Subclasses Of Order Research Articles

Contemporary group alternatives for genetic evaluation of milk yield in small populations of dairy cattle

In the present study, different random regression models, focussed on the nature of the effect of the contemporary group, fixed or random, were compared for genetic evaluation of test-day milk yield in dairy Gir breed, whose herds are characterised by relatively reduced numbers of cows. Cows were assigned to the same contemporary group if they were tested in the same herd, year and month. In one of the evaluated models, the contemporary group was investigated as a fixed effect and a clustering procedure was adopted to group herd-year subclasses in order to, at the end of the process, all contemporary groups contained at least three cows. The models were compared by the quality of fit, the accuracy of the predicted breeding values and their estimates of genetic parameters. The clustering procedure did not significantly improve the accuracy of predicted breeding values. Moreover, the fit to the data for this model was negatively affected. Therefore, this strategy should not be further implemented. The model including the herd-calving period effect and contemporary group treated as random effect showed similar characteristics to its equivalent, where the contemporary group was modelled as a fixed effect. However, the fit to the data for this model was slightly worse. Thus, the results suggest a random regression model including the herd-calving period effect and the fixed effect of contemporary group for the genetic evaluations of production traits in dairy Gir cattle. These findings could be extended to small dairy cattle populations whose herds are small-sized.

MR contingency supplement prior for joint estimation of activity and attenuation in non‐time‐of‐flight positron emission tomography/MR

Maximum likelihood reconstruction of activity and attenuation (MLAA) from emission data only suffered from the inherent cross-talk between the estimated attenuation and activity distributions. The authors proposed an improved MLAA algorithm by utilising tissue prior atlas (TPA) and a Gibbs prior as prior knowledge. TPA determines the plausible region for each of the typical attenuation coefficients; hence, it imposes statistical condition as a supplement for the exclusive magnetic resonance (MR) information on the reconstruction process of attenuation map. Therefore, along with the soft tissue distribution provided by the segmentation of MR images, an air mask and a bone probability map breakdown the MR low-signal class into four subclasses in order to favour recognition of air and bone. Estimations on attenuation coefficients are realised as a mixture of pseudo-Gaussian distributions. The proposed algorithm is evaluated using the simulated 3D emission data. The proposed MLAA-TPA algorithm is compared with the MR-MLAA algorithm proposed by Heuser et al. Their results demonstrate that the MR-MLAA algorithm performance depends heavily on the MR segmentation accuracy well handled by MLAA-TPA. The quantification results illustrated that the MLAA-TPA outperformed the MR-MLAA algorithm owing to reduction of misclassification and more precise tissue detection.

Novel human IgG1 and IgG4 Fc-engineered antibodies with completely abolished immune effector functions.

Recombinant human IgG antibodies (hIgGs) completely devoid of binding to Fcγ receptors (FcγRs) and complement protein C1q, and thus with abolished immune effector functions, are of use for various therapeutic applications in order to reduce FcγR activation and Fc-mediated toxicity. Fc engineering approaches described to date only partially achieve this goal or employ a large number of mutations, which may increase the risk of anti-drug antibody generation. We describe here two new, engineered hIgG Fc domains, hIgG1-P329G LALA and hIgG4-P329G SPLE, with completely abolished FcγR and C1q interactions, containing a limited number of mutations and with unaffected FcRn interactions and Fc stability. Both 'effector-silent' Fc variants are based on a novel Fc mutation, P329G that disrupts the formation of a proline sandwich motif with the FcγRs. As this motif is present in the interface of all IgG Fc/FcγR complexes, its disruption can be applied to all human and most of the other mammalian IgG subclasses in order to create effector silent IgG molecules.

On the Canonical Representation of Order 3 Discrete Phase Type Distributions

In spite of the fact that discrete phase type (DPH) distributions are used almost as often as continuous phase type (CPH) distributions canonical representation is not available for general (cyclic) order 3 DPH distributions yet.In this paper we investigate the canonical representation of DPH distributions of order 3. During the course of this investigation we find that the problem of canonical representation of order 3 DPH distributions is far more complex than the one of order 3 CPH distribution. As a result we needed to distinguish 8 different subclasses of order 3 DPH distributions, while it was enough to distinguish 3 subclasses of order 3 CPH distributions for their canonical representation. Additionally, we were not able to prove all subclasses of DPH distributions with the relatively simple methodology which was sufficient for the canonical representation of order 3 DPH distributions.

Immunological and Immunogenetic Changes in Children with Autistic Disorder in Republic of Macedonia

AIM: The aim of the study was to present our results about immunological and immunogenetic investigations in children with autistic disorder in Republic of Macedonia.METHODS: Infantile autism was diagnosed by DSM-IV and ICD-10 criteria. Plasma samples were collected from 35 autistic subjects, and their 21 siblings (biological brothers and sisters) who served as healthy controls. Plasma samples were separated by centrifugation and stored at –20°C until the determination. Plasma immunoglobulin classes (IgM, IgA, IgG) and subclasses (IgG1, IgG2, IgG3, IgG4) were determined using a nephelometer Analyzer. Specific IgA and IgG antibodies against some food allergens, as well as total IgE have been determined with automated immunofluorescent device with solid phase - UniCAP 100 (AmershamBiosciences). HLA DNA typing of class I genes was performed using a Reverse Line Strip method (RLS), and the Sequencing Based Typing method (SBT) was used for typing of class II genes.RESULTS: Children with autism had significantly higher plasma concentrations of IgG4 (p<0.001) compared to their siblings (healthy brothers or sisters). IgE specific antibodies, as well as plasma concentration of total IgE were statistically significant higher in plasma of participants with autism. Multiple comparisons for the IgA variable have shown statistically significant differences between children with autistic disorder from the fathers and mothers (p < 0.001), and healthy brothers and sisters from the fathers and mothers (p < 0.001). Our results showed significantly increased frequencies of HLA-C*03 (OR = 2.74*; c2= 4.68; p = 0.03), and HLA-DRB1*01 (OR = 3.10*; c2= 6.26; p = 0.012) alleles in autistic patients when compared to the controls.CONCLUSION: Children with autism have increased plasma concentration of immunoglobulines. Our results demonstrate an association of HLA-C*03 and HLA-DRB1*01 alleles with Macedonian autistic patients. Comparison between healthy children and children with autistic disorder from the same family should be tested for immunoglobulin classes and subclasses in order to avoid differences between generations.

Context-sensitive intra-class clustering

This paper describes a new semi-supervised learning algorithm for intra-class clustering (ICC). ICC partitions each class into sub-classes in order to minimize overlap across clusters from different classes. This is achieved by allowing partitioning of a certain class to be assisted by data points from other classes in a context-dependent fashion. The result is that overlap across sub-classes (both within- and across class) is greatly reduced. ICC is particularly useful when combined with algorithms that assume that each class has a unimodal Gaussian distribution (e.g., Linear Discriminant Analysis (LDA), quadratic classifiers), an assumption that is not always true in many real-world situations. ICC can help partition non-Gaussian, multimodal distributions to overcome such a problem. In this sense, ICC works as a preprocessor. Experiments with our ICC algorithm on synthetic data sets and real-world data sets indicated that it can significantly improve the performance of LDA and quadratic classifiers. We expect our approach to be applicable to a broader class of pattern recognition problems where class-conditional densities are significantly non-Gaussian or multi-modal.

Significant IgG subclass heterogeneity in HLA-specific antibodies: Implications for pathogenicity, prognosis, and the rejection response

IgG subclasses differ in their ability to fix complement and bind Fc receptors. This study describes a detailed analysis of the distribution of HLA-specific IgG subclasses in order to define how this varies in sensitised waiting-list patients. We found significant variation in the level, presence and combinations of each HLA-specific IgG subclass between and within individuals and this is influenced by the type of sensitising event. Graft failure in particular provokes higher levels of IgG1 (vs transfusion, p=0.071 and pregnancy, p=0.042), IgG2 (vs transfusion, p=0.001 and pregnancy, p=0.016), and IgG4 (vs transfusion, p=0.052). Both graft failure and pregnancy tend to stimulate multiple IgG subclass responses against HLA, whereas transfusion stimulated antibodies are dominated by responses limited to IgG1 (p=0.033) and have a low incidence of IgG4 (p=0.046). In marked contrast, IgG4 characterised nearly all HLA DQ-specific antibodies stimulated by graft rejection (p=0.006). Such widely varying IgG subclass heterogeneity is likely to be due to underlying immunological processes dependent on the route of sensitisation. This diversity, which implies functional variation, may help explain why HLA-specific antibodies are an obstacle to transplantation in some circumstances but not others. The subclass association with rejection has potential as a biomarker for chronic rejection.

Family analysis of immunoglobulin classes and subclasses in children with autistic disorder.

Autistic disorder is a severe neurodevelopment disorder characterized by a triad of impairments in reciprocal social interaction, verbal and nonverbal communication, and a pattern of repetitive stereotyped activities, behaviours and interests. There are strong lines of evidence to suggest that the immune system plays an important role in the pathogenesis of autistic disorder. The aim of this study was to analyze quantitative plasma concentration of immunoglobulin classes, and subclasses in autistic patients and their families. The investigation was performed retrospectively in 50 persons with autistic disorder in the Republic of Macedonia. Infantile autistic disorder was diagnosed by DSM-IV and ICD-10 criteria. Plasma immunoglobulin classes (IgM, IgA, and IgG) and subclasses (IgG1, IgG2, IgG3, and IgG4) were determined using Nephelometer Analyzer BN-100. Multiple comparisons for the IgA variable have shown statistically significant differences between three pairs: male autistic from the fathers (p = 0,001), female autistic from the mothers (p = 0,008), as well as healthy sisters from the fathers (p = 0,011). Statistically significant differences found between three groups regarding autistic disorder (person with autistic disorder, father/mother of a person with autistic disorder, and brother/sister) independent of sex belongs to IgA, IgG2, and IgG3 variables. Multiple comparisons for the IgA variable have shown statistically significant differences between children with autistic disorder from the fathers and mothers (p < 0,001), and healthy brothers and sisters from the fathers and mothers (p < 0,001). Comparison between healthy children and children with autistic disorder from the same family should be tested for immunoglobulin classes and subclasses in order to avoid differences between generations.

Unsupervised named-entity extraction from the Web: An experimental study

The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KnowItAll's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KnowItAll extracted over 50,000 class instances, but suggested a challenge: How can we improve KnowItAll's recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., “chemist” and “biologist” are identified as sub-classes of “scientist”). List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KnowItAll's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KnowItAll a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.

Implicit parallel peer methods for stiff initial value problems

A class of implicit two-step integration methods is introduced having s stages which may be computed in parallel. Since all stage solutions are approximations with equal accuracy and stability properties these methods were attributed as ‘peer’ methods. Using a special result on Vandermonde matrices we identify one subclass of order s − 1 which is zero stable for general stepsize sequences. A further analysis for singularly perturbed problems shows that no order reduction occurs and the accuracy is essentially determined by the regularity of the smooth component. These results are backed by several numerical examples even including one differential algebraic problem.

Natural antibodies in paracoccidioidomycosis

Recent attention has been focused on the natural antibodies as a component of natural immunity and as integral part of the idiotypic network. However, their functional role in different infections has rarely been studied. This work was undertaken to investigate the presence of natural antibodies in paracoccidioidomycosis (PCM). In addition, we analyzed anti-P. brasiliensis antibodies and their distribution in IgG subclasses in order to acquire better knowledge about the humoral immune response in this mycosis. Our findings show that the natural antibody response is not very much increased in PCM when compared with other parasite infections and this response is restricted to a few specificities, suggesting that P. brasiliensis moderately triggers CD5+ B cells. The anti-actin antibody was the main antibody specificity found in PCM. Specific antibodies to P. brasiliensis were mainly found in the IgG1 subclass in chronic patients of PCM.

The central engine of Seyfert 2 galaxies with and without polarized broad lines

Abstract We study the hard X-ray emission of two subsamples of Seyfert 2 galaxies with and without Polarized Broad Lines (PBL hereafter). In the hard X-ray domain, absorption effects do not significantly modify the intrinsic emission allowing us a direct access to the central engine. The purpose of this study is to compare the primary emission of these two subclasses in order to investigate the nature of their central engine and to test unified models according to which they both have a hidden Sy 1 nucleus. We compute the average hard X-ray spectra of Seyfert 2 galaxies with and without PBL observed with BeppoSAX/PDS (15-136 keV). The two spectra have a common general behavior at first sight but investigating deeper we find differences in the intrinsic properties of the two categories of Seyfert 2 galaxies. Sy 2 with PBL have physical conditions close to that of Sy 1 galaxies whereas Sy 2 without PBL differ substantially, suggesting that they may have a particular place in the scheme of Seyfert galaxies.

Investigating the central engine of Seyfert 2 galaxieswithandwithoutPolarized Broad Lines

We study the hard X-ray emission of two samples of Seyfert 2 galaxies with and without Polarized Broad Lines (PBL). In the hard X-ray domain, absorption effects do not significantly modify the intrinsic emission allowing us a direct access to the central engine. The purpose of this study is to compare the primary emission of the two Seyfert 2 subclasses in order to investigate the nature of their central engine and to test unified models according to which they both have a hidden Seyfert 1 nucleus. We compute the average hard X-ray spectra of Seyfert 2 galaxies with and without PBL observed with BeppoSAX/PDS (15-136 keV). The two spectra have a common general behavior at first sight, but investigating deeper we find differences in the intrinsic properties of the two categories of Seyfert 2 galaxies. Sy 2 with polarized broad lines have physical conditions close to those of Sy 1 galaxies whereas Sy 2 without PBL differ substantially, suggesting that they may have a particular place in the scheme of Seyfert galaxies.

On model and filter sensitivity

It is shown that there is a stochastic version of the fact that the standard IIR, AR, ARX and ARMAX model and filter parametrizations suffer from a worst-case exponential growth of the required relative coefficient accuracy to preserve model and filter stability. This means that, inevitably, such standard model and filter parametrizations become infeasible to use for a subclass of order n filters and models when n is not small.