Scanning Windows Research Articles

Validation of a Simplified Tissue-to-Reference Ratio Measurement Using SUVR to Assess Synaptic Density Alterations in Alzheimer Disease with [11C]UCB-J PET.

Simplified methods of acquisition and quantification would facilitate the use of synaptic density imaging in multicenter and longitudinal studies of Alzheimer disease (AD). We validated a simplified tissue-to-reference ratio method using SUV ratios (SUVRs) for estimating synaptic density with [11C]UCB-J PET. Methods: Participants included 31 older adults with AD and 16 with normal cognition. The distribution volume ratio (DVR) using simplified reference tissue model 2 was compared with SUVR at short scan windows using a whole-cerebellum reference region. Results: Synaptic density was reduced in AD participants using DVR or SUVR. SUVR using later scan windows (60-90 or 70-90 min) was minimally biased, with the strongest correlation with DVR. Effect sizes using SUVR at these late time windows were minimally reduced compared with effect sizes with DVR. Conclusion: A simplified tissue-to-reference method may be useful for multicenter and longitudinal studies seeking to measure synaptic density in AD.

Integrating myocardial metabolic imaging and stress myocardial contrast echocardiography to improve the diagnosis of coronary microvascular diseases in rabbits.

Persistent challenges associated with misdiagnosis and underdiagnosis of coronary microvascular disease (CMVD) necessitate the exploration of noninvasive imaging techniques to enhance diagnostic accuracy. Therefore, we aimed to integrate multimodal imaging approaches to achieve a higher diagnostic rate for CMVD using high-quality myocardial metabolism imaging (MMI) and myocardial contrast echocardiography (MCE). This combination diagnostic strategy may help address the urgent need for improved CMVD diagnosis. In this study, we established five distinct pretreatment groups, each consisting of nine male rabbit: a fasted group, a nonfasted group, a sugar load group, an acipimox group, and a combination group of nonfasted rabbits administered insulin. Moreover, positron emission tomography-computed tomography (PET/CT) scan windows were established at 30-, 60-, and 90-minute intervals. We developed 10 CMVD models and conducted a diagnosis of CMVD through an integrated analysis of MMI and MCE, including image acquisition and processing. For each heart segment, we calculated the standardized uptake value (SUV) based on body weight (SUVbw), as well as certain ratios of SUV including SUV of the heart (SUVheart) to that of the liver (SUVliver) and SUVheart to SUV of the lung (SUVlung). Additionally, we obtained three coronary SUVbw uptake values. To clarify the relationship between SUVbw uptake values and echocardiographic parameters of the myocardial contrast agent more thoroughly, we conducted a comprehensive analysis across different pretreatment protocols. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic accuracy of each parameter in the context of CMVD. In the context of MMI, the nonfasted-plus-insulin group, as observed during the 60-minute examination, exhibited a noteworthy total 18F-fluorodeoxyglucose (18F-FDG) uptake of 47.44±6.53 g/mL, which was found to be statistically different from the other groups. To ascertain the reliability of the results, two double-blind investigators independently assessed the data and achieved a good level of agreement, according to the intraclass correlation coefficient (ICC) (0.957). The SUVbw of the nonfasted-plus-insulin group exhibited a moderate correlation with the microvascular blood flow reserve (MBFR) parameters derived from the MCE examination, as evidenced by a r value of 0.686. For the diagnosis of CMVD disease, the diagnostic accuracy of the combined diagnostic method [area under the curve (AUC) =0.789; 95% confidence interval (CI): 0.705-0.873] was significantly higher than that of the MBFR (AUC =0.697; 95% CI: 0.597-0.797) and SUVbw (AUC =0.715; 95% CI: 0.622-0.807) methods (P<0.05). Our study demonstrated the feasibility of a simple premedication approach involving free feeding and intravenous insulin in producing high-quality gated heart 18F-FDG PET/CT images in adult male New Zealand white rabbits. This technique holds considerable potential for ischemic heart disease research in rabbits and can enhance CMVD diagnosis via the comprehensive assessment of myocardial metabolism and perfusion.

3D Data Processing and Entropy Reduction for Reconstruction from Low-Resolution Spatial Coordinate Clouds in a Technical Vision System.

This paper proposes an advancement in the application of a Technical Vision System (TVS), which integrates a laser scanning mechanism with a single light sensor to measure 3D spatial coordinates. In this application, the system is used to scan and digitalize objects using a rotating table to explore the potential of the system for 3D scanning at reduced resolutions. The experiments undertaken searched for optimal scanning windows and used statistical data filtering techniques and regression models to find a method to generate a 3D scan that was still recognizable with the least amount of 3D points, balancing the number of points scanned and time, while at the same time reducing effects caused by the particularities of the TVS, such as noise and entropy in the form of natural distortion in the resulting scans. The evaluation of the experimentation results uses 3D point registration methods, joining multiple faces from the original volume scanned by the TVS and aligning it to the ground truth model point clouds, which are based on a commercial 3D camera to verify that the reconstructed 3D model retains substantial detail from the original object. This research finds it is possible to reconstruct sufficiently detailed 3D models obtained from the TVS, which contain coarsely scanned data or scans that initially lack high definition or are too noisy.

Digital Fracture Porosity Measurement using Digital Outcrop Modelling (DOM)

Abstract Fracture measurements for porosity calculations often experience problems such as barely accessible locations, limited area coverage, and a long time to carry out field data measurements. Therefore, refined alternative methods are necessary to obtain the most favourable data. This study demonstrates fracture measurements through application of Digital Outcrop Modelling (DOM) using the Structure from Motion (SfM) method to utilize digital photogrammetry techniques. This method was chosen because data collection can be done remotely using Light Detection and Ranging (LiDAR) or digital photogrammetry to obtain point cloud data that is used to perform three-dimensional outcrop modeling. In addition, SfM also produces high-resolution data without the need for experts, is cost-efficient and safer than other methods. Hand-held platform Canon 700D digital single lens reflex (DSLR) camera was used in this study to capture data from an area of a square meter located on the riverbed that requires detailed modeling. DOM using SfM is applied to conduct aperture measurements in fractures inside the scan window, later used as data for calculating basement fracture porosity predictions. Field data collection is also carried out to validate data generated by DOM. Calculations achieved from field data and DOM data will be compared to determine differences of the porosity prediction values. Field data collection was carried out on 3 scan windows, each one is an area of 1 m2 and in totals obtained 117 fracture data, consist of 44 extension joints, 34 release joints, 7 shear joints, and 32 non-systematic joints. In this study, 115 photos were taken from scanwindow 1, 162 photos from scanwindow 2, and 158 photos from scanwindow 3 to construct the DOM using agisoft metashape software. The difference in these values can be used as a correction formula or evaluation for further research. The results of measuring basement fracture porosity using data generated by DOM will remain in the range of less than 1%.

Assisted Slotless Neighbor Discovery Based on Additional Scan Windows

Assisted Slotless Neighbor Discovery Based on Additional Scan Windows

Outlet Ventricular Septal Defect: A Case Report on the Importance of Multi-Window Sonographic Assessment

Congenital heart disease (CHD) is the existence of structural heart problems since the formation of the heart prebirth. There are different categories of CHD, all having their own unique frequency and severity characteristics. One type of CHD is a ventricular septal defect (VSD), more specifically outlet VSD. A VSD is the most common congenital heart defect; however, their presentation can be quite complex. Color and spectral Doppler transthoracic echocardiography (TTE) are valuable tools used in the diagnosis of VSDs. The often-challenging presentation of these defects requires sonographers to confirm their existence through the parasternal long-axis, parasternal short-axis, apical four chamber, apical three chamber, and apical five chamber scanning windows. Although VSDs are the most common congenital heart defect, their wide-ranging uniqueness and location along the interventricular septum (IVS) to assess all areas along the IVS on all exams, specifically those that demonstrate left-to-right shunting. The objective of this case report was to stress the importance of multi-window assessment during the sonographer’s interrogation of outlet VSDs, due to the potential implications on adjacent anatomy and the potential for surgical intervention.

Calibrating the Scan Statistic: Finite Sample Performance Versus Asymptotics

Abstract We consider the problem of detecting an elevated mean on an interval with unknown location and length in the univariate Gaussian sequence model. Recent results have shown that using scale-dependent critical values for the scan statistic allows to attain asymptotically optimal detection simultaneously for all signal lengths, thereby improving on the traditional scan, but this procedure has been criticised for losing too much power for short signals. We explain this discrepancy by showing that these asymptotic optimality results will necessarily be too imprecise to discern the performance of scan statistics in a practically relevant way, even in a large sample context. Instead, we propose to assess the performance with a new finite sample criterion. We then present three calibrations for scan statistics that perform well across a range of relevant signal lengths: The first calibration uses a particular adjustment to the critical values and is therefore tailored to the Gaussian case. The second calibration uses a scale-dependent adjustment to the significance levels rather than to the critical values and this adjustment is therefore applicable to arbitrary known null distributions. The third calibration restricts the scan to a particular sparse subset of the scan windows and then applies a weighted Bonferroni adjustment to the corresponding test statistics. This Bonferroni scan is also applicable to arbitrary null distributions and in addition is very simple to implement. We show how to apply these calibrations for scanning in a number of distributional settings: for normal observations with an unknown baseline and a known or unknown constant variance, for observations from a natural exponential family, for potentially heteroscadastic observations from a symmetric density by employing self-normalisation in a novel way, and for exchangeable observations using tests based on permutations, ranks or signs.

Determinants of place birth: a multinomial logistic regression and spatial analysis of the Ethiopian mini demographic and health survey data, 2019

BackgroundMaternal and neonatal health significantly improves when birth is attended at health institutions where there are quality services and skilled attendants. In contrary, home birth results in high rates of maternal and neonatal mortality. Thus, this study aimed to determine the spatial distribution of home birth and to identify determinants of place of birth in Ethiopia based on the recent national survey.MethodsEthiopian mini-DHS-2019 data was used in this analysis. A weighted sample of 5423 mothers were included. While health facility was a reference, home and health post were used as comparison categories to identify determinants of place of birth in a survey multinomial logistic regression model. An adjusted relative risk ratio, marginal effect, and a corresponding 95% confidence interval and a p-value of < 0.05 were used to declare statistical significance. The Global Moran’s I analysis was done by using ArcMap 10.8 to evaluate the clustering of home birth. The prevalence of home birth was predicted by ordinary kriging interpolation. Then, scanning was done by SaTScan V.9.6 software to detect scanning windows with low or high rates of home birth.ResultPrevalence of home birth in Ethiopia was 52.19% (95% CI: 46.49 – 57.83). Whereas, only 2.99% (95% CI: 1.68 – 5.25) of mothers gave birth in the health posts. Bigger family size, family wealth, multiparity, none and fewer antenatal visits, and low coverage of cluster level 4 + antenatal visits were predictors of home birth. Also, home birth was clustered across enumeration areas and it was over 40% in most parts of the country with > 75% in the Somali region. SaTScan analysis detected most likely primary clusters in the Somali region and secondary clusters in the rest five regions of the country.ConclusionHome birth is a common practice in Ethiopia. Among public health facilities, health posts are the least utilized institutions for labor and delivery care. Nationally, implementing the 2016 WHO’s recommendations on antenatal care for a positive pregnancy experience and providing quality antenatal and delivery care in public facilities by qualified providers and back-up systems in place could be supportive.

MCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences.

In the past decade, convolutional neural networks (CNNs) have been used as powerful tools by scientists to solve visual data tasks. However, many efforts of convolutional neural networks in solving protein function prediction and extracting useful information from protein sequences have certain limitations. In this research, we propose a new method to improve the weaknesses of the previous method. mCNN-ETC is a deep learning model which can transform the protein evolutionary information into image-like data composed of 20 channels, which correspond to the 20 amino acids in the protein sequence. We constructed CNN layers with different scanning windows in parallel to enhance the useful pattern detection ability of the proposed model. Then we filtered specific patterns through the 1-max pooling layer before inputting them into the prediction layer. This research attempts to solve a basic problem in biology in terms of application: predicting electron transporters and classifying their corresponding complexes. The performance result reached an accuracy of 97.41%, which was nearly 6% higher than its predecessor. We have also published a web server on http://bio219.bioinfo.yzu.edu.tw, which can be used for research purposes free of charge.

Resuscitative transesophageal echocardiography in the diagnosis of post-CABG loculated pericardial clot causing cardiac tamponade

BackgroundPericardial effusion is a known complication of post-open cardiac surgery which can progress to life-threatening cardiac tamponade. Classical signs of tamponade such as hypotension and pulsus paradoxus are often absent. Diagnosing acute cardiac tamponade with transthoracic echocardiography (TTE) can be challenging in post-cardiac surgical patients due to distorted anatomy and limited scanning windows by the presence of surgical dressings or scar. Additionally, this patient population is more likely to have a loculated pericardial effusion, or an effusion that is isoechoic in appearance secondary to clotted blood. These findings can be challenging to visualize with traditional TTE. Missed diagnosis of cardiac tamponade due to loculated pericardial clot can result in delayed diagnosis and clinical management.Case presentationWe report a case series that illustrates the diagnostic challenge and value of resuscitative transesophageal echocardiography (TEE) in the emergency department (ED) for the diagnosis of cardiac tamponade due to posterior loculated pericardial clot in post-surgical coronary artery bypass graft (CABG) patients.ConclusionsCardiac tamponade due to loculated posterior pericardial clot post-CABG requires prompt diagnosis and appropriate management to avoid the potential for hemodynamic instability. Transesophageal echocardiography allows a rapid diagnosis, early appropriate referral and an opportunity to institute appropriate therapeutic measures.

Fast Focus Ultrasound Liver Technique for the Assessment of Cystic Echinococcosis in Sheep.

Simple SummaryCystic echinococcosis (CE) is a parasitic zoonosis caused by the larval stage of the tapeworm Echinococcus granulosus. The liver ultrasonography can be considered as the gold standard for the in vivo diagnosis detection of CE. Nevertheless, control programs against E. granulosus are considered long-term actions that require an integrated approach and the high expenditure of time and financial resources. This parasitosis still causes severe economic losses both in human healthcare and in the entire livestock breeding system, with loss in milk, meat, and wool production. Considering the impact of this zoonosis in the modern sheep’s breeding system, a reliable hepatic ultrasound could represent an innovative strategy to control and even eradicate the disease in sheep farms. Based on these considerations, the present study aimed to evaluate a fast-focused technique for hepatic CE detection in different sheep breeds and compare the performance of the latter with another fast-scan (previously evaluated in Sarda sheep breed), the complete ultrasound liver examination, and the anatomopathological examination.A complete ultrasound examination (cUS) of the liver was performed on 172 female sheep and compared to the performance of a fast-focused ultrasound technique to diagnose echinococcal cysts. The scanned area was divided in: HYP (right hypocondrium), zone (Z)1 from HYP to the 11th intercostal space (IS), Z2 (10th–8th IS) and Z3 (7th–5th IS). Contiguous scans were also examined (HYP + Z1, Z1 + Z2, Z2 + Z3). Furthermore, during the procedures, the sheep were divided into three groups according to the body weight: Group (G) 1 (lighter), G2 (medium), and G3 (heavier). Finally, diagnostic outcomes were compared with necropsy findings. cUS obtained the highest values of sensitivity (Se) (91%), Specificity (Sp) (80%), and positive-zones (124/138, 90%), as compared to the other scans. cUS was also characterized by high values of Se and Sp and was able to identify a great number of positive-zones, when sheep were divided by body-weight groups. Similar performances were obtained in G1 by HYP (Se: 91%–Sp: 82%; 18/20, 90% of positive-zones) and HYP + Z1 scans (Se: 91%–Sp: 82; 90% of positive-zones, 18/20). Thus, in lighter breeds, the examination of HYP and HYP + Z1 scan windows could be considered reliable techniques for identifying the infected animals, while in heavier sheep the cUS still represents the best option.

Investigation of ancestral alleles in the Bovinae subfamily

BackgroundIn evolutionary theory, divergence and speciation can arise from long periods of reproductive isolation, genetic mutation, selection and environmental adaptation. After divergence, alleles can either persist in their initial state (ancestral allele - AA), co-exist or be replaced by a mutated state (derived alleles -DA). In this study, we aligned whole genome sequences of individuals from the Bovinae subfamily to the cattle reference genome (ARS.UCD-1.2) for defining ancestral alleles necessary for selection signatures study.ResultsAccommodating independent divergent of each lineage from the initial ancestral state, AA were defined based on fixed alleles on at least two groups of yak, bison and gayal-gaur-banteng resulting in ~ 32.4 million variants. Using non-overlapping scanning windows of 10 Kb, we counted the AA observed within taurine and zebu cattle. We focused on the extreme points, regions with top 0. 1% (high count) and regions without any occurrence of AA (null count). High count regions preserved gene functions from ancestral states that are still beneficial in the current condition, while null counts regions were linked to mutated ones. For both cattle, high count regions were associated with basal lipid metabolism, essential for survival of various environmental pressures. Mutated regions were associated to productive traits in taurine, i.e. higher metabolism, cell development and behaviors and in immune response domain for zebu.ConclusionsOur findings suggest that retaining and losing AA in some regions are varied and made it species-specific with possibility of overlapping as it depends on the selective pressure they had to experience.

Towards practical 2D grapevine bud detection with fully convolutional networks

In Viticulture, visual inspection of the plant is a necessary task for measuring relevant variables. In many cases, these visual inspections are susceptible to automation through computer vision methods. Bud detection is one such visual task, central for the measurement of important variables such as: measurement of bud sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, bud area, and bud development stage, among others. This paper presents a computer method for grapevine bud detection based on a Fully Convolutional Networks MobileNet architecture (FCN-MN). To validate its performance, this architecture was compared in the detection task with a strong method for bud detection, Scanning Windows (SW) based on a patch classifier, showing improvements over three aspects of detection: segmentation, correspondence identification and localization. The best version of FCN-MN showed a detection F1-measure of 88.6% (for true positives defined as detected components whose intersection-over-union with the true bud is above 0.5), and false positives that are small and near the true bud. Splits –false positives overlapping the true bud– showed a mean segmentation precision of 89.3%(21.7), while false alarms –false positives not overlapping the true bud– showed a mean pixel area of only 8% the area of a true bud, and a distance (between mass centers) of 1.1 true bud diameters. The paper concludes by discussing how these results for FCN-MN would produce sufficiently accurate measurements of bud variables such as bud number, bud area, and internode length, suggesting a good performance in a practical setup.

Study on spatial clustering characteristics of tuberculosis in China, 2013-2018

Objective: To explore the tuberculosis (TB) clustering areas and changing tendency in China from 2013 to 2018, so as to provide evidence for improving the control and prevention programs on TB. Methods: Spatial distribution of TB in China from 2013 to 2018 was studied using data from TB information management system of Chinese disease prevention and control information system. The SaTScan 9.6 software was used to detect and analyze the TB clusters and relative risks. Likelihood ratio and P value under the scan windows were used as the main index to define the clusters. Results: The TB registration rate declined in China from 2013 to 2018. High-risk clusters were mainly located in western regions of Xinjiang, Tibet, Shaanxi, Sichuan, Chongqing, Yunnan, Guizhou, Guangxi provinces and central and eastern regions of Inner Mongolia, Hubei, Hunan, Jiangxi, Guangdong and Hainan provinces. The number of counties with TB clusters declined slowly, from 1 112 in 2013 to 806 in 2018. In comparison with areas other than the clusters, the RR of TB in the clusters remained at high levels in southern Xinjiang and western Tibet from 2013 to 2018 (RR>3.0). Conclusions: The number of the counties with TB clustering declined slowly from 2013 to 2018, and most of them were located in western and central China. Early detection and health education should be focused on TB patients to prevent the spread of TB. Management programs set for TB patients should be strengthened, especially in the western parts of the country where TB clusters and high relative risks existed.

Sensitive and Comprehensive Lipid Mediator Analysis using Advanced Scheduled MRM with Polarity Switching and QTRAP Enhanced Product Ion Scanning

Using a QTRAP® 6500+ system coupled with an ExionLC™ System, we developed a targeted panel of 88 compounds for comprehensive profiling of lipid mediators and pathway markers. A Kinetex® Polar C18 column was used for LC based separation of lipid mediators, epimers and other isoelemental structures within a 20 min run time. Advanced scheduled sMRM was used to optimize scanning windows and dwell weighting. The negative mode lipid mediator panel includes pro‐inflammatory prostaglandins, leukotrienes and their primary metabolites, specialized pro‐resolving mediators (SPM) including resolvins, protectins, maresins, and lipoxins, biomarkers of ROS and NOS, and fatty acid and mono‐hydroxy fatty acid precursors. The positive mode panel includes cysteinyl leukotrienes, PAF, and maresin and protectin conjugates in tissue regeneration (MCTRs and PCTRs).Concentration curves were generated by injecting 3 replicate injections of 8 different concentrations of each lipid mediator standard. Excellent linearity is observed with r2 of at least 0.997 for all analytes. Excellent sensitivity was obtained for all lipid mediator species with LLOQ between 0.05 and 1 ng/mL and CV &lt; 30%. The unique qualitative/quantitative QTRAP platform also enabled MRM triggered EPI experiments which provided high sensitivity MS/MS data for confirmation of low level analytes. Baseline separation of LXA4 and 15‐epi‐LXA4 well as RvD1 and 17‐epi‐RvD1 enables the differentiation of enzymatic pathway utilization for pro‐resolving lipid mediator production.

Detecting anomalies in fibre systems using 3-dimensional image data

We consider the problem of detecting anomalies in the directional distribution of fibre materials observed in 3D images. We divide the image into a set of scanning windows and classify them into two clusters: homogeneous material and anomaly. Based on a sample of estimated local fibre directions, for each scanning window we compute several classification attributes, namely the coordinate wise means of local fibre directions, the entropy of the directional distribution, and a combination of them. We also propose a new spatial modification of the Stochastic Approximation Expectation-Maximization (SAEM) algorithm. Besides the clustering we also consider testing the significance of anomalies. To this end, we apply a change point technique for random fields and derive the exact inequalities for tail probabilities of a test statistic. The proposed methodology is first validated on simulated images. Finally, it is applied to a 3D image of a fibre reinforced polymer.

Prediction of Lee filter performance for Sentinel-1 SAR images

Synthetic aperture radar (SAR) images are corrupted by a specific noise-like phenomenon called speckle that prevents efficient processing of remote sensing data. There are many denoising methods already proposed including well known (local statistic) Lee filter. Its performance in terms of different criteria depends on several factors including image complexity where it sometimes occurs useless to process complex structure images (containing texture regions). We show that performance of the Lee filter can be predicted before starting image filtering and which can be done faster than the filtering itself. For this purpose, we propose to apply a trained neural network that employs analysis of image statistics and spectral features in a limited number of scanning windows. We show that many metrics including visual quality metrics can be predicted for SAR images acquired by Sentinel-1 sensor recently put into operation.

Identifying high‐density regions of pests within an orchard

AbstractThis paper proposes a statistical method for identifying high‐density regions of pests, so‐called hot spots, within an orchard. Our method uses scanning windows to search for clusters of high counts within the sampled data. The proposed method enables a localized alternative for treatment that could be faster, less costly, and more environmentally friendly. R code that implements the hot spot identification method is provided as online supplementary material. The method is illustrated through simulated examples and a real data on counts of cottony cushion scales from an orchard.

PENERAPAN CYLINDRICAL DAN FLEXIBLE SPACE TIME SCAN STATISTIC DALAM MENGIDENTIFIKASI KANTONG KEMISKINAN DI PULAU JAWA TAHUN 2011-2015

The Indonesian government formed the National Team for the Acceleration of Poverty Reduction (TNP2K) to eradicate poverty. TNP2K requires identification of priority areas or poverty hotspots so that the program can be targeted. Scan statistic is one of the most widely used methods to identify poverty hotspots. Cylindrical STSS uses cylindrical scanning windows while most geographical areas are not circular. Flexible STSS is able to detect poverty hotspots in a flexible form. This study aims to identify poverty hotspots using Cylindrical and Flexible STSS then compare the results of both and then determine the best STSS method. Cylindrical STSS tends to have wider hotspots than Flexible STSS. There are a number of districts that are not eligible to be included as poverty Flexible STSS is able to produce better poverty hotspots by not including these districts Poverty hotspots produced by Flexible STSS have higher LLR values. The more suitable STSS method has optimal K values and high suitability with TNP2K priority areas. Cylindrical STSS has an optimal K value when K = 8 and 9. Flexible STSS has a constant LLR value. Flexible STSS has a higher LLR value than Cylindrical STSS at each K value. Flexible STSS with K = 9 has optimal K and high suitability with TNP2K priority areas so that it is the more suitable STSS method to identify poverty hotspots in Java.

Space-time cluster analysis to improve classical swine fever risk-based surveillance in Ecuador

Space-time cluster analysis to improve classical swine fever risk-based surveillance in Ecuador