A multichannel sampling method for 2-D finite-rate-of-innovation signals

We address the problem of sampling and reconstruction of two-dimensional (2-D) finite-rate-of-innovation (FRI) signals. We propose a three-channel sampling method for efficiently solving the problem. We consider the sampling of a stream of 2-D Dirac impulses and a sum of 2-D unit-step functions. We propose a 2-D causal exponential function as the sampling kernel. By causality in 2-D, we mean that the function has its support restricted to the first quadrant. The advantage of using a multichannel sampling method with causal exponential sampling kernel is that standard annihilating filter or root-finding algorithms are not required. Further, the proposed method has inexpensive hardware implementation and is numerically stable as the number of Dirac impulses increases.

Spectral properties of a double-quantum-dot structure: A causal Green's function approach

Spectral properties of a double quantum dot (QD) structure are studied by a causal Green's function (GF) approach. The double QD system is modeled by an Anderson-type Hamiltonian in which both the intra- and interdot Coulomb interactions are taken into account. The GF's are derived by an equation-of-motion method and the real-space renormalization-group technique. The numerical results show that the average occupation number of electrons in the QD exhibits staircase features and the local density of states depends appreciably on the electron occupation of the dot.

Inferring the conservative causal core of gene regulatory networks

Background
Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.

Results
In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.

Conclusions
For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

The causal attributions of nursing students towards adolescent survivors of brain injury

Background: The hidden nature of brain injury means that it is often difficult for people to understand the sometimes challenging behaviors that individuals exhibit. The misattribution of these behaviors may lead to a lack of consideration and public censure if the individual is seen as simply misbehaving.

Objective: The aim of this study was to explore the impact of visual cues indicating the presence or absence of brain injury on prejudice, desire for social interaction, and causal attributions of nursing and computing science students.

Method: An independent-groups design was employed in this research, which recruited 190 first-year nursing students and 194 first-year computing science students from a major university in Belfast, UK. A short passage describing an adolescent’s behavior after a brain injury, together with one of three images portraying a young adolescent with a scar, a head dressing, or neither of these, was given to participants. They were then asked to answer questions relating to prejudice, social interaction, locus of control, and causal attributions. The attributional statements suggested that the character’s behavior could be the result of brain injury or adolescence.

Results: Analysis of variance demonstrated a statistically significant difference between the student groups, where nursing students (M = 45.17, SD = 4.69) desired more social interaction with the fictional adolescent than their computer science peers (M = 38.64, SD = 7.69). Further, analysis of variance showed a main effect of image on the attributional statement that described adolescence as a suitable explanation for the character’s lack of self-confidence.

Discussion: Attributions of brain injury were influenced by the presence of a visible but potentially specious indicator of injury. This suggests that survivors of brain injury who do not display any outward indicator may receive less care and face expectations to behave in a manner consistent with the norms of society. If their injury does not allow them to meet with these expectations, they may face public censure and discrimination.

Identification of benzimidazole resistance in Cladobotryum dendroides using a PCR-based method

Cladobotryutn dendroides, causal agent of cobweb disease of Agaricus bisporus, has become increasingly resistant to methylbenzimidazole carbamate (MBC) fungicides following the extensive use of MBC in cultivated mushroom production in Ireland. Of 38 isolates of C. dendroides obtained from Irish mushroom units, 34 were resistant to carbendazim. Primers based on conserved regions of the -tubulin gene were used to amplify and sequence a portion of the -tubulin gene in C. dendroides. A point mutation was detected at codon 50 in isolates resistant to benzimidazole fungicides, causing an amino acid substitution from tyrosine to cysteine. Species-specific PCR primers were designed to amplify the region of the -tubulin gene containing this substitution. The point mutation removed an Ace I restriction site in the -tubulin gene sequence of resistant isolates. Digestion of the PCR product with Ace I thus provides a rapid diagnostic test to differentiate sensitive and resistant isolates of this fungus. EMBL accession number: YI2256.

DNA variation in a 13-Mb region including the F9 gene: inferring the genealogical history and causal role of a hemophilia B mutation (IVS 5+13 A -> G)

About 5.5% of all UK hemophilia B patients have the base substitution IVS 5+13 A-->G as the only change in their factor (F)IX gene (F9). This generates a novel donor splice site which fits the consensus better than the normal intron 5 donor splice. Use of the novel splice site should result in a missense mutation followed by the abnormal addition of four amino acids to the patients' FIX. In order to explain the prevalence of this mutation, its genealogical history is examined. Analysis of restriction fragment length polymorphism in the 21 reference UK individuals (from different families) with the above mutation showed identical haplotypes in 19 while two differed from the rest and from each other. In order to investigate the history of the mutation and to verify that it had occurred independently more than once, the sequence variation in 1.5-kb segments scattered over a 13-Mb region including F9 was examined in 18 patients and 15 controls. This variation was then analyzed with a recently developed Bayesian approach that reconstructs the genealogy of the gene investigated while providing evidence of independent mutations that contribute disconnected branches to the genealogical tree. The method also provides minimum estimates of the age of the mutation inherited by the members of coherent trees. This revealed that 17 or 18 mutant genes descend from a founder who probably lived 450 years ago, while one patient carries an independent mutation. The independent recurrence of the IVS5+13 A-->G mutation strongly supports the conclusion that it is the cause of these patients' mild hemophilia.

Ensembling MML causal discovery

This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with any single learner in terms of learning accuracy and correctness; (2) Among all the ensemble causal induction algorithms examined, the weighted voting without seeding algorithm outperforms all the rest; (3) It seems that the ensembled CI algorithms could alleviate the local minimum problem. The only drawback of this method is that the time complexity is increased by δ times, where δ is the ensemble size.

An empirical study of encoding schemes and search strategies in discovering causal networks

Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithm proposed by Wallace et al. [10] has demonstrated its ability in discovering Linear Causal Models from data. To explore the ways to improve efficiency, this research examines three different encoding schemes and four searching strategies. The experimental results reveal that (1) specifying parents encoding method is the best among three encoding methods we examined; (2) In the discovery of linear causal models, local Hill climbing works very well compared to other more sophisticated methods, like Markov Chain Monte Carto (MCMC), Genetic Algorithm (GA) and Parallel MCMC searching.

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.

Discovering linear causal model from incomplete data

One common drawback in algorithms for learning Linear Causal Models is that they can not deal with incomplete data set. This is unfortunate since many real problems involve missing data or even hidden variable. In this paper, based on multiple imputation, we propose a three-step process to learn linear causal models from incomplete data set. Experimental results indicate that this algorithm is better than the single imputation method (EM algorithm) and the simple list deletion method, and for lower missing rate, this algorithm can even find models better than the results from the greedy learning algorithm MLGS working in a complete data set. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.

The discovery of generalized causal models with mixed variables using MML criterion

One major difficulty frustrating the application of linear causal models is that they are not easily adapted to cope with discrete data. This is unfortunate since most real problems involve both continuous and discrete variables. In this paper, we consider a class of graphical models which allow both continuous and discrete variables, and propose the parameter estimation method and a structure discovery algorithm based on Minimum Message Length and parameter estimation. Experimental results are given to demonstrate the potential for the application of this method.

Using causal mapping to find new avenue for organizational fit research

Although organizational fit is strongly linked to important psychological outcomes such as motivation, satisfaction, turnover and performance, there is still a lot of confusion about definitions and conceptualizations of the construct. One reason for this is that fit researchers have almost exclusively conducted theory-driven nomothetic studies that have utilized varying approaches to the term. In this paper, we call for exploratory research that listens to how workers construct their own sense of fit and suggest that researchers should adopt idiographic data gathering techniques, coupled with nomothetic analysis tools, to do so. To enable this, we explain how fit researchers might use causal maps and thereby develop a stronger understanding of organizational fit that is grounded in how people conceive it.

Realist synthesis : illustrating the method for implementation research

Background
Realist synthesis is an increasingly popular approach to the review and synthesis of evidence, which focuses on understanding the mechanisms by which an intervention works (or not). There are few published examples of realist synthesis. This paper therefore fills a gap by describing, in detail, the process used for a realist review and synthesis to answer the question 'what interventions and strategies are effective in enabling evidence-informed healthcare?' The strengths and challenges of conducting realist review are also considered.
Methods
The realist approach involves identifying underlying causal mechanisms and exploring how they work under what conditions. The stages of this review included: defining the scope of the review (concept mining and framework formulation); searching for and scrutinising the evidence; extracting and synthesising the evidence; and developing the narrative, including hypotheses.
Results
Based on key terms and concepts related to various interventions to promote evidenceinformed healthcare, we developed an outcome-focused theoretical framework. Questions were tailored for each of four theory/intervention areas within the theoretical framework and were used to guide development of a review and data extraction process. The search for literature within our first theory area, change agency, was executed and the screening procedure resulted in inclusion of 52 papers. Using the questions relevant to this theory area, data were extracted by one reviewer and validated by a second reviewer. Synthesis involved organisation of extracted data into evidence tables, theming and formulation of chains of inference, linking between the chains of inference, and hypothesis formulation. The narrative was developed around the hypotheses generated within the change agency theory area.
Conclusions
Realist synthesis lends itself to the review of complex interventions because it accounts for context as well as outcomes in the process of systematically and transparently synthesising relevant literature. While realist synthesis demands flexible thinking and the ability to deal with complexity, the rewards include the potential for more pragmatic conclusions than alternative approaches to systematic reviewing. A separate publication will report the findings of the review.

Controle alternativo de Colletotrichum acutatum agente causal da queda prematura dos frutos cítricos

A queda prematura dos frutos cítricos (QPFC), causada por Colletotrichum acutatum, dados os grandes prejuízos que têm causado aos produtores, constitui-se numa doença de grande importância econômica. O controle da doença é feito predominantemente mediante uso de fungicidas, que eleva o custo de produção e afeta negativamente o meio ambiente. Diante disso, este trabalho teve por objetivo buscar um método alternativo de controle da QPFC, mediante o uso de agentes de biocontrole ou de biofertilizantes. Diferentes concentrações de biofertilizantes (originários de duas fontes distintas e denominados de Bio1 e Bio 2); três isolados de Bacillus subtilis (ACB-69; 72 e 77) e três isolados de Trichoderma spp. (ACB-14; 37 e 39) foram testados, isoladamente ou em combinação, sob condições de laboratório, quanto à capacidade inibitória da germinação de conídios de C. acutatum. Estudaram-se, ainda, a produção de metabólitos termoestáveis por B. subtilis e o efeito sobre a germinação do patógeno. Quinze isolados de B. subtilis foram testados quanto à capacidade de prevenir a infecção por C. acutatum em flores destacadas de lima- ácida 'Tahiti' e, no campo, foram instalados dois experimentos, visando a testar ACBs e biofertilizantes no controle da doença. Verificou-se que o isolado ACB-72 (B. subtilis) e ACB-37 (T. pseudokoningii) foram os que mais inibiram a germinação do patógeno. Quanto à produção de metabólitos termoestáveis, ACB-69 e 77 foram os mais eficientes em produzir substâncias antifúngicas, e em quantidades suficientes para inibirem a germinação do patógeno. A mistura dos quatro isolados de Bacillus (ACBs: 69; 72; 77 e AP3) foi o que apresentou maior porcentagem de inibição (73%). Os biofertilizantes (Bio1 e Bio2), em concentrações acima de 10% e, quando em associação com isolados de Trichoderma spp., promoveram maiores inibições na germinação de C. acutatum. em testes com flores destacadas, verificou-se que, onde foram aplicados os ACBs 69; 76; 74 e 77, as porcentagens de pétalas sem sintomas de infecção por C. acutatum foram de 83; 92; 92 e 97%, respectivamente. Mediante avaliações a campo, verificou-se a potencialidade de B. subtilis e de biofertilizantes em controlar a doença.

Burkitt's lymphoma in Brazil: Strong association with Epstein-Barr virus

The incidence of Burkitt's lymphoma (BL) in Brazil is intermediate between the endemic form of equatorial Africa and the sporadic form in the United States, To characterize these lymphomas, we evaluated the clinical, morphologic, and immunohistochemical features of 24 Brazilian cases of BL, We also analyzed the cases for the presence of Epstein-Barr virus (EBV)-RNA using a highly sensitive and specific method of in situ hybridization, Most cases presented with involvement of intraabdominal organs, similar to the sporadic form of BL, EBV-RNA was detected in 17 of 24 cases (71%) in all or virtually all the tumor cells, This prevalence of EBV-positivity in our cases is intermediate between the endemic form of BL in equatorial Africa (100%) and the sporadic form in the United States (30%), These findings suggest that EBV plays an important role in the pathogenesis of BL in Brazil, This intermediate incidence of EBV infection may explain in part the differences of incidence of BL in different geographic regions.