Journal articles on the topic 'Complex Networks of treaties'
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Denters, Erik, and Tarcisio Gazzini. "The Role of African Regional Organizations in the Promotion and Protection of Foreign Investment." Journal of World Investment & Trade 18, no. 3 (December 26, 2017): 449–92. http://dx.doi.org/10.1163/22119000-12340048.
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A complex, fragmented and heterogeneous network of domestic and international legal instruments promotes and protects foreign investment in Africa. While bilateral treaties seem to be increasingly unpopular, regionalism is clearly on the rise in the continent. The article examines how regional treaties have contributed to upgrade the current regulation of foreign investment. From this perspective, Africa can be seen as a normative laboratory. Regional treaties, most prominently those concluded within the Economic Community of West African States (ECOWAS) and the Southern African Development Community (SADC), contain several important novelties meant to rebalance the rights and obligations of the various stakeholders as well as to safeguard host State policy space. The content of these treaties has been brought more in line with the evolution of international law, especially with regard to the protection of the environment, social and human rights, transparency, corruption, public scrutiny, economic development, and corporate responsibility.
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Fang, Xinli, Qiang Yang, and Wenjun Yan. "Outer Synchronization between Complex Networks with Nonlinear Time-Delay Characteristics and Time-Varying Topological Structures." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/437673.
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This paper exploits the network outer synchronization problem in a generic context for complex networks with nonlinear time-delay characteristics and nonidentical time-varying topological structures. Based on the classic Lyapunov stability theory, the synchronization criteria and adaptive control strategy are presented, respectively, by adopting an appropriate Lyapunov-Krasovskii energy function and the convergence of the system error can also be well proved. The existing results of network outer synchronization can be obtained by giving certain conditions, for example, treating the coupling matrices as time-invariant, and by applying the suggested generic synchronization criteria and control scheme. The numerical simulation experiments for networks scenarios with dynamic chaotic characteristics and time-varying topologies are carried out and the result verifies the correctness and effectiveness of the proposed control solution.
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Liu, Xiangrong, Zengyan Hong, Juan Liu, Yuan Lin, Alfonso Rodríguez-Patón, Quan Zou, and Xiangxiang Zeng. "Computational methods for identifying the critical nodes in biological networks." Briefings in Bioinformatics 21, no. 2 (February 12, 2019): 486–97. http://dx.doi.org/10.1093/bib/bbz011.
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Abstract A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.
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SMITH, REGINALD D. "THE DYNAMICS OF INTERNET TRAFFIC: SELF-SIMILARITY, SELF-ORGANIZATION, AND COMPLEX PHENOMENA." Advances in Complex Systems 14, no. 06 (December 2011): 905–49. http://dx.doi.org/10.1142/s0219525911003451.
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The Internet is one of the largest and most complex communication and information exchange networks ever created. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, BGP storms, and comparisons of the Internet and biological models will be covered.
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Sacco, Pier Luigi, Alex Arenas, and Manlio De Domenico. "The Resilience of the Multirelational Structure of Geopolitical Treaties is Critically Linked to Past Colonial World Order and Offshore Fiscal Havens." Complexity 2023 (January 7, 2023): 1–9. http://dx.doi.org/10.1155/2023/5280604.
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The governance of the political and economic world order builds on a complex architecture of international treaties at various geographical scales. In a historical phase of high institutional turbulence, assessing the stability of such architecture with respect to the unilateral defection of single countries and the breakdown of single treaties is important. We carry out this analysis on the whole global architecture and find that the countries with the highest disruption potential are mostly medium-small and micro countries. Political stability is highly dependent on many former colonial overseas territories that are today part of the global network of fiscal havens, as well as on emerging economies, mostly from South-East Asia. Economic stability depends on medium-sized European and African countries. Single global treaties have surprisingly less disruptive potential, with the major exception of the WTO. Our results suggest that the potential fragility of the world order seems to be more directly related to global inequality and fiscal injustice than commonly believed and that the legacy of the colonial world order is still strong in the current international relations scenario. In particular, vested interests related to tax avoidance seem to have a structural role in the political architecture of global governance.
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Wiley, David J., Ilona Juan, Hao Le, Xiaodong Cai, Lisa Baumbach, Christine Beattie, and Gennaro D'Urso. "Yeast Augmented Network Analysis (YANA): a new systems approach to identify therapeutic targets for human genetic diseases." F1000Research 3 (June 2, 2014): 121. http://dx.doi.org/10.12688/f1000research.4188.1.
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Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets.Here we introduce our Yeast Augmented Network Analysis (YANA) approach and test it with the X-linked spinal muscular atrophy (SMA) disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish) SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.
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Arel-Bundock, Vincent. "The Unintended Consequences of Bilateralism: Treaty Shopping and International Tax Policy." International Organization 71, no. 2 (2017): 349–71. http://dx.doi.org/10.1017/s0020818317000108.
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AbstractThe international tax system is a complex regime composed of thousands of bilateral tax treaties. These agreements coordinate policies between countries to avoid double taxation and encourage international investment. I argue that by solving this coordination problem on a bilateral basis, states have inadvertently created opportunities for treaty shopping by multinationals. These opportunities, in turn, reduce the potency of fiscal policy, put pressure on governments to change their domestic tax laws, and ultimately constrain state autonomy. This constraint is theoretically distinct from the usual race-to-the-bottom story and it generates different testable implications. I use a motivating case study to show how multinationals leverage the structure of the treaty network to reduce their tax burden. Then, I develop a new measure of treaty-shopping opportunities for firms in 164 countries. Where the proliferation of tax treaties allows multinationals to engage in treaty shopping, states’ fiscal autonomy is limited, and governments tend to maintain lower tax rates.
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Borschberg, Peter. "Luso-Johor-Dutch Relations in the Straits of Malacca and Singapore, c. 1600-1623." Itinerario 28, no. 2 (July 2004): 15–43. http://dx.doi.org/10.1017/s0165115300019471.
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The study of the early European colonial presence in Asia has been stimulated in recent years by a series of excellent works. These have been both of general and specialist nature, written not only by historians, but also by political scientists as well as specialists of international relations. The truly excellent study published in 2002 by Edward Keene, can be taken as a point in case. Central to his revisitation of seventeenth-century treaties of the United Dutch East India Company (VOC) with the Emperor of Kandy, is the notion of divided sovereignty expounded by Hugo Grotius around 1600-1610. It was against the backdrop of such concepts of divided sovereignty that the VOC could ultimately conclude its complex web of treaty relationships that broadly characterise the Dutch colonial empire in the East Indies up the advent of the French Revolution and the Napoleonic Wars. There is some legitimacy in contending that Keene's postulations effectively rework and reinterpret, at the level of international relations, what was once conveniently dubbed the ‘Age of Partnership’, i.e. an age characterised by trade-driven colonial empires that grew upon a complex, sometimes self-contradictory network of treaty relationships as well as formal and informal cooperation garnered from native elites. Admittedly such relations were often but not always based on unequal power and treaty relationships. Despite the uneven playing fields created by many such Euro-Asian treaties, especially those forged in the late sixteenth and early seventeenth centuries, the conclusion of treaties was assumed on the basis of the nominal co-equality of sovereigns and plenipotentiary agents acting on their behalf. European and Asian treaty partners were accepted as contracting equals, and this is particularly stunning given that the feudal world of European power politics at the time was, by comparison, probably more complex and legally structured than Asia. Certainly, the underlying power relations behind these early modern agreements were completely different from those imposed by the mature colonial powers on Asia at the zenith of nineteenth-century imperialism!
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ZHENG DA-FANG, SHEN SHUN-QING, and TAO RUI-BAO. "AN EXTENDED GENERATING FUNCTION TECHNIQUE FOR TREATING RANDOM WALKS ON COMPLEX NETWORKS." Acta Physica Sinica 37, no. 11 (1988): 1823. http://dx.doi.org/10.7498/aps.37.1823.
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Kim, Rakhyun E. "Is Global Governance Fragmented, Polycentric, or Complex? The State of the Art of the Network Approach." International Studies Review 22, no. 4 (September 18, 2019): 903–31. http://dx.doi.org/10.1093/isr/viz052.
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Abstract International institutions such as treaties and organizations shape, and are shaped by, the large web-like architecture of global governance. Yet we know little about what this architecture looks like, why certain structures are observed, and how they are linked to the functioning of international institutions as well as the overall effectiveness of global governance. Over the past decade, network science has emerged as a promising and indispensable approach to unraveling structural nuances and complexities of the system of international institutions. This article presents a state-of-the-art review of this emerging field of research and seeks to stimulate its further development. In this article, I draw connections between various network analyses of global governance that are found in different bodies of literature. In so doing, I integrate three separate but overlapping strands of work on institutional fragmentation, polycentricity, and complexity and bring much-needed conceptual clarity to the debate. Building on previous studies, I propose a framework for operationalizing fragmentation, polycentricity, and complexity in network terms in order to enable systematic and comparative analysis of global governance systems. This article argues that there is much potential in the network approach and makes a case for advancing the “network science of global governance.”
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Passban, Peyman, Qun Liu, and Andy Way. "Providing Morphological Information for SMT Using Neural Networks." Prague Bulletin of Mathematical Linguistics 108, no. 1 (June 1, 2017): 271–82. http://dx.doi.org/10.1515/pralin-2017-0026.
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Abstract Treating morphologically complex words (MCWs) as atomic units in translation would not yield a desirable result. Such words are complicated constituents with meaningful subunits. A complex word in a morphologically rich language (MRL) could be associated with a number of words or even a full sentence in a simpler language, which means the surface form of complex words should be accompanied with auxiliary morphological information in order to provide a precise translation and a better alignment. In this paper we follow this idea and propose two different methods to convey such information for statistical machine translation (SMT) models. In the first model we enrich factored SMT engines by introducing a new morphological factor which relies on subword-aware word embeddings. In the second model we focus on the language-modeling component. We explore a subword-level neural language model (NLM) to capture sequence-, word- and subword-level dependencies. Our NLM is able to approximate better scores for conditional word probabilities, so the decoder generates more fluent translations. We studied two languages Farsi and German in our experiments and observed significant improvements for both of them.
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Magruk, Andrzej. "The Desirable Systemic Uncertainty in Complex IoT Sensor Networks—General Anticipatory Foresight Perspective." Sensors 22, no. 5 (February 22, 2022): 1698. http://dx.doi.org/10.3390/s22051698.
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A wide methodological spectrum regarding future research is offered by anticipation studies, with a special role of foresight studies. Many studies of this type focus on generating the desired future, taking into account the fact that it is accompanied by uncertainty. The author of this publication postulates treating uncertainty as an equivalent—in relation to the future—research object. This approach allows us to formulate general assumptions for a model of the anticipatory management of systemic uncertainty in IoT networks. The goal of such a model will not be to eliminate or even minimize uncertainty, but to regulate it to a desired level. Such an action can bring many more benefits than trying to zero out uncertainty. On the general side, uncertainty can be studied in two ways: (1) as an abstract idea, or (2) as a feature of a particular structure, also with elements of research on its abstract component. In this paper the attention is focused on the second approach. The main research area is the IoT network in its broadest sense, with a particular role of the social construct, in the context of the study of systemic uncertainty in relation to selected anticipatory actions. The overarching goal is to define a desired state, or to determine what such a desired state is, of anticipatory IoT uncertainty.
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Besnard, Eric, Adeline Schmitz, Hamid Hefazi, and Rahul Shinde. "Constructive Neural Networks and Their Application to Ship Multidisciplinary Design Optimization." Journal of Ship Research 51, no. 04 (December 1, 2007): 297–312. http://dx.doi.org/10.5957/jsr.2007.51.4.297.
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This paper presents a neural network-based response surface method for reducing the cost of computer-intensive optimizations for applications in ship design. In the approach, complex or costly analyses are replaced by a neural network, which is used to instantaneously estimate the value of the function(s) of interest. The cost of the optimization is shifted to the generation of (smaller) data sets used for training the network. The focus of the paper is on the use and analysis of constructive networks, as opposed to networks of fixed size, for treating problems with a large number of variables, say around 30. The advantages offered by constructive networks are emphasized, leading to the selection and discussion of the cascade correlation algorithm. This topology allows for efficient neural network determination when dealing with function representation over large design spaces without requiring prior experience from the user. During training, the network grows until the error on a small set (validation set), different from that used in the training itself (training set), starts increasing. The method is validated for a mathematical function for dimensions ranging from 5 to 30, and the importance of analyzing the error on a set other than the training set is emphasized. The approach is then applied to the automated computational fluid dynamics-based shape optimization of a fast ship configuration known as the twin H-body. The classical approach yields a design improvement of 26%, whereas the neural network-based method allows reaching a 34% improvement at one fifth of the cost of the former. Based on the analysis of the results, areas for future improvements and research are outlined. The results demonstrate the potential of the method in saving valuable development cycle time and increasing the performance of future ship designs.
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Deng, Xiaodong, Yuhua Liang, Jianmei Hu, and Yuhui Yang. "Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology." Natural Product Communications 16, no. 1 (January 2021): 1934578X2098213. http://dx.doi.org/10.1177/1934578x20982138.
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Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.
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Hey, Ellen. "The International Regime for the Protection of the North Sea: From Functional Approaches to a More Integrated Approach." International Journal of Marine and Coastal Law 17, no. 3 (2002): 325–50. http://dx.doi.org/10.1163/157180802x00107.
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AbstractThe international regime for the North Sea functions within a complex network of multilateral and regional treaties and institutions, without the existence of a treaty that focuses on the overall protection of the North Sea ecosystem. Nevertheless, a North Sea regime can be identified. This essay shows that that regime has moved from more functional approaches to a more integrated approach. It describes the legal and institutional framework in which the North Sea regime is situated, discusses relevant geographical and geopolitical factors and analyses how the process of change emerged and developed. It concludes that the transition process is about attaining perceptual changes and that in that process law, and rules and regulations, must be regarded as both substantive content, in the sense of standard or rule, and as process, in the sense of procedures for changing or testing the standards and rules.
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Liu, Pei-Pei, Ya-Jie Xu, Zhao-Qian Teng, and Chang-Mei Liu. "Polycomb Repressive Complex 2: Emerging Roles in the Central Nervous System." Neuroscientist 24, no. 3 (December 14, 2017): 208–20. http://dx.doi.org/10.1177/1073858417747839.
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The polycomb repressive complex 2 (PRC2) is responsible for catalyzing both di- and trimethylation of histone H3 at lysine 27 (H3K27me2/3). The subunits of PRC2 are widely expressed in the central nervous system (CNS). PRC2 as well as H3K27me2/3, play distinct roles in neuronal identity, proliferation and differentiation of neural stem/progenitor cells, neuronal morphology, and gliogenesis. Mutations or dysregulations of PRC2 subunits often cause neurological diseases. Therefore, PRC2 might represent a common target of different pathological processes that drive neurodegenerative diseases. A better understanding of the intricate and complex regulatory networks mediated by PRC2 in CNS will help to develop new therapeutic approaches and to generate specific brain cell types for treating neurological diseases.
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O’Connor, Manjula. "Dowry-related domestic violence and complex posttraumatic stress disorder: a case report." Australasian Psychiatry 25, no. 4 (March 28, 2017): 351–53. http://dx.doi.org/10.1177/1039856217700464.
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Objectives: This paper draws attention to the mental health impact of coercive practice of dowry demands, associated with domestic violence (DV) in an immigrant woman. Methods: This study was based on a case report and selective literature review. Results: This case history illustrates the serious mental health impacts of repeated emotional and physical trauma inflicted by a husband who was dissatisfied with his wife’s dowry. Bio-psycho-social / cultural aspects of mental health treatments needed to be augmented with attention to safety, advocacy, and access to support networks. Conclusions: Cultural factors are important determinants of mental illness. Psychiatrists need to be aware of DV and dowry when treating immigrant women.
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von Stein, Jana. "Exploring the Universe of UN Human Rights Agreements." Journal of Conflict Resolution 62, no. 4 (August 8, 2017): 871–99. http://dx.doi.org/10.1177/0022002717721395.
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The international human rights (HR) regime is vast and complex. Yet, most of what we know about it draws from a handful of agreements, often chosen for their prominence and/or perceived centrality to the HR project. This article argues that HR research needs to expand its scope to encompass all agreements in this realm, and presents a new data resource that enables scholars to accomplish that goal. Using the data, I demonstrate that the literature has painted an unrepresentative portrait of HR agreements. In addition to making comprehensive analysis possible, the database moves the literature forward by (1) taking into account important legal distinctions in the process of making treaties binding, (2) providing information on treaty design, and (3) considering relationships between agreements. I present several applications and discuss future areas of inquiry. Network analysis and the linking of treaty participation to HR outcomes are two notable areas of interest.
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Rixen, Thomas, and Ingo Rohlfing. "The Institutional Choice of Bilateralism and Multilateralism in International Trade and Taxation." International Negotiation 12, no. 3 (2007): 389–414. http://dx.doi.org/10.1163/138234007x240718.
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AbstractTrade relations are governed by a multilateral agreement, whereas the avoidance of double taxation rests on a network of about 2000 separate bilateral treaties. What accounts for the difference in the institutional form? Distinguishing between the bargaining and agreement stage of international cooperation, we first show that the institutional design of both regimes is more complex than commonly assumed. Both exhibit a mix of bilateral and multilateral bargaining that precedes multilateral agreement in trade and bilateral agreement in taxation. We demonstrate that in both regimes, governmental concerns for the distribution of benefits can best be achieved through bilateral bargaining. Multilateral bargaining serves to reduce the high transaction costs of bilateral bargains. Multilateral agreement in trade helps to overcome the problem of free-riding that results from a particular interaction of concerns on distribution and enforcement problems. Since no such problem exists in double tax avoidance, agreement is therefore bilateral in nature.
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Notarianni, MaryAnn, and Fardous Hosseiny. "Accounting for Complexity and Context in Implementation through Coaching, Convening, and Co-Design: A New Intermediary’s Approach." Canadian Journal of Community Mental Health 40, no. 3 (November 1, 2021): 81–85. http://dx.doi.org/10.7870/cjcmh-2021-024.
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The Centre of Excellence on PTSD (the Centre) is a new Canadian intermediary established in part to support the uptake of evidence-based practices among service providers treating veterans. Given the unique and complex landscape for veteran mental health service delivery, the Centre is developing networks and prioritizing co-design to address anticipated implementation challenges.
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Ren, Keguo, Xining Li, and Qimin Zhang. "Near-optimal control and threshold behavior of an avian influenza model with spatial diffusion on complex networks." Mathematical Biosciences and Engineering 18, no. 5 (2021): 6452–83. http://dx.doi.org/10.3934/mbe.2021321.
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<abstract><p>Near-optimization is as sensible and important as optimization for both theory and applications. This paper concerns the near-optimal control of an avian influenza model with saturation on heterogeneous complex networks. Firstly, the basic reproduction number $ \mathcal{R}_{0} $ is defined for the model, which can be used to govern the threshold dynamics of influenza disease. Secondly, the near-optimal control problem was formulated by slaughtering poultry and treating infected humans while keeping the loss and cost to a minimum. Thanks to the maximum condition of the Hamiltonian function and the Ekeland's variational principle, we establish both necessary and sufficient conditions for the near-optimality by several delicate estimates for the state and adjoint processes. Finally, a number of examples presented to illustrate our theoretical results.</p></abstract>
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Ma, Gang, Yuhang Chen, Wenliang Zhu, Lesong Zheng, Hui Tang, Yong Yu, and Lirong Wang. "Evaluating and Visualizing the Contribution of ECG Characteristic Waveforms for PPG-Based Blood Pressure Estimation." Micromachines 13, no. 9 (August 31, 2022): 1438. http://dx.doi.org/10.3390/mi13091438.
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Non-invasive continuous blood pressure monitoring is of great significance for the preventing, diagnosing, and treating of cardiovascular diseases (CVDs). Studies have demonstrated that photoplethysmogram (PPG) and electrocardiogram (ECG) signals can effectively and continuously predict blood pressure (BP). However, most of the BP estimation models focus on the waveform features of the PPG signal, while the peak value of R-wave in ECG is only used as a time reference, and few references investigated the ECG waveforms. This paper aims to evaluate the influence of three characteristic waveforms in ECG on the improvement of BP estimation. PPG is the primary signal, and five input combinations are formed by adding ECG, P wave, QRS complex, T wave, and none. We employ five common convolutional neural networks (CNN) to validate the consistency of the contribution. Meanwhile, with the visualization of Gradient-weighted class activation mapping (Grad-CAM), we generate the heat maps and further visualize the distribution of CNN’s attention to each waveform of PPG and ECG. The heat maps show that networks pay more attention to the QRS complex and T wave. In the comparison results, the QRS complex and T wave have more contribution to minimizing errors than P wave. By separately adding P wave, QRS complex, and T wave, the average MAE of these networks reaches 7.87 mmHg, 6.57 mmHg, and 6.21 mmHg for systolic blood pressure (SBP), and 4.27 mmHg, 3.65 mmHg, and 3.73 mmHg, respectively, for diastolic blood pressure (DBP). The results of the experiment show that QRS complex and T wave deserves more attention and feature extraction like PPG waveform features in the continuous BP estimation.
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Moosavi, S. Amin, and Wilson Truccolo. "Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures." PLOS Computational Biology 19, no. 2 (February 7, 2023): e1010852. http://dx.doi.org/10.1371/journal.pcbi.1010852.
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The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model’s computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.
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van der Lee, Marije L., and Melanie P. J. Schellekens. "Capturing the complexity of mental disorders in the medically ill: the network approach on behavioral medicine." Translational Behavioral Medicine 10, no. 3 (February 12, 2019): 812–16. http://dx.doi.org/10.1093/tbm/ibz019.
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Abstract Since Descartes introduced dualism, body and mind have been seen as separate entities. The latent disease model, the view that symptoms are caused by an underlying disease, was possible within the dualistic paradigm. This paradigm, although successful in some aspects of medicine, is also assumed to underlie psychiatric disorders. As an alternative to the latent disease model, the network approach conceptualizes disorders as complex networks of causally connected symptoms. It offers a new way of understanding psychiatric disorders by directing attention away from the underlying cause and towards the symptoms and their functional interconnectedness, making the distinction between mental and physical symptoms obsolete. This article discusses how the network perspective helps us to overcome some of the problems we have faced when diagnosing and treating psychopathology in the medically ill. Furthermore, we describe how the network perspective can stimulate new research to better understand psychopathology in medically ill patients and how it can help deliver the most suitable treatment to the individual patient.
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Jeevani, M. "Prediction of Retinal diseases using Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 449–52. http://dx.doi.org/10.22214/ijraset.2022.45242.
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Abstract: Eye diseases are the common diseases people are facing nowadays. Even people of all ages ranging from children to old are affected due to eye diseases. Some diseases will become very complex if they are untreated at an early stage. To overcome such problems early detection and treatment is required which could prevent vision loss. For many, eye disorders have become almost a part of their daily lives. Traditionally, retinal disease detection techniques were based on manual description. The WHO (World Health Organization) finds that there are millions of visually impaired people all over the world. The retinal diseases include Cataract, Crossed Eyes, Bulging Eyes, Uveitis and Glaucoma can be predicted using convolutional neural networks in this project. Medical Imaging has a huge scope for the application of recent advancements in computation. With the emerging computer technology, the development of an automatic system for diseases using Convolutional neural Networks in association with different optimization methods (SGDM, RMS-PROP, and ADAM). Treating the disease is now possible especially if the patient’s area and medical services are limited. Thus, the idea is to build an algorithm that automatically identifies whether a patient is suffering from any eye diseases or not by looking at the images
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Sanchez-Sanchez, Paola A., José Rafael García-González, and Juan Manuel Rúa Ascar. "Automatic migraine classification using artificial neural networks." F1000Research 9 (June 16, 2020): 618. http://dx.doi.org/10.12688/f1000research.23181.1.
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Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.
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Sanchez-Sanchez, Paola A., José Rafael García-González, and Juan Manuel Rúa Ascar. "Automatic migraine classification using artificial neural networks." F1000Research 9 (July 17, 2020): 618. http://dx.doi.org/10.12688/f1000research.23181.2.
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Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.
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Dong, Weifan, Xin Chen, Xi Huang, Adam Fekete, Lu-yang Wang, and Hongwei Liu. "CNSC-28. IDENTIFICATION OF EAG2-KVß2 POTASSIUM CHANNEL COMPLEX IN NEURON-GBM COMMUNICATIONS REVEALS GBM VULNERABILITY TO DESIGNER INTERFERENCE PEPTIDE." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii28. http://dx.doi.org/10.1093/neuonc/noac209.109.
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Abstract Glioblastomas (GBMs) are the most aggressive brain tumors. GBM cells form extensive tumoral networks to communicate with each other and with surrounding neurons. Neuronal activity promotes GBM cell proliferation by secreting protumorigenic factors and triggering neuronal-activity-dependent Ca2+ transients, which are persisted and transmitted via tumor networks to promote overall tumor growth and therapy resistance. While how these processes are regulated is largely unknown. Here we show the GBM networks and Ca2+ transients are regulated by a voltage-gated potassium channel complex comprised of EAG2 and Kvβ2. GBM cells selectively overexpress Kvβ2 isoform 4 to facilitate tumor-specific Kvβ2-EAG2 interaction, which regulates neuron-GBM contact-dependent localization of EAG2. Rationally designed interfering peptide K90-114TAT blocks EAG2-Kvβ2 interaction, leading to reduced tumor cell proliferation, increased apoptosis, and prolonged survival of patient-derived xenograft mouse models with no evidence of toxicity to normal tissue. Single-cell RNA sequencing revealed a subgroup of GBM cells is highly sensitive to K90-114TAT treatment. These cells exhibit neuronal signatures and are highly associated with TMZ resistance and worse prognosis. In accordance with the notion, we found neurons promote TMZ resistance of GBM cells, which is regulated by the EAG2-Kvβ2 complex. Treating TMZ-resistant GBM xenograft mice with K90-114TAT yielded significant tumor burden reduction and prolonged survival. Together, our findings revealed the EAG2-Kvβ2 channel complex as a key regulator of neuron promoted GBM progression and designed an interfering peptide with anti-GBM efficacy and low general toxicity, which may have significant clinical impact.
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Havbro Faber, Michael, Inger B. Kroon, Eva Kragh, David Bayly, and Patrick Decosemaeker. "Risk Assessment of Decommissioning Options Using Bayesian Networks." Journal of Offshore Mechanics and Arctic Engineering 124, no. 4 (October 22, 2002): 231–38. http://dx.doi.org/10.1115/1.1491974.
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For complex and costly decommissioning activities, it is beneficial, if not necessary, that all relevant risks are identified and assessed on an overall basis, treating and assessing all risks within the same theoretical framework. Only then may the different options be consistently compared and the risks associated with decommissioning demonstrated and documented to the different parties of interest. The present paper suggests an approach for assessing the risks associated with the decommissioning of offshore facilities. The approach takes basis in a discrete point in time representation of the considered decommissioning options where important phases of the options are represented in terms of event scenarios. Using the possibilities of Bayesian Probabilistic Networks (BPN), the failure probabilities and risk events involved in the modeling of an option may then be analyzed for each phase and added up time-wise over the entire decommissioning process. The principles of BPNs are shortly described, and the proposed approach is illustrated by an example linking the operational and structural risks in connection with a re-float decommissioning option for a concrete offshore platform. It is shown how the sensitivity may be evaluated on the basis of the BPNs, thus providing a valuable framework to first improve the risk model in terms of the representation of important scenarios, then for deciding where to apply additional safety measures most effectively, and last but not least, to demonstrate and document the contributions to the mission failure probability.
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Guscott, Martin R., Stacy L. Cooper, Poonam Bagai, Archie Bleyer, Randal K. Buddington, Dan Douer, Tina B. Chong, et al. "Research and Care Networks in Acute Leukemia As a Model for Global Healthcare Collaboration." Blood 138, Supplement 1 (November 5, 2021): 4966. http://dx.doi.org/10.1182/blood-2021-147876.
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Abstract Introduction: Clinicians in academia face four major career challenges: Gaining clinical advice from colleagues experienced in a particular disease or treatment Experiencing Life-long coaching and mentoring from senior experienced clinicians Accessing high quality continuing medical education relevant for patient care Support to carry out medical research All four challenges have been adversely impacted during the Covid-19 pandemic as traditional face-to-face networks have become harder to access. This is especially pertinent when treating complex or rare diseases like acute lymphoblastic leukemia (ALL). Atypical or refractory patients, and those who experience toxicities often benefit from timely input from experts with considerable experience managing ALL. Online networks offer a robust pandemic-proof source of health and care support and advice. Until recently there have been few dedicated professional networks that provide a regular online forum dedicated to research and care on specific diseases across countries and none related to ALL. Methods: We describe the Resonance ALL Research and Care Network (ALL RCaN; https://resonancehealth.org/networks/all-rcan ) which includes a network of colleagues and a weekly, multidisciplinary online forum that brings together pediatric and adult hematologists and oncologists from around the world to share data, discuss cases and support patient care. In addition, there is a monthly 'Fellows Fourth Friday' to help fellows build their own professional network and gain scientific and clinical advice. The network was born out of a monthly meeting of study chairs (the "Study Chair Affinity Group") for ALL research protocols which had been running for 10 years. Results: The network launched formally in June 2020 with 30 founding members but has expanded rapidly through word of mouth. The Acute Leukemia network grew by 850% by Dec 2020 and 1460% by March 2021 and as of July 2021 has 579 members across 18 time zones. It has succeeded in 'Building ALL Bridges" between physicians that treat adults and pediatricians for joint discussions in acute leukemia. This collaboration has been severely lacking in the past. The network also presents selected abstracts from major national and international conferences every 4 weeks. This model has been replicated for other cancers including the Global Neuroblastoma Network (resonancehealth.org/networks/gnn) and High-dose Methotrexate Quality Improvement Network (resonancehealth.org/networks/hdmtx). Network software development, video conferencing, meeting coordination, and content hosting have been funded by volunteer network leaders, many volunteer presenters, philanthropic contributions, and unrestricted educational grants. Now that the Resonance infrastructure is fully developed (and available to all at no cost), most Networks function well without funding. Conclusions: Providing free video conferencing, content hosting, and network management tools combined with dedicated leadership and clinically relevant discussions and presentations has led to massive growth of the ALL Research and Care Network, which continues to grow. Networks for other cancers are in various stages of development since the tool set and methodology easily scales to new groups of colleagues and new diseases. Figure 1 Figure 1. Disclosures Guscott: EUSA Pharma UK Ltd: Ended employment in the past 24 months. Douer: Amgen: Consultancy, Speakers Bureau; Servier: Consultancy, Speakers Bureau; Jazz: Consultancy; Adaptive: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company, Speakers Bureau. Howard: Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees; Servier: Consultancy.
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Boulter, Luke, Esme Bullock, Zeanap Mabruk, and Valerie G. Brunton. "The fibrotic and immune microenvironments as targetable drivers of metastasis." British Journal of Cancer 124, no. 1 (November 26, 2020): 27–36. http://dx.doi.org/10.1038/s41416-020-01172-1.
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AbstractAlthough substantial progress has been made over the past 40 years in treating patients with cancer, effective therapies for those who are diagnosed with advanced metastatic disease are still few and far between. Cancer cells do not exist in isolation: rather, they exist within a complex microenvironment composed of stromal cells and extracellular matrix. Within this tumour microenvironment exists an interplay between the two main stromal cell subtypes, cancer-associated fibroblasts (CAFs) and immune cells, that are important in controlling metastasis. A complex network of paracrine signalling pathways between CAFs, immune cells and tumour cells are involved at multiple stages of the metastatic process, from invasion and intravasation at the primary tumour site to extravasation and colonisation in the metastatic site. Heterogeneity and plasticity within stromal cell populations also contribute to the complexity. Although many of these processes are likely to be common to a number of metastatic sites, we will describe in detail the interplay within the liver, a preferred site of metastasis for many tumours. A greater understanding of these networks provides opportunities for the design of new therapeutic approaches for targeting the metastatic disease.
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Sadirbajevs, Felikss. "A Nullclines Approach to the Study of 2D Artificial Network." Contemporary Mathematics 1, no. 1 (November 4, 2019): 1–11. http://dx.doi.org/10.37256/cm.11201976.1-11.
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The system of two the first order ordinary differential equations arising in the gene regulatory networks theory is studied. The structure of attractors for this system is described for three important behavioral cases: activation, inhibition, mixed activation-inhibition. The geometrical approach combined with the vector field analysis allows treating the problem in full generality. A number of propositions are stated and the proof is geometrical, avoiding complex analytic. Although not all the possible cases are considered, the instructions are given what to do in any particular situation.
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Ratliff, M., S. Schlieper-Scherf, D. Hausmann, E. Jung, E. Maier, T. M. Ratliff, N. Etminan, and F. Winkler. "P13.12 Effect of tumor treating fields on tumor microtubes in glioma." Neuro-Oncology 23, Supplement_2 (September 1, 2021): ii35. http://dx.doi.org/10.1093/neuonc/noab180.120.
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Abstract BACKGROUND Tumor microtubes (TMs) are ultralong membrane protrusions of tumor cells in astrocytic gliomas, including glioblastomas. TMs are used as routes for brain invasion and for cells to interconnect over long distances resulting in a functional network that allows multicellular communication. This network mediates resistance against the cytotoxicity of radiation and chemotherapy. One explanation for TM network protection is a better homeostasis of calcium ions that would otherwise increase to toxic intracellular levels in response to these therapies. Our working hypothesis is that interfering with the integrity of the glioblastoma cell network is key to a potential breakthrough in glioma therapy. Many cellular structures are polarized and composed of charged elements and are thus potential subjects to electrical forces; this might also influence the complex intercellular calcium waves (ICWs) that are characteristic for glioma networks. We were therefore interested in the effect of TTF on glioma network maintenance. MATERIAL AND METHODS To examine the effect of TTF on glioma TMs we have established a 2D in vitro glioma model using glioblastoma stem cells (GBSCs) grown in high-glucose medium and a 3D model using glioma tumor organoids. Both models reliably reproduce functionality and complexity of morphological features we observe in our mouse model. We analyzed the disruption of tumor network complexity and disruption of functionality by measuring intercellular calcium waves. Tumor cell death and proliferation was investigated in the 2D in vitro glioma model using the inovitroTM-System. RESULTS A peculiar “cricked-TM” phenotype that rarely (0.2% ±0.14) occurred under standard or control conditions was observed in TTF-treated cells (16.22% ±5.12). Cell number was reduced by 75% in two lines of GBSCs after 5 days of TTF exposure; predominantly TM-rich GBSCs (> 4 TMs) were affected. This reduction in tumor cell number corresponded with an increase in cell death (0.3% ±0.09 in untreated cells; 1.4% ±0.45 at day 5 of TTF exposure). The frequency of intercellular calcium transients, a measurement for calcium wave frequency in the glioma networks, was instantly reduced after TTF exposure to 58% ±20.42 of control levels in the primary GBSC 2D culture, and to 57.78% ±12.34 in tumor organoids derived from 3 glioblastoma patients. CONCLUSION This data suggests a potential effect of TTF application on tumor cell networks, at least in vitro. Interestingly, particularly those glioblastoma cells that have so far been proven to be resistant to radio- and chemotherapy appeared to be affected. We will confirm the observed effects of TTFs on tumor cell calcium signaling in our in vivo chronic cranial window mouse model. We anticipate that the results of our project will provide important insights into the underlying mechanism of TTF therapy.
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Ali, Sarwan, Muhammad Haroon Shakeel, Imdadullah Khan, Safiullah Faizullah, and Muhammad Asad Khan. "Predicting Attributes of Nodes Using Network Structure." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (March 2021): 1–23. http://dx.doi.org/10.1145/3442390.
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In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute a , using all attributes of its h -hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute a . We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy.
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Batalov, A. A. "The Sources of International Air Law: Current Issues of Theory and Practice." Moscow Journal of International Law, no. 3 (December 26, 2020): 64–90. http://dx.doi.org/10.24833/0869-0049-2020-3-64-90.
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INTRODUCTION. Within the context of the theory of ‘the concordance of the wills of States’ developed in the Russian doctrine of international law and on the basis of the contemporary practice of States and international aviation organizations (in particular, International Civil Aviation Organization (ICAO)) the author examines the process of rule-making and the sources of international air law.MATERIALS AND METHODS. The present research has been conducted on the basis of international air law treaties, the ICAO documents, as well as the works of Russian and foreign international law experts. The methodology of the research is based upon general scientific and special methods, including dialectical method, methods of analysis and synthesis, deduction and induction, comparative legal method.RESEARCH RESULTS. On the basis of his research the author has been able to identify and systematize the present-day sources of international air law, i.e. the forms, in which the norms of this branch of interna- tional law exist. Theoretical and practical problems relating to the development and implementation of these sources of international air law have been examined.DISCUSSION AND CONCLUSIONS. On the basis of the theory of ‘the concordance of the wills of States’ developed in the Russian doctrine of international law and the contemporary practice of States and international aviation organizations the following sources of international air law have been identified: (i) international treaties; (ii) international customs; (iii) ICAO international standards; (iv) Technical Instructions for the Safe Transport of Dangerous Goods by Air. Each of these sources of international air law has been examined in detail both from the point of view of the rulemaking process and within the context of its role played in regulation of cooperation between States in the area of international civil aviation. In particular, the author shows the specific role in international air law of such traditional sources as international treaties and international customs. As for the international standards and the Technical Instructions for the Safe Transport of Dangerous Goods by Air, they are described as special complex forms of creation of norms of international air law emerged as a result of rapid development of civil aviation in the second half of the 20th century and its transformation into the global passengers and cargo transportation network. Overall examination of the currently existing rule-making processes and sources of the international air law demonstrates that they are rather dynamic and well take into account the contemporary development of global aviation and the interests of different States.
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Jha, Kailash, and Manish Kumar Mishra. "Object-oriented integrated algorithms for efficient water pipe network by modified Hardy Cross technique." Journal of Computational Design and Engineering 7, no. 1 (February 1, 2020): 56–64. http://dx.doi.org/10.1093/jcde/qwaa006.
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Abstract In this work, object-oriented integrated algorithms for an efficient flow analysis of the water pipe network are developed. This is achieved by treating the pipe network as a graph data structure with its nodes as the graph’s nodes and the pipes as the edges. The algorithm for cycle (real cycle or pseudo-cycle) extraction has been developed using nested breadth-first search that gives ordered cycles. Pseudo-loops are found using the shortest path algorithm between the nodes. Pipes are initialized loop by loop using conservation of mass at nodes. A modified Hardy Cross method is used in the proposed work with third-order convergence. The friction factor is updated for every change in discharges. The pressure calculation has been done by the graph traversal algorithm between the reference nodes and node where the pressure is to be calculated using the energy equation. The pressure at all intermediate nodes is obtained in the course of the traversal. Balanced discharges and nodal pressure in the pipe network are compared with the simultaneous loop flow adjustment method and EPANET software. The proposed work gives more efficient flow analysis than the traditional Newton–Raphson-based techniques for complex networks.
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Oh, Ki-Kwang, Ye-Rin Choi, Haripriya Gupta, Raja Ganesan, Satya Priya Sharma, Sung-Min Won, Jin-Ju Jeong, et al. "Identification of Gut Microbiome Metabolites via Network Pharmacology Analysis in Treating Alcoholic Liver Disease." Current Issues in Molecular Biology 44, no. 7 (July 19, 2022): 3253–66. http://dx.doi.org/10.3390/cimb44070224.
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Alcoholic liver disease (ALD) is linked to a broad spectrum of diseases, including diabetes, hypertension, atherosclerosis, and even liver carcinoma. The ALD spectrum includes alcoholic fatty liver disease (AFLD), alcoholic hepatitis, and cirrhosis. Most recently, some reports demonstrated that the pathogenesis of ALD is strongly associated with metabolites of human microbiota. AFLD was the onset of disease among ALDs, the initial cause of which is alcohol consumption. Thus, we analyzed the significant metabolites of microbiota against AFLD via the network pharmacology concept. The metabolites from microbiota were retrieved by the gutMGene database; sequentially, AFLD targets were identified by public databases (DisGeNET, OMIM). The final targets were utilized for protein–protein interaction (PPI) networks and signaling pathway analyses. Then, we performed a molecular docking test (MDT) to verify the affinity between metabolite(s) and target(s) utilizing the Autodock 1.5.6 tool. From a holistic viewpoint, we integrated the relationships of microbiota-signaling pathways-targets-metabolites (MSTM) using the R Package. We identified the uppermost six key targets (TLR4, RELA, IL6, PPARG, COX-2, and CYP1A2) against AFLD. The PPI network analysis revealed that TLR4, RELA, IL6, PPARG, and COX-2 had equivalent degrees of value (4); however, CYP1A2 had no associations with the other targets. The bubble chart showed that the PI3K-Akt signaling pathway in nine signaling pathways might be the most significant mechanism with antagonistic functions in the treatment of AFLD. The MDT confirmed that Icaritin is a promising agent to bind stably to RELA (known as NF-Κb). In parallel, Bacterium MRG-PMF-1, the PI3K-Akt signaling pathway, RELA, and Icaritin were the most significant components against AFLD in MSTM networks. In conclusion, we showed that the Icaritin–RELA complex on the PI3K-Akt signaling pathway by bacterial MRG-PMF-1 might have promising therapeutic effects against AFLD, providing crucial evidence for further research.
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Dovgan, K. E. "The Development of Framework Legal Regulation in the Field of Delimitation of Subjects of Jurisdiction and Powers of Federal Government Bodies of the Russian Federation and Its Subjects." Siberian Law Review 19, no. 3 (September 20, 2022): 233–44. http://dx.doi.org/10.19073/2658-7602-2022-19-3-233-244.
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The article analyzes individual legal acts regulating the issues of delimitation of the jurisdiction and powers of the Russian Federation and the constituent entities of the Russian Federation in terms of legal technique, in particular, the presence of elements of a framework legal regulation. Separate aspects of framework legal regulation from the point of view of constitutional law, the purpose of this study is to study the legal technique, tools of framework legal regulation. The mechanism of framework legal regulation exists in the field of delimitation of the subjects of jurisdiction and powers of federal government bodies and its subjects. From the point of view of legal technique, signs of framework legal regulation can be traced in the legislation of the USSR, however, conceptual changes in the development of federal relations began to occur at the end of the 20th century. An analysis of the legal acts that were signed before the popular vote on the adoption of the Constitution of the Russian Federation is essential in the process of determining the legal tools that the legislator used in regulating federal relations in the Russian Federation. Regulatory legal acts, including the Federal Treaty, were of great importance in the process of centralization of state power in our country, contributed to preventing the withdrawal of the autonomous union republics of the RSFSR from its composition. The Federal Treaty has certain features, on the technical and legal side, the text contains polysemy, relatively certain norms that create uncertainty in legal regulation, on the one hand, and on the other hand, in some Treaties there is a condition for further specification of legal relations, which allows taking into account the local features of the regions in further legal regulation. In complex states, the relationship between federal authorities and subjects of the federation is important, first of all, in order to carry out the functions of the state. From the point of view of the methodological concept of historicism, it is possible to explore the development of legal means of framework legal regulation, which were laid down during the formation and development of federal relations in Russia. Based on the results of the study, certain problems were identified, as well as generalizing conclusions were made.
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Weber, W. J. "Distributed optimal technology networks: a concept and strategy for potable water sustainability." Water Science and Technology 46, no. 6-7 (September 1, 2002): 241–46. http://dx.doi.org/10.2166/wst.2002.0685.
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Viable strategies for ensuring adequate supplies of potable water are essential to long-term societal sustainability. The steadily increasing necessity for multiple reuse of water in urban societies is even now taxing our technical and financial abilities to meet ongoing needs for water suitable for human consumption. As a consequence, the current practice of treating the entire water demands of urban communities to the increasingly stringent standards required for drinking water is becoming an unsustainable practice, and thus a questionable strategy for planning and development of urban water systems. An innovative technology-based concept for implementation of a more sustainable strategy and practice for potable water is developed here. The concept is predicated on the inherent advantages of flexibility and responsiveness associated with decentralization of complex functions and operations. Specifically, it calls for strategic dispersal of flexible advanced treatment and control technologies throughout urban water transport and storage networks. This is in direct contradistinction to current strategies and practices of centralized and inflexible monolithic facilities. By integrating use-related satellite systems with critical components of existing systems and infrastructures, the concept can enable and facilitate optimal cost-effective applications of highly sophisticated advanced treatment and on-line monitoring and control technologies to in-place infrastructures in a holistic and sustainable manner.
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Sadarangani, Tina, Jeannette Beasley, and Shannon E. Jarrott. "LEVERAGING COMMUNITIES AS NETWORKS TO REDUCE MALNUTRITION IN OLDER ADULTS." Innovation in Aging 3, Supplement_1 (November 2019): S226—S227. http://dx.doi.org/10.1093/geroni/igz038.834.
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Abstract Malnutrition in older adults, while ubiquitous, remains largely underrecognized and undertreated. In community-dwelling older adults, 25% of those at risk of over or under nutrition do not receive any dietary interventions; routine screenings for malnutrition are not typically required in community-based settings. In this interdisciplinary symposium, we explore issues focused on the delivery of evidence-based nutrition interventions to meet the needs of community-dwelling older adults. Using national survey data, we begin by underscoring the importance of treating the complex needs of adults at risk of malnutrition by examining health sequelae, specifically hospitalizations, in community-dwelling adults receiving home-delivered meals. We subsequently examine approaches to malnutrition screening in community-based settings, focusing on the utility of the DETERMINE checklist. We explore barriers and facilitators of providing person-centered nutrition to ethnically diverse Asian American older adults in the adult day healthcare setting. Finally, we shift our focus to overnutrition, discussing the dissemination of a telehealth diabetes prevention program, BRInging the Diabetes prevention program to GEriatric populations (BRIDGE) among older adult meal program recipients. Older adults in community-based health settings are at risk of malnutrition, and among them, those who are prone to social isolation, are at highest risk for adverse outcomes. While congregate settings can facilitate social interaction, honoring food preferences and facilitating choice to address undernutrition, is challenging. Conversely, telehealth interventions may present a feasible approach for addressing overnutrition. We conclude by discussing how current and future research can inform innovative person-centered community-based approaches to identify and treat malnutrition.
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Xie, Fuda, Mingxiang Xie, Yibing Yang, Miaomiao Zhang, Xiaojie Xu, Na Liu, Wei Xiao, and Jiangyong Gu. "Assessing the Anti-inflammatory Mechanism of Reduning Injection by Network Pharmacology." BioMed Research International 2020 (December 16, 2020): 1–13. http://dx.doi.org/10.1155/2020/6134098.
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Reduning Injection (RDNI) is a traditional Chinese medicine formula indicated for the treatment of inflammatory diseases. However, the molecular mechanism of RDNI is unclear. The information of RDNI ingredients was collected from previous studies. Targets of them were obtained by data mining and molecular docking. The information of targets and related pathways was collected in UniProt and KEGG. Networks were constructed and analyzed by Cytoscape to identify key compounds, targets, and pathways. Data mining and molecular docking identified 11 compounds, 84 targets, and 201 pathways that are related to the anti-inflammatory activity of RDNI. Network analysis identified two key compounds (caffeic acid and ferulic acid), five key targets (Bcl-2, eNOS, PTGS2, PPARA, and MMPs), and four key pathways (estrogen signaling pathway, PI3K-AKT signaling pathway, cGMP-PKG signaling pathway, and calcium signaling pathway) which would play critical roles in the treatment of inflammatory diseases by RDNI. The cross-talks among pathways provided a deeper understanding of anti-inflammatory effect of RDNI. RDNI is capable of regulating multiple biological processes and treating inflammation at a systems level. Network pharmacology is a practical approach to explore the therapeutic mechanism of TCM for complex disease.
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Wei, Wei, Todd K. Leen, and Etienne Barnard. "A Fast Histogram-Based Postprocessor That Improves Posterior Probability Estimates." Neural Computation 11, no. 5 (July 1, 1999): 1235–48. http://dx.doi.org/10.1162/089976699300016430.
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Although the outputs of neural network classifiers are often considered to be estimates of posterior class probabilities, the literature that assesses the calibration accuracy of these estimates illustrates that practical networks often fall far short of being ideal estimators. The theorems used to justify treating network outputs as good posterior estimates are based on several assumptions: that the network is sufficiently complex to model the posterior distribution accurately, that there are sufficient training data to specify the network, and that the optimization routine is capable of finding the global minimum of the cost function. Any or all of these assumptions may be violated in practice. This article does three things. First, we apply a simple, previously used histogram technique to assess graphically the accuracy of posterior estimates with respect to individual classes. Second, we introduce a simple and fast remapping procedure that transforms network outputs to provide better estimates of posteriors. Third, we use the remapping in a real-world telephone speech recognition system. The remapping results in a 10% reduction of both word-level error rates (from 4.53% to 4.06%) and sentence-level error rates (from 16.38% to 14.69%) on one corpus, and a 29% reduction at sentence-level error (from 6.3% to 4.5%) on another. The remapping required negligible additional overhead (in terms of both parameters and calculations). McNemar's test shows that these levels of improvement are statistically significant.
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Moskow, Danielle M., Clarissa W. Ong, Steven C. Hayes, and Stefan G. Hofmann. "Process-based therapy: A personalized approach to treatment." Journal of Experimental Psychopathology 14, no. 1 (January 2023): 204380872311528. http://dx.doi.org/10.1177/20438087231152848.
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Historically, evidence-based treatment has followed the latent disease model, which emphasizes using specific protocols tied to diagnoses. Today, the field continues to move towards an individual approach with models of treatment based on change processes. Here, we describe Process-Based Therapy (PBT), a new way of thinking that is moving away from nomothetic studies of diagnosis-driven interventions toward an individual approach to treatment that recognizes the complexity of human suffering. In PBT, therapists select from a wide range of evidence-based interventions, tailoring treatment to meet a person’s needs at a given point in time. PBT is used to analyze intra-individual changes at the level of complex networks of biopsychosocial events, then gathering these into subpopulation and overall population parameters using theory and experimental analysis. PBT emphasizes tracking patient progress over time and treating symptoms based on current experiences, as well as understanding a patient’s past and predicting future experiences. Through specific analyses that aid in this process, therapists can use PBT to create a network with clients to visualize symptoms over time and areas of change.
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Yu, Shui-Lian, Woon-Pang Kuan, Chun-Kwok Wong, Edmund K. Li, and Lai-Shan Tam. "Immunopathological Roles of Cytokines, Chemokines, Signaling Molecules, and Pattern-Recognition Receptors in Systemic Lupus Erythematosus." Clinical and Developmental Immunology 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/715190.
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Systemic lupus erythematosus (SLE) is an autoimmune disease with unknown etiology affecting more than one million individuals each year. It is characterized by B- and T-cell hyperactivity and by defects in the clearance of apoptotic cells and immune complexes. Understanding the complex process involved and the interaction between various cytokines, chemokines, signaling molecules, and pattern-recognition receptors (PRRs) in the immune pathways will provide valuable information on the development of novel therapeutic targets for treating SLE. In this paper, we review the immunopathological roles of novel cytokines, chemokines, signaling molecules, PRRs, and their interactions in immunoregulatory networks and suggest how their disturbances may implicate pathological conditions in SLE.
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Kessler, Steven, Dennis Schroeder, Sergej Korlakov, Vincent Hettlich, Sebastian Kalkhoff, Sobhan Moazemi, Artur Lichtenberg, Falko Schmid, and Hug Aubin. "Predicting readmission to the cardiovascular intensive care unit using recurrent neural networks." DIGITAL HEALTH 9 (January 2023): 205520762211495. http://dx.doi.org/10.1177/20552076221149529.
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If a patient can be discharged from an intensive care unit (ICU) is usually decided by the treating physicians based on their clinical experience. However, nowadays limited capacities and growing socioeconomic burden of our health systems increase the pressure to discharge patients as early as possible, which may lead to higher readmission rates and potentially fatal consequences for the patients. Therefore, here we present a long short-term memory-based deep learning model (LSTM) trained on time series data from Medical Information Mart for Intensive Care (MIMIC-III) dataset to assist physicians in making decisions if patients can be safely discharged from cardiovascular ICUs. To underline the strengths of our LSTM we compare its performance with a logistic regression model, a random forest, extra trees, a feedforward neural network and with an already known, more complex LSTM as well as an LSTM combined with a convolutional neural network. The results of our evaluation show that our LSTM outperforms most of the above models in terms of area under receiver operating characteristic curve. Moreover, our LSTM shows the best performance with respect to the area under precision-recall curve. The deep learning solution presented in this article can help physicians decide on patient discharge from the ICU. This may not only help to increase the quality of patient care, but may also help to reduce costs and to optimize ICU resources. Further, the presented LSTM-based approach may help to improve existing and develop new medical machine learning prediction models.
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Georgiev, Dejan, Harith Akram, and Marjan Jahanshahi. "Deep brain stimulation for psychiatric disorders: role of imaging in identifying/confirming DBS targets, predicting, and optimizing outcome and unravelling mechanisms of action." Psychoradiology 1, no. 3 (September 2021): 118–51. http://dx.doi.org/10.1093/psyrad/kkab012.
Full textAbstract:
Abstract Following the established application of deep brain stimulation (DBS) in the treatment of movement disorders, new non-neurological indications have emerged, such as for obsessive–compulsive disorders, major depressive disorder, dementia, Gilles de la Tourette Syndrome, anorexia nervosa, and addictions. As DBS is a network modulation surgical treatment, the development of DBS for both neurological and psychiatric disorders has been partly driven by advances in neuroimaging, which has helped explain the brain networks implicated. Advances in magnetic resonance imaging connectivity and electrophysiology have led to the development of the concept of modulating widely distributed, complex brain networks. Moreover, the increasing number of targets for treating psychiatric disorders have indicated that there may be a convergence of the effect of stimulating different targets for the same disorder, and the effect of stimulating the same target for different disorders. The aim of this paper is to review the imaging studies of DBS for psychiatric disorders. Imaging, and particularly connectivity analysis, offers exceptional opportunities to better understand and even predict the clinical outcomes of DBS, especially where there is a lack of objective biomarkers that are essential to properly guide DBS pre- and post-operatively. In future, imaging might also prove useful to individualize DBS treatment. Finally, one of the most important aspects of imaging in DBS is that it allows us to better understand the brain through observing the changes of the functional connectome under neuromodulation, which may in turn help explain the mechanisms of action of DBS that remain elusive.
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Milovanović, Miroslav, Alexandru Oarcea, Saša Nikolić, Andjela Djordjević, and Miodrag Spasić. "An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks." Applied Sciences 12, no. 11 (May 26, 2022): 5372. http://dx.doi.org/10.3390/app12115372.
Full textAbstract:
Currently, artificial intelligence and intelligent algorithms for the control of dynamic systems are the main focus for building Industry 4.0 services and developing novel, innovative industrial solutions. This paper proposes a novel intelligent control structure specifically tailored for treating environmental stimuli and disturbances in operational environments of dynamic systems. The structure is based on the Orthogonal Endocrine Neural Network (OENN) and Artificial Orthogonal Glands (AOGs). The operational mechanism of each AOG acquires and processes environmental stimuli and generates artificial hormone concentration values at the gland output. These values are introduced to the appropriate OENN layer to provoke the network with collected environmental insights. To verify the applicability of the proposed structure on a complex dynamical nonlinear system, it was tested in a laboratory environment on the laboratory magnetic levitation system (MLS). The main experimental goal was to test the tracking performance of a levitation object when the new control logic was applied. The results were compared with two additional intelligent algorithms and a default linear quadratic (LQ) control logic. OENN + AOG structure showed improved tracking performances compared with traditional LQ control and better adaptability to environmental conditions compared with similar existing solutions.
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Filippello, Agnese, Stefania Di Mauro, Alessandra Scamporrino, Roberta Malaguarnera, Sebastiano Alfio Torrisi, Gian Marco Leggio, Antonino Di Pino, Roberto Scicali, Francesco Purrello, and Salvatore Piro. "High Glucose Exposure Impairs L-Cell Differentiation in Intestinal Organoids: Molecular Mechanisms and Clinical Implications." International Journal of Molecular Sciences 22, no. 13 (June 22, 2021): 6660. http://dx.doi.org/10.3390/ijms22136660.
Full textAbstract:
Intestinal organoids are used to analyze the differentiation of enteroendocrine cells (EECs) and to manipulate their density for treating type 2 diabetes. EEC differentiation is a continuous process tightly regulated in the gut by a complex regulatory network. However, the effect of chronic hyperglycemia, in the modulation of regulatory networks controlling identity and differentiation of EECs, has not been analyzed. This study aimed to investigate the effect of glucotoxicity on EEC differentiation in small intestinal organoid platforms. Mouse intestinal organoids were cultured in the presence/absence of high glucose concentrations (35 mM) for 48 h to mimic glucotoxicity. Chronic hyperglycemia impaired the expression of markers related to the differentiation of EEC progenitors (Ngn3) and L-cells (NeuroD1), and it also reduced the expression of Gcg and GLP-1 positive cell number. In addition, the expression of intestinal stem cell markers was reduced in organoids exposed to high glucose concentrations. Our data indicate that glucotoxicity impairs L-cell differentiation, which could be associated with decreased intestinal stem cell proliferative capacity. This study provides the identification of new targets involved in new molecular signaling mechanisms impaired by glucotoxicity that could be a useful tool for the treatment of type 2 diabetes.
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Nuzziello, Nicoletta, Francesco Craig, Marta Simone, Arianna Consiglio, Flavio Licciulli, Lucia Margari, Giorgio Grillo, Sabino Liuni, and Maria Liguori. "Integrated Analysis of microRNA and mRNA Expression Profiles: An Attempt to Disentangle the Complex Interaction Network in Attention Deficit Hyperactivity Disorder." Brain Sciences 9, no. 10 (October 22, 2019): 288. http://dx.doi.org/10.3390/brainsci9100288.
Full textAbstract:
Attention Deficit Hyperactivity Disorder (ADHD) is a childhood-onset neurodevelopmental disorder, whose etiology and pathogenesis are still largely unknown. In order to uncover novel regulatory networks and molecular pathways possibly related to ADHD, we performed an integrated miRNA and mRNA expression profiling analysis in peripheral blood samples of children with ADHD and age-matched typically developing (TD) children. The expression levels of 13 miRNAs were evaluated with microfluidic qPCR, and differentially expressed (DE) mRNAs were detected on an Illumina HiSeq 2500 genome analyzer. The miRNA targetome was identified using an integrated approach of validated and predicted interaction data extracted from seven different bioinformatic tools. Gene Ontology (GO) and pathway enrichment analyses were carried out. Results showed that six miRNAs (miR-652-3p, miR-942-5p, let-7b-5p, miR-181a-5p, miR-320a, and miR-148b-3p) and 560 genes were significantly DE in children with ADHD compared to TD subjects. After correction for multiple testing, only three miRNAs (miR-652-3p, miR-148b-3p, and miR-942-5p) remained significant. Genes known to be associated with ADHD (e.g., B4GALT2, SLC6A9 TLE1, ANK3, TRIO, TAF1, and SYNE1) were confirmed to be significantly DE in our study. Integrated miRNA and mRNA expression data identified critical key hubs involved in ADHD. Finally, the GO and pathway enrichment analyses of all DE genes showed their deep involvement in immune functions, reinforcing the hypothesis that an immune imbalance might contribute to the ADHD etiology. Despite the relatively small sample size, in this study we were able to build a complex miRNA-target interaction network in children with ADHD that might help in deciphering the disease pathogenesis. Validation in larger samples should be performed in order to possibly suggest novel therapeutic strategies for treating this complex disease.
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N, Jothy, Jayanthi K, and Gunasundari R. "A Combined Modified OpportunisticNeighbor Selection and Vehicle Localization Routing Approach for Improved Connectivity in Indian Road Scenario." International Journal of Engineering & Technology 7, no. 3.16 (July 26, 2018): 52. http://dx.doi.org/10.14419/ijet.v7i3.4.16182.
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In the recent years, VANET is becoming a spectacular research area in wireless networks. The high mobility vehicular node in VANET dynamically changes the network topology resulting in highly unstable vehicle connectivity. This induces network partitioning and hence ensuring link availability remains to be a challenging task. To surpass these issues, design of efficient VANET routing algorithms is necessary. The routing design for VANET scenario is highly complex and challenging making the existing AODV, greedy, cluster based routing algorithms to suffer from degraded link quality resulting in high end-to-end delay and significant packet loss. Although Opportunistic Neighbor Selection (ONS) scheme proves to be a better routing logic, it does not seem to always ensure link availability at road intersections, particularly in Indian road scenario, where multi road lane discipline is very hard to implement. To overcome these limitations, a combination of Modified Opportunistic Neighbor Selection (MONS) and Vehicle Localization (VL) routing logic for adoption in Indian road sector has been proposed in this paper. This paper addresses the connectivity challenges and provides better solution to achieve improved performance. In this work, two specific scenarios namely: varied mobility/node density rates is considered by treating the other fixed inorder to evaluate the suitability of the proposed logic in terms of packet delivery ratio, end-to-end delay.