Scan Databases Research Articles

R-8 Dispersion Interaction: Derivation and Application to the Effective Fragment Potential Method.

The anisotropic and isotropic R-8 dispersion contributions (disp8) are derived and implemented within the framework of the effective fragment potential (EFP) method formulated with imaginary frequency-dependent Cartesian polarizability tensors distributed at the centroids of the localized molecular orbitals (LMOs). Two forms of damping functions, intermolecular overlap-based and Tang-Toennies, are extended for disp8. To obtain LMO polarizability tensors centered at LMO centroids, an origin-shifting transformation is derived and implemented for the dipole-octopole polarizability tensor and the quadrupole-quadrupole polarizability tensor. The analytic gradient is derived and implemented for the isotropic disp8 contribution. Relative to the previously implemented empirical EFP disp8 energy, the isotropic disp8 component of the interaction energy improves the overall agreement of the EFP dispersion energies with the symmetry-adapted perturbation theory (SAPT) benchmarks, reducing the mean absolute errors (MAEs) and mean absolute percentage errors for most of the databases examined in this work. While the anisotropic disp8 can further enhance the accuracy of the EFP dispersion energy and yield smaller MAEs, significantly overbound dispersion energies are predicted by the anisotropic disp8 when the maximum element in the intermolecular overlap matrix is greater than 0.1, possibly due to the breakdown of the approximations made in the EFP dispersion derivation at a short range. For potential energy scan databases, the newly developed EFP dispersion model with isotropic disp8 yields the overall correct curvature and good agreement with SAPT benchmarks around equilibrium and longer but overestimates the dispersion interactions at a short range. While the overlap-based dispersion-damping functions produce better MAEs than Tang-Toennies damping functions, further improvement is needed to better screen the large attractive dispersion energies at a short range (overlap >0.1).

Effective lung cancer detection using deep learning network

The use of a computer-assisted diagnosis system was crucial to the results of the clinical study conducted to determine the nature of the human illness. When compared to other disorders, lung cancer requires extra caution during the examination process. This is because the mortality rate from lung cancer is higher because it affects both men and women. Poor image resolution has hampered previous lung cancer detection technologies, preventing them from achieving the requisite degree of dependability. Therefore, in this study, we provide a unique approach to lung cancer prognosis that makes use of improved machine learning and processing of images. Images of lung disease from CT scan databases created using quasi cells are used for diagnosis. Multilayer illumination was used to analyse the generated images, which improved the precision of the lungs' depiction by probing each and every one of their pixels while simultaneously decreasing the amount of background noise. Lung CT images are pre-processed to remove noise, and then a more advanced deep learning network is used to isolate the affected region. The territory is partitioned into subnetworks according to the number of existing networks, from which different features are subsequently extracted. Next, an ensemble classifier should be used to correctly diagnose lung diseases. Using MATLAB simulation, the authors examine how the provided technique improves the rate at which lung cancer could be diagnosed.

FaceTuneGAN: Face autoencoder for convolutional expression transfer using neural generative adversarial networks

In this paper, we present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. We propose a first adaptation of image-to-image translation networks, that have successfully been used in the 2D domain, to 3D face geometry. Leveraging recently released large face scan databases (FaceScape and CoMA), a neural network has been trained to decouple factors of variations with a better knowledge of the face, enabling facial expressions transfer and neutralization of expressive faces. Specifically, we design an adversarial architecture adapting the base architecture of FUNIT and using SpiralNet++ for our convolutional and sampling operations. Applied on these two datasets, FaceTuneGAN has a better identity decomposition and face neutralization than state-of-the-art techniques. It also outperforms classical deformation transfer approach by predicting blendshapes closer to ground-truth data and with less of undesired artifacts due to too different facial morphologies between source and target.

Assessment of Significant Pathway Signaling and Prognostic Value of GNG11 in Ovarian Serous Cystadenocarcinoma.

BackgroundGNG11 (G protein subunit gamma 11) is a member of guanine nucleotide-binding protein (G protein) gamma family. Few studies elucidated the role of GNG11 in human disease, especially in tumors. The present study initially analyzed the function of GNG11 in ovarian serous cystadenocarcinoma.MethodsThe differential expression of GNG11 mRNA in ovarian cancer and normal tissues was evaluated through Oncomine, CCLE, Gepia, UCSC Xena and UALCAN databases. The protein expression of GNG11 was assessed via HPA database. Prognosis analysis was performed by Kaplan–Meier Plotter. Restrict survival analysis to subtypes including tumor grade, cancer stage and TP53 mutation status was then carried out. GSEA enrichment analysis was performed to explore the significant pathways associated with GNG11 in ovarian cancer. Finally, the upstream miRNAs of GNG11 were predicted by DIANA, Target Scan, miRDB and miRWalk databases, and the potential key KEGG pathways were subsequently determined by DIANA.ResultsThe mRNA expression of GNG11 was down-regulated in ovarian cancer patients (P<0.05). The cancer stage of patients correlated with the expression of GNG11 (P<0.05). Survival analysis indicated that GNG11 high expression statistically shortened the overall survival time of patients (HR=1.26, P=0.0043) compared with low expression group, especially for the patients with earlier stage (HR=2.48, P=0.035) and lower grade (HR=1.72, P=0.0016). Subsequently, the consistent upstream miRNA of GNG11, hsa-miR-22-5p, was predicted from 4 databases. The differential expression profile of hsa-miR-22-5p in blood was observed in ovarian cancer patients. According to the GSEA analysis on GNG11 and KEGG analysis on hsa-miR-22-5p, the consistent pathway of ECM-receptor interaction was observed (all P<0.01). ECM-receptor interaction pathway and differential expression of hsa-miR-22-5p in blood suggested the migration risk of ovarian cancer.ConclusionHigh expression of GNG11 indicated the poor prognosis of ovarian cancer patients. GNG11 might play a crucial role in the biological process of ovarian serous cystadenocarcinoma by ECM-receptor interaction pathway, thus affecting the prognosis of patients.

COVID-19 Diagnostic System Using Medical Image Classification and Retrieval: A Novel Method for Image Analysis

With the rapid increase in the number of people infected with COVID-19 disease in the entire world, and with the limited medical equipment used to detect it (testing kit), it becomes necessary to provide another detection method that mainly relies on Artificial Intelligence and radiographic Image Analysis to determine the disease infection. In this study, we proposed a diagnosis system that detects the COVID-19 using chest X-ray or computed tomography (CT) scan images knowing that this system does not eliminate the reverse transcription-polymerase chain reaction test but rather complements it. The proposed system consists of the following steps, starting with extracting the image’s features using Visual Words Fusion of ResNet-50 (deep neural network) and Histogram of Oriented Gradient descriptors based on Bag of Visual Word methodology. Then training the Adaptive Boosting classifier to classify the image to COVID-19 or NOTCOVID-19 and finally retrieving the most similar images. We implemented our work on X-ray and CT scan databases, and the experimental results demonstrate the effectiveness of the proposed system. The performance of the classification task in terms of accuracy was as follows: 100% for classifying the input image to X-ray or CT scan, 99.18% for classifying X-ray image to COVID-19 or NOTCOVID-19 and 97.84% for classifying CT scan to COVID-19 or NOTCOVID-19.

Content Based Image Retrieval: A Review

Image recovery was one of the most thrilling and vibrant fields of computer vision science. Content-based image retrieval systems (CBIR) are used to catalog, scan, download and access image databases automatically. Color &amp; texture features are significant properties for content-based image recovery systems. The content-based image retrieval (CBIR) is therefore an attractive source of accurate and quick retrieval. Number of techniques has been established in recent years to improve the performance of CBIR. This paper discusses why CBIR is important nowadays along with the limitations and benefits. Apart from applications, various feature extraction techniques used in CBIR are also discussed.

Long noncoding RNA ZFPM2-AS1 acts as a miRNA sponge and promotes cell invasion through regulation of miR-139/GDF10 in hepatocellular carcinoma

BackgroundEmerging evidence has shown that dysregulated expression of long noncoding RNAs (lncRNAs) is implicated in liver hepatocellular carcinoma (HCC). However, the role and molecular mechanism of differentially expressed lncRNAs in HCC has not been fully explained.MethodsThe expression profiles of lncRNAs in HCC samples were derived from microarrays analysis or downloaded from The Cancer Genome Atlas (TCGA), and their correlation with prognosis and clinical characteristics were further analyzed. Silencing of lncRNA ZFPM2-AS1 was conducted to assess the effect of ZFPM2-AS1 in vitro. The miRcode and Target Scan databases were used to determine the lncRNA-miRNA-mRNA interactions. The biological functions were demonstrated by luciferase reporter assay, western blotting, PCR and rescue experiments.ResultsThe expression level of lncRNA ZFPM2-AS1 was significantly higher in HCC tissues than in adjacent normal tissues, and higher ZFPM2-AS1 was remarkably related to poor survival. Functionally, silencing of lncRNA ZFPM2-AS1 inhibited cell proliferation, migration, invasion and promoted cell apoptosis in vitro. Bioinformatics analysis based on the miRcode and TargetScan databases showed that lncRNA ZFPM2-AS1 regulated GDF10 expression by competitively binding to miR-139. miR-139 and downregulated GDF10 reversed cell phenotypes caused by lncRNA ZFPM2-AS1 by rescue analysis.ConclusionsZFPM2-AS1, an upregulated lncRNA in HCC, was associated with malignant tumor phenotypes and worse patient survival. ZFPM2-AS1 regulated the progression of HCC by acting as a competing endogenous RNA (ceRNA) to competitively bind to miR-139 and regulate GDF10 expression. Our study provides new insight into the posttranscriptional regulation mechanism of lncRNA ZFPM2-AS1 and suggests that ZFPM2-AS1/miR-139/GDF10 may act as a potential therapeutic target and prognostic biomarker for HCC.

The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2

Abstract. The Aerosol Robotic Network (AERONET) Version 3 (V3) aerosol retrieval algorithm is described, which is based on the Version 2 (V2) algorithm with numerous updates. Comparisons of V3 aerosol retrievals to those of V2 are presented, along with a new approach to estimate uncertainties in many of the retrieved aerosol parameters. Changes in the V3 aerosol retrieval algorithm include (1) a new polarized radiative transfer code (RTC), which replaced the scalar RTC of V2, (2) detailed characterization of gas absorption by adding NO2 and H2O to specify total gas absorption in the atmospheric column, specification of vertical profiles of all the atmospheric species, (3) new bidirectional reflectance distribution function (BRDF) parameters for land sites adopted from the MODIS BRDF/Albedo product, (4) a new version of the extraterrestrial solar flux spectrum, and (5) a new temperature correction procedure of both direct Sun and sky radiance measurements. The potential effect of each change in V3 on single scattering albedo (SSA) retrievals was analyzed. The operational almucantar retrievals of V2 versus V3 were compared for four AERONET sites: GSFC, Mezaira, Mongu, and Kanpur. Analysis showed very good agreement in retrieved parameters of the size distributions. Comparisons of SSA retrievals for dust aerosols (Mezaira) showed a good agreement in 440 nm SSA, while for longer wavelengths V3 SSAs are systematically higher than those of V2, with the largest mean difference at 675 nm due to cumulative effects of both extraterrestrial solar flux and BRDF changes. For non-dust aerosols, the largest SSA deviation is at 675 nm due to differences in extraterrestrial solar flux spectrums used in each version. Further, the SSA 675 nm mean differences are very different for weakly (GSFC) and strongly (Mongu) absorbing aerosols, which is explained by the lower sensitivity to a bias in aerosol scattering optical depth by less absorbing aerosols. A new hybrid (HYB) sky radiance measurement scan is introduced and discussed. The HYB combines features of scans in two different planes to maximize the range of scattering angles and achieve scan symmetry, thereby allowing for cloud screening and spatial averaging, which is an advantage over the principal plane scan that lacks robust symmetry. We show that due to an extended range of scattering angles, HYB SSA retrievals for dust aerosols exhibit smaller variability with solar zenith angles (SZAs) than those of almucantar (ALM), which allows extension of HYB SSA retrievals to SZAs less than 50∘ to as small as 25∘. The comparison of SSA retrievals from closely time-matched HYB and ALM scans in the 50 to 75∘ SZA range showed good agreement with the differences below ∼0.005. We also present an approach to estimate retrieval uncertainties which utilizes the variability in retrieved parameters generated by perturbing both measurements and auxiliary input parameters as a proxy for retrieval uncertainty. The perturbations in measurements and auxiliary inputs are assumed as estimated biases in aerosol optical depth (AOD), radiometric calibration of sky radiances combined with solar spectral irradiance, and surface reflectance. For each set of Level 2 Sun/sky radiometer observations, 27 inputs corresponding to 27 combinations of biases were produced and separately inverted to generate the following statistics of the inversion results: average, standard deviation, minimum and maximum values. From these statistics, standard deviation (labeled U27) is used as a proxy for estimated uncertainty, and a lookup table (LUT) approach was implemented to reduce the computational time. The U27 climatological LUT was generated from the entire AERONET almucantar (1993–2018) and hybrid (2014–2018) scan databases by binning U27s in AOD (440 nm), Angström exponent (AE, 440–870 nm), and SSA (440, 675, 870, 1020 nm). Using this LUT approach, the uncertainty estimates U27 for each individual V3 Level 2 retrieval can be obtained by interpolation using the corresponding measured and inverted combination of AOD, AE, and SSA.

MiR-644-5p carried by bone mesenchymal stem cell-derived exosomes targets regulation of p53 to inhibit ovarian granulosa cell apoptosis

BackgroundThis article aims to reveal the therapeutic effects and potential mechanisms of bone mesenchymal stem cell (BMSC)-derived exosomes on premature ovarian failure (POF).MethodsExosomes were collected from BMSCs and were used to treat cisplatin-induced POF mouse models. Pathological changes of ovarian tissue were detected by using HE staining and by Western blot that detected the expression of apoptosis-related proteins. In cisplatin-induced primary granulosa cell injury, exosomes were co-cultured with the granulosa cells. The apoptosis or viability of granulosa cells was analyzed by flow cytometry or MTT, respectively. In Target scan and microT-CDS databases, an intersection of miRNAs targeting to p53 was found. The expressions of miRNAs in BMSC-derived exosomes were detected by qRT-PCR. Besides, miR-664-5p targeted to p53 of cells was verified by dual-luciferase reporter assay.ResultsBMSC-derived exosomes improved the follicular morphology of POF mice and inhibited the expression of apoptosis-related protein. By co-culture of exosomes and primary granulosa cells, BMSC-derived exosomes repressed cisplatin-induced granulosa cells apoptosis and increased cells viability, while these effects were abrogated after the exosome-containing RNA was degraded by RNase. By Target scan, microT-CDS and qRT-PCR, miR-664-5p was regarded as the dominated RNA in BMSC-derived exosomes. By dual-luciferase reporter assay, miR-664-5p negatively regulated p53 luciferase activity. After shRNA interfering miR-664-5p of BMSC, BMSC-derived exosomes exerted no protective effect on cisplatin-induced granulosa cell apoptosis.ConclusionOur results indicated that miR-644-5p carried by BMSC-derived exosomes inhibited the apoptosis of ovarian granulosa cell by targeting p53 of cells, suggesting that miR-644-5p had the potential to treat POF and restore ovarian function.

Metformin Regulates Key MicroRNAs to Improve Endometrial Receptivity Through Increasing Implantation Marker Gene Expression in Patients with PCOS Undergoing IVF/ICSI.

To some extent, the use of metformin may improve endometrial receptivity and pregnancy outcomes of women with polycystic ovarian syndrome (PCOS) undergoing in vitro fertilization/intracytoplasmic sperm injection. However, the mechanism is not well-known. The endometrium of metformin-treated group (metformin-treated patients with PCOS) and the control group (non-metformin-treated patients with PCOS) were analyzed for the expression of homeobox A10 (HOXA10) and integrin beta-3 (ITGB3) and differential micro RNA (miRNA) expression profiles. On this basis, miRDB and Target Scan databases were used to predict and screen out that miR-491-3p and miR-1910-3p may target HOXA10 and ITGB3. Furthermore, we verified the effects of metformin on the expression of HOXA10 and ITGB3, and regulatory effects of miR-1910-3p and miR-491-3p on HOXA10 and ITGB3 using Ishikawa cell line. Metformin induced a significant dose-dependent upregulation of HOXA10 and ITGB3. The results from the microarray analyses showed there were 40 differentially expressed miRNAs between the 2 groups. Among them, miR-1910-3p and miR-491-3p were the 2 significantly downregulated miRNAs. Bioinformatics prediction indicated that HOXA10 and ITGB3 are potential target genes for miR-1910-3p and miR-491-3p. In Ishikawa cells transfected with miR-491-3p mimics, the expression of HOXA10 and ITGB3 on both messenger RNA (mRNA) and protein level were lower than those in control group (P < .001). Also, the expression of HOXA10 mRNA and protein was lower in Ishikawa cells transfected with miR-1910-3p mimics (P < .001). However, no significant changes in ITGB3 levels were observed in cells transfected with miR-1910-3p mimics (P > .05). Metformin likely improves endometrial receptivity through downregulating the expression of miR-491-3p and miR-1910-3p, thereby increasing the expression of HOXA10 and ITGB3 in the endometrium of PCOS women.

Identification of biomarkers to response of Tripterygium Glycosides Tablets acting on rheumatoid arthritis by integrating transcriptional data mining and biomolecular network analysis

Growing clinical evidence shows that a partial rheumatoid arthritis( RA) patient treated with Tripterygium Glycosides Tablets( TGT) may fail to achieve clinical improvement. It is of great clinical significance to predict the therapeutic effect of TGT in RA. Therefore,the aim of the current study was to identify potential biomarkers for TGT treatment in RA. Affymetrix EG1.0 arrays were applied to detect gene expression in peripheral blood mononuclear cells obtained from 6 RA patients( 3 responders and 3 non-responders) treated with TGT. By integrating differential expression data analysis and biomolecular network analysis,360 mRNAs( 185 up-regulated and 175 down-regulated) and 24 miRNAs( 7 up-regulated and 17 down-regulated) which were differentially expressed between TGT responder and non-responder groups were identified. A total of 206 candidate target genes for the differentially expressed miRNAs were obtained based on miRanada and Target Scan databases,and then the miRNA target gene coexpression network and miRNA-mediated gene signal transduction network were constructed. Following the network analyses,three candidate miRNAs biomarkers( hsa-miR-4720-5 p,hsa-miR-374 b-5 p,hsa-miR-185-3 p) were identified as candidate biomarkers predicting individual response to TGT. Partialleast-squares( PLS) was applied to construct a model for predicting response to TGT based on the expression levels of the candidate gene biomarkers in RA patients. The five-fold cross-validation showed that the prediction accuracy( ACC) of this PLS-based model efficacy was 100.00%,100.00%,100.00%,66.67% and 66.67% respectively,and all the area under the receiver operating characteristic curve( AUC) were 1.00,indicating the highly predictive efficiency of this PLS-based model. In conclusion,the integrating transcription data mining and biomolecular network investigation show that hsa-mir-4720-5 p,hsa-mir-374 b-5 p and hsa-mir-185-3 p may be candidate biomarkers predicting individual response to TGT. In addition,the PLS model based on the expression levels of these candidate biomarkers may be helpful for the clinical screen of RA patients,which potentially benefit individualized therapy of RA in a daily clinical setting.

Is computed tomography of the head justified in patients with minor head trauma presenting with Glasgow Coma Scale 15/15?

BackgroundIn keeping with radiology departments in tertiary referral hospitals in developing countries offering computed tomography (CT) head scan services, the radiology department at Groote Schuur Hospital (GSH) in the Western Cape of South Africa undertakes several such scans annually. Of these scans, many are undertaken for post-trauma patients with minor head injury (MHI). While there is agreement that MHI patients with Glasgow Coma Scale (GCS) scores of 13–14/15 may well benefit, there is doubt as to the clinical utility of routine CT head scanning in MHI patients with GCS scores of 15/15.ObjectivesThis retrospective descriptive study of patient records was undertaken to determine the frequency and clinical significance of any abnormalities found on CT head scans of 460 patients with MHI and GCS scores of 15/15, scanned at GSH between 2012 and 2014.MethodEthical clearance was obtained and the records of 460 MHI patients with GCS scores of 15/15, loss of consciousness (LOC) and amnesia who underwent CT head scanning at GSH between 2012 and 2014 were then retrieved from the Philips picture archiving and communication system (PACS). Patient records, containing illegible referral forms or technically inadequate CT head scans, were excluded from the study. Patients’ biographical, clinical and CT head scan data were entered into sequentially numbered data collection forms. These data were tabulated and summed as percentage distributions. Patients’ CT head scan findings were reviewed and classified as either showing normal or abnormal features. Abnormalities detected on CT head scans were classified as being either clinically significant or clinically non-significant.ResultsReferral forms and CT scan reports were obtained for 460 MHI patients from a sample of 497 patients, calculated by using the equation for estimating a single proportion from a large sample (precision 1.5%). The sample obtained yielded an acceptable response rate of 460/497 (92.6%). Of 460 (100%) scan reports, 320 (69.6%) showed no abnormality, while 140 (30.4%) showed abnormality. Of the 140 abnormal scans, 107 (23.3%) showed clinically non-significant abnormality, while 33 (7.2%) revealed clinically significant abnormality. Twenty-two (4.8%) of these clinically significant scans showed brain contusion and 11(2.4%) showed skull fracture. No subdural or extradural haematoma, shift or herniation were reported and none of the 33 patients whose CT scans showed clinically significant abnormality underwent urgent neurosurgical intervention.ConclusionOf the 460 CT head scans performed at GSH for MHI with LOC but normal GCS between 2012 and 2014, none required urgent neurosurgical intervention. This is in accordance with the 2012 Kimberley Hospital Rule (KHR), a management protocol which indicates that CT head scanning in patients with MHI and GCS scores of 15/15 can safely be delayed for 8 h. An audit of the records of patients excluded from this study coupled with an analysis of data from other Western Cape hospital CT head scan databases could help ensure that this scarce resource is used cost-beneficially for all head-injured patients in the Western Cape catchment area.

Building statistical shape spaces for 3D human modeling

Statistical models of 3D human shape and pose learned from scan databases have developed into valuable tools to solve a variety of vision and graphics problems. Unfortunately, most publicly available models are of limited expressiveness as they were learned on very small databases that hardly reflect the true variety in human body shapes. In this paper, we contribute by rebuilding a widely used statistical body representation from the largest commercially available scan database, and making the resulting model available to the community (visit http://humanshape.mpi-inf.mpg.de). As preprocessing several thousand scans for learning the model is a challenge in itself, we contribute by developing robust best practice solutions for scan alignment that quantitatively lead to the best learned models. We make implementations of these preprocessing steps also publicly available. We extensively evaluate the improved accuracy and generality of our new model, and show its improved performance for human body reconstruction from sparse input data.

3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.

Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods.

DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets

Frequent closed itemsets (FCI) play an important role in pruning redundant rules fast. Therefore, a lot of algorithms for mining FCI have been developed. Algorithms based on vertical data formats have some advantages in that they require scan databases once and compute the support of itemsets fast. Recent years, BitTable (Dong & Han, 2007) and IndexBitTable (Song, Yang, & Xu, 2008) approaches have been applied for mining frequent itemsets and results are significant. However, they always use a fixed size of Bit-Vector for each item (equal to number of transactions in a database). It leads to consume more memory for storage Bit-Vectors and the time for computing the intersection among Bit-Vectors. Besides, they only apply for mining frequent itemsets, algorithm for mining FCI based on BitTable is not proposed. This paper introduces a new method for mining FCI from transaction databases. Firstly, Dynamic Bit-Vector (DBV) approach will be presented and algorithms for fast computing the intersection between two DBVs are also proposed. Lookup table is used for fast computing the support (number of bits 1 in a DBV) of itemsets. Next, subsumption concept for memory and computing time saving will be discussed. Finally, an algorithm based on DBV and subsumption concept for mining frequent closed itemsets fast is proposed. We compare our method with CHARM, and recognize that the proposed algorithm is more efficient than CHARM in both the mining time and the memory usage.

Bringing Water Data Together

We are living through an information revolution. We are instantly connected to information and services through the internet, and communicate through e-mail with colleagues spread all over the globe, without regard to distance or their computer system. How can the power of this information revolution be brought to bear on water resources? This editorial presents some perspectives I have derived from my service as leader of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. CUAHSI Hydrologic Information System HIS project, a Nation Science Foundation NSF -supported effort to improve hydrologic science through better data access and organization at the nation’s universities. The circulation of the waters of the earth through the hydrologic cycle is a very complex phenomenon. Many agencies and individuals collect water observations data on streamflow, water quality, groundwater levels, precipitation, snow conditions, and climate. The largest repository of such data in the United States is the United States Geological Survey USGS National Water Information System NWIS , which contains information from about 1.6 million sites for streamflow, water quality, and groundwater. Other large federal repositories of water observations data are the Environmental Protection Agency EPA Storet Storage and Retrieval for water quality, the National Climatic Data Center’s Climate Data Online, and the United States Department of Agriculture USDA -National Resources Conservation Service NRCS Snotel and Soil Climate Analysis Network SCAN databases. Agencies such as the Bureau of Reclamation and the Corps of Engineers store water operations data at reservoirs and other facilities. This panoply of federal data is augmented by similar data collected by state and local water agencies. Academic and research investigators collect data for individual research projects, and contribute significantly to subjects, such as aquatic biology and continuous monitoring of water quality, that are not the traditional focus of the large water agencies. The aggregation of all this water observations information defines the historical record of the water conditions of the nation. The task of assembling all the relevant information for a particular purpose in a chosen geographic region is greatly facilitated by the publication of much of the data on Web sites. However, every organization follows its own method in constructing its Web site and formatting the data presented there, so it still requires a great deal of effort to discover, learn, and operate all these various Web data sources. And, much of the data collected by academic and research investigators is no longer accessible once research studies are completed. Water conditions have been measured in the nation at several million locations, but no comprehensive window on water data exists. The growth of information sharing across the internet has led to the definition of standards for communication among computers. One of these standards is the HyperText Markup Language HTML used for communication of information visible in Web

Automated Detection of Small Pulmonary Nodules in Whole Lung CT Scans

The objective of this work was to develop and evaluate a robust algorithm that automatically detects small solid pulmonary nodules in whole lung helical CT scans from a lung cancer screening study. We developed a three-stage detection algorithm for both isolated and attached nodules. The algorithm consisted of nodule search space demarcation, nodule candidates' generation, and a sequential elimination of false positives. Isolated nodules are nodules that are surrounded by lung parenchyma, whereas attached nodules are connected to large, dense structures such as pleural and/or mediastinal surface. Two large well-documented whole lung CT scan databases (Databases A and B) were created to train and test the detection algorithm. Database A contains 250 sequentially selected scans with 2.5-mm slice thickness that were obtained at Weill Medical College of Cornell University. With equipment upgrade at this college, a second database, Database B, was created containing 250 scans with a 1.25-mm slice thickness. A total of 395 and 482 nodules were identified in Databases A and B, respectively. In both databases, the majority of the nodules were isolated, comprising 72.1% and 82.3% of nodules in Databases A and B, respectively. The detection algorithm was trained and tested on both Databases A and B. For isolated nodules with sizes 4 mm or larger, the algorithm achieved 94.0% sensitivity and 7.1 false positives per case (FPPC) for Database A (2.5 mm). Similarly, the algorithm achieved 91% sensitivity and 6.9 FPPC for Database B (1.25 mm). The algorithm achieved 92% sensitivity with 17.4 FPPC and 89% sensitivity with 5.5 FFPC for attached nodules with sizes 3 mm or larger in the Database A (2.5 mm) and Database B (1.25 mm), respectively. The developed algorithm achieved practical performance for automated detection of both isolated and the more challenging attached nodules. The automated system will be a useful tool to assist radiologists in identifying nodules from whole lung CT scans in a clinical setting.