HAMAP in 2015: updates to the protein family classification and annotation system.

HAMAP (High-quality Automated and Manual Annotation of Proteins-available at http://hamap.expasy.org/) is a system for the automatic classification and annotation of protein sequences. HAMAP provides annotation of the same quality and detail as UniProtKB/Swiss-Prot, using manually curated profiles for protein sequence family classification and expert curated rules for functional annotation of family members. HAMAP data and tools are made available through our website and as part of the UniRule pipeline of UniProt, providing annotation for millions of unreviewed sequences of UniProtKB/TrEMBL. Here we report on the growth of HAMAP and updates to the HAMAP system since our last report in the NAR Database Issue of 2013. We continue to augment HAMAP with new family profiles and annotation rules as new protein families are characterized and annotated in UniProtKB/Swiss-Prot; the latest version of HAMAP (as of 3 September 2014) contains 1983 family classification profiles and 1998 annotation rules (up from 1780 and 1720). We demonstrate how the complex logic of HAMAP rules allows for precise annotation of individual functional variants within large homologous protein families. We also describe improvements to our web-based tool HAMAP-Scan which simplify the classification and annotation of sequences, and the incorporation of an improved sequence-profile search algorithm

An improved method for measuring muon energy using the truncated mean of dE/dx

The measurement of muon energy is critical for many analyses in large Cherenkov detectors, particularly those that involve separating extraterrestrial neutrinos from the atmospheric neutrino background. Muon energy has traditionally been determined by measuring the specific energy loss (dE/dx) along the muon's path and relating the dE/dx to the muon energy. Because high-energy muons (E_mu > 1 TeV) lose energy randomly, the spread in dE/dx values is quite large, leading to a typical energy resolution of 0.29 in log10(E_mu) for a muon observed over a 1 km path length in the IceCube detector. In this paper, we present an improved method that uses a truncated mean and other techniques to determine the muon energy. The muon track is divided into separate segments with individual dE/dx values. The elimination of segments with the highest dE/dx results in an overall dE/dx that is more closely correlated to the muon energy. This method results in an energy resolution of 0.22 in log10(E_mu), which gives a 26% improvement. This technique is applicable to any large water or ice detector and potentially to large scintillator or liquid argon detectors.Comment: 12 pages, 16 figure

Bacterial virus ontology; Coordinating across databases

Bacterial viruses, also called bacteriophages, display a great genetic diversity and utilize unique processes for infecting and reproducing within a host cell. All these processes were investigated and indexed in the ViralZone knowledge base. To facilitate standardizing data, a simple ontology of viral life-cycle terms was developed to provide a common vocabulary for annotating data sets. New terminology was developed to address unique viral replication cycle processes, and existing terminology was modified and adapted. Classically, the viral life-cycle is described by schematic pictures. Using this ontology, it can be represented by a combination of successive events: entry, latency, transcription/replication, host-virus interactions and virus release. Each of these parts is broken down into discrete steps. For example enterobacteria phage lambda entry is broken down in: viral attachment to host adhesion receptor, viral attachment to host entry receptor, viral genome ejection and viral genome circularization. To demonstrate the utility of a standard ontology for virus biology, this work was completed by annotating virus data in the ViralZone, UniProtKB and Gene Ontology databases.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

The relationships between Depression Spatial Clusters and Mental Health planning in Catalonia(Spain)

This study aims to analyse potential risk factors which could influence the occurrence of hot spots of depression. They cannot only be explained through municipal socio-demographic characteristics and which is why causes at catchment area level should also be studied. Indicators at both spatial levels were analysed by a multi-level regression model. The analysis included various sociodemographic, geographical and service allocation indicators. According to scientific literature, unemployment and rurality were identified as risk factors for depression and, therefore, for hot spots. On the other hand, low educational levels and poor accessibility showed little relationship here while other studies indicated otherwise. Preliminary results described diverse risk factors at two levels which were related to a high likelihood of hot spots, although more indepth analysis will be needed

Unidade de contexto e observação social sistemática em saúde: conceitos e métodos Context unit and systematic social observation: a review of concepts and methods

Assumimos que "onde você mora é importante para sua saúde, para além de quem você é". Entendemos que o impacto do local de moradia ou unidade de contexto (UC) na saúde das populações se deve à heterogeneidade dos atributos do entorno físico e social da UC, para além das características individuais ou agregadas daqueles ali aninhados. Estes atributos, embora dependentes dos indivíduos, são tipicamente externos a eles e potencialmente modificáveis. As UC são compreendidas como unidades ecológicas inseridas em conjuntos sucessivamente mais amplos e interdependentes. Quando relevante para a hipótese do estudo, unidades geográficas administrativas podem ser utilizadas como aproximações da UC. Outra alternativa é a que utiliza a percepção de seus moradores, a "vizinhança percebida". O ressurgimento do interesse com relação à determinação dos efeitos da UC sobre a saúde correlaciona com novas tendências na área da saúde coletiva: incorporação de novos níveis hierárquicos de exposição, as iniqüidades e seus determinantes, a urbanização e seus efeitos e a avaliação de intervenções multi-setoriais. Nosso objetivo central é rever opções para a escolha da UC a ser investigada além de estratégias para a aferição de seus atributos físicos e sociais, utilizando a observação social sistemática (OSS). A combinação de dados originárias de dados administrativos, da vizinhança percebida, dos inquéritos populacionais e da OSS ainda necessita de maiores elaborações conceitual, metodológica e analítica. Entretanto, a compreensão da distribuição dos atributos físicos e sociais da UC permite compor níveis hierárquicos de complexidade relevantes para o entendimento da ocorrência dos eventos relacionados à saúde nas populações.<br>We understand that "where one lives makes a difference to health in addition to who you are", and that the effects of the place of residence or context unit (CU) on public health are due to the heterogeneity of the physical and social environment characteristics, in addition to the individual and aggregate attributes of the population nested in the CU. Those attributes, although intrinsically dependent on the individuals, are typically external to them and susceptible to intervention. Also, the UC's are understood as ecological units nested within successively larger communities. Depending on the study hypothesis, census-defined areas may be used as proxy for the CU. Alternatively, the CU may be defined by the individual's perception of his/her neighborhood. The renewed interest on the health effects of the CU are associated with new trends in public health, namely: new hierarchical levels of exposure beyond individual level characteristics, inequalities and social determinants of health, urbanization and the need to evaluate interventions not traditionally associated to public health. Our objective was, first, to review options while choosing the relevant CU and second, to review strategies to determine and quantify the characteristics of the CU using social systematic observation (SSO). The combination of census-defined data, information on the neighborhood defined by the local population, surveys and SSO still needs conceptual, methodological and analytical development. However, the distribution of the physical and social attributes of the CU will permit to incorporate other hierarchical level of complexity to better understand the incidence and prevalence of health related events in populations