Antigen-driven T-cell turnover.

A mathematical model is developed to characterize the distribution of cell turnover rates within a population of T lymphocytes. Previous models of T-cell dynamics have assumed a constant uniform turnover rate; here we consider turnover in a cell pool subject to clonal proliferation in response to diverse and repeated antigenic stimulation. A basic framework is defined for T-cell proliferation in response to antigen, which explicitly describes the cell cycle during antigenic stimulation and subsequent cell division. The distribution of T-cell turnover rates is then calculated based on the history of random exposures to antigens. This distribution is found to be bimodal, with peaks in cell frequencies in the slow turnover (quiescent) and rapid turnover (activated) states. This distribution can be used to calculate the overall turnover for the cell pool, as well as individual contributions to turnover from quiescent and activated cells. The impact of heterogeneous turnover on the dynamics of CD4(+) T-cell infection by HIV is explored. We show that our model can resolve the paradox of high levels of viral replication occurring while only a small fraction of cells are infected

Case reports: A helping hand to generalists

Clinical decision making can be challenging for both generalists and specialists. Case reports may assist the decision making process either by providing guidance to generalists on identifying rarer conditions or a searchable database for looking up seemingly disparate symptoms. This editorial highlights the innovations being implemented by Journal of Medical Case Reports and Cases Journal in developing an educational resource to help clinicians in decision-making

‘Okunen’ in a long, hot orogen: a timeline of tectonometamorphic activity in southern LützowHolm Bay

第3回極域科学シンポジウム/第32回極域地学シンポジウム 11月30日（金） 統計数理研究所 ３階セミナー

Identification of Urinary Biomarkers of Colon Inflammation in IL10−/− Mice Using Short-Column LCMS Metabolomics

The interleukin-10-deficient (IL10−/−) mouse develops colon inflammation in response to normal intestinal microflora and has been used as a model of Crohn's disease. Short-Column LCMS metabolite profiling of urine from IL10−/− and wild-type (WT) mice was used, in two independent experiments, to identify mass spectral ions differing in intensity between these two genotypes. Three differential metabolites were identified as xanthurenic acid and as the glucuronides of xanthurenic acid and of α-CEHC (2,5,7,8-tetramethyl-2-(2′-carboxyethyl)-6-hydroxychroman). The significance of several differential metabolites as potential biomarkers of colon inflammation was evaluated in an experiment which compared metabolite concentrations in IL10−/− and WT mice housed, either under conventional conditions and dosed with intestinal microflora, or maintained under specific pathogen-free (SPF) conditions. Concentrations of xanthurenic acid, α-CEHC glucuronide, and an unidentified metabolite m/z 495−/497+ were associated with the degree of inflammation in IL10−/− mice and may prove useful as biomarkers of colon inflammation

Experiments in cross-domain few-shot learning for image classification: extended abstract

We summarise experiments (Wang et al., 2022) evaluating cross-domain few-shot learning (CDFSL) with feature extractors trained on ImageNet. The work explores the transfer performance of extracted features on five target domains with different degrees of similarity to ImageNet. These experiments compare robust classifiers and normalisation methods, consider multi-instance learning algorithms, and evaluate the effect of using features extracted by different ResNet backbones at various levels of their convolutional hierarchies. The cosine similarity classifier and 1-vs-rest logistic regression with ℓ2 regularisation are the top-performing robust classifiers in the evaluation, and ℓ2 normalisation improves performance on all five target domains when using LDA as the robust classifier. The results also show that feature extractors with the highest capacity do not always achieve the best CDFSL performance. Lastly, simple multi-instance learning methods are shown to improve classifier accuracy

Whose Power to Control? Some Reflections on Seed Systems and Food Security in a Changing World

Four key words are essential in understanding the changing global food system: power, control, risks and benefits. The interplay between state and private actors vying to influence the direction of change, and use whatever tools for control they can, is at the heart of the contention for the future control of food. It is one shaped by history and influenced by a changing geopolitics. This interplay has led to the creation of a range of global rules affecting food, agriculture and biodiversity in which those on ‘intellectual property’ or IP are central. These rules come from a system dominated by the interests of the biggest players. Also important are the changing understandings and nature of food security and the pathways to innovation in agri?food systems that are most likely to lead to a just, healthy and sustainable future for all. Developments in food and farming are central to this and are the context in which the political economy of cereal seed systems in Africa is grounded

Analysis of high-molecular-weight fructan polymers in crude plant extracts by high-resolution LC-MS

The main water-soluble carbohydrates in temperate forage grasses are polymeric fructans. Fructans consist of fructose chains of various chain lengths attached to sucrose as a core molecule. In grasses, fructans are a complex mixture of a large number of isomeric oligomers with a degree of polymerisation ranging from 3 to >100. Accurate monitoring and unambiguous peak identification requires chromatographic separation coupled to mass spectrometry. The mass range of ion trap mass spectrometers is limited, and we show here how monitoring selected multiply charged ions can be used for the detection and quantification of individual isomers and oligomers of high mass, particularly those of high degree of polymerization (DP > 20) in complex plant extracts. Previously reported methods using linear ion traps with low mass resolution have been shown to be useful for the detection of fructans with a DP up to 49. Here, we report a method using high-resolution mass spectrometry (MS) using an Exactive Orbitrap MS which greatly improves the signal-to-noise ratio and allows the detection of fructans up to DP = 100. High-sugar (HS) Lolium perenne cultivars with high concentrations of these fructans have been shown to be of benefit to the pastoral agricultural industry because they improve rumen nitrogen use efficiency and reduce nitrous oxide emissions from pastures. We demonstrate with our method that these HS grasses not only contain increased amounts of fructans in leaf blades but also accumulate fructans with much higher DP compared to cultivars with normal sugar levels

The Impact of Monitoring HIV Patients Prior to Treatment in Resource-Poor Settings: Insights from Mathematical Modelling

Using a stochastic model based on data from Africa, Timothy Hallett and colleagues find that starting HIV treatment based on regular CD4 monitoring, rather than on symptoms, would substantially increase survival

Biomarkers of coagulation, endothelial function, and fibrinolysis in critically ill patients with COVID-19: A single-center prospective longitudinal study

Background: Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in coronavirus disease 2019 (COVID-19). We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential. Methods: We performed a single-center prospective study of 14 adult COVID-19(+) intensive care unit patients who were age- and sex-matched to 14 COVID-19(−) intensive care unit patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Baseline values for 10 biomarkers of interest were compared between the three groups, and visualized using Fisher\u27s linear discriminant function. Linear repeated-measures mixed models were used to screen biomarkers for associations with mortality. Selected biomarkers were further explored and entered into an unsupervised longitudinal clustering machine learning algorithm to identify trends and targets that may be used for future predictive modelling efforts. Results: Elevated D-dimer was the strongest contributor in distinguishing COVID-19 status; however, D-dimer was not associated with survival. Variable selection identified clot lysis time, and antigen levels of soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), and plasminogen as biomarkers associated with death. Longitudinal multivariate k-means clustering on these biomarkers alone identified two clusters of COVID-19(+) patients: low (30%) and high (100%) mortality groups. Biomarker trajectories that characterized the high mortality cluster were higher clot lysis times (inhibited fibrinolysis), higher sTM and PAI-1 levels, and lower plasminogen levels. Conclusions: Longitudinal trajectories of clot lysis time, sTM, PAI-1, and plasminogen may have predictive ability for mortality in COVID-19