Classification of cellular malignancy associated changes in cervical cancer

Cervical cancer is the leading cause of death and disease from malignant neoplasms among women in developing countries. Even though the Pap smear has significantly decreased the number of deaths from cervical cancer in the past years, it has its limitations. Researchers have developed an automated screening machine which can potentially detect abnormal cases that are overlooked by conventional screening. The goal of quantitative cytology is to classify the patient's tissue sample based on quantitative measurements of the individual cells. It is also much cheaper and potentially can take less time. One of the major challenges of collecting cells with a cytobrush is the possibility of not sampling any existing dysplastic cells on the cervix. Being able to correctly classify patients who have disease without the presence of dysplastic cells could improve the accuracy of quantitative cytology algorithms. Subtle morphologic changes in normal-appearing tissues adjacent to or distant from malignant tumors have been shown to exist, but a comparison of various statistical methods, including many recent advances in the statistical learning field, has not previously been done. The objective of this thesis is to use different classification methods applied to quantitative cytology data for the detection of malignancy associated changes (MACs). In this thesis, Elastic Net is the best algorithm. When we applied the Elastic Net algorithm to the test set, we combined the training set and validation set as "training" set and used 5-fold cross validation to choose the parameter for Elastic Net. It has a sensitivity of 47% at 80% specificity, an AUC 0.52, and a partial AUC 0.10 (95% CI 0.09-0.11).^

Mechanisms of perceptual learning of depth discrimination in random dot stereograms.

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

Molecular mechanisms underlying a cellular analog of operant reward learning.

Operant conditioning is a ubiquitous but mechanistically poorly understood form of associative learning in which an animal learns the consequences of its behavior. Using a single-cell analog of operant conditioning in neuron B51 of Aplysia, we examined second-messenger pathways engaged by activity and reward and how they may provide a biochemical association underlying operant learning. Conditioning was blocked by Rp-cAMP, a peptide inhibitor of PKA, a PKC inhibitor, and by expressing a dominant-negative isoform of Ca2+-dependent PKC (apl-I). Thus, both PKA and PKC were necessary for operant conditioning. Injection of cAMP into B51 mimicked the effects of operant conditioning. Activation of PKC also mimicked conditioning but was dependent on both cAMP and PKA, suggesting that PKC acted at some point upstream of PKA activation. Our results demonstrate how these molecules can interact to mediate operant conditioning in an individual neuron important for the expression of the conditioned behavior.

Cerebellar mechanisms for motor learning: Testing predictions from a large-scale computer simulation

The cerebellum is the major brain structure that contributes to our ability to improve movements through learning and experience. We have combined computer simulations with behavioral and lesion studies to investigate how modification of synaptic strength at two different sites within the cerebellum contributes to a simple form of motor learning—Pavlovian conditioning of the eyelid response. These studies are based on the wealth of knowledge about the intrinsic circuitry and physiology of the cerebellum and the straightforward manner in which this circuitry is engaged during eyelid conditioning. Thus, our simulations are constrained by the well-characterized synaptic organization of the cerebellum and further, the activity of cerebellar inputs during simulated eyelid conditioning is based on existing recording data. These simulations have allowed us to make two important predictions regarding the mechanisms underlying cerebellar function, which we have tested and confirmed with behavioral studies. The first prediction describes the mechanisms by which one of the sites of synaptic modification, the granule to Purkinje cell synapses (gr → Pkj) of the cerebellar cortex, could generate two time-dependent properties of eyelid conditioning—response timing and the ISI function. An empirical test of this prediction using small, electrolytic lesions of the cerebellar cortex revealed the pattern of results predicted by the simulations. The second prediction made by the simulations is that modification of synaptic strength at the other site of plasticity, the mossy fiber to deep nuclei synapses (mf → nuc), is under the control of Purkinje cell activity. The analysis predicts that this property should confer mf → nuc synapses with resistance to extinction. Thus, while extinction processes erase plasticity at the first site, residual plasticity at mf → nuc synapses remains. The residual plasticity at the mf → nuc site confers the cerebellum with the capability for rapid relearning long after the learned behavior has been extinguished. We confirmed this prediction using a lesion technique that reversibly disconnected the cerebellar cortex at various stages during extinction and reacquisition of eyelid responses. The results of these studies represent significant progress toward a complete understanding of how the cerebellum contributes to motor learning. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

SIGNALING MECHANISMS THAT CONTROL GAP JUNCTIONAL COUPLING BETWEEN RETINAL NEURONS

Gap junctions between neurons form the structural substrate for electrical synapses. Connexin 36 (Cx36, and its non-mammalian ortholog connexin 35) is the major neuronal gap junction protein in the central nervous system (CNS), and contributes to several important neuronal functions including neuronal synchronization, signal averaging, network oscillations, and motor learning. Connexin 36 is strongly expressed in the retina, where it is an obligatory component of the high-sensitivity rod photoreceptor pathway. A fundamental requirement of the retina is to adapt to broadly varying inputs in order to maintain a dynamic range of signaling output. Modulation of the strength of electrical coupling between networks of retinal neurons, including the Cx36-coupled AII amacrine cell in the primary rod circuit, is a hallmark of retinal luminance adaptation. However, very little is known about the mechanisms regulating dynamic modulation of Cx36-mediated coupling. The primary goal of this work was to understand how cellular signaling mechanisms regulate coupling through Cx36 gap junctions. We began by developing and characterizing phospho-specific antibodies against key regulatory phosphorylation sites on Cx36. Using these tools we showed that phosphorylation of Cx35 in fish models varies with light adaptation state, and is modulated by acute changes in background illumination. We next turned our focus to the well-studied and readily identifiable AII amacrine cell in mammalian retina. Using this model we showed that increased phosphorylation of Cx36 is directly related to increased coupling through these gap junctions, and that the dopamine-stimulated uncoupling of the AII network is mediated by dephosphorylation of Cx36 via protein kinase A-stimulated protein phosphatase 2A activity. We then showed that increased phosphorylation of Cx36 on the AII amacrine network is driven by depolarization of presynaptic ON-type bipolar cells as well as background light increments. This increase in phosphorylation is mediated by activation of extrasynaptic NMDA receptors associated with Cx36 gap junctions on AII amacrine cells and by Ca2+-calmodulin-dependent protein kinase II activation. Finally, these studies indicated that coupling is regulated locally at individual gap junction plaques. This work provides a framework for future study of regulation of Cx36-mediated coupling, in which increased phosphorylation of Cx36 indicates increased neuronal coupling.

Learning reward timing in cortex through reward dependent expression of synaptic plasticity.

The ability to represent time is an essential component of cognition but its neural basis is unknown. Although extensively studied both behaviorally and electrophysiologically, a general theoretical framework describing the elementary neural mechanisms used by the brain to learn temporal representations is lacking. It is commonly believed that the underlying cellular mechanisms reside in high order cortical regions but recent studies show sustained neural activity in primary sensory cortices that can represent the timing of expected reward. Here, we show that local cortical networks can learn temporal representations through a simple framework predicated on reward dependent expression of synaptic plasticity. We assert that temporal representations are stored in the lateral synaptic connections between neurons and demonstrate that reward-modulated plasticity is sufficient to learn these representations. We implement our model numerically to explain reward-time learning in the primary visual cortex (V1), demonstrate experimental support, and suggest additional experimentally verifiable predictions.

Spike timing dependent plasticity: a consequence of more fundamental learning rules.

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse - the "first law" of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory.

Decades of research on the cellular mechanisms of memory have led to the widely held view that memories are stored as modifications of synaptic strength. These changes involve presynaptic processes, such as direct modulation of the release machinery, or postsynaptic processes, such as modulation of receptor properties. Parallel studies have revealed that memories might also be stored by nonsynaptic processes, such as modulation of voltage-dependent membrane conductances, which are expressed as changes in neuronal excitability. Although in some cases nonsynaptic changes can function as part of the engram itself, they might also serve as mechanisms through which a neural circuit is set to a permissive state to facilitate synaptic modifications that are necessary for memory storage.

Mechanisms of TGF-beta1-induced neuronal plasticity in Aplysia

Transforming growth factor beta-1 (TGF-β1) is a cytokine and neurotrophic factor whose neuromodulatory effects in Aplysia californica were recently described. Previous results demonstrated that TGF-β1 induces long-term increases in the efficacy of sensorimotor synapses, a neural correlate of sensitization of the defensive tail withdrawal reflex. These results provided the first evidence that a neurotrophic factor regulates neuronal plasticity associated with a simple form of learning in Aplysia, and raised many questions regarding the nature of the modulation. No homologs of TGF-β had previously been identified in Aplysia, and thus, it was not known whether components of TGF-β1 signaling pathways were present in Aplysia. Furthermore, the signaling mechanisms engaged by TGF-β1 had not been identified, and it was not known whether TGF-β1 regulated other aspects of neuronal function.^ The present investigation into the actions of TGF-β1 was initiated by examining the distribution of the type II TGF-β1 receptor, the ligand binding receptor. The receptor was widely distributed in the CNS and most neurons exhibited somatic and neuritic immunoreactivity. In addition, the ability of TGF-β1 to activate the cAMP/PKA and MAPK pathways, known to regulate several important aspects of neuronal function, was examined. TGF-β1 acutely decreased cAMP levels in sensory neurons, activated MAPK and triggered translocation of MAPK to the nucleus. MAPK activation was critical for both short- and long-term regulation of neuronal function by TGF-β1. TGF-β1 acutely decreased synaptic depression induced by low frequency stimuli in a MAPK-dependent manner. This regulation may result, at least in part, from the modulation of synapsin, a major peripheral synaptic vesicle protein. TGF-β1 stimulated MAPK-dependent phosphorylation of synapsin, a process believed to regulate synaptic vesicle mobilization from reserve to readily-releasable pools of neurotransmitter. In addition to its acute effect on synaptic efficacy, TGF-β1 also induced long-term increases in sensory neuron excitability. Whereas transient exposure to TGF-β1 was not sufficient to drive short-or long-term changes in excitability, prolonged exposure to TGF-β1 induced long-term changes in excitability that depended on MAPK. The results of these studies represent significant progress toward an understanding of the role of TGF-β1 in neuronal plasticity. ^

Cellular mechanisms of operant and classical conditioning

The ability to associate a predictive stimulus with a subsequent salient event (i.e., classical conditioning) and the ability to associate an expressed behavior with the consequences (i.e., operant conditioning) allow for a predictive understanding of a changing environment. Although they are operationally distinct, there has been considerable debate whether at some fundamental level classical and operant conditioning are mechanistically distinct or similar. Feeding behavior of Aplysia (i.e., biting) was chosen as the model system and was successfully conditioned with appetitive forms of both operant and classical conditioning. The neuronal circuitry responsible for feeding is well understood and is suitable for cellular analyses, thus providing for a mechanistic comparison between these two forms of associative learning. ^ Neuron B51 is part of the feeding circuitry of Aplysia and is critical for the expression of ingestive behaviors. B51 also is a locus of plasticity following both operant and classical conditioning. Both in vivo and in vitro operant conditioning increased the input resistance and the excitability of B51. No pairing-specific changes in the input resistance were observed following both in vivo and in vitro classical conditioning. However, classical conditioning decreased the excitability of B51. Thus, both operant and classical conditioning modified the threshold level for activation of neuron B51, but in opposite directions, revealing key differences in the cellular mechanisms underlying these two forms of associative learning. ^ Next, the cellular mechanisms underlying operant conditioning were investigated in more detail using a single-cell analogue. The single-cell analogue successfully recapitulated the previous in vivo and in vitro operant conditioning results by increasing the input resistance and the excitability of B51. Both PKA and PKC were necessary for operant conditioning. Dopamine appears to be the transmitter mediating the reinforcement signal in this form of conditioning. A D1 dopamine receptor antibody revealed that the D1receptor localizes to the axon hillock, which is also the region that gives the strongest response when iontophoresing dopamine. ^ The studies presented herein, thus, provide for a greater understanding of the mechanisms underlying both of these forms of associative learning and demonstrate that they likely operate through distinct cellular mechanisms. ^

Mechanisms of heterosynaptic modulation of transmission in Aplysia

Heterosynaptic plasticity has received considerable attention as a means to induce and maintain cell-wide, as opposed to synapse-specific, learning-related modifications. Modulatory neurotransmitters are thought to provide the attentional and motivational state for memory formation. However, the cellular and molecular mechanisms mediating the effects of most of these modulators on synaptic plasticity and learning remain unclear. A well established system for the study of heterosynaptic plasticity is the Aplysia sensorimotor synapse, which is subject regulation by at least two neuromodulators, serotonin (5-HT) and FMRFa. ^ 5-HT engages multiple second messenger cascades to induce short- and long-term facilitation (STF and LTF, respectively) of synaptic transmission. One mechanism proposed to be involved in STF is mobilization of synaptic vesicles from a storage pool to a releasable pool. To investigate this hypothesis, we examined the involvement of the protein synapsin, a central element in the regulation of the storage pool of vesicles in nerve terminals, in STF. 5-HT induced phosphorylation of synapsin and modified its subcellular distribution via PKA and p42/44 MAPK. Electrophysiological experiments and computer simulations suggested that synapsin can support heterosynaptic plasticity by regulating vesicle mobilization. ^ FMRFa induce short- and long-term synaptic depression in Aplysia . Long-term depression (LTD) correlates with morphological changes, the mechanisms of which remain elusive. LTD is also transcription- and translation-dependent, but little is known about the genes expressed and their regulation. We investigated the role of protein degradation via the ubiquitin-proteasome system and the regulation of one of its components, ubiquitin C-terminal hydrolase (ap-uch), in LTD. LTD was sensitive to inhibition of the proteasome and was associated with upregulation of ap-uch mRNA and protein. This upregulation appeared to be mediated by the transcription factor CREB2, which is generally regarded as a transcription repressor. These results suggest that proteasome-mediated protein degradation is engaged in LTD and that CREB2 may act as a transcription activator under certain conditions. ^ These and additional studies on the interaction of the 5-HT and FMRFa-activated pathways suggest that different neuromodulators, by activating several and sometimes overlapping signaling cascades, can exercise bidirectional control on synaptic gain and information processing.^

Calcium-dependent mechanisms and long-term potentiation: Role of NMDA receptors, voltage -dependent calcium channels and persistent protein kinase activity

Long-term potentiation (LTP) is a rapidly induced and long lasting increase in synaptic strength and is the leading cellular model for learning and memory in the mammalian brain. LTP was first identified in the hippocampus, a structure implicated in memory formation. LTP induction is dependent on postsynaptic Ca2+ increases mediated by N-methyl-D-aspartate (NMDA) receptors. Activation of other postsynaptic routes of Ca2+ entry, such as voltage-dependent Ca2+ channels (VDCCs) have subsequently been shown to induce a long-lasting increase in synaptic strength. However, it is unknown if VDCC-induced LTP utilized similar cellular mechanisms as the classical NMDA receptor-dependent LTP and if these two forms of LTP display similar properties. This dissertation determines the similarities and differences in VDCC and NMDA receptor-dependent LTP in area CA1 of hippocampal slices and demonstrates that VDCCs and NMDA receptors activate similar cellular mechanisms, such as protein kinases, to induce LTP. However, VDCC and NMDA receptor activated LTP induction mechanisms are compartmentalized in the postsynaptic neuron, such that they do not interact. Consistent with activation properties of NMDA receptors and VDCCs, NMDA receptor and VDCC-dependent LTP have different induction properties. In contrast to NMDA-dependent LTP, VDCC-induced potentiation does not require evoked presynaptic stimulation or display input specificity. These results indicate that there are two different routes of postsynaptic Ca2+ which can induce LTP and the compartmentation of VDCCs and NMDA receptors and/or their resulting Ca2+ increases may account for the distinction between these LTP induction mechanisms.^ One of the molecular targets for postsynaptic Ca2+ that is required for the induction of LTP is protein kinases. Evidence for the role of protein kinase activity in LTP expression is either correlational or controversial. We have utilized a broad range and potent inhibitors of protein kinases to systematically examine the temporal requirement for protein kinases in the induction and expression of LTP. Our results indicate that there is a critical period of persistent protein kinase activity required for LTP induction activated by tetanic stimulation and extending until 20 min after HFS. In addition, our results suggest that protein kinase activity during and immediately after HFS is not sufficient for LTP induction. These results provide evidence for persistent and/or Ca2+ independent protein kinase activity involvement in LTP induction. ^