Expression of an Activation Antigen, Mo3e, Associated With the Cellular Response to Migration Inhibitory Factor by HL‐60 Promyelocytes Undergoing Monocyte‐Macrophage Differentiation

HL‐60 promyelocytic cells acquire the surface expression of the Mo3e antigenic determinant after exposure to PMA or compounds that raise intracellular concentrations of cyclic AMP (dibutyryl cyclic AMP or a combination of cholera toxin and IBMX). The expression of Mo3e by these stimulated HL‐60 cells coincides with the development of features of monocyte‐macrophage differentiation (characteristic morphology, nonspecific esterase activity, and respiratory burst activity). During in vitro monocyte‐macrophage differentiation, HL‐60 cells become responsive to migration inhibitory factor (MIF); the MIF responsiveness of differentiated HL‐60 cells is blocked by anti‐Mo3e monoclonal antibody. These findings further support the relationship between the expression of Mo3e and the cellular response to MIF.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141671/1/jlb0492.pd

Soluble and cell-associated transferrin receptor in lung cancer.

The expression of transferrin receptor (TfR) has been identified in many malignant tumours. In lung cancer, lymphoma and breast cancer, it has been shown that the expression of TfR correlates with tumour differentiation, probably implying some prognostic value. A soluble form of TfR (sTfR) in human serum has been shown to be proportional to the number of cellular TfRs. Based on these data we examined the utility of measuring sTfR in the serum and bronchoalveolar lavage (BAL) fluid of patients with lung cancer (n = 32) and patients with chronic obstructive pulmonary disease (n = 22). BAL fluid was centrifuged to separate the supernatant from the cellular component. Cells were lysed in a detergent and cell-associated TfR was measured by enzyme-linked immunosorbent assay (ELISA) and expressed as ng 10(-6) cells in this cellular component. There was no difference in serum sTfR between the cancer and chronic obstructive pulmonary disease (COPD) groups. A higher level of cell-associated TfR was found in BAL of non-small-cell lung cancer patients than in COPD patients (P = 0.01). The calculated number of TfR molecules per cell in BAL correlated positively with the percentage of macrophages in BAL (P < 0.0001), suggesting that cell-associated TfR in BAL originates primarily from macrophages in this fluid. No correlation existed between BAL cell-associated TfR and tumour size, nodal status, the presence of metastases and serum sTfR. BAL cell-associated TfR was negatively correlated with BAL supernatant neuron-specific enolase (NSE) (P = 0.01). A combination of BAL supernatant NSE and cell-associated TfR detected lung cancer with a sensitivity of 91%, a specificity of 59% and positive and negative predictive values of 81% and 71% respectively. In conclusion, BAL cell-associated TfR may help in the differential diagnosis of lung cancer vs pneumonia

Identification of Candidate Driver Genes in Common Focal Chromosomal Aberrations of Microsatellite Stable Colorectal Cancer

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Chromosomal instability (CIN) is a major driving force of microsatellite stable (MSS) sporadic CRC. CIN tumours are characterised by a large number of somatic chromosomal copy number aberrations (SCNA) that frequently affect oncogenes and tumour suppressor genes. The main aim of this work was to identify novel candidate CRC driver genes affected by recurrent and focal SCNA. High resolution genome-wide comparative genome hybridisation (CGH) arrays were used to compare tumour and normal DNA for 53 sporadic CRC cases. Context corrected common aberration (COCA) analysis and custom algorithms identified 64 deletions and 32 gains of focal minimal common regions (FMCR) at high frequency (>10%). Comparison of these FMCR with published genomic profiles from CRC revealed common overlap (42.2% of deletions and 34.4% of copy gains). Pathway analysis showed that apoptosis and p53 signalling pathways were commonly affected by deleted FMCR, and MAPK and potassium channel pathways by gains of FMCR. Candidate tumour suppressor genes in deleted FMCR included RASSF3, IFNAR1, IFNAR2 and NFKBIA and candidate oncogenes in gained FMCR included PRDM16, TNS1, RPA3 and KCNMA1. In conclusion, this study confirms some previously identified aberrations in MSS CRC and provides in silico evidence for some novel candidate driver gene

U.S. stock market interaction network as learned by the Boltzmann Machine

We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented analysis shows that binarization preserves market correlation structure. Properties of distributions of external fields and couplings as well as industry sector clustering structure are studied for different historical dates and moving window sizes. We found that a heavy positive tail in the distribution of couplings is responsible for the sparse market clustering structure. We also show that discrepancies between the model parameters might be used as a precursor of financial instabilities.Comment: 15 pages, 17 figures, 1 tabl

Statistical pairwise interaction model of stock market

Financial markets are a classical example of complex systems as they comprise many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price of restrictive assumptions on the market dynamics or others are agent-based models with rules designed in order to recover some empirical behaviours. Here we show that the pairwise model is actually a statistically consistent model with observed first and second moments of the stocks orientation without making such restrictive assumptions. This is done with an approach based only on empirical data of price returns. Our data analysis of six major indices suggests that the actual interaction structure may be thought as an Ising model on a complex network with interaction strengths scaling as the inverse of the system size. This has potentially important implications since many properties of such a model are already known and some techniques of the spin glass theory can be straightforwardly applied. Typical behaviours, as multiple equilibria or metastable states, different characteristic time scales, spatial patterns, order-disorder, could find an explanation in this picture.Comment: 11 pages, 8 figure

Are there biological differences between screen-detected and interval colorectal cancers in the English Bowel Cancer Screening Programme?

Background: We measured biomarkers of tumour growth and vascularity in interval and screen-detected colorectal cancers (CRCs) in the English Bowel Cancer Screening Programme in order to determine whether rapid tumour growth might contribute to interval CRC (a CRC diagnosed between a negative guaiac stool test and the next scheduled screening episode). Methods: Formalin-fixed, paraffin-embedded sections from 71 CRCs (screen-detected 43, interval 28) underwent immunohistochemistry for CD31 and Ki-67, in order to measure the microvessel density (MVD) and proliferation index (PI), respectively, as well as microsatellite instability (MSI) testing. Results: Interval CRCs were larger (P=0.02) and were more likely to exhibit venous invasion (P=0.005) than screen-detected tumours. There was no significant difference in MVD or PI between interval and screen-detected CRCs. More interval CRCs displayed MSI-high (14%) compared with screen-detected tumours (5%). A significantly (P=0.005) higher proportion (51%) of screen-detected CRC resection specimens contained at least one polyp compared with interval CRC (18%) resections. Conclusions: We found no evidence of biological differences between interval and screen-detected CRCs, consistent with the low sensitivity of guaiac stool testing as the main driver of interval CRC. The contribution of synchronous adenomas to occult blood loss for screening requires further investigation

Broad ligament cystic lymphangioma: A case report

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

ПРИМЕНЕНИЕ ЭВОЛЮЦИОННОЙ ПАРАДИГМЫ К ПРОЕКТИРОВАНИЮ АРХИТЕКТУРЫ НЕЙРОННОЙ СЕТИ ДЛЯ РАСПОЗНАВАНИЯ ИСКАЖЁННОГО ТЕКСТА

The paper presents an attempt to apply of evolutionary methods to the design and training of a system for recognizing distorted text.Over the past decades, artificial neural networks are widely used in many areas of artificial intelligence, such as forecasting, optimization, data analysis, pattern recognition and decision making. Nevertheless, the traditional heuristic approaches to design of multi-layer neural networks are based on the recombination of already existing neural network architectures.This approach allows us to solve a wide range of problems, but implies compliance with specific conditions for the quality work of algorithms.The natural analogues of such intelligent systems in living nature, however, are universal enough to adapt to virtually any habitat.Despite their extreme complexity and limited ability to study their structures, it is known that these structures were formed as a result of the evolutionary process. And if today it is impossible to determine the exact architecture of the links in biological neural systems, then at least one can try to reproduce the very process of their formation in order to obtain a more universal algorithm than those developed to the present moment.In opposite to them we form the final structure of the core of the classification system by evolutionary process, taking into account the knowledge about the features of the development and construction of the nervous system of vertebrates.Applying of the approach makes it possible to abstract from the limitations of existing neural network algorithms, caused by the scope of application of specific types of their structures.В течение последних десятилетий искусственные нейронные сети хорошо себя зарекомендовали во многих областях искусственного интеллекта, таких, например, как прогнозирование, оптимизация, анализ данных, распознавание образов и принятие решений. Тем не менее, традиционные эвристические подходы к разработке топологии многослойных нейронных сетей основываются на рекомбинации уже существующих нейросетевых архитектур. Такой подход позволяет решать широкий спектр задач, но подразумевает соблюдение специфических условий для качественной работы алгоритмов.Естественные аналоги подобных интеллектуальных систем в живой природе, однако, достаточно универсальны, чтобы адаптироваться практически к любой среде обитания.Несмотря на их чрезвычайную сложность и ограниченные возможности к исследованию их структур, известно, что эти конструкции были сформированы в результате эволюционного процесса. И если на сегодняшний день невозможно определить точную архитектуру связей в биологических нейросистемах, то, по крайней мере, можно попытаться воспроизвести сам процесс их формирования с целью получения более универсального алгоритма, чем те, что разработаны к настоящему моменту.В рассматриваемой работе окончательная структура ядра системы классификации образуется в результате эволюционного процесса, с учетом известных сегодня знаний об особенностях развития и строения нервной системы позвоночных.Использование описываемого подхода позволяет абстрагироваться от ограничений существующих нейросетевых алгоритмов, обусловленных сферой применения конкретных типов их структур.

Нейроэволюционное подкрепляющее обучение нейронных сетей

The article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. The last two are used as the primary method for reducing the dimension of the input signal of the system and simplifying the process of its training in terms of computational complexity.In the presented work, the neural network structure of the control device of the modeled system is formed in the course of the evolutionary process, taking into account the currently known structural and developmental features of self-learning systems that take place in living nature. This method of constructing it makes it possible to bypass the specific limitations of models created on the basis of recombination of already known topologies of neural networks.В статье представлены результаты объединения 4-х различных типов обучения нейронных сетей: эволюционного, с подкреплением, глубокого и экстраполирующего. Последние два используются в качестве первичного метода уменьшения размерности входного сигнала системы и упрощения процесса её обучения с точки зрения вычислительной сложности.В представленной работе нейросетевая структура управляющего устройства моделируемой системы формируется в ходе эволюционного процесса, с учётом известных на текущий момент особенностей строения и развития самообучающихся систем, имеющих место в живой природе. Данный способ его конструирования даёт возможность обойти специфические ограничения моделей, созданных на основе рекомбинации уже известных топологий нейронных сетей