Routine screening of harmful microorganisms in beach sands: implications to public health

Beaches worldwide provide recreational opportunities to hundreds of millions of people and serve as important components of coastal economies. Beach water is often monitored for microbiological quality to detect the presence of indicators of human sewage contamination so as to prevent public health outbreaks associated with water contact. However, growing evidence suggests that beach sand can harbor microbes harmful to human health, often in concentrations greater than the beach water. Currently, there are no standards for monitoring, sampling, analyzing, or managing beach sand quality. In addition to indicator microbes, growing evidence has identified pathogenic bacteria, viruses, and fungi in a variety of beach sands worldwide. The public health threat associated with these populations through direct and indirect contact is unknown because so little research has been conducted relating to health outcomes associated with sand quality. In this manuscript, we present the consensus findings of a workshop of experts convened in Lisbon, Portugal to discuss the current state of knowledge on beach sand microbiological quality and to develop suggestions for standardizing the evaluation of sand at coastal beaches. The expert group at the "Microareias 2012" workshop recommends that 1) beach sand should be screened for a variety of pathogens harmful to human health, and sand monitoring should then be initiated alongside regular water monitoring; 2) sampling and analysis protocols should be standardized to allow proper comparisons among beach locations; and 3) further studies are needed to estimate human health risk with exposure to contaminated beach sand. Much of the manuscript is focused on research specific to Portugal, but similar results have been found elsewhere, and the findings have worldwide implications.

Optimization of immunocytochemistry in cytology: comparison of two protocols for fixation and preservation on cytospin and smear preparations

Objective: A new protocol for fixation and slide preservation was evaluated in order to improve the quality of immunocytochemical reactions on cytology slides. Methods: The quality of immunoreactions was evaluated retrospectively on 186 cytology slides (130 direct smears, 56 cytospins) prepared from different cytology samples. Ninety-three of the slides were air dried, stored at -20 °C and fixed in acetone for 10 minutes (Protocol 1), whereas the other 93 were immediately fixed in methanol at -20 °C for at least 30 minutes, subsequently protected with polyethylene glycol (PEG) and stored at room temperature (Protocol 2). Immunocytochemical staining, with eight primary antibodies, was performed on a Ventana BenchMark Ultra instrument using an UltraView Universal DAB Detection Kit. The following parameters were evaluated for each immunoreaction: morphology preservation, intensity of specific staining, background and counterstain. The slides were blinded and independently scored by four observers with marks from 0 to 20. Results: The quality of immunoreactions was better on methanol-fixed slides protected with PEG than on air-dried slides stored in the freezer: X¯ = 14.44 ± 3.58 versus X¯ = 11.02 ± 3.86, respectively (P < 0.001). Conclusion: Immediate fixation of cytology slides in cold methanol with subsequent application of PEG is an easy and straightforward procedure that improves the quality of immunocytochemical reactions and allows the storage of the slides at room temperature.

ECG Analysis Using Consensus Clustering

Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.

Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.