Temporal Cluster Analysis of Deep Sternal Wound Infection in a Regional Quality Collaborative.

INTRODUCTION: Deep sternal wound infection (DSWI) is a rare complication associated with high mortality. Seasonal variability in surgical site infections has been demonstrated, however, these patterns have not been applied to DSWI. The purpose of this study was to assess temporal clustering of DSWIs. METHODS: All cardiac surgery patients who underwent sternotomy were queried from a regional Society of Thoracic Surgeons database from 17 centers from 2001 to 2019. All patients with the diagnosis of DSWI were then identified. Cluster analysis was performed at varying time intervals (monthly, quarterly, and yearly) at the hospital and regional level. DSWI rates were calculated by year and month, and compared using mixed-effects negative binomial regression. RESULTS: A total of 134,959 patients underwent a sternotomy for cardiac surgery, of whom 469 (0.35%) developed a DSWI. Rates of DSWI per hospital across all years ranged from 0.12% to 0.69%. Collaborative-level rates of DSWIs were the greatest in September (0.44%) and the lowest in January (0.30%). Temporal clustering was not seen across seasonal quarters (high rate in preceeding quarter was not associated with a high rate in the next quarter) (P = 0.39). There were yearly differences across all institutions in the DSWI rates. A downward trend in DSWI rates was seen from 2001 to 2019 (P < 0.001). A difference among hospitals in the cohort was observed (P < 0.001). CONCLUSIONS: DSWI are a rare event within our region. Unlike other surgical site infection, there does not appear to be a seasonal pattern associated with DSWI.

Temporal clustering of neuroblastic tumours in children and young adults from Ontario, Canada.

BACKGROUND: The aetiology of neuroblastic tumours is likely to involve both genetic and environmental factors. A number of possible environmental risk factors have been suggested, including infection. If an irregular temporal pattern in incidence is found, this might suggest that a transient agent, such as an infection, is implicated. Previous work has found evidence for temporal clustering in children and young adults living in northern England. METHODS: We examined data from a second population-based registry from Ontario, Canada to determine whether there was evidence of temporal clustering of neuroblastic tumours. Cases diagnosed in children and young adults aged 0-19 years between 1985 and 2016 were extracted from the population-based Pediatric Oncology Group of Ontario Networked Information System (POGONIS). A modified version of the Potthoff-Whittinghill method was used to test for temporal clustering. Estimates of extra-Poisson variation (EPV) and standard errors (SE) were obtained. RESULTS: Eight hundred seventy-six cases of neuroblastic tumours were diagnosed during the study period. Overall, no evidence of temporal clustering was found between fortnights, between months or between quarters within years. However, significant EPV was found between years within the full study period (EPV = 1.05, SE = 0.25; P = 0.005). CONCLUSIONS: The findings are consistent with the possibility that a transient agent, such as an infection that is characterised by 'peaks and troughs' in its occurrence, might be implicated in the aetiology of neuroblastic tumours. However, this pattern may also reflect a long-term increase in the numbers of cases, rather than peaks and troughs.

Neurons in the human medial temporal lobe track multiple temporal contexts during episodic memory processing.

Episodic memory requires associating items with temporal context, a process for which the medial temporal lobe (MTL) is critical. This study uses recordings from 27 human subjects who were undergoing surgical intervention for intractable epilepsy. These same data were also utilized in Umbach et al. (2020). We identify 103 memory-sensitive neurons in the hippocampus and entorhinal cortex, whose firing rates predicted successful episodic memory encoding as subjects performed a verbal free recall task. These neurons exhibit important properties. First, as predicted from the temporal context model, they demonstrate reinstatement of firing patterns observed during encoding at the time of retrieval. The magnitude of reinstatement predicted the tendency of subjects to cluster retrieved memory items according to input serial position. Also, we found that spiking activity of these neurons was locked to the phase of hippocampal theta oscillations, but that the mean phase of spiking shifted between memory encoding versus retrieval. This unique observation is consistent with predictions of the "Separate Phases at Encoding And Retrieval (SPEAR)" model. Together, the properties we identify for memory-sensitive neurons characterize direct electrophysiological mechanisms for the representation of contextual information in the human MTL.

Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD.

PURPOSE: To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using temporal clustering, tissue composition, and total variation (CCTV). METHODS: Three-dimensional multi-echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion-weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue-type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ-based OEF and CMRO2 were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region-of-interest analysis, Kruskal-Wallis test, and post hoc Wilcoxson rank sum test. RESULTS: In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ-based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast-to-noise ratio between DWI-defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 (P = .01) versus 0.7 ± 0.5 (P < .001). CONCLUSION: The CCTV mapping significantly improves the robustness of QQ-based OEF against noise.

Analysis of spatial-temporal distribution of notifiable respiratory infectious diseases in Shandong Province, China during 2005-2014.

BACKGROUND: Little comprehensive information on overall epidemic trend of notifiable respiratory infectious diseases is available in Shandong Province, China. This study aimed to determine the spatiotemporal distribution and epidemic characteristics of notifiable respiratory infectious diseases. METHODS: Time series was firstly performed to describe the temporal distribution feature of notifiable respiratory infectious diseases during 2005-2014 in Shandong Province. GIS Natural Breaks (Jenks) was applied to divide the average annual incidence of notifiable respiratory infectious diseases into five grades. Spatial empirical Bayesian smoothed risk maps and excess risk maps were further used to investigate spatial patterns of notifiable respiratory infectious diseases. Global and local Moran's I statistics were used to measure the spatial autocorrelation. Spatial-temporal scanning was used to detect spatiotemporal clusters and identify high-risk locations. RESULTS: A total of 537,506 cases of notifiable respiratory infectious diseases were reported in Shandong Province during 2005-2014. The morbidity of notifiable respiratory infectious diseases had obvious seasonality with high morbidity in winter and spring. Local Moran's I analysis showed that there were 5, 23, 24, 4, 20, 8, 14, 10 and 7 high-risk counties determined for influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella, respectively. The spatial-temporal clustering analysis determined that the most likely cluster of influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella included 74, 66, 58, 56, 22, 64, 2, 75 and 56 counties, and the time frame was November 2009, March 2008, January 2007, February 2005, July 2007, December 2011, November 2009, June 2012 and May 2005, respectively. CONCLUSIONS: There were obvious spatiotemporal clusters of notifiable respiratory infectious diseases in Shandong during 2005-2014. More attention should be paid to the epidemiological and spatiotemporal characteristics of notifiable respiratory infectious diseases to establish new strategies for its control.

Pollution zone identification research during ozone pollution processes.

Identifying an ozone pollution zone during the pollution processes is significant for ozone pollution management and environmental health risk assessment. However, few studies have focused on ozone pollution zone identification during pollution processes. A spatial-temporal clustering framework for identifying pollution zones during ozone pollution processes was initially proposed in this study, and an ozone pollution process in China in May 2017 was selected as a case. The results showed that the framework can help selecting one more accurate method to identify the pollution zone according to the pollution characteristics of air pollution process. In addition, different ozone pollution zone identification methods work well in different scenarios: The self-organizing map (SOM) method was suitable for identifying the zone with the duration of pollution between 24 and 48 h, the image fusion based on wavelet transform (IFbWT) method for the zone with the duration of pollution over 48 h and the Apriori method for the zone with obvious boundaries between high-value and low-value ozone concentrations. The proposed procedure can also be applied to identify the pollution zone of the pollution process of other pollutants.

Temporal clustering of extreme climate events drives a regime shift in rocky intertidal biofilms.

Research on regime shifts has focused primarily on how changes in the intensity and duration of press disturbances precipitate natural systems into undesirable, alternative states. By contrast, the role of recurrent pulse perturbations, such as extreme climatic events, has been largely neglected, hindering our understanding of how historical processes regulate the onset of a regime shift. We performed field manipulations to evaluate whether combinations of extreme events of temperature and sediment deposition that differed in their degree of temporal clustering generated alternative states in rocky intertidal epilithic microphytobenthos (biofilms) on rocky shores. The likelihood of biofilms to shift from a vegetated to a bare state depended on the degree of temporal clustering of events, with biofilm biomass showing both states under a regime of non-clustered (60 d apart) perturbations while collapsing in the clustered (15 d apart) scenario. Our results indicate that time since the last perturbation can be an important predictor of collapse in systems exhibiting alternative states and that consideration of historical effects in studies of regime shifts may largely improve our understanding of ecosystem dynamics under climate change.

Instability of the steady state solution in cell cycle population structure models with feedback.

We show that when cell-cell feedback is added to a model of the cell cycle for a large population of cells, then instability of the steady state solution occurs in many cases. We show this in the context of a generic agent-based ODE model. If the feedback is positive, then instability of the steady state solution is proved for all parameter values except for a small set on the boundary of parameter space. For negative feedback we prove instability for half the parameter space. We also show by example that instability in the other half may be proved on a case by case basis.

Traffic Speed Prediction: An Attention-Based Method.

Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.

Recognizing spatial and temporal clustering patterns of dengue outbreaks in Taiwan.

BACKGROUND: Dengue fever is the most common arboviral infection in humans, with viral transmissions occurring in more than 100 countries in tropical regions. A global strategy for dengue prevention and control was established more than 10 years ago. However, the factors that drive the transmission of the dengue virus and subsequent viral infection continue unabated. The largest dengue outbreaks in Taiwan since World War II occurred in two recent successive years: 2014 and 2015. METHODS: We performed a systematic analysis to detect and recognize spatial and temporal clustering patterns of dengue incidence in geographical areas of Taiwan, using the map-based pattern recognition procedure and scan test. Our aim was to recognize geographical heterogeneity patterns of varying dengue incidence intensity and detect hierarchical incidence intensity clusters. RESULTS: Using the map-based pattern recognition procedure, we identified and delineated two separate hierarchical dengue incidence intensity clusters that comprise multiple mutually adjacent geographical units with high dengue incidence rates. We also found that that dengue incidence tends to peak simultaneously and homogeneously among the neighboring geographic units with high rates in the same cluster. CONCLUSION: Beyond significance testing, this study is particularly desired by and useful for health authorities who require optimal characteristics of disease incidence patterns on maps and over time. Among the integrated components for effective prevention and control of dengue and dengue hemorrhagic fever are active surveillance and community-based integrated mosquito control, for which this study provides valuable inferences. Effective dengue prevention and control programs in Taiwan are critical, and have the added benefit of controlling the potential emergence of Zika.

Spatio-temporal patterns of highly pathogenic avian influenza virus subtype H5N8 spread, France, 2016 to 2017.

IntroductionFrance is one of Europe's foremost poultry producers and the world's fifth largest producer of poultry meat. In November 2016, highly pathogenic avian influenza (HPAI) virus subtype H5N8 emerged in poultry in the country. As of 23 March 2017, a total of 484 confirmed outbreaks were reported, with consequences on animal health and socio-economic impacts for producers. Methods: We examined the spatio-temporal distribution of outbreaks that occurred in France between November 2016 and March 2017, using the space-time K-function and space-time permutation model of the scan statistic test. Results: Most outbreaks affected duck flocks in south-west France. A significant space-time interaction of outbreaks was present at the beginning of the epidemic within a window of 8 km and 13 days. This interaction disappeared towards the epidemic end. Five spatio-temporal outbreak clusters were identified in the main poultry producing areas, moving sequentially from east to west. The average spread rate of the epidemic front wave was estimated to be 5.5 km/week. It increased from February 2017 and was negatively associated with the duck holding density. Conclusion: HPAI-H5N8 infections varied over time and space in France. Intense transmission events occurred at the early stages of the epidemic, followed by long-range jumps in the disease spread towards its end. Findings support strict control strategies in poultry production as well as the maintenance of high biosecurity standards for poultry holdings. Factors and mechanisms driving HPAI spread need to be further investigated.

Legacy effects and memory loss: how contingencies moderate the response of rocky intertidal biofilms to present and past extreme events.

Understanding how historical processes modulate the response of ecosystems to perturbations is becoming increasingly important. In contrast to the growing interest in projecting biodiversity and ecosystem functioning under future climate scenarios, how legacy effects originating from historical conditions drive change in ecosystems remains largely unexplored. Using experiments in combination with stochastic antecedent modelling, we evaluated how extreme warming, sediment deposition and grazing events modulated the ecological memory of rocky intertidal epilithic microphytobenthos (EMPB). We found memory effects in the non-clustered scenario of disturbance (60 days apart), where EMPB biomass fluctuated in time, but not under clustered disturbances (15 days apart), where EMPB biomass was consistently low. A massive grazing event impacted on EMPB biomass in a second run of the experiment, also muting ecological memory. Our results provide empirical support to the theoretical expectation that stochastic fluctuations promote ecological memory, but also show that contingencies may lead to memory loss.

Space-time patterns in maternal and mother mortality in a rural South African population with high HIV prevalence (2000-2014): results from a population-based cohort.

BACKGROUND: International organs such as, the African Union and the South African Government view maternal health as a dominant health prerogative. Even though most countries are making progress, maternal mortality in South Africa (SA) significantly increased between 1990 and 2015, and prevented the country from achieving Millennium Development Goal 5. Elucidating the space-time patterns and risk factors of maternal mortality in a rural South African population could help target limited resources and policy guidelines to high-risk areas for the greatest impact, as more generalized interventions are costly and often less effective. METHODS: Population-based mortality data from 2000 to 2014 for women aged 15-49 years from the Africa Centre Demographic Information System located in the Umkhanyakude district of KwaZulu-Natal Province, South Africa were analysed. Our outcome was classified into two definitions: Maternal mortality; the death of a woman while pregnant or within 42 days of cessation of pregnancy, regardless of the duration and site of the pregnancy, from any cause related to or exacerbated by the pregnancy or its management but not from unexpected or incidental causes; and 'Mother death'; death of a mother whilst child is less than 5 years of age. Both the Kulldorff and Tango spatial scan statistics for regular and irregular shaped cluster detection respectively were used to identify clusters of maternal mortality events in both space and time. RESULTS: The overall maternal mortality ratio was 650 per 100,000 live births, and 1204 mothers died while their child was less than or equal to 5 years of age, of a mortality rate of 370 per 100,000 children. Maternal mortality declined over the study period from approximately 600 per 100,000 live births in 2000 to 400 per 100,000 live births in 2014. There was no strong evidence of spatial clustering for maternal mortality in this rural population. However, the study identified a significant spatial cluster of mother deaths in childhood (p = 0.022) in a peri-urban community near the national road. Based on our multivariable logistic regression model, HIV positive status (Adjusted odds ratio [aOR] = 2.5, CI 95%: [1.5-4.2]; primary education or less (aOR = 1.97, CI 95%: [1.04-3.74]) and parity (aOR = 1.42, CI 95%: [1.24-1.63]) were significant predictors of maternal mortality. CONCLUSIONS: There has been an overall decrease in maternal and mother death between 2000 and 2014. The identification of a clear cluster of mother deaths shows the possibility of targeting intervention programs in vulnerable communities, as population-wide interventions may be ineffective and too costly to implement.

Dynamic epigenetic mode analysis using spatial temporal clustering.

BACKGROUND: Differentiation of human embryonic stem cells requires precise control of gene expression that depends on specific spatial and temporal epigenetic regulation. Recently available temporal epigenomic data derived from cellular differentiation processes provides an unprecedented opportunity for characterizing fundamental properties of epigenomic dynamics and revealing regulatory roles of epigenetic modifications. RESULTS: This paper presents a spatial temporal clustering approach, named STCluster, which exploits the temporal variation information of epigenomes to characterize dynamic epigenetic mode during cellular differentiation. This approach identifies significant spatial temporal patterns of epigenetic modifications along human embryonic stem cell differentiation and cluster regulatory sequences by their spatial temporal epigenetic patterns. CONCLUSIONS: The results show that this approach is effective in capturing epigenetic modification patterns associated with specific cell types. In addition, STCluster allows straightforward identification of coherent epigenetic modes in multiple cell types, indicating the ability in the establishment of the most conserved epigenetic signatures during cellular differentiation process.

Delineating potential epileptogenic areas utilizing resting functional magnetic resonance imaging (fMRI) in epilepsy patients.

Seizure localization includes neuroimaging like electroencephalogram, and magnetic resonance imaging (MRI) with limited ability to characterize the epileptogenic network. Temporal clustering analysis (TCA) characterizes epileptogenic network congruent with interictal epileptiform discharges by clustering together voxels with transient signals. We generated epileptogenic areas for 12 of 13 epilepsy patients with TCA, congruent with different areas of seizure onset. Resting functional MRI (fMRI) scans are noninvasive, and can be acquired quickly, in patients with different levels of severity and function. Analyzing resting fMRI data using TCA is quick and can complement clinical methods to characterize the epileptogenic network.

The COVID-19 pandemic and changes in spatio-temporal patterns of suicide: monthly variations among localities in Argentina.

The aim of this study was to assess space-time clustering of suicide in Argentina between 2017 and 2020. A spatio-temporal scan statistic using a space-time permutation model was employed to retrospectively detect clusters of total suicides by sex and by age group. From 2017 to 2020, six statistically significant spatio-temporal clusters were identified in Argentina with either more or less suicides than expected. Two clusters extended from May 2019 to July and September 2020, while the remaining four clusters were detected only during 2020. The start of the COVID-19 pandemic seems to have changed the spatio-temporal patterns of suicide in Argentina. Despite the national decrease in the number of suicides during 2020 compared to previous years, the spatio-temporal analysis revealed both areas with decreases and increases in suicide. This heterogeneous scenario highlights the need for the study of local effects behind the emergence of these spatio-temporal suicide clusters.

Spatio-temporal clustering analysis of COVID-19 cases in Johor.

At the end of the year 2019, a virus named SARS-CoV-2 induced the coronavirus disease, which is very contagious and quickly spread around the world. This new infectious disease is called COVID-19. Numerous areas, such as the economy, social services, education, and healthcare system, have suffered grave consequences from the invasion of this deadly virus. Thus, a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster. In this research, the daily reported COVID-19 cases in 92 sub-districts in Johor state, Malaysia, as well as the population size associated to each sub-district, are used to study the propagation of COVID-19 disease across space and time in Johor. The time frame of this research is about 190 days, which started from August 5, 2021, until February 10, 2022. The clustering technique known as spatio-temporal clustering, which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level. The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre (Bandar Johor Bahru), and during the festive season. These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays. On the other hand, the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.

The temporal and spatial pattern evolution of provincial industrial carbon intensity under the carbon neutral target: evidence from China.

Industry is a core area to achieve the carbon neutrality target for most developing countries including China. Hence, it is of great practical significance to study the spatio-temporal characteristics of China's industrial carbon intensity and its evolution. The exploratory spatial data analysis methods were adopted to conduct global and local spatial correlation analysis in this paper. The result shows that (1) the industrial carbon emission intensity decreases year by year, with high industrial carbon emission intensity in the West and low in the East. (2) There is a correlation in the spatial distribution of industrial carbon intensity, with the Moran index experiencing the stage of descending first and then ascending. (3) The local spatial clustering of industrial carbon intensity is obvious. (4) Half of the provinces have experienced a leap, with the majority located in the western part of China. Based on these findings, it is concluded that industrial emission reduction policy synergy between provinces is particularly important, such as low-carbon industrial production policy and green industry development policy.

Dynamical changes of tea metabolites fermented by Aspergillus cristatus, Aspergillus neoniger and mixed fungi: A temporal clustering strategy for untargeted metabolomics.

Dark tea fermentation involves various fungi, but studies focusing on the mixed fermentation in tea remain limited. This study investigated the influences of single and mixed fermentation on the dynamical alterations of tea metabolites. The differential metabolites between unfermented and fermented teas were determined using untargeted metabolomics. Dynamical changes in metabolites were explored by temporal clustering analysis. Results indicated that Aspergillus cristatus (AC) at 15 days, Aspergillus neoniger (AN) at 15 days, and mixed fungi (MF) at 15 days had respectively 68, 128 and 135 differential metabolites, compared with unfermentation (UF) at 15 days. Most of metabolites in the AN or MF group showed a down-regulated trend in cluster 1 and 2, whereas most of metabolites in the AC group showed an up-regulated trend in cluster 3 to 6. The three key metabolic pathways mainly composed of flavonoids and lipids included flavone and flavonol biosynthesis, glycerophospholipid metabolism and flavonoid biosynthesis. Based on the dynamical changes and metabolic pathways of the differential metabolites, AN showed a predominant status in MF compared with AC. Together, this study will advance the understanding of dynamic changes in tea fermentation and provide valuable insights into the processing and quality control of dark tea.