Kurzes Lehrbuch der Chemie: Anorganische Chemie. von Dr. F. Krafft, Professor an der Universität Heidelberg. 525 Seiten mit zahlreichen Holzschnitten und einer Spektraltafel. 5. vermehrte und verbesserte Auflage. Preis geh. 9 Mk. Franz Deuticke, Leipzig und Wien. 1904

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Expedition S. M. Schiff "Pola"︁ in das Rote Meer, nördliche Hälfte (Oktober 1895-Mai 1896). IX. Chemische Untersuchungen, von KONRAD NATTERER. 65. Band der Denkschriften der mathematischnaturwissenschaftlichen Klasse der Kaiserlichen Akademie der Wissensc

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Practical Dictionary of Electrical Engineering and Chemistry in German, English and Spanish. By P. heyne. Vol. II. English-spanisch-german. 209 Seiten. (Dresden, G. Kühtmann. Preis geb. 4.80 M.)

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Predictive Value of Bronchoalveolar Lavage in Excluding a Diagnosis of Pneumocystis carinii Pneumonia During Prophylaxis with Aerosolized Pentamidine

We assessed the negative predictive value of bronchoalveolar lavage (BAL) for Pneumocystis carinii pneumonia (PCP) during prophylaxis with aerosolized pentamidine. On the basis of the assumption that undiagnosed and untreated PCP would progress and become clinically apparent, for 3 months we prospectively followed 34 consecutive cases in which BAL had not detected PCP. All patients were immunodeficient, had a symptomatic human immunodeficiency virus infection, and were evaluated for possible PCP during prophylaxis with aerosolized pentamidine. No transbronchial biopsies were performed. In 32 of 34 cases, a diagnosis of PCP could be excluded because of other definite diagnoses or improvement during the follow-up. Despite negative results of an examination of their BAL fluid, two patients received empirical treatment that was active against PCP; these patients were regarded as possibly having undiagnosed PCP. Thus, the negative predictive value of BAL alone was at least 94% (32 of 34 cases) in excluding a diagnosis of PCP during prophylaxis with aerosolized pentamidin

Hadronic Parity Violation and Inelastic Electron-Deuteron Scattering

We compute contributions to the parity-violating (PV) inelastic electron-deuteron scattering asymmetry arising from hadronic PV. While hadronic PV effects can be relatively important in PV threshold electro- disintegration, we find that they are highly suppressed at quasielastic kinematics. The interpretation of the PV quasielastic asymmetry is, thus, largely unaffected by hadronic PV.Comment: 27 pages, 13 figures, uses REVTeX and BibTe

Personalised depression forecasting using mobile sensor data and ecological momentary assessment

Introduction Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients’ individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising. Methods We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention (N=65 patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models’ ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day. Results In our experiments, we achieve a best mean-absolute-error (MAE) of 0.801 (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline (MAE=1.062). For one day ahead-forecasting, we can improve the baseline of 1.539 by 12% to a MAE of 1.349 using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level. Discussion Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression

Modeling and enacting complex data dependencies in business processes

Enacting business processes in process engines requires the coverage of control flow, resource assignments, and process data. While the first two aspects are well supported in current process engines, data dependencies need to be added and maintained manually by a process engineer. Thus, this task is error-prone and time-consuming. In this report, we address the problem of modeling processes with complex data dependencies, e.g., m:n relationships, and their automatic enactment from process models. First, we extend BPMN data objects with few annotations to allow data dependency handling as well as data instance differentiation. Second, we introduce a pattern-based approach to derive SQL queries from process models utilizing the above mentioned extensions. Therewith, we allow automatic enactment of data-aware BPMN process models. We implemented our approach for the Activiti process engine to show applicability. Keywords: Process Modeling, Data Modeling, Process Enactment, BPMN, SQ