Recognizing novel chemicals/drugs for anatomical therapeutic chemical classes with a heat diffusion algorithm

Drug Anatomical Therapeutic Chemical (ATC) classification system is a widely used and accepted drug classification system. It is recommended and maintained by World Health Organization (WHO). Each drug in this system is assigned one or more ATC codes, indicating which classes it belongs to in each of five levels. Given a chemical/drug, correct identification of its ATC codes in such system can be helpful to understand its therapeutic effects. Several computational methods have been proposed to identify the first level ATC classes for any drug. Most of them built multi-label classifiers in this regard. One previous study proposed a quite different scheme, which contained two network methods, based on shortest path (SP) and random walk with restart (RWR) algorithms, respectively, to infer novel chemicals/drugs for each first level class. However, due to the limitations of SP and RWR algorithms, there still exist lots of hidden chemicals/drugs that above two methods cannot discover. This study employed another classic network algorithm, Laplacian heat diffusion (LHD) algorithm, to construct a new computational method for recognizing novel latent chemicals/drugs of each first level ATC class. This algorithm was applied on a chemical network, which containing lots of chemical interaction information, to evaluate the associations of candidate chemicals/drugs and each ATC class. Three screening tests, which measured the specificity and association to one ATC class, followed to yield more reliable potential members for each class. Some hidden chemicals/drugs were recognized, which cannot be found out by previous methods, and they were extensively analyzed to confirm that they can be novel members in the corresponding ATC class.     

Multiclass molecular cancer classification by kernel subspace methods with effective kernel parameter selection

Microarray techniques provide new insights into molecular classification of cancer types, which is critical for cancer treatments and diagnosis. Recently, an increasing number of supervised machine learning methods have been applied to cancer classification problems using gene expression data. Support vector machines (SVMs), in particular, have become one of the most effective and leading methods. However, there exist few studies on the application of other kernel methods in the literature. We apply a kernel subspace (KS) method to multiclass cancer classification problems, and assess its validity by comparing it with multiclass SVMs. Our comparative study using seven multiclass cancer datasets demonstrates that the KS method has high performance that is comparable to multiclass SVMs. Furthermore, we propose an effective criterion for kernel parameter selection, which is shown to be useful for the computation of the KS method.     

Assessment of the Automobile Assembly Paint Process for Energy, Environmental, and Economic Improvement

A coat of paint adds considerable value to an automobile. In addition to consuming up to 60% of the energy needed by automobile assembly plants, however, the painting process also creates both economic and environmental impacts. This study investigated the degree of cost and environmental impact improvement that can be expected when modifications are considered for existing paint processes through heat integration. In order to accomplish this goal, a mathematical model was created to describe the energy use, costs, and environmental impacts from energy consumption in an automobile assembly painting facility. The model agrees with measured energy consumption data for process heating and electricity demand to within about 15% for one Michigan truck facility from which model input parameters were obtained. Thermal pinch analysis determined an energy conservation target of 58% of paint process energy demand. A heat exchanger network optimization study was conducted in order to determine how closely the network design could achieve this target. The resulting heat exchanger network design was profitable based on a discounted cash flow analysis and may achieve reductions in total corporate energy consumption of up to 16% if implemented corporatewide at a major automobile manufacturer.     

Limitations on arteriovenous cooling of the blood supply to the human brain

Arteriovenous heat transfer (AVHT) is a thermoregulatory phenomenon which enhances tolerance to thermal stress in a variety of animals. Several authors have speculated that human responses to thermal stress reflect AVHT in the head and neck, even though primates lack the specialized vascular arrangements which characterize AVHT in other animals. We modeled heat transfer based on the anatonmical relationships and blood flows for the carotid artery and associated venous channels in the human neck and cavernous sinus. Heat transfer rate was predicted using the effectiveness-number of transfer units method for heat exchanger analysis. Modeling showed that AVHT is critically dependent upon (1) heat exchanger effectiveness and (2) arteriovenous inlet temperature difference. Predicted heat exchanger effectiveness is less than 5.5% for the neck and 0.3% for the cavernous sinus. These very low values reflect both the small arteriovenous interface for heat exchange and the high flow rate in the carotid artery. In addition, humans lack the strong venous temperature depression required to drive heat exchange; both the cavernous sinus and the internal jugular vein carry a large proportion of venous blood warmed by its passage through the brain as well as a small contribution from the face and scalp, whose temperature varies with environmental conditions. Under the most optimistic set of assumptions, carotid artery temperature would be lowered by less than 0.1° C during its passage from the aorta to the base of the brain. Physiologically significant cooling of the blood supply to the brain cannot occur in the absence of a suitably scaled site specialized for heat exchange.     

Moderate heat stress reduces the pH component of the transthylakoid proton motive force in light-adapted, intact tobacco leaves

We measured the Δ Ψ and ΔpH components of the transthylakoid proton motive force ( pmf ) in light-adapted, intact tobacco leaves in response to moderate heat. The Δ Ψ causes an electrochromic shift (ECS) in carotenoid absorbance spectra. The light–dark difference spectrum has a peak at 518 nm and the two components of the pmf were separated by following the ECS for 25 s after turning the light off. The ECS signal was deconvoluted by subtracting the effects of zeaxanthin formation (peak at 505 nm) and the qE-related absorbance changes (peak at 535 nm) from a signal measured at 520 nm. Heat reduced ΔpH while Δ Ψ slightly increased. Elevated temperature accelerated ECS decay kinetics likely reflecting heat-induced increases in proton conductance and ion movement. Energy-dependent quenching (qE) was reduced by heat. However, the reduction of qE was less than expected given the loss of ΔpH. Zeaxanthin did not increase with heat in light-adapted leaves but it was higher than would be predicted given the reduced ΔpH found at high temperature. The results indicate that moderate heat stress can have very large effects on thylakoid reactions.     

Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller

Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.     

生态土地分类研究进展

土地分类是土地评价,替代和管理的基础,对林业资源可持续利用具有重要的意义。生态土地分类是目前土地分类的主要方向和趋势。国际上早已开展此项研究。随着景观生态学和3S技术的发展,生态分类系统的研制已成为生态土地分类的重点。本文对生态土地分类的定义,特点,发展历史,生态分类系统理论,以及国际生态分类系统的发展趋势进行了系统的综述,并指出以资源（森林,土地,水域等）多目标管理为基础,定量与定性相结合的多因子多层次综合是未来生态土地研究发展的总趋势,有必要在我国大力开展相关研究。     

中国西北及中亚主要绿洲农区分类

应用生态学原理以水为主线建立绿洲农区树形分类体系,将中国西北及中亚主要绿洲农区采用五级指标分类并进行区划。一级分类指标为水热配合状况,分水热同期型或不同期型;二级指标为水汽主要来源,分西风环流带来水汽型或太平洋季风带来水汽型;三级指标为灌溉水来源,分为"山地-绿洲-荒漠系统"产生河流灌溉型或由"系统"外来河流灌溉型;四级指标为不同河流类型灌溉形成的土壤,分地面河灌溉形成灌淤土型或地下径流(河)井、泉清水灌溉形成灌耕土型;五级指标为地理位置名称及当地的气候带。将分类结果用亿图图示专家软件绘成树形分类图,再用Arc GIS9.3软件制成分类分布图。经分析比较得出:中国西北水热同期型较中亚水热不同期型绿洲农区的光、温、水资源耦合效应更优越。     

Reconfigurable control system design for fault diagnosis and accommodation

The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on the type of system dynamical structure or failure scenarios. The sufficient conditions for system on-line stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon the existing control theory and the modern computational intelligence techniques, an on-line fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Theoretical analysis indicates that the control problem of interest can be solved on-line without a complete realization of the unknown failure dynamics provided an on-line estimator satisfies certain conditions. Through the on-line estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. Several on-line simulation studies have been presented to demonstrate the effectiveness of the proposed strategy. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in hardware under the real-time environment, an on-line fault tolerant control test bed has been constructed to validate the proposed technology. Both on-line simulations and the real-time experiment show encouraging results and promising futures of on-line real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure dynamics.     

Joint estimation in batch culture by using unscented kalman filter

The disturbances caused by uncertain factors are inevitable in microbial fermentation. In this paper, we study the joint estimation problem for state and parameter in the bio-dissimulation process of glycerol to 1,3-PD in batch culture. Based on the nonlinear stochastic dynamic system model, we establish the corresponding iteration equations of Joint Unscented Kalman Filter (UKF) by referring to the Extended Kalman Filter (EKF), which is generally applied in microbial fermentation. Through numerical computation, both the state estimations and the uncertain model parameter estimations are obtained. Furthermore, the results of different parameter identification methods are compared. The results show that Joint UKF is more feasible for the process of controlling the glycerol fermentation.     

Temperature regulation of the testes of the bottlenose dolphin (Tursiops truncatus): evidence from colonic temperatures

Dolphins possess a countercurrent heat exchanger that functions to cool their intra-abdominal testes. spermatic arteries in the posterior abdomen are juxtaposed to veins returning cooled blood from the surfaces of the dorsal fin and flukes. A rectal probe housing a linear array of five copper-constantan thermocouples was designed to measure colonic temperatures simultaneously at positions anterior to, within, and posterior to the region of the colon flanked by the countercurrent heat exchanger. Colonic temperatures adjacent to the countercurrent heat exchanger were maximally 1.3°C cooler than temperatures measured outside this region. Temporary heating and cooling of the dorsal fin and flukes affected temperatures at the countercurrent heat exchanger, but had little or no effect on temperatures posterior to its position. These measurements support the hypothesis that cooled blood is introduced into the deep abdominal cavity and functions specifically to regulate the temperature of arterial blood flow to the dolphin testes.     

Optimisation of biocide dose as a function of residual biocide in a heat exchanger pilot plant effluent

Biofouling is one of the most serious problems facing numerous industrial processes. In the case of a heat exchanger unit, biological deposits adhering to the inside surface of its tubes reduce heat transfer and, thus, the thermal performance of the cycle. Control of this phenomenon is proving fundamental for both land and marine equipment to operate in optimum working conditions. Hence, it is necessary to apply antifouling methods capable of keeping surfaces free of any kind of biofouling. This paper reports on the behaviour resulting from use of the flow inversion method vs that obtained by using various chemical treatments. The study compares the effectiveness of certain chemical treatments (Na hypochlorite, peracetic acid and a compound formed by Na bromide + Na hypochlorite) for removing a biofouling film that has already formed on the inside surfaces of tubes in a heat exchanger pilot plant. The paper also addresses the issue of optimising the concentration of biocide dose as a function of the residual biocide in order minimise the environmental impact caused by effluent from industrial plants. The results indicate that it is possible to eliminate a biofilm formed on the inside surfaces of tubes by the use of intermittent doses of chemical treatments at low concentrations and over long application times. Furthermore, once the stabilisation phase is reached 6 d after starting the treatment, it is possible to maintain the conditions achieved using only 20% of the initial dosage.     

东海大陆架鱼类群落的空间结构

根据1997～2000年东海大陆架水深30～200米海域4个航次的底拖网调查资料,运用多元分析(聚类分析和非度量多维标度(NMDS))、SIMPER以及BIO-ENV方法分析了东海鱼类群落的空间格局以及与非生物环境因子之间的关系。结果表明,东海大陆架海域鱼类群落在空间上可分为3个群落类型,分别为东海近海群落、东海大陆架外缘群落以及这两个群落之间的东海大陆架混合群落。东海大陆架鱼类群落的种类组成沿着水深梯度的变化明显,每种鱼类均有一定的水深范围。不同的流系具有不同的温盐特征,影响了鱼类的分布及其洄游,从而控制了不同鱼类群落类型的种类组成。东海大陆架鱼类群落类型中,大陆架外缘群落类型各调查季节在空间分布和种类组成上相对稳定,而东海近海群落类型和东海大陆架混合群落类型在夏季融合在一起,其它季节保持各自的空间结构。同时东海陆架区只在一种群落类型中出现的鱼类相对较少,多数鱼类同时出现在两个群落类型中或3个群落类型中,但是它们相对较集中的区域明显不同,在不同群落类型中的生物量和出现频率有很大的差异,这些鱼类在其不同的生长发育阶段由于对海洋环境的不同需求,它们根据繁殖或摄食需求而在东海陆架区范围内洄游以寻找最适的海洋环境,因此对3个群落类型定义是相对的,很难严格确定一个永久的鱼类群落地理区域,以形成相互隔离的鱼类群落,而且鱼类群落之间的边界随着季节不同发生变化。     

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.     

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.     

A comparison of two fouling‐resistant heat exchangers

Fouling cannot always be prevented; it is important to consider the design of fouling‐resistant heat exchangers. To examine these exchangers, a test fluid whose fouling behaviour is understood should be used. Experiments have been conducted to examine the response of two model systems, a pulsatile flow and a fluid bed heat exchanger, to fouling from whey protein concentrates. Both systems are effective in certain cases, although the enhanced mass transfer possible in the pulsatile flow exchanger can increase fouling when mass transfer controls deposition. This demonstrates the possible danger in installing “antifouling”; systems. The possible mechanisms by which antifouling exchangers operate is discussed; they may work both by slowing the kinetics of fouling or enhancing the heat transfer coefficient. A simple model to demonstrate the design of antifouling exchangers is presented.     

Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence

Process monitoring and profile analysis are crucial in detecting various abnormal events in semiconductor manufacturing, which consists of highly complex, interrelated, and lengthy wafer fabrication processes for yield enhancement and quality control. To address real requirements, this study aims to develop a framework for semiconductor fault detection and classification (FDC) to monitor and analyze wafer fabrication profile data from a large number of correlated process variables to eliminate the cause of the faults and thus reduce abnormal yield loss. Multi-way principal component analysis and data mining are used to construct the model to detect faults and to derive the rules for fault classification. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the model. Use of the proposed framework can effectively detect abnormal wafers based on a controlled limit and the derived simple rules. The extracted information can be used to aid fault diagnosis and process recovery. The proposed solution has been implemented in the semiconductor company. This has simplified the monitoring process in the FDC system through the fewer key variables. The results demonstrate the practical viability of the proposed approach.     

Electroconvulsive Shock Increases α1b-but Not α1a-Adrenoceptor Binding Sites in Rat Cerebral Cortex

Repeated administration of electroconvulsive shock (ECS) increases [3H]prazosin binding to alpha 1-adrenoceptors in rat cerebral cortex. In contrast, [3H]WB4101 binding in cortex has been reported to be unchanged after ECS. [3H]Prazosin labels two alpha 1-adrenoceptor subtypes, termed alpha 1a and alpha 1b, whereas [3H]WB4101 labels the alpha 1a subtype preferentially. The purpose of this study was to determine whether ECS increases one or both alpha 1-adrenoceptor subtypes in rat cerebral cortex. We found that treatment of rats with ECS once daily for 10-12 days increased [3H]prazosin binding in cortex by about 25% but did not significantly alter [3H]WB4101 binding to alpha 1-adrenoceptors. Measurement of alpha 1a and alpha 1b receptors by competition analysis of the selective alpha 1a antagonist 5-methylurapidil against [3H]prazosin and measurement of [3H]prazosin binding in homogenates preincubated with chlorethylclonidine, which alkylates alpha 1b binding sites, also indicated that the ECS-induced increase in alpha 1-adrenoceptors is confined to the alpha 1b subtype. In contrast to its effect on [3H]prazosin binding, ECS did not increase phosphoinositide hydrolysis as measured by [3H]inositol 1-phosphate accumulation in slices of rat cerebral cortex stimulated by either norepinephrine or phenylephrine. The failure of ECS to increase [3H]inositol 1-phosphate accumulation stimulated by phenylephrine, which is a partial agonist for this response, suggests that spare receptors do not account for the apparent absence of effect of ECS on alpha 1-adrenoceptor-mediated phosphoinositide hydrolysis.     

中国东海微型鞭毛虫群落的分布特征及其环境影响因子

海洋微型鞭毛虫是海洋原生生物中一类高度异质化的类群,物种多样性高,具有多种营养方式,在全球海洋生态系统中占据广阔的生态位,在生物地球化学循环中发挥着关键作用。然而关于其生物多样性和群落结构的认识十分有限,特别是有关环境因子与其生物地理分布关系的研究更为罕见。为了探究微型鞭毛虫群落多样性、群落结构以及影响其生物地理分布格局的环境因素,将高通量测序技术与传统的显微镜观测方法相结合,全面调查了中国东海春季和秋季微型鞭毛虫的群落特征,并深入探讨了与环境因子之间的关系。结果表明：东海微型鞭毛虫的丰度平均为2.27×10~3个/mL,表现为近岸处较高、随离岸距离的增加而下降的趋势;Shannon多样性指数呈现表层低于底层、近岸区低于陆架区的特征,反映了生物群落的稳定程度以及对环境条件的适应程度;不同类群的鞭毛虫具有各自独特的营养模式和相对固定的粒级,表现出对温度、盐度、溶解氧等环境因素的不同响应,从而使群落的物种组成和分布模式呈现明显的季节变化和生境差异。研究结果可为深入认识东海海洋微型鞭毛虫的群落结构、分布格局以及环境影响因素提供理论依据。