Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning

In this paper we explore a unique, high-value spatio-temporal dataset that results from the fusion of three data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed), the corresponding fish catch reports (i.e., the quantity and type of fish caught), and relevant environmental data. The result of that fusion is a set of semantic trajectories describing the fishing activities in Northern Adriatic Sea over two years. We present early results from an exploratory analysis of these semantic trajectories, as well as from initial predictive modeling using Machine Learning. Our goal is to predict the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation useful for fisheries management. Our predictive results are preliminary in both the temporal data horizon that we are able to explore and in the limited set of learning techniques that are employed on this task. We discuss several approaches that we plan to apply in the near future to learn from such data, evidence, and knowledge that will be useful for fisheries management. It is likely that other centers of intense fishing activities are in possession of similar data and could use the methods similar to the ones proposed here in their local context

The Opportunistic Pathogen Propionibacterium acnes: Insights into Typing, Human Disease, Clonal Diversification and CAMP Factor Evolution

We previously described a Multilocus Sequence Typing (MLST) scheme based on eight genes that facilitates population genetic and evolutionary analysis of P. acnes. While MLST is a portable method for unambiguous typing of bacteria, it is expensive and labour intensive. Against this background, we now describe a refined version of this scheme based on two housekeeping (aroE; guaA) and two putative virulence (tly; camp2) genes (MLST4) that correctly predicted the phylogroup (IA1, IA2, IB, IC, II, III), clonal complex (CC) and sequence type (ST) (novel or described) status for 91% isolates (n = 372) via cross-referencing of the four gene allelic profiles to the full eight gene versions available in the MLST database (http:// pubmlst.org/pacnes/). Even in the small number of cases where specific STs were not completely resolved, the MLST4 method still correctly determined phylogroup and CC membership. Examination of nucleotide changes within all the MLST loci provides evidence that point mutations generate new alleles approximately 1.5 times as frequently as recombination; although the latter still plays an important role in the bacterium’s evolution. The secreted/cell-associated ‘virulence’ factors tly and camp2 show no clear evidence of episodic or pervasive positive selection and have diversified at a rate similar to housekeeping loci. The co-evolution of these genes with the core genome might also indicate a role in commensal/normal existence constraining their diversity and preventing their loss from the P. acnes population. The possibility that members of the expanded CAMP factor protein family, including camp2, may have been lost from other propionibacteria, but not P. acnes, would further argue for a possible role in niche/host adaption leading to their retention within the genome. These evolutionary insights may prove important for discussions surrounding camp2 as an immunotherapy target for acne, and the effect such treatments may have on commensal lineages

Supramolecular recognition of estrogens via molecularly imprinted polymers

The isolation and preconcentration of estrogens from new types of biological samples (acellular and protein-free simulated body fluid) by molecularly imprinted solid-phase extraction has been described. In this technique, supramolecular receptors, namely molecularly imprinted polymers (MIPs) are used as a sorbent material. The recognition sites of MIPs were prepared by non-covalent multiple interactions and formed with the target 17β-estradiol as a template molecule. High-performance liquid chromatography with spectroscopic UV, selective, and a sensitive electrochemical CoulArray detector was used for the determination of 17β-estradiol, estrone, and estriol in simulated body fluid which mimicked human plasma

Pharmacokinetics and target attainment of intravenous posaconazole in critically ill patients during extracorporeal membrane oxygenation

Background: Posaconazole is an antifungal drug used for prophylaxis and treatment of invasive fungal infections. Severe influenza has been identified as a risk factor for invasive pulmonary aspergillosis in critically ill patients. In this population, extracorporeal membrane oxygenation (ECMO) is used as rescue therapy, although little is known about the pharmacokinetics (PK) of posaconazole during ECMO. Objectives: To determine the PK and target attainment of six patients treated with IV posaconazole under ECMO and to develop a population PK model that can be used to simulate the PTA. Methods: Critically ill patients treated with posaconazole and ECMO were included in this study. Plasma samples were collected at several timepoints within one dosing interval on two occasions: an early (Day 2-3) and a late (Day 4-7) sampling day. Daily trough concentrations were measured. Results: The median (IQR) AUC(0-24), CL and V-d were 34.3 (28.3-37.7) mg.h/L, 8.7 (8.0-10.6) L/h and 389 (314-740) L, if calculated with non-compartmental analysis based on the observed concentrations. All measured trough concentrations were >= 0.7 mg/L and 11/16 were >= 1 mg/L, which are the haematological thresholds for prophylaxis and treatment of invasive aspergillosis, respectively. The targeted PTA (>90%) was attained for prophylaxis but not for treatment. Conclusions: ECMO does not appear to influence posaconazole exposure compared with haematology patients. However, some trough levels were below the lower limit for treatment. An a priori dose adjustment does not appear to be necessary but drug monitoring is recommended

Drug-induced acute myocardial infarction: identifying 'prime suspects' from electronic healthcare records-based surveillance system.

BACKGROUND: Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings. OBJECTIVE: To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network. METHODS: Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible. RESULTS: Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate. LIMITATIONS: Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out. CONCLUSION: A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation