Ticks transmit more pathogens to humans and animals than any other arthropod. We describe the 2.1 Gbp nuclear genome of the tick, Ixodes scapularis (Say), which vectors pathogens that cause Lyme disease, human granulocytic anaplasmosis, babesiosis and other diseases. The large genome reflects accumulation of repetitive DNA, new lineages of retro-transposons, and gene architecture patterns resembling ancient metazoans rather than pancrustaceans. Annotation of scaffolds representing ∼57% of the genome, reveals 20,486 protein-coding genes and expansions of gene families associated with tick–host interactions. We report insights from genome analyses into parasitic processes unique to ticks, including host ‘questing', prolonged feeding, cuticle synthesis, blood meal concentration, novel methods of haemoglobin digestion, haem detoxification, vitellogenesis and prolonged off-host survival. We identify proteins associated with the agent of human granulocytic anaplasmosis, an emerging disease, and the encephalitis-causing Langat virus, and a population structure correlated to life-history traits and transmission of the Lyme disease agent.

Identifying chemical–disease relations (CDR) from biomedical literature could improve chemical safety and toxicity studies. This article proposes a novel syntactic and semantic information exploitation method for CDR extraction. The proposed method consists of a feature-based model, a tree kernel-based model and a neural network model. The feature-based model exploits lexical features, the tree kernel-based model captures syntactic structure features, and the neural network model generates semantic representations. The motivation of our method is to fully utilize the nice properties of the three models to explore diverse information for CDR extraction. Experiments on the BioCreative V CDR dataset show that the three models are all effective for CDR extraction, and their combination could further improve extraction performance. Database URL: http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/.

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Background This study aimed to investigate the factors associated with sarcopenia in elderly residents in three nursing homes in Suzhou City, East China including the association with nutrition and physical exercise. Material/Methods Elderly residents (n=316) from three nursing homes included 112 men and 204 women. The appendicular skeletal muscle index (ASMI), grip strength, and movements were measured to diagnose sarcopenia. The correlation between sarcopenia with age, sex, body mass index (BMI), ASMI, upper arm circumference, calf circumference, muscle content, grip strength, dietary intake, degree and duration of movement were also assessed. Results The prevalence of sarcopenia was 28.8% (30.4% for men and 27.9% for women). Patients with sarcopenia were older compared with controls. Height, BMI, upper arm circumference, calf circumference and arm muscle mass, lower limb muscle mass, limb skeletal muscle index and ASMI, grip strength, and pace of movement were lower than controls. The prevalence of sarcopenia correlated with the intake of meat, fish, eggs, and milk, and duration of weekly aerobic and resistance exercise. Logistic regression analysis showed a positive correlation between the prevalence of sarcopenia and age, and a negative correlation between BMI and consumption of meat, eggs, and milk. Conclusions The prevalence of sarcopenia in elderly residents in three nursing homes in Suzhou City was 28.8%. Increasing age was a risk factor for sarcopenia. Increased BMI and a diet containing meat, eggs, and milk were protective factors. The findings from this study provide support that adequate dietary protein can prevent sarcopenia in the elderly.