Academic literature on the topic 'Spatiotemporal clinical database'

During early mammalian embryo development, different epigenetic marks undergo reprogramming and play crucial roles in the mediation of gene expression. Currently, several databases provide multi-omics information on early embryos. However, how interconnected epigenetic markers function together to coordinate the expression of the genetic code in a spatiotemporal manner remains difficult to analyze, markedly limiting scientific and clinical research. Here, we present dbEmbryo, an integrated and interactive multi-omics database for human and mouse early embryos. dbEmbryo integrates data on gene expression, DNA methylation, histone modifications, chromatin accessibility, and higher-order chromatin structure profiles for human and mouse early embryos. It incorporates customized analysis tools, such as “multi-omics visualization,” “Gene&Peak annotation,” “ZGA gene cluster,” “cis-regulation,” “synergistic regulation,” “promoter signal enrichment,” and “3D genome.” Users can retrieve gene expression and epigenetic profile patterns to analyze synergistic changes across different early embryo developmental stages. We showed the uniqueness of dbEmbryo among extant databases containing data on early embryo development and provided an overview. Using dbEmbryo, we obtained a phase-separated model of transcriptional control during early embryo development. dbEmbryo offers web-based analytical tools and a comprehensive resource for biologists and clinicians to decipher molecular regulatory mechanisms of human and mouse early embryo development.

The aim of the present systematic review was to describe the gait pattern in people with multiple sclerosis (MS) by compiling the main findings obtained from studies using three-dimensional capture systems of human movement. The search was carried out in PubMed, Web of Science, Physiotherapy Evidence Database (PEDro), and the Cumulative Index to Nursing and Allied Health (CINAHL) databases. Studies that used three-dimensional gait analysis systems and that analyzed spatiotemporal, kinematic, kinetic, or electromyographic parameters, were included. The quality of the studies was assessed using the Critical Review Form–Quantitative Studies scale. 12 articles were included with 523 (342 women and 181 men) people with a diagnosis of MS. The present work suggests that people with MS have a decrease in speed and stride length, as well as an increase in double-stance intervals during gait. Likewise, it is common to observe a decrease in hip extension during the stance period, a decrease in knee flexion in the swing period, a decrease in ankle dorsiflexion in the initial contact and a decrease in ankle plantar flexion during the pre-swing phase. The subjects with MS decrease the hip extensor moment and the ankle power during the stance period of walking.

In the present work, an optical motion-capture system combined with software for 2D clinical gait analysis is utilized to determine spatiotemporal gait parameters such as stride-length, cadence, cycle-time, and speed as well as joint angles. The developed system consists of a video camera with a maximum speed of 90fps, LED markers, PC and technical computing software, which are developed for tracking markers attached to human body during motion and to calculate kinematics and kinetics parameters of human gait. Gait data of 60 subjects within the age group between 18 to 49 years are measured as part of an effort to develop normal walking database of Indonesian people. In the experiments, the subject is instructed to walk in a specially-arranged measurement area, which is calibrated using the Direct Linear Transformation (DLT) method. Before the measurement, the body posture of each subject is evaluated to ensure normalcy. To validate the system, the obtained gait data is compared to the available normal walking database, and the results obtained by the system show good compatibility.

BackgroundClinical disease caused by the bovine lungworm (Dictyocaulus viviparus) causes significant welfare and economic problems for the livestock industry. Anecdotal reports suggest that the number of clinical cases has increased, particularly in Northern England and Scotland. However, these spatiotemporal changes have not been quantified and the current impact that the disease is having across Great Britain remains unclear.MethodsHere, we report a retrospective analysis of the spatial distribution, seasonality and age of lungworm cases reported by the Veterinary Investigation Diagnosis Analysis database from 1975 to 2014.ResultsA sharp overall increase in the dictyocaulosis diagnostic rate (DR, cases / 1000 submissions) was observed, with, for example, median 2010–2014 DR 3.5 times as high as 1980–1984 DR. Such increases were most pronounced for Scotland, which became the region with the highest proportion of cases by 2009. Cases were increasingly diagnosed during the winter months (December–February).ConclusionThe apparent spatiotemporal changes in lungworm epidemiology pose new challenges to cattle farmers in Great Britain. Farmers and veterinarians need to remain vigilant for this disease and consider it as a possible cause for milk production losses at any time of the year. Awareness levels may have to be raised particularly in northern England and Scotland.

Objective. To establish better diagnosis thinking and provide advanced understanding of MSK, the CT imaging features, clinical characteristics, and the expression of suspected genes in the kidney spatiotemporal immune zonation and fetal renal development were investigated. Methods. 17 patients with MSK hospitalized in our hospital were selected as our research subjects. Human Phenotype Ontology, MalaCards: The Human Disease Database, GeneCards: The Human Gene Database, Human Protein Atlas, and Single Cell Expression Atlas were used to analyze this disease. Results. In our 17 patients, the incidence of MSK tended to be the same in male and female, and the onset age of MSK was probably 31-50 years old. The top one related disease of MSK was nephrocalcinosis and the most frequent phenotype related to MSK was nephrolithiasis. In addition, the expression of HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 has been implicated in both human kidney immune zonation and fetal kidney development. Conclusions. HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 could be independent indicators for the diagnosis and preventive intervention of MSK patients, and abnormal kidney development due to mutations in key genes was the underlying cause of MSK.

The plant U-box (PUB) protein family plays an important role in plant growth and development. The U-box gene family has been well studied in Arabidopsis thaliana, Brassica rapa, rice, etc., but there have been no systematic studies in Brassica oleracea. In this study, we performed genome-wide identification and evolutionary analysis of the U-box protein family of B. oleracea. Firstly, based on the Brassica database (BRAD) and the Bolbase database, 99 Brassica oleracea PUB genes were identified and divided into seven groups (I–VII). The BoPUB genes are unevenly distributed on the nine chromosomes of B. oleracea, and there are tandem repeat genes, leading to family expansion from the A. thaliana genome to the B. oleracea genome. The protein interaction network, GO annotation, and KEGG pathway enrichment analysis indicated that the biological processes and specific functions of the BoPUB genes may mainly involve abiotic stress. RNA-seq transcriptome data of different pollination times revealed spatiotemporal expression specificity of the BoPUB genes. The differential expression profile was consistent with the results of RT-qPCR analysis. Additionally, a large number of pollen-specific cis-acting elements were found in promoters of differentially expressed genes (DEG), which verified that these significantly differentially expressed genes after self-pollination (SP) were likely to participate in the self-incompatibility (SI) process, including gene encoding ARC1, a well-known downstream protein of SI in B. oleracea. Our study provides valuable information indicating that the BoPUB genes participates not only in the abiotic stress response, but are also involved in pollination.

e12656 Background: Predictive models of the efficacy of different tumor therapies will provide significant enhancements to current standard of care practices. Predicting a given tumor’s growth and treatment response, however, is an intricate process that requires not only an understanding of the tumor's intrinsic characteristics, but also spatial- and temporal-resolved tumor shape descriptions, surrounding tissue dynamics, and a complete account for the milieu of diffusible molecules that drive tumor behaviors and interactions. Here we report an ongoing retrospective study designed to validate SimBioSys TumorScope as a computational tumor therapy prediction model in a real-world clinical setting. Methods: Fully-deidentified and HIPAA-compliant data were assessed from real-world clinical records and cases. Subjects comprised early stage breast cancer patients who were treated with neoadjuvant chemotherapy (NACT) and subsequent surgical resection. Data fields included imaging data, biomarker status, tumor sizing, demographic data, digital pathology, and genetic lab test data. Half of the data, including all data fields, were used as a training dataset for TumorScope. The second half of the data, with blinded diagnoses and results, will be used to test TumorScope’s prediction accuracy. Simulations will be initialized with pre-treatment MRI data and processed through the entirety of each patient's specified treatment regimen. Predicted tumor volume and longest dimension will be compared against measured values at several time-points after therapy initiation. Overall accuracy will be statistically assessed by the Pearson correlation coefficient between predicted and measured tumor volume and longest dimension at each time-point, as well as their root-mean-squared-errors. Results: Final statistical analysis is currently underway. Thus far, SimBioSys TumorScope has trended high accuracy levels with the non-blinded “training” cohort just as it has in previous database studies, with a Pearson correlation coefficient greater than 0.94. Conclusions: SimBioSys TumorScope for Breast Cancer accurately predicts patient NACT responses via spatio-temporal modeling of drug and nutrient perfusion, metabolic behavior, and the physio-chemical interactions between surrounding tissues. Future prospective studies may assess TumorScope’s capacity to support efficient patient treatments and enhance overall standard of care.

Background Accurate and timely surveillance and diagnosis of health care facility onset Clostridium difficile infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations. Objectives The objective of this study was to integrate spatiotemporal factors with HO-CDI cases and to develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI. Methods Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over 2 years were extracted from the information warehouse of a large academic medical center (AMC) and processed according to the Center for Disease Control National Healthcare Safety Network definitions to classify CDI cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this AMC. Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey. Results The simple classification algorithm identified all 265 HO-CDI cases from January 1, 2015 to November 30, 2015 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6%. All users “strongly agreed” that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present hospital-acquired infection information to others more efficiently. Conclusion The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.

The P-type plasma membrane (PM) H+-ATPase plays a major role during the growth and development of a plant. It is also involved in plant resistance to a variety of biotic and abiotic factors, including salt stress. The PM H+-ATPase gene family has been well characterized in Arabidopsis and other crop plants such as rice, cucumber, and potato; however, the same cannot be said in sunflower (Helianthus annuus). In this study, a total of thirteen PM H+-ATPase genes were screened from the recently released sunflower genome database with a comprehensive genome-wide analysis. According to a systematic phylogenetic classification with a previously reported species, the sunflower PM H+-ATPase genes (HHAs) were divided into four sub-clusters (I, II, IV, and V). In addition, systematic bioinformatics analyses such as gene structure analysis, chromosome location analysis, subcellular localization predication, conserved motifs, and Cis-acting elements of promoter identification were also done. Semi-quantitative PCR analysis data of HHAs in different sunflower tissues revealed the specificity of gene spatiotemporal expression and sub-cluster grouping. Those belonging to sub-cluster I and II exhibited wide expression in almost all of the tissues studied while sub-cluster IV and V seldom showed expression. In addition, the expression of HHA4, HHA11, and HHA13 was shown to be induced by salt stress. The transgenic plants overexpressing HHA4 and HHA11 showed higher salinity tolerance compared with wild-type plants. Further analysis showed that the Na+ content of transgenic Arabidopsis plants decreased under salt stress, which indicates that PM H+ ATPase participates in the physiological process of Na+ efflux, resulting in salt resistance of the plants. This study is the first to identify and analyze the sunflower PM H+ ATPase gene family. It does not only lay foundation for future research but also demonstrates the role played by HHAs in salt stress tolerance.

Background and objectivesDialysis patients have a high mortality risk after coronavirus disease 2019 (COVID-19) and an altered immunologic response to vaccines, but vaccine clinical effectiveness remains unknown in this population.Design, setting, participants, & measurementsUsing Bayesian multivariable spatiotemporal models, we estimated the association between vaccine exposure and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) severe infections (with hospital admission) in dialysis patients from simultaneous incidence in the general population. For dialysis patients, cases were reported within the French end-stage kidney disease REIN registry from March 11, 2020, to April 29, 2021, and vaccine exposure (first dose) was reported in weekly national surveys since January 2021. Cases in the general population were obtained from the national exhaustive inpatient surveillance system (SI-VIC database), and vaccination coverage (first dose) was obtained from the national surveillance system (VAC-SI database).ResultsDuring the first wave, incidence in dialysis patients was approximately proportional to the general population. However, we showed a lower relative incidence for dialysis patients during the second wave (compared with that observed in nondialysis patients), suggesting an effect of prevention measures. Moreover, from the beginning of the vaccination rollout, incidence in dialysis patients was lower compared with predictions based on the first and second waves. Adding vaccination coverages in dialysis and nondialysis patients as predictors allowed the reported cases to be fit correctly (3685 predicted cases, 95% confidence interval, 3552 to 3816, versus 3620 reported). Incidence rate ratios were 0.37 (95% confidence interval, 0.18 to 0.71) for vaccine exposure in dialysis patients and 0.50 (95% confidence interval, 0.40 to 0.61) per 10% higher in vaccination coverage in the same-age general population, meaning that vaccine exposure in dialysis patients and the general population was independently associated with lower hospitalization rate of dialysis patients.ConclusionsOur findings suggest that vaccination may yield a protective effect against severe forms of COVID-19 in dialysis patients, despite altered immunologic vaccine responses.

This dissertation focused on examining murder-suicide in the United States through descriptive, time-series and spatiotemporal analyses using a self-created and herein verified national database that spanned the years 1999 through 2009. Chapter 2, "Establishment and validation of a national database for murder-suicide in the United States: 1999-2009," describes the methods and sources used in the creation of a national database of murder-suicide. The database was validated using less geographically and/or temporally expansive databases through the use of capture-recapture methods in two ways: the number of events identified in specified space and time was compared and cases were matched using the perpetrator's name. Victim and perpetrator characteristics were then described as compared to previous studies. Chapter 3, "A time-series analysis of murder, suicide and murder-suicide in the United States, 1999-2007" utilized time-series analysis techniques to investigate the impact of time varying covariates on murder, suicide and murder-suicide. Analyses were conducted in the United States at the national level from January 1999 to December 2007. Johansen's multivariable cointegration analysis showed that two-month time lagged murder was positively associated with murder, suicide and murder-suicide. Two-month time lagged suicide was negatively associated with murder, suicide and murder-suicide. Two-month time lagged murder-suicide was not related to any of the three events. Chapter 4, "Spatiotemporal relationships among murder, suicide and murder-suicide in the United States: 1999-2008" examined space, time, and spatiotemporal relationships among murder, suicide and murder-suicide using a spatiotemporal scan statistic from SaTScanTM. Thirty-five temporal and spatiotemporal clusters of murder, suicide, murder/murder-suicide, suicide/murder-suicide and murder/suicide/murder-suicide were identified. No purely spatial clusters, clusters of murder/suicide without murder-suicide, or purely murder-suicide were identified. The murder-suicide database, that will be made public in 2012, will be a novel source of information for investigators interested in studying murder-suicides with the inclusion of date, place, perpetrator and victim characteristics. Its validation along with the time-series and spatiotemporal analyses provides greater understanding of murder-suicide by itself and compared to murder and suicide.

In molti campi di ricerca, i ricercatori hanno la necessità di memorizzare, gestire e interrogare dati spazio-temporali. Tali dati sono classici dati alfanumerici arricchiti però con una o più componenti temporali, spaziali e spazio-temporali che, con diversi possibili significati, li localizzano nel tempo e/o nello spazio. Ambiti in cui tali dati spazio-temporali devono essere raccolti e gestiti sono, per esempio, la gestione del territorio o delle risorse naturali, l'epidemiologia, l'archeologia e la geografia. Più in dettaglio, per esempio nelle ricerche epidemiologiche, i dati spazio-temporali possono servire a rappresentare diversi aspetti delle malattie e delle loro caratteristiche, quali per esempio la loro origine, espansione ed evoluzione e i fattori di rischio potenzialmente connessi alle malattie e al loro sviluppo. Le componenti spazio-temporali dei dati possono essere considerate come dei "meta-dati" che possono essere sfruttati per introdurre nuovi tipi di analisi sui dati stessi. La gestione di questi "meta-dati" può avvenire all'interno di diversi framework proposti in letteratura. Uno dei concetti proposti a tal fine è quello delle granularità. In letteratura c'è ampio consenso sul concetto di granularità temporale, di cui esistono framework basati su diversi approcci. D'altro canto, non esiste invece un consenso generale sulla definizione di un framework completo, come quello delle granularità temporali, per le granularità spaziali e spazio-temporali. Questa tesi ha lo scopo di riempire questo vuoto proponendo un framework per le granularità spaziali e, basandosi su questo e su quello già presente in letteratura per le granularità temporali, un framework per le granularità spazio-temporali. I framework proposti vogliono essere completi, per questo, oltre alle definizioni dei concetti di granularità spaziale e spazio-temporale, includono anche la definizione di diversi concetti legati alle granularità, quali per esempio le relazioni e le operazioni tra granularità. Le relazioni permettono di conoscere come granularità diverse sono legate tra loro, costruendone anche una gerarchia. Tali informazioni sono poi utili al fine di conoscere se e come è possibile confrontare dati associati e rappresentati con granularità diverse. Le operazioni permettono invece di creare nuove granularità a partire da altre granularità già definite nel sistema, manipolando o selezionando alcune loro componenti. Basandosi su questi framework, l'obiettivo della tesi si sposta poi sul mostrare come le granularità possano essere utilizzate per arricchire basi di dati spazio-temporali già esistenti al fine di una loro migliore e più ricca gestione e interrogazione. A tal fine, proponiamo qui una base di dati per la gestione dei dati riguardanti le granularità temporali, spaziali e spazio-temporali. Nella base di dati proposta possono essere rappresentate tutte le componenti di una granularità come definito nei framework proposti. La base di dati può poi essere utilizzata per estendere una base di dati spazio-temporale esistente aggiungendo alle tuple di quest'ultima delle referenze alle granularità dove quei dati possono essere localizzati nel tempo e/o nel spazio. Per dimostrare come ciò possa essere fatto, nella tesi introduciamo la base di dati sviluppata ed utilizzata dal Servizio Psichiatrico Territoriale (SPT) di Verona. Tale base di dati memorizza le informazioni su tutti i pazienti venuti in contatto con l'SPT negli ultimi 30 anni e tutte le informazioni sui loro contatti con il servizio stesso (per esempio: chiamate telefoniche, visite a domicilio, ricoveri). Parte di tali informazioni hanno una componente spazio-temporale e possono essere quindi analizzate studiandone trend e pattern nel tempo e nello spazio. Nella tesi quindi estendiamo questa base di dati psichiatrica collegandola a quella proposta per la gestione delle granularità. A questo punto i dati psichiatrici possono essere interrogati anche sulla base di vincoli spazio-temporali basati su granularità. L'interrogazione di dati spazio-temporali associati a granularità richiede l'utilizzo di un linguaggio d'interrogazione che includa, oltre a strutture, operatori e funzioni spazio-temporali per la gestione delle componenti spazio-temporali dei dati, anche costrutti per l'utilizzo delle granularità nelle interrogazioni. Quindi, partendo da un linguaggio d'interrogazione spazio-temporale già presente in letteratura, in questa tesi proponiamo anche un linguaggio d'interrogazione che permetta ad un utente di recuperare dati da una base di dati spazio-temporale anche sulla base di vincoli basati su granularità. Il linguaggio viene introdotto fornendone la sintassi e la semantica. Inoltre per mostrare l'effettivo ruolo delle granularità nell'interrogazione di una base di dati clinica, mostreremo diversi esempi di interrogazioni, scritte con il linguaggio d'interrogazione proposto, sulla base di dati psichiatrica dell'SPT di Verona. Tali interrogazioni spazio-temporali basate su granularità possono essere utili ai ricercatori ai fini di analisi epidemiologiche dei dati psichiatrici.
In several research fields, temporal, spatial, and spatio-temporal data have to be managed and queried with several purposes. These data are usually composed by classical data enriched with a temporal and/or a spatial qualification. For instance, in epidemiology spatio-temporal data may represent surveillance data, origins of disease and outbreaks, and risk factors. In order to better exploit the time and spatial dimensions, spatio-temporal data could be managed considering their spatio-temporal dimensions as meta-data useful to retrieve information. One way to manage spatio-temporal dimensions is by using spatio-temporal granularities. This dissertation aims to show how this is possible, in particular for epidemiological spatio-temporal data. For this purpose, in this thesis we propose a framework for the definition of spatio-temporal granularities (i.e., partitions of a spatio-temporal dimension) with the aim to improve the management and querying of spatio-temporal data. The framework includes the theoretical definitions of spatial and spatio-temporal granularities (while for temporal granularities we refer to the framework proposed by Bettini et al.) and all related notions useful for their management, e.g., relationships and operations over granularities. Relationships are useful for relating granularities and then knowing how data associated with different granularities can be compared. Operations allow one to create new granularities from already defined ones, manipulating or selecting their components. We show how granularities can be represented in a database and can be used to enrich an existing spatio-temporal database. For this purpose, we conceptually and logically design a relational database for temporal, spatial, and spatio-temporal granularities. The database stores all data about granularities and their related information we defined in the theoretical framework. This database can be used for enriching other spatio-temporal databases with spatio-temporal granularities. We introduce the spatio-temporal psychiatric case register, developed by the Verona Community-based Psychiatric Service (CPS), for storing and managing information about psychiatric patient, their personal information, and their contacts with the CPS occurred in last 30 years. The case register includes both clinical and statistical information about contacts, that are also temporally and spatially qualified. We show how the case register database can be enriched with spatio-temporal granularities both extending its structure and introducing a spatio-temporal query language dealing with spatio-temporal data and spatio-temporal granularities. Thus, we propose a new spatio-temporal query language, by defining its syntax and semantics, that includes ad-hoc features and constructs for dealing with spatio-temporal granularities. Finally, using the proposed query language, we report several examples of spatio-temporal queries on the psychiatric case register showing the ``usage'' of granularities and their role in spatio-temporal queries useful for epidemiological studies.