The brain of the Malayan bear (Helarctos malayanus)

This little known ursid has a brain that shows very progressive encephalization and neocorticalization, reminiscent of the giant panda's. But, somewhat paradoxically, it is also characterized by proportionately large rhinencephalon and brain stem, and relatively small striatum. This quantitative study suggests a general contrast between Asian and American bears. It invites further comparative investigations of the brain in all ursiform mammals, as an especially promising material from combined evolutionary and neuroethological standpoints.     

Thermoregulation in Malayan sun bears (Helarctos malayanus) and its consequences for in situ conservation

Thermoregulation in Malayan sun bears is not fully understood. Therefore, in this study the effect of meteorological variables on both behavioural and autonomic thermoregulatory mechanisms in sun bears was examined in order to identify temperature thresholds for the activation of various thermoregulatory mechanisms. Infrared thermography was used to non‒invasively determine body surface temperature (T_S) distribution in relation to ambient temperature (T_A) and to determine the thermoneutral zone (TNZ) of sun bears. Thermographic measurements were performed on 10 adult sun bears at T_A between 5 °C and 30 °C in three European zoos. To assess behaviours that contribute to thermoregulation, nine adult sun bears were observed at T_A ranging from 5 °C to 34 °C by instantaneous scan sampling in 60 s intervals for a total of 787 h. Thermographic measurements revealed that the TNZ of sun bears lies between 24 °C and 28 °C and that heat is equally dissipated over the body surface. Behavioural data showed that behaviours related to thermoregulation occurred in advance of energetically costly autonomic mechanisms, and were highly correlated with T_A and solar radiation. While the temperature threshold for the onset of thermoregulatory behaviours below the TNZ lies around 15 °C, which is well below the lower critical temperature (T_LC) assessed by thermography, the onset for behaviours to prevent overheating occurred at 28 °C, which was closer to the estimated upper critical temperature (T_UC) of sun bears. These findings provide useful data on the thermal requirements of sun bears with respect to the species potential to cope with the effects of climate change and deforestation which are occurring in their natural range. Furthermore, these results may have important implications for the care and welfare of bears in captivity and should be taken into consideration, when designing and managing facilities.     

Longitudinal monitoring of fecal testosterone in male Malayan Sun bears (U. malayanus)

Fecal steroid monitoring was applied as a non‐invasive method to investigate testicular cycles and seasonality in the Malayan Sun bear (Ursus malayanus), an endangered ursid from South East Asia. Fecal testosterone was analyzed by radioimmunoassay in samples collected from male Sun bears (n=8) housed in zoological parks in North America and New Zealand, over periods of <27 months. Testosterone levels were often, but not exclusively, elevated during mating periods with peaks accompanying breeding behavior and copulation. There was a significant effect of age with older bears having clearly higher concentrations of fecal testosterone (P<0.001). Testosterone concentrations fluctuated throughout the year, with no significant effect of season (P>0.05). All bears did, however, share a common pattern of annual excretion that suggests a potential role for non‐photoperiodic seasonal influences on testicular cycles. Levels were generally lower early in the year with regular increases occurring at 3–4‐month intervals. Grouped data suggest an association between cycles of testosterone production in males and months of peak reproductive activity in captivity. Zoo Biol 0:1–15, 2005. © 2005 Wiley‐Liss, Inc.     

The primary structure of the hemoglobin of Malayan sun bear (Helarctos malayanus, Carnivora) and structural comparison to other hemoglobin sequences

The complete primary structure of the alpha- and beta-chains of the hemoglobin of Malayan Sun Bear (Helarctos malayanus) is presented. After cleavage of the heme-protein link and chain separation by RP-HPLC, amino-acid sequences were determined by Edman degradation in liquid- and gas-phase sequenators. An interesting result of this work is the demonstration that the hemoglobin of Malayan Sun Bear is identical to the hemoglobins of Polar Bear (Ursus maritimus) and Asiatic Black Bear (Ursus tibetanus). The paper gives an updated table of identical hemoglobin chains from different species. This paper may be considered as a compilation of work on the genetic relationship of Pandas.     

Fecal steroid analysis for monitoring reproduction in the sun bear (Helarctos malayanus)

Fecal steroid analyses were conducted on captive (n = 10) and free-ranging (n = 2) sun bears (Helarctos malayanus) in order to establish a noninvasive technique for monitoring endocrine profiles during the estrous cycle and pregnancy. Secondly, the effect of the contraceptive porcine zona pellucida protein (PZP) on reproductive function was studied. Finally, we investigated whether the sun bear, naturally living in the aseasonal tropical forests of Southeast Asia, is a seasonal breeder. Fecal samples were collected over periods of 7-48 months in captive untreated (n = 8) and PZP-treated (n = 2) female sun bears. In addition samples were collected over a period of 12 months from radio-collared free-ranging females (n = 2) in their natural habitat in Indonesian Borneo. Androgens, precursors of estrogens, were found to be reliable indicators of the follicular phase, whereas estrogens were found unsuitable. Pregnanediol assay was found to be a reliable indicator of luteal function. Results indicate that sun bears are polyestrous, nonseasonal breeders. Interestrus intervals in nonpregnant animals (n = 2), which were monitored for 27 months, were between 140 and 216 days. Luteal phases (89.6 +/- 3.7 days; n = 9) were preceded by androgen peaks of 15.2 +/- 1.0 days (n = 10). Hormonal profiles of two females treated with PZP indicated missing ovarian activity in one, and persistent follicular and luteal activity in another animal. However, extended periods of missing ovarian, and persistent follicular and luteal activity were also observed in other sun bears studied.     

Rediscovery of the sun bear (Helarctos malayanus)in Yingjiang County,Yunnan Province,China

DEAR EDITOR, The sun bear,Helarctos malayanus (Raffles,1821),is a forestdependent bear species distributed in tropical Southeast Asia.The species was previously reported from scattered localities in southwestern China,which is at the northeastern edge of its global range.Due to the scarcity of reliable recent records,some authorities cast doubt on the continued existence of sun bear in China.Here we present the rediscovery of this species in Yingjiang County,western Yunnan Province,China,near the international border with Myanmar's Kachin State.     

Analyzing the Distribution of Decay Constants in Pulse-Fluorimetry Using the Maximum Entropy Method

The maximum entropy method (MEM) is used to analyze time-resolved pulse-fluorescence spectrometry. The central problem in such analyses is the recovery of the distribution of exponentials describing the decay of the fluorescence (i.e., inverting the Laplace transform) which is, in turn, convolved by the shape of the excitation flash. MEM is shown to give high quality results from both computer-generated “noisy” data and experimental data from chemical and biological molecules.

The use of the Shannon-Jaynes entropy function is justified and both the theoretical and practical advantages of MEM are presented. The MEM results are easy to interpret and can help to overcome some experimental limitations. In particular MEM could be a powerful tool to analyze the heterogeneity of fluorescent emission of biological macromolecules which can be correlated with their conformational dynamics in solution.

     

Combining Experiments and Simulations Using the Maximum Entropy Principle

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.     

Frugivory in sun bears (Helarctos malayanus) is linked to El Niño-related fluctuations in fruiting phenology, East Kalimantan, Indonesia

Sun bear ( Helarctos malayanus ) frugivory and fruiting phenology was investigated in a lowland dipterocarp forest in East Kalimantan, Indonesia. Two mast fruiting events, both coinciding with El Niño/Southern Oscillation events, occurred 4 years apart, resulting in large fluctuations in fruit availability. Sun bear fruit availability decreased from 13 trees ha⁻¹ fruiting month⁻¹ during the mast fruiting to 1.6 trees ha⁻¹ fruiting month⁻¹ during the intermast period. Almost 100% of sun bear diet consisted of fruit during mast fruiting period, whereas sun bear diet was predominantly insectivorous during intermast periods. The majority of sun bear fruit trees displayed 'mast-fruiting' and 'supra-annual' fruiting patterns, indicating sporadic productivity. Sun bears fed on 115 fruit species covering 54 genera and 30 families, with Ficus (Moraceae) being the main fallback fruit. The families Moraceae, Burseraceae, and Myrtaceae contributed more than 50% to the sun bear fruit diet. Sun bear fruit feeding observations were unevenly distributed over forest types with more observations in high-dry forest type despite fewer fruiting events, possibly due to a side-effect of high insect abundance that causes bears to use these areas more intensively. The possible evolutionary pathways of sun bears in relation to the Sundaic environment are discussed.  © 2006 The Linnean Society of London, Biological Journal of the Linnean Society , 2006, 89 , 489–508.     

Maximum Entropy Principle Based Estimation of Performance Distribution in Queueing Theory

In related research on queuing systems, in order to determine the system state, there is a widespread practice to assume that the system is stable and that distributions of the customer arrival ratio and service ratio are known information. In this study, the queuing system is looked at as a black box without any assumptions on the distribution of the arrival and service ratios and only keeping the assumption on the stability of the queuing system. By applying the principle of maximum entropy, the performance distribution of queuing systems is derived from some easily accessible indexes, such as the capacity of the system, the mean number of customers in the system, and the mean utilization of the servers. Some special cases are modeled and their performance distributions are derived. Using the chi-square goodness of fit test, the accuracy and generality for practical purposes of the principle of maximum entropy approach is demonstrated.     

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.     

Maximum Likelihood Mapping of Quantitative Trait Loci Using Full-Sib Families

A maximum likelihood method is presented for the detection of quantitative trait loci (QTL) using flanking markers in full-sib families. This method incorporates a random component for common family effects due to additional QTL or the environment. Simulated data have been used to investigate this method. With a fixed total number of full sibs power of detection decreased substantially with decreasing family size. Increasing the number of alleles at the marker loci (i.e., polymorphism information content) and decreasing the interval size about the QTL increased power. Flanking markers were more powerful than single markers. In testing for a linked QTL the test must be made against a model which allows for between family variation (i.e., including an unlinked QTL or a between family variance component) or the test statistic may be grossly inflated. Mean parameter estimates were close to the simulated values in all situations when fitting the full model (including a linked QTL and common family effect). If the common family component was omitted the QTL effect was overestimated in data in which additional genetic variance was simulated and when compared with an unlinked QTL model there was reduced power. The test statistic curves, reflecting the likelihood of the QTL at each position along the chromosome, have discontinuities at the markers caused by adjacent pairs of markers providing different amounts of information. This must be accounted for when using flanking markers to search for a QTL in an outbred population.     

Predictive Modeling of Coral Disease Distribution within a Reef System

Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1) coral diseases show distinct associations with multiple environmental factors, 2) incorporating interactions (synergistic collinearities) among environmental variables is important when predicting coral disease spatial patterns, and 3) modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value) will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA), Porites tissue loss (PorTL), Porites trematodiasis (PorTrem), and Montipora white syndrome (MWS), and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT) within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response), led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to particular environmental conditions. Predictive statistical modeling can help to increase our understanding of coral disease ecology worldwide.     

应用最大熵原理通过酶控制通量预测突变体的通量分布

在代谢工程和系统生物学领域, 计算机模拟比以往更为有效的应用于生物过程的分析和优化。胞内代谢通量可以用代谢通量分析和基元模式分析来估算。由于测定数据的不足和误差, 以及基元途径的冗余, 经常很难得到准确的代谢通量分布数据。本研究提出一种基于最大熵原理的算法来计算基元模式系数。欠定和不确定条件下, 通过胞外代谢通量数据估算胞内代谢通量分布。为了检验算法的可行性, 对杂交瘤细胞、枯草芽孢杆菌和大肠杆菌的胞内代谢通量分布做了估算。本研究提出的基于最大熵原理的优化算法避免了对细胞状态的生理学假设。与其他目标函数相比, 可以更为可靠和可行的估算胞内代谢通量分布。     

离散性状QTL区间定位的最大似然方法

采用最大似然区间定位法对阈模型与一般线性模型的QTL定位效率进行了比较,并对影响离散性状QTL检测效率的主要因素（QTL效应、性状的遗传力和表型发生率）进行了模拟研究,实验设计为多个家系的女儿设计．资源群体大小为500头。研究结果表明：在QTL参数估计及检验功效方面,阈模型方法具有较大的优势,对离散性状QTL定位的效率明显高于LM（Linear Model）方法,定位的准确性也较高。另外,性状遗传力、QTL效应的大小和性状表型发生率对QTL定位的准确度也有直接的影响,随着性状遗传力和表型发生率的提高,随着QTL效应的增大,QTL定位的效率也进一步提高。     

Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features

Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755–0.771) to 0.769 (95% CI: 0.761–0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.     

基于广义线性模型和最大熵模型的黑龙江省林火空间分布模拟

林火分布模型是在较大区域上描述林火空间分布的强有力工具,并可以确定影响林火分布的控制因子.本研究基于黑龙江省1996-2006年的历史火烧记录数据,分别采用广义线性模型和最大熵模型分析了地形、人类活动和土地覆被类型等环境控制因子对黑龙江省林火空间分布的影响,并比较了模型预测精度、评价环境变量重要性及预测火点概率分布图等.结果表明:两个模型的预测精度达中等水平,而最大熵模型的预测精度要略高于广义线性模型.总体而言,与人类活动相关的变量是林火分布模型最佳的环境变量,地形变量次之.尽管两个模型在预测精度和环境变量重要性方面都有很大的相似性,但最大熵模型产生的火点概率图空间格局与广义线性模型产生的明显不同.本研究说明,为了更加精确地确定森林火灾发生的热点地区,应该采用不同模型进行比较,或者有选择性地进行组合以产生综合的预测结果,从而为森林防火工作提供更加合理高效的建议.