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Size and complexity of social networks among substance abusers: childhood and current correlates.

The objective of this study was to identify parental, childhood, demographic, and social function factors associated with social network size and complexity among substance abusers using retrospective data regarding family and childhood history and current data regarding demographic characteristics and psychosocial function. The authors interviewed 505 voluntary patients with substance abuse at two university medical centers in Minnesota and Oklahoma with alcohol-drug programs located within departments of psychiatry. Data collection instruments included a childhood questionnaire, a demographic checklist, and two psychiatric rating scales of psychosocial function. The authors found that years of education, current residence with others, being actively occupied at work or school, and higher psychosocial function on two psychiatrist-rated scales were associated with increased social network size and complexity. Loss of mother, out-of-home placement, and runaway before age 18 were associated with smaller social networks in adulthood. Age, gender, and current marital status were not associated with social network. Regression analysis indicated that network size (i.e., the number of individuals in the network) was associated with higher psychosocial function over the last year but not over the last two weeks, whereas network complexity (ie, the number of subgroups in the network) was related to psychosocial function over both the last year and the last two weeks. These data indicate that in addicted persons, both childhood factors and current social factors affect network size and complexity. Network complexity may be amenable to short-term change, whereas network size may be more related to longer-term coping.

Adaptation, Psychological↗
PubMed2001

Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits.

Circuits and their involvement in complex dynamics are described in differential terms in Part I of this work. Here, we first explain why it may be appropriate to use a logical description, either by itself or in symbiosis with the differential description. The major problem of a logical description is to find an adequate way to involve time. The procedure we adopted differs radically from the classical one by its fully asynchronous character. In Sec. II we describe our "naive" logical approach, and use it to illustrate the major laws of circuitry (namely, the involvement of positive circuits in multistationarity and of negative circuits in periodicity) and in a biological example. Already in the naive description, the major steps of the logical description are to: (i) describe a model as a set of logical equations, (ii) derive the state table from the equations, (iii) derive the graph of the sequences of states from the state table, and (iv) determine which of the possible pathways will be actually followed in terms of time delays. In the following sections we consider multivalued variables where required, the introduction of logical parameters and of logical values ascribed to the thresholds, and the concept of characteristic state of a circuit. This generalized logical description provides an image whose qualitative fit with the differential description is quite remarkable. A major interest of the generalized logical description is that it implies a limited and often quite small number of possible combinations of values of the logical parameters. The space of the logical parameters is thus cut into a limited number of boxes, each of which is characterized by a defined qualitative behavior of the system. Our analysis tells which constraints on the logical parameters must be fulfilled in order for any circuit (or combination of circuits) to be functional. Functionality of a circuit will result in multistationarity (in the case of a positive circuit) or in a cycle (in the case of a negative circuit). The last sections deal with "more about time delays" and "reverse logic," an approach that aims to proceed rationally from facts to models. (c) 2001 American Institute of Physics.

Journal Article↗
PubMed2000

Artificial neural networks compared to factor analysis for low-dimensional classification of high-dimensional body fat topography data of healthy and diabetic subjects.

Subcutaneous adipose tissue thickness was measured in 590 healthy subjects at 15 specific body sites by means of the new optical device, lipometer, providing a high-dimensional and partly highly intercorrelated set of data, which had been analyzed by factor analysis previously. N-2-N back-propagation neural networks are able to perform low-dimensional display of high-dimensional data as a special application. We report about the performance of such a 15-2-15 network and compare its results with the output of factor analysis. As test data for verification, measurement values on women with proven diabetes mellitus type II (NIDDM) are used. Surprisingly our 15-2-15 neural network is able to reproduce the classification pattern resulting from factor analysis very precisely. After extracting the network weights the classification of new subjects is even more simple with the neural network as compared with factor analysis. In addition, the network weights are able to cluster highly correlated body sites nicely to different groups, corresponding to different regions of the human body. Thus, the analysis of these weights provides additional information about the structure of the data. Therefore, N-2-N networks seem to be a good alternative method for analyzing high-dimensional data with strong intercorrelation.

Adipose Tissue↗
PubMed2005

[Improving partial least square regression precision in NIR multi-component analysis using artificial neural network].

The present paper presents a new NIR multi-component analysis method with Artificial Neural Network(ANN) and Partial Least Square Regression(PLS). First, this method divides the concentration range of training samples into some sub-ranges, and respectively computes a PLS correlation model in each sub-range with the sub-range's training samples. Then, the authors classify prediction samples according to its concentration sub-range with ANN and judge which sub-range theprediction sample belongs to. Finally, the authors compute the concentration of prediction component with the PLS correlation model of the sub-range according to ANN. The experiment and the result of data processing show that this method improves the model's applicability, and evidently enhances prediction precision compared to traditional PLS.

Least-Squares Analysis↗
PubMed2000

Analysis of Myc/Max/Mad network members in adipogenesis: inhibition of the proliferative burst and differentiation by ectopically expressed Mad1.

Transcription factors of the Myc/Max/Mad network affect multiple aspects of cellular behavior, including proliferation, differentiation, and apoptosis. Recent studies have shown that Mad proteins can inhibit cellular growth and transformation and thus antagonize the function of Myc proteins. To define further the contribution of these proteins to cellular growth control, we have studied the expression of the respective genes and proteins in 3T3-L1 cells, both upon serum stimulation of quiescent cells and during adipocytic differentiation in response to insulin, dexamethasone, and isobutylmethylxanthine. We found distinct expression patterns for the mad genes. Mad4 was induced when cells exit the cell cycle and, together with mad1, during the late phase of differentiation. In contrast, mad3 expression was associated with progression through S phase and the proliferative burst of differentiating preadipocytes, overlapping in part c-myc expression. DNA binding analyses revealed that the most prominent network complex both in cycling and in differentiating cells was Mnt/Max, whereas c-Myc/Max complexes were detectable only during peak c-Myc expression periods. Ectopic expression of Mad1 in preadipocytes resulted in the inhibition of S phase and the proliferation associated with the proliferative burst; as a consequence, adipocytic differentiation was significantly inhibited. Our findings suggest that the precise temporal regulation of Myc/Max/Mad network proteins is critical for determining cellular behavior.

1-Methyl-3-isobutylxanthine↗
PubMed2003

Hormone replacement therapy and cancer risk: a systematic analysis from a network of case-control studies.

To provide comprehensive and quantitative information on the benefits and risks of hormone replacement therapy (HRT) on several cancer sites, we systematically examined the relation between HRT use and the risk of various cancers in women aged 45-79 by using data from a framework of case-control studies conducted in Italy between 1983 and 1999. The overall data set included the following incident, histologically confirmed neoplasms: oral cavity, pharynx, larynx and esophagus (n = 253), stomach (n = 258), colon (n = 886), rectum (n = 488), liver (n = 105), gallbladder (n = 31), pancreas (n = 122), breast (n = 4,713), endometrium (n = 704), ovary (n = 1,614), urinary bladder (n = 106), kidney (n = 102), thyroid (n = 65), Hodgkin's disease (n = 26), non-Hodgkin's lymphomas (n = 145), multiple myeloma (n = 65) and sarcomas (n = 78). The control group comprised 6,976 women aged 45-79 years, admitted for a wide spectrum of acute, nonneoplastic conditions. Odds ratios (OR) and the corresponding 95% confidence intervals (CI) for use of HRT were derived from multiple logistic regression equations. There was an inverse association between ever use of HRT and colon (OR = 0.7), rectum (OR = 0.5) and liver cancer (OR = 0.2), with a consistent pattern of protection for duration of use. An excess risk was found for gallbladder (OR = 3.2), breast (OR = 1.1), endometrial (OR = 3.0) and urinary bladder cancer (OR = 2.0). These data from a southern European population add some useful information on the risk-benefit assessment of HRT among postmenopausal women.

Aged↗
PubMed2003

Synthesis and physicochemical analysis of interpenetrating networks containing modified gelatin and poly(ethylene glycol) diacrylate.

The interrelated effects of gelatin modification, content, and poly(ethylene glycol) molecular weight on the melting temperature, surface hydrophilicity, tensile properties, swelling/degradation, and drug-release kinetics of a novel interpenetrating network (IPN) system containing gelatin and poly(ethylene glycol) diacrylate were evaluated. Gelatin content had a large effect on the IPN melting temperature and Delta H. Modifying gelatin with ethylenediaminetetraacetic acid and/or monomethoxy poly(ethylene glycol) monoacetate ester as well as increasing poly(ethylene glycol) diacrylate molecular weight increased the surface hydrophilicity. Increasing the gelatin weight percent increased the IPN elasticity at room temperature. When buffer and elevated temperature were present in the testing environment, the elasticity of all IPNs tested decreased. IPNs showed an enhanced elasticity and strength when compared with glutaraldehyde-fixed gelatin hydrogels. The extent of IPN swelling and degradation was increased by increasing the gelatin content or by modifying gelatin. The time to complete sample degradation was longer for IPNs when compared with gelatin crosslinked with glutaraldehyde. Modifications to the IPN system increased the maximum percent of chlorhexidine digluconate released from the IPNs. The rate of complete drug release was slower from IPNs than from glutaraldehyde-fixed gelatin matrices. A wide range of IPN physicochemical properties was obtained through formulation changes and chemical modifications.

Biocompatible Materials↗
PubMed1990

Analysis of neuronal networks: a review of techniques for labeling axonal projections.

In order to analyze connections between neurons in the vertebrate central nervous system, methods have been developed to label a given population of axons of known origin so that they can be differentiated from other, non-labeled structures. Three such methods are reviewed here: experimentally induced orthograde (Wallerian) degeneration, axon transport of radioactive proteins demonstrated by autoradiography, and axon transport of macromolecules that can be reacted histochemically to yield a visible reaction product. Each of the methods has particular strengths and weaknesses. Degeneration methods may differentiate between different functional classes of axons which have different fiber diameters. However, degeneration distorts the morphology of axon terminals, making them more difficult to interpret, and degenerating terminals may be removed rapidly by phagocytosis. Autoradiography of radioactive terminals preserves normal fine structure, but the necessary exposure times extend the method by weeks or months, and care must be exercised to distinguish labeled axons from other structures exhibiting background or transneuronal radioactivity. Histochemical methods, such as those used to demonstrate horseradish peroxidase conjugated to wheat germ lectin (WGA-HRP), are sensitive and rapid, but the injection site must be carefully characterized, and the presence of transneuronal label may make interpretation of the results difficult. Experimental methods of axonal labeling have been invaluable in studying neuronal networks. Each of the methods described here may be of particular value, given the nature of the system to be analyzed.

Animals↗
PubMed1979

Activity analysis of neural networks.

In a series of articles (Leung et al., 1973, 1974; Ogŭztöreli, 1972, 1975, 1978, 1979; Stein et al., 1974) we have investigated some of the physiologically significant properties of a general neural model. In these papers the nature of the oscillations occuring in the model has been briefly analyzed by omitting the effects of the discrete time-lags in the interaction of neurons, although these time-lags were incoporated in the general model. In the present work we investigate the effects of the time-lags on the oscillations which are intrinsic to the neural model, depending on the structural parameters such as external inputs, interaction coefficients, self-inhibition, self-excitation and self-adaptation coefficients. The numerical solution of the neural model, the computation of the steady-state solutions and the natural modes of the oscillations around the steady-state solutions are described.

Humans↗
PubMed1978

Analysis of neuronal networks in the visual system of the cat using statistical signals--simple and complex cells. Part II.

Superimposing additively a two-dimensional noise process to deterministic input signals (bars) the neurons of area 17 show a class-specific reaction for the task of signal extraction. Moving both parts of the signals simultaneously and varying the signal to noise ratio (S/N) the simple cells achieve the same performance as resulted from the psychophysical experiment. Type I complex cells extract moving deterministic signals (i.e. bars) from the stationary noise, whereas in the answers of Type II complex cells the statistical parts of the signals predominate. Considering the different cell types each as a series of a linear and a nonlinear system one obtains the cell specific space-time frequency and the amplitude characteristics.

Animals↗
PubMed2005

Biological significance of autoregulation through steady state analysis of genetic networks.

Autoregulation of regulatory proteins is a recurring theme in genetic networks. Autoregulation is an important component of a genetic regulatory network besides protein-protein and protein-DNA interactions, stoichiometry, multiple binding sites and cooperativity. Although the biological significance of autoregulation has been studied before, its significance in presence of other mechanisms is not clearly enumerated. We have analyzed at steady state the significance of autoregulation in presence of other molecular mechanisms by considering hypothetical genetic networks. We demonstrate that autoregulation of a regulatory protein can impart amplification to the response. Further, autoregulation of an activator binding to the DNA as a dimer can introduce bistability, thus forcing the system to reside in two distinct steady states. In combination with autoregulation, cooperative binding can further increase the sensitivity and can yield a highly ultrasensitive response. We conclude that autoregulation with the help of other molecular mechanisms can impart distinct system level properties such as amplification, sensitivity and bistability. The results are further discussed in relation to various examples of genetic networks that exist in biological systems.

DNA↗
PubMed2004

Conservation analysis in biochemical networks: computational issues for software writers.

Large scale genomic studies are generating significant amounts of data on the structure of cellular networks. This is in contrast to kinetic data, which is frequently absent, unreliable or fragmentary. There is, therefore, a desire by many in the community to investigate the potential rewards of analyzing the more readily available topological data. This brief review is concerned with a particular property of biological networks, namely structural conservations (e.g. moiety conserved cycles). There has been much discussion in the literature on these cycles but a review on the computational issues related to conserved cycles has been missing. This review is concerned with the detection and characterization of conservation relations in arbitrary networks and related issues, which impinge on simulation simulation software writers. This review will not address flux balance constraints or small-world type analyses in any significant detail.

Biochemical Phenomena↗
PubMed2026

SLC1A5 and NUMA1 are potential regulators and therapeutic targets of ferroptosis in diffuse large B-cell lymphoma.

BACKGROUND: Ferroptosis, a form of regulated cell death driven by iron-dependent lipid peroxidation, has emerged as a potential therapeutic target in various cancers, including diffuse large B-cell lymphoma (DLBCL). This study aimed to identify and characterize ferroptosis-related panel genes with prognostic value in DLBCL. METHODS: Transcriptomic data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) in DLBCL samples. Gene set variation analysis (GSVA) and network topology analysis were performed to identify key ferroptosis-related genes. Lasso regression was utilized to construct a prognostic model based on the identified panel genes. In vitro experiments, including gene silencing, overexpression, and ferroptosis induction, were conducted to evaluate the functional roles of the identified genes, NUMA1 and SLC1A5, in DLBCL cells. RESULTS: A panel of ferroptosis-related genes with prognostic value, including NUMA1 and SLC1A5, was identified in DLBCL samples. Silencing SLC1A5 or overexpressing NUMA1 in DLBCL cells enhanced sensitivity to ferroptosis inducers, increased intracellular labile iron and lipid peroxidation levels, promoted mitochondrial damage, and modulated the expression of key ferroptosis markers. Furthermore, SLC1A5 silencing or NUMA1 overexpression augmented radiation-induced ferroptosis in DLBCL cells. CONCLUSION: NUMA1 and SLC1A5 are potential ferroptosis regulators and therapeutic targets in DLBCL. Silencing the ferroptosis-suppressive transporter SLC1A5 or restoring NUMA1 expression promotes lipid peroxidation and ferroptotic cell death, thereby sensitizing DLBCL cells to ferroptosis and enhancing radiosensitivity-providing a rationale for novel ferroptosis-based therapeutic strategies.

Humans↗
PubMed2005

Language network specializations: an analysis with parallel task designs and functional magnetic resonance imaging.

Although the classical core regions of the language system (Broca's and Wernicke's areas) were defined over a century ago, it took the advent of functional imaging to sharpen our understanding of how these regions and adjacent parts of the brain are associated with particular aspects of language. One limitation of such studies has been the need to compare results across different subject groups, each performing a different type of language task. Thus, this study was designed to examine overlapping versus segregated brain activations associated with three fundamental language tasks, orthography, phonology and semantics performed by the same subjects during a single experimental session. The results demonstrate a set of primarily left-sided core language regions in ventrolateral frontal, supplementary motor, posterior mid-temporal, occipito-temporal and inferior parietal areas, which were activated for all language tasks. Segregated task-specific activations were demonstrated within the ventrolateral frontal, mid-temporal and inferior parietal areas. Within the inferior frontal cortex (Broca's regional complex), segregated activations were seen for the semantic and phonological tasks. These findings demonstrate both common and task specific activations within the language system.

Adult↗