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Innovations in diffusion analysis and imaging techniques have gradually revealed the ubiquity and importance of extracellular space.

Freshly removed from the skull, the human brain looks like a cauliflower with the consistency of a creme caramel flan. This mundane object conceals the most complex structure ever discovered. When the interior of the brain is examined with a resolution of a few micrometers, using appropriate staining techniques and light microscopy, it is seen to be composed of a vast number of cells. The full complexity of the ensemble of cells is only finally revealed at the submicron level using electron microscopy. This chapter will show how the application of methods and models based on diffusion can lead to an understanding of how brain cells pack together and some of the remarkable properties of the narrow spaces that separate them.

Hurricanes and tropical storms are natural hazards with the capacity to devastate coastal regions. Understanding this catastrophic potential is critical to support informed decision-making at local, state, and federal levels. The primary objective of this research is to use machine learning to rigorously explore and quantify the relationship of tropical cyclone characteristics to their destructive outcomes on the coast of the USA. Historical data on hurricanes and tropical storms are identified and curated to support supervised learning. A novel storm damage ratio is introduced to address the inherent challenge of comparing damage to regions with distinct assets and population. Multiple mathematical models to predict economic impacts from tropical events are created using machine learning methods, and the results are compared. Finally, the storm features that most influence the accuracy of predictions are identified and ranked.

Key points Extracellular space (ECS) rapid volume pulsation (RVP) accompanying epileptiform activity is described for the first time. Such RVP occurs robustly in several in vitro and in vivo mouse models of epileptiform activity. In the in vitro 4-aminopyridine model of epileptiform activity, RVP depends on the activity of the electrogenic Na+ /HCO3 - cotransporter (NBCe1). NBCe1 pharmacological inhibition suppresses RVP and epileptiform activity. Inhibition of changes in ECS volume may represent a useful target in epilepsy patients who are resistant to current treatments. Abstract The extracellular space (ECS) of the brain shrinks persistently by approximately 35% during epileptic seizures. Here we report the discovery of rapid volume pulsation (RVP), further transient drops in ECS volume which accompany events of epileptiform activity. These transient ECS contractions were observed in multiple mouse models of epileptiform activity both in vivo (Bicuculline Methiodide model) and in vitro (Hyaluronan synthase 3 knock-out, Picrotoxin, Bicuculline, and 4-Aminopyridine models). By using the probe transients quantification (PTQ) method we show that individual pulses of RVP shrank the ECS by almost 15% in vivo. In the 4-Aminopyridine in vitro model, the individual pulses of RVP shrank the ECS by more than 4%, and these transient changes were superimposed on a persistent ECS shrinkage of 36% measured with the real-time iontophoretic method. In this in vitro model, we investigated several channels and transporters that may be required for the generation of RVP and epileptiform activity. Pharmacological blockages of Na+ /K+ /2Cl- cotransporter type 1 (NKCC1), K+ /Cl- cotransporter (KCC2), the water channel Aquaporin-4 (AQP4) and inwardly-rectifying potassium channel 4.1 (Kir4.1) were ineffective in halting the RVP and the epileptiform activity. In contrast, pharmacological blockade of the electrogenic Na+ /HCO3 - cotransporter (NBCe1) by 4,4'-diisothiocyano-2,2'-stilbenedisulfonic acid (DIDS) eliminated both the RVP and the persistent ECS shrinkage. Importantly, this blocker also stopped the epileptiform activity. These results demonstrate that RVP is closely associated with epileptiform activity across several models of epileptiform activity and therefore the underlying mechanism could potentially represent a novel target for epilepsy management and treatment. This article is protected by copyright. All rights reserved.

The combined records of two 20th-century observers (Martin Bourgoin and Julian Ford) of Night Parrots Pezoporus occidentalis at permanent springs on the Ethel River amount to more published records of this elusive bird over a longer timespan (1912–1964) than anywhere else in Western Australia. Likely Night Parrot habitats persisting in the Ethel River area in 2019–2020 include extensive areas of mature Limestone Spinifex Triodia wiseana with other native perennial grasses, hard spinifex on sandplains, patchy perennial tussock grasses on minor floodplains and chenopod shrublands including perennial saltbush. We evaluate these habitats for their past uses and likely continuing changes that might have affected Night Parrots. With a mine under construction upstream in the sub-catchment headwaters and indications that the species is not extinct in the district, there is an urgent need to raise awareness of a uniquely important area and to survey the area for the species. Monitoring and coordinating protective measures across intersecting land uses is likely to improve the future of rare permanent springs and sensitive habitats.

We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.

Bridges are an important type of transportation infrastructures. Once more than one bridge in a transportation network is extensively or completely damaged in a severe earthquake, the traffic capac...

Maritime transportation systems are responsible for the transportation of the vast majority of global overseas trade. Ports are fundamental agents of the maritime transportation system, being the p...

The real-time iontophoretic method has measured volume fraction and tortuosity of the interstitial component of extracellular space in many regions and under different conditions. To interpret these data computer models of the interstitial space (ISS) of the brain are constructed by representing cells as Basic Cellular Structures (BCS) surrounded by a layer of ISS and replicating this combination to make a 3D ensemble that approximates brain tissue with a specified volume fraction. Tortuosity in such models is measured by releasing molecules of zero size into the ISS and allowing them to execute random walks in the ISS of the ensemble using a Monte Carlo algorithm. The required computational resources for such simulations may be high and here we show that in many situations the 3D problem may be reduced to a quasi-1D problem with consequent reduction in resources. We take the simplest BCS in the form of cubes and use MCell software to perform the Monte Carlo simulations but the analysis described here may be extended in principle to more complex BCS and an ISS that has a defined viscosity and an extracellular matrix that interacts with diffusing molecules. In the course of this study we found that the original analytical description of the relation between volume fraction and tortuosity for an ensemble of cubes may require a small correction.

Background Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs of the clinical and cancer-registry domains. Two TNM ontologies developed specifically for cancer registries were designed for different purposes and have limitations for serving wider application. A unified ontology is proposed to serve the various cancer registry TNM-related tasks and reduce the multiplication effects of different ontologies serving specific tasks. The ontology is comprehensive of the rules for TNM edition 7 as required by cancer registries and designed on a modular basis to allow extension to other TNM editions. Results A unified ontology was developed building on the experience and design of the existing ontologies. It follows a modular approach allowing plug in of components dependent upon any particular TNM edition. A Java front-end was developed to interface with the ontology via the Web Ontology Language application programme interface and enables batch validation or classification of cancer registry records. The programme also allows the means of automated error correction in some instances. Initial tests verified the design concept by correctly inferring TNM stage and successfully handling the TNM-related validation checks on a number of cancer case records, with a performance similar to that of an existing ontology dedicated to the task. Conclusions The unified ontology provides a multi-purpose tool for TNM-related tasks in a cancer registry and is scalable for different editions of TNM. It offers a convenient way of quickly checking validity of cancer case stage information and for batch processing of multi-record data via a dedicated front-end programme. The ontology is adaptable to many uses, either as a standalone TNM module or as a component in applications of wider focus. It provides a first step towards a single, unified TNM ontology for cancer registries.

Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is non-trivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identify failed components, especially the critical ones affecting the overall system performance., Appears in IEEE BigData 2020. Preliminary version in NeurIPS 2020 AI + HADR

The National Survey on Drug Use and Health (NSDUH) contains a large number of responses and many features. This study aims to identify features from within NSDUH that are important in classifying heroin use. Proper implementation of random forest (RF) techniques copes with the highly imbalanced nature of heroin usage among respondents to identify features that are prominent in classification models involving nonlinear combinations of predictive variables. To date, methods for the proper application of RF to imbalanced medical datasets have not been defined.

The multi-modal freight transportation network plays a vital role in maintaining commodity flows across multiple industries and multiple regions. As such, the effects of large-scale disruptive events could result in the closure of key transportation nodes and links, causing disruptions in commodity flows and larger disruptions to industries requiring those commodities for economic productivity. This work integrates a multi-commodity network flow formulation with an economic interdependency model to quantify the multi-industry impacts of a disrupted transportation network to devise contingent rerouting plans to strengthen the network's adaptive capacity. The formulation proposed here is illustrated with a freight transportation planning case study in the state of Oklahoma, considering disruptive scenarios in which a network component is lost and how the proposed approach improves total economic productivity following a disruption.

Brain extracellular space is the narrow microenvironment that surrounds every cell of the central nervous system. It contains a solution that closely resembles cerebrospinal fluid with the addition of extracellular matrix molecules. The space provides a reservoir for ions essential to the electrical activity of neurons and forms an intercellular chemical communication channel. Attempts to reveal the size and structure of the extracellular space using electron microscopy have had limited success; however, a biophysical approach based on diffusion of selected probe molecules has proved useful. A point-source paradigm, realized in the real-time iontophoresis method using tetramethylammonium, as well as earlier radiotracer methods, have shown that the extracellular space occupies ∼20% of brain tissue and small molecules have an effective diffusion coefficient that is two-fifths that in a free solution. Monte Carlo modeling indicates that geometrical constraints, including dead-space microdomains, contribute to the hindrance to diffusion. Imaging the spread of macromolecules shows them increasingly hindered as a function of size and suggests that the gaps between cells are predominantly ∼40 nm with wider local expansions that may represent dead-spaces. Diffusion measurements also characterize interactions of ions and proteins with the chondroitin and heparan sulfate components of the extracellular matrix; however, the many roles of the matrix are only starting to become apparent. The existence and magnitude of bulk flow and the so-called glymphatic system are topics of current interest and controversy. The extracellular space is an exciting area for research that will be propelled by emerging technologies.

Theory, methodology, and applications of risk analysis contribute to the quantification and management of resilience. For risk analysis, numerous complementary frameworks, guidelines, case studies, etc., are available in the literature. For resilience, the documented applications are sparse relative to numerous untested definitions and concepts. This essay on resilience analytics motivates the methodology, tools, and processes that will achieve resilience of real systems. The paper describes how risk analysts will lead in the modeling, quantification, and management of resilience for a variety of systems subject to future conditions, including technologies, economics, environment, health, developing regions, regulations, etc. The paper identifies key gaps where methods innovations are needed, presenting resilience of interdependent infrastructure networks as an example. Descriptive, predictive, and prescriptive analytics are differentiated. A key outcome will be the recognition, adoption, and adva...

Analyzing network vulnerability is a key element of network planning in order to be prepared for any disruptive event that might impact the performance of the network. Hence, many importance measures have been proposed to identify the important components in a network with respect to vulnerability and rank them accordingly based on individual importance measure. However, in this paper, we propose a new approach to identify the most important network components based on multiple importance measures using a multi criteria decision making (MCDM) method, namely the technique for order performance by similarity to ideal solution (TOPSIS), able to take into account the preferences of decision-makers. We consider multiple edge-specific flow-based importance measures provided as the multiple criteria of a network where the alternatives are the edges. Accordingly, TOPSIS is used to rank the edges of the network based on their importance considering multiple different importance measures. The proposed approach is illustrated through different networks with different densities along with the effects of weighs.

This paper presents a novel resilience-based framework to optimise the scheduling of the post-disaster recovery actions for road-bridge transportation networks. The methodology systematically incor...

Display Omitted A search heuristic is proposed for reverse engineering of gene regulatory networks.The approach is based on the variable neighborhood search metaheuristic.Search neighborhood structures are defined based on permissible move types.Empirically it outperforms greedy techniques in both speed and solution quality. A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.

One strategy to mitigate social and economic vulnerabilities of communities to natural disasters is to enhance the current infrastructure underlying the community. Decisions regarding allocation of limited resources to improve infrastructure components are complex and involve various trade-offs. In this study, an efficient multi-objective optimization model is proposed to support decisions regarding building retrofits within a community. In particular, given a limited budget and a heterogeneous commercial and residential building stock, solutions to the proposed model allow a detailed analysis of the trade-offs between direct economic loss and the competing objective of minimizing immediate population dislocation. The developed mathematical model is informed by earthquake simulation modeling as well as population dislocation modeling from the field of social science. The model is applied to the well-developed virtual city, Centerville, designed collaboratively by a team of engineering experts, eco...

Introduce a statistical learning approach to a traditional optimization problem.Problem is reformulated as a linear relaxation based on model predictions.Method can be part of an exact solution strategy and used as a primal heuristic.Empirical tests demonstrate improved solutions over leading commercial software.Incremental solution time is negligible for large problems. A new heuristic procedure for the fixed charge network flow problem is proposed. The new method leverages a probabilistic model to create an informed reformulation and relaxation of the FCNF problem. The technique relies on probability estimates that an edge in a graph should be included in an optimal flow solution. These probability estimates, derived from a statistical learning technique, are used to reformulate the problem as a linear program which can be solved efficiently. This method can be used as an independent heuristic for the fixed charge network flow problem or as a primal heuristic. In rigorous testing, the solution quality of the new technique is evaluated and compared to results obtained from a commercial solver software. Testing demonstrates that the novel prediction-based relaxation outperforms linear programming relaxation in solution quality and that as a primal heuristic the method significantly improves the solutions found for large problem instances within a given time limit.

A predicative model is investigated to determine whether or not arcs are selected in an optimal solution of a FCNF problem.The accuracy of the predictive mode is very high.The model has useful explanatory power regarding the predictors defined.Component importance measure is developed to rank the arcs in the network. The fixed charge network flow (FCNF) problem is a classical NP-hard combinatorial problem with wide spread applications. To the best of our knowledge, this is the first paper that employs a statistical learning technique to analyze and quantify the effect of various network characteristics relating to the optimal solution of the FCNF problem. In particular, we create a probabilistic classifier based on 18 network related variables to produce a quantitative measure that an arc in the network will have a non-zero flow in an optimal solution. The predictive model achieves 85% cross-validated accuracy. An application employing the predictive model is presented from the perspective of identifying critical network components based on the likelihood of an arc being used in an optimal solution.

Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies. When compared to hand specimens, thin sections provide better and more accurate means for analysis of mineral proportion, distribution, texture, pore space analysis, and cement composition. Most petrographic analysis relies on visual inspection of rock thin sections under a microscope, a task that is laborious even for experienced geologists. Large projects with a tight time frame requiring the analysis of a large amount of thin sections may require multiple petrographers, thereby risking the introduction of inconsistency in the analysis. To address this challenge, we explore the use of deep convolutional neural networks (CNN) as a tool for acceleration and automatization of microfacies classification. We make use of transfer learning based on robust and reliable CNN models trained with a large amount of non-geological images. With a relatively small number of labeled thin sections used in “fine-tuning” training we are able to adapt CNN models that achieve low error levels (

This research investigates the adaptive capacity enhancement problem of network systems and proposes a framework for allocating limited resources during the response phase in the aftermath ...

This study proposes a framework for the probabilistic prediction of building portfolio functionality loss (BPFL) in a community following an earthquake hazard. Building functionality is joi...

This work develops and compares several flow-based vulnerability measures to prioritize important network edges for the implementation of preparedness options. These network vulnerability measures quantify different characteristics and perspectives on enabling maximum flow, creating bottlenecks, and partitioning into cutsets, among others. The efficacy of these vulnerability measures to motivate preparedness options against experimental geographically located disruption simulations is measured. Results suggest that a weighted flow capacity rate, which accounts for both (i) the contribution of an edge to maximum network flow and (ii) the extent to which the edge is a bottleneck in the network, shows most promise across four instances of varying network sizes and densities.

The objective scaling ensemble approach is a novel two-phase heuristic for integer linear programming problems shown to be effective on a wide variety of integer linear programming problems. The technique identifies and aggregates multiple partial solutions to modify the problem formulation and significantly reduce the search space. An empirical analysis on publicly available benchmark problems demonstrate the efficacy of our approach by outperforming standard solution strategies implemented in modern optimization software., Comment: journal

Diet influences dopamine transmission in motor- and reward-related basal ganglia circuitry. In part, this reflects diet-dependent regulation of circulating and brain insulin levels. Activation of striatal insulin receptors amplifies axonal dopamine release in brain slices, and regulates food preference in vivo. The effect of insulin on dopamine release is indirect, and requires striatal cholinergic interneurons that express insulin receptors. However, insulin also acts directly on dopamine axons to increase dopamine uptake by promoting dopamine transporter (DAT) surface expression, counteracting enhanced dopamine release. Here we determined the functional consequences of acute insulin exposure and chronic diet-induced changes in insulin on DAT activity after evoked dopamine release in striatal slices from adult ad-libitum fed (AL) rats and mice, and food-restricted (FR) or high-fat/high-sugar obesogenic (OB) diet rats. Uptake kinetics were assessed by fitting evoked dopamine transients to the Michaelis-Menten equation and extracting C(peak) and V(max). Insulin (30 nM) increased both parameters in the caudate putamen and nucleus accumbens core of AL rats in an insulin receptor- and PI3-kinase-dependent manner. A pure effect of insulin on uptake was unmasked using mice lacking striatal acetylcholine, in which increased V(max) caused a decrease in C(peak). Diet also influenced V(max), which was lower in FR versus AL. The effects of insulin on C(peak) and V(max) were amplified by FR but blunted by OB, consistent with opposite consequences of these diets on insulin levels and insulin receptor sensitivity. Overall, these data reveal acute and chronic effects of insulin and diet on dopamine release and uptake that will influence brain reward pathways.

This review describes the basic concepts and protocol to perform the real-time iontophoresis (RTI) method, the gold-standard to explore and quantify the extracellular space (ECS) of the living brain. The ECS surrounds all brain cells and contains both interstitial fluid and extracellular matrix. The transport of many substances required for brain activity, including neurotransmitters, hormones, and nutrients, occurs by diffusion through the ECS. Changes in the volume and geometry of this space occur during normal brain processes, like sleep, and pathological conditions, like ischemia. However, the structure and regulation of brain ECS, particularly in diseased states, remains largely unexplored. The RTI method measures two physical parameters of living brain: volume fraction and tortuosity. Volume fraction is the proportion of tissue volume occupied by ECS. Tortuosity is a measure of the relative hindrance a substance encounters when diffusing through a brain region as compared to a medium with no obstructions. In RTI, an inert molecule is pulsed from a source microelectrode into the brain ECS. As molecules diffuse away from this source, the changing concentration of the ion is measured over time using an ion-selective microelectrode positioned roughly 100 µm away. From the resulting diffusion curve, both volume fraction and tortuosity can be calculated. This technique has been used in brain slices from multiple species (including humans) and in vivo to study acute and chronic changes to ECS. Unlike other methods, RTI can be used to examine both reversible and irreversible changes to the brain ECS in real time.

As the prevalence of social media real-time communication grows among the public, research has increased regarding its use in various domains of study, including human behavior with respect to natural disasters. Various metrics, whether related to message posting frequency, origination proximity to the disaster, and/or the sentiment of the messages themselves are commonly studied. To the best of our knowledge, no study has been conducted to determine the sensitivity of social media to different types or magnitudes of natural disasters under various circumstances. We select four types of natural disasters (tornadoes, winter storms, wildfires, and floods) and for each we examine multiple recent events along with the associated Twitter behavior to evaluate multiple aspects: duration of social media attention, frequency shifts, frequency shifts for different social vulnerability levels, tweet proximity to the disasters, and sentiment. The results demonstrate that Twitter is indeed a social sensor with different sensitivity levels to natural disasters and depending on the event circumstances, a diverse pattern of social media behavior should be expected.

The narrow spaces between the cells of the brain are a highway for diffusing molecular traffic. In this issue, Xiao et al. (1) use experiments and Monte Carlo modeling to show that molecular transport in the granular layer of the rat cerebellum must be described by anomalous diffusion mediated by anatomical structures called glomeruli. The extracellular space (ECS) of the brain comprises all the territory that resides outside the neurons and glia cells, most importantly the interstitial space between neighboring cells.

Accumulation of toxic protein aggregates-amyloid-β (Aβ) plaques and hyperphosphorylated tau tangles-is the pathological hallmark of Alzheimer disease (AD). Aβ accumulation has been hypothesized to result from an imbalance between Aβ production and clearance; indeed, Aβ clearance seems to be impaired in both early and late forms of AD. To develop efficient strategies to slow down or halt AD, it is critical to understand how Aβ is cleared from the brain. Extracellular Aβ deposits can be removed from the brain by various clearance systems, most importantly, transport across the blood-brain barrier. Findings from the past few years suggest that astroglial-mediated interstitial fluid (ISF) bulk flow, known as the glymphatic system, might contribute to a larger portion of extracellular Aβ (eAβ) clearance than previously thought. The meningeal lymphatic vessels, discovered in 2015, might provide another clearance route. Because these clearance systems act together to drive eAβ from the brain, any alteration to their function could contribute to AD. An understanding of Aβ clearance might provide strategies to reduce excess Aβ deposits and delay, or even prevent, disease onset. In this Review, we describe the clearance systems of the brain as they relate to proteins implicated in AD pathology, with the main focus on Aβ.

Nature Reviews Neurology 11, 457–470 (2015) In the initially published version of this article, Figure 1 had an incomplete credit line. The correct credit line reads: Redrawn from Nedergaard, M. Garbage truck of the brain. Science 340, 1529–1530 (2013). Reprinted with permission from AAAS. This error has been corrected in the HTML and PDF versions of the article.