QuantiDOPA: A Quantification Software for Dopaminergic Neurotransmission SPECT

Quantification of neurotransmission Single-Photon Emission Computed Tomography (SPECT) studies of the dopaminergic system can be used to track, stage and facilitate early diagnosis of the disease. The aim of this study was to implement QuantiDOPA, a semi-automatic quantification software of application in clinical routine to reconstruct and quantify neurotransmission SPECT studies using radioligands which bind the dopamine transporter (DAT). To this end, a workflow oriented framework for the biomedical imaging (GIMIAS) was employed. QuantiDOPA allows the user to perform a semiautomatic quantification of striatal uptake by following three stages: reconstruction, normalization and quantification. QuantiDOPA is a useful tool for semi-automatic quantification inDAT SPECT imaging and it has revealed simple and flexible

Efficient hybrid monocular-stereo approach to on-board, video-based traffic sign detection and tracking

In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Singular value decomposition for genome-wide expression data processing and modeling

We describe the use of singular value decomposition in transforming genome-wide expression data from genes × arrays space to reduced diagonalized “eigengenes” × “eigenarrays” space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Suppression subtractive hybridization: a method for generating differentially regulated or tissue-specific cDNA probes and libraries.

A new and highly effective method, termed suppression subtractive hybridization (SSH), has been developed for the generation of subtracted cDNA libraries. It is based primarily on a recently described technique called suppression PCR and combines normalization and subtraction in a single procedure. The normalization step equalizes the abundance of cDNAs within the target population and the subtraction step excludes the common sequences between the target and driver populations. In a model system, the SSH technique enriched for rare sequences over 1,000-fold in one round of subtractive hybridization. We demonstrate its usefulness by generating a testis-specific cDNA library and by using the subtracted cDNA mixture as a hybridization probe to identify homologous sequences in a human Y chromosome cosmid library. The human DNA inserts in the isolated cosmids were further confirmed to be expressed in a testis-specific manner. These results suggest that the SSH technique is applicable to many molecular genetic and positional cloning studies for the identification of disease, developmental, tissue-specific, or other differentially expressed genes.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.

Towards automatic tweet generation: A comparative study from the text summarization perspective in the journalism genre

In recent years, Twitter has become one of the most important microblogging services of the Web 2.0. Among the possible uses it allows, it can be employed for communicating and broadcasting information in real time. The goal of this research is to analyze the task of automatic tweet generation from a text summarization perspective in the context of the journalism genre. To achieve this, different state-of-the-art summarizers are selected and employed for producing multi-lingual tweets in two languages (English and Spanish). A wide experimental framework is proposed, comprising the creation of a new corpus, the generation of the automatic tweets, and their assessment through a quantitative and a qualitative evaluation, where informativeness, indicativeness and interest are key criteria that should be ensured in the proposed context. From the results obtained, it was observed that although the original tweets were considered as model tweets with respect to their informativeness, they were not among the most interesting ones from a human viewpoint. Therefore, relying only on these tweets may not be the ideal way to communicate news through Twitter, especially if a more personalized and catchy way of reporting news wants to be performed. In contrast, we showed that recent text summarization techniques may be more appropriate, reflecting a balance between indicativeness and interest, even if their content was different from the tweets delivered by the news providers.

Automatic Fiscal Stabiliser: Make it happen! EPC Discussion Paper, 2 May 2016

The eurozone crisis triggered a whole new series of innovations in EU economic governance in order to make the Union more resilient for the next economic downswing. But one of the more persistent issues are the socio-economic divergences between member states, identified by the Five Presidents’ Report as a major problem in the functioning of the Economic and Monetary Union (EMU). Debates took place in recent years about automatic stabilisers, and more specifically about the possibility of introducing an unemployment insurance within the EMU. While the need for some form of fiscal risk-sharing has become a dominant view in expert circles, there has been much less progress among the main political parties and stakeholders. In this study, Regula Hess and László Andor analyse the political feasibility of the adoption of an automatic fiscal stabiliser (AFS) for the eurozone by evaluating actors’ positions towards three distinctive proposals: 1) cyclical shock insurance, 2) reinsurance, 3) a European basic unemployment insurance; they included an empirical case study of France and Germany as the most relevant players within the intergovernmental bargaining constellation. Although the authors realise the current political context makes the adoption of an AFS improbable, Hess and Andor encourage stakeholders to further pursue the discussion, as windows of opportunities can open at any time, and even give some suggestions on what the parameters of the most feasible proposal might be.

Domestic engineering and the journal of mechanical contracting.

June 27, 1931-March 1935, 1941- include section: Automatic heat and air conditioning section (varies); also issued separately September 1933- June 1940.

Modernisation in small steps? Comparing the organisational reforms of the British Conservative Party and the German CDU

Research on political parties has long identified “environmental” pressures upon parties to undertake organisational and programmatic reforms – this applies in particular to “catch-all” parties or Volksparteien. Changed social and media structures, the decline of organisations traditionally associated with the parties, and the growth in alternative possibilities of political participation create significant organisational – as well as programmatic – challenges. This paper compares the German CDU and the British Conservatives in two respects: in particular it focuses on their organisational responses to the election defeats they suffered at the end of the 1990s, examining those reforms which took place and consider whether these match the expectations of organisational reforms anticipated by proponents of the “cartel party thesis”. While in both cases there are similarities, but (in particular in the German case) it is important not to understate the extent of internal party resistance to reform, and thus the difficulties with which aspiring party reformers are confronted. This conclusion suggests, more broadly, that in reality the process of party change is more than an almost automatic, isomorphic, and inevitable response to a changing environment. Rather it is punctuated, messy, and often contingent on events and agents.

Spatial and temporal dependencies of cross-orientation suppression in human vision.

A well-known property of orientation-tuned neurons in the visual cortex is that they are suppressed by the superposition of an orthogonal mask. This phenomenon has been explained in terms of physiological constraints (synaptic depression), engineering solutions for components with poor dynamic range (contrast normalization) and fundamental coding strategies for natural images (redundancy reduction). A common but often tacit assumption is that the suppressive process is equally potent at different spatial and temporal scales of analysis. To determine whether it is so, we measured psychophysical cross-orientation masking (XOM) functions for flickering horizontal Gabor stimuli over wide ranges of spatio-temporal frequency and contrast. We found that orthogonal masks raised contrast detection thresholds substantially at low spatial frequencies and high temporal frequencies (high speeds), and that small and unexpected levels of facilitation were evident elsewhere. The data were well fit by a functional model of contrast gain control, where (i) the weight of suppression increased with the ratio of temporal to spatial frequency and (ii) the weight of facilitatory modulation was the same for all conditions, but outcompeted by suppression at higher contrasts. These results (i) provide new constraints for models of primary visual cortex, (ii) associate XOM and facilitation with the transient magno- and sustained parvostreams, respectively, and (iii) reconcile earlier conflicting psychophysical reports on XOM.

Measurement and compensation of pivot point positional error on five-axis machines

Measuring and compensating the pivot points of five-axis machine tools is always challenging and very time consuming. This paper presents a newly developed approach for automatic measurement and compensation of pivot point positional errors on five-axis machine tools. Machine rotary axis errors are measured using a circular test. This method has been tested on five-axis machine tools with swivel table configuration. Results show that up to 99% of the positional errors of the rotary axis can be compensated by using this approach.

Radio frequency identification and its application in e-commerce

This chapter presents Radio Frequency Identification (RFID), which is one of the Automatic Identification and Data Capture (AIDC) technologies (Wamba and Boeck, 2008) and discusses the application of RFID in E-Commerce. Firstly RFID is defined and the tag and reader components of the RFID system are explained. Then historical context of RFID is briefly discussed. Next, RFID is contrasted with other AIDC technologies, especially the use of barcodes which are commonly applied in E-Commerce. Lastly, RFID applications in E-Commerce are discussed with the focus on achievable benefits and obstacles to successful applications of RFID in E-Commerce, and ways to alleviate them.

Androgyny versus Patriarchy: A Historicist‐Psychoanalytic Reading of Hemingway's The Garden of Eden and Wilkie Collins's The Woman in White

This paper will examine how male and female character interactions in Ernest Hemingway’s The Garden of Eden and Wilkie Collins’s The Woman in White expose the internalization, normalization, and perpetuation of current modes of patriarchy in terms of gender roles through their presentations of androgyny. This paper highlights the parallels of gender construction and the interaction within the social relations depicted in these two novels, which have not been compared previously. The premise, based on the psychoanalytic theories of Jacques Lacan and cultural materialism of Raymond Williams, is that fiction reflects historical and contemporary social relations. Lacanian and feminist interpretations have both been conducted on literature written by Collins and Hemingway; however, neither look at these particular novels as two examples for the same contemporary phenomenon of 21st century patriarchal interpellation. This paper most similarly follows the work of Slavoj Žižek who analyzes contemporary social relations through film (including classics such as Casablanca and works by Alfred Hitchcock) and other aspects of popular culture. This project’s contribution and uniqueness lie with the way it applies theory to these particular literary works, specifically concerning gender relations and the prevalence of androgyny in widely read works by well-known authors in two very different literary and historical eras. My interpretation of these two novels provides an evaluation of historical and contemporary patriarchal norms and a radical potentiality for subverting the idea of static gender roles that has remained prevalent throughout the three centuries of these texts’ existence.

Glaisdale Beck diversion scheme - channel change and suspended sediment response

This paper describes the implementation of a novel mitigation approach and subsequent adaptive management, designed to reduce the transfer of fine sediment in Glaisdale Beck; a small upland catchment in the UK. Hydro-meteorological and suspended sediment datasets are collected over a two year period spanning pre- and post-diversion periods in order to assess the impact of the channel reconfiguration scheme on the fluvial suspended sediment dynamics. Analysis of the river response demonstrates that the fluvial sediment system has become more restrictive with reduced fine sediment transfer. This is characterised by reductions in flow-weighted mean suspended sediment concentrations from 77.93 mg/l prior to mitigation, to 74.36 mg/l following the diversion. A Mann-Whitney U test found statistically significant differences (p < 0.001) between the pre- and post-monitoring median SSCs. Whilst application of one-way analysis of covariance (ANCOVA) on the coefficients of sediment rating curves developed before and after the diversion found statistically significant differences (p < 0.001), with both Log a and b coefficients becoming smaller following the diversion. Non-parametric analysis indicates a reduction in residuals through time (p < 0.001), with the developed LOWESS model over-predicting sediment concentrations as the channel stabilises. However, the channel is continuing to adjust to the reconfigured morphology, with evidence of a headward propagating knickpoint which has migrated 120 m at an exponentially decreasing rate over the last 7 years since diversion. The study demonstrates that channel reconfiguration can be effective in mitigating fine sediment flux in upland streams but the full value of this may take many years to achieve whilst the fluvial system, slowly readjusts.