Földrengés-populációk komplex statisztikus vizsgálata, kapcsolódásokkal földrengésprognózishoz = Complex statistical studies of earthquake populations, with possible applications to earthquake prediction

Vizsgáltuk három eltérő szeizmotektonikai környezet földrengéseinek statisztikus jellemzőit, különös tekintettel a rengések térben, időben és energia szerinti előfordulásának fraktálstatisztikájára, azon belül pedig a keletkezési időpontok kérdéskörére. A rengések előfordulása időben egyenletlen, klaszteresedő, a legerősebb rengések előfordulásai sem felelnek meg a Poisson valószínűségi eloszlásnak. A klaszteresedés mértékét a teljes vizsgált időskálán jellemző "globális" fraktáldimenzió nagysága eltér az időskála kisebb szakaszain mutatott inhomogenitásokat leíró "lokális" fraktáldimenzióktól, az időbeli előfordulások struktúrája multiskálázott, a jelenség multifraktál. A szeizmicitás területi eloszlása is mindhárom térségben fraktáljellegű. A területi eloszlásra jellemző fraktáldimenzió és az energia szerinti gyakorisággal kapcsolatos szeizmikus paraméter közötti összefüggést vizsgálva, korrelációs kapcsolatot mutattunk ki a két paraméter között. A korreláció jellegének valószínűsíthetően geodinamikai háttere lehet. Eredményeink szerint a szeizmicitás mind térben, mind időben mutatott eloszlási mintázatát egy és ugyanaz a globális fraktáldimenzió kontrollálja, ennek konkrét értékeire a szeizmogenezist önszerveződésű kritikus folyamatként értelmezve sikerült magyarázatot találnunk. Neurális számítástechnikán alapuló numerikus modellszámításokat végeztünk erős földrengések keletkezési időpontjainak prognosztizálására. Az eredmények arra utalnak, hogy a neurális hálózatokkal történő modellezésnek ígéretes alkalmazásai lehetnek földrengéskutatásban. | Statistical characteristics, with special respect to fractal statistics, of earthquake occurrences in time, space and energy domain, have been investigated in three different seismotectonic environments. Particular attention was payed to the problem of earhquake occurrence times. Earthquakes occur clustered in time, even the strongest events do not follow the Poissonian probability distribution. The ?global? and ?local? fractal dimensions, characterising the degree of clustering on different sections of the time scale, show different values, the occurrence of earthquakes is a multiscaled process, multifractal. The areal distribution of seismicity also exhibits fractal features in all three areas. Considering possible relations between fractal dimension of earthquake epicenter distribution and well known in seismology seismic b-parameter, correlations between these characteristics have been found. The nature of the obtained correlations can possibly be attributable to geodynamic background. Results hint that the pattern of distribution of seismicity in both space and time is controlled by one and the same global fractal dimension, the particular values of which at the different regions can be interpreted in the frames of the approach of self-organized criticality to seismogenesis. Neural-network modelling studies have been carried out with the aim to predict the occurrence times of strong earthquakes. The remarkably good predicting power of the constructed neural networks suggests the usefulness of this tool in earthquake prediction problems

USING THE EMBL-EBI CLUSTAL OMEGA TOOL TO CALCULATE DIVERSITY OF HEAVY CHAIN PHAGE-DISPLAY LIBRARIES

Here we show that traditional Sanger sequencing combined with analysis tools available from the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), specifically EMBOSS Transeq and Clustal Omega, is extremely effective in the analysis of naïve phage display antibody libraries for the determination of library size and diversity. The free tools are easy to use and require little manipulation of reads by hand, allowing analysis to be performed on a standard personal computer. Utilization of this technique has applicability to researchers with limited access to deep sequencing. The primary drawback to this analysis methodology is that antibodies with particular molecular or binding properties, desirable or otherwise, are not identifiable by their sequences

Black Soldier Fly (Hermetia illucens Linnaeus) as Feed for the American Alligator (Alligator mississippiensis Daudin)

Larvae of Hermetia illucens Linnaeus (Diptera: Stratiomyidae), the black soldier fly (BSF), or one of two commercial feeds were offered to three randomized groups of hatchling American Alliga­tors (Alligator mississippiensis Daudin) (Crocodylia: Alligatoridae) for three months and their growth monitored. Statistical analysis of weight (g) (Wilcoxon/Kruskal-Wallis Test) and snout-vent length (cm) (Welch ANOVA F-test) increases were significantly greater in groups of alligators fed 56% protein/11 % fat or 45% protein/8% fat com­mercial feeds versus alligators fed BSF. Based on these results, BSF can not be recommended as a complete replacement for commercial feeds in young alligators

Preliminary Investigation into the Use of Logistic Regression to Predict Parasite Intermediate Hosts. Case Study: Dujardinascaris waltoni (Nemtatoda: Ascarididae) in the American Alligator (Alligator mississippiensis)

We present here a case study that illustrates the potential for the use of logistic regression to facilitate identification of a parasite’s intermediate host. We used a full factorial logistic regression to estimate the probability that adult American alligators (Alligator mississippiensis) were infected with the ascarid nematode Dujardinascaris waltoni given the alligators’ physical characteristics and stomach contents. The strength of association between a fish-based diet and infection predict fish as the intermediate host taxon. Significant but weaker associations with reptile-mammal and reptile-crustacean diets are likely due to the high percentage of alligator remains present in the stomachs examined, with infection likely the result of vertical transmission of the parasites, not the ingestion of these other prey categories. This case study strongly suggests logistic regression has the potential to determine parasite intermediate hosts if definitive host stomach contents can be routinely and adequately sampled and the food items identified to taxa

Detrended fluctuation analysis as a statistical tool to monitor the climate

Detrended fluctuation analysis is used to investigate power law relationship between the monthly averages of the maximum daily temperatures for different locations in the western US. On the map created by the power law exponents, we can distinguish different geographical regions with different power law exponents. When the power law exponents obtained from the detrended fluctuation analysis are plotted versus the standard deviation of the temperature fluctuations, we observe different data points belonging to the different climates, hence indicating that by observing the long-time trends in the fluctuations of temperature we can distinguish between different climates.Comment: 8 pages, 4 figures, submitted to JSTA