Towards Probabilistic Tensor Canonical Polyadic Decomposition 2.0: Automatic Tensor Rank Learning Using Generalized Hyperbolic Prior

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential but challenging problem. In particular, since the tensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task. Rather than exhaustively searching for the best tensor rank via trial-and-error experiments, Bayesian inference under the Gaussian-gamma prior was introduced in the context of probabilistic CPD modeling and it was shown to be an effective strategy for automatic tensor rank determination. This triggered flourishing research on other structured tensor CPDs with automatic tensor rank learning. As the other side of the coin, these research works also reveal that the Gaussian-gamma model does not perform well for high-rank tensors or/and low signal-to-noise ratios (SNRs). To overcome these drawbacks, in this paper, we introduce a more advanced generalized hyperbolic (GH) prior to the probabilistic CPD model, which not only includes the Gaussian-gamma model as a special case, but also provides more flexibilities to adapt to different levels of sparsity. Based on this novel probabilistic model, an algorithm is developed under the framework of variational inference, where each update is obtained in a closed-form. Extensive numerical results, using synthetic data and real-world datasets, demonstrate the excellent performance of the proposed method in learning both low as well as high tensor ranks even for low SNR cases

Revealing the photophysics of gold-nanobeacons via time-resolved fluorescence spectroscopy

We demonstrate that time-resolved fluorescence spectroscopy is a powerful tool to investigate the conformation states of hairpin DNA on the surface of gold nanoparticles (AuNPs) and energy transfer processes in Au-nanobeacons. Long-range fluorescence quenching of Cy5 by AuNPs has been found to be in good agreement with electrodynamics modelling. Moreover, time-correlated single-photon counting (TCSPC) is shown to be promising for real-time monitoring of the hybridization kinetics of Au-nanobeacons, with up to 60% increase in decay time component and 300% increase in component fluorescence fraction observed. Our results also indicate the importance of the stem and spacer designs for the performance of Au-nanobeacons

Cell Treatment for Stroke in Type Two Diabetic Rats Improves Vascular Permeability Measured by MRI

Treatment of stroke with bone marrow stromal cells (BMSC) significantly enhances brain remodeling and improves neurological function in non-diabetic stroke rats. Diabetes is a major risk factor for stroke and induces neurovascular changes which may impact stroke therapy. Thus, it is necessary to test our hypothesis that the treatment of stroke with BMSC has therapeutic efficacy in the most common form of diabetes, type 2 diabetes mellitus (T2DM). T2DM was induced in adult male Wistar rats by administration of a high fat diet in combination with a single intraperitoneal injection (35mg/kg) of streptozotocin. These rats were then subjected to 2h of middle cerebral artery occlusion (MCAo). T2DM rats received BMSC (5x106, n = 8) or an equal volume of phosphate-buffered saline (PBS) (n = 8) via tail-vein injection at 3 days after MCAo. MRI was performed one day and then weekly for 5 weeks post MCAo for all rats. Compared with vehicle treated control T2DM rats, BMSC treatment of stroke in T2DM rats significantly (

Combination of Decitabine and a Modified Regimen of Cisplatin, Cytarabine and Dexamethasone: A Potential Salvage Regimen for Relapsed or Refractory Diffuse Large B-Cell Lymphoma After Second-Line Treatment Failure

ObjectiveThe prognosis for patients with relapsed or refractory diffuse large B-cell lymphoma (R/R-DLBCL) after second-line treatment failure is extremely poor. This study prospectively observed the efficacy and safety of decitabine with a modified cisplatin, cytarabine, and dexamethasone (DHAP) regimen in R/R-DLBCL patients who failed second-line treatment.MethodsTwenty-one R/R-DLBCL patients were enrolled and treated with decitabine and a modified DHAP regimen. The primary endpoints were overall response rate (ORR) and safety. The secondary endpoints were progression-free survival (PFS) and overall survival (OS).ResultsORR reached 50% (complete response rate, 35%), five patients (25%) had stable disease (SD) with disease control rate (DCR) of 75%. Subgroup analysis revealed patients over fifty years old had a higher complete response rate compared to younger patients (P = 0.005), and relapsed patients had a better complete response rate than refractory patients (P = 0.031). Median PFS was 7 months (95% confidence interval, 5.1-8.9 months). Median OS was not achieved. One-year OS was 59.0% (95% CI, 35.5%-82.5%), and two-year OS was 51.6% (95% confidence interval, 26.9%-76.3%). The main adverse events (AEs) were grade 3/4 hematologic toxicities such as neutropenia (90%), anemia (50%), and thrombocytopenia (70%). Other main non-hematologic AEs were grade 1/2 nausea/vomiting (40%) and infection (50%). No renal toxicity or treatment-related death occurred.ConclusionDecitabine with a modified DHAP regimen can improve the treatment response and prognosis of R/R-DLBCL patients with good tolerance to AEs, suggesting this regimen has potential as a possible new treatment option for R/R-DLBCL patients after second-line treatment failure.Clinical Trial RegistrationClinicalTrials.gov, identifier: NCT03579082