Predictive text entry methods for mobile phones

This paper reports initial user tests comparing text entry methods, analysis of word clashes with dictionary based methods and keystroke level modelling of the different input methods

Validation of a bovine rectal palpation simulator for training veterinary students

No abstract available

Expression of nitric oxide synthase and transforming growth factor-beta in crush-injured tendon and synovium.

This study examined the expression of inducible nitric oxide synthase (iNOS) and transforming growth factor-beta (TGF-beta) in macrophage infiltrates within crush-injured digital flexor tendon and synovium of control rats and rats treated with N(G)-nitro-1-arginine methyl ester (L-NAME) (5 mg/kg). Release of TGF-beta from organ cultures of tendon, muscle, and synovium, and the effects of L-NAME treatment (in vitro and in vivo), on adhesion of peritoneal macrophages to epitenon monolayers were also investigated. The results showed that during normal tendon healing the levels of TGF-beta are high at first and gradually decrease after 3 weeks of injury to slightly above control uninjured levels. However, when L-NAME was administered at the time of injury, the macrophage infiltrates were expressing high levels of TGF-beta even at 5 weeks after the injury, with no evidence of reduction. In the standard injury, iNOS activity was greatest at the acute phase of the inflammatory response and then gradually returned to normal. Treatment with L-NAME, however, resulted in inhibition of iNOS activity at 3 days and a reduction in the activity at the later time points examined after injury. We also found greatly increased levels of adhesion of peritoneal macrophages from L-NAME-treated rats to epitenon monolayers in vitro, which reflect a chronic imbalance in expression of TGF-beta, which is overexpressed, and nitric oxide, which is underexpressed. The results of the current study show that formation of nitric oxide is an important event in the course of tendon healing since its inhibition results in chronic inflammation and fibrosis due to an imbalance in TGF-beta expression in vivo

Posttransplant lymphoproliferative disorders in neuronal xenotransplanted macaques

Posttransplant lymphoproliferative disorders (PTLDs) are a heterogeneous group of lymphoid proliferations that occur in the setting of depressed T-cell function due to immunosuppressive therapy used following solid organ transplantation, hematopoietic stem cell transplantation, and also xenotransplantation. In the present study, 28 immunosuppressed parkinsonian Macaca fascicularis were intracerebrally injected with wild-type or CTLA4-Ig transgenic porcine xenografts to identify a suitable strategy to enable long-term cell survival, maturation, and differentiation. Nine of 28 (32%) immunosuppressed primates developed masses compatible with PTLD, located mainly in the gastrointestinal tract and/or nasal cavity. The masses were classified as monomorphic PTLD according to the World Health Organization classification. Immunohistochemistry and polymerase chain reaction (PCR) analyses revealed that the PTLDs were associated with macaca lymphocryptovirus as confirmed by double-labeling immunohistochemistry for CD20 and Epstein-Barr nuclear antigen 2 (EBNA-2), where the viral protein was located within the CD20+ neoplastic B cells. In sera from 3 distinct phases of the experimental life of the primates, testing by quantitative PCR revealed a progression of the viral load that paralleled the PTLD progression and no evidence of zoonotic transmission of porcine lymphotropic herpesvirus through xenoneuronal grafts. These data suggest that monitoring the variation of macaca lymphocryptovirus DNA in primates could be used as a possible early diagnostic tool for PTLD progression, allowing preemptive treatment such as immunosuppression therapy reduction

Late Cretaceous ammonoids show that drivers of diversification are regionally heterogeneous

Palaeontologists have long sought to explain the diversification of individual clades to whole biotas at global scales. Advances in our understanding of the spatial distribution of the fossil record through geological time, however, has demonstrated that global trends in biodiversity were a mosaic of regionally heterogeneous diversification processes. Drivers of diversification must presumably have also displayed regional variation to produce the spatial disparities observed in past taxonomic richness. Here, we analyse the fossil record of ammonoids, pelagic shelled cephalopods, through the Late Cretaceous, characterised by some palaeontologists as an interval of biotic decline prior to their total extinction at the Cretaceous-Paleogene boundary. We regionally subdivide this record to eliminate the impacts of spatial sampling biases and infer regional origination and extinction rates corrected for temporal sampling biases using Bayesian methods. We then model these rates using biotic and abiotic drivers commonly inferred to influence diversification. Ammonoid diversification dynamics and responses to this common set of diversity drivers were regionally heterogeneous, do not support ecological decline, and demonstrate that their global diversification signal is influenced by spatial disparities in sampling effort. These results call into question the feasibility of seeking drivers of diversity at global scales in the fossil record

Organizational learning and emotion: constructing collective meaning in support of strategic themes

Missing in the organizational learning literature is an integrative framework that reflects the emotional as well as the cognitive dynamics involved. Here, we take a step in this direction by focusing in depth over time (five years) on a selected organization which manufactures electronic equipment for the office industry. Drawing on personal construct theory, we define organizational learning as the collective re-construal of meaning in the direction of strategically significant themes. We suggest that emotions arise as members reflect on progress or lack of progress in achieving organizational learning. Our evidence suggests that invalidation – where organizational learning fails to correspond with expectations – gives rise to anxiety and frustration, while validation – where organizational learning is aligned with or exceeds expectations – evokes comfort or excitement. Our work aims to capture the key emotions involved as organizational learning proceeds

Can majority support save an endangered language? A case study of language attitudes in Guernsey

Many studies of minority language revitalisation focus on the attitudes and perceptions of minorities, but not on those of majority group members. This paper discusses the implications of these issues, and presents research into majority andf minority attitudes towards the endangered indigenous vernacular of Guernsey, Channel Islands. The research used a multi-method approach (questionnaire and interview) to obtain attitudinal data from a representative sample of the population that included politicians and civil servants (209 participants). The findings suggested a shift in language ideology away from the post-second world war ‘culture of modernisation’ and monolingual ideal, towards recognition of the value of a bi/trilingual linguistic heritage. Public opinion in Guernsey now seems to support the maintenance of the indigenous language variety, which has led to a degree of official support. The paper then discusses to what extent this ‘attitude shift’ is reflected in linguistic behaviour and in concrete language planning measures

Improved protein structure prediction using potentials from deep learning

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13 Submissions were made by three free-modelling methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on-par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 free-modelling assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group. (An average GDT_TS of 61.4.) The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 free-modelling domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods