Analysing the implication of the EU 20-10-20 targets for world vegetable oil production

The European Commission proposes a minimum of 10 % biofuels in the total transport fuel use by 2020. The new 10% minimum target in 2020 is combined with the existing regulation, which fixes the target at 5.75% in 2010. This paper will in particular investigates how a full implementation of the 20- 10-20 targets would affect production and trade of oil plants in the EU and its main trade partners on this commodity markets, particularly Malaysia and Indonesia. The global general equilibrium model GLOBE is used to carry out the policy scenarios and to assess the effects on oil palm plantation area in Malaysia and Indonesia. The results show that the increased EU bio-diesel target will not significantly influence the expansion of palm oil production in Indonesia and Malaysia.Crop Production/Industries, International Relations/Trade, Resource /Energy Economics and Policy,

Use of a single bipolar electrode in the posterior arytenoid muscles for bilateral monitoring of the recurrent laryngeal nerves in thyroid surgery

The aims were to assess the technical feasibility of using a single electrode in the posterior arytenoid muscles (PAM) for intraoperative monitoring of the recurrent laryngeal nerve (RLN) in thyroid surgery, to validate the new method against the insertion of electrodes placed in the vocal cord muscle, and to report the results of the clinical application of the new concept. A total of 52 patients were enrolled. The handling and safety of RLN monitoring was tested by simultaneous registration of the EMG response from vocal fold electrodes and PAM electrodes. Acoustically and electromyographically we found nearly the same values for the arytenoid muscles as for the vocal folds, although the signals taken from the vocal folds were slightly stronger. PAM recording using a single bipolar electrode is technically feasible and as reliable compared to the standard vocal cord monitorin

Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning -- A Residual Physics Approach

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection to computation, potentially offer significant benefits. This work presents a novel and versatile approach to detector optimization using machine learning (ML) and residual physics. We apply the concept to positron emission tomography (PET), intending to improve the coincidence time resolution (CTR). PET visualizes metabolic processes in the body by detecting photons with scintillation detectors. Improved CTR performance offers the advantage of reducing radioactive dose exposure for patients. Modern PET detectors with sophisticated concepts and read-out topologies represent complex physical and electronic systems requiring dedicated calibration techniques. Traditional methods primarily depend on analytical formulations successfully describing the main detector characteristics. However, when accounting for higher-order effects, additional complexities arise matching theoretical models to experimental reality. Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach. We present a residual physics-based strategy using gradient tree boosting and physics-guided data generation. The explainable AI framework SHapley Additive exPlanations (SHAP) was used to identify known physical effects with learned patterns. In addition, the models were tested against basic physical laws. We were able to improve the CTR significantly (more than 20%) for clinically relevant detectors of 19 mm height, reaching CTRs of 185 ps (450-550 keV)

Funnel control for systems with relative degree two

PublishedJournal ArticleTracking of reference signals yref (·) by the output y(·) of linear (as well as a considerably large class of nonlinear) single-input, single-output systems is considered. The system is assumed to have strict relative degree two with (weakly) stable zero dynamics. The control objective is tracking of the error e = y - yref and its derivative e within two prespecified performance funnels, respectively. This is achieved by the so-called funnel controller u(t) = -k0(t)2e(t)-k 1(t)e(t), where the simple proportional error feedback has gain functions k0 and k1 designed in such a way to preclude contact of e and e with the funnel boundaries, respectively. The funnel controller also ensures boundedness of all signals. We also show that the same funnel controller (i) is applicable to relative degree one systems, (ii) allows for input constraints provided a feasibility condition (formulated in terms of the system data, the saturation bounds, the funnel data, bounds on the reference signal, and the initial state) holds, (iii) is robust in terms of the gap metric: if a system is sufficiently close to a system with relative degree two, stable zero dynamics, and positive high-frequency gain, but does not necessarily have these properties, then for small initial values the funnel controller also achieves the control objective. Finally, we illustrate the theoretical results by experimental results: the funnel controller is applied to a rotatory mechanical system for position control. © 2013 Society for Industrial and Applied Mathematics

Test anxiety, working memory, and cognitive performance: Supportive effects of sequential demands

Substantial evidence suggests that test anxiety is associated with poor performance in complex tasks. Based on the differentiation of coordinative and sequential demands on working memory (Mayr & Kliegl, 1993), two studies examined the effects of sequential demands on the relationship between test anxiety and cognitive performance. Both studies found that high sequential demands had beneficial effects on the speed and accuracy of the performance of test-anxious participants. It is suggested that the more frequent memory updates associated with high sequential demands may represent external processing aids that compensate for the restricted memory capacity of individuals with high test anxiet

Cleavage efficiency of the intramembrane protease γ-secretase is reduced by the palmitoylation of a substrate's transmembrane domain

The intramembrane protease gamma-secretase has broad physiological functions, but also contributes to Notch-dependent tumors and Alzheimer's disease. While gamma-secretase cleaves numerous membrane proteins, only few nonsubstrates are known. Thus, a fundamental open question is how gamma-secretase distinguishes substrates from nonsubstrates and whether sequence-based features or post-translational modifications of membrane proteins contribute to substrate recognition. Using mass spectrometry-based proteomics, we identified several type I membrane proteins with short ectodomains that were inefficiently or not cleaved by gamma-secretase, including 'pituitary tumor-transforming gene 1-interacting protein' (PTTG1IP). To analyze the mechanism preventing cleavage of these putative nonsubstrates, we used the validated substrate FN14 as a backbone and replaced its transmembrane domain (TMD), where gamma-cleavage occurs, with the one of nonsubstrates. Surprisingly, some nonsubstrate TMDs were efficiently cleaved in the FN14 backbone, demonstrating that a cleavable TMD is necessary, but not sufficient for cleavage by gamma-secretase. Cleavage efficiencies varied by up to 200-fold. Other TMDs, including that of PTTG1IP, were still barely cleaved within the FN14 backbone. Pharmacological and mutational experiments revealed that the PTTG1IP TMD is palmitoylated, which prevented cleavage by gamma-secretase. We conclude that the TMD sequence of a membrane protein and its palmitoylation can be key factors determining substrate recognition and cleavage efficiency by gamma-secretase. The intramembrane protease gamma-secretase has broad physiological functions. However, a fundamental open question is how gamma-secretase distinguishes substrates from nonsubstrates and whether sequence-based features or post-translational modifications of membrane proteins contribute to substrate recognition. Using mass spectrometry-based proteomics and domain swap experiments, this study demonstrates that palmitoylation within the C-terminal half of a substrate's transmembrane domain constitutes a new mechanism that can suppress cleavage by gamma-secretase.imag

[COMMODE] a large-scale database of molecular descriptors using compounds from PubChem

BACKGROUND: Molecular descriptors have been extensively used in the field of structure-oriented drug design and structural chemistry. They have been applied in QSPR and QSAR models to predict ADME-Tox properties, which specify essential features for drugs. Molecular descriptors capture chemical and structural information, but investigating their interpretation and meaning remains very challenging. RESULTS: This paper introduces a large-scale database of molecular descriptors called COMMODE containing more than 25 million compounds originated from PubChem. About 2500 DRAGON-descriptors have been calculated for all compounds and integrated into this database, which is accessible through a web interface at http://commode.i-med.ac.at