COVID-19 in non-hospitalised adults caused by either SARS-CoV-2 sub-variants Omicron BA.1, BA.2, BA.4/5 or Delta associates with similar illness duration, symptom severity and viral kinetics, irrespective of vaccination history.

BACKGROUND: SARS-CoV-2 variant Omicron rapidly evolved over 2022, causing three waves of infection due to sub-variants BA.1, BA.2 and BA.4/5. We sought to characterise symptoms and viral loads over the course of COVID-19 infection with these sub-variants in otherwise-healthy, vaccinated, non-hospitalised adults, and compared data to infections with the preceding Delta variant of concern (VOC). METHODS: In a prospective, observational cohort study, healthy vaccinated UK adults who reported a positive polymerase chain reaction (PCR) or lateral flow test, self-swabbed on alternate weekdays until day 10. We compared participant-reported symptoms and viral load trajectories between infections caused by VOCs Delta and Omicron (sub-variants BA.1, BA.2 or BA.4/5), and tested for relationships between vaccine dose, symptoms and PCR cycle threshold (Ct) as a proxy for viral load using Chi-squared (χ2) and Wilcoxon tests. RESULTS: 563 infection episodes were reported among 491 participants. Across infection episodes, there was little variation in symptom burden (4 [IQR 3-5] symptoms) and duration (8 [IQR 6-11] days). Whilst symptom profiles differed among infections caused by Delta compared to Omicron sub-variants, symptom profiles were similar between Omicron sub-variants. Anosmia was reported more frequently in Delta infections after 2 doses compared with Omicron sub-variant infections after 3 doses, for example: 42% (25/60) of participants with Delta infection compared to 9% (6/67) with Omicron BA.4/5 (χ2 P < 0.001; OR 7.3 [95% CI 2.7-19.4]). Fever was less common with Delta (20/60 participants; 33%) than Omicron BA.4/5 (39/67; 58%; χ2 P = 0.008; OR 0.4 [CI 0.2-0.7]). Amongst infections with an Omicron sub-variants, symptoms of coryza, fatigue, cough and myalgia predominated. Viral load trajectories and peaks did not differ between Delta, and Omicron, irrespective of symptom severity (including asymptomatic participants), VOC or vaccination status. PCR Ct values were negatively associated with time since vaccination in participants infected with BA.1 (ß = -0.05 (CI -0.10-0.01); P = 0.031); however, this trend was not observed in BA.2 or BA.4/5 infections. CONCLUSION: Our study emphasises both the changing symptom profile of COVID-19 infections in the Omicron era, and ongoing transmission risk of Omicron sub-variants in vaccinated adults. TRIAL REGISTRATION: NCT04750356.

Early inflammatory markers as prognostic indicators following allogeneic stem cell transplantation.

Allogeneic stem cell transplantation is used widely in the treatment of hematopoietic malignancy although graft versus host disease and relapse remain major complications. We measured the serum protein expression of 92 inflammation-related markers from 49 patients at Day 0 (D0) and 154 patients at Day 14 (D14) following transplantation and related values to subsequent clinical outcomes. Low levels of 7 proteins at D0 were linked to GvHD whilst high levels of 7 proteins were associated with relapse. The concentration of 38 proteins increased over 14 days and higher inflammatory response at D14 was strongly correlated with patient age. A marked increment in protein concentration during this period associated with GvHD but reduced risk of disease relapse, indicating a link with alloreactive immunity. In contrast, patients who demonstrated low dynamic elevation of inflammatory markers during the first 14 days were at increased risk of subsequent disease relapse. Multivariate time-to-event analysis revealed that high CCL23 at D14 was associative of AGvHD, CXCL10 with reduced rate of relapse, and high PD-L1 with reduced overall survival. This work identifies a dynamic pattern of inflammatory biomarkers in the very early post-transplantation period and reveals early protein markers that may help to guide patient management.

The impact of built environment on mental health: A COVID-19 lockdown perspective.

Tackling mental health has become a priority for governments around the world because it influences not only individuals but also the whole society. As people spend a majority of their time (i.e., around 90%) in buildings, it is pivotal to understand the relationship between built environment and mental health, particularly during COVID-19 when people have experienced recurrent local and national lockdowns. Despite the demonstration by previous research that the design of the built environment can affect mental health, it is not clear if the same influence pattern remains when a 'black swan' event (e.g., COVID-19) occurs. To this end, we performed logistic regression and hierarchical regression analyses to examine the relationship between built environment and mental health utilising a data sample from the United Kingdom (UK) residents during the COVID-19 lockdown while considering their social demographics. Our results show that compared with depression and anxiety, people were more likely to feel stressed during the lockdown period. Furthermore, general house type, home workspace, and neighbourhood environment and amenity were identified to have significantly contributed to their mental health status. With the ensuing implications, this study represents one of the first to inform policymakers and built environment design professionals of how built environment should be designed to accommodate features that could mitigate mental health problems in any future crisis. As such, it contributes to the body of knowledge of built environment planning by considering mental health during the COVID-19 lockdown.

African Hydroclimate During the Early Eocene From the DeepMIP Simulations.

The early Eocene (∼56-48 Myr ago) is characterized by high CO2 estimates (1,200-2,500 ppmv) and elevated global temperatures (∼10°C-16°C higher than modern). However, the response of the hydrological cycle during the early Eocene is poorly constrained, especially in regions with sparse data coverage (e.g., Africa). Here, we present a study of African hydroclimate during the early Eocene, as simulated by an ensemble of state-of-the-art climate models in the Deep-time Model Intercomparison Project (DeepMIP). A comparison between the DeepMIP pre-industrial simulations and modern observations suggests that model biases are model- and geographically dependent, however, these biases are reduced in the model ensemble mean. A comparison between the Eocene simulations and the pre-industrial suggests that there is no obvious wetting or drying trend as the CO2 increases. The results suggest that changes to the land sea mask (relative to modern) in the models may be responsible for the simulated increases in precipitation to the north of Eocene Africa. There is an increase in precipitation over equatorial and West Africa and associated drying over northern Africa as CO2 rises. There are also important dynamical changes, with evidence that anticyclonic low-level circulation is replaced by increased south-westerly flow at high CO2 levels. Lastly, a model-data comparison using newly compiled quantitative climate estimates from paleobotanical proxy data suggests a marginally better fit with the reconstructions at lower levels of CO2.

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.

Identification of New Natural Sources of Flavour and Aroma Metabolites from Solid-State Fermentation of Agro-Industrial By-Products.

Increasing consumer demand for natural flavours and fragrances has driven up prices and increased pressure on natural resources. A shift in consumer preference towards more sustainable and economical sources of these natural additives and away from synthetic production has encouraged research into alternative supplies of these valuable compounds. Solid-state fermentation processes support the natural production of secondary metabolites, which represents most flavour and aroma compounds, while agro-industrial by-products are a low-value waste stream with a high potential for adding value. Accordingly, four filamentous fungi species with a history of use in the production of fermented foods and food additives were tested to ferment nine different agro-industrial by-products. Hundreds of volatile compounds were produced and identified using headspace (HS) solid-phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Four compounds of interest, phenylacetaldehyde, methyl benzoate, 1-octen-3-ol, and phenylethyl alcohol, were extracted and quantified. Preliminary yields were encouraging compared to traditional sources. This, combined with the low-cost substrates and the high-value natural flavours and aromas produced, presents a compelling case for further optimisation of the process.

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care.

OBJECTIVES: Existing UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score. DESIGN: Candidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection. SETTING: Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. PARTICIPANTS: Patients with COVID-19 admitted to UHB January-August 2020 were included. MAIN OUTCOME MEASURES: Death and ITU admission within 28 days of admission. RESULTS: 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785). CONCLUSIONS: The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient's probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

Asymmetric excitation of left- and right-tail extreme events probed using a Hawkes model: Application to financial returns.

We construct a two-tailed peaks-over-threshold Hawkes model that captures asymmetric self- and cross-excitation in and between left- and right-tail extreme values within a time series. We demonstrate its applicability by investigating extreme gains and losses within the daily log-returns of the S&P 500 equity index. We find that the arrivals of extreme losses and gains are described by a common conditional intensity to which losses contribute twice as much as gains. However, the contribution of the former decays almost five times more quickly than that of the latter. We attribute these asymmetries to the different reactions of market traders to extreme upward and downward movements of asset prices: an example of negativity bias, wherein trauma is more salient than euphoria.

Immunogenicity of single vaccination with BNT162b2 or ChAdOx1 nCoV-19 at 5-6 weeks post vaccine in participants aged 80 years or older: an exploratory analysis.

BACKGROUND: In several countries, extended interval COVID-19 vaccination regimens are now used to accelerate population coverage, but the relative immunogenicity of different vaccines in older people remains uncertain. In this study we aimed to assess the antibody and cellular responses of older people after a single dose of either the BNT162b2 vaccine (tozinameran; Pfizer-BioNTech) or ChAdOx1 nCoV-19 vaccine (Oxford University-AstraZeneca). METHODS: Participants aged 80 years or older, who did not live in a residential or care home or require assisted living, and had received a single dose of either the BNT162b2 vaccine or ChAdOx1 nCoV-19 vaccine were eligible to participate. Participants were recruited through local primary care networks in the West Midlands, UK. Blood samples and dried blood spots were taken 5-6 weeks after vaccination to assess adaptive immune responses using Elecsys electrochemiluminescence immunoassay and cellular responses by ELISpot. Primary endpoints were percentage response and quantification of adaptive immunity. FINDINGS: Between Dec 29, 2020, and Feb 28, 2021, 165 participants were recruited and included in the analysis. 76 participants had received BNT162b2 (median age 84 years, IQR 82-89; range 80-98) and 89 had received ChAdOx1 nCoV-19 (median age 84 years, 81-87; 80-99). Antibody responses against the spike protein were detectable in 69 (93%) of 74 BNT162b2 vaccine recipients and 77 (87%) of 89 ChAdOx1 nCoV-19 vaccine recipients. Median antibody titres were of 19·3 U/mL (7·4-79·4) in the BNT162b2 vaccine recipients and 19·6 U/mL (6·1-60·0) in the ChAdOx1 nCoV-19 vaccine recipients (p=0·41). Spike protein-specific T-cell responses were observed in nine (12%) of 73 BNT162b2 vaccine recipients and 27 (31%) of 88 ChAdOx1 nCoV-19 vaccine recipients, and median responses were three-times higher in ChAdOx1 nCoV-19 vaccine recipients (24 spots per 1 × 106 peripheral blood mononuclear cells) than BNT162b2 vaccine recipients (eight spots per 1 × 106 peripheral blood mononuclear cells; p<0·0001). Humoral and cellular immune responses against spike protein were correlated in both cohorts. Evidence of previous SARS-CoV-2 infection was seen in eight participants (n=5 BNT162b2 recipients and n=3 ChAdOx1 nCoV-19 recipients), and was associated with 691-times and four-times increase in humoral and cellular immune responses across the whole cohort. INTERPRETATION: Single doses of either BNT162b2 or ChAdOx1 nCoV-19 in older people induces humoral immunity in most participants, and is markedly enhanced by previous infection. Cellular responses were weaker, but showed enhancement after the ChAdOx1 nCoV-19 vaccine at the 5-6 week timepoint. FUNDING: Medical Research Council, National Institute for Health Research, and National Core Studies.

Ultimate Spatial Resolution Realisation in Optical Frequency Domain Reflectometry with Equal Frequency Resampling.

A method based on equal frequency resampling is proposed to suppress laser nonlinear frequency sweeping for the ultimate spatial resolution in optical frequency domain reflectometry. Estimation inaccuracy of the sweeping frequency distribution caused by the finite sampling rate in the auxiliary interferometer can be efficiently compensated by the equal frequency resampling method. With the sweeping range of 130 nm, a 12.1 µm spatial resolution is experimentally obtained. In addition, the sampling limitation of the auxiliary interferometer-based correction is discussed. With a 200 m optical path delay in the auxiliary interferometer, a 21.3 µm spatial resolution is realised at the 191 m fibre end. By employing the proposed resampling and a drawing tower FBG array to enhance the Rayleigh backscattering, a distributed temperature sensing over a 105 m fibre with a sensing resolution of 1 cm is achieved. The measured temperature uncertainty is limited to ±0.15 °C.

Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II.

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.

Metabolomics Approach to Assess the Relative Contributions of the Volatile and Non-volatile Composition to Expert Quality Ratings of Pinot Noir Wine Quality.

Wine flavor and quality are determined by the assessment of multiple sensory stimuli, including aroma, taste, and mouthfeel. It is therefore important to consider the contribution of as many metabolites as possible when attempting to relate wine composition to quality. In this study, partial least squares regression of the volatile (untargeted headspace solid-phase microextraction coupled with gas chromatography time-of-flight mass spectrometry), non-volatile (untargeted reverse-phase ultra-high-performance liquid chromatography mass spectrometry), and combined metabolite profiles were used to predict Pinot Noir wine quality ratings as assessed by experts. Non-volatile metabolite profiles predicted wine quality ratings better than volatile metabolite profiles, suggesting that the non-volatile composition of Pinot Noir wines contributes to quality perception to a greater extent than the volatile composition. This was underscored by descriptive sensory analysis, which found that taste and mouthfeel attributes were better correlated with wine quality ratings than aroma attributes. Important predictors of Pinot Noir wine quality were also characterized. Some new relationships between wine metabolites and quality ratings were found: dipeptides and unsaturated fatty acids were positively related to Pinot Noir wine quality, while N-(3-methylbutyl)acetamide and xanthine were negatively associated.

Dietary palaeoecology of an Early Cretaceous armoured dinosaur (Ornithischia; Nodosauridae) based on floral analysis of stomach contents.

The exceptionally well-preserved holotype of the armoured dinosaur Borealopelta markmitchelli (Ornithischia; Nodosauridae) from the Early Cretaceous (Clearwater Formation) of northern Alberta preserves a distinct mass within the abdominal cavity. Fourteen independent criteria (including: co-allochthony, anatomical position, gastroliths) support the interpretation of this mass as ingested stomach contents-a cololite. Palynomorphs in the cololite are a subset of the more diverse external sample. Analysis of the cololite documents well-preserved plant material dominated by leaf tissue (88%), including intact sporangia, leaf cross-sections and cuticle, but also including stems, wood and charcoal. The leaf fraction is dominated (85%) by leptosporangiate ferns (subclass Polypodiidae), with low cycad-cycadophyte (3%) and trace conifer foliage. These data represent the most well-supported and detailed direct evidence of diet in an herbivorous dinosaur. Details of the dietary palaeoecology of this nodosaur are revealed, including: selective feeding on ferns; preferential ingestion of leptosporangiate ferns to the exclusion of Osmundaceae and eusporangiate ferns such as Marattiaceae; and incidental consumption of cycad-cycadophyte and conifer leaves. The presence of significant (6%) charcoal may represent the dietary use of recently burned conifer forest undergoing fern succession, early evidence of a fire succession ecology, as is associated with many modern large herbivores.

Salivary Microbiome Shifts in Response to Periodontal Treatment Outcome.

PURPOSE: Periodontitis is linked to a localized dysbiotic microbial shift. This trending may often not be evident due to deep taxonomic changes of low abundance organisms and lack of consideration of variations in the treatment response. By using next generation sequencing this study aims to evaluate the salivary microbiome dynamics of periodontal treatment and the implication of treatment outcome EXPERIMENTAL DESIGN: Patients with generalized aggressive periodontitis are treated non-surgically and followed up for 6 months. Saliva is collected for microbiome profiling by next generation sequencing and diversity analysis, as well as quantitative real-time polymerase chain reaction (qPCR). The treatment outcome on the first follow-up is also considered. RESULTS: Clinical parameters are significantly improved following treatment, but with no accompanying relative abundance changes on the phylum, genus and species levels, or diversity indices. Distinctive differences are observed on species level when the sensitive qPCR is used. Patients responding poorly to treatment display a marginally lower microbiome profile distance from baseline, compared to those responding favorably. CONCLUSION AND CLINICAL RELEVANCE: Periodontal treatment does not alter the broader salivary microbiome profile, but may have selective implications on the species level. Treatment outcome can be impactful in the microbiome profile, as reduced microbiome changes may be associated with poorer clinical responses.

Anticancer Characteristics of Fomitopsis pinicola Extract in a Xenograft Mouse Model-a Preliminary Study.

Introduction: Medicinal mushrooms have been used for the treatment of diseases and general promotion of health for many centuries. Recent pharmacological research into medicinal mushrooms has identified various therapeutic properties, with applications in modern medicine.Aim: To evaluate the anti-cancer activities of Fomitopsis pinicola (F. pinicola) alcoholic extract in an in vivo setting.Methods: The anti-tumour effect of the F. pinicola extract was tested in a xenograft immune-compromised Rag-1 mouse model. This was followed by RT-PCR and metabolomics analyses.Results: There were no observable differences in tumor growth between treated and non-treated groups. The bioactive components were not detected in the mouse plasma or the tumor site.Conclusions: The extract was poorly absorbed; this is likely due to the timing of treatment, dosage levels and modifications made to the extract where the alcohol-based solvent was replaced with water. This, in combination with fractionation studies which identified most anti-cancer compounds to be hydrophobic, largely explained the lack of anti-cancer activities in vivo.

Periodontal disease: From the lenses of light microscopy to the specs of proteomics and next-generation sequencing.

Periodontal disease entails the inflammatory destruction of the tooth supporting (periodontal) tissues as a result of polymicrobial colonization of the tooth surface in the form of biofilms. Extensive data collected over the past decades on this chronic disease demonstrate that its progression is infrequent and episodic, and the susceptibility to it can vary among individuals. Physical assessments of previously occurring damage to periodontal tissues remain the cornerstone of detection and diagnosis, whereas traditionally used diagnostic procedures do neither identify susceptible individuals nor distinguish between disease-active and disease-inactive periodontal sites. Thus, more sensitive and accurate "measurable biological indicators" of periodontal diseases are needed in order to place diagnosis (e.g., the presence or stage) and management of the disease on a more rational less empirical basis. Contemporary "omics" technologies may help unlock the path to this quest. High throughput nucleic acid sequencing technologies have enabled us to examine the taxonomic distribution of microbial communities in oral health and disease, whereas proteomic technologies allowed us to decipher the molecular state of the host in disease, as well as the interactive cross-talk of the host with the microbiome. The newly established field of metaproteomics has enabled the identification of the repertoire of proteins that oral microorganisms use to compete or co-operate with each other. Vast such data is derived from oral biological fluids, including gingival crevicular fluid and saliva, which is progressively completed and catalogued as the analytical technologies and bioinformatics tools progressively advance. This chapter covers the current "omics"-derived knowledge on the microbiome, the host and their "interactome" with regard to periodontal diseases, and addresses challenges and opportunities ahead.

Induction of Microbial Oxidative Stress as a New Strategy to Enhance the Enzymatic Degradation of Organic Micropollutants in Synthetic Wastewater.

Organic micropollutants (OMPs) are pervasive anthropogenic contaminants of receiving waters where they can induce various adverse effects to aquatic life. Their ubiquitous environmental occurrence is primarily attributed to discharge from wastewater treatment plants due to incomplete removal by common biological wastewater treatment processes. Here, we assess a new strategy for promoting the degradation of six representative OMPs (i.e., sulfamethoxazole, carbamazepine, tylosin, atrazine, naproxen, and ibuprofen) by intentionally stimulating the production of microbial oxidoreductases to counter oxidative stress caused by oxygen perturbations. Mixed microbial cultures from a dairy farm wastewater were subjected to cyclic perturbations of dissolved oxygen (DO). A distance-based redundancy analysis was used to show that DO perturbations correlate with the abundance of Pseudomonadaceae and Rhodocyclaceae families, activities of peroxidases and cytochromes, and the degradation of OMPs. DO perturbation of 0.25 and 0.5 cycles/h led to most abundance of Pseudomonadaceae and Rhodocyclaceae families, showed higher activity of peroxidase and cytochrome, and gave largest removal of OMPs (removal of 92 ± 3% for sulfamethoxazole, 84 ± 3% for naproxen, 82 ± 3% for ibuprofen, 66 ± 2% for carbamazepine, 57 ± 15% for tylosin, and 88 ± 1% for atrazine).