Porous silica spheres as indoor air pollutant scavengers

Porous silica spheres were investigated for their effectiveness in removing typical indoor air pollutants, such as aromatic and carbonyl-containing volatile organic compounds (VOCs), and compared to the commercially available polymer styrene-divinylbenzene (XAD-4). The silica spheres and the XAD-4 resin were coated on denuder sampling devices and their adsorption efficiencies for volatile organic compounds evaluated using an indoor air simulation chamber. Real indoor sampling was also undertaken to evaluate the affinity of the silica adsorbents for a variety of indoor VOCs. The silica sphere adsorbents were found to have a high affinity for polar carbonyls and found to be more efficient than the XAD-4 resin at adsorbing carbonyls in an indoor environment

Optimal population-level infection detection strategies for malaria control and elimination in a spatial model of malaria transmission

Mass campaigns with antimalarial drugs are potentially a powerful tool for local elimination of malaria, yet current diagnostic technologies are insufficiently sensitive to identify all individuals who harbor infections. At the same time, overtreatment of uninfected individuals increases the risk of accelerating emergence of drug resistance and losing community acceptance. Local heterogeneity in transmission intensity may allow campaign strategies that respond to index cases to successfully target subpatent infections while simultaneously limiting overtreatment. While selective targeting of hotspots of transmission has been proposed as a strategy for malaria control, such targeting has not been tested in the context of malaria elimination. Using household locations, demographics, and prevalence data from a survey of four health facility catchment areas in southern Zambia and an agent-based model of malaria transmission and immunity acquisition, a transmission intensity was fit to each household based on neighborhood age-dependent malaria prevalence. A set of individual infection trajectories was constructed for every household in each catchment area, accounting for heterogeneous exposure and immunity. Various campaign strategies (mass drug administration, mass screen and treat, focal mass drug administration, snowball reactive case detection, pooled sampling, and a hypothetical serological diagnostic) were simulated and evaluated for performance at finding infections, minimizing overtreatment, reducing clinical case counts, and interrupting transmission. For malaria control, presumptive treatment leads to substantial overtreatment without additional morbidity reduction under all but the highest transmission conditions. Selective targeting of hotspots with drug campaigns is an ineffective tool for elimination due to limited sensitivity of available field diagnostics

Near-ultraviolet absorption cross sections of nitrophenols and their potential influence on tropospheric oxidation capacity

Nitrophenols and methylnitrophenols have been identified as photolytic precursors of nitrous acid, HONO, but their gas-phase absorption has not previously been reported. In this study, the absorption cross sections of 2-nitrophenol, 3-methyl-2-nitrophenol, and 4-methyl-2-nitrophenol were measured from 320 to 450 nm using incoherent broad-band cavity enhanced absorption spectroscopy (IBBCEAS). The benzaldehyde absorption spectrum wasmeasured to validate the approach and was in good agreement with literature spectra. The nitrophenol absorption cross sections are large (ca. 10-17 cm2 molecule-1)  and blue-shifted about 20 nm compared to previously measured solution spectra. Besides forming HONO, nitrophenol absorption influences other photochemistry by reducing the available actinic flux. The magnitudes of both effects are evaluated as a function of solar zenith angle, and nitrophenol absorption is shown to lower the photolysis rates of O3 and NO2

PITX1, a specificity determinant in the HIF-1α-mediated transcriptional response to hypoxia

Hypoxia is an important developmental cue for multicellular organisms but it is also a contributing factor for several human pathologies, such as stroke, cardiovascular diseases and cancer. In cells, hypoxia activates a major transcriptional program coordinated by the Hypoxia Inducible Factor (HIF) family. HIF can activate more than one hundred targets but not all of them are activated at the same time, and there is considerable cell type variability. In this report we identified the paired-like homeodomain pituitary transcription factor (PITX1), as a transcription factor that helps promote specificity in HIF-1α dependent target gene activation. Mechanistically, PITX1 associates with HIF-1β and it is important for the induction of certain HIF-1 dependent genes but not all. In particular, PITX1 controls the HIF-1α-dependent expression of the histone demethylases; JMJD2B, JMJD2A, JMJD2C and JMJD1B. Functionally, PITX1 is required for the survival and proliferation responses in hypoxia, as PITX1 depleted cells have higher levels of apoptotic markers and reduced proliferation. Overall, our study identified PITX1 as a key specificity factor in HIF-1α dependent responses, suggesting PITX1 as a protein to target in hypoxic cancers

Prospectus, September 8, 1999

https://spark.parkland.edu/prospectus_1999/1020/thumbnail.jp

Malaria elimination campaigns in the Lake Kariba region of Zambia: a spatial dynamical model

Background As more regions approach malaria elimination, understanding how different interventions interact to reduce transmission becomes critical. The Lake Kariba area of Southern Province, Zambia, is part of a multi-country elimination effort and presents a particular challenge as it is an interconnected region of variable transmission intensities. Methods In 2012-13, six rounds of mass-screen-and-treat drug campaigns were carried out in the Lake Kariba region. A spatial dynamical model of malaria transmission in the Lake Kariba area, with transmission and climate modeled at the village scale, was calibrated to the 2012-13 prevalence survey data, with case management rates, insecticide-treated net usage, and drug campaign coverage informed by surveillance. The model was used to simulate the effect of various interventions implemented in 2014-22 on reducing regional transmission, achieving elimination by 2022, and maintaining elimination through 2028. Findings The model captured the spatio-temporal trends of decline and rebound in malaria prevalence in 2012-13 at the village scale. Simulations predicted that elimination required repeated mass drug administrations coupled with simultaneous increase in net usage. Drug campaigns targeted only at high-burden areas were as successful as campaigns covering the entire region. Interpretation Elimination in the Lake Kariba region is possible through coordinating mass drug campaigns with high-coverage vector control. Targeting regional hotspots is a viable alternative to global campaigns when human migration within an interconnected area is responsible for maintaining transmission in low-burden areas

LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding

Deductive coding is a widely used qualitative research method for determining the prevalence of themes across documents. While useful, deductive coding is often burdensome and time consuming since it requires researchers to read, interpret, and reliably categorize a large body of unstructured text documents. Large language models (LLMs), like ChatGPT, are a class of quickly evolving AI tools that can perform a range of natural language processing and reasoning tasks. In this study, we explore the use of LLMs to reduce the time it takes for deductive coding while retaining the flexibility of a traditional content analysis. We outline the proposed approach, called LLM-assisted content analysis (LACA), along with an in-depth case study using GPT-3.5 for LACA on a publicly available deductive coding data set. Additionally, we conduct an empirical benchmark using LACA on 4 publicly available data sets to assess the broader question of how well GPT-3.5 performs across a range of deductive coding tasks. Overall, we find that GPT-3.5 can often perform deductive coding at levels of agreement comparable to human coders. Additionally, we demonstrate that LACA can help refine prompts for deductive coding, identify codes for which an LLM is randomly guessing, and help assess when to use LLMs vs. human coders for deductive coding. We conclude with several implications for future practice of deductive coding and related research methods

Prospectus, October 6, 1999

https://spark.parkland.edu/prospectus_1999/1024/thumbnail.jp