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Non-spherical gravity plays a crucial role in the LEO satellite orbit determination and prediction. In recent years, several new gravity models have been proposed with more comprehensive ground and space-borne data. The impact of the gravity models has been extensively studied while its impact on the orbit prediction has not attracted enough attention. With the risen of the mega LEO constellation, new applications such as the LEO navigation requires real-time precise orbit, which increases the importance of the precise orbit prediction. In this study, we selected six popular gravity models, namely JGM3, EGM2008, EGM96, EIGEN2, GL04C, and GGM03S, and compared their performance in different LEO orbit predictions. The comparison results indicate that there is no single optimal gravity model for all LEO orbit prediction scenarios. For short-term prediction, JGM3, EGM2008, GL04C models perform better while in long-term prediction JGM3, EGM96, EIGEN2 have more potential. The results also reveal that the optimal model changed with time. In addition, the impact of the gravity order on the orbit prediction is investigated, the results indicate that for satellites with lower orbital heights, the gravitational field order required to achieve a certain truncation error is higher than for satellites with higher orbital heights. The authors also explore the effect of gravitational field-associated permanent tides on orbital prediction. In one day, for satellites with an orbital altitude of about 970km, the effect of permanent tides on 3D RMS is 6.92m; for satellites around 710km, the effect of permanent tides on 3D RMS is 4.20m; for satellites around 970km, the effect of permanent tides on 3D RMS is 2.07m.

Wi-Fi fingerprint positioning is widely used because of its ready hardware and high accuracy. However, its application is considerably restricted by time-consuming and labor-intensive works of offline collection and irregular fluctuation of signals. To address the above problems, we proposed a novel method to deploy the Wi-Fi fingerprint database based on implicit crowdsourcing and improved the weighted k-nearest neighbor (WKNN) algorithm to eliminate the influence of neighbor mismatching and device heterogeneity. First, ordinary users continuously gather Wi-Fi information instead of collecting one point after another. Meanwhile, video surveillance cameras record users’ trajectories without any intervention and use monocular vision based on plane constraints to obtain users’ location at the moment of each scanning. At the localization phase, the morphology similarity distance instead of the Euclidean distance is used to measure the similarity of signals to solve the problem of device heterogeneity. Outlier detection is also utilized for a secondary selection of neighbor points. Finally, geometric and signal morphology similarity distances are used to determine the combined weight of all neighbors after the dimensionless treatment. Results of the experiments conducted in a real indoor environment show that the proposed strategy improves the efficiency of fingerprint collection and achieves higher positioning accuracy.

CD3+CD56+ natural killer T (NKT)-like cells are a group of CD3+ T cells sharing characteristics of NK and T cells and constitute a major component of host antitumor immune response in human cancer. However, the nature, function and clinical relevance of CD3+CD56+ NKT-like cells in human gastric cancer (GC) remain unclear. In this study, we showed that the frequencies of CD3+CD56+NKT-like cells in GC tumors were significantly decreased and low levels of tumor-infiltrating CD3+CD56+ NKT-like cells were positively correlated with poor survival and disease progression. Most CD3+CD56+NKT-like cells in GC tumors were CD45RA-CD27+/- central/effectormemory cells with decreased activity and lower expression levels of CD69, NKG2D and DNAM-1 than those in non-tumor tissues. We further observed that tumor-infiltrating CD3+CD56+ NKT-like cells had impaired effector function as shown by decreased IFN-γ, TNF-α, granzyme B and Ki-67 expression. Moreover, in vitro studies showed that soluble factors released from GC tumors could induce the functional impairment of CD3+CD56+ NKT-like cells. Collectively, our data indicate that decreased tumorinfiltrating CD3+CD56+ NKT-like cells with impaired effector function are associated with tumor progression and poor survival of GC patients, which may contribute to immune escape of GC.

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for Weakly Interacting Massive Particles (WIMPs), while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector., 77 pages, 40 figures, 1262 references

Neutrophils constitute abundant cellular components in human gastric cancer (GC) tissues, but their protumorigenic subset in pathogenesis of GC progression is unclear. Here, it is found that patients with GC show significantly higher neutrophil infiltration in tumors that is regulated by CXCL12-CXCR4 chemotaxis. These tumor-infiltrating neutrophils express high level immunosuppressive molecules FasL and PD-L2, and this FasL+PD-L2+ neutrophil subset with a unique phenotype constitutes at least 20% of all neutrophils in advanced GC and predicts poor patient survival. Tumor induces neutrophils to express FasL and PD-L2 proteins with similar phenotype to those in GC tumors in both time-dependent and dose-dependent manners. Mechanistically, Th17 cell-derived IL-17A and tumor cell-derived G-CSF can significantly induce neutrophil FasL and PD-L2 expression via activating ERK-NF-κB and JAK-STAT3 signaling pathway, respectively. Importantly, upon over-expressing FasL and PD-L2, neutrophils acquire immunosuppressive functions on tumor-specific CD8+ T-cells and promote the growth and progression of human GC tumors in vitro and in vivo, which can be reversed by blocking FasL and PD-L2 on these neutrophils. Thus, the work identifies a novel protumorigenic FasL+PD-L2+ neutrophil subset in GC and provides new insights for human cancer immunosuppression and anti-cancer therapies targeting these pathogenic cells.

The E-310 experiment at the Facility for Advanced Accelerator Experimental Tests II (FACET-II) at SLAC National Accelerator Laboratory aims to demonstrate the creation of high brightness beams from a plasma photocathode. Betatron radiation will be measured by a Compton spectrometer, currently under development at UCLA, to provide single-shot, nondestructive beam diagnostics. We give a brief overview of this spectrometer as well as double differential spectrum reconstruction from the spectrometer image and beam parameter reconstruction from this double differential spectrum. We discuss three models for betatron radiation: an idealized particle tracking code which computes radiation from Li\'enard-Wiechert potentials, a quasi-static particle-in-cell (PIC) code which computes radiation from Li\'enard-Wiechert potentials, and a full PIC code which computes radiation using a Monte Carlo QED method. Spectra computed by the three models for a simple case are compared., Comment: Proceedings of the Advanced Accelerator Concepts Seminar Series 2020

Background: SMP30, as a calmodulin, is differentially expressed in hepatocellular carcinoma（HCC） tissues and adjacent tissues. Previous studies have indicated that the expression of SMP30 is closely related to the prognosis of patients with HCC. However, little is known about the detailed role of SMP30 on HCC. Methods: The SMP30 overexpression and silenced cell lines were constructed through lentivirus transfection，the effects of SMP30 on migration and invasion of HCC were observed by Transwell assay. Then subcloned cell line with different migration abilities were undergone four rounds of screening from SMP30 overexpressing cell lines, and the transcriptome and proteome were detected and analyzed. The interactions of proteins with SMP30 were validated by AP-MS and COIP-WB techniques. Results: High expression of SMP30 can effectively inhibit the migration and invasion of HCC, but has no significant effect on cell proliferation and cell cycle. The cell lines with higher metastatic abilities have significantly higher calcium ion concentrations than those with lower metastatic abilities and higher expression of MMP9, MMP14 and MMP15 proteins. There is an interaction between SMP30 and tumor metastasis-related protein ROCK1, which reduces the phosphorylation level of cytoskeleton-related MLC protein. The SMP30 regulatesthe downstream molecule MLC protein phosphorylation and affects the formation of cytoskeletal structure. Conclusion: On the one hand, SMP30 regulates the expression of MMPs protein by regulating the Ca2 + concentration; on the other hand, SMP30 affects the phosphorylation level of MLC protein by interacting with ROCK1 protein. The combined effect of the two aspects inhibits the metastatic ability of HCC.

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods., Comment: accepted by AAAI2021

© 1999-2012 IEEE. Recent skeleton-based action recognition approaches achieve great improvement by using recurrent neural network (RNN) models. Currently, these approaches build an end-to-end network from coordinates of joints to class categories and improve accuracy by extending RNN to spatial domains. First, while such well-designed models and optimization strategies explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, according to the evolution of previous work, we select a set of simple geometric features, and then seperately feed each type of features to a three-layer LSTM framework. Second, we propose a multistream LSTM architecture with a new smoothed score fusion technique to learn classification from different geometric feature streams. Furthermore, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and the fusion results achieve the state-of-the-art performance on four datasets. We also show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.

© 2018 American Chemical Society. A spark ignition (SI) engine with an ethanol direct injection (EDI) and gasoline port injection (GPI) system has been studied quite recently. In order to investigate its application, experiments were carried out on a single-cylinder 250 cm3 short stroke motorcycle SI engine, which is supplied with the ethanol-gasoline dual injection system. Experimental findings have revealed that EDI helped increase the lean combustion limit owing to the combination effects of the dual injection and ethanol fuel. At the identical engine speed and load, the lean burn limit in the dual injection model was, on average, 0.2 times higher than that in the corresponding single GPI model. Increasing ethanol energy ratio (EER) gave rise to the increase in the lean burn limit; nevertheless, the increase rate began reducing when the EER was above 48% and 42% at 4000 and 3500 rpm, correspondingly. Subsequently, late ethanol direct injection (LEDI), following the intake valve closing slightly extended the lean burn limit, probably, owing to the unsatisfactory mixture quality. Early ethanol direct injection (EEDI) prior to the intake valve closing was more helpful in increasing lean burn limit as compared with the LEDI. Nonetheless, further advance of spark timing earlier than 35 crank angle degress before top dead center (CAD BTDC) in LEDI situation, in addition 30 CAD BTDC in EEDI situation, might lead to reduction of the lean burn limit.

© 2016 IEEE. It is observed that distinct words in a given document have either strong or weak ability in delivering facts (i.e., the objective sense) or expressing opinions (i.e., the subjective sense) depending on the topics they associate with. Motivated by the intuitive assumption that different words have varying degree of discriminative power in delivering the objective sense or the subjective sense with respect to their assigned topics, a model named as identified objective-subjective latent Dirichlet allocation (LDA) (iosLDA) is proposed in this paper. In the iosLDA model, the simple Pólya urn model adopted in traditional topic models is modified by incorporating it with a probabilistic generative process, in which the novel 'Bag-of-Discriminative-Words' (BoDW) representation for the documents is obtained; each document has two different BoDW representations with regard to objective and subjective senses, respectively, which are employed in the joint objective and subjective classification instead of the traditional Bag-of-Topics representation. The experiments reported on documents and images demonstrate that: 1) the BoDW representation is more predictive than the traditional ones; 2) iosLDA boosts the performance of topic modeling via the joint discovery of latent topics and the different objective and subjective power hidden in every word; and 3) iosLDA has lower computational complexity than supervised LDA, especially under an increasing number of topics.

The involvement of host factors is critical to our understanding of underlying mechanisms of transposition and the applications of transposon-based technologies. Modified piggyBac (PB) is one of the most potent transposon systems in mammals. However, varying transposition efficiencies of PB among different cell lines have restricted its application. We discovered that the DNA-PK complex facilitates PB transposition by binding to PB transposase (PBase) and promoting paired-end complex formation. Mass spectrometry analysis and coimmunoprecipitation revealed physical interaction between PBase and the DNA-PK components Ku70, Ku80, and DNA-PKcs. Overexpression or knockdown of DNA-PK components enhances or suppresses PB transposition in tissue culture cells, respectively. Furthermore, germ-line transposition efficiency of PB is significantly reduced in Ku80 heterozygous mutant mice, confirming the role of DNA-PK in facilitating PB transposition in vivo. Fused dimer PBase can efficiently promote transposition. FRET experiments with tagged dimer PBase molecules indicated that DNA-PK promotes the paired-end complex formation of the PB transposon. These data provide a mechanistic explanation for the role of DNA-PK in facilitating PB transposition and suggest a transposition-promoting manipulation by enhancing the interaction of the PB ends. Consistent with this, deletions shortening the distance between the two PB ends, such as PB vectors with closer ends (PB-CE vectors), have a profound effect on transposition efficiency. Taken together, our study indicates that in addition to regulating DNA repair fidelity during transposition, DNA-PK also affects transposition efficiency by promoting paired-end complex formation. The approach of CE vectors provides a simple practical solution for designing efficient transposon vectors.

The diagnosis of tuberculosis remains among public health concerns due to shortcomings of the purified protein derivative (PPD). Recombinant truncated 38 kDa protein (rTPA38) of Mycobacterium tuberculosis was evaluated to screen new tuberculosis-specific tuberculin. 539 patients, 1133 healthy controls, and 55 guinea pigs were recruited to assess their sensitivity and specificity to rTPA38; 221 healthy controls, with negative responses to rTPA38 and PPD, were vaccinated with M. bovis BCG to determine their cross-reactions with M. bovis BCG. The Mantoux technique was adopted to perform skin tests. No difference in the sensitivity of skin tests was detected between rTPA38 and PPD (78.2% vs 83.4%), but there was a significant difference in the specificity of skin tests between rTPA38 and PPD (75.2% vs 47.0%). Compared to PPD, rTPA38 elicited low positive responses for those recruitments vaccinated with M. bovis BCG. The rTPA38 had significant skin reactions in M. tuberculosis-sensitized guinea pigs, and the opposite was true for both M. fortuitum- and M. kansasii-sensitized guinea pigs. These findings indicate that rTPA38 may have potential as a tuberculosis-specific skin test antigen.

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces path ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it structured embedding via pairwise relation and /ong-range interactions (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.

Plants growing on soil contaminated with 14C can receive the radionuclide from both direct uptake through the roots or indirect uptake from the atmosphere. The contribution from the atmosphere depends on the rate of volatilization from the soil, the size of the source area, and the meteorological dispersion conditions. An atmospheric dispersion relationship was used to calculate the air concentration for a given area source term. The air concentration was then used to estimate the flux to foliage, which was compared with direct plant uptake through the roots. The ratio of atmospheric to root uptake pathways depended on plant height and source area, exceeding unity when the fetch was greater than a few meters for bean plants, but much greater for maize. A very large fetch, greater than 1000 m, is required before a uniform specific activity throughout the local biosphere can be assumed for a soil source. This quantitative analysis describes the limitations for calculations and experiments used to define simplified transfer coefficients for 14C from soil to vegetation.

We investigated the ability of fluorodeoxyglucose positron emission tomography (FDG PET) imaging to serially monitor macrophage content in a rabbit model of atherosclerosis. Atherosclerosis was induced in rabbits (n = 8) by a combination of atherogenic diet and balloon denudation of the aorta. At the end of nine months, the rabbits were randomized to a further six months of the same atherogenic diet (progression group) or normal diet (regression group). In vivo uptake of FDG by the thoracic aorta was measured using aortic uptake-to-blood radioactivity ratios at the start and end of the randomized period. A significant increase in FDG uptake of the progression group after continued cholesterol feeding (aortic uptake-to-blood radioactivity: 0.57 +/- 0.02 to 0.68 +/- 0.02, P = 0.001), and a corresponding fall in FDG uptake of the regression group after returning to a normal chow diet (aortic uptake-to-blood radioactivity ratios: 0.67 +/- 0.02 to 0.53 +/- 0.02, P0.0001). FDG PET can quantify in vivo macrophage content and serially monitor changes in FDG activity in this rabbit model.

To examine the virulence factors of Mycobacterium tuberculosis H37Rv, the proteome was used to characterize the differences in protein expression between virulent M. tuberculosis H37Rv and attenuated M. tuberculosis H37Ra. Two-dimensional gel electrophoresis was performed to separate culture supernatant proteins extracted from M. tuberculosis H37Rv and M. tuberculosis H37Ra. The protein spots of interest were identified by mass spectrometry, and then the genes encoding the identified proteins were cloned and sequenced. Comparison of silver-stained gels showed that three well-resolved protein spots were present in M. tuberculosis H37Rv but absent from M. tuberculosis H37Ra. Protein spot no. 1 was identified as Rv2346c. Protein spot no. 2 was identified as Rv2347c, Rv1197, Rv1038c, and Rv3620c, which shared significant homology and had the same peptide fingerprinting using tryptic digestion. No M. tuberculosis protein matched protein spot no. 3. Rv2346c, Rv2347c, Rv1038c, and Rv3620c of M. tuberculosis H37Rv were located on the M. tuberculosis H37Ra chromosome, and multiple mutations were observed in the corresponding areas of M. tuberculosis H37Ra. Codon 59 (CAG, Gln) of Rv2347c and Rv3620c was replaced by termination codon (TAG) in M. tuberculosis H37Ra, which probably terminated the polypeptide elongation. These results demonstrate the importance of studying the gene products of M. tuberculosis and show that subtle differences in isogenic mutant strains might play an important role in identifying the attenuating mutations.

Kinetics and distribution of i.v. human pancreatic phospholipase A2 (h-PLA2) were determined in intact and nephrectomized rats, and tissue localization of rat pancreatic PLA2 (r-PLA2) was studied by immunohistochemistry in experimental acute pancreatitis. The concentration of h-PLA2 and the catalytic activity of phospholipase A2 in plasma decreased exponentially in intact and nephrectomized animals after the injection. The initial 15-min half-life was considerably longer in nephrectomized animals, and higher h-PLA2 concentrations and PLA2 catalytic activities were found in plasma. h-PLA2 was localized in endocytotic vesicles and apical cytoplasmic vacuoles in proximal tubule cells of the kidney. The intensity of the immunoreaction decreased considerably between 15 and 50 min in these cells. No signs of tubular damage were seen by light microscopy. Neither immunoreactive h-PLA2 nor PLA2 catalytic activity was found in urine. r-PLA2 was observed in proximal tubule cells 15 min after an injection of sodium taurocholate (necrotizing pancreatitis group) or saline (edematous pancreatitis group) into the pancreatic duct. Signs of tubular damage were present in necrotizing pancreatitis, but tubular morphology was normal in the animals with edematous pancreatitis. We conclude that the proximal tubule cells of the kidney participate in the metabolism of circulating pancreatic PLA2, and considerably higher PLA2 levels persist in plasma in nephrectomized animals. Endogenous pancreatic PLA2 is detected in kidneys in acute pancreatitis.

Ethanol fuel, as a renewable fuel can play an important role in addressing the critical issue of energy resources if it is used in a proper way. Ethanol direct injection plus gasoline port injection (EDI+GPI) is such a new way to enable substantial improvement in engine efficiency and emission reduction in spark ignition engines. This paper reports our preliminary investigation to the combustion and emissions in this new dual fuel injection system. Experiments were conducted on a single-cylinder spark ignition engine equipped with EDI+GPI. In the experiments, the ethanol/gasoline volumetric percentage (EVP) was varied from 0% (gasoline fuel only) to 71%. Mass burnt fraction and indicated mean effective pressure (IMEP) were calculated from the measured cylinder pressure for analysing the combustion process. The variance of IMEP, reduced with the increased EVP, showed that the combustion stability was improved by the direct injection of ethanol fuel. The effect of EVP on initial, early and major combustion time periods showed that ethanol fuel's higher combustion velocity and low ignition energy might contribute to accelerating the flame propagating, shortening the combustion periods and reducing the combustion temperature when EVP was less than 48%. However further increase of EVP when it was over 48% resulted in a negative effect on combustion which might be caused by the ethanol fuel's over cooling effect. Hydrocarbon and carbon monoxide emission increased and nitric oxide emission decreased with the increase of EVP.

Over the past few years, a large family of manifold learning algorithms have been proposed, and applied to various applications. While designing new manifold learning algorithms has attracted much research attention, fewer research efforts have been focused on out-of-sample extrapolation of learned manifold. In this paper, we propose a novel algorithm of manifold learning. The proposed algorithm, namely Local and Global Regressive Mapping (LGRM), employs local regression models to grasp the manifold structure. We additionally impose a global regression term as regularization to learn a model for out-of-sample data extrapolation. Based on the algorithm, we propose a new manifold learning framework. Our framework can be applied to any manifold learning algorithms to simultaneously learn the low dimensional embedding of the training data and a model which provides explicit mapping of the out-of-sample data to the learned manifold. Experiments demonstrate that the proposed framework uncover the manifold structure precisely and can be freely applied to unseen data.

Ethanol has been used as a renewable fuel in internal combustion (IC) engines. However, the existing method of blending gasoline and ethanol fuels does not take the advantages of ethanol fuel, such as its high Octane number and great latent heat of vaporization, to increase the engine compression ratio and consequently the thermal efficiency. Ethanol direct injection plus gasoline port injection (EDI+GPI) is a new technology for using ethanol fuel more effectively and efficiently in IC engine. To experimentally investigate this new technique, a research engine has been developed by modifying a commercial product representing the cylinder capacity of a down sized passenger car engine. In the development of this research engine, two major tasks were addressed: the two separate fuel systems and the electronic control unit (ECU). The operation of both fuel systems including the high pressure pump and the common rail fuel pressure are electronically controlled. The ECU also controls the throttle position and fuel flow rates in an open loop to provide the flexibility of manual adjustments of engine speed, load and lambda. Sample results are reported to show that the developed engine system has met the basic requirements of experiments in this investigation.

© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Automatically recognizing a large number of action categories from videos is of significant importance for video understanding. Most existing works focused on the design of more discriminative feature representation, and have achieved promising results when the positive samples are enough. However, very limited efforts were spent on recognizing a novel action without any positive exemplars, which is often the case in the real settings due to the large amount of action classes and the users' queries dramatic variations. To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning. Different from other zero-shot learning approaches, which exploit attributes as the intermediate layer for the knowledge transfer, our main contribution is SIR, which directly leverages the semantic inter-class relationships between the known and unknown actions followed by label transfer learning. The inter-class semantic relationships are automatically measured by continuous word vectors, which learned by the skip-gram model using the large-scale text corpus. Extensive experiments on the UCF101 dataset validate the superiority of our method over fully-supervised approaches using few positive exemplars.

Non-negative tensor factorization (NTF) has attracted great attention in the machine learning community. In this paper, we extend traditional non-negative tensor factorization into a supervised discriminative decomposition, referred as Supervised Non-negative Tensor Factorization with Maximum-Margin Constraint (SNTFM2). SNTFM2 formulates the optimal discriminative factorization of non-negative tensorial data as a coupled least-squares optimization problem via a maximum-margin method. As a result, SNTFM2 not only faithfully approximates the tensorial data by additive combinations of the basis, but also obtains a strong generalization power to discriminative analysis (in particular for classification in this paper). The experimental results show the superiority of our proposed model over state-of-the-art techniques on both toy and real world data sets. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Purpose The objective of this study was to explore whether killer immunoglobulin-like receptor (KIR) genotypes and haplotypes are associated with dry eye disease (DED) in a Han Chinese population. Methods Polymerase chain reaction with sequence-specific primers (PCR-SSP) method was used to genotype KIR genes in 106 patients with DED and 220 healthy controls. Results Twenty-three KIR genotypes were observed in the DED patient and healthy control groups, ten of which had not been described previously. The genotype G and haplotype 4 were associated with increased risk of DED, and the odds ratio (OR) and 95% confidence interval (95% CI) were 2.58, 1.10–6.02 and 2.48, 1.31–4.69, respectively; while haplotype 2 appeared to have an inverse association with the disease (OR, 0.64; 95% CI, 0.44–0.92). Genotype B/B was also associated with increased risk of DED, and the OR and 95% CI were 2.35 and 1.09–5.10, respectively. KIR haplotypes A and B have distinctive centromeric (Cen) and telomeric (Tel) gene-content motifs, and Cen-B/B was associated with increased risk of DED (OR, 2.38; 95% CI, 1.03–5.49). However, all frequencies of these KIR genotypes and haplotypes were no longer statistically significant between the two groups after the Bonferroni correction was applied for multiple testing. Conclusions There was a possible association between certain KIR genotypes and haplotypes with DED in a Han Chinese population. However, additional confirmation is required.

Stem-cell functions require activation of stem-cell-intrinsic transcriptional programs and extracellular interaction with a niche microenvironment. How the transcriptional machinery controls residency of stem cells in the niche is unknown. Here we show that Id proteins coordinate stem-cell activities with anchorage of neural stem cells (NSCs) to the niche. Conditional inactivation of three Id genes in NSCs triggered detachment of embryonic and postnatal NSCs from the ventricular and vascular niche, respectively. The interrogation of the gene modules directly targeted by Id deletion in NSCs revealed that Id proteins repress bHLH-mediated activation of Rap1GAP, thus serving to maintain the GTPase activity of RAP1, a key mediator of cell adhesion. Preventing the elevation of the Rap1GAP level countered the consequences of Id loss on NSC-niche interaction and stem-cell identity. Thus, by preserving anchorage of NSCs to the extracellular environment, Id activity synchronizes NSC functions to residency in the specialized niche.

High-grade gliomas (HGGs) are incurable brain tumors that are characterized by the presence of glioma-initiating cells (GICs). GICs are essential to tumor aggressiveness and retain the capacity for self-renewal and multilineage differentiation as long as they reside in the perivascular niche. ID proteins are master regulators of stemness and anchorage to the extracellular niche microenvironment, suggesting that they may play a role in maintaining GICs. Here, we modeled the probable therapeutic impact of ID inactivation in HGG by selective ablation of Id in tumor cells and after tumor initiation in a new mouse model of human mesenchymal HGG. Deletion of 3 Id genes induced rapid release of GICs from the perivascular niche, followed by tumor regression. GIC displacement was mediated by derepression of Rap1gap and subsequent inhibition of RAP1, a master regulator of cell adhesion. We identified a signature module of 5 genes in the ID pathway, including RAP1GAP, which segregated 2 subgroups of glioma patients with markedly different clinical outcomes. The model-informed survival analysis together with genetic and functional studies establish that ID activity is required for the maintenance of mesenchymal HGG and suggest that pharmacological inactivation of ID proteins could serve as a therapeutic strategy.

This paper presents our recent and current work on expressive speech synthesis and recognition as enabling technologies for affective robot-child interaction. We show that current expression recognition systems could be used to discriminate between several archetypical emotions, but also that the old adage ”there’s no data like more data” is more than ever valid in this field. A new speech synthesizer was developed that is capable of high quality concatenative synthesis. This system will be used in the robot to synthesize expressive nonsense speech by using prosody transplantation and a recorded database with expressive speech examples. With these enabling components lining up, we are getting ready to start experiments towards hopefully effective child-machine communication of affect and emotion.

Epitaxial ZnSe/CdTe/ZnSe heterostructures were successfully fabricated by molecular beam epitaxy on GaAs(001) wafers despite the large in-plane lattice mismatch (14.3%) between the two II–VI materials. X-ray reciprocal space maps and selected area diffraction results indicate single-phase, (111)-oriented growth of CdTe onto the lower ZnSe(001) cladding layer, and single-phase, (111)-oriented growth of the topmost ZnSe layer, with a small inhomogeneous residual strain within the CdTe layer. Cross-sectional transmission electron micrographs reveal a distribution of rotational microtwins within the (111)-oriented layers near each interface. The low-temperature near-band-edge photoluminescence from the CdTe layer is free-exciton related, and exhibits a linewidth of only 5–6 meV.