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1.
The National Institute of General Medical Sciences (NIGMS) at the U.S. National Institutes of Health (NIH) is committed to supporting the safety of the nation’s biomedical research and training environments. Institutional training grants affect many trainees and can have a broad influence across their parent institutions, making them good starting points for our initial efforts to promote the development and maintenance of robust cultures of safety at U.S. academic institutions. In this Perspective, we focus on laboratory safety, although many of the strategies we describe for improving laboratory safety are also applicable to other forms of safety including the prevention of harassment, intimidation, and discrimination. We frame the problem of laboratory safety using a number of recent examples of tragic accidents, highlight some of the lessons that have been learned from these and other events, discuss what NIGMS is doing to address problems related to laboratory safety, and outline steps that institutions can take to improve their safety cultures.All new funding opportunity announcements (FOAs) for training programs supported by the National Institute of General Medical Sciences (NIGMS) contain the expectation that the programs will promote “inclusive, safe and supportive scientific and training environments.” In this context, the word “safe” refers to several aspects of safety. First, we mean an environment free from harassment and intimidation, in which everyone participating is treated in a respectful and supportive manner, optimized for productive learning and research. We also mean that institutions should ensure that their campuses are as safe as possible so that individuals can focus on their studies and research. Finally, we mean safety in the laboratory and clinical spaces. In this Perspective, we focus on this last issue and describe some of the approaches NIGMS is taking to help the biomedical research community move toward an enhanced culture of safety in which core values and the behaviors of leadership, principal investigators (PIs), research staff, and trainees emphasize safety over competing goals. 相似文献
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- The experiment was conducted in the 2016/17 crop season in a greenhouse at Passo Fundo University, Brazil. We hypothesised that the morphological characteristics and biochemical and anatomical composition of soybean roots and shoots, when competing with weeds during different growth periods, are negatively affected, so current concepts of competition between plants should also consider changes in plant roots.
- The soybean cultivar P 95R51 and horseweed (Conyza bonariensis) were used. The treatments consisted of the presence or absence of weeds during different coexistence periods of soybean with horseweed. The periods were V0–V3, V0–V6, V0–R2, V3–R6, V6–R6 and R2–R6, where V0 was the date of soybean sowing and V3, V6, R2 and R6 were phenological stages of the crop. Two fresh roots were used to examine morphological traits. Four roots were used for quantification of dry matter and secondary metabolites.
- Root length was reduced by 21%, 14% and 20% when competing with a weed in the V0–V3, V0–V6 and R2–R6 coexistence periods, respectively. Total phenol content in the V0–V6 and V0–R2 periods was reduced when plants were in competition with weeds; a similar trend was found for flavonoids in the V0–V6 period.
- Soybean–horseweed competition from crop emergence to the V6 stage, in general, affects shoot and root morphological traits and the biochemical composition of the soybean roots. The presence of horseweed at the V3, V6 and R2 stages does not negatively alter the traits evaluated. Root anatomical composition is not modified during all coexistence periods with horseweed.
3.
Spliceosome-targeted therapies trigger an antiviral immune response in triple-negative breast cancer
Elizabeth A. Bowling Jarey H. Wang Fade Gong William Wu Nicholas J. Neill Ik Sun Kim Siddhartha Tyagi Mayra Orellana Sarah J. Kurley Rocio Dominguez-Vidaña Hsiang-Ching Chung Tiffany Y.-T. Hsu Julien Dubrulle Alexander B. Saltzman Heyuan Li Jitendra K. Meena Gino M. Canlas Srinivas Chamakuri Thomas F. Westbrook 《Cell》2021,184(2):384-403.e21
4.
Wanwisa Dejnirattisai Daming Zhou Helen M. Ginn Helen M.E. Duyvesteyn Piyada Supasa James Brett Case Yuguang Zhao Thomas S. Walter Alexander J. Mentzer Chang Liu Beibei Wang Guido C. Paesen Jose Slon-Campos César López-Camacho Natasha M. Kafai Adam L. Bailey Rita E. Chen Baoling Ying Gavin R. Screaton 《Cell》2021,184(8):2183-2200.e22
5.
Ryan A. Flynn Julia A. Belk Yanyan Qi Yuki Yasumoto Jin Wei Mia Madel Alfajaro Quanming Shi Maxwell R. Mumbach Aditi Limaye Peter C. DeWeirdt Cameron O. Schmitz Kevin R. Parker Elizabeth Woo Howard Y. Chang Tamas L. Horvath Jan E. Carette Carolyn R. Bertozzi Craig B. Wilen Ansuman T. Satpathy 《Cell》2021,184(9):2394-2411.e16
6.
Richard J. Mills Sean J. Humphrey Patrick R.J. Fortuna Mary Lor Simon R. Foster Gregory A. Quaife-Ryan Rebecca L. Johnston Troy Dumenil Cameron Bishop Rajeev Rudraraju Daniel J. Rawle Thuy Le Wei Zhao Leo Lee Charley Mackenzie-Kludas Neda R. Mehdiabadi Christopher Halliday Dean Gilham James E. Hudson 《Cell》2021,184(8):2167-2182.e22
7.
D.L. Ambriz-Pérez E.E. Orozco-Guillen N.D. Galán-Hernández K.D. Luna-Avelar A. Valdez-Ortiz D.U. Santos-Ballardo 《Letters in applied microbiology》2021,73(3):343-351
The development of microalgae culture technology has been an integral part to produce biomass feedstock to biofuel production. Due to this, numerous attempts have been made to improve some operational parameters of microalgae production. Despite this, specialized research in cell growth monitoring, considered as a fundamental parameter to achieve profitable applications of microalgae for biofuels production, presents some opportunity areas mainly related to the development of specific and accurate methodologies for growth monitoring. In this work, predictive models were developed through statistical tools that correlate a specific micro-algal absorbance with cell density measured by cell count (cells∙per ml), for three species of interest for biofuels production. The results allow the precise prediction of cell density through a logistic model based on spectrophotometry, valid for all the kinetics analysed. The adjusted determination coefficients () for the developed models were 0·993, 0·995 and 0·994 for Dunaliella tertiolecta, Nannochloropsis oculata and Chaetoceros muelleri respectively. The results showed that the equations obtained here can be used with an extremely low error (≤2%) for all the cell growth ranges analysed, with low operational cost and high potential of automation. Finally, a user-friendly software was designed to give practical use to the developed predictive models. 相似文献
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