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Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

Implementation of policies to reduce forest loss challenges the Earth observation community to improve forest monitoring. An important avenue for progress is the use of new satellite missions and the combining of optical and synthetic aperture radar sensor data.

COVID-19 oral treatments require initiation within 5 days of symptom onset. Although antigen tests are less sensitive than RT-PCR, rapid results could facilitate entry to treatment. We collected anterior nasal swabs for BinaxNOW and RT-PCR testing and clinical data at a walk-up, community site in San Francisco, California between January and June 2022. SARS-CoV-2 genomic sequences were generated from positive samples and classified according to subtype and variant. Monte Carlo simulations were conducted to estimate the expected proportion of SARS-CoV-2 infected persons who would have been diagnosed within 5 days of symptom onset using RT-PCR versus BinaxNOW testing. Among 25,309 persons tested with BinaxNOW, 2,799 had concomitant RT-PCR. 1137/2799 (40.6%) were SARS-CoV-2 RT-PCR positive. We identified waves of predominant omicron BA.1, BA.2, BA.2.12, BA.4, and BA.5 among 720 sequenced samples. Among 1,137 RT-PCR positive samples, 788/1137 (69%) were detected by BinaxNOW; 94% (669/711) of those with Ct value <30 were detected by BinaxNOW. BinaxNOW detection was consistent over lineages. In analyses to evaluate entry to treatment, BinaxNOW detected 81.7% (361/442, 95% CI: 77–85%) of persons with COVID-19 within 5 days of symptom onset. In comparison, RT-PCR (24-hour turnaround) detected 84.2% (372/442, 95% CI: 80–87%) and RT-PCR (48-hour turnaround) detected 67.0% (296/442, 95% CI: 62–71%) of persons with COVID-19 within 5 days of symptom onset. BinaxNOW detected high viral load from anterior nasal swabs consistently across omicron sublineages emerging between January and June of 2022. Simulations support BinaxNOW as an entry point for COVID-19 treatment in a community field setting.

Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure.

Abstract We found no significant difference in cycle threshold values between vaccinated and unvaccinated persons infected with severe acute respiratory syndrome coronavirus 2 Delta, overall or stratified by symptoms. Given the substantial proportion of asymptomatic vaccine breakthrough cases with high viral levels, interventions, including masking and testing, should be considered in settings with elevated coronavirus disease 2019 transmission.

Auto-antibodies (Abs) to type I interferons (IFNs) are found in up to 25% of patients with severe COVID-19, and are implicated in disease pathogenesis. It has remained unknown, however, whether type I IFN auto-Abs are unique to COVID-19, or are also found in other types of severe respiratory illnesses. To address this, we studied a prospective cohort of 284 adults with acute respiratory failure due to causes other than COVID-19. We measured type I IFN auto-Abs by radio ligand binding assay and screened for respiratory viruses using clinical PCR and metagenomic sequencing. Three patients (1.1%) tested positive for type I IFN auto-Abs, and each had a different underlying clinical presentation. Of the 35 patients found to have viral infections, only one patient tested positive for type I IFN auto-Abs. Together, our data suggest that type I IFN auto-Abs are uncommon in critically ill patients with acute respiratory failure due to causes other than COVID-19.

Some individuals do not return to baseline health following SARS-CoV-2 infection, leading to a condition known as long COVID. The underlying pathophysiology of long COVID remains unknown. Given that autoantibodies have been found to play a role in severity of SARS-CoV-2 infection and certain other post-COVID sequelae, their potential role in long COVID is important to investigate. Here, we apply a well-established, unbiased, proteome-wide autoantibody detection technology (T7 phage-display assay with immunoprecipitation and next-generation sequencing, PhIP-Seq) to a robustly phenotyped cohort of 121 individuals with long COVID, 64 individuals with prior COVID-19 who reported full recovery, and 57 pre-COVID controls. While a distinct autoreactive signature was detected that separated individuals with prior SARS-CoV-2 infection from those never exposed to SARS-CoV-2, we did not detect patterns of autoreactivity that separated individuals with long COVID from individuals fully recovered from COVID-19. These data suggest that there are robust alterations in autoreactive antibody profiles due to infection; however, no association of autoreactive antibodies and long COVID was apparent by this assay.

Importance Characterizing the clinical symptoms and evolution of community-based SARS-CoV-2 infections may inform health practitioners and public health officials in a rapidly changing landscape of population immunity and viral variants. Objectives To compare COVID-19 symptoms among people testing positive with a rapid antigen test (RAT) during the Omicron BA.1 variant period (December 1, 2021, to January 30, 2022) with the pre-Delta (January 10 to May 31, 2021) and Delta (June 1 to November 30, 2021) variant periods and to assess the duration of RAT positivity during the Omicron BA.1 surge. Design, Setting, and Participants This cross-sectional study was conducted from January 10, 2021, to January 31, 2022, at a walk-up community COVID-19 testing site in San Francisco, California. Participants included children and adults seeking COVID-19 testing with an RAT, regardless of age, vaccine status, or symptoms. Main Outcomes and Measures Fisher exact tests or χ2tests were used to compare COVID-19 symptoms during the Omicron BA.1 period with the pre-Delta and Delta periods for vaccination status and age group. Among people returning for repeated testing during the Omicron period, the proportion with a positive RAT between 4 and 14 days from symptom onset or since first positive test if asymptomatic was estimated. Results Among 63 277 persons tested (median [IQR] age, 32 [21-44] years, with 12.0% younger than 12 years; 52.0% women; and 68.5% Latinx), a total of 18 301 people (28.9%) reported symptoms, of whom 4565 (24.9%) tested positive for COVID-19. During the Omicron BA.1 period, 3032 of 7283 symptomatic participants (41.6%) tested positive, and the numbers of these reporting cough and sore throat were higher than during pre-Delta and Delta periods (cough: 2044 [67.4%] vs 546 [51.3%] of 1065 participants,P < .001 for pre-Delta, and 281 [60.0%] of 468 participants,P = .002, for Delta; sore throat: 1316 [43.4%] vs 315 [29.6%] of 1065 participants,P < .001 for pre-Delta, and 136 [29.1%] of 468 participants,P < .001, for Delta). Compared with the 1065 patients with positive test results in the pre-Delta period, congestion among the 3032 with positive results during the Omicron BA.1 period was more common (1177 [38.8%] vs 294 [27.6%] participants,P < .001), and loss of taste or smell (160 [5.3%] vs 183 [17.2%] participants,P < .001) and fever (921 [30.4%] vs 369 [34.7%] participants,P = .01) were less common. In addition, during the Omicron BA.1 period, fever was less common among the people with positive test results who had received a vaccine booster compared with those with positive test results who were unvaccinated (97 [22.5%] of 432 vs 42 [36.2%] of 116 participants,P = .003), and fever and myalgia were less common among participants who had received a booster compared with those with positive results who had received only a primary series (fever: 97 [22.5%] of 432 vs 559 [32.8%] of 1705 participants,P < .001; myalgia: 115 [26.6%] of 432 vs 580 [34.0%] of 1705 participants,P = .003). During the Omicron BA.1 period, 5 days after symptom onset, 507 of 1613 people (31.1%) with COVID-19 stated that their symptoms were similar, and 95 people (5.9%) reported worsening symptoms. Among people testing positive, 80.2% of participants who were symptomatic and retested remained positive 5 days after symptom onset. Conclusions and Relevance In this cross-sectional study, COVID-19 upper respiratory tract symptoms were more commonly reported during the Omicron BA.1 period than during the pre-Delta and Delta periods, with differences by vaccination status. Rapid antigen test positivity remained high 5 days after symptom onset, supporting guidelines requiring a negative test to inform the length of the isolation period.

In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false-negative viral PCR test results. Such tests are also susceptible to false-positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses, and nonviral conditions ( n  = 318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with nonviral conditions, such as IFI6 , and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5 . The IFI6 + GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort ( n  = 553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improved accuracy of COVID-19 diagnostics. IMPORTANCE In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. Both types of error carry serious consequences of either unrecognized viral transmission or unnecessary isolation and contact tracing. This work is directly relevant to the ongoing COVID-19 pandemic given the continued emergence of viral variants and the continued challenges of false-positive PCR assays. It also suggests the feasibility of pan-respiratory virus host-based diagnostics that would have value in congregate settings, such as hospitals and nursing homes, where unrecognized respiratory viral transmission is of particular concern.

Multisystem inflammatory syndrome in children (MIS-C) is a severe, post-infectious sequela of SARS-CoV-2 infection 1 , 2 , yet the pathophysiological mechanism connecting the infection to the broad inflammatory syndrome remains unknown. Here we leveraged a large set of samples from patients with MIS-C to identify a distinct set of host proteins targeted by patient autoantibodies including a particular autoreactive epitope within SNX8, a protein involved in regulating an antiviral pathway associated with MIS-C pathogenesis. In parallel, we also probed antibody responses from patients with MIS-C to the complete SARS-CoV-2 proteome and found enriched reactivity against a distinct domain of the SARS-CoV-2 nucleocapsid protein. The immunogenic regions of the viral nucleocapsid and host SNX8 proteins bear remarkable sequence similarity. Consequently, we found that many children with anti-SNX8 autoantibodies also have cross-reactive T cells engaging both the SNX8 and the SARS-CoV-2 nucleocapsid protein epitopes. Together, these findings suggest that patients with MIS-C develop a characteristic immune response to the SARS-CoV-2 nucleocapsid protein that is associated with cross-reactivity to the self-protein SNX8, demonstrating a mechanistic link between the infection and the inflammatory syndrome, with implications for better understanding a range of post-infectious autoinflammatory diseases. A cross-reactive antibody and T cell response is identified in a large portion of patients with multisystem inflammatory syndrome in children.