Expressionsunterschiede verschiedener miRNAs bei chronisch-entzündlichen Darmerkrankungen

Chronisch-entzündliche Darmerkrankungen konfrontieren unsere heutige Gesellschaft mit hohen Inzidenzraten in der westlichen Welt und zunehmend steigenden Inzidenzraten im asiatischen Raum. Die Folgen für die Patienten sind eine starke Beeinträchtigung der Lebensqualität, mit sozialen und wirtschaftlichen Folgen sowie ein erhöhtes Risiko für die Entwicklung kolorektaler Karzinome. Durch die Entdeckung von 22 nt langen, regulierenden RNAs, auch genannt miRNAs, wurde ein neuer Baustein im Verständnis zellulärer Regelprozesse und der Differenzierung und Aktivierung von Antworten etwa des Immunsystems entdeckt. Somit stellt sich die Frage nach der Bedeutung von miRNAs im Rahmen von chronisch-entzündlichen Darmerkrankungen. Hierzu wurden in dieser Arbeit über ein miRNA-Array System 12 miRNAs als potentiell relevante Ziele identifiziert und an einem Kollektiv aus insgesamt 131 Patienten und 163 Biopsien aus dem Bereich des Darmes überprüft. Es zeigte sich hierbei, dass im Rahmen eines Morbus Crohn mit Befall des Dickdarms die miRNAs let-7d und miR-22 in gesteigerter Expression vorlagen. Da im terminalen Ileum eine gesonderte Immunsituation vorliegt, wurde dieser Bereich zusätzlich bei der Erkrankung Morbus Crohn untersucht. Es zeigten sich Expressionsveränderungen für die miRNAs miR-30e, miR-185, miR-374b und miR-424. Bei Patienten mit einer Colitis ulcerosa waren die miRNAs let-7d, miR-185 und miR-424 in ihrem Expressionsverhalten verändert. Zusätzlich konnte gezeigt werden, dass in Abhängigkeit vom Entzündungsgrad bei bestehender Colitis ulcerosa eine zunehmenden Überexpression der miRNAs let-7d, miR-185 und miR-424 erfolgte. Die miRNAs miR-18a und miR-185 wiesen unter Remissionsbedingungen Expressionsveränderungen auf und lassen somit den Verdacht eines protektiven Effektes aufkommen. Mit Hilfe von computerbasierten Datenbankanalysen konnten gemeinsam regulierenden miRNAs Proteine und Pathways zugeordnet werden, welche einen Zusammenhang mit bereits pathogenetisch bestätigten Signalwegen wie etwa dem nF-ĸB und MAPK-Signalweg nahelegen. Auch konnte herausgearbeitet werden, dass einige, der von diesen miRNAs regulierten Proteine, bereits in veröffentlichten Arbeiten als fehlreguliert festgestellt wurden, jedoch blieb die Ursache dieser Fehlregulation gänzlich unbekannt. Mit den in dieser Arbeit erhobenen Daten konnte gezeigt werden, dass eine Kongruenz der Befunde vorliegt, welche einen Zusammenhang der miRNA-Expression mit der Fehlregulation bestimmter Proteine nicht nur nahelegt, sondern darüber hinaus auch noch einige weitere potentielle Proteinziele für weitere Untersuchungen aufführt. Dazu ist es jedoch notwendig, die Relevanz der hier entdeckten, computerbasierten Proteine in zukünftigen Untersuchungen einer genauen Prüfung zu unterziehen.

Search for high mass resonances decaying into electron-positron pairs in proton-proton collisions at sqrt(s) = 7 TeV with the ATLAS detector

The Standard Model of particle physics was developed to describe the fundamental particles, which form matter, and their interactions via the strong, electromagnetic and weak force. Although most measurements are described with high accuracy, some observations indicate that the Standard Model is incomplete. Numerous extensions were developed to solve these limitations. Several of these extensions predict heavy resonances, so-called Z' bosons, that can decay into an electron positron pair. The particle accelerator Large Hadron Collider (LHC) at CERN in Switzerland was built to collide protons at unprecedented center-of-mass energies, namely 7 TeV in 2011. With the data set recorded in 2011 by the ATLAS detector, a large multi-purpose detector located at the LHC, the electron positron pair mass spectrum was measured up to high masses in the TeV range. The properties of electrons and the probability that other particles are mis-identified as electrons were studied in detail. Using the obtained information, a sophisticated Standard Model expectation was derived with data-driven methods and Monte Carlo simulations. In the comparison of the measurement with the expectation, no significant deviations from the Standard Model expectations were observed. Therefore exclusion limits for several Standard Model extensions were calculated. For example, Sequential Standard Model (SSM) Z' bosons with masses below 2.10 TeV were excluded with 95% Confidence Level (C.L.).

High-mass Drell-Yan cross-section and search for new phenomena in multi-electron/positron final states with the ATLAS detector

The Standard Model of particle physics is a very successful theory which describes nearly all known processes of particle physics very precisely. Nevertheless, there are several observations which cannot be explained within the existing theory. In this thesis, two analyses with high energy electrons and positrons using data of the ATLAS detector are presented. One, probing the Standard Model of particle physics and another searching for phenomena beyond the Standard Model.rnThe production of an electron-positron pair via the Drell-Yan process leads to a very clean signature in the detector with low background contributions. This allows for a very precise measurement of the cross-section and can be used as a precision test of perturbative quantum chromodynamics (pQCD) where this process has been calculated at next-to-next-to-leading order (NNLO). The invariant mass spectrum mee is sensitive to parton distribution functions (PFDs), in particular to the poorly known distribution of antiquarks at large momentum fraction (Bjoerken x). The measurementrnof the high-mass Drell-Yan cross-section in proton-proton collisions at a center-of-mass energy of sqrt(s) = 7 TeV is performed on a dataset collected with the ATLAS detector, corresponding to an integrated luminosity of 4.7 fb-1. The differential cross-section of pp -> Z/gamma + X -> e+e- + X is measured as a function of the invariant mass in the range 116 GeV < mee < 1500 GeV. The background is estimated using a data driven method and Monte Carlo simulations. The final cross-section is corrected for detector effects and different levels of final state radiation corrections. A comparison isrnmade to various event generators and to predictions of pQCD calculations at NNLO. A good agreement within the uncertainties between measured cross-sections and Standard Model predictions is observed.rnExamples of observed phenomena which can not be explained by the Standard Model are the amount of dark matter in the universe and neutrino oscillations. To explain these phenomena several extensions of the Standard Model are proposed, some of them leading to new processes with a high multiplicity of electrons and/or positrons in the final state. A model independent search in multi-object final states, with objects defined as electrons and positrons, is performed to search for these phenomenas. Therndataset collected at a center-of-mass energy of sqrt(s) = 8 TeV, corresponding to an integrated luminosity of 20.3 fb-1 is used. The events are separated in different categories using the object multiplicity. The data-driven background method, already used for the cross-section measurement was developed further for up to five objects to get an estimation of the number of events including fake contributions. Within the uncertainties the comparison between data and Standard Model predictions shows no significant deviations.

Applications of LiDAR remote sensing of forest structure in the Upper Great Lakes region, USA

We used active remote sensing technology to characterize forest structure in a northern temperate forest on a landscape- and local-level in the Upper Peninsula of Michigan. Specifically, we used a form of active remote sensing called light detection and ranging (e.g., LiDAR) to aid in the depiction of current forest structural stages and total canopy gap area estimation. On a landscape-level, LiDAR data are shown not only to be a useful tool in characterizing forest structure, in both coniferous and deciduous forest cover types, but also as an effective basis for data-driven surrogates for classification of forest structure. On a local-level, LiDAR data are shown to be a benchmark reference point to evaluate field-based canopy gap area estimations, due to the highly accurate nature of such remotely sensed data. The application of LiDAR remote sensed data can help facilitate current and future sustainable forest management.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

Effects of Ownership on Hospital Efficiency in Germany

The objective of our study was to evaluate the efficiency of public, private for-profit, and private non-profit hospitals in Germany. First, bootstrapped data envelopment analysis (DEA) was used to evaluate the efficiency of a panel (n = 1,046) of public, private for-profit, and private non-profit hospitals between 2002 and 2006. This was followed by a second-step truncated linear regression model with bootstrapped DEA efficiency scores as dependent variable. The results show that public hospitals performed significantly better than their private for-profit and non-profit counterparts. In addition, we found a significant positive association between hospital size and efficiency, and that competitive pressure had a significant negative impact on hospital efficiency.

Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts

Prompted reports of recall of spontaneous, conscious experiences were collected in a no-input, no-task, no-response paradigm (30 random prompts to each of 13 healthy volunteers). The mentation reports were classified into visual imagery and abstract thought. Spontaneous 19-channel brain electric activity (EEG) was continuously recorded, viewed as series of momentary spatial distributions (maps) of the brain electric field and segmented into microstates, i.e. into time segments characterized by quasi-stable landscapes of potential distribution maps which showed varying durations in the sub-second range. Microstate segmentation used a data-driven strategy. Different microstates, i.e. different brain electric landscapes must have been generated by activity of different neural assemblies and therefore are hypothesized to constitute different functions. The two types of reported experiences were associated with significantly different microstates (mean duration 121 ms) immediately preceding the prompts; these microstates showed, across subjects, for abstract thought (compared to visual imagery) a shift of the electric gravity center to the left and a clockwise rotation of the field axis. Contrariwise, the microstates 2 s before the prompt did not differ between the two types of experiences. The results support the hypothesis that different microstates of the brain as recognized in its electric field implement different conscious, reportable mind states, i.e. different classes (types) of thoughts (mentations); thus, the microstates might be candidates for the `atoms of thought'.

Library Assessment Trends Report, 2010–2011

The field of library assessment continues to grow. The annual Library Assessment Trends Report provides a brief synopsis of the more important trends in library assessment. It is hoped these brief reports will facilitate the Dean of the Library’s understanding of assessment trends. These reports provide information that supports data driven decisions. Additionally, the reports are an outreach method that supports a greater institutional understanding of library assessment. Library assessment supports strategic planning, improved processes, and a greater understanding of our users’ needs.

Epoetin and Darbepoetin for Managing Anemia in Patients Undergoing Cancer Treatment: Comparative Effectiveness Update

Objectives: To update the 2006 systematic review of the comparative benefits and harms of erythropoiesis-stimulating agent (ESA) strategies and non-ESA strategies to manage anemia in patients undergoing chemotherapy and/or radiation for malignancy (excluding myelodysplastic syndrome and acute leukemia), including the impact of alternative thresholds for initiating treatment and optimal duration of therapy. Data sources: Literature searches were updated in electronic databases (n=3), conference proceedings (n=3), and Food and Drug Administration transcripts. Multiple sources (n=13) were searched for potential gray literature. A primary source for current survival evidence was a recently published individual patient data meta-analysis. In that meta-analysis, patient data were obtained from investigators for studies enrolling more than 50 patients per arm. Because those data constitute the most currently available data for this update, as well as the source for on-study (active treatment) mortality data, we limited inclusion in the current report to studies enrolling more than 50 patients per arm to avoid potential differential endpoint ascertainment in smaller studies. Review methods: Title and abstract screening was performed by one or two (to resolve uncertainty) reviewers; potentially included publications were reviewed in full text. Two or three (to resolve disagreements) reviewers assessed trial quality. Results were independently verified and pooled for outcomes of interest. The balance of benefits and harms was examined in a decision model. Results: We evaluated evidence from 5 trials directly comparing darbepoetin with epoetin, 41 trials comparing epoetin with control, and 8 trials comparing darbepoetin with control; 5 trials evaluated early versus late (delay until Hb ≤9 to 11 g/dL) treatment. Trials varied according to duration, tumor types, cancer therapy, trial quality, iron supplementation, baseline hemoglobin, ESA dosing frequency (and therefore amount per dose), and dose escalation. ESAs decreased the risk of transfusion (pooled relative risk [RR], 0.58; 95% confidence interval [CI], 0.53 to 0.64; I2 = 51%; 38 trials) without evidence of meaningful difference between epoetin and darbepoetin. Thromboembolic event rates were higher in ESA-treated patients (pooled RR, 1.51; 95% CI, 1.30 to 1.74; I2 = 0%; 37 trials) without difference between epoetin and darbepoetin. In 14 trials reporting the Functional Assessment of Cancer Therapy (FACT)-Fatigue subscale, the most common patient-reported outcome, scores decreased by −0.6 in control arms (95% CI, −6.4 to 5.2; I2 = 0%) and increased by 2.1 in ESA arms (95% CI, −3.9 to 8.1; I2 = 0%). There were fewer thromboembolic and on-study mortality adverse events when ESA treatment was delayed until baseline Hb was less than 10 g/dL, in keeping with current treatment practice, but the difference in effect from early treatment was not significant, and the evidence was limited and insufficient for conclusions. No evidence informed optimal duration of therapy. Mortality was increased during the on-study period (pooled hazard ratio [HR], 1.17; 95% CI, 1.04 to 1.31; I2 = 0%; 37 trials). There was one additional death for every 59 treated patients when the control arm on-study mortality was 10 percent and one additional death for every 588 treated patients when the control-arm on-study mortality was 1 percent. A cohort decision model yielded a consistent result—greater loss of life-years when control arm on-study mortality was higher. There was no discernible increase in mortality with ESA use over the longest available followup (pooled HR, 1.04; 95% CI, 0.99 to 1.10; I2 = 38%; 44 trials), but many trials did not include an overall survival endpoint and potential time-dependent confounding was not considered. Conclusions: Results of this update were consistent with the 2006 review. ESAs reduced the need for transfusions and increased the risk of thromboembolism. FACT-Fatigue scores were better with ESA use but the magnitude was less than the minimal clinically important difference. An increase in mortality accompanied the use of ESAs. An important unanswered question is whether dosing practices and overall ESA exposure might influence harms.

CHARACTERIZING, ASSESSING AND IMPROVING HEALTHCARE REFERRAL COMMUNICATION

Similar to other health care processes, referrals are susceptible to breakdowns. These breakdowns in the referral process can lead to poor continuity of care, slow diagnostic processes, delays and repetition of tests, patient and provider dissatisfaction, and can lead to a loss of confidence in providers. These facts and the necessity for a deeper understanding of referrals in healthcare served as the motivation to conduct a comprehensive study of referrals. The research began with the real problem and need to understand referral communication as a mean to improve patient care. Despite previous efforts to explain referrals and the dynamics and interrelations of the variables that influence referrals there is not a common, contemporary, and accepted definition of what a referral is in the health care context. The research agenda was guided by the need to explore referrals as an abstract concept by: 1) developing a conceptual definition of referrals, and 2) developing a model of referrals, to finally propose a 3) comprehensive research framework. This dissertation has resulted in a standard conceptual definition of referrals and a model of referrals. In addition a mixed-method framework to evaluate referrals was proposed, and finally a data driven model was developed to predict whether a referral would be approved or denied by a specialty service. The three manuscripts included in this dissertation present the basis for studying and assessing referrals using a common framework that should allow an easier comparative research agenda to improve referrals taking into account the context where referrals occur.

Three decades of global methane sources and sinks

Methane is an important greenhouse gas, responsible for about 20 of the warming induced by long-lived greenhouse gases since pre-industrial times. By reacting with hydroxyl radicals, methane reduces the oxidizing capacity of the atmosphere and generates ozone in the troposphere. Although most sources and sinks of methane have been identified, their relative contributions to atmospheric methane levels are highly uncertain. As such, the factors responsible for the observed stabilization of atmospheric methane levels in the early 2000s, and the renewed rise after 2006, remain unclear. Here, we construct decadal budgets for methane sources and sinks between 1980 and 2010, using a combination of atmospheric measurements and results from chemical transport models, ecosystem models, climate chemistry models and inventories of anthropogenic emissions. The resultant budgets suggest that data-driven approaches and ecosystem models overestimate total natural emissions. We build three contrasting emission scenarios � which differ in fossil fuel and microbial emissions � to explain the decadal variability in atmospheric methane levels detected, here and in previous studies, since 1985. Although uncertainties in emission trends do not allow definitive conclusions to be drawn, we show that the observed stabilization of methane levels between 1999 and 2006 can potentially be explained by decreasing-to-stable fossil fuel emissions, combined with stable-to-increasing microbial emissions. We show that a rise in natural wetland emissions and fossil fuel emissions probably accounts for the renewed increase in global methane levels after 2006, although the relative contribution of these two sources remains uncertain.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

Sensory and conceptual representations in memory: Motor images which cannot be imaged

The paper argues for a distinction between sensory-and conceptual-information storage in the human information-processing system. Conceptual information is characterized as meaningful and symbolic, while sensory information may exist in modality-bound form. Furthermore, it is assumed that sensory information does not contribute to conscious remembering and can be used only in data-driven process reptitions, which can be accompanied by a kind of vague or intuitive feeling. Accordingly, pure top-down and willingly controlled processing, such as free recall, should not have any access to sensory data. Empirical results from different research areas and from two experiments conducted by the authors are presented in this article to support these theoretical distinctions. The experiments were designed to separate a sensory-motor and a conceptual component in memory for two-digit numbers and two-letter items, when parts of the numbers or items were imaged or drawn on a tablet. The results of free recall and recognition are discussed in a theoretical framework which distinguishes sensory and conceptual information in memory.