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A newly identified protein may be one of the biggest obstacles holding CAR T-cell therapy back。 Researchers found that NFIL3 causes these engineered immune cells to become exhausted and lose their cancer-fighting power over time。 When NFIL3 was disabled, the cells remained stronger for longer and controlled tumors more effectively in animal models

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Supported metal nanostructures are the most widely used type of heterogeneous catalyst in industrial processes. The size of metal particles is a key factor in determining the performance of such catalysts. In particular, because low-coordinated metal atoms often function as the catalytically active sites, the specific activity per metal atom usually increases with decreasing size of the metal particles. However, the surface free energy of metals increases significantly with decreasing particle size, promoting aggregation of small clusters. Using an appropriate support material that strongly interacts with the metal species prevents this aggregation, creating stable, finely dispersed metal clusters with a high catalytic activity, an approach industry has used for a long time. Nevertheless, practical supported metal catalysts are inhomogeneous and usually consist of a mixture of sizes from nanoparticles to subnanometer clusters. Such heterogeneity not only reduces the metal atom efficiency but also frequently leads to undesired side reactions. It also makes it extremely difficult, if not impossible, to uniquely identify and control the active sites of interest. The ultimate small-size limit for metal particles is the single-atom catalyst (SAC), which contains isolated metal atoms singly dispersed on supports. SACs maximize the efficiency of metal atom use, which is particularly important for supported noble metal catalysts. Moreover, with well-defined and uniform single-atom dispersion, SACs offer great potential for achieving high activity and selectivity. In this Account, we highlight recent advances in preparation, characterization, and catalytic performance of SACs, with a focus on single atoms anchored to metal oxides, metal surfaces, and graphene. We discuss experimental and theoretical studies for a variety of reactions, including oxidation, water gas shift, and hydrogenation. We describe advances in understanding the spatial arrangements and electronic properties of single atoms, as well as their interactions with the support. Single metal atoms on support surfaces provide a unique opportunity to tune active sites and optimize the activity, selectivity, and stability of heterogeneous catalysts, offering the potential for applications in a variety of industrial chemical reactions.

Optical detection and spectroscopy of single molecules and single nanoparticles have been achieved at room temperature with the use of surface-enhanced Raman scattering. Individual silver colloidal nanoparticles were screened from a large heterogeneous population for special size-dependent properties and were then used to amplify the spectroscopic signatures of adsorbed molecules. For single rhodamine 6G molecules adsorbed on the selected nanoparticles, the intrinsic Raman enhancement factors were on the order of 10(14) to 10(15), much larger than the ensemble-averaged values derived from conventional measurements. This enormous enhancement leads to vibrational Raman signals that are more intense and more stable than single-molecule fluorescence.

We present a method for detecting objects in images us-ing a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of bounding box priors over different aspect ratios and scales per feature map location. At prediction time, the network generates confidences that each prior corre-sponds to objects of interest and produces adjustments to the prior to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of var-ious sizes. Our SSD model is simple relative to methods that requires object proposals, such as R-CNN and Multi-Box, because it completely discards the proposal generation step and encapsulates all the computation in a single net-work. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on ILSVRC DET and PASCAL VOC dataset confirm that SSD has comparable performance with methods that utilize an additional object proposal step and yet is 100-1000 × faster. Compared to other single stage methods, SSD has similar or better performance, while pro-viding a unified framework for both training and inference. 1.

To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

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Metal species with different size (single atoms, nanoclusters, and nanoparticles) show different catalytic behavior for various heterogeneous catalytic reactions. It has been shown in the literature that many factors including the particle size, shape, chemical composition, metal-support interaction, and metal-reactant/solvent interaction can have significant influences on the catalytic properties of metal catalysts. The recent developments of well-controlled synthesis methodologies and advanced characterization tools allow one to correlate the relationships at the molecular level. In this Review, the electronic and geometric structures of single atoms, nanoclusters, and nanoparticles will be discussed. Furthermore, we will summarize the catalytic applications of single atoms, nanoclusters, and nanoparticles for different types of reactions, including CO oxidation, selective oxidation, selective hydrogenation, organic reactions, electrocatalytic, and photocatalytic reactions. We will compare the results obtained from different systems and try to give a picture on how different types of metal species work in different reactions and give perspectives on the future directions toward better understanding of the catalytic behavior of different metal entities (single atoms, nanoclusters, and nanoparticles) in a unifying manner.

Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of "marker" genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3' mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods.

We report the measurement of the thermal conductivity of a suspended single-layer graphene. The room temperature values of the thermal conductivity in the range approximately (4.84+/-0.44)x10(3) to (5.30+/-0.48)x10(3) W/mK were extracted for a single-layer graphene from the dependence of the Raman G peak frequency on the excitation laser power and independently measured G peak temperature coefficient. The extremely high value of the thermal conductivity suggests that graphene can outperform carbon nanotubes in heat conduction. The superb thermal conduction property of graphene is beneficial for the proposed electronic applications and establishes graphene as an excellent material for thermal management.

By exploiting the extremely large effective cross sections ( ${10}^{\ensuremath{-}17}--{10}^{\ensuremath{-}16}{\mathrm{cm}}^{2}/\mathrm{molecule}$) available from surface-enhanced Raman scattering (SERS), we achieved the first observation of single molecule Raman scattering. Measured spectra of a single crystal violet molecule in aqueous colloidal silver solution using one second collection time and about $2\ifmmode\times\else\texttimes\fi{}{10}^{5}\mathrm{W}/{\mathrm{cm}}^{2}$ nonresonant near-infrared excitation show a clear ``fingerprint'' of its Raman features between 700 and $1700{\mathrm{cm}}^{\ensuremath{-}1}$. Spectra observed in a time sequence for an average of 0.6 dye molecule in the probed volume exhibited the expected Poisson distribution for actually measuring 0, 1, 2, or 3 molecules.