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Epidemiological data for acute kidney injury are scarce, especially in low-income countries (LICs) and lower-middle-income countries (LMICs). We aimed to assess regional differences in acute kidney injury recognition, management, and outcomes. In this multinational cross-sectional study, 322 physicians from 289 centres in 72 countries collected prospective data for paediatric and adult patients with confirmed acute kidney injury in hospital and non-hospital settings who met criteria for acute kidney injury. Signs and symptoms at presentation, comorbidities, risk factors for acute kidney injury, and process-of-care data were obtained at the start of acute kidney injury, and need for dialysis, renal recovery, and mortality recorded at 7 days, and at hospital discharge or death, whichever came earlier. We classified countries into high-income countries (HICs), upper-middle-income countries (UMICs), and combined LICs and LMICs (LLMICs) according to their 2014 gross national income per person. Between Sept 29 and Dec 7, 2014, data were collected from 4018 patients. 2337 (58%) patients developed community-acquired acute kidney injury, with 889 (80%) of 1118 patients in LLMICs, 815 (51%) of 1594 in UMICs, and 663 (51%) of 1241 in HICs (for HICs vs UMICs p=0.33; p<0.0001 for all other comparisons). Hypotension (1615 [40%] patients) and dehydration (1536 [38%] patients) were the most common causes of acute kidney injury. Dehydration was the most frequent cause of acute kidney injury in LLMICs (526 [46%] of 1153 vs 518 [32%] of 1605 in UMICs vs 492 [39%] of 1260 in HICs) and hypotension in HICs (564 [45%] of 1260 vs 611 [38%%] of 1605 in UMICs vs 440 [38%] of 1153 LLMICs). Mortality at 7 days was 423 (11%) of 3855, and was higher in LLMICs (129 [12%] of 1076) than in HICs (125 [10%] of 1230) and UMICs (169 [11%] of 1549). We identified common aetiological factors across all countries, which might be amenable to a standardised approach for early recognition and treatment of acute kidney injury. Study limitations include a small number of patients from outpatient settings and LICs, potentially under-representing the true burden of acute kidney injury in these areas. Additional strategies are needed to raise awareness of acute kidney injury in community health-care settings, especially in LICs. International Society of Nephrology.

Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries, leading to a better understanding of the universal properties of phase transitions. However, the small-world property of complex networks complicates application of the renormalization group by introducing correlations between coexisting scales. Here, we provide a framework for the investigation of complex networks at different resolutions. The approach is based on geometric representations, which have been shown to sustain network navigability and to reveal the mechanisms that govern network structure and evolution. We define a geometric renormalization group for networks by embedding them into an underlying hidden metric space. We find that real scale-free networks show geometric scaling under this renormalization group transformation. We unfold the networks in a self-similar multilayer shell that distinguishes the coexisting scales and their interactions. This in turn offers a basis for exploring critical phenomena and universality in complex networks. It also affords us immediate practical applications, including high-fidelity smaller-scale replicas of large networks and a multiscale navigation protocol in hyperbolic space, which betters those on single layers.

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms. In this scenario, data mining and machine learning techniques have widely contribute to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, existing public gene-expression databases are characterized by the unfavorable imbalance between the huge number of genes (in the order of tenths of thousands) and the small number of samples (in the order of a few hundreds) available. Despite diverse feature selection and extraction strategies have been traditionally applied to surpass derived over-fitting issues, the efficacy of standard machine learning pipelines is far from being satisfactory for the prediction of relevant clinical outcomes like follow-up end-points or patient's survival. Using the public Pan-Cancer dataset, in this study we pre-train convolutional neural network architectures for survival prediction on a subset composed of thousands of gene-expression samples from thirty-one tumor types. The resulting architectures are subsequently fine-tuned to predict lung cancer progression-free interval. The application of convolutional networks to gene-expression data has many limitations, derived from the unstructured nature of these data. In this work we propose a methodology to rearrange RNA-seq data by transforming RNA-seq samples into gene-expression images, from which convolutional networks can extract high-level features. As an additional objective, we investigate whether leveraging the information extracted from other tumor-type samples contributes to the extraction of high-level features that improve lung cancer progression prediction, compared to other machine learning approaches.

We introduce Mercator, a reliable embedding method to map real complex networks into their hyperbolic latent geometry. The method assumes that the structure of networks is well described by the popularity × similarity S 1 H 2 static geometric network model, which can accommodate arbitrary degree distributions and reproduces many pivotal properties of real networks, including self-similarity patterns. The algorithm mixes machine learning and maximum likelihood (ML) approaches to infer the coordinates of the nodes in the underlying hyperbolic disk with the best matching between the observed network topology and the geometric model. In its fast mode, Mercator uses a model-adjusted machine learning technique performing dimensional reduction to produce a fast and accurate map, whose quality already outperforms other embedding algorithms in the literature. In the refined Mercator mode, the fast mode embedding result is taken as an initial condition in a ML estimation, which significantly improves the quality of the final embedding. Apart from its accuracy as an embedding tool, Mercator has the clear advantage of systematically inferring not only node orderings, or angular positions, but also the hidden degrees and global model parameters, and has the ability to embed networks with arbitrary degree distributions. Overall, our results suggest that mixing machine learning and ML techniques in a model-dependent framework can boost the meaningful mapping of complex networks.

Concentrating solar power plants use sensible thermal energy storage, a mature technology based on molten salts, due to the high storage efficiency (up to 99%). Both parabolic trough collectors and the central receiver system for concentrating solar power technologies use molten salts tanks, either in direct storage systems or in indirect ones. But even though this is a mature technology, it still shows challenges in its implementation and operation. This paper underscores the critical importance of stringent design criteria for molten salt tanks in thermal storage technology. Focusing on the potential ramifications of design failures, the study explores various dimensions where an inadequate design can lead to severe consequences, even jeopardizing the viability of the entire technology. Key areas discussed include structural integrity, corrosion, thermal shock, thermal expansions, and others. By elucidating the multifaceted risks associated with design shortcomings, this paper aims to emphasize the necessity of thorough reviews and adherence to robust design principles for ensuring the success, safety, and sustainability of thermal storage technology.

Background Geospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. High quality census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth. Methods We use census data from a bed-net mass-distribution campaign on Bioko Island, Equatorial Guinea, conducted by the Bioko Island Malaria Elimination Program as a gold standard to evaluate LandScan (LS), WorldPop Constrained (WP-C) and WorldPop Unconstrained (WP-U), Gridded Population of the World (GPW), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the gold-standard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model. Results Specific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL and WP-C matched best at all other lower population densities. GPW and WP-U performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels. Discussion The metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city.

The ASC (apoptosis-associated speck-like protein containing a caspase recruitment domain (CARD)) protein is an scaffold component of different inflammasomes, intracellular multiprotein platforms of the innate immune system that are activated in response to pathogens or intracellular damage. The formation of ASC specks, initiated by different inflammasome receptors, promotes the recruitment and activation of procaspase-1, thereby triggering pyroptotic inflammatory cell death and pro-inflammatory cytokine release. Here we describe MM01 as the first-in-class small-molecule inhibitor of ASC that interferes with ASC speck formation. MM01 inhibition of ASC oligomerization prevents activation of procaspase-1 in vitro and inhibits the activation of different ASC-dependent inflammasomes in cell lines and primary cultures. Furthermore, MM01 inhibits inflammation in vivo in a mouse model of inflammasome-induced peritonitis. Overall, we highlight MM01 as a novel broad-spectrum inflammasome inhibitor for the potential treatment of multifactorial diseases involving the dysregulation of multiple inflammasomes.

Acute kidney injury (AKI) is increasingly encountered in community settings and contributes to morbidity, mortality, and increased resource utilization worldwide. In low-resource settings, lack of awareness of and limited access to diagnostic and therapeutic interventions likely influence patient management. We evaluated the feasibility of the use of point-of-care (POC) serum creatinine and urine dipstick testing with an education and training program to optimize the identification and management of AKI in the community in 3 low-resource countries. Patients presenting to healthcare centers (HCCs) from 1 October 2016 to 29 September 2017 in the cities Cochabamba, Bolivia; Dharan, Nepal; and Blantyre, Malawi, were assessed utilizing a symptom-based risk score to identify patients at moderate to high AKI risk. POC testing for serum creatinine and urine dipstick at enrollment were utilized to classify these patients as having chronic kidney disease (CKD), acute kidney disease (AKD), or no kidney disease (NKD). Patients were followed for a maximum of 6 months with repeat POC testing. AKI development was assessed at 7 days, kidney recovery at 1 month, and progression to CKD and mortality at 3 and 6 months. Following an observation phase to establish baseline data, care providers and physicians in the HCCs were trained with a standardized protocol utilizing POC tests to evaluate and manage patients, guided by physicians in referral hospitals connected via mobile digital technology. We evaluated 3,577 patients, and 2,101 were enrolled: 978 in the observation phase and 1,123 in the intervention phase. Due to the high number of patients attending the centers daily, it was not feasible to screen all patients to assess the actual incidence of AKI. Of enrolled patients, 1,825/2,101 (87%) were adults, 1,117/2,101 (53%) were females, 399/2,101 (19%) were from Bolivia, 813/2,101 (39%) were from Malawi, and 889/2,101 (42%) were from Nepal. The age of enrolled patients ranged from 1 month to 96 years, with a mean of 43 years (SD 21) and a median of 43 years (IQR 27-62). Hypertension was the most common comorbidity (418/2,101; 20%). At enrollment, 197/2,101 (9.4%) had CKD, and 1,199/2,101 (57%) had AKD. AKI developed in 30% within 7 days. By 1 month, 268/978 (27%) patients in the observation phase and 203/1,123 (18%) in the intervention phase were lost to follow-up. In the intervention phase, more patients received fluids (observation 714/978 [73%] versus intervention 874/1,123 [78%]; 95% CI 0.63, 0.94; p = 0.012), hospitalization was reduced (observation 578/978 [59%] versus intervention 548/1,123 [49%]; 95% CI 0.55, 0.79; p < 0.001), and admitted patients with severe AKI did not show a significantly lower mortality during follow-up (observation 27/135 [20%] versus intervention 21/178 [11.8%]; 95% CI 0.98, 3.52; p = 0.057). Of 504 patients with kidney function assessed during the 6-month follow-up, de novo CKD arose in 79/484 (16.3%), with no difference between the observation and intervention phase (95% CI 0.91, 2.47; p = 0.101). Overall mortality was 273/2,101 (13%) and was highest in those who had CKD (24/106; 23%), followed by those with AKD (128/760; 17%), AKI (85/628; 14%), and NKD (36/607; 6%). The main limitation of our study was the inability to determine the actual incidence of kidney dysfunction in the health centers as it was not feasible to screen all the patients due to the high numbers seen daily. This multicenter, non-randomized feasibility study in low-resource settings demonstrates that it is feasible to implement a comprehensive program utilizing POC testing and protocol-based management to improve the recognition and management of AKI and AKD in high-risk patients in primary care.

Here, we present the World Trade Atlas 1870–2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, based on a gravity model predicting the existence of significant trade channels, are such that the closer countries are in trade space, the greater their chance of becoming connected. The atlas provides us with information regarding the long-term evolution of the international trade system and demonstrates that, in terms of trade, the world is not flat but hyperbolic, as a reflection of its complex architecture. The departure from flatness has been increasing since World War I, meaning that differences in trade distances are growing and trade networks are becoming more hierarchical. Smaller-scale economies are moving away from other countries except for the largest economies; meanwhile those large economies are increasing their chances of becoming connected worldwide. At the same time, Preferential Trade Agreements do not fit in perfectly with natural communities within the trade space and have not necessarily reduced internal trade barriers. We discuss an interpretation in terms of globalization, hierarchization, and localization; three simultaneous forces that shape the international trade system.

POLITICS OF DISINFORMATION Discover a comprehensive exploration of the underlying theories of disinformation, and their impact, from leading voices in the field Politics of Disinformation delivers a thorough discussion of the overwhelming problem of modern fake news in the political arena. The book reviews fundamental theoretical concepts of disinformation and analyzes the impact of new techniques of misinformation and the dissemination of false information in the public space. A group of distinguished authors provide case studies throughout the text to illustrate the effect of disinformation all around the world; including, but not limited to Europe, the Middle East, and South America. The chapters include examination of topics such as the rise of populism, the increasing political influence of social networks, the use of fact checking to combat fake news and echo chambers, and comparative analyses of how disinformation affects conservatives and liberals. A final case study examines all of these factors as they relate to the recent Spanish election of 2019 and how they affected the results. This book also includes: A thorough introduction to the politics of disinformation and the relationship between disinformation and populism An exploration of the democratic implications of networked persona construction and the likely reaction to disinformation by future journalists Discussions of the third person effect and fake news in Spain, as well as perceptions, views, and definitions of fake news among Israeli conservatives and liberals A treatment of disinformation in campaigns in France, Brazil, and Spain Perfect for use as a reference book for students and scholars of political communication and political science, Politics of Disinformation will also earn a place in the libraries of practicing journalists and students of journalism and media studies, as well as those studying or working in communications.