Botanical drugs, synergy, and network pharmacology: forth and back to intelligent mixtures

For centuries the science of pharmacognosy has dominated rational drug development until it was gradually substituted by target-based drug discovery in the last fifty years. Pharmacognosy stems from the different systems of traditional herbal medicine and its "reverse pharmacology" approach has led to the discovery of numerous pharmacologically active molecules and drug leads for humankind. But do botanical drugs also provide effective mixtures? Nature has evolved distinct strategies to modulate biological processes, either by selectively targeting biological macromolecules or by creating molecular promiscuity or polypharmacology (one molecule binds to different targets). Widely claimed to be superior over monosubstances, mixtures of bioactive compounds in botanical drugs allegedly exert synergistic therapeutic effects. Despite evolutionary clues to molecular synergism in nature, sound experimental data are still widely lacking to support this assumption. In this short review, the emerging concept of network pharmacology is highlighted, and the importance of studying ligand-target networks for botanical drugs is emphasized. Furthermore, problems associated with studying mixtures of molecules with distinctly different pharmacodynamic properties are addressed. It is concluded that a better understanding of the polypharmacology and potential network pharmacology of botanical drugs is fundamental in the ongoing rationalization of phytotherapy.

Further Evidence for Hemisity Sorting During Career Specialization

Hemisity refers to binary thinking and behavioral style differences between right and left brain-oriented individuals. The inevitability of hemisity became clear when it was discovered by magnetic resonance imaging (MRI) that an anatomical element of the executive system was unilaterally embedded in either the right or the left side of the ventral gyrus of the anterior cingulate cortex in an idiosyncratic manner that was congruent with an individual's inherent hemisity subtype. Based upon the MRI-calibrated hemisity of many individuals, a set of earlier biophysical and questionnaire hemisity assays was calibrated for accuracy and found appropriate for use in the investigation of the hemisity of individuals and groups. It had been reported that a partial sorting of individuals into hemisity right and left brain-oriented subgroups occurred during the process of higher education and professional development. Here, these results were extended by comparison of the hemisity of a putative unsorted population of 1,049 high school upper classmen, with that of 228 university freshmen. These hemisity outcomes were further compared with that of 15 university librarians, here found to be predominantly left brain-oriented, and 91 academically trained musicians, including 47 professional pianists, here found to be mostly right brainers. The results further supported the existence of substantial hemisity selection occurring during the process of higher education and in professional development.

Swiss residents' speciality choices--impact of gender, personality traits, career motivation and life goals

BACKGROUND: The medical specialties chosen by doctors for their careers play an important part in the development of health-care services. This study aimed to investigate the influence of gender, personality traits, career motivation and life goal aspirations on the choice of medical specialty. METHODS: As part of a prospective cohort study of Swiss medical school graduates on career development, 522 fourth-year residents were asked in what specialty they wanted to qualify. They also assessed their career motivation and life goal aspirations. Data concerning personality traits such as sense of coherence, self-esteem, and gender role orientation were collected at the first assessment, four years earlier, in their final year of medical school. Data analyses were conducted by univariate and multivariate analyses of variance and covariance. RESULTS: In their fourth year of residency 439 (84.1%) participants had made their specialty choice. Of these, 45 (8.6%) subjects aspired to primary care, 126 (24.1%) to internal medicine, 68 (13.0%) to surgical specialties, 31 (5.9%) to gynaecology & obstetrics (G&O), 40 (7.7%) to anaesthesiology/intensive care, 44 (8.4%) to paediatrics, 25 (4.8%) to psychiatry and 60 (11.5%) to other specialties. Female residents tended to choose G&O, paediatrics, and anaesthesiology, males more often surgical specialties; the other specialties did not show gender-relevant differences of frequency distribution. Gender had the strongest significant influence on specialty choice, followed by career motivation, personality traits, and life goals. Multivariate analyses of covariance indicated that career motivation and life goals mediated the influence of personality on career choice. Personality traits were no longer significant after controlling for career motivation and life goals as covariates. The effect of gender remained significant after controlling for personality traits, career motivation and life goals. CONCLUSION: Gender had the greatest impact on specialty and career choice, but there were also two other relevant influencing factors, namely career motivation and life goals. Senior physicians mentoring junior physicians should pay special attention to these aspects. Motivational guidance throughout medical training should not only focus on the professional career but also consider the personal life goals of those being mentored.

Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.

EVALUATION OF INTELLIGENT COMPACTION CONTROL IN THE M-189 RECONSTRUCTION PROJECT AT IRON RIVER, MICHIGAN

This research evaluated an Intelligent Compaction (IC) unit on the M-189 highway reconstruction project at Iron River, Michigan. The results from the IC unit were compared to several traditional compaction measurement devices including Nuclear Density Gauge (NDG), Geogauge, Light Weight Deflectometer (LWD), Dynamic Cone Penetrometer (DCP), and Modified Clegg Hammer (MCH). The research collected point measurements data on a test section in which 30 test locations on the final Class II sand base layer and the 22A gravel layer. These point measurements were compared with the IC measurements (ICMVs) on a point-to-point basis through a linear regression analysis. Poor correlations were obtained among different measurements points using simple regression analysis. When comparing the ICMV to the compaction measurements points. Factors attributing to the weak correlation include soil heterogeneity, variation in IC roller operation parameters, in-place moisture content, the narrow range of the compaction devices measurement ranges and support conditions of the support layers. After incorporating some of the affecting factors into a multiple regression analysis, the strength of correlation significantly improved, especially on the stiffer gravel layer. Measurements were also studied from an overall distribution perspective in terms of average, measurement range, standard deviation, and coefficient of variance. Based on data analysis, on-site project observation and literature review, conclusions were made on how IC performed in regards to compaction control on the M-189 reconstruction project.

THE IMPACTS OF MITEP ON MY CAREER IN EDUCATION

MiTEP, the Michigan Teacher Excellence Program, provides current teachers the opportunity to partner with Michigan Technological University to obtain graduate credit towards a Master’s degree in applied science education. In exchange, the university collects data on the implementation of inquiry and earth science concepts into science classrooms. This paper documents my experience within this program, including how it has affected my personal and professional learning.

Intelligent Virtual Patients for Training Clinical Skills

The article presents the design process of intelligent virtual human patients that are used for the enhancement of clinical skills. The description covers the development from conceptualization and character creation to technical components and the application in clinical research and training. The aim is to create believable social interactions with virtual agents that help the clinician to develop skills in symptom and ability assessment, diagnosis, interview techniques and interpersonal communication. The virtual patient fulfills the requirements of a standardized patient producing consistent, reliable and valid interactions in portraying symptoms and behaviour related to a specific clinical condition.

Hanging Together, Together Hung? Career Implications of Interpersonal Ties Between CEOs and Top Managers

Is it good or bad for senior executives to have strong interpersonal ties to the CEO? We argue that a strong relationship with the CEO raises the likelihood that a top manager stays in office or makes an upward career move when the CEO leaves office voluntarily. At the same time, such interpersonal ties also reinforce the negative spillover effects of a dismissal of the CEO on the career prospects of the manager concerned. Our empirical analysis lends support to both arguments. We contribute to managerial succession research by underlining the ambivalence of interpersonal ties within top management teams.

Semantic Methods for Intelligent Distributed Design Environments

Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.