Flow graphs: interweaving dynamics and structure

The behavior of complex systems is determined not only by the topological organization of their interconnections but also by the dynamical processes taking place among their constituents. A faithful modeling of the dynamics is essential because different dynamical processes may be affected very differently by network topology. A full characterization of such systems thus requires a formalization that encompasses both aspects simultaneously, rather than relying only on the topological adjacency matrix. To achieve this, we introduce the concept of flow graphs, namely weighted networks where dynamical flows are embedded into the link weights. Flow graphs provide an integrated representation of the structure and dynamics of the system, which can then be analyzed with standard tools from network theory. Conversely, a structural network feature of our choice can also be used as the basis for the construction of a flow graph that will then encompass a dynamics biased by such a feature. We illustrate the ideas by focusing on the mathematical properties of generic linear processes on complex networks that can be represented as biased random walks and also explore their dual consensus dynamics.Comment: 4 pages, 1 figur

Estimating sampling biases in citizen science datasets

The rise of citizen science (also called community science) has led to vast quantities of species observation data collected by members of the public. Citizen science data tend to be unevenly distributed across space and time, but the treatment of sampling bias varies between studies, and interactions between different biases are often overlooked. We present a method for conceptualizing and estimating spatial and temporal sampling biases, and interactions between them. We use this method to estimate sampling biases in an example ornithological citizen science dataset from eBird in Brisbane City, Australia. We then explore the effects of these sampling biases on subsequent model inference of population trends, using both a simulation study and an application of the same trend models to the Brisbane eBird dataset. We find varying levels of sampling bias in the Brisbane eBird dataset across temporal and spatial scales, and evidence for interactions between biases. Several of the sampling biases we identified differ from those described in the literature for other datasets, with protected areas being undersampled in the city, and only limited seasonal sampling bias. We demonstrate variable performance of trend models under different sampling bias scenarios, with more complex biases being associated with typically poorer trend estimates. Sampling biases are important to consider when analysing ecological datasets, and analysts can use this method to ensure that any biologically relevant sampling biases are detected and given due consideration during analysis. With appropriate model specification, the effects of sampling biases can be reduced to yield reliable information about biodiversity.Peer reviewe

Using citizen science to identify Australia’s least known birds and inform conservation action

Citizen science is a popular approach to biodiversity surveying, whereby data that are collected by volunteer naturalists may help analysts to understand the distribution and abundance of wild organisms. In Australia, birdwatchers have contributed to two major citizen science programs, eBird (run by the Cornell Lab of Ornithology) and Birdata (run by Birdlife Australia), which collectively hold more than 42 million records of wild birds from across the country. However, these records are not evenly distributed across space, time, or taxonomy, with particularly significant variation in the number of records of each species in these datasets. In this paper, we explore this variation and seek to determine which Australian bird species are least known as determined by rates of citizen science survey detections. We achieve this by comparing the rates of survey effort and species detection across each Australian bird species? range, assigning all 581 species to one of the four groups depending on their rates of survey effort and species observation. We classify 56 species into a group considered the most poorly recorded despite extensive survey effort, with Coxen?s Fig Parrot Cyclopsitta coxeni, Letter-winged Kite Elanus scriptus, Night Parrot Pezoporus occidentalis, Buff-breasted Buttonquail Turnix olivii and Red-chested Buttonquail Turnix pyrrhothorax having the very lowest numbers of records. Our analyses provide a framework to identify species that are poorly represented in citizen science datasets. We explore the reasons behind why they may be poorly represented and suggest ways in which targeted approaches may be able to help fill in the gaps.Publisher PDFPeer reviewe

Optimizing offshore wind export cable routing using GIS-based environmental heat maps

In the United States, there are plans to produce up to 30 GW of offshore wind power by the year 2030, resulting in numerous seabed lease areas which are currently going through the leasing or construction and operations phase. A key challenge associated with offshore wind is optimal routing and installation of the subsea power cables, which transmit power from the main offshore wind energy production area to a land-based station, where it connects to the electrical grid. By traversing a vast extent of the seafloor, the installation and operational phases of subsea power cables have the potential to result in a range of environmental impacts, which may negatively affect sensitive biological, physical, human and/or cultural resource receptors. Presented here is a case study from southeastern North Carolina to identify optimal seabed cable routes and coastal landfalls for a recently leased offshore wind farm by using a combination of publicly available data, coupled with standard environmental impact assessment methodologies and geographic information system (GIS)-based heat maps. The study identified a range of high-risk areas, in addition to a number of potential low-risk routes and landfall areas which minimize seabed user conflicts and impacts on environmentally sensitive locations. Although additional high-resolution and site-specific environmental, geological and biological surveys are required to develop a robust cable installation plan, the preliminary steps from this research optimize early-phase marine spatial planning for offshore wind projects and other similar subsea industries.</p

An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

Neurosteroid Activation of GABA-A Receptors: A Potential Treatment Target for Symptoms in Primary Biliary Cholangitis?

\ua9 2022 Aaron Wetten et al. Background and Aims. A third of patients with primary biliary cholangitis (PBC) experience poorly understood cognitive symptoms, with a significant impact on quality of life (QOL), and no effective medical treatment. Allopregnanolone, a neurosteroid, is a positive allosteric modulator of gamma-aminobutyricacid-A (GABA-A) receptors, associated with disordered mood, cognition, and memory. This study explored associations between allopregnanolone and a disease-specific QOL scoring system (PBC-40) in PBC patients. Method. Serum allopregnanolone levels were measured in 120 phenotyped PBC patients and 40 age and gender-matched healthy controls. PBC subjects completed the PBC-40 at recruitment. Serum allopregnanolone levels were compared across PBC-40 domains for those with none/mild symptoms versus severe symptoms. Results. There were no overall differences in allopregnanolone levels between healthy controls (median = 0.03 ng/ml (IQR = 0.025)) and PBC patients (0.031 (0.42), p=0.42). Within the PBC cohort, higher allopregnanolone levels were observed in younger patients (r (120) = -0.53, p&lt;0.001) but not healthy controls (r (39) = -0.21, p=0.21). Allopregnanolone levels were elevated in the PBC-40 domains, cognition (u = 1034, p=0.02), emotional (u = 1374, p=0.004), and itch (u = 795, p=0.03). Severe cognitive symptoms associated with a younger age: severe (50 (12)) vs. none (60 (13); u = 423 p=0.001). Conclusion. Elevated serum allopregnanolone is associated with severe cognitive, emotional, and itch symptoms in PBC, in keeping with its known action on GABA-A receptors. Existing novel compounds targeting allopregnanolone could offer new therapies in severely symptomatic PBC, satisfying a significant unmet need

GEMSEC: Graph Embedding with Self Clustering

Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC -- a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. GEMSEC is a general extension of earlier work in the domain of sequence-based graph embedding. GEMSEC places nodes in an abstract feature space where the vertex features minimize the negative log-likelihood of preserving sampled vertex neighborhoods, and it incorporates known social network properties through a machine learning regularization. We present two new social network datasets and show that by simultaneously considering the embedding and clustering problems with respect to social properties, GEMSEC extracts high-quality clusters competitive with or superior to other community detection algorithms. In experiments, the method is found to be computationally efficient and robust to the choice of hyperparameters

Circadian hormone secretory profiles in women with severe premenstrual tension syndrome.

The circadian secretory profiles of serum prolactin, growth hormone and cortisol were measured in two women suffering from severe premenstrual tension syndrome and in two asymptomatic control subjects. Subjects and controls were screened and included after a rigorous selection process. Blood samples were obtained every 30 min over a period of 24 h in each woman both on day 9 (follicular phase) and day 26 (luteal phase) of the menstrual cycle. There was no relationship between the hormonal secretory profiles and the premenstrual tension syndrome.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75119/1/j.1471-0528.1984.tb04785.x.pd

Health-related quality of life in patients with β-thalassemia: Data from the phase 3 BELIEVE trial of luspatercept

BACKGROUND: Patients with transfusion-dependent (TD) β-thalassemia require long-term red blood cell transfusions (RBCTs) that lead to iron overload, impacting health-related quality of life (HRQoL). METHODS: The impact of luspatercept, a first-in-class erythroid maturation agent, versus placebo on HRQoL of patients with TD β-thalassemia was evaluated in the phase 3 BELIEVE trial. HRQoL was assessed at baseline and every 12 weeks using the 36-item Short Form Health Survey (SF-36) and Transfusion-dependent Quality of Life questionnaire (TranQol). Mean change in HRQoL was evaluated from baseline to week 48 for patients receiving luspatercept + best supportive care (BSC) and placebo + BSC and between luspatercept responders and non-responders. RESULTS: Through week 48, for both groups, mean scores on SF-36 and TranQol domains were stable over time and did not have a clinically meaningful change. At week 48, more patients who achieved clinical response (≥50% reduction in RBCT burden over 24 weeks) in the luspatercept + BSC group had improvement in SF-36 Physical Function compared with placebo + BSC (27.1% vs. 11.5%; p = .019). CONCLUSIONS: Luspatercept + BSC reduced transfusion burden while maintaining patients' HRQoL. HRQoL domain improvements from baseline through 48 weeks were also enhanced for luspatercept responders