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Based on morphological features of the adult female, the Brazilian soft scale, Mesolecanium ferum Hempel, 1920 (= Toumeyella ferum) (Hemiptera: Coccomorpha: Coccidae) is transferred to the genus Neotoumeyella Kondo and Williams, 2009, as Neotoumeyella ferum (Hempel), comb. nov. The genus Neotoumeyella is rediagnosed in order to accommodate the unique features of M. ferum and an updated key to the six species of the genus is provided. An English translation of the Spanish redescription of M. ferum (as T. ferum) by Granara de Willink (2012) is also provided, with the author’s comments and interpretations.
In the Twilight of the Revolution : The Pan Africanist Congress of Azania (South Africa) 1959-1994
(2009)
This book is a long-overdue history of the Pan Africanist Congress of Azania (PAC) and the rise of the Africanist ideology in South Africa. From its formation in 1959, the PAC underground inside South Africa and in exile shaped the dynamics of the anti-apartheid movement and liberation struggle by framing alternative ideologies. Kwandiwe Kondlo analyses the radical traditions, the structural contradictions and the internal conflicts of this rival to the African National Congress (ANC), South Africa?s dominant liberation organisation. The contributions of some of the PAC leaders, including Robert Sobukhwe, Potlake Kitchener Leballo, Vusumzi Make and John Nyathi Pokela, are reconstructed as are the PAC?s experiences in exile and the strategies pursued by its military wing, the Azanian People?s Liberation Party (APLA). The role of the PAC in the power-sharing negotiations leading to the historic 1994 elections in South Africa round off the narrative. The PAC story is a highly controversial one, as the perspectives are wide and various. This book seeks to present a balanced picture which includes diverse views in a comprehensive narrative.
The prevention of tau protein aggregations is a therapeutic goal for the treatment of Alzheimer's disease (AD), and hydromethylthionine (HMT) (also known as leucomethylthioninium-mesylate [LMTM]), is a potent inhibitor of tau aggregation in vitro and in vivo. In two Phase 3 clinical trials in AD, HMT had greater pharmacological activity on clinical endpoints in patients not receiving approved symptomatic treatments for AD (acetylcholinesterase (AChE) inhibitors and/or memantine) despite different mechanisms of action. To investigate this drug interaction in an animal model, we used tau-transgenic L1 and wild-type NMRI mice treated with rivastigmine or memantine prior to adding HMT, and measured changes in hippocampal acetylcholine (ACh) by microdialysis. HMT given alone doubled hippocampal ACh levels in both mouse lines and increased stimulated ACh release induced by exploration of the open field or by infusion of scopolamine. Rivastigmine increased ACh release in both mouse lines, whereas memantine was more active in tau-transgenic L1 mice. Importantly, our study revealed a negative interaction between HMT and symptomatic AD drugs: the HMT effect was completely eliminated in mice that had been pre-treated with either rivastigmine or memantine. Rivastigmine was found to inhibit AChE, whereas HMT and memantine had no effects on AChE or on choline acetyltransferase (ChAT). The interactions observed in this study demonstrate that HMT enhances cholinergic activity in mouse brain by a mechanism of action unrelated to AChE inhibition. Our findings establish that the drug interaction that was first observed clinically has a neuropharmacological basis and is not restricted to animals with tau aggregation pathology. Given the importance of the cholinergic system for memory function, the potential for commonly used AD drugs to interfere with the treatment effects of disease-modifying drugs needs to be taken into account in the design of clinical trials.
This thesis contributes to the field of machine learning with a specific focus on the methods for learning relations between the inputs. Learning relationships between images is the most common primitive in vision. There are many vision tasks in which relationships across images play an important role. Some of them are motion estimation, activity recognition, stereo vision, multi-view geometry and visual odometry. Many of such tasks mainly depend on motion and disparity cues, which are inferred based on the relations across multiple image pairs. The approaches presented in this thesis mainly deal with, but are not limited to, learning of the representations for motion and depth. This thesis by articles consists of five articles which present relational feature learning models along with their applications in computer vision. In the first article, we present an approach for encoding motion in videos. To this end, we show that the detection of spatial transformations can be viewed as detection of coincidence or synchrony between the given sequence of frames and a sequence of features which are related by the transformation we wish to detect. Learning to detect synchrony is possible by introducing "multiplicative interactions'' into the hidden units of single layered sparse coding models.
We show that the learned motion representations employed for the task of activity recognition achieve competitive performance on multiple benchmarks. Stereo vision is an important challenge in computer vision and useful for many applications in that field. In the second article, we extend the energy based learning models, which were previously used for motion encoding, to the context of depth perception. Given the common architecture of the models for encoding motion and depth, we show that it is possible to define a single model for learning a unified representation for both the cues. Our experimental results show that learning a combined representation for depth and motion makes it possible to achieve state-of-the-art performance at the task of 3-D activity analysis, and to perform better than the existing hand-engineered 3-D motion features. Autoencoder is a popular unsupervised learning method for learning efficient encoding for a given set of data samples. Typically, regularized autoencoders which are used to learn over-complete and sparse representations for the input data, were shown to fail on intrinsically high dimensional data like videos. In the third article, we investigate the reason for such a behavior. It can be observed that the regularized autoencoders typically learn negative hidden unit biases. We show that the learning of negative biases is the result of hidden units being responsible for both the sparsity and the representation of the input data. It is shown that, as a result, the behavior of the model resembles clustering methods which would require exponentially large number of features to model intrinsically high dimensional data. Based on this understanding, we propose a new activation function which decouples the roles of hidden layer and uses linear encoding. This allows to learn representations on data with very high intrinsic dimensionality. We also show that gating connections in the bi-linear models and the single layer models from articles one and two of this thesis can be thought of as a way to attain a linear encoding scheme which allows them to learn good representations on videos. Visual odometry is the task of inferring egomotion of a moving object from visual information such as images and videos. It can primarily be used for the task of localization and has many applications in the fields of robotics and navigation. The work in article four was motivated by the idea of using deep learning techniques, which are successful methods for many vision tasks, for visual odometry. The visual odometry task mainly requires inference of motion and depth information from visual input which can then be mapped to velocity and change in direction. We use relational feature models presented in the articles one and two for inferring a combined motion and depth representation from stereo video sequences. The combined representation is then mapped to discrete velocity and change in direction labels using convolutional neural networks. Our approach is an end-to-end deep learning-based architecture which uses a single type of computational model and learning rule. Preliminary results show that the architecture is capable of learning the mapping from input video to egomotion. Activity recognition is a challenging computer vision task with many real world applications. It is well know that it is a hard task to use computer vision research for real-time applications. In the fifth article of this thesis, we present a real-time activity recognition system based on deep learning based methods. Our approach uses energy based relational feature learning models for the computation of local motion features directly from videos. A bag-of-words over the local motion features is used for the analysis of activity in a given video sequence. We implement this system on a distributed computational platform and demonstrate its performance on the iCub robot. Using GPUs we demonstrate real time performance which makes the deployment of activity recognition systems in real world scenarios possible.
Background: Subarachnoid hemorrhage (SAH) is mainly caused by ruptured cerebral aneurysms but in up to 15% of patients with SAH no bleeding source could be identified. Our objective was to analyze patient characteristics, clinical outcome and prognostic factors in patients suffering from non-aneurysmal SAH.
Methods: From 1999 to 2009, data of 125 patients with non-aneurysmal SAH were prospectively entered into a database. All patients underwent repetitive cerebral angiography. Outcome was assessed according to the modified Rankin Scale (mRS) (mRS 0-2 favorable vs. 3-6 unfavorable). Also, patients were divided in two groups according to the distribution of blood in the CT scan (perimesencephalic and non-perimesencephalic SAH).
Results: 106 of the 125 patients were in good WFNS grade (I-III) at admission (85%). Overall, favorable outcome was achieved in 104 of 125 patients (83%). Favorable outcome was associated with younger age (P < 0.001), good admission status (P < 0.0001), and absence of hydrocephalus (P = 0.001).73 of the 125 patients suffered from perimesencephalic SAH, most patients (90%) were in good grade at admission, and 64 achieved favorable outcome.52 of the 125 patients suffered from non-perimesencephalic SAH and 40 were in good grade at admission. Also 40 patients achieved favorable outcome.
Conclusions: Patients suffering from non-aneurysmal SAH have better prognosis compared to aneurysm related SAH and poor admission status was the only independent predictor of unfavorable outcome in the multivariate analysis. Patients with a non-perimesencephalic SAH have an increased risk of a worse neurological outcome. These patients should be monitored attentively.
Event-by-event multiplicity fluctuations in nucleus-nucleus collisions are studied within the HSD and UrQMD transport models. The scaled variances of negative, positive, and all charged hadrons in Pb+Pb at 158 AGeV are analyzed in comparison to the data from the NA49 Collaboration. We find a dominant role of the fluctuations in the nucleon participant number for the final hadron multiplicity fluctuations. This fact can be used to check di erent scenarios of nucleus-nucleus collisions by measuring the final multiplicity fluctuations as a function of collision centrality. The analysis reveals surprising e ects in the recent NA49 data which indicate a rather strong mixing of the projectile and target hadron production sources even in peripheral collisions. PACS numbers: 25.75.-q,25.75.Gz,24.60.-k
The multiplicity fluctuations in A+A collisions at SPS and RHIC energies are studied within the HSD transport approach. We find a dominant role of the fluctuations in the nucleon participant number for the final fluctuations. In order to extract physical fluctuations one should decrease the fluctuations in the participants number. This can be done considering very central collisions. The system size dependence of the multiplicity fluctuations in central A+A collisions at the SPS energy range – obtained in the HSD and UrQMD transport models – is presented. The results can be used as a ‘background’ for experimental measurements of fluctuations as a signal of the critical point. Event-by-event fluctuations of the K/p , K/p and p/p ratios in A+A collisions are also studied. Event-by-event fluctuations of the kaon to pion number ratio in nucleus-nucleus collisions are studied for SPS and RHIC energies. We find that the HSD model can qualitatively reproduce the measured excitation function for the K/p ratio fluctuations in central Au+Au (or Pb+Pb) collisions from low SPS up to top RHIC energies. The forward-backward correlation coefficient measured by the STAR Collaboration in Au+Au collisions at RHIC is also studied. We discuss the effects of initial collision geometry and centrality bin definition on correlations in nucleus-nucleus collisions. We argue that a study of the dependence of correlations on the centrality bin definition as well as the bin size may distinguish between these ‘trivial’ correlations and correlations arising from ‘new physics’. 5th International Workshop on Critical Point and Onset of Deconfinement - CPOD 2009, June 08 - 12 2009 Brookhaven National Laboratory, Long Island, New York, USA
Event-by-event fluctuations of the net baryon number and electric charge in nucleus-nucleus collisions are studied in Pb+Pb at SPS energies within the HSD transport model. We reveal an important role of the fluctuations in the number of target nucleon participants. They strongly influence all measured fluctuations even in the samples of events with rather rigid centrality trigger. This fact can be used to check different scenarios of nucleus-nucleus collisions by measuring the multiplicity fluctuations as a function of collision centrality in fixed kinematical regions of the projectile and target hemispheres. The HSD results for the event-by-event fluctuations of electric charge in central Pb+Pb collisions at 20, 30, 40, 80 and 158 A GeV are in a good agreement with the NA49 experimental data and considerably larger than expected in a quark-gluon plasma. This demonstrate that the distortions of the initial fluctuations by the hadronization phase and, in particular, by the final resonance decays dominate the observable fluctuations.
The current thesis is devoted to a systematic study of fluctuations and correlations in heavy-ion collisions, which might be considered as probes for the phase transition and the critical point in the phase diagram, within the Hadron-String- Dynamics (HSD) microscopic transport approach. This is a powerful tool to study nucleus-nucleus collisions and allows to completely simulate experimental collisions on an event-by-event basis. Thus, the transport model has been used to study fluctuations and correlations including the influence of experimental acceptance as well as centrality, system size and collision energy. The comparison to experimental data can separate the effects induced by a phase transition since there is no phase transition in the HSD version used here. Firstly the centrality dependence of multiplicity fluctuations has been studied. Different centrality selections have been performed in the analysis in correspondence to the experimental situation. For the fixed target experiment NA49 events with fixed numbers of the projectile participants have been studied while in the collider experiment PHENIX centrality classes of events have been defined by the multiplicity in certain phase space region. A decrease of participant number fluctuations (and thus volume fluctuations) in more central collisions for both experiments has been obtained. Another area of this work addresses to transport model calculations of multiplicity fluctuations in nucleus-nucleus collisions as a function of colliding energy and system size. This study is in full correspondence to the experimental program of the NA61 Collaboration at the SPS. Central C+C, S+S, In+In, and Pb+Pb nuclear collisions at Elab = 10, 20, 30, 40, 80, 158 AGeV have been investigated. The expected enhanced fluctuations - attributed to the critical point and phase transition - can be observed experimentally on top of a monotonic and smooth ‘hadronic background’. These findings should be helpful for the optimal choice of collision systems and collision energies for the experimental search of the QCD critical point. Other observables are fluctuations of ratios of hadrons (e.g. pions, kaons, protons, etc.) which are not so much affected by volume fluctuations. In particular HSD results for the kaon-to-pion ratio fluctuations, which has been regarded as promising observable for a long time, are presented from low SPS energies up to high energies at RHIC. In addition to the HSD calculations statistical model is also used in terms of microcanonical, canonical and grand canonical ensembles. Further a study of the system size event-by-event fluctuations causing rapidity forward-backward correlations in relativistic heavy-ion collisions is presented. The HSD simulations reveal strong forward-backward correlations and reproduce the main qualitative features of the STAR data in A+A collisions at RHIC energies. It has been shown that strong forward-backward correlations arise due to an averaging over many different events that belong to one centrality bin. An optimization of the experimental selection of centrality classes is presented, which is relevant for the program of the NA61 collaboration at CERN, the low-energy program at RHIC, as well as future experiments at FAIR.
Event-by-event multiplicity fluctuations in nucleus-nucleus collisions from low SPS up to RHIC energies have been studied within the HSD transport approach. Fluctuations of baryonic number and electric charge also have been explored for Pb+Pb collisions at SPS energies in comparison to the experimental data from NA49. We find a dominant role of the fluctuations in the nucleon participant number for the final hadron multiplicity fluctuations and a strong influence of the experimental acceptance on the final results. Critical Point and Onset of Deconfinement - 4th International Workshop July 9 - 13, 2007 Darmstadt, Germany