Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment

Background Individualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke. Methods A decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data. Results The model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA. Conclusions We have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

Mechanisms to engage an online community in crowdsourcing: insights from an idea contest in training

Knowledge sharing is particularly important for co-creating, discussing, or acquiring innovative ideas. Crowdsourcing, as an enabler of open innovation, has raised the question about the kind of organising forms and/or managerial interventions it may require or underpin. However, there is little consensus in management studies on how to best design a crowdsourcing initiative (contest) with regard to the mechanisms to engage an online community. In this paper, starting from an exploratory case study on the project “Stati Generali della Formazione e del Lavoro” (General Assembly on Training and Work)—a crowdsourcing experience designed for a large community of professional trainers, planned and managed by University of Milano-Bicocca and AIF Academy (Associazione Italiana Formatori), a broad representative association of Italian trainers—we study the factors influencing the decision of the participants (a.k.a., solvers) to become involved (and to what extent) in a contest. The study could contribute to the debate on crowdsourcing by both underlining important governance factors involved and providing empirical evidence of the link between management strategies and crowdsourcing success

Parameterization of a linear vibronic coupling model with multiconfigurational electronic structure methods to study the quantum dynamics of photoexcited pyrene

With this work, we present a protocol for the parameterization of a Linear Vibronic Coupling (LVC) Hamiltonian for quantum dynamics using highly accurate multiconfigurational electronic structure methods such as RASPT2/RASSCF, combined with a maximum-overlap diabatization technique. Our approach is fully portable and can be applied to many medium-size rigid molecules whose excited state dynamics requires a quantum description. We present our model and discuss the details of the electronic structure calculations needed for the parameterization, analyzing critical situations that could arise in the case of strongly interacting excited states. The protocol was applied to the simulation of the excited state dynamics of the pyrene molecule, starting from either the first or the second bright state (S2 or S5). The LVC model was benchmarked against state-of-the-art quantum mechanical calculations with optimizations and energy scans and turned out to be very accurate. The dynamics simulations, performed including all active normal coordinates with the multilayer multiconfigurational time-dependent Hartree method, show good agreement with the available experimental data, endorsing prediction of the excited state mechanism, especially for S5, whose ultrafast deactivation mechanism was not yet clearly understood

Enabling monocular depth perception at the very edge

Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs - precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge

Coupled Electronic and Nuclear Motions during Azobenzene Photoisomerization Monitored by Ultrafast Electron Diffraction

Ultrafast electron diffraction is a powerful technique that can resolve molecular structures with femtosecond and angstrom resolutions. We demonstrate theoretically how it can be used to monitor conical intersection dynamics in molecules. Specific contributions to the signal are identified which vanish in the absence of vibronic coherence and offer a direct window into conical intersection paths. A special focus is on hybrid scattering from nuclei and electrons, a process that is unique to electron (rather than X-ray) diffraction and monitors the strongly coupled nuclear and electronic motions in the vicinity of conical intersections. An application is made to the cis to trans isomerization of azobenzene, computed with exact quantum dynamics wavepacket propagation in a reactive two-dimensional nuclear space

Rainfall Thresholding and Susceptibility assessment of rainfall induced landslides: application to landslide management in St Thomas, Jamaica

The final publication is available at Springer via http://dx.doi.org/10.1007/s10064-009-0232-zThe parish of St Thomas has one of the highest densities of landslides in Jamaica, which impacts the residents, local economy and the built and natural environment. These landslides result from a combination of steep slopes, faulting, heavy rainfall and the presence of highly weathered volcanics, sandstones, limestones and sandstone/shale series and are particularly prevalent during the hurricane season (June–November). The paper reports a study of the rainfall thresholds and landslide susceptibility assessment to assist the prediction, mitigation and management of slope instability in landslide-prone areas of the parish

Light chain deposition disease presenting as paroxysmal atrial fibrillation: a case report

<p>Abstract</p> <p>Introduction</p> <p>Light chain deposition disease (LCDD) can involve the heart and cause severe heart failure. Cardiac involvement is usually described in the advanced stages of the disease. We report the case of a woman in whom restrictive cardiomyopathy due to LCDD presented with paroxysmal atrial fibrillation.</p> <p>Case presentation</p> <p>A 55-year-old woman was admitted to our emergency department because of palpitations. In a recent blood test, serum creatinine was 1.4 mg/dl. She was found to have high blood pressure, left ventricular hypertrophy and paroxysmal atrial fibrillation. An ACE-inhibitor was prescribed but her renal function rapidly worsened and she was admitted to our nephrology unit. On admission serum creatinine was 9.4 mg/dl, potassium 6.8 mmol/l, haemoglobin 7.7 g/dl, N-terminal pro-brain natriuretic peptide 29894 pg/ml. A central venous catheter was inserted and haemodialysis was started. She underwent a renal biopsy which showed kappa LCDD. Bone marrow aspiration and bone biopsy demonstrated kappa light chain multiple myeloma. Echocardiographic findings were consistent with restrictive cardiomyopathy. Thalidomide and dexamethasone were prescribed, and a peritoneal catheter was inserted. Peritoneal dialysis has now been performed for 15 months without complications.</p> <p>Discussion</p> <p>Despite the predominant tubular deposition of kappa light chain, in our patient the first clinical manifestation of LCDD was cardiac disease manifesting as atrial fibrillation and the correct diagnosis was delayed. The clinical management initially addressed the cardiovascular symptoms without paying sufficient attention to the pre-existing slight increase in our patient's serum creatinine. However cardiac involvement is a quite uncommon presentation of LCDD, and this unusual case suggests that the onset of acute arrhythmias associated with restrictive cardiomyopathy and impaired renal function might be related to LCDD.</p

Discovery and functional characterization of neuropeptides in crinoid echinoderms.

Neuropeptides are one of the largest and most diverse families of signaling molecules in animals and, accordingly, they regulate many physiological processes and behaviors. Genome and transcriptome sequencing has enabled the identification of genes encoding neuropeptide precursor proteins in species from a growing variety of taxa, including bilaterian and non-bilaterian animals. Of particular interest are deuterostome invertebrates such as the phylum Echinodermata, which occupies a phylogenetic position that has facilitated reconstruction of the evolution of neuropeptide signaling systems in Bilateria. However, our knowledge of neuropeptide signaling in echinoderms is largely based on bioinformatic and experimental analysis of eleutherozoans-Asterozoa (starfish and brittle stars) and Echinozoa (sea urchins and sea cucumbers). Little is known about neuropeptide signaling in crinoids (feather stars and sea lilies), which are a sister clade to the Eleutherozoa. Therefore, we have analyzed transcriptome/genome sequence data from three feather star species, Anneissia japonica, Antedon mediterranea, and Florometra serratissima, to produce the first comprehensive identification of neuropeptide precursors in crinoids. These include representatives of bilaterian neuropeptide precursor families and several predicted crinoid neuropeptide precursors. Using A. mediterranea as an experimental model, we have investigated the expression of selected neuropeptides in larvae (doliolaria), post-metamorphic pentacrinoids and adults, providing new insights into the cellular architecture of crinoid nervous systems. Thus, using mRNA in situ hybridization F-type SALMFamide precursor transcripts were revealed in a previously undescribed population of peptidergic cells located dorso-laterally in doliolaria. Furthermore, using immunohistochemistry a calcitonin-type neuropeptide was revealed in the aboral nerve center, circumoral nerve ring and oral tube feet in pentacrinoids and in the ectoneural and entoneural compartments of the nervous system in adults. Moreover, functional analysis of a vasopressin/oxytocin-type neuropeptide (crinotocin), which is expressed in the brachial nerve of the arms in A. mediterranea, revealed that this peptide causes a dose-dependent change in the mechanical behavior of arm preparations in vitro-the first reported biological action of a neuropeptide in a crinoid. In conclusion, our findings provide new perspectives on neuropeptide signaling in echinoderms and the foundations for further exploration of neuropeptide expression/function in crinoids as a sister clade to eleutherozoan echinoderms