12,243 research outputs found
Ixazomib enhances parathyroid hormone-induced β-catenin/T-cell factor signaling by dissociating β-catenin from the parathyroid hormone receptor.
The anabolic action of PTH in bone is mostly mediated by cAMP/PKA and Wnt-independent activation of β-catenin/T-cell factor (TCF) signaling. β-Catenin switches the PTH receptor (PTHR) signaling from cAMP/PKA to PLC/PKC activation by binding to the PTHR. Ixazomib (Izb) was recently approved as the first orally administered proteasome inhibitor for the treatment of multiple myeloma; it acts in part by inhibition of pathological bone destruction. Proteasome inhibitors were reported to stabilize β-catenin by the ubiquitin-proteasome pathway. However, how Izb affects PTHR activation to regulate β-catenin/TCF signaling is poorly understood. In the present study, using CRISPR/Cas9 genome-editing technology, we show that Izb reverses β-catenin-mediated PTHR signaling switch and enhances PTH-induced cAMP generation and cAMP response element-luciferase activity in osteoblasts. Izb increases active forms of β-catenin and promotes β-catenin translocation, thereby dissociating β-catenin from the PTHR at the plasma membrane. Furthermore, Izb facilitates PTH-stimulated GSK3β phosphorylation and β-catenin phosphorylation. Thus Izb enhances PTH stimulation of β-catenin/TCF signaling via cAMP-dependent activation, and this effect is due to its separating β-catenin from the PTHR. These findings provide evidence that Izb may be used to improve the therapeutic efficacy of PTH for the treatment of osteoporosis and other resorptive bone diseases
Tensor network and (-adic) AdS/CFT
We use the tensor network living on the Bruhat-Tits tree to give a concrete
realization of the recently proposed -adic AdS/CFT correspondence (a
holographic duality based on the -adic number field ). Instead
of assuming the -adic AdS/CFT correspondence, we show how important features
of AdS/CFT such as the bulk operator reconstruction and the holographic
computation of boundary correlators are automatically implemented in this
tensor network.Comment: 59 pages, 18 figures; v3: improved presentation, added figures and
reference
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
Knowledge Graph (KG) contains entities and the relations between entities.
Due to its representation ability, KG has been successfully applied to support
many medical/healthcare tasks. However, in the medical domain, knowledge holds
under certain conditions. For example, symptom \emph{runny nose} highly
indicates the existence of disease \emph{whooping cough} when the patient is a
baby rather than the people at other ages. Such conditions for medical
knowledge are crucial for decision-making in various medical applications,
which is missing in existing medical KGs. In this paper, we aim to discovery
medical knowledge conditions from texts to enrich KGs.
Electronic Medical Records (EMRs) are systematized collection of clinical
data and contain detailed information about patients, thus EMRs can be a good
resource to discover medical knowledge conditions. Unfortunately, the amount of
available EMRs is limited due to reasons such as regularization. Meanwhile, a
large amount of medical question answering (QA) data is available, which can
greatly help the studied task. However, the quality of medical QA data is quite
diverse, which may degrade the quality of the discovered medical knowledge
conditions. In the light of these challenges, we propose a new truth discovery
method, MedTruth, for medical knowledge condition discovery, which incorporates
prior source quality information into the source reliability estimation
procedure, and also utilizes the knowledge triple information for trustworthy
information computation. We conduct series of experiments on real-world medical
datasets to demonstrate that the proposed method can discover meaningful and
accurate conditions for medical knowledge by leveraging both EMR and QA data.
Further, the proposed method is tested on synthetic datasets to validate its
effectiveness under various scenarios.Comment: Accepted as CIKM2019 long pape
On the Performance Gain of NOMA over OMA in Uplink Communication Systems
In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of
non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in
uplink cellular communication systems. A base station equipped with a
single-antenna, with multiple antennas, and with massive antenna arrays is
considered both in single-cell and multi-cell deployments. In particular, in
single-antenna systems, we identify two types of gains brought about by NOMA:
1) a large-scale near-far gain arising from the distance discrepancy between
the base station and users; 2) a small-scale fading gain originating from the
multipath channel fading. Furthermore, we reveal that the large-scale near-far
gain increases with the normalized cell size, while the small-scale fading gain
is a constant, given by = 0.57721 nat/s/Hz, in Rayleigh fading
channels. When extending single-antenna NOMA to -antenna NOMA, we prove that
both the large-scale near-far gain and small-scale fading gain achieved by
single-antenna NOMA can be increased by a factor of for a large number of
users. Moreover, given a massive antenna array at the base station and
considering a fixed ratio between the number of antennas, , and the number
of users, , the ESG of NOMA over OMA increases linearly with both and
. We then further extend the analysis to a multi-cell scenario. Compared to
the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more
severe inter-cell interference due to the non-orthogonal transmissions.
Besides, we unveil that a large cell size is always beneficial to the ergodic
sum-rate performance of NOMA in both single-cell and multi-cell systems.
Numerical results verify the accuracy of the analytical results derived and
confirm the insights revealed about the ESG of NOMA over OMA in different
scenarios.Comment: 51 pages, 7 figures, invited paper, submitted to IEEE Transactions on
Communication
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