Detecting Wage Under-reporting Using a Double Hurdle Model

We estimate a double hurdle (DH) model of the Hungarian wage distribution assuming censoring at the minimum wage and wage under-reporting (i.e. compensation consisting of the minimum wage, subject to taxation, and an unreported cash supplement). We estimate the probability of under-reporting for minimum wage earners, simulate their genuine earnings and classify them and their employers as 'cheaters' and 'non-cheaters'. In the possession of the classification we check how cheaters and non-cheaters reacted to the introduction of a minimum social security contribution base, equal to 200 per cent of the minimum wage, in 2007. The findings suggest that cheaters were more likely to raise the wages of their minimum wage earners to 200 per cent of the minimum wage thereby reducing the risk of tax audit. Cheating firms also experienced faster average wage growth and slower output growth. The results suggest that the DH model is able to identify the loci of wage under-reporting with some precision.tax evasion, double hurdle model, Hungary

Explaining the elongated shape of 'Oumuamua by the Eikonal abrasion model

The photometry of the minor body with extrasolar origin (1I/2017 U1) 'Oumuamua revealed an unprecedented shape: Meech et al. (2017) reported a shape elongation b/a close to 1/10, which calls for theoretical explanation. Here we show that the abrasion of a primordial asteroid by a huge number of tiny particles ultimately leads to such elongated shape. The model (called the Eikonal equation) predicting this outcome was already suggested in Domokos et al. (2009) to play an important role in the evolution of asteroid shapes.Comment: Accepted by the Research Notes of the AA

Static properties and spin dynamics of the ferromagnetic spin-1 Bose gas in magnetic field

Properties of spin-1 Bose gases with ferromagnetic interaction in the presence of a nonzero magnetic field are studied. The equation of state and thermodynamic quantities are worked out with the help of a mean-field approximation. The phase diagram besides Bose-Einstein condensation contains a first order transition where two values of the magnetization coexist. The dynamics is investigated with the help of the Random Phase Approximation. The soft mode corresponding to the critical point of the magnetic phase transition is found to behave like in conventional theory.Comment: 8 pages and 3 figures included in text, submitted to Physical Review

Intrinsic protein disorder in histone lysine methylation

Histone lysine methyltransferases (HKMTs), catalyze mono-, di- and trimethylation of lysine residues, resulting in a regulatory pattern that controls gene expression. Their involvement in many different cellular processes and diseases makes HKMTs an intensively studied protein group, but scientific interest so far has been concentrated mostly on their catalytic domains. In this work we set out to analyze the structural heterogeneity of human HKMTs and found that many contain long intrinsically disordered regions (IDRs) that are conserved through vertebrate species. Our predictions show that these IDRs contain several linear motifs and conserved putative binding sites that harbor cancer-related SNPs. Although there are only limited data available in the literature, some of the predicted binding regions overlap with interacting segments identified experimentally. The importance of a disordered binding site is illustrated through the example of the ternary complex between MLL1, menin and LEDGF/p75. Our suggestion is that intrinsic protein disorder plays an as yet unrecognized role in epigenetic regulation, which needs to be further elucidated through structural and functional studies aimed specifically at the disordered regions of HKMTs. Reviewers: This article was reviewed by Arne Elofsson and Piotr Zielenkiewicz. © 2016 The Author(s)

NANOPARTICLES: TOXICITY AND PENETRATION ACROSS BIOLOGICAL BARRIERS

Nanoparticles provide new opportunities for drug delivery and human therapy. To fulfill the therapeutical potential of nanoparticles two major aspects, toxicity and penetration across barriers of the body need to be studied. Different ex vivo and in vitro cell culture based models of the skin, nasal, lung, intestinal and blood-brain barriers have been established in our laboratory that can be used for both purposes. Three different types of nanoparticles were tested on the different models. Amorphous nanoparticles from the antiinflammatory drug meloxicam were obtained by by co-grinding with polyvinylpyrrolidone. Nanosized bilayered vesicles of non-ionic surfactants bearing glucose and amino acid ligands were prepared to specifically target solute carriers on the blood-brain barrier [1]. Poly(ferrocenyl silane) redox responsive polymer nanocarriers were also studied [2]. Several methods were applied parallelly to measure the toxicity of nanoparticles. In addition to colorimetric tests like MTT dye reduction assay, release of the cytoplasmic enzyme lactate dehydrogenase cellular events were also monitored in real time. By measuring impedance across microelectrodes covered with cells quantitative information on cell viability and intercellular adherence indicating paracellular permeability could be obtained. Co-culture models of the barriers prepared from primary cultures or human cell lines [3] served for permeability experiments to test the penetration of nanocarriers across cell layers. In the case of the blood-brain barrier a kinetic in vivo study in mice was also performed by near infrared fluorescence time-domain optical imaging. The results indicate that (i) toxicity measurements are very important to obtain the optimal dose of nanoparticles on living cells, (ii) nanonization of drugs can improve drug dissolution, absorption and pharmacokinetics, (iii) targeting of microvesicles increases their penetration across barriers

Brain protein expression changes in WAG/Rij rats, a genetic rat model of absence epilepsy after peripheral lipopolysaccharide treatment

Peripheral injection of bacterial lipopolysaccharide (LPS) facilitates 8-10Hz spike-wave discharges (SWD) characterizing absence epilepsy in WAG/Rij rats. It is unknown however, whether peripherally administered LPS is able to alter the generator areas of epileptic activity at the molecular level. We injected 1mg/kg dose of LPS intraperitoneally into WAG/Rij rats, recorded the body temperature and EEG, and examined the protein expression changes of the proteome 12h after injection in the fronto-parietal cortex and thalamus. We used fluorescent two-dimensional differential gel electrophoresis to investigate the expression profile. We found 16 differentially expressed proteins in the fronto-parietal cortex and 35 proteins in the thalamus. It is known that SWD genesis correlates with the transitional state of sleep-wake cycle thus we performed meta-analysis of the altered proteins in relation to inflammation, epilepsy as well as sleep. The analysis revealed that all categories are highly represented by the altered proteins and these protein-sets have considerable overlap. Protein network modeling suggested that the alterations in the proteome were largely induced by the immune response, which invokes the NFkB signaling pathway. The proteomics and computational analysis verified the known functional interplay between inflammation, epilepsy and sleep and highlighted proteins that are involved in their common synaptic mechanisms. Our physiological findings support the phenomenon that high dose of peripheral LPS injection increases SWD-number, modifies its duration as well as the sleep-wake stages and decreases body temperature

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates