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Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

Key Points Negative regulatory receptors, such as PD1 and LAG3, are expressed on 'exhausted' T cells. However, not all cells that express these receptors are exhausted. Therapeutic blockade of the PD1 pathway shows durable clinical responses in patients with melanoma and other types of cancer. The presumed mechanism of action of PD1 blockade is prevention of the interaction between PD1 on tumour-infiltrating T cells and PDL1 expressed on tumour cells. However, PDL1 expression by tumour cells is not an absolute biomarker of clinical response. PD1 has many other functions and pathways that could also be affected by PD1–PDL1 blockade: for example, PD1 and PDL1 are expressed by a variety of cell types in response to a variety of stimuli. PD1 blockade may also perturb other receptor–ligand interactions. Furthermore, 'reverse signalling' can occur through PDL1. The clinical activity of blocking LAG3 is not yet known, but this could potentially induce anti-tumour responses. Triggering of LAG3 on T cells by MHC class II ligands downregulates T cell function, but may also have other immunomodulatory roles. In addition, soluble LAG3 exhibits immune adjuvant activity. Here, two receptors that inhibit T cell functions — programmed cell death protein 1 (PD1) and lymphocyte activation gene 3 protein (LAG3) — are reviewed. Their mechanisms of action are discussed in the context of clinical blockade for cancer therapy and potential biomarkers of the efficacy of therapeutic blockade are proposed. Dysfunctional T cells can render the immune system unable to eliminate infections and cancer. Therapeutic targeting of the surface receptors that inhibit T cell function has begun to show remarkable success in clinical trials. In this Review, we discuss the potential mechanisms of action of the clinical agents that target two of these receptors, programmed cell death protein 1 (PD1) and lymphocyte activation gene 3 protein (LAG3). We also suggest correlative studies that may define the predominant mechanisms of action and identify predictive biomarkers.

Having geographical proximity and a high volume of trade with China, the first country to record an outbreak of the new Coronavirus disease (COVID-19), Vietnam was expected to have a high risk of transmission. However, as of 4 April 2020, in comparison to attempts to containing the disease around the world, responses from Vietnam are seen as prompt and effective in protecting the interests of its citizens, with 239 confirmed cases and no fatalities. This study analyzes the situation in terms of Vietnam’s policy response, social media and science journalism. A self-made web crawl engine was used to scan and collect official media news related to COVID-19 between the beginning of January and April 4, yielding a comprehensive dataset of 14,952 news items. The findings shed light on how Vietnam—despite being under-resourced—has demonstrated political readiness to combat the emerging pandemic since the earliest days. Timely communication on any developments of the outbreak from the government and the media, combined with up-to-date research on the new virus by the Vietnamese science community, have altogether provided reliable sources of information. By emphasizing the need for immediate and genuine cooperation between government, civil society and private individuals, the case study offers valuable lessons for other nations concerning not only the concurrent fight against the COVID-19 pandemic but also the overall responses to a public health crisis.

As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digital abilities. Results show that economic status and parents’ level of education are positively correlated with digital literacy. Students from urban schools have only a slightly higher level of digital literacy than their rural counterparts, suggesting that school location may not be a defining explanatory element in the variation of digital literacy and resilience among Vietnamese students. Students’ digital literacy and, especially resilience, also have associations with their gender. Moreover, as students are digitally literate, they are more likely to be digitally resilient. Following SDG4, i.e., Quality Education, it is advisable for schools, and especially parents, to seriously invest in creating a safe, educational environment to enhance digital literacy among students.

Background and aims Extreme climate events, including flooding and prolonged drought, may establish long-lasting (legacy) effects on soil abiotic and biotic properties, potentially influencing soil N-cycling, microbial communities, and plant productivity. Nitrogen (N) fertilizer often stimulates plant growth, but it remains unknown whether N addition can alleviate the impact of legacy drought or waterlogging events on crops. Our aim was to assess the interactive effects of legacy extreme climate events and N-addition on these processes. Methods Using cotton as a model system, soils previously exposed to waterlogging and prolonged drought were used to examine potential legacy impacts of extreme climate on soil N process rates, abundance and structure of associated microbial communities, and cotton growth and productivity under different levels of N fertilizer application (0, 100, 200 and 300 kg N/ha). Results The deleterious legacy effects of prolonged drought on plant productivity were due to negative impacts on microbial abundance and community structure, and soil nutrient availability, thereby negatively influencing the rate of nitrification, and consequently plant available N. The legacy impacts of prolonged drought persisted throughout the experiment despite fertiliser applications of up to 300 kg of N/ha. The only observed legacy impacts of waterlogging were low NO3− levels in soils without N-addition and shifts in the abundance and structure of the N2O-reducing community. Conclusions There were strong legacy impacts of prolonged drought, but minor legacy impacts of waterlogging, on soils and crop yields which could not be fully counteracted by the high rates of N fertilizer application. This study provides critical knowledge contributing to the development of adaptation and soil N management strategies to minimize the loss of farm productivity, within the context of increased frequencies and intensities of extreme weather events.

Durability is the most pressing issue preventing the efficient commercialization of polymer electrolyte membrane fuel cell (PEMFC) stationary and transportation applications. A big barrier to overcoming the durability limitations is gaining a better understanding of failure modes for user profiles. In addition, durability test protocols for determining the lifetime of PEMFCs are important factors in the development of the technology. These methods are designed to gather enough data about the cell/stack to understand its efficiency and durability without causing it to fail. They also provide some indication of the cell/stack’s age in terms of changes in performance over time. Based on a study of the literature, the fundamental factors influencing PEMFC long-term durability and the durability test protocols for both PEMFC stationary and transportation applications were discussed and outlined in depth in this review. This brief analysis should provide engineers and researchers with a fast overview as well as a useful toolbox for investigating PEMFC durability issues.

Incomplete adherence to tuberculosis (TB) treatment increases the risk of delayed culture conversion with continued transmission in the community, as well as treatment failure, relapse, and development or amplification of drug resistance. We conducted a systematic review and meta-analysis of adherence interventions, including directly observed therapy (DOT), to determine which approaches lead to improved TB treatment outcomes. We systematically reviewed Medline as well as the references of published review articles for relevant studies of adherence to multidrug treatment of both drug-susceptible and drug-resistant TB through February 3, 2018. We included randomized controlled trials (RCTs) as well as prospective and retrospective cohort studies (CSs) with an internal or external control group that evaluated any adherence intervention and conducted a meta-analysis of their impact on TB treatment outcomes. Our search identified 7,729 articles, of which 129 met the inclusion criteria for quantitative analysis. Seven adherence categories were identified, including DOT offered by different providers and at various locations, reminders and tracers, incentives and enablers, patient education, digital technologies (short message services [SMSs] via mobile phones and video-observed therapy [VOT]), staff education, and combinations of these interventions. When compared with DOT alone, self-administered therapy (SAT) was associated with lower rates of treatment success (CS: risk ratio [RR] 0.81, 95% CI 0.73-0.89; RCT: RR 0.94, 95% CI 0.89-0.98), adherence (CS: RR 0.83, 95% CI 0.75-0.93), and sputum smear conversion (RCT: RR 0.92, 95% CI 0.87-0.98) as well as higher rates of development of drug resistance (CS: RR 4.19, 95% CI 2.34-7.49). When compared to DOT provided by healthcare providers, DOT provided by family members was associated with a lower rate of adherence (CS: RR 0.86, 95% CI 0.79-0.94). DOT delivery in the community versus at the clinic was associated with a higher rate of treatment success (CS: RR 1.08, 95% CI 1.01-1.15) and sputum conversion at the end of two months (CS: RR 1.05, 95% CI 1.02-1.08) as well as lower rates of treatment failure (CS: RR 0.56, 95% CI 0.33-0.95) and loss to follow-up (CS: RR 0.63, 95% CI 0.40-0.98). Medication monitors improved adherence and treatment success and VOT was comparable with DOT. SMS reminders led to a higher treatment completion rate in one RCT and were associated with higher rates of cure and sputum conversion when used in combination with medication monitors. TB treatment outcomes improved when patient education, healthcare provider education, incentives and enablers, psychological interventions, reminders and tracers, or mobile digital technologies were employed. Our findings are limited by the heterogeneity of the included studies and lack of standardized research methodology on adherence interventions. TB treatment outcomes are improved with the use of adherence interventions, such as patient education and counseling, incentives and enablers, psychological interventions, reminders and tracers, and digital health technologies. Trained healthcare providers as well as community delivery provides patient-centered DOT options that both enhance adherence and improve treatment outcomes as compared to unsupervised, SAT alone.

Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains underexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accuracy. Our results show that alternative FE techniques can outperform PCA, improving classification accuracy and computational efficiency. Techniques like LDA and NCA effectively capture nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM.

ObjectiveThe enteric nervous system (ENS) undergoes neuronal loss and degenerative changes with age. The cause of this neurodegeneration is poorly understood. Muscularis macrophages residing in close proximity to enteric ganglia maintain neuromuscular function via direct crosstalk with enteric neurons and have been implicated in the pathogenesis of GI motility disorders like gastroparesis and postoperative ileus. The aim of this study was to assess whether ageing causes alterations in macrophage phenotype that contributes to age-related degeneration of the ENS.DesignLongitudinal muscle and myenteric plexus from small intestine of young, mid-aged and old mice were dissected and prepared for whole mount immunostaining, flow cytometry, Luminex immunoassays, western blot analysis, enteric neural stem cell (ENSC) isolation or conditioned media. Bone marrow derived macrophages were prepared and polarised to classic (M1) or alternative (M2) activation states. Markers for macrophage phenotype were measured using quantitative RT-PCR.ResultsAgeing causes a shift in macrophage polarisation from anti-inflammatory ‘M2’ to proinflammatory ‘M1’ that is associated with a rise in cytokines and immune cells in the ENS. This phenotypic shift is associated with a neural response to inflammatory signals, increase in apoptosis and loss of enteric neurons and ENSCs, and delayed intestinal transit. An age-dependent decrease in expression of the transcription factor FoxO3, a known longevity gene, contributes to the loss of anti-inflammatory behaviour in macrophages of old mice, and FoxO3-deficient mice demonstrate signs of premature ageing of the ENS.ConclusionsA shift by macrophages towards a proinflammatory phenotype with ageing causes inflammation-mediated degeneration of the ENS.