Variation analysis of photonic lantern mode control ability under mode oscillation: a new approach.

In this Letter, we present a novel, to the best of our knowledge, image-based approach to analyze the mode control ability of a photonic lantern employed in diode laser beam combining, aiming to achieve a stable beam output. The proposed method is founded on theories of power flow and mode coupling and is validated through experiments. The findings demonstrate that the analysis of the beam combining process is highly reliable when the main mode component of the output light is the fundamental mode. Moreover, it is experimentally demonstrated that the mode control performance of the photonic lantern significantly influences the beam combining loss and the fundamental mode purity. In the essence of the variation-based analysis, a key advantage of the proposed method is its applicability even in the situation of a poor combined beam stability. The experiment only requires the collection of the far-field light images of the photonic lantern to characterize the model control ability, achieving an accuracy greater than 98%.

'They are conspiring against us': How outgroup conspiracy theories stimulate environmental neglect in intergroup resource dilemmas.

Natural resources are limited, and people often share these limited resources in groups, which creates an intergroup resource dilemma. To understand individuals' sustainable behaviours in intergroup resource dilemmas in the context of group interactions, the present research systematically investigates the effect of outgroup conspiracy theories on sustainable behaviours and preliminarily explores the internal mechanism underlying this effect. First, a survey study (Study 1) relying on real-world intergroup relations first confirmed the negative correlation between outgroup conspiracy beliefs and sustainable intentions in intergroup resource dilemmas. Then, an online experimental study that utilized the real situation of a region in China (Study 2) tested the causal relationship between exposure to an outgroup conspiracy theory and sustainable intentions, as well as showing the mediating role of intergroup threat perception underlying this relationship. Finally, a preregistered experimental laboratory study (Study 3) further verified the causal effect of exposure to an outgroup conspiracy theory on sustainable behaviours, again confirming the mediating role of intergroup threat perception. In general, our research demonstrates that exposure to an outgroup conspiracy theory stimulates individuals' environmental neglect and reduces their sustainable behaviours by increasing their perceptions of intergroup threat when faced with intergroup resource dilemmas.

Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms.

Background: Short-term prediction of COVID-19 epidemics is crucial to decision making. We aimed to develop supervised machine-learning algorithms on multiple digital metrics including symptom search trends, population mobility, and vaccination coverage to predict local-level COVID-19 growth rates in the UK. Methods: Using dynamic supervised machine-learning algorithms based on log-linear regression, we explored optimal models for 1-week, 2-week, and 3-week ahead prediction of COVID-19 growth rate at lower tier local authority level over time. Model performance was assessed by calculating mean squared error (MSE) of prospective prediction, and naïve model and fixed-predictors model were used as reference models. We assessed real-time model performance for eight five-weeks-apart checkpoints between 1st March and 14th November 2021. We developed an online application (COVIDPredLTLA) that visualised the real-time predictions for the present week, and the next one and two weeks. Results: Here we show that the median MSEs of the optimal models for 1-week, 2-week, and 3-week ahead prediction are 0.12 (IQR: 0.08-0.22), 0.29 (0.19-0.38), and 0.37 (0.25-0.47), respectively. Compared with naïve models, the optimal models maintain increased accuracy (reducing MSE by a range of 21-35%), including May-June 2021 when the delta variant spread across the UK. Compared with the fixed-predictors model, the advantage of dynamic models is observed after several iterations of update. Conclusions: With flexible data-driven predictors selection process, our dynamic modelling framework shows promises in predicting short-term changes in COVID-19 cases. The online application (COVIDPredLTLA) could assist decision-making for control measures and planning of healthcare capacity in future epidemic growths.

Interactions of the bacteriome, virome, and immune system in the nose.

Emerging evidence suggests that the nasal microbiome may influence host susceptibility to initial development and severity of respiratory viral infections. While not as extensively studied as the microbiota of the alimentary tract, it is now clearly established that the microbial composition of this niche is influenced by medical, social and pharmacological influences, predisposing some sub-populations to respiratory infections. The resulting specific microbial profiles may explain variance in susceptibility to viral infection. This review summaries the evolution and constituents of the commensal nasal microbiome; the bacterial-virus, bacterial-host and interbacterial interactions which potentiate disease; and considers the effects of interventions such as vaccination and probiotics.

Current COVID-19 treatments: Rapid review of the literature.

BACKGROUND: As SARS-CoV-2 continues to spread worldwide, it has already resulted in over 110 million cases and 2.5 million deaths. Currently, there are no effective COVID-19 treatments, although numerous studies are under way. SARS-CoV-2, however, is not the first coronavirus to cause serious outbreaks. COVID-19 can be compared with previous human coronavirus diseases, such as Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), to better understand the development of treatments. METHODS: Databases Medline, Embase and WHO COVID-19 was systematically searched on 9 February 2021 for studies reporting on therapeutic effect of COVID-19 treatments. Clinical trials, case reports, observational studies and systematic reviews in the English language were eligible. RESULTS: 1416 studies were identified and 40 studies were included in this review. Therapies included are: remdesivir, convalescent plasma, hydroxychloroquine, lopinavir/ ritonavir, interferon, corticosteroids, cytokine storm inhibitors and monoclonal antibodies. Remdesivir, convalescent plasma and interferon seems to provide some clinical benefits such as faster recovery time and reduced mortality, but these effects are not clinically significant. Some corticosteroids are effective in reducing mortality in severe COVID-19 patients. Hydroxychloroquine do not convey any beneficial, and therapies such as cytokine storm inhibitors and monoclonal antibodies were also not effective and require further investigation. CONCLUSIONS: There is no single therapy effective against COVID-19. However, a combination of therapies administered at different stages of infection may provide some benefit. This conclusion is reflected in the limited effects of these treatments in previous human coronaviruses.

SARS-CoV-2 transmission in schools: An updated living systematic review (version 2; November 2020).

BACKGROUND: Better understanding of SARS-CoV-2 transmission risks is needed to support decision-making around mitigation measures for COVID-19 in schools. METHODS: We updated a living systematic review and meta-analysis to investigate the extent of SARS-CoV-2 transmission in schools. In this update we modified our inclusion criteria to include: 1) cohort studies; 2) cross-sectional studies that investigated and cross-assessed SARS-COV-2 positivity rates in schools and communities; and 3) pre-post studies. We performed risk of bias evaluation for all included studies using the Newcastle-Ottawa Scale (NOS). RESULTS: 6270 articles were retrieved and six new studies were added in this update. In total from the two updates and using the new inclusion criteria, we identified 11 cohort studies (1st update: n = 5; 2nd update: n = 6) and one cross-sectional study (1st update: n = 1; 2nd update: n = 0). We performed a meta-analysis on nine of the 11 cohort studies investigating IAR in schools. Nine cohort studies reported a total of 91 student and 52 staff index cases that exposed 5698 contacts with 101 secondary infections (overall infection attack rate (IAR) = 1.45%, 95% CI = 0.31%-3.26%). IARs for students and school staff were 1.66% (95% CI = 0.08%-4.78%) and 1.18% (95% CI = 0.00%-4.43%) respectively. The risk of bias was found to be high for most studies identified, limiting the confidence in results. CONCLUSIONS: There is limited high-quality evidence available to quantify the extent of SARS-CoV-2 transmission in schools or to compare it to community transmission. Emerging evidence suggests the overall IAR and SARS-CoV-2 positivity rate in school settings are low. Higher IAR were found in students, compared to staff. NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. This is the second version of the original article published on 23 December 2020 (J Glob Health 2020;11:021104), and previous versions can be found as data supplements. When citing this paper please consider adding the version number and date of access for clarity.