Contamination of recreational waters with cyanobacterial toxins continues to increase and presents a risk to animals and humans. Although cases of acute hepato- and neurotoxicoses in dogs following cyanotoxin exposure exist, no reports of skin-related reactions in dogs exist. A 5-year-old female spayed 34 kg Bracco Italiano was initially presented for rapid onset of severe pruritus and urticaria. Marked excoriation and erythema were noted over the chest and neck, while urticaria was noted in the inguinal regions and ventral abdomen. Initial basic dermatology work-up excluded parasitic, fungal, and bacterial organisms. Due to the severity and progression of urticaria, the dog received IV dexamethasone and IM diphenhydramine. Improvement of the urticaria and the dog's clinical status was noted over the next 45 min. Assessment of the dog's environment revealed access to a lake on the property with visible algal bloom. Water from the lake was submitted for toxicology testing and revealed the presence of debromoaplysiatoxin. Access to the lake was discontinued and follow-up evaluation over the next few weeks revealed a complete resolution of the skin irritation. To the authors' knowledge, this is the first case report of debromoaplysiatoxin exposure in a dog after swimming in cyanobacteria-contaminated water. Veterinarians should recognize the potential harm that contaminated waters may cause in terms of dermal, hepatic, and neurological conditions. In addition, more prudent oversight of contaminated recreational waters is recommended for animals and humans to prevent adverse events and intoxications.

The Cancer Genome Atlas Pan-Cancer Analysis Working Group collaborated on the Synapse software platform to share and evolve data, results and methodologies while performing integrative analysis of molecular profiling data from 12 tumor types. The group's work serves as a pilot case study that provides (i) a template for future large collaborative studies; (ii) a system to support collaborative projects; and (iii) a public resource of highly curated data, results and automated systems for the evaluation of community-developed models.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.