Efficient validation of teaching and learning using multiple-choice exams

One purpose of this study was to quantify, by means of single-format, multiple-choice questions at the beginning and end of the course, the extent to which first-year medical students learn neuroscience material from an introductory course in their curriculum. Compared with their precourse test performance (mean = 41.8%), collectively, the students nearly doubled their grade by the end of the course (mean = 81.4%). Their scores in subcategories of the material improved in inverse proportion to what they knew initially. A second goal was to evaluate a two-dimensional, computer-generated matrix as a way to assess test question validity and value. The evaluation of individual test questions as assessed from the matrix often, but not always, was similar to the classical pedagogical analysis that uses difficulty and discrimination indexes. Strengths of the matrix are its ability to render data as a gestalt, as well as flexibility and intuitive ease of use.     

Tailoring science outreach through E-matching using a community-based participatory approach

In an effort to increase science exposure for pre-college (K-12) students and as part of the science education reform agenda, many biomedical research institutions have established university-community partnerships. Typically, these science outreach programs consist of pre-structured, generic exposure for students, with little community engagement. However, the use of a medium that is accessible to both teachers and scientists, electronic web-based matchmaking (E-matching) provides an opportunity for tailored outreach utilizing a community-based participatory approach (CBPA), which involves all stakeholders in the planning and implementation of the science outreach based on the interests of teachers/students and scientists. E-matching is a timely and urgent endeavor that provides a rapid connection for science engagement between teachers/students and experts in an effort to fill the science outreach gap. National Lab Network (formerly National Lab Day), an ongoing initiative to increase science equity and literacy, provides a model for engaging the public in science via an E-matching and hands-on learning approach. We argue that science outreach should be a dynamic endeavor that changes according to the needs of a target school. We will describe a case study of a tailored science outreach activity in which a public school that serves mostly under-represented minority students from disadvantaged backgrounds were E-matched with a university, and subsequently became equal partners in the development of the science outreach plan. In addition, we will show how global science outreach endeavors may utilize a CBPA, like E-matching, to support a pipeline to science among under-represented minority students and students from disadvantaged backgrounds. By merging the CBPA concept with a practical case example, we hope to inform science outreach practices via the lens of a tailored E-matching approach.     

Common student misconceptions in exercise physiology and biochemistry

The present study represents a preliminary investigation designed to identify common misconceptions in students' understanding of physiological and biochemical topics within the academic domain of sport and exercise sciences. A specifically designed misconception inventory (consisting of 10 multiple-choice questions) was administered to a cohort of level 1, 2, and 3 undergraduate students enrolled in physiology and biochemistry-related modules of the BSc Sport Science degree at the authors' institute. Of the 10 misconceptions proposed by the authors, 9 misconceptions were confirmed. Of these nine misconceptions, only one misconception appeared to have been alleviated by the current teaching strategy employed during the progression from level 1 to 3 study. The remaining eight misconceptions prevailed throughout the course of the degree program, suggesting that students enter and leave university with the same misconceptions in certain areas of exercise physiology and biochemistry. The possible origins of these misconceptions are discussed, as are potential teaching strategies to prevent and/or remediate them for future years.     

A systems biology approach to learning autophagy

With its relevance to our understanding of eukaryotic cell function in the normal and disease state, autophagy is an important topic in modern cell biology; yet, few textbooks discuss autophagy beyond a two- or three-sentence summary. Here, we report an undergraduate/graduate class lesson for the in-depth presentation of autophagy using an active learning approach. By our method, students will work in small groups to solve problems and interpret an actual data set describing genes involved in autophagy. The problem-solving exercises and data set analysis will instill within the students a much greater understanding of the autophagy pathway than can be achieved by simple rote memorization of lecture materials; furthermore, the students will gain a general appreciation of the process by which data are interpreted and eventually formed into an understanding of a given pathway. As the data sets used in these class lessons are largely genomic and complementary in content, students will also understand first-hand the advantage of an integrative or systems biology study: No single data set can be used to define the pathway in full-the information from multiple complementary studies must be integrated in order to recapitulate our present understanding of the pathways mediating autophagy. In total, our teaching methodology offers an effective presentation of autophagy as well as a general template for the discussion of nearly any signaling pathway within the eukaryotic kingdom.     

The biochemistry of learning and memory

Molecular and Cellular Biochemistry - An overview of some of the biochemical and molecular events involved in the process of learning and memory are presented in a short review. Two invertebrate...     

A data-driven predictive approach for drug delivery using machine learning techniques

In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.     

A problem-based learning approach to teaching medical genetics.

A newly developed problem-based medical genetics course that was integrated into the fourth-year medical school curriculum of the University of Texas Health Science Center at San Antonio is described. To provide a basic genetic background for the clinical rotations, a supplemental computer tutorial is required during the second year. These two formats prepare the medical students to recognize genetic diseases, to provide basic genetic counseling in their daily practice, and to appropriately refer patients to genetic specialists.     

A Bayesian approach to the evolution of social learning

There has been much interest in understanding the evolution of social learning. Investigators have tried to understand when natural selection will favor individuals who imitate others, how imitators should deal with the fact that available models may exhibit different behaviors, and how social and individual learning should interact. In all of this work, social learning and individual learning have been treated as alternative, conceptually distinct processes. Here we present a Bayesian model in which both individual and social learning arise from a single inferential process. Individuals use Bayesian inference to combine social and nonsocial cues about the current state of the environment. This model indicates that natural selection favors individuals who place heavy weight on social cues when the environment changes slowly or when its state cannot be well predicted using nonsocial cues. It also indicates that a conformist bias should be a universal aspect of social learning.     

Neural muscle activation detection: A deep learning approach using surface electromyography

The timing of muscles activation which is a key parameter in determining plenty of medical conditions can be greatly assessed by the surface EMG signal which inherently carries an immense amount of information. Many techniques for measuring muscle activity detection exist in the literature. However, due to the complex nature of the EMG signal as well as the interference from other muscles that is observed during the measurement of the EMG signal, the accuracy of these techniques is compromised. In this paper, we introduce the neural muscle activation detection (NMAD) framework that detects the muscle activation based on deep learning. The main motivation behind using deep learning is to allow the neural network to detect based on the appropriate signal features instead of depending on certain assumptions. Not only the presented approach significantly improves the accuracy of timing detection, but because of the training nature, it can adapt to operate under different levels of interference and signal-to-noise ratio.     

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data

Background  

A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.     

A machine learning information retrieval approach to protein fold recognition

MOTIVATION: Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequence-structure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical machine learning methods provide tools for integrating multiple features, but so far these methods have been used primarily for protein and fold classification, rather than addressing the retrieval problem of fold recognition-finding a proper template for a given query protein. RESULTS: Here we present a two-stage machine learning, information retrieval, approach to fold recognition. First, we use alignment methods to derive pairwise similarity features for query-template protein pairs. We also use global profile-profile alignments in combination with predicted secondary structure, relative solvent accessibility, contact map and beta-strand pairing to extract pairwise structural compatibility features. Second, we apply support vector machines to these features to predict the structural relevance (i.e. in the same fold or not) of the query-template pairs. For each query, the continuous relevance scores are used to rank the templates. The FOLDpro approach is modular, scalable and effective. Compared with 11 other fold recognition methods, FOLDpro yields the best results in almost all standard categories on a comprehensive benchmark dataset. Using predictions of the top-ranked template, the sensitivity is approximately 85, 56, and 27% at the family, superfamily and fold levels respectively. Using the 5 top-ranked templates, the sensitivity increases to 90, 70, and 48%.     

Toward a landscape approach in seagrass beds: using macroalgal accumulation to address questions of scale

An experimental investigation of drift macroalgal accumulation in seagrass beds was conducted to determine if the relationship between passively dispersed plant structure and the spatial arrangement of rooted macrophytes differed when examined across two spatial scales. Experiments were performed from December 1992 to April 1993 at four different sites in Tampa Bay, Florida, utilizing artificial seagrass units (ASUs) of uniform shoot length and density but with different areal dimensions [1 m² (S) versus 4 m² (L)]. Drift macroalgae were also collected from 1 m×1 m plots of natural seagrass at each of the experimental sites from November 1990 to May 1992 to determine the relationship between macroalgal abundance and structural characteristics of natural seagrass. Disproportionately higher amounts of macroalgae were captured in L compared to S plots suggesting that macroalgal accumulation does not scale up directly with the areal dimensions of ASU patches. Higher amounts of algae recovered in L plots is in accordance with patterns expected if algae accumulate in zones of attenuated water flow. Neither seagrass shoot density nor blade length could adequately describe the patterns of algal accumulation. These combined results suggest that explanations for trapping/retention of passively dispersed particles should extend beyond traditional measures of vegetation complexity.     

PBL与图表相结合的教学法在《基础生物化学》课程教学中的初步实践

针对生物化学课程的特点,以改革当前传统的课堂讲授式教学模式作为突破口,采用基于问题学习（PBL）与图表结合的教学模式在《基础生物化学》中进行教学尝试。初步教学实践表明,通过PBL与图表教学法的互补结合,基本能克服《基础生物化学》教学活动中存在的两类矛盾,使传统的以＂教＂为主的教学模式,转变为以＂学＂与＂教＂相互平衡和促进的教学模式,使学生在掌握所学课程核心内容的同时又能获得学习方法、提高学习兴趣和学习主动性。