing non-verbal IQ, short-term memory, articulation, receptive grammar, reading abilities and spelling. Self-report measures demonstrated a higher prevalence of language and literacy impairments in parents of affected probands (32%) compared with parents of unaffected probands (6%). The two groups of parents differed significantly in their performance on the non-word repetition, oromotor and digit span tasks. Non-word repetition gave the best discrimination between the parent groups even when the data from the parents who actually were impaired as ascertained by direct testing or self-report were removed from the analyses. This suggests that non-word repetition serves as a marker of a family risk for language impairment. The paper concludes with a discussion of issues associated with ascertainment of specific language impairment (SLI).     

DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

In the medical domain, it is very significant to develop a rule-based classification model. This is because it has the ability to produce a comprehensible and understandable model that accounts for the predictions. Moreover, it is desirable to know not only the classification decisions but also what leads to these decisions. In this paper, we propose a novel dynamic quantitative rule-based classification model, namely DQB, which integrates quantitative association rule mining and the Artificial Bee Colony (ABC) algorithm to provide users with more convenience in terms of understandability and interpretability via an accurate class quantitative association rule-based classifier model. As far as we know, this is the first attempt to apply the ABC algorithm in mining for quantitative rule-based classifier models. In addition, this is the first attempt to use quantitative rule-based classification models for classifying microarray gene expression profiles. Also, in this research we developed a new dynamic local search strategy named DLS, which is improved the local search for artificial bee colony (ABC) algorithm. The performance of the proposed model has been compared with well-known quantitative-based classification methods and bio-inspired meta-heuristic classification algorithms, using six gene expression profiles for binary and multi-class cancer datasets. From the results, it can be concludes that a considerable increase in classification accuracy is obtained for the DQB when compared to other available algorithms in the literature, and it is able to provide an interpretable model for biologists. This confirms the significance of the proposed algorithm in the constructing a classifier rule-based model, and accordingly proofs that these rules obtain a highly qualified and meaningful knowledge extracted from the training set, where all subset of quantitive rules report close to 100% classification accuracy with a minimum number of genes. It is remarkable that apparently (to the best of our knowledge) several new genes were discovered that have not been seen in any past studies. For the applicability demand, based on the results acqured from microarray gene expression analysis, we can conclude that DQB can be adopted in a different real world applications with some modifications.     

Infectious complications associated with renal transplantation: an analysis of risk factors.

To assess the multiple risk factors reported to be associated with onset of serious bacterial, fungal, viral, and protozoal infections in renal allograft recipients, a retrospective study of all renal transplantations performed at Yale-New Haven Medical Center from the inception of the transplantation program in December, 1967, to December, 1975, was undertaken. Ninety-six renal allograft transplants in 85 patients were available for evaluation during this study period. Renal allograft recipients were evaluated for incidence of infection from time of transplantation until transplant nephrectomy, death, or January 1, 1976. All infections were characterized by type of infection, organism, site, and time of onset post-transplantation. Recipients with infections were also evaluated for their donor type, living-related or cadaveric, age at time of transplantation, granulocytopenia, corticosteroid therapy, and rejection episodes. There were 215 infections, 92 of which were defined as serious, in 78 of the 96 renal allograft recipients. Eighteen renal allograft recipients had no infections. Granulocytopenia, but not rejection, correlated with serious infections at some time in the patient''s course. However, no significant temporal relationship between serious infections and episodes of granulocytopenia or rejection could be established. Mortality rate and incidence of serious infection was higher in the group receiving high dose corticosteroid therapy compared with the group receiving lower doses of corticosteroids. The mortality rate in these 85 transplant recipients was 33%. Seventy-four percent of these deaths were directly related to infection (24% of 85 patients).     

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ₁-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ₂-norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.     

Substrate preference of transglutaminase 2 revealed by logistic regression analysis and intrinsic disorder examination

Tissue transglutaminase (TG2) catalyzes the Ca²⁺-dependent posttranslational modification of proteins via formation of isopeptide bonds between their glutamine and lysine residues. Although substrate specificity of TG2 has been studied repeatedly at the sequence level, no clear consensus sequences have been determined so far. With the use of the extensive structural information on TG2 substrate proteins listed in TRANSDAB Wiki database†, a slight preference of TG2 for glutamine and lysine residues situated in turns could be observed. When the spatial environment of the favored glutamine and lysine residues was analyzed with logistic regression, the presence of specific amino acid patterns was identified. By using the occurrence of the predictor amino acids as selection criteria, several polypeptides were predicted and later identified as novel in vitro substrates for TG2. By studying the sequence of TG2 substrate proteins lacking available crystal structure, the strong favorable influence on substrate selection of the presence of substrate glutamine and lysine residues in intrinsically disordered regions could also be revealed. The collected structural data have provided novel understanding of how this versatile enzyme selects its substrates in various cell compartments and tissues.     

Automated detection of connective tissue by tissue counter analysis and classification and regression trees.

OBJECTIVE: To evaluate the feasibility of the CART (Classification and Regression Tree) procedure for the recognition of microscopic structures in tissue counter analysis. METHODS: Digital microscopic images of H & E; stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements) and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets. RESULTS: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 and r=0.94, respectively; p<0.001). CONCLUSIONS: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.     

A potential link among biogenic amines-based pesticides,learning and memory,and colony collapse disorder: A unique hypothesis

Pesticides are substances that have been widely used throughout the world to kill, repel, or control organisms such as certain forms of plants or animals considered as pests. Depending on their type, dose, and persistence in the environment, they can have impact even on non-target species such as beneficial insects (honeybees) in different ways, including reduction in their survival rate and interference with their reproduction process. Honeybee Apis mellifera is a major pollinator and has substantial economical and ecological values. Colony collapse disorder (CCD) is a mysterious phenomenon in which adult honeybee workers suddenly abandon from their hives, leaving behind food, brood, and queen. It is lately drawing a lot of attention due to pollination crisis as well as global agriculture and medical demands. If the problem of CCD is not resolved soon enough, this could have a major impact on food industry affecting world’s economy a big time. Causes of CCD are not known. In this overview, I discuss CCD, biogenic amines-based-pesticides (neonicotinoids and formamidines), and their disruptive effects on biogenic amine signaling causing olfactory dysfunction in honeybees. According to my hypothesis, chronic exposure of biogenic amines-based-pesticides to honeybee foragers in hives and agricultural fields can disrupt neural cholinergic and octopaminergic signaling. Abnormality in biogenic amines-mediated neuronal signaling impairs their olfactory learning and memory, therefore foragers do not return to their hive – a possible cause of CCD. This overview is an attempt to discuss a hypothetical link among biogenic amines-based pesticides, olfactory learning and memory, and CCD.     

Multiple regression analysis on factors associated with nasal carriage of antibiotic-resistant Staphylococcus aureus and it's representation as a graphical model

The results of a multiple regression analysis on the nasal carriage of antibiotic-resistant Staphylococcus aureus and the four major patient parameters, age, length of stay in hospital, sex and antibiotic use, is reported. The multiple correlation coefficient of the regression analysis was high and all the regression coefficients of the independent variables were statistically significant, confirming the validity of the analysis. Further, an analysis on the four patient parameters confirmed that the parameters are not correlated, hence the possibility of a bias being introduced into the regression model through interactions between the parameters is excluded. The resulting regression model was then studied and an application given with respect to an infection.     

Metabolite profile analysis: from raw data to regression and classification

Successful metabolic profile analysis will aid in the fundamental understanding of physiology. Here, we present a possible analysis workflow. Initially, the procedure to transform raw data into a data matrix containing relative metabolite levels for each sample is described. Given that, because of experimental issues in the technical equipment, the levels of some metabolites cannot be universally determined or that different experiments need to be compared, missing value estimation and normalization are presented as helpful preprocessing steps. Regression methods are presented in this review as tools to relate metabolite levels with other physiological properties like biomass and gene expression. As the number of measured metabolites often exceeds the number of samples, dimensionality reduction methods are required. Two of these methods are discussed in detail in this review. Throughout this article, practical examples illustrating the application of the aforementioned methods are given. We focus on the uncovering the relationship between metabolism and growth-related properties.     

Identification of factors associated with risk of fall using a force platform and power spectrum analysis technique

The purpose of this retrospective study was to investigate some parameters of neuromuscular performance of the lower limbs in a population cross-section and their relationship to the risk of falls, using a force platform (FP). Individuals from the Lower Franconia population were invited by public advertisement. Out of a total of 1720 invited subjects 50-90 years of age, the successful completion of all tests were achieved by 807 women, age 66.4±9.3, and 442 men, age 64.0±9.2. A novel FP measured the time series of vertical forces over 10 s during 3 kinds of tests: tandem stand with eyes closed, knee bends, and chair rise. Proprietary software captured the peak force and calculated the power density distribution (PSD), intended to characterize balance and power through the FP. Grip strength as a common geriatric force test was dynamometrically measured for comparison. The parameters were related to the number of falls in the past 12 months in both genders. Mean PSD showed little age dependency and was not related to falls in tandem stance. Peak forces and power over 10 s knee bends showed a larger age-related decrease in men than in women and these parameters were related to falls (p<0.001), whereas they were not related to falls in the chair rise test. Chair rise time and grip strength was related to falls in women (p<0.01). The PSD obtained from the tandem test with eyes closed did not provide a sensitive parameter associated with falls. Knee bends may be a meaningful FP screening test that justifies further studies of physical performance related to the risk of falls, whereas chair rise and grip measurements provided inferior information in this study.