Severe Outbreaks Research Articles

New emerging Pseudomonads as causal agents of bean blight disease

Common bean (Phaseolus vulgaris) production is negatively affected by several bacterial plant diseases. A severe outbreak of bean blight was observed in the nine major bean-producing provinces of Iran during 2021–2022. Necrotic spots were observed on leaves which become necrotic with chlorosis beyond it. One hundred-sixty bacterial strains with a greyish-white colony appearance were consistently isolated from the one-hundred eighteen symptomatic bean leaf, pod, seed and root samples on nutrient agar. All strains were screened for their ability to cause disease bean leaves among which 134 strains induced chlorosis three days after inoculation on bean leaves of cv. Yaghoot followed by necrotic spots on the inoculated sites one week after inoculation. The pathogenic strains clustered in three groups based on phenotypic characters and multilocus sequencing analyses. Three housekeeping genes including gyrB, rpoB, and rpoD of representative strains were partially sequenced. In the phylogenetic tree based on concatenated sequences of these three genes or each gene individually, the strains were divided into three clusters each with high bootstrap support. The first group of the strains clustered with Pseudomonas simiae CCUG 50988T. This is the first report of this species as causal agent of bean blight. The second group clustered close to Pseudomonas alloputida NMI5768T which apparently belong to a new species. The third group clustered in a separate clade in Pseudomonas chlororaphis species which belongs to a new subspecies based on pathogenicity on bean, phenotypic characters, sequences of five housekeeping genes which named as subsp. phaseoli subsp. nov. with the type strain UT-M315.

Assessing the validity of social media in fighting COVID-19 in Africa

The severe outbreak of the COVID-19 pandemic has had a significant impact on global health, the economy, and society as a whole. To this effect, various measures both national and international were taken to respond to the pandemic. Among these measures was the rapid involvement of social media which played a vital role in the context of information dissemination, coordination of medical resources, promotion of public health campaigns, and population management and disease tracing. Besides these facilities put forward by social media, there were some weaknesses like misinformation, lack of guidance, and information leakage. Based on these numerous controversies brought about by social media during the pandemic periods, we aim to examine in this study the socio-economic effect of social media in the fight against Pandemics with particular attention on the case of the COVID-19 outbreak in Africa. Using Driscoll and Kraay's fixed effects and the Lewbel-2SLS estimators on 30 African countries, the results reveal that social media in Africa through the penetration rate of Facebook, Twitter, Instagram, Pinterest, and YouTube contribute significantly in disseminating COVID-19 in Africa. The findings equally reveal the mixed effect of other determinants of COVID-19 in this nexus. While Diabetes prevalence, the proportion of people aged 65 + , and population density contribute to the increased spread of the disease, GDP per capita and Hand Washing facilities instead prove a reducing effect of the disease. Sensitivity analysis reveals that this effect is homogenous for regional considerations of the overall sample. Based on this, appropriate suggestions are made to the various governments regarding the use of social media to prevent pandemics in the future.

How increasing temperature affects the innate immune system of sea urchin Paracentrotus lividus (Lamarck, 1816) reared in a RAS system

The purple sea urchin, Paracentrotus lividus, is the most exploited and economically important in Southern Europe due to the high value of its gonads. Temperature generally affects several physiological functions of marine invertebrates and the ocean warming has been linked to increasing frequency and severity of disease outbreaks in several echinoderms. Sea urchins have an innate immune system consisting of coelomocytes, the cellular components responsible for the immune response, supported by proteases and lysozymes. The present study aimed to investigate the effect of increasing seawater temperature on the immunological response of P. lividus. In this experiment, the animals were exposed to an increase in temperature up to 24 °C for 36 days, after which cellular and humoral immunity parameters were measured. The number of coelomocytes in the animals increased with the temperature rise, mainly the phagocytes and the colourless granulocytes. In the humoral response of the animals, only the concentration of lysozyme responded to the increase in temperature.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

A systematic review of epidemiological modelling in response to lumpy skin disease outbreaks.

Lumpy skin disease (LSD) is an infectious disease currently spreading worldwide and poses a serious global threat. However, there is limited evidence and understanding to support the use of models to inform decision-making in LSD outbreak responses. This review aimed to identify modelling approaches that can be used before and during an outbreak of LSD, examining their characteristics and priorities, and proposing a structured workflow. We conducted a systematic review and identified 60 relevant publications on LSD outbreak modelling. The review identified six categories of question to be addressed following outbreak detection (origin, entry pathway, outbreak severity, risk factors, spread, and effectiveness of control measures), and five analytical techniques used to address them (descriptive epidemiology, risk factor analysis, spatiotemporal analysis, dynamic transmission modelling, and simulation modelling). We evaluated the questions each analytical technique can address, along with their data requirements and limitations, and accordingly assigned priorities to the modelling. Based on this, we propose a structured workflow for modelling during an LSD outbreak. Additionally, we emphasise the importance of pre-outbreak preparation and continuous updating of modelling post-outbreak for effective decision-making. This study also discusses the inherent limitations and uncertainties in the identified modelling approaches. To support this workflow, high-quality data must be collected in standardised formats, and efforts should be made to reduce inherent uncertainties of the models. The suggested modelling workflow can be used as a process to support rapid response for countries facing their first LSD occurrence and can be adapted to other transboundary diseases.

Mass mortality associated with carpione rhabdovirus in golden pompano (Trachinotus ovatus) in China: First report

From August to November 2023, a severe epizootic outbreak occurred in an offshore cultured golden pompano (Trachinotus ovatus) population in Zhanjiang City, Guangdong Province, China, resulting in a cumulative mortality rate of 65–82 %. The diseased fish showed clinical signs of lethargy, anorexia, whirling movements, and hemorrhage at the base of the fins and in the upper and lower jaws before mass mortality. A rhabdovirus strain, designated as CARPV2023, was successfully isolated using fathead minnow (FHM) cells. The cytopathic effects of the tissue filtrate from the diseased golden pompano on FHM and epithelioma papulosum cyprini (EPC) cell monolayers were characterized by rounded cells, grape-like cluster formation, detachment, and lysis. Histopathology revealed congestion, hemorrhage, and necrosis in the spleen, liver, trunk kidney, and intestinal tissues of naturally and experimentally infected golden pompanos. Electron microscopy showed that bullet-shaped viral particles, measuring 183.5–217.6 nm in length and 57.3–82.7 nm in width, budded from the cell membrane or aggregated around the infected FHM cells. The virus remained stable for 7 d at 4–33 °C and replicated optimally at 28 °C. Whole-genome sequencing and subsequent phylogenetic analysis further indicated that the carpione rhabdovirus (CARPV)2023 strain is a new carpione rhabdovirus, and is related to novirhabdoviruses, however, is thus far unclassified. A specific nested reverse transcription-polymerase chain reaction assay confirmed the presence of CARPV in the affected golden pompano. Significantly higher mortality was observed in challenged golden pompanos than that in the controls through intraperitoneal injection and immersion infection. Fluorescence in situ hybridization showed showed positive reactions for RNA in multiple tissues (liver, spleen, trunk kidney, brain, heart, gill, and muscle) from infected fish. The highest expression level was confirmed in the spleen through qPCR. This is the first study to report natural CARPV infections in marine fish.

Epidemiology of COVID-19 in Berlin-Neukölln nursing homes

BackgroundThe COVID-19 pandemic has affected various urban population groups in different ways. Earlier studies have shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disproportionally impacts nursing home residents by increasing morbidity and mortality following viral exposure. However, little is known about the epidemiology of this disease in detail. Therefore, the objective of this study is to analyze the development of the COVID-19 pandemic in 14 nursing homes across Berlin-Neukölln, Germany, during pandemic waves 1 to 5 (Feb 2020 - May 2022). MethodsReporting data to the Neukölln Department of Public Health on COVID-19 cases in connection with nursing homes were extracted from the SORMAS database. The case fatality rates (CFRs) and odds ratios (ORs) of demographic parameters, prevalent variants of concern (VOCs) and vaccine availability were calculated. In addition, the temporal course in waves 1–5 in Neukölln and the relevant government measures were examined. ResultsData collected from nursing homes providing age-dependent physical care revealed that 1.9 % of the total 108,600 cases registered in Berlin-Neukölln during the study period were related one of the 14 facilities. Compared to the general population in Neukölln, nursing homes exhibited a 20-fold increase in the CFR. Notably, nursing homes with higher bed capacities displayed a greater CFR than did smaller nursing homes. Similarly, elderly residents living in nursing homes faced a much greater mortality rate than did their counterparts living outside of medical settings (OR = 3.5). The original wild-type SARS-CoV-2 strain had the most severe direct impact, with a CFR of 16.7 %, compared to the alpha (CFR = 6.9 %), delta (CFR = 10.2 %) and omicron (CFR = 2.8 %) variants in nursing homes. Interestingly, the number of infections increased following vaccination campaigns, but this trend was accompanied by a decrease in the number of deaths from 2.6 to 1.1 per week. As a result, the CFR significantly decreased from 18.4 to 5.5, while still exceeding the mean CFR compared to that of the general population of Neukölln. ConclusionsOur findings reveal the changing patterns of outbreak frequency and severity across the five pandemic waves. They highlight the crucial role of rapid vaccination programs for residents, staff, visitors, and third-party services in safeguarding nursing homes. Additionally, improvements in containment and cluster strategies are essential in prevaccination scenarios to prevent future infection traps for elderly individuals in long-term care facilities. The presented data highlight the importance of tailored protection measures for one of the most vulnerable populations in our society.

Grid Search Optimized Machine Learning based Modeling of CO2 Emissions Prediction from Cars for Sustainable Environment

Carbon emissions have increased dramatically because of industrialization, trapping heat in the atmosphere and hastening climate change. This is a serious threat to the wealth, security, and well-being of the world. The effects are extensive, ranging from severe weather, disease outbreaks, and economic disruption to food insecurity and water scarcity. The World Health Organization (WHO) has determined that climate change poses the greatest threat to public health in the twenty-first century. Thus, precise CO2 emissions have emerged as a crucial concern in recent times. Several studies have tried to forecast the amount CO2 from industry and power plant using statistical analysis. Efficiency, robustness and diverse application was the limitation of the study. In this study, we have proposed an AI based model that is able to predict the amounts of CO2 emissions from cars. We applied a grid search-optimized machine learning approach using the publicly available Canadian dataset. Incorporation of different statistical analyses and preprocessing techniques such as duplicate data management, outlier rejection, scaling contributed to enhance the quality of the dataset. Later, grid search techniques were applied to tune the KNN, RF, and SVR models. The approach has enhanced the performance of CO2 emissions prediction. In the study, we further used the explainability of the random forest model to check the bias and fairness of predictability. MSE, RMSE, and R-squared metrics of the proposed approach were the highest as the state of the art.

Clonostachys rosea, a Pathogen of Brown Rot in Gastrodia elata in China.

Gastrodia elata, commonly known as Tian Ma, is a perennial mycoheterotrophic orchid. Qianyang Tian Ma (QTM), a geographical indication agricultural product from Hongjiang City, Hunan Province, China, is primarily characterized by the red variety, G. elata f. elata. A severe outbreak of tuber brown rot disease was documented in QTM during the harvesting season in Hunan. The fungal pathogen associated with the disease was isolated on potato saccharose agar (PSA) and identified through morphological and phylogenetic analyses. Pathogenicity tests were performed on healthy tubers of G. elata f. elata. The results showed that the representative isolate, named TMB, produced white hyphal colonies with a ring structure, broom-like phialides, partially curved ellipsoidal conidia, and orange-yellow spherical ascocarps on PSA. Phylogenetic analysis of the ITS, LSU, rpb2 and tub2 sequences using Bayesian and maximum-likelihood methods identified the isolate TMB as Clonostachys rosea, based on morphological and phylogenetic data. Pathogenicity tests revealed typical disease symptoms on healthy G. elata tubers 15 days post-inoculation with the isolate TMB. C. rosea is known to cause diseases in economically important crops, but there are no reports of its occurrence on G. elata f. elata in China. This study provides valuable insights into the occurrence, prevention, and control of brown rot disease in G. elata f. elata.

Sensitivity analysis and global stability of epidemic between Thais and tourists for Covid -19

This study employs a mathematical model to analyze and forecast the severe outbreak of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2), focusing on the socio-economic ramifications within the Thai population and among foreign tourists. Specifically, the model examines the impact of the disease on various population groups, including susceptible (S), exposed (E), infected (I), quarantined (Q), and recovered (R) individuals among tourists visiting the country. The stability theory of differential equations is utilized to validate the mathematical model. This involves assessing the stability of both the disease-free equilibrium and the endemic equilibrium using the basic reproduction number. Emphasis is placed on local stability, the positivity of solutions, and the invariant regions of solutions. Additionally, a sensitivity analysis of the model is conducted. The computation of the basic reproduction number (R0) reveals that the disease-free equilibrium is locally asymptotically stable when R0 is less than 1, whereas the endemic equilibrium is locally asymptotically stable when R0 exceeds 1. Notably, both equilibriums are globally asymptotically stable under the same conditions. Through numerical simulations, the study concludes that the outcome of COVID-19 is most sensitive to reductions in transmission rates. Furthermore, the sensitivity of the model to all parameters is thoroughly considered, informing strategies for disease control through various intervention measures.

Risk factors associated with the intensity of COVID-19 outbreaks in Canadian community settings: a retrospective analysis of outbreak-level surveillance data

BackgroundThe severity of COVID-19 outbreaks is disproportionate across settings (e.g., long-term care facilities (LTCF), schools) across Canada. Few studies have examined factors associated with outbreak severity to inform prevention and response. Our study objective was to assess how outbreak severity, as measured using outbreak intensity and defined as number of outbreak-associated cases divided by outbreak duration, differed by setting and factors known to influence SARS-CoV-2 transmission.MethodsWe described outbreak intensity trends in 2021 using data from the Canadian COVID-19 Outbreak Surveillance System from seven provinces/territories, representing 93% of the Canadian population. A negative binomial fixed-effects model was used to assess for associations between the outcome, outbreak intensity, and characteristics of outbreaks: setting type, median age of cases, number at risk, and vaccination coverage of at least 1 dose. Also included were variables previously reported to influence SARS-CoV-2 transmission: stringency of non-pharmaceutical interventions (NPI) and the predominant SARS-CoV-2 variant detected by surveillance.ResultsThe longest outbreaks occurred in LTCF (mean = 25.4 days) and correctional facilities (mean = 20.6 days) which also reported the largest outbreaks (mean = 29.6 cases per outbreak). Model results indicated that outbreak intensity was highest in correctional facilities. Relative to correctional facilities (referent), the second highest adjusted intensity ratio was in childcare centres (intensity ratio = 0.58 [95% CI: 0.51–0.66]), followed by LTCF (0.56 [95% CI: 0.51–0.66]). Schools had the lowest adjusted intensity ratio (0.46 [95% CI: 0.40–0.53]) despite having the highest proportion of outbreaks (37.5%). An increase in outbreak intensity was associated with increases in median age, the number at risk, and stringency of NPI. Greater vaccination coverage with at least 1 dose was associated with reduced outbreak intensity.ConclusionDescriptive and multivariable model results indicated that in Canada during 2021, outbreak intensity was greatest in closed congregate living facilities: correctional facilities and LTCF. Findings from this study support the importance of vaccination in reducing outbreak intensity when vaccines are effective against infection with circulating variants, which is especially important for closed congregate living facilities where NPIs are more challenging to implement.

Changes in the epidemiological patterns of respiratory syncytial virus and human metapneumovirus infection among pediatric patients and their correlation with severe cases: a long-term retrospective study.

We aim to investigate the prevalence of respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) among pediatric patients with acute respiratory tract illness (ARTI) in southern China both pre- and post-COVID-19 pandemic, as well as identify associated risk factors for severe infections. The study conducted a real-time PCR analysis on hospitalized children with ARTI from 2012 to 2023, specifically targeting RSV, hMPV, and other respiratory pathogens. Additionally, demographic data was collected during this analysis. The prevalence of RSV occurs triennially, and likewise, the temporal pattern of hMPV outbreaks mirrors that of RSV. The peak infection rates of RSV and hMPV occurred during and following the implementation of COVID-19 epidemic prevention and control measures. The incidence of RSV infection exhibited bimodal peaks in 2022, while hMPV demonstrated seasonal peaks during the spring, fall, and winter periods post-COVID-19 pandemic. After the COVID-19 outbreak, there has been an upward trend in the proportion of female patients and patients aged one year and older presenting with ARTI, RSV infections, and hMPV infections. Infant (OR = 4.767, 95%CI: [3.888-5.846], p < 0.0001), presence of co-infection (OR = 0.540, 95%CI: [0.404-0.722], p < 0.0001), and existence of comorbidities (OR = 1.582, 95%CI: [1.285-1.949], p < 0.0001) was the risk ratio for the severity of RSV infection. Children infected with hMPV under the age of 1 year (OR = 0.322, 95%CI: [0.180 - 0.575], p < 0.0001), as well as those with comorbidities (OR = 8.809, 95%CI: [4.493 - 17.272], p < 0.0001), have a higher risk of developing severe illness. The changing epidemiological patterns have the potential to lead to widespread severe outbreaks among children, particularly those with underlying medical conditions who may experience more severe symptoms. Conducting surveillance for pneumoviridae viruses in children is an imperative measure to establish a robust foundation for future epidemic prevention and treatment strategies.

Transboundary migration of Loxostege sticticalis (Lepidoptera: Crambidae) among China, Russia and Mongolia.

The beet webworm, Loxostege sticticalis, a worldwide pest of many crops, performs a seasonal migration, causing periodic outbreaks in Asia, Europe and North America. Although long-distance migration is well documented in China, patterns of transboundary migration among China, Russia and Mongolia are largely unknown. We performed a phase analysis of L. sticticalis periodic outbreaks among three countries based on 30 years of historical population data, analyzed the wind systems during migration over boundary regions, and traced the migratory routes in a case study of outbreaks in 2008 by trajectory simulation. Highly synchronized outbreak years of L. sticticalis were observed between China and Mongolia, China and eastern Siberia, China and western Siberia, Mongolia and eastern Siberia, eastern Siberia and western Siberia from 1978 to 2008, indicating possible transboundary migration between these regions. Winds at 300-600 m altitude, where adult migration usually occurs, also showed a high probability of northwestern winds in Haila'er (China), Chita (Russia) and Choybalsan (Mongolia), favoring successful adult migration from these areas to northern and northeastern China. Back trajectory analysis further showed that the first-generation adults that caused the severe outbreak of second-generation larvae in 2008 originated from eastern Siberia, eastern Mongolia, and the boundary regions of China-Russia and China-Mongolia. Our findings demonstrated that the source of L. sticticalis outbreaks in northern China was closely related to the outbreaks in Siberia and Mongolia via long-distance transboundary windborne migration. This information will help guide international monitoring and management strategies against this notorious pest. © 2024 Society of Chemical Industry.

Wild bird mass mortalities in eastern Canada associated with the Highly Pathogenic Avian Influenza A(H5N1) virus, 2022

AbstractIn 2022, a severe outbreak of disease caused by clade 2.3.4.4b Highly Pathogenic Avian Influenza (HPAI) H5N1 virus resulted in unprecedented mortality among wild birds in eastern Canada. Tens of thousands of birds were reported sick or dead, prompting a comprehensive assessment of mortality spanning the breeding season between April 1 and September 30, 2022. Mortality reports were collated from federal, Indigenous, provincial, and municipal agencies, the Canadian Wildlife Health Cooperative, and other nongovernmental organizations, universities, and citizen science platforms. A scenario analysis was conducted to refine mortality estimates, accounting for potential double counts from multiple sources under a range of spatial and temporal overlaps. Correcting for double counting, HPAI is estimated to have caused 40,391 wild bird mortalities in eastern Canada during the spring and summer of 2022; however, this figure underestimates total mortality as it excludes unreported deaths on land and at sea. Seabirds and sea ducks, long‐lived species that are slow to recover from perturbations, accounted for 98.7% of estimated mortalities. Our study provides estimates of bird mortality, with Northern Gannets (Morus bassanus; 25,669), Common Murres (Uria aalge; 8133), and American Common Eiders (Somateria mollissima dresseri; 1894) exhibiting the highest mortality figures. We then compare these mortality estimates with recent population estimates and trends and make an initial assessment of whether biologically meaningful population‐level impacts are possible. Specifically, we focus on the Northern Gannet, a species that has suffered significant global mortality, and two harvested species, Common Murre and American Common Eider, to inform management decisions. Our analysis suggests population‐level impacts in eastern Canada are possible for Northern Gannets and American Common Eiders, but are unlikely for Common Murres. This study demonstrates a comprehensive approach to assessing mortality and underscores the urgent need for further research to understand the broader ecological ramifications of the HPAI outbreak on wild bird populations.

A retrospective single institutional analysis of outpatient chemotherapy in patients with cancer during the COVID-19 pandemic.

Providing treatment to patients with cancer, even during the coronavirus disease (COVID-19) pandemic, is essential. In collaboration with infectious disease specialists, we established guidelines for the management of patients with cancer receiving ambulatory treatment during the pandemic on April 8, 2020. This study examined the practice and management of ambulatory chemotherapy under emergency conditions. Following the guidelines, our Breast and Medical oncology department developed a chemotherapy strategy for the phases. Additionally, to distinguish fever during chemotherapy, we developed a flow chart for fever. As part of a fact-finding survey, the status of outpatient chemotherapy was investigated: (1) whether there was any change in the number of chemotherapies before and after the declaration of a state of emergency by the Tokyo Metropolitan Government and (2) the frequency and severity of febrile neutropenia (FN) cases. Compared to before the first declaration of the state of emergency, the number of chemotherapies decreased except after the declaration, but no decrease was observed during the rest of the period; no difference was observed in the frequency or severity of FN outbreaks or in the use of pegfilgrastim for primary prevention before and after the epidemic. With appropriate treatment guidelines, routine chemotherapy can be performed in an outpatient setting during an outbreak.

Quantifying wildland fire resources deployed during the compound threat of COVID-19

Fire agencies across the United States must make complex resource allocation decisions to manage wildfires using a national network of shared firefighting resources. Firefighters play a critical role in suppressing fires and protecting vulnerable communities. However, they are exposed to health and safety risks associated with fire, smoke inhalation, and infectious disease transmission. The COVID-19 pandemic further complicated these risks, prompting fire agencies to propose resource management adaptations to minimize COVID-19 exposure and transmission. It is unclear if and how the pandemic may have operationally influenced wildland firefighting personnel resource use given compounding wildfire and COVID-19 risks. Therefore, we developed generalized linear mixed models that were fit using multiple integrated datasets to detect changes in personnel resource use for years prior to and during the COVID-19 pandemic, while controlling for historical fire and landscape conditions, societal risks, and management objectives. Analyses of observed and predicted firefighting resource use revealed reductions in the mean personnel resources used per wildfire per day during the pandemic for models developed across the western U.S. and for various western U.S. fire regions. Notably, the Northern California and the Great Basin Coordination Centers showed statistically significant reductions in ground personnel use during the COVID-19 pandemic. Learning from wildland fire management strategies and resource use trends that occurred during the COVID-19 pandemic, fire agencies can better anticipate resource constraints that may arise during the compounding threats of severe wildland fire activity and infectious disease outbreaks to proactively prepare and adapt suppression management strategies.

Leveraging social media data for pandemic detection and prediction

Governments and healthcare institutions are increasingly recognizing the value of leveraging social media data to address disease outbreaks. This is due to the rapid dissemination and rich content of social media data, which includes real-time reactions and calls for help from people. However, current research on which social media data can be utilized for information support, as well as the underlying reasons why social media data can be utilized for information support, remains limited. This study aims to make up for the aforementioned limitation by investigating which social media information is more likely to reflect the severity of an outbreak through empirical and prediction models, while also elucidating why social media data has the ability to reflect pandemic through content analysis. The COVID-19 outbreak was utilized as a case example in this study because it has the advantage of enhancing the universality of results and promoting the validation of the model with multiple waves of data. The empirical model results indicate that social media activity from public users is more likely to reflect the ground truth during pandemic. In particular, it was found that negative sentiment expressed in blog posts by public users during pandemic aligns more closely with the severity of disease outbreak. Then, a prediction model was proposed to further validate these findings of the empirical model. Finally, a content analysis was conducted based on the conclusions drawn from empirical model and prediction model. The content analysis revealed that the predictive capability of social media data for pandemic originates from individual self-reporting of illness. This study provides contributions and insights into which types of information can be used for pandemic monitoring and forecasting. The findings of our research have significant implications for governments and healthcare institutions in leveraging social media data for pandemic monitoring and forecasting.

First Report of Colletotrichum fioriniae Causing Anthracnose on Persimmon in China.

Persimmons (Diospyros kaki Thunb.) have a longstanding history of cultivation in China. Both aesthetically pleasing and edible, they often symbolize a sweet and fulfilling life. During the summer of 2022, a severe outbreak of anthracnose was observed on the lower leaves of persimmon trees in the National Field Genebank for Persimmon (NFGP), located in Yangling, Shaanxi, China (34°17'42.80″ N, 108°04'08.21″ E). The estimated incidence rate of this disease within the NFGP was approximately 30%. The typical symptoms of the disease included the presence of irregular lesions on leaves, and oval sunken lesions on infected fruit. Under high humidity conditions, pink sticky substances appeared in the affected areas. The presence of numerous lesions led to softening and detachment of persimmon fruit. To identify the causal pathogen, 5 × 5mm samples of the diseased leaves were collected from the interface between the infected and healthy leaves. The leaves were disinfected with 70% alcohol for 20 s, followed by rinsing with sterile water. Subsequently, the leaves were immersed in 1% NaClO for 2 to 3 minutes, rinsed with sterile water three times, dried using sterile absorbent paper, and the leaf samples were then transferred onto potato dextrose agar (PDA) medium, and cultured in 25°C incubators. Once the colony reached a certain size, small pieces of hyphae were extracted from edge and transferred for purification and repeated three times. After being cultured on PDA for 7 days, the colony showed a white spongy surface with a pink-orange center. The conidia displayed a fusiform shape and were transparent, measuring 4.58 to 6.53 μm × 9.27 to 13.11 μm (n=50). The isolates share morphological similarities with Colletotrichum fioriniae. The representative isolate HY-7 was selected for molecular identification. The internal transcribed spacers (ITS) region, chitin synthase (CHS-1), actin (ACT), beta-tubulin 2 (TUB2), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene were amplified using ITS1/4 (White et al. 1990), CHS-79F/CHS-345R (Carbone & Kohn, 1999), ACT512F/ACT (Carbone & Kohn, 1999), T1/BT2B (Glass & Donaldson 1995, O'Donnell et al., 1997), and GDF/GDR (Templeton et al. 1992), respectively. The generated sequences were deposited at GenBank under accession numbers OR878056 (ITS), OR766019 (CHS-1), OR766021(TUB2), OR766018 (ACT) and OR766020 (GAPDH). BLAST analysis revealed the sequences were 100% identical to C. fioriniae (MH865005 for ITS, JQ948953 for CHS-1, JQ949613 for ACT, JQ949943 for TUB2 and JQ948622 for GAPDH). The morphological characteristics and molecular analyses of the isolate matched the description of C. fioriniae. To fulfill Koch's postulates, the twigs and leaves of 'Fupingjianshi' in four different directions were inoculated without wounding in the field, and 10 healthy fruits were selected for wound inoculation. The concentration of conidia used for inoculation was about 1 × 106 conidia/ml, and sterilized water was used as control. The experiment was replicated three times under the same conditions. One week after inoculation, characteristic symptoms resembling those observed on the leaves of primary diseased persimmon trees appeared on the leaves and fruits. No symptoms were observed on the leaves, twigs and fruits in the control treatment. The pathogen from the artificially infected leaves and fruits were reisolated and identified as C. fiorinae based on morphological and molecular characteristics. Persimmon anthracnose is a common disease in regions where the fruit is grown, to the best of our knowledge, this is the first documented occurrence of C. fioriniae-induced anthracnose on persimmons in China, which should be paid more attentions. This report will help identify disease symptoms in the field and provides a basis for determining the occurrence, distribution, and control of C. fioriniae on persimmon leaves and fruits.

Screening and Identification of Natural Compounds as Potential Inhibitors of Glutamate Racemase and an Emerging Drug Target of Food Pathogen E. coli O157:H7: An In silico Approach to Combat Increasing Drug Resistance.

Shiga Toxin-Producing Escherichia coli (E. coli O157:H7), capable of causing serious food-borne illnesses, is extensively studied and is known to be transmitted through animal reservoirs or person-to-person contact, leading to severe disease outbreaks. The emergence of antibiotic resistance in these strains, coupled with increased adverse effects of existing therapeutics, underscores the urgent need for alternative therapeutic strategies. This study aims to evaluate Glutamate Racemase (MurI protein) of the food-path-ogenic E. coli O157:H7 (EC MurI) as a novel drug target. Furthermore, the study seeks to identify new compounds with potential inhibitory effects against this protein. Using computational tools, the study identified inhibitor binding sites on EC MurI and identified relevant inhibitors capable of binding to these sites. Molecular docking tech-niques were employed to assess potential hits, and selected compounds were further analyzed for their structural activity and binding affinity to the protein. The results of the study revealed that Frigocyclinone and Deslanoside, exhibited the best binding affinity with EC-MurI. Subsequent molecular dynamic (MD) simulations of the selected complexes indicated that both compounds were stable. This suggests that Frigocy-clinone and Deslanoside have the potential to serve as potent inhibitors of EC-MurI. In summary, this study highlights the urgent need for alternative therapies against food-pathogenic E. coli, focusing on E. coli O157:H7. Evaluation of Glutamate Race-mase as a drug target identified Frigocyclinone and Deslanoside as promising inhibitors. MD simulations indicated their stability, suggesting their potential as lead molecules for further research and treatment development.