Urban energy exchanges monitoring from space

One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. However, satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (-64.1, +69.3 W m-2 for ±2 K perturbation); and also underestimate anthropogenic heat flux. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling

Distinct Mechanisms for Induction and Tolerance Regulate the Immediate Early Genes Encoding Interleukin 1β and Tumor Necrosis Factor α

Interleukin-1β and Tumor Necrosis Factor α play related, but distinct, roles in immunity and disease. Our study revealed major mechanistic distinctions in the Toll-like receptor (TLR) signaling-dependent induction for the rapidly expressed genes (IL1B and TNF) coding for these two cytokines. Prior to induction, TNF exhibited pre-bound TATA Binding Protein (TBP) and paused RNA Polymerase II (Pol II), hallmarks of poised immediate-early (IE) genes. In contrast, unstimulated IL1B displayed very low levels of both TBP and paused Pol II, requiring the lineage-specific Spi-1/PU.1 (Spi1) transcription factor as an anchor for induction-dependent interaction with two TLR-activated transcription factors, C/EBPβ and NF-κB. Activation and DNA binding of these two pre-expressed factors resulted in de novo recruitment of TBP and Pol II to IL1B in concert with a permissive state for elongation mediated by the recruitment of elongation factor P-TEFb. This Spi1-dependent mechanism for IL1B transcription, which is unique for a rapidly-induced/poised IE gene, was more dependent upon P-TEFb than was the case for the TNF gene. Furthermore, the dependence on phosphoinositide 3-kinase for P-TEFb recruitment to IL1B paralleled a greater sensitivity to the metabolic state of the cell and a lower sensitivity to the phenomenon of endotoxin tolerance than was evident for TNF. Such differences in induction mechanisms argue against the prevailing paradigm that all IE genes possess paused Pol II and may further delineate the specific roles played by each of these rapidly expressed immune modulators. © 2013 Adamik et al

Symmetry and topology in antiferromagnetic spintronics

Antiferromagnetic spintronics focuses on investigating and using antiferromagnets as active elements in spintronics structures. Last decade advances in relativistic spintronics led to the discovery of the staggered, current-induced field in antiferromagnets. The corresponding N\'{e}el spin-orbit torque allowed for efficient electrical switching of antiferromagnetic moments and, in combination with electrical readout, for the demonstration of experimental antiferromagnetic memory devices. In parallel, the anomalous Hall effect was predicted and subsequently observed in antiferromagnets. A new field of spintronics based on antiferromagnets has emerged. We will focus here on the introduction into the most significant discoveries which shaped the field together with a more recent spin-off focusing on combining antiferromagnetic spintronics with topological effects, such as antiferromagnetic topological semimetals and insulators, and the interplay of antiferromagnetism, topology, and superconductivity in heterostructures.Comment: Book chapte

Mechanistic insight into the sensing of nitroaromatic compounds by metal-organic frameworks

There has been extensive research on the sensing of explosive nitroaromatic compounds (NACs) using fluorescent metal-organic frameworks (MOFs). However, ambiguity in the sensing mechanism has hampered the development of efficient explosive sensors. Here we report the synthesis of a hydroxyl-functionalized MOF for rapid and efficient sensing of NACs and examine in detail its fluorescence quenching mechanisms. In chloroform, quenching takes place primarily by exciton migration to the ground-state complex formed between the MOF and the analytes. A combination of hydrogen-bonding interactions and ??????? stacking interactions are responsible for fluorescence quenching, and this observation is supported by single-crystal structures. In water, the quenching mechanism shifts toward resonance energy transfer and photo-induced electron transfer, after exciton migration as in chloroform. This study provides insight into florescence-quenching mechanisms for the selective sensing of NACs and reduces the ambiguity regarding the nature of interactions between the MOF and NACs

Methods to study splicing from high-throughput RNA Sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde

HMMSplicer: A Tool for Efficient and Sensitive Discovery of Known and Novel Splice Junctions in RNA-Seq Data

Background: High-throughput sequencing of an organism’s transcriptome, or RNA-Seq, is a valuable and versatile new strategy for capturing snapshots of gene expression. However, transcriptome sequencing creates a new class of alignment problem: mapping short reads that span exon-exon junctions back to the reference genome, especially in the case where a splice junction is previously unknown. Methodology/Principal Findings: Here we introduce HMMSplicer, an accurate and efficient algorithm for discovering canonical and non-canonical splice junctions in short read datasets. HMMSplicer identifies more splice junctions than currently available algorithms when tested on publicly available A. thaliana, P. falciparum, and H. sapiens datasets without a reduction in specificity. Conclusions/Significance: HMMSplicer was found to perform especially well in compact genomes and on genes with low expression levels, alternative splice isoforms, or non-canonical splice junctions. Because HHMSplicer does not rely on prebuilt gene models, the products of inexact splicing are also detected. For H. sapiens, we find 3.6 % of 39 splice sites and 1.4% of 59 splice sites are inexact, typically differing by 3 bases in either direction. In addition, HMMSplicer provides a score for every predicted junction allowing the user to set a threshold to tune false positive rates depending on the needs of the experiment. HMMSplicer is implemented in Python. Code and documentation are freely available a

FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts, such as misalignment or random pairing of transcript fragments, and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including eight cancers with and without known rearrangements

Digital Gene Expression Analysis Based on Integrated De Novo Transcriptome Assembly of Sweet Potato [Ipomoea batatas (L.) Lam.]

Background: Sweet potato (Ipomoea batatas L. [Lam.]) ranks among the top six most important food crops in the world. It is widely grown throughout the world with high and stable yield, strong adaptability, rich nutrient content, and multiple uses. However, little is known about the molecular biology of this important non-model organism due to lack of genomic resources. Hence, studies based on high-throughput sequencing technologies are needed to get a comprehensive and integrated genomic resource and better understanding of gene expression patterns in different tissues and at various developmental stages. Methodology/Principal Findings: Illumina paired-end (PE) RNA-Sequencing was performed, and generated 48.7 million of 75 bp PE reads. These reads were de novo assembled into 128,052 transcripts ($100 bp), which correspond to 41.1 million base pairs, by using a combined assembly strategy. Transcripts were annotated by Blast2GO and 51,763 transcripts got BLASTX hits, in which 39,677 transcripts have GO terms and 14,117 have ECs that are associated with 147 KEGG pathways. Furthermore, transcriptome differences of seven tissues were analyzed by using Illumina digital gene expression (DGE) tag profiling and numerous differentially and specifically expressed transcripts were identified. Moreover, the expression characteristics of genes involved in viral genomes, starch metabolism and potential stress tolerance and insect resistance were also identified

Study of B+- --> D_CP K+- and D^*_CP K+- decays

We report a study of the modes D_CP K+- and D^*_CP K+- where D^(*) decays to CP eigenstates. The data sample used contains 275 x 10^6 BB events at the Upsilon(4S) resonance collected by the Belle detector at the KEKB energy-asymmetric e^+ e^- collider. The CP asymmetries obtained for D_CP K are: A_1 = 0.06 +- 0.14 (stat) +- 0.05 (sys), A_2 = -0.12 +- 0.14 (stat) +- 0.05 (sys) and for D^*_CP K : A_1^* = -0.20 +- 0.22 (stat) +- 0.04 (sys), A_2^* = 0.13 +- 0.30 (stat) +- 0.08 (sys).Comment: 10 pages, 7 figures, submitted to Phys.Rev.D (Rapid Communications