Supersymmetric dS/CFT

We put forward new explicit realisations of dS/CFT that relate ${\cal N}=2$ supersymmetric Euclidean vector models with reversed spin-statistics in three dimensions to specific supersymmetric Vasiliev theories in four-dimensional de Sitter space. The partition function of the free supersymmetric vector model deformed by a range of low spin deformations that preserve supersymmetry appears to specify a well-defined wave function with asymptotic de Sitter boundary conditions in the bulk. In particular we find the wave function is globally peaked at undeformed de Sitter space, with a low amplitude for strong deformations. This suggests that supersymmetric de Sitter space is stable in higher-spin gravity and in particular free from ghosts. We speculate this is a limiting case of the de Sitter realizations in exotic string theories.Comment: V2: references and comments added, typos corrected, version published in JHEP; 27 pages, 3 figures, 1 tabl

Image1_An ensemble model for short-term wind power prediction based on EEMD-GRU-MC.TIF

As a kind of clean and renewable energy, wind power is of great significance for alleviating energy crisis and environmental pollution. However, the strong randomness and large volatility of wind power bring great challenges to the dispatching and safe operation of the power grid. Hence, accurate and reliable short-term prediction of wind power is crucial for the power grid dispatching department arranging reasonable day-ahead generation schedules. Targeting the problem of low model prediction accuracy caused by the strong intermittency and large volatility of wind power, this paper develops a novel ensemble model for short-term wind power prediction which integrates the ensemble empirical mode decomposition (EEMD) algorithm, the gated recurrent unit (GRU) model and the Markov chain (MC) technique. Firstly, the EEMD algorithm is used to decompose the historical wind power sequence into a group of relatively stationary subsequences to reduce the influence of random fluctuation components and noise. Then, the GRU model is employed to predict each subsequence, and the predicted values of each subsequence are aggregated to get the preliminary prediction results. Finally, to further enhance the prediction accuracy, the MC is used to modified the prediction results. A large number of numerical examples indicates that the proposed EEMD-GRU-MC model outperforms the six benchmark models (i.e., LSTM, GRU, EMD-LSTM, EMD-GRU, EEMD-LSTM and EEMD-GRU) in terms of multiple evaluation indicators. Taking the spring dataset of the ZMS wind farm, for example, the MAE, RMSE and MAPE of the EEMD-GRU-MC model is 1.37 MW, 1.97 MW, and from 1.76%, respectively. Moreover, the mean prediction error of the developed model in all scenarios is less than or close to 2%. After 30 iterations, the proposed model uses an average of about 35 min to accurately predict the wind power of the next day, proving its high computation efficiency. It can be concluded that the ensemble model based on EEMD-GRU-MC is a promising prospect for short-term wind power prediction.</p

Low-Dose Arsenic Trioxide Modulates the Differentiation of Mouse Embryonic Stem Cells

Arsenic (As) is a well-known environmental pollutant, while arsenic trioxide (ATO) has been proven to be an effective treatment for acute promyelocytic leukemia, however, the mechanism underlying its dual effects is not fully understood. Embryonic stem cells (ESCs) exhibit properties of stemness and serve as a popular model to investigate epigenetic modifiers including environmental pollutants. Herein, the effects of low-dose ATO on differentiation were evaluated <i>in vitro</i> using a mouse ESCs (mESCs) cell line, CGR8. Cells treated with 0.2–0.5 μM ATO for 3–4 days had slight inhibition of proliferation with elevation of apoptosis, but obvious alterations of differentiation by morphological checking and alkaline phosphatase (AP) staining. Moreover, ATO exposure significantly decreased the mRNA expression of the stemness maintenance genes including <i>Oct4</i>, <i>Nanog</i>, and <i>Rex-1</i> (<i>P</i> < 0.01), whereas obviously increased some tissue-specific differentiation marker genes such as <i>Gata4</i>, <i>Gata-6</i>, <i>AFP</i>, and <i>IHH</i>. These alterations were consistent with the differentiation phenotype induced by retinoic acid (RA) and the expression patterns of distinct pluripotency markers such as SSEA-1 and Oct4. Furthermore, low-dose ATO led to a quantitative increase in Caspase 3 (CASP3) activation and subsequent cleavage of Nanog around 27 kDa, which corresponded with the mouse Nanog cleaved by CASP3 in a tube cleavage assay. Taken together, we suggest that low-dose ATO exposure will induce differentiation, other than apoptosis, of ESCs, such effects might be tuned partially by ATO-induced CASP3 activation and Nanog cleavage coupling with other differentiation related genes involved. The present findings provide a preliminary action mechanism of arsenic on the cell fate determination

Additional file 5: Figure S5. of Massively parallel nanowell-based single-cell gene expression profiling

Percentage of mitochondrial transcripts plotted against total number of detected transcripts for mouse Ba/F3 cells (a), human cell lines (b), mouse cell lines (c), and pancreatic islets (d). Dashed lines indicate the minimum number of detected transcripts required as a cell QC filter for each data set. (PDF 409 kb

Additional file 2: Figure S2. of Massively parallel nanowell-based single-cell gene expression profiling

Checkerboard assay. (a) Image of a microchip where the right half contains negative control master mix (NTC wells, n = 2520) and the left half contains lambda DNA master mix master (Positive wells, n = 1024) and negative control master mix (Test wells, n = 1496) in a checkerboard pattern. (b) Number of Test wells with signal, number of NTC wells with signal, and calculated misalignment rate for 11 MSNDs and 19 microchips. (PDF 1288 kb

Additional file 4: Figure S4. of Massively parallel nanowell-based single-cell gene expression profiling

Heatmaps illustrating the total number of detected transcripts for each well selected for downstream processing. Data are for three microchips, each with 5184 wells arranged in a 72 × 72 square layout. Microchips 72,618 and 72,598 were used for profiling human and mouse cell lines (names of cell lines indicated in the plot). Microchip 72,625 was used for profiling pancreatic islets. For microchips with multiple dispensed samples, the dispense area for each sample is indicated. (PDF 93 kb