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Wielding the power of knowledge
No full textThis article examines how anti-gender mobilizations strategically (mis)use knowledge and epistemic authority to legitimize their claims. Focusing on the manual Children Are at Stake! A Manual for Mothers, Fathers, and Teachers to Protect Children from LGBTQIA+ Ideology , we situate our analysis within broader global patterns of anti-gender campaigns that target schools, universities, and academic freedom. While previous scholarship has described such practices as ‘troll science’ or ‘counterknowledge’, our study advances this work by identifying the specific mechanisms through which such knowledge is produced and mobilized. Using a mixed-methods approach, we coded 24 references cited in the manual and analysed the relationship between these sources and the claims they were used to support. The results show three distinct techniques of knowledge (mis)use: the Torsion Technique (twisting knowledge through interpretative extension or distortion), the Magician’s Technique (fabricating relevance through phantomized or invented connections), and the Echo Technique (accurately summarizing ideologically aligned but questionable sources). Together, these strategies demonstrate how anti-gender actors exploit the symbolic authority of science, often stripped of context and sensationalized, to produce a parallel politics of knowledge. We argue that these practices not only delegitimize critical scholarship but also generate alternative epistemologies that reinforce retrotopian visions of social order
Primerjava metod za inferenco genskih regulatornih omrežij
No full textThe main goal of our work was to systematically evaluate and compare different methods for gene regulatory network (GRN) inference. GRN inference is a challenging task due to the high dimensionality of gene expression data and the complex regulatory interactions between transcription factors (TFs) and their target genes.
We studied several modern methods for GRN inference, including PANDA, OTTER, GENIE3, ARACNe, and CLR. For each method, we described the required input data, the computational approach used, and the results obtained. We evaluated the effectiveness of individual methods by comparing reconstructed networks with a reference network, using metrics such as MSE, accuracy, sensitivity, and F1. In addition, we analysed how noise and sample size affect the performance of each method.
Since the “true” underlying network of gene regulation that could serve as a gold standard for verifying the results is not available, we decided to create our own simulated test networks. The first, smaller network was used to test the impact of sample size and noise on the performance of individual methods. The second, larger network is based on the knowledge network of gene interactions in model plant Arabidopsis thaliana and represents realistic biological data. Based on these networks, we simulated gene expression using a multivariate normal distribution. In doing so, we ensured that characteristic correlations between TFs and their target genes were preserved, including activation and inhibition relationships and self-regulation.
Our work contributes to a better understanding of the effectiveness of different methods for GRN reconstruction. It also represents progress, as we test the methods on more complex organisms, multicellular plants such as Arabidopsis thaliana, and not just on bacteria or yeast, as has been common in comparative evaluations of methods such as in the DREAM5 challenges.
Our results thus enable the comparison of methods under controlled conditions, assess their robustness at different noise levels and sample sizes, and provide guidelines for selecting appropriate approaches for the analysis of real biological data.Glavni cilj našega dela je bilo sistematično ovrednotiti in primerjati različne metode za inferenco genskih regulatornih omrežij. Zaradi visoke dimenzionalnosti podatkov o genski izraženosti ter kompleksnih regulatornih interakcij med transkripcijskimi faktorji in njihovimi tarčnimi geni inferenca genskih regulatornih omrežij predstavlja velik izziv.
Preučevali smo več sodobnih metod, in sicer PANDA, OTTER, GENIE3, ARACNe in CLR. Za vsako od navedenih metod smo predstavili zahtevane vhodne podatke, uporabljen računski pristop in rezultate. Učinkovitost posameznih metod smo ocenili s primerjavo rekonstruiranih omrežij z referenčnim omrežjem, pri čemer smo uporabili metrike, kot so MSE, točnost, občutljivost in F1. Poleg tega smo analizirali tudi, kako na delovanje posamezne metode vplivata šum in velikost vzorca.
Ker za genska regulatorna omrežja običajno nimamo popolnega resničnega omrežja, ki bi služilo kot zlati standard za preverjanje rezultatov, smo se odločili simulirati testna omrežja. Prvo, manjše omrežje, je bilo uporabljeno za testiranje vpliva velikosti vzorca in količine šuma na delovanje posameznih metod. Drugo, večje omrežje, temelji na omrežju Arabidopsis thaliana in predstavlja realistične biološke podatke. Na podlagi teh omrežij smo simulirali izražanje genov z multivariatno normalno porazdelitvijo. Pri tem smo ohranili značilnosti funkcijskih povezav med transkripcijskimi faktorji in njihovimi tarčnimi geni, vključno z aktivacijskimi in inhibicijskimi povezavami ter samoregulacijo.
Naše delo prispeva k boljšemu razumevanju učinkovitosti različnih metod za rekonstrukcijo genskih regulatornih omrežij. Prav tako predstavlja napredek, saj metode testiramo na kompleksnejših organizmih, večceličnih rastlinah, kot je Arabidopsis thaliana, in ne zgolj na bakterijah ali kvasovkah, kot je bilo doslej običajno pri primerjavi metod v okviru izzivov DREAM5. Rezultati našega dela omogočajo primerjavo metod pod nadzorovanimi pogoji, ocenjujejo njihovo robustnost pri različnih nivojih šuma in različnih velikostih vzorcev ter predstavljajo smernice za izbiro primernih pristopov pri analizi realnih bioloških podatkov iz kompleksnejših organizmov
An analysis of the definitions of insolvency and threatened insolvency across EU member states
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The impact of financial literacy and risk tolerance on the portfolio diversification of Slovenian investors
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