Workflow automation in liquid chromatography mass spectrometry

We describe the fully automated workflow path developed for the ingest and analysis of liquid chromatography mass spectrometry (LCMS) data. With the help of this computational workflow, we were able to replace two human work days to analyze data with two hours of unsupervised computation time. In addition, this tool also can compute confidence intervals for all its results, based on the noise level present in the data. We leverage only open source tools and libraries in this workflow

Taking the self out of self-rule

Many philosophers believe that agents are self-ruled only when ruled by their (authentic) selves. Though this view is rarely argued for explicitly, one tempting line of thought suggests that self-rule is just obviously equivalent to rule by the self. However, the plausibility of this thought evaporates upon close examination of the logic of ‘self-rule’ and similar reflexives. Moreover, attempts to rescue the account by recasting it in negative terms are unpromising. In light of these problems, this paper instead proposes that agents are self-ruled only when not ruled by others. One reason for favouring this negative social view is its ability to yield plausible conclusions concerning various manipulation cases that are notoriously problematic for nonsocial accounts of self-rule. A second reason is that the account conforms with ordinary usage. It is concluded that self-rule may be best thought of as an essentially social concept

Validation and clinical application of molecular methods for the identification of molds in tissue

Background. Invasive fungal infections due to less-common molds are an increasing problem, and accurate diagnosis is difficult.Methods. We used our previously established molecular method, which allows species identification of molds in histological tissue sections, to test sequential specimens from 56 patients with invasive fungal infections who were treated at our institution from 1982 to 2000.Results. The validity of the method was demonstrated with the establishment of a molecular diagnosis in 52 cases (93%). Confirmation of the causative organism was made in all cases in which a mold had been cultured from the tissue specimen. Less-common molds were identified in 7% of cases and appear to be an increasing problem.Conclusions. Our previously established method has proven to be of value in determining the incidence of invasive infection caused by less-common molds. Institutions should continue to pursue diagnosis of invasive fungal infections by means of tissue culture and microbiologic analysis

Genetic algorithms with self-organizing behaviour in dynamic environments

Copyright @ 2007 Springer-VerlagIn recent years, researchers from the genetic algorithm (GA) community have developed several approaches to enhance the performance of traditional GAs for dynamic optimization problems (DOPs). Among these approaches, one technique is to maintain the diversity of the population by inserting random immigrants into the population. This chapter investigates a self-organizing random immigrants scheme for GAs to address DOPs, where the worst individual and its next neighbours are replaced by random immigrants. In order to protect the newly introduced immigrants from being replaced by fitter individuals, they are placed in a subpopulation. In this way, individuals start to interact between themselves and, when the fitness of the individuals are close, one single replacement of an individual can affect a large number of individuals of the population in a chain reaction. The individuals in a subpopulation are not allowed to be replaced by individuals of the main population during the current chain reaction. The number of individuals in the subpopulation is given by the number of individuals created in the current chain reaction. It is important to observe that this simple approach can take the system to a self-organization behaviour, which can be useful for GAs in dynamic environments.Financial support was obtained from FAPESP (Proc. 04/04289-6)

Catalase epitopes vaccine design for Helicobacter pylori: A bioinformatics approach

Bioinformatics tools are helpful for epitopes prediction directly from the genomes of pathogens in order to design a vaccine. Epitopes are sub-sequences of proteins (8 to 10 mer peptides) which bind to MHC to interact with the T cell receptors and stimulate immune responses. Finding a suitable vaccine against Helicobacter pylori is necessary, because of high prevalence of the infection (25 to 90%). Moreover, this bacteria has been classified as a grade I carcinogen by WHO since 1994. Catalase, an important enzyme in the virulence of H. pylori, could be a suitable candidate for vaccine design because it is highly conserved, which is important for the survival of H. pylori; it is expressed in high level and it is exposed on the surface of the bacteria. In this study, we designed epitope-based vaccine for catalase specific regions of H. pylori by means of immunobioinformatic tools. H. pylori (26695) catalase has been compared with human catalase in order to select specific regions. Afterwards, epitopes of catalase were determined by propred software. Among predicted epitopes, three epitopes were selected including, MVNKDVKQTT, VLLQSTWFL and FHPFDVTKI. Three candidates out of 51catalase antigen epitopes had the highest score for reactivating with MHC II MHC in propred software. The candidate epitopes for vaccine design should be rather a composition of considering epitopes: MVNKDVKQTTKKVLLQSTWFLKKFHPFDVTKI. In this manner, 39 of 51 alleles of MHC class ІІ were involved and stimulated T-cell responses. We believe prediction of catalase epitopes by the immunoinformatics tools would be valuable for developing new immuoprophylatic strategy against H. pylori infection.Key words: Helicobacter pylori, catalase, epitopes

Multinational Enterprise Strategies for Addressing Sustainability: the Need for Consolidation

© 2018, Springer Nature B.V. This paper examines the growing number of publications on multinational enterprise management of sustainability issues. Based on an integrative literature review and thematic analysis, the paper analyses and synthesises the current state of knowledge about main issues arising. Key issues identified include the following: choice of sustainability strategies; management of the views of headquarters towards sustainability; local cultural sustainability perspectives in developed and developing host countries; MNEs with home in developing/emerging countries; and resource availability for implementing sustainability initiatives. Findings indicate that although the literature is tending towards growing acceptance about sustainability and its challenges most researchers have focused on corporate social responsibility and investigate their own niche problem, industry, and country, using their own chosen theory and do not consider the need for consolidation and integration of social, environmental and economic performance. Avenues for future research are identified which will provide a means for the ethical foundations of theory and practice to be improved

The PML-RAR alpha transcript in long-term follow-up of acute promyelocytic leukemia patients

Background and Objectives. Detection of PML-RAR alpha transcripts by RT-PCR is now established as a rapid and sensitive method for diagnosis of acute promyelocytic leukemia (APL), Although the majority of patients in longterm clinical remission are negative by consecutive reverse transcription polymerase chain reaction (RT-PCR) assays, negative tests are still observed in patients who ultimately relapse. Conversion from negative to positive PCR has been observed after consolidation and found to be a much stronger predictor of relapse. This study reports on 47 APL patients to determine the correlation between minimal residual disease (MRD) status and clinical outcome in our cohort of patients. Design and Methods. The presence of PML-RAR alpha t transcripts was investigated in 47 APL patients (37 adults and 10 children) using a semi-nested reverse transcriptase-polymerase chain reaction to evaluate the prognostic value of RT-PCR tests. Results. All patients achieved complete clinical remission (CCR) following induction treatment with all-trans retinoic acid (ATRA) and chemotherapy (CHT) or ATRA alone. Patients were followed up between 2 and 117.6 months (median: 37 months). Relapses occurred in 11 patients (9 adults and 2 children) between 11.4 and 19 months after diagnosis (median: 15.1 months) while 36 patients (28 adults and 8 children) remained in CCR, Seventy-five percent of patients carried the PML-RARa long isoform (bcr 1/2) which also predominated among the relapsed cases (9 of 11) but did not associate with any adverse outcome (p = 0.37), For the purpose of this analysis, minimal residual disease tests were clustered into four time-intervals: 0-2 months, 3-5 months, 5-9 months and 10-24 months. Interpretation and Conclusions. Children showed persisting disease for longer than adults during the first 2 months of treatment, At 2 months, 10 (50%) of 20 patients who remained in CCR and 4 (80%) of 5 patients who subsequently relapsed were positive. Patients who remained in CCR had repeatedly negative results beyond 5.5 months from diagnosis. A positive MRD test preceded relapse in 3 of 4 tested patients. The ability of a negative test to predict CCR (predictive negative value, PNV) was greater after 6 months (> 83%), while the ability of a positive test to predict relapse (predictive positive value, PPV) was most valuable only beyond 10 months (100%). This study (i) highlights the prognostic value of RT-PCR monitoring after treatment of APL patients but only from the end of treatment, (ii) shows an association between conversion to a positive test and relapse and (iii) suggests that PCR assessments should be carried out at 3-month intervals to provide a more accurate prediction of hematologic relapses but only after the end of treatment, (C) 2001, Ferrata Storti Foundatio

Using pharmacy dispensing data to predict falls in older individuals

Aims Associations between individual medication use and falling in older individuals are well-documented. However, a comprehensive risk score that takes into account overall medication use and that can be used in daily pharmacy practice is lacking. We, therefore, aimed to determine whether pharmacy dispensing records can be used to predict falls. Methods A retrospective cohort study was conducted using pharmacy dispensing data and self-reported falls among 3454 Dutch individuals aged >= 65 years. Two different methods were used to classify medication exposure for each person: the drug burden index (DBI) for cumulative anticholinergic and sedative medication exposure as well as exposure to fall risk-increasing drugs (FRIDs). Multinomial regression analyses, adjusted for age and sex, were conducted to investigate the association between medication exposure and falling classified as nonfalling, single falling and recurrent falling. The predictive performances of the DBI and FRIDs exposure were estimated by the polytomous discrimination index (PDI). Results There were 521 single fallers (15%) and 485 recurrent fallers (14%). We found significant associations between a DBI >= 1 and single falling (adjusted odds ratio: 1.30 [95% confidence interval {CI}: 1.02-1.66]) and recurrent falling (adjusted odds ratio: 1.60 [95%CI: 1.25-2.04]). The PDI of the DBI model was 0.41 (95%CI: 0.39-0.42) and the PDI of the FRIDs model was 0.45 (95%CI: 0.43-0.47), indicating poor discrimination between fallers and nonfallers. Conclusion The study shows significant associations between medication use and falling. However, the medication-based models were insufficient and other factors should be included to develop a risk score for pharmacy practice

Saliency-driven system models for cell analysis with deep learning.

Background and objectivesSaliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert's visual attention, there is little evidence about how these models perform when used to predict the cytopathologist's eye fixations. Saliency models may be the key to instrumenting fast object detection on large Pap smear slides under real noisy conditions, artifacts, and cell occlusions. This paper describes how our computational schemes retrieve regions of interest (ROI) of clinical relevance using visual attention models. We also compare the performance of different computed saliency models as part of cell screening tasks, aiming to design a computer-aided diagnosis systems that supports cytopathologists.MethodWe record eye fixation maps from cytopathologists at work, and compare with 13 different saliency prediction algorithms, including deep learning. We develop cell-specific convolutional neural networks (CNN) to investigate the impact of bottom-up and top-down factors on saliency prediction from real routine exams. By combining the eye tracking data from pathologists with computed saliency models, we assess algorithms reliability in identifying clinically relevant cells.ResultsThe proposed cell-specific CNN model outperforms all other saliency prediction methods, particularly regarding the number of false positives. Our algorithm also detects the most clinically relevant cells, which are among the three top salient regions, with accuracy above 98% for all diseases, except carcinoma (87%). Bottom-up methods performed satisfactorily, with saliency maps that enabled ROI detection above 75% for carcinoma and 86% for other pathologies.ConclusionsROIs extraction using our saliency prediction methods enabled ranking the most relevant clinical areas within the image, a viable data reduction strategy to guide automatic analyses of Pap smear slides. Top-down factors for saliency prediction on cell images increases the accuracy of the estimated maps while bottom-up algorithms proved to be useful for predicting the cytopathologist's eye fixations depending on parameters, such as the number of false positive and negative. Our contributions are: comparison among 13 state-of-the-art saliency models to cytopathologists' visual attention and deliver a method that the associate the most conspicuous regions to clinically relevant cells