University of Manitoba

About: University of Manitoba is a education organization based out in Winnipeg, Manitoba, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 31888 authors who have published 66592 publications receiving 2095493 citations.

High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method

Abstract: Here we describe a high-efficiency version of the lithium acetate/single-stranded carrier DNA/PEG method of transformation of Saccharomyces cerevisiae. This method currently gives the highest efficiency and yield of transformants, although a faster protocol is available for small number of transformations. The procedure takes up to 1.5 h, depending on the length of heat shock, once the yeast culture has been grown. This method is useful for most transformation requirements.

Studies on the transformation of intact yeast cells by the LiAc/SS‐DNA/PEG procedure

Abstract: An improved lithium acetate (LiAc)/single-stranded DNA (SS-DNA)/polyethylene glycol (PEG) protocol which yields >1 × 106 transformants/μg plasmid DNA and the original protocol described by Schiestl and Gietz (1989) were used to investigate aspects of the mechanism of LiAc/SS-DNA/PEG transformation. The highest transformation efficiency was observed when 1 × 108 cells were transformed with 100 ng plasmid DNA in the presence of 50 μg SS carrier DNA. The yield of transformants increased linearly up to 5 μg plasmid per transformation. A 20-min heat shock at 42°C was necessary for maximal yields. PEG was found to deposit both carrier DNA and plasmid DNA onto cells. SS carrier DNA bound more effectively to the cells and caused tighter binding of 32P-labelled plasmid DNA than did double-stranded (DS) carrier. The LiAc/SS-DNA/PEG transformation method did not result in cell fusion. DS carrier DNA competed with DS vector DNA in the transformation reaction. SS plasmid DNA transformed cells poorly in combination with both SS and DS carrier DNA. The LiAc/SS-DNA/PEG method was shown to be more effective than other treatments known to make cells transformable. A model for the mechanism of transformation by the LiAc/SS-DNA/PEG method is discussed.

Guidelines for the Diagnosis and Management of Food Allergy in the United States: Summary of the NIAID-Sponsored Expert Panel Report

Abstract: Food allergy is an important public health problem that affects children and adults and may be increasing in prevalence. Despite the risk of severe allergic reactions and even death, there is no current treatment for food allergy: the disease can only be managed by allergen avoidance or treatment of symptoms. The diagnosis and management of food allergy also may vary from one clinical practice setting to another. Finally, because patients frequently confuse nonallergic food reactions, such as food intolerance, with food allergies, there is an unfounded belief among the public that food allergy prevalence is higher than it truly is. In response to these concerns, the National Institute of Allergy and Infectious Diseases, working with 34 professional organizations, federal agencies, and patient advocacy groups, led the development of clinical guidelines for the diagnosis and management of food allergy. These Guidelines are intended for use by a wide variety of health care professionals, including family practice physicians, clinical specialists, and nurse practitioners. The Guidelines include a consensus definition for food allergy, discuss comorbid conditions often associated with food allergy, and focus on both IgE-mediated and non-IgE-mediated reactions to food. Topics addressed include the epidemiology, natural history, diagnosis, and management of food allergy, as well as the management of severe symptoms and anaphylaxis. These Guidelines provide 43 concise clinical recommendations and additional guidance on points of current controversy in patient management. They also identify gaps in the current scientific knowledge to be addressed through future research.

Infliximab Maintenance Therapy for Fistulizing Crohn's Disease

Abstract: BACKGROUND: Infliximab, a monoclonal antibody against tumor necrosis factor, is an effective maintenance therapy for patients with Crohn's disease without fistulas. It is not known whether infliximab is an effective maintenance therapy for patients with fistulas. METHODS: We performed a multicenter, double-blind, randomized, placebo-controlled trial to evaluate the efficacy of infliximab maintenance therapy in 306 adult patients with Crohn's disease and one or more draining abdominal or perianal fistulas of at least three months' duration. Patients received 5 mg of infliximab per kilogram of body weight intravenously on weeks 0, 2, and 6. A total of 195 patients who had a response at weeks 10 and 14 and 87 patients who had no response were then randomly assigned to receive placebo or 5 mg of infliximab per kilogram every eight weeks and to be followed to week 54. The primary analysis was the time to the loss of response among patients who had a response at week 14 and underwent randomization. RESULTS: The time to loss of response was significantly longer for patients who received infliximab maintenance therapy than for those who received placebo maintenance (more than 40 weeks vs. 14 weeks, P<0.001). At week 54, 19 percent of patients in the placebo maintenance group had a complete absence of draining fistulas, as compared with 36 percent of patients in the infliximab maintenance group (P=0.009). CONCLUSIONS: Patients with fistulizing Crohn's disease who have a response to induction therapy with infliximab have an increased likelihood of a sustained response over a 54-week period if infliximab treatment is continued every 8 weeks.

Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

Abstract: A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.