An economic assessment of alternative antimicrobial use scenarios on pig farms

This paper explores the theoretical economic outcome of management changes that result in different levels of antimicrobial use (AMU) in two types of UK pig farm. A static farm economic pig production model (FEPM) was used on a representative ‘Top-third’ most profitable farm and a representative ‘Mid-range’ profitable farm. Three AMU theoretical management scenarios were investigated; (a) management changes leading to a reduction of AMU by 35% (AMU35); (b) more extensive management changes leading to a reduction of AMU by 95% (AMU95); and (c) implementing depopulation of the herd (AMU Depop). A sensitivity analysis was conducted to determine the effect of increases or decreases in pig revenue and feed price on farm gross margin under these scenarios. Over a single year, the AMU35 scenario was estimated to have a small positive impact (+3%) on both farm types. The other two AMU reduction scenarios had higher AMU reduction on farms but required higher variable cost and hence they resulted in lower farm profitability. There was a substantial reduction (up to −50%) in farm gross margin under these two AMU reduction scenarios in the modeled short-term time-period. The impact of the alternative AMU scenarios was slightly higher on a farm representing the ‘Top-third’ farm type, reducing farm gross margin further by 7% compared to the ‘Mid-range’ farm. Nevertheless, both farm types stay profitable under all three AMU scenarios. The results showed that in the modeled short-term implementing management changes that result in a reduction of on-farm AMU by 35% had a good economic outcome. In practice, the other two scenarios would be considered as longer-term strategies. Although both require higher initial costs to implement, the improved biosecurity and hygiene will benefit from lower disease occurrence for a longer term. Farm gross margins were, however, found to be highly sensitive to changes on market prices especially increasing feed prices. An increase of more than 15% in feed price moved a profitable farm into a loss-making farm. It will be economically challenging for uptakes of these, or similar, AMU reduction scenarios on farms if the market prices become un-favorable to pig farmers

Anthropogenic impacts on the water chemistry of a transboundary river system in Southeast Asia

The Red River originating from Yunnan province, China is the second largest river in Vietnam in terms of length and discharge. Combination of water chemistry monitoring data of 4 years (2018–2022) from different sub-basins of the Red River (the Da, Lo, Thao, Tra Ly, and Day) with historical datasets indicates a decline in pH from 8.1 in 2000 to 7.7 in 2021, greater CO2 concentrations and a shift from waters naturally dominated by carbonate weathering to waters dominated by evaporite weathering. Such changes were most apparent in the delta area where heavy human activities have increased influxes of most dissolved chemicals, except SiO2. Evaporite weathering is particularly enhanced by mining and deforestation occurring in upstream regions of both China and Vietnam. Pyrite oxidation, alongside silicate weathering, is enhanced along the Red River Fault Zone but reduced in tributaries with a higher proportion of hydropower reservoirs. Longer water residence times in these large reservoirs (total volume > 2.7x1010 m3) located in the Da and Lo sub-basins have also increased primary productivity, leading to higher evasion/uptake of CO2 and SiO2, lower total dissolved solids (TDS), and higher pH. The total physical and chemical denudation rates of upstream mountain tributaries ranged between 0.107 \ub1 0.108 and 0.139 \ub1 0.137 mm yr−1, mainly due to reservoir implementation and instream aquatic biogeochemistry changes. Our findings demonstrate that anthropogenic activities are profound factors impacting the water chemistry of the Red River system

Catalyst-Controlled Chemoselective Arylation of 2-Aminobenzimidazoles

What N would you like? The chemoselective and complementary Pd- and Cu-catalyzed N-arylation of 2-aminobenzimidazoles is described. Selective N-arylation of the amino group was achieved with a Pd-catalyzed method, while selective N-arylation of azole nitrogen was achieved with a Cu-catalyzed procedure (see scheme).National Institutes of Health (U.S.) (GM58160

Integration of molecular characterization of microorganisms in a global antimicrobial resistance surveillance program

© 2001 by the Infectious Diseases Society of America. All rights reserved.The SENTRY Antimicrobial Surveillance Program has incorporated molecular strain typing and resistance genotyping as a means of providing additional information that may be useful for understanding pathogenic microorganisms worldwide. Resistance phenotypes of interest include multidrug-resistant pathogens, extended-spectrum β-lactamase (ESBL)–producing Enterobacteriaceae, methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci, and fluoroquinolone-resistant (FQR) strains of gram-negative bacilli and Streptococcus pneumoniae. Clusters of 2 isolates within a given resistance profile that are linked temporally and by hospital location are flagged for DNA fingerprinting. Further characterization of organisms with respect to resistance genotype is accomplished with use of polymerase chain reaction and DNA sequencing. This process has been highly successful in identifying clonal spread within clusters of multiresistant pathogens. Between 50% and 90% of MRSA clusters identified by phenotypic screening contained evidence of clonal spread. Among the Enterobacteriaceae, ESBL-producing strains of Escherichia coli and Klebsiella pneumoniae are the most common pathogens causing clusters of infection, and 50% of recognized clusters demonstrate clonal spread. Clusters of Pseudomonas aeruginosa, Acinetobacter species, and Stenotrophomonas maltophilia have been noted with clonal spread among patients with urinary tract, respiratory, and bloodstream infections. Characterization of mutations in the FQR-determining region of phenotypically susceptible isolates of E. coli and S. pneumoniae has identified first-stage mutants among as many as 40% of isolates. The ability to characterize organisms phenotypically and genotypically is extremely powerful and provides unique information that is important in a global antimicrobial surveillance program.M. A. Pfaller, J. Acar, R. N. Jones, J. Verhoef, J. Turnidge, and H. S. Sade

Fingerprinting the Substrate Specificity of M1 and M17 Aminopeptidases of Human Malaria, Plasmodium falciparum

Plasmodium falciparum, the causative agent of human malaria, expresses two aminopeptidases, PfM1AAP and PfM17LAP, critical to generating a free amino acid pool used by the intraerythrocytic stage of the parasite for proteins synthesis, growth and development. These exopeptidases are potential targets for the development of a new class of anti-malaria drugs.To define the substrate specificity of recombinant forms of these two malaria aminopeptidases we used a new library consisting of 61 fluorogenic substrates derived both from natural and unnatural amino acids. We obtained a detailed substrate fingerprint for recombinant forms of the enzymes revealing that PfM1AAP exhibits a very broad substrate tolerance, capable of efficiently hydrolyzing neutral and basic amino acids, while PfM17LAP has narrower substrate specificity and preferentially cleaves bulky, hydrophobic amino acids. The substrate library was also exploited to profile the activity of the native aminopeptidases in soluble cell lysates of P. falciparum malaria.This data showed that PfM1AAP and PfM17LAP are responsible for majority of the aminopeptidase activity in these extracts. These studies provide specific substrate and mechanistic information important for understanding the function of these aminopeptidases and could be exploited in the design of new inhibitors to specifically target these for anti-malaria treatment

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page