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Spin of a test particle is a fundamental property that can affect its motion in a gravitational field. In this work we consider the effect of particle spin on its deflection angle and gravitational lensing in the equatorial plane of arbitrary stationary and axisymmetric spacetimes. To do this we developed a perturbative method that can be applied to spinning signals with arbitrary asymptotic velocity and takes into account the finite distance effect of the source and the observer. The deflection angle $\Delta\varphi$ and total travel time $\Delta t$ are expressed as (quasi-)power series whose coefficients are polynomials of the asymptotic expansion coefficients of the metric functions. It is found that when the spin and orbital angular momenta are parallel (or antiparallel), the deflection angle is decreased (or increased). Apparent angles $\theta$ of the images in gravitational lensing and their time delays are also solved. In Kerr spacetime, spin affects the apparent angle $\theta_K$ in a way similar to its effect on $\Delta\varphi_K$. The time delay between signals with opposite spins is found to be proportional to the signal spin at leading order. These time delays might be used to constrain the spin to mass ratio of neutrinos., Comment: 33 pages, 7 figures, to match the published version

Additional file 1.

With rapid growth of wind power integration into the modern power system, Wind Power Prediction (WPP) plays an increasingly important role in the planning and operation of electric power system. However, the wind power time series always exhibits nonlinear and non-stationary characteristics, which is still with great challenge to be predicted accurately. To overcome the challenge, a Stacked Denoising Auto Encoders (SDAE) based deep learning approach for WPP is proposed in this paper. Firstly, SDAE with three hidden layers is designed to capture the nonlinear and complex characteristics from the reference data sequence, and the optimal initial connection weights of the deep neural network is obtained by the layer-wise pre-training process. Secondly, the back propagation algorithm is applied to fine-tune the weights of the whole network. To achieve the optimal network architecture, the Bat Algorithm (BA) is adopted to identify the number of neurons of the hidden layers for each denoising auto encoder (DAE). Finally, the proposed method is evaluated by the data from a real wind farm and compared with the Back-propagation Neural Network (BPNN) and the Support Vector Machine (SVM). The results show that SDAE is with the ability to learn the nonlinear and non-stationary characteristics of wind power data and the WPP Root Mean Square Error (RMSE) are reduced by 3.49% and 1.59% in comparison with BPNN and SVM, respectively, which is applicable for practical applications in electric power system.

Mice carrying a hypomorphic point mutation in the Ptpn6 gene (Ptpn6spin mice) develop an inflammatory skin disease that resembles neutrophilic dermatosis in humans. Here, we demonstrated that interleukin (IL)-1α signaling through IL-1R and MyD88 in both stromal and immune cells drive inflammation in Ptpn6spin mice. We further identified SYK as a critical kinase that phosphorylates MyD88, promoted MyD88-dependent signaling and mediates dermatosis in Ptpn6spin mice. Our studies further demonstrated that SHP1 encoded by Ptpn6 binds and suppresses SYK activation to inhibit MyD88 phosphorylation. Downstream of SHP1 and SYK-dependent counterregulation of MyD88 tyrosine phosphorylation, we have demonstrated that the scaffolding function of receptor interacting protein kinase 1 (RIPK1) and tumor growth factor-β activated kinase 1 (TAK1)-mediating signaling were required to spur inflammatory disease. Overall, these studies identify SHP1 and SYK crosstalk as a critical regulator of MyD88 post-translational modifications and IL-1-driven inflammation.

Using Normalized Difference Vegetation Index(NDVI) which was derived from MODIS and GIMMS to characterize the vegetation cover in Zhejiang Province,this study has investigated the responses of vegetation cover variation to climate change at yearly and monthly time scales,by integrating NDVI with climate factors(temperature,precipitation and humid index) of sixty-three meteorological sites.The results indicated the following viewpoint.In the past 30 years,the climate of Zhejiang Province presented a process of distinct warming and drying.The vegetation cover had a slowly decreasing trend which mainly results from damaging forest and restraining agriculture activity.The area with NDVI significant decreasing occupied 29.1% of the total land area of Zhejiang.Vegetation cover reducing mainly happened in June to November.Precipitation and dry-wet condition had a crucial effect on NDVI inter-annual variation.The relationship of NDVI and humidity is stronger than that of NDVI with precipitation and temperature,whereas,its coefficient and partial coefficient all passed the confidence test of 0.05 levels.The close relativity between NDVI and humidity indicated that humid increasing could promote vegetation growing.At monthly scale,the response of vegetation variation to climatic factors suggested that temperature is a key factor for impacting vegetation growth in winter,and vegetation variation lag nearly two months responding to precipitation and dry-wet condition in summer.This study also highlighted that human activities influencing on vegetation cover variation should not be ignored because the NDVI value of the crop planting area was increasing with agricultural production level promoting in Zhejiang Province.

Outlier detection is an important task in data mining and has been well studied on precise data. However, outlier detection on uncertain objects is particularly challenging. In this paper, firstly, the conceptions about density-based top-k uncertain outlier detection are defined. Secondly, an algorithm of density-based Top-k outlier detection on uncertain objects is proposed, the time complexity of which is polynomial. Finally, the experiment illustrates the effectiveness and efficiency of the algorithm.

The antiapoptotic Bcl-2 family member Bfl-1 is up-regulated in many human tumors in which nuclear factor-κB (NF-κB) is implicated and contributes significantly to tumor cell survival and chemoresistance. We previously found that NF-κB induces transcription of bfl-1 and that the Bfl-1 protein is also regulated by ubiquitin-mediated proteasomal degradation. However, the role that dysregulation of Bfl-1 turnover plays in cancer is not known. Here we show that ubiquitination-resistant mutants of Bfl-1 display increased stability and greatly accelerated tumor formation in a mouse model of leukemia/lymphoma. We also show that tyrosine kinase Lck is up-regulated and activated in these tumors and leads to activation of the IkappaB kinase, Akt, and extracellular signal-regulated protein kinase signaling pathways, which are key mediators in cancer. Coexpression of Bfl-1 and constitutively active Lck promoted tumor formation, whereas Lck knockdown in tumor-derived cells suppressed leukemia/lymphomagenesis. These data demonstrate that ubiquitination is a critical tumor suppression mechanism regulating Bfl-1 function and suggest that mutations in bfl-1 or in the signaling pathways that control its ubiquitination may predispose one to cancer. Furthermore, because bfl-1 is up-regulated in many human hematopoietic tumors, this finding suggests that strategies to promote Bfl-1 ubiquitination may improve therapy.

This paper presents an application method of Bayesian networks (BN) to the reliability assessment of power systems. Bayesian networks provide a flexible framework to represent probabilistic information and to make inference on it. Uncertainty and dependency of the components' information in a system are easily incorporated in the analysis. The flexibility of the probabilistic inference algorithms in Bayesian networks permit to compute both the system's reliability indices and the mutual affection on reliability indices of all components. However, a BN cannot be constructed easily based on the topology of the relating power system. The paper gives a new method to construct a Bayesian network based on the assessed system's fault tree or its minimal path set. The method is efficient and can compute components failure probabilities on the condition of the system failure. Its advantages are demonstrated through two examples.