8,684 research outputs found
Plant Homeo Domain Finger Protein 8 Regulates Mesodermal and Cardiac Differentiation of Embryonic Stem Cells Through Mediating the Histone Demethylation of pmaip1
published_or_final_versio
High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry
published_or_final_versio
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion;
often, motion helps to resolve ambiguities in object structure that arise when
we observe object appearance only. There are particular scenarios, however,
where neither appearance nor spatial-temporal motion signals are informative:
occluding twigs may look connected and have almost identical movements, though
they belong to different, possibly disconnected branches. We propose to tackle
this problem through spectrum analysis of motion signals, because vibrations of
disconnected branches, though visually similar, often have distinctive natural
frequencies. We propose a novel formulation of tree structure based on a
physics-based link model, and validate its effectiveness by theoretical
analysis, numerical simulation, and empirical experiments. With this
formulation, we use nonparametric Bayesian inference to reconstruct tree
structure from both spectral vibration signals and appearance cues. Our model
performs well in recognizing hierarchical tree structure from real-world videos
of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://tree.csail.mit.edu
Nanofiltration membranes with dually charged composite layer exhibiting super-high multivalent-salt rejection
Moving from evidence-based medicine to evidence-based health.
While evidence-based medicine (EBM) has advanced medical practice, the health care system has been inconsistent in translating EBM into improvements in health. Disparities in health and health care play out through patients' limited ability to incorporate the advances of EBM into their daily lives. Assisting patients to self-manage their chronic conditions and paying attention to unhealthy community factors could be added to EBM to create a broader paradigm of evidence-based health. A perspective of evidence-based health may encourage physicians to consider their role in upstream efforts to combat socially patterned chronic disease
Solving Quantum Ground-State Problems with Nuclear Magnetic Resonance
Quantum ground-state problems are computationally hard problems; for general
many-body Hamiltonians, there is no classical or quantum algorithm known to be
able to solve them efficiently. Nevertheless, if a trial wavefunction
approximating the ground state is available, as often happens for many problems
in physics and chemistry, a quantum computer could employ this trial
wavefunction to project the ground state by means of the phase estimation
algorithm (PEA). We performed an experimental realization of this idea by
implementing a variational-wavefunction approach to solve the ground-state
problem of the Heisenberg spin model with an NMR quantum simulator. Our
iterative phase estimation procedure yields a high accuracy for the
eigenenergies (to the 10^-5 decimal digit). The ground-state fidelity was
distilled to be more than 80%, and the singlet-to-triplet switching near the
critical field is reliably captured. This result shows that quantum simulators
can better leverage classical trial wavefunctions than classical computers.Comment: 11 pages, 13 figure
Blockchain-based privacy preservation for 5G-enabled drone communications
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record5G-enabled drones have potential applications in a
variety of both military and civilian settings (e.g., monitoring
and tracking of individuals in demonstrations and/or enforcing of social / physical distancing during pandemics such as
COVID-19). Such applications generally involve the collection
and dissemination of (massive) data from the drones to remote
data centres for storage and analysis, for example via 5G
networks. Consequently, there are security and privacy considerations underpinning 5G-enabled drone communications. We
posit the potential of leveraging blockchain to facilitate privacy
preservation, and therefore in this article we will review existing
blockchain-based solutions after introducing the architecture for
5G-enabled drone communications and blockchain. We will also
review existing legislation and data privacy regulations that need
to be considered in the design of blockchain-based solutions, as
well as identifying potential challenges and open issues which
will hopefully inform future research agenda
Why do Particle Clouds Generate Electric Charges?
Grains in desert sandstorms spontaneously generate strong electrical charges;
likewise volcanic dust plumes produce spectacular lightning displays. Charged
particle clouds also cause devastating explosions in food, drug and coal
processing industries. Despite the wide-ranging importance of granular charging
in both nature and industry, even the simplest aspects of its causes remain
elusive, because it is difficult to understand how inert grains in contact with
little more than other inert grains can generate the large charges observed.
Here, we present a simple yet predictive explanation for the charging of
granular materials in collisional flows. We argue from very basic
considerations that charge transfer can be expected in collisions of identical
dielectric grains in the presence of an electric field, and we confirm the
model's predictions using discrete-element simulations and a tabletop granular
experiment
“Reputational Heuristics” Violate Rationality: New Empirical Evidence in an Online Multiplayer Game
Functional Mapping of Dynamic Traits with Robust t-Distribution
Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate -distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis
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