Residents of the Tibetan Plateau show heritable adaptations to extreme altitude. We sequenced 50 exomes of ethnic Tibetans, encompassing coding sequences of 92% of human genes, with an average coverage of 18X per individual. Genes showing population-specific allele frequency changes, which represent strong candidates for altitude adaptation, were identified. The strongest signal of natural selection came from EPAS1, a transcription factor involved in response to hypoxia. One SNP at EPAS1 shows a 78% frequency difference between Tibetan and Han samples, representing the fastest allele frequency change observed at any human gene to date. This SNP’s association with erythrocyte abundance supports the role of EPAS1 in adaptation to hypoxia. Thus, a population genomic survey has revealed a functionally important locus in genetic adaptation to high altitude.

We studied the effect of anchoring groups on the conductance of single molecules using alkanes terminated with dithiol, diamine, and dicarboxylic-acid groups as a model system. We created a large number of molecular junctions mechanically and analyzed the statistical distributions of the conductance values of the molecular junctions. Multiple sets of conductance values were found in each case. The I-V characteristics, temperature independence, and exponential decay of the conductance with the molecular length all indicate tunneling as the conduction mechanism for these molecules. The prefactor of the exponential decay function, which reflects the contact resistance, is highly sensitive to the anchoring group, and the decay constant is weakly dependent on the anchoring group. These observations are attributed to different electronic couplings between the molecules and the electrodes and alignments of the molecular energy levels relative to the Fermi energy level of the electrodes introduced by different anchoring groups. For diamine and dicarboxylic-acid groups, the conductance values are sensitive to pH due to protonation and deprotonation of the anchoring groups. Further insight into the binding strengths of these anchoring groups to gold electrodes is obtained by statistically analyzing the stretching length of molecular junctions.

Recalcitrance to saccharification is a major limitation for conversion of lignocellulosic biomass to ethanol. In stems of transgenic alfalfa lines independently downregulated in each of six lignin biosynthetic enzymes, recalcitrance to both acid pretreatment and enzymatic digestion is directly proportional to lignin content. Some transgenics yield nearly twice as much sugar from cell walls as wild-type plants. Lignin modification could bypass the need for acid pretreatment and thereby facilitate bioprocess consolidation.

Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing. Existing approaches are either top-down, which start from a gist of an image and convert it into words, or bottom-up, which come up with words describing various aspects of an image and then combine them. In this paper, we propose a new algorithm that combines both approaches through a model of semantic attention. Our algorithm learns to selectively attend to semantic concept proposals and fuse them into hidden states and outputs of recurrent neural networks. The selection and fusion form a feedback connecting the top-down and bottom-up computation. We evaluate our algorithm on two public benchmarks: Microsoft COCO and Flickr30K. Experimental results show that our algorithm significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.

Chimeric antigen receptor (CAR)-modified T cells with anti-CD19 specificity are a highly effective novel immune therapy for relapsed/refractory acute lymphoblastic leukemia. Cytokine release syndrome (CRS) is the most significant and life-threatening toxicity. To improve understanding of CRS, we measured cytokines and clinical biomarkers in 51 CTL019-treated patients. Peak levels of 24 cytokines, including IFNγ, IL6, sgp130, and sIL6R, in the first month after infusion were highly associated with severe CRS. Using regression modeling, we could accurately predict which patients would develop severe CRS with a signature composed of three cytokines. Results were validated in an independent cohort. Changes in serum biochemical markers, including C-reactive protein and ferritin, were associated with CRS but failed to predict development of severe CRS. These comprehensive profiling data provide novel insights into CRS biology and, importantly, represent the first data that can accurately predict which patients have a high probability of becoming critically ill. SIGNIFICANCE: CRS is the most common severe toxicity seen after CAR T-cell treatment. We developed models that can accurately predict which patients are likely to develop severe CRS before they become critically ill, which improves understanding of CRS biology and may guide future cytokinedirected therapy. Cancer Discov;6(6);

Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual's genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics.

Switchgrass is a leading dedicated bioenergy feedstock in the United States because it is a native, high-yielding, perennial prairie grass with a broad cultivation range and low agronomic input requirements. Biomass conversion research has developed processes for production of ethanol and other biofuels, but they remain costly primarily because of the intrinsic recalcitrance of biomass. We show here that genetic modification of switchgrass can produce phenotypically normal plants that have reduced thermal-chemical (≤180°C), enzymatic, and microbial recalcitrance. Down-regulation of the switchgrass caffeic acid O-methyltransferase gene decreases lignin content modestly, reduces the syringyl:guaiacyl lignin monomer ratio, improves forage quality, and, most importantly, increases the ethanol yield by up to 38% using conventional biomass fermentation processes. The down-regulated lines require less severe pretreatment and 300-400% lower cellulase dosages for equivalent product yields using simultaneous saccharification and fermentation with yeast. Furthermore, fermentation of diluted acid-pretreated transgenic switchgrass using Clostridium thermocellum with no added enzymes showed better product yields than obtained with unmodified switchgrass. Therefore, this apparent reduction in the recalcitrance of transgenic switchgrass has the potential to lower processing costs for biomass fermentation-derived fuels and chemicals significantly. Alternatively, such modified transgenic switchgrass lines should yield significantly more fermentation chemicals per hectare under identical process conditions.ignocellulosic biomass is an abundant, domestic, renewable feedstock source that can be converted to liquid transportation fuels and other chemicals by fermentation. Cellulosic ethanol is a promising near-term technological option to reduce transportation sector greenhouse gas emissions (1). Because lignocellulosic biomass is made up of the complex structures of cellulose, hemicellulose, and lignin, such feedstock is highly recalcitrant to bioconversion of its carbohydrates into ethanol compared with starch (2, 3). Current biomass fermentation processes for fuels and chemicals have a relatively high cost primarily because of this recalcitrance, which in turn has limited commercialization of biomass ethanol (4). To achieve sustainable energy production, it is necessary to overcome the chemical and structural properties of biomass that inhibit its deconstruction in dedicated bioenergy crops (5).The conversion of lignocellulosic biomass to ethanol is a threestep process that involves pretreatment followed by polysaccharide hydrolysis to simple sugars followed by sugar fermentation to ethanol (6). The presence of lignin in cell walls negatively impacts these conversion steps (7,8). Examination of natural variation in alfalfa, switchgrass, canarygrass, and sorghum has shown that decreased lignin levels improve in vitro enzyme hydrolysis (9, 10). Lignin pathway modification in alfalfa generated transgenic lines with increased enzymati...

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