Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors.

Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster-based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores >75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs.

Epidemic spreading on mixing group with face-to-face interaction.

The mixing groups gathered in the enclosed space form a complex contact network due to face-to-face interaction, which affects the status and role of different groups in social communication. The intricacies of epidemic spreading in mixing groups are intrinsically complicated. Multiple interactions and transmission add to the difficulties of understanding and forecasting the spread of infectious diseases in mixing groups. Despite the critical relevance of face-to-face interactions in real-world situations, there is a significant lack of comprehensive study addressing the unique issues of mixed groups, particularly those with complex face-to-face interactions. We introduce a novel model employing an agent-based approach to elucidate the nuances of face-to-face interactions within mixing groups. In this paper, we apply a susceptible-infected-susceptible process to mixing groups and integrate a temporal network within a specified time window to distinguish between individual movement patterns and epidemic spreading dynamics. Our findings highlight the significant impact of both the relative size of mixing groups and the groups' mixing patterns on the trajectory of disease spread within the mixing groups. When group sizes differ significantly, high inter-group contact preference limits disease spread. However, if the minority reduces their intra-group preferences while the majority maintains high inter-group contact, disease spread increases. In balanced group sizes, high intra-group contact preferences can limit transmission, but asymmetrically reducing any group's intra-group preference can lead to increased spread.

Tratamiento de las enfermedades infecciosas 2024-2026. Novena edición

Esta guía de Tratamiento de las enfermedades infecciosas 2024-2026 es una referencia útil para guiar la prescripción de antimicrobianos en medicina familiar y comunitaria, así como para orientar a especialistas en formación. La publicación contiene recomendaciones sobre el tratamiento más apropiado de las enfermedades infecciosas, considerando la epidemiología de los microorganismos causales y los patrones de sensibilidad en los países de América Latina y el Caribe, a la vez que se busca contribuir a la contención de la resistencia que surge del uso excesivo o incorrecto de fármacos antimicrobianos. Para la presente edición, se hizo una revisión exhaustiva del tratamiento de la sepsis y el síndrome de sepsis, con hincapié en su detección y tratamiento tempranos para reducir la morbilidad y mortalidad por sepsis. También se han incorporado principios del sistema AWaRe (Acceso, Precaución y Reserva) de la Organización Mundial de la Salud, que proporciona orientación concisa, con base en información comprobada, sobre el tratamiento de las 30 infecciones más comunes de niños y adultos.

Concurrent disease burden from multiple infectious diseases and the influence of social determinants in the contiguous United States.

Social determinants of health are known to underly excessive burden from infectious diseases. However, it is unclear if social determinants are strong enough drivers to cause repeated infectious disease clusters in the same location. When infectious diseases are known to co-occur, such as in the co-occurrence of HIV and TB, it is also unknown how much social determinants of health can shift or intensify the co-occurrence. We collected available data on COVID-19, HIV, influenza, and TB by county in the United States from 2019-2022. We applied the Kulldorff scan statistic to examine the relative risk of each disease by year depending on the data available. Additional analyses using the percent of the county that is below the US poverty level as a covariate were conducted to examine how much clustering is associated with poverty levels. There were three counties identified at the centers of clusters in both the adjusted and unadjusted analysis. In the poverty-adjusted analysis, we found a general shift of infectious disease burden from urban to rural clusters.

Prediction of infectious diseases using sentiment analysis on social media data.

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.

Impact of twelve immunization-preventable infectious diseases on population health using disability-adjusted life years (DALYs) in Spain.

BACKGROUND: The objective of this study is to estimate the burden of selected immunization-preventable infectious diseases in Spain using the Burden of Communicable Diseases in Europe (BCoDE) methodology, as well as focusing on the national immunization programme and potential new inclusions. METHODS: The BCoDE methodology relies on an incidence and pathogen-based approach to calculate disease burden via disability-adjusted life year (DALY) estimates. It considers short and long-term sequelae associated to an infection via outcome trees. The BCoDE toolkit was used to populate those trees with Spanish-specific incidence estimates, and de novo outcome trees were developed for four infections (herpes zoster, rotavirus, respiratory syncytial virus [RSV], and varicella) not covered by the toolkit. Age/sex specific incidences were estimated based on data from the Spanish Network of Epidemiological Surveillance; hospitalisation and mortality rates were collected from the Minimum Basic Data Set. A literature review was performed to design the de novo models and obtain the rest of the parameters. The methodology, assumptions, data inputs and results were validated by a group of experts in epidemiology and disease modelling, immunization and public health policy. RESULTS: The total burden of disease amounted to 163.54 annual DALYs/100,000 population. Among the selected twelve diseases, respiratory infections represented around 90% of the total burden. Influenza exhibited the highest burden, with 110.00 DALYs/100,000 population, followed by invasive pneumococcal disease and RSV, with 25.20 and 10.57 DALYs/100,000 population, respectively. Herpes zoster, invasive meningococcal disease, invasive Haemophilus influenza infection and hepatitis B virus infection ranked lower with fewer than 10 DALYs/100,000 population each, while the rest of the infections had a limited burden (< 1 DALY/100,000 population). A higher burden of disease was observed in the elderly (≥ 60 years) and children < 5 years, with influenza being the main cause. In infants < 1 year, RSV represented the greatest burden. CONCLUSIONS: Aligned with the BCoDE study, the results of this analysis show a persisting high burden of immunization-preventable respiratory infections in Spain and, for the first time, highlight a high number of DALYs due to RSV. These estimates provide a basis to guide prevention strategies and make public health decisions to prioritise interventions and allocate healthcare resources in Spain.

How to correctly fit an SIR model to data from an SEIR model?

In epidemiology, realistic disease dynamics often require Susceptible-Exposed-Infected-Recovered (SEIR)-like models because they account for incubation periods before individuals become infectious. However, for the sake of analytical tractability, simpler Susceptible-Infected-Recovered (SIR) models are commonly used, despite their lack of biological realism. Bridging these models is crucial for accurately estimating parameters and fitting models to observed data, particularly in population-level studies of infectious diseases. This paper investigates stochastic versions of the SEIR and SIR frameworks and demonstrates that the SEIR model can be effectively approximated by a SIR model with time-dependent infection and recovery rates. The validity of this approximation is supported by the derivation of a large-population Functional Law of Large Numbers (FLLN) limit and a finite-population concentration inequality. To apply this approximation in practice, the paper introduces a parameter inference methodology based on the Dynamic Survival Analysis (DSA) survival analysis framework. This method enables the fitting of the SIR model to data simulated from the more complex SEIR dynamics, as illustrated through simulated experiments.

The Burden and Risk Factors Associated with Infectious Diseases among Refugees in a Camp for Migrants in Porto Alegre: A Cross-Sectional Survey.

Refugees usually face a disproportionate burden of infectious diseases. Recently, Brazil has experienced an influx of refugees which demands the need for scaling up public health efforts to address the challenges. The research sought to study the burden and risk factors associated with infectious diseases among refugees received in the city of Porto Alegre. This was a cross-sectional study of 261 newly arrived refugees. The study sample was predominantly composed of Venezuelans (50.6%) and Haitians (44%), male (146: 56.7%), single (30.7%), with an average age of 33.38 (± 7.30) years. The average schooling was 10.42 (± 2.09) years. Diseases with the highest prevalence were influenza, whooping cough, diphtheria, and tuberculosis. There was significant association between the country of origin and presence of symptoms for infectious and contagious diseases, which warrants targeted interventions for reducing the incidence of these diseases among refugees in Brazil.

Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example.

OBJECTIVE: In order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing. METHODS: This study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model. RESULTS: We have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas. CONCLUSION: The model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.

Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic.

BACKGROUND: Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. METHODS: The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. RESULTS: In comparing AI models' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P = .012). The same trend was observed in Arabic, albeit without statistical significance (P = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models' performance in English was rated as "excellent", significantly outperforming their "above-average" Arabic counterparts (P = .002). CONCLUSIONS: Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.

Lessons from ancient pathogens.

Ancient infectious diseases and microbes can be used to address contemporary disease.

Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics.

OBJECTIVE: Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. RESULTS: OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

Possible counter-intuitive impact of local vaccine mandates for vaccine-preventable infectious diseases.

We modeled the impact of local vaccine mandates on the spread of vaccine-preventable infectious diseases, which in the absence of vaccines will mainly affect children. Examples of such diseases are measles, rubella, mumps, and pertussis. To model the spread of the pathogen, we used a stochastic SIR (susceptible, infectious, recovered) model with two levels of mixing in a closed population, often referred to as the household model. In this model, individuals make local contacts within a specific small subgroup of the population (e.g., within a household or a school class), while they also make global contacts with random people in the population at a much lower rate than the rate of local contacts. We considered what would happen if schools were given freedom to impose vaccine mandates on all of their pupils, except for the pupils that were exempt from vaccination because of medical reasons. We investigated first how such a mandate affected the probability of an outbreak of a disease. Furthermore, we focused on the probability that a pupil that was medically exempt from vaccination, would get infected during an outbreak. We showed that if the population vaccine coverage was close to the herd-immunity level, then both probabilities may increase if local vaccine mandates were implemented. This was caused by unvaccinated pupils possibly being moved to schools without mandates.

Healthcare-seeking behavior and out-of-pocket payments in Erbil, Kurdistan Region of Iraq.

BACKGROUND: Understanding healthcare-seeking behavior and examining health expenditures can help determine possible barriers to accessing healthcare and direct more effective and inclusive healthcare systems. This study aimed to evaluate healthcare-seeking behavior and out-of-pocket healthcare expenditure in a sample of the population in Erbil, Iraq. METHODS: We conducted this cross-sectional study in Erbil, Kurdistan Region of Iraq, from October to December 2023. A convenience sample of 414 adults completed a self-administered online survey. The following data were collected: recent illness, sociodemographic characteristics, type of healthcare received, and cost of healthcare. RESULTS: The most common health conditions reported were communicable diseases (16.3%), musculoskeletal problems (13.1%), and noncommunicable diseases (12.7%). Approximately 85% of patients with health conditions requiring care sought healthcare; most visited private clinics (46.3%) and private hospitals (18.6%). The median total out-of-pocket healthcare expenditure in US dollars was 117.3 (interquartile range (IQR) = 45.6-410.0). The median total cost was much greater for participants who first visited a private health facility (USD 135.5, IQR = 57.3-405.6) than those who first visited a public facility (USD 76.8, IQR = 16.1-459.7). Participants ≥ 60 years spent significantly more than those < 14 years (USD 332, 95% CI = 211-453, p < 0.001). Evermarried participants spent significantly more than unmarried (USD 97, 95% CI = 1 to 192, p = 0.047). Health expenditures were significantly greater for noncommunicable diseases than infectious diseases (USD 232, 95% CI = 96-368, p = 0.001). After adjusting for covariates, age ≥ 60 years was independently associated with higher spending (USD 305, 95% CI = 153-457, p < 0.001). CONCLUSIONS: Most participants sought care from formal health services, preferring the private sector. Seeking care from private facilities incurred significantly higher costs than seeking care from public ones, which suggests potential barriers to accessing healthcare, particularly affordability. The findings underscore the importance of evaluating existing healthcare policies to enhance effectiveness and identify areas for improvement. This study can help policymakers and healthcare providers design effective interventions, allocate resources efficiently, and improve healthcare delivery.

Infectious disease responses to human climate change adaptations.

Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes; they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have underappreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes nonintuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that support pathogens. Here, we consider the ways that human responses to climate change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under-resourced communities, and call attention to the need for socio-ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases.

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

The impact of population influx on infectious diseases - from the mediating effect of polluted air transmission.

The global population influx during the COVID-19 pandemic poses significant challenges to public health, making the prevention and control of infectious diseases a pressing concern. This paper aims to examine the impact of population influx on the spread of infectious diseases, with a specific emphasis on the mediating role of air pollution in this process. A theoretical analysis is conducted to explore the relationship between population influx, air pollution, and infectious diseases. Additionally, we establish a series of econometric models and employ various empirical tests and analytical techniques, including mediation effect test, threshold effect test, and systematic GMM test, to evaluate our hypotheses. The results indicate that: (1) Population influx directly and indirectly impacts infectious diseases. Specifically, population influx not only directly elevates the risk of infectious diseases, but also indirectly increases the incidence rate of infectious diseases by intensifying air pollution. (2) The impact of population inflow on infectious diseases exhibits regional heterogeneity. Compared to central and western China, the eastern regions exhibit a significantly higher risk of infectious diseases, exceeding the national average. (3) External factors influence the relationship between population influx and infectious diseases differently. Personal income and medical resources both help mitigate the risk of infectious diseases due to population influx, with medical resources having a more substantial effect. Contrary to expectations, abundant educational resources have not reduced the risk, instead, they have exacerbated the risk associated with population influx. This paper provides a scientific basis for formulating effective strategies for the prevention and control of infectious diseases.

rtestim: Time-varying reproduction number estimation with trend filtering.

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases.

BACKGROUND: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. METHODS: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. RESULTS: When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9). CONCLUSION: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.