Divergent Class Research Articles

Microbial Danger Signals Control Transcriptional Induction of Distinct MHC Class I L Lineage Genes in Atlantic Salmon.

Antigen processing and presentation by major histocompatibility complex (MHC) molecules is a cornerstone in vertebrate immunity. Like mammals, teleosts possess both classical MHC class I and multiple families of divergent MHC class I genes. However, while certain mammalian MHC class I-like molecules have proven to be integral in immune regulation against a broad array of pathogens, the biological relevance of the different MHC class I lineages in fish remains elusive. This work focuses on MHC class I L lineage genes and reveals unique regulatory patterns of six genes (Sasa-lia, Sasa-lda, Sasa-lca, Sasa-lga, Sasa-lha, and Sasa-lfa) in antimicrobial immunity of Atlantic salmon (Salmo salar L.). Using two separate in vivo challenge models with different kinetics and immune pathologies combined with in vitro stimulation using viral and bacterial TLR ligands, we show that de novo synthesis of different L lineage genes is distinctly regulated in response to various microbial stimuli. Prior to the onset of classical MHC class I gene expression, lia was rapidly and systemically induced in vivo by the single-stranded (ss) RNA virus salmonid alpha virus 3 (SAV3) but not in response to the intracellular bacterium Piscirickettsia salmonis. In contrast, lga expression was upregulated in response to both viral and bacterial stimuli. A role for distinct MHC class I L-lineage genes in anti-microbial immunity in salmon was further substantiated by a marked upregulation of lia and lga gene expression in response to type I IFNa stimulation in vitro. Comparably, lha showed no transcriptional induction in response to IFNa stimulation but was strongly induced in response to a variety of viral and bacterial TLR ligands. In sharp contrast, lda showed no response to viral or bacterial challenge. Similarly, induction of lca, which is predominantly expressed in primary and secondary lymphoid tissues, was marginal with the exception of a strong and transient upregulation in pancreas following SAV3 challenge Together, these findings suggest that certain Atlantic salmon MHC class I L lineage genes play important and divergent roles in early anti-microbial response and that their regulation, in response to different activation signals, represents a system for selectively promoting the expression of distinct non-classical MHC class I genes in response to different types of immune challenges.

Gaussian Mixture Model Based Hierarchical Clustering in Prediction of Autism Spectrum Disorder

Autism is one of the most complex and divergent class disorders which accompany various lacking in symptoms needed for classification, societal interaction, abridged verbal communication, and monotonous behavior. Timely and proper diagnosis of Autism Spectrum Disorder can ensure the offering of medical treatment and guidance to get cure. In this paper, Gaussian Mixture Model based Hierarchical Clustering is proposed for efficiently predicting the Autism Spectrum Disorder. Also, Flexible splitting concept was proposed for hierarchical clustering in order to increase the quality of guessing and classification accuracy. The proposed algorithm is validated to check the performance against the existing method. The results shows that the proposed algorithm outperforms the existing algorithm in terms of classification accuracy.

Unitarity of the infinite-volume three-particle scattering amplitude arising from a finite-volume formalism

In a previous publication, two of us derived a relation between the scattering amplitude of three identical bosons, $\mathcal M_3$, and a real function referred to as the {divergence-free} K matrix and denoted $\mathcal K_{\text{df},3}$. The result arose in the context of a relation between finite-volume energies and $\mathcal K_{\text{df},3}$, derived to all orders in the perturbative expansion of a generic low-energy effective field theory. In this work we set aside the role of the finite volume and focus on the infinite-volume relation between $\mathcal K_{\text{df},3}$ and $\mathcal M_3$. We show that, for any real choice of $\mathcal K_{\text{df},3}$, $\mathcal M_3$ satisfies the three-particle unitarity constraint to all orders. Given that $\mathcal K_{\text{df},3}$ is also free of a class of kinematic divergences, the function may provide a useful tool for parametrizing three-body scattering data. Applications include the phenomenological analysis of experimental data (where the connection to the finite volume is irrelevant) as well as calculations in lattice quantum chromodynamics (where the volume plays a key role).

The Kullback-Leibler Divergence Class in Decoding the Chest Sound Pattern

Kullback-Leibler Divergence Class or relative entropy is a special case of broader divergence. It represents a calculation of how one probability distribution diverges from another one, expected probability distribution. Kullback-Leibler divergence has a lot of real-time applications. Even though there is a good progress in the field of medicine, there is a need for a statistical analysis for supporting the emerging requirements. In this paper, we are discussing the application of Kullback-Leibler divergence as a possible method for predicting hypertension by using chest sound recordings and machine learning algorithms. It would have a major out-reached benefit in emergency health care systems. Decoding the chest sound pattern has a wide degree in distinguishing different irregularities and wellbeing states of a person in the medicinal field. The proposed method for the estimation of blood pressure is chest sound analysis using a method that creates a record of sounds delivered by the contracting heart, coming about because of valves and related vessels vibration and analyzing it with the help of Kullback-Leibler divergence and machine algorithm. An analysis using the Kullback-Leibler divergence method will allow finding the difference in chest sound recordings which can be evaluated by a machine learning algorithm. The report also proposes the method for analysis of chest sound recordings in Kullback-Leibler divergence class.

A new class of entropic information measures, formal group theory and information geometry.

In this work, we study generalized entropies and information geometry in a group-theoretical framework. We explore the conditions that ensure the existence of some natural properties and at the same time of a group-theoretical structure for a large class of entropies. In addition, a method for defining new entropies, using previously known ones with some desired group-theoretical properties is proposed. In the second part of this work, the information geometrical counterpart of the previous construction is examined and a general class of divergences are proposed and studied. Finally, a method of constructing new divergences from known ones is discussed; in particular, some results concerning the Riemannian structure associated with the class of divergences under investigation are formulated.

Power and level robustness of a test for composite hypotheses under independent non-homogeneous data

We consider a class of density power divergence based tests for composite hypotheses under non-homogeneous data. This note provides a rigorous derivation of the power and level influence functions to theoretically justify their robustness with applications to fixed-carrier linear regressions.

Parent-Adolescent Concordance in Borderline Pathology and why it Matters.

The aims of the current study were to: 1) identify patterns of agreement between parent-adolescent dyads on reports of adolescent borderline personality features utilizing latent class analysis (LCA) and 2) examine the clinical implications of class membership for indices of psychiatric severity and internal psychological resources. The sample included 643 adolescent inpatients. Borderline personality features were assessed by both adolescents and parents using the Borderline Personality Features Scale - Child (BPFS-C; Crick et al. 2005) and Borderline Personality Features Scale - Parent (BPFS-P; Sharp et al. 2010), respectively. Following recommended statistical approaches for evaluating rater concordance, LCA was utilized to identify distinct classes of parent-adolescent dyads based on concordance/discrepancy in BPFS reports. The subsequent classes were then related to outcome measures of psychiatric severity and internal psychological resources (emotion regulation and experiential acceptance). LCA identified 3 classes of parent-adolescent dyads: 2 convergent classes demonstrating BPFS-P and BPFS-C agreement at a moderate and high level and a divergent class consisting of dyads reporting clinically significant scores on the BPFS-P but clinically negligible BPFS-C scores. Both convergent classes evidenced higher rates of psychiatric severity and lower access to internal resources. The current study is the first to use LCA to examine the relation between informant concordance on reports of DSM-based adolescent borderline pathology in a clinical sample. The significance of the discrepancies within and between classes is discussed with relation to psychosocial outcomes, the diagnosis of borderline personality disorder and implications for what it means when parents and adolescents disagree.

Information Geometry for Covariance Estimation in Heterogeneous Clutter with Total Bregman Divergence.

This paper presents a covariance matrix estimation method based on information geometry in a heterogeneous clutter. In particular, the problem of covariance estimation is reformulated as the computation of geometric median for covariance matrices estimated by the secondary data set. A new class of total Bregman divergence is presented on the Riemanian manifold of Hermitian positive-definite (HPD) matrix, which is the foundation of information geometry. On the basis of this divergence, total Bregman divergence medians are derived instead of the sample covariance matrix (SCM) of the secondary data. Unlike the SCM, resorting to the knowledge of statistical characteristics of the sample data, the geometric structure of matrix space is considered in our proposed estimators, and then the performance can be improved in a heterogeneous clutter. At the analysis stage, numerical results are given to validate the detection performance of an adaptive normalized matched filter with our estimator compared with existing alternatives.

Mobility closure in the upper class: assessing time and forms of capital.

Most understandings of the ways classes become social groupings centre on processes of mobility closure whereby mutual appreciation and recognition within classes arise from homogenous experiences over time. The mapping of such structured biographies, however, remains understudied. This paper explores intra- and intergenerational mobility patterns in the upper strata of the Norwegian class structure and aims to include temporal processes and multiple forms of capital in the quantification of class trajectories. By combining multiple correspondence analysis and social sequence analysis, two important but often neglected aspects of recruitment to the upper class are emphasized: first, by introducing multiple forms of capital, different ways of maintaining mobility closure are demonstrated; second, different pathways to power are highlighted by distinguishing between divergent class careers. A key aim of the analysis is to explore internal divisions within the upper class in forms of parental capital (an 'origin space') and link these divisions to a typology of 'destination careers' in adulthood. The analysis suggests that individuals from modest origins are more likely to have careers that feature a biographically late arrival and/or short-term affiliations to upper-class positions whereas individuals from families rich in capital are more likely to have stable careers in the upper-class fractions from which they originate. The analysis thus reveals important divisions in the trajectories of Norwegians who reach the upper class; not only are there differences in their upbringing in terms of the availability of different amounts and types of capital but such divisions also seem linked to their own class careers later in adulthood.

A Criterion for Local Model Selection

In this paper, we introduce a class of local divergences between two probability distributions and illustrate its usefulness in model selection. Explicit expressions of the proposed local divergences are derived when the underlying distributions are members of the exponential family of distributions or they are described by multivariate normal models. In addition, a local model selection criterion, termed the local divergence information criterion (LDiv.IC), is proposed. Simulations and applications are presented in order to study and exemplify the performance of the proposed criterion.

Heterosis in relation to genetic diversity in pearl millet (Pennisetum glaucum L.)

In the advent of failure of some transgenics at the field level and concerns due to biosafety and bioethical issues, there much thrust on natural plant genetic diversity as a means of adaptive value. Plant breeding basing on natural genetic diversity has gained momentum along with marker assisted breeding technologies for increased production and sustenance to various kinds of stresses. In the present investigation, divergence classification (DC) – a method devised by Arunachalam and Bandyopadhyay (1984) were used to classify 28 Pearl millet genotypes to draw a limit of parental diversity in expressing maximum heterosis. Mahalanobis D2 (D2 Values) statistic was used to measure the genetic diversity and then the genotypes were classified in to four divergence classes based on mean and standard deviation of all D2 values. According to divergence classification DCI involved most distantly and DCIV the most closely related parents where as DCII and DCIII involved the medium divergent parents. A scoring system was adopted to work out the relative order of importance of the divergence classes. The overall scores for each divergence classes based on the proportion of crosses (q) showing significant heterosis (heterosis over better parent) in desired direction and mean (y) of such crosses for ten characters were carried out to rank the divergence classes. According to the scoring system, the most desirable class would be with the lowest total score. Results clearly showed the superiority of class DCIII followed by DCII, as both the classes received low overall score and maximum number of heterotic cross combinations. In conclusion it can be said that divergence classification appears to be effective in clubbing the pearl millet genotypes for parental diversity and suggested that pearl millet parents with intermediate diversity would be used to produce heterotic cross combination.

Geometric target detection based on total Bregman divergence

This paper develops a geometric detection approach based upon the total Bregman divergence on the Riemannian manifold of Hermitian Positive-Definite (HPD) matrices to realize target detection in a clutter. First of all, the radar received clutter data in each range cell in one coherent processing interval is modeled and mapped into an HPD matrix space, which can be described as a complex Riemannian manifold. Each point of this manifold is an HPD matrix. Then, a class of total Bregman divergences are presented to measure the closeness between HPD matrices. Based on these divergences, the medians for a finite collection of HPD matrices are derived. Furthermore, the three divergences, namely the total square loss, the total log-determinant divergence, and the total von Neumann divergence are deduced, and their corresponding geometric detection methods are designed. The principle of detection is that if a location has enough dissimilarity from the median estimated by its neighboring locations, targets are supposed to appear at this location. Numerical experiments and real clutter data are given to demonstrate the effectiveness of the proposed geometric detection methods.

Asymptotic minimum scoring rule prediction

Most of the methods nowadays employed in forecast problems are based on scoring rules. There is a divergence function associated to each scoring rule, that can be used as a measure of discrepancy between probability distributions. This approach is commonly used in the literature for comparing two competing predictive distributions on the basis of their relative expected divergence from the true distribution. In this paper we focus on the use of scoring rules as a tool for finding predictive distributions for an unknown of interest. The proposed predictive distributions are asymptotic modifications of the estimative solutions, obtained by minimizing the expected divergence related to a general scoring rule. The asymptotic properties of such predictive distributions are strictly related to the geometry induced by the considered divergence on a regular parametric model. In particular, the existence of a global optimal predictive distribution is guaranteed for invariant divergences, whose local behaviour is similar to well known $\alpha $-divergences. We show that a wide class of divergences obtained from weighted scoring rules share invariance properties with $\alpha $-divergences. For weighted scoring rules it is thus possible to obtain a global solution to the prediction problem. Unfortunately, the divergences associated to many widely used scoring rules are not invariant. Still for these cases we provide a locally optimal predictive distribution, within a specified parametric model.

The Kullback-Leibler Divergence Used in Machine Learning Algorithms for Health Care Applications and Hypertension Prediction: A Literature Review

Kullback-Leibler divergence class or relative entropy is an exceptional instance of a more extensive divergence. It is an estimation of how a particular dissemination wanders from another, normal likelihood appropriation. Kullback-Leibler divergence has a considerable measure of ongoing applications. Despite the fact that there is an advance in the drug field, it still requires a measurable examination for supporting developing prerequisites. This paper discusses the use of Kullback-Leibler divergence as a conceivable technique to foresee hypertension, utilizing chest sound accounts and machine-learning calculations. It would have a unique, elevated advantage in the crisis medical services framework. Interpreting the chest sound example gives a wide and varying degree of recognition concerning different abnormalities and prosperity conditions of the restorative field. The proposed technique to estimate circulatory strain is chest sound examination, using a strategy that makes a record of sounds conveyed by the contracting heart, coming to fruition in view of valves and related vessel vibration, finally investigating it with the assistance of Kullback-Leibler divergence and machine calculation. An investigation employing the Kullback-Leibler divergence strategy will permit finding the distinction in chest sound chronicles, which can be assessed by a machine-learning calculation. Likewise, the report proposes the strategy for examining the chest sound accounts in Kullback-Leibler divergence class.

Genetic Variability, Heritability and Expected Genetic Advance as Indices for Selection in Soybean [<i>Glycine max</i> (L.) Merrill] Varieties

Genetic variability, heritability and genetic advance under selection studies were conducted at Assosa on 49 soybean genotypes. A field study laid out in 7x7 simple lattice design with two replications at Assosa Agricultural Research Center with the objective of estimating genetic variability, heritability, expected genetic advance, and to estimate genetic divergence, thereby, to cluster the test genotypes in to genetically divergent classes. The result of this study indicated variations for all the traits evaluated. The highest heritability value was recorded for days to 50% flowering followed by days to maturity and days to pod setting. Wide range of mean values was observed in all the characters evaluated. This indicates that the characters can be improved through selection. Divergence analysis grouped the 49 soybean genotypes into three. The principal component analysis revealed that five principal components PC1 to PC5 with Eigen values 4.27, 2.53, 1.91, 1.28 and 1.08 respectively, have accounted for 73.81% of the total variation.

Genetic Variability, Heritability and Expected Genetic Advance in Soybean [<i>Glycine max</i> (L.) Merrill] Genotypes

Information on the extent and pattern of genetic variability, heritability, and genetic advance under selection are essential to design breeding strategies in the available germplasm of soybean and helps to identify elite genotypes that will be incorporated in to soybean crop improvement programs to address the growing demand of the crop in Ethiopia. Forty-nine soybean (Glycine max (L.) Merrill) genotypes were evaluated in a field study in 7x7 simple lattice design with two replications at Jimma Agricultural Research Center (JARC) with the objective of estimating genetic variability, heritability, expected genetic advance, and to estimate genetic divergence, thereby, to cluster the test genotypes in to genetically divergent classes. The result indicated substantial variations for all the traits evaluated. Analysis of variance revealed that there was statistically significant difference among the forty-nine genotypes for most of the traits studied except root volume and root dry weight. The highest heritability value was recorded for total nodules per plant followed by effective nodules per plant and plant height. Significant wide range of mean values was observed in all the characters evaluated. This indicates that the characters can be improved through selection. The Divergence analysis grouped the 49 soybean genotypes into five which shows crossing between genotypes which fall in to different classes would result in hybrid vigour. The principal component analysis revealed that 6 components have accounted for 79.90% of the total variation among the genotypes.

Testing Composite Hypothesis Based on the Density Power Divergence

In any parametric inference problem, the robustness of the procedure is a real concern. A procedure which retains a high degree of efficiency under the model and simultaneously provides stable inference under data contamination is preferable in any practical situation over another procedure which achieves its efficiency at the cost of robustness or vice versa. The density power divergence family of Basu et al. (Biometrika 85, 549–559 1998) provides a flexible class of divergences where the adjustment between efficiency and robustness is controlled by a single parameter β. In this paper we consider general tests of parametric hypotheses based on the density power divergence. We establish the asymptotic null distribution of the test statistic and explore its asymptotic power function. Numerical results illustrate the performance of the theory developed.

Dissecting the molecular assembly of the Toxoplasma gondii MyoA motility complex

Apicomplexan parasites such as Toxoplasma gondii rely on a unique form of locomotion known as gliding motility. Generating the mechanical forces to support motility are divergent class XIV myosins (MyoA) coordinated by accessory proteins known as light chains. Although the importance of the MyoA-light chain complex is well-established, the detailed mechanisms governing its assembly and regulation are relatively unknown. To establish a molecular blueprint of this dynamic complex, we first mapped the adjacent binding sites of light chains MLC1 and ELC1 on the MyoA neck (residues 775-818) using a combination of hydrogen-deuterium exchange mass spectrometry and isothermal titration calorimetry. We then determined the 1.85 Å resolution crystal structure of MLC1 in complex with its cognate MyoA peptide. Structural analysis revealed a bilobed architecture with MLC1 clamping tightly around the helical MyoA peptide, consistent with the stable 10 nm Kd measured by isothermal titration calorimetry. We next showed that coordination of calcium by an EF-hand in ELC1 and prebinding of MLC1 to the MyoA neck enhanced the affinity of ELC1 for the MyoA neck 7- and 8-fold, respectively. When combined, these factors enhanced ELC1 binding 49-fold (to a Kd of 12 nm). Using the full-length MyoA motor (residues 1-831), we then showed that, in addition to coordinating the neck region, ELC1 appears to engage the MyoA converter subdomain, which couples the motor domain to the neck. These data support an assembly model where staged binding events cooperate to yield high-affinity complexes that are able to maximize force transduction.

Emergence and loss of spliceosomal twin introns

BackgroundIn the primary transcript of nuclear genes, coding sequences—exons—usually alternate with non-coding sequences—introns. In the evolution of spliceosomal intron–exon structure, extant intron positions can be abandoned and new intron positions can be occupied. Spliceosomal twin introns (“stwintrons”) are unconventional intervening sequences where a standard “internal” intron interrupts a canonical splicing motif of a second, “external” intron. The availability of genome sequences of more than a thousand species of fungi provides a unique opportunity to study spliceosomal intron evolution throughout a whole kingdom by means of molecular phylogenetics.ResultsA new stwintron was encountered in Aspergillus nidulans and Aspergillus niger. It is present across three classes of Leotiomyceta in the transcript of a well-conserved gene encoding a putative lipase (lipS). It occupies the same position as a standard intron in the orthologue gene in species of the early divergent classes of the Pezizomycetes and the Orbiliomycetes, suggesting that an internal intron has appeared within a pre-extant intron. On the other hand, the stwintron has been lost from certain taxa in Leotiomycetes and Eurotiomycetes at several occasions, most likely by a mechanism involving reverse transcription and homologous recombination. Another ancient stwintron present across whole Pezizomycotina orders—in the transcript of the bifunctional biotin biosynthesis gene bioDA—occurs at the same position as a standard intron in many species of non-Dikarya. Nevertheless, also the bioDA stwintron has disappeared from certain lineages within the taxa where it occurs, i.e., Sordariomycetes and Botryosphaeriales. Intriguingly, only the internal intron was lost from the Sordariomycetes bioDA stwintron at all but one occasion, leaving a standard intron in the same position, while where the putative lipase stwintron was lost, no intronic sequences remain.ConclusionsMolecular phylogeny of the peptide product was used to monitor the existence and fate of a stwintron in the transcripts of two neatly defined fungal genes, encoding well conserved proteins. Both defining events—stwintron emergence and loss—can be explained with extant models for intron insertion and loss. We thus demonstrate that stwintrons can serve as model systems to study spliceosomal intron evolution.

MECHANISMS OF ENVIRONMENTAL COMPLIANCE OF DIFFERENT CATEGORIES OF THE POOR PEOPLE IN CAIRO - AN ECOLOGICAL ANALYSIS

This study drives at identifying the mechanisms of environmental convenience for divergent classes of Cairo city. The sample is applied on (150) male/female individuals using the Scale of Environmental Convenience as a means for collecting data to be applied on a sample of the poor (Monshaat Naser quarter – Roud El-Farag quarter – Al-Megawreen cemetery). The researcher uses the social survey method for deducing social reality and analyzes the problem ecologically and for giving a conception of quality of life. The researcher uses multiple theories such as (Psychological analysis of Freud – The behavioral school – The human school – the ecological system theory). The research comes to several results which most important are: there are significant statistical differences between the study sample (tomb’ residents/slum quarter) on Scale of Environmental Convenience. The study recommends giving more consideration and concern to that marginalized category in order to improve their conditions and overcome the diseases they are exposed to because of lack of cleanness, establishing as well some projects to overcome the unemployment problem and improve the condition.