Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

Over the past two decades, the study of resting-state functional magnetic resonance imaging (fMRI) has revealed the existence of multiple brain areas displaying synchronous functional blood oxygen level-dependent signals (BOLD) - resting-state networks (RSNs). The variation in functional connectivity between the different areas of a resting-state network or between multiple networks, have been extensively studied and linked to cognitive states and pathologies. However, the white matter connections supporting each network remain only partially described. In this work, we developed a data-driven method to systematically map the white and grey matter contributing to resting-state networks. Using the Human Connectome Project, we generated an atlas of 30 resting-state networks, each with two maps: white matter and grey matter. By integrating structural and functional neuroimaging data, this method builds an atlas that unlocks the joint anatomical exploration of white and grey matter to resting-state networks. The method also allows highlighting the overlap between networks, which revealed that most (89%) of the brain's white matter is shared amongst multiple networks, with 16% shared by at least 7 resting-state networks. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the correlations and the communication within resting-state networks. We provide an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage on RSNs.

Web Service Applications and Consumer Environments Based on ICT-Driven Optimization

Networking is the use of physical links to connect individual isolated workstations or hosts together to form data links for the purpose of resource sharing and communication. In the field of web service application and consumer environment optimization, it has been shown that the introduction of network embedding methods can effectively alleviate the problems such as data sparsity in the recommendation process. However, existing network embedding methods mostly target a specific structure of network and do not collaborate with multiple relational networks from the root. Therefore, this paper proposes a service recommendation model based on the hybrid embedding of multiple networks and designs a multinetwork hybrid embedding recommendation algorithm. First, the user social relationship network and the user service heterogeneous information network are constructed; then, the embedding vectors of users and services in the same vector space are obtained through multinetwork hybrid embedding learning; finally, the representation vectors of users and services are applied to recommend services to target users. To verify the effectiveness of this paper’s method, a comparative analysis is conducted with a variety of representative service recommendation methods on three publicly available datasets, and the experimental results demonstrate that this paper’s multinetwork hybrid embedding method can effectively collaborate with multirelationship networks to improve service recommendation quality, in terms of recommendation efficiency and accuracy.

Cosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture data

Understanding soil moisture dynamics at the sub-kilometre scale is increasingly important, especially with the continuous development of hyper-resolution land surface and hydrological models. Cosmic-ray neutron sensors (CRNSs) are able to provide estimates of soil moisture at this elusive scale, and networks of these sensors have been expanding across the world over the previous decade. However, each network currently implements its own protocol when processing raw data into soil moisture estimates. As a consequence, this lack of a harmonised global data set can ultimately lead to limitations in the global assessment of the CRNS technology from multiple networks. Here, we present crspy, an open-source Python tool that is designed to facilitate the processing of raw CRNS data into soil moisture estimates in an easy and harmonised way. We outline the basic structure of this tool, discussing the correction methods used as well as the metadata that crspy can create about each site. Metadata can add value to global-scale studies of field-scale soil moisture estimates by providing additional routes to understanding catchment similarities and differences. We demonstrate that current differences in processing methodologies can lead to misinterpretations when comparing sites from different networks and that having a tool to provide a harmonised data set can help to mitigate these issues. By being open source, crspy can also serve as a development and testing tool for new understanding of the CRNS technology as well as being used as a teaching tool for the community.

Delivery of Ecosystem Services by Community Woodland Groups and Their Networks

The Millennium Ecosystem Assessment stresses that it is possible to manage ecosystems so as to strengthen their capacity to provide a range of goods and services. In reality, the delivery of ecosystem services reflects policy and delivery mechanisms, the environment, and the objectives of landowners and managers. Amid gradual changes to forest policy and more recent periods of austerity, the management of treescapes by locally led groups, such as Community Woodland Groups (CWGs), has become increasingly common. Through document analysis and interviews we explore the objectives and activities of British-based CWGs, and the implications these have for the delivery of ecosystem services. Additionally, we explore CWGs involvement with three types of networks and the ways in which each facilitate CWGs’ establishment, operations and ecosystem service provision. We conclude that, while CWGs are capable of delivering a range of ecosystem services, their focus is typically on: (i) cultural services for the benefit of the local community, and (ii) biodiversity. Since these foci parallel the goods and services emphasised in contemporary forest policy agendas, it is apparent that CWGs represent a promising model for woodland management. However, to realise their potential and confront management challenges, CWGs often rely on access to advice, labour, equipment and funding from across multiple networks.

Approach to Tremor Disorders

Tremor disorders are diverse and complex. Historical clues and examination features play a major role in diagnosing these disorders, but diagnosis can be challenging due to phenotypic overlap. Ancillary testing, such as neuroimaging or laboratory testing, is driven by the history and examination, and should be performed particularly when there are other neurological or systemic manifestations. The pathophysiology of tremor is not entirely understood, but likely involves multiple networks along with the cerebello-thalamo-cortical pathways. Treatment options include medications, botulinum toxin, surgery, and nonpharmacologic interventions utilizing physical and occupational therapies and assistive devices. Further work is needed in developing accurate diagnostic tests and better treatment options for tremor disorders.

Explainable Neural Network Ensembles

Neural networks are known for providing impressive classification performance, and the ensemble learning technique is further acting as a catalyst to enhance this performance by integrating multiple networks. But like neural networks, neural network ensembles are also considered as a black-box because they cannot explain their decision making process. So, despite having high classification performance, neural networks and their ensembles are not suited for some applications which require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks with the power to justify their classification responses through explainable classification rules. Several rule extraction algorithms exist to extract classification rules from neural networks, but only a few of them generates rules using neural network ensembles. So this paper proposes an algorithm named Rule Extraction using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles through rule extraction. RE-E-NNES extracts classification rules by ensembling several neural network ensembles. Results show the efficacy of the proposed RE-E-NNES algorithm compared to different existing rule extraction algorithms.

Multiplex Embedding of Biological Networks Using Topological Similarity of Different Layers

Network embedding techniques, which provide low dimensional representations of the nodes in a network, have been commonly applied to many machine learning problems in computational biology. In most of these applications, multiple networks (e.g., different types of interactions/associations or semantically identical networks that come from different sources) are available. Multiplex network embedding aims to derive strength from these data sources by integrating multiple networks with a common set of nodes. Existing approaches to this problem treat all layers of the multiplex network equally while performing integration, ignoring the differences in the topology and sparsity patterns of different networks. Here, we formulate an optimization problem that accounts for inner-network smoothness, intra-network smoothness, and topological similarity of networks to compute diffusion states for each network. To quantify the topological similarity of pairs of networks, we use Gromov-Wasserteins discrepancy. Finally, we integrate the resulting diffusion states and apply dimensionality reduction (singular value decomposition after log-transformation) to compute node embeddings. Our experimental results in the context of drug repositioning and drug-target prediction show that the embeddings computed by the resulting algorithm, Hattusha, consistently improve predictive accuracy over algorithms that do not take into account the topological similarity of different networks.

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability, to better understand if hubs are (a) relatively conserved across people, (b) locations with malleable connectivity, leading individuals to show variable hub profiles, or (c) artifacts arising from cross-person variation. To answer this question, we compared the locations of hubs and regions of strong idiosyncratic functional connectivity ("variants") in both the Midnight Scan Club and Human Connectome Project datasets. Group hubs defined based on the participation coefficient did not overlap strongly with variants. These hubs have relatively strong similarity across participants and consistent cross-network profiles. Consistency across participants was further improved when participation coefficient hubs were allowed to shift slightly in local position. Thus, our results demonstrate that group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density, which are based on spatial proximity and show higher correspondence to locations of individual variability.

Effective framework to tackle urban unemployment by e-government: an IoT solution for smart/metro cities in developing nation

Purpose With the advancement in technology, the day-to-day life of people has gone through an immense transformation. The use of smart devices for day-to-day life is greater than before, and people are moving towards smart work rather than doing hard work. In this paper, a novel framework is proposed named Online Service Provider in Metro City (OSPMC) for IoT. The purpose of this study is to provide a theoretical framework for the E-Government in order to sustain or minimize the unemployment rate. Design/methodology/approach The utilization of the Web in the upcoming years would create further opportunities for smart work. Internet of Things (IoT) plays an essential part in a system of multiple networks that aims to connect all things in the world that are capable of being connected through the internet. OSPMC framework can be developed on ASP.NET through (visual C#) 3.0 and Microsoft SQL Server with frontend and backend languages, respectively, under a web-based environment built on .Net platform. This lucrative facility is available mainly for the people living in the smart city. Findings Rural people are coming to smart cities in search of jobs, better education and a healthy lifestyle. India is also coping up with the modern world. The Government of India has taken an initiative for the making of 100 smart cities where the residents are relied upon to use Information and Communication Technology with the assistance of Web. Social implications OSPMC promises to eliminate multiple evils like social injustice, crime, unemployment, tax fraud and would promote women empowerment. Also, provide opportunities to start-ups in order to grow and enhance their skill work. Originality/value The objective of OSPMC can be found useful for opening new job opportunities for urban/rural people while also encouraging people to learn skill work. For smart cities’ success in developing nations, it is important to recognize the elements influencing it. The motivation behind OSPMC is to identify those variables influencing the successful usage of allowing IoT in the smart cities by E-Government of India and to use IoT to help urban smart cities.