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BACKGROUND: Feedback processes are crucial for learning, guiding improvement, and enhancing performance. In workplace-based learning settings, diverse teaching and assessment activities are advocated to be designed and implemented, generating feedback that students use, with proper guidance, to close the gap between current and desired performance levels. Since productive feedback processes rely on observed information regarding a student's performance, it is imperative to establish structured feedback activities within undergraduate workplace-based learning settings. However, these settings are characterized by their unpredictable nature, which can either promote learning or present challenges in offering structured learning opportunities for students. This scoping review maps literature on how feedback processes are organised in undergraduate clinical workplace-based learning settings, providing insight into the design and use of feedback. METHODS: A scoping review was conducted. Studies were identified from seven databases and ten relevant journals in medical education. The screening process was performed independently in duplicate with the support of the StArt program. Data were organized in a data chart and analyzed using thematic analysis. The feedback loop with a sociocultural perspective was used as a theoretical framework. RESULTS: The search yielded 4,877 papers, and 61 were included in the review. Two themes were identified in the qualitative analysis: (1) The organization of the feedback processes in workplace-based learning settings, and (2) Sociocultural factors influencing the organization of feedback processes. The literature describes multiple teaching and assessment activities that generate feedback information. Most papers described experiences and perceptions of diverse teaching and assessment feedback activities. Few studies described how feedback processes improve performance. Sociocultural factors such as establishing a feedback culture, enabling stable and trustworthy relationships, and enhancing student feedback agency are crucial for productive feedback processes. CONCLUSIONS: This review identified concrete ideas regarding how feedback could be organized within the clinical workplace to promote feedback processes. The feedback encounter should be organized to allow follow-up of the feedback, i.e., working on required learning and performance goals at the next occasion. The educational programs should design feedback processes by appropriately planning subsequent tasks and activities. More insight is needed in designing a full-loop feedback process, in which specific attention is needed in effective feedforward practices.

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Metastasis is a multi-step process that leads to the dissemination of tumor cells to new sites and, consequently, to multi-organ neoplasia. Although most lethal breast cancer cases are related to metastasis occurrence, little is known about the dysregulation of each step, and clinicians still lack reliable therapeutic targets for metastasis impairment. To fill these gaps, we constructed and analyzed gene regulatory networks for each metastasis step (cell adhesion loss, epithelial-to-mesenchymal transition, and angiogenesis). Through topological analysis, we identified E2F1, EGR1, EZH2, JUN, TP63, and miR-200c-3p as general hub-regulators, FLI1 for cell-adhesion loss specifically, and TRIM28, TCF3, and miR-429 for angiogenesis. Applying the FANMOD algorithm, we identified 60 coherent feed-forward loops regulating metastasis-related genes associated with distant metastasis-free survival prediction. miR-139-5p, miR-200c-3p, miR-454-3p, and miR-1301-3p, among others, were the FFL's mediators. The expression of the regulators and mediators was observed to impact overall survival and to go along with metastasis occurrence. Lastly, we selected 12 key regulators and observed that they are potential therapeutic targets for canonical and candidate antineoplastics and immunomodulatory drugs, like trastuzumab, goserelin, and calcitriol. Our results highlight the relevance of miRNAs in mediating feed-forward loops and regulating the expression of metastasis-related genes. Altogether, our results contribute to understanding the multi-step metastasis complexity and identifying novel therapeutic targets and drugs for breast cancer management.

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The quality control for fruit maturity inspection is a key issue in fruit packaging and international trade. The quantification of Soluble Solids (SS) in fruits gives a good approximation of the total sugar concentration at the ripe stage, and on the other hand, SS alone or in combination with acidity is highly related to the acceptability of the fruit by consumers. The non-destructive analysis based on Visible (VIS) and Near-Infrared (NIR) spectroscopy has become a popular technique for the assessment of fruit quality. To improve the accuracy of fruit maturity inspection, VIS−NIR spectra models based on machine learning techniques are proposed for the non-destructive evaluation of soluble solids in considering a range of variations associated with varieties of stones fruit species (peach, nectarine, and plum). In this work, we propose a novel approach based on a Convolutional Neural Network (CNN) for the classification of the fruits into species and then a Feedforward Neural Network (FNN) to extract the information of VIS−NIR spectra to estimate the SS content of the fruit associated to several varieties. A classification accuracy of 98.9% was obtained for the CNN classification model and a correlation coefficient of Rc>0.7109 for the SS estimation of the FNN models was obtained. The results reported show the potential of this method for a fast and on-line classification of fruits and estimation of SS concentration.

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OBJECTIVE: To identify and evaluate the best set of acoustic measures to discriminate among healthy, rough, breathy, and strained voices. METHODS: This study used the vocal samples of the sustained /ε/ vowel from 251 patients with the vocal complaints, among which 51, 80, 63, and 57 patients exhibited healthy, rough, breathy, and strained voices, respectively. Twenty-two acoustic measures were extracted, and feature selection was applied to reduce the number of combinations of acoustic measures and obtain an optimal subset of measures according to the information gain attribute ranking algorithm. To classify signals as a function of predominant voice quality, a feedforward neural network was applied using a Levenberg-Marquardt supervised learning algorithm. RESULTS: The best results were obtained from 11 combinations, with each combination presenting six acoustic measures. Kappa indices ranged from 0.7527 to 0.7743, the overall hit rates are 81.67%-83.27%, and the hit rates of healthy, rough, breathy, and strained voices are 74.51%-84.31%, 78.75%-90.00%, 85.71%-98.41%, and 68.42%-82.46%, respectively. CONCLUSIONS: We obtained the best results from 11 combinations, with each combination exhibiting six acoustic measures for discriminating among healthy, rough, breathy, and strained voices. These sets exhibited good Kappa performance and a good overall hit rate. The hit rate varied between acceptable and good for healthy voices, acceptable and excellent for rough voices, good and excellent for breathy voices, and poor and good for strained voices.

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This paper investigates whether a group of regular Yoga practitioners shows postural control differences compared with healthy controls while performing single-leg Yoga postures. Ten Yoga practitioners were compared with a control group of 10 nonpractitioners performing two single-leg support Yoga postures: Vrksasana (tree posture) and Natarajasana (dancer posture). Rambling and trembling decomposition of the center of pressure trajectories was implemented using a genetic algorithm spectral optimization that avoids using horizontal forces and was validated with bipedal posture data. Additionally, the center of mass was estimated from body kinematics using OpenSim and compared with the rambling outputs. During Natarajasana, no postural control adaptations were observed. For Vrksasana, the Yoga practitioners showed a lower center of pressure ellipse confidence interval area, center of pressure anteroposterior SD, and smaller rambling SD in the mediolateral direction, suggesting possible supraspinal feed-forward motor adaptations associated with Yoga training.

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The striatum is the largest entrance to the basal ganglia. Diverse neuron classes make up striatal microcircuit activity, consisting in the sequential activation of neuronal ensembles. How different neuron classes participate in generating ensemble sequences is unknown. In control mus musculus brain slices in vitro, providing excitatory drive generates ensemble sequences. In Parkinsonian microcircuits captured by a highly recurrent ensemble, a cortical stimulus causes a transitory reconfiguration of neuronal groups alleviating Parkinsonism. Alternation between neuronal ensembles needs interconnectivity, in part due to interneurons, preferentially innervated by incoming afferents. One main class of interneuron expresses parvalbumin (PV+ neurons) and mediates feed-forward inhibition. However, its more global actions within the microcircuit are unknown. Using calcium imaging in ex vivo brain slices simultaneously recording dozens of neurons, we aimed to observe the actions of PV+ neurons within the striatal microcircuit. PV+ neurons in active microcircuits are 5%-11% of the active neurons even if, anatomically, they are <1% of the total neuronal population. In resting microcircuits, optogenetic activation of PV+ neurons turns on circuit activity by activating or disinhibiting, more neurons than those actually inhibited, showing that feed-forward inhibition is not their only function. Optostimulation of PV+ neurons in active microcircuits inhibits and activates different neuron sets, resulting in the reconfiguration of neuronal ensembles by changing their functional connections and ensemble membership, showing that neurons may belong to different ensembles at different situations. Our results show that PV+ neurons participate in the mechanisms that generate alternation of neuronal ensembles, therefore provoking ensemble sequences.

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BACKGROUND: The transcription factor MYC regulates several biological cellular processes, and its target gene network comprises approximately 15% of all human genes, including microRNAs (miRNAs), that also contribute to MYC regulatory activity. Although miRNAs are emerging as key regulators of immune functions, the specific roles of miRNAs in the regulation/dysregulation of germinal centre B-cells and B-cell lymphomas are still being uncovered. The regulatory network that integrates MYC, target genes and miRNAs is a field of intense study, highlighting potential pathways to be explored in the context of future clinical approaches. METHODS: The scientific literature that is indexed in PUBMED was consulted for publications involving MYC and miRNAs with validated bioinformatics analyses or experimental protocols. Additionally, seminal studies on germinal centre B-cell functions and lymphomagenesis were reported. CONCLUSIONS: This review summarizes the interactions between MYC and miRNAs through regulatory loops and circuits involving target genes in germinal centre B-cell lymphomas with MYC alterations. Moreover, we provide an overview of the understanding of the regulatory networks between MYC and miRNAs, highlighting the potential implication of this approach for the comprehension of germinal centre B-cell lymphoma pathogenesis. Therefore, circuits involving MYC, target genes and miRNAs provide novel insight into lymphomagenesis that could be useful for new improved therapeutic strategies.

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Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reliable forms of our Extreme Learning Machines (ELM) classifiers, used to maintain a more consistent signal classification. To perform the signal processing, we explore the use of a stochastic filter based on the Antonyan Vardan Transform (AVT) in combination with two variations of our Reliable classifiers (denoted R-ELM and R-Regularized ELM (RELM), respectively), to derive a reliability metric from the system, which autonomously selects the most reliable samples for the signal classification. To validate and compare our database and classifiers with related papers, we performed the classification of the whole of Databases 1, 2, and 6 (DB1, DB2, and DB6) of the NINAProdatabase. Our database presented consistent results, while the reliable forms of ELM classifiers matched or outperformed related papers, reaching average accuracies higher than 99 % for the IEEdatabase, while average accuracies of 75 . 1 % , 79 . 77 % , and 69 . 83 % were achieved for NINAPro DB1, DB2, and DB6, respectively.

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Object manipulation depends on a refined control of grip force (GF) and load force (LF). After a brain injury, the GF control is altered in the paretic hand but what happens with the non-paretic hand is still unclear. In this study, we compared the GF control and GF-LF coordination of the non-paretic hand of 10 stroke individuals who suffered right brain damage (RBD) and 10 who suffered left brain damage (LBD), with 20 healthy individuals during lifting and oscillation task, using an instrumented object. GF was recorded with a force transducer, and LF was estimated from the object weight and acceleration. Overall, the ipsilesional hand of stroke individuals, independent of the lesion side, presented similar GF control and GF-LF coordination. However, LBD individuals took longer to start lifting the object, which may be due to the need of more time to obtain somatosensory information from the contact with the object. The findings indicate that stroke individuals preserve their ability to control and coordinate GF and LF when using their ipsilesional hand for object manipulation and the left hemisphere may play an essential role in the processing of somatosensory information needed for the GF control.

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Abstract Introduction The aim of this study was to predict 3D ground reaction force signals based on accelerometer data during gait, using a feed-forward neural network (MLP). Methods Seventeen healthy subjects were instructed to walk at a self-selected speed with a 3D accelerometer attached to the distal and anterior part of the shank. A force plate was embedded into the middle of the walkway. MLP neural networks with one hidden layer and three output layers were selected to simulate the anteroposterior (AP), vertical (Vert) and mediolateral (ML) ground reaction forces (GRF). The input layer was composed of fourteen inputs obtained from accelerometer signals, selected based on previous studies. Principal component analysis (PCA) was used to compare the simulated and collected curves. The Pearson correlation coefficient and the mean absolute deviation (MAD) between signals were calculated. Results PCA identified small, but significant differences between collected and simulated signals in the loading response phases of AP and ML GRF, while Vert did not show differences. The correlation between the simulated and collected signals was high (AP: 0.97; Vert: 0.98; ML: 0.80). MAD was 1.8%BW for AP, 4.5%BW for Vert and 1.4%BW for ML. Conclusion This study confirmed that multilayer perceptron neural network can predict the highly non-linear relationship of shank acceleration parameters and ground reaction forces, as well as other studies have done using plantar pressure devices. The greater advantages of this device are the low cost and the possibility of use outside the laboratory environment.

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We used biotinylated dextran amine (BDA) to anterogradely label individual axons projecting from primary somatosensory cortex (S1) to four different cortical areas in rats. A major goal was to determine whether axon terminals in these target areas shared morphometric similarities based on the shape of individual terminal arbors and the density of two bouton types: en passant (Bp) and terminaux (Bt). Evidence from tridimensional reconstructions of isolated axon terminal fragments (n=111) did support a degree of morphological heterogeneity establishing two broad groups of axon terminals. Morphological parameters associated with the complexity of terminal arbors and the proportion of beaded Bp vs stalked Bt were found to differ significantly in these two groups following a discriminant function statistical analysis across axon fragments. Interestingly, both groups occurred in all four target areas, possibly consistent with a commonality of presynaptic processing of tactile information. These findings lay the ground for additional work aiming to investigate synaptic function at the single bouton level and see how this might be associated with emerging properties in postsynaptic targets.

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El "CORE" es un concepto funcional que engloba la integración de tres sistemas cuyo óptimo funcionamiento garantiza la realización de tareas con una mayor eficacia y seguridad a nivel raquídeo, permitiendo adecuados niveles de estabilidad y control del movimiento. En este sentido, a fin de afrontar con éxito retos que demanden un control dinámico de la columna y la pelvis, el SNC debe aplicar estrategias diferentes, sopesando as fuerzas internas y externas con el fin de proporcionar una respuesta muscular que permita un movimiento óptimo y resista cualquier posible perturbación. En el presente manuscrito se revisa de forma aplicada, las bases, atendiendo a la información disponible actualmente, de los mecanismos básicos de control motor y las posibles alteraciones en los mismos a ser considerados por los especialistas en ejercicio respecto a su intervención mediante programas de ejercicio para la mejora de la capacidad de estabilización raquídea

The "CORE" is a funcional concept that englobes the integration of three systems which optimal operation guarantees better eficiency and security in tasks related with the spine, allowing appropiate stability and movement control levels. In order to successfully addres challenges which demand a dynamic control of the spine and the pelvis, the SNC must use diferent strategies, weighing the internal and external forces in order to provide a muscular response to allow an appropiate movement and resist any possible disturbance. This article reviews the foundations based on the information currently available about the basic mechanisms of motor control and posible changes in them, to be considered by exercise specialists regarding to their exercise intervention programs to improve spinal stabilization capacity

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Autism diagnosis requires validated diagnostic tools employed by mental health professionals with expertise in autism spectrum disorders. This conventionally requires lengthy information processing and technical understanding of each of the areas evaluated in the tools. Classifying the impact of these areas and proposing a system that can aid experts in the diagnosis is a complex task. This paper presents the methodology used to find the most significant items from the ADOS-G tool to detect Autism Spectrum Disorders through Feed-forward Artificial Neural Networks with back-propagation training. The number of cases for the network training data was determined by using the Taguchi method with Orthogonal Arrays reducing the sample size from 531,441 to only 27. The trained network provides an accuracy of 100% with 11 different cases used only for validation, which provides a specificity and sensitivity of 1. The network was used to classify the 12 items from the ADOS-G tool algorithm into three levels of impact for Autism diagnosis: High, Medium and Low. It was found that the items "Showing", "Shared enjoyment in Interaction" and "Frequency of vocalization directed to others", are the areas of highest impact for Autism diagnosis. The methodology here presented can be replicated to different Autism diagnosis tests to classify their impact areas as well.

El diagnóstico del autismo requiere del uso de herramientas de diagnóstico validadas internacionalmente que son utilizadas por los profesionales de la salud expertos en trastornos del espectro autista, lo cual requiere de procesamiento de mucha información y un entendimiento técnico de cada una de las áreas evaluadas en ellas. La clasificación del impacto que tienen cada una de estas áreas, así como la propuesta de un sistema que pueda ayudar a los expertos en el diagnóstico, es una tarea compleja, por lo que en este artículo se presenta una metodología utilizada para encontrar los elementos más significativos de la herramienta de diagnóstico de autismo ADOS-G a través de redes neuronales artificiales entrenadas con retropropagación del error. El número de casos para entrenamiento de la red se seleccionó utilizando el método de Taguchi con arreglos ortogonales, reduciendo el tamaño de la muestra de 531,441 a solo 27 casos. La red entrenada tiene una exactitud del 100% validada con 11 casos diferentes de niños evaluados para diagnóstico de trastorno del espectro autista con lo que se obtuvo una especificidad y sensibilidad de 1. La red neuronal artificial se utilizó para clasificar los 12 elementos del algoritmo de la herramienta ADOS-G en tres niveles de impacto: Alto, Medio y Bajo. Se encontró que los elementos "Mostrar", "Placer compartido durante la interacción" y "Frecuencia de vocalizaciones dirigidas a otros" son las áreas de mayor impacto para el diagnóstico de autismo. La metodología presentada puede ser replicada para diferentes herramientas de diagnóstico de autismo para clasificar sus áreas de mayor impacto también.

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In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and single-path assumption, but also by demonstrating the importance of considering temporal dynamics, and specifically introducing the usefulness of Boolean network models and also reviewing some key properties of network approaches.

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Gaseous ethanol may be recovered from the effluent gas mixture of the sugar cane fermentation process using a staged absorption column. In the present work, the development of a nonlinear controller, based on a neural network inverse model (ANN controller), was proposed and tested to manipulate the absorbent flow rate in order to control the residual ethanol concentration in the effluent gas phase. Simulation studies were carried out, in which a noise was applied to the ethanol concentration signals from the rigorous model. The ANN controller outperformed the dynamic matrix control (DMC) when step disturbances were imposed to the gas mixture composition. A security device, based on a conventional feedback algorithm, and a digital filter were added to the proposed strategy to improve the system robustness when unforeseen operating and environmental conditions occured. The results demonstrated that ANN controller was a robust and reliable tool to control the absorption column.

Deseja-se recuperar o etanol perdido por evaporação durante o processo de fermentação da cana-de-açúcar. Para tanto, faz-se uso de uma coluna de absorção. O controle da concentração de etanol no efluente gasoso da coluna é realizado pela manipulação da vazão de solvente, sendo esta determinada pelo controlador não linear proposto, baseado em um modelo inverso de redes neurais (controlador ANN). Foram feitas simulações adicionando-se um sinal de ruído a medida de concentração de etanol na fase gasosa. Quando perturbações degrau foram inseridas na mistura gasosa afluente, o controlador ANN demonstrou desempenho superior ao controle por matriz dinâmica (DMC). Um dispositivo de segurança, baseado em um controlador feedback convencional, e um filtro digital foram implementados à estratégia de controle proposta para agregar robustez no tratamento de distúrbios ocorridos no ambiente operacional. Os resultados demonstraram que o controlador ANN é uma ferramenta robusta e confiável no controle de uma coluna de absorção.