Bt cotton is spreading very rapidly in China, in response to demand from farmers for technology that will reduce both the cost of pesticide applications and exposure to pesticides, and will free up time for other tasks. Based on surveys of hundreds of farmers in the Yellow River cotton-growing region in northern China in 1999, 2000 and 2001, over 4 million smallholders have been able to increase yield per hectare, and reduce pesticide costs, time spent spraying dangerous pesticides, and illnesses due to pesticide poisoning. The expansion of this cost-saving technology is increasing the supply of cotton and pushing down the price, but prices are still sufficiently high for adopters of Bt cotton to make substantial gains in net income.
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http://dx.doi.org/10.1046/j.1365-313x.2002.01401.x | DOI Listing |
Cureus
December 2024
Anesthesiology, Asahi General Hospital, Asahi, JPN.
The gagging reflex during dental treatment is a common concern for dentists and patients. Herein, we describe a novel approach to managing severe gagging reflex, termed the "KOJIMA program," using a systematic desensitization technique combined with an ultrasound-guided selective glossopharyngeal nerve block (UGSGNB). After performing the UGSGNB, the participants were trained to touch the inside of their mouths with a cotton swab.
View Article and Find Full Text PDFPLoS One
January 2025
Entomology & Biothreat Management Division, Defense Research Laboratory (DRL-DRDO), Tezpur, Assam, India.
Cotton leaf curl disease (CLCuD) is a major constraint for production of cotton (Gossypium sp.) in Northwest India. CLCuD is caused by a monopartite, circular ssDNA virus belonging to the genus Begomovirus in association with betasatellites and alphasatellites, and ttransmitted by a whitefly vector (Bemisia tabaci).
View Article and Find Full Text PDFJAMA Oncol
January 2025
Division of Hematologic Malignancies, Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Importance: Although sharing care with local oncologists after allogeneic hematopoietic cell transplantation (HCT) has been proposed for patients living far from HCT centers, it is not known whether a shared strategy is safe or improves patient quality of life (QOL).
Objective: To determine the efficacy and safety of sharing follow-up care after HCT between the HCT specialty center and local oncologists.
Design, Setting, And Participants: This was a multicenter collaborative randomized clinical trial of patients undergoing HCT at Dana-Farber Cancer Institute (DFCI)-a high volume HCT center in Boston (Massachusetts)-and 8 local oncology practices.
J Trauma Acute Care Surg
January 2025
From the Department of Surgery (J.-M.V., T.W.C., B.A.C.), McGovern Medical School, University of Texas Health Science Center, Houston, Texas; Department of Epidemiology (B.L.R.-R., S.R.W.) and Department of Surgery (J.W.C.), University of Pennsylvania, Philadelphia, Pennsylvania; Donald D. Trunkey Center for Civilian and Combat Casualty Care (M.A.S.), Oregon Health & Science University, Portland, Oregon; Department of Surgery, Ernest E. Moore Shock Trauma Center at Denver Health (E.E.M.), University of Colorado Health Sciences Center, Denver, Colorado; Department of Surgery (N.N.), University of Miami/Jackson Memorial Hospital, Miami, Florida; and Department of Surgery (J.L.S.), Trauma and Transfusion Medicine Research Center, University of Pittsburgh, Pittsburgh, Pennsylvania.
Background: Blood shortages and utilization stewardship have motivated the trauma community to evaluate futility cutoffs during massive transfusions (MTs). Recent single-center studies have confirmed meaningful survival in ultra-MT (≥20 U) and super-MT (≥50 U), while others advocate for earlier futility cut points. We sought to evaluate whether transfusion volume and intensity cut points could predict 100% mortality in a multicenter analysis.
View Article and Find Full Text PDFSci Rep
January 2025
College of Information Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
In modern agriculture, the proliferation of weeds in cotton fields poses a significant threat to the healthy growth and yield of crops. Therefore, efficient detection and control of cotton field weeds are of paramount importance. In recent years, deep learning models have shown great potential in the detection of cotton field weeds, achieving high-precision weed recognition.
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