Untersuchungen zur Biologie und den natürlichen Feinden von Deanolis sublimbalis SNELLEN (Lepidoptera, Pyralidae) an Mango in Papua New Guinea

Deanolis sublimbalis SNELLEN (Lepidoptera: Pyralidae), the red banded mango caterpillar (RMBC), is a Southeast Asian insect species. It is now widely distributed throughout this region (India, Burma, Thailand, China, Brunei, Philippines, Indonesia, and Papua New Guinea) and was recently detected for the first time on mainland Australia, but so far has not been recorded in Pakistan, Nepal, and Malaysia (WATERHOUSE 1998). In Papua New Guinea (PNG) it is nowadays widely distributed throughout the mainland and islands (WATERHOUSE 1998). Although infestation levels of 40 – 50 % were recorded in the Philippines (TIPON 1979), very little is known about the biology of this pest, and there are only few references in the literature, the results differing significantly (WATERHOUSE 1998). Due to the contradictory opinions on the biology published, the following study was undertaken to obtain the basic information required for the development of an appropriate management technique for this pest. In particular, the fundamental data to clarify in the course of this study were: 1. life history and behaviour of RMBC, especially when mango is out of season. 2. search for other host plants of RMBC. 3. search for natural enemies of RMBC.Dt. Titel: Untersuchungen zur Biologie und den natürlichen Feinden von Deanolis sublimbalis SNELLEN (Lepidoptera, Pyralidae) an Mango in Papua New Guinea An vier Standorten in der Central Province von Papua Neuguinea wurde D. sublimbalis untersucht. Eier wurden fast immer am Fruchtstiel unter vertrockneten Blütenblättern abgelegt, meist zu 2-4. Eiablage auf die Früchte war sehr selten. Larven wurden in den Früchten von Mangifera indica, M. minor und M. odorata gefunden, nie an den anderen Anacardiaceen Spondias spp. und Anacardium occidentale oder den Myrtaceen Syzygium spp. Im Labor entwickelten sich in Früchten von A. occidentale (Cashew-Baum) nur 5 % der Larven zur Imago. Präpuppen und Puppen wurden nur unter der Rinde der Mangobäume gefunden oder in tiefen Rissen der Rinde. Parasitoide oder Pathogene der Eier oder Larven wurden nicht festgestellt. Die Prädatoren, wie die Weberameise Oecophylla smaragdina (F.), spielten bei D. sublimbalis keine Rolle

Initial Public Offerings, Subsequent Seasoned Equity Offerings, and Long-Run Performance: Evidence from IPOs in Germany

The objective of this study is to investigate the long-run performance of initial public offerings (IPO) in Germany for the period from 1977 to 1995. Of particular interest is to examine whether underpricing and the timing of subsequent seasoned equty offerings (SEO) may help to explain why some firms have substantial positive and others have substantial negative long-run abnormal holding period returns after going public. We find significant empirical evidence that firms that raised additional funds after an IPO through a seasoned equity offering outperformed the market. There is a significant difference in returns relative to the firms that had no subsequent equity offering. A comparison of seasoned equity offerings of IPOs and of established firms suggests that the information asymmetry is more pronounced for IPO firms

Changes in herbivore control in arable fields by detrital subsidies depend on predator species and vary in space

Prey from the decomposer subsystem may help sustain predator populations in arable fields. Adding organic residues to agricultural systems may therefore enhance pest control. We investigated whether resource addition (maize mulch) strengthens aboveground trophic cascades in winter wheat fields. Evaluating the flux of the maize-borne carbon into the food web after 9 months via stable isotope analysis allowed differentiating between prey in predator diets originating from the above- and belowground subsystems. Furthermore, we recorded aphid populations in predator-reduced and control plots of no-mulch and mulch addition treatments. All analyzed soil dwelling species incorporated maize-borne carbon. In contrast, only 2 out of 13 aboveground predator species incorporated maize carbon, suggesting that these 2 predators forage on prey from the above- and belowground systems. Supporting this conclusion, densities of these two predator species were increased in the mulch addition fields. Nitrogen isotope signatures suggested that these generalist predators in part fed on Collembola thereby benefiting indirectly from detrital resources. Increased density of these two predator species was associated by increased aphid control but the identity of predators responsible for aphid control varied in space. One of the three wheat fields studied even lacked aphid control despite of mulch-mediated increased density of generalist predators. The results suggest that detrital subsidies quickly enter belowground food webs but only a few aboveground predator species include prey out of the decomposer system into their diet. Variation in the identity of predator species benefiting from detrital resources between sites suggest that, depending on locality, different predator species are subsidised by prey out of the decomposer system and that these predators contribute to aphid control. Therefore, by engineering the decomposer subsystem via detrital subsidies, biological control by generalist predators may be strengthened

Comparing meat and meat alternatives: an analysis of nutrient quality in five European countries

Abstract Objective: To assess and compare the (macro-)nutritional composition of red meat (RM) and poultry meat (PM) products with the emerging category of meat substitutes. Design: We use information on nutritional values per 100 g to estimate the differences in the nutritional composition between RM, PM, vegan meat substitute (VMS) and non-vegan meat substitute (NVMS) and derive six unique meat product clusters to enhance the comparability. Setting: Meat markets from five major European countries: France, Germany, UK, Italy and Spain. Participants/Data: Product innovation data for 19 941 products from Mintel’s Global New Product Database from 2010 to 2020. Results: Most of the innovations in the sample are RM products (55 %), followed by poultry (30 %), VMS (11 %) and NVMS (5 %). RM products exhibit a significantly higher energy content in kcal/100 g as well as fat, saturated fat, protein and salt all in g/100 g than the meatless alternatives, while the latter contain significantly more carbohydrates and fibre than either poultry or RM. However, results differ to a certain degree when products are grouped into more homogeneous clusters like sausages, cold cuts and burgers. This indicates that general conclusions regarding the health effects of substituting meat with plant-based alternatives should only be drawn in relation to comparable products. Conclusions: Meat substitutes, both vegan and non-vegan, are rated as ultra-processed foods. However, compared with RM products, they and also poultry products both can provide a diet that contains fewer nutrients-to-limit, like salt and saturated fats

Prolonged rather than hasty decision-making in schizophrenia using the box task. Must we rethink the jumping to conclusions account of paranoia?

Accepted manuscript version, licensed CC BY-NC-ND 4.0. Jumping to conclusions (JTC) is the best established cognitive bias in schizophrenia and is increasingly targeted in interventions aimed to improve positive symptoms. To address shortcomings of the standard measure to capture JTC, the beads task, we developed a new variant—the box task—which was subsequently validated in people with elevated psychotic-like experiences. For the first time, the box task was administered in a sample of individuals with manifest schizophrenia. We hypothesized that patients with schizophrenia would display an elevated JTC bias relative to controls. Method - We recruited a large sample of 101 patients with schizophrenia and matched them to an online sample recruited from the general population. In the box task, participants must decide which of two kinds of colored balls are presented more often. Participants are told that the task may end prematurely, and that task performance will be counted as an error if no decision had been made before that point. The primary measure was the number of draws to decision (DTD), where fewer DTD corresponds to greater JTC. Results - In contrast to expectations, participants with schizophrenia showed significantly higher DTD (i.e., reduced JTC). Consistent with our previous findings, patients also displayed a lowered decision threshold compared to controls. Response confidence for the final decision was lower in patients and correlated with self-esteem and positive symptoms. While there was evidence that previous knowledge of the box task lowered DTD, exclusion of participants with experience on the box task did not substantially change results. Discussion - The study fits a growing body of experiments casting doubt on the generalizability of the JTC effect in schizophrenia across different tasks. While the study tentatively supports a liberal acceptance account of psychosis, caution is warranted and we recommend that research should explore and control for potentially important mediators (e.g., task difficulty, stress, test-taking attitudes)

Quality of life and metabolic outcomes after total pancreatectomy and simultaneous islet autotransplantation

Background Pancreas surgery remains technically challenging and is associated with considerable morbidity and mortality. Identification of predictive risk factors for complications have led to a stratified surgical approach and postoperative management. The option of simultaneous islet autotransplantation (sIAT) allows for significant attenuation of long-term metabolic and overall complications and improvement of quality of life (QoL). The potential of sIAT to stratify a priori the indication for total pancreatectomy is yet not adequately evaluated. Methods The aim of this analysis was to evaluate the potential of sIAT in patients undergoing total pancreatectomy to improve QoL, functional and overall outcome and therefore modify the surgical strategy towards earlier and extended indications. A center cohort of 24 patients undergoing pancreatectomy were simultaneously treated with IAT. Patients were retrospectively analyzed regarding in-hospital and overall mortality, postoperative complications, ICU stay, hospital stay, metabolic outcome, and QoL. Results Here we present that all patients undergoing primary total pancreatectomy or surviving complicated two-stage pancreas resection and receiving sIAT show excellent metabolic outcome (33% insulin independence, 66% partial graft function; HbA1c 6,1 ± 1,0%) and significant benefit regarding QoL. Primary total pancreatectomy leads to significantly improved overall outcome and a significant reduction in ICU- and hospital stay compared to a two-stage completion pancreatectomy approach. Conclusions The findings emphasize the importance of risk-stratified pancreas surgery. Feasibility of sIAT should govern the indication for primary total pancreatectomy particularly in high-risk patients. In rescue completion pancreatectomy sIAT should be performed whenever possible due to tremendous metabolic benefit and associated QoL

Ultralow‐parameter denoising: trainable bilateral filter layers in computed tomography

Background Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. Purpose Most data-driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. Methods This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. Results Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal-to-noise ratio values of 33.17 and 43.07 on the respective data sets. Conclusions Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures

Specific Targeting of Lymphoma Cells Using Semisynthetic Anti-Idiotype Shark Antibodies

The B-cell receptor (BCR) is a key player of the adaptive immune system. It is a unique part of immunoglobulin (Ig) molecules expressed on the surface of B cells. In case of many B- cell lymphomas, the tumor cells express a tumor-speci fi c and functionally active BCR, also known as idiotype. Utilizing the idiotype as target for lymphoma therapy has emerged to be demanding since the idiotype differs from patient to patient. Previous studies have shown that shark-derived antibody domains (vNARs) isolated from a semi-synthetic CDR3-randomized library allow for the rapid generation of anti-idiotype binders. In this study, we evaluated the potential of generating patient-speci fi c binders against the idiotype of lymphomas. To this end, the BCRs of three different lymphoma cell lines SUP-B8, Daudi, and IM-9 were identi fi ed, the variable domains were reformatted and the resulting monoclonal antibodies produced. The SUP-B8 BCR served as antigen in fl uorescence-activated cell sorting (FACS)-based screening of the yeast-displayed vNAR libraries which resulted after three rounds of screening in the enrichment of antigen-binding vNARs. Five vNARs were expressed as Fc fusion proteins and consequently analyzed for their binding to soluble antigen using biolayer interferometry (BLI) revealing binding constants in the lower single-digit nanomolar range. These variants showed speci fi c binding to the parental SUP-B8 cell line con fi rming a similar folding of the recombinantly expressed proteins compared with the native cell surface-presented BCR. First initial experiments to utilize the generated vNAR-Fc variants for BCR-clustering to induce apoptosis or ADCC/ADCP did not result in a signi fi cant decrease of cell viability. Here, we report an alternative approach for a personalized B-cell lymphoma therapy based on the construction of vNAR-Fc antibody-drug conjugates to enable speci fi c killing of malignant B cells, which may widen the therapeutic window for B-cell lymphoma therapy

Realizing a Low-latency Virtual Reality Environment for Motor Learning

Waltemate T, Hülsmann F, Pfeiffer T, Kopp S, Botsch M. Realizing a Low-latency Virtual Reality Environment for Motor Learning. In: Proceedings of the 21st ACM Symposium on Virtual Reality Software and Technology. VRST '15. New York, NY, USA: ACM; 2015: 139-147.Virtual Reality (VR) has the potential to support motor learning in ways exceeding beyond the possibilities provided by real world environments. New feedback mechanisms can be implemented that support motor learning during the performance of the trainee and afterwards as a performance review. As a consequence, VR environments excel in controlled evaluations, which has been proven in many other application scenarios. However, in the context of motor learning of complex tasks, including full-body movements, questions regarding the main technical parameters of such a system, in particular that of the required maximum latency, have not been addressed in depth. To fill this gap, we propose a set of requirements towards VR systems for motor learning, with a special focus on motion capturing and rendering. We then assess and evaluate state-of-the-art techniques and technologies for motion capturing and rendering, in order to provide data on latencies for different setups. We focus on the end-to-end latency of the overall system, and present an evaluation of an exemplary system that has been developed to meet these requirements

Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT

Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline's ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding two well-established DL-based denoisers (RED-CNN/QAE) in our pipeline, the denoising performance is improved by $10\,\%$/$82\,\%$ (RMSE) and $3\,\%$/$81\,\%$ (PSNR) in regions containing metal and by $6\,\%$/$78\,\%$ (RMSE) and $2\,\%$/$4\,\%$ (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines