Malaria remains a major global health problem, creating a constant need for research to identify druggable weaknesses in P. falciparum biology. As important components of cellular redox biology, members of the Thioredoxin (Trx) superfamily of proteins have received interest as potential drug targets in Apicomplexans. However, the function and essentiality of endoplasmic reticulum (ER)-localized Trx-domain proteins within P. falciparum has not been investigated. We generated conditional mutants of the protein PfJ2-an ER chaperone and member of the Trx superfamily-and show that it is essential for asexual parasite survival. Using a crosslinker specific for redox-active cysteines, we identified PfJ2 substrates as PfPDI8 and PfPDI11, both members of the Trx superfamily as well, which suggests a redox-regulatory role for PfJ2. Knockdown of these PDIs in PfJ2 conditional mutants show that PfPDI11 may not be essential. However, PfPDI8 is required for asexual growth and our data suggest it may work in a complex with PfJ2 and other ER chaperones. Finally, we show that the redox interactions between these Trx-domain proteins in the parasite ER and their substrates are sensitive to small molecule inhibition. Together these data build a model for how Trx-domain proteins in the P. falciparum ER work together to assist protein folding and demonstrate the suitability of ER-localized Trx-domain proteins for antimalarial drug development.Pseudomonas aeruginosa is a ubiquitous opportunistic pathogen which relies on a highly adaptable metabolism to achieve broad pathogenesis. In one example of this flexibility, to catalyze the NADHquinone oxidoreductase step of the respiratory chain, P. aeruginosa has three different enzymes NUO, NQR and NDH2, all of which carry out the same redox function but have different energy conservation and ion transport properties.  https://www.selleckchem.com/products/hydroxychloroquine-sulfate.html  In order to better understand the roles of these enzymes, we constructed two series of mutants (i) three single deletion mutants, each of which lacks one NADH dehydrogenase and (ii) three double deletion mutants, each of which retains only one of the three enzymes. All of the mutants grew approximately as well as wild type, when tested in rich and minimal medium and in a range of pH and [Na+] conditions, except that the strain with only NUO (ΔnqrFΔndh) has an extended lag phase. During exponential phase, the NADH dehydrogenases contribute to total wild-type activity in the following order NQR > NDH2 > NUO. Some mutants, including the strain without NQR (ΔnqrF) had increased biofilm formation, pyocyanin production, and killed more efficiently in both macrophage and mouse infection models. Consistent with this, ΔnqrF showed increased transcription of genes involved in pyocyanin production.With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012-2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network ( less then 1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.It is urgent to understand how to effectively communicate public health messages during the COVID-19 pandemic. Previous work has focused on how to formulate messages in terms of style and content, rather than on who should send them. In particular, little is known about the impact of spokesperson selection on message propagation during times of crisis. We report on the effectiveness of different public figures at promoting social distancing among 12,194 respondents from six countries that were severely affected by the COVID-19 pandemic at the time of data collection. Across countries and demographic strata, immunology expert Dr. Anthony Fauci achieved the highest level of respondents' willingness to reshare a call to social distancing, followed by a government spokesperson. Celebrity spokespersons were least effective. The likelihood of message resharing increased with age and when respondents expressed positive sentiments towards the spokesperson. These results contribute to the development of evidence-based knowledge regarding the effectiveness of prominent official and non-official public figures in communicating public health messaging in times of crisis. Our findings serve as a reminder that scientific experts and governments should not underestimate their power to inform and persuade in times of crisis and underscore the crucial importance of selecting the most effective messenger in propagating messages of lifesaving information during a pandemic.Breast cancer presents high incidence and mortality rates, being considered an important public health issue. Analyze the spatial distribution pattern of late stage diagnosis and mortality for breast cancer and its correlation with socioeconomic and health service offer-related population indicators. Ecological study, developed with 161 Intermediate Region of Urban Articulation (IRUA). Mortality data were collected from the Mortality Information System (MIS). Tumor staging data were extracted from the Hospital Cancer Registry (HCR). Socioeconomic variables were obtained from the Atlas of Human Development in Brazil; data on medical density and health services were collected from the National Registry of Health Institutions (NRHI) and Supplementary National Health Agency. Global Moran's Index and Local Indicator of Spatial Association (LISA) were utilized to verify the existence of territorial clusters. Multivariate analysis used models with global spatial effects. The proportion of late stage diagnosis of breast cancer was 39.