Related Data Research Articles

The Localisation of a Tracheoesophageal Shunt during Laryn(-gopharyn)gectomy Determines the Risk of Shunt Insufficiency.

Tracheoesophageal shunt insufficiency (TESI) is a common and potentially life-threatening complication after laryn(-gopharyn)gectomy (L(P)E). We investigated whether TESI could be the result of a specific shunt location. A monocentric, retrospective cohort analysis of 171 consecutively treated L(P)E patients was performed. Patients with a secondary prosthesis instillation and patients with insufficient postoperative imaging were excluded. Disease related data as well as location of primary voice prosthesis were assessed. The cohort was divided into 62 TESI-positive and 109 TESI-negative individuals. The mean time from surgery to TESI was 32 months. No differences were observed in gender, age, tumor localization, T/R/M-status. Surgery without adjuvant therapy was more often performed in TESI-negative individuals when compared with their positive counterparts. However, Cox regression including T/N status, therapy and categorized distance of the tracheoesophageal shunt to the manubrium (≤1.5 cm vs. >1.5 cm) revealed that a distance of ≤1.5 cm was associated with a 2.1-fold increased risk of TESI, while all other parameters did not influence the event-free survival. Primary shunt positioning ≤1.5 cm to the ridge of the manubrium is associated with an increased risk of TESI. In these individuals secondary shunt operation resulting in a position >1.5 cm distant to the manubrium should be recommended.

Biobanking in the hospital of a multidisciplinary research medical center as a potential for a wide research range. Part II. Specifics and first results of developing a described collection of biomaterial

In connection with the development of genetic and other types of research in translational medicine, collections containing a significant number of biospecimens and large data sets are required. Since in-depth examination of patients is most often carried out during hospitalization, the creation of collections based on electronic medical records (EMR) data is promising. In this regard, the project "Total biobanking in the hospital of the National Medical Research Center for Therapy and Preventive Medicine" was initiated, aimed at total biobanking of blood and related products from patients in inpatient departments and the creation of a unified system that combines information on biosamples, clinical and other types of data.Aim. To describe the collection of biospecimens collected within the project "Total biobanking in the hospital of the National Medical Research Center for Therapy and Preventive Medicine" for August 2023, and to analyze the process of biospecimen description.Material and methods. Recruitment of patients is carried out on the basis of inpatient departments of the National Medical Research Center for Therapy and Preventive Medicine, only if the patient signs informed consent. After blood is taken, the biomaterial enters the biobank, where sample preparation and storage is carried out, as well as data about each biosample is entered into a special FreezerPRO system. At the same time, clinical data about the patient is collected using EMR, which are combined in the Medialog medical information system.Results. A special software for data exchange between the medical information system and the biobank has been developed, which combines information about patients entering the Medialog medical information system and the FreezerPro biospecimen management system, which contains detailed information about biospecimens. As of August 25, 2023, biomaterial from 3996 patients was collected, which was accompanied by a detailed clinical summary. In particular, data is available on diagnoses (for 100% of patients), laboratory test data (100%), paraclinical data, in particular, electrocardiography (100%), echocardiography (76,8%), 24-hour Holter monitoring (59,8%), esophagogastroduodenoscopy (27,3%), etc. The most common diseases were hypertension (76,0%), atherosclerosis (69,9%), coronary artery disease (52,9%), arrhythmias (50,6%), diabetes (19,2%). In addition, 25,0% of patients had prior stenting.Conclusion. Total biobanking within the hospital of a multidisciplinary research medical center allows for the rapid and high-quality collection of biomaterial and related data. A collection of biospecimens with comprehensive and detailed clinical annotations has been collected. A primary analysis of the collection was carried, which showed that it can be used for a wide range of studies.

Bioclimatic design recommendations for novel cluster analysis-based mapping for humid climates with altitudinal gradient variations

Proper planning of urban environments in construction projects requires an accurate understanding of the prevailing climates in an area. However, the need for sufficient climate records in the Global South countries can make this characterization difficult. This study proposes using satellite information and cluster analysis to define the climatic behavior in Colombia, specifically in areas with intertropical climates with altitudinal gradient impact. The methodology used the statistical analysis of hourly temperature and relative humidity data for recent typical meteorological year (TMY) files. These data were obtained from the National Renewable Energy Laboratory (NREL). Subsequently, cluster analysis was applied to group regions with similar climatic characteristics. Standardization of the climatic variables ensured that all contributed equally to the research. The study results present an atlas with nine different climatic strategy zones, turning Colombia's climate into eight main clusters, each with specific characteristics and recommendations for the passive design of buildings. Identifying these zones made it possible to define the distribution of bioclimatic strategies in the different thermal floors of the region studied. This approach provides a framework for urban planners and city officials to develop climate-responsive building design guidelines adapted to the specific climatic conditions of each zone.

Effect of Confining Pressure on CO2-Brine Relative Permeability Characteristics of Sandstone in Ordos Basin

CO2-brine relative permeability significantly impacts CO2 injection and is a key parameter for carbon dioxide storage simulation in saline aquifers. In the study of relative permeability, factors such as temperature, pressure, and reservoir rock physical properties play a crucial role. To better understand the impact of confining pressure on the CO2-brine relative permeability characteristics of sandstone in the Ordos Basin, five sets of CO2-brine relative permeability data were obtained through unsteady-state displacement tests conducted at various confining pressures ranging from 12 to 20 MPa. The research findings indicate that with an increase in confining pressure there is a slight decrease in irreducible brine saturation. Furthermore, the CO2 relative permeability in the irreducible brine state decreased by 57% as the pressure increased from 12 MPa to 20 MPa. The study demonstrates notable differences in the CO2-brine relative permeability curves under varying confining pressure conditions. As the confining pressure increases, the CO2 relative permeability curve decreases, while the brine relative permeability increases. The change in brine relative permeability is not as pronounced as that of CO2. These experimental results offer essential support for subsequent numerical calculations and practical applications in engineering. Experimental research holds significant importance in the assessment of storage potential and the prediction of the evolutionary patterns of CO2 migration.

COVID-19 infection analysis framework using novel boosted CNNs and radiological images

COVID-19, a novel pathogen that emerged in late 2019, has the potential to cause pneumonia with unique variants upon infection. Hence, the development of efficient diagnostic systems is crucial in accurately identifying infected patients and effectively mitigating the spread of the disease. However, the system poses several challenges because of the limited availability of labeled data, distortion, and complexity in image representation, as well as variations in contrast and texture. Therefore, a novel two-phase analysis framework has been developed to scrutinize the subtle irregularities associated with COVID-19 contamination. A new Convolutional Neural Network-based STM-BRNet is developed, which integrates the Split-Transform-Merge (STM) block and Feature map enrichment (FME) techniques in the first phase. The STM block captures boundary and regional-specific features essential for detecting COVID-19 infectious CT slices. Additionally, by incorporating the FME and Transfer Learning (TL) concept into the STM blocks, multiple enhanced channels are generated to effectively capture minute variations in illumination and texture specific to COVID-19-infected images. Additionally, residual multipath learning is used to improve the learning capacity of STM-BRNet and progressively increase the feature representation by boosting at a high level through TL. In the second phase of the analysis, the COVID-19 CT scans are processed using the newly developed SA-CB-BRSeg segmentation CNN to accurately delineate infection in the images. The SA-CB-BRSeg method utilizes a unique approach that combines smooth and heterogeneous processes in both the encoder and decoder. These operations are structured to effectively capture COVID-19 patterns, including region-homogenous, texture variation, and border. By incorporating these techniques, the SA-CB-BRSeg method demonstrates its ability to accurately analyze and segment COVID-19 related data. Furthermore, the SA-CB-BRSeg model incorporates the novel concept of CB in the decoder, where additional channels are combined using TL to enhance the learning of low contrast regions. The developed STM-BRNet and SA-CB-BRSeg models achieve impressive results, with an accuracy of 98.01%, recall of 98.12%, F-score of 98.11%, Dice Similarity of 96.396%, and IOU of 98.85%. The proposed framework will alleviate the workload and enhance the radiologist's decision-making capacity in identifying the infected region of COVID-19 and evaluating the severity stages of the disease.

Implementation of a population-based cancer registry network in Mexico 2017-2020

The growing cancer burden particularly among less developed countries requires local data to plan and evaluate cancer control measures. This article describes the development of a population-based cancer registry network (PBCRN) in Mexico that took place between 2017 and 2020 and present related data. The PBCRN, led by the National Cancer Institute (Incan), included nine registries representing 11.3% of the Mexican population. Definitions, coding, and operative processes were based on international standards. All cities were visited to set up local structure; personnel were hired by Incan and trained in basic cancer registration in Merida. A specific software was developed. Regular virtual meetings took place for data verification and quality control. Data collection included institutions of the public and private health system. Personnel included 34 registrars, nine local leaders, and 12 staff members at the Incan. A total of 13 517 cases were recorded between 2017-2020, 64% percent of them were among females. Breast cancer was the more frequent malignancy (23.3%), followed by digestive organs with (18.4%) and female genital cancers (13.5%). Childhood (0-14 years) and adolescents cancer represented 4.4% of the total new cancer cases. The network was suspended in 2020. The present effort lacked sustainability and data were only partial. However, the experience provides valuable insights to be considered for the renewed cancer registration efforts that are currently ongoing in Mexico.

Anthropogenic impacts on urban blue space and its reciprocal effect on human and socio-ecological health

Quantifying anthropogenic impacts on blue space (BS) and its effect on human and socio-ecological health was least explored. The present study aimed to do this in reference to the urban BS transformation scenario of Eastern India. To measure BS transformation, Landsat image-based water indices were run from 1990 to 2021. Anthropogenic impact score (AIS) and 7 components scores of 78 selected BS on 70 parameters related data driven from the field. Total 345 respondents were taken for human and socio-ecological health assessment. For this, depression (DEP), anxiety (ANX), stress (STR), physical activities (PA), social capital (SC), therapeutic landscape (TL) and environment building (EB) parameters were taken. The result exhibited that BS was reduced. About 50% of urban core BS was reported highly impacted. Human and socio-ecological health was identified as good in proximity to BS, but it was observed better in the cases of larger peripheral BS. AIS on BS was found to be positively associated with mental health (0.47–0.63) and negatively associated with PA, SC, TL and EB (−0.50 to −0.90). Standard residual in ordinary least square was reported low (−1.5 to 1.5) in 95% BS. Therefore, BS health restoration and management is crucial for sustaining the living environment.

The OREGANO knowledge graph for computational drug repurposing

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project (https://gitub.u-bordeaux.fr/erias/oregano).

A tumor cell specific Zona Pellucida glycoprotein 3 RNA transcript encodes an intracellular cancer antigen.

Expression of Zona Pellucida glycoprotein 3 (ZP3) in healthy tissue is restricted to the extracellular Zona Pellucida layer surrounding oocytes of ovarian follicles and to specific cells of the spermatogenic lineage. Ectopic expression of ZP3 has been observed in various types of cancer, rendering it a possible therapeutic target. To support its validity as therapeutic target, we extended the cancer related data by investigating ZP3 expression using immunohistochemistry (IHC) of tumor biopsies. We performed a ZP3 transcript specific analysis of publicly available RNA-sequencing (RNA-seq) data of cancer cell lines (CCLs) and tumor and normal tissues, and validated expression data by independent computational analysis and real-time quantitative PCR (qPCR). A correlation between the ZP3 expression level and pathological and clinical parameters was also investigated. IHC data for several cancer types showed abundant ZP3 protein staining, which was confined to the cytoplasm, contradicting the extracellular protein localization in oocytes. We noticed that an alternative ZP3 RNA transcript, which we term 'ZP3-Cancer', was annotated in gene databases that lacks the genetic information encoding the N-terminal signal peptide that governs entry into the secretory pathway. This explains the intracellular localization of ZP3 in tumor cells. Analysis of publicly available RNA-seq data of 1339 cancer cell lines (CCLs), 10386 tumor tissues (The Cancer Genome Atlas) and 7481 healthy tissues (Genotype-Tissue Expression) indicated that ZP3-Cancer is the dominant ZP3 RNA transcript in tumor cells and is highly enriched in many cancer types, particularly in rectal, ovarian, colorectal, prostate, lung and breast cancer. Expression of ZP3-Cancer in tumor cells was confirmed by qPCR. Higher levels of the ZP3-Cancer transcript were associated with more aggressive tumors and worse survival of patients with various types of cancer. The cancer-restricted expression of ZP3-Cancer renders it an attractive tumor antigen for the development of a therapeutic cancer vaccine, particularly using mRNA expression technologies.

Association of long-term ozone air pollution and age-related macular degeneration in older Chinese population

BackgroundAge-related macular degeneration (AMD) is the leading cause of legal blindness. It remains unclear whether and to what extent the ambient ozone pollution could increase the risk of AMD. MethodsA nationwide cross-sectional survey was conducted in 129 major cities in 27 of 31 provincial regions across China from 2018 to 2021. Data in relation to demographics, residential address, and medical histories were collected. The exposure-response relationship between ozone exposure and AMD was explored using the restricted cubic splines. A piecewise logistic regression model was used to examine the magnitudes of the association, after adjusting demographic, social-economic and co-pollutants. Residential ozone exposures were estimated using a satellite-based model. ResultsA total of 624,167 middle-aged and older participants were included in the final analyses, the overall prevalence of AMD was 16.76 %. The risk of AMD was consistently increasing with higher warm-season ozone concentration, and the risk became much larger after the cut-off of 110 μg/m3 (approximately 50 ppb). Every 10 μg/m3 increment in warm-season ozone concentration, the adjusted odds ratio (OR) for AMD were 1.15 (1.13, 1.16) and 1.66 (1.63, 1.69) when the warm-season ozone concentration was below or above 110 μg/m3, respectively. ConclusionThis large-scale nationwide study provides the first epidemiological evidence demonstrating significant associations between long-term residential ozone exposure and AMD prevalence. Based on our findings, in conjunction with WHO global air quality guidelines, we suggest that a warm-season ozone of 110 μg/m3 should be adopted for middle-aged and older populations to reduce the risk of AMD. Ongoing efforts to reduce ozone exposure in communities through improved air quality regulations and public education are essential for the improvement of public health.

OpenSVBRDF: A Database of Measured Spatially-Varying Reflectance

We present the first large-scale database of measured spatially-varying anisotropic reflectance, consisting of 1,000 high-quality near-planar SVBRDFs, spanning 9 material categories such as wood, fabric and metal. Each sample is captured in 15 minutes, and represented as a set of high-resolution texture maps that correspond to spatially-varying BRDF parameters and local frames. To build this database, we develop a novel integrated system for robust, high-quality and -efficiency reflectance acquisition and reconstruction. Our setup consists of 2 cameras and 16,384 LEDs. We train 64 lighting patterns for efficient acquisition, in conjunction with a network that predicts per-point reflectance in a neural representation from carefully aligned two-view measurements captured under the patterns. The intermediate results are further fine-tuned with respect to the photographs acquired under 63 effective linear lights, and finally fitted to a BRDF model. We report various statistics of the database, and demonstrate its value in the applications of material generation, classification as well as sampling. All related data, including future additions to the database, can be downloaded from https://opensvbrdf.github.io/.

Stochastic inversion of geophysical data by a conditional variational autoencoder

Recovering geologically realistic physical property models by geophysical inversion is a long-standing challenge. Generative neural networks offer a promising path to meet this challenge because they can produce spatially complex models that exhibit the characteristics of a set of training models, even when those characteristics are difficult to quantify. In the context of geophysical inversion, these characteristics may include faults, layers, and sharp contacts between rock units. Here, we develop a framework for incorporating prior geologic knowledge into geophysical inversions using conditional variational autoencoders (CVAEs). We train a CVAE to reconstruct training density models while honoring relative gravity data. Once trained, the decoder network of the CVAE inverts gravity data. The inputs to the decoder are observed gravity data and a set of latent variables that are sampled from a standard normal distribution. The decoder maps from the observed data and latent variables to density models such that the resulting models are consistent with the training models and the input data. Consequently, the inversion fits the observed data and incorporates the information embedded in the training models. The decoder can produce many inverted models instantaneously, sampling an approximation to the posterior model distribution efficiently. We find that the latent variables correspond to independent interpretable ways in which the model can vary while still honoring the observed data. We draw a connection to linear inverse theory, positing that the latent variables are analogous to the principal components of the local posterior model covariance.

Epidemiological characteristics of respiratory viruses in children during the COVID-19 epidemic in Chengdu, China.

During the coronavirus disease 2019 epidemic, the Chinese government launched and used a series of nonpharmaceutical interventions (NPIs), including banning social gatherings, wearing face masks, home isolation, and maintaining hand hygiene, to control the disease spread. Whether and how NPIs influence other respiratory viruses in children remain unclear. In this article, we analyzed relative data and found that the number of samples and positive proportion of respiratory viruses decreased significantly compared with that before the epidemic. Clinicians and public health policymakers should pay attention to changes in the epidemic trends and types of respiratory viruses and maintain monitoring of respiratory-related viruses to avoid possible abnormal rebounds and epidemic outbreaks of these viruses.

Distribution incidence, mortality of tuberculosis and human development index in Iran: estimates from the global burden of disease study 2019

BackgroundTuberculosis is one of the most serious challenges facing the global healthcare system. This study aims to investigate the incidence and mortality of tuberculosis in Iran from 2010 to 2019 as well as its relationship with the human development index (HDI).MethodsThe present study is an ecological study aiming at investigating the incidence and mortality of tuberculosis in Iran during the years 2010 to 2019. The related data were extracted from the Global Burden of Disease (GBD) website. The spatial pattern attributed to tuberculosis in the provinces of Iran was analyzed using ArcGIS software. In this study, the two-variable correlation method was used to analyze the data extracted to study the correlation between Tuberculosis and HDI.ResultBased on the results recorded in GBD, the incidence of tuberculosis in 2010, that is, 14.61 (12.72, 16.74), declined compared to 2019, namely 12.29 (10.71, 14.09). The age-standardized mortality rate which was 1.63 (1.52, 1.73) in 2010, has decreased compared to 2019: 1.17 (1.07, 1.32). The incidence and mortality rates of tuberculosis in Iran in all age groups have decreased in 2019 compared to 2010. The highest incidence and mortality among tuberculosis patients were recorded in Sistan and Baluchistan and Golestan provinces. The results indicated that there was a negative and significant correlation between the mortality rate of tuberculosis and the human development index in 2010 (r = -0.509, P-value = 0.003) and 2019 (r = -0.36, P-value = 0.001); however, this correlation between incidence and human development index was not significant (p > 0.05).ConclusionSince mortality is mostly observed in areas with low HDI, health system policymakers must pay more attention to these areas in order to improve care and perform screenings to diagnose and treat patients thus reducing the mortality rate of tuberculosis and preventing an increase in its incidence in Iran.

Climate Change Impacts on Shallow Landslide Events and on the Performance of the Regional Shallow Landslide Early Warning System of Piemonte (Northwestern Italy)

Shallow landslides are one of the most dangerous gravitational phenomena. They are responsible for more than 50% of causalities due to landslides in northwestern Italy in the last century. The aim of the research study presented here is focused on understanding if and how climate change influences the occurrence and behavior of this landslide type. A total of 120 widespread shallow landslide events have been analyzed from 1960 to 2019, taking into account the spatial and time distribution in association with related rainfall historical data elaborated by the Optimal Interpolation (OI) model. Results underline that shallow landslide events’ number (aggregated per five-year intervals) is characterized by a weak trend consisting of a slight increase in the Alps and a more pronounced decrease in the hilly and Apennines environments. In addition, the trend of the annual accumulated rainfall average shows a weak drop in the winter season of about 9 mm in ten years. Moreover, the rainy days have generally decreased over the hills and Apennines, while in the Alps, only in the summer season, with a decreasing rate of about 1.5 days every ten years. The rainfall trends are in accordance with those of shallow landslide events, pointing out the close and direct dependence of the shallow landslide events on the rainfall regime variations. The results obtained were also used to validate the robustness of the performance of the Regional Shallow Landslide Early Warning System adopted in Piemonte over the investigated period, confirming the effectiveness of the trigger thresholds used for the entire historical series and for different geographical areas.

Exploring needs and wishes regarding sleep support from people with dementia and their informal caregiver at home

AbstractBackgroundMost people with dementia live in community with an informal caregiver (IC) for primary support (Greenwoord & Smith, 2019). Caring for someone with dementia can be emotionally and cognitively demanding and can influence the overall health and wellbeing negatively (Fonareva & Oken, 2014; Vasileiou et al., 2017). Behavioural and psychological symptoms of dementia (BPSD) are very common in people with dementia (PwD) and are one of the main factors that cause strain on an IC (Chiao et al., 2015). BPSD is related to sleep, for example, supporting sleep can improve cognitive function and BPSD (Shin et al., 2014). In all types of dementia sleep rhythms are changing. Sleep problems of a person with dementia can affect the sleep of an IC. Sleep disturbances can exacerbate changes in mental, physical, and cognitive health (Petrovsky et al., 2018), and may lead to nursing home admission (e.g. Livingston et al., 2017). This qualitative study explored the experiences around sleep and the wishes and needs of PwD and their IC to improve their sleep quality. This is a starting point for working toward appropriate sleep support using non‐pharmacological interventions.MethodA total of 28 interviews are performed with 31 person (PwD, IC, and professionals) comprising three duos. Thematic analysis (Braun & Clarke, 2006) is used to identify patterns within and across data in relation to participants’ lived experiences, views, perspectives, behaviour, and practices.ResultsThe first preliminary results show the following themes that are relevant: personal situation including other diseases and personal characteristics, day structure and daily activities, sleep behaviour including sleep rhythm, problems while sleeping, sleeping/naps during the day and caregiver experiences and supporting tools. The results show that these themes are related to the sleep of PwD and IC.ConclusionThe findings of this study contribute to giving insight into the experience flow and are helpful to determine the priority of subjects in order to support the IC and PwD. This study show that within the target group there are many diverse experiences and that this can vary greatly from one situation to another and from one person to another.

Abstract B127: The impact of sociodemographic and lifestyle factors on the vaginal microbiome and cervical cancer disparities in Native American women

Abstract Cervical cancer morbidity and mortality are continually experienced by Native American women compared to white women. Human papillomavirus (HPV) is the most common sexually transmitted infection and can cause cervical cancer in women when HPV is not cleared by an individual. Depletion of Lactobacillus species in the vaginal microbiome (VMB) is linked to HPV acquisition and persistence of HPV infection and development/progression of cervical neoplasm. Most Lactobacillus spp. protect the host against invading pathogens of the genital mucosa, including the cervix. Changes in the VMB composition to dysbiotic anaerobes decrease an individual’s ability to clear HPV, increasing susceptibility to malignancy. Depletion of Lactobacilli is associated with race and ethnicity, despite research showing genetics to be statistically similar across ethnicities. However, there may be other factors that drive changes in the microbiome, including demographic, socio-economic, lifestyle and sexual & reproductive health factors. Here, we aim to determine which demographic, socioeconomic, lifestyle and sexual & reproductive health factors impact the microbial community within the VMB, regardless of race and ethnicity. These factors have not been studied in the context and inclusion of NA women. In this study, 31 women at the Native American for Community Action Clinic in Flagstaff, Arizona, U.S. were enrolled, and participants completed self-reported surveys. The survey assessed the individual’s socioeconomic, demographics, lifestyle, reproductive health, as well as their knowledge on HPV and VMB. Vaginal swabs were also collected for microbiome analysis via 16S rRNA sequencing. Surveys were collected and stored in REDCap. Surveys were statistically analyzed using t-test, Fisher’s exact, or Chi-square tests. Most women identified as NA (n=16), white (n=13), and Hispanic (n=5). We identified bacterial profiles of 28 of the 31 participants to be Lactobacillus-dominant (n=16) and non-Lactobacillus-dominant (n=12) and stratified them into two groups. Significant differences were observed, suggesting association of multiple people in a household (p=0.0228), lower level of education (p=0.0093), and high parity (p=0.0283) to undesirable non-Lactobacillus-dominant VMB. It is noteworthy that there were no differences between groups regarding race/ethnicity, age, body mass index, marital status, employment status, household income, hrHPV positivity, vaccination status, use of contraceptives, sexual activity, number of partners and knowledge of HPV and VMB. Yet, the non-Lactobacillus group tended to have more women with high-risk HPV infection. Ongoing analysis includes global health and perceived stress data in relation to the VMB and HPV status. Upon full analysis, we will report on trends between the VMB composition and sociodemographic, lifestyle factors and quality of life. The findings will improve ways for people to find solutions to decrease HPV infections and cervical cancer in Native American populations. Citation Format: Tawnjerae Joe, Paweł Łaniewski, Tristen Eddie, Skyler Bordeaux, Verity Quiroz, Haiyan Cui, Denise Roe, Donna Peace, J. Gregory Caporaso, Naomi Lee, Melissa M. Herbst-Kralovetz. The impact of sociodemographic and lifestyle factors on the vaginal microbiome and cervical cancer disparities in Native American women [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B127.

Role of nutrition and metabolic regulation in multidomain trials for dementia risk reduction: findings from FINGER and FINGER 2.0 studies

AbstractBackgroundThe successful FINGER multidomain intervention model combined dietary intervention (healthy Nordic diet) with exercise, cognitive training, social activities, and cardiovascular risk monitoring. The FINGER model is being tested and optimized in the World‐Wide FINGERS network of multidomain dementia prevention trials (45+ countries). Advanced FINGER 2.0 models combine lifestyle interventions with multi‐nutrients and/or putative disease‐modifying drugs.MethodThe 11‐year extended follow‐up of FINGER trial participants (N:1260, at‐risk general population) was completed in Q1‐2023. The MIND‐AD pilot‐trial (N:93, prodromal Alzheimer’s disease (AD)) included medical food (Fortasyn Connect), tested alone or in combination with multidomain lifestyle intervention for 6 months. The MET‐FINGER trial (N:600, at‐risk general population enriched with APOE4‐carriers, 2‐year intervention) includes different dosages of metformin (in combination with upgraded FINGER lifestyle intervention).ResultNew nutrition/metabolic related data will be presented from FINGER where eg. adherence to healthier diet was still relatively high in the multidomain arm after the extended follow‐up. Higher adherence was also related to better cognition. Several dietary and metabolic biomarkers have been analyzed revealing underlying mechanisms and responders (eg persons with insulin resistance, higher HOMA, responded less). In the MIND‐AD trial, participants from 4 European countries were randomized to the multidomain lifestyle intervention alone (N:32), or in combination with medical food (N:31), and 30 to the control group. Adherence was high for all intervention domains (78‐87%). Compared with the control group, the group with lifestyle+medical food showed a decrease in vascular risk burden (P = 0.027) and an increase in healthy diet patterns (P = 0.045), and better cognition after the intervention (CDR‐SOB). There were no significant differences between the lifestyle only and control group. MET‐FINGER trial recruitment started in January 2023 (preliminary data presented).ConclusionLonger‐term dietary and lifestyle changes are feasible and effective in older adults in the at‐risk spectrum of AD/dementia. Beneficial effects (adherence to and effects of interventions) seemed to improve with the inclusion of medical food in prodromal AD. The unique long follow‐up of FINGER provides important information about long‐term effects and methodological aspects. The MET‐FINGER trial will provide data on synergistic effects between FINGER intervention and a drug targeting both glucose metabolism and AD pathology.

On the use of big data frameworks in big service management

AbstractOver the last few years, big data have emerged as a paradigm for processing and analyzing a large volume of data. Coupled with other paradigms, such as cloud computing, service computing, and Internet of Things, big data processing takes advantage of the underlying cloud infrastructure, which allows hosting and managing massive amounts of data, while service computing allows to process and deliver various data sources as on‐demand services. This synergy between multiple paradigms has led to the emergence of big services, as a cross‐domain, large‐scale, and big data‐centric service model. Apart from the adaptation issues (e.g., need of high reaction to changes) inherited from other service models, the massiveness and heterogeneity of big services add a new factor of complexity to the way such a large‐scale service ecosystem is managed in case of execution deviations. Indeed, big services are often subject to frequent deviations at both the functional (e.g., service failure, QoS degradation, and IoT resource unavailability) and data (e.g., data source unavailability or access restrictions) levels. Handling these execution problems is beyond the capacity of traditional web/cloud service management tools, and the majority of big service approaches have targeted specific management operations, such as selection and composition. To maintain a moderate state and high quality of their cross‐domain execution, big services should be continuously monitored and managed in a scalable and autonomous way. To cope with the absence of self‐management frameworks for large‐scale services, the goal of this work is to design an autonomic management solution that takes the whole control of big services in an autonomous and distributed lifecycle process. We combine autonomic computing and big data processing paradigms to endow big services with self‐* and parallel processing capabilities. The proposed management framework takes advantage of the well‐known MapReduce programming model and Apache Spark and manages big service's related data using knowledge graph technology. We also define a scalable embedding model that allows processing and learning latent big service knowledge in a distributed manner. Finally, a cooperative decision mechanism is defined to trigger non‐conflicting management policies in response to the captured deviations of the running big service. Big services' management tasks (monitoring, embedding, and decision), as well as the core modules (autonomic managers' controller, embedding module, and coordinator), are implemented on top of Apache Spark as MapReduce jobs, while the processed data are represented as resilient distributed dataset (RDD) structures. To exploit the shared information exchanged between the workers and the master node (coordinator), and for further resolution of conflicts between management policies, we endowed the proposed framework with a lightweight communication mechanism that allows transferring useful knowledge between the running map‐reduce tasks and filtering inappropriate intermediate data (e.g., conflicting actions). The experimental results proved the increased quality of embeddings and the high performance of autonomic managers in a parallel and cooperative setting, thanks to the shared knowledge.

Disclosure Quality and Risk of Share Price Collapse

In recent years, the stock crash has become a serious financial crisis, which can seriously undermine the personal gains of investors and the stability of society, as well as bring about long-term negative impacts on society. Therefore, it is necessary to strengthen the regulation in order to prevent the emergence of such crisis. Through empirical analysis, we can see that when companies strengthen internal control and improve information disclosure, it will help to effectively reduce the possibility of future stock crash. Through this study, we can see that the corporate internal control disclosure system implemented in China in recent years has been able to help companies avoid the crisis of stock plunge as well as provide strong support for the sustainable growth of China's capital market. This study adopts a state-of-the-art methodology, utilizing a number of data about internal information control of listed companies and its related data, as well as related indicators about stock crashes, thus greatly expanding out the development trend in recent years. This provides theoretical references on how companies can avoid stock price crashes and whether the level of internal control disclosure needs to change.