An effective risk management approach to prevent bee damage due to the emission of abraded seed treatment particles during sowing of seeds treated with bee toxic insecticides

contribution to session V Honey bee poisoning incidents and monitoring schemes In spring of 2008, a bee incident occurred in the Upper Rhine Valley (Germany) during drilling of corn: bees were exposed to dust from abraded particles of the seed-coating containing the insecticide clothianidin. An inspection of drilled seed batches for resistance to abrasion and a geographical correlation analysis between specified seed batches and reported bee damages revealed that the incident was caused by improperly dressed batches of corn seeds with excessive abrasion of seed treatment particles which were subsequently emitted via the outlet air stream of the pneumatic drilling machines. Concerns raised by local beekeepers regarding effects on bees from foraging in seed-treated corn fields during bloom could be dispelled by a large-scale survey of clothianidin residues in pollen from the treated crop and an accompanying monitoring of bee hives exposed to flowering corn fields. In order to ensure the bee safety of seed-dressing products, technical improvements of seed treatment quality and drilling technology were developed resulting in a minimization of formation and emission of dust from abraded seed treatment particles. The efficacy of these improvements was proven in field trials. Keywords: seed treatment, drilling machines, corn, clothianidin, dust, honey bee

Feasibility and Efficacy of Adjuvant Chemotherapy With Gemcitabine After Liver Transplantation for Perihilar Cholangiocarcinoma: A Multi-Center, Randomized, Controlled Trial (pro-duct001)

Background Liver transplantation (LT) is considered a therapeutic option for unresectable perihilar cholangiocarcinoma (PHC) within defined criteria. It remains uncertain whether patients can safely receive adjuvant chemotherapy after LT. Methods We performed a prospective, multi-center, randomized, non-blinded two-arm trial (pro-duct001). Patients after LT for unresectable PHC within defined criteria were randomized to adjuvant gemcitabine (LT-Gem group) and LT alone (LT alone group). The primary objective was to investigate if adjuvant chemotherapy is feasible in ≥ 85% of patients after LT. The primary endpoint was the percentage of patients completing the 24 weeks course of adjuvant chemotherapy. Secondary endpoints included overall survival (OS) and disease-free (DFS), and complication rates. Results Twelve patients underwent LT for PHC, of which six (50%) were eligible for randomization (LT-Gem: three patients, LT alone: three patients). Two out of three patients discontinued adjuvant chemotherapy after LT due to intolerance. The study was prematurely terminated due to slow enrollment. One patient with PHC had underlying primary sclerosing cholangitis (PSC). Tumor-free margins could be achieved in all patients. In both the LT-Gem and the LT alone group, the cumulative 1-, 3-, and 5-year OS and DFS rates were 100%, 100%, 67%, and 100%, 67% and 67%, respectively. Conclusions This prospective, multi-center study was prematurely terminated due to slow enrollment and a statement on the defined endpoints cannot be made. Nevertheless, long-term survival data are consistent with available retrospective data and confirm defined criteria for LT. Since more evidence of LT per se in unresectable PHC is urgently needed, a prospective, non-randomized follow-up study (pro-duct002) has since been launched

A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids

Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (lazar) algorithms, were applied to a new Salmonella mutagenicity dataset with 8,290 unique chemical structures utilizing MolPrint2D and Chemistry Development Kit (CDK) descriptors. Crossvalidation accuracies of all investigated models ranged from 80 to 85% which is comparable with the interlaboratory variability of the Salmonella mutagenicity assay. Pyrrolizidine alkaloid predictions showed a clear distinction between chemical groups, where otonecines had the highest proportion of positive mutagenicity predictions and monoesters the lowest

Tamino - An Internet Database System

Abstract. Software AG's Tamino is a novel database server designed to fit the needs of electronic business and worldwide information exchange via the Internet. It is not just an on-top solution based on a database system originally designed for use in other application areas. Rather, it is entirely designed for the specific scenario of HTTP-based access to data represented in XML. These data can stem from various sources, and can be combined on the fly when a corresponding request is encountered. This paper sketches the architecture and the functional features of Tamino, and justifies its various design decisions

Machine Learning-Based Analysis Reveals Triterpene Saponins and Their Aglycones in Cimicifuga racemosa as Critical Mediators of AMPK Activation.

Cimicifuga racemosa (CR) extracts contain diverse constituents such as saponins. These saponins, which act as a defense against herbivores and pathogens also show promise in treating human conditions such as heart failure, pain, hypercholesterolemia, cancer, and inflammation. Some of these effects are mediated by activating AMP-dependent protein kinase (AMPK). Therefore, comprehensive screening for activating constituents in a CR extract is highly desirable. Employing machine learning (ML) techniques such as Deep Neural Networks (DNN), Logistic Regression Classification (LRC), and Random Forest Classification (RFC) with molecular fingerprint MACCS descriptors, 95 CR constituents were classified. Calibration involved 50 randomly chosen positive and negative controls. LRC achieved the highest overall test accuracy (90.2%), but DNN and RFC surpassed it in precision, sensitivity, specificity, and ROC AUC. All CR constituents were predicted as activators, except for three non-triterpene compounds. The validity of these classifications was supported by good calibration, with misclassifications ranging from 3% to 17% across the various models. High sensitivity (84.5-87.2%) and specificity (84.1-91.4%) suggest suitability for screening. The results demonstrate the potential of triterpene saponins and aglycones in activating AMP-dependent protein kinase (AMPK), providing the rationale for further clinical exploration of CR extracts in metabolic pathway-related conditions

Safe and Trustful AI for Closed-Loop Control Systems

In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS