718 research outputs found
Subgraph Games in the Semi-Random Graph Process and Its Generalization to Hypergraphs
The semi-random graph process is a single-player game that begins with an
empty graph on vertices. In each round, a vertex is presented to the
player independently and uniformly at random. The player then adaptively
selects a vertex and adds the edge to the graph. For a fixed monotone
graph property, the objective of the player is to force the graph to satisfy
this property with high probability in as few rounds as possible.
We focus on the problem of constructing a subgraph isomorphic to an
arbitrary, fixed graph . Let be any function tending to
infinity as . In (Omri Ben-Eliezer et al. "Semi-random graph
process". In: Random Structures & Algorithms 56.3 (2020), pp. 648-675) it was
proved that asymptotically almost surely one can construct in less than
rounds where is the degeneracy of . It was
also proved that the result is sharp for , that is, asymptotically
almost surely it takes at least rounds to create
. Moreover, the authors conjectured that their general upper bound is
sharp for all graphs . We prove this conjecture here.
We also consider a natural generalization of the process to -uniform
hypergraphs, the semi-random hypergraph process in which vertices are
presented at random, and the player then selects vertices to form
an edge of size~. Our results for graphs easily generalize to hypergraphs
when ; the threshold for constructing a fixed -uniform hypergraph
is, again, determined by the degeneracy of . However, new challenges are
mounting when ; thresholds are not even known for complete
hypergraphs. We provide bounds for this family and determine thresholds for
some sparser hypergraphs
Relationship between ATSR fire counts and CO vertical column densities retrieved from SCIAMACHY onboard ENVISAT
SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric ChartographY) is the first instrument to allow retrieval of CO by measuring absorption in the near infrared from reflected and scattered sunlight instead of from thermal emission. Thus, in contrast to thermal-infrared satellites (MOPITT), SCIAMACHY is highly sensitive to the lower layers of the troposphere where the sources, such as biomass burning, are located, and where the bulk of the CO is usually found. In many regions of the world, the burning of vegetation has a repeating seasonal pattern, but the amount of CO emitted from biomass burning varies considerably from place to place. Here we present a study on the relationship between fire counts and CO vertical column densities (VCD) in different regions. These results are compared with the CO VCD from MOPITT, aerosol index, and NO_2 tropospheric VCD (TVCD) from SCIAMACHY, and the coupled chemistry climate model (CCM) ECHAM5/MESSY
Confirmed case of encephalitis caused by Murray Valley encephalitis virus infection in a horse
A five year old Australian stock horse in Monto, Queensland, developed neurological signs and was euthanized after a six day course of illness. Histological examination of the brain and spinal cord revealed moderate to severe, subacute, non-suppurative encephalomyelitis. Sections of spinal cord stained positively in immunohistochemistry with a flavivirus-specific monoclonal antibody. Reverse transcription-polymerase chain reaction assay targeting the envelope gene of flavivirus yielded positive results from brain, spinal cord, cerebrospinal fluid and facial nerve. A flavivirus was isolated from the cerebrum and spinal cord. Nucleotide sequences obtained from amplicons from both tissues and virus isolated in cell culture were compared with those in GenBank, and had 96-98% identity with Murray Valley encephalitis virus. The partial envelope gene sequence of the viral isolate clustered into Genotype 1, and was most closely related to a previous Queensland isolate. This is the first confirmed case of naturally-occurring equine encephalitis attributable to Murray Valley encephalitis virus infection
Boreal forest fires in 1997 and 1998: a seasonal comparison using transport model simulations and measurement data
Forest fire emissions have a strong impact on the concentrations of trace gases and aerosols in the atmosphere. In order to quantify the influence of boreal forest fire emissions on the atmospheric composition, the fire seasons of 1997 and 1998 are compared in this paper. Fire activity in 1998 was very strong, especially over Canada and Eastern Siberia, whereas it was much weaker in 1997. According to burned area estimates the burning in 1998 was more than six times as intense as in 1997. Based on hot spot locations derived from ATSR (Along Track Scanning Radiometer) data and official burned area data, fire emissions were estimated and their transport was simulated with a Lagrangian tracer transport model. Siberian and Canadian forest fire tracers were distinguished to investigate the transport of both separately. The fire emissions were transported even over intercontinental distances. Due to the El Niño induced meteorological situation, transport from Siberia to Canada was enhanced in 1998. Siberian fire emissions were transported towards Canada and contributed concentrations more than twice as high as those due to Canada's own CO emissions by fires. In 1998 both tracers arrive at higher latitudes over Europe, which is due to a higher North Atlantic Oscillation (NAO) index in 1998. The simulated emission plumes are compared to CMDL (Climate Monitoring and Diagnostics Laboratory) CO<sub>2</sub> and CO data, Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI) data and Global Ozone Monitoring Experiment (GOME) tropospheric NO<sub>2</sub> and HCHO columns. All the data show clearly enhanced signals during the burning season of 1998 compared to 1997. The results of the model simulation are in good agreement with ground-based as well as satellite-based measurements
A machine learning pipeline for discriminant pathways identification
Motivation: Identifying the molecular pathways more prone to disruption
during a pathological process is a key task in network medicine and, more in
general, in systems biology.
Results: In this work we propose a pipeline that couples a machine learning
solution for molecular profiling with a recent network comparison method. The
pipeline can identify changes occurring between specific sub-modules of
networks built in a case-control biomarker study, discriminating key groups of
genes whose interactions are modified by an underlying condition. The proposal
is independent from the classification algorithm used. Three applications on
genomewide data are presented regarding children susceptibility to air
pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's.
Availability: Details about the software used for the experiments discussed
in this paper are provided in the Appendix
Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization
Background:
The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations.
Methodology and Principal Findings:
We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets.
Conclusions:
The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters
Managing affect in learners' questions in undergraduate science
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 Society for Research into Higher Education.This article aims to position students' classroom questioning within the literature surrounding affect and its impact on learning. The article consists of two main sections. First, the act of questioning is discussed in order to highlight how affect shapes the process of questioning, and a four-part genesis to question-asking that we call CARE is described: the construction, asking, reception and evaluation of a learner's question. This work is contextualised through studies in science education and through our work with university students in undergraduate chemistry, although conducted in the firm belief that it has more general application. The second section focuses on teaching strategies to encourage and manage learners' questions, based here upon the conviction that university students in this case learn through questioning, and that an inquiry-based environment promotes better learning than a simple ‘transmission’ setting. Seven teaching strategies developed from the authors' work are described, where university teachers ‘scaffold’ learning through supporting learners' questions, and working with these to structure and organise the content and the shape of their teaching. The article concludes with a summary of the main issues, highlighting the impact of the affective dimension of learning through questioning, and a discussion of the implications for future research
NEAT: An efficient network enrichment analysis test
Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. Results: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. Conclusions: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat )
Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms
Motivation :Reconstructing the topology of a gene regulatory network is one
of the key tasks in systems biology. Despite of the wide variety of proposed
methods, very little work has been dedicated to the assessment of their
stability properties. Here we present a methodical comparison of the
performance of a novel method (RegnANN) for gene network inference based on
multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER),
focussing our analysis on the prediction variability induced by both the
network intrinsic structure and the available data.
Results: The extensive evaluation on both synthetic data and a selection of
gene modules of "Escherichia coli" indicates that all the algorithms suffer of
instability and variability issues with regards to the reconstruction of the
topology of the network. This instability makes objectively very hard the task
of establishing which method performs best. Nevertheless, RegnANN shows MCC
scores that compare very favorably with all the other inference methods tested.
Availability: The software for the RegnANN inference algorithm is distributed
under GPL3 and it is available at the corresponding author home page
(http://mpba.fbk.eu/grimaldi/regnann-supmat
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