Cell Surface Markers in HTLV-1 Pathogenesis

The phenotype of HTLV-1-transformed CD4+ T lymphocytes largely depends on defined viral effector molecules such as the viral oncoprotein Tax. In this review, we exemplify the expression pattern of characteristic lineage markers, costimulatory receptors and ligands of the tumor necrosis factor superfamily, cytokine receptors, and adhesion molecules on HTLV-1-transformed cells. These molecules may provide survival signals for the transformed cells. Expression of characteristic surface markers might therefore contribute to persistence of HTLV-1-transformed lymphocytes and to the development of HTLV-1-associated disease

Low hydrological connectivity after summer drought inhibits DOC export in a forested headwater catchment

Understanding the controls on event-driven dissolved organic carbon (DOC) export is crucial as DOC is an important link between the terrestrial and the aquatic carbon cycles. We hypothesized that topography is a key driver of DOC export in headwater catchments because it influences hydrological connectivity, which can inhibit or facilitate DOC mobilization. To test this hypothesis, we studied the mechanisms controlling DOC mobilization and export in the Große Ohe catchment, a forested headwater in a mid-elevation mountainous region in southeastern Germany. Discharge and stream DOC concentrations were measured at an interval of 15 min using in situ UV-Vis (ultraviolet–visible) spectrometry from June 2018 until October 2020 at two topographically contrasting subcatchments of the same stream. At the upper location (888 m above sea level, a.s.l.), the stream drains steep hillslopes, whereas, at the lower location (771 m a.s.l.), it drains a larger area, including a flat and wide riparian zone. We focus on four events with contrasting antecedent wetness conditions and event size. During the events, in-stream DOC concentrations increased up to 19 mg L−1 in comparison to 2–3 mg L−1 during baseflow. The concentration–discharge relationships exhibited pronounced but almost exclusively counterclockwise hysteresis loops which were generally wider in the lower catchment than in the upper catchment due to a delayed DOC mobilization in the flat riparian zone. The riparian zone released considerable amounts of DOC, which led to a DOC load up to 7.4 kg h−1. The DOC load increased with the total catchment wetness. We found a disproportionally high contribution to the total DOC export of the upper catchment during events following a long dry period. We attribute this to the low hydrological connectivity in the lower catchment during drought, which inhibited DOC mobilization, especially at the beginning of the events. Our data show that not only event size but also antecedent wetness conditions strongly influence the hydrological connectivity during events, leading to a varying contribution to DOC export of subcatchments, depending on topography. As the frequency of prolonged drought periods is predicted to increase, the relative contribution of different subcatchments to DOC export may change in the future when hydrological connectivity will be reduced more often.</p

AI-based structure-function correlation in age-related macular degeneration

Sensitive and robust outcome measures of retinal function are pivotal for clinical trials in age-related macular degeneration (AMD). A recent development is the implementation of artificial intelligence (AI) to infer results of psychophysical examinations based on findings derived from multimodal imaging. We conducted a review of the current literature referenced in PubMed and Web of Science among others with the keywords 'artificial intelligence' and 'machine learning' in combination with 'perimetry', 'best-corrected visual acuity (BCVA)', 'retinal function' and 'age-related macular degeneration'. So far AI-based structure-function correlations have been applied to infer conventional visual field, fundus-controlled perimetry, and electroretinography data, as well as BCVA, and patient-reported outcome measures (PROM). In neovascular AMD, inference of BCVA (hereafter termed inferred BCVA) can estimate BCVA results with a root mean squared error of ~7-11 letters, which is comparable to the accuracy of actual visual acuity assessment. Further, AI-based structure-function correlation can successfully infer fundus-controlled perimetry (FCP) results both for mesopic as well as dark-adapted (DA) cyan and red testing (hereafter termed inferred sensitivity). Accuracy of inferred sensitivity can be augmented by adding short FCP examinations and reach mean absolute errors (MAE) of ~3-5 dB for mesopic, DA cyan and DA red testing. Inferred BCVA, and inferred retinal sensitivity, based on multimodal imaging, may be considered as a quasi-functional surrogate endpoint for future interventional clinical trials in the future

Vaccines for protecting infants from bacterial causes of diarrheal disease

The global diarrheal disease burden fo