A Recipe for Successful Metastasis: Transition and Migratory Modes of Ovarian Cancer Cells.

One of the characteristic features of ovarian cancer is its early dissemination. Metastasis and the invasiveness of ovarian cancer are strongly dependent on the phenotypical and molecular determinants of cancer cells. Invasive cancer cells, circulating tumor cells, and cancer stem cells, which are responsible for the metastatic process, may all undergo different modes of transition, giving rise to mesenchymal, amoeboid, and redifferentiated epithelial cells. Such variability is the result of the changing needs of cancer cells, which strive to survive and colonize new organs. This would not be possible if not for the variety of migration modes adopted by the transformed cells. The most common type of metastasis in ovarian cancer is dissemination through the transcoelomic route, but transitions in ovarian cancer cells contribute greatly to hematogenous and lymphatic dissemination. This review aims to outline the transition modes of ovarian cancer cells and discuss the migratory capabilities of those cells in light of the known ovarian cancer metastasis routes.

Mutual information of spin systems from autoregressive neural networks.

We describe a direct method to estimate the bipartite mutual information of a classical spin system based on Monte Carlo sampling enhanced by autoregressive neural networks. It enables us to study arbitrary geometries of subsystems, and it can be generalized to classical field theories. We demonstrate it on the Ising model for four partitionings, including a multiply connected even-odd division. We show that the area law is satisfied for temperatures away from the critical temperature: the constant term is universal, whereas the proportionality coefficient is different for the even-odd partitioning.

Simulating first-order phase transition with hierarchical autoregressive networks.

We apply the hierarchical autoregressive neural network sampling algorithm to the two-dimensional Q-state Potts model and perform simulations around the phase transition at Q=12. We quantify the performance of the approach in the vicinity of the first-order phase transition and compare it with that of the Wolff cluster algorithm. We find a significant improvement as far as the statistical uncertainty is concerned at a similar numerical effort. In order to efficiently train large neural networks we introduce the technique of pretraining. It allows us to train some neural networks using smaller system sizes and then employ them as starting configurations for larger system sizes. This is possible due to the recursive construction of our hierarchical approach. Our results serve as a demonstration of the performance of the hierarchical approach for systems exhibiting bimodal distributions. Additionally, we provide estimates of the free energy and entropy in the vicinity of the phase transition with statistical uncertainties of the order of 10^{-7} for the former and 10^{-3} for the latter based on a statistics of 10^{6} configurations.

Analysis of autocorrelation times in neural Markov chain Monte Carlo simulations.

We provide a deepened study of autocorrelations in neural Markov chain Monte Carlo (NMCMC) simulations, a version of the traditional Metropolis algorithm which employs neural networks to provide independent proposals. We illustrate our ideas using the two-dimensional Ising model. We discuss several estimates of autocorrelation times in the context of NMCMC, some inspired by analytical results derived for the Metropolized independent sampler (MIS). We check their reliability by estimating them on a small system where analytical results can also be obtained. Based on the analytical results for MIS, we propose a loss function and study its impact on the autocorrelation times. Although, this function's performance is a bit inferior to the traditional Kullback-Leibler divergence, it offers two training algorithms which in some situations may be beneficial. By studying a small 4×4 system, we gain access to the dynamics of the training process, which we visualize using several observables. Furthermore, we quantitatively investigate the impact of imposing global discrete symmetries of the system in the neural network training process on the autocorrelation times. Eventually, we propose a scheme which incorporates partial heat-bath updates, which considerably improves the quality of the training. The impact of the above enhancements is discussed for a 16×16 spin system. The summary of our findings may serve as guidance to the implementation of NMCMC simulations for more complicated models.

The role of phoenixin in the proliferation and migration of ectopic epithelial cells in vitro.

AIM: Endometriosis is one of the most common gynecologic diseases in women of reproductive age. The pathophysiology of endometriosis is still not fully understood. Phoenixin (PNX-14) is a newly discovered neuropeptide that regulates the hypothalamo-pituitary-gonadal (HPG) axis and reproductive functions. Recently, we reported that PNX-14, its precursor protein and receptor were expressed in human endometrium. Moreover, PNX-14 serum levels in endometriosis were reduced. This study aimed to evaluate the in vitro biological functions of physiological PNX-14 concentrations on the ectopic endometrium Z12 cells. METHODS: The proliferation and migration of Z12 cells were assessed using the xCELLigence® RTCA DP system following 72 h of stimulation with 0.05 and 0.2 nM of PNX-14. GPR173 and small integral membrane protein 20 (SMIM20) gene expression was evaluated using quantitative polymerase chain reaction (qPCR) and the protein levels of GPR173 were analyzed using Western blot analysis. RESULTS: PNX-14 at the concentration observed in the serum of patients with endometriosis (0.05 nM) reduced GPR173 and increased SMIM20 expression, while protein levels of GPR173 remained unchanged. Cell proliferation was increased by the 0.02 nM PNX-14- the concentration found in healthy subjects. The 0.2 nM of PNX-14 decreased SMIM20 expression with no change to GPR173 expression and reduced ectopic epithelial cell proliferation during the first 5 h after stimulation. However, at 72 h, the proliferation increased. CONCLUSIONS: This study shows that PNX-14 at endometriosis specific concentration desensitized ectopic epithelium to PNX-14, and increased the expression of SMIM20 to restore the physiological levels of PNX-14.

Rényi entropy of zeta urns.

We calculate analytically the Rényi entropy for the zeta-urn model with a Gibbs measure definition of the microstate probabilities. This allows us to obtain the singularities in the Rényi entropy from those of the thermodynamic potential, which is directly related to the free-energy density of the model. We enumerate the various possible behaviors of the Rényi entropy and its singularities, which depend on both the value of the power law in the zeta urn and the order of the Rényi entropy under consideration.

Random allocation models in the thermodynamic limit.

We discuss the phase transition and critical exponents in the random allocation model (urn model) for different statistical ensembles. We provide a unified presentation of the statistical properties of the model in the thermodynamic limit, uncover relationships between the thermodynamic potentials, and fill some lacunae in previous results on the singularities of these potentials at the critical point and behavior in the thermodynamic limit. The presentation is intended to be self-contained, so we carefully derive all formulas step by step throughout. Additionally, we comment on a quasiprobabilistic normalization of configuration weights, which was considered in some recent studies.

Negative psychological aspects of working with experimental animals in scientific research.

The aim of the study was to reveal the negative psychological aspects of using animals by scientists and to determine whether the emotional tensions and stress are associated with performing experiments on animals. All 150 participants of the study conduct experiments on animals in their work. Computer-assisted web interviewing, was used to collect the data. Correlation matrices for factorial analysis of main component loads and cluster analysis have been calculated as grouping methods revealed two different categories of researchers, which were mostly distinguished by acceptance and aversion to animal testing and animal welfare. The main findings demonstrated, that there is a group of respondents who feel discomfort when performing experiments on animals. Especially young people involved in animal testing, feel remorse, emotional tension and helplessness.

The Study of the Expression of CGB1 and CGB2 in Human Cancer Tissues.

Human chorionic gonadotropin (hCG) is a well-known hormone produced by the trophoblast during pregnancy as well as by both trophoblastic and non-trophoblastic tumors. hCG is built from two subunits: α (hCGα) and ß (hCGß). The hormone-specific ß subunit is encoded by six allelic genes: CGB3, CGB5, CGB6, CGB7, CGB8, and CGB9, mapped to the 19q13.32 locus. This gene cluster also encompasses the CGB1 and CGB2 genes, which were originally considered to be pseudogenes, but as documented by several studies are transcriptionally active. Even though the protein products of these genes have not yet been identified, based on The Cancer Genome Atlas (TCGA) database analysis we showed that the mutual presence of CGB1 and CGB2 transcripts is a characteristic feature of cancers of different origin, including bladder urothelial carcinoma, cervical squamous cell carcinoma, esophageal carcinoma, head and neck squamous cell carcinoma, ovarian serous cystadenocarcinoma, lung squamous cell carcinoma, pancreatic adenocarcinoma, rectum adenocacinoma, testis germ cell tumors, thymoma, uterine corpus endometrial carcinoma and uterine carcinosarcoma.

Regulation of human chorionic gonadotropin beta subunit expression in ovarian cancer.

PURPOSE: Expression of human chorionic gonadotropin beta subunit by cancers is extensively documented, yet regulation of the multiple genes that can code for this protein is poorly understood. The aim of the study was to examine the mechanisms regulating CGB gene expression in ovarian cancer. METHODS: Expression of CGB genes and SP1, SP3, TFAP2A transcription factor genes was evaluated by RT-qPCR. The methylation status of CGB genes promoter regions was examined by methylation-specific PCR. RESULTS: mRNA arising from multiple CGB genes was detected in both ovarian control and malignant tissues. However, expression of CGB3-9 genes was shown to be significantly higher in malignant than healthy ovarian tissues. CGB1 and CGB2 transcripts were shown to be present in 20% of ovarian cancers, but were not detected in any of the control samples. Malignant tissues were characterized by DNA demethylation of CGB promoter regions. In ovarian cancer CGB expression positively correlated with TFAP2A transcripts level and expression of TFAP2A transcription factor was significantly higher in cancer than in control tissues. In contrast SP3 expression level was significantly lower in ovarian tumours than in control ovarian tissue. CONCLUSIONS: In ovarian cancers increased expression of human chorionic gonadotropin beta subunit is associated with demethylation of CGB promoter regions. CGB3-9 expression level strongly correlates with expression of the TFAP2A transcription factor. Presence of mRNA arising from CGB1 and CGB2 genes appears to be a unique feature of a subset of ovarian cancers.

The association between irisin and muscle metabolism in different thyroid disorders.

BACKGROUND: Irisin is a new adipo-myokine, encoded by the FNDC5 gene. Currently, there is a discussion regarding the relation between thyroid function and irisin concentration. This prospective study assesses the influence of thyrometabolic changes on serum irisin concentration in association with altered muscle metabolism. This is performed on a large cohort of patients affected by severe hypo- or hyperthyroidism, as well as by the expression of the FNDC5 gene in thyroid tissue affected by different pathologies. METHODS: The study group comprised 119 patients with newly diagnosed severe hyperthyroidism or hypothyroidism, and a control group of 45 healthy subjects. Body composition, serum irisin concentrations, and thyroid-related hormones, creatine kinase, dystrophin and titin concentrations were evaluated. FNDC5 expression was also analysed in tissue samples from 80 patients with nontoxic multinodular goitre, toxic goitre, Graves' disease and papillary thyroid cancer. RESULTS: Irisin concentration was lower in patients with prolonged hypothyroidism. There was a tendency towards lower dystrophin and titin concentrations in patients with hypothyroidism and hyperthyroidism. Restoration of euthyroidism in patients with hypothyroidism resulted in a decreased muscle mass with an increase in irisin concentrations, while the hyperthyroid group showed an increase in fat mass. Statistically significant overexpression of FNDC5 gene was found in patients with toxic goitre as compared to Graves' disease, papillary thyroid cancer and controls. CONCLUSIONS: The presented data support the theory that irisin concentration changes are associated with prolonged hypothyroidism and might primarily constitute the result of prolonged myopathy. These changes are most likely not related to the expression of the FNDC5 gene in the thyroid gland.

Novel scintillating material 2-(4-styrylphenyl)benzoxazole for the fully digital and MRI compatible J-PET tomograph based on plastic scintillators.

A novel plastic scintillator is developed for the application in the digital positron emission tomography (PET). The novelty of the concept lies in application of the 2-(4-styrylphenyl)benzoxazole as a wavelength shifter. The substance has not been used as scintillator dopant before. A dopant shifts the scintillation spectrum towards longer wavelengths making it more suitable for applications in scintillators of long strips geometry and light detection with digital silicon photomultipliers. These features open perspectives for the construction of the cost-effective and MRI-compatible PET scanner with the large field of view. In this article we present the synthesis method and characterize performance of the elaborated scintillator by determining its light emission spectrum, light emission efficiency, rising and decay time of the scintillation pulses and resulting timing resolution when applied in the positron emission tomography. The optimal concentration of the novel wavelength shifter was established by maximizing the light output and it was found to be 0.05 ‰ for cuboidal scintillator with dimensions of 14 mm x 14 mm x 20 mm.

Fluorescence resonance energy transfer usage to track the sequence promoter changes in CGB5 gene in ovarian cancer patients.

PURPOSE: The survival rates for ovarian cancer patients remain very low, often as a result of late diagnosis due to the asymptomatic course of the early stage disease. Based on the important biological contribution of human chorionic gonadotropin to various key processes including; cell cycle control, DNA repair, cellular differentiation and developmental processes, we hypothesized that genetic polymorphisms in the genes promoter could be associated with ovarian cancer risk. Thus, the purpose of the study was to determine whether particular polymorphisms occur in the promoter region of the human chorionic gonadotropin polypeptide 5 encoding gene, and if so, are they associated with ovarian cancer outcome. PATIENTS AND METHODS: We analyzed Central European females diagnosed with ovarian cancer (n=95) and controls (n=76) for the occurrence of at least one of three polymorphisms (rs7260002, rs7246045, rs540432391) and their impact on cancer risk. The fluorescence resonance energy transfer technique was used in order to conduct single nucleotide polymorphisms genotyping. RESULTS: The occurrence of two studied polymorphisms, rs7260002 and rs540432391 present in the 5' upstream region of the chorionic gonadotropin (CG) gene were associated with an increased risk of ovarian cancer. The former polymorphism had a minor impact on cancer risk (P=0.049; OR=1.95; 95% CI=0.97-3.92), while the latter had a much larger impact and may be of great importance in the evaluation of cancer development in the analyzed population (p<0.001; OR 8.5; 95% CI 3.59-20.23). CONCLUSIONS: The fluorescence resonance energy transfer application used in tracking the sequence promoter variations of genes expressed during tumorigenesis may be an important factor in early prediction of ovarian cancer. Taking under consideration the elevated CG expression associated with several different cancer types it seems reasonable to estimate if the analyzed polymorphisms could affect cancer outcome.

hCG - related molecules and their measurement.

Measurements of human chorionic gonadotropin (hCG) synthesized by trophoblast cells is a powerful tool of pregnancy monitoring. It was showed that similarly to pregnancy also trophoblastic and nontrophoblastic malignancies produce variety of hCG molecules. In urine and serum of both pregnant women and tumors patients a fifteen various forms of hCG, such as: regular hCG, hyperglycosylated hCG and predominant hyperglycosylated hCG free ß, were identified. These forms might be useful in order to recognize between physiological and pathological pregnancies as well as cancers. Even the presence of these different hormone variants is well documented the commercially available biochemical tests detecting hCG failed to identified and distinguish among these forms. Especially hard is to identify glycan chains linked to heterodimer. Thus, a detailed analysis of hCG-related molecules produced during physiological and pathological condition, together with a new tests development are needed.

A high frequency steady-state visually evoked potential based brain computer interface using consumer-grade EEG headset.

This work evaluates a possibility of creating a high-frequency, SSVEP-based brain computer interface using a low cost EEG recording hardware - an Emotiv EEG Neuro-headset. Both above aspects are crucial to enable deploying the BCI technology in the consumer market. High frequencies can be used to create a non-tiring and more pleasant interface. Commercial EEG systems, as the Emotiv EEG, although demonstrating large underperformance, are much more affordable than standard, clinical-grade EEG amplifiers. A system classifying between two stimuli and rest is designed and tested in two experiments: on five and ten subject respectively. First, the accuracy of the system is compared for frequencies in lower range (17Hz, 19Hz, 23Hz, 25Hz) and higher range (31Hz, 33Hz, 37Hz, 40Hz). The mean online accuracy is 80%±15% for the former and 67%±12% for the latter. Second, a more thorough investigation is done by evaluating the system for frequencies within a set of 35Hz-40Hz. Although the mean accuracy, 64% ± 22%, is relatively low, most of the users were able to achieve satisfying accuracy, with the mean reaching 82%±5%, which would allow for an efficient, and yet pleasant, usage of the BCI system. In each case a user dependent approach is applied, with a calibration session lasting about five minutes. EEG feature extraction is done using common spatial pattern (CSP) filtering, canonical correlation analysis (CCA), and linear discrimination analysis (LDA).

Respiratory-cycle related analysis of the EEG-spectrum during sleep: a healthy population study.

Recent research has shown the EEG's spectral changes that occur in synchrony with the respiratory-cycle. During wakefulness, and for healthy subjects it is reported that the EEG power in several frequency bands changes between the expiratory and inspiratory phases. For sleep-disordered breathing (SDB) patients, it is reported that the amplitude of changes in normalized EEG power (referred to as respiratory-cycle related EEG changes RCREC) within a respiratory-cycle decreases after a successful intervention to alleviate the SDB condition. In this paper, we focus on analyzing the changes in the sleep's EEG spectrum related to the respiratory-cycle for a healthy population comprising 39 subjects. For 3 sleep stages (N2, N3, REM), 6 EEG channels, and 7 frequency bands, two types of EEG spectral analyzes were considered: 1) the ratio between the EEG power during expiration and that during inspiration, and 2) the RCREC. For the first type of analysis and at the population level, no statistically significant difference was found between the EEG power during expiration and that during inspiration. For the second type of analysis, the RCREC for all conditions is at a level that is statistically significantly larger than 0.1. The latter being the value at which the RCREC decreased after successful SDB intervention.

Long-range disassortative correlations in generic random trees.

We explicitly calculate the distance dependent correlation functions in a maximal entropy ensemble of random trees. We show that correlations remain disassortative at all distances and vanish only as a second inverse power of the distance. We discuss in detail the example of scale-free trees where the diverging second moment of the degree distribution leads to some interesting phenomena.

Correlations in connected random graphs.

We study the properties of the giant connected component in random graphs with arbitrary degree distribution. We concentrate on the degree-degree correlations. We show that the adjoining nodes in the giant connected component are correlated and derive analytic formulas for the joint nearest-neighbor degree probability distribution. Using those results we describe correlations in maximal entropy connected random graphs. We show that connected graphs are disassortative and that correlations are strongly related to the presence of one-degree nodes (leaves). We propose an efficient algorithm for generating connected random graphs. We illustrate our results with several examples.