Intermediate (field) (direct and general support) and depot level maintenance : generator set, diesel engine driven, tactical skid MTD., 200 KW, 3 phase, 4 wire, 120/208 and 240/416 volts : DoD model MEP009A, MEP108A, class utility, precise ... 6115-00-403-3762.

"20 October 1977."

Pushball game, class competition

On verso: Pushball contest; class games. University of Michigan News Service; Negative Number 3629. Photograph is in 1910 Ensian

UM Engineering Class of 1916, assembled outside

too large for a single scan, so 2 scans (one of right side, one of left) sharing the same housescan number and suffix 1of2 and 2of2. Also, one merged in photoshop, suffix "merged"

A new class of critical sets in Latin squares

Previously the process of finding critical sets in Latin squares has been inside cumbersome by the complexity and number of Latin trades that, must be constructed. In this paper we develop a theory of Latin trades that yields more transparent constructions. We use these Latin trades to find a new class of critical sets for Latin squares which are a product of the Latin square of order 2 with a. back circulant Latin square of odd order.

MHC class I bound to an immunodominant Theileria parva epitope demonstrates unconventional presentation to T cell receptors

T cell receptor (TCR) recognition of peptide-MHC class I (pMHC) complexes is a crucial event in the adaptive immune response to pathogens. Peptide epitopes often display a strong dominance hierarchy, resulting in focusing of the response on a limited number of the most dominant epitopes. Such T cell responses may be additionally restricted by particular MHC alleles in preference to others. We have studied this poorly understood phenomenon using Theileria parva, a protozoan parasite that causes an often fatal lymphoproliferative disease in cattle. Despite its antigenic complexity, CD8+ T cell responses induced by infection with the parasite show profound immunodominance, as exemplified by the Tp1(214-224) epitope presented by the common and functionally important MHC class I allele N*01301. We present a high-resolution crystal structure of this pMHC complex, demonstrating that the peptide is presented in a distinctive raised conformation. Functional studies using CD8+ T cell clones show that this impacts significantly on TCR recognition. The unconventional structure is generated by a hydrophobic ridge within the MHC peptide binding groove, found in a set of cattle MHC alleles. Extremely rare in all other species, this feature is seen in a small group of mouse MHC class I molecules. The data generated in this analysis contribute to our understanding of the structural basis for T cell-dependent immune responses, providing insight into what determines a highly immunogenic p-MHC complex, and hence can be of value in prediction of antigenic epitopes and vaccine design.

MHC class II binding prediction:a little help from a friend

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

Alzheimer's disease: size class frequency distribution of senile plaques: do they indicate when a brain tissue was affected?

The size class frequency distribution of a sample of senile plaques (SP) was determined in a total of 20 brain regions from 5 elderly cases of Alzheimer's disease (AD). The purpose of the study was to determine whether a comparison of the frequency distributions could be used to determine the chronology of SP development in the AD brain. SP from 10 microns to a maximum diameter of 160 microns were present in the tissue and the size class frequency distributions were positively skewed. The frequency distributions varied between brain regions in: (1) the size class containing the mode, (2) the degree of positive skew, and (3) the ratio of large to small SP. In most patients the ratio of large to small SP was higher in the hippocampus or adjacent gyrus compared with temporal, parietal and frontal neocortex. If the diameter of a SP reflects its age in the tissue than the data suggest that SP formed earlier either in the hippocampus or adjacent gyrus compared with the other neocortical tissues. However, this conclusion rests on a number of assumptions including: (1) that SP diameter is directly related to age, (2) that SP development occurs at similar rates in different brain regions and (3) that, once formed, SP are not removed from the tissue by astrocytes.

A class of perfect fluids in general relativity

This thesis is concerned with exact solutions of Einstein's field equations of general relativity, in particular, when the source of the gravitational field is a perfect fluid with a purely electric Weyl tensor. General relativity, cosmology and computer algebra are discussed briefly. A mathematical introduction to Riemannian geometry and the tetrad formalism is then given. This is followed by a review of some previous results and known solutions concerning purely electric perfect fluids. In addition, some orthonormal and null tetrad equations of the Ricci and Bianchi identities are displayed in a form suitable for investigating these space-times. Conformally flat perfect fluids are characterised by the vanishing of the Weyl tensor and form a sub-class of the purely electric fields in which all solutions are known (Stephani 1967). The number of Killing vectors in these space-times is investigated and results presented for the non-expanding space-times. The existence of stationary fields that may also admit 0, 1 or 3 spacelike Killing vectors is demonstrated. Shear-free fluids in the class under consideration are shown to be either non-expanding or irrotational (Collins 1984) using both orthonormal and null tetrads. A discrepancy between Collins (1984) and Wolf (1986) is resolved by explicitly solving the field equations to prove that the only purely electric, shear-free, geodesic but rotating perfect fluid is the Godel (1949) solution. The irrotational fluids with shear are then studied and solutions due to Szafron (1977) and Allnutt (1982) are characterised. The metric is simplified in several cases where new solutions may be found. The geodesic space-times in this class and all Bianchi type 1 perfect fluid metrics are shown to have a metric expressible in a diagonal form. The position of spherically symmetric and Bianchi type 1 space-times in relation to the general case is also illustrated.

Secretin family (Class B) G protein-coupled receptors - from molecular to clinical perspectives

Family B G protein-coupled receptors represent an important but under-researched group of receptors. This edition of the British Journal of Pharmacology considers the roles and pharmacology of a number of these receptors. Whilst common themes emerge, it is clear that more work is needed to understand the details of each receptor in order to properly exploit them therapeutically.

Protein-protein interactions classification from text via local learning with class priors

Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semisupervised learning algorithms such as SVMand it also performs better than local learning without incorporating class priors.

Student morningness-eveningness type and performance:does class timing matter?

Circadian rhythms have often been linked to people’s performance outcomes, although this link has not been examined within the context of University students. We therefore sought to test whether students’ perceptions of their morning-evening (ME) type had an influence on their performance on modules. We tested this hypothesis using students from a number of modules at two UK Universities. Results indicated that, contrary to our hypothesis, the further the discrepancy between a student’s ME type and the teaching time of the class, the better the student’s performance. These results have implications for teaching as student ME type could be taken into account for timetabling especially if modules need to be taught multiple times. We also provide implications for those seeking to measure ME, as our results are consistent with a 5-item ME scale, a 3-item ME scale, and a single-item ME scale.

Toward bacterial protein sub-cellular location prediction:single-class discrimminant models for all gram- and gram+ compartments

Based on Bayesian Networks, methods were created that address protein sequence-based bacterial subcellular location prediction. Distinct predictive algorithms for the eight bacterial subcellular locations were created. Several variant methods were explored. These variations included differences in the number of residues considered within the query sequence - which ranged from the N-terminal 10 residues to the whole sequence - and residue representation - which took the form of amino acid composition, percentage amino acid composition, or normalised amino acid composition. The accuracies of the best performing networks were then compared to PSORTB. All individual location methods outperform PSORTB except for the Gram+ cytoplasmic protein predictor, for which accuracies were essentially equal, and for outer membrane protein prediction, where PSORTB outperforms the binary predictor. The method described here is an important new approach to method development for subcellular location prediction. It is also a new, potentially valuable tool for candidate subunit vaccine selection.

Predicting class II MHC-Peptide binding:a kernel based approach using similarity scores

Background - Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. Results - The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database. Conclusion - The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Class I T-cell epitope prediction:improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.

On a Class of Vertex Folkman Numbers

Let a1 , . . . , ar, be positive integers, i=1 ... r, m = ∑(ai − 1) + 1 and p = max{a1 , . . . , ar }. For a graph G the symbol G → (a1 , . . . , ar ) means that in every r-coloring of the vertices of G there exists a monochromatic ai -clique of color i for some i ∈ {1, . . . , r}. In this paper we consider the vertex Folkman numbers F (a1 , . . . , ar ; m − 1) = min |V (G)| : G → (a1 , . . . , ar ) and Km−1 ⊂ G} We prove that F (a1 , . . . , ar ; m − 1) = m + 6, if p = 3 and m ≧ 6 (Theorem 3) and F (a1 , . . . , ar ; m − 1) = m + 7, if p = 4 and m ≧ 6 (Theorem 4).