Relative Incidence Research Articles

The associations between invasive group A streptococcal disease and infection with influenza, varicella, or hepatitis C viruses: A data linkage study, Victoria, Australia

ObjectivesTo quantify the associations between invasive group A streptococcal disease (iGAS) incidence and influenza, varicella, and chronic hepatitis C virus (HCV). MethodsWe used individual-level linked data of iGAS cases from Victoria, Australia (2007-2017) to assess associations between these viral infections and iGAS. A self-controlled case series method was used to estimate the relative incidence of iGAS following an influenza or varicella infection, while the relative incidence of iGAS among HCV cases, and HCV cases who inject drugs, was estimated using population-level data and a negative binomial regression model. ResultsOf the 1949 individuals with at least one iGAS diagnosis, 82 were diagnosed with influenza at least once, 30 with varicella, and 118 with HCV during the study period. The relative incidence of iGAS increased substantially following infection with influenza (incidence rate ratio [IRR]: 34.5, 95% confidence interval [CI]: 21.3-55.8) or varicella (IRR: 22.4, 95% CI: 10.3-48.8). iGAS incidence was higher among HCV cases (IRR: 5.7, 95% CI: 4.4-7.3) compared to individuals without HCV. iGAS incidence was also higher among HCV cases who inject drugs (IRR: 17.9, 95% CI: 13.0-24.4) compared to individuals without HCV who did not inject drugs. ConclusionsWe found a significantly higher risk of iGAS following an influenza or varicella infection and for chronic HCV cases, particularly those who inject drugs. These findings are relevant to public health practice and support the timely identification of iGAS cases.

An analysis of the poverty reduction effects on land transfer: Evidence from rural areas in China.

This paper develops a multidimensional poverty index (MPI) evaluation system using multiple measures. We use the China Family Panel Study (CFPS) data to build balanced panel data from 2012 to 2018. Employing the probit model to analyze the impact of land transfer on relative poverty incidence, as well as utilizing the two-way fixed effects model and the logit model, we approach the issue from the perspective of multidimensional relative poverty identification. Our study indicates a decrease in relative poverty among rural households since 2012. Nonetheless, the overall incidence of relative poverty among rural households in China remains high at 20.6%, highlighting the severity of this issue in rural China. Moreover, we examine the heterogeneity of the poverty reduction effects of land transfer-in and land transfer-out. Land transfer can significantly reduce the incidence of relative poverty among rural households, with distinct mechanisms for land transfer-in and land transfer-out. Land transfer-in primarily reduces the relative poverty incidence of rural households through the education, housing, and land dimensions, while land transfer-out focuses on the quality-of-life dimension. Overall, land transfer-out has a more significant poverty reduction effect than land transfer-in. Furthermore, our study reveals that the reduction effect of land transfer on the incidence of relative poverty among rural households persists for at least two years, but by the fourth year, this effect disappears.

Time series analysis of malaria cases to assess the impact of various interventions over the last three decades and forecasting malaria in India towards the 2030 elimination goals

BackgroundDespite the progress made in this decade towards malaria elimination, it remains a significant public health concern in India and many other countries in South Asia and Asia Pacific region. Understanding the historical trends of malaria incidence in relation to various commodity and policy interventions and identifying the factors associated with its occurrence can inform future intervention strategies for malaria elimination goals.MethodsThis study analysed historical malaria cases in India from 1990 to 2022 to assess the annual trends and the impact of key anti-malarial interventions on malaria incidence. Factors associated with malaria incidence were identified using univariate and multivariate linear regression analyses. Generalized linear, smoothing, autoregressive integrated moving averages (ARIMA) and Holt’s models were used to forecast malaria cases from 2023 to 2030.ResultsThe reported annual malaria cases in India during 1990–2000 were 2.38 million, which dropped to 0.73 million cases annually during 2011–2022. The overall reduction from 1990 (2,018,783) to 2022 (176,522) was 91%. The key interventions of the Enhanced Malaria Control Project (EMCP), Intensified Malaria Control Project (IMCP), use of bivalent rapid diagnostic tests (RDT-Pf/Pv), artemisinin-based combination therapy (ACT), and involvement of the Accredited Social Health Activists (ASHAs) as front-line workers were found to result in the decline of malaria significantly. The ARIMA and Holt’s models projected a continued decline in cases with the potential for reaching zero indigenous cases by 2027–2028. Important factors influencing malaria incidence included tribal population density, literacy rate, health infrastructure, and forested and hard-to-reach areas.ConclusionsStudies aimed at assessing the impact of major commodity and policy interventions on the incidence of disease and studies of disease forecasting will inform programmes and policymakers of steps needed during the last mile phase to achieve malaria elimination. It is proposed that these time series and disease forecasting studies should be performed periodically using granular (monthly) and meteorological data to validate predictions of prior studies and suggest any changes needed for elimination efforts at national and sub-national levels.

Acute disseminated encephalomyelitis and transverse myelitis following COVID-19 vaccination – A self-controlled case series analysis

Acute Disseminated Encephalomyelitis (ADEM) and Transverse Myelitis (TM) are within the group of immune mediated disorders of acquired demyelinating syndromes. Both have been described in temporal association following various vaccinations in case reports and case series and have been evaluated in observational studies. A recent analysis conducted by The Global Vaccine Data Network (GVDN) observed an excess of ADEM and TM cases following the adenoviral vectored ChAdOx1 nCoV-19 (AZD1222) and mRNA-1273 vaccines, compared with historically expected background rates from prior to the pandemic. Further epidemiologic studies were recommended to explore these potential associations.We utilized an Australian vaccine datalink, Vaccine Safety Health-Link (VSHL), to perform a self-controlled case series analysis for this purpose. VSHL was selected for this analysis as while VSHL data are utilised for GVDN association studies, they were not included in the GVDN observed expected analyses. The VSHL dataset contains vaccination records sourced from the Australian Immunisation Register, and hospital admission records from the Victorian Admitted Episodes Dataset for 6.7 million people. These datasets were used to determine the relative incidence (RI) of G040 (ADEM) and G373 (TM) ICD-10-AM coded admissions in the 42-day risk window following COVID-19 vaccinations as compared to control periods either side of the risk window.We observed associations between ChAdOx1 adenovirus vector COVID-19 vaccination and ADEM (all dose RI: 3.74 [95 %CI 1.02,13.70]) and TM (dose 1 RI: 2.49 [95 %CI: 1.07,5.79]) incident admissions. No associations were observed between mRNA COVID-19 vaccines and ADEM or TM. These findings translate to an extremely small absolute risk of ADEM (0.78 per million doses) and TM (1.82 per million doses) following vaccination; any potential risk of ADEM or TM should be weighed against the well-established protective benefits of vaccination against COVID-19 disease and its complications.This study demonstrates the value of the GVDN collaboration leveraging large population sizes to examine important vaccine safety questions regarding rare outcomes, as well as the value of linked population level datasets, such as VSHL, to rapidly explore associations that are identified.

Efficient crowd density estimation with edge intelligence via structural reparameterization and knowledge transfer

Crowd stampedes and related incidents pose significant dangers to public safety and have resulted in numerous fatalities over the past few decades. Estimating crowd density in real-time can help avoid stampedes by providing early warning systems to prevent overcrowding and manage the flow of people. With the increasing prevalence of Internet of Things (IoT), the application of edge computing for field pedestrian density estimation can help to enhance security and efficiency of system. To deal with the issue of crowd scale variation, most previous works rely on heavy backbone networks or complex module, which require high runtime consumption and severely limits the deployment scope of their work. To overcome this issue, we propose multi-branch model Repmobilenet. For multi-scale spatial feature extraction. Repmobilenet equips with lightweight multi-branch depthwise separable convolutional block (DSBlock), which can effectively extract multi-scale feature of dense crowd to cope with scale variation. In the inference phase, the multi-branch structure can be transformed into a single-branch feed-forward structure through structural reparameterization. By this way, Repmobilenet can use multi-branch over-parameterized topology to capture more dense spatial features during training stage and decrease inference latency during inference stage. We also added dilated convolutions in the backend to expand the receptive field of the model. Comparison to state-of-the-art methods, proposed Repmobilenet is able to achieve comparable counting performance while maintaining small model size and low inference latency in ShanghaiTech benchmark. At last, we introduce a multi-layer knowledge distillation method to further increase the model’s fitting capability. By imitating the feature of multiple intermediate layers and final output soft ground truth of the teacher model, the student model can learn compact and efficient knowledge without increasing model size and inference latency. The code can be found in https://github.com/lcxxxiii/Repmobilenet.

(364) Development of a Men’s Health Report Card for the State of Florida

Abstract Introduction Collecting regional health data that identifies causes of morbidity and mortality is imperative in order to guide the planning and implementation of programs and policies that will ultimately improve men’s health. Unintentional impact accidents are a major source of morbidity and mortality for young males across the United States. Other potentially reversible behaviors that have adverse behaviors that n’s health include smoking, vaping, and failing to undergo recommended cancer screenings. Corroborating this data on a regional level within our state of Florida, as well as understanding the prevalence of activities and behaviors that may adversely impact men’s health is important to build a strategy that reverses these trends. Objective The overarching goal was to develop a Men’s Health Report Card for the State of Florida which elucidated important health data that could be used to drive an evidence-based men’s health initiative for our state. In this study, we sought to evaluate the impact of accidental injuries on mortality and define specifics associated with those accidental injuries that may be used to mitigate the incidence of accidental deaths, Methods Data was obtained from various sources to develop a Men's Health Report Card for the State of Florida. The sources include the United States Census Bureau (USCB), the State Department of Health, the Center for Disease Control and Prevention (CDC) and the Florida Department of Health (FDH). The demographic data obtained from the USCB includes information on the population size, age distribution, race, and ethnicity breakdown within Florida. FDH contributed health indicator data from multiple sources. This data includes information from death certificates, providing insight into the leading causes of death for men in Florida, including COVID-19 death reports specific to gender and age. USCB American Community Survey provides data on social determinants of health, such as high school graduation rates and income levels falling below the federal poverty guidelines. CDC National Center for Health Statistics Mortality in the United States Report provides data on life expectancy in Florida and the United States organized by race and sex. The data spanned from the years 2015 and 2018. Results Figure 1 highlights some of the important information that was gleaned with regard to the state of Men’s Health in Florida. On average, Floridian men had a 5- year shorter life expectancy at birth than their female counterparts. Leading causes of male deaths in our state include heart disease, cancer, unintentional injuries, chronic lung disease, and cerebrovascular disease. The relative incidence of these sources of mortality is age dependent. It is notable that among young males, ages 18-54 years old, unintentional injuries, homicide, and suicide accounted for approximately 75% of male deaths. With respect to health behaviors: smoking and vaping, a sedentary lifestyle, obesity, alcohol abuse, and not wearing a seat belt remain highly prevalent among Floridian males. Notable among the cancer screenings, 30% of Floridian men over 50 years have never had a colonoscopy and 40% of men 50 years and older have never had a serum PSA test. Finally, it is notable that 15% of Floridian males could not see a physician due to cost, and close to 20% had no healthcare insurance coverage. Conclusions The State of Florida Men’s Health Report card defines significant causes of morbidity and mortality for Floridian males. As can be seen in this report, many of the sources of morbidity and mortality arise from unhealthy lifestyle choices, which if addressed could potentially reverse some of these trends. Racial, ethnic, and geographic health disparities among Floridian males exist and will require further work in the future to remedy this situation. Disclosure No.

Bailout Options for Intra-operative Implant and Instrumentation Related Incidents and Complications During Arthroscopic Knee Surgery-A Retrospective Study.

Arthroscopic knee procedures are one amongst the common surgical interventions for problems in the knee. It is technically more demanding than an open procedure and is associated with several potential complications. During arthroscopy procedures, several technical challenges may arise, and even experienced surgeons may encounter new issues. However, careful attention to the surgical technique can help prevent or resolve them. The study was conducted on all patients who underwent knee arthroscopy procedure during study period. We recorded details of the implants used and any unexpected situations related to them, as well as how they were managed. Instrumentation-related parameters such as screwdriver issues, radiofrequency ablator issues, scope damages, shaver complications, probe complications, and meniscus suture passing devices were also assessed. In total, there were 12 (3.73%) implant and instrument-related incidents and complications, of which 5 (1.55%) were implant-related and 7 (2.17%) were instrument related. Among the instrumentation-related incidents and complications, two (0.62%) were screwdriver breakage incidents, two (0.62%) were radiofrequency ablator-related incidents, one was arthroscopic probe (0.31%) related incident, one (0.31%) was meniscus suture passing device related complication and one (0.31%) was arthroscope related incident. Surgeons must be ready to anticipate and effectively manage any technical difficulties that may arise during the procedure, maintaining composure in the face of unexpected challenges and guiding the team. In most cases, incidents can be addressed intra operatively and may not have long-term effects on patient outcomes. It is crucial to have multiple implant and instrument backup options available for successful surgery.

Comparative incidence of Ganoderma basal stem rot and upper stem rot disease in oil palm in relation to soil type and geographical distribution

The greatest danger to oil palm sustainability in Indonesia is posed by basal stem rot (BSR) and upper stem rot (USR). However, information on their comparative incidence in relation to soil types and geo-distribution remains scarce. As such, data from annual surveys carried out over 95,000 ha of first-generation oil palms planted in Asian Agri estates was reviewed and analyzed. The highest infection levels occurred in North Sumatra, followed by Riau and Jambi. Likewise, infection levels were highest in peat (24.79 palms/ha), followed by alluvial (13.41 palms/ha), volcanic (7.25 palms/ha), and inland (0.07 palms/ha) soil types. Averaged over three provinces and all four soil types, infection due to USR was more dominant than BSR, comprising 52.3% and 47.7%, respectively. Molecular assays of infected tissue confirmed that both BSR and USR were caused by the genus Ganoderma with the major species being G. boninense. Longitudinal dissections of oil palm trunks infected by USR indicated no infection connectivity with the basal portion of the trunk, unlike BSR, where the infection is through root contact. Transmission through basidiospores is likely to play an essential role in USR epidemiology. Understanding pathogen distribution, species identity, and its transmission epidemiology are prerequisites to control diseases more effectively.

The risk of acute disseminated encephalomyelitis (ADEM) following covid-19 vaccination in England: A self-controlled case-series analysis

ABSTRACT Acute disseminated encephalomyelitis (ADEM) has been identified as an Adverse Event of Special Interest in the COVID-19 vaccine programme due to its long-standing temporal association with a wide range of other vaccines. Case reports of ADEM shortly following COVID−19 vaccination have now been documented. There were 217 ADEM admissions in 215 individuals in the period 8th December 2020 to 31st March 2023. An increased risk of ADEM following the first dose of ChAdOx1 vaccine was observed (relative incidence (RI) = 3.13, 95% Confidence Interval (CI) [1.56–6.25]) with a vaccine attributable risk of 0.39 per million doses. When doses 1 and 2 were combined this increased risk remained just significant (1.96 [95%CI 1.01–3.82]). No significant increased risk was observed with any other vaccine or dose. This small, elevated risk after the first dose of ChAdOx1-S vaccine demonstrates how large national electronic datasets can be used to identify very rare risks and provides reassurance that any risk of ADEM following the ChAdOx1-S COVID-19 vaccination is extremely small. Given the rarity of this risk, further studies in settings with access to data on large populations should be carried out to verify these findings.

Modelling Data Poisoning Attacks Against Convolutional Neural Networks

Cybersecurity has become a great concern in many real-world applications involving adversaries with Machine Learning (ML) algorithms being more widely used. This concern is more challenging in Internet of Things (IoT) platforms. As IoT-enabled applications are growing at a rapid pace in every sector there are growing security related incidents as well. ML algorithms are widely deployed to perform data analysis, reasoning and decision-making over the data emanating from IoT devices. Security of this data while collection, communication and computing is a major challenge. Various attackers are trying to find weaknesses in the ML algorithms and trying to deceive these ML algorithms to learn the wrong information from the data. Countermeasures need to be developed to evaluate the security of the ML models. To develop such countermeasures, one needs to understand all possible attacks on the ML models. Data poisoning attacks are a class of adversarial attacks on ML where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier to satisfy certain objectives. In order to develop an attack-resistant ML model, one needs to know all possible attacks on these models. The recent data poisoning technique such as the Fast Gradient Sign Method (FGSM) is static and provides very micro control to the attacker on creating adversarial data. In this research, we develop a more robust data poisoning technique for deep neural networks using Generative Adversarial Networks (GANs) to create a data poisoning attack. We then evaluate the performance of the proposed algorithm and also compare the results obtained by FGSM.

NAVMAT: an AI-powered pathway to knowledge sharing on material failures

The paper describes the product development methodology, the architecture, the modules and features of the operating prototype, as well as future challenges of NAVMAT, a naval materials failure management system. The fundamental compound of the knowledge management platform is the recording and classification of a failure incident. NAVMAT supports different types of users, from the first Reporter to the Analyst and Forensic Engineer through appropriate workflows. In these workflows the incident-related information can be provided as text, files, images and videos, which can be easily associated to the incident and provide structured information. NAVMAT provides a number of Artificial Intelligence (AI)-enabled helpers. During the incident recording, NAVMAT brings into play reactive real-time search which suggests related incidents and literature to facilitate the editor. It also speeds up the classification of incidents by providing AI-suggested labels, chosen from the (multi-lingual) concepts contained in the system Ontology. On the other hand, the intelligent indexing and search infrastructure of the system supports easy identification and retrieval of past incidents, reports and publications, by applying Natural Language Processing. The prototype of the described system has been embedded as a web application validated by potential Users and is being prepared for Operation in a fleet environment.

An investigation of the effects of meteorological factors on the incidence of tuberculosis

To explore the influence of meteorological factors on the incidence of tuberculosis (TB) in Yingjisha County, Kashgar Region, Xinjiang, and to provide a scientific basis for the prevention and control of TB. The Spearman correlation analysis and distribution lag nonlinear model analysis were conducted on the number of daily reported cases of TB from 2016 to 2023 to study the association effect of various meteorological factors and the daily incidence number of TB in Yingjisha County. A total of 13,288 TB cases were reported from January 2016 to June 2023, and June to October is the peak period of annual TB incidence. Spearman correlation analysis revealed that average daily temperature (AT) and average daily wind speed (WS) were positively correlated with TB incidence (rAT = 0.110, rWS = 0.090); and average daily relative humidity (RH) and TB incidence was negatively correlated (rRH = − 0.093). When AT was − 15 °C, the RR reached a maximum of 2.20 (95% CI: 0.77–6.29) at a lag of 21 days. When RH was 92%, the RR reached a maximum of 1.05 (95% CI: 0.92–1.19) at a lag of 6 days. When WS was 5.2 m/s, the RR reached a maximum of 1.30 (95% CI: 0.78–2.16) at a lag of 16 days. There is a non-linearity and a certain lag between meteorological factors and the occurrence and prevalence of TB in the population, which is mainly manifested in the fact that the risk of incidence of TB decreases with the increase of the daily AT, has a hazardous effect within a certain range of humidity as the average daily RH rises, and gradually increases with the increase of the average daily WS. Local residents are advised to pay attention to climate change so as to take appropriate preventive measures, especially women and middle and old age group should pay close attention to climate change and add more clothes in time, minimise travelling in hazy weather and windy and sandy weather, maintain good nutrition, adequate sleep and moderate exercise in daily life to enhance their immunity, wash hands frequently and ventilate the air, and try to avoid staying in humid and confined spaces in order to reduce the risk of latent TB patients developing the disease.

Spatial and moderating effects of greenspace on the association between air pollution and lung cancer incidence

Lung cancer remains the primary cause of death globally. Studies have increasingly explored the role of greenspace in mitigating lung cancer risks, yet research gaps persist. First, while the direct spatial effect of greenspace has received attention, its potential spillover effects, driven by human mobility and air pollution dispersion, remain underexamined. Second, despite prevalent assertions of greenspace as an air purifier, the extent to which it moderates the air pollution-lung cancer association has yet to be fully understood. Third, the evaluation of greenspace's effects, predominately analyzed linearly a priori, demands exploration into their potential nonlinearity. We utilize three-year lung cancer datasets from 228 counties in China, to investigate the spatial, moderating, and threshold effects of greenspaces on lung cancer incidence in relation to air pollution. Employing spatial econometric and threshold models, our findings indicate that greenspace reduces lung cancer incidence in both local and neighboring counties. We also observe a diminution in the detrimental impact of air pollution on lung cancer incidence in areas with higher greenspace, especially when the Normalized Difference Vegetation Index surpasses a given threshold. These insights contribute to an enhanced understanding of greenspace's role in lung cancer prevention and could inform policies on greenspace expansion prioritization.

The impact of local cost-of-living differences on relative poverty incidence: an application using retail scanner data and small area estimation models

Estimating economic poverty indicators at the local level is essential for well-targeted data-driven welfare policies. However, Italy is a country characterized by strong geographical heterogeneity represented by unequal price levels among different areas, and computing poverty indicators with a national monetary poverty threshold can be misleading. This work proposes a novel approach to estimate monetary poverty incidence at the provincial level in Italy considering the different price levels within national boundaries. To account for local price variation, Spatial Price Indices (SPIs) are computed using scanner data on retail prices. The SPIs are estimated in two ways, referring to the mean local prices and using the 20th percentile of the prices. These two kinds of SPIs are used to adjust the national poverty line when computing the poverty incidence at the provincial level using Small Area Estimation (SAE) models. Our findings suggest that adjusting the national poverty line using the SPIs to compute a monetary poverty index can modify the poverty mapping results from the map produced with the traditional national poverty line that ignores price differences.

Post-marketing active surveillance of Guillain Barré Syndrome following COVID-19 vaccination in persons aged ≥12 years in Italy: A multi-database self-controlled case series study.

Recently published studies have reported association of COVID-19 vaccine ChAdOx1-S (Vaxzevria) with Guillain Barré Syndrome (GBS). Less is known about the safety of other COVID-19 vaccines with respect to GBS outcome. This study investigated the association of COVID-19 vaccines with GBS in more than 15 million persons aged ≥12 years in Italy. Study population was all individuals aged ≥12 years who received at least one dose of COVID-19 vaccines, admitted to emergency care/hospital for GBS from 27 December 2020-30 September 2021 in Italy. Identification of GBS cases and receipt of at least one dose of mRNA-1273 (Elasomeran), BNT162b2 (Tozinameran), ChAdOx1-S (Vaxzevria) and Ad26.COV2.S (Janssen) through record linkage between regional health care and vaccination registries. Relative Incidence (RI) was estimated Self-controlled case series method adapted to event-dependent exposure using in the 42-day exposure risk period after each dose compared with other observation periods. Increased risk of GBS was found after first (RI = 6.83; 95% CI 2.14-21.85) and second dose (RI = 7.41; 2.35-23.38) of mRNA-1273 and first dose of ChAdOx1-S (RI = 6.52; 2.88-14.77). Analysis by age found an increased risk among those aged≥60 years after first (RI = 8.03; 2.08-31.03) and second dose (RI = 7.71; 2.38-24.97) of mRNA-1273. The first dose of ChAdOx1-S was associated with GBS in those aged 40-59 (RI = 4.50; 1.37-14.79) and in those aged ≥ 60 years (RI = 6.84; 2.56-18.28). mRNA-1273 and ChAdOx1-S vaccines were associated with an increased risk of GBS however this risk resulted in a small number of excess cases. Limitations were loss of GBS outpatient cases and imprecision of the estimates in the subgroup analysis due to a low number of events.

Beta-blocker adjunct therapy as a prospective anti-metastatic with cardio-oncologic regulation.

The prevailing treatment stratagem in cancer therapy still challenges the dilemma of a probable metastatic spread following an initial diagnosis. Including an anti-metastatic agent demands a significant focus to overrule the incidence of treatment failures. Adrenergic stimulation underlying the metastatic spread paved the way for beta blockers as a breakthrough in repurposing as an anti-metastatic agent. However, the current treatment approach fails to fully harness the versatile potential of the drug in inhibiting probable metastasis. The beta blockers were seen to show a myriad of grip over the pro-metastatic and prognostic parameters of the patient. Novel interventions in immune therapy, onco-hypertension, surgery-induced stress, induction of apoptosis and angiogenesis inhibition have been used as evidence to interpret our objective of discussing the potential adjuvant role of the drug in the existing anti-cancer regimens. Adding weight to the relative incidence of onco-hypertension as an unavoidable side effect from chemotherapy, the slot for an anti-hypertensive agent is necessitated, and we try to suggest beta-blockers to fill this position. However, pointing out the paucity in the clinical study, we aim to review the current status of beta blockers under this interest to state how the drug should be included as a drug of choice in every patient undergoing cancer treatment.

Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states.

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

Dose-dependent incidence of agranulocytosis in patients treated with methimazole and propylthiouracil.

Agranulocytosis is a serious adverse effect of methimazole (MMI) and propylthiouracil (PTU), and although there have been reports suggesting a dose-dependent incidence in relation to both drugs, the evidence has not been conclusive. The objective of our study was to determine whether the incidences of agranulocytosis induced by MMI and PTU exhibit dose-dependency. The subjects were 27,784 patients with untreated Graves' disease, 22,993 of whom were on an antithyroid drug treatment regimen for more than 90 days. Within this subset, 18,259 patients had been treated with MMI, and 4,734 had been treated with PTU. The incidence of agranulocytosis according to dose in the MMI group was 0.13% at 10 mg/day, 0.20% at 15 mg/day, 0.32% at 20 mg/day, and 0.47% at 30 mg/day, revealing a significant dose-dependent increase. In the PTU group, there were 0 cases of agranulocytosis at doses of 125 mg/day and below, 0.33% at 150 mg/day, 0.31% at 200 mg/day, and 0.81% at 300 mg/day, also revealing a significant dose-dependent increase. The incidence of agranulocytosis at MMI 15 mg and PTU 300 mg, i.e., at the same potency in terms of hormone synthesis inhibition, was 0.20% and 0.81%, respectively, and significantly higher in the PTU group. Our findings confirm a dose-dependent increase in the incidence of agranulocytosis with both drugs, but that at comparable thyroid hormone synthesis inhibitory doses PTU has a considerably higher propensity to induce agranulocytosis than MMI does.

A Survey of Xylella fastidiosa in the U.S. State of Virginia Reveals Wide Distribution of Both Subspecies fastidiosa and multiplex in Grapevine.

Global travel and trade in combination with climate change are expanding the geographic distribution of plant pathogens. The bacterium Xylella fastidiosa is a prime example. Native to the Americas, it has spread to Europe, Asia, and the Middle East. To assess the risk that pathogen introductions pose to crops in newly invaded areas, it is key to survey their diversity, host range, and disease incidence in relation to climatic conditions where they are already present. We performed a survey of X. fastidiosa in grapevine in Virginia using a combination of quantitative PCR, multilocus sequencing, and metagenomics. We also analyzed samples from deciduous trees with leaf scorch symptoms. X. fastidiosa subspecies fastidiosa was identified in grapevines in all regions of the state, even in Northern Virginia, where the temperature was below -9°C for 10 days per year on average in the years preceding sampling. Unexpectedly, we also found for the first time grapevine samples infected with X. fastidiosa subspecies multiplex (Xfm). The Xfm lineage found in grapevines had been previously isolated from blueberries in the Southeastern United States and was distinct from that found in deciduous trees in Virginia. The obtained results will be important for risk assessment of X. fastidiosa introductions in other parts of the world.

Correlation of genome methylation of fig tree accessions with natural nematode and rust incidence.

Commercial fig tree cultivation in Brazil involves a single cultivar, 'Roxo-de-Valinhos'. The use of a single cultivar results in serious diseases and related problems. The aim of this study was to characterize fig accessions by analyzing the natural root-knot nematode and leaf rust incidence in relation to the epigenomic profile of the plant, since epigenetic variations affect plant-pathogen interactions. All plants were attacked by nematodes, indicating susceptibility; Meloidogyne incognita was the root-knot nematode species involved. Joint analysis of data showed that methylation and leaf rust incidence were correlated when observed in the same phenological phase, presenting initial evidence of the same factorial pressure loads in genotypes, suggesting similar behavior within these genotypes.