Northwestern University

About: Northwestern University is a education organization based out in Evanston, Illinois, United States. It is known for research contribution in the topics: Population & Medicine. The organization has 75430 authors who have published 188857 publications receiving 9463252 citations. The organization is also known as: Northwestern & NU.

Strategies for the Construction of Supramolecular Compounds through Coordination Chemistry

Abstract: Synthetic organic chemists enjoy the luxury of having a large collection of reliable reactions at their disposal for preparing small molecules, mesoscopic structures, and polymers. Coordination chemists, on the other hand, are faced with the fact that transition metal chemistry, when normalized for the number of transition metals, has relatively few high-yielding reactions, when compared to the chemistry of carbon, for preparing even small molecule structures. This lack of control is manifested, in large part, in the weak metal-ligand interactions found in coordination complexes as compared with the strong covalent bonds in organic compounds. Weak bonding often translates into many reaction pathways that are not substantially different from an energetic point of view, and therefore, results in poor selectivity. As a result, many coordination chemists in recent years have come to the realization that it may be easier and more productive to develop straightforward and reliable routes to mesoscopic supramolecular structures by capitalizing on the modest collection of high-yielding reactions in coordination chemistry, the directional bonding afforded by metal centers, and strategies aimed at taking advantage of the weak metal bonds found in coordination complexes. Three emerging synthetic strategies, the symmetry-interaction, directional-bonding, and weak-link synthetic approaches, all use metal centers as structural building blocks to rationally assemble molecular components into supramolecular metallocyclophanes. These three approaches are discussed herein, and the fundamental principles underlying each as well as their capabilities are compared and contrasted.

A critical review of simulation-based medical education research: 2003-2009

Abstract: Objectives This article reviews and critically evaluates historical and contemporary research on simulation-based medical education (SBME). It also presents and discusses 12 features and best practices of SBME that teachers should know in order to use medical simulation technology to maximum educational benefit. Methods This qualitative synthesis of SBME research and scholarship was carried out in two stages. Firstly, we summarised the results of three SBME research reviews covering the years 1969–2003. Secondly, we performed a selective, critical review of SBME research and scholarship published during 2003–2009. Results The historical and contemporary research synthesis is reported to inform the medical education community about 12 features and best practices of SBME: (i) feedback; (ii) deliberate practice; (iii) curriculum integration; (iv) outcome measurement; (v) simulation fidelity; (vi) skill acquisition and maintenance; (vii) mastery learning; (viii) transfer to practice; (ix) team training; (x) high-stakes testing; (xi) instructor training, and (xii) educational and professional context. Each of these is discussed in the light of available evidence. The scientific quality of contemporary SBME research is much improved compared with the historical record. Conclusions Development of and research into SBME have grown and matured over the past 40 years on substantive and methodological grounds. We believe the impact and educational utility of SBME are likely to increase in the future. More thematic programmes of research are needed. Simulation-based medical education is a complex service intervention that needs to be planned and practised with attention to organisational contexts. Medical Education 2010: 44: 50–63

Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data

Abstract: Summary Background Locally advanced rectal cancer is usually treated with preoperative chemoradiation. After chemoradiation and surgery, 15–27% of the patients have no residual viable tumour at pathological examination, a pathological complete response (pCR). This study established whether patients with pCR have better long-term outcome than do those without pCR. Methods In PubMed, Medline, and Embase we identified 27 articles, based on 17 different datasets, for long-term outcome of patients with and without pCR. 14 investigators agreed to provide individual patient data. All patients underwent chemoradiation and total mesorectal excision. Primary outcome was 5-year disease-free survival. Kaplan-Meier survival functions were computed and hazard ratios (HRs) calculated, with the Cox proportional hazards model. Subgroup analyses were done to test for effect modification by other predicting factors. Interstudy heterogeneity was assessed for disease-free survival and overall survival with forest plots and the Q test. Findings 484 of 3105 included patients had a pCR. Median follow-up for all patients was 48 months (range 0–277). 5-year crude disease-free survival was 83·3% (95% CI 78·8–87·0) for patients with pCR (61/419 patients had disease recurrence) and 65·6% (63·6–68·0) for those without pCR (747/2263; HR 0·44, 95% CI 0·34–0·57; p Q test and forest plots did not suggest significant interstudy variation. The adjusted HR for pCR for failure was 0·54 (95% CI 0·40–0·73), indicating that patients with pCR had a significantly increased probability of disease-free survival. The adjusted HR for disease-free survival for administration of adjuvant chemotherapy was 0·91 (95% CI 0·73–1·12). The effect of pCR on disease-free survival was not modified by other prognostic factors. Interpretation Patients with pCR after chemoradiation have better long-term outcome than do those without pCR. pCR might be indicative of a prognostically favourable biological tumour profile with less propensity for local or distant recurrence and improved survival. Funding None.

Full-potential self-consistent linearized-augmented-plane-wave method for calculating the electronic structure of molecules and surfaces: O 2 molecule

Abstract: The linearized-augmented-plane-wave (LAPW) method for thin films is generalized by removing the remaining shape approximation to the potential inside the atomic spheres. A new technique for solving Poisson's equation for a general charge density and potential is described and implemented in the film LAPW method. In the resulting full-potential LAPW method (FLAPW), all contributions to the potential are completely taken into account in the Hamiltonian matrix elements. The accuracy of the method---already well known for clean metal surfaces---is demonstrated for the case of a nearly free (noninteracting) ${\mathrm{O}}_{2}$ molecule which is a severe test case of the method because of its large anisotropic charge distribution. Detailed comparisons show that the accuracy of the FLAPW results for ${\mathrm{O}}_{2}$ exceeds that of existing state-of-the-art local-density linear-combination-of-atomic-orbitals (LCAO)-type calculations, and that taking the full potential LAPW results as a reference, the LCAO basis can be improved by adding off-site functions. Thus the full-potential LAPW is a unified method which is ideally suited to test not only molecular adsorption on surfaces, but also the components of the same system separately, i.e., the extreme limits of the molecule and the clean surface.

Inside Case-Based Reasoning

Abstract: Case-based reasoning, broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents is using case-based reasoning. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving. Case-based reasoning (CBR) has been formalized as a four-step process:N 1. Retrieve: Given a target problem, retrieve cases from memory that are relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. 2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue -- an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. 4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his newfound procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule-induction algorithmsP of machine learning.N Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. For instance, when Fred mapped his procedure for plain pancakes to blueberry pancakes, he decided to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization. For instance, if a rule-induction algorithm were given recipes for plain pancakes, Dutch apple pancakes, and banana pancakes as its training examples, it would have to derive, at training time, a set of general rules for making all types of pancakes. It would not be until testing time that it would be given, say, the task of cooking blueberry pancakes. The difficulty for the rule-induction algorithm is in anticipating the different directions in which it should attempt to generalize its training examples. This is in contrast to CBR, which delays (implicit) generalization of its cases until testing time -- a strategy of lazy generalization. In the pancake example, CBR has already been given the target problem of cooking blueberry pancakes; thus it can generalize its cases exactly as needed to cover this situation. CBR therefore tends to be a good approach for rich, complex domains in which there are myriad ways to generalize a case.