Core Subset Research Articles

Checks and balances: constraint solving without surprises in object-constraint programming languages

Object-constraint programming systems integrate declarative constraint solving with imperative, object-oriented languages, seamlessly providing the power of both paradigms. However, experience with object-constraint systems has shown that giving too much power to the constraint solver opens up the potential for solutions that are surprising and unintended as well as for complex interactions between constraints and imperative code. On the other hand, systems that overly limit the power of the solver, for example by disallowing constraints involving mutable objects, object identity, or polymorphic message sends, run the risk of excluding the core object-oriented features of the language from the constraint part, and consequently not being able to express declaratively a large set of interesting problem solutions. In this paper we present design principles that tame the power of the constraint solver in object-constraint languages to avoid difficult corner cases and surprising solutions while retaining the key features of the approach, including constraints over mutable objects, constraints involving object identity, and constraints on the results of message sends. We present our solution concretely in the context of the Babelsberg object-constraint language framework, providing both an informal description of the resulting language and a formal semantics for a core subset of it. We validate the utility of this semantics with an executable version that allows us to run test programs and to verify that they provide the same results as existing implementations of Babelsberg in JavaScript, Ruby, and Smalltalk.

Featherweight VeriFast

VeriFast is a leading research prototype tool for the sound modular verification of safety and correctness properties of single-threaded and multithreaded C and Java programs. It has been used as a vehicle for exploration and validation of novel program verification techniques and for industrial case studies; it has served well at a number of program verification competitions; and it has been used for teaching by multiple teachers independent of the authors. However, until now, while VeriFast's operation has been described informally in a number of publications, and specific verification techniques have been formalized, a clear and precise exposition of how VeriFast works has not yet appeared. In this article we present for the first time a formal definition and soundness proof of a core subset of the VeriFast program verification approach. The exposition aims to be both accessible and rigorous: the text is based on lecture notes for a graduate course on program verification, and it is backed by an executable machine-readable definition and machine-checked soundness proof in Coq.

Genetic diversity of safflower (Carthamus tinctoriusL.) germplasm as revealed by SSR markers

Safflower is a multi-purpose oilseed crop, primarily known for good quality oil containing highest polyunsaturated fatty acid content (>80%) among edible oils. In this study, a core subset of 148 safflower accessions representing 15 countries, predominantly of Indian origin, was evaluated for agronomic traits and characterized for genetic diversity, population structure and linkage disequilibrium (LD) using 44 simple sequence repeat (SSR) loci across 11 linkage groups to enable its utilization in breeding and genetic mapping purposes. The collection had substantial variation for seed yield-related traits. SSR allelic variation was low as indicated by average number of alleles (3.6) per locus, gene diversity (0.314) and polymorphism information content (0.284). Cluster analysis (neighbour-joining tree) revealed five major genotypic groups with very low bootstrap support. STRUCTURE analysis showed recognizable population structure; based on membership coefficients ( ≥ 0.75), 52% accessions were classified into four populations (K= 4) and the remaining 48% accessions into admixture group. HighFstvalues (0.30–0.48) suggested that the populations were substantially differentiated. Analysis of molecular variance results showed that maximum of genetic variation (85%) was explained between individuals within the population suggesting that the population structure was only weak. About 1.9% of SSR locus pairs were in LD, which appeared to be low. High phenotypic variation, mild population structure and low level of LD among unlinked loci suggested that the core subset can be explored for association mapping of seed yield components in safflower.

The Impact of the Branched-Chain Ketoacid Dehydrogenase Complex on Amino Acid Homeostasis in Arabidopsis.

The branched-chain amino acids (BCAAs) Leu, Ile, and Val are among nine essential amino acids that must be obtained from the diet of humans and other animals, and can be nutritionally limiting in plant foods. Despite genetic evidence of its importance in regulating seed amino acid levels, the full BCAA catabolic network is not completely understood in plants, and limited information is available regarding its regulation. In this study, transcript coexpression analyses revealed positive correlations among BCAA catabolism genes in stress, development, diurnal/circadian, and light data sets. A core subset of BCAA catabolism genes, including those encoding putative branched-chain ketoacid dehydrogenase subunits, is highly expressed during the night in plants on a diel cycle and in prolonged darkness. Mutants defective in these subunits accumulate higher levels of BCAAs in mature seeds, providing genetic evidence for their function in BCAA catabolism. In addition, prolonged dark treatment caused the mutants to undergo senescence early and overaccumulate leaf BCAAs. These results extend the previous evidence that BCAAs can be catabolized and serve as respiratory substrates at multiple steps. Moreover, comparison of amino acid profiles between mature seeds and dark-treated leaves revealed differences in amino acid accumulation when BCAA catabolism is perturbed. Together, these results demonstrate the consequences of blocking BCAA catabolism during both normal growth conditions and under energy-limited conditions.

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g‐index, q2‐index, and hr‐index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.

Genetic Diversity among Wheat Accessions from the USDA National Small Grains Collection

ABSTRACTAccessions of common wheat (Triticum aestivum L. subsp. aestivum) from the USDA–ARS National Small Grains Collection (NSGC) are a resource for wheat scientists worldwide. The genetic diversity of the wheat core subset, representing approximately 10% of the collection's 42,138 T. aestivum accessions, was examined using 390 diversity arrays technology (DArT) markers, 4941 single nucleotide polymorphisms (SNPs), and descriptor data. The marker profiles revealed duplicates, which were excluded to form an informative core (iCore) of 3230 accessions. The iCore population structure and diversity within various subgroups were examined with analysis of molecular variance, principal coordinate analysis, cluster analysis, and by ranking the contribution of individual accessions to overall diversity. Accession groups based on molecular marker data corresponded well to their geographic origin, and population structure was accounted for primarily by differences between Iranian landrace accessions and the rest of the accessions. Accessions classified as breeding lines were overrepresented among those ranked as most diverse based on SNP data, whereas Iranian landraces were underrepresented. Although less diverse as a group, Iranian landrace accessions had a higher frequency of resistance to bunt diseases and Russian wheat aphid compared with the iCore as a whole. The present study provides support for establishing core subsets based on geographic origin of accessions and will be a basis for further study of diversity among NSGC wheats.

16S rRNA Amplicon Sequencing Demonstrates that Indoor-Reared Bumblebees (Bombus terrestris) Harbor a Core Subset of Bacteria Normally Associated with the Wild Host.

A MiSeq multiplexed 16S rRNA amplicon sequencing of the gut microbiota of wild and indoor-reared Bombus terrestris (bumblebees) confirmed the presence of a core set of bacteria, which consisted of Neisseriaceae (Snodgrassella), Orbaceae (Gilliamella), Lactobacillaceae (Lactobacillus), and Bifidobacteriaceae (Bifidobacterium). In wild B. terrestris we detected several non-core bacteria having a more variable prevalence. Although Enterobacteriaceae are unreported by non next-generation sequencing studies, it can become a dominant gut resident. Furthermore the presence of some non-core lactobacilli were associated with the relative abundance of bifidobacteria. This association was not observed in indoor-reared bumblebees lacking the non-core bacteria, but having a more standardized microbiota compared to their wild counterparts. The impact of the bottleneck microbiota of indoor-reared bumblebees when they are used in the field for pollination purpose is discussed.

An analysis of metamodeling practices for MOF and OCL

The definition of a metamodel that precisely captures domain knowledge for effective know-how capitalization is a challenging task. A major obstacle for domain experts who want to build a metamodel is that they must master two radically different languages: an object-oriented, MOF-compliant, modeling language to capture the domain structure and first order logic (the Object Constraint Language) for the definition of well-formedness rules. However, there are no guidelines to assist the conjunct usage of both paradigms, and few tools support it. Consequently, we observe that most metamodels have only an object-oriented domain structure, leading to inaccurate metamodels. In this paper, we perform the first empirical study, which analyzes the current state of practice in metamodels that actually use logical expressions to constrain the structure. We analyze 33 metamodels including 995 rules coming from industry, academia and the Object Management Group, to understand how metamodelers articulate both languages. We implement a set of metrics in the OCLMetrics tool to evaluate the complexity of both parts, as well as the coupling between both. We observe that all metamodels tend to have a small, core subset of concepts, which are constrained by most of the rules, in general the rules are loosely coupled to the structure and we identify the set of OCL constructs actually used in rules.

Assessment of apple core collections constructed using phenotypic and genotypic data.

Several types of information can be used to select core collections, including passport data, agronomic data, and molecular data. However, little is known about the ability of core collections to retain the genetic diversity and structure of the whole collection for characters that were not considered during the selection, particularly when molecular markers are used. In this study, two core subsets were established for the apple (Malus spp) germplasm bank curated at the Apple Research Station, National Institute of Horticultural and Herbal Science, Korea, based upon genetic diversity estimated with 14 simple sequence repeat markers, and phenotypic diversity based on 23 traits. Comparisons between these two subsets and with the whole collection were used to determine the effect of the data used in the selection on phenotypic and genetic diversity, and population structure. The two subsets had a similar diversity and did not differ from the original collection, according to the Nei and Shannon diversity indices. Allele and class frequencies were also maintained in the two subsets. Overall, the type of data used to construct the core collection had little influence on the phenotypic and genetic diversity retained. Therefore, in the case of apple collections, the use of molecular markers is preferable, because they allow rapid and reliable characterization.

SCREENING A CORE COLLECTION OF CITRUS GENETIC RESOURCES FOR RESISTANCE TO FUSARIUM SOLANI

A causal agent for Dry root rot (DRR) of citrus has not been definitively identified, but the organism most consistently associated with DRR is Fusarium solani (Mart.) Sacc. To efficiently screen a citrus germplasm collection for resistance to F. solani, a core subset of the collection was evaluated. Seedlings of forty-five accessions were wounded by girdling. Treatment consisted of inoculation with F. solani, with uninoculated controls. Three parameters were evaluated: recovery from girdling (0 - 3); amount of DRR (0 - 4); and percentage of healthy feeder roots. Twenty-four accessions showed no significant difference in DRR between inoculated and uninoculated seedlings, and 12 of these accessions showed low development of DRR ( 2.7). Twenty-four accessions showed no significant difference in % healthy feeder roots between inoculated and uninoculated seedlings and eight of these showed a high percentage (> 80 %) of healthy feeder roots. Five accessions were superior in all three parameters: 'Fremont' mandarin (PI 539507), 'Lamas' lemon (PI 539226), 'Mato buntan' pummelo (PI 529398), 'Olivelands' sour orange (PI 539164), and 'South Coast Field Station' citron (PI 539435). These accessions would probably represent the best candidates for further study of F. solani resistance.

Cytoplasmic-nuclear variation in a diversity-fixed foundation set ofBrassica juncea (L.) Czern & Coss.

A core subset of genetically fixed lines was selected through stratified sampling to represent the allelic variations within Brassica juncea gene pools. It included germplasm accessions from India, China, and Australia, as well as B. Juncea introgression lines carrying alien genetic variation and/or cytoplasm from wild crucifers, namely Diplotaxis cardaminoides, Diplotaxis tenuisiliqua, and Erucastrum abyssinicum. The aim of the study was to infer the influence of underlying cytoplasm on population structure and trait variation. Molecular characterization using nuclear SSR markers (158) and chloroplast SSR markers (9) revealed interesting germplasm patterns and trait variations. Diversity groups obtained using distance-based clustering were not fully compliant with structure analysis, especially for the nuclear genetic variation. Association between population structure and phenotypic variation was indicated by significant interactions between chlorotype and seed/biological yields. Thus, there may be a need to factor in the impact of background cytoplasm on population structure in association studies of crops where different cytoplasmic lineages are known.

Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction.

We are facing an era with annotated biological data rapidly and continuously generated. How to effectively incorporate new annotated data into the learning step is crucial for enhancing the performance of a bioinformatics prediction model. Although machine-learning-based methods have been extensively used for dealing with various biological problems, existing approaches usually train static prediction models based on fixed training datasets. The static approaches are found having several disadvantages such as low scalability and impractical when training dataset is huge. In view of this, we propose a dynamic learning framework for constructing query-driven prediction models. The key difference between the proposed framework and the existing approaches is that the training set for the machine learning algorithm of the proposed framework is dynamically generated according to the query input, as opposed to training a general model regardless of queries in traditional static methods. Accordingly, a query-driven predictor based on the smaller set of data specifically selected from the entire annotated base dataset will be applied on the query. The new way for constructing the dynamic model enables us capable of updating the annotated base dataset flexibly and using the most relevant core subset as the training set makes the constructed model having better generalization ability on the query, showing "part could be better than all" phenomenon. According to the new framework, we have implemented a dynamic protein-ligand binding sites predictor called OSML (On-site model for ligand binding sites prediction). Computer experiments on 10 different ligand types of three hierarchically organized levels show that OSML outperforms most existing predictors. The results indicate that the current dynamic framework is a promising future direction for bridging the gap between the rapidly accumulated annotated biological data and the effective machine-learning-based predictors. OSML web server and datasets are freely available at: http://www.csbio.sjtu.edu.cn/bioinf/OSML/ for academic use.

IDENTIFYING IMPORTANT CLASSES OF LARGE SOFTWARE SYSTEMS THROUGH K-CORE DECOMPOSITION

In a large software project, the number of classes, and the dependencies between them, generally increase as software evolves. The size and scale of the system often makes it difficult to easily identify the important components in a particular software product. To address this problem, we model software as a network, where the classes are the vertices in the network and the dependencies are the edges, and apply K-core decomposition to identify a core subset of vertices as potentially important classes. We study three open source Java projects over a 10-year period and demonstrate, using different metrics, that the K-core decomposition of the network can help us identify the key classes of the corresponding software. Specifically, we show that the vertices with the highest core number represent the important classes and demonstrate that the core-numbers of classes with similar functionalities evolve at similar trends.

The AquaVIT-1 intercomparison of atmospheric water vapor measurement techniques

Abstract. The AquaVIT-1 intercomparison of atmospheric water vapor measurement techniques was conducted at the aerosol and cloud simulation chamber AIDA (Aerosol Interaction and Dynamics in the Atmosphere) at the Karlsruhe Institute of Technology, Germany, in October 2007. The overall objective was to intercompare state-of-the-art and prototype atmospheric hygrometers with each other and with independent humidity standards under controlled conditions. This activity was conducted as a blind intercomparison with coordination by selected referees. The effort was motivated by persistent discrepancies found in atmospheric measurements involving multiple instruments operating on research aircraft and balloon platforms, particularly in the upper troposphere and lower stratosphere, where water vapor reaches its lowest atmospheric values (less than 10 ppm). With the AIDA chamber volume of 84 m3, multiple instruments analyzed air with a common water vapor mixing ratio, by extracting air into instrument flow systems, by locating instruments inside the chamber, or by sampling the chamber volume optically. The intercomparison was successfully conducted over 10 days during which pressure, temperature, and mixing ratio were systematically varied (50 to 500 hPa, 185 to 243 K, and 0.3 to 152 ppm). In the absence of an accepted reference instrument, the absolute accuracy of the instruments was not established. To evaluate the intercomparison, the reference value was taken to be the ensemble mean of a core subset of the measurements. For these core instruments, the agreement between 10 and 150 ppm of water vapor is considered good with variation about the reference value of about ±10% (±1σ). In the region of most interest between 1 and 10 ppm, the core subset agreement is fair with variation about the reference value of ±20% (±1σ). The upper limit of precision was also derived for each instrument from the reported data. The implication for atmospheric measurements is that the substantially larger differences observed during in-flight intercomparisons stem from other factors associated with the moving platforms or the non-laboratory environment. The success of AquaVIT-1 provides a template for future intercomparison efforts with water vapor or other species that are focused on improving the analytical quality of atmospheric measurements on moving platforms.

An Intrinsic Encoding of a Subset of C and its Application to TLS Network Packet Processing

TLS is such a widespread security protocol that errors in its implementation can have disastrous consequences. This responsibility is mostly borne by programmers, caught between specifications with the ambiguities of natural language and error-prone low-level parsing of network packets. We report here on the construction in the Coq proof-assistant of libraries to model, specify, and verify C programs to process TLS packets. We provide in particular an encoding of the core subset of C whose originality lies in its use of dependent types to guarantee statically well-formedness of datatypes and correct typing. We further equip this encoding with a Separation logic that enables byte-level reasoning and also provide a logical view of data structures. We also formalize a significant part of the RFC for TLS, again using dependent types to capture succinctly constraints that are left implicit in the prose document. Finally, we apply the above framework to an existing implementation of TLS (namely, PolarSSL) of which we specify and verify a parsing function for network packets. Thanks to this experiment, we were able to spot ambiguities in the RFC and to correct bugs in the C source code.

Pathogenicity of Mycobacterium tuberculosis is expressed by regulating metabolic thresholds of the host macrophage.

The success of Mycobacterium tuberculosis as a pathogen derives from its facile adaptation to the intracellular milieu of human macrophages. To explore this process, we asked whether adaptation also required interference with the metabolic machinery of the host cell. Temporal profiling of the metabolic flux, in cells infected with differently virulent mycobacterial strains, confirmed that this was indeed the case. Subsequent analysis identified the core subset of host reactions that were targeted. It also elucidated that the goal of regulation was to integrate pathways facilitating macrophage survival, with those promoting mycobacterial sustenance. Intriguingly, this synthesis then provided an axis where both host- and pathogen-derived factors converged to define determinants of pathogenicity. Consequently, whereas the requirement for macrophage survival sensitized TB susceptibility to the glycemic status of the individual, mediation by pathogen ensured that the virulence properties of the infecting strain also contributed towards the resulting pathology.

Global protein-protein interaction network of rice sheath blight pathogen.

Rhizoctonia solani is the major pathogenic fungi of rice sheath blight. It is responsible for the most serious disease of rice (Oryza sativa L.) and causes significant yield losses in rice-growing countries. Identifying the protein-protein interaction (PPI) maps of R. solani can provide insights into the potential pathogenic mechanisms and assign putative functions to unknown genes. Here, we exploited a PPI map of R. solani anastomosis group 1 IA (AG-1 IA) based on the interolog and domain-domain interaction methods. We constructed a core subset of high-confidence protein networks consisting of 6705 interactions among 1773 proteins. The high quality of the network was revealed by comprehensive methods, including yeast two-hybrid experiments. Pathogenic interaction subnetwork, secreted proteins subnetwork, and mitogen-activated protein kinase (MAPK) cascade subnetwork and their interacting partners were constructed and analyzed. Moreover, to exactly predict the pathogenic factors, the expression levels of the interaction proteins were investigated by analyzing RNA sequences that consisted of samples from the entire infection progress. The PPIs offer an exceptionally rich source of data that can be used to understand the gene functions and biological processes of this serious disease at the system level.

Fully Functional Static and Dynamic Succinct Trees

We propose new succinct representations of ordinal trees and match various space/time lower bounds. It is known that any n -node static tree can be represented in 2 n + o ( n ) bits so that a number of operations on the tree can be supported in constant time under the word-RAM model. However, the data structures are complicated and difficult to dynamize. We propose a simple and flexible data structure, called the range min-max tree , that reduces the large number of relevant tree operations considered in the literature to a few primitives that are carried out in constant time on polylog-sized trees. The result is extended to trees of arbitrary size, retaining constant time and reaching 2 n + O ( n /polylog( n )) bits of space. This space is optimal for a core subset of the operations supported and significantly lower than in any previous proposal. For the dynamic case, where insertion/deletion (indels) of nodes is allowed, the existing data structures support a very limited set of operations. Our data structure builds on the range min-max tree to achieve 2 n + O ( n /log n ) bits of space and O (log n ) time for all operations supported in the static scenario, plus indels. We also propose an improved data structure using 2 n + O ( n log log n /log n ) bits and improving the time to the optimal O (log n /log log n ) for most operations. We extend our support to forests, where whole subtrees can be attached to or detached from others, in time O (log 1+ϵ n ) for any ϵ > 0. Such operations had not been considered before. Our techniques are of independent interest. An immediate derivation yields an improved solution to range minimum/maximum queries where consecutive elements differ by ± 1, achieving n + O ( n /polylog( n )) bits of space. A second one stores an array of numbers supporting operations sum and search and limited updates, in optimal time O (log n /log log n ). A third one allows representing dynamic bitmaps and sequences over alphabets of size σ, supporting rank/select and indels, within zero-order entropy bounds and time O (log n log σ/(log log n ) 2 ) for all operations. This time is the optimal O (log n /log log n ) on bitmaps and polylog-sized alphabets. This improves upon the best existing bounds for entropy-bounded storage of dynamic sequences, compressed full-text self-indexes, and compressed-space construction of the Burrows-Wheeler transform.

Medial prefrontal cortex: genes linked to bipolar disorder and schizophrenia have altered expression in the highly social maternal phenotype.

The transition to motherhood involves CNS changes that modify sociability and affective state. However, these changes also put females at risk for post-partum depression and psychosis, which impairs parenting abilities and adversely affects children. Thus, changes in expression and interactions in a core subset of genes may be critical for emergence of a healthy maternal phenotype, but inappropriate changes of the same genes could put women at risk for post-partum disorders. This study evaluated microarray gene expression changes in medial prefrontal cortex (mPFC), a region implicated in both maternal behavior and psychiatric disorders. Post-partum mice were compared to virgin controls housed with females and isolated for identical durations. Using the Modular Single-set Enrichment Test (MSET), we found that the genetic landscape of maternal mPFC bears statistical similarity to gene databases associated with schizophrenia (5 of 5 sets) and bipolar disorder (BPD, 3 of 3 sets). In contrast to previous studies of maternal lateral septum (LS) and medial preoptic area (MPOA), enrichment of autism and depression-linked genes was not significant (2 of 9 sets, 0 of 4 sets). Among genes linked to multiple disorders were fatty acid binding protein 7 (Fabp7), glutamate metabotropic receptor 3 (Grm3), platelet derived growth factor, beta polypeptide (Pdgfrb), and nuclear receptor subfamily 1, group D, member 1 (Nr1d1). RT-qPCR confirmed these gene changes as well as FMS-like tyrosine kinase 1 (Flt1) and proenkephalin (Penk). Systems-level methods revealed involvement of developmental gene networks in establishing the maternal phenotype and indirectly suggested a role for numerous microRNAs and transcription factors in mediating expression changes. Together, this study suggests that a subset of genes involved in shaping the healthy maternal brain may also be dysregulated in mental health disorders and put females at risk for post-partum psychosis with aspects of schizophrenia and BPD.

Effect of Environment and Genotype on Commercial Maize Hybrids Using LC/MS-Based Metabolomics

We recently applied gas chromatography coupled to time-of-flight mass spectrometry (GC/TOF-MS) and multivariate statistical analysis to measure biological variation of many metabolites due to environment and genotype in forage and grain samples collected from 50 genetically diverse nongenetically modified (non-GM) DuPont Pioneer commercial maize hybrids grown at six North American locations. In the present study, the metabolome coverage was extended using a core subset of these grain and forage samples employing ultra high pressure liquid chromatography (uHPLC) mass spectrometry (LC/MS). A total of 286 and 857 metabolites were detected in grain and forage samples, respectively, using LC/MS. Multivariate statistical analysis was utilized to compare and correlate the metabolite profiles. Environment had a greater effect on the metabolome than genetic background. The results of this study support and extend previously published insights into the environmental and genetic associated perturbations to the metabolome that are not associated with transgenic modification.