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The need to increase food production for a growing world population makes an assessment of global agricultural water productivities and virtual water flows important. Using the hydrology and agro-biosphere model LPJmL, we quantify at 0.5 degrees resolution the amount of blue and green water (irrigation and precipitation water) needed to produce one unit of crop yield, for 11 of the world's major crop types. Based on these, we also quantify the agricultural water footprints (WFP) of all countries, for the period 1998-2002, distinguishing internal and external WFP (virtual water imported from other countries) and their blue and green components, respectively. Moreover, we calculate water savings and losses, and for the first time also land savings and losses, through international trade with these products. The consistent separation of blue and green water flows and footprints shows that green water globally dominates both the internal and external WFP (84% of the global WFP and 94% of the external WFP rely on green water). While no country ranks among the top ten with respect to all water footprints calculated here, Pakistan and Iran demonstrate high absolute and per capita blue WFP, and the US and India demonstrate high absolute green and blue WFPs. The external WFPs are relatively small (6% of the total global blue WFP, 16% of the total global green WFP). Nevertheless, current trade of the products considered here saves significant water volumes and land areas (similar to 263 km(3) and similar to 41 Mha, respectively, equivalent to 5% of the sowing area of the considered crops and 3.5% of the annual precipitation on this area). Relating the proportions of external to internal blue/green WFP to the per capita WFPs allows recognizing that only a few countries consume more water from abroad than from their own territory and have at the same time above-average WFPs. Thus, countries with high per capita water consumption affect mainly the water availability in their own country. Finally, this study finds that flows/savings of both virtual water and virtual land need to be analysed together, since they are intrinsically related.
A poorly understood lag time of 15-20 m.y. exists between the initial Arabia-Eurasia continental collision in late Eocene to early Oligocene time and the acceleration of tectonic and sedimentary processes across the collision zone in the early to late Miocene. The late Eocene to Miocene-Pliocene clastic and shallow-marine sedimentary rocks of the Kond, Eyvanekey, and Semnan Basins in the Alborz Mountains (northern Iran) offer the possibility to track the evolution of this orogen in the framework of collision processes. A transition from volcaniclastic submarine deposits to shallow-marine evaporites and terrestrial sediments occurred shortly after 36 Ma in association with reversals in sediment provenance, strata tilting, and erosional unroofing. These events followed the termination of subduction arc magmatism and marked a changeover from an extensional to a contractional regime in response to initiation of continental collision with the subduction of stretched Arabian lithosphere. This early stage of collision produced topographic relief associated with shallow foreland basins, suggesting that shortening and tectonic loading occurred at low rates. Starting from the early Miocene (17.5 Ma), flexural subsidence in response to foreland basin initiation occurred. Fast sediment accumulation rates and erosional unroofing trends point to acceleration of shortening by the early Miocene. We suggest that the lag time between the initiation of continental collision (36 Ma) and the acceleration of regional deformation (20-17.5 Ma) reflects a two-stage collision process, involving the "soft" collision of stretched lithosphere at first and "hard" collision following the arrival of unstretched Arabian continental litho sphere in the subduction zone.
Genome sequencing of closely related individuals has yielded valuable insights that link genome evolution to phenotypic variations. However, advancement in sequencing technology has also led to an escalation in the number of poor quality-drafted genomes assembled based on reference genomes that can have highly divergent or haplotypic regions. The self-fertilizing nature of Arabidopsis thaliana poses an advantage to sequencing projects because its genome is mostly homozygous. To determine the accuracy of an Arabidopsis drafted genome in less conserved regions, we performed a resequencing experiment on a similar to 371-kb genomic interval in the Landsberg erecta (Ler-0) accession. We identified novel structural variations (SVs) between Ler-0 and the reference accession Col-0 using a long-range polymerase chain reaction approach to generate an Illumina data set that has positional information, that is, a data set with reads that map to a known location. Positional information is important for accurate genome assembly and the resolution of SVs particularly in highly duplicated or repetitive regions. Sixty-one regions with misassembly signatures were identified from the Ler-0 draft, suggesting the presence of novel SVs that are not represented in the draft sequence. Sixty of those were resolved by iterative mapping using our data set. Fifteen large indels (> 100 bp) identified from this study were found to be located either within protein-coding regions or upstream regulatory regions, suggesting the formation of novel alleles or altered regulation of existing genes in Ler-0. We propose future genome-sequencing experiments to follow a clone-based approach that incorporates positional information to ultimately reveal haplotype-specific differences between accessions.
Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system specification and a corresponding workflow model used as implementation have to be consistent. Another example would be the analysis to what degree a process log of executed business operations is consistent with the corresponding normative process model. Typically, existing notions of behaviour equivalence, such as bisimulation and trace equivalence, are applied as consistency notions. Still, these notions are exponential in computation and yield a Boolean result. In many cases, however, a quantification of behavioural deviation is needed along with concepts to isolate the source of deviation.
In this article, we propose causal behavioural profiles as the basis for a consistency notion. These profiles capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities of a process model. Consistency based on these profiles is weaker than trace equivalence, but can be computed efficiently for a broad class of models. In this article, we introduce techniques for the computation of causal behavioural profiles using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S-or T-nets. We also elaborate on the findings of applying our technique to three industry model collections.
We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered.
Speech perception requires rapid extraction of the linguistic content from the acoustic signal. The ability to efficiently process rapid changes in auditory information is important for decoding speech and thereby crucial during language acquisition. Investigating functional networks of speech perception in infancy might elucidate neuronal ensembles supporting perceptual abilities that gate language acquisition. Interhemispheric specializations for language have been demonstrated in infants. How these asymmetries are shaped by basic temporal acoustic properties is under debate. We recently provided evidence that newborns process non-linguistic sounds sharing temporal features with language in a differential and lateralized fashion. The present study used the same material while measuring brain responses of 6 and 3 month old infants using simultaneous recordings of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). NIRS reveals that the lateralization observed in newborns remains constant over the first months of life. While fast acoustic modulations elicit bilateral neuronal activations, slow modulations lead to right-lateralized responses. Additionally, auditory-evoked potentials and oscillatory EEG responses show differential responses for fast and slow modulations indicating a sensitivity for temporal acoustic variations. Oscillatory responses reveal an effect of development, that is, 6 but not 3 month old infants show stronger theta-band desynchronization for slowly modulated sounds. Whether this developmental effect is due to increasing fine-grained perception for spectrotemporal sounds in general remains speculative. Our findings support the notion that a more general specialization for acoustic properties can be considered the basis for lateralization of speech perception. The results show that concurrent assessment of vascular based imaging and electrophysiological responses have great potential in the research on language acquisition.
Linear mixed models (LMMs) provide a still underused methodological perspective on combining experimental and individual-differences research. Here we illustrate this approach with two-rectangle cueing in visual attention (Egly et al., 1994). We replicated previous experimental cue-validity effects relating to a spatial shift of attention within an object (spatial effect), to attention switch between objects (object effect), and to the attraction of attention toward the display centroid (attraction effect), also taking into account the design-inherent imbalance of valid and other trials. We simultaneously estimated variance/covariance components of subject-related random effects for these spatial, object, and attraction effects in addition to their mean reaction times (RTs). The spatial effect showed a strong positive correlation with mean RT and a strong negative correlation with the attraction effect. The analysis of individual differences suggests that slow subjects engage attention more strongly at the cued location than fast subjects. We compare this joint LMM analysis of experimental effects and associated subject-related variances and correlations with two frequently used alternative statistical procedures.
Spatial numerical associations (SNAs) are prevalent yet their origin is poorly understood. We first consider the possible prime role of reading habits in shaping SNAs and list three observations that argue against a prominent influence of this role: (1) directional reading habits for numbers may conflict with those for non-numerical symbols, (2) short-term experimental manipulations can overrule the impact of decades of reading experience, (3) SNAs predate the acquisition of reading. As a promising alternative, we discuss behavioral, neuroscientific, and neuropsychological evidence in support of finger counting as the most likely initial determinant of SNAs. Implications of this "manumerical cognition" stance for the distinction between grounded, embodied, and situated cognition are discussed.
SLocX predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
(2011)
Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.
Large-scale co-expression approach to dissect secondary cell wall formation across plant species
(2011)
Plant cell walls are complex composites largely consisting of carbohydrate-based polymers, and are generally divided into primary and secondary walls based on content and characteristics. Cellulose microfibrils constitute a major component of both primary and secondary cell walls and are synthesized at the plasma membrane by cellulose synthase (CESA) complexes. Several studies in Arabidopsis have demonstrated the power of co-expression analyses to identify new genes associated with secondary wall cellulose biosynthesis. However, across-species comparative co-expression analyses remain largely unexplored. Here, we compared co-expressed gene vicinity networks of primary and secondary wall CESAsin Arabidopsis, barley, rice, poplar, soybean, Medicago, and wheat, and identified gene families that are consistently co-regulated with cellulose biosynthesis. In addition to the expected polysaccharide acting enzymes, we also found many gene families associated with cytoskeleton, signaling, transcriptional regulation, oxidation, and protein degradation. Based on these analyses, we selected and biochemically analyzed T-DNA insertion lines corresponding to approximately twenty genes from gene families that re-occur in the co-expressed gene vicinity networks of secondary wall CESAs across the seven species. We developed a statistical pipeline using principal component analysis and optimal clustering based on silhouette width to analyze sugar profiles. One of the mutants, corresponding to a pinoresinol reductase gene, displayed disturbed xylem morphology and held lower levels of lignin molecules. We propose that this type of large-scale co-expression approach, coupled with statistical analysis of the cell wall contents, will be useful to facilitate rapid knowledge transfer across plant species.