Biotechnology approaches to overcome biotic and abiotic stress constraints in legumes

Biotic and abiotic stresses cause significant yield losses in legumes and can significantly affect their productivity. Biotechnology tools such as marker-assisted breeding, tissue culture, in vitro mutagenesis and genetic transformation can contribute to solve or reduce some of these constraints. However, only limited success has been achieved so far. The emergence of “omic” technologies and the establishment of model legume plants such as Medicago truncatula and Lotus japonicus are promising strategies for understanding the molecular genetic basis of stress resistance, which is an important bottleneck for molecular breeding. Understanding the mechanisms that regulate the expression of stress-related genes is a fundamental issue in plant biology and will be necessary for the genetic improvement of legumes. In this review, we describe the current status of biotechnology approaches in relation to biotic and abiotic stresses in legumes and how these useful tools could be used to improve resistance to important constraints affecting legume crops.E. Prats is funded by an European Marie Curie Reintegration Grant, N. Rispail by (FP5) Eufaba project. Our work in this area is supported by Spanish CICYT project AGL-2002-03248 and European Union project FP6-2002-FOOD-1-506223. K. Singh’s work in this area is supported in part by the Grains Research and Development Corporation (GRDC) and the Department of Education, Science and Training (DEST) in Australia.Peer reviewe

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio