Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

<p>Abstract</p> <p>Background</p> <p>Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy <it>C</it>-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function.</p> <p>Results</p> <p>Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops.</p> <p>Conclusions</p> <p>HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.</p

Fee Arrangements and Fee Shifting: Lessons From the Experience in Ontario

About one-third of oestrogen receptor alpha-positive breast cancer patients treated with tamoxifen relapse. Here we identify the nuclear receptor retinoic acid receptor alpha as a marker of tamoxifen resistance. Using quantitative mass spectrometry-based proteomics, we show that retinoic acid receptor alpha protein networks and levels differ in a tamoxifen-sensitive (MCF7) and a tamoxifen-resistant (LCC2) cell line. High intratumoural retinoic acid receptor alpha protein levels also correlate with reduced relapse-free survival in oestrogen receptor alpha-positive breast cancer patients treated with adjuvant tamoxifen solely. A similar retinoic acid receptor alpha expression pattern is seen in a comparable independent patient cohort. An oestrogen receptor alpha and retinoic acid receptor alpha ligand screening reveals that tamoxifen-resistant LCC2 cells have increased sensitivity to retinoic acid receptor alpha ligands and are less sensitive to oestrogen receptor alpha ligands compared with MCF7 cells. Our data indicate that retinoic acid receptor alpha may be a novel therapeutic target and a predictive factor for oestrogen receptor alpha-positive breast cancer patients treated with adjuvant tamoxifen

Child and adolescent psychiatric patients and later criminality

<p>Abstract</p> <p>Background</p> <p>Sweden has an extensive child and adolescent psychiatric (CAP) research tradition in which longitudinal methods are used to study juvenile delinquency. Up to the 1980s, results from descriptions and follow-ups of cohorts of CAP patients showed that children's behavioural disturbances or disorders and school problems, together with dysfunctional family situations, were the main reasons for families, children, and youth to seek help from CAP units. Such factors were also related to registered criminality and registered alcohol and drug abuse in former CAP patients as adults. This study investigated the risk for patients treated 1975–1990 to be registered as criminals until the end of 2003.</p> <p>Methods</p> <p>A regional sample of 1,400 former CAP patients, whose treatment occurred between 1975 and 1990, was followed to 2003, using database-record links to the Register of Persons Convicted of Offences at the National Council for Crime Prevention (NCCP).</p> <p>Results</p> <p>Every third CAP patient treated between 1975 and 1990 (every second man and every fifth woman) had entered the Register of Persons Convicted of Offences during the observation period, which is a significantly higher rate than the general population.</p> <p>Conclusion</p> <p>Results were compared to published results for CAP patients who were treated between 1953 and 1955 and followed over 20 years. Compared to the group of CAP patients from the 1950s, the results indicate that the risk for boys to enter the register for criminality has doubled and for girls, the risk seems to have increased sevenfold. The reasons for this change are discussed. Although hypothetical and perhaps speculative this higher risk of later criminality may be the result of lack of social control due to (1) rising consumption of alcohol, (2) changes in organisation of child social welfare work, (3) the school system, and (4) CAP methods that were implemented since 1970.</p

Intrauterine Growth Retarded Progeny of Pregnant Sows Fed High Protein:Low Carbohydrate Diet Is Related to Metabolic Energy Deficit

High and low protein diets fed to pregnant adolescent sows led to intrauterine growth retardation (IUGR). To explore underlying mechanisms, sow plasma metabolite and hormone concentrations were analyzed during different pregnancy stages and correlated with litter weight (LW) at birth, sow body weight and back fat thickness. Sows were fed diets with low (6.5%, LP), adequate (12.1%, AP), and high (30%, HP) protein levels, made isoenergetic by adjusted carbohydrate content. At −5, 24, 66, and 108 days post coitum (dpc) fasted blood was collected. At 92 dpc, diurnal metabolic profiles were determined. Fasted serum urea and plasma glucagon were higher due to the HP diet. High density lipoprotein cholesterol (HDLC), %HDLC and cortisol were reduced in HP compared with AP sows. Lowest concentrations were observed for serum urea and protein, plasma insulin-like growth factor-I, low density lipoprotein cholesterol, and progesterone in LP compared with AP and HP sows. Fasted plasma glucose, insulin and leptin concentrations were unchanged. Diurnal metabolic profiles showed lower glucose in HP sows whereas non-esterified fatty acids (NEFA) concentrations were higher in HP compared with AP and LP sows. In HP and LP sows, urea concentrations were 300% and 60% of AP sows, respectively. Plasma total cholesterol was higher in LP than in AP and HP sows. In AP sows, LW correlated positively with insulin and insulin/glucose and negatively with glucagon/insulin at 66 dpc, whereas in HP sows LW associated positively with NEFA. In conclusion, IUGR in sows fed high protein∶low carbohydrate diet was probably due to glucose and energy deficit whereas in sows with low protein∶high carbohydrate diet it was possibly a response to a deficit of indispensable amino acids which impaired lipoprotein metabolism and favored maternal lipid disposal

Patterns of Diversity in Soft-Bodied Meiofauna: Dispersal Ability and Body Size Matter

Background: Biogeographical and macroecological principles are derived from patterns of distribution in large organisms, whereas microscopic ones have often been considered uninteresting, because of their supposed wide distribution. Here, after reporting the results of an intensive faunistic survey of marine microscopic animals (meiofauna) in Northern Sardinia, we test for the effect of body size, dispersal ability, and habitat features on the patterns of distribution of several groups.Methodology/Principal Findings: As a dataset we use the results of a workshop held at La Maddalena (Sardinia, Italy) in September 2010, aimed at studying selected taxa of soft-bodied meiofauna (Acoela, Annelida, Gastrotricha, Nemertodermatida, Platyhelminthes and Rotifera), in conjunction with data on the same taxa obtained during a previous workshop hosted at Tjärnö (Western Sweden) in September 2007. Using linear mixed effects models and model averaging while accounting for sampling bias and potential pseudoreplication, we found evidence that: (1) meiofaunal groups with more restricted distribution are the ones with low dispersal potential; (2) meiofaunal groups with higher probability of finding new species for science are the ones with low dispersal potential; (3) the proportion of the global species pool of each meiofaunal group present in each area at the regional scale is negatively related to body size, and positively related to their occurrence in the endobenthic habitat.Conclusion/Significance: Our macroecological analysis of meiofauna, in the framework of the ubiquity hypothesis for microscopic organisms, indicates that not only body size but mostly dispersal ability and also occurrence in the endobenthic habitat are important correlates of diversity for these understudied animals, with different importance at different spatial scales. Furthermore, since the Western Mediterranean is one of the best-studied areas in the world, the large number of undescribed species (37%) highlights that the census of marine meiofauna is still very far from being complete

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Hierarchical multivariate regression-based sensitivity analysis reveals complex parameter interaction patterns in dynamic models

AbstractDynamic models of biological systems often possess complex and multivariate mappings between input parameters and output state variables, posing challenges for comprehensive sensitivity analysis across the biologically relevant parameter space. In particular, more efficient and robust ways to obtain a solid understanding of how the sensitivity to each parameter depends on the values of the other parameters are sorely needed.We report a new methodology for global sensitivity analysis based on Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR)-based approximations (metamodelling) of the input–output mappings of dynamic models, which we expect to be generic, efficient and robust, even for systems with highly nonlinear input–output relationships. The two-step HC-PLSR metamodelling automatically separates the observations (here corresponding to different combinations of input parameter values) into groups based on the dynamic model behaviour, then analyses each group separately with Partial Least Squares Regression (PLSR). This produces one global regression model comprising all observations, as well as regional regression models within each group, where the regression coefficients can be used as sensitivity measures. Thereby a more accurate description of complex interactions between inputs to the dynamic model can be revealed through analysis of how a certain level of one input parameter affects the model sensitivity to other inputs. We illustrate the usefulness of the HC-PLSR approach on a dynamic model of a mouse heart muscle cell, and demonstrate how it reveals interaction patterns of probable biological significance not easily identifiable by a global regression-based sensitivity analysis alone.Applied for sensitivity analysis of a complex, high-dimensional dynamic model of the mouse heart muscle cell, several interactions between input parameters were identified by the two-step HC-PLSR analysis that could not be detected in the single-step global analysis. Hence, our approach has the potential to reveal new biological insight through the identification of complex parameter interaction patterns. The HC-PLSR metamodel complexity can be adjusted according to the nonlinear complexity of the input–output mapping of the analysed dynamic model through adjustment of the number of regional regression models included. This facilitates sensitivity analysis of dynamic models of varying complexities