In vitro three-dimensional (3D) cell culture models, such as organoids and spheroids, are valuable tools for many applications including development and disease modeling, drug discovery, and regenerative medicine. To fully exploit these models, it is crucial to study them at cellular and subcellular levels. However, characterizing such in vitro 3D cell culture models can be technically challenging and requires specific expertise to perform effective analyses. Here, this paper provides detailed, robust, and complementary protocols to perform staining and subcellular resolution imaging of fixed in vitro 3D cell culture models ranging from 100 µm to several millimeters. These protocols are applicable to a wide variety of organoids and spheroids that differ in their cell-of-origin, morphology, and culture conditions. From 3D structure harvesting to image analysis, these protocols can be completed within 4-5 days. Briefly, 3D structures are collected, fixed, and can then be processed either through paraffinembedding and histological/immunohistochemical staining, or directly immunolabeled and prepared for optical clearing and 3D reconstruction (200 µm depth) by confocal microscopy.
Currently, research on gesture recognition systems has been on the rise due to the capabilities these systems provide to the field of human–machine interaction, however, gesture recognition in prosthesis and orthesis has been carried out through the use of an extensive amount of channels and electrodes to acquire the EMG (Electromyography) signals, increasing the cost and complexity of these systems. The scientific literature shows different approaches related to gesture recognition based on the analysis of EMG signals using deep learning models, highlighting the recurrent neural networks with deep learning structures. This paper presents the implementation of a Recurrent Neural Network (RNN) model using Long-short Term Memory (LSTM) units and dense layers to develop a gesture classifier for hand prosthesis control, aiming to decrease the number of EMG channels and the overall model complexity, in order to increase its scalability for embedded systems. The proposed model requires the use of only four EMG channels to recognize five hand gestures, greatly reducing the number of electrodes compared to other approaches found in the literature. The proposed model was trained using a dataset for each gesture EMG signals, which were recorded for 20 s using a custom EMG armband. The model reached an accuracy of to 99% for the training and validation stages, and an accuracy of 87 ± 7% during real-time testing. The results obtained by the proposed model establish a general methodology for the reduction of complexity in the recognition of gestures intended for human.machine interaction for different computational devices.
"All men can see these tactics whereby I conquer, but what none can see is the strategy out of which victory is evolved. "Tzun Tzu.Abstract. This work attempts to underline that motivating and managing change in the mentality of its environment, and being in tune with society's changing needs is the basis of successful strategic planning. Our aim is to highlight planning as a way of learning, that is, planning implies changing ways of thinking, not making plans. Strategic learning requires releasing the mind in order to slip flexibly into the continuous line and to achieve the creation of possible action courses from a fertile dialogue between thought and action. Using our insights from the two literatures, we propose a dynamic, integrative conceptual model of change based on organizational learning. This practice has been analysed in three Spanish cities where important events have taken place.
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