Safety, tolerability, pharmacokinetics, and neutralisation activities of the anti-HIV-1 monoclonal antibody PGT121.414.LS administered alone and in combination with VRC07-523LS in adults without HIV in the USA (HVTN 136/HPTN 092): a first-in-human, open-label, randomised controlled phase 1 trial.

Background: Multiple broadly neutralising monoclonal antibodies (mAbs) are in development for HIV-1 prevention. The aim of this trial was to test the PGT121.414.

Conformational variability in the D2 loop of Apical Membrane antigen 1.

Apical Membrane Antigen 1 (AMA1) plays a vital role in the invasion of the host erythrocyte by the malaria parasite, . It is thus an important target for vaccine and anti-malaria therapeutic strategies that block the invasion process. AMA1, present on the surface of the parasite, interacts with RON2, a component of the parasite's rhoptry neck (RON) protein complex, which is transferred to the erythrocyte membrane during invasion.

Prevalence of detectable HIV-DNA and HIV-RNA in cerebrospinal fluid of youth with perinatal HIV and impaired cognition on antiretroviral therapy.

Objective: Central nervous system (CNS) HIV infection can impact cognition and may be an obstacle to cure in adolescents and young adults with perinatal HIV (AYAPHIV). IMPAACT2015 enrolled AYAPHIV on suppressive antiretroviral therapy (ART) with cognitive impairment to detect and quantify HIV in blood and cerebrospinal fluid (CSF).

Design: IMPAACT2015 was a U.

Inhibition profile of three biological nitrification inhibitors and their response to soil pH modification in two contrasting soils.

Up to 70% of the nitrogen (N) fertilizer applied to agricultural soils is lost through microbially mediated processes, such as nitrification. This can be counteracted by synthetic and biological compounds that inhibit nitrification. However, for many biological nitrification inhibitors (BNIs), the interaction with soil properties, nitrifier specificity, and effective concentrations are unclear.

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches.