Historiografija u političkoj komunikaciji – kako Josip Horvat pišući o prošlom želi mijenjati suvremenost1

Jedan od modusa na koji se politička poruka može komunicirati recipijentima jest posredovanje ideologije kroz priču o prošlim događajima, a u ovom se radu analizira konkretan slučaj – biografije historijskih ličnosti novinara i publicista Josipa Horvata iz 30-ih godina 20. stoljeća. Uklapanjem naravnog u određenu narativnu shemu Horvat želi oblikovati određenu političku zajednicu. Horvat svoj povijesni diskurs gradi, s jedne strane, na postupcima koji ovjerovljavaju njegovu priču, a s druge ga usmjerava kako bi postigao određeni ideološki efekt. Kao istinosni postupci izdvajaju se citiranje, kronologija, toponimija i sl, a kao postupci koji naglašavaju fikcionalnost antiteza, inscenacija, ispuštanje događaja i rekonstrukcija nepoznatog. Sudjelujući u društvenoj komunikaciji koja je tematizirala pitanja organizacije društva i raspodjele moći, Horvat je slikovitošću proizvodio ekspresivnost te usmjeravao imaginaciju čitatelja s namjerom izazivanja političke mobilizacije. Spajajući kontingentno i literarno proizvodi hibrid: priču napisanu s predumišljajem koja snagu uvjeravanja čitatelja crpi na svojoj vezi s istinitim, a sve u svrhu posredovanja svoje liberalne ideologije

The usefulness of twenty-four molecular markers in predicting treatment outcome with combination therapy of amodiaquine plus sulphadoxine-pyrimethamine against falciparum malaria in Papua New Guinea

<p>Abstract</p> <p>Background</p> <p>In Papua New Guinea (PNG), combination therapy with amodiaquine (AQ) or chloroquine (CQ) plus sulphadoxine-pyrimethamine (SP) was introduced as first-line treatment against uncomplicated malaria in 2000.</p> <p>Methods</p> <p>We assessed <it>in vivo </it>treatment failure rates with AQ+SP in two different areas in PNG and twenty-four molecular drug resistance markers of <it>Plasmodium falciparum </it>were characterized in pre-treatment samples. The aim of the study was to investigate the association between infecting genotype and treatment response in order to identify useful predictors of treatment failure with AQ+SP.</p> <p>Results</p> <p>In 2004, Day-28 treatment failure rates for AQ+SP were 29% in the Karimui and 19% in the South Wosera area, respectively. The strongest independent predictors for treatment failure with AQ+SP were <it>pfmdr1 </it>N86Y (OR = 7.87, <it>p </it>< 0.01) and <it>pfdhps </it>A437G (OR = 3.44, <it>p </it>< 0.01). Mutations found in CQ/AQ related markers <it>pfcrt </it>K76T, A220S, N326D, and I356L did not help to increase the predictive value, the most likely reason being that these mutations reached almost fixed levels. Though mutations in SP related markers <it>pfdhfr </it>S108N and C59R were not associated with treatment failure, they increased the predictive value of <it>pfdhps </it>A437G. The difference in treatment failure rate in the two sites was reflected in the corresponding genetic profile of the parasite populations, with significant differences seen in the allele frequencies of mutant <it>pfmdr1 </it>N86Y, <it>pfmdr1 </it>Y184F, <it>pfcrt </it>A220S, and <it>pfdhps </it>A437G.</p> <p>Conclusion</p> <p>The study provides evidence for high levels of resistance to the combination regimen of AQ+SP in PNG and indicates which of the many molecular markers analysed are useful for the monitoring of parasite resistance to combinations with AQ+SP.</p

Lack of multiple copies of pfmdr1 gene in Papua New Guinea

We describe here the results of an analysis of Plasmodium falciparum multidrug resistance protein 1 (pfmdr1) gene copy number from 440 field isolates from Papua New Guinea. No multiple copies of the gene were found, which corresponds to the lack of usage of mefloquine. These data extend regional knowledge about the distribution of multidrug-resistant P. falciparu

Plasmodium falciparum resistance to anti-malarial drugs in Papua New Guinea: evaluation of a community-based approach for the molecular monitoring of resistance

ABSTRACT: BACKGROUND: Molecular monitoring of parasite resistance has become an important complementary tool in establishing rational anti-malarial drug policies. Community surveys provide a representative sample of the parasite population and can be carried out more rapidly than accrual of samples from clinical cases, but it is not known whether the frequencies of genetic resistance markers in clinical cases differ from those in the overall population, or whether such community surveys can provide good predictions of treatment failure rates. METHODS: Between 2003 and 2005, in vivo drug efficacy of amodiaquine or chloroquine plus sulphadoxine-pyrimethamine was determined at three sites in Papua New Guinea. The genetic drug resistance profile (i.e., 33 single nucleotide polymorphisms in Plasmodium falciparum crt, mdr1, dhfr, dhps, and ATPase6) was concurrently assessed in 639 community samples collected in the catchment areas of the respective health facilities by using a DNA microarray-based method. Mutant allele and haplotype frequencies were determined and their relationship with treatment failure rates at each site in each year was investigated. RESULTS: PCR-corrected in vivo treatment failure rates were between 12% and 28% and varied by site and year with variable longitudinal trends. In the community samples, the frequencies of mutations in pfcrt and pfmdr1 were high and did not show significant changes over time. Mutant allele frequencies in pfdhfr were moderate and those in pfdhps were low. No mutations were detected in pfATPase6. There was much more variation between sites than temporal, within-site, variation in allele and haplotype frequencies. This variation did not correlate well with treatment failure rates. Allele and haplotype frequencies were very similar in clinical and community samples from the same site. CONCLUSIONS: The relationship between parasite genetics and in vivo treatment failure rate is not straightforward. The frequencies of genetic anti-malarial resistance markers appear to be very similar in community and clinical samples, but cannot be used to make precise predictions of clinical outcome. Thus, indicators based on molecular data have to be considered with caution and interpreted in the local context, especially with regard to prior drug usage and level of pre-existing immunity. Testing community samples for molecular drug resistance markers is a complementary tool that should help decision-making for the best treatment options and appropriate potential alternative

Transformations of Infrastructure Systems: Report of the second International Conference of the Research Training Group KRITIS at the Technical University Darmstadt, Germany

On the 4th and 5th of November 2021, more than 50 scholars from different disciplines and countries came together in an online conference to discuss the multiple aspects of Transformations of Infrastructure Systems at the second international conference organized by the Research Training Group KRITIS at the Technical University of Darmstadt, Germany. The focus of this conference was on the dynamic and changing nature of infrastructure systems and describing, understanding, and explaining transformation processes of infrastructures. Within the four multidisciplinary panels (Safety, Cultures, Governance, and both Temporality and Spatiality) the participants shared their research and knowledge on various aspects of transformation of infrastructure Systems. The conference gave an insight into the triggers of transformations and highlighted the conditions under which they take place and the consequences. The keynote lectures by Prof. Dr. Timothy Moss (Humboldt University of Berlin), Dr. Anique Hommels (Maastricht University), and Niklas Vespermann (Federal Network Agency, Germany) further highlighted and deepened the aspects relevant to this context

Quantifying the Evolution and Impact of Antimalarial Drug Resistance: Drug Use, Spread of Resistance, and Drug Failure over a 12-Year Period in Papua New Guinea

Background. Antimalarial use is a key factor driving drug resistance and reduced treatment effectiveness in Plasmodium falciparum malaria, but there are few formal, quantitative analyses of this process. Methods. We analyzed drug usage, drug failure rates, and the frequencies of mutations and haplotypes known to be associated with drug resistance over a 12-year period (1991-2002) in a site in Papua New Guinea. This period included 2 successive treatment policies: amodiaquine (AQ) or chloroquine (CQ) from 1991 through 2000 and their subsequent replacement by sulfadoxine-pyrimethamine (SP) plus AQ or SP plus CQ. Results. Drug use approximated 1 treatment per person-year and was associated with increasing frequencies of pfcrt and pfmdr1 mutations and of treatment failure. The frequency of pfdhfr mutations also increased, especially after the change in treatment policy. Treatment failure rates multiplied by 3.5 between 1996 and 2000 but then decreased dramatically after treatment policy change. Conclusions. With high levels of resistance to CQ, AQ, and SP, the deployment of the combination of both drugs appears to increase clinical effectiveness but does not decelerate growth of resistance. Our estimates of mutation and haplotype frequencies provide estimates of selection coefficients acting in this environment, which are key parameters for understanding the dynamics of resistanc

Automated identification of local contamination in remote atmospheric composition time series

Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be "tuned" to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).Peer reviewe

Information recovery from low coverage whole-genome bisulfite sequencing.

The cost of whole-genome bisulfite sequencing (WGBS) remains a bottleneck for many studies and it is therefore imperative to extract as much information as possible from a given dataset. This is particularly important because even at the recommend 30X coverage for reference methylomes, up to 50% of high-resolution features such as differentially methylated positions (DMPs) cannot be called with current methods as determined by saturation analysis. To address this limitation, we have developed a tool that dynamically segments WGBS methylomes into blocks of comethylation (COMETs) from which lost information can be recovered in the form of differentially methylated COMETs (DMCs). Using this tool, we demonstrate recovery of ∼30% of the lost DMP information content as DMCs even at very low (5X) coverage. This constitutes twice the amount that can be recovered using an existing method based on differentially methylated regions (DMRs). In addition, we explored the relationship between COMETs and haplotypes in lymphoblastoid cell lines of African and European origin. Using best fit analysis, we show COMETs to be correlated in a population-specific manner, suggesting that this type of dynamic segmentation may be useful for integrated (epi)genome-wide association studies in the future