3,078 research outputs found
Gradient Bounds for Solutions of Stochastic Differential Equations Driven by Fractional Brownian Motions
We study some functional inequalities satisfied by the distribution of the
solution of a stochastic differential equation driven by fractional Brownian
motions. Such functional inequalities are obtained through new integration by
parts formulas on the path space of a fractional Brownian motion.Comment: The paper is dedicated to Pr. David Nualart 60th's birthda
Visualization of the distribution of autophosphorylated calcium/calmodulin-dependent protein kinase II after tetanic stimulation in the CA1 area of the hippocampus
Autophosphorylation of calcium/calmodulin-dependent protein kinase II (CaMKII) at threonine-286 produces Ca2+-independent kinase activity and has been proposed to be involved in induction of long-term potentiation by tetanic stimulation in the hippocampus. We have used an immunocytochemical method to visualize and quantify the pattern of autophosphorylation of CaMKII in hippocampal slices after tetanization of the Schaffer collateral pathway. Thirty minutes after tetanic stimulation, autophosphorylated CaM kinase II (P-CaMKII) is significantly increased in area CA1 both in apical dendrites and in pyramidal cell somas. In apical dendrites, this increase is accompanied by an equally significant increase in staining for nonphosphorylated CaM kinase II. Thus, the increase in P-CaMKII appears to be secondary to an increase in the total amount of CaMKII. In neuronal somas, however, the increase in P-CaMKII is not accompanied by an increase in the total amount of CaMKII. We suggest that tetanic stimulation of the Schaffer collateral pathway may induce new synthesis of CaMKII molecules in the apical dendrites, which contain mRNA encoding its alpha-subunit. In neuronal somas, however, tetanic stimulation appears to result in long-lasting increases in P-CaMKII independent of an increase in the total amount of CaMKII. Our findings are consistent with a role for autophosphorylation of CaMKII in the induction and/or maintenance of long-term potentiation, but they indicate that the effects of tetanus on the kinase and its activity are not confined to synapses and may involve induction of new synthesis of kinase in dendrites as well as increases in the level of autophosphorylated kinase
Catalytic hollow fiber membranes prepared using layer-by-layer adsorption of polyelectrolytes and metal nanoparticles
Immobilization of metalnanoparticles in hollowfibermembranes via alternating adsorption of polyelectrolytes and negatively charged Au nanoparticles yields catalytic reactors with high surface areas. SEM images show that this technique deposits a high density of unaggregated metalnanoparticles both on the surfaces and in the pores of the hollowfibers. Catalytic reduction of 4-nitrophenol with NaBH4, which can be easily monitored by UV–vis spectrophotometry, demonstrates that the nanoparticles in the hollowfibermembrane are highly catalytically active. In a single pass through the membrane, >99% of the 4-nitrophenol is reduced to 4-aminophenol, but this conversion decreases over time. The conversion decline may stem from catalyst fouling caused by by-products of 4-aminophenol oxidation
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
Multimodal Foundation Models For Echocardiogram Interpretation
Multimodal deep learning foundation models can learn the relationship between
images and text. In the context of medical imaging, mapping images to language
concepts reflects the clinical task of diagnostic image interpretation, however
current general-purpose foundation models do not perform well in this context
because their training corpus have limited medical text and images. To address
this challenge and account for the range of cardiac physiology, we leverage
1,032,975 cardiac ultrasound videos and corresponding expert interpretations to
develop EchoCLIP, a multimodal foundation model for echocardiography. EchoCLIP
displays strong zero-shot (not explicitly trained) performance in cardiac
function assessment (external validation left ventricular ejection fraction
mean absolute error (MAE) of 7.1%) and identification of implanted intracardiac
devices (areas under the curve (AUC) between 0.84 and 0.98 for pacemakers and
artificial heart valves). We also developed a long-context variant (EchoCLIP-R)
with a custom echocardiography report text tokenizer which can accurately
identify unique patients across multiple videos (AUC of 0.86), identify
clinical changes such as orthotopic heart transplants (AUC of 0.79) or cardiac
surgery (AUC 0.77), and enable robust image-to-text search (mean cross-modal
retrieval rank in the top 1% of candidate text reports). These emergent
capabilities can be used for preliminary assessment and summarization of
echocardiographic findings
Compilation by stochastic Hamiltonian sparsification
Simulation of quantum chemistry is expected to be a principal application of
quantum computing. In quantum simulation, a complicated Hamiltonian describing
the dynamics of a quantum system is decomposed into its constituent terms,
where the effect of each term during time-evolution is individually computed.
For many physical systems, the Hamiltonian has a large number of terms,
constraining the scalability of established simulation methods. To address this
limitation we introduce a new scheme that approximates the actual Hamiltonian
with a sparser Hamiltonian containing fewer terms. By stochastically
sparsifying weaker Hamiltonian terms, we benefit from a quadratic suppression
of errors relative to deterministic approaches. Relying on optimality
conditions from convex optimisation theory, we derive an appropriate
probability distribution for the weaker Hamiltonian terms, and compare its
error bounds with other probability ansatzes for some electronic structure
Hamiltonians. Tuning the sparsity of our approximate Hamiltonians allows our
scheme to interpolate between two recent random compilers: qDRIFT and
randomized first order Trotter. Our scheme is thus an algorithm that combines
the strengths of randomised Trotterisation with the efficiency of qDRIFT, and
for intermediate gate budgets, outperforms both of these prior methods.Comment: 17 pages, 1 figure, 1 algorith
Evolutionary Signatures of Common Human Cis-Regulatory Haplotypes
Variation in gene expression may give rise to a significant fraction of inter-individual phenotypic variation. Studies searching for the underlying genetic controls for such variation have been conducted in model organisms and humans in recent years. In our previous effort of assessing conserved underlying haplotype patterns across ethnic populations, we constructed common haplotypes using SNPs having conserved linkage disequilibrium (LD) across ethnic populations. These common haplotypes cluster into a simple evolutionary structure based on their frequencies, defining only up to three conserved clusters termed ‘haplotype frameworks’. One intriguing preliminary finding was that a significant portion of reported variants strongly associated with cis-regulation tags these globally conserved haplotype frameworks. Here we expand the investigation by collecting genes showing stringently determined cis-association between genotypes and expression phenotypes from major studies. We conducted phylogenetic analysis of current major haplotypes along with the corresponding haplotypes derived from chimpanzee reference sequences. Our analysis reveals that, for the vast majority of such cis-regulatory genes, the tagging SNPs showing the strongest association also tag the haplotype lineages directly separated from ancestry, inferred from either chimpanzee reference sequences or the allele frequency-derived haplotype frameworks, suggesting that the differentially expressed phenotypes were evolved relatively early in human history. Such evolutionary signatures provide keys for a more effective identification of globally-conserved candidate regulatory haplotypes across human genes in future epidemiologic and pharmacogenetic studies
Analysis of Bridges for Seismic Hazard Mitigation in Kentucky
The priority routes have been selected for Western Kentucky which shares the most hazardous New Madrid seismic zone. As the vital links on the priority routes, bridges need to be protected from collapse during earthquakes in order to maintain the access to the route for subsequent emergency traffic. In this paper, a support-loss type of bridge collapse due to earthquake induced abutment sliding is analyzed and corresponding criteria to this type of collapse is established. The analysis methods for existing bridge abutment are advanced. A computer program based on the methods is developed and applied to evaluate the potential earthquake induced damage of 276 bridges on the priority routes
The KMO Method for Solving Non-homogenous, mth Order Differential Equations
This paper shows a simple tabular procedure \added{derived from the method of undetermined coefficients} for finding a particular solution to differential equations of the form \sum_{j=0}^m a_j\frac{d^j y}{dx^j} = P(x)e^{\alpha{}x}. This procedure reduces the derivatives of the product of an arbitrary polynomial and an exponential to rows of constants representing the coefficients of the terms. The rows are each multiplied by aj and summed to produce a mth order differential equation such that its solution is the polynomial part of the particular solution of the above equation. Solving this corresponding differential equation determines the coefficients of the polynomial. The underlying algebra of this conversion and its formulaic implication are then discussed. Using the formula derived, the particular solution is found. This procedure is based on but different than the method of undetermined coefficients because while the method of undetermined coefficients requires substitution of a product of a polynomial, Q, and an exponential into the differential equation immediately, this procedure is derived from the examination of the substitution of the product of any function and an exponential. This allows for a richer understanding of the relationship between the differential equation for y and the differential equation for Q. Ultimately this method is better than the method of undetermined coefficients because it is more straightforward. In any case, both methods solve the same problem but KMO is faster
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