Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors

Neural coding of gait intent and continuous gait kinematics have advanced brain computer interface (BCI) technology for detection and predicting human upright walking movement. However, the dynamics of cortical involvement in upright walking and upright standing has not been clearly understood especially with the focus of off-laboratory assessments. In this study, wearable low-cost mobile phone accelerometers were used to extract position and velocity at 12 joints during walking and the cortical changes involved during gait phases of walking were explored using non-invasive electroencephalogram (EEG). Extracted gait data included, accelerometer values proximal to brachium of arm, antecubitis, carpus, coxal, femur and tarsus by considering physical parameters including height, weight and stride length. Including EEG data as features, the spectral and temporal features were used to classify and predict the swing and stance instances for healthy subjects. While focusing on stance and swing classification in healthy subjects, this chapter relates to gait features that help discriminate walking movement and its neurophysiological counterparts. With promising initial results, further exploration of gait may help change detection of movement neurological conditions in regions where specialists and clinical facilities may not be at par

Diversity and Neocolonialism in Big Data Research. Avoiding extractivism while struggling with paternalism.

The extractive logic of Big Data-driven technology and knowledge production has raised serious concerns. While most criticism initially focused on the impacts on Western societies, attention is now increasingly turning to the consequences for communities in the Global South. To date, debates have focused on private-sector activities. In this article, we start from the conviction that publicly funded knowledge and technology production must also be scrutinized for their potential neocolonial entanglements. To this end, we analyze the dynamics of collaboration in an European Union-funded research project that collects data for developing a social platform focused on diversity. The project includes pilot sites in China, Denmark, the United Kingdom, India, Italy, Mexico, Mongolia, and Paraguay. We present the experience at four field sites and reflect on the project’s initial conception, our collaboration, challenges, progress, and results. We then analyze the different experiences in comparison. We conclude that while we have succeeded in finding viable strategies to avoid contributing to the dynamics of unilateral data extraction as one side of the neocolonial circle, it has been infinitely more difficult to break through the much more subtle but no less powerful mechanisms of paternalism that we find to be prevalent in data-driven North–South relations. These mechanisms, however, can be identified as the other side of the neocolonial circle.</p

Temporal constrained objects for modelling neuronal dynamics

Background Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. Methods In this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. Results We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron. Discussion Temporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits

RSCDNet: A Robust Deep Learning Architecture for Change Detection From Bi-Temporal High Resolution Remote Sensing Images

Accurate change detection from high-resolution satellite and aerial images is of great significance in remote sensing for precise comprehension of Land cover (LC) variations. The current methods compromise with the spatial context; hence, they fail to detect and delineate small change areas and are unable to capture the difference between features of the bi-temporal images. This paper proposes Remote Sensing Change Detection Network (RSCDNet) - a robust end-to-end deep learning architecture for pixel-wise change detection from bi-temporal high-resolution remote-sensing (HRRS) images. The proposed RSCDNet model is based on an encoder-decoder framework integrated with the Modified Self-Attention (MSA) andthe Gated Linear Atrous Spatial Pyramid Pooling (GL-ASPP) blocks; both efficient mechanisms to regulate the field-of-view while finding the most suitable trade-off between accurate localization and context assimilation. The paper documents the design and development of the proposed RSCDNet model and compares its qualitative and quantitative results with state-of-the-art HRRS change detection architectures. The above mentioned novelties in the proposed architecture resulted in an F1-score of 98%, 98%, 88%, and 75% on the four publicly available HRRS datasets namely, Staza-Tisadob, Onera, CD-LEVIR, and WHU. In addition to the improvement in the performance metrics, the strategic connections in the proposed GL-ASPP and MSA units significantly reduce the prediction time per image (PTPI) and provide robustness against perturbations. Experimental results yield that the proposed RSCDNet model outperforms the most recent change detection benchmark models on all four HRRS datasets

Measurement of Recent Exposure to Phlebotomus argentipes, the Vector of Indian Visceral Leishmaniasis, by Using Human Antibody Responses to Sand Fly Saliva

Antibody (IgG) responses to the saliva of Phlebotomus argentipes were investigated using serum samples from regions of India endemic and non-endemic for visceral leishmaniasis (VL). By pre-adsorbing the sera against the saliva of the competing human-biting but non-VL vector P. papatasi, we significantly improved the specificity of a P. argentipes saliva enzyme-linked immunosorbent assay. Using this method, we observed a statistically significant correlation between antibodies to P. argenitpes saliva and the average indoor density of female sand flies. Additionally, the method was able to detect recent changes in vector exposure when sera from VL patients were assayed before, during, and after hospitalization and protected from sand fly bites under untreated bed nets. Collectively, these results highlight the utility of antibodies to P. argentipes saliva as an important tool to evaluate VL vector control programs

An appeal to the global health community for a tripartite innovation: an ‘‘Essential Diagnostics List,’’ ‘‘Health in All Policies,’’ and ‘‘See-Through 21st Century Science and Ethics"

Diagnostics spanning a wide range of new biotechnologies, including proteomics, metabolomics, and nanotechnology, are emerging as companion tests to innovative medicines. In this Opinion, we present the rationale for promulgating an ‘‘Essential Diagnostics List.’’ Additionally, we explain the ways in which adopting a vision for ‘‘Health in All Policies’’ could link essential diagnostics with robust and timely societal outcomes such as sustainable development, human rights, gender parity, and alleviation of poverty. We do so in three ways. First, we propose the need for a new, ‘‘see through’’ taxonomy for knowledge-based innovation as we transition from the material industries (e.g., textiles, plastic, cement, glass) dominant in the 20th century to the anticipated knowledge industry of the 21st century. If knowledge is the currency of the present century, then it is sensible to adopt an approach that thoroughly examines scientific knowledge, starting with the production aims, methods, quality, distribution, access, and the ends it purports to serve. Second, we explain that this knowledge trajectory focus on innovation is crucial and applicable across all sectors, including public, private, or public–private partnerships, as it underscores the fact that scientific knowledge is a co-product of technology, human values, and social systems. By making the value systems embedded in scientific design and knowledge co-production transparent, we all stand to benefit from sustainable and transparent science. Third, we appeal to the global health community to consider the necessary qualities of good governance for 21st century organizations that will embark on developing essential diagnostics. These have importance not only for science and knowledge based innovation, but also for the ways in which we can build open, healthy, and peaceful civil societies today and for future generations

Effect of Village-wide Use of Long-Lasting Insecticidal Nets on Visceral Leishmaniasis Vectors in India and Nepal: A Cluster Randomized Trial

Visceral leishmaniasis (VL) is a vector-borne disease causing at least 60,000 deaths each year amongst an estimated half million cases, and until recently there have been no significant initiatives to reduce this burden. However, in 2005, the governments of India, Bangladesh and Nepal signed a memorandum of understanding at the World Health Assembly in Geneva for the elimination of the disease by 2015. In the absence of an effective vaccine, the program will rely on the active detection and prompt treatment of cases throughout the endemic region, combined with a recurrent indoor residual spraying (IRS) of all villages at risk. Vector control programs based on IRS are notorious for failing to maintain comprehensive spray coverage over time owing to logistical problems and lack of compliance by householders. Long-lasting insecticidal nets (LNs) have been postulated as an alternative or complement to IRS. Here we describe how comprehensive coverage of LN in trial communities reduced the indoor density of sand flies by 25% compared to communities without LNs. This provides an indication that LNs could be usefully deployed as a component of the VL control program in the Indian subcontinent

Local Field Potential Modeling Predicts Dense Activation in Cerebellar Granule Cells Clusters under LTP and LTD Control

Local field-potentials (LFPs) are generated by neuronal ensembles and contain information about the activity of single neurons. Here, the LFPs of the cerebellar granular layer and their changes during long-term synaptic plasticity (LTP and LTD) were recorded in response to punctate facial stimulation in the rat in vivo. The LFP comprised a trigeminal (T) and a cortical (C) wave. T and C, which derived from independent granule cell clusters, co-varied during LTP and LTD. To extract information about the underlying cellular activities, the LFP was reconstructed using a repetitive convolution (ReConv) of the extracellular potential generated by a detailed multicompartmental model of the granule cell. The mossy fiber input patterns were determined using a Blind Source Separation (BSS) algorithm. The major component of the LFP was generated by the granule cell spike Na+ current, which caused a powerful sink in the axon initial segment with the source located in the soma and dendrites. Reproducing the LFP changes observed during LTP and LTD required modifications in both release probability and intrinsic excitability at the mossy fiber-granule cells relay. Synaptic plasticity and Golgi cell feed-forward inhibition proved critical for controlling the percentage of active granule cells, which was 11% in standard conditions but ranged from 3% during LTD to 21% during LTP and raised over 50% when inhibition was reduced. The emerging picture is that of independent (but neighboring) trigeminal and cortical channels, in which synaptic plasticity and feed-forward inhibition effectively regulate the number of discharging granule cells and emitted spikes generating “dense” activity clusters in the cerebellar granular layer

Cerebellum and its disorders

The cerebellum has been known for its roles in motor coordination and timing and has been recently implicated in normal cognitive functions such as facial recognition, emotion and directed attention. Neurological disorders such as ataxia, autism, Alzheimer's dementia, Parkinson's disease, dyskinesia have been linked with dysfunctions of cerebellum and interconnected circuits. In this chapter, we discuss the roles and computational models of cerebellar network along with its interactions with other circuits including basal ganglia and cortico-cerebellar-thalamic-cortical circuits. The dynamics of such large-scale reconstructions allow for simulating several interlocking levels at single neuron and network circuits for modeling disease conditions. The chapter provides an overview of the computational properties of the cerebellar circuits and potential reconstruction of emergent behavior. Mathematical modeling of local field response during control and diseased condition has been indicated to simulate information processing in disease conditions. We also discuss models of ataxia, autism and epileptic conditions as simulated using the cerebellar granular layer