Perspectives on European organic apple breeding and propagation under the frame of LIVESEED Project

Apples are the most prominent fruits produced in Europe. However, their organic production is very challenging and most cultivars can only be produced economically with copper application to combat fungal diseases. Therefore, the existing cultivars need to be replaced by more resilient genotypes also considering improvement of the rootstocks for better adaptation to organic soil, fertility conditions and new diseases. The objective of LIVESEED Horizon 2020 project is to improve the performance and sustainability of organic agriculture by boosting organic seed and plant breeding efforts, and increasing the availability of cultivars adapted to organic growing conditions. LIVESEED apple task aims to improve breeding for organic apple production through a network that will join forces, share knowledge and genetic resources. Organic apple breeding priorities until today should concentrate on several key issues, the main ones being: high quality and regularly bearing apple varieties with durable disease and pest resistances. Ongoing initiatives will be connected and a European network for testing candidate genotypes under organic conditions will be established to join forces, breeding strategies and share knowledge and genetic resources

Robotic Single-Port Cholecystectomy Using a New Platform: Initial Clinical Experience

Background: The technique of single-port laparoscopy was developed over the past years in an attempt to reduce the invasiveness of surgery. A reduction of incisions and their overall size might result in enhanced postoperative cosmesis and potentially reduce pain when compared to conventional techniques. While manual single-port laparoscopy is technically challenging, a newly approved robotic platform used with the da Vinci Si System (Intuitive Surgical, Sunnyvale, CA, USA) might overcome some of the difficulties of this technique. Methods: Patients with cholelithiasis were scheduled for robotic single-port cholecystectomy in an initial clinical trial. Demographic data, intra- and short-term postoperative results were assessed prospectively. Results: Twenty-eight patients (22 females/6 males; median age, 48years) underwent robotic single-port cholecystectomy in our first week of clinical cases. Median OR time was 80min with a median docking time of 8min and median robotic console time of 53min. Two patients underwent intraoperative cholangiography. Eight cases presented with adhesions, tissue alterations, or anatomical abnormalities. No conversions, intra- or postoperative complications occurred. Conclusion: Robotic single-port cholecystectomy appears feasible and safe in our early experience. The robotic approach to single-port surgery seems to overcome some of the technical difficulties of manual single-port surgery. This robotic platform may facilitate completion of more complex case

Deep Drainage Lowers Methane and Nitrous Oxide Emissions from Rice Fields in a Semi-Arid Environment in Rwanda

Few studies have explored greenhouse gas (GHG) emissions from arable land in sub-Saharan Africa (SSA), and particularly from rice paddy fields, which can be a major source of methane (CH4) and nitrous oxide (N2O) emissions. This study examined the effect of drainage on CH4 and N2O emissions from rice fields in Rwanda under shallow drainage to 0.6 m, with the drain weir open four times per week, and deep drainage to 1.2 m with the weir open four times or two times per week. CH4 and N2O fluxes from the soil surface were measured on nine occasions during rice flowering and ripening, using a closed chamber method. Measured fluxes made only a minor contribution to total GHG emissions from rice fields. However, drainage depth had significant effects on CH4 emissions, with shallow drainage treatment giving significantly higher emissions (~0.8 kg ha−1 or ~26 kg CO2-equivalents ha−1) than deep drainage (0.0 kg) over the 44-day measurement period. No treatment effect was observed for N2O fluxes, which ranged from low uptake to low release, and were generally not significantly different from zero, probably due to low nitrogen (N) availability in soil resulting from low N fertilization rate (in the region). Overall, the results suggest that deep drainage can mitigate CH4 emissions compared with traditional shallow drainage, while not simultaneously increasing N2O emissions

Reducing Cost of Surgery by Avoiding Complications: the Model of Robotic Roux-en-Y Gastric Bypass

Background: Robotic surgery is a complex technology offering technical advantages over conventional methods. Still, clinical outcomes and financial issues have been subjects of debate. Several studies have demonstrated higher costs for robotic surgery when compared to laparoscopy or open surgery. However, other studies showed fewer costly anastomotic complications after robotic Roux-en-Y gastric bypass (RYGBP) when compared to laparoscopy. Methods: We collected data for our gastric bypass patients who underwent open, laparoscopic, or robotic surgery from June 1997 to July 2010. Demographic data, BMI, complications, mortality, intensive care unit stay, hospitalization, and operating room (OR) costs were analyzed and a cost projection completed. Sensitivity analyses were performed for varied leak rates during laparoscopy, number of robotic cases per month, number of additional staplers during robotic surgery, and varied OR times for robotic cases. Results: Nine-hundred ninety patients underwent gastric bypass surgery at the University Hospital Geneva from June 1997 to July 2010. There were 524 open, 323 laparoscopic, and 143 robotic cases. Significantly fewer anastomotic complications occurred after open and robotic RYGBP when compared to laparoscopy. OR material costs were slightly less for robotic surgery (USD 5,427) than for laparoscopy (USD 5,494), but more than for the open procedure (USD 2,251). Overall, robotic gastric bypass (USD 19,363) was cheaper when compared to laparoscopy (USD 21,697) and open surgery (USD 23,000). Conclusions: Robotic RYGBP can be cost effective due to balancing greater robotic overhead costs with the savings associated with avoiding stapler use and costly anastomotic complication

Automated Segmentation of Olivine Phenocrysts in a Volcanic Rock Thin Section Using a Fully Convolutional Neural Network

An example of automated characterization and interpretation of the textural and compositional characteristics of solids phases in thin sections using machine learning (ML) is presented. In our study, we focus on the characterization of olivine in volcanic rocks, which is a phase that is often chemically zoned with variable Mg/(Mg + Fe) ratios, so-called magnesian number or mg#. As the olivine crystals represent only less than 10 vol% of the volcanic rock, a pre-processing step is necessary to automatically detect the phases of interest in the images on a pixel level, which is achieved using Deep Learning. A major contribution of the presented approach is to use backscattered electron (BSE) images to: 1) automatically segment all olivine crystals present in the thin section; 2) determine quantitatively their mg#; and 3) identify different populations depending on zoning type (e.g., normal vs reversal zoning) and textural characteristics (e.g., microlites vs phenocrysts). The segmentation of the olivine crystals is implemented with a pretrained fully convolutional neural network model with DeepLabV3 architecture. The model is trained to identify olivine crystals in backscattered electron images using automatically generated training data. The training data are generated automatically from images which can easily be created from X-Ray element maps. Once the olivines are identified in the BSE images, the relationship between BSE intensity value and mg# is determined using a simple regression based on a set of microprobe measurements. This learned functional relationship can then be applied to all olivine pixels of the thin section. If the highest possible map resolution (1 micron per 1 pixel) is selected for the data acquisition, the full processing time of an entire thin section of (Formula presented.) containing more than 1,500 phenocrysts and 20.000 microliths required 140 h of data acquisition (BSE + X-Ray element maps), 8 h of training and 16 h of segmentation and classification. Our further tests demonstrated that the 140 h of data acquisition can be reduced at least by a factor of 4 since only a part of the thin section area (25% or even less) needs to be used for training. The characterization of each additional thin section would only require the BSE data acquisition time (less than 48 h for a whole thin section), without an additional training step. The paper describes the training and processing in detail, shows analytical results and outlines the potential of this Deep Learning approach for petrological applications, resulting in the automatic characterization and interpretation of mineral textures and compositions with an unprecedented high resolution. Copyright © 2022 Leichter, Almeev, Wittich, Beckmann, Rottensteiner, Holtz and Sester

Clinical and epidemiological characteristics of multiple sclerosis patients receiving disease-modifying treatment in Poland

Aim of study. The aim of this study was to collect and analyse data on relapsing-remitting multiple sclerosis (RRMS) patients receiving disease-modifying therapies (DMTs) in Poland.Material and methods. This observational, multicentre study with prospective data collection included RRMS patients receiving DMTs reimbursed by the National Health Fund (NFZ) in Poland, monitored by the Therapeutic Programme Monitoring System (SMPT). Demographic profiles, disability status, and treatment modalities were analysed.Results. Data from 11,632 RRMS patients was collected (from 15,368 new prescriptions), including 10,649 patients in the first-line and 983 in the second-line therapeutic programme of DMTs. The proportion of females to males was 2.39 in the first-line and 1.91 in the second-line. The mean age at DMTs start was 36.6 years in the first-line and 35.1 in the second-line. The median time from the first symptoms to MS diagnosis was 7.4 months, and from MS diagnosis to treatment it was 18.48 months. A total of 43.4% of MS patients started DMT during the 12 months following diagnosis. There was a positive correlation between the duration from MS diagnosis to the start of DMT and a higher initial EDSS value [correlation 0.296 (p < 0.001)]. About 10% of patients stopped DMTs. In Poland, about one third of all MS patients are treated in both lines, and the choice of first-line treatment depends on the region of the country.Conclusions. In Poland there is a need to increase MS patient access to DMTs by improving the organisation of drug programmes

Sequential generation of olfactory bulb glutamatergic neurons by Neurog2-expressing precursor cells

<p>Abstract</p> <p>Background</p> <p>While the diversity and spatio-temporal origin of olfactory bulb (OB) GABAergic interneurons has been studied in detail, much less is known about the subtypes of glutamatergic OB interneurons.</p> <p>Results</p> <p>We studied the temporal generation and diversity of Neurog2-positive precursor progeny using an inducible genetic fate mapping approach. We show that all subtypes of glutamatergic neurons derive from Neurog2 positive progenitors during development of the OB. Projection neurons, that is, mitral and tufted cells, are produced at early embryonic stages, while a heterogeneous population of glutamatergic juxtaglomerular neurons are generated at later embryonic as well as at perinatal stages. While most juxtaglomerular neurons express the T-Box protein Tbr2, those generated later also express Tbr1. Based on morphological features, these juxtaglomerular cells can be identified as tufted interneurons and short axon cells, respectively. Finally, targeted electroporation experiments provide evidence that while the majority of OB glutamatergic neurons are generated from intrabulbar progenitors, a small portion of them originate from extrabulbar regions at perinatal ages.</p> <p>Conclusions</p> <p>We provide the first comprehensive analysis of the temporal and spatial generation of OB glutamatergic neurons and identify distinct populations of juxtaglomerular interneurons that differ in their antigenic properties and time of origin.</p

Perspetiva europeia de melhoramento e propagação de macieira em agricultura biológica no âmbito do projeto LIVESEED

A maçã é um dos frutos de maior destaque na fruticultura europeia. No entanto, a sua produção em Agricultura Biológica (AB) é um desafio constante, dada a problemática associada à proteção das culturas, num quadro de forte limitação à aplicação de produtos fitossanitários. Deste modo, torna-se necessária a produção de material vegetal melhor adaptado às condições edafoclimáticas e ao modo de produção biológica. O objetivo do LIVESEED é melhorar o desempenho e a sustentabilidade em AB, impulsionando os esforços de produção de diásporos e de melhoramento de plantas em modo de produção biológica

Selective Pharmacological Targeting of a DEAD Box RNA Helicase

RNA helicases represent a large family of proteins implicated in many biological processes including ribosome biogenesis, splicing, translation and mRNA degradation. However, these proteins have little substrate specificity, making inhibition of selected helicases a challenging problem. The prototypical DEAD box RNA helicase, eIF4A, works in conjunction with other translation factors to prepare mRNA templates for ribosome recruitment during translation initiation. Herein, we provide insight into the selectivity of a small molecule inhibitor of eIF4A, hippuristanol. This coral-derived natural product binds to amino acids adjacent to, and overlapping with, two conserved motifs present in the carboxy-terminal domain of eIF4A. Mutagenesis of amino acids within this region allowed us to alter the hippuristanol-sensitivity of eIF4A and undertake structure/function studies. Our results provide an understanding into how selective targeting of RNA helicases for pharmacological intervention can be achieved

Wie schnell steigt Magma auf? : Maschinelles Lernen (ML) hilft bei der Antwort

Wissenschaftler*innen der Geoinformatik und der Geowissenschaften bündeln ihre Expertisen, um Georisiken zu minimieren. In diesem Beitrag zeigen sie, dass maschinelles Lernen (ML) für die Auswertung von großen analytischen Datensätzen unumgänglich ist, um Prozesse in Magma-Reservoiren nachzuvollziehen. Der Einsatz von ML ermöglicht ein schnelles Reagieren bei laufenden vulkanischen Eruptionen, wie zum Beispiel La Palma