Use of particle counter system for the optimization of sampling ,identification and decontamination procedures for biological aerosols dispersion in confined environment

Abstract In a CBRNe (Chemical, Biological, Radiological, Nuclear and explosive) scenario, biological agents hardly allow efficient detection/identification because of the incubation time that provides a lag in symptoms outbreak following their dissemination. The detection of atmospheric dispersion of biological agents (i.e.: toxins, viruses, bacteria and so on) is a key issue for the safety of people and security of environment. Another fundamental aspect is related to the efficiency of the sampling method, which leads to the identification of the agent released, in fact an effective sampling method is needed either to identify the contamination and to check for the decontamination procedure. Environmental monitoring is one of the ways to improve fast detection of biological agents; for instance, particle counters with the ability of discriminating between biological and non-biological particles are used for a first warning when the amount of biological particles exceeds a particular threshold. Nevertheless, these systems are not able to distinguish between pathogen and non-pathogen organisms, thus, classical “laboratory” assays are still required to unambiguously identify the particle which triggered the warning signal. In this work, a combination of commercially available equipment for detection and identification of the atmospheric dispersion of biological agents was evaluated in partnership between the Italian Army, the Department of Industrial Engineering and the School of Medicine and Surgery of the University of Rome “Tor Vergata”. The aim of this work, whose results are presented here, was to conduce preliminary studies on the dynamics of biological aerosols fallout after its dispersion, to improve detection, sampling and identification techniques. This will help minimizing the impact of the release of biological agents, guarantee environmental, and people safety and securit

Marginal Structural Models with Dose-Delay Joint-Exposure for Assessing Variations to Chemotherapy Intensity

Marginal Structural Models (MSMs) are causal models designed to adjust for time-dependent confounders in observational studies with dynamically-adjusted treatments. They are robust tools to assess causality in complex longitudinal data. In this paper a MSM is proposed with an innovative dose-delay joint-exposure model for Inverse-Probability-of-Treatment Weighted (IPTW) estimation of the causal effect of alterations to the therapy intensity. The model is motivated by a precise clinical question concerning the possibility of reducing dosages in a regimen. It is applied to data from a randomised trial of chemotherapy in osteosarcoma, an aggressive primary bone-tumour. Chemotherapy data are complex because their longitudinal nature encompasses many clinical details like composition and organisation of multi-drug regimens, or dynamical therapy adjustments. This manuscript focuses on the clinical dynamical process of adjusting the therapy according to the patient’s toxicity history, and the causal effect on the outcome of interest of such therapy modifications. Depending on patients’ toxicity levels, variations to therapy intensity may be achieved by physicians through the allocation of either a reduction or a delay of the next planned dose. Thus, a negative feedback is present between exposure to cytotoxic agents and toxicity levels, which acts as time-dependent confounders. The construction of the model is illustrated highlighting the high complexity and entanglement of chemotherapy data. Built to address dosage reductions, the model also shows that delays should not be introduced in the therapy administration. The last aspect makes sense from the cytological point of view, but it is seldom addressed in the literature

Method to measure the mismatch between target and achieved received dose intensity of chemotherapy in cancer trials: a retrospective analysis of the MRC BO06 trial in osteosarcoma

OBJECTIVES: In cancer studies, the target received dose intensity (tRDI) for any regimen, the intended dose and time for the regimen, is commonly taken as a proxy for achieved RDI (aRDI), the actual individual dose and time for the regimen. Evaluating tRDI/aRDI mismatches is crucial to assess study results whenever patients are stratified on allocated regimen. The manuscript develops a novel methodology to highlight and evaluate tRDI/aRDI mismatches. DESIGN: Retrospective analysis of a randomised controlled trial, MRC BO06 (EORTC 80931). SETTING: Population-based study but proposed methodology can be applied to other trial designs. PARTICIPANTS: A total of 497 patients with resectable high-grade osteosarcoma, of which 19 were excluded because chemotherapy was not started or the estimated dose was abnormally high (>1.25 × prescribed dose). INTERVENTIONS: Two regimens with the same anticipated cumulative dose (doxorubicin 6×75 mg/m2/week; cisplatin 6×100 mg/m2/week) over different time schedules: every 3 weeks in regimen-C and every 2 weeks in regimen-DI. PRIMARY AND SECONDARY OUTCOME MEASURES: tRDI distribution was measured across groups of patients derived from k-means clustering of treatment data. K-means creates groups of patients who are aRDI-homogeneous. The main outcome is the proportion of tRDI values in groups of homogeneous aRDI. RESULTS: For nearly half of the patients, there is a mismatch between tRDI and aRDI; for 21%, aRDI was closer to the tRDI of the other regimen. CONCLUSIONS: For MRC BO06, tRDI did not predict well aRDI. The manuscript offers an original procedure to highlight the presence of and quantify tRDI/aRDI mismatches. Caution is required to interpret the effect of chemotherapy-regimen intensification on survival outcome at an individual level where such a mismatch is present.The study relevance lies in the use of individual realisation of the intended treatment, which depends on individual delays and/or dose reductions reported throughout the treatment. TRIAL REGISTRATION NUMBER: ISRCTN86294690

The approximability of the String Barcoding problem

The String Barcoding (SBC) problem, introduced by Rash and Gusfield (RECOMB, 2002), consists in finding a minimum set of substrings that can be used to distinguish between all members of a set of given strings. In a computational biology context, the given strings represent a set of known viruses, while the substrings can be used as probes for an hybridization experiment via microarray. Eventually, one aims at the classification of new strings (unknown viruses) through the result of the hybridization experiment. In this paper we show that SBC is as hard to approximate as Set Cover. Furthermore, we show that the constrained version of SBC (with probes of bounded length) is also hard to approximate. These negative results are tight

On the fixed parameter tractability and approximability of the minimum error correction problem

Haplotype assembly is the computational problem of reconstructing the two parental copies, called haplotypes, of each chromosome starting from sequencing reads, called fragments, possibly affected by sequencing errors. Minimum Error Correction (MEC) is a prominent computational problem for haplotype assembly and, given a set of fragments, aims at reconstructing the two haplotypes by applying the minimum number of base corrections. By using novel combinatorial properties of MEC instances, we are able to provide new results on the fixed-parameter tractability and approximability of MEC. In particular, we show that MEC is in FPT when parameterized by the number of corrections, and, on “gapless” instances, it is in FPT also when parameterized by the length of the fragments, whereas the result known in literature forces the reconstruction of complementary haplotypes. Then, we show that MEC cannot be approximated within any constant factor while it is approximable within factor O(log nm) where nm is the size of the input. Finally, we provide a practical 2-approximation algorithm for the Binary MEC, a variant of MEC that has been applied in the framework of clustering binary data

A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories

Understanding the protein folding mechanism remains a grand challenge in structural biology. In the past several years, computational theories in molecular dynamics have been employed to shed light on the folding process. Coupled with high computing power and large scale storage, researchers now can computationally simulate the protein folding process in atomistic details at femtosecond temporal resolution. Such simulation often produces a large number of folding trajectories, each consisting of a series of 3D conformations of the protein under study. As a result, effectively managing and analyzing such trajectories is becoming increasingly important. In this article, we present a spatio-temporal mining approach to analyze protein folding trajectories. It exploits the simplicity of contact maps, while also integrating 3D structural information in the analysis. It characterizes the dynamic folding process by first identifying spatio-temporal association patterns in contact maps, then studying how such patterns evolve along a folding trajectory. We demonstrate that such patterns can be leveraged to summarize folding trajectories, and to facilitate the detection and ordering of important folding events along a folding path. We also show that such patterns can be used to identify a consensus partial folding pathway across multiple folding trajectories. Furthermore, we argue that such patterns can capture both local and global structural topology in a 3D protein conformation, thereby facilitating effective structural comparison amongst conformations. We apply this approach to analyze the folding trajectories of two small synthetic proteins-BBA5 and GSGS (or Beta3S). We show that this approach is promising towards addressing the above issues, namely, folding trajectory summarization, folding events detection and ordering, and consensus partial folding pathway identification across trajectories

Application of Real Time PCR to identify residual bio-decontamination pf confined environments after hydrogen peroxide vapor treatment :preliminary results.

Abstract This study was conducted to assess the effectiveness of hydrogen peroxide vapor (HPV) to remove biological contamination in a confined environment. Decontamination after the dispersion of biological aerosol is a main issue from a civilian, public health and military perspective. Despite the effectiveness of aggressive substances, eco-friendly but still efficient methods for decontamination are a relevant demand. The hydrogen peroxide vapor (HPV) decontamination method is among the most recent technologies in the field. Microbiological and molecular biology techniques are commonly used to detect and identify biological contamination, but many of them are time consuming and requires advanced training for the operators who perform the analysis. In case of CBRN (Chemical, Biological, Radiological and Nuclear) event, detection,identification and removal of the hazardous agent is paramount;in this kind of scenario, civilian and military forces are the first actor involved and they are responsible for these actions. Thus, itis essential that these operations becomes as quick and easy as possible.In this work, a combination of commercially available equipment for detection, identification and decontamination, was evaluated in partnership between the Italian Army, the Department of Industrial Engineering and the School of Medicine and Surgery of the University of Rome “Tor Vergata with the aim of finding a preliminary setup to implement in case of Biological events. This work was focused on evaluating a) the effectiveness of HPV as bio-decontaminant in case of biological aerosol dispersion in a confined environment, and b) the usefulness of Real-Time PCR as a technique to identify residual bio-.contamination.Preliminary results for decontamination with HPV show that, despite the death of the microorganisms, nucleic acids are not completely degraded, suggesting the need for further efforts to identify a more efficient, eco-friendly method for biological decontamination