Mariachi and Spanish speaking English learners: District initiatives,models, and education policy

ABSTRACT

Districts nationwide are challenged with how best to address the needs of an increasing number of non-English or limited-English speaking students. These young people are similarly challenged as they face new environs, unfamiliar cultural settings, and significant communication issues. Findings have indicated that an arts-rich education can assist limited English speakers, many of them considered to be at-risk students, to achieve at higher levels. This article examines a number of districts with growing Hispanic populations that have implemented standards-based mariachi programs and found success in engaging Spanish speaking English learners, their parents, and the Hispanic community at large in the process. Some of these districts had considerable issues needing to be resolved but innovative solutions have led to student success. One district that serves as a model to be replicated is chronicled in detail due to how its creative program implementation subsequently informed policy. School climate data is also provided by another district, which broke down its reporting according to subgroup. Although the data do not measure only those who were enrolled solely in mariachi courses, the major findings are significant in that they indicate how music coursework impacts Latino students specifically. The impact of participating as an active music-maker in any ensemble by any participant cannot be overlooked, but for Spanish speaking English learners, the standards-based mariachi program provides a culturally familiar and welcoming setting, facilitates new patterns of learning, and addresses the priority of attaining English language proficiency.     

Learning and optimization in biological systems

A model of sequential actions implying learning is proposed, starting from the concept of acybernetical action introduced in a previous paper (Teodorescu, 1977). Sequential actions of this kind (e.g. defence actions occurring repeatedly in a biological population, some motor subroutines such as walking, swimming etc.) are always goal-oriented ones. Furthermore, in performing them, some constraints must be observed. Finally, each action must be optimal with respect to a given criterion, otherwise it becomes highly expensive (or, perhaps, inefficient) for the living organism(s). To take into account these rather natural features of any cybernetical action, a specific tool is used, namely, themessage operator defined in (Teodorescu, 1976b). The procedure of deriving an action model with the aid of message operators is illustrated by means of an experimental paradigm concerning the visually induced behaviour in insects (Reichardt and Poggio, 1976). Next, by taking into account certain recovery processes, the concept of alearning sequence (i.e. model of sequential action involving learning) is defined, and some examples are given to illustrate learning in biological systems. The related concepts offull subsequence, node andmemory cycle are also defined, and some theorems concerning memory cycles are stated and proved. Furthermore,a measure of the learning effort is suggested. It is used to show that, under certain conditions, learning occurs even in the case of many different active messages. Finally, by using the proposed models, the relationship between learning and optimization is investigated, proving thatoptimization is deeply implicated in any learning process. More exctly, as far as sequential actions with recovery are concerned, optimization is a necessary (but not sufficient) condition for learning. Thus, the proposed model of action may be regarded as a useful tool, which enables to investigate some unknown aspects related to learning in living organisms, in a general, computeroriented way.     

An RNA Holliday junction? Structural and dynamic considerations of the bacteriophage T4 gene 60 interruption

A novel interrupted gene motif has been reported in which a 50-nucleotide insertion into bacteriophage T4 gene 60 appears to be present in the message at the time of translation, yet it is not translated. We present here a dynamic model for how translation may be occurring in the neighborhood of the interruption. The model involves formation of an RNA structure with similarities to a Holliday junction, followed by migration of the branch point in a strand exchange between message and interruption. The advantage of this model over previous ones is that at no time is a new tRNA required to pair with a discontinuous template.     

Visual immersion for cultural understanding and multimodal literacy

ABSTRACT

When considering inclusive art curriculum that accommodates all learners, including English language learners, two distinct yet inseparable issues come to mind. The first is that English language learner students can use visual language and visual literacy skills inherent in visual arts curriculum to scaffold learning in and through the arts. Second, in facilitating a sense of belonging for students whose home language and cultural aesthetic may be different from those of the dominant school culture, an authentically developed multicultural art curriculum can guide self-efficacy and inclusiveness. Both aspects of teaching art for English language learners can have the added benefits of facilitating collaborative learning opportunities and increasing worldviews for all students.     

From the science of learning (and development) to learning engineering

ABSTRACT

In this issue, Cantor and colleagues synthesize a broad representation of the literature on the science of learning, and how learning changes over the course of development. Their perspective highlights three important factors about the emerging field of science of learning and development: (1) that it draws insights from increasingly diverse fields of research inquiry, from neuroscience and social science to computer science and adversity science; (2) that it provides a means to understand principles that generalize across learners, and yet also allow individual differences in learning to emerge and inform; and (3) that it recognizes that learning occurs in context, and is thus a shared responsibility between the learner, the instructor, and the environment. Here I discuss how this complex systems dynamical perspective can be integrated with the emerging framework of ‘learning engineering’ to provide a blueprint for significant innovations in education.     

Hormone multifunctionalities: a theory of endocrine signaling, command and control

A theory is presented outlining how organisms can function and benefit from multifunctionality of hormones in order to enhance greatly the information-carrying potential of endocrine signaling. Hormones are produced continuously as micropulses, and intermittently as larger pulses. It is generally believed that micropulses generate fluctuating basal hormone concentrations, which may consistently elicit particular responses among diverse variables. Evidence is discussed suggesting that in contrast to the hormone micropulses, the larger endogenous hormone pulses may elicit responses which may differ from one pulse to another and may therefore serve different physiological functions. In this paper we postulate that an endogenous hormone pulse is a specific form of a multisignal message that serves a certain physiological function. Different pulses of a hormone may be signals of diverse multisignal messages that serve different functions. A multisignal message may elicit congruous responses by selectively enhancing some actions and suppressing other actions of the component signals. Various roles of signals of multisignal messages are discussed, as well as processes that may be involved in the diversity and selectivity of actions of different pulses of a hormone. Hormones also are converted into other hormones; we analyze how precursor and derived hormones may function independently of each other, and how precursor hormones may give rise to permissive effects. Mechanisms involved in therapeutic and adverse effects of hormone administrations are analyzed, and a strategy is suggested for developing more selective hormonal therapies.     

Coexistence of individual and social learners during range expansion

Individual learning and social learning are two primary abilities supporting cultural evolution. Conditions for their evolution have mostly been studied by investigating gene frequency dynamics, which essentially implies constant population size. Predictions from such “static” models may only be of partial relevance to the evolution of advanced individual learning in modern humans, because modern humans have experienced rapid population growth and range expansion during “out-of-Africa.” Here we model the spatial population dynamics of individual and social learners by a reaction–diffusion system. One feature of our model is the inclusion of the possibility that social learners may fail to find an exemplar to copy in regions where the population density is low. Due to this attenuation effect, the invasion speed of social learners is diminished, and various kinds of invasion dynamics are observed. Our primary findings are: (1) individual learners can persist indefinitely when invading environmentally homogeneous infinite space; (2) the occurrence of individual learners at the front may inhibit the spread of social learners. These results suggest that “out-of-Africa” may have driven the evolution of advanced individual learning ability in modern humans.     

Optimal deep brain stimulation of the subthalamic nucleus—a computational study

Deep brain stimulation (DBS) of the subthalamic nucleus, typically with periodic, high frequency pulse trains, has proven to be an effective treatment for the motor symptoms of Parkinson’s disease (PD). Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963–2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211–235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells’ auto- and cross-correlograms. In both cases, a nonlinear closed-loop learning algorithm identifies effective DBS inputs that are optimized to have minimal strength. The network dynamics that result differ from the regular, entrained firing which some previous studies have associated with conventional high-frequency DBS. This type of optimized solution is also found with heterogeneity in both the intrinsic network dynamics and the strength of DBS inputs received at various cells. Such alternative DBS inputs could potentially be identified, guided by the model-free learning algorithm, in experimental or eventual clinical settings. Action Editor: Steven J. Schiff Xiao-Jiang Feng and Eric Shea-Brown contributed equally to this work.     

Myocardial iron homeostasis and hepcidin expression in a rat model of heart failure at different levels of dietary iron intake

BackgroundUp to 50% of patients with chronic heart failure (HF) have systemic iron deficiency, which contributes to symptoms and poor prognosis. Myocardial iron deficiency (MID) in HF patients has been recently documented, but its causes and consequences are unknown. The goal of our study was to address these questions in a well-defined rat HF model induced by volume overload due to aorto-caval fistula.MethodsModulation of dietary iron content in a rat model of HF has been used to address how iron status affects cardiac iron levels, heart structure and function, and how the presence of HF affects cardiac expression of hepcidin and other iron-related genes.ResultsMID developed in the rat model of heart failure. Iron supplementation did not normalize the myocardial iron content; however, it improved survival of HF animals compared to animals fed diet with normal iron content. We observed marked upregulation of hepcidin mRNA expression in HF animals, which was not associated with systemic or cardiac iron levels but strongly correlated with markers and parameters of heart injury. Identical iron-independent pattern was observed for expression of several iron-related genes.ConclusionsMID is not caused by defective iron absorption or decreased systemic iron levels, but rather by intrinsic myocardial iron deregulation. Altered cardiac expression of hepcidin and other iron-related genes is driven by iron-independent stimuli in the failing heart.General significanceUnderstanding of the causes and consequences of MID is critical for finding strategies how to improve cardiac iron stores and in HF patients.     

Arduino-Assisted robotic and coding applications in science teaching: Pulsimeter activity in compliance with the 5E learning model

Abstract

The purpose of the current study is to design and develop an Arduino-assisted robotic and coding activity in compliance with the 5E learning model for middle school students to construct knowledge and realize learning in science teaching. The study was conducted on 6^th grade students attending a private middle school in Turkey in the second term of the 2018 and 2019 school year. Application of the designed activity was carried out in the science laboratory with the participation of 10 students who were the members of the STEM club. The activity was designed to teach the concept of pulse addressed within the subject of circulatory system in the science curriculum in line with the 5E learning model. With this activity developed, systematic contraction and relaxation movements of the heart while working were modeled through robotics and coding. In addition, through this activity, it was intended to introduce the students to coding, to improve their attitudes towards technology, to develop their information processing skills and to help them to relate the concept of pulse to daily life. In this way, the students were provided with opportunities to experience Arduino-assisted robotic and coding applications and to integrate such applications into their daily life.     

Learning from Instructional Explanations: Effects of Prompts Based on the Active-Constructive-Interactive Framework

Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which interactive learning activities are at the top; these are then followed by constructive and active learning activities, respectively. Against this background, we combined instructional explanations with different types of prompts that were designed to elicit these learning activities and tested the central predictions of the active-constructive-interactive framework. In Experiment 1, N = 83 students were randomly assigned to one of four combinations of instructional explanations and prompts. To test the active < constructive learning hypothesis, the learners received either (1) complete explanations and engaging prompts designed to elicit active activities or (2) explanations that were reduced by inferences and inference prompts designed to engage learners in constructing the withheld information. Furthermore, in order to explore how interactive learning activities can be elicited, we gave the learners who had difficulties in constructing the prompted inferences adapted remedial explanations with either (3) unspecific engaging prompts or (4) revision prompts. In support of the active < constructive learning hypothesis, we found that the learners who received reduced explanations and inference prompts outperformed the learners who received complete explanations and engaging prompts. Moreover, revision prompts were more effective in eliciting interactive learning activities than engaging prompts. In Experiment 2, N = 40 students were randomly assigned to either (1) a reduced explanations and inference prompts or (2) a reduced explanations and inference prompts plus adapted remedial explanations and revision prompts condition. In support of the constructive < interactive learning hypothesis, the learners who received adapted remedial explanations and revision prompts as add-ons to reduced explanations and inference prompts acquired more conceptual knowledge.     

Aberrant Function of Learning and Cognitive Control Networks Underlie Inefficient Cognitive Flexibility in Anorexia Nervosa: A Cross-Sectional fMRI Study

ObjectivesPeople with Anorexia Nervosa exhibit difficulties flexibly adjusting behaviour in response to environmental changes. This has previously been attributed to problematic behavioural shifting, characterised by a decrease in fronto-striatal activity. Additionally, alterations of instrumental learning, which relies on fronto-striatal networks, may contribute to the observation of inflexible behaviour. The authors sought to investigate the neural correlates of cognitive flexibility and learning in Anorexia Nervosa.MethodThirty-two adult females with Anorexia Nervosa and thirty-two age-matched female control participants completed the Wisconsin Card Sorting Task whilst undergoing functional magnetic resonance imaging. Event-related analysis permitted the comparison of cognitive shift trials against those requiring maintenance of rule-sets and allowed assessment of trials representing learning.ResultsAlthough both groups performed similarly, we found significant interactions in the left middle frontal gyrus, precuneus and superior parietal lobule whereby blood-oxygenated-level dependent response was higher in Anorexia Nervosa patients during shifting but lower when maintaining rule-sets, as compared to healthy controls. During learning, posterior cingulate cortex activity in healthy controls decreased whilst increasing in the Anorexia Nervosa group, whereas the right precuneus exhibited the opposite pattern. Furthermore, learning was associated with lower blood-oxygenated-level dependent response in the caudate body, as compared to healthy controls.ConclusionsPeople with Anorexia Nervosa display widespread changes in executive function. Whilst cognitive flexibility appears to be associated with aberrant functioning of the fronto-parietal control network that mediates between internally and externally directed cognition, fronto-striatal alterations, particularly within the caudate body, were associated with instrumental learning. Together, this shows how perseverative tendencies could be a substrate of multiple high-order processes that may contribute to the maintenance of Anorexia Nervosa.     

Computer vision for LCA foreground modelling—an initial pipeline and proof of concept software,lcopt-cv

Purpose

The majority of LCA studies begin with the drawing of a process flow diagram, which then needs to be translated manually into an LCA model. This study presents an initial image processing pipeline, implemented in an open-source software package, called lcopt-cv, which can be used to identify the boxes and links in a photograph of a hand-drawn process flow diagram and automatically create an LCA foreground model.

Methods

The computer vision pipeline consists of a total of 15 steps, beginning with loading the image file and conversion to greyscale. The background is equalised, then the foreground of the image is extracted from the background using thresholding. The lines are then dilated and closed to account for drawing errors. Contours in the image are detected and simplified, and rectangles (contours with four corners) are identified from the simplified contours as ‘boxes’. Links between these boxes are identified using a flood-filling technique. Heuristic processing, based on knowledge of common practice in drawing of process flow diagrams, is then performed to more accurately identify the typology of the identified boxes and the direction of the links between them.

Results and discussion

The performance of the image processing pipeline was tested on four flow diagrams of increasing difficulty: one simple computer drawn diagram and three photographs of hand-drawn diagrams (a simple diagram, a complex diagram and a diagram with merged lines). A set of default values for the variables which define the pipeline was developed through trial and error. For the two simple flow charts, all boxes and links were identified using the default settings. The complex diagram required minor tweaks to the default values to detect all boxes and links. An ‘unstacking’ heuristic allowed the diagram with merged lines to be correctly processed. After some manual reclassification of link directions and process types, the diagrams were turned into LCA models and exported to open-source LCA software packages (lcopt and Brightway) to be verified and analysed.

Conclusions

This study demonstrates that it is possible to generate a fully functional LCA model from a picture of a flow chart. This has potentially important implications not only for LCA practitioners as a whole, but in particular for the teaching of LCA. Skipping the steep learning curve required by most LCA software packages allows teachers to focus on important LCA concepts, while participants maintain the benefits of experiential learning by doing a ‘real’ LCA.

     

Morningness-eveningness preferences,learning approach and academic achievement of undergraduate medical students

ABSTRACT

Several studies have focused on determining the effect of chronotype and learning approach on academic achievement separately indicating that morning types have an academic advantage over the evening types and so have the deep learners over the surface learners. But, surprisingly none have assessed the possible relationship between chronotype and learning approach. So, the current study aimed to evaluate this association and their individual influence on academic performance as indicated by the Cumulative Grade Point Average (CGPA) as well as the effect of their interaction on academic performance. The study included 345 undergraduate medical students who responded to reduced Morningness-Eveningness Questionnaire and Biggs Revised Two-Factor Study Process Questionnaire. Morning types indulged in deep learning while evening types in surface learning. Morning and evening types did not differ on academic performance but deep learners had better academic outcomes than their counterparts. The interaction between chronotype and learning approach was significant on determining academic achievement. Our findings gave the impression that chronotype could have an impact on academic performance not directly but indirectly through learning approaches.     

A neural model for conditioned avoidance learning

A mathematical model for learning of a conditioned avoidance behavior is presented. An identification of the net excitation of a neural model (Rashevsky, N., 1960.Mathematical Biophysics. Vol. II. New York: Dover Publications, Inc.) with the instantaneous probability of response is introduced and its usefulness in discussing block-trial learning performances in the conditioned avoidance situation is outlined for normal and brain-operated animals, using experimental data collected by the author. Later, the model is applied to consecutive trial learning and connection is made with the approach of H. D. Landahl (1964. “An Avoidance Learning Situation. A Neural Net Model.”Bull. Math. Biophysics,26, 83–89; and 1965, “A Neural Net Model for Escape Learning.”Bull. Math. Biophysics,27, Special Edition, 317–328) wherein lie further data with which the model can be compared.     

In vitro investigation of the factors affecting pulse oximetry

The effect of a number of physiological parameters on pulse oximetry accuracy has been investigated in an in vitro model. We have found that above 50% saturation, pulse oximeters will not be affected by variations in haematocrit, blood flow rate, tissue blood content and pulse amplitude. At low saturations, however, it is known that the accuracy of pulse oximeters decreases and our in vitro results suggest how this may be corrected.     

Evolution and plasticity: Divergence of song discrimination is faster in birds with innate song than in song learners in Neotropical passerine birds

Plasticity is often thought to accelerate trait evolution and speciation. For example, plasticity in birdsong may partially explain why clades of song learners are more diverse than related clades with innate song. This “song learning” hypothesis predicts that (1) differences in song traits evolve faster in song learners, and (2) behavioral discrimination against allopatric song (a proxy for premating reproductive isolation) evolves faster in song learners. We tested these predictions by analyzing acoustic traits and conducting playback experiments in allopatric Central American sister pairs of song learning oscines (N = 42) and nonlearning suboscines (N = 27). We found that nonlearners evolved mean acoustic differences slightly faster than did leaners, and that the mean evolutionary rate of song discrimination was 4.3 times faster in nonlearners than in learners. These unexpected results may be a consequence of significantly greater variability in song traits in song learners (by 54–79%) that requires song‐learning oscines to evolve greater absolute differences in song before achieving the same level of behavioral song discrimination as nonlearning suboscines. This points to “a downside of learning” for the evolution of species discrimination, and represents an important example of plasticity reducing the rate of evolution and diversification by increasing variability.     

A conserved GXXXG motif in the transmembrane domain of CLIC proteins is essential for their cholesterol-dependant membrane interaction

BackgroundSterols have been reported to modulate conformation and hence the function of several membrane proteins. One such group is the Chloride Intracellular Ion Channel (CLIC) family of proteins. The CLIC protein family consists of six evolutionarily conserved protein members in vertebrates. These proteins exist as both monomeric soluble proteins and as membrane bound proteins. To date, the structure of their membrane-bound form remains unknown. In addition to several studies indicating cellular redox environment and pH as facilitators of CLIC1 insertion into membranes, we have also demonstrated that the spontaneous membrane insertion of CLIC1 is regulated by membrane cholesterol.MethodWe have performed Langmuir-film, Impedance Spectroscopy and Molecular Docking Simulations to study the role of this GXXXG motif in CLIC1 interaction with cholesterol.ResultsUnlike CLIC1-wild-type protein, the G18A and G22A mutants, that form part of the GXXXG motif, showed much slower initial kinetics and lower ion channel activity compared to the native protein. This difference can be attributed to the significantly reduced membrane interaction and insertion rate of the mutant proteins and/or slower formation of the final membrane configuration of the mutant proteins once in the membrane.Conclusion: In this study, our findings uncover the identification of a GXXXG motif in CLIC1, which likely serves as the cholesterol-binding domain, that facilitates the protein's membrane interaction and insertion. Furthermore, we were able to postulate a model by which CLIC1 can autonomously insert into membranes to form functional ion channels.General significanceMembers of the CLIC family of proteins demonstrate unusual structural and dual functional properties – as ion channels and enzymes. Elucidating how the CLIC proteins' interact with membranes, thus allowing them to switch between their soluble and membrane form, will provide key information as to a mechanism of moonlighting activity and a novel regulatory role for cholesterol in such a process.     

A meta-learning approach for B-cell conformational epitope prediction

Background

One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.

Results

We developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor.

Conclusions

Computational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0378-y) contains supplementary material, which is available to authorized users.