Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional ...differences between cells. We introduce the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for experimental spike-in controls. Analyzing changes in relative transcript counts led to dramatic improvements in accuracy compared to normalized read counts and enabled new statistical tests for identifying developmentally regulated genes. Census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances. We reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation. Census is freely available through our updated single-cell analysis toolkit, Monocle 2.
Robust Face Recognition via Sparse Representation Wright, J.; Yang, A.Y.; Ganesh, A. ...
IEEE transactions on pattern analysis and machine intelligence,
02/2009, Volume:
31, Issue:
2
Journal Article
Peer reviewed
Open access
We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one ...of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C 1 -minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.
This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces ...with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace passing through the point. When the number of subspaces is known, we show that these polynomials can be estimated linearly from data; hence, subspace segmentation is reduced to classifying one point per subspace. We select these points optimally from the data set by minimizing certain distance function, thus dealing automatically with moderate noise in the data. A basis for the complement of each subspace is then recovered by applying standard PCA to the collection of derivatives (normal vectors). Extensions of GPCA that deal with data in a high-dimensional space and with an unknown number of subspaces are also presented. Our experiments on low-dimensional data show that GPCA outperforms existing algebraic algorithms based on polynomial factorization and provides a good initialization to iterative techniques such as k-subspaces and expectation maximization. We also present applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views.
To improve energy conversion efficiency, the development of active electrocatalysts with similar structural features to photosynthesis II systems (PS‐II), which can efficiently catalyze the oxygen ...evolution reaction (OER), have received great research interest. Crystalline cobalt phosphate nanosheets are designed as an efficient OER catalyst in neutral media, showing outstanding performance that even outperforms the noble RuO2 benchmark. The correlation of experimental and computational results reveals that the active sites are the edge‐sharing CoO9 structural motif, akin to the molecular geometry of PS‐II. This unique structure can facilitate reaction intermediate adsorption and decrease the reaction energy barrier, thus improving the OER kinetics.
Crystalline Co3(PO4)2 nanosheets are designed for electrocatalytic oxygen evolution reaction with high efficiency, wherein the biomimetic edge‐sharing CoO9 subunits are the active sites, leading to a low energy barrier towards water splitting.
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Photocatalytic hydrogen production is crucial for solar‐to‐chemical conversion process, wherein high‐efficiency photocatalysts lie in the heart of this area. A photocatalyst of hierarchically ...mesoporous titanium phosphonate based metal–organic frameworks, featuring well‐structured spheres, a periodic mesostructure, and large secondary mesoporosity, are rationally designed with the complex of polyelectrolyte and cathodic surfactant serving as the template. The well‐structured hierarchical porosity and homogeneously incorporated phosphonate groups can favor the mass transfer and strong optical absorption during the photocatalytic reactions. Correspondingly, the titanium phosphonates exhibit significantly improved photocatalytic hydrogen evolution rate along with impressive stability. This work can provide more insights into designing advanced photocatalysts for energy conversion and render a tunable platform in photoelectrochemistry.
A multi‐structured photocatalyst: A metal–organic framework (MOF) nanostructure synthesized by a surfactant‐directed strategy features a stable framework of titanium phosphates, a well‐defined sphere, and hierarchical nanopores. These features ensure competitive photoactivity in evolving hydrogen under both visible light and full‐spectrum simulator irradiation, along with high durability.
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ZnCo2O4 quantum dots anchored on nitrogen‐doped carbon nanotubes (N‐CNT) retain the high catalytic activity of ZnCo2O4 to oxidize water while enabling an efficient oxygen reduction performance ...thereby combining these desirable features. These advantages realize a bifunctional catalytic activity for ZnCo2O4/N‐CNT that can be used in rechargeable zinc–air batteries.
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A new class of highly efficient oxygen evolution catalysts has been synthesized through the self‐assembly of graphitic carbon nitride nanosheets and carbon nanotubes, driven by π–π stacking and ...electrostatic interactions. Remarkably, the catalysts exhibit higher catalytic oxygen evolution activity and stronger durability than Ir‐based noble‐metal catalysts and display the best performance among the reported nonmetal catalysts. This good result is attributed to the high nitrogen content and the efficient mass and charge transfer in the porous three‐dimensional nanostructure.
Combining C and N to make O: 3D porous graphitic carbon nitride nanosheet–carbon nanotube composites have been synthesized through a spontaneous assembly process. The high nitrogen content, enhanced electron conductivity, and improved mass transport result in excellent catalytic oxygen evolution activity and strong durability, superior to those reported for other nonmetal catalysts and noble‐metal catalysts (see figure; OER: oxygen evolution reaction).
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Abstract
Scalable and sustainable solar hydrogen production through photocatalytic water splitting requires highly active and stable earth-abundant co-catalysts to replace expensive and rare ...platinum. Here we employ density functional theory calculations to direct atomic-level exploration, design and fabrication of a MXene material, Ti
3
C
2
nanoparticles, as a highly efficient co-catalyst. Ti
3
C
2
nanoparticles are rationally integrated with cadmium sulfide via a hydrothermal strategy to induce a super high visible-light photocatalytic hydrogen production activity of 14,342 μmol h
−1
g
−1
and an apparent quantum efficiency of 40.1% at 420 nm. This high performance arises from the favourable Fermi level position, electrical conductivity and hydrogen evolution capacity of Ti
3
C
2
nanoparticles. Furthermore, Ti
3
C
2
nanoparticles also serve as an efficient co-catalyst on ZnS or Zn
x
Cd
1−
x
S. This work demonstrates the potential of earth-abundant MXene family materials to construct numerous high performance and low-cost photocatalysts/photoelectrodes.
Rational design and exploration of robust and low‐cost bifunctional oxygen reduction/evolution electrocatalysts are greatly desired for metal–air batteries. Herein, a novel high‐performance oxygen ...electrode catalyst is developed based on bimetal FeCo nanoparticles encapsulated in in situ grown nitrogen‐doped graphitic carbon nanotubes with bamboo‐like structure. The obtained catalyst exhibits a positive half‐wave potential of 0.92 V (vs the reversible hydrogen electrode, RHE) for oxygen reduction reaction, and a low operating potential of 1.73 V to achieve a 10 mA cm−2 current density for oxygen evolution reaction. The reversible oxygen electrode index is 0.81 V, surpassing that of most highly active bifunctional catalysts reported to date. By combining experimental and simulation studies, a strong synergetic coupling between FeCo alloy and N‐doped carbon nanotubes is proposed in producing a favorable local coordination environment and electronic structure, which affords the pyridinic N‐rich catalyst surface promoting the reversible oxygen reactions. Impressively, the assembled zinc–air batteries using liquid electrolytes and the all‐solid‐state batteries with the synthesized bifunctional catalyst as the air electrode demonstrate superior charging–discharging performance, long lifetime, and high flexibility, holding great potential in practical implementation of new‐generation powerful rechargeable batteries with portable or even wearable characteristic.
Bamboo‐like FeCo alloy encapsulated in nitrogen‐doped carbon nanotubes exhibits superior catalytic oxygen reduction and oxygen evolution performance than that of noble metal benchmarks, which benefits from the nitrogen‐rich and defect‐rich catalyst surface. The all‐solid‐state zinc–air batteries equipped by the synthesized materials show low charging/discharging overpotentials, long lifetime, and high flexibility, suitable for practical application.
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