An evolutionary hybrid cellular automaton model of solid tumour growth

We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour, this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level.     

Evaluation of PAPNET-assisted cervical rescreening

Evaluation of PAPNET-assisted cervical rescreening
We have compared the results of targeted manual rescreening of 1211 randomly selected smears with the results of PAPNET-assisted rescreening of 1613 cervical smears, containing at least 6.3% low-grade squamous intraepithelial lesion (SIL). PAPNET diagnosis and the targeted rescreening diagnosis were compared with the initial report, issued on the corresponding smear. Reproducibility scores for inadequacy, presence of endocervical and endometrial cells, specific infections and squamous cell abnormalities were determined. The reproducibility scores for the diagnosis of inadequate smears and specific infections were lower with the PAPNET-assisted rescreening. The detection of squamous cell abnormalities was excellent for both methods (>0.95), with a higher detection rate for false-negative smears with the PAPNET testing system.     

Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

Background

Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.

Results

Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G₁ fixed point was found for deterministic updating schemes.

Conclusions

In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.     

Mono- and dinuclear manganese(II) complexes derived from the cis/trans isomers of 2,4,5-tris(2-pyridyl)-4,5-dihydroimidazole

Two new manganese(II) complexes, [Mn(L1)(L1H)(ClO₄)(H₂O)][ClO₄]₂·0.5CH₃CN·H₂O (1) [L1 = trans-(±)2-(2,5-di(pyridin-2-yl)-4,5-dihydro-1H-imidazol-4-yl)pyridine)] and [Mn₂(μ-L2)₂(H₂O)₃(CH₃CN)₃][ClO₄]₄·2CH₃CN (2) [L2 = cis-(±)2-(2,5-di(pyridin-2-yl)-4,5-dihydro-1H-imidazol-4-yl)pyridine)], have been prepared and examined by single-crystal X-ray diffraction analysis, showing that complex 1 is a mononuclear compound, whereas complex 2 is a dinuclear species. The cis/trans isomers L1 and L2 have similar coordination properties, but behave as bidentate and tridentate chelating ligands, respectively, giving distorted octahedral metal coordination geometries. X-ray diffraction studies revealed that the molecular and crystal structures are stabilized by a series of intra- and intermolecular interactions. In both cases extended supramolecular networks are generated, in compound 1 through O-H···O, O-H···N, N-H···O, N-H···N, C-H···O, C-H···N, C-H···π and π···π interactions, and in compound 2 through O-H···O, O-H···N, C-H···O and π···π interactions. The observed structural differences between the two metal complexes might be a consequence of these stabilizing effects.     

Evidence for new Neanderthal teeth in Tabun Cave (Israel) by the application of self-organizing maps (SOMs)

Morphological and metrical study suggested that seven human teeth from Tabun Cave, Israel were part of the upper dentition of a single, probably Neanderthal, individual renumbered as Tabun BC7. An enamel fragment gave ESR age estimates of 82+/-14 ka (early U-uptake) and 92+/-18 ka (linear uptake) and an age estimate of 90(+30)(-16) ka using U-series disequilibrium. Although metrical analyses suggested Neanderthal affinities, definitive assessment was difficult as the values often fell into the ranges of both Neanderthal and Levantine early modern human samples. Therefore, two further classification analyses were conducted (neural networks using self-organizing maps and homogeneity analysis). Both identify Tabun BC7 as a Neanderthal. Neural networks are a promising tool for paleoanthropological studies as they can provide reliable classifications even with incomplete data.     

Alignments grow, secondary structure prediction improves.

Using information from sequence alignments significantly improves protein secondary structure prediction. Typically, more divergent profiles yield better predictions. Recently, various groups have shown that accuracy can be improved significantly by using PSI-BLAST profiles to develop new prediction methods. Here, we focused on the influences of various alignment strategies on two 8-year-old PHD methods. The following results stood out. (i) PHD using pairwise alignments predicts about 72% of all residues correctly in one of the three states: helix, strand, and other. Using larger databases and PSI-BLAST raised accuracy to 75%. (ii) More than 60% of the improvement originated from the growth of current sequence databases; about 20% resulted from detailed changes in the alignment procedure (substitution matrix, thresholds, and gap penalties). Another 20% of the improvement resulted from carefully using iterated PSI-BLAST searches. (iii) It is of interest that we failed to improve prediction accuracy further when attempting to refine the alignment by dynamic programming (MaxHom and ClustalW). (iv) Improvement through family growth appears to saturate at some point. However, most families have not reached this saturation. Hence, we anticipate that prediction accuracy will continue to rise with database growth.     

Goals and objectives of the Italian Network for Tumor Biotherapy (NIBIT)

The explosion in the immuno-oncology field, exemplified by the clinical implementation of immune checkpoint inhibitor blockade and other immunotherapeutic strategies was quickly recognized by the Italian biomedical community, thanks to the networking activities of the Italian Network for Tumor Biotherapy (NIBIT), which has been active since 2004 in the diffusion of new scientific and clinical findings in the fields of tumor immunology and immunotherapy. Numerous activities of NIBIT have also helped to overcome the hurdles associated with the clinical implementation of cancer immune-biotherapeutic strategies at the national and international levels. Looking forward, a concerted interaction of NIBIT with existing European networks focused on cancer bio-immunotherapy will further contribute to the development of improved therapies in the immuno-oncology field. This Introduction briefly summarizes the history and objectives of NIBIT, as well as the current activities of the Network.     

Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information

In this paper a systematic attempt has been made to develop a better method for predicting alpha-turns in proteins. Most of the commonly used approaches in the field of protein structure prediction have been tried in this study, which includes statistical approach "Sequence Coupled Model" and machine learning approaches; i) artificial neural network (ANN); ii) Weka (Waikato Environment for Knowledge Analysis) Classifiers and iii) Parallel Exemplar Based Learning (PEBLS). We have also used multiple sequence alignment obtained from PSIBLAST and secondary structure information predicted by PSIPRED. The training and testing of all methods has been performed on a data set of 193 non-homologous protein X-ray structures using five-fold cross-validation. It has been observed that ANN with multiple sequence alignment and predicted secondary structure information outperforms other methods. Based on our observations we have developed an ANN-based method for predicting alpha-turns in proteins. The main components of the method are two feed-forward back-propagation networks with a single hidden layer. The first sequence-structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position specific scoring matrices. The initial predictions obtained from the first network and PSIPRED predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. The final network yields an overall prediction accuracy of 78.0% and MCC of 0.16. A web server AlphaPred (http://www.imtech.res.in/raghava/alphapred/) has been developed based on this approach.