Seroepidemiology of emerging tickborne infectious diseases in a Northern California community.

PubMed

Fritz, C L; Kjemtrup, A M; Conrad, P A; Flores, G R; Campbell, G L; Schriefer, M E; Gallo, D; Vugia, D J

1997-06-01

A seroprevalence and risk factor study of emerging tickborne infectious diseases (Lyme disease, ehrlichiosis, and babesiosis) was conducted among 230 residents of a semirural community in Sonoma County, California. Over 50% of residents reported finding a tick on themselves in the preceding 12 months. Samples from 51(23%) residents were seroreactive to antigens from one or more tickborne disease agents: 1.4% to Borrelia burgdorferi, 0.4% to Ehrlichia equi, 4.6% to Ehrlichia chaffeensis, and 17.8% to the Babesia-like piroplasm WA1. Only 14 (27%) of these seroreactive residents reported one or more symptoms compatible with these diseases. Seroreactivity was significantly associated with younger age (<16 years), longer residence in the community (11-20 years), and having had a physician's diagnosis of Lyme disease. In northern California, the risk of infection with these emerging tickborne diseases, particularly in children, may be greater than previously recognized.

Gene expression profile and immunological evaluation of unique hypothetical unknown proteins of Mycobacterium leprae by using quantitative real-time PCR.

PubMed

Kim, Hee Jin; Prithiviraj, Kalyani; Groathouse, Nathan; Brennan, Patrick J; Spencer, John S

2013-02-01

The cell-mediated immunity (CMI)-based in vitro gamma interferon release assay (IGRA) of Mycobacterium leprae-specific antigens has potential as a promising diagnostic means to detect those individuals in the early stages of M. leprae infection. Diagnosis of leprosy is a major obstacle toward ultimate disease control and has been compromised in the past by the lack of specific markers. Comparative bioinformatic analysis among mycobacterial genomes identified potential M. leprae-specific proteins called "hypothetical unknowns." Due to massive gene decay and the prevalence of pseudogenes, it is unclear whether any of these proteins are expressed or are immunologically relevant. In this study, we performed cDNA-based quantitative real-time PCR to investigate the expression status of 131 putative open reading frames (ORFs) encoding hypothetical unknowns. Twenty-six of the M. leprae-specific antigen candidates showed significant levels of gene expression compared to that of ESAT-6 (ML0049), which is an important T cell antigen of low abundance in M. leprae. Fifteen of 26 selected antigen candidates were expressed and purified in Escherichia coli. The seroreactivity to these proteins of pooled sera from lepromatous leprosy patients and cavitary tuberculosis patients revealed that 9 of 15 recombinant hypothetical unknowns elicited M. leprae-specific immune responses. These nine proteins may be good diagnostic reagents to improve both the sensitivity and specificity of detection of individuals with asymptomatic leprosy.

Gene Expression Profile and Immunological Evaluation of Unique Hypothetical Unknown Proteins of Mycobacterium leprae by Using Quantitative Real-Time PCR

PubMed Central

Prithiviraj, Kalyani; Groathouse, Nathan; Brennan, Patrick J.; Spencer, John S.

2013-01-01

The cell-mediated immunity (CMI)-based in vitro gamma interferon release assay (IGRA) of Mycobacterium leprae-specific antigens has potential as a promising diagnostic means to detect those individuals in the early stages of M. leprae infection. Diagnosis of leprosy is a major obstacle toward ultimate disease control and has been compromised in the past by the lack of specific markers. Comparative bioinformatic analysis among mycobacterial genomes identified potential M. leprae-specific proteins called “hypothetical unknowns.” Due to massive gene decay and the prevalence of pseudogenes, it is unclear whether any of these proteins are expressed or are immunologically relevant. In this study, we performed cDNA-based quantitative real-time PCR to investigate the expression status of 131 putative open reading frames (ORFs) encoding hypothetical unknowns. Twenty-six of the M. leprae-specific antigen candidates showed significant levels of gene expression compared to that of ESAT-6 (ML0049), which is an important T cell antigen of low abundance in M. leprae. Fifteen of 26 selected antigen candidates were expressed and purified in Escherichia coli. The seroreactivity to these proteins of pooled sera from lepromatous leprosy patients and cavitary tuberculosis patients revealed that 9 of 15 recombinant hypothetical unknowns elicited M. leprae-specific immune responses. These nine proteins may be good diagnostic reagents to improve both the sensitivity and specificity of detection of individuals with asymptomatic leprosy. PMID:23239802

Seroprevalence of Bartonella species, Coxiella burnetii and Toxoplasma gondii among patients with hematological malignancies: A pilot study in Romania.

PubMed

Messinger, C J; Gurzau, E S; Breitschwerdt, E B; Tomuleasa, C I; Trufan, S J; Flonta, M M; Maggi, R G; Berindan-Neagoe, I; Rabinowitz, P M

2017-09-01

Patients receiving immunosuppressive cancer treatments in settings where there is a high degree of human-animal interaction may be at increased risk for opportunistic zoonotic infections or reactivation of latent infections. We sought to determine the seroprevalence of selected zoonotic pathogens among patients diagnosed with haematologic malignancies and undergoing chemotherapeutic treatments in Romania, where much of the general population lives and/or works in contact with livestock. A convenience sample of 51 patients with haematologic cancer undergoing chemotherapy at a referral clinic in Cluj-Napoca, Romania, was surveyed regarding animal exposures. Blood samples were obtained and tested for evidence of infection with Bartonella species, Coxiella burnetii and Toxoplasma gondii, which are important opportunistic zoonotic agents in immunocompromised individuals. 58.8% of participants reported living or working on a farm, and living or working on a farm was associated with contact with livestock and other animals. 37.5% of participants were IgG seroreactive against one or more of five Bartonella antigens, and seroreactivity was statistically associated with living on farms. Farm dwellers were 3.6 times more likely to test IgG seroreactive to Bartonella antibodies than non-farm dwellers. 47.1% of the participants tested T. gondii IgG positive and 13.7% tested C. burnetii IgG positive, indicating past or latent infection. C. burnetii IgM antibodies were detected in four participants (7.8%), indicating possible recent infection. These results indicate that a large proportion of patients with haematologic cancer in Romania may be at risk for zoonotic infections or for reactivation of latent zoonotic infections, particularly with respect to Bartonella species. Special attention should be paid to cancer patients' exposure to livestock and companion animals in areas where much of the population lives in rural settings. © 2017 Blackwell Verlag GmbH.

Strongyloides stercoralis seroprevalence in Vietnam.

PubMed

Diep, Nguyen Thi Ngoc; Thai, Pham Quang; Trang, Nghiem Nguyen Minh; Jäger, Julia; Fox, Annette; Horby, Peter; Phuong, Hoang Vu Mai; Anh, Dang Duc; Mai, LE Thi Quynh; VAN Doorn, H Rogier; Nadjm, Behzad

2017-11-01

Strongyloidiasis is a neglected tropical disease caused by the roundworm Strongyloides stercoralis affecting 30-100 million people worldwide. Many Southeast-Asian countries report a high prevalence of S. stercoralis infection, but there are little data from Vietnam. Here, we evaluated the seroprevalence of S. stercoralis related to geography, sex and age in Vietnam through serological testing of anonymized sera. Sera (n = 1710, 1340 adults and 270 children) from an anonymized age-stratified serum bank from four regions in Vietnam between 2012 and 2013 were tested using a commercial Strongyloides ratti immunoglobulin G ELISA. Seroreactivity was found in 29·1% (390/1340) of adults and 5·5% (15/270) of children. Male adults were more frequently seroreactive than females (33·3% vs. 24·9%, P = 0·001). The rural central highlands had the highest seroprevalence (42·4% of adults). Seroreactivity in the other regions was 29·9% (Hue) and 26·0% and 18·2% in the large urban centres of Hanoi and Ho Chi Minh City, respectively. We conclude that seroprevalence of S. stercoralis was high in the Vietnamese adult population, especially in rural areas.

Natural immune responses against eight oncogenic human papillomaviruses in the ASCUS-LSIL triage study

PubMed Central

Wilson, Lauren E.; Pawlita, Michael; Castle, Phillip E.; Waterboer, Tim; Sahasrabuddhe, Vikrant; Gravitt, Patti E.; Schiffman, Mark; Wentzensen, Nicolas

2014-01-01

Only a subset of women with human papillomavirus (HPV) infections will become seropositive, and the factors influencing seroconversion are not well-understood. We used a multiplex serology assay in women with mildly abnormal cytology results to examine seroreactivity to oncogenic HPV genotypes. An unbiased subset of women in the atypical squamous cell of undetermined significance /low-grade squamous intraepithelial lesion Triage Study (ALTS) provided blood samples at trial enrollment for serological testing. A Luminex assay based on GST-L1 fusion proteins as antigens was used to test seroreactivity against eight carcinogenic HPV genotypes (16, 18, 31, 33, 35, 45, 52, 58). We analyzed the relationship between seroprevalence in women free of precancer (N=2464) and HPV DNA status, age, sexual behavior, and other HPV-related risk factors. The overall seroprevalence was 24.5% for HPV16 L1 and ~ 20% for 18L1 and 31L1. Among women free of precancer, seroprevalence peaked in women less than 29 years and decreased with age. Type-specific seroprevalence was associated with baseline DNA detection for HPV16 (OR= 1.36, 95%CI: 1.04–1.79) and HPV18 (OR= 2.31, 95%CI: 1.61–3.32), as well as for HPV52 and HPV58. Correlates of sexual exposure were associated with increased seroprevalence across most genotypes. Women who were current or former smokers were less likely to be seropositive for all eight of the tested oncogenic genotypes. The multiplex assay showed associations between seroprevalence and known risk factors for HPV infection across nearly all tested HPV genotypes but associations between DNA- and serostatus were weak, suggesting possible misclassification of the participants’ HPV serostatus. PMID:23588935

Identification of Novel Seroreactive Antigens in Johne's Disease Cattle by Using the Mycobacterium tuberculosis Protein Array

PubMed Central

Campo, Joseph J.; Li, Lingling; Randall, Arlo; Pablo, Jozelyn; Praul, Craig A.; Raygoza Garay, Juan Antonio; Stabel, Judith R.

2017-01-01

ABSTRACT Johne's disease, a chronic gastrointestinal inflammatory disease caused by Mycobacterium avium subspecies paratuberculosis, is endemic in dairy cattle and other ruminants worldwide and remains a challenge to diagnose using traditional serological methods. Given the close phylogenetic relationship between M. avium subsp. paratuberculosis and the human pathogen Mycobacterium tuberculosis, here, we applied a whole-proteome M. tuberculosis protein array to identify seroreactive and diagnostic M. avium subsp. paratuberculosis antigens. A genome-scale pairwise analysis of amino acid identity levels between orthologous proteins in M. avium subsp. paratuberculosis and M. tuberculosis showed an average of 62% identity, with more than half the orthologous proteins sharing >75% identity. Analysis of the M. tuberculosis protein array probed with sera from M. avium subsp. paratuberculosis-infected cattle showed antibody binding to 729 M. tuberculosis proteins, with 58% of them having ≥70% identity to M. avium subsp. paratuberculosis orthologs. The results showed that only 4 of the top 40 seroreactive M. tuberculosis antigens were orthologs of previously reported M. avium subsp. paratuberculosis antigens, revealing the existence of a large number of previously unrecognized candidate diagnostic antigens. Enzyme-linked immunosorbent assay (ELISA) testing of 20 M. avium subsp. paratuberculosis recombinant proteins, representing reactive and nonreactive M. tuberculosis orthologs, further confirmed that the M. tuberculosis array has utility as a screening tool for identifying candidate antigens for Johne's disease diagnostics. Additional ELISA testing of field serum samples collected from dairy herds around the United States revealed that MAP2942c had the strongest seroreactivity with Johne's disease-positive samples. Collectively, our studies have considerably expanded the number of candidate M. avium subsp. paratuberculosis proteins with potential utility in the next generation of rationally designed Johne's disease diagnostic assays. PMID:28515134

Identification of Novel Seroreactive Antigens in Johne's Disease Cattle by Using the Mycobacterium tuberculosis Protein Array.

PubMed

Bannantine, John P; Campo, Joseph J; Li, Lingling; Randall, Arlo; Pablo, Jozelyn; Praul, Craig A; Raygoza Garay, Juan Antonio; Stabel, Judith R; Kapur, Vivek

2017-07-01

Johne's disease, a chronic gastrointestinal inflammatory disease caused by Mycobacterium avium subspecies paratuberculosis , is endemic in dairy cattle and other ruminants worldwide and remains a challenge to diagnose using traditional serological methods. Given the close phylogenetic relationship between M. avium subsp. paratuberculosis and the human pathogen Mycobacterium tuberculosis , here, we applied a whole-proteome M. tuberculosis protein array to identify seroreactive and diagnostic M. avium subsp. paratuberculosis antigens. A genome-scale pairwise analysis of amino acid identity levels between orthologous proteins in M. avium subsp. paratuberculosis and M. tuberculosis showed an average of 62% identity, with more than half the orthologous proteins sharing >75% identity. Analysis of the M. tuberculosis protein array probed with sera from M. avium subsp. paratuberculosis -infected cattle showed antibody binding to 729 M. tuberculosis proteins, with 58% of them having ≥70% identity to M. avium subsp. paratuberculosis orthologs. The results showed that only 4 of the top 40 seroreactive M. tuberculosis antigens were orthologs of previously reported M. avium subsp. paratuberculosis antigens, revealing the existence of a large number of previously unrecognized candidate diagnostic antigens. Enzyme-linked immunosorbent assay (ELISA) testing of 20 M. avium subsp. paratuberculosis recombinant proteins, representing reactive and nonreactive M. tuberculosis orthologs, further confirmed that the M. tuberculosis array has utility as a screening tool for identifying candidate antigens for Johne's disease diagnostics. Additional ELISA testing of field serum samples collected from dairy herds around the United States revealed that MAP2942c had the strongest seroreactivity with Johne's disease-positive samples. Collectively, our studies have considerably expanded the number of candidate M. avium subsp. paratuberculosis proteins with potential utility in the next generation of rationally designed Johne's disease diagnostic assays. Copyright © 2017 American Society for Microbiology.

Serological response to Helicobacter pylori infection among Latin American populations with contrasting risks of gastric cancer

PubMed Central

Camargo, M. Constanza; Beltran, Mauricio; Conde-Glez, Carlos; Harris, Paul R.; Michel, Angelika; Waterboer, Tim; Flórez, Astrid Carolina; Torres, Javier; Ferreccio, Catterina; Sampson, Joshua N.; Pawlita, Michael; Rabkin, Charles S.

2015-01-01

Gastric cancer is a rare outcome of chronic Helicobacter pylori infection. Serologic profiles may reveal bacterial, environmental and/or host factors associated with cancer risk. We therefore compared specific anti-H. pylori antibodies among populations with at least 2-fold differences in gastric cancer mortality from Mexico, Colombia and Chile. Our study included 1,776 adults (mean age 42 years) from three nationally representative surveys, equally divided between residents of high- and low-risk areas. Antibodies to 15 immunogenic H. pylori antigens were measured by fluorescent bead-based multiplex assays; results were summarized to identify overall H. pylori seropositivity. We used logistic regression to model associations between antibody seroreactivity and regional cancer risk (high vs. low), adjusting for country, age and sex. Both risk areas had similar H. pylori seroprevalence. Residents in high- and low-risk areas were seroreactive to a similar number of antigens (means 8.2 vs. 7.9, respectively; adjusted-odds ratio, OR: 1.02, p=0.05). Seroreactivities to Catalase and the known virulence proteins CagA and VacA were each significantly (p<0.05) associated with residence in high-risk areas, but ORs were moderate (1.26, 1.42, and 1.41, respectively) and their discriminatory power was low (ROC area under curve <0.6). The association of Catalase was independent from effects of either CagA or VacA. Sensitivity analyses for antibody associations restricted to H. pylori-seropositive individuals generally replicated significant associations. Our findings suggest that humoral responses to H. pylori are insufficient to distinguish high and low gastric cancer risk in Latin America. Factors determining population variation of gastric cancer burden remain to be identified. PMID:26178251

Profiling the humoral immune response of acute and chronic Q fever by protein microarray.

PubMed

Vigil, Adam; Chen, Chen; Jain, Aarti; Nakajima-Sasaki, Rie; Jasinskas, Algimantas; Pablo, Jozelyn; Hendrix, Laura R; Samuel, James E; Felgner, Philip L

2011-10-01

Antigen profiling using comprehensive protein microarrays is a powerful tool for characterizing the humoral immune response to infectious pathogens. Coxiella burnetii is a CDC category B bioterrorist infectious agent with worldwide distribution. In order to assess the antibody repertoire of acute and chronic Q fever patients we have constructed a protein microarray containing 93% of the proteome of Coxiella burnetii, the causative agent of Q fever. Here we report the profile of the IgG and IgM seroreactivity in 25 acute Q fever patients in longitudinal samples. We found that both early and late time points of infection have a very consistent repertoire of IgM and IgG response, with a limited number of proteins undergoing increasing or decreasing seroreactivity. We also probed a large collection of acute and chronic Q fever patient samples and identified serological markers that can differentiate between the two disease states. In this comparative analysis we confirmed the identity of numerous IgG biomarkers of acute infection, identified novel IgG biomarkers for acute and chronic infections, and profiled for the first time the IgM antibody repertoire for both acute and chronic Q fever. Using these results we were able to devise a test that can distinguish acute from chronic Q fever. These results also provide a unique perspective on isotype switch and demonstrate the utility of protein microarrays for simultaneously examining the dynamic humoral immune response against thousands of proteins from a large number of patients. The results presented here identify novel seroreactive antigens for the development of recombinant protein-based diagnostics and subunit vaccines, and provide insight into the development of the antibody response.

T2 Magnetic Resonance Assay-Based Direct Detection of Three Lyme Disease-Related Borrelia Species in Whole-Blood Samples.

PubMed

Snyder, Jessica L; Giese, Heidi; Bandoski-Gralinski, Cheryl; Townsend, Jessica; Jacobson, Beck E; Shivers, Robert; Schotthoefer, Anna M; Fritsche, Thomas R; Green, Clayton; Callister, Steven M; Branda, John A; Lowery, Thomas J

2017-08-01

In early Lyme disease (LD), serologic testing is insensitive and seroreactivity may reflect active or past infection. In this study, we evaluated a novel assay for the direct detection of three species of Borrelia spirochetes in whole blood. The T2 magnetic resonance (T2MR) assay platform was used to amplify Borrelia DNA released from intact spirochetes and to detect amplicon. Analytical sensitivity was determined from blood spiked with known concentrations of spirochetes, and the assay's limit of detection was found to be in the single-cell-per-milliliter range: 5 cells/ml for B. afzelii and 8 cells/ml for Borrelia burgdorferi and Borrelia garinii Clinical samples ( n = 66) from confirmed or suspected early LD patients were also analyzed. B. burgdorferi was detected using T2MR in 2/2 (100%) of blood samples from patients with confirmed early LD, based on the presence of erythema migrans and documentation of seroconversion or a positive real-time blood PCR. T2MR detected B. burgdorferi in blood samples from 17/54 (31%) of patients with probable LD, based on the presence of erythema migrans without documented seroconversion or of documented seroconversion in patients with a compatible clinical syndrome but without erythema migrans. Out of 21 clinical samples tested by real-time PCR, only 1 was positive and 13 were negative with agreement with T2MR. An additional 7 samples that were negative by real-time PCR were positive with T2MR. Therefore, T2MR enables a low limit of detection (LoD) for Borrelia spp. in whole blood samples and is able to detect B. burgdorferi in clinical samples. Copyright © 2017 American Society for Microbiology.

T2 Magnetic Resonance Assay-Based Direct Detection of Three Lyme Disease-Related Borrelia Species in Whole-Blood Samples

PubMed Central

Giese, Heidi; Bandoski-Gralinski, Cheryl; Townsend, Jessica; Jacobson, Beck E.; Shivers, Robert; Schotthoefer, Anna M.; Fritsche, Thomas R.; Green, Clayton; Callister, Steven M.; Branda, John A.

2017-01-01

ABSTRACT In early Lyme disease (LD), serologic testing is insensitive and seroreactivity may reflect active or past infection. In this study, we evaluated a novel assay for the direct detection of three species of Borrelia spirochetes in whole blood. The T2 magnetic resonance (T2MR) assay platform was used to amplify Borrelia DNA released from intact spirochetes and to detect amplicon. Analytical sensitivity was determined from blood spiked with known concentrations of spirochetes, and the assay's limit of detection was found to be in the single-cell-per-milliliter range: 5 cells/ml for B. afzelii and 8 cells/ml for Borrelia burgdorferi and Borrelia garinii. Clinical samples (n = 66) from confirmed or suspected early LD patients were also analyzed. B. burgdorferi was detected using T2MR in 2/2 (100%) of blood samples from patients with confirmed early LD, based on the presence of erythema migrans and documentation of seroconversion or a positive real-time blood PCR. T2MR detected B. burgdorferi in blood samples from 17/54 (31%) of patients with probable LD, based on the presence of erythema migrans without documented seroconversion or of documented seroconversion in patients with a compatible clinical syndrome but without erythema migrans. Out of 21 clinical samples tested by real-time PCR, only 1 was positive and 13 were negative with agreement with T2MR. An additional 7 samples that were negative by real-time PCR were positive with T2MR. Therefore, T2MR enables a low limit of detection (LoD) for Borrelia spp. in whole blood samples and is able to detect B. burgdorferi in clinical samples. PMID:28566314

Serologic evidence for tick-borne pathogens other than Borrelia burgdorferi (TOBB) in Lyme borreliosis patients from midwestern Germany.

PubMed

Hunfeld, K P; Allwinn, R; Peters, S; Kraiczy, P; Brade, V

1998-12-23

The seroprevalence of antibodies against the human granulocytic ehrlichiosis agent (HGE) and Babesia microti was retrospectively determined in 76 Lyme borreliosis patients and in 44 asymptomatic individuals with a positive borreliosis serology, in comparison to 100 healthy blood donors from the Rhein-Main area. Additionally, seroreactivity for tick-borne encephalitis virus (TBEV) was investigated. For antibody detection, commercially available immunofluorescence assays (MRL Diagnostics, USA) and a TBEV-ELISA (Immuno, Germany) were used. In the control group, the positivity rate for anti-Borrelia burgdorferi (IgG/IgM) and anti-Babesia microti-antibodies in the population of the Rhein-Main area (Midwestern Germany) may be estimated at 15% and 8%, respectively. Examination for both HGE and TBEV demonstrated seroreactivity (IgG) in 1% of tested individuals. Specific anti-HGE IgG and/or IgM antibodies were more often discovered in cases of early Borrelia infection (stage I: 13.6%, stage II: 18.4%) than in patients with stage III disease (0%) or in seropositive but asymptomatic patients (6.8%). Investigation for TBEV revealed seroreactivity for IgG in 13% of these cases. No TBEV-IgM was found. Interestingly, the prevalence of anti-HGE and anti-TBEV antibodies among Lyme borreliosis patients and seropositive patients without active Lyme disease symptoms was significantly higher than that in the control group of healthy blood donors (p < 0.05). Likewise, antibody titers reflecting a recent infection with Babesia microti could be demonstrated more often in patients with Lyme borreliosis stage I or II (p < 0.05). Analysis of 50 samples from patients with florid or recent syphilis infection revealed no crossreactivity between Babesia microti, HGE and Treponema pallidum. Our findings suggest that concomitant or serial infection due to TOBB may be common in tick exposed patients from the Rhein-Main area and in European countries in general. Hence, in addition to TBEV, human babesiosis and HGE should always be considered by European physicians in the differential diagnosis of acute febrile illness following a tick bite.

Chlamydia trachomatis serotype A infections in the Amazon region of Brazil: prevalence, entry and dissemination.

PubMed

Ishak, Marluísa de Oliveira Guimarães; Costa, Maurimélia Mesquita; Almeida, Núbia Caroline Costa de; Santiago, Angélica Menezes; Brito, William Botelho de; Vallinoto, Antonio Carlos Rosário; Azevedo, Vânia Nakauth; Ishak, Ricardo

2015-01-01

Chlamydia infection is associated with debilitating human diseases including trachoma, pneumonia, coronary heart disease and urogenital diseases. Serotypes of C. trachomatis show a fair correlation with the group of diseases they cause, and their distribution follows a well-described geographic pattern. Serotype A, a trachoma-associated strain, is known for its limited dissemination in the Middle East and Northern Africa. However, knowledge on the spread of bacteria from the genus Chlamydia as well as the distribution of serotypes in Brazil is quite limited. Blood samples of 1,710 individuals from ten human population groups in the Amazon region of Brazil were examined for antibodies to Chlamydia using indirect immunofluorescence and microimmunofluorescence assays. The prevalence of antibodies to Chlamydia ranged from 23.9% (Wayana-Apalai) to 90.7% (Awa-Guaja) with a mean prevalence of 50.2%. Seroreactivity was detected to C. pneumoniae and to all serotypes of C. trachomatis tested; furthermore, we report clear evidence of the as-yet-undescribed occurrence of serotype A of C. trachomatis. Specific seroreactivity not only accounts for the large extent of dissemination of C. trachomatis in the Amazon region of Brazil but also shows an expanded area of occurrence of serotype A outside the epidemiological settings previously described. Furthermore, these data suggest possible routes of Chlamydia introduction into the Amazon region from the massive human migration that occurred during the 1,700s.

Serological and molecular evidence for spotted fever group Rickettsia and Borrelia burgdorferi sensu lato co-infections in The Netherlands.

PubMed

Koetsveld, Joris; Tijsse-Klasen, Ellen; Herremans, Tineke; Hovius, Joppe W R; Sprong, Hein

2016-03-01

Only a few reported cases indicate that Rickettsia helvetica and Rickettsia monacensis can cause disease in humans. Exposure to these two spotted fever group (SFG) rickettsiae occurs through bites of Ixodes ricinus, also the primary vector of Lyme borreliosis in Europe. To date, it is unclear how often exposure to these two microorganisms results in infection or disease. We show that of all the Borrelia burgdorferi s.l.-positive ticks, 25% were co-infected with rickettsiae. Predominantly R. helvetica was detected while R. monacensis was only found in approximately 2% of the ticks. In addition, exposure to tick-borne pathogens was compared by serology in healthy blood donors, erythema migrans (EM)-patients, and patients suspected of Lyme neuroborreliosis (LNB). As could be expected, seroreactivity against B. burgdorferi sensu lato was lower in blood donors (6%) compared to EM patients (34%) and suspected LNB cases (64%). Interestingly, seroreactivity against SFG Rickettsia antigens was not detected in serum samples from blood donors (0%), but 6% of the EM patients and 21% of the LNB suspects showed anti-rickettsial antibodies. Finally, the presence of B. burgdorferi s.l. and Rickettsia spp. in cerebrospinal fluid samples of a large cohort of patients suspected of LNB (n=208) was investigated by PCR. DNA of B. burgdorferi s.l., R. helvetica and R. monacensis was detected in seventeen, four and one patient, respectively. In conclusion, our data show that B. burgdorferi s.l. and SFG rickettsiae co-infection occurs in Dutch I. ricinus and that Lyme borreliosis patients, or patients suspected of Lyme borreliosis, are indeed exposed to both tick-borne pathogens. Whether SFG rickettsiae actually cause disease, and whether co-infections alter the clinical course of Lyme borreliosis, is not clear from our data, and warrants further investigation. Copyright © 2015 Elsevier GmbH. All rights reserved.

Patterns of orthopox virus wild rodent hosts in South Germany.

PubMed

Essbauer, Sandra; Hartnack, Sonja; Misztela, Krystian; Kiessling-Tsalos, Judith; Bäumler, Walter; Pfeffer, Martin

2009-06-01

Although cowpox virus (CPXV) infections in a variety of dead-end hosts have been investigated in Germany for more than 50 years, data on species and geographical distribution of CPXV in reservoir hosts are sparse. Here we present the first comprehensive study of 825 rodents that have been collected in Bavaria, Southern Germany. In summary, six different rodent species (Apodemus flavicollis, Myodes glareolus, Microtus arvalis, Apodemus sylvaticus, Microtus agrestis, and Arvicola amphibius) were trapped at three main trapping sites and investigated using a serum neutralization test (SNT). Prevalence of orthopox virus (OPV)-neutralizing antibodies was (with exception of one trapping site) highest in bank voles, ranging from 24.5% to 42.4%; often with SNT titers > or =96. Two up to 25% of yellow-necked mice were OPV sero-positive, but wood mice only at one site with 5.5%. Up to 7.7% of common voles were found to be OPV seroreactive, while M. agrestis and A. amphibius only sporadically showed seroreactivity. Further analyses of a subset of 450 bank voles and yellow-necked mice trapped at one site over a 18-month period revealed that male yellow-necked mice and female gravid yellow-necked mice had significantly more OPV-neutralizing antibodies. Mean body weight and OPV-seroreactivity were significantly negatively associated in male A. flavicollis. This was not due to shorter body length or smaller body mass index, but previously OPV-infected male A. flavicollis had dramatically reduced mean kidney weights. Seroreactivity in female bank voles was positively related to lung weights. We also found that both male yellow-necked mice and male bank voles with positive SNT titers had higher infestation rates with ectoparasites. We here show for the first time that A. flavicollis beside M. glareolus is a hypothetic host for CPXV, and that there are big geographical and spatial variations concerning the seroprevalence in rodent populations in South Germany.

Hematology, plasma chemistry, serology, and Chlamydophila status of the waved albatross (Phoebastria irrorata) on the Galapagos Islands.

PubMed

Padilla, Luis R; Huyvaert, Kathryn P; Merkel, Jane; Miller, R Eric; Parker, Patricia G

2003-09-01

Venipuncture was performed on 50 adult, free-ranging waved albatrosses (Phoebastria irrorata) on Española, Galapagos Islands, Ecuador, to establish hematologic and plasma biochemistry reference ranges and to determine the prevalence of exposure to important domestic avian pathogens. Weights and plasma creatine phosphokinase activities differed significantly between males and females. Serum was tested for evidence of exposure to avian influenza, avian paramyxoviruses 1, 2, and 3, avian cholera, adenovirus groups 1 and 2, avian encephalomyelitis, Marek's disease, infectious bursal disease, and infectious bronchitis virus (Connecticut and Massachusetts strains). Of 44 birds, 29 (66%) seroreacted to adenovirus group 1, and four seroreacted to avian encephalomyelitis. Cloacal swabs were negative for Chlamydophila psittaci DNA.

Recall of symptoms and treatment of syphilis and yaws by healthy blood donors screening positive for syphilis in Kumasi, Ghana.

PubMed

Sarkodie, Francis; Owusu-Dabo, Ellis; Hassall, Oliver; Bates, Imelda; Bygbjerg, Ib C; Ullum, Henrik

2016-09-01

To describe the recalled medical history, clinical manifestations, and treatment of yaws and syphilis by syphilis seroreactive blood donors in Kumasi, Ghana. Of the blood donors at Komfo Anokye Teaching Hospital, Kumasi, Ghana tested with the syphilis rapid diagnostic test (RDT) and later by rapid plasma reagin (RPR) test, 526 were seroreactive. Four hundred and seventy-one (89.5%) of these subjects were confirmed with the Ortho-Vitros Syphilis TP test as the gold standard and were interviewed to determine past or present clinical manifestations of yaws and syphilis. Of the 471 respondent donors, 28 (5.9%) gave a history of skin lesions and sores; four (14.3%) of these subjects, who were all male and RPR-positive, recalled a diagnosis of syphilis. All four reported having had skin lesions/bumps with slow-healing sores, but only one of them had had these symptoms before the age of 15 years. A small proportion of confirmed seroreactive donors in this sample had any recall of symptoms or treatment for yaws or syphilis. These data suggest that clinical questioning adds little further information to the current screening algorithm. The relative contribution of yaws and syphilis to frequent positive tests in endemic areas remains speculative. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Blood transfusion transmitted infections in multiple blood transfused patients of Beta thalassaemia.

PubMed

Vidja, Prakash J; Vachhani, J H; Sheikh, S S; Santwani, P M

2011-06-01

Transfusion Transmitted Infection (TTI) continue to be a problem in many parts of world and multi-transfused patients of beta thalassaemia major are at a particularly increased risk of TTI. This study is aimed to estimate the prevalence of blood TTI in multiple blood transfused patients of beta thalassaemia major. Cross-sectional study of 200 multi-transfused patients of beta thalassaemia major, who were interviewed using a structured questionnaire and history was taken regarding sero-status of HIV (Human Immunodeficiency Virus), HBV (Hepatitis B Virus), HCV (Hepatitis C Virus) infection from their case papers. This study was conducted at the department of Pathology, M.P. Shah medical college, Jamnagar and Thalassemia ward, G.G. Hospital, Jamnagar (Gujarat, India) from March to May 2010. Out of 200 multiple blood transfused patients 7% patients were infected with TTI. Total 9 male patients and 5 female patients were infected with TTI. The seroreactivity for HIV was 3% (06/200); 1% (02/200) were males and 2% (04/200) were females. The seroreactivity for HBV was 2% (04/200) all were males. The seroreactivity for HCV was 2% (04/200); 1.5% (03/200) were males and 0.5% (01/200) was female. HIV, HBV, HCV infections are most prevalent TTI among multiple blood transfused patients of beta thalassemia major, and remains a major health problem for these patients.

Peripartum dynamics of Coxiella burnetii infections in intensively managed dairy goats associated with a Q fever outbreak in Australia.

PubMed

Muleme, Michael; Stenos, John; Vincent, Gemma; Wilks, Colin R; Devlin, Joanne M; Campbell, Angus; Cameron, Alexander; Stevenson, Mark A; Graves, Stephen; Firestone, Simon M

2017-04-01

Coxiella burnetii may cause reproduction disorders in pregnant animals but subclinical infection in other animals. Unrecognised disease may delay implementation of control interventions, resulting in transmission of infection to other livestock and to humans. Seroreactivity to C. burnetii phase-specific antigens, is routinely used to interpret the course of human Q fever. This approach could be similarly useful in identifying new and existing infections in livestock herds to help describe risk factors or production losses associated with the infections and the implementation of disease-control interventions. This study aimed to elucidate the dynamics of C. burnetii infections using seroreactivity to phase-specific antigens and to examine the impact of infection on milk yield in goats in an endemically-infected farm that was associated with a Q fever outbreak in Australia. Seroreactivity pre- and post-partum and milk yield were studied in 164 goats (86 nulliparous and 78 parous). Post-partum, the seroprevalence of antibodies to C. burnetti increased from 4.7% to 31.4% throughout goats' first kiddings and from 47.4% to 55.1% in goats kidding for the second or greater time. Of 123 goats that were seronegative pre-partum, 26.8% seroconverted over the three-month peri-partum period, highlighting the importance of controlling infection throughout this time. The risk of seroconversion was comparable in first or later kidders, suggesting constant risk irrespective of parity. No loss in milk production associated with seroconversion to phase 2 was observed within the first nine weeks of lactation. However, seroconversion to only phase 1 was associated with extra 0.276L of milk per day (95% Confidence Interval: 0.010, 0.543; P=0.042), which warrants further investigation to ascertain whether or not the association is causal. Further studies on seroreactivity and milk production over longer periods are required, as milk production loss caused by C. burnetti may be an additional reason to control the disease in goat herds. Copyright © 2017 Elsevier B.V. All rights reserved.

Seroprevalence and factors associated with bovine viral diarrhea virus (BVDV) infection in dairy cattle in three milksheds in Ethiopia.

PubMed

Aragaw, Kassaye; Sibhat, Berhanu; Ayelet, Gelagay; Skjerve, Eystein; Gebremedhin, Endrias Z; Asmare, Kassahun

2018-05-31

This work was conducted to estimate the seroprevalence, to identify potential factors that influence seroprevalence of bovine viral diarrhea virus (BVDV), and to investigate the association between BVDV serostatus and occurrence of reproductive disorders in dairy cattle in three milksheds in Ethiopia. A total of 1379 serum samples were obtained from cattle randomly selected from 149 herds from three milksheds representing central, southern, and western Ethiopia. Sera samples were examined for bovine viral diarrhea virus (BVDV) antibodies using commercial competitive enzyme-linked immunosorbent assay (ELISA) kit. Logistic regression analysis was employed to investigate associations between risk factors and the risk of BVDV seroprevalence, and BVDV serostatus and reproductive disorders. Seroreaction to BVDV antigens was detected in 32.6% of the 1379 cattle and 69.8% of the 149 herds sampled. Factors associated with BVDV seroplevalence were age, breed, and herd size (P < 0.05). Adult cattle ≥ 18 months old had 2.1 (95% CI 1.5, 3.1) times the odds of BVDV seroreaction than younger cattle. Holstein-Friesian (HF) local crosses (OR = 2.1, 95% CI 1.3, 3.4) and HFs (OR = 1.3, 95% CI 0.9, 1.9) were more likely to be seropositive than Jersey and the odds of seropositivity in cattle in large herds with 11 or more animals were higher (OR = 1.8, 95% CI 1.3, 2.5) than the odds of BVDV seropositivity in smaller herds. Seroprevalence was not associated with geographical region (P > 0.05). Risk of reproductive disorders was not affected by BVDV serostatus, except for repeat breeding (P > 0.05). The present study demonstrated that BVDV has wide distribution in the country being detected in all the 15 conurbations and 69.8% of herds involved in the study.

Seroprevalence of Borrelia burgdorferi in patients with Behçet's disease.

PubMed

Onen, Fatos; Tuncer, Dilek; Akar, Servet; Birlik, Merih; Akkoc, Nurullah

2003-11-01

Turkey is one of the countries where Behçet's disease is most prevalent. Although its pathogenesis is not defined clearly, infectious agents are thought to play a role in the etiology. In one study of a group of uveitis patients, including those with Behçet's disease, increased seropositivity to B. burgdorferi was reported by enzyme-linked immunosorbent assay (ELISA). The seroprevalence of B. burgdorferi has been found to be as high as 36% in some rural areas of Turkey, although Lyme disease caused by B. burgdorferi is quite rare. In this study, we investigated the seroreactivity to B. burgdorferi antigens in patients with Behçet's disease and compared it with that of healthy and disease controls. This study was conducted in Izmir in western Turkey. B. burgdorferi immunoglobulin (Ig)M and IgG antibodies were tested by ELISA in the sera of patients with Behçet's disease ( n=30), rheumatoid arthritis patients as disease controls ( n=31), and healthy controls ( n=31). Positive results were confirmed by Western blotting. The difference in B. burgdorferi seropositivity between the groups was not significant by any method. Seroreactivity to B. burgdorferi antigens by ELISA was detected in 26.7% of the patients with Behçet's disease, 35.5% of those with rheumatoid arthritis, and 19.4% of the healthy controls. Immunoblots were positive in 13.3% of the Behçet's disease patients, 22.6% of the rheumatoid arthritis patients, and 12.9% of healthy controls. These results suggest no association between Behçet's disease and B. burgdorferi infection.

Interaction of allergy history and antibodies to specific Varicella zoster virus proteins on glioma risk

PubMed Central

Lee, Seung-Tae; Bracci, Paige; Zhou, Mi; Rice, Terri; Wiencke, John; Wrensch, Margaret; Wiemels, Joseph

2014-01-01

Glioma is the most common cancer of the central nervous system but with few confirmed risk factors. Glioma has been inversely associated with chicken pox, shingles, and seroreactivity to varicella virus (VZV), as well as to allergies and allergy-associated IgE. The role of antibody reactivity against individual VZV antigens has not been assessed. Ten VZV-related proteins, selected for high immunogenicity or known function, were synthesized and used as targets for antibody measurements in the sera of 143 glioma cases and 131 healthy controls selected from the San Francisco Bay Area Adult Glioma Study. Glioma cases exhibited significantly reduced seroreactivity compared to controls for six antigens, including proteins IE63 (OR = 0.26, 95%CI:0.12-0.58, comparing lowest quartile to highest), and the VZV-unique protein ORF2p (OR = 0.44, 95%CI:0.21-0.96, lowest quartile to highest). When stratifying the study population into those with low and high self-reported allergy history, VZV protein seroreactivity was only associated inversely with glioma among individuals self-reporting more than two allergies. The data provide insight into both allergy and VZV effects on glioma: strong anti-VZV reactions in highly allergic individuals is associated with reduced occurrence of glioma. This result suggests a role for specificity in the anti-VZV immunity in brain tumor suppression for both individual VZV antigens and in the fine-tuning of the immune response by allergy. Anti-VZV reactions may also be a biomarker of effective CNS immunosurveillance due to the tropism of the virus. PMID:24127236

Coinfection of Ugandan Red Colobus (Procolobus [Piliocolobus] rufomitratus tephrosceles) with Novel, Divergent Delta-, Lenti-, and Spumaretroviruses ▿

PubMed Central

Goldberg, Tony L.; Sintasath, David M.; Chapman, Colin A.; Cameron, Kenneth M.; Karesh, William B.; Tang, Shaohua; Wolfe, Nathan D.; Rwego, Innocent B.; Ting, Nelson; Switzer, William M.

2009-01-01

Nonhuman primates host a plethora of potentially zoonotic microbes, with simian retroviruses receiving heightened attention due to their roles in the origins of human immunodeficiency viruses type 1 (HIV-1) and HIV-2. However, incomplete taxonomic and geographic sampling of potential hosts, especially the African colobines, has left the full range of primate retrovirus diversity unexplored. Blood samples collected from 31 wild-living red colobus monkeys (Procolobus [Piliocolobus] rufomitratus tephrosceles) from Kibale National Park, Uganda, were tested for antibodies to simian immunodeficiency virus (SIV), simian T-cell lymphotrophic virus (STLV), and simian foamy virus (SFV) and for nucleic acids of these same viruses using genus-specific PCRs. Of 31 red colobus tested, 22.6% were seroreactive to SIV, 6.4% were seroreactive to STLV, and 97% were seroreactive to SFV. Phylogenetic analyses of SIV polymerase (pol), STLV tax and long terminal repeat (LTR), and SFV pol and LTR sequences revealed unique SIV and SFV strains and a novel STLV lineage, each divergent from corresponding retroviral lineages previously described in Western red colobus (Procolobus badius badius) or black-and-white colobus (Colobus guereza). Phylogenetic analyses of host mitochondrial DNA sequences revealed that red colobus populations in East and West Africa diverged from one another approximately 4.25 million years ago. These results indicate that geographic subdivisions within the red colobus taxonomic complex exert a strong influence on retroviral phylogeny and that studying retroviral diversity in closely related primate taxa should be particularly informative for understanding host-virus coevolution. PMID:19692478

Detection of IFN-γ Secretion by T Cells Collected Before and After Successful Treatment of Early Lyme Disease.

PubMed

Callister, Steven M; Jobe, Dean A; Stuparic-Stancic, Aleksandra; Miyamasu, Misato; Boyle, Jeff; Dattwyler, Raymond J; Arnaboldi, Paul M

2016-05-15

Current serodiagnostics for Lyme disease lack sensitivity during early disease, and cannot determine treatment response. We evaluated an assay based on QuantiFERON technology utilizing peptide antigens derived from Borrelia burgdorferi to stimulate interferon-gamma (IFN-γ) release as an alternative to serodiagnosis for the laboratory detection of Lyme disease. Blood was obtained from patients with erythema migrans before (n = 29) and 2 months after (n = 27) antibiotic therapy. IFN-γ release was measured by enzyme-linked immunosorbent assay (ELISA) following overnight stimulation of whole blood with the peptide antigens, and compared to the results of standard serological assays (C6, ELISA, and Western blot). IFN-γ release was observed in pretreatment blood of 20 of 29 (69%) patients with Lyme disease. Following antibiotic treatment, IFN-γ was significantly reduced (P = .0002), and was detectable in only 4 of 20 (20%) initially positive patients. By contrast, anti-C6 antibodies were detected in pretreatment sera from 17 of 29 (59%) subjects, whereas only 5 of 29 (17%) patients had positive Western blot seroreactivity. Antibody responses persisted and expanded following treatment. Our findings suggest that measurement of IFN-γ after incubating blood with Borrelia antigens could be useful in the laboratory diagnosis of early Lyme disease. Also, after antibiotic treatment, this response appears to be short lived. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Untreated maternal syphilis and adverse outcomes of pregnancy: a systematic review and meta-analysis

PubMed Central

Kamb, Mary L; Newman, Lori M; Mark, Jennifer; Broutet, Nathalie; Hawkes, Sarah J

2013-01-01

Abstract Objective To perform a systematic review and meta-analysis of reported estimates of adverse pregnancy outcomes among untreated women with syphilis and women without syphilis. Methods PubMed, EMBASE and Cochrane Libraries were searched for literature assessing adverse pregnancy outcomes among untreated women with seroreactivity for Treponema pallidum infection and non-seroreactive women. Adverse pregnancy outcomes were fetal loss or stillbirth, neonatal death, prematurity or low birth weight, clinical evidence of syphilis and infant death. Random-effects meta-analyses were used to calculate pooled estimates of adverse pregnancy outcomes and, where appropriate, heterogeneity was explored in group-specific analyses. Findings Of the 3258 citations identified, only six, all case-control studies, were included in the analysis. Pooled estimates showed that among untreated pregnant women with syphilis, fetal loss and stillbirth were 21% more frequent, neonatal deaths were 9.3% more frequent and prematurity or low birth weight were 5.8% more frequent than among women without syphilis. Of the infants of mothers with untreated syphilis, 15% had clinical evidence of congenital syphilis. The single study that estimated infant death showed a 10% higher frequency among infants of mothers with syphilis. Substantial heterogeneity was found across studies in the estimates of all adverse outcomes for both women with syphilis (66.5% [95% confidence interval, CI: 58.0–74.1]; I2 = 91.8%; P < 0.001) and women without syphilis (14.3% [95% CI: 11.8–17.2]; I2 = 95.9%; P < 0.001). Conclusion Untreated maternal syphilis is associated with adverse pregnancy outcomes. These findings can inform policy decisions on resource allocation for the detection of syphilis and its timely treatment in pregnant women. PMID:23476094

Helicobacter pylori serological biomarkers of gastric cancer risk in the MCC-Spain case-control Study.

PubMed

Fernández de Larrea-Baz, Nerea; Pérez-Gómez, Beatriz; Michel, Angelika; Romero, Beatriz; Lope, Virginia; Pawlita, Michael; Fernández-Villa, Tania; Moreno, Victor; Martín, Vicente; Willhauck-Fleckenstein, Martina; López-Abente, Gonzalo; Castilla, Jesús; Fernández-Tardón, Guillermo; Dierssen-Sotos, Trinidad; Santibáñez, Miguel; Peiró, Rosana; Jiménez-Moleón, José Juan; Navarro, Carmen; Castaño-Vinyals, Gemma; Kogevinas, Manolis; Pollán, Marina; de Sanjosé, Silvia; Del Campo, Rosa; Waterboer, Tim; Aragonés, Nuria

2017-10-01

Helicobacter pylori infection is one of the main risk factors for non-cardia gastric cancer. However, only a minority of infected persons develop the disease. This study aims at identifying H. pylori related serological biomarkers of risk for gastric cancer. Incident gastric cancer cases and population controls (age, sex and region frequency-matched) from the MCC-Spain multicase-control Study were included. Seroreactivities against 16H. pylori proteins were determined using multiplex serology. Infection was defined as seropositivity against≥4 proteins. Relation of serological results to non-cardia and cardia gastric cancer was assessed using multivariable mixed logistic regression and principal components analysis. Seroprevalence was 88% among 2071 controls, 95% among 202 non-cardia gastric cancer cases (OR=1.9 (95% CI: 1.0-3.6)) and 85% among 62 cardia cancer cases (OR=0.5 (95% CI: 0.3-1.1)). In infected subjects, seropositivity for UreA, HP231, NapA and Cagδ was associated with lower non-cardia gastric cancer risk, while seropositivity for CagA and VacA was associated with higher risk. Seropositivity for CagA and seronegativity for Cagδ maintained the association after additional adjustment by serostatus of significant proteins. We identified two antibody reactivity patterns: the "virulent-pattern", related to a threefold higher risk of non-cardia gastric cancer and the "non-virulent pattern", related to a 60% decreased risk (4th vs. first quartile). In our population, people seropositive for H. pylori were characterized by two patterns of antibody reactivity against H. pylori proteins: 1) Combined high seroreactivity against several proteins, associated with a lower non-cardia gastric cancer risk, and 2) High seroreactivity against CagA and VacA, associated with an increased risk. Copyright © 2017 Elsevier Ltd. All rights reserved.

Anemia and Helicobacter pylori Seroreactivity in a Rural Haitian Population

PubMed Central

Shak, Joshua R.; Sodikoff, Jamie B.; Speckman, Rebecca A.; Rollin, Francois G.; Chery, Marie P.; Cole, Conrad R.; Suchdev, Parminder S.

2011-01-01

Anemia is a significant health concern worldwide and can be the result of nutritional, environmental, social, and infectious etiologies. We estimated the prevalence of anemia in 336 pre-school children and 132 adults in the rural Central Plateau of Haiti and assessed associations with age, sex, household size, water source, sanitation, and Helicobacter pylori seroreactivity using logistic regression analysis; 80.1% (269/336) of children and 63.6% (84/132) of adults were anemic. Among children, younger age was associated with increased prevalence of anemia (adjusted odds ratio [aOR] = 4.1, 95% confidence interval [CI] = 1.5–11.1 for children 6–11 months compared with children 48–59 months). Among adults, 50.8% were H. pylori-seropositive, and seropositivity was inversely associated with anemia (aOR = 0.4, 95% CI = 0.2–0.9). Anemia prevalence in this region of Haiti is very high and not attributable to sanitary conditions or a high prevalence of H. pylori infection. PMID:22049049

Anemia and Helicobacter pylori seroreactivity in a rural Haitian population.

PubMed

Shak, Joshua R; Sodikoff, Jamie B; Speckman, Rebecca A; Rollin, Francois G; Chery, Marie P; Cole, Conrad R; Suchdev, Parminder S

2011-11-01

Anemia is a significant health concern worldwide and can be the result of nutritional, environmental, social, and infectious etiologies. We estimated the prevalence of anemia in 336 pre-school children and 132 adults in the rural Central Plateau of Haiti and assessed associations with age, sex, household size, water source, sanitation, and Helicobacter pylori seroreactivity using logistic regression analysis; 80.1% (269/336) of children and 63.6% (84/132) of adults were anemic. Among children, younger age was associated with increased prevalence of anemia (adjusted odds ratio [aOR] = 4.1, 95% confidence interval [CI] = 1.5-11.1 for children 6-11 months compared with children 48-59 months). Among adults, 50.8% were H. pylori-seropositive, and seropositivity was inversely associated with anemia (aOR = 0.4, 95% CI = 0.2-0.9). Anemia prevalence in this region of Haiti is very high and not attributable to sanitary conditions or a high prevalence of H. pylori infection.

Identification of novel seroreactive antigens in Johne’s disease cattle using the Mycobacterium tuberculosis protein array

USDA-ARS?s Scientific Manuscript database

Johne’s disease, a chronic gastrointestinal inflammatory disease caused by Mycobacterium avium subspecies paratuberculosis (Map), is endemic in dairy cattle and other ruminants worldwide and remains a challenge to diagnose using traditional serological methods. Given the close phylogenetic relations...

Potentially Novel Ehrlichia Species in Horses, Nicaragua

PubMed Central

O’Nion, Victoria L.; Montilla, Hernan J.; Qurollo, Barbara A.; Maggi, Ricardo G.; Hegarty, Barbara C.; Tornquist, Susan J.

2015-01-01

Ehrlichia sp. DNA was amplified from 4 Ehrlichia-seroreactive horses from Mérida, Nicaragua. Sequencing of 16S rDNA, sodB, and groEL genes indicated that the bacterium is most likely a novel Ehrlichia species. The tick vector and the potential for canine and human infection remain unknown. PMID:25625228

Exploiting the Immunological Effects of Standard Treatments In Prostate Cancer

DTIC Science & Technology

2011-04-01

seroreactivity that emerged after EBRT (Fig. 1B). Thus, in most cases, EBRT is associated with retention or even enhancement of hormone therapy–induced...479396 Notch signaling Outer dense fiber of sperm tails 2 (ODF2)* Hs.129055 Maintains the elastic structure and recoil of the sperm tail Serologically

Sparse evidence for equine or avian influenza virus infections among Mongolian adults with animal exposures.

PubMed

Khurelbaatar, Nyamdavaa; Krueger, Whitney S; Heil, Gary L; Darmaa, Badarchiin; Ulziimaa, Daramragchaa; Tserennorov, Damdindorj; Baterdene, Ariungerel; Anderson, Benjamin D; Gray, Gregory C

2013-11-01

In recent years, Mongolia has experienced recurrent epizootics of equine influenza virus (EIV) among its 2·1 million horses and multiple incursions of highly pathogenic avian influenza (HPAI) virus via migrating birds. No human EIV or HPAI infections have been reported. In 2009, 439 adults in Mongolia were enrolled in a population-based study of zoonotic influenza transmission. Enrollment sera were examined for serological evidence of infection with nine avian, three human, and one equine influenza virus strains. Seroreactivity was sparse among participants suggesting little human risk of zoonotic influenza infection. © 2013 John Wiley & Sons Ltd.

Identification of sero-reactive antigens for the early diagnosis of Johne's disease in cattle

USDA-ARS?s Scientific Manuscript database

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne’s disease (JD), a chronic intestinal inflammatory disease of cattle and other ruminants. JD has a high herd prevalence rate and is recognized as a serious animal health problem and a cause of significant economic loss ...

Central African Hunters Exposed to Simian Immunodeficiency Virus

PubMed Central

Wolfe, Nathan D.; Ndongmo, Clement B.; McNicholl, Janet; Robbins, Kenneth E.; Aidoo, Michael; Fonjungo, Peter N.; Alemnji, George; Zeh, Clement; Djoko, Cyrille F.; Mpoudi-Ngole, Eitel; Burke, Donald S.; Folks, Thomas M.

2005-01-01

HIV-seronegative Cameroonians with exposure to nonhuman primates were tested for simian immunodeficiency virus (SIV) infection. Seroreactivity was correlated with exposure risk (p<0.001). One person had strong humoral and weak cellular immune reactivity to SIVcol peptides. Humans are exposed to and possibly infected with SIV, which has major public health implications. PMID:16485481

Prospective Study of Serologic Tests for Lyme Disease

PubMed Central

Steere, Allen C.; McHugh, Gail; Damle, Nitin; Sikand, Vijay K.

2017-01-01

Background Tests to determine serum antibody levels—the 2-tier sonicate immunoglobulin M (IgM) and immunoglobulin G (IgG) enzyme-linked immunosorbent assay (ELISA) and Western blot method or the IgG of the variable major protein-like sequence-expressed (VlsE) sixth invariant region (C6) peptide ELISA method—are the major tests available for support of the diagnosis of Lyme disease. However, these tests have not been assessed prospectively. Methods We used these tests prospectively to determine serologic responses in 134 patients with various manifestations of Lyme disease, 89 patients with other illnesses (with or without a history of Lyme disease), and 136 healthy subjects from areas of endemicity and areas in which the infection was not endemic. Results With 2-tier tests and the C6 peptide ELISA, only approximately one-third of 76 patients with erythema migrans had results that were positive for IgM or IgG seroreactivity with Borrelia burgdorferi in acute-phase samples. During convalescence, 3–4 weeks later, almost two-thirds of patients had seroreactivity with the spirochete B. burgdorferi. The frequencies of seroreactivity were significantly greater among patients with spirochetal dissemination than they were among those who lacked evidence of disseminated disease. Of the 44 patients with Lyme disease who had neurologic, heart, or joint involvement, all had positive C6 peptide ELISA results, 42 had IgG responses with 2-tier tests, and 2 patients with facial palsy had only IgM responses. However, among the control groups, the IgG Western blot was slightly more specific than the C6 peptide ELISA. The differences between the 2 test systems (2-tier testing and C6 peptide ELISA) with respect to sensitivity and specificity were not statistically significant. Conclusions Except in patients with erythema migrans, both test systems were sensitive for support of the diagnosis of Lyme disease. However, with current methods, 2-tier testing was associated with slightly better specificity. PMID:18532885

Convalescent Plasmodium falciparum-specific seroreactivity does not correlate with paediatric malaria severity or Plasmodium antigen exposure.

PubMed

Kessler, Anne; Campo, Joseph J; Harawa, Visopo; Mandala, Wilson L; Rogerson, Stephen J; Mowrey, Wenzhu B; Seydel, Karl B; Kim, Kami

2018-04-25

Antibody immunity is thought to be essential to prevent severe Plasmodium falciparum infection, but the exact correlates of protection are unknown. Over time, children in endemic areas acquire non-sterile immunity to malaria that correlates with development of antibodies to merozoite invasion proteins and parasite proteins expressed on the surface of infected erythrocytes. A 1000 feature P. falciparum 3D7 protein microarray was used to compare P. falciparum-specific seroreactivity during acute infection and 30 days after infection in 23 children with uncomplicated malaria (UM) and 25 children with retinopathy-positive cerebral malaria (CM). All children had broad P. falciparum antibody reactivity during acute disease. IgM reactivity decreased and IgG reactivity increased in convalescence. Antibody reactivity to CIDR domains of "virulent" PfEMP1 proteins was low with robust reactivity to the highly conserved, intracellular ATS domain of PfEMP1 in both groups. Although children with UM and CM differed markedly in parasite burden and PfEMP1 exposure during acute disease, neither acute nor convalescent PfEMP1 seroreactivity differed between groups. Greater seroprevalence to a conserved Group A-associated ICAM binding extracellular domain was observed relative to linked extracellular CIDRα1 domains in both case groups. Pooled immune IgG from Malawian adults revealed greater reactivity to PfEMP1 than observed in children. Children with uncomplicated and cerebral malaria have similar breadth and magnitude of P. falciparum antibody reactivity. The utility of protein microarrays to measure serological recognition of polymorphic PfEMP1 antigens needs to be studied further, but the study findings support the hypothesis that conserved domains of PfEMP1 are more prominent targets of cross reactive antibodies than variable domains in children with symptomatic malaria. Protein microarrays represent an additional tool to identify cross-reactive Plasmodium antigens including PfEMP1 domains that can be investigated as strain-transcendent vaccine candidates.

DETECTION OF Leptospira spp. AND Brucella abortus ANTIBODIES IN FREE-LIVING JAGUARS (Panthera onca) IN TWO PROTECTED AREAS OF NORTHERN PANTANAL, BRAZIL

PubMed Central

ONUMA, Selma Samiko Miyazaki; KANTEK, Daniel Luis Zanella; CRAWSHAW, Peter Gransden; MORATO, Ronaldo Gonçalves; MAY-JÚNIOR, Joares Adenilson; de MORAIS, Zenaide Maria; FERREIRA, José Soares; de AGUIAR, Daniel Moura

2015-01-01

 This study aimed to assess the exposure of free-living jaguars (Panthera onca) to Leptospira spp. and Brucella abortus in two conservation units in the Pantanal of Mato Grosso, Brazil. The presence of antibodies in blood samples of eleven jaguars was investigated using autochthonous antigens isolated in Brazil added to reference antigen collection applied to diagnosis of leptospirosis by Microscopic Agglutination Test (MAT). The Rose Bengal test was applied for B. abortus antibodies. Two (18.2%) jaguars were seroreactive for the Leptospira spp. antigen and the serovar considered as most infective in both animals was a Brazilian isolate of serovar Canicola (L01). All jaguars were seronegative for B. abortus. These data indicate that the inclusion of autochthonous antigens in serological studies can significantly increase the number of reactive animals, as well as modify the epidemiological profile of Leptospira spp. infection. PMID:25923900

Detection of Leptospira spp. and Brucella abortus antibodies in free-living jaguars (Panthera onca) in two protected areas of northern Pantanal, Brazil.

PubMed

Onuma, Selma Samiko Miyazaki; Kantek, Daniel Luis Zanella; Crawshaw Júnior, Peter Gransden; Morato, Ronaldo Gonçalves; May-Júnior, Joares Adenilson; Morais, Zenaide Maria de; Ferreira Neto, José Soares; Aguiar, Daniel Moura de

2015-01-01

This study aimed to assess the exposure of free-living jaguars (Panthera onca) to Leptospira spp. and Brucella abortus in two conservation units in the Pantanal of Mato Grosso, Brazil. The presence of antibodies in blood samples of eleven jaguars was investigated using autochthonous antigens isolated in Brazil added to reference antigen collection applied to diagnosis of leptospirosis by Microscopic Agglutination Test (MAT). The Rose Bengal test was applied for B. abortus antibodies. Two (18.2%) jaguars were seroreactive for the Leptospira spp. antigen and the serovar considered as most infective in both animals was a Brazilian isolate of serovar Canicola (L01). All jaguars were seronegative for B. abortus. These data indicate that the inclusion of autochthonous antigens in serological studies can significantly increase the number of reactive animals, as well as modify the epidemiological profile of Leptospira spp. infection.

Legionnaire's disease: a nosocomial outbreak in Turkey.

PubMed

Ozerol, I H; Bayraktar, M; Cizmeci, Z; Durmaz, R; Akbas, E; Yildirim, Z; Yologlu, S

2006-01-01

Six nosocomial cases of Legionella pneumophila occurred over a two-week period, with one further case being diagnosed retrospectively after 30 days. Strains isolated from the hospital water system were clonally related to a single sputum isolate. A sero-epidemiological investigation into legionella exposure amongst staff and inpatients was undertaken at the eight-year-old Inonu University Medical Centre in Turkey, which has 600 beds and central air conditioning. There is no disinfection programme for the hospital water system. A total of 500 serum samples (400 hospital staff and 100 inpatients) were screened for antibody to L. pneumophila by enzyme-linked immunosorbent assay (ELISA). Seroreactive cases were confirmed by a four-fold antibody rise in ELISA, a high indirect immunofluorescent assay (IFA) antibody titre or a positive urinary antigen test. ELISA showed that 24 (6%) of the 400 hospital staff and seven (7%) of the 100 inpatients had antibody titres higher than the cut-off value. ELISA-seroreactive cases were followed for two to four weeks. Of these subjects, seven (three patients and four staff) showed a four-fold rise in antibody titre by ELISA, six (three patients and three staff) had a high IFA titre, three patients with pneumonia had a positive urinary antigen test, and one of these patients also had a positive sputum culture. In addition, 22 water distribution systems were screened for the presence of L. pneumophila by culture. L. pneumophila was isolated from 15 sites. Pulsed-field gel electrophoresis typing indicated that all strains isolated from water systems were identical and clonally related to the strain isolated from sputum. Superheating and flushing of water systems were undertaken with legionella being re-isolated from four sites. Repeated superheating and flushing eliminated legionella completely. This study demonstrated that rapid detection of L. pneumophila and adequate superheating and flushing of water systems are effective for elimination and reduction of spread of this organism.

High prevalence of tick-borne pathogens in dogs from an Indian reservation in northeastern Arizona.

PubMed

Diniz, Pedro Paulo V P; Beall, Melissa J; Omark, Karina; Chandrashekar, Ramaswamy; Daniluk, Daryn A; Cyr, Katie E; Koterski, James F; Robbins, Richard G; Lalo, Pamela G; Hegarty, Barbara C; Breitschwerdt, Edward B

2010-03-01

We evaluated the serological and molecular prevalence of selected organisms in 145 dogs during late spring (May/June) of 2005 and in 88 dogs during winter (February) of 2007 from the Hopi Indian reservation. Additionally, in 2005, 442 ticks attached to dogs were collected and identified as Rhipicephalus sanguineus. Infection with or exposure to at least one organism was detected in 69% and 66% of the dogs in May/June 2005 and February 2007, respectively. Exposure to spotted fever group (SFG) rickettsiae was detected in 66.4% (2005) and 53.4% (2007) of dogs, but rickettsial DNA was not detected using polymerase chain reaction. Active Ehrlichia canis infection (by polymerase chain reaction) was identified in 36.6% (2005) and 36.3% (2007) of the dogs. E. canis infection was associated with SFG rickettsiae seroreactivity (p < 0.001). Anaplasma platys DNA was detected in 8.3% (2005) and 4.5% (2007) of the dogs. Babesia canis and Bartonella vinsonii berkhoffii seroprevalences were 6.7% and 1% in 2005, whereas in 2007 prevalences were 0% and 1.1%, respectively. No Bartonella spp., Ehrlichia chaffeensis, or Ehrlichia ewingii DNA was detected. Dogs on this Hopi Indian reservation were most frequently infected with E. canis or A. platys; however, more than half of the dogs were exposed to a SFG-Rickettsia species.

Immunogenicity and reactivity of novel Mycobacterium avium subsp. paratuberculosis PPE MAP1152 and conserved MAP1156 proteins with sera from experimentally and naturally infected animals

USDA-ARS?s Scientific Manuscript database

Mycobacterium avium subsp. paratuberculosis (MAP) causes Johne’s Disease (JD) in ruminants. Development of genetic tools and completion of the MAP genome sequencing project expanded opportunities for antigen discovery. In this study, we determined the seroreactivity of two proteins encoded for at th...

Experimental Infection of Rattus norvegicus by the Group II Intermediate Pathogen, Leptospira licerasiae.

PubMed

Fernandez, Carla; Lubar, Aristea A; Vinetz, Joseph M; Matthias, Michael A

2018-06-25

Leptospira licerasiae serovar Varillal, a group II intermediate pathogen species/serovar discovered in the Peruvian Amazon city of Iquitos, is commonly recognized in this region by sera from humans (at least 40% seroprevalence) without a known clinical history of leptospirosis. This high frequency of human seroreactivity remains unexplained. To test the hypothesis that the oral route of infection might explain the high rate of human seroreactivity against L. licerasiae , an experimental infection model using Rattus norvegicus was developed, given that rats were one of the original reservoir hosts identified as being colonized by this leptospire. Sprague-Dawley rats were experimentally exposed via mucosa, direct gastric gavage, or parenteral inoculation with nine different isolates of L. licerasiae originally isolated from Peruvian humans, peridomiciliary rodents, and wildlife. As shown by quantitative polymerase chain reaction of kidney tissue, Leptospira infection via these routes of infection was equally successful. Importantly, the data show that L. licerasiae infects R. norvegicus via the oral route demonstration, leading to renal colonization. Not only do these findings confirm the infectiousness of group II Leptospira , but also they underscore the potential importance of oral as well as mucosal and transcutaneous routes of Leptospira infection.

Tuberculosis in Laos, who is at risk: the mahouts or their elephants?

PubMed

Lassausaie, J; Bret, A; Bouapao, X; Chanthavong, V; Castonguay-Vanier, J; Quet, F; Mikota, S K; Théorêt, C; Buisson, Y; Bouchard, B

2015-04-01

SUMMARY Tuberculosis (TB) in elephants has the potential to infect humans and is an increasing public health concern. Lao PDR is one of the last countries where elephants are still used for timber extraction and where they live in close contact with their mahouts. There are 500 animals at work in the country, some interacting with wild herds. Although human TB prevalence is known to be high in Laos, studies on elephant TB had yet to be undertaken. From January to July 2012, screening was performed using the ElephantTB Stat-Pak assay on 80 elephants working around the Nam Pouy National Park in Sayaboury Province. This represents more than 18% of the total registered national working elephant population. Here we report that 36% of the elephants were seroreactive to the test. Of these, 31% had contacts with wild individuals, which suggests potential transmission of mycobacteria to the local wild herds. Clinical examination, chest X-rays, sputum microscopy and culture were performed on their 142 mahouts or owners. Despite high TB seroreactivity in elephants, no participant was smear- or culture-positive for Mycobacterium tuberculosis or M. bovis, although atypical mycobacteria were isolated from 4% of participants.

Leptospirosis is strongly associated to estrus repetition on cattle.

PubMed

Libonati, H A; Santos, G B; Souza, G N; Brandão, F Z; Lilenbaum, W

2018-05-02

Although prevalent, the exact impact of infectious diseases on reproductive failures remains to be determined. Among them, leptospirosis has commonly been reported as cause of abortions on outbreaks. Nevertheless, the majority of the animals present a chronic, silent form of the disease, which is characterized by low reproductive efficiency and is frequently neglected. In that context, we conducted a study that aims to estimate the impact of chronic leptospirosis on reproductive disorders on cattle. A total of 25 different dairy herds with history of reproductive losses from Rio de Janeiro, Brazil, were selected. From each herd, a questionnaire was applied and sera from 20 cows were randomly tested for leptospirosis (totaling 500 cows). Chi-square was performed to estimate the association of seroreactivity with reproductive disorders. A total of 32% of the herds were positive, all of them against serogroup Sejroe. Estrus repetition was the most important reported reproductive problem and it was strongly associated to seroreactivity against leptospirosis. Besides, specific vaccination against leptospirosis was an important protection factor against that disorder. In conclusion, control programs including, but not limited to, vaccines must be implemented on those herds in order to reduce reproductive losses, particularly estrus repetition.

Q Fever, Scrub Typhus, and Rickettsial Diseases in Children, Kenya, 2011-2012.

PubMed

Maina, Alice N; Farris, Christina M; Odhiambo, Antony; Jiang, Ju; Laktabai, Jeremiah; Armstrong, Janice; Holland, Thomas; Richards, Allen L; O'Meara, Wendy P

2016-05-01

To increase knowledge of undifferentiated fevers in Kenya, we tested paired serum samples from febrile children in western Kenya for antibodies against pathogens increasingly recognized to cause febrile illness in Africa. Of patients assessed, 8.9%, 22.4%, 1.1%, and 3.6% had enhanced seroreactivity to Coxiella burnetii, spotted fever group rickettsiae, typhus group rickettsiae, and scrub typhus group orientiae, respectively.

Mass spectrometry-based identification of allergens from Curvularia pallescens, a prevalent aerospore in India.

PubMed

Dey, Debarati; Saha, Bodhisattwa; Sircar, Gaurab; Ghosal, Kavita; Bhattacharya, Swati Gupta

2016-07-01

The worldwide prevalence of fungal allergy in recent years has augmented mining allergens from yet unexplored ones. Curvularia pallescens (CP) being a dominant aerospore in India and a major sensitiser on a wide range of allergic population, pose a serious threat to human health. Therefore, we aimed to identify novel allergens from CP in our present study. A cohort of 22 CP-sensitised patients was selected by positive Skin prick grade. Individual sera exhibited elevated specific IgE level and significant histamine release on a challenge with antigenic extract of CP. First gel-based profiling of CP proteome was done by 1- and 2-dimensional gel. Parallel 1- and 2-dimensional immunoblot were performed applying individual as well as pooled patient sera. Identification of the sero-reactive spots from the 2-dimensional gel was found to be challenging as CP was not previously sequenced. Hence, mass spectrometry-based proteomic workflow consisting of conventional database search was not alone sufficient. Therefore, de novo sequencing preceded homology search was implemented for further identification. Altogether 11 allergenic proteins including Brn-1, vacuolar protease, and fructose-bis-phosphate aldolase were identified with high statistical confidence (p<0.05). This is the first study to report on any allergens from CP. This kind of proteome-based analysis provided a catalogue of CP allergens that would lead an improved way of diagnosis and therapy of CP-related allergy. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection of wild animals as carriers of Leptospira by PCR in the Pantanal biome, Brazil.

PubMed

Vieira, Anahi S; Narduche, Lorena; Martins, Gabriel; Schabib Péres, Igor A H F; Zimmermann, Namor P; Juliano, Raquel S; Pellegrin, Aiesca O; Lilenbaum, Walter

2016-11-01

Leptospiral infection is widespread in wildlife. In this context, wild ecosystems in tropical countries hold a vast biodiversity, including several species that may act as potential reservoirs of leptospires. The Pantanal biome presents highly favorable environmental conditions for the occurrence of leptospirosis, such as high temperatures, constant flooding, and high biodiversity. The purpose of this study was to detect wild animals as carriers of Leptospira sp. using direct methods (PCR and culture) in the Pantanal biome, Brazil. A total of 35 animals were studied, namely Cerdocyon thous, Nasua nasua, Ozotoceros bezoarticus, and Sus scrofa species. Blood for serology (MAT) and urine for bacteriological culturing and PCR was sampled. The most prevalent serogroups were Javanica and Djasiman. Additionally, 40.6% of these animals presented PCR positive reactions. Seroreactivity associated with the high frequency of leptospiral carriers among the different studied species suggests a high level of exposure of the studied animals to pathogenic Leptospira strains. Our results are still limited and the actual role of the studied animals in the epidemiology of leptospirosis in the Pantanal region remains to be elucidated. Copyright © 2016 Elsevier B.V. All rights reserved.

Thick Smear is a Good Substitute for the Thin Smear in Parasitological Confirmation of Canine Visceral Leishmaniasis.

PubMed

de Mello, Cintia Xavier; Figueiredo, Fabiano Borges; Mendes Júnior, Artur Augusto Velho; Miranda, Luciana de Freitas Campos; de Oliveira, Raquel de Vasconcellos Carvalhaes; Madeira, Maria de Fátima

2016-07-06

Although direct examination methods are important for diagnosing leishmaniasis, such methods are often neglected because of their low sensitivity relative to other techniques. Our study aimed to evaluate the performance of bone marrow (BM) thick smears and cytocentrifugation tests as alternatives to direct examination for diagnosing canine visceral leishmaniasis (CVL). Ninety-two dogs exhibiting leishmaniasis seroreactivity were evaluated. The animals were euthanized; and healthy skin, spleen, popliteal lymph node, and BM puncture samples were cultured. BM cultures were used as the reference standard. Of the 92 dogs studied, 85.9% exhibited positive cultures, and Leishmania infantum (synonym Leishmania chagasi) was confirmed in all positive culture cases. The sensitivity rates for cytocentrifugation as well as thin and thick smears were 47.1%, 52.8%, and 77%, respectively. However, no association between the dogs' clinical status and culture or direct examination results was found. To our knowledge, this was the first study to use thick smears and cytocentrifugation for diagnosing CVL. Our results indicate that BM thick smears have a good sensitivity and their use reduces the time required to read slides. Therefore, thick smears can provide a rapid and safe alternative to parasitological confirmation of seroreactive dogs. © The American Society of Tropical Medicine and Hygiene.

Borrelia miyamotoi sensu lato seroreactivity and seroprevalence in the northeastern United States.

PubMed

Krause, Peter J; Narasimhan, Sukanya; Wormser, Gary P; Barbour, Alan G; Platonov, Alexander E; Brancato, Janna; Lepore, Timothy; Dardick, Kenneth; Mamula, Mark; Rollend, Lindsay; Steeves, Tanner K; Diuk-Wasser, Maria; Usmani-Brown, Sahar; Williamson, Phillip; Sarksyan, Denis S; Fikrig, Erol; Fish, Durland

2014-07-01

Borrelia miyamotoi sensu lato, a relapsing fever Borrelia sp., is transmitted by the same ticks that transmit B. burgdorferi (the Lyme disease pathogen) and occurs in all Lyme disease-endemic areas of the United States. To determine the seroprevalence of IgG against B. miyamotoi sensu lato in the northeastern United States and assess whether serum from B. miyamotoi sensu lato-infected persons is reactive to B. burgdorferi antigens, we tested archived serum samples from area residents during 1991-2012. Of 639 samples from healthy persons, 25 were positive for B. miyamotoi sensu lato and 60 for B. burgdorferi. Samples from ≈10% of B. miyamotoi sensu lato-seropositive persons without a recent history of Lyme disease were seropositive for B. burgdorferi. Our results suggest that human B. miyamotoi sensu lato infection may be common in southern New England and that B. burgdorferi antibody testing is not an effective surrogate for detecting B. miyamotoi sensu lato infection.

Identification of sero-reactive antigens for the early diagnosis of Johne’s disease in cattle

PubMed Central

Randall, Arlo; Grohn, Yrjo T.; Katani, Robab; Schilling, Megan; Radzio-Basu, Jessica

2017-01-01

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne’s disease (JD), a chronic intestinal inflammatory disease of cattle and other ruminants. JD has a high herd prevalence and causes serious animal health problems and significant economic loss in domesticated ruminants throughout the world. Since serological detection of MAP infected animals during the early stages of infection remains challenging due to the low sensitivity of extant assays, we screened 180 well-characterized serum samples using a whole proteome microarray from Mycobacterium tuberculosis (MTB), a close relative of MAP. Based on extensive testing of serum and milk samples, fecal culture and qPCR for direct detection of MAP, the samples were previously assigned to one of 4 groups: negative low exposure (n = 30, NL); negative high exposure (n = 30, NH); fecal positive, ELISA negative (n = 60, F+E-); and fecal positive, ELISA positive (n = 60, F+E+). Of the 740 reactive proteins, several antigens were serologically recognized early but not late in infection, suggesting a complex and dynamic evolution of the MAP humoral immune response during disease progression. Ordinal logistic regression models identified a subset of 47 candidate proteins with significantly different normalized intensity values (p<0.05), including 12 in the NH and 23 in F+E- groups, suggesting potential utility for the early detection of MAP infected animals. Next, the diagnostic utility of four MAP orthologs (MAP1569, MAP2942c, MAP2609, and MAP1272c) was assessed and reveal moderate to high diagnostic sensitivities (range 48.3% to 76.7%) and specificity (range 96.7% to 100%), with a combined 88.3% sensitivity and 96.7% specificity. Taken together, the results of our analyses have identified several candidate MAP proteins of potential utility for the early detection of MAP infection, as well individual MAP proteins that may serve as the foundation for the next generation of well-defined serological diagnosis of JD in cattle. PMID:28863177

Improving global estimates of syphilis in pregnancy by diagnostic test type: A systematic review and meta-analysis.

PubMed

Ham, D Cal; Lin, Carol; Newman, Lori; Wijesooriya, N Saman; Kamb, Mary

2015-06-01

"Probable active syphilis," is defined as seroreactivity in both non-treponemal and treponemal tests. A correction factor of 65%, namely the proportion of pregnant women reactive in one syphilis test type that were likely reactive in the second, was applied to reported syphilis seropositivity data reported to WHO for global estimates of syphilis during pregnancy. To identify more accurate correction factors based on test type reported. Medline search using: "Syphilis [Mesh] and Pregnancy [Mesh]," "Syphilis [Mesh] and Prenatal Diagnosis [Mesh]," and "Syphilis [Mesh] and Antenatal [Keyword]. Eligible studies must have reported results for pregnant or puerperal women for both non-treponemal and treponemal serology. We manually calculated the crude percent estimates of subjects with both reactive treponemal and reactive non-treponemal tests among subjects with reactive treponemal and among subjects with reactive non-treponemal tests. We summarized the percent estimates using random effects models. Countries reporting both reactive non-treponemal and reactive treponemal testing required no correction factor. Countries reporting non-treponemal testing or treponemal testing alone required a correction factor of 52.2% and 53.6%, respectively. Countries not reporting test type required a correction factor of 68.6%. Future estimates should adjust reported maternal syphilis seropositivity by test type to ensure accuracy. Published by Elsevier Ireland Ltd.

Proteome Analysis and Serological Characterization of Surface-Exposed Proteins of Rickettsia heilongjiangensis

PubMed Central

Qi, Yong; Xiong, Xiaolu; Wang, Xile; Duan, Changsong; Jia, Yinjun; Jiao, Jun; Gong, Wenping; Wen, Bohai

2013-01-01

Background Rickettsia heilongjiangensis, the agent of Far-Eastern spotted fever (FESF), is an obligate intracellular bacterium. The surface-exposed proteins (SEPs) of rickettsiae are involved in rickettsial adherence to and invasion of host cells, intracellular bacterial growth, and/or interaction with immune cells. They are also potential molecular candidates for the development of diagnostic reagents and vaccines against rickettsiosis. Methods R. heilongjiangensis SEPs were identified by biotin-streptavidin affinity purification and 2D electrophoreses coupled with ESI-MS/MS. Recombinant SEPs were probed with various sera to analyze their serological characteristics using a protein microarray and an enzyme-linked immune sorbent assay (ELISA). Results Twenty-five SEPs were identified, most of which were predicted to reside on the surface of R. heilongjiangensis cells. Bioinformatics analysis suggests that these proteins could be involved in bacterial pathogenesis. Eleven of the 25 SEPs were recognized as major seroreactive antigens by sera from R. heilongjiangensis-infected mice and FESF patients. Among the major seroreactive SEPs, microarray assays and/or ELISAs revealed that GroEL, OmpA-2, OmpB-3, PrsA, RplY, RpsB, SurA and YbgF had modest sensitivity and specificity for recognizing R. heilongjiangensis infection and/or spotted fever. Conclusions Many of the SEPs identified herein have potentially important roles in R. heilongjiangensis pathogenicity. Some of them have potential as serodiagnostic antigens or as subunit vaccine antigens against the disease. PMID:23894656

Exposure to vector-borne pathogens in candidate blood donor and free-roaming dogs of northeast Italy.

PubMed

Vascellari, Marta; Ravagnan, Silvia; Carminato, Antonio; Cazzin, Stefania; Carli, Erika; Da Rold, Graziana; Lucchese, Laura; Natale, Alda; Otranto, Domenico; Capelli, Gioia

2016-06-29

Many vector-borne pathogens including viruses, bacteria, protozoa and nematodes occur in northeast Italy, representing a potential threat to animal and human populations. Little information is available on the circulation of the above vector-borne pathogens in dogs. This work aims to (i) assess exposure to and circulation of pathogens transmitted to dogs in northeast Italy by ticks, sandflies, and mosquitoes, and (ii) drive blood donor screening at the newly established canine blood bank of the Istituto Zooprofilattico Sperimentale delle Venezie. Blood samples from 150 privately-owned canine candidate blood donors and 338 free-roaming dogs were screened by serology (IFA for Leishmania infantum, Ehrlichia canis, Anaplasma phagocythophilum, Babesia canis, Rickettsia conorii, R. rickettsii), microscopic blood smear examination, and blood filtration for Dirofilaria spp. All candidate donors and seropositive free-roaming dogs were tested by PCR for L. infantum, E. canis, A. phagocythophilum, Babesia/Theileria and Rickettsia spp. The dogs had no clinical signs at the time of sampling. Overall, 40 candidate donors (26.7 %) and 108 free-roaming dogs (32 %) were seroreactive to at least one vector-borne pathogen. Seroprevalence in candidate donors vs free-roaming dogs was: Leishmania infantum 6.7 vs 7.1 %; Anaplasma phagocytophilum 4.7 vs 3.3 %; Babesia canis 1.3 vs 2.7 %; Ehrlichia canis none vs 0.9 %; Rickettsia conorii 16 vs 21.3 % and R. rickettsii 11 vs 14.3 %. Seroreactivity to R. rickettsii, which is not reported in Italy, is likely a cross-reaction with other rickettsiae. Filariae, as Dirofilaria immitis (n = 19) and D. repens (n = 2), were identified in free-roaming dogs only. No significant differences were observed between candidate donors and free-roaming dogs either in the overall seroprevalence of vector-borne pathogens or for each individual pathogen. All PCRs and smears performed on blood were negative. This study demonstrated that dogs are considerably exposed to vector-borne pathogens in northeast Italy. Although the dog owners reported regularly using ectoparasiticides against fleas and ticks, their dogs had similar exposure to vector-borne pathogens as free-roaming dogs. This prompts the need to improve owner education on the use of insecticidal and repellent compounds in order to reduce the risk of arthropod bites and exposure to vector-borne pathogens. Based on the absence of pathogens circulating in the blood of healthy dogs, the risk of transmission of these pathogens by blood transfusion seems to be low, depending also on the sensitivity of the tests used for screening.

Urinary PCR as an increasingly useful tool for an accurate diagnosis of leptospirosis in livestock.

PubMed

Hamond, C; Martins, G; Loureiro, A P; Pestana, C; Lawson-Ferreira, R; Medeiros, M A; Lilenbaum, W

2014-03-01

The aim of the present study was to consider the wide usage of urinary PCR as an increasingly useful tool for an accurate diagnosis of leptospirosis in livestock. A total of 512 adult animals (300 cattle, 138 horses, 59 goats and 15 pigs), from herds/flocks with reproductive problems in Rio de Janeiro, Brazil was studied by serology and urinary PCR. From the 512 serum samples tested, 223 (43.5 %) were seroreactive (cattle: 45.6 %, horses: 41.3 %, goats: 34%and pigs: 60 %). PCR detected leptospiral DNA in 32.4 % (cattle: 21.6 %, horses: 36.2 %, goats: 77.4 % and pigs: 33.3 %. To our knowledge there is no another study including such a large number of samples (512) from different species, providing a comprehensive analysis of the usage of PCR for detecting leptospiral carriers in livestock. Serological and molecular results were discrepant, regardless the titre, what was an expected outcome. Nevertheless, it is impossible to establish agreement between these tests, since the two methodologies are conducted on different samples (MAT - serum; PCR - urine). Additionally, the MAT is an indirect method and PCR is a direct one. In conclusion, we have demonstrated that urinary PCR should be considered and encouraged as an increasingly useful tool for an accurate diagnosis of leptospirosis in livestock.

Seroprevalence of IgA anti Epstein-Barr virus is high among family members of nasopharyngeal cancer patients and individuals presenting with chronic complaints in head and neck area

PubMed Central

Argy, Gabriella; Indrasari, Sagung Rai; Indrawati, Luh Putu Lusy; Paramita, Dewi Kartikawati; Jati, Theodola Baning Rahayu; Middeldorp, Jaap M.

2017-01-01

Epstein-Barr (EBV) infection and presence of a nasopharyngeal cancer (NPC) case in the family increases the risk of developing NPC. Aberrant anti-EBV immunoglobulin A (IgA) antibodies (EBV-IgA) may be present in the sera of non-cancer individuals and predict NPC. Limited studies report the presence of EBV-IgA antibodies within non-cancer individuals in Indonesia where the disease is prevalent. This study aimed at exploring whether EBV-IgA was found more frequently among first degree relatives of NPC patients and individuals presenting with chronic symptoms in the head and neck area compared to healthy controls. A total of 967 non-cancer subjects were recruited, including 509 family members of NPC cases, 196 individuals having chronic complaints in the head and neck region, and 262 healthy donors of the local blood bank. Sera were analyzed using a standardized peptide-based EBV-IgA ELISA. Overall, 61.6% of all individuals had anti-EBV IgA reactivity equal to or below cut off value (CoV). Seroreactivity above CoV was significantly higher in females (38.7%) compared to males (28.7%) (p = 0.001). Older individuals had more seroreactivity above CoV (42.5%) than the younger ones (26.4%) (p< 0.001). Seroprevalence was significantly higher in family members of NPC patients (41.7%), compared to 32.7% of individuals with chronic head and neck problems (p = 0.028) and 16.4% healthy blood donors (p< 0.001). As conclusion, this study showed a significant higher seroprevalence in healthy family members of NPC cases and subjects presenting with chronic symptoms in the head and neck area compared to healthy individuals from the general community. This finding indicates that both groups have elevated risk of developing NPC and may serve as targets for a regional NPC screening program. PMID:28800616

Seroprevalence of IgA anti Epstein-Barr virus is high among family members of nasopharyngeal cancer patients and individuals presenting with chronic complaints in head and neck area.

PubMed

Hutajulu, Susanna Hilda; Fachiroh, Jajah; Argy, Gabriella; Indrasari, Sagung Rai; Indrawati, Luh Putu Lusy; Paramita, Dewi Kartikawati; Jati, Theodola Baning Rahayu; Middeldorp, Jaap M

2017-01-01

Epstein-Barr (EBV) infection and presence of a nasopharyngeal cancer (NPC) case in the family increases the risk of developing NPC. Aberrant anti-EBV immunoglobulin A (IgA) antibodies (EBV-IgA) may be present in the sera of non-cancer individuals and predict NPC. Limited studies report the presence of EBV-IgA antibodies within non-cancer individuals in Indonesia where the disease is prevalent. This study aimed at exploring whether EBV-IgA was found more frequently among first degree relatives of NPC patients and individuals presenting with chronic symptoms in the head and neck area compared to healthy controls. A total of 967 non-cancer subjects were recruited, including 509 family members of NPC cases, 196 individuals having chronic complaints in the head and neck region, and 262 healthy donors of the local blood bank. Sera were analyzed using a standardized peptide-based EBV-IgA ELISA. Overall, 61.6% of all individuals had anti-EBV IgA reactivity equal to or below cut off value (CoV). Seroreactivity above CoV was significantly higher in females (38.7%) compared to males (28.7%) (p = 0.001). Older individuals had more seroreactivity above CoV (42.5%) than the younger ones (26.4%) (p< 0.001). Seroprevalence was significantly higher in family members of NPC patients (41.7%), compared to 32.7% of individuals with chronic head and neck problems (p = 0.028) and 16.4% healthy blood donors (p< 0.001). As conclusion, this study showed a significant higher seroprevalence in healthy family members of NPC cases and subjects presenting with chronic symptoms in the head and neck area compared to healthy individuals from the general community. This finding indicates that both groups have elevated risk of developing NPC and may serve as targets for a regional NPC screening program.

Human Exposure to Anaplasma phagocytophilum in Two Cities of Northwestern Morocco.

PubMed

Elhamiani Khatat, Sarah; Sahibi, Hamid; Hing, Mony; Alaoui Moustain, Ismail; El Amri, Hamid; Benajiba, Mohammed; Kachani, Malika; Duchateau, Luc; Daminet, Sylvie

2016-01-01

Anaplasma phagocytophilum is an emerging tick-borne zoonosis with extensive increased interest. Epidemiological data are available in several regions of the USA, Europe and Asia in contrast to other parts of the world such as North Africa. Blood samples of 261 healthy individuals divided in two groups i.e., dog handlers and blood donors were analysed. Indirect immunofluorescent assay using a commercial kit was performed to detect specific A. phagocytophilum IgG. Two dilutions were used to assess the prevalence of seroreactive samples. Demographic variables were assessed as potential risk factors using exact logistic regression. Seropositivity rates reached 37% and 27% in dog handlers and 36% and 22% in blood donors. No statistically significant differences were found in the prevalence rates between the two groups. Analysis of risk factors such as gender, age groups, outdoor activities, self-reported previous exposure to ticks, or contact with domestic animals (dogs, cats, ruminants and horses) did not shown any significant difference. A. phagocytophilum exposure was common in both high-risk population and blood donors in Morocco.

Human Exposure to Anaplasma phagocytophilum in Two Cities of Northwestern Morocco

PubMed Central

Elhamiani Khatat, Sarah; Sahibi, Hamid; Hing, Mony; Alaoui Moustain, Ismail; El Amri, Hamid; Benajiba, Mohammed; Kachani, Malika; Duchateau, Luc; Daminet, Sylvie

2016-01-01

Anaplasma phagocytophilum is an emerging tick-borne zoonosis with extensive increased interest. Epidemiological data are available in several regions of the USA, Europe and Asia in contrast to other parts of the world such as North Africa. Blood samples of 261 healthy individuals divided in two groups i.e., dog handlers and blood donors were analysed. Indirect immunofluorescent assay using a commercial kit was performed to detect specific A. phagocytophilum IgG. Two dilutions were used to assess the prevalence of seroreactive samples. Demographic variables were assessed as potential risk factors using exact logistic regression. Seropositivity rates reached 37% and 27% in dog handlers and 36% and 22% in blood donors. No statistically significant differences were found in the prevalence rates between the two groups. Analysis of risk factors such as gender, age groups, outdoor activities, self-reported previous exposure to ticks, or contact with domestic animals (dogs, cats, ruminants and horses) did not shown any significant difference. A. phagocytophilum exposure was common in both high-risk population and blood donors in Morocco. PMID:27532208

Phage display and synthetic peptides as promising biotechnological tools for the serological diagnosis of leprosy.

PubMed

Alban, Silvana Maria; de Moura, Juliana Ferreira; Thomaz-Soccol, Vanete; Bührer Sékula, Samira; Alvarenga, Larissa Magalhães; Mira, Marcelo Távora; Olortegui, Carlos Chávez; Minozzo, João Carlos

2014-01-01

The diagnosis of leprosy is primarily based on clinical manifestations, and there is no widely available laboratory test for the early detection of this disease, which is caused by Mycobacterium leprae. In fact, early detection and treatment are the key elements to the successful control of leprosy. Peptide ligands for antibodies from leprosy patients were selected from phage-displayed peptide libraries. Three peptide sequences expressed by reactive phage clones were chemically synthesized. Serological assays that used synthetic peptides were evaluated using serum samples from leprosy patients, household contacts (HC) of leprosy patients, tuberculosis patients and endemic controls (EC). A pool of three peptides identified 73.9% (17/23) of multibacillary (MB) leprosy patients using an enzyme-linked immunosorbent assay (ELISA). These peptides also showed some seroreactivities to the HC and EC individuals. The peptides were not reactive to rabbit polyclonal antisera against the different environmental mycobacteria. The same peptides that were conjugated to the carrier protein bovine serum albumin (BSA) induced the production of antibodies in the mice. The anti-peptide antibodies that were used in the Western blotting analysis of M. leprae crude extracts revealed a single band of approximately 30 kDa in one-dimensional electrophoresis and four 30 kDa isoforms in the two-dimensional gel. The Western blotting data indicated that the three peptides are derived from the same bacterial protein. These new antigens may be useful in the diagnosis of MB leprosy patients. Their potentials as diagnostic reagents must be more extensively evaluated in future studies using a large panel of positive and negative sera. Furthermore, other test approaches using peptides should be assessed to increase their sensitivity and specificity in detecting leprosy patients. We have revealed evidence in support of phage-displayed peptides as promising biotechnological tools for the design of leprosy diagnostic serological assays.

Seroprevalence of Streptococcal Inhibitor of Complement (SIC) suggests association of streptococcal infection with chronic kidney disease

PubMed Central

2013-01-01

Background Group A streptococcus (GAS) is an etiological agent for the immune mediated sequela post streptococcal glomerulonephritis (PSGN). In some populations PSGN is recognized as a risk factor for chronic kidney disease (CKD) and end-stage renal disease (ESRD). It was found that a significantly greater proportion of subjects with past history of PSGN than without the history exhibited seroreactions to streptococcal antigens called streptococcal inhibitor of complement (SIC) and to distantly related SIC (DRS). These antigens are expressed by major PSGN-associated GAS types. We therefore predicted that in populations such as India, which is endemic for streptococcal diseases and which has high prevalence of CKD and ESRD, greater proportions of CKD and ESRD patients exhibit seroreaction to SIC and DRS than healthy controls. Methods To test this we conducted a SIC and DRS seroprevalence study in subjects from Mumbai area. We recruited 100 CKD, 70 ESRD and 70 healthy individuals. Results Nineteen and 35.7% of CKD and ESRD subjects respectively were SIC antibody-positive, whereas only 7% of healthy cohort was seropositive to SIC. Furthermore, significantly greater proportion of the ESRD patients than the CKD patients is seropositive to SIC (p=0.02; odds ratio 2.37). No association was found between the renal diseases and DRS-antibody-positivity. Conclusions Past infection with SIC-positive GAS is a risk factor for CKD and ESRD in Mumbai population. Furthermore, SIC seropositivity is predictive of poor prognosis of CKD patients. PMID:23642030

Effects of sex, parity, and sequence variation on seroreactivity to candidate pregnancy malaria vaccine antigens.

PubMed

Oleinikov, Andrew V; Rossnagle, Eddie; Francis, Susan; Mutabingwa, Theonest K; Fried, Michal; Duffy, Patrick E

2007-07-01

Plasmodium falciparum-infected erythrocytes adhere to chondroitin sulfate A (CSA) to sequester in the human placenta, and pregnancy malaria (PM) is associated with the development of disease in and the death of both mother and child. A PM vaccine appears to be feasible, because women become protected as they develop antibodies against placental infected erythrocytes (IEs). Two IE surface molecules, VAR1CSA and VAR2CSA, bind CSA in vitro and are potential vaccine candidates. We expressed all domains of VAR1CSA and VAR2CSA as mammalian cell surface proteins, using a novel approach that allows rapid purification, immobilization, and quantification of target antigen. For serum samples from East Africa, we measured reactivity to all domains, and we examined the effects of host sex and parity, as well as the effects of parasite antigenic variation. Serum samples obtained from multigravid women had a higher reactivity to all VAR2CSA domains than did those obtained from primigravid women or from men. Conversely, serum samples obtained from men had consistently higher reactivity to VAR1CSA domains than did those obtained from gravid women. Seroreactivity was strongly influenced by antigenic variation of VAR2CSA Duffy binding-like domains. Women acquire antibodies to VAR2CSA over successive pregnancies, but they lose reactivity to VAR1CSA. Serum reactivity to VAR2CSA is variant specific, and future studies should examine the degree to which functional antibodies, such as binding-inhibition antibodies, are variant specific.

Association of leptospiral seroreactivity and breed with uveitis and blindness in horses: 372 cases (1986-1993).

PubMed

Dwyer, A E; Crockett, R S; Kalsow, C M

1995-11-15

Recurrent uveitis, a leading cause of blindness in horses, often develops as a sequela to systemic leptospirosis. Over a 7-year period, 63 of 112 (56%) horses with uveitis were seropositive for Leptospira interrogans serovar pomona, but only 23 of 260 (9%) horses without uveitis were seropositive. Odds-ratio analysis revealed that seropositive horses were 13.2 times more likely to have uveitis than were seronegative horses. Of the 63 seropositive horses with uveitis, 59% developed blindness, compared with only 24% in the 49 seronegative horses with uveitis that lost vision in 1 or both eyes during the same period. Odds-ratio analysis revealed that seropositive horses with uveitis were 4.4 times more likely to lose vision than were seronegative horses with uveitis. Of the 112 horses with uveitis, 28 (25%) were Appaloosas, compared with only 10 of the 260 (4%) horses without uveitis (odds ratio, 8.3). In addition, 19 of the 28 (68%) Appaloosas with uveitis developed blindness, compared with only 30 of the 84 (36%) non-Appaloosas with uveitis that lost vision in 1 or both eyes (odds ratio, 3.8). This field study therefore confirmed a strong positive relationship between uveitis and leptospiral seroreactivity in horses. Furthermore, the data suggested that seropositive horses with uveitis were at increased risk of losing vision, compared with that in seronegative horses with uveitis, and that Appaloosas were at increased risk of developing uveitis and associated blindness, compared with that in non-Appaloosas.

Molecular and serologic evidence for Babesia bovis-like parasites in white-tailed deer (Odocoileus virginianus) in south Texas.

PubMed

Ramos, Christina M; Cooper, Susan M; Holman, Patricia J

2010-09-20

The current study was undertaken to determine if white-tailed deer in south Texas harbor Babesia bovis, a causative agent of bovine babesiosis. Blood samples from free-ranging white-tailed deer (Odocoileus virginianus) on two ranches in LaSalle and Webb Counties were screened for B. bovis and other hemoparasites by the polymerase chain reaction (PCR) to detect the piroplasm 18S rDNA. Serology was conducted on selected samples to detect antibody activity to B. bovis by the immunofluorescent antibody test (IFAT). PCR revealed that 16% of the LaSalle County samples and 4% of the Webb County samples were positive for B. bovis. Five of the LaSalle County and the two Webb County B. bovis 18S rDNA amplicons were cloned and sequenced. The resulting clones shared 99% identity to B. bovis 18S rRNA gene sequences derived from cattle isolates. Weak seroreactivity to B. bovis was shown by the IFAT. The samples were also screened for additional hemoparasites of deer including Theileria cervi, Babesia odocoilei and other Babesia spp. A genotypically unique Theileria sp. was found, along with T. cervi and B. odocoilei. The finding of putative B. bovis in white-tailed deer necessitates further study to determine if deer may act as a transient host or even a reservoir of infection for B. bovis pathogenic to cattle.

Serologic evidence of the exposure of small mammals to spotted-fever Rickettsia and Rickettsia bellii in Minas Gerais, Brazil.

PubMed

Coelho, Marcella Gonçalves; Ramos, Vanessa do Nascimento; Limongi, Jean Ezequiel; de Lemos, Elba Regina Sampaio; Guterres, Alexandro; da Costa Neto, Sócrates Fraga; Rozental, Tatiana; Bonvicino, Cibele Rodrigues; D'Andrea, Paulo Sérgio; Moraes-Filho, Jonas; Labruna, Marcelo Bahia; Szabó, Matias Pablo Juan

2016-03-31

Sources of pathogenic Rickettsia in wildlife are largely unknown in Brazil. In this work, potential tick vectors and seroreactivity of small mammals against four spotted-fever group Rickettsia (R. rickettsii, R. parkeri, R. amblyommii and R. rhipicephali) and Rickettsia bellii from peri-urban areas of Uberlândia, a major town in Brazil, are described for the first time. Small mammals were captured and blood samples collected. Ticks were collected from the surface of the host and the environment and posteriorly identified. Reactivity of small mammal sera to Rickettsia was tested by indirect immunofluorescence assay (IFA) using crude antigens from five Brazilian Rickettsia isolates. Information was obtained from 416 small mammals (48 Marsupialia and 368 Rodentia). Forty-eight animals were parasitized and two tick species, Ixodes loricatus and Amblyomma dubitatum, were found on several host species, with a few tick-host relationships described for the first time. From the 416 tested sera, 70 reacted to at least one Rickettsia antigen (prevalence of 16.8%) and from these, 19 (27.1%) reacted to two or more antigens. Seroprevalence was higher for marsupials (39.6%) than for rodents (13.8%). Marsupial and Rhipidomys spp. sera reacted mainly (highest seroprevalence and titers) to R. bellii, and that of Necromys lasiurus mainly to R. rickettsii. Although the serologic assays poorly discriminate between closely related spotted-fever group Rickettsia, the observed small mammal seroreactivity suggests the circulation of Rickettsia in the peri-urban area of Uberlândia, albeit at low levels.

Mother-Newborn Pairs in Malawi Have Similar Antibody Repertoires to Diverse Malaria Antigens.

PubMed

Boudová, Sarah; Walldorf, Jenny A; Bailey, Jason A; Divala, Titus; Mungwira, Randy; Mawindo, Patricia; Pablo, Jozelyn; Jasinskas, Algis; Nakajima, Rie; Ouattara, Amed; Adams, Matthew; Felgner, Philip L; Plowe, Christopher V; Travassos, Mark A; Laufer, Miriam K

2017-10-01

Maternal antibodies may play a role in protecting newborns against malaria disease. Plasmodium falciparum parasite surface antigens are diverse, and protection from infection requires allele-specific immunity. Although malaria-specific antibodies have been shown to cross the placenta, the extent to which antibodies that respond to the full repertoire of diverse antigens are transferred from the mother to the infant has not been explored. Understanding the breadth of maternal antibody responses and to what extent these antibodies are transferred to the child can inform vaccine design and evaluation. We probed plasma from cord blood and serum from mothers at delivery using a customized protein microarray that included variants of malaria vaccine target antigens to assess the intensity and breadth of seroreactivity to three malaria vaccine candidate antigens in mother-newborn pairs in Malawi. Among the 33 paired specimens that were assessed, mothers and newborns had similar intensity and repertoire of seroreactivity. Maternal antibody levels against vaccine candidate antigens were the strongest predictors of infant antibody levels. Placental malaria did not significantly impair transplacental antibody transfer. However, mothers with placental malaria had significantly higher antibody levels against these blood-stage antigens than mothers without placental malaria. The repertoire and levels of infant antibodies against a wide range of malaria vaccine candidate antigen variants closely mirror maternal levels in breadth and magnitude regardless of evidence of placental malaria. Vaccinating mothers with an effective malaria vaccine during pregnancy may induce high and potentially protective antibody repertoires in newborns. Copyright © 2017 American Society for Microbiology.

Going Wild: Lessons from Naturally Occurring T-Lymphotropic Lentiviruses

PubMed Central

VandeWoude, Sue; Apetrei, Cristian

2006-01-01

Over 40 nonhuman primate (NHP) species harbor species-specific simian immunodeficiency viruses (SIVs). Similarly, more than 20 species of nondomestic felids and African hyenids demonstrate seroreactivity against feline immunodeficiency virus (FIV) antigens. While it has been challenging to study the biological implications of nonfatal infections in natural populations, epidemiologic and clinical studies performed thus far have only rarely detected increased morbidity or impaired fecundity/survival of naturally infected SIV- or FIV-seropositive versus -seronegative animals. Cross-species transmissions of these agents are rare in nature but have been used to develop experimental systems to evaluate mechanisms of pathogenicity and to develop animal models of HIV/AIDS. Given that felids and primates are substantially evolutionarily removed yet demonstrate the same pattern of apparently nonpathogenic lentiviral infections, comparison of the biological behaviors of these viruses can yield important implications for host-lentiviral adaptation which are relevant to human HIV/AIDS infection. This review therefore evaluates similarities in epidemiology, lentiviral genotyping, pathogenicity, host immune responses, and cross-species transmission of FIVs and factors associated with the establishment of lentiviral infections in new species. This comparison of consistent patterns in lentivirus biology will expose new directions for scientific inquiry for understanding the basis for virulence versus avirulence. PMID:17041142

A systematic review of leptospirosis on wild animals in Latin America.

PubMed

Vieira, Anahi S; Pinto, Priscila S; Lilenbaum, Walter

2018-02-01

Leptospirosis is a bacterial systemic infection which affects domestic animals and wildlife, as well as humans. Many wild animals act as reservoirs of leptospires. Nevertheless, the real role of wildlife animals as source of infection to livestock and humans, as well as the most important reservoirs and leptospiral strains remains unclear. This systematic review assesses the available data about wildlife and their biomes in Latin America, concerning to leptospiral infection. In addition, we discuss the development of the research on leptospirosis in wildlife in this region. After the application of exclusion criteria, 79 papers were analyzed, comprising 186 species, 122 genus, 53 families, and 19 orders from four classes. Mammals were the most studied class, followed by Amphibian, Reptile, and Aves. The Icterohaemorrhagiae serogroup was predominant in most biomes and many orders. A small number of antigens detected the majority of seroreactive animals of each class, and a smaller panel may be used at microscopic agglutination test. Further studies must always consider edaphoclimatic conditions besides only host class or species, in order to obtain a broader understanding of the wild epidemiological cycle of leptospirosis in the region. In conclusion, direct and indirect evidences demonstrate that leptospirosis is largely widespread among wildlife in all biomes of Latin America. Moreover, more research on the role of wildlife on the epidemiology of leptospirosis and its impact on livestock and public health are required, particularly focusing on direct detection of the agent.

Association of TNF-alpha (-308 A/G) and IFN-gamma (+874 A/T) gene polymorphisms in response to spontaneous and treatment induced viral clearance in HCV infected multitransfused thalassemic patients.

PubMed

Biswas, Aritra; Gupta, Nabyendu; Gupta, Debanjali; Datta, Abira; Firdaus, Rushna; Chowdhury, Prosanto; Bhattacharyya, Maitreyee; Sadhukhan, Provash C

2018-06-01

Multitransfused thalassemic individuals are at high risk of developing transfusion transmitted Hepatitis C virus (HCV) infection. The aim of the study was to correlate the effects of host cytokine single nucleotide polymorphisms of TNF-α (-308 A/G) and IFN-γ (+874 A/T) in spontaneous or IFN induced treatment response in the HCV infected thalassemic individuals. A total of 427 HCV sero-reactive thalassemic individuals were processed for HCV viral genomic diversity and host gene polymorphisms analysis of TNF-α (-308 A/G) and IFN-γ (+874 A/T). Out of 427 HCV sero-reactive individuals, 69.09% were found to be HCV RNA positive with genotype 3 as the predominant infecting strain (94.29%). Study highlighted that, A allele was significantly associated with (p < .05) spontaneous clearance of HCV infection and G allele was correlated with viral persistence at TNF-α (-308) gene polymorphism. Whereas in case of IFN-γ (+874) SNPs, A allele was significantly responsible (p < .05) for spontaneous clearance than T allele. Our study also indicated that in relapsed cases, IFN-γ (+874) T allele is more responsible than A allele. Though no significant correlation was found at both TNF-α (-308) and IFN-γ (+874) gene polymorphism among SVR and relapsed thalassemic patients. A allele at both TNF-α (-308) and IFN-γ (+874) were strongly associated with spontaneous clearance among this population. But in case of SVR and relapsed cases no significant association was found. This cytokine gene polymorphisms pattern will help clinicians to take an informed decision about therapeutic management of HCV infected thalassemic individuals. Copyright © 2017 Elsevier Ltd. All rights reserved.

Seroprevalence and risk factors associated with Ehrlichia canis, Anaplasma spp., Borrelia burgdorferi sensu lato, and D. immitis in hunting dogs from southern Italy.

PubMed

Piantedosi, Diego; Neola, Benedetto; D'Alessio, Nicola; Di Prisco, Francesca; Santoro, Mario; Pacifico, Laura; Sgroi, Giovanni; Auletta, Luigi; Buch, Jesse; Chandrashekar, Ramaswamy; Breitschwerdt, Edward B; Veneziano, Vincenzo

2017-10-01

Canine vector-borne diseases (CVBDs) are caused by a range of pathogens transmitted to dogs by arthropods. The present study investigates Ehrlichia canis, Anaplasma spp., Borrelia burgdorferi sensu lato, and Dirofilaria immitis seroprevalences in hunting dogs from southern Italy. Dogs (no. 1335) were tested using a commercial in-clinic enzyme-linked immunosorbent assay kit. Odds ratios (ORs) were calculated by logistic regression analysis to identify risk factors. Overall, 138/1335 dogs (10.3%) were seroreactive to at least one CVBD pathogen. E. canis, Anaplasma spp., B. burgdorferi s.l., and D. immitis seroprevalences were 7.6, 4.4, 0.3, and 0.2%, respectively. E. canis and Anaplasma spp. co-exposures were found in 30 dogs (2.2%), compared with Anaplasma spp. and B. burgdorferi s.l. co-exposures in 2 dogs (0.1%). Adult age was a risk factor for E. canis (OR 2.35) seroreactivity whereas hunting fur-bearing animals for E. canis (OR 4.75) and Anaplasma spp. (OR 1.87), respectively. The historical presence of tick infestation was identified as a risk factor for positivity to E. canis (OR 2.08) and Anaplasma spp. (OR 2.15). Finally, larger dog pack size was significantly associated with E. canis (OR 1.85) and Anaplasma spp. (OR 2.42) exposures. The results of the present survey indicated that hunting dog populations are at relative risk of CVBDs in southern Italy. Further studies are needed to evaluate the role of hunting dogs in the epidemiology of vector-borne organisms due to sharing common environments with wild, sympatric animal populations.

Epidemiology of Rickettsia sp. strain Atlantic rainforest in a spotted fever-endemic area of southern Brazil.

PubMed

Barbieri, Amalia R M; Filho, Jonas M; Nieri-Bastos, Fernanda A; Souza, Julio C; Szabó, Matias P J; Labruna, Marcelo B

2014-10-01

The present study was performed in Vila Itoupava, an area of the state of Santa Catarina, southern Brazil, in which a tick-borne spotted fever illness has been endemic since 2003. Notably, both the etiological agent and the vector of these spotted fever cases remain unknown. During January 2011, humans, domestic dogs, and their ticks were sampled in households that are typically surrounded by highly preserved Atlantic rainforest fragments. Ticks collected from dogs were Amblyomma ovale (34% prevalence), Amblyomma aureolatum (18.9%), and Rhipicephalus sanguineus (3.8%). A total of 7.8% (6/77) A. ovale and 9.3% (4/43) A. aureolatum were infected by Rickettsia sp. strain Atlantic rainforest, a Rickettsia parkeri-like agent recently shown to cause spotted fever illness in southeastern Brazil. Overall, 67.3% (35/52) of the dogs were seroreactive to spotted fever group rickettsiae, mostly with highest endpoint titers to R. parkeri. Among humans, 46.7% (7/15) reacted serologically to rickettsiae at low to moderate endpoint titers. Because canine seroreactivity to R. parkeri was strongly associated with frequent contact with forests (the preferred habitat for A. ovale and A. aureolatum), it is concluded that sampled dogs have been infected by strain Atlantic rainforest through the parasitism of these tick species. The present study provides epidemiological evidence that the spotted fever in the study area has been caused by Rickettsia sp. strain Atlantic rainforest, transmitted to humans by either A. ovale or A. aureolatum. Further studies encompassing direct diagnostic methods on clinical specimens from patients are needed to confirm the above epidemiological evidence. Copyright © 2014 Elsevier GmbH. All rights reserved.

Characterization of culture filtrate proteins Rv1197 and Rv1198 of ESAT-6 family from Mycobacterium tuberculosis H37Rv.

PubMed

Pandey, Himanshu; Tripathi, Sarita; Srivastava, Kanchan; Tripathi, Dinesh K; Srivastava, Mrigank; Kant, Surya; Srivastava, Kishore K; Arora, Ashish

2017-02-01

We have characterized two immunogenic proteins, Rv1197 and Rv1198, of the Esx-5 system of the ESAT-6 family of Mycobacterium tuberculosis H37Rv. The complex formation between Rv1197 and Rv1198 was characterized by biophysical techniques. The reactivity of serum from TB patients towards these proteins was characterized by ELISA. Lymphocyte proliferation and cytokine induction were followed in restimulated splenocytes from immunized mice by using MTT assay and CBA flowcytometry, respectively. Rv1197 and Rv1198 strongly interact to form a heterodimeric complex under reducing conditions, which is characterized by a dissociation constant of 97×10 -9 M and melting temperature, Tm, of 50.5°C. Strong humoral responses to Rv1197, Rv1198, CFP-10 and MoaC1 (Rv3111) antigens were found in Indian patients with active pulmonary tuberculosis (n=44), in comparison to non-infected healthy individuals (n=20). The seroreactivity to Rv1198 was characterized by a sensitivity of 75% and specificity of 90%. In BALB/c mice, immunization with Rv1198-FIA induced a pro-inflammatory response with elevated levels of TNF and IL-6, along with low induction of IFN-γ, IL-2 and IL-10, but no induction of IL-4. Rv1197 and Rv1198 form a stable complex, which is regulated by the redox state of Rv1198. Rv1198 is immunogenic with highly specific seroreactivity towards TB patients' serum. Rv1198 elicits a pro-inflammatory recall response in immunized mice. This study characterizes the interaction of Rv1197 and Rv1198, and establishes the immunogenic nature of Rv1198. Copyright © 2016 Elsevier B.V. All rights reserved.

Readressing the role of Toll-like receptor-4 alleles in inflammatory bowel disease: colitis, smoking, and seroreactivity.

PubMed

Manolakis, Anastassios C; Kapsoritakis, Andreas N; Kapsoritaki, Anastasia; Tiaka, Elisavet K; Oikonomou, Konstantinos A; Lotis, Vassilis; Vamvakopoulou, Dimitra; Davidi, Ioanna; Vamvakopoulos, Nikolaos; Potamianos, Spyros P

2013-02-01

Toll-like receptor (TLR) polymorphisms, and especially TLR-4 Asp299Gly and TLR-4 Thr399Ile, have been linked with Crohn's disease (CD) and to a lesser extent with ulcerative colitis (UC), CD behavior, and compromised seroreactivity to microbial antigens. Available data, however, are conflicting. To address these issues, the distribution of TLR-4 polymorphic alleles was assessed in patients with UC, CD, and healthy controls (HC), considering patient and disease characteristics as well as related serological markers. TLR-4 Asp299Gly and TLR-4 Thr399Ile polymorphisms were determined in 187 UC and 163 CD patients and 274 randomly selected HC. C reactive protein, anti-Saccharomyces cerevisiae mannan antibodies, anti-mannobioside carbohydrate antibodies, anti-laminariobioside carbohydrate antibodies IgG, and anti-chitobioside carbohydrate antibodies (ACCA) IgA levels were also assessed. UC and especially pancolitis patients carried the mutant alleles more frequently compared to CD patients and HC or UC patients with different disease extents (P = 0.002 and P < 0.0001, respectively). Involvement of the colon was more frequent in CD patients with mutant TLR-4 compared to those with wild-type alleles (P = 0.004). Levels and positivity rates of ACCA IgA were lower in inflammatory bowel disease (IBD) patients carrying the mutant compared to those with wild-type alleles (0.075 < P < 0.05). Despite the mutant TLR-4 predisposition for UC pancolitis, smoking was associated with more limited disease (P < 0.001). The presence of TLR-4 Asp299Gly and TLR-4 Thr399Ile polymorphisms is related to UC pancolitis, involvement of the colon in CD, and lower ACCA IgA levels. Smoking reduces the extent of UC, even in the presence of mutant alleles.

Half a decade of mini-pool nucleic acid testing: Cost-effective way for improving blood safety in India

PubMed Central

Chandrashekar, Shivaram

2014-01-01

Background and Objectives: It is well established that Nucleic acid testing (NAT) reduces window phase of transfusion transmissible infections (TTI) and helps improve blood safety. NAT testing can be done individually or in pools. The objectives of this study were to determine the utility, feasibility and cost effectiveness of an in-house minipool-NAT(MP-NAT). Materials and Methods: Blood donors were screened by history, tested by ELISA and sero-negative samples were subjected to an in-house NAT by using reverse transcriptase-polymerase chain reaction (RT-PCR). Testing was done in mini-pools of size eight (8). Positive pools were repeated with individual samples. Results: During the study period of Oct 2005-Sept 2010 (5 years) all blood donors (n=53729) were screened by ELISA. Of which 469 (0.87%) were positive for HIV-1, HBV or HCV. Sero-negative samples (n=53260) were screened by in-house MP-NAT. HIV-NAT yield was 1/53260 (n=1) and HBV NAT yield (n=2) was 1/26630. Conclusion: NAT yield was lower than other India studies possibly due to the lower sero-reactivity amongst our donors. Nevertheless it intercepted 9 lives including the components prepared. The in-house assay met our objective of improving blood safety at nominal cost and showed that it is feasible to set up small molecular biology units in medium-large sized blood banks and deliver blood within 24-48 hours. The utility of NAT (NAT yield) will vary based on the donor population, the type of serological test used, the nature of kit employed and the sensitivity of NAT test used. The limitations of our in-house MP-NAT consisted of stringent sample preparation requirements, with labor and time involved. The benefits of our MP-NAT were that it acted as a second level of check for ELISA tests, was relatively inexpensive compared to ID-NAT and did not need sophisticated equipment. PMID:24678172

Myocarditis in mice and guinea pigs experimentally infected with a canine-origin Borrelia isolate from Florida.

PubMed

Breitschwerdt, E B; Geoly, F J; Meuten, D J; Levine, J F; Howard, P; Hegarty, B C; Stafford, L C

1996-04-01

To characterize the pathogenic potential of a unique Borrelia isolate obtained from a dog from Florida (FCB isolate). Prospective experimental infection. 32 preweanling Swiss Webster mice and 12 adult male Hartley guinea pigs were injected intraperitoneally with 10(5) spirochetes. Mice were used as controls and blood recipients, and at 3- to 4-day intervals, 1 control mouse and 2 infected mice were necropsied, tissues were cultured, and a recipient mouse was inoculated with blood. Guinea pigs were randomized to 4 groups and inoculated intradermally with 10(0), 10(2), 10(3), or 10(4) spirochetes. For 48 days, clinical, hematologic, serologic, and microbiologic tests were performed on them, after which they were necropsied. In mice, spirochetemia was detectable between postinoculation days (PID) 3 and 13, and seroreactivity to homologous antigen was detectable during PID 10 through 31. Compared with control mice, infected mouse spleens were 2 to 3 times larger. Histologic lesions included lymphoid hyperplasia, neutrophilic panniculitis, epicarditis, and myocarditis, with intralesional spirochetes detected from PID 3 through 6. During PID 10 through 31, nonsuppurative epicarditis developed. Signs of illness and hematologic abnormalities were not observed in guinea pigs, despite isolating spirochetes from blood during PID 7 to 27. When necropsied on PID 48, histologic lesions included lymphoid hyperplasia and lymphocytic plasmacytic epicarditis. The FCB isolate causes spirochetemia, lymphoid hyperplasia, dermatitis, and myocardial injury in Swiss Webster mice and can be transmitted by blood inoculation. In Hartley guinea pigs, the isolate causes spirochetemia, lymphoid hyperplasia, and epicarditis. Documentation of disease in mice, guinea pigs, and, presumably, dogs raises the level of concern that the FCB isolate might be pathogenic for man and other animal species.

Anaplasma spp. in dogs and owners in north-western Morocco.

PubMed

Elhamiani Khatat, Sarah; Daminet, Sylvie; Kachani, Malika; Leutenegger, Christian M; Duchateau, Luc; El Amri, Hamid; Hing, Mony; Azrib, Rahma; Sahibi, Hamid

2017-04-24

Anaplasma phagocytophilum is an emerging tick-borne zoonotic pathogen of increased interest worldwide which has been detected in northern Africa. Anaplasma platys is also present in this region and could possibly have a zoonotic potential. However, only one recent article reports on the human esposure to A. phagocytophilum in Morocco and no data are available on canine exposure to both bacteria. Therefore, we conducted a cross-sectional epidemiological study aiming to assess both canine and human exposure to Anaplasma spp. in Morocco. A total of 425 dogs (95 urban, 160 rural and 175 working dogs) and 11 dog owners were sampled from four cities of Morocco. Canine blood samples were screened for Anaplasma spp. antibodies by an enzyme-linked immunosorbent assay (ELISA) and for A. phagocytophilum and A. platys DNA by a real-time polymerase chain reaction (RT-PCR) targeting the msp2 gene. Human sera were tested for specific A. phagocytophilum immunoglobulin G (IgG) using a commercial immunofluorescence assay (IFA) kit. Anaplasma spp. antibodies and A. platys DNA were detected in 21.9 and 7.5% of the dogs, respectively. Anaplasma phagocytophilum DNA was not amplified. Anaplasma platys DNA was significantly more frequently amplified for working dogs. No statistically significant differences in the prevalence of Anaplasma spp. antibodies or A. platys DNA detection were observed between sexes, age classes or in relation to exposure to ticks. A total of 348 Rhipicephalus sanguineus (sensu lato) ticks were removed from 35 urban and working dogs. The majority of dog owners (7/10) were seroreactive to A. phagoyctophilum IgG (one sample was excluded because of hemolysis). This study demonstrates the occurrence of Anaplasma spp. exposure and A. platys infection in dogs, and A. phagocytophilum exposure in humans in Morocco.

Seronegativity of bovines face to their own recovered leptospiral isolates.

PubMed

Libonati, Hugo; Pinto, Priscila S; Lilenbaum, Walter

2017-07-01

Leptospirosis is an important cause of reproductive failure in cattle. The standard diagnostic tool (MAT) is recommended for herd but not for individual diagnosis. The aim of this study was to evaluate the humoral response of bovines face to their own recovered isolates. A total of 25 bovine from which leptospires were recovered were tested by MAT against reference strains and their own isolates. Only three cows (12%) presented seroreactivity against their own isolates. This study demonstrates that cattle may not react against their own isolates and highlights the importance of interpreting serological negative results with caution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detection of antileishmanial antibodies in blood sampled from blood bank donors in Istanbul.

PubMed

Ates, Sezen Canim; Bagirova, Malahat; Allahverdiyev, Adil M; Baydar, Serap Yesilkir; Koc, Rabia Cakir; Elcicek, Serhat; Abamor, Emrah Sefik; Oztel, Olga Nehir

2012-06-01

According to the WHO, only 5-20% of the total cases of leishmaniasis are symptomatic leishmaniasis; the other cases are identified as asymptomatic leishmaniasis. In recent studies, it has been demonstrated that donor blood plays an important role in the epidemiology of asymptomatic leishmaniasis. However, the number of the studies on this subject is still insufficient. Additionally, donor blood samples obtained from Istanbul, which is the biggest metropolitan area in Turkey, have not been investigated with regard to Leishmania. Moreover, there is no information about the sensitivity of noninvasive serological methods that are used in the detection of leishmaniasis donor blood samples. Accordingly, this study aimed to investigate the presence of antileishmanial antibodies in blood samples obtained from blood bank donors in Istanbul, by using different serologic methods, and to determine the most sensitive detection method. Blood samples were taken from 188 healthy blood bank donors to the Capa Turkish Red Crescent Blood Bank (Istanbul, Turkey), and the presence of antileishmanial antibodies was measured by indirect immunofluorescent antibody test (IFAT), ELISA, immunochromatographic dipstick rapid test, and western blot (WB). Antileishmanial antibodies were determined in 12 out of 188 samples by IFAT (6.4%), and six out of these 12 donors were found to be positive at diagnostic titer 1:128 (3.2%). One hundred and eighty eight samples were investigated by ELISA and one (0.5%) of them gave a positive result. None of 188 samples provided a positive result by immunochromatographic test. WB applied to the 12 seroreactive donors showed that three out of 12 donors were positive. In this study, the presence of antileishmanial antibodies in blood samples of blood bank donors from Istanbul has been demonstrated by using feasible and low-cost serological methods. Additionally, in comparison with other simple and low-cost detection methods, WB was used for confirmation. IFAT has a higher sensitivity and therefore may be preferred as a prescreening method in endemic or nonendemic areas.

Intestinal parasites and vector-borne pathogens in stray and free-roaming cats living in continental and insular Greece

PubMed Central

Diakou, Anastasia; Di Cesare, Angela; Accettura, Paolo Matteo; Barros, Luciano; Iorio, Raffaella; Paoletti, Barbara; Frangipane di Regalbono, Antonio; Halos, Lénaïg; Beugnet, Frederic; Traversa, Donato

2017-01-01

This survey investigated the distribution of various intestinal parasites and vector-borne pathogens in stray and free-roaming cats living in four regions of Greece. A total number of one hundred and fifty cats living in three Islands (Crete, Mykonos and Skopelos) and in Athens municipality was established as a realistic aim to be accomplished in the study areas. All cats were examined with different microscopic, serological and molecular assays aiming at evaluating the occurrence of intestinal parasites, and exposure to or presence of vector-borne infections. A total of 135 cats (90%) was positive for one or more parasites and/or pathogens transmitted by ectoparasites. Forty-four (29.3%) cats were positive for one single infection, while 91 (60.7%) for more than one pathogen. A high number of (n. 53) multiple infections caused by feline intestinal and vector-borne agents including at least one zoonotic pathogen was detected. Among them, the most frequently recorded helminths were roundworms (Toxocara cati, 24%) and Dipylidium caninum (2%), while a high number of examined animals (58.8%) had seroreaction for Bartonella spp., followed by Rickettsia spp. (43.2%) and Leishmania infantum (6.1%). DNA-based assays revealed the zoonotic arthropod-borne organisms Bartonella henselae, Bartonella clarridgeiae, Rickettsia spp., and L. infantum. These results show that free-ranging cats living in areas of Greece under examination may be exposed to a plethora of internal parasites and vector-borne pathogens, some of them potentially able to infect humans. Therefore, epidemiological vigilance and appropriate control measures are crucial for the prevention and control of these infections and to minimize the risk of infection for people. PMID:28141857

Intestinal parasites and vector-borne pathogens in stray and free-roaming cats living in continental and insular Greece.

PubMed

Diakou, Anastasia; Di Cesare, Angela; Accettura, Paolo Matteo; Barros, Luciano; Iorio, Raffaella; Paoletti, Barbara; Frangipane di Regalbono, Antonio; Halos, Lénaïg; Beugnet, Frederic; Traversa, Donato

2017-01-01

This survey investigated the distribution of various intestinal parasites and vector-borne pathogens in stray and free-roaming cats living in four regions of Greece. A total number of one hundred and fifty cats living in three Islands (Crete, Mykonos and Skopelos) and in Athens municipality was established as a realistic aim to be accomplished in the study areas. All cats were examined with different microscopic, serological and molecular assays aiming at evaluating the occurrence of intestinal parasites, and exposure to or presence of vector-borne infections. A total of 135 cats (90%) was positive for one or more parasites and/or pathogens transmitted by ectoparasites. Forty-four (29.3%) cats were positive for one single infection, while 91 (60.7%) for more than one pathogen. A high number of (n. 53) multiple infections caused by feline intestinal and vector-borne agents including at least one zoonotic pathogen was detected. Among them, the most frequently recorded helminths were roundworms (Toxocara cati, 24%) and Dipylidium caninum (2%), while a high number of examined animals (58.8%) had seroreaction for Bartonella spp., followed by Rickettsia spp. (43.2%) and Leishmania infantum (6.1%). DNA-based assays revealed the zoonotic arthropod-borne organisms Bartonella henselae, Bartonella clarridgeiae, Rickettsia spp., and L. infantum. These results show that free-ranging cats living in areas of Greece under examination may be exposed to a plethora of internal parasites and vector-borne pathogens, some of them potentially able to infect humans. Therefore, epidemiological vigilance and appropriate control measures are crucial for the prevention and control of these infections and to minimize the risk of infection for people.

Similar Patterns of Infection with Bovine Foamy Virus in Experimentally Inoculated Calves and Sheep

PubMed Central

Hechler, Torsten; Löchelt, Martin; Kuźmak, Jacek

2013-01-01

Foamy viruses (FVs) are the least known retroviruses commonly found in primates, cats, horses, and cattle. Although FVs are considered apathogenic, simian and feline FVs have been shown to be associated with some transient health abnormalities in animal models. Currently, data regarding the course of infection with bovine FV (BFV) are not available. In this study, we conducted experimental infections of natural (cattle) and heterologous (sheep) hosts with the BFV100 isolate and monitored infection patterns in both hosts during the early phase postinoculation as well as after long-term infection. Four calves and six sheep inoculated with BFV100 showed no signs of pathology but developed persistent infection, as confirmed by virus rescue, consistent detection of BFV-specific antibodies, and presence of viral DNA. In both hosts, antibodies against BFV Gag and Bet appeared early after infection and persisted at high and stable levels while seroreactivity toward Env was consistently detectable only in BFV-infected sheep. Interestingly, the BFV proviral DNA load was highest in lung, spleen, and liver and moderate in leukocytes, while salivary glands contained either low or undetectable DNA loads in calves or sheep, respectively. Additionally, comparison of partial BFV sequences from inoculum and infected animals demonstrated very limited changes after long-term infection in the heterologous host, clearly less than those found in BFV field isolates. The persistence of BFV infection in both hosts suggests full replication competence of the BFV100 isolate with no requirement of genetic adaptation for productive replication in the authentic and even in a heterologous host. PMID:23325680

Similar patterns of infection with bovine foamy virus in experimentally inoculated calves and sheep.

PubMed

Materniak, Magdalena; Hechler, Torsten; Löchelt, Martin; Kuzmak, Jacek

2013-03-01

Foamy viruses (FVs) are the least known retroviruses commonly found in primates, cats, horses, and cattle. Although FVs are considered apathogenic, simian and feline FVs have been shown to be associated with some transient health abnormalities in animal models. Currently, data regarding the course of infection with bovine FV (BFV) are not available. In this study, we conducted experimental infections of natural (cattle) and heterologous (sheep) hosts with the BFV(100) isolate and monitored infection patterns in both hosts during the early phase postinoculation as well as after long-term infection. Four calves and six sheep inoculated with BFV(100) showed no signs of pathology but developed persistent infection, as confirmed by virus rescue, consistent detection of BFV-specific antibodies, and presence of viral DNA. In both hosts, antibodies against BFV Gag and Bet appeared early after infection and persisted at high and stable levels while seroreactivity toward Env was consistently detectable only in BFV-infected sheep. Interestingly, the BFV proviral DNA load was highest in lung, spleen, and liver and moderate in leukocytes, while salivary glands contained either low or undetectable DNA loads in calves or sheep, respectively. Additionally, comparison of partial BFV sequences from inoculum and infected animals demonstrated very limited changes after long-term infection in the heterologous host, clearly less than those found in BFV field isolates. The persistence of BFV infection in both hosts suggests full replication competence of the BFV(100) isolate with no requirement of genetic adaptation for productive replication in the authentic and even in a heterologous host.

Q fever as a cause of fever of unknown origin and thrombocytosis: first molecular evidence of Coxiella burnetii in Brazil.

PubMed

Lemos, Elba R S; Rozental, Tatiana; Mares-Guia, Maria Angélica M; Almeida, Daniele N P; Moreira, Namir; Silva, Raphael G; Barreira, Jairo D; Lamas, Cristiane C; Favacho, Alexsandra R; Damasco, Paulo V

2011-01-01

We report a case of Q fever in a man who presented with fever of 40 days duration associated with thrombocytosis. Serological and molecular analysis (polymerase chain reaction) confirmed infection with Coxiella burnetii. A field study was conducted by collecting blood samples from the patient's family and from the animals in the patient's house. The patient's wife and 2 of 13 dogs showed seroreactivity. Our data indicate that C. burnetii may be an underrecognized cause of fever in Brazil and emphasize the need for clinicians to consider Q fever in patients with a febrile illness, particularly those with a history of animal contact.

Seroreactive marker for inflammatory bowel disease and associations with antibodies to dietary proteins in bipolar disorder.

PubMed

Severance, Emily G; Gressitt, Kristin L; Yang, Shuojia; Stallings, Cassie R; Origoni, Andrea E; Vaughan, Crystal; Khushalani, Sunil; Alaedini, Armin; Dickerson, Faith B; Yolken, Robert H

2014-05-01

Immune sensitivity to wheat glutens and bovine milk caseins may affect a subset of individuals with bipolar disorder. Digested byproducts of these foods are exorphins that have the potential to impact brain physiology through action at opioid receptors. Inflammation in the gastrointestinal (GI) tract might accelerate exposure of food antigens to systemic circulation and help explain elevated gluten and casein antibody levels in individuals with bipolar disorder. We measured a marker of GI inflammation, anti-Saccharomyces cerevisiae antibodies (ASCA), in non-psychiatric controls (n = 207), in patients with bipolar disorder without a recent onset of psychosis (n = 226), and in patients with bipolar disorder with a recent onset of psychosis (n = 38). We compared ASCA levels to antibodies against gluten, casein, Epstein-Barr virus (EBV), herpes simplex virus 1 (HSV-1), influenza A, influenza B, measles, and Toxoplasma gondii. Elevated ASCA conferred a 3.5-4.4-fold increased odds ratio of disease association (age-, race-, and gender-corrected multinomial logistic regressions, p ≤ 0.00001) that was independent of type of medication received. ASCA correlated with food antibodies in both bipolar disorder groups (R(2)  = 0.29-0.59, p ≤ 0.0005), and with measles and T. gondii immunoglobulin G (IgG) in the recent onset psychosis bipolar disorder group (R(2)  = 0.31-0.36, p ≤ 0.004-0.01). Elevated seropositivity of a GI-related marker and its association with antibodies to food-derived proteins and self-reported GI symptoms suggest a GI comorbidity in at least a subgroup of individuals with bipolar disorder. Marker seroreactivity may also represent part of an overall heightened activated immune state inherent to this mood disorder. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Seroreactivity to Dirofilaria antigens in people from different areas of Serbia.

PubMed

Tasić-Otašević, Suzana A; Gabrielli, Simona V; Tasić, Aleksandar V; Miladinovićtasić, Nataša L; Kostić, Jovana T; Ignjatović, Aleksandra M; Popović Dragonjić, Lidija D; Milošević, Zoran G; Arsić-Arsenijević, Valentina S; Cancrini, Gabriella A

2014-02-08

The Northern part of Serbia is hyperendemic-endemic for canine dirofilarioses. Considering this fact, many human dirofilarial infections could be expected, however only about 30 cases in Serbia have been described until today. Aims of this survey were to assess the people reactivity to the antigens of D. repens and D. immitis and to identify risk factors for the contact exposure. Investigation included sera taken from 297 people (179 women and 118 men) living in different areas of Serbia (Pančevo, Novi Sad, Zaječar, Leskovac, Vranje, Niš, Pirot). Sera were analysed by means of two indirect enzyme-linked immunosorbent (ELISA) home-designed that use as antigens adult somatic/metabolic polyproteins of D. repens (DR) and D. immitis (DI), respectively. The results were elaborated using the statistical method of descriptive and quantitative analysis. Significant differences by area in the reactivity of human sera to dirofilarial antigens were not observed (p = 0.056). A high seroreactivity was demonstrated in people from the towns of northern Serbia (Pančevo = 27,1%; Novi Sad = 16,3%), as well as in people from Zaječar (eastern Serbia = 15,8%) and Vranje (southern Serbia = 15,1%). No differences were evidenced between people reactivity to polyproteins of the two dirofilarial species, nor differences related to the gender of examinees. Factor risks evidenced were: i) place of residence; ii) spending work time outdoors during the mosquito season; iii) spending time outdoors and nearby rivers, lakes, swamps or canals; unespectedly, iv) cat owning. The findings emerging from this investigation indicate that clinicians and public health authorities should pay greater attention to this zoonosis. Continuing education and training of physicians will greatly contribute to the knowledge of the actual impact of filarial worms on animal and public health, and allow for the planning of suitable measures to prevent the infections.

Seroreactivity to Dirofilaria antigens in people from different areas of Serbia

PubMed Central

2014-01-01

Background The Northern part of Serbia is hyperendemic-endemic for canine dirofilarioses. Considering this fact, many human dirofilarial infections could be expected, however only about 30 cases in Serbia have been described until today. Aims of this survey were to assess the people reactivity to the antigens of D. repens and D. immitis and to identify risk factors for the contact exposure. Methods Investigation included sera taken from 297 people (179 women and 118 men) living in different areas of Serbia (Pančevo, Novi Sad, Zaječar, Leskovac, Vranje, Niš, Pirot). Sera were analysed by means of two indirect enzyme-linked immunosorbent (ELISA) home-designed that use as antigens adult somatic/metabolic polyproteins of D. repens (DR) and D. immitis (DI), respectively. The results were elaborated using the statistical method of descriptive and quantitative analysis. Results Significant differences by area in the reactivity of human sera to dirofilarial antigens were not observed (p = 0.056). A high seroreactivity was demonstrated in people from the towns of northern Serbia (Pančevo = 27,1%; Novi Sad = 16,3%), as well as in people from Zaječar (eastern Serbia = 15,8%) and Vranje (southern Serbia = 15,1%). No differences were evidenced between people reactivity to polyproteins of the two dirofilarial species, nor differences related to the gender of examinees. Factor risks evidenced were: i) place of residence; ii) spending work time outdoors during the mosquito season; iii) spending time outdoors and nearby rivers, lakes, swamps or canals; unespectedly, iv) cat owning. Conclusion The findings emerging from this investigation indicate that clinicians and public health authorities should pay greater attention to this zoonosis. Continuing education and training of physicians will greatly contribute to the knowledge of the actual impact of filarial worms on animal and public health, and allow for the planning of suitable measures to prevent the infections. PMID:24507413

Persistent infection in Neotoma fuscipes (Muridae: Sigmodontinae) with Ehrlichia phagocytophila sensu lato.

PubMed

Castro, M B; Nicholson, W L; Kramer, V L; Childs, J E

2001-10-01

Dusky-footed wood rats (Neotoma fuscipes Baird) and two species of Peromyscus mice (P. maniculatus Wagner and P. truei Shufeldt) were collected over a 16-month period from three sites in Sonoma County, California. Blood was collected from 93 wood rats and 177 mice and serum or plasma was tested for seroreactivity with Ehrlichia phagocytophila sensu lato (also known as the human granulocytic ehrlichiosis agent). Thirty-five (37.6%) wood rats and 15 (8.5%) mice were seropositive. Positive Neotoma serology by site ranged from 9.4% to 62.1%. Polymerase chain reaction (PCR) testing for the Ehrlichia groESL heat shock operon was performed on all the seropositive and selected seronegative wood rats; 24 (68.6%) seropositive animals were PCR positive. Two seroconversions and no seroreversions were detected among 18 of the seropositive wood rats that were recaptured and tested multiple times (range = 2-6). Fourteen (77.8%) of the 18 were also PCR positive with six of these positive at every testing point (range = 2-6). One wood rat remained serologically and PCR positive in six specimens collected over a 14-month period. One male of 84 questing adult Ixodes pacificus Cooley & Kohls collected was PCR-positive for E. phagocytophila. Borrelia burgdorferi, the agent of Lyme disease, was cultured from ear punch biopsies from six of seven E. phagocytophila seropositive and one of four seronegative wood rats.

Serological detection of Tick-Borne Relapsing Fever in Texan domestic dogs

PubMed Central

Snell, Chloe B.; Adetunji, Shakirat A.; Piccione, Julie

2017-01-01

Tick-Borne Relapsing Fever (TBRF) is caused by spirochetes in the genus Borrelia. Very limited information exists on the incidence of this disease in humans and domestic dogs in the United States. The main objective of this study is to evaluate exposure of dogs to Borrelia turicatae, a causative agent of TBRF, in Texas. To this end, 878 canine serum samples were submitted to Texas A&M Veterinary Medical Diagnostic Laboratory from October 2011 to September 2012 for suspected tick-borne illnesses. The recombinant Borrelial antigen glycerophosphodiester phosphodiesterase (GlpQ) was expressed, purified, and used as a diagnostic antigen in both ELISA assays and Immunoblot analysis. Unfortunately, due to significant background reaction, the use of GlpQ as a diagnostic marker in the ELISA assay was not effective in discriminating dogs exposed to B. turicatae. Nevertheless, immunoblot assays showed that 17 out of 853 samples tested were considered to be seropositive, which constitutes 1.99% of all Texas samples tested in this study. The majority of positive samples were from central and southern Texas. Exposure to TBRF spirochetes may be seasonal, with 70.59% (12 out of 17) of the cases detected between June and December. In addition, 2 out of the 17 sero-reactive cases (11.76%) showed reactivity to both B. burgdorferi (causative agent of Lyme disease) and B. turicatae (a causative agent of TBRF). This is the first report of TBRF sero-prevalence in companion animals in an endemic area. Our findings further indicate that B. turicatae is maintained in domestic canids in Texas in regions where human disease also occurs, suggesting that domestic dogs could serve as sentinels for this disease. PMID:29232415

Clinical validation of a novel diagnostic HIV-2 total nucleic acid qualitative assay using the Abbott m2000 platform: Implications for complementary HIV-2 nucleic acid testing for the CDC 4th generation HIV diagnostic testing algorithm.

PubMed

Chang, Ming; Wong, Audrey J S; Raugi, Dana N; Smith, Robert A; Seilie, Annette M; Ortega, Jose P; Bogusz, Kyle M; Sall, Fatima; Ba, Selly; Seydi, Moussa; Gottlieb, Geoffrey S; Coombs, Robert W

2017-01-01

The 2014 CDC 4th generation HIV screening algorithm includes an orthogonal immunoassay to confirm and discriminate HIV-1 and HIV-2 antibodies. Additional nucleic acid testing (NAT) is recommended to resolve indeterminate or undifferentiated HIV seroreactivity. HIV-2 NAT requires a second-line assay to detect HIV-2 total nucleic acid (TNA) in patients' blood cells, as a third of untreated patients have undetectable plasma HIV-2 RNA. To validate a qualitative HIV-2 TNA assay using peripheral blood mononuclear cells (PBMC) from HIV-2-infected Senegalese study participants. We evaluated the assay precision, sensitivity, specificity, and diagnostic performance of an HIV-2 TNA assay. Matched plasma and PBMC samples were collected from 25 HIV-1, 30 HIV-2, 8 HIV-1/-2 dual-seropositive and 25 HIV seronegative individuals. Diagnostic performance was evaluated by comparing the outcome of the TNA assay to the results obtained by the 4th generation HIV screening and confirmatory immunoassays. All PBMC from 30 HIV-2 seropositive participants tested positive for HIV-2 TNA including 23 patients with undetectable plasma RNA. Of the 30 matched plasma specimens, one was HIV non-reactive. Samples from 50 non-HIV-2 infected individuals were confirmed as non-reactive for HIV-2 Ab and negative for HIV-2 TNA. The agreement between HIV-2 TNA and the combined immunoassay results was 98.8% (79/80). Furthermore, HIV-2 TNA was detected in 7 of 8 PBMC specimens from HIV-1/HIV-2 dual-seropositive participants. Our TNA assay detected HIV-2 DNA/RNA in PBMC from serologically HIV-2 reactive, HIV indeterminate or HIV undifferentiated individuals with undetectable plasma RNA, and is suitable for confirming HIV-2 infection in the HIV testing algorithm. Copyright © 2016 Elsevier B.V. All rights reserved.

Maternal and congenital syphilis in rural Haiti.

PubMed

Lomotey, Chaylah J; Lewis, Judy; Gebrian, Bette; Bourdeau, Royneld; Dieckhaus, Kevin; Salazar, Juan C

2009-09-01

A study was conducted to assess the prevalence of maternal syphilis and estimate the rate of congenital syphilis in five rural villages surrounding Jeremie, Haiti. This research was a retrospective observational study. Data were extracted from the Haitian Health Foundation's public health database and verified through original clinical paper records, death certificates, midwife reports, and discussions with community health workers. Data were analyzed by chi-square analysis, bivariate correlations, and two-tailed t-test for independent samples. Of the 410 women tested for syphilis, 31 (7.6%) were sero-reactive. Average gestation at time of testing was 25 weeks, which correlated with entry into prenatal care at an average of 23 weeks. Women who tested positive during pregnancy were more likely to have had a negative pregnancy outcome than those who did not (chi square = 16.4; P < 0.0001). The estimated rate of congenital syphilis in the region was 767 per 100,000 live births. Maternal syphilis is prevalent in rural Haiti. This prevalence combined with late entry into prenatal care contributes to adverse pregnancy outcomes and a high estimated rate of congenital syphilis. More research is needed on congenital syphilis and prenatal-careseeking practices of rural Haitian women in order to understand the impact of maternal syphilis in the region and improve pregnancy outcomes.

Novel antibody binding determinants on the capsid surface of serotype O foot-and-mouth disease virus

PubMed Central

Asfor, Amin S.; Upadhyaya, Sasmita; Knowles, Nick J.; King, Donald P.; Paton, David J.

2014-01-01

Five neutralizing antigenic sites have been described for serotype O foot-and-mouth disease viruses (FMDV) based on monoclonal antibody (mAb) escape mutant studies. However, a mutant virus selected to escape neutralization of mAb binding at all five sites was previously shown to confer complete cross-protection with the parental virus in guinea pig challenge studies, suggesting that amino acid residues outside the mAb binding sites contribute to antibody-mediated in vivo neutralization of FMDV. Comparison of the ability of bovine antisera to neutralize a panel of serotype O FMDV identified three novel putative sites at VP2-74, VP2-191 and VP3-85, where amino acid substitutions correlated with changes in sero-reactivity. The impact of these positions was tested using site-directed mutagenesis to effect substitutions at critical amino acid residues within an infectious copy of FMDV O1 Kaufbeuren (O1K). Recovered viruses containing additional mutations at VP2-74 and VP2-191 exhibited greater resistance to neutralization with both O1K guinea pig and O BFS bovine antisera than a virus that was engineered to include only mutations at the five known antigenic sites. The changes at VP2-74 and VP3-85 are adjacent to critical amino acids that define antigenic sites 2 and 4, respectively. However VP2-191 (17 Å away from VP2-72), located at the threefold axis and more distant from previously identified antigenic sites, exhibited the most profound effect. These findings extend our knowledge of the surface features of the FMDV capsid known to elicit neutralizing antibodies, and will improve our strategies for vaccine strain selection and rational vaccine design. PMID:24584474

Antibody responses to synthetic peptides from cytomegalovirus phosphoprotein 150.

PubMed Central

Sundqvist, V A; Xu, W; Wahren, B

1992-01-01

We have identified antigenic regions within phosphoprotein 150 of human cytomegalovirus (CMV pp150) to which seroreactivity appears in patients with active CMV infection or persists in seropositive persons. A range of 8.3 to 61.6% of healthy CMV-seropositive blood donors were immunoglobulin G positive for single peptides, while 91.6% reacted to a mixture of four peptides. All convalescent-phase serum samples from 26 patients with active CMV infection reacted with either of two peptides encompassing amino acids (aa) 594 to 623 and aa 614 to 643. Patients with a primary CMV infection had patterns of reactivity to single peptides different from those of patients with reactivated CMV infection. The immunoglobulin M antibodies reacted preferentially with the peptides encompassing aa 594 to 663 of CMV pp150. PMID:1328283

Improved assessment of accuracy and performance using a rotational paper-based device for multiplexed detection of heavy metals.

PubMed

Sun, Xiange; Li, Bowei; Qi, Anjin; Tian, Chongguo; Han, Jinglong; Shi, Yajun; Lin, Bingcheng; Chen, Lingxin

2018-02-01

In this work, a novel rotational microfluidic paper-based device was developed to improve the accuracy and performance of the multiplexed colorimetric detection by effectively avoiding the diffusion of colorimetric reagent on the detection zone. The integrated paper-based rotational valves were used to control the connection or disconnection between detection zones and fluid channels. Based on the manipulation of the rotational valves, this rotational paper-based device could prevent the random diffusion of colorimetric reagent and reduce the error of quantitative analysis considerably. The multiplexed colorimetric detection of heavy metals Ni(II), Cu(II) and Cr(VI) were implemented on the rotational device and the detection limits could be found to be 4.8, 1.6, and 0.18mg/L, respectively. The developed rotational device showed the great advantage in improving the detection accuracy and was expected to be a low-cost, portable analytical platform for the on-site detection. Copyright © 2017 Elsevier B.V. All rights reserved.

Blood donor notification and counseling: Our experience from a tertiary care hospital in India

PubMed Central

Kotwal, Urvershi; Doda, Veena; Arora, Satyam; Bhardwaj, Swati

2015-01-01

Aims: To evaluate the response rate of transfusion-transmissible infection (TTI)-reactive donors after notification of their abnormal test results for the year 2012. Materials and Methods: This study is an observational descriptive study performed in our department over a period of 1 year. We evaluated the response rate of TTI-reactive donors after notification of their abnormal test results over 1 year as per the existing strategy (three telephonic and two postal communications). Results: During the study period, among the annual donation of 15,322 units, 464 blood donors were found to be seroreactive. Of these 464 seroreactive cases, 47 were HIV positive, 284 were reactive for Hepatitis B surface antigen (HBsAg), 49 were Hepatitis C (HCV) positive and 84 were VDRL reactive. The TTI-reactive donors (464) for various markers were contacted: 229 (49.4%) telephonically and the remaining 235 (50.6%) not contacted on phone were informed by post. Of the 229 contacted donors, the response rate was 98.2% as only 225 donors reported (221 on the first, three on second and one on the third call) for one to one counseling. The remaining four non-responders were - one HIV and three HBsAg reactive. The remaining 235 (50.6%) reactive donors did not respond to any communication. Conclusion: Donor notification and post-donation counseling are an essential aspect of the blood bank that entails provision of information on serological status, assess the impact of test results on the donor and finally referral for medical care. As in our data only 49.4% of the blood donors could be contacted successfully, incomplete demographic details was the major limiting factor in communicating with rest. Of the 229 contacted donors, the response rate was 98.2%. A large majority (94.75%) of the notified donors in our study contacted their health care provider when given clear instructions to do so. These results are encouraging because they indicate that a major element of the notification message is acted upon when it is worded clearly. The very high response rate of the contacted donors ensured their concern for knowing their test result status. PMID:25722567

Serum Immunoproteomics Combined With Pathological Reassessment of Surgical Specimens Identifies TCP-1ζ Autoantibody as a Potential Biomarker in Thyroid Neoplasia.

PubMed

Belousov, Pavel V; Bogolyubova, Apollinariya V; Kim, Yan S; Abrosimov, Alexander Y; Kopylov, Arthur T; Tvardovskiy, Andrey A; Lanshchakov, Kirill V; Sazykin, Alexei Y; Dvinskikh, Nina Y; Bobrovskaya, Yana I; Selivanova, Lilia S; Shilov, Evgeniy S; Schwartz, Anton M; Shebzukhov, Yuriy V; Severskaia, Natalya V; Vanushko, Vladimir E; Moshkovskii, Sergei A; Nedospasov, Sergei A; Kuprash, Dmitry V

2015-09-01

Current methods of preoperative diagnostics frequently fail to discriminate between benign and malignant thyroid neoplasms. In encapsulated follicular-patterned tumors (EnFPT), this discrimination is challenging even using histopathological analysis. Autoantibody response against tumor-associated antigens is a well-documented phenomenon with prominent diagnostic potential; however, autoantigenicity of thyroid tumors remains poorly explored. Objectives were exploration of tumor-associated antigen repertoire of thyroid tumors and identification of candidate autoantibody biomarkers capable of discrimination between benign and malignant thyroid neoplasms. Proteins isolated from FTC-133 cells were subjected to two-dimensional Western blotting using pooled serum samples of patients originally diagnosed with either papillary thyroid carcinoma (PTC) or EnFPT represented by apparently benign follicular thyroid adenomas, as well as healthy individuals. Immunoreactive proteins were identified using liquid chromatography-tandem mass-spectrometry. Pathological reassessment of EnFPT was performed applying nonconservative criteria for capsular invasion and significance of focal PTC nuclear changes (PTC-NCs). Recombinant T-complex protein 1 subunitζ (TCP-1ζ) was used to examine an expanded serum sample set of patients with various thyroid neoplasms (n = 89) for TCP-1ζ autoantibodies. All patients were included in tertiary referral centers. A protein demonstrating a distinct pattern of EnFPT-specific seroreactivity was identified as TCP-1ζ protein. A subsequent search for clinicopathological correlates of TCP-1ζ seroreactivity revealed nonclassical capsular invasion or focal PTC-NC in all TCP-1ζ antibody-positive cases. Further studies in an expanded sample set confirmed the specificity of TCP-1ζ autoantibodies to malignant EnFPT. We identified TCP-1ζ autoantibodies as a potential biomarker for presurgical discrimination between benign and malignant encapsulated follicular-patterned thyroid tumors. Our results suggest the use of nonconservative morphological criteria for diagnosis of malignant EnFPT in biomarker identification studies and provide a peculiar example of uncovering the diagnostic potential of a candidate biomarker using incorporation of pathological reassessment in the pipeline of immunoproteomic research.

Research on Aircraft Target Detection Algorithm Based on Improved Radial Gradient Transformation

NASA Astrophysics Data System (ADS)

Zhao, Z. M.; Gao, X. M.; Jiang, D. N.; Zhang, Y. Q.

2018-04-01

Aiming at the problem that the target may have different orientation in the unmanned aerial vehicle (UAV) image, the target detection algorithm based on the rotation invariant feature is studied, and this paper proposes a method of RIFF (Rotation-Invariant Fast Features) based on look up table and polar coordinate acceleration to be used for aircraft target detection. The experiment shows that the detection performance of this method is basically equal to the RIFF, and the operation efficiency is greatly improved.

Multi-input multioutput orthogonal frequency division multiplexing radar waveform design for improving the detection performance of space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Hongyan

2017-04-01

This paper addresses the waveform optimization problem for improving the detection performance of multi-input multioutput (MIMO) orthogonal frequency division multiplexing (OFDM) radar-based space-time adaptive processing (STAP) in the complex environment. By maximizing the output signal-to-interference-and-noise-ratio (SINR) criterion, the waveform optimization problem for improving the detection performance of STAP, which is subjected to the constant modulus constraint, is derived. To tackle the resultant nonlinear and complicated optimization issue, a diagonal loading-based method is proposed to reformulate the issue as a semidefinite programming one; thereby, this problem can be solved very efficiently. In what follows, the optimized waveform can be obtained to maximize the output SINR of MIMO-OFDM such that the detection performance of STAP can be improved. The simulation results show that the proposed method can improve the output SINR detection performance considerably as compared with that of uncorrelated waveforms and the existing MIMO-based STAP method.

An improved three-dimensional non-scanning laser imaging system based on digital micromirror device

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Lei, Jieyu; Zhai, Yu; Timofeev, Alexander N.

2018-01-01

Nowadays, there are two main methods to realize three-dimensional non-scanning laser imaging detection, which are detection method based on APD and detection method based on Streak Tube. However, the detection method based on APD possesses some disadvantages, such as small number of pixels, big pixel interval and complex supporting circuit. The detection method based on Streak Tube possesses some disadvantages, such as big volume, bad reliability and high cost. In order to resolve the above questions, this paper proposes an improved three-dimensional non-scanning laser imaging system based on Digital Micromirror Device. In this imaging system, accurate control of laser beams and compact design of imaging structure are realized by several quarter-wave plates and a polarizing beam splitter. The remapping fiber optics is used to sample the image plane of receiving optical lens, and transform the image into line light resource, which can realize the non-scanning imaging principle. The Digital Micromirror Device is used to convert laser pulses from temporal domain to spatial domain. The CCD with strong sensitivity is used to detect the final reflected laser pulses. In this paper, we also use an algorithm which is used to simulate this improved laser imaging system. In the last, the simulated imaging experiment demonstrates that this improved laser imaging system can realize three-dimensional non-scanning laser imaging detection.

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

PubMed Central

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-01-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment. PMID:29494524

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement.

PubMed

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Bai, Xiangzhi; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-03-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.

Seroprevalence of transfusion transmissible infections (TTI), in first time blood donors in Abeokuta, Nigeria.

PubMed

Motayo, Babatunde Olanrewaju; Faneye, Adedayo Omotayo; Udo, Usen Asuquo; Olusola, Babatunde Adebiyi; Ezeani, Isreal; Ogiogwa, Joseph Iruobe

2015-03-01

Transfusion transmissible infections, such as HIV, HBV, HCV and syphilis are on the rise and pose a threat to blood safety. To determine prevalence and demographic profiles of TTI's among first time blood donors in Abeokuta, Nigeria. The study was conducted between February to November 2013; 130 first time blood donors were tested for the presence of HIV, HBsAg, HCV antibodies and Treponema palidium antibodies using EIA based rapid immunochromatographic kits. Data analysis was done using SPSS with a level of significance of p<0.05. Prevalence rates to HIV, HBsAg, HCV antibody, were 6.2% (n=8), 10% (n=13) and 1.5% (n=2), there was 0% prevalence to Treponema palidium antibodies. Group specific prevalence rates revealed that educational status was associated with HBsAg positivity (p = 0.028), donors with a history of previous blood transfusion was also statistically associated with HIV sero-reactivity (p = 0.013). High levels of HBsAg and HIV were observed, there is need to revise the donor testing algorithm in Nigeria in line with the prevalence of TTI's. We also advocate that a National surveillance system for TTI's be established through our National blood transfusion service (NBTS) program, a second serological test is also suggested to reduce the risk of occult HBV infection in Nigeria.

Parasites and vector-borne pathogens in client-owned dogs in Albania. Blood pathogens and seroprevalences of parasitic and other infectious agents.

PubMed

Hamel, Dietmar; Shukullari, Enstela; Rapti, Dhimitër; Silaghi, Cornelia; Pfister, Kurt; Rehbein, Steffen

2016-02-01

Knowledge on the epidemiology of parasitic and vector-borne infections is still very limited for Albania, a country located in the Balkan Peninsula in southeast Europe. Recent publications indicated prevalence rates of up to 52% for vector-borne infections in less-cared dogs in Albania. To provide data on the epidemiological situation in dogs under veterinary care, a total of 602 client-owned dogs presented to four small animal clinics between March 2010 and April 2011 in Tirana, Albania, were screened by examination of Giemsa-stained blood smears, PCR, and serological methods for the presence of arthropod-borne infections, as well as Neospora caninum and Toxoplasma gondii. Eight different pathogens, namely Babesia vogeli, Hepatozoon canis, Leishmania infantum, Dirofilaria immitis, Anaplasma phagocytophilum, Anaplasma platys, Ehrlichia canis, and Mycoplasma haemocanis, were detected by direct methods with prevalence rates ranging from 1 to 9%. Seroprevalence for Babesia spp., L. infantum, Anaplasma spp., and E. canis were 6.6, 5.1, 24.1, and 20.8%, respectively. Dogs >1 year of age were positive for vector-borne infections significantly more often than younger dogs (p = 0.003). More than half (51.7%) of the dogs were seroreactive to T. gondii and 18.3% to N. caninum. This is the first report on the detection of A. phagocytophilum, A. platys, E. canis, and M. haemocanis by PCR as well as the serological confirmation of exposure of dogs to N. caninum and T. gondii in Albania. The spectrum of pathogens and the seroprevalences for N. caninum and T. gondii in client-owned dogs from Tirana, Albania, are comparable to that reported in other countries in the Mediterranean Basin. The prevalence rates of vector-borne pathogens are at the lower range of that reported in studies from this geographical region. This is probably due to increased awareness of the owners of pet dogs, including better husbandry conditions and ectoparasiticidal treatment, thus limiting exposure of dogs to vectors.

A conceptual model to empower software requirements conflict detection and resolution with rule-based reasoning

NASA Astrophysics Data System (ADS)

Ahmad, Sabrina; Jalil, Intan Ermahani A.; Ahmad, Sharifah Sakinah Syed

2016-08-01

It is seldom technical issues which impede the process of eliciting software requirements. The involvement of multiple stakeholders usually leads to conflicts and therefore the need of conflict detection and resolution effort is crucial. This paper presents a conceptual model to further improve current efforts. Hence, this paper forwards an improved conceptual model to assist the conflict detection and resolution effort which extends the model ability and improves overall performance. The significant of the new model is to empower the automation of conflicts detection and its severity level with rule-based reasoning.

Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer — EDRN Public Portal

Cancer.gov

Our long term goal is to improve survival of patients with head and neck squamous cell carcinoma (HNSCC) through early detection using simple noninvasive serum assays in an ELISA-like platform. The objective of this proposal is to improve and confirm the validity of a diagnostic serum assay based on a panel of cancer-specific biomarkers for early cancer detection in patients with HNSCC. Our central hypothesis is that the detection of antibody responses to HNSCC-specific antigens, using a panel of biomarkers, can provide sufficient sensitivity and specificity suitable for clinical testing in the primary setting to screen and diagnose HNSCC in high risk populations to improve early detection.

Structures of the glycopeptidolipid antigens of two animal pathogens: Mycobacterium senegalense and Mycobacterium porcinum.

PubMed

López Marín, L M; Lanéelle, M A; Promé, D; Daffé, M

1993-08-01

The structures of the major glycolipid antigens of two animal pathogens Mycobacterium senegalense and Mycobacterium porcinum were elucidated by a combination of fast-atom bombardment mass spectrometry, nuclear magnetic resonance spectroscopy, chemical analyses and radiolabeling experiments. Five glycoconjugates belonging to the class of C-mycoside glycopeptidolipids were characterized in each species. They shared with those recently described in M. peregrinum the same unusual distribution of the disaccharides on the alaninol end of the molecules. Both species showed the presence of the novel sulfated glycopeptidolipid. In addition, some acetylated forms of the glycolipids were also present in the species examined. Identical seroreactivities were observed between the glycolipid antigens extracted from M. senegalense, M. porcinum and M. peregrinum and an antiserum raised against the whole lipid antigens of M. peregrinum. These data reinforce the close taxonomic relationships between the three mycobacterial species and demonstrate the antigenicity of the new variants of mycobacterial glycopeptidolipids.

Co-infection with Anaplasma platys, Bartonella henselae and Candidatus Mycoplasma haematoparvum in a veterinarian.

PubMed

Maggi, Ricardo G; Mascarelli, Patricia E; Havenga, Lauren N; Naidoo, Vinny; Breitschwerdt, Edward B

2013-04-15

During a two year period, a 27-year-old female veterinarian experienced migraine headaches, seizures, including status epilepticus, and other neurological and neurocognitive abnormalities. Prior to and during her illness, she had been actively involved in hospital-based work treating domestic animals, primarily cats and dogs, in Grenada and Ireland and anatomical research requiring the dissection of wild animals (including lions, giraffe, rabbits, mongoose, and other animals), mostly in South Africa. The woman reported contact with fleas, ticks, lice, biting flies, mosquitoes, spiders and mites and had also been scratched or bitten by dogs, cats, birds, horses, reptiles, rabbits and rodents. Prior diagnostic testing resulted in findings that were inconclusive or within normal reference ranges and no etiological diagnosis had been obtained to explain the patient's symptoms. PCR assays targeting Anaplasma sp. Bartonella sp. and hemotopic Mycoplasma sp. were used to test patient blood samples. PCR positive amplicons were sequenced directly and compared to Gen Bank sequences. In addition, Bartonella alpha Proteobacteria growth medium (BAPGM) enrichment blood culture was used to facilitate bacterial growth and Bartonella spp. serology was performed by indirect fluorescent antibody testing. Anaplasma platys, Bartonella henselae and Candidatus Mycoplasma haematoparvum DNA was amplified and sequenced from the woman's blood, serum or blood culture samples. Her serum was variably seroreactive to several Bartonella sp. antigens. Despite symptomatic improvement, six months of doxycycline most likely failed to eliminate the B. henselae infection, whereas A. platys and Candidatus M. haematoparvum DNA was no longer amplified from post-treatment samples. As is typical of many veterinary professionals, this individual had frequent exposure to arthropod vectors and near daily contact with persistently bacteremic reservoir hosts, including cats, the primary reservoir host for B. henselae, and dogs, the presumed primary reservoir host for A. platys and Candidatus Mycoplasma haematoparvum. Physicians caring for veterinarians should be aware of the occupational zoonotic risks associated with the daily activities of these animal health professionals.

Co-infection with Anaplasma platys, Bartonella henselae and Candidatus Mycoplasma haematoparvum in a veterinarian

PubMed Central

2013-01-01

Background During a two year period, a 27-year-old female veterinarian experienced migraine headaches, seizures, including status epilepticus, and other neurological and neurocognitive abnormalities. Prior to and during her illness, she had been actively involved in hospital-based work treating domestic animals, primarily cats and dogs, in Grenada and Ireland and anatomical research requiring the dissection of wild animals (including lions, giraffe, rabbits, mongoose, and other animals), mostly in South Africa. The woman reported contact with fleas, ticks, lice, biting flies, mosquitoes, spiders and mites and had also been scratched or bitten by dogs, cats, birds, horses, reptiles, rabbits and rodents. Prior diagnostic testing resulted in findings that were inconclusive or within normal reference ranges and no etiological diagnosis had been obtained to explain the patient’s symptoms. Methods PCR assays targeting Anaplasma spp. Bartonella spp. and hemotopic Mycoplasma spp. were used to test patient blood samples. PCR positive amplicons were sequenced directly and compared to GenBank sequences. In addition, Bartonella alpha Proteobacteria growth medium (BAPGM) enrichment blood culture was used to facilitate bacterial growth and Bartonella spp. serology was performed by indirect fluorescent antibody testing. Results Anaplasma platys, Bartonella henselae and Candidatus Mycoplasma haematoparvum DNA was amplified and sequenced from the woman’s blood, serum or blood culture samples. Her serum was variably seroreactive to several Bartonella sp. antigens. Despite symptomatic improvement, six months of doxycycline most likely failed to eliminate the B. henselae infection, whereas A. platys and Candidatus M. haematoparvum DNA was no longer amplified from post-treatment samples. Conclusions As is typical of many veterinary professionals, this individual had frequent exposure to arthropod vectors and near daily contact with persistently bacteremic reservoir hosts, including cats, the primary reservoir host for B. henselae, and dogs, the presumed primary reservoir host for A. platys and Candidatus Mycoplasma haematoparvum. Physicians caring for veterinarians should be aware of the occupational zoonotic risks associated with the daily activities of these animal health professionals. PMID:23587235

Recent Advances on Luminescent Enhancement-Based Porous Silicon Biosensors.

PubMed

Jenie, S N Aisyiyah; Plush, Sally E; Voelcker, Nicolas H

2016-10-01

Luminescence-based detection paradigms have key advantages over other optical platforms such as absorbance, reflectance or interferometric based detection. However, autofluorescence, low quantum yield and lack of photostability of the fluorophore or emitting molecule are still performance-limiting factors. Recent research has shown the need for enhanced luminescence-based detection to overcome these drawbacks while at the same time improving the sensitivity, selectivity and reducing the detection limits of optical sensors and biosensors. Nanostructures have been reported to significantly improve the spectral properties of the emitting molecules. These structures offer unique electrical, optic and magnetic properties which may be used to tailor the surrounding electrical field of the emitter. Here, the main principles behind luminescence and luminescence enhancement-based detections are reviewed, with an emphasis on europium complexes as the emitting molecule. An overview of the optical porous silicon microcavity (pSiMC) as a biosensing platform and recent proof-of-concept examples on enhanced luminescence-based detection using pSiMCs are provided and discussed.

Synergistic electron transfer effect-based signal amplification strategy for the ultrasensitive detection of dopamine.

PubMed

Lu, Qiujun; Chen, Xiaogen; Liu, Dan; Wu, Cuiyan; Liu, Meiling; Li, Haitao; Zhang, Youyu; Yao, Shouzhuo

2018-05-15

The selective and sensitive detection of dopamine (DA) is of great significance for the identification of schizophrenia, Huntington's disease, and Parkinson's disease from the perspective of molecular diagnostics. So far, most of DA fluorescence sensors are based on the electron transfer from the fluorescence nanomaterials to DA-quinone. However, the limited electron transfer ability of the DA-quinone affects the level of detection sensitivity of these sensors. In this work, based on the DA can reduce Ag + into AgNPs followed by oxidized to DA-quinone, we developed a novel silicon nanoparticles-based electron transfer fluorescent sensor for the detection of DA. As electron transfer acceptor, the AgNPs and DA-quinone can quench the fluorescence of silicon nanoparticles effectively through the synergistic electron transfer effect. Compared with traditional fluorescence DA sensors, the proposed synergistic electron transfer-based sensor improves the detection sensitivity to a great extent (at least 10-fold improvement). The proposed sensor shows a low detection limit of DA, which is as low as 0.1 nM under the optimal conditions. This sensor has potential applicability for the detection of DA in practical sample. This work has been demonstrated to contribute to a substantial improvement in the sensitivity of the sensors. It also gives new insight into design electron transfer-based sensors. Copyright © 2018. Published by Elsevier B.V.

Post-processing for improving hyperspectral anomaly detection accuracy

NASA Astrophysics Data System (ADS)

Wu, Jee-Cheng; Jiang, Chi-Ming; Huang, Chen-Liang

2015-10-01

Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed-Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.

A novel rail defect detection method based on undecimated lifting wavelet packet transform and Shannon entropy-improved adaptive line enhancer

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Zhang, Xin; Wang, Yan; Shen, Yi; Makis, Viliam

2018-07-01

Acoustic emission (AE) technology is sensitive to subliminal rail defects, however strong wheel-rail contact rolling noise under high-speed condition has gravely impeded detecting of rail defects using traditional denoising methods. In this context, the paper develops an adaptive detection method for rail cracks, which combines multiresolution analysis with an improved adaptive line enhancer (ALE). To obtain elaborate multiresolution information of transient crack signals with low computational cost, lifting scheme-based undecimated wavelet packet transform is adopted. In order to feature the impulsive property of crack signals, a Shannon entropy-improved ALE is proposed as a signal enhancing approach, where Shannon entropy is introduced to improve the cost function. Then a rail defect detection plan based on the proposed method for high-speed condition is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in enhancing the rail defect AE signal and reducing the strong background noise, offering an effective multiresolution approach for rail defect detection under high-speed and strong-noise condition.

Rickettsia spp. among wild mammals and their respective ectoparasites in Pantanal wetland, Brazil.

PubMed

de Sousa, Keyla Carstens Marques; Herrera, Heitor Miraglia; Rocha, Fabiana Lopes; Costa, Francisco Borges; Martins, Thiago Fernandes; Labruna, Marcelo Bahia; Machado, Rosangela Zacarias; André, Marcos Rogério

2018-01-01

The genus Rickettsia comprises obligatory intracellular bacteria, well known to cause zoonotic diseases around the world. The present work aimed to investigate the occurrence of Rickettsia spp. in wild animals, domestic dogs and their respective ectoparasites in southern Pantanal region, central-western Brazil, by molecular and serological techniques. Between August 2013 and March 2015, serum, whole blood and/or spleen samples were collected from 31 coatis, 78 crab-eating foxes, seven ocelots, 42 dogs, 110 wild rodents, and 30 marsupials. Serum samples from canids, felids, rodents and marsupials were individually tested by indirect fluorescent antibody test (IFAT) in order to detect IgG antibodies to Rickettsia rickettsii, Rickettsia parkeri and Rickettsia amblyommatis. DNA samples from mammals and ectoparasites were submitted to a multiplex qPCR assay in order to detect and quantify spotted fever group (SFG) and typhus group (TG) rickettsiae and Orientia tsutsugamushi. Positive samples in qPCR assays were submitted to conventional PCR assays targeting gltA, ompA, ompB and htrA genes, followed by sequencing and phylogenetic analyses. The ticks collected (1582) from animals belonged to the species Amblyomma sculptum, Amblyomma parvum, Amblyomma ovale, Amblyomma tigrinum, Rhipicephalus (Boophilus) microplus, Rhipicephalus sanguineus sensu lato and Amblyomma auricularium. Overall, 27 (64.2%) dogs, 59 (75.6%) crab-eating foxes and six (85.7%) ocelots were seroreactive (titer≥64) to at least one Rickettsia species. For 17 (40.4%) dogs, 33 (42.3%) crab-eating foxes, and two (33.3%) ocelots, homologous reactions to R. amblyommatis or a closely related organism were suggested. One hundred and sixteen (23.5%) tick samples and one (1.2%) crab-eating fox blood sample showed positivity in qPCR assays for SFG Rickettsia spp. Among SFG Rickettsia-positive ticks samples, 93 (80.2%) belonged to A. parvum, 14 (12%) belonged to A. sculptum species, three (2.5%) belonged to A. auricularim, and six (5.2%) were Amblyomma larval pools. Thirty samples out of 117 qPCR positive samples for SFG Rickettsia spp. also showed positivity in cPCR assays based on gltA, htrA and/or ompB genes. The Blast analyses showed 100% identity with 'Candidatus Rickettsia andeanae' in all 30 sequences obtained from gltA, htrA and/or ompB genes. The concatenated phylogenetic analysis based on gltA and 17-kDa htrA genes grouped the Rickettsia sequences obtained from tick samples in the same clade of 'Candidatus Rickettsia andeanae'. The present study revealed that wild and domestic animals in southern Pantanal region, Brazil, are exposed to SFG rickettsiae agents. Future studies regarding the pathogenicity of these agents are necessary in order to prevent human cases of rickettsiosis in Brazilian southern Pantanal. Copyright © 2017 Elsevier GmbH. All rights reserved.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

PubMed

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Prediagnostic Helicobacter pylori Antibodies and Colorectal Cancer Risk in an Elderly, Caucasian Population.

PubMed

Blase, Jennifer L; Campbell, Peter T; Gapstur, Susan M; Pawlita, Michael; Michel, Angelika; Waterboer, Tim; Teras, Lauren R

2016-12-01

Study results on overall seroprevalence of Helicobacter pylori and colorectal cancer risk have been inconsistent. However, one study found positive associations with antibodies to specific H. pylori proteins. To follow up on those findings, we assessed associations of 15 H. pylori specific proteins with colorectal cancer incidence in the prospective Cancer Prevention Study-II Nutrition Cohort. Participants in this nested case-control study included 392 cases and 774 controls who were predominantly elderly (median age at blood draw: 71 years) and Caucasian (98%). Seroreactivity against 15 H. pylori proteins was assessed by fluorescent bead-based multiplex serology and associations with colorectal cancer were estimated using conditional logistic regression. Helicobacter pylori serostatus was not associated with colorectal cancer incidence (odds ratio (OR), 1.17, 95% confidence interval (95% CI), 0.91-1.50). Among individual antigens, GroEl serostatus was associated with colorectal cancer risk (OR, 1.32, 95% CI: 1.03-1.70), whereas CagM was associated with colon cancer risk only (OR, 1.35, 95% CI: 1.01-1.80). No dose-response relationships were observed for any of the antigens, including GroEl and CagM. The results of our study do not support an association between H. pylori infection and colorectal cancer risk in this elderly, mostly Caucasian population. © 2016 John Wiley & Sons Ltd.

Corner detection and sorting method based on improved Harris algorithm in camera calibration

NASA Astrophysics Data System (ADS)

Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

2016-11-01

In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

A Real-Time System for Lane Detection Based on FPGA and DSP

NASA Astrophysics Data System (ADS)

Xiao, Jing; Li, Shutao; Sun, Bin

2016-12-01

This paper presents a real-time lane detection system including edge detection and improved Hough Transform based lane detection algorithm and its hardware implementation with field programmable gate array (FPGA) and digital signal processor (DSP). Firstly, gradient amplitude and direction information are combined to extract lane edge information. Then, the information is used to determine the region of interest. Finally, the lanes are extracted by using improved Hough Transform. The image processing module of the system consists of FPGA and DSP. Particularly, the algorithms implemented in FPGA are working in pipeline and processing in parallel so that the system can run in real-time. In addition, DSP realizes lane line extraction and display function with an improved Hough Transform. The experimental results show that the proposed system is able to detect lanes under different road situations efficiently and effectively.

Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications

PubMed Central

Lee, Young-Sook; Chung, Wan-Young

2012-01-01

Vision-based abnormal event detection for home healthcare systems can be greatly improved using visual sensor-based techniques able to detect, track and recognize objects in the scene. However, in moving object detection and tracking processes, moving cast shadows can be misclassified as part of objects or moving objects. Shadow removal is an essential step for developing video surveillance systems. The goal of the primary is to design novel computer vision techniques that can extract objects more accurately and discriminate between abnormal and normal activities. To improve the accuracy of object detection and tracking, our proposed shadow removal algorithm is employed. Abnormal event detection based on visual sensor by using shape features variation and 3-D trajectory is presented to overcome the low fall detection rate. The experimental results showed that the success rate of detecting abnormal events was 97% with a false positive rate of 2%. Our proposed algorithm can allow distinguishing diverse fall activities such as forward falls, backward falls, and falling asides from normal activities. PMID:22368486

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

Radioactive threat detection using scintillant-based detectors

NASA Astrophysics Data System (ADS)

Chalmers, Alex

2004-09-01

An update to the performance of AS&E's Radioactive Threat Detection sensor technology. A model is presented detailing the components of the scintillant-based RTD system employed in AS&E products aimed at detecting radiological WMD. An overview of recent improvements in the sensors, electrical subsystems and software algorithms are presented. The resulting improvements in performance are described and sample results shown from existing systems. Advanced and future capabilities are described with an assessment of their feasibility and their application to Homeland Defense.

An Extension to the Kalman Filter for an Improved Detection of Unknown Behavior

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Narasimhan, Sriram

2005-01-01

The use of Kalman filter (KF) interferes with fault detection algorithms based on the residual between estimated and measured variables, since the measured values are used to update the estimates. This feedback results in the estimates being pulled closer to the measured values, influencing the residuals in the process. Here we present a fault detection scheme for systems that are being tracked by a KF. Our approach combines an open-loop prediction over an adaptive window and an information-based measure of the deviation of the Kalman estimate from the prediction to improve fault detection.

Human polyomaviruses and incidence of cutaneous squamous cell carcinoma in the New Hampshire skin cancer study.

PubMed

Gossai, Anala; Waterboer, Tim; Hoen, Anne G; Farzan, Shohreh F; Nelson, Heather H; Michel, Angelika; Willhauck-Fleckenstein, Martina; Christensen, Brock C; Perry, Ann E; Pawlita, Michael; Karagas, Margaret R

2016-06-01

Squamous cell carcinoma (SCC) of the skin is a malignancy arising from epithelial keratinocytes. Experimental and epidemiologic evidence raise the possibility that human polyomaviruses (PyV) may be associated with the occurrence of SCC. To investigate whether the risk for SCC was associated with PyV infection, seropositivity to 10 PyV types was assessed following diagnosis in a population-based case-control study conducted in the United States. A total of 253 SCC cases and 460 age group and gender-matched controls were included. Antibody response against each PyV was measured using a multiplex serology-based glutathione S-transferase capture assay of recombinantly expressed VP1 capsid proteins. Odds ratios (OR) for SCC associated with seropositivity to each PyV type were estimated using logistic regression, with adjustment for potentially confounding factors. SCC cases were seropositive for a greater number of PyVs than controls (P = 0.049). Those who were JC seropositive had increased odds of SCC when compared to those who were JC seronegative (OR = 1.37, 95% CI: 0.98-1.90), with an increasing trend in SCC risk with increasing quartiles of seroreactivity (P for trend = 0.04). There were no clear associations between SCC risk and serostatus for other PyV types. This study provides limited evidence that infection with certain PyVs may be related to the occurrence of SCC in the general population of the United States. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Airborne Particulate Threat Assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patrick Treado; Oksana Klueva; Jeffrey Beckstead

Aerosol threat detection requires the ability to discern between threat agents and ambient background particulate matter (PM) encountered in the environment. To date, Raman imaging technology has been demonstrated as an effective strategy for the assessment of threat agents in the presence of specific, complex backgrounds. Expanding our understanding of the composition of ambient particulate matter background will improve the overall performance of Raman Chemical Imaging (RCI) detection strategies for the autonomous detection of airborne chemical and biological hazards. Improving RCI detection performance is strategic due to its potential to become a widely exploited detection approach by several U.S. governmentmore » agencies. To improve the understanding of the ambient PM background with subsequent improvement in Raman threat detection capability, ChemImage undertook the Airborne Particulate Threat Assessment (APTA) Project in 2005-2008 through a collaborative effort with the National Energy Technology Laboratory (NETL), under cooperative agreement number DE-FC26-05NT42594. During Phase 1 of the program, a novel PM classification based on molecular composition was developed based on a comprehensive review of the scientific literature. In addition, testing protocols were developed for ambient PM characterization. A signature database was developed based on a variety of microanalytical techniques, including scanning electron microscopy, FT-IR microspectroscopy, optical microscopy, fluorescence and Raman chemical imaging techniques. An automated particle integrated collector and detector (APICD) prototype was developed for automated collection, deposition and detection of biothreat agents in background PM. During Phase 2 of the program, ChemImage continued to refine the understanding of ambient background composition. Additionally, ChemImage enhanced the APICD to provide improved autonomy, sensitivity and specificity. Deliverables included a Final Report detailing our findings and APICD Gen II subsystems for automated collection, deposition and detection of ambient particulate matter. Key findings from the APTA Program include: Ambient biological PM taxonomy; Demonstration of key subsystems needed for autonomous bioaerosol detection; System design; Efficient electrostatic collection; Automated bioagent recognition; Raman analysis performance validating Td<9 sec; Efficient collection surface regeneration; and Development of a quantitative bioaerosol defection model. The objective of the APTA program was to advance the state of our knowledge of ambient background PM composition. Operation of an automated aerosol detection system was enhanced by a more accurate assessment of background variability, especially for sensitive and specific sensing strategies like Raman detection that are background-limited in performance. Based on this improved knowledge of background, the overall threat detection performance of Raman sensors was improved.« less

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

NASA Astrophysics Data System (ADS)

Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi

2015-12-01

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

NASA Astrophysics Data System (ADS)

Hao, Xiaohong; Zhang, Xiaofeng

2018-01-01

In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

Antibody levels correlate with detection of Trypanosoma cruzi DNA by sensitive PCR assays in seropositive blood donors and possible resolution of infection over time

PubMed Central

Sabino, E.C.; Lee, T.H.; Montalvo, L.; Nguyen, M.L.; Leiby, D.A.; Carrick, D.M.; Otani, M.M.; Vinelli, E.; Wright, D.; Stramer, S.L.; Busch, M.

2013-01-01

Background The clinical significance of anti-T. cruzi low-level reactive samples is incompletely understood. PCR-positive rates and antibody levels among seropositive blood donors in three countries are described. Methods Follow-up whole blood and plasma samples were collected from T. cruzi-seropositive donors from 2008-2010 in the US (n=195) and Honduras (n=58). Also 143 samples from Brazil in 1996-2002, originally positive by three serological assays, were available and paired with contemporary follow-up samples from these donors. All samples were retested with the FDA-approved Ortho ELISA. PCR assays were performed on coded sample panels by two laboratories (BSRI and ARC) that amplified kinetoplast minicircle DNA sequences of T. cruzi. Results PCR testing at BSRI yielded slightly higher overall sensitivity and specificity (33% and 98%) compared with the ARC lab (28% and 94%). Among seropositive donors, PCR-positive rates varied by country (p<0.0001) for the BSRI laboratory: Brazil (57%), Honduras (32%) and the US (14%). ELISA signal/cutoff (S/CO) ratios were significantly higher for PCR-positive compared to PCR-negative donors (p<0.05 for all comparisons). Additionally, PCR-negative Brazilian donors exhibited greater frequencies of antibody decline over time versus PCR-positive donors (p=0.003). Conclusion For all three countries, persistent DNA positivity correlated with higher ELISA S/CO values, suggesting that high-level seroreactivity reflects chronic parasitemia. The higher rate of PCR positivity for Brazilian donors was likely attributable to required reactivity on three assays available a decade ago. Significant S/CO declines in 10% of the PCR-negative Brazilian donors may indicate seroreversion following parasite clearance in the absence of treatment. PMID:23002996

Interferon regulatory factor 5 is a potential target of autoimmune response triggered by Epstein-barr virus and Mycobacterium avium subsp. paratuberculosis in rheumatoid arthritis: investigating a mechanism of molecular mimicry.

PubMed

Bo, Marco; Erre, Gian Luca; Niegowska, Magdalena; Piras, Marco; Taras, Loredana; Longu, Maria Giovanna; Passiu, Giuseppe; Sechi, Leonardo A

2018-01-01

Rheumatoid arthritis (RA) is a chronic disease characterised by a pro-inflammatory cytokines linked erosive joint damage and by humoral and cellular response against a broad range of self-peptides. Molecular mimicry between Epstein-Barr virus (EBV), Mycobacterium avium subsp. paratuberculosis (MAP) and host peptides has long been regarded as an RA pathogenetic mechanism. Using bioinformatic analysis we identified high sequence homology among interferon regulatory factor 5 (IRF5), EBV antigen BOLF1 and MAP antigen MAP_4027. Our objective was to evaluate the presence in sera of RA patients of antibodies (Abs) directed against human homologous IRF5 cross-reacting with BOLF1 and MAP_4027. Frequency of reactivity against IRF5424-434, BOLF1305-320 and MAP_402718-32 was tested by indirect ELISA in sera from 71 RA patients and 60 healthy controls (HCs). RA sera show a remarkable high frequency of reactivity against IRF5424-434 in comparison to HCs (69% vs. 8%; p<0.0001). Similarly, seroreactivity against BOLF1305-320 was more frequently detected in RA sera than in HCs counterpart (58% vs. 8%; p<0.0001). Frequency of Abs against MAP_402718-32 was 17% in RA sera vs. 5% in HCs with a p-value at the threshold level (p<0.051). Prevalence of Abs against at least one of the assessed epitopes reached 72% in RA patients and 15% among HCs. Levels of Abs in RA patients were significantly related to systemic inflammation. IRF5 is a potential autoimmune target of RA. Our results support the hypothesis that EBV and MAP infections may be involved in the pathogenesis of RA, igniting a secondary immune response that cross-reacts against RA self-peptides.

Nano-immunoassay with improved performance for detection of cancer biomarkers

DOE PAGES

Krasnoslobodtsev, Alexey V.; Torres, Maria P.; Kaur, Sukhwinder; ...

2015-01-01

Nano-immunoassay utilizing surface-enhanced Raman scattering (SERS) effect is a promising analytical technique for the early detection of cancer. In its current standing the assay is capable of discriminating samples of healthy individuals from samples of pancreatic cancer patients. Further improvements in sensitivity and reproducibility will extend practical applications of the SERS-based detection platforms to wider range of problems. In this report, we discuss several strategies designed to improve performance of the SERS-based detection system. We demonstrate that reproducibility of the platform is enhanced by using atomically smooth mica surface as a template for preparation of capture surface in SERS sandwichmore » immunoassay. Furthermore, the assay's stability and sensitivity can be further improved by using either polymer or graphene monolayer as a thin protective layer applied on top of the assay addresses. The protective layer renders the signal to be more stable against photo-induced damage and carbonaceous contamination.« less

Improved wavelet de-noising method of rail vibration signal for wheel tread detection

NASA Astrophysics Data System (ADS)

Zhao, Quan-ke; Zhao, Quanke; Gao, Xiao-rong; Luo, Lin

2011-12-01

The irregularities of wheel tread can be detected by processing acceleration vibration signal of railway. Various kinds of noise from different sources such as wheel-rail resonance, bad weather and artificial reasons are the key factors influencing detection accuracy. A method which uses wavelet threshold de-noising is investigated to reduce noise in the detection signal, and an improved signal processing algorithm based on it has been established. The results of simulations and field experiments show that the proposed method can increase signal-to-noise ratio (SNR) of the rail vibration signal effectively, and improve the detection accuracy.

Improved PCR-Based Detection of Soil Transmitted Helminth Infections Using a Next-Generation Sequencing Approach to Assay Design.

PubMed

Pilotte, Nils; Papaiakovou, Marina; Grant, Jessica R; Bierwert, Lou Ann; Llewellyn, Stacey; McCarthy, James S; Williams, Steven A

2016-03-01

The soil transmitted helminths are a group of parasitic worms responsible for extensive morbidity in many of the world's most economically depressed locations. With growing emphasis on disease mapping and eradication, the availability of accurate and cost-effective diagnostic measures is of paramount importance to global control and elimination efforts. While real-time PCR-based molecular detection assays have shown great promise, to date, these assays have utilized sub-optimal targets. By performing next-generation sequencing-based repeat analyses, we have identified high copy-number, non-coding DNA sequences from a series of soil transmitted pathogens. We have used these repetitive DNA elements as targets in the development of novel, multi-parallel, PCR-based diagnostic assays. Utilizing next-generation sequencing and the Galaxy-based RepeatExplorer web server, we performed repeat DNA analysis on five species of soil transmitted helminths (Necator americanus, Ancylostoma duodenale, Trichuris trichiura, Ascaris lumbricoides, and Strongyloides stercoralis). Employing high copy-number, non-coding repeat DNA sequences as targets, novel real-time PCR assays were designed, and assays were tested against established molecular detection methods. Each assay provided consistent detection of genomic DNA at quantities of 2 fg or less, demonstrated species-specificity, and showed an improved limit of detection over the existing, proven PCR-based assay. The utilization of next-generation sequencing-based repeat DNA analysis methodologies for the identification of molecular diagnostic targets has the ability to improve assay species-specificity and limits of detection. By exploiting such high copy-number repeat sequences, the assays described here will facilitate soil transmitted helminth diagnostic efforts. We recommend similar analyses when designing PCR-based diagnostic tests for the detection of other eukaryotic pathogens.

Fetal heart rate deceleration detection using a discrete cosine transform implementation of singular spectrum analysis.

PubMed

Warrick, P A; Precup, D; Hamilton, E F; Kearney, R E

2007-01-01

To develop a singular-spectrum analysis (SSA) based change-point detection algorithm applicable to fetal heart rate (FHR) monitoring to improve the detection of deceleration events. We present a method for decomposing a signal into near-orthogonal components via the discrete cosine transform (DCT) and apply this in a novel online manner to change-point detection based on SSA. The SSA technique forms models of the underlying signal that can be compared over time; models that are sufficiently different indicate signal change points. To adapt the algorithm to deceleration detection where many successive similar change events can occur, we modify the standard SSA algorithm to hold the reference model constant under such conditions, an approach that we term "base-hold SSA". The algorithm is applied to a database of 15 FHR tracings that have been preprocessed to locate candidate decelerations and is compared to the markings of an expert obstetrician. Of the 528 true and 1285 false decelerations presented to the algorithm, the base-hold approach improved on standard SSA, reducing the number of missed decelerations from 64 to 49 (21.9%) while maintaining the same reduction in false-positives (278). The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series.

Serologic Evidence of Human Monocytic and Granulocytic Ehrlichiosis in Israel

PubMed Central

Keysary, Avi; Amram, Lili; Keren, Gershon; Sthoeger, Zev; Potasman, Israel; Jacob, Amir; Strenger, Carmella; Dawson, Jacqueline E.

1999-01-01

We conducted a retrospective serosurvey of 1,000 persons in Israel who had fever of undetermined cause to look for Ehrlichia chaffeensis antibodies. Four of five cases with antibodies reactive to E. chaffeensis were diagnosed in the summer, when ticks are more active. All patients had influenzalike symptoms with high fever. None of the cases was fatal. Three serum samples were also seroreactive for antibodies to E. canis, and one was also reactive to the human granulocytic ehrlichiosis (HGE) agent. The titer to the HGE agent in this patient was higher than the serum titer to E. chaffeensis, and the Western blot analysis also indicated that the HGE agent was the primary cause of infection. We present the first serologic evidence that the agents of human monocytic ehrlichiosis (HME) and HGE are present in Israel. Therefore, human ehrlichiosis should be included in the differential diagnoses for persons in Israel who have been exposed to ticks and have influenzalike symptoms. PMID:10603210

[Confirmation of West Nile virus seroreactivity in central nervous system infections of unknown etiology from Ankara Province, Central Anatolia, Turkey].

PubMed

Ergünay, Koray; Özkul, Aykut

2011-04-01

West Nile virus (WNV) infections may trigger febrile conditions and/or neuroinvasive disease in a portion of the exposed individuals. Serosurveillance data from various regions of Turkey indicate WNV activity. The aim of this study was to confirm the antibody specificity of the serum samples via virus neutralization assay, previously reported to be reactive for WNV IgM. The samples originated from two individuals with the preliminary diagnosis of aseptic meningitis/encephalitis of unknown etiology in 2009 and had been classified as probable WNV infections. Cerebrospinal fluid and sera samples of these patients had been evaluated as negative for WNV RNA and IgG antibodies. Only one serum sample could be included in the neutralization assay due to the limited amounts in the current investigation. The sample was observed as positive in dilutions of 1/20 and 1/40, thus confirming the diagnosis of WNV-related central nervous system infection in a 62 year-old female patient from Ankara, Central Anatolia, Turkey.

Mutation in a locus linked to penB-nmp causes suppression of the Mtr phenotype of Neisseria gonorrhoeae.

PubMed Central

Shinners, E N; Catlin, B W

1988-01-01

The chromosomal locus mtr, which encodes low-level resistance to multiple antibacterial agents in Neisseria gonorrhoeae, is subject to phenotypic suppression by env mutations that increase the permeability of the envelope. We have identified a new locus, mom (for modifier of Mtr), which is located on the chromosome very close to penB and nmp, loci known to be linked to each other and to spc. Phenotypic suppression of Mtr was recognized by reductions of resistance to benzylpenicillin and also to oxacillin and the hydrophobic agents novobiocin and erythromycin. The resistance to each of these antibiotics returned to the Mtr levels in mom+ transformants isolated by selection for increased resistance to either novobiocin or erythromycin; the accompanying change of the outer membrane protein I seroreactions confirmed the proximity of nmp and mom. Thus, some mutant gonococci display wild-type antibiotic susceptibilities but can express multiple resistance following a mom+ mutation that releases the suppressed Mtr phenotype. PMID:3142343

Distribution of Leptospira serogroups in cattle herds and dogs in France.

PubMed

Ayral, Florence C; Bicout, Dominique J; Pereira, Helena; Artois, Marc; Kodjo, Angeli

2014-10-01

A retrospective study was conducted to identify and describe the distribution pattern of Leptospira serogroups in domestic animals in France. The population consisted of cattle herds and dogs with clinically suspected leptospirosis that were tested at the "Laboratoire des Leptospires" between 2008 and 2011. The laboratory database was queried for records of cattle and dogs in which seroreactivity in Leptospira microagglutination tests was consistent with a recent or current infection, excluding vaccine serogroups in dogs. A total of 394 cattle herds and 232 dogs were diagnosed with clinical leptospirosis, and the results suggested infection by the Leptospira serogroup Australis in 43% and 63%, respectively; by the Leptospira serogroup Grippotyphosa in 17% and 9%, respectively; and by the Leptospira serogroup Sejroe in 33% and 6%, respectively. This inventory of infecting Leptospira serogroups revealed that current vaccines in France are not fully capable of preventing the clinical form of the disease. © The American Society of Tropical Medicine and Hygiene.

Immunological response to quadrivalent HPV vaccine in treatment of recurrent respiratory papillomatosis.

PubMed

Tjon Pian Gi, Robin E A; San Giorgi, Michel R M; Pawlita, Michael; Michel, Angelika; van Hemel, Bettien M; Schuuring, Ed M D; van den Heuvel, Edwin R; van der Laan, Bernard F A M; Dikkers, Frederik G

2016-10-01

Aim of this study was to explore influence of the quadrivalent HPV vaccine (Gardasil(®)) on the immune status of recurrent respiratory papillomatosis (RRP) patients. In retrospective observational study, six RRP patients who received the quadrivalent HPV vaccine and whose HPV seroreactivity was measured were included. Multiplex HPV Serology was used to determine HPV-specific antibodies pre- and post-vaccination. Surgical interventions and patient records were analyzed. Five HPV6 and 1 HPV11 infected patient were included. Mean antibody reactivity against the associated HPV type rose from 1125 median fluorescence intensity (MFI) pre-vaccination to 4690 MFI post-vaccination (p < 0.001). Median post-vaccination follow-up was 4 years. Poisson regression analysis showed that the quadrivalent HPV vaccine decreased the incidence rate of surgeries. The immune system of RRP patients is able to increase antibody reactivity against the associated HPV type. A double blind randomized controlled trial is needed to determine whether this immunological increase can cause decrease in number of surgeries.

Seroepidemiology of helminths and the association with severe malaria among infants and young children in Tanzania.

PubMed

Kwan, Jennifer L; Seitz, Amy E; Fried, Michal; Lee, Kun-Lin; Metenou, Simon; Morrison, Robert; Kabyemela, Edward; Nutman, Thomas B; Prevots, D Rebecca; Duffy, Patrick E

2018-03-01

The disease burden of Wuchereria bancrofti and Plasmodium falciparum malaria is high, particularly in Africa, and co-infection is common. However, the effects of filarial infection on the risk of severe malaria are unknown. We used the remaining serum samples from a large cohort study in Muheza, Tanzania to describe vector-borne filarial sero-reactivity among young children and to identify associations between exposure to filarial parasites and subsequent severe malaria infections. We identified positive filarial antibody responses (as well as positive antibody responses to Strongyloides stercoralis) among infants as young as six months. In addition, we found a significant association between filarial seropositivity at six months of age and subsequent severe malaria. Specifically, infants who developed severe malaria by one year of age were 3.9 times more likely (OR = 3.9, 95% CI: 1.2, 13.0) to have been seropositive for filarial antigen at six months of age compared with infants who did not develop severe malaria.

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

Detection of abnormal item based on time intervals for recommender systems.

PubMed

Gao, Min; Yuan, Quan; Ling, Bin; Xiong, Qingyu

2014-01-01

With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from "shilling" attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ(2)). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users.

Improvement of the Mutation-Discrimination Threshold for Rare Point Mutations by a Separation-Free Ligase Detection Reaction Assay Based on Fluorescence Resonance Energy Transfer.

PubMed

Hagihara, Kenta; Tsukagoshi, Kazuhiko; Nakajima, Chinami; Esaki, Shinsuke; Hashimoto, Masahiko

2016-01-01

We previously developed a separation-free ligase detection reaction assay based on fluorescence resonance energy transfer from a donor quantum dot to an acceptor fluorescent dye. This assay could successfully detect one cancer mutation among 10 wild-type templates. In the current study, the mutation-discrimination threshold was improved by one order of magnitude by replacing the original acceptor dye (Alexa Fluor 647) with another fluorescent dye (Cyanine 5) that was spectrally similar but more fluorescent.

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

An Improved Vision-based Algorithm for Unmanned Aerial Vehicles Autonomous Landing

NASA Astrophysics Data System (ADS)

Zhao, Yunji; Pei, Hailong

In vision-based autonomous landing system of UAV, the efficiency of target detecting and tracking will directly affect the control system. The improved algorithm of SURF(Speed Up Robust Features) will resolve the problem which is the inefficiency of the SURF algorithm in the autonomous landing system. The improved algorithm is composed of three steps: first, detect the region of the target using the Camshift; second, detect the feature points in the region of the above acquired using the SURF algorithm; third, do the matching between the template target and the region of target in frame. The results of experiment and theoretical analysis testify the efficiency of the algorithm.

MPI Runtime Error Detection with MUST: Advances in Deadlock Detection

DOE PAGES

Hilbrich, Tobias; Protze, Joachim; Schulz, Martin; ...

2013-01-01

The widely used Message Passing Interface (MPI) is complex and rich. As a result, application developers require automated tools to avoid and to detect MPI programming errors. We present the Marmot Umpire Scalable Tool (MUST) that detects such errors with significantly increased scalability. We present improvements to our graph-based deadlock detection approach for MPI, which cover future MPI extensions. Our enhancements also check complex MPI constructs that no previous graph-based detection approach handled correctly. Finally, we present optimizations for the processing of MPI operations that reduce runtime deadlock detection overheads. Existing approaches often require ( p ) analysis time permore » MPI operation, for p processes. We empirically observe that our improvements lead to sub-linear or better analysis time per operation for a wide range of real world applications.« less

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Webb, Virgil H.; Bradley, Scott R.; Hansen, Christopher A.

1998-07-01

An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over-the-Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug-smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model-based neural network that combines advantages of neural network and model-based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track-before-detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal-to-noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the "Einsteinian" likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.

Organic electrochemical transistor based immunosensor for prostate specific antigen (PSA) detection using gold nanoparticles for signal amplification.

PubMed

Kim, Duck-Jin; Lee, Nae-Eung; Park, Joon-Shik; Park, In-Jun; Kim, Jung-Gu; Cho, Hyoung J

2010-07-15

We demonstrated a highly sensitive organic electrochemical transistor (OECT) based immunosensor with a low detection limit for prostate specific antigen/alpha1-antichymotrypsin (PSA-ACT) complex. The poly(styrenesulfonate) doped poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) based OECT with secondary antibody conjugated gold nanoparticles (AuNPs) provided a detection limit of the PSA-ACT complex as low as 1pg/ml, as well as improved sensitivity and a dynamic range, due to the role of AuNPs in the signal amplification. The sensor performances were particularly improved in the lower concentration range where the detection is clinically important for the preoperative diagnosis and screening of prostate cancer. This result shows that the OECT-based immunosensor can be used as a transducer platform acceptable to the point-of-care (POC) diagnostic systems and demonstrates adaptability of organic electronics to clinical applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Resolution-improved in situ DNA hybridization detection based on microwave photonic interrogation.

PubMed

Cao, Yuan; Guo, Tuan; Wang, Xudong; Sun, Dandan; Ran, Yang; Feng, Xinhuan; Guan, Bai-ou

2015-10-19

In situ bio-sensing system based on microwave photonics filter (MPF) interrogation method with improved resolution is proposed and experimentally demonstrated. A microfiber Bragg grating (mFBG) is used as sensing probe for DNA hybridization detection. Different from the traditional wavelength monitoring technique, we use the frequency interrogation scheme for resolution-improved bio-sensing detection. Experimental results show that the frequency shift of MPF notch presents a linear response to the surrounding refractive index (SRI) change over the range of 1.33 to 1.38, with a SRI resolution up to 2.6 × 10(-5) RIU, which has been increased for almost two orders of magnitude compared with the traditional fundamental mode monitoring technique (~3.6 × 10(-3) RIU). Due to the high Q value (about 27), the whole process of DNA hybridization can be in situ monitored. The proposed MPF-based bio-sensing system provides a new interrogation method over the frequency domain with improved sensing resolution and rapid interrogation rate for biochemical and environmental measurement.

Research on Taxiway Path Optimization Based on Conflict Detection

PubMed Central

Zhou, Hang; Jiang, Xinxin

2015-01-01

Taxiway path planning is one of the effective measures to make full use of the airport resources, and the optimized paths can ensure the safety of the aircraft during the sliding process. In this paper, the taxiway path planning based on conflict detection is considered. Specific steps are shown as follows: firstly, make an improvement on A * algorithm, the conflict detection strategy is added to search for the shortest and safe path in the static taxiway network. Then, according to the sliding speed of aircraft, a time table for each node is determined and the safety interval is treated as the constraint to judge whether there is a conflict or not. The intelligent initial path planning model is established based on the results. Finally, make an example in an airport simulation environment, detect and relieve the conflict to ensure the safety. The results indicate that the model established in this paper is effective and feasible. Meanwhile, make comparison with the improved A*algorithm and other intelligent algorithms, conclude that the improved A*algorithm has great advantages. It could not only optimize taxiway path, but also ensure the safety of the sliding process and improve the operational efficiency. PMID:26226485

Research on Taxiway Path Optimization Based on Conflict Detection.

PubMed

Zhou, Hang; Jiang, Xinxin

2015-01-01

Taxiway path planning is one of the effective measures to make full use of the airport resources, and the optimized paths can ensure the safety of the aircraft during the sliding process. In this paper, the taxiway path planning based on conflict detection is considered. Specific steps are shown as follows: firstly, make an improvement on A * algorithm, the conflict detection strategy is added to search for the shortest and safe path in the static taxiway network. Then, according to the sliding speed of aircraft, a time table for each node is determined and the safety interval is treated as the constraint to judge whether there is a conflict or not. The intelligent initial path planning model is established based on the results. Finally, make an example in an airport simulation environment, detect and relieve the conflict to ensure the safety. The results indicate that the model established in this paper is effective and feasible. Meanwhile, make comparison with the improved A*algorithm and other intelligent algorithms, conclude that the improved A*algorithm has great advantages. It could not only optimize taxiway path, but also ensure the safety of the sliding process and improve the operational efficiency.

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

Automatic food intake detection based on swallowing sounds.

PubMed

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-11-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions.

Automatic food intake detection based on swallowing sounds

PubMed Central

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-01-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions. PMID:23125873

Improved forest change detection with terrain illumination corrected landsat images

USDA-ARS?s Scientific Manuscript database

An illumination correction algorithm has been developed to improve the accuracy of forest change detection from Landsat reflectance data. This algorithm is based on an empirical rotation model and was tested on the Landsat imagery pair over Cherokee National Forest, Tennessee, Uinta-Wasatch-Cache N...

Community structure detection based on the neighbor node degree information

NASA Astrophysics Data System (ADS)

Tang, Li-Ying; Li, Sheng-Nan; Lin, Jian-Hong; Guo, Qiang; Liu, Jian-Guo

2016-11-01

Community structure detection is of great significance for better understanding the network topology property. By taking into account the neighbor degree information of the topological network as the link weight, we present an improved Nonnegative Matrix Factorization (NMF) method for detecting community structure. The results for empirical networks show that the largest improved ratio of the Normalized Mutual Information value could reach 63.21%. Meanwhile, for synthetic networks, the highest Normalized Mutual Information value could closely reach 1, which suggests that the improved method with the optimal λ can detect the community structure more accurately. This work is helpful for understanding the interplay between the link weight and the community structure detection.

Fire flame detection based on GICA and target tracking

NASA Astrophysics Data System (ADS)

Rong, Jianzhong; Zhou, Dechuang; Yao, Wei; Gao, Wei; Chen, Juan; Wang, Jian

2013-04-01

To improve the video fire detection rate, a robust fire detection algorithm based on the color, motion and pattern characteristics of fire targets was proposed, which proved a satisfactory fire detection rate for different fire scenes. In this fire detection algorithm: (a) a rule-based generic color model was developed based on analysis on a large quantity of flame pixels; (b) from the traditional GICA (Geometrical Independent Component Analysis) model, a Cumulative Geometrical Independent Component Analysis (C-GICA) model was developed for motion detection without static background and (c) a BP neural network fire recognition model based on multi-features of the fire pattern was developed. Fire detection tests on benchmark fire video clips of different scenes have shown the robustness, accuracy and fast-response of the algorithm.

An immunity-based anomaly detection system with sensor agents.

PubMed

Okamoto, Takeshi; Ishida, Yoshiteru

2009-01-01

This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

Binocular Vision-Based Position and Pose of Hand Detection and Tracking in Space

NASA Astrophysics Data System (ADS)

Jun, Chen; Wenjun, Hou; Qing, Sheng

After the study of image segmentation, CamShift target tracking algorithm and stereo vision model of space, an improved algorithm based of Frames Difference and a new space point positioning model were proposed, a binocular visual motion tracking system was constructed to verify the improved algorithm and the new model. The problem of the spatial location and pose of the hand detection and tracking have been solved.

Detecting crop growth stages of maize and soybeans by using time-series MODIS data

NASA Astrophysics Data System (ADS)

Sakamoto, T.; Wardlow, B. D.; Gitelson, A. A.; Verma, S. B.; Suyker, A. E.; Arkebauer, T. J.

2009-12-01

The crop phenological stages are one of essential parameters for evaluating crop productivity based on a crop simulation model. In this study, we improved a method named the Wavelet-based Filter for detecting Crop Phenology (WFCP) for detecting the specific phenological dates of maize and soybeans. The improved method was applied to MODIS-derived Wide Dynamic Range Vegetation Index (WDRVI) over a 6-year period (2003 to 2008) for three experimental fields planted to either maize or soybeans as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln (UNL). Using the ground-based crop growth stage observations collected by the CSP, it was confirmed that the improved method can estimate the specific phenological dates of maize (V2.5, R1, R5 and R6) and soybeans (V1, R5, R6 and R7) with reasonable accuracy.

Computer assisted diagnostic system in tumor radiography.

PubMed

Faisal, Ahmed; Parveen, Sharmin; Badsha, Shahriar; Sarwar, Hasan; Reza, Ahmed Wasif

2013-06-01

An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.

Target Detection of Quantum Illumination Receiver Based on Photon-subtracted Entanglement State

NASA Astrophysics Data System (ADS)

Chi, Jiao; Liu, HongJun; Huang, Nan; Wang, ZhaoLu

2017-12-01

We theoretically propose a quantum illumination receiver based on the ideal photon-subtracted two-mode squeezed state (PSTMSS) to efficiently detect the noise-hidden target. This receiver is generated by applying an optical parametric amplifier (OPA) to the cross correlation detection. With analyzing the output performance, it is found that OPA as a preposition technology of the receiver can contribute to the PSTMSS by significantly reducing the error probability than that of the general two-mode squeezed state (TMSS). Comparing with TMSS, the signal-to-noise ratio of quantum illumination based on ideal PSTMSS and OPA is improved more than 4 dB under an optimal gain of OPA. This work may provide a potential improvement in the application of accurate target detection when two kinds of resource have the identical real squeezing parameter.

Efficient hybrid monocular-stereo approach to on-board video-based traffic sign detection and tracking

NASA Astrophysics Data System (ADS)

Marinas, Javier; Salgado, Luis; Arróspide, Jon; Camplani, Massimo

2012-01-01

In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion.

An Improved Harmonic Current Detection Method Based on Parallel Active Power Filter

NASA Astrophysics Data System (ADS)

Zeng, Zhiwu; Xie, Yunxiang; Wang, Yingpin; Guan, Yuanpeng; Li, Lanfang; Zhang, Xiaoyu

2017-05-01

Harmonic detection technology plays an important role in the applications of active power filter. The accuracy and real-time performance of harmonic detection are the precondition to ensure the compensation performance of Active Power Filter (APF). This paper proposed an improved instantaneous reactive power harmonic current detection algorithm. The algorithm uses an improved ip -iq algorithm which is combined with the moving average value filter. The proposed ip -iq algorithm can remove the αβ and dq coordinate transformation, decreasing the cost of calculation, simplifying the extraction process of fundamental components of load currents, and improving the detection speed. The traditional low-pass filter is replaced by the moving average filter, detecting the harmonic currents more precisely and quickly. Compared with the traditional algorithm, the THD (Total Harmonic Distortion) of the grid currents is reduced from 4.41% to 3.89% for the simulations and from 8.50% to 4.37% for the experiments after the improvement. The results show the proposed algorithm is more accurate and efficient.

Immune Control of Burkholderia pseudomallei––Common, High-Frequency T-Cell Responses to a Broad Repertoire of Immunoprevalent Epitopes

PubMed Central

Nithichanon, Arnone; Rinchai, Darawan; Buddhisa, Surachat; Saenmuang, Pornpun; Kewcharoenwong, Chidchamai; Kessler, Bianca; Khaenam, Prasong; Chetchotisakd, Ploenchan; Maillere, Bernard; Robinson, John; Reynolds, Catherine J.; Boyton, Rosemary J.; Altmann, Daniel M.; Lertmemongkolchai, Ganjana

2018-01-01

Burkholderia pseudomallei (Bp) is an environmental bacterial pathogen that causes potentially lethal sepsis in susceptible individuals and is considered a Category B, Tier-1 biothreat agent. As such, it is crucial to gain an improved understanding of protective immunity and potential vaccine candidates. The nature of immune correlates dictating why most exposed individuals in endemic regions undergo asymptomatic seroconversion while others succumb to life-threatening sepsis is largely uncharted. Bp seroreactive, immunogenic proteins have previously been identified by antigen microarray. We here set out to conduct an analysis of T-cell recognition of the Bp immunome using serodominant antigens represented in the original antigen microarray, examining immune correlates of disease in healthy seropositive individuals and those with acute disease or in convalescence. By screening a library of 739 overlapping peptides representing the sequences of 20 different Bp antigens, we aimed to define immune correlates of protection at the level of immunoprevalent T-cell epitopes. Responses to a large number of epitopes were common in healthy seropositive individuals: we found remarkably broad responsiveness to Bp epitopes, with 235 of 739 peptides recognized by ≥80% of all tested donors. The cumulative response to Bp epitopes in healthy, seropositive, donors from this endemic region were of the order of thousands of spot forming cells per million cells, making Bp recognition a significant component of the T-cell repertoire. Noteworthy among our findings, analysis revealed 10 highly immunoprevalent T-cell epitopes, able to induce Bp-specific IFNγ responses that were high in responding T-cell frequency within the repertoire, and also common across individuals with different human leukocyte antigen types. Acute melioidosis patients showed poor T-cell responses to the immunoprevalent epitopes, but acquired responsiveness following recovery from infection. Our findings suggest that a large repertoire of CD4 T cells, high in frequency and with broad coverage of antigens and epitopes, is important in controlling Bp infection. This offers an attractive potential strategy for subunit or epitope-based vaccines. PMID:29616023

Exposure to Sarcocystis spp. in horses from Spain determined by Western blot analysis using Sarcocystis neurona merozoites as heterologous antigen.

PubMed

Arias, M; Yeargan, M; Francisco, I; Dangoudoubiyam, S; Becerra, P; Francisco, R; Sánchez-Andrade, R; Paz-Silva, A; Howe, D K

2012-04-30

Horses serve as an intermediate host for several species of Sarcocystis, all of which utilize canids as the definitive host. Sarcocystis spp. infection and formation of latent sarcocysts in horses often appears to be subclinical, but morbidity can occur, especially when the parasite burden is large. A serological survey was conducted to determine the presence of antibodies against Sarcocystis spp. in seemingly healthy horses from the Galicia region of Spain. Western blot analyses using Sarcocystis neurona merozoites as heterologous antigen suggested greater than 80% seroprevalance of Sarcocystis spp. in a sample set of 138 horses. The serum samples were further tested with enzyme-linked immunosorbent assays (ELISAs) based on recombinant S. neurona-specific surface antigens (rSnSAGs). As expected for horses from the Eastern Hemisphere, less than 4% of the serum samples were positive when analyzed with either the rSnSAG2 or the rSnSAG4/3 ELISAs. An additional 246 horses were tested using the rSnSAG2 ELISA, which revealed that less than 3% of the 384 samples were seropositive. Collectively, the results of this serologic study suggested that a large proportion of horses from this region of Spain are exposed to Sarcocystis spp. Furthermore, the anti-Sarcocystis seroreactivity in these European horses could be clearly distinguished from anti-S. neurona antibodies using the rSnSAG2 and rSnSAG4/3 ELISAs. Copyright © 2011 Elsevier B.V. All rights reserved.

Nanoprobe-Enhanced, Split Aptamer-Based Electrochemical Sandwich Assay for Ultrasensitive Detection of Small Molecules.

PubMed

Zhao, Tao; Liu, Ran; Ding, Xiaofan; Zhao, Juncai; Yu, Haixiang; Wang, Lei; Xu, Qing; Wang, Xuan; Lou, Xinhui; He, Miao; Xiao, Yi

2015-08-04

It is quite challenging to improve the binding affinity of antismall molecule aptamers. We report that the binding affinity of anticocaine split aptamer pairs improved by up to 66-fold by gold nanoparticles (AuNP)-attached aptamers due to the substantially increased local concentration of aptamers and multiple and simultaneous ligand interactions. The significantly improved binding affinity enables the detection of small molecule targets with unprecedented sensitivity, as demonstrated in nanoprobe-enhanced split aptamer-based electrochemical sandwich assays (NE-SAESA). NE-SAESA replaces the traditional molecular reporter probe with AuNPs conjugated to multiple reporter probes. The increased binding affinity allowed us to use 1,000-fold lower reporter probe concentrations relative to those employed in SAESA. We show that the near-elimination of background in NE-SAESA effectively improves assay sensitivity by ∼1,000-100,000-fold for ATP and cocaine detection, relative to equivalent SAESA. With the ongoing development of new strategies for the selection of aptamers, we anticipate that our sensor platform should offer a generalizable approach for the high-sensitivity detection of diverse targets. More importantly, we believe that NE-SAESA represents a novel strategy to improve the binding affinity between a small molecule and its aptamer and potentially can be extended to other detection platforms.

Underwater electric field detection system based on weakly electric fish

NASA Astrophysics Data System (ADS)

Xue, Wei; Wang, Tianyu; Wang, Qi

2018-04-01

Weakly electric fish sense their surroundings in complete darkness by their active electric field detection system. However, due to the insufficient detection capacity of the electric field, the detection distance is not enough, and the detection accuracy is not high. In this paper, a method of underwater detection based on rotating current field theory is proposed to improve the performance of underwater electric field detection system. First of all, we built underwater detection system based on the theory of the spin current field mathematical model with the help of the results of previous researchers. Then we completed the principle prototype and finished the metal objects in the water environment detection experiments, laid the foundation for the further experiments.

Development of a HIV-1 Virus Detection System Based on Nanotechnology.

PubMed

Lee, Jin-Ho; Oh, Byung-Keun; Choi, Jeong-Woo

2015-04-27

Development of a sensitive and selective detection system for pathogenic viral agents is essential for medical healthcare from diagnostics to therapeutics. However, conventional detection systems are time consuming, resource-intensive and tedious to perform. Hence, the demand for sensitive and selective detection system for virus are highly increasing. To attain this aim, different aspects and techniques have been applied to develop virus sensor with improved sensitivity and selectivity. Here, among those aspects and techniques, this article reviews HIV virus particle detection systems incorporated with nanotechnology to enhance the sensitivity. This review mainly focused on four different detection system including vertically configured electrical detection based on scanning tunneling microscopy (STM), electrochemical detection based on direct electron transfer in virus, optical detection system based on localized surface plasmon resonance (LSPR) and surface enhanced Raman spectroscopy (SERS) using plasmonic nanoparticle.

Rhythm-based heartbeat duration normalization for atrial fibrillation detection.

PubMed

Islam, Md Saiful; Ammour, Nassim; Alajlan, Naif; Aboalsamh, Hatim

2016-05-01

Screening of atrial fibrillation (AF) for high-risk patients including all patients aged 65 years and older is important for prevention of risk of stroke. Different technologies such as modified blood pressure monitor, single lead ECG-based finger-probe, and smart phone using plethysmogram signal have been emerging for this purpose. All these technologies use irregularity of heartbeat duration as a feature for AF detection. We have investigated a normalization method of heartbeat duration for improved AF detection. AF is an arrhythmia in which heartbeat duration generally becomes irregularly irregular. From a window of heartbeat duration, we estimate the possible rhythm of the majority of heartbeats and normalize duration of all heartbeats in the window based on the rhythm so that we can measure the irregularity of heartbeats for both AF and non-AF rhythms in the same scale. Irregularity is measured by the entropy of distribution of the normalized duration. Then we classify a window of heartbeats as AF or non-AF by thresholding the measured irregularity. The effect of this normalization is evaluated by comparing AF detection performances using duration with the normalization, without normalization, and with other existing normalizations. Sensitivity and specificity of AF detection using normalized heartbeat duration were tested on two landmark databases available online and compared with results of other methods (with/without normalization) by receiver operating characteristic (ROC) curves. ROC analysis showed that the normalization was able to improve the performance of AF detection and it was consistent for a wide range of sensitivity and specificity for use of different thresholds. Detection accuracy was also computed for equal rates of sensitivity and specificity for different methods. Using normalized heartbeat duration, we obtained 96.38% accuracy which is more than 4% improvement compared to AF detection without normalization. The proposed normalization method was found useful for improving performance and robustness of AF detection. Incorporation of this method in a screening device could be crucial to reduce the risk of AF-related stroke. In general, the incorporation of the rhythm-based normalization in an AF detection method seems important for developing a robust AF screening device. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

NASA Astrophysics Data System (ADS)

Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

2016-01-01

The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

Photonic Crystal Enhanced Fluorescence for Early Breast Cancer Biomarker Detection

PubMed Central

Cunningham, Brian T.; Zangar, Richard C.

2013-01-01

Photonic crystal surfaces offer a compelling platform for improving the sensitivity of surface-based fluorescent assays used in disease diagnostics. Through the complementary processes of photonic crystal enhanced excitation and enhanced extraction, a periodic dielectric-based nanostructured surface can simultaneously increase the electric field intensity experienced by surface-bound fluorophores and increase the collection efficiency of emitted fluorescent photons. Through the ability to inexpensively fabricate photonic crystal surfaces over substantial surface areas, they are amenable to single-use applications in biological sensing, such as disease biomarker detection in serum. In this review, we will describe the motivation for implementing high-sensitivity, multiplexed biomarker detection in the context of breast cancer diagnosis. We will summarize recent efforts to improve the detection limits of such assays though the use of photonic crystal surfaces. Reduction of detection limits is driven by low autofluorescent substrates for photonic crystal fabrication, and detection instruments that take advantage of their unique features. PMID:22736539

Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Earthquake Detection

NASA Astrophysics Data System (ADS)

Bergen, K.; Yoon, C. E.; OReilly, O. J.; Beroza, G. C.

2015-12-01

Recent improvements in computational efficiency for waveform correlation-based detections achieved by new methods such as Fingerprint and Similarity Thresholding (FAST) promise to allow large-scale blind search for similar waveforms in long-duration continuous seismic data. Waveform similarity search applied to datasets of months to years of continuous seismic data will identify significantly more events than traditional detection methods. With the anticipated increase in number of detections and associated increase in false positives, manual inspection of the detection results will become infeasible. This motivates the need for new approaches to process the output of similarity-based detection. We explore data mining techniques for improved detection post-processing. We approach this by considering similarity-detector output as a sparse similarity graph with candidate events as vertices and similarities as weighted edges. Image processing techniques are leveraged to define candidate events and combine results individually processed at multiple stations. Clustering and graph analysis methods are used to identify groups of similar waveforms and assign a confidence score to candidate detections. Anomaly detection and classification are applied to waveform data for additional false detection removal. A comparison of methods will be presented and their performance will be demonstrated on a suspected induced and non-induced earthquake sequence.

Virtual-Lattice Based Intrusion Detection Algorithm over Actuator-Assisted Underwater Wireless Sensor Networks

PubMed Central

Yan, Jing; Li, Xiaolei; Luo, Xiaoyuan; Guan, Xinping

2017-01-01

Due to the lack of a physical line of defense, intrusion detection becomes one of the key issues in applications of underwater wireless sensor networks (UWSNs), especially when the confidentiality has prime importance. However, the resource-constrained property of UWSNs such as sparse deployment and energy constraint makes intrusion detection a challenging issue. This paper considers a virtual-lattice-based approach to the intrusion detection problem in UWSNs. Different from most existing works, the UWSNs consist of two kinds of nodes, i.e., sensor nodes (SNs), which cannot move autonomously, and actuator nodes (ANs), which can move autonomously according to the performance requirement. With the cooperation of SNs and ANs, the intruder detection probability is defined. Then, a virtual lattice-based monitor (VLM) algorithm is proposed to detect the intruder. In order to reduce the redundancy of communication links and improve detection probability, an optimal and coordinative lattice-based monitor patrolling (OCLMP) algorithm is further provided for UWSNs, wherein an equal price search strategy is given for ANs to find the shortest patrolling path. Under VLM and OCLMP algorithms, the detection probabilities are calculated, while the topology connectivity can be guaranteed. Finally, simulation results are presented to show that the proposed method in this paper can improve the detection accuracy and save the energy consumption compared with the conventional methods. PMID:28531127

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

Improved signal recovery for flow cytometry based on ‘spatially modulated emission’

NASA Astrophysics Data System (ADS)

Quint, S.; Wittek, J.; Spang, P.; Levanon, N.; Walther, T.; Baßler, M.

2017-09-01

Recently, the technique of ‘spatially modulated emission’ has been introduced (Baßler et al 2008 US Patent 0080181827A1; Kiesel et al 2009 Appl. Phys. Lett. 94 041107; Kiesel et al 2011 Cytometry A 79A 317-24) improving the signal-to-noise ratio (SNR) for detecting bio-particles in the field of flow cytometry. Based on this concept, we developed two advanced signal processing methods which further enhance the SNR and selectivity for cell detection. The improvements are achieved by adapting digital filtering methods from RADAR technology and mainly address inherent offset elimination, increased signal dynamics and moreover reduction of erroneous detections due to processing artifacts. We present a comprehensive theory on SNR gain and provide experimental results of our concepts.

The energy ratio mapping algorithm: a tool to improve the energy-based detection of odontocete echolocation clicks.

PubMed

Klinck, Holger; Mellinger, David K

2011-04-01

The energy ratio mapping algorithm (ERMA) was developed to improve the performance of energy-based detection of odontocete echolocation clicks, especially for application in environments with limited computational power and energy such as acoustic gliders. ERMA systematically evaluates many frequency bands for energy ratio-based detection of echolocation clicks produced by a target species in the presence of the species mix in a given geographic area. To evaluate the performance of ERMA, a Teager-Kaiser energy operator was applied to the series of energy ratios as derived by ERMA. A noise-adaptive threshold was then applied to the Teager-Kaiser function to identify clicks in data sets. The method was tested for detecting clicks of Blainville's beaked whales while rejecting echolocation clicks of Risso's dolphins and pilot whales. Results showed that the ERMA-based detector correctly identified 81.6% of the beaked whale clicks in an extended evaluation data set. Average false-positive detection rate was 6.3% (3.4% for Risso's dolphins and 2.9% for pilot whales).

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

PubMed

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-05-15

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

PubMed Central

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-01-01

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. PMID:28505135

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

PubMed

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-04-28

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids

PubMed Central

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-01-01

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability. PMID:28452925

Interference Information Based Power Control for Cognitive Radio with Multi-Hop Cooperative Sensing

NASA Astrophysics Data System (ADS)

Yu, Youngjin; Murata, Hidekazu; Yamamoto, Koji; Yoshida, Susumu

Reliable detection of other radio systems is crucial for systems that share the same frequency band. In wireless communication channels, there is uncertainty in the received signal level due to multipath fading and shadowing. Cooperative sensing techniques in which radio stations share their sensing information can improve the detection probability of other systems. In this paper, a new cooperative sensing scheme that reduces the false detection probability while maintaining the outage probability of other systems is investigated. In the proposed system, sensing information is collected using multi-hop transmission from all sensing stations that detect other systems, and transmission decisions are based on the received sensing information. The proposed system also controls the transmit power based on the received CINRs from the sensing stations. Simulation results reveal that the proposed system can reduce the outage probability of other systems, or improve its link success probability.

A novel underwater dam crack detection and classification approach based on sonar images

PubMed Central

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments. PMID:28640925

A novel underwater dam crack detection and classification approach based on sonar images.

PubMed

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

Digitized locksmith forensics: automated detection and segmentation of toolmarks on highly structured surfaces

NASA Astrophysics Data System (ADS)

Clausing, Eric; Vielhauer, Claus

2014-02-01

Locksmith forensics is an important area in crime scene forensics. Due to new optical, contactless, nanometer range sensing technology, such traces can be captured, digitized and analyzed more easily allowing a complete digital forensic investigation. In this paper we present a significantly improved approach for the detection and segmentation of toolmarks on surfaces of locking cylinder components (using the example of the locking cylinder component 'key pin') acquired by a 3D Confocal Laser Scanning Microscope. This improved approach is based on our prior work1 using a block-based classification approach with textural features. In this prior work1 we achieve a solid detection rate of 75-85% for the detection of toolmarks originating from illegal opening methods. Here, in this paper we improve, expand and fuse this prior approach with additional features from acquired surface topography data, color data and an image processing approach using adapted Gabor filters. In particular we are able of raising the detection and segmentation rates above 90% with our test set of 20 key pins with approximately 700 single toolmark traces of four different opening methods. We can provide a precise pixel- based segmentation as opposed to the rather imprecise segmentation of our prior block-based approach and as the use of the two additional data types (color and especially topography) require a specific pre-processing, we furthermore propose an adequate approach for this purpose.

Synthesis of improved upconversion nanoparticles as ultrasensitive fluorescence probe for mycotoxins.

PubMed

Chen, Quansheng; Hu, Weiwei; Sun, Cuicui; Li, Huanhuan; Ouyang, Qin

2016-09-28

Rare earth-doped upconversion nanoparticles (UCNPs) have promising potentials in biodetection due to their unique frequency upconverting capability and high detection sensitivity. This paper reports an improved UCNPs-based fluorescence probe for dual-sensing of Aflatoxin B1 (AFB1) and Deoxynivalenol (DON) using a magnetism-induced separation and the specific formation of antibody-targets complex. Herein, the improved UCNPs, which were namely NaYF4:Yb/Ho/Gd and NaYF4:Yb/Tm/Gd, were systematically studied based on the optimization of reaction time, temperature and the concentration of dopant ions with simultaneous phase and size controlled NaYF4 nanoparticles; and the targets were detected using the pattern of competitive combination assay. Under an optimized condition, the advanced fluorescent probes revealed stronger fluorescent properties, broader biological applications and better storage stabilities compared to traditional UCNPs-based ones; and ultrasensitive determinations of AFB1 and DON were achieved under a wide sensing range of 0.001-0.1 ng ml(-1) with the limit of detection (LOD) of 0.001 ng ml(-1). Additionally, the applicability of the improved nanosensor for the detection of mycotoxins was also confirmed in adulterated oil samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Extracting information from the text of electronic medical records to improve case detection: a systematic review

PubMed Central

Carroll, John A; Smith, Helen E; Scott, Donia; Cassell, Jackie A

2016-01-01

Background Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall). PMID:26911811

Membrane-based lateral flow immunochromatographic strip with nanoparticles as reporters for detection: A review.

PubMed

Huang, Xiaolin; Aguilar, Zoraida P; Xu, Hengyi; Lai, Weihua; Xiong, Yonghua

2016-01-15

Membrane-based lateral flow immunochromatographic strip (LFICS) is widely used in various fields because of its simplicity, rapidity (detection within 10min), and low cost. However, early designs of membrane-based LFICS for preliminary screening only provide qualitative ("yes/no" signal) or semi-quantitative results without quantitative information. These designs often suffer from low-signal intensity and poor sensitivity and are only capable of single analyte detection, not simultaneous multiple detections. The performance of existing techniques used for detection using LFICS has been considerably improved by incorporating different kinds of nanoparticles (NPs) as reporters. NPs can serve as alternative labels and improve analytical sensitivity or limit of detection of LFICS because of their unique properties, such as optical absorption, fluorescence spectra, and magnetic properties. The controlled manipulation of NPs allows simultaneous or multiple detections by using membrane-based LFICS. In this review, we discuss how colored (e.g., colloidal gold, carbon, and colloidal selenium NPs), luminescent (e.g., quantum dots, up-converting phosphor NPs, and dye-doped NPs), and magnetic NPs are integrated into membrane-based LFICS for the detection of target analytes. Gold NPs are also featured because of their wide applications. Different types and unique properties of NPs are briefly explained. This review focuses on examples of NP-based LFICS to illustrate novel concepts in various devices with potential applications as screening tools. This review also highlights the superiority of NP-based approaches over existing conventional strategies for clinical analysis, food safety, and environmental monitoring. This paper is concluded by a short section on future research trends regarding NP-based LFICS. Copyright © 2015 Elsevier B.V. All rights reserved.

Generalised Category Attack—Improving Histogram-Based Attack on JPEG LSB Embedding

NASA Astrophysics Data System (ADS)

Lee, Kwangsoo; Westfeld, Andreas; Lee, Sangjin

We present a generalised and improved version of the category attack on LSB steganography in JPEG images with straddled embedding path. It detects more reliably low embedding rates and is also less disturbed by double compressed images. The proposed methods are evaluated on several thousand images. The results are compared to both recent blind and specific attacks for JPEG embedding. The proposed attack permits a more reliable detection, although it is based on first order statistics only. Its simple structure makes it very fast.

An integrative 'omics' solution to the detection of recombinant human erythropoietin and blood doping.

PubMed

Pitsiladis, Yannis P; Durussel, Jérôme; Rabin, Olivier

2014-05-01

Administration of recombinant human erythropoietin (rHumanEPO) improves sporting performance and hence is frequently subject to abuse by athletes, although rHumanEPO is prohibited by the WADA. Approaches to detect rHumanEPO doping have improved significantly in recent years but remain imperfect. A new transcriptomic-based longitudinal screening approach is being developed that has the potential to improve the analytical performance of current detection methods. In particular, studies are being funded by WADA to identify a 'molecular signature' of rHumanEPO doping and preliminary results are promising. In the first systematic study to be conducted, the expression of hundreds of genes were found to be altered by rHumanEPO with numerous gene transcripts being differentially expressed after the first injection and further transcripts profoundly upregulated during and subsequently downregulated up to 4 weeks postadministration of the drug; with the same transcriptomic pattern observed in all participants. The identification of a blood 'molecular signature' of rHumanEPO administration is the strongest evidence to date that gene biomarkers have the potential to substantially improve the analytical performance of current antidoping methods such as the Athlete Biological Passport for rHumanEPO detection. Given the early promise of transcriptomics, research using an 'omics'-based approach involving genomics, transcriptomics, proteomics and metabolomics should be intensified in order to achieve improved detection of rHumanEPO and other doping substances and methods difficult to detect such a recombinant human growth hormone and blood transfusions.

Design and characterization of a dead-time regime enhanced early photon projection imaging system

NASA Astrophysics Data System (ADS)

Sinha, L.; Fogarty, M.; Zhou, W.; Giudice, A.; Brankov, J. G.; Tichauer, K. M.

2018-04-01

Scattering of visible and near-infrared light in biological tissue reduces spatial resolution for imaging of tissues thicker than 100 μm. In this study, an optical projection imaging system is presented and characterized that exploits the dead-time characteristics typical of photon counting modules based on single photon avalanche diodes (SPADs). With this system, it is possible to attenuate the detection of more scattered late-arriving photons, such that detection of less scattered early-arriving photons can be enhanced with increased light intensity, without being impeded by the maximum count rate of the SPADs. The system has the potential to provide transmittance-based anatomical information or fluorescence-based functional information (with slight modification in the instrumentation) of biological samples with improved resolution in the mesoscopic domain (0.1-2 cm). The system design, calibration, stability, and performance were evaluated using simulation and experimental phantom studies. The proposed system allows for the detection of very-rare early-photons at a higher frequency and with a better signal-to-noise ratio. The experimental results demonstrated over a 3.4-fold improvement in the spatial resolution using early photon detection vs. conventional detection, and a 1000-fold improvement in imaging time using enhanced early detection vs. conventional early photon detection in a 4-mm thick phantom with a tissue-equivalent absorption coefficient of μa = 0.05 mm-1 and a reduced scattering coefficient of μs' = 5 mm-1.

Reducing sojourn points from recurrence plots to improve transition detection: Application to fetal heart rate transitions.

PubMed

Zaylaa, Amira; Charara, Jamal; Girault, Jean-Marc

2015-08-01

The analysis of biomedical signals demonstrating complexity through recurrence plots is challenging. Quantification of recurrences is often biased by sojourn points that hide dynamic transitions. To overcome this problem, time series have previously been embedded at high dimensions. However, no one has quantified the elimination of sojourn points and rate of detection, nor the enhancement of transition detection has been investigated. This paper reports our on-going efforts to improve the detection of dynamic transitions from logistic maps and fetal hearts by reducing sojourn points. Three signal-based recurrence plots were developed, i.e. embedded with specific settings, derivative-based and m-time pattern. Determinism, cross-determinism and percentage of reduced sojourn points were computed to detect transitions. For logistic maps, an increase of 50% and 34.3% in sensitivity of detection over alternatives was achieved by m-time pattern and embedded recurrence plots with specific settings, respectively, and with a 100% specificity. For fetal heart rates, embedded recurrence plots with specific settings provided the best performance, followed by derivative-based recurrence plot, then unembedded recurrence plot using the determinism parameter. The relative errors between healthy and distressed fetuses were 153%, 95% and 91%. More than 50% of sojourn points were eliminated, allowing better detection of heart transitions triggered by gaseous exchange factors. This could be significant in improving the diagnosis of fetal state. Copyright © 2014 Elsevier Ltd. All rights reserved.

Molecular Methods for the Detection of Mycoplasma and Ureaplasma Infections in Humans

PubMed Central

Waites, Ken B.; Xiao, Li; Paralanov, Vanya; Viscardi, Rose M.; Glass, John I.

2012-01-01

Mycoplasma and Ureaplasma species are well-known human pathogens responsible for a broad array of inflammatory conditions involving the respiratory and urogenital tracts of neonates, children, and adults. Greater attention is being given to these organisms in diagnostic microbiology, largely as a result of improved methods for their laboratory detection, made possible by powerful molecular-based techniques that can be used for primary detection in clinical specimens. For slow-growing species, such as Mycoplasma pneumoniae and Mycoplasma genitalium, molecular-based detection is the only practical means for rapid microbiological diagnosis. Most molecular-based methods used for detection and characterization of conventional bacteria have been applied to these organisms. A complete genome sequence is available for one or more strains of all of the important human pathogens in the Mycoplasma and Ureaplasma genera. Information gained from genome analyses and improvements in efficiency of DNA sequencing are expected to significantly advance the field of molecular detection and genotyping during the next few years. This review provides a summary and critical review of methods suitable for detection and characterization of mycoplasmas and ureaplasmas of humans, with emphasis on molecular genotypic techniques. PMID:22819362

An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

NASA Astrophysics Data System (ADS)

Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

2018-01-01

In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

Spatial Probability Dynamically Modulates Visual Target Detection in Chickens

PubMed Central

Sridharan, Devarajan; Ramamurthy, Deepa L.; Knudsen, Eric I.

2013-01-01

The natural world contains a rich and ever-changing landscape of sensory information. To survive, an organism must be able to flexibly and rapidly locate the most relevant sources of information at any time. Humans and non-human primates exploit regularities in the spatial distribution of relevant stimuli (targets) to improve detection at locations of high target probability. Is the ability to flexibly modify behavior based on visual experience unique to primates? Chickens (Gallus domesticus) were trained on a multiple alternative Go/NoGo task to detect a small, briefly-flashed dot (target) in each of the quadrants of the visual field. When targets were presented with equal probability (25%) in each quadrant, chickens exhibited a distinct advantage for detecting targets at lower, relative to upper, hemifield locations. Increasing the probability of presentation in the upper hemifield locations (to 80%) dramatically improved detection performance at these locations to be on par with lower hemifield performance. Finally, detection performance in the upper hemifield changed on a rapid timescale, improving with successive target detections, and declining with successive detections at the diagonally opposite location in the lower hemifield. These data indicate the action of a process that in chickens, as in primates, flexibly and dynamically modulates detection performance based on the spatial probabilities of sensory stimuli as well as on recent performance history. PMID:23734188

Locomotive track detection for underground

NASA Astrophysics Data System (ADS)

Ma, Zhonglei; Lang, Wenhui; Li, Xiaoming; Wei, Xing

2017-08-01

In order to improve the PC-based track detection system, this paper proposes a method to detect linear track for underground locomotive based on DSP + FPGA. Firstly, the analog signal outputted from the camera is sampled by A / D chip. Then the collected digital signal is preprocessed by FPGA. Secondly, the output signal of FPGA is transmitted to DSP via EMIF port. Subsequently, the adaptive threshold edge detection, polar angle and radius constrain based Hough transform are implemented by DSP. Lastly, the detected track information is transmitted to host computer through Ethernet interface. The experimental results show that the system can not only meet the requirements of real-time detection, but also has good robustness.

Task-based statistical image reconstruction for high-quality cone-beam CT

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

PubMed

Levman, Jacob E D; Gallego-Ortiz, Cristina; Warner, Ellen; Causer, Petrina; Martel, Anne L

2016-02-01

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre. This study compares the performance of a proposed method for computer-aided detection (based on the second-order spatial derivative of the relative signal intensity) with the signal enhancement ratio (SER) on MRI-based breast screening examinations. Comparison is performed using receiver operating characteristic (ROC) curve analysis as well as free-response receiver operating characteristic (FROC) curve analysis. A modified computer-aided detection system combining the proposed approach with the SER method is also presented. The proposed method provides improvements in the rates of false positive markings over the SER method in the detection of breast cancer (as assessed by FROC analysis). The modified computer-aided detection system that incorporates both the proposed method and the SER method yields ROC results equal to that produced by SER while simultaneously providing improvements over the SER method in terms of false positives per noncancerous exam. The proposed method for identifying malignancies outperforms the SER method in terms of false positives on a challenging dataset containing many small lesions and may play a useful role in breast cancer screening by MRI as part of a computer-aided detection system.

[Seroreactivity to hepatitis B virus surface antigen among different population groups in the City of Durango, Mexico].

PubMed

Alvarado-Esquivel, Cosme; Sifuentes-Alvarez, Antonio; Pérez-Ochoa, José Francisco; García-Corral, Nora; Rodríguez-Briones, Alfredo; González-Castañeda, José Luis; Alonso-Muñoz, Citlaly María Teresa; Bracho-Huemoeller, Antonio

2008-01-01

To determine the seroprevalence of hepatitis B surface antigen (HBsAg) in several groups of populations in Durango City, Mexico. An observational and comparative study was conducted in 6 groups of population in a total of 775 persons in Durango City, Mexico. The groups studied were 141 registered female sex workers, 100 medical students, 150 blood donors, 104 persons applying for medical certificates, 100 pregnant women, and 180 drug addicts. Serum samples of participants were analyzed for HBsAg by an immunoassay. HBsAg confirmation was performed by neutralization assay. Out of the 775 participants, 13 (1.7%) were positive by the immunoassay, and only 1 (0.1%) resulted positive by the confirmatory assay. This positive case was a drug addict and had a history of surgery and national and international trips. The seroprevalence of HBsAg in several groups of population in Durango City is low; the seroprevalence is comparable to or lower than those informed in other Mexican cities. It is strongly recommended to perform the HBsAg confirmation test due to low specificity of the immunoassay.

Significance of MPEG-7 textural features for improved mass detection in mammography.

PubMed

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

Item Purification Does Not Always Improve DIF Detection: A Counterexample with Angoff's Delta Plot

ERIC Educational Resources Information Center

Magis, David; Facon, Bruno

2013-01-01

Item purification is an iterative process that is often advocated as improving the identification of items affected by differential item functioning (DIF). With test-score-based DIF detection methods, item purification iteratively removes the items currently flagged as DIF from the test scores to get purified sets of items, unaffected by DIF. The…

A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging.

PubMed

Salehi, Leila; Azmi, Reza

2014-07-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

A Viola-Jones based hybrid face detection framework

NASA Astrophysics Data System (ADS)

Murphy, Thomas M.; Broussard, Randy; Schultz, Robert; Rakvic, Ryan; Ngo, Hau

2013-12-01

Improvements in face detection performance would benefit many applications. The OpenCV library implements a standard solution, the Viola-Jones detector, with a statistically boosted rejection cascade of binary classifiers. Empirical evidence has shown that Viola-Jones underdetects in some instances. This research shows that a truncated cascade augmented by a neural network could recover these undetected faces. A hybrid framework is constructed, with a truncated Viola-Jones cascade followed by an artificial neural network, used to refine the face decision. Optimally, a truncation stage that captured all faces and allowed the neural network to remove the false alarms is selected. A feedforward backpropagation network with one hidden layer is trained to discriminate faces based upon the thresholding (detection) values of intermediate stages of the full rejection cascade. A clustering algorithm is used as a precursor to the neural network, to group significant overlappings. Evaluated on the CMU/VASC Image Database, comparison with an unmodified OpenCV approach shows: (1) a 37% increase in detection rates if constrained by the requirement of no increase in false alarms, (2) a 48% increase in detection rates if some additional false alarms are tolerated, and (3) an 82% reduction in false alarms with no reduction in detection rates. These results demonstrate improved face detection and could address the need for such improvement in various applications.

Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller.

PubMed

Cruz, Aniana; Pires, Gabriel; Nunes, Urbano J

2018-01-01

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.

On-line early fault detection and diagnosis of municipal solid waste incinerators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao Jinsong; Huang Jianchao; Sun Wei

A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows thatmore » automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI.« less

Low cost charged-coupled device (CCD) based detectors for Shiga toxins activity analysis

USDA-ARS?s Scientific Manuscript database

To improve food safety there is a need to develop simple, low-cost sensitive devices for detection of foodborne pathogens and their toxins. We describe a simple and relatively low-cost webcam-based detector which can be used for various optical detection modalities, including fluorescence, chemilumi...

"Dip-and-read" paper-based analytical devices using distance-based detection with color screening.

PubMed

Yamada, Kentaro; Citterio, Daniel; Henry, Charles S

2018-05-15

An improved paper-based analytical device (PAD) using color screening to enhance device performance is described. Current detection methods for PADs relying on the distance-based signalling motif can be slow due to the assay time being limited by capillary flow rates that wick fluid through the detection zone. For traditional distance-based detection motifs, analysis can take up to 45 min for a channel length of 5 cm. By using a color screening method, quantification with a distance-based PAD can be achieved in minutes through a "dip-and-read" approach. A colorimetric indicator line deposited onto a paper substrate using inkjet-printing undergoes a concentration-dependent colorimetric response for a given analyte. This color intensity-based response has been converted to a distance-based signal by overlaying a color filter with a continuous color intensity gradient matching the color of the developed indicator line. As a proof-of-concept, Ni quantification in welding fume was performed as a model assay. The results of multiple independent user testing gave mean absolute percentage error and average relative standard deviations of 10.5% and 11.2% respectively, which were an improvement over analysis based on simple visual color comparison with a read guide (12.2%, 14.9%). In addition to the analytical performance comparison, an interference study and a shelf life investigation were performed to further demonstrate practical utility. The developed system demonstrates an alternative detection approach for distance-based PADs enabling fast (∼10 min), quantitative, and straightforward assays.

Evidence-Based Assessment in Case Management to Improve Abnormal Cancer Screen Follow-Up

ERIC Educational Resources Information Center

Vourlekis, Betsy; Ell, Kathleen; Padgett, Deborah

2005-01-01

The authors describe an evidence-based assessment protocol for intensive case management to improve screening diagnostic follow-up developed through a research project in breast and cervical cancer early detection funded by the Centers for Disease Control and Prevention. Three components of an evidence-based approach to assessment are presented…

Research about Memory Detection Based on the Embedded Platform

NASA Astrophysics Data System (ADS)

Sun, Hao; Chu, Jian

As is known to us all, the resources of memory detection of the embedded systems are very limited. Taking the Linux-based embedded arm as platform, this article puts forward two efficient memory detection technologies according to the characteristics of the embedded software. Especially for the programs which need specific libraries, the article puts forwards portable memory detection methods to help program designers to reduce human errors,improve programming quality and therefore make better use of the valuable embedded memory resource.

Adaptation of red blood cell lysis represents a fundamental breakthrough that improves the sensitivity of Salmonella detection in blood

PubMed Central

Boyd, MA; Tennant, SM; Melendez, JH; Toema, D; Galen, JE; Geddes, CD; Levine, MM

2015-01-01

Aims Isolation of Salmonella Typhi from blood culture is the standard diagnostic for confirming typhoid fever but it is unavailable in many developing countries. We previously described a Microwave Accelerated Metal Enhanced Fluorescence (MAMEF)-based assay to detect Salmonella in medium. Attempts to detect Salmonella in blood were unsuccessful, presumably due to the interference of erythrocytes. The objective of this study was to evaluate various blood treatment methods that could be used prior to PCR, real-time PCR or MAMEF to increase sensitivity of detection of Salmonella. Methods and Results We tested ammonium chloride and erythrocyte lysis buffer, water, Lymphocyte Separation Medium, BD Vacutainer® CPT™ Tubes and dextran. Erythrocyte lysis buffer was the best isolation method as it is fast, inexpensive and works with either fresh or stored blood. The sensitivity of PCR- and real-time PCR detection of Salmonella in spiked blood was improved when whole blood was first lysed using erythrocyte lysis buffer prior to DNA extraction. Removal of erythrocytes and clotting factors also enabled reproducible lysis of Salmonella and fragmentation of DNA, which are necessary for MAMEF sensing. Conclusions Use of the erythrocyte lysis procedure prior to DNA extraction has enabled improved sensitivity of Salmonella detection by PCR and real-time PCR and has allowed lysis and fragmentation of Salmonella using microwave radiation (for future detection by MAMEF). Significance and Impact of the Study Adaptation of the blood lysis method represents a fundamental breakthrough that improves the sensitivity of DNA-based detection of Salmonella in blood. PMID:25630831

UV plasmonic device for sensing ethanol and acetone

NASA Astrophysics Data System (ADS)

Honda, Mitsuhiro; Ichikawa, Yo; Rozhin, Alex G.; Kulinich, Sergei A.

2018-01-01

In the present study, we demonstrate efficient detection of volatile organic vapors with improved sensitivity, exploiting the localized surface plasmon resonance of indium nanograins in the UV range (UV-LSPR). The sensitivity of deep-UV-LSPR measurements toward ethanol was observed to be 0.004 nm/ppm, which is 10 times higher than that of a previously reported visible-LSPR device based on Ag nanoprisms [Sensors 11, 8643 (2011)]. Although practical issues such as improving detection limits are still remaining, the results of the present study suggest that the new approach based on UV-LSPR may open new avenues to the detection of organic molecules in solid, liquid, and gas phases using plasmonic sensors.

THz QCL-Based Cryogen-Free Spectrometer for in Situ Trace Gas Sensing

PubMed Central

Consolino, Luigi; Bartalini, Saverio; Beere, Harvey E.; Ritchie, David A.; Vitiello, Miriam Serena; De Natale, Paolo

2013-01-01

We report on a set of high-sensitivity terahertz spectroscopy experiments making use of QCLs to detect rotational molecular transitions in the far-infrared. We demonstrate that using a compact and transportable cryogen-free setup, based on a quantum cascade laser in a closed-cycle Stirling cryostat, and pyroelectric detectors, a considerable improvement in sensitivity can be obtained by implementing a wavelength modulation spectroscopy technique. Indeed, we show that the sensitivity of methanol vapour detection can be improved by a factor ≈ 4 with respect to standard direct absorption approaches, offering perspectives for high sensitivity detection of a number of chemical compounds across the far-infrared spectral range. PMID:23478601

THz QCL-based cryogen-free spectrometer for in situ trace gas sensing.

PubMed

Consolino, Luigi; Bartalini, Saverio; Beere, Harvey E; Ritchie, David A; Vitiello, Miriam Serena; De Natale, Paolo

2013-03-11

We report on a set of high-sensitivity terahertz spectroscopy experiments making use of QCLs to detect rotational molecular transitions in the far-infrared. We demonstrate that using a compact and transportable cryogen-free setup, based on a quantum cascade laser in a closed-cycle Stirling cryostat, and pyroelectric detectors, a considerable improvement in sensitivity can be obtained by implementing a wavelength modulation spectroscopy technique. Indeed, we show that the sensitivity of methanol vapour detection can be improved by a factor ≈ 4 with respect to standard direct absorption approaches, offering perspectives for high sensitivity detection of a number of chemical compounds across the far-infrared spectral range.

The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy.

PubMed

Fleming, Alan D; Goatman, Keith A; Philip, Sam; Williams, Graeme J; Prescott, Gordon J; Scotland, Graham S; McNamee, Paul; Leese, Graham P; Wykes, William N; Sharp, Peter F; Olson, John A

2010-06-01

Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy. Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection. Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload. Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.

Terahertz wave electro-optic measurements with optical spectral filtering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ilyakov, I. E., E-mail: igor-ilyakov@mail.ru; Shishkin, B. V.; Kitaeva, G. Kh.

We propose electro-optic detection techniques based on variations of the laser pulse spectrum induced during pulse co-propagation with terahertz wave radiation in a nonlinear crystal. Quantitative comparison with two other detection methods is made. Substantial improvement of the sensitivity compared to the standard electro-optic detection technique (at high frequencies) and to the previously shown technique based on laser pulse energy changes is demonstrated in experiment.

Unsupervised iterative detection of land mines in highly cluttered environments.

PubMed

Batman, Sinan; Goutsias, John

2003-01-01

An unsupervised iterative scheme is proposed for land mine detection in heavily cluttered scenes. This scheme is based on iterating hybrid multispectral filters that consist of a decorrelating linear transform coupled with a nonlinear morphological detector. Detections extracted from the first pass are used to improve results in subsequent iterations. The procedure stops after a predetermined number of iterations. The proposed scheme addresses several weaknesses associated with previous adaptations of morphological approaches to land mine detection. Improvement in detection performance, robustness with respect to clutter inhomogeneities, a completely unsupervised operation, and computational efficiency are the main highlights of the method. Experimental results reveal excellent performance.

Note: An improved 3D imaging system for electron-electron coincidence measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Yun Fei; Lee, Suk Kyoung; Adhikari, Pradip

We demonstrate an improved imaging system that can achieve highly efficient 3D detection of two electrons in coincidence. The imaging system is based on a fast frame complementary metal-oxide semiconductor camera and a high-speed waveform digitizer. We have shown previously that this detection system is capable of 3D detection of ions and electrons with good temporal and spatial resolution. Here, we show that with a new timing analysis algorithm, this system can achieve an unprecedented dead-time (<0.7 ns) and dead-space (<1 mm) when detecting two electrons. A true zero dead-time detection is also demonstrated.

Note: An improved 3D imaging system for electron-electron coincidence measurements

NASA Astrophysics Data System (ADS)

Lin, Yun Fei; Lee, Suk Kyoung; Adhikari, Pradip; Herath, Thushani; Lingenfelter, Steven; Winney, Alexander H.; Li, Wen

2015-09-01

We demonstrate an improved imaging system that can achieve highly efficient 3D detection of two electrons in coincidence. The imaging system is based on a fast frame complementary metal-oxide semiconductor camera and a high-speed waveform digitizer. We have shown previously that this detection system is capable of 3D detection of ions and electrons with good temporal and spatial resolution. Here, we show that with a new timing analysis algorithm, this system can achieve an unprecedented dead-time (<0.7 ns) and dead-space (<1 mm) when detecting two electrons. A true zero dead-time detection is also demonstrated.

Research on improved edge extraction algorithm of rectangular piece

NASA Astrophysics Data System (ADS)

He, Yi-Bin; Zeng, Ya-Jun; Chen, Han-Xin; Xiao, San-Xia; Wang, Yan-Wei; Huang, Si-Yu

Traditional edge detection operators such as Prewitt operator, LOG operator and Canny operator, etc. cannot meet the requirements of the modern industrial measurement. This paper proposes a kind of image edge detection algorithm based on improved morphological gradient. It can be detect the image using structural elements, which deals with the characteristic information of the image directly. Choosing different shapes and sizes of structural elements to use together, the ideal image edge information can be detected. The experimental result shows that the algorithm can well extract image edge with noise, which is clearer, and has more detailed edges compared with the previous edge detection algorithm.

Rapid surface defect detection based on singular value decomposition using steel strips as an example

NASA Astrophysics Data System (ADS)

Sun, Qianlai; Wang, Yin; Sun, Zhiyi

2018-05-01

For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection. Experimental results confirm its validity.

Improvement in QEPAS system utilizing a second harmonic based wavelength calibration technique

NASA Astrophysics Data System (ADS)

Zhang, Qinduan; Chang, Jun; Wang, Fupeng; Wang, Zongliang; Xie, Yulei; Gong, Weihua

2018-05-01

A simple laser wavelength calibration technique, based on second harmonic signal, is demonstrated in this paper to improve the performance of quartz enhanced photoacoustic spectroscopy (QEPAS) gas sensing system, e.g. improving the signal to noise ratio (SNR), detection limit and long-term stability. Constant current, corresponding to the gas absorption line, combining f/2 frequency sinusoidal signal are used to drive the laser (constant driving mode), a software based real-time wavelength calibration technique is developed to eliminate the wavelength drift due to ambient fluctuations. Compared to conventional wavelength modulation spectroscopy (WMS), this method allows lower filtering bandwidth and averaging algorithm applied to QEPAS system, improving SNR and detection limit. In addition, the real-time wavelength calibration technique guarantees the laser output is modulated steadily at gas absorption line. Water vapor is chosen as an objective gas to evaluate its performance compared to constant driving mode and conventional WMS system. The water vapor sensor was designed insensitive to the incoherent external acoustic noise by the numerical averaging technique. As a result, the SNR increases 12.87 times in wavelength calibration technique based system compared to conventional WMS system. The new system achieved a better linear response (R2 = 0 . 9995) in concentration range from 300 to 2000 ppmv, and achieved a minimum detection limit (MDL) of 630 ppbv.

Rapid detection of Salmonella in pet food: design and evaluation of integrated methods based on real-time PCR detection.

PubMed

Balachandran, Priya; Friberg, Maria; Vanlandingham, V; Kozak, K; Manolis, Amanda; Brevnov, Maxim; Crowley, Erin; Bird, Patrick; Goins, David; Furtado, Manohar R; Petrauskene, Olga V; Tebbs, Robert S; Charbonneau, Duane

2012-02-01

Reducing the risk of Salmonella contamination in pet food is critical for both companion animals and humans, and its importance is reflected by the substantial increase in the demand for pathogen testing. Accurate and rapid detection of foodborne pathogens improves food safety, protects the public health, and benefits food producers by assuring product quality while facilitating product release in a timely manner. Traditional culture-based methods for Salmonella screening are laborious and can take 5 to 7 days to obtain definitive results. In this study, we developed two methods for the detection of low levels of Salmonella in pet food using real-time PCR: (i) detection of Salmonella in 25 g of dried pet food in less than 14 h with an automated magnetic bead-based nucleic acid extraction method and (ii) detection of Salmonella in 375 g of composite dry pet food matrix in less than 24 h with a manual centrifugation-based nucleic acid preparation method. Both methods included a preclarification step using a novel protocol that removes food matrix-associated debris and PCR inhibitors and improves the sensitivity of detection. Validation studies revealed no significant differences between the two real-time PCR methods and the standard U.S. Food and Drug Administration Bacteriological Analytical Manual (chapter 5) culture confirmation method.

A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

PubMed

Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

2016-09-01

Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05). Copyright © 2016 Elsevier Ltd. All rights reserved.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

PubMed Central

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-01-01

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography (FFDM) images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793±0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions. PMID:27997380

Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

PubMed

Li, Ye; Whelan, Michael; Hobbs, Leigh; Fan, Wen Qi; Fung, Cecilia; Wong, Kenny; Marchand-Austin, Alex; Badiani, Tina; Johnson, Ian

2016-06-27

In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention. A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease. Historical data were used as baseline to assess known outbreaks. Regression models were used to model seasonality and CUSUM was applied to the difference between observed and expected counts. An interactive web application was developed allowing program staff to directly interact with data and tune the parameters of CUSUM algorithms using their expertise on the epidemiology of each disease. Using these parameters, a CUSUM detection system was applied prospectively and the results were compared to the outputs generated by EARS. The outcome was the detection of outbreaks, or the start of a known seasonal increase and predicting the peak in activity. The CUSUM algorithms detected provincial outbreaks earlier than the EARS algorithm, identified the start of the influenza season in advance of traditional methods, and had fewer false positive alerts. Additionally, having staff involved in the creation of the algorithms improved their understanding of the algorithms and improved use in practice. Using interactive web-based technology to tune CUSUM improved the sensitivity and specificity of detection algorithms.

A highly selective and simple fluorescent sensor for mercury (II) ion detection based on cysteamine-capped CdTe quantum dots synthesized by the reflux method.

PubMed

Ding, Xiaojie; Qu, Lingbo; Yang, Ran; Zhou, Yuchen; Li, Jianjun

2015-06-01

Cysteamine (CA)-capped CdTe quantum dots (QDs) (CA-CdTe QDs) were prepared by the reflux method and utilized as an efficient nano-sized fluorescent sensor to detect mercury (II) ions (Hg(2+) ). Under optimum conditions, the fluorescence quenching effect of CA-CdTe QDs was linear at Hg(2+) concentrations in the range of 6.0-450 nmol/L. The detection limit was calculated to be 4.0 nmol/L according to the 3σ IUPAC criteria. The influence of 10-fold Pb(2+) , Cu(2+) and Ag(+) on the determination of Hg(2+) was < 7% (superior to other reports based on crude QDs). Furthermore, the detection sensitivity and selectivity were much improved relative to a sensor based on the CA-CdTe QDs probe, which was prepared using a one-pot synthetic method. This CA-CdTe QDs sensor system represents a new feasibility to improve the detection performance of a QDs sensor by changing the synthesis method. Copyright © 2014 John Wiley & Sons, Ltd.

An improved NSGA - II algorithm for mixed model assembly line balancing

NASA Astrophysics Data System (ADS)

Wu, Yongming; Xu, Yanxia; Luo, Lifei; Zhang, Han; Zhao, Xudong

2018-05-01

Aiming at the problems of assembly line balancing and path optimization for material vehicles in mixed model manufacturing system, a multi-objective mixed model assembly line (MMAL), which is based on optimization objectives, influencing factors and constraints, is established. According to the specific situation, an improved NSGA-II algorithm based on ecological evolution strategy is designed. An environment self-detecting operator, which is used to detect whether the environment changes, is adopted in the algorithm. Finally, the effectiveness of proposed model and algorithm is verified by examples in a concrete mixing system.

Method for oil pipeline leak detection based on distributed fiber optic technology

NASA Astrophysics Data System (ADS)

Chen, Huabo; Tu, Yaqing; Luo, Ting

1998-08-01

Pipeline leak detection is a difficult problem to solve up to now. Some traditional leak detection methods have such problems as high rate of false alarm or missing detection, low location estimate capability. For the problems given above, a method for oil pipeline leak detection based on distributed optical fiber sensor with special coating is presented. The fiber's coating interacts with hydrocarbon molecules in oil, which alters the refractive indexed of the coating. Therefore the light-guiding properties of the fiber are modified. Thus pipeline leak location can be determined by OTDR. Oil pipeline lead detection system is designed based on the principle. The system has some features like real time, multi-point detection at the same time and high location accuracy. In the end, some factors that probably influence detection are analyzed and primary improving actions are given.

Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

NASA Astrophysics Data System (ADS)

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Qian, Wei; Zheng, Bin

2016-03-01

Current commercialized CAD schemes have high false-positive (FP) detection rates and also have high correlations in positive lesion detection with radiologists. Thus, we recently investigated a new approach to improve the efficacy of applying CAD to assist radiologists in reading and interpreting screening mammograms. Namely, we developed a new global feature based CAD approach/scheme that can cue the warning sign on the cases with high risk of being positive. In this study, we investigate the possibility of fusing global feature or case-based scores with the local or lesion-based CAD scores using an adaptive cueing method. We hypothesize that the information from the global feature extraction (features extracted from the whole breast regions) are different from and can provide supplementary information to the locally-extracted features (computed from the segmented lesion regions only). On a large and diverse full-field digital mammography (FFDM) testing dataset with 785 cases (347 negative and 438 cancer cases with masses only), we ran our lesion-based and case-based CAD schemes "as is" on the whole dataset. To assess the supplementary information provided by the global features, we used an adaptive cueing method to adaptively adjust the original CAD-generated detection scores (Sorg) of a detected suspicious mass region based on the computed case-based score (Scase) of the case associated with this detected region. Using the adaptive cueing method, better sensitivity results were obtained at lower FP rates (<= 1 FP per image). Namely, increases of sensitivities (in the FROC curves) of up to 6.7% and 8.2% were obtained for the ROI and Case-based results, respectively.

B-Spline Filtering for Automatic Detection of Calcification Lesions in Mammograms

NASA Astrophysics Data System (ADS)

Bueno, G.; Sánchez, S.; Ruiz, M.

2006-10-01

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Waveform design for detection of weapons based on signature exploitation

NASA Astrophysics Data System (ADS)

Ahmad, Fauzia; Amin, Moeness G.; Dogaru, Traian

2010-04-01

We present waveform design based on signature exploitation techniques for improved detection of weapons in urban sensing applications. A single-antenna monostatic radar system is considered. Under the assumption of exact knowledge of the target orientation and, hence, known impulse response, matched illumination approach is used for optimal target detection. For the case of unknown target orientation, we analyze the target signatures as random processes and perform signal-to-noise-ratio based waveform optimization. Numerical electromagnetic modeling is used to provide the impulse responses of an AK-47 assault rifle for various target aspect angles relative to the radar. Simulation results depict an improvement in the signal-to-noise-ratio at the output of the matched filter receiver for both matched illumination and stochastic waveforms as compared to a chirp waveform of the same duration and energy.

An ensemble deep learning based approach for red lesion detection in fundus images.

PubMed

Orlando, José Ignacio; Prokofyeva, Elena; Del Fresno, Mariana; Blaschko, Matthew B

2018-01-01

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our system is publicly available at https://github.com/ignaciorlando/red-lesion-detection. Copyright © 2017 Elsevier B.V. All rights reserved.

Laser-Induced Breakdown Spectroscopy: A Review of Applied Explosive Detection

DTIC Science & Technology

2013-09-01

Based Techniques ..........................................................................................7 2.5 Ion Mobility and Mass Spectrometry...proximal trace detection. We show that the algorithms for material identification could be improved by including the critical signatures (e.g., C2...IMS), desorption electrospray ionization (DESI), laser electrospray mass spectrometry (LEMS), emerging efforts like antibody/antigen-based efforts

A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

PubMed

Raman, M R Gauthama; Somu, Nivethitha; Kirthivasan, Kannan; Sriram, V S Shankar

2017-08-01

Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS. The Helly property of Hypergraph was exploited for the identification of the optimal feature subset and the arithmetic residue of the optimal feature subset was used to train the PNN. The performance of HG AR-PNN was evaluated using KDD CUP 1999 intrusion dataset. Experimental results prove the dominance of HG AR-PNN classifier over the existing classifiers with respect to the stability and improved detection rate for less frequent attacks. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2014-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan Walker

2015-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

Research on vehicle detection based on background feature analysis in SAR images

NASA Astrophysics Data System (ADS)

Zhang, Bochuan; Tang, Bo; Zhang, Cong; Hu, Ruiguang; Yun, Hongquan; Xiao, Liping

2017-10-01

Aiming at vehicle detection on the ground through low resolution SAR images, a method is proposed for determining the region of the vehicles first and then detecting the target in the specific region. The experimental results show that this method not only reduces the target detection area, but also reduces the influence of terrain clutter on the detection, which greatly improves the reliability of the target detection.

Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs

NASA Technical Reports Server (NTRS)

Bloomberg, Jacob; Reschke, Millard; Mulavara, Ajitkumar; Wood, Scott; Serrador, Jorge; Fiedler, Matthew; Kofman, Igor; Peters, Brian T.; Cohen, Helen

2012-01-01

Crewmembers returning from long-duration space flight face significant challenges due to the microgravity-induced inappropriate adaptations in balance/ sensorimotor function. The Neuroscience Laboratory at JSC is developing a method based on stochastic resonance to enhance the brain s ability to detect signals from the balance organs of the inner ear and use them for rapid improvement in balance skill, especially when combined with balance training exercises. This method involves a stimulus delivery system that is wearable/portable providing imperceptible electrical stimulation to the balance organs of the human body. Stochastic resonance (SR) is a phenomenon whereby the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular non-zero level of noise. This phenomenon of SR is based on the concept of maximizing the flow of information through a system by a non-zero level of noise. Application of imperceptible SR noise coupled with sensory input in humans has been shown to improve motor, cardiovascular, visual, hearing, and balance functions. SR increases contrast sensitivity and luminance detection; lowers the absolute threshold for tone detection in normal hearing individuals; improves homeostatic function in the human blood pressure regulatory system; improves noise-enhanced muscle spindle function; and improves detection of weak tactile stimuli using mechanical or electrical stimulation. SR noise has been shown to improve postural control when applied as mechanical noise to the soles of the feet, or when applied as electrical noise at the knee and to the back muscles.

A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare

PubMed Central

Huang, Chih-Ning; Chan, Chia-Tai

2014-01-01

Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person. PMID:24743841

An improved Abbott ARCHITECT assay for the detection of hepatitis B virus surface antigen (HBsAg).

PubMed

Lou, Sheng C; Pearce, Sandra K; Lukaszewska, Teresa X; Taylor, Russell E; Williams, Gregg T; Leary, Thomas P

2011-05-01

The sensitive and accurate detection of hepatitis B virus surface antigen (HBsAg) is critical to the identification of infection and the prevention of transfusion transmitted disease. Improvement in HBsAg assay sensitivity is essential to reduce the window to detect an acute HBV infection. Additionally, the sensitive detection of HBsAg mutants that continue to evolve due to vaccine escape, immune selection and an error prone reverse transcriptase is a necessity. A fully automated HBsAg prototype assay on the Abbott ARCHITECT instrument was developed to improve sensitivity and mutant detection. This magnetic microparticle-based assay utilizes anti-HBsAg monoclonal antibodies to capture antigen present in serum or plasma. Captured antigen is then detected using anti-HBsAg antibody conjugated with the chemiluminescent compound, acridinium. The sensitivity of the ARCHITECT HBsAg prototype assay was improved as compared to the current ARCHITECT, PRISM, and competitor HBsAg assays. The enhancement in assay sensitivity was demonstrated by the use of commercially available HBV seroconversion panels. The prototype assay detected more panel members (185 of 383) vs. the current ARCHITECT (171), PRISM (181), or competitor HBsAg assays (73/140 vs. 62/140, respectively). The ARCHITECT prototype assay also efficiently detected all mutants evaluated. Finally, the sensitivity improvement did not compromise the specificity of the assay (99.94%). An improved Abbott ARCHITECT HBsAg prototype assay with enhanced detection of HBsAg and HBsAg mutants, as well as equivalent specificity was developed for the detection, diagnosis, and management of HBV infection. Copyright © 2011 Elsevier B.V. All rights reserved.

Binaural comodulation masking release: Effects of masker interaural correlation

PubMed Central

Hall, Joseph W.; Buss, Emily; Grose, John H.

2007-01-01

Binaural detection was examined for a signal presented in a narrow band of noise centered on the on-signal masking band (OSB) or in the presence of flanking noise bands that were random or comodulated with respect to the OSB. The noise had an interaural correlation of 1.0 (No), 0.99 or 0.95. In No noise, random flanking bands worsened Sπ detection and comodulated bands improved Sπ detection for some listeners but had no effect for other listeners. For the 0.99 or 0.95 interaural correlation conditions, random flanking bands were less detrimental to Sπ detection and comodulated flanking bands improved Sπ detection for all listeners. Analyses based on signal detection theory indicated that the improvement in Sπ thresholds obtained with comodulated bands was not compatible with an optimal combination of monaural and binaural cues or to across-frequency analyses of dynamic interaural phase differences. Two accounts consistent with the improvement in Sπ thresholds in comodulated noise were (1) envelope information carried by the flanking bands improves the weighting of binaural cues associated with the signal; (2) the auditory system is sensitive to across-frequency differences in ongoing interaural correlation. PMID:17225415

Radar fall detection using principal component analysis

NASA Astrophysics Data System (ADS)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Optimized feature-detection for on-board vision-based surveillance

NASA Astrophysics Data System (ADS)

Gond, Laetitia; Monnin, David; Schneider, Armin

2012-06-01

The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.

Combining spatial and spectral information to improve crop/weed discrimination algorithms

NASA Astrophysics Data System (ADS)

Yan, L.; Jones, G.; Villette, S.; Paoli, J. N.; Gée, C.

2012-01-01

Reduction of herbicide spraying is an important key to environmentally and economically improve weed management. To achieve this, remote sensors such as imaging systems are commonly used to detect weed plants. We developed spatial algorithms that detect the crop rows to discriminate crop from weeds. These algorithms have been thoroughly tested and provide robust and accurate results without learning process but their detection is limited to inter-row areas. Crop/Weed discrimination using spectral information is able to detect intra-row weeds but generally needs a prior learning process. We propose a method based on spatial and spectral information to enhance the discrimination and overcome the limitations of both algorithms. The classification from the spatial algorithm is used to build the training set for the spectral discrimination method. With this approach we are able to improve the range of weed detection in the entire field (inter and intra-row). To test the efficiency of these algorithms, a relevant database of virtual images issued from SimAField model has been used and combined to LOPEX93 spectral database. The developed method based is evaluated and compared with the initial method in this paper and shows an important enhancement from 86% of weed detection to more than 95%.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Yanmei; Li, Xinli; Bai, Yan

The measurement of multiphase flow parameters is of great importance in a wide range of industries. In the measurement of multiphase, the signals from the sensors are extremely weak and often buried in strong background noise. It is thus desirable to develop effective signal processing techniques that can detect the weak signal from the sensor outputs. In this paper, two methods, i.e., lock-in-amplifier (LIA) and improved Duffing chaotic oscillator are compared to detect and process the weak signal. For sinusoidal signal buried in noise, the correlation detection with sinusoidal reference signal is simulated by using LIA. The improved Duffing chaoticmore » oscillator method, which based on the Wigner transformation, can restore the signal waveform and detect the frequency. Two methods are combined to detect and extract the weak signal. Simulation results show the effectiveness and accuracy of the proposed improved method. The comparative analysis shows that the improved Duffing chaotic oscillator method can restrain noise strongly since it is sensitive to initial conditions.« less

Research on capability of detecting ballistic missile by near space infrared system

NASA Astrophysics Data System (ADS)

Lu, Li; Sheng, Wen; Jiang, Wei; Jiang, Feng

2018-01-01

The infrared detection technology of ballistic missile based on near space platform can effectively make up the shortcomings of high-cost of traditional early warning satellites and the limited earth curvature of ground-based early warning radar. In terms of target detection capability, aiming at the problem that the formula of the action distance based on contrast performance ignores the background emissivity in the calculation process and the formula is only valid for the monochromatic light, an improved formula of the detecting range based on contrast performance is proposed. The near space infrared imaging system parameters are introduced, the expression of the contrastive action distance formula based on the target detection of the near space platform is deduced. The detection range of the near space infrared system for the booster stage ballistic missile skin, the tail nozzle and the tail flame is calculated. The simulation results show that the near-space infrared system has the best effect on the detection of tail-flame radiation.

Management approaches for suspected and established Lyme disease used at the Lyme disease diagnostic center.

PubMed

Wormser, Gary P; McKenna, Donna; Nowakowski, John

2016-01-14

2015 marks the 27th year that the Lyme Disease Diagnostic Center, located in New York State in the United States, has provided care for patients with suspected or established deer tick-transmitted infections. There are five deer tick-transmitted infectious in this geographic area of which Lyme disease is the most common.For patients with erythema migrans, we do not obtain any laboratory testing. However, if the patient is febrile at the time of the visit or reports rigors and high-grade fevers, we consider the possibility of a co-infection and order pertinent laboratory tests.Our preferred management for Lyme disease-related facial palsy and/or radiculopathy is a 2-week course of doxycycline. Patients who are hospitalized for Lyme meningitis are usually treated at least initially with ceftriaxone. We have not seen convincing cases of encephalitis or myelitis solely due to Borrelia burgdorferi infection in the absence of laboratory evidence of concomitant deer tick virus infection (Powassan virus). We have also never seen Lyme encephalopathy or a diffuse axonal peripheral neuropathy and suggest that these entities are either very rare or nonexistent.We have found that Lyme disease rarely presents with fever without other objective clinical manifestations. Prior cases attributed to Lyme disease may have overlooked an asymptomatic erythema migrans skin lesion or the diagnosis may have been based on nonspecific IgM seroreactivity. More research is needed on the appropriate management and significance of IgG seropositivity in asymptomatic patients who have no history of Lyme disease.

Higher frequencies of HLA DQB1*05:01 and anti-glycosphingolipid antibodies in a cluster of severe Guillain-Barré syndrome.

PubMed

Schirmer, L; Worthington, V; Solloch, U; Loleit, V; Grummel, V; Lakdawala, N; Grant, D; Wassmuth, R; Schmidt, A H; Gebhardt, F; Andlauer, T F M; Sauter, J; Berthele, A; Lunn, M P; Hemmer, Bernhard

2016-10-01

Few regional and seasonal Guillain-Barré syndrome (GBS) clusters have been reported so far. It is unknown whether patients suffering from sporadic GBS differ from GBS clusters with respect to clinical and paraclinical parameters, HLA association and antibody response to glycosphingolipids and Campylobacter jejuni (Cj). We examined 40 consecutive patients with GBS from the greater Munich area in Germany with 14 of those admitted within a period of 3 months in fall 2010 defining a cluster of GBS. Sequencing-based HLA typing of the HLA genes DRB1, DQB1, and DPB1 was performed, and ELISA for anti-glycosphingolipid antibodies was carried out. Clinical and paraclinical findings (Cj seroreactivity, cerebrospinal fluid parameters, and electrophysiology) were obtained and analyzed. GBS cluster patients were characterized by a more severe clinical phenotype with more patients requiring mechanical ventilation and higher frequencies of autoantibodies against sulfatide, GalC and certain ganglioside epitopes (54 %) as compared to sporadic GBS cases (13 %, p = 0.017). Cj seropositivity tended to be higher within GBS cluster patients (69 %) as compared to sporadic cases (46 %, p = 0.155). We noted higher frequencies of HLA class II allele DQB1*05:01 in the cluster cohort (23 %) as compared to sporadic GBS patients (3 %, p = 0.019). Cluster of severe GBS was defined by higher frequencies of autoantibodies against glycosphingolipids. HLA class II allele DQB1*05:01 might contribute to clinical worsening in the cluster patients.

Improving adaptive/responsive signal control performance : implications of non-invasive detection and legacy timing practices : final report.

DOT National Transportation Integrated Search

2017-02-01

This project collected and analyzed event based vehicle detection data from multiple technologies at four different sites across Oregon to provide guidance for deployment of non-invasive detection for use in adaptive control, as well as develop a tru...

Natural gas pipeline leak detector based on NIR diode laser absorption spectroscopy.

PubMed

Gao, Xiaoming; Fan, Hong; Huang, Teng; Wang, Xia; Bao, Jian; Li, Xiaoyun; Huang, Wei; Zhang, Weijun

2006-09-01

The paper reports on the development of an integrated natural gas pipeline leak detector based on diode laser absorption spectroscopy. The detector transmits a 1.653 microm DFB diode laser with 10 mW and detects a fraction of the backscatter reflected from the topographic targets. To eliminate the effect of topographic scatter targets, a ratio detection technique was used. Wavelength modulation and harmonic detection were used to improve the detection sensitivity. The experimental detection limit is 50 ppmm, remote detection for a distance up to 20 m away topographic scatter target is demonstrated. Using a known simulative leak pipe, minimum detectable pipe leak flux is less than 10 ml/min.

Systematic evaluation of deep learning based detection frameworks for aerial imagery

NASA Astrophysics Data System (ADS)

Sommer, Lars; Steinmann, Lucas; Schumann, Arne; Beyerer, Jürgen

2018-04-01

Object detection in aerial imagery is crucial for many applications in the civil and military domain. In recent years, deep learning based object detection frameworks significantly outperformed conventional approaches based on hand-crafted features on several datasets. However, these detection frameworks are generally designed and optimized for common benchmark datasets, which considerably differ from aerial imagery especially in object sizes. As already demonstrated for Faster R-CNN, several adaptations are necessary to account for these differences. In this work, we adapt several state-of-the-art detection frameworks including Faster R-CNN, R-FCN, and Single Shot MultiBox Detector (SSD) to aerial imagery. We discuss adaptations that mainly improve the detection accuracy of all frameworks in detail. As the output of deeper convolutional layers comprise more semantic information, these layers are generally used in detection frameworks as feature map to locate and classify objects. However, the resolution of these feature maps is insufficient for handling small object instances, which results in an inaccurate localization or incorrect classification of small objects. Furthermore, state-of-the-art detection frameworks perform bounding box regression to predict the exact object location. Therefore, so called anchor or default boxes are used as reference. We demonstrate how an appropriate choice of anchor box sizes can considerably improve detection performance. Furthermore, we evaluate the impact of the performed adaptations on two publicly available datasets to account for various ground sampling distances or differing backgrounds. The presented adaptations can be used as guideline for further datasets or detection frameworks.

Improving membrane based multiplex immunoassays for semi-quantitative detection of multiple cytokines in a single sample

PubMed Central

2014-01-01

Background Inflammatory mediators can serve as biomarkers for the monitoring of the disease progression or prognosis in many conditions. In the present study we introduce an adaptation of a membrane-based technique in which the level of up to 40 cytokines and chemokines can be determined in both human and rodent blood in a semi-quantitative way. The planar assay was modified using the LI-COR (R) detection system (fluorescence based) rather than chemiluminescence and semi-quantitative outcomes were achieved by normalizing the outcomes using the automated exposure settings of the Odyssey readout device. The results were compared to the gold standard assay, namely ELISA. Results The improved planar assay allowed the detection of a considerably higher number of analytes (n = 30 and n = 5 for fluorescent and chemiluminescent detection, respectively). The improved planar method showed high sensitivity up to 17 pg/ml and a linear correlation of the normalized fluorescence intensity with the results from the ELISA (r = 0.91). Conclusions The results show that the membrane-based technique is a semi-quantitative assay that correlates satisfactorily to the gold standard when enhanced by the use of fluorescence and subsequent semi-quantitative analysis. This promising technique can be used to investigate inflammatory profiles in multiple conditions, particularly in studies with constraints in sample sizes and/or budget. PMID:25022797

Building Change Detection from LIDAR Point Cloud Data Based on Connected Component Analysis

NASA Astrophysics Data System (ADS)

Awrangjeb, M.; Fraser, C. S.; Lu, G.

2015-08-01

Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

Improved detection sensitivity of γ-aminobutyric acid based on graphene oxide interface on an optical microfiber.

PubMed

Zhou, Jun; Huang, Yunyun; Chen, Chaoyan; Xiao, Aoxiang; Guo, Tuan; Guan, Bai-Ou

2018-05-11

Interfacing bio-recognition elements to optical materials is a longstanding challenge to manufacture sensitive biosensors and inexpensive diagnostic devices. In this work, a graphene oxide (GO) interface has been constructed between silica microfiber and bio-recognition elements to develop an improved γ-aminobutyric acid (GABA) sensing approach. The GO interface, which was located at the site with the strongest evanescent field on the microfiber surface, improved the detection sensitivity by providing a larger platform for more bio-recognition element immobilization, and amplifying surface refractive index change caused by combination between bio-recognition elements and target molecules. Owing to the interface improvement, the microfiber has a three times improved sensitivity of 1.03 nm/log M for GABA detection, and hence a lowest limit of detection of 2.91 × 10-18 M, which is 7 orders of magnitude higher than that without the GO interface. Moreover, the micrometer-sized footprint and non-radioactive nature enable easy implantation in human brains for in vivo applications.

A Videotape-Based Training Method for Improving the Detection of Depression in Residents of Long-Term Care Facilities

ERIC Educational Resources Information Center

Wood, Stacey; Cummings, Jeffrey L.; Schnelle, Betha; Stephens, Mary

2002-01-01

Purpose: This article reviews the effectiveness of a new training program for improving nursing staffs' detection of depression within long-term care facilities. The course was designed to increase recognition of the Minimal Data Set (MDS) Mood Trigger items, to be brief, and to rely on images rather than didactics. Design and Methods: This study…

Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields.

PubMed

Cabezas, M; Corral, J F; Oliver, A; Díez, Y; Tintoré, M; Auger, C; Montalban, X; Lladó, M; Pareto, D; Rovira, À

2016-06-09

Detection of disease activity, defined as new/enlarging T2 lesions on brain MR imaging, has been proposed as a biomarker in MS. However, detection of new/enlarging T2 lesions can be hindered by several factors that can be overcome with image subtraction. The purpose of this study was to improve automated detection of new T2 lesions and reduce user interaction to eliminate inter- and intraobserver variability. Multiparametric brain MR imaging was performed at 2 time points in 36 patients with new T2 lesions. Images were registered by using an affine transformation and the Demons algorithm to obtain a deformation field. After affine registration, images were subtracted and a threshold was applied to obtain a lesion mask, which was then refined by using the deformation field, intensity, and local information. This pipeline was compared with only applying a threshold, and with a state-of-the-art approach relying only on image intensities. To assess improvements, we compared the results of the different pipelines with the expert visual detection. The multichannel pipeline based on the deformation field obtained a detection Dice similarity coefficient close to 0.70, with a false-positive detection of 17.8% and a true-positive detection of 70.9%. A statistically significant correlation (r = 0.81, P value = 2.2688e-09) was found between visual detection and automated detection by using our approach. The deformation field-based approach proposed in this study for detecting new/enlarging T2 lesions resulted in significantly fewer false-positives while maintaining most true-positives and showed a good correlation with visual detection annotations. This approach could reduce user interaction and inter- and intraobserver variability. © 2016 American Society of Neuroradiology.

Retinal hemorrhage detection by rule-based and machine learning approach.

PubMed

Di Xiao; Shuang Yu; Vignarajan, Janardhan; Dong An; Mei-Ling Tay-Kearney; Kanagasingam, Yogi

2017-07-01

Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.

A prospective study of risk-based colposcopy demonstrates improved detection of cervical precancers.

PubMed

Wentzensen, Nicolas; Walker, Joan; Smith, Katie; Gold, Michael A; Zuna, Rosemary; Massad, L Stewart; Liu, Angela; Silver, Michelle I; Dunn, S Terence; Schiffman, Mark

2018-06-01

Sensitivity for detection of precancers at colposcopy and reassurance provided by a negative colposcopy are in need of systematic study and improvement. We sought to evaluate whether selecting the appropriate women for multiple targeted cervical biopsies based on screening cytology, human papillomavirus testing, and colposcopic impression could improve accuracy and efficiency of cervical precancer detection. In all, 690 women aged 18-67 years referred to colposcopy subsequent to abnormal cervical cancer screening results were included in the study (ClinicalTrials.gov: NCT00339989). Up to 4 cervical biopsies were taken during colposcopy to evaluate the incremental benefit of multiple biopsies. Cervical cytology, human papillomavirus genotyping, and colposcopy impression were used to establish up to 24 different risk strata. Outcomes for the primary analysis were cervical precancers, which included p16 + cervical intraepithelial neoplasia 2 and all cervical intraepithelial neoplasia 3 that were detected by colposcopy-guided biopsy during the colposcopy visit. Later outcomes in women without cervical intraepithelial neoplasia 2 + at baseline were abstracted from electronic medical records. The risk of detecting precancer ranged from 2-82% across 24 strata based on colposcopy impression, cytology, and human papillomavirus genotyping. The risk of precancer in the lowest stratum increased only marginally with multiple biopsies. Women in the highest-risk strata had risks of precancer consistent with immediate treatment. In other risk strata, multiple biopsies substantially improved detection of cervical precancer. Among 361 women with cervical intraepithelial neoplasia <2 at baseline, 195 (54%) had follow-up cytology or histology data with a median follow-up time of 508 days. Lack of detection of precancer at initial colposcopy that included multiple biopsies predicted low risk of precancer during follow-up. Risk assessment at the colposcopy visit makes identification of cervical precancers more effective and efficient. Not finding precancer after a multiple-biopsy protocol provides high reassurance and allows releasing women back to regular screening. Published by Elsevier Inc.

Prevalence and risk factors of hepatitis B and C virus infections among the general population and blood donors in Morocco

PubMed Central

2013-01-01

Background Viral hepatitis is a serious public health problem affecting billions of people globally. Limited information is available on this issue in Morocco. This cross-sectional study was undertaken with the aim of determining the seroprevalence and risk factors of hepatitis B virus (HBV) and hepatitis C virus (HCV) among the general population and among blood donors. Methods Blood samples from volunteers, have been screened with ELISA tests for detecting the hepatitis-B surface antigen (HBsAg) and anti-HCV. Within the seroreactive patients for HCV in the general population, RT-PCR was performed by the Cobas Ampliprep/Cobas Amplicor. Results HCV and HBV-seropositivity was documented in 1.58% and 1.81% out of 41269 and 23578 participants respectively from the general population. Two patients were found to be co-infected. HCV-RNA was detected by PCR in 70.9% of the 195 anti-HCV positive subjects. The anti-HCV prevalence was not different among males and females (P = 0.3). It increased with age; the highest prevalence was observed among subjects with >50 years old (3.12%). Various risk factors for acquiring HCV infection were identified; age, dental treatment, use of glass syringes and surgical history. In addition to these factors, gender and sexual risk behaviors were found to be associated with higher prevalence of hepatitis B. The HBV positivity was significantly higher among males than females participants in all age groups (P < 0.01). The peak was noticed among males aged 30–49 years (2.4%). None of the 152 persons younger than 20 years had HBsAg or anti-HCV. The prevalence of anti-HCV and HBsAg among 169605 blood donors was 0.62% and 0.96% respectively. Conclusions Our study provided much important information concerning hepatitis B and C prevalence and risk factors; it confirmed the intermediate endemicity for HCV infection and pointed to a decreasing trend of HBV incidence, which might reclassify Morocco in low HBV endemicity area. This could be attributed primarily to the universal HBV vaccination among infants and healthcare workers over the past 13 years. HCV and HBV infections in the present survey were mainly associated with nosocomial exposures. Prevention and control of HBV infection are needed to reduce HBV transmission between adults. PMID:23331910

Improvement and implementation for Canny edge detection algorithm

NASA Astrophysics Data System (ADS)

Yang, Tao; Qiu, Yue-hong

2015-07-01

Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

PubMed

Ganapathy, S; Yogesh, P; Kannan, A

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

PubMed

Emam, Mahmoud; Han, Qi; Zhang, Hongli

2018-01-01

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

PubMed Central

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Indoor air quality inspection and analysis system based on gas sensor array

NASA Astrophysics Data System (ADS)

Gao, Xiang; Wang, Mingjiang; Fan, Binwen

2017-08-01

A detection and analysis system capable of measuring the concentration of four major gases in indoor air is designed. It uses four gas sensors constitute a gas sensor array, to achieve four indoor gas concentration detection, while the detection of data for further processing to reduce the cross-sensitivity between the gas sensor to improve the accuracy of detection.

Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point

NASA Astrophysics Data System (ADS)

Yang, Liqin; Sang, Nong; Gao, Changxin

2018-03-01

Vehicle parts detection plays an important role in public transportation safety and mobility. The detection of vehicle parts is to detect the position of each vehicle part. We propose a new approach by combining Faster RCNN and three level cascaded convolutional neural network (DCNN). The output of Faster RCNN is a series of bounding boxes with coordinate information, from which we can locate vehicle parts. DCNN can precisely predict feature point position, which is the center of vehicle part. We design an output strategy by combining these two results. There are two advantages for this. The quality of the bounding boxes are greatly improved, which means vehicle parts feature point position can be located more precise. Meanwhile we preserve the position relationship between vehicle parts and effectively improve the validity and reliability of the result. By using our algorithm, the performance of the vehicle parts detection improve obviously compared with Faster RCNN.

Integrity Verification for SCADA Devices Using Bloom Filters and Deep Packet Inspection

DTIC Science & Technology

2014-03-27

prevent intrusions in smart grids [PK12]. Parthasarathy proposed an anomaly detection based IDS that takes into account system state. In his implementation...Security, 25(7):498–506, 10 2006. [LMV12] O. Linda, M. Manic, and T. Vollmer. Improving cyber-security of smart grid systems via anomaly detection and...6 2012. 114 [PK12] S. Parthasarathy and D. Kundur. Bloom filter based intrusion detection for smart grid SCADA. In Electrical & Computer Engineering

Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain

NASA Astrophysics Data System (ADS)

Nougarou, François; Massicotte, Daniel; Descarreaux, Martin

2012-12-01

The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are needed. However, this phenomenon is clearly more difficult to detect for LBP patients than for healthy patients. The main goal of this study is to develop an automated method based on wavelet transformation that would improve time point limits detection of surface electromyography signals of the FRP in case of LBP patients. Conventional visual identification and proposed automated methods of time point limits detection of relaxation phase were compared on experimental data using criteria of accuracy and repeatability based on physiological properties. The evaluation demonstrates that the use of wavelet transform (WT) yields better results than methods without wavelet decomposition. Furthermore, methods based on wavelet per packet transform are more effective than algorithms employing discrete WT. Compared to visual detection, in addition to demonstrating an obvious saving of time, the use of wavelet per packet transform improves the accuracy and repeatability in the detection of the FRP limits. These results clearly highlight the value of the proposed technique in identifying onset and offset of the flexion relaxation response in LBP subjects.

Improved Sensor Fault Detection, Isolation, and Mitigation Using Multiple Observers Approach

PubMed Central

Wang, Zheng; Anand, D. M.; Moyne, J.; Tilbury, D. M.

2017-01-01

Traditional Fault Detection and Isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analyzing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a 5-state suspension system. PMID:28924303

A Technology Analysis to Support Acquisition of UAVs for Gulf Coalition Forces Operations

DTIC Science & Technology

2017-06-01

their selection of the most suitable and cost-effective unmanned aerial vehicles to support detection operations. This study uses Map Aware Non ...being detected by Gulf Coalition Forces and improved time to detect them, support the use of UAVs in detection missions. Computer experimentations and...aerial vehicles to support detection operations. We use Map Aware Non - Uniform Automata, an agent-based simulation software platform, for the

Community detection enhancement using non-negative matrix factorization with graph regularization

NASA Astrophysics Data System (ADS)

Liu, Xiao; Wei, Yi-Ming; Wang, Jian; Wang, Wen-Jun; He, Dong-Xiao; Song, Zhan-Jie

2016-06-01

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

New pediatric vision screener employing polarization-modulated, retinal-birefringence-scanning-based strabismus detection and bull's eye focus detection with an improved target system: opto-mechanical design and operation

NASA Astrophysics Data System (ADS)

Irsch, Kristina; Gramatikov, Boris I.; Wu, Yi-Kai; Guyton, David L.

2014-06-01

Amblyopia ("lazy eye") is a major public health problem, caused by misalignment of the eyes (strabismus) or defocus. If detected early in childhood, there is an excellent response to therapy, yet most children are detected too late to be treated effectively. Commercially available vision screening devices that test for amblyopia's primary causes can detect strabismus only indirectly and inaccurately via assessment of the positions of external light reflections from the cornea, but they cannot detect the anatomical feature of the eyes where fixation actually occurs (the fovea). Our laboratory has been developing technology to detect true foveal fixation, by exploiting the birefringence of the uniquely arranged Henle fibers delineating the fovea using retinal birefringence scanning (RBS), and we recently described a polarization-modulated approach to RBS that enables entirely direct and reliable detection of true foveal fixation, with greatly enhanced signal-to-noise ratio and essentially independent of corneal birefringence (a confounding variable with all polarization-sensitive ophthalmic technology). Here, we describe the design and operation of a new pediatric vision screener that employs polarization-modulated, RBS-based strabismus detection and bull's eye focus detection with an improved target system, and demonstrate the feasibility of this new approach.

New pediatric vision screener employing polarization-modulated, retinal-birefringence-scanning-based strabismus detection and bull's eye focus detection with an improved target system: opto-mechanical design and operation.

PubMed

Irsch, Kristina; Gramatikov, Boris I; Wu, Yi-Kai; Guyton, David L

2014-06-01

Amblyopia ("lazy eye") is a major public health problem, caused by misalignment of the eyes (strabismus) or defocus. If detected early in childhood, there is an excellent response to therapy, yet most children are detected too late to be treated effectively. Commercially available vision screening devices that test for amblyopia's primary causes can detect strabismus only indirectly and inaccurately via assessment of the positions of external light reflections from the cornea, but they cannot detect the anatomical feature of the eyes where fixation actually occurs (the fovea). Our laboratory has been developing technology to detect true foveal fixation, by exploiting the birefringence of the uniquely arranged Henle fibers delineating the fovea using retinal birefringence scanning (RBS), and we recently described a polarization-modulated approach to RBS that enables entirely direct and reliable detection of true foveal fixation, with greatly enhanced signal-to-noise ratio and essentially independent of corneal birefringence (a confounding variable with all polarization-sensitive ophthalmic technology). Here, we describe the design and operation of a new pediatric vision screener that employs polarization-modulated, RBS-based strabismus detection and bull's eye focus detection with an improved target system, and demonstrate the feasibility of this new approach.

A New Moving Object Detection Method Based on Frame-difference and Background Subtraction

NASA Astrophysics Data System (ADS)

Guo, Jiajia; Wang, Junping; Bai, Ruixue; Zhang, Yao; Li, Yong

2017-09-01

Although many methods of moving object detection have been proposed, moving object extraction is still the core in video surveillance. However, with the complex scene in real world, false detection, missed detection and deficiencies resulting from cavities inside the body still exist. In order to solve the problem of incomplete detection for moving objects, a new moving object detection method combined an improved frame-difference and Gaussian mixture background subtraction is proposed in this paper. To make the moving object detection more complete and accurate, the image repair and morphological processing techniques which are spatial compensations are applied in the proposed method. Experimental results show that our method can effectively eliminate ghosts and noise and fill the cavities of the moving object. Compared to other four moving object detection methods which are GMM, VIBE, frame-difference and a literature's method, the proposed method improve the efficiency and accuracy of the detection.

A Framework of Simple Event Detection in Surveillance Video

NASA Astrophysics Data System (ADS)

Xu, Weiguang; Zhang, Yafei; Lu, Jianjiang; Tian, Yulong; Wang, Jiabao

Video surveillance is playing more and more important role in people's social life. Real-time alerting of threaten events and searching interesting content in stored large scale video footage needs human operator to pay full attention on monitor for long time. The labor intensive mode has limit the effectiveness and efficiency of the system. A framework of simple event detection is presented advance the automation of video surveillance. An improved inner key point matching approach is used to compensate motion of background in real-time; frame difference are used to detect foreground; HOG based classifiers are used to classify foreground object into people and car; mean-shift is used to tracking the recognized objects. Events are detected based on predefined rules. The maturity of the algorithms guarantee the robustness of the framework, and the improved approach and the easily checked rules enable the framework to work in real-time. Future works to be done are also discussed.

Imaging and Elastometry of Blood Clots Using Magnetomotive Optical Coherence Tomography and Labeled Platelets.

PubMed

Oldenburg, Amy L; Wu, Gongting; Spivak, Dmitry; Tsui, Frank; Wolberg, Alisa S; Fischer, Thomas H

2011-07-21

Improved methods for imaging and assessment of vascular defects are needed for directing treatment of cardiovascular pathologies. In this paper, we employ magnetomotive optical coherence tomography (MMOCT) as a platform both to detect and to measure the elasticity of blood clots. Detection is enabled through the use of rehydrated, lyophilized platelets loaded with superparamagnetic iron oxides (SPIO-RL platelets) that are functional infusion agents that adhere to sites of vascular endothelial damage. Evidence suggests that the sensitivity for detection is improved over threefold by magnetic interactions between SPIOs inside RL platelets. Using the same MMOCT system, we show how elastometry of simulated clots, using resonant acoustic spectroscopy, is correlated with the fibrin content of the clot. Both methods are based upon magnetic actuation and phase-sensitive optical monitoring of nanoscale displacements using MMOCT, underscoring its utility as a broad-based platform to detect and measure the molecular structure and composition of blood clots.

A controlled phantom study of a noise equalization algorithm for detecting microcalcifications in digital mammograms.

PubMed

Gürün, O O; Fatouros, P P; Kuhn, G M; de Paredes, E S

2001-04-01

We report on some extensions and further developments of a well-known microcalcification detection algorithm based on adaptive noise equalization. Tissue equivalent phantom images with and without labeled microcalcifications were subjected to this algorithm, and analyses of results revealed some shortcomings in the approach. Particularly, it was observed that the method of estimating the width of distributions in the feature space was based on assumptions which resulted in the loss of similarity preservation characteristics. A modification involving a change of estimator statistic was made, and the modified approach was tested on the same phantom images. Other modifications for improving detectability such as downsampling and use of alternate local contrast filters were also tested. The results indicate that these modifications yield improvements in detectability, while extending the generality of the approach. Extensions to real mammograms and further directions of research are discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bueno, G.; Ruiz, M.; Sanchez, S

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Image processing improvement for optical observations of space debris with the TAROT telescopes

NASA Astrophysics Data System (ADS)

Thiebaut, C.; Theron, S.; Richard, P.; Blanchet, G.; Klotz, A.; Boër, M.

2016-07-01

CNES is involved in the Inter-Agency Space Debris Coordination Committee (IADC) and is observing space debris with two robotic ground based fully automated telescopes called TAROT and operated by the CNRS. An image processing algorithm devoted to debris detection in geostationary orbit is implemented in the standard pipeline. Nevertheless, this algorithm is unable to deal with debris tracking mode images, this mode being the preferred one for debris detectability. We present an algorithm improvement for this mode and give results in terms of false detection rate.

Portable Nanoparticle-Based Sensors for Food Safety Assessment

PubMed Central

Bülbül, Gonca; Hayat, Akhtar; Andreescu, Silvana

2015-01-01

The use of nanotechnology-derived products in the development of sensors and analytical measurement methodologies has increased significantly over the past decade. Nano-based sensing approaches include the use of nanoparticles (NPs) and nanostructures to enhance sensitivity and selectivity, design new detection schemes, improve sample preparation and increase portability. This review summarizes recent advancements in the design and development of NP-based sensors for assessing food safety. The most common types of NPs used to fabricate sensors for detection of food contaminants are discussed. Selected examples of NP-based detection schemes with colorimetric and electrochemical detection are provided with focus on sensors for the detection of chemical and biological contaminants including pesticides, heavy metals, bacterial pathogens and natural toxins. Current trends in the development of low-cost portable NP-based technology for rapid assessment of food safety as well as challenges for practical implementation and future research directions are discussed. PMID:26690169

Highly sensitive detection for proteins using graphene oxide-aptamer based sensors.

PubMed

Gao, Li; Li, Qin; Li, Raoqi; Yan, Lirong; Zhou, Yang; Chen, Keping; Shi, Haixia

2015-07-07

In recent years, the detection of proteins by using bare graphene oxide (GO) to quench the fluorescence of fluorescein-labeled aptamers has been reported. However, the proteins can be adsorbed on the surface of bare GO to prevent the sensitivity from further being improved. In order to solve this problem, polyethylene glycol (PEG)-protected GO was used to prevent the proteins using thrombin as an example from nonspecific binding. The detection limit was improved compared to bare GO under the optimized ratio of GO to PEG concentration. The results show that our method is a promising technique for the detection of proteins.

Detecting persons concealed in a vehicle

DOEpatents

Tucker, Jr., Raymond W.

2005-03-29

An improved method for detecting the presence of humans or animals concealed within in a vehicle uses a combination of the continuous wavelet transform and a ratio-based energy calculation to determine whether the motion detected using seismic sensors placed on the vehicle is due to the presence of a heartbeat within the vehicle or is the result of motion caused by external factors such as the wind. The method performs well in the presence of light to moderate ambient wind levels, producing far fewer false alarm indications. The new method significantly improves the range of ambient environmental conditions under which human presence detection systems can reliably operate.

Immunocapture reverse transcription-polymerase chain reaction combined with nested PCR greatly increases the detection of Prunus necrotic ring spot virus in the peach.

PubMed

Helguera, P R; Taborda, R; Docampo, D M; Ducasse, D A

2001-06-01

A detection system based on nested PCR after IC-RT-PCR (IC-RT-PCR-Nested PCR) was developed to improve indexing of Prunus necrotic ringspot virus in peach trees. Inhibitory effects and inconsistencies of the standard IC-RT-PCR were overcome by this approach. IC-RT-PCR-Nested PCR improved detection by three orders of magnitude compared with DAS-ELISA for the detection of PNRSV in leaves. Several different tissues were evaluated and equally consistent results were observed. The main advantages of the method are its consistency, high sensitivity and easy application in quarantine programs.

Peak tree: a new tool for multiscale hierarchical representation and peak detection of mass spectrometry data.

PubMed

Zhang, Peng; Li, Houqiang; Wang, Honghui; Wong, Stephen T C; Zhou, Xiaobo

2011-01-01

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

NASA Astrophysics Data System (ADS)

Ghaffarian, S.; Ghaffarian, S.

2014-08-01

This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

Surface modification of alignment layer by ultraviolet irradiation to dramatically improve the detection limit of liquid-crystal-based immunoassay for the cancer biomarker CA125.

PubMed

Su, Hui-Wen; Lee, Mon-Juan; Lee, Wei

2015-05-01

Liquid crystal (LC)-based biosensing has attracted much attention in recent years. We focus on improving the detection limit of LC-based immunoassay techniques by surface modification of the surfactant alignment layer consisting of dimethyloctadecyl[3-(trimethoxysilyl)propyl]ammonium chloride (DMOAP). The cancer biomarker CA125 was detected with an array of anti-CA125 antibodies immobilized on the ultraviolet (UV)-modified DMOAP monolayer. Compared with a pristine counterpart, UV irradiation enhanced the binding affinity of the CA125 antibody and reproducibility of immunodetection in which a detection limit of 0.01 ng∕ml for the cancer biomarker CA125 was achieved. Additionally, the optical texture observed under a crossed polarized microscope was correlated with the analyte concentration. In a proof-of-concept experiment using CA125-spiked human serum as the analyte, specific binding between the CA125 antigen and the anti-CA125 antibody resulted in a distinct and concentration-dependent optical response despite the high background caused by nonspecific binding of other biomolecules in the human serum. Results from this study indicate that UVmodification of the alignment layer, as well as detection with LCs of large birefringence, contributes to the enhanced performance of the label-free LC-based immunodetection, which may be considered a promising alternative to conventional label-based methods.

Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

NASA Astrophysics Data System (ADS)

Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

2016-03-01

Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

Is 3D MPRAGE better than the combination DIR/PSIR for cortical lesion detection at 3T MRI?

PubMed

Nelson, Flavia; Poonawalla, Aziz; Datta, Sushmita; Wolinsky, Jerry; Narayana, Ponnada

2014-03-01

Based on the application of newer magnetic resonance imaging (MRI) acquisition sequences, the detection of cortical lesions (CL) in multiple sclerosis (MS) has significantly improved. Double inversion recovery (DIR) at 3T has increased the detection sensitivity and classification specificity when combined with phase sensitive inversion recovery (PSIR). Previous findings with 3D magnetization prepared rapid acquisition with gradient echo (MPRAGE) sequences, showed improved classification specificity of purely intracortical (IC) and mixed (MX) lesions, compared to the classification based on DIR/PSIR. Direct comparison between the detection of CL by 3D MPRAGE and by DIR/PSIR at 3T has not been evaluated. Eleven subjects were imaged on a 3T magnet. DIR/PSIR and 3D MPRAGE images were reviewed independently. Each image set was reviewed twice; only lesions detected on both sessions were scored. Review time per scan was ~5min for DIR/PSIR and ~15min for 3D MPRAGE. We identified 141 CL (62 IC+79 MX) based on DIR/PSIR images vs. 93 (38 IC+55 MX) based on MPRAGE from all eleven patients. MPRAGE under-detected the number of CL in seven cases and over-detected the number of CL in three, only one case had the same number of CL on both sets of images. Combination DIR/PSIR at 3T is superior to 3D MPRAGE for detection of cortical gray matter lesions in MS. The contrast-to-noise ratio of CL appears to be inferior on the MPRAGE images relative to DIR/PSIR. © 2013 Published by Elsevier B.V.

Microwave photonic link with improved phase noise using a balanced detection scheme

NASA Astrophysics Data System (ADS)

Hu, Jingjing; Gu, Yiying; Tan, Wengang; Zhu, Wenwu; Wang, Linghua; Zhao, Mingshan

2016-07-01

A microwave photonic link (MPL) with improved phase noise performance using a dual output Mach-Zehnder modulator (DP-MZM) and balanced detection is proposed and experimentally demonstrated. The fundamental concept of the approach is based on the two complementary outputs of DP-MZM and the destructive combination of the photocurrent in balanced photodetector (BPD). Theoretical analysis is performed to numerical evaluate the additive phase noise performance and shows a good agreement with the experiment. Experimental results are presented for 4 GHz, 8 GHz and 12 GHz transmission link and an 11 dB improvement of phase noise performance at 10 MHz offset is achieved compared to the conventional intensity-modulation and direct-detection (IMDD) MPL.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner–Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments. PMID:25905704

Sensitive elemental detection using microwave-assisted laser-induced breakdown imaging

NASA Astrophysics Data System (ADS)

Iqbal, Adeel; Sun, Zhiwei; Wall, Matthew; Alwahabi, Zeyad T.

2017-10-01

This study reports a sensitive spectroscopic method for quantitative elemental detection by manipulating the temporal and spatial parameters of laser-induced plasma. The method was tested for indium detection in solid samples, in which laser ablation was used to generate a tiny plasma. The lifetime of the laser-induced plasma can be extended to hundreds of microseconds using microwave injection to remobilize the electrons. In this novel method, temporal integrated signal of indium emission was significantly enhanced. Meanwhile, the projected detectable area of the excited indium atoms was also significantly improved using an interference-, instead of diffraction-, based technique, achieved by directly imaging microwave-enhanced plasma through a novel narrow-bandpass filter, exactly centered at the indium emission line. Quantitative laser-induce breakdown spectroscopy was also recorded simultaneously with the new imaging method. The intensities recorded from both methods exhibit very good mutual linear relationship. The detection intensity was improved to 14-folds because of the combined improvements in the plasma lifetime and the area of detection.

Improved wavelength coded optical time domain reflectometry based on the optical switch.

PubMed

Zhu, Ninghua; Tong, Youwan; Chen, Wei; Wang, Sunlong; Sun, Wenhui; Liu, Jianguo

2014-06-16

This paper presents an improved wavelength coded time-domain reflectometry based on the 2 × 1 optical switch. In this scheme, in order to improve the signal-noise-ratio (SNR) of the beat signal, the improved system used an optical switch to obtain wavelength-stable, low-noise and narrow optical pulses for probe and reference. Experiments were set up to demonstrate a spatial resolution of 2.5m within a range of 70km and obtain the beat signal with line width narrower than 15 MHz within a range of 50 km in fiber break detection. A system for wavelength-division-multiplexing passive optical network (WDM-PON) monitoring was also constructed to detect the fiber break of different channels by tuning the current applied on the gating section of the distributed Bragg reflector (DBR) laser.

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

ASCS online fault detection and isolation based on an improved MPCA

NASA Astrophysics Data System (ADS)

Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

2014-09-01

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

Research on Daily Objects Detection Based on Deep Neural Network

NASA Astrophysics Data System (ADS)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model

NASA Astrophysics Data System (ADS)

Shi, Aiye; Wang, Chao; Shen, Shaohong; Huang, Fengchen; Ma, Zhenli

2016-10-01

Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as "salt and pepper" noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.

Validation of an automated seizure detection algorithm for term neonates

PubMed Central

Mathieson, Sean R.; Stevenson, Nathan J.; Low, Evonne; Marnane, William P.; Rennie, Janet M.; Temko, Andrey; Lightbody, Gordon; Boylan, Geraldine B.

2016-01-01

Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. PMID:26055336

Set-membership fault detection under noisy environment with application to the detection of abnormal aircraft control surface positions

NASA Astrophysics Data System (ADS)

El Houda Thabet, Rihab; Combastel, Christophe; Raïssi, Tarek; Zolghadri, Ali

2015-09-01

The paper develops a set membership detection methodology which is applied to the detection of abnormal positions of aircraft control surfaces. Robust and early detection of such abnormal positions is an important issue for early system reconfiguration and overall optimisation of aircraft design. In order to improve fault sensitivity while ensuring a high level of robustness, the method combines a data-driven characterisation of noise and a model-driven approach based on interval prediction. The efficiency of the proposed methodology is illustrated through simulation results obtained based on data recorded in several flight scenarios of a highly representative aircraft benchmark.

Co-detection: ultra-reliable nanoparticle-based electrical detection of biomolecules in the presence of large background interference.

PubMed

Liu, Yang; Gu, Ming; Alocilja, Evangelyn C; Chakrabartty, Shantanu

2010-11-15

An ultra-reliable technique for detecting trace quantities of biomolecules is reported. The technique called "co-detection" exploits the non-linear redundancy amongst synthetically patterned biomolecular logic circuits for deciphering the presence or absence of target biomolecules in a sample. In this paper, we verify the "co-detection" principle on gold-nanoparticle-based conductimetric soft-logic circuits which use a silver-enhancement technique for signal amplification. Using co-detection, we have been able to demonstrate a great improvement in the reliability of detecting mouse IgG at concentration levels that are 10(5) lower than the concentration of rabbit IgG which serves as background interference. Copyright © 2010 Elsevier B.V. All rights reserved.

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

PubMed

Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R; Ktonas, Periklis Y; Frost, James D; Hrachovy, Richard A; Mizrahi, Eli M

2006-04-01

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

Availability of tissue rinse liquid-based cytology for the rapid diagnosis of sentinel lymph node metastasis and improved bilateral detection by photodynamic eye camera.

PubMed

Kato, Hidenori; Ohba, Yoko; Yamazaki, Hiroyuki; Minobe, Shin-Ichiro; Sudo, Satoko; Todo, Yukiharu; Okamoto, Kazuhira; Yamashiro, Katsushige

2015-08-01

On sentinel lymph node navigation surgery for early invasive cervical cancers, to gain high sensitivity and specificity, the sentinel nodes should be detected bilaterally and pathological diagnosis should be sensitive to detect micrometastasis. To improve these problems, we tried tissue rinse liquid-based cytology and the photodynamic eye. From 2005 to 2013, 102 patients with Stage Ib1 uterine cervical cancer were subjected to sentinel lymph node navigation surgery with Technetium-99 m colloid and blue dye. For the recent 11 patients with whom bilateral sentinel node detection was not available, the photodynamic eye was selectively examined. The detected sentinel node was cut along the minor axis into 2 mm slices, soaked in 10 ml CytoRich red and then subjected to tissue rinse liquid-based cytology at the time of surgery. With the accumulation of 102 Ib1 patients subjected to sentinel lymph node navigation surgery, the bilateral sentinel node detection rate was 67.7%. The photodynamic eye was examined for the recent 11 patients who did not have bilateral signals. Out of the 11, 10 patients obtained bilateral signals successfully. During the period of examining the photodynamic eye, a total of 34 patients were subjected to sentinel lymph node navigation surgery. Thus, the overall bilateral detection rate increased to 97% in this subset. Two hundred and five lymph nodes were available as sentinel nodes. The sensitivity of tissue rinse liquid-based cytology was 91.7%, and the specificity was 100%. False positivity was 0% and false negativity was 8.3%. Detection failure was observed only with one micrometastasis and one case of isolated tumor cells. Combination of photodynamic eye detection and tissue rinse liquid-based cytology pathology can be a promising method for more rewarding sentinel node detection. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Fabric defect detection based on faster R-CNN

NASA Astrophysics Data System (ADS)

Liu, Zhoufeng; Liu, Xianghui; Li, Chunlei; Li, Bicao; Wang, Baorui

2018-04-01

In order to effectively detect the defects for fabric image with complex texture, this paper proposed a novel detection algorithm based on an end-to-end convolutional neural network. First, the proposal regions are generated by RPN (regional proposal Network). Then, Fast Region-based Convolutional Network method (Fast R-CNN) is adopted to determine whether the proposal regions extracted by RPN is a defect or not. Finally, Soft-NMS (non-maximum suppression) and data augmentation strategies are utilized to improve the detection precision. Experimental results demonstrate that the proposed method can locate the fabric defect region with higher accuracy compared with the state-of- art, and has better adaptability to all kinds of the fabric image.

Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images

NASA Astrophysics Data System (ADS)

Ghaffarian, Saman; Ghaffarian, Salar

2014-11-01

This paper proposes an improved FastICA model named as Purposive FastICA (PFICA) with initializing by a simple color space transformation and a novel masking approach to automatically detect buildings from high resolution Google Earth imagery. ICA and FastICA algorithms are defined as Blind Source Separation (BSS) techniques for unmixing source signals using the reference data sets. In order to overcome the limitations of the ICA and FastICA algorithms and make them purposeful, we developed a novel method involving three main steps: 1-Improving the FastICA algorithm using Moore-Penrose pseudo inverse matrix model, 2-Automated seeding of the PFICA algorithm based on LUV color space and proposed simple rules to split image into three regions; shadow + vegetation, baresoil + roads and buildings, respectively, 3-Masking out the final building detection results from PFICA outputs utilizing the K-means clustering algorithm with two number of clusters and conducting simple morphological operations to remove noises. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.6% and 85.5% overall pixel-based and object-based precision performances, respectively.

Localized Surface Plasmon Resonance Biosensing: Current Challenges and Approaches

PubMed Central

Unser, Sarah; Bruzas, Ian; He, Jie; Sagle, Laura

2015-01-01

Localized surface plasmon resonance (LSPR) has emerged as a leader among label-free biosensing techniques in that it offers sensitive, robust, and facile detection. Traditional LSPR-based biosensing utilizes the sensitivity of the plasmon frequency to changes in local index of refraction at the nanoparticle surface. Although surface plasmon resonance technologies are now widely used to measure biomolecular interactions, several challenges remain. In this article, we have categorized these challenges into four categories: improving sensitivity and limit of detection, selectivity in complex biological solutions, sensitive detection of membrane-associated species, and the adaptation of sensing elements for point-of-care diagnostic devices. The first section of this article will involve a conceptual discussion of surface plasmon resonance and the factors affecting changes in optical signal detected. The following sections will discuss applications of LSPR biosensing with an emphasis on recent advances and approaches to overcome the four limitations mentioned above. First, improvements in limit of detection through various amplification strategies will be highlighted. The second section will involve advances to improve selectivity in complex media through self-assembled monolayers, “plasmon ruler” devices involving plasmonic coupling, and shape complementarity on the nanoparticle surface. The following section will describe various LSPR platforms designed for the sensitive detection of membrane-associated species. Finally, recent advances towards multiplexed and microfluidic LSPR-based devices for inexpensive, rapid, point-of-care diagnostics will be discussed. PMID:26147727

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

PubMed

Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

2017-01-01

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

A Cyber-Attack Detection Model Based on Multivariate Analyses

NASA Astrophysics Data System (ADS)

Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi

In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.

A hybrid method based on Band Pass Filter and Correlation Algorithm to improve debris sensor capacity

NASA Astrophysics Data System (ADS)

Hong, Wei; Wang, Shaoping; Liu, Haokuo; Tomovic, Mileta M.; Chao, Zhang

2017-01-01

The inductive debris detection is an effective method for monitoring mechanical wear, and could be used to prevent serious accidents. However, debris detection during early phase of mechanical wear, when small debris (<100 um) is generated, requires that the sensor has high sensitivity with respect to background noise. In order to detect smaller debris by existing sensors, this paper presents a hybrid method which combines Band Pass Filter and Correlation Algorithm to improve sensor signal-to-noise ratio (SNR). The simulation results indicate that the SNR will be improved at least 2.67 times after signal processing. In other words, this method ensures debris identification when the sensor's SNR is bigger than -3 dB. Thus, smaller debris will be detected in the same SNR. Finally, effectiveness of the proposed method is experimentally validated.

Toward multimodal signal detection of adverse drug reactions.

PubMed

Harpaz, Rave; DuMouchel, William; Schuemie, Martijn; Bodenreider, Olivier; Friedman, Carol; Horvitz, Eric; Ripple, Anna; Sorbello, Alfred; White, Ryen W; Winnenburg, Rainer; Shah, Nigam H

2017-12-01

Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation. Four data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates. Limited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7-22 months relative to labeling revision dates from a time-indexed benchmark. The results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals. Copyright © 2017 Elsevier Inc. All rights reserved.

A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection.

PubMed

Guvensan, M Amac; Dusun, Burak; Can, Baris; Turkmen, H Irem

2017-12-30

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people's daily activities including transportation types and duration by taking advantage of individual's smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.

False star detection and isolation during star tracking based on improved chi-square tests.

PubMed

Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Yang, Yanqiang; Su, Guohua

2017-08-01

The star sensor is a precise attitude measurement device for a spacecraft. Star tracking is the main and key working mode for a star sensor. However, during star tracking, false stars become an inevitable interference for star sensor applications, which may result in declined measurement accuracy. A false star detection and isolation algorithm in star tracking based on improved chi-square tests is proposed in this paper. Two estimations are established based on a Kalman filter and a priori information, respectively. The false star detection is operated through adopting the global state chi-square test in a Kalman filter. The false star isolation is achieved using a local state chi-square test. Semi-physical experiments under different trajectories with various false stars are designed for verification. Experiment results show that various false stars can be detected and isolated from navigation stars during star tracking, and the attitude measurement accuracy is hardly influenced by false stars. The proposed algorithm is proved to have an excellent performance in terms of speed, stability, and robustness.

Improved detection and false alarm rejection using FLGPR and color imagery in a forward-looking system

NASA Astrophysics Data System (ADS)

Havens, Timothy C.; Spain, Christopher J.; Ho, K. C.; Keller, James M.; Ton, Tuan T.; Wong, David C.; Soumekh, Mehrdad

2010-04-01

Forward-looking ground-penetrating radar (FLGPR) has received a significant amount of attention for use in explosivehazards detection. A drawback to FLGPR is that it results in an excessive number of false detections. This paper presents our analysis of the explosive-hazards detection system tested by the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD). The NVESD system combines an FLGPR with a visible-spectrum color camera. We present a target detection algorithm that uses a locally-adaptive detection scheme with spectrum-based features. The remaining FLGPR detections are then projected into the camera imagery and image-based features are collected. A one-class classifier is then used to reduce the number of false detections. We show that our proposed FLGPR target detection algorithm, coupled with our camera-based false alarm (FA) reduction method, is effective at reducing the number of FAs in test data collected at a US Army test facility.

Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

Overview of MPLNET Version 3 Cloud Detection

NASA Technical Reports Server (NTRS)

Lewis, Jasper R.; Campbell, James; Welton, Ellsworth J.; Stewart, Sebastian A.; Haftings, Phillip

2016-01-01

The National Aeronautics and Space Administration Micro Pulse Lidar Network, version 3, cloud detection algorithm is described and differences relative to the previous version are highlighted. Clouds are identified from normalized level 1 signal profiles using two complementary methods. The first method considers vertical signal derivatives for detecting low-level clouds. The second method, which detects high-level clouds like cirrus, is based on signal uncertainties necessitated by the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multitemporal averaging scheme is used to improve cloud detection under conditions of a weak signal-to-noise ratio. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, Maryland) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5 km, above MSL), which increase in occurrence by over 5%. There is also an increase in the detection of multilayered cloud profiles from 9% to 19%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which the cirrus cloud optical depth could be reliably estimated occurs between 0.5 and 0.8. A comparison using collocated CALIPSO measurements at the Goddard Space Flight Center and Singapore Micro Pulse Lidar Network (MPLNET) sites indicates improvements in cloud occurrence frequencies and layer heights.

Improving Fall Detection Using an On-Wrist Wearable Accelerometer

PubMed Central

Chira, Camelia; González, Víctor M.; de la Cal, Enrique

2018-01-01

Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms. PMID:29701721

Breast cancer statistics and prediction methodology: a systematic review and analysis.

PubMed

Dubey, Ashutosh Kumar; Gupta, Umesh; Jain, Sonal

2015-01-01

Breast cancer is a menacing cancer, primarily affecting women. Continuous research is going on for detecting breast cancer in the early stage as the possibility of cure in early stages is bright. There are two main objectives of this current study, first establish statistics for breast cancer and second to find methodologies which can be helpful in the early stage detection of the breast cancer based on previous studies. The breast cancer statistics for incidence and mortality of the UK, US, India and Egypt were considered for this study. The finding of this study proved that the overall mortality rates of the UK and US have been improved because of awareness, improved medical technology and screening, but in case of India and Egypt the condition is less positive because of lack of awareness. The methodological findings of this study suggest a combined framework based on data mining and evolutionary algorithms. It provides a strong bridge in improving the classification and detection accuracy of breast cancer data.

LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Zhao, Zhili; Wei, Jiaxuan; Hu, Rongjing

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

An electromagnetic induction method for underground target detection and characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bartel, L.C.; Cress, D.H.

1997-01-01

An improved capability for subsurface structure detection is needed to support military and nonproliferation requirements for inspection and for surveillance of activities of threatening nations. As part of the DOE/NN-20 program to apply geophysical methods to detect and characterize underground facilities, Sandia National Laboratories (SNL) initiated an electromagnetic induction (EMI) project to evaluate low frequency electromagnetic (EM) techniques for subsurface structure detection. Low frequency, in this case, extended from kilohertz to hundreds of kilohertz. An EMI survey procedure had already been developed for borehole imaging of coal seams and had successfully been applied in a surface mode to detect amore » drug smuggling tunnel. The SNL project has focused on building upon the success of that procedure and applying it to surface and low altitude airborne platforms. Part of SNL`s work has focused on improving that technology through improved hardware and data processing. The improved hardware development has been performed utilizing Laboratory Directed Research and Development (LDRD) funding. In addition, SNL`s effort focused on: (1) improvements in modeling of the basic geophysics of the illuminating electromagnetic field and its coupling to the underground target (partially funded using LDRD funds) and (2) development of techniques for phase-based and multi-frequency processing and spatial processing to support subsurface target detection and characterization. The products of this project are: (1) an evaluation of an improved EM gradiometer, (2) an improved gradiometer concept for possible future development, (3) an improved modeling capability, (4) demonstration of an EM wave migration method for target recognition, and a demonstration that the technology is capable of detecting targets to depths exceeding 25 meters.« less

Ship Detection in SAR Image Based on the Alpha-stable Distribution

PubMed Central

Wang, Changcheng; Liao, Mingsheng; Li, Xiaofeng

2008-01-01

This paper describes an improved Constant False Alarm Rate (CFAR) ship detection algorithm in spaceborne synthetic aperture radar (SAR) image based on Alpha-stable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution. PMID:27873794

Direct Detection of Nucleic Acid with Minimizing Background and Improving Sensitivity Based on a Conformation-Discriminating Indicator.

PubMed

Zhu, Lixuan; Qing, Zhihe; Hou, Lina; Yang, Sheng; Zou, Zhen; Cao, Zhong; Yang, Ronghua

2017-08-25

As is well-known, the nucleic acid indicator-based strategy is one of the major approaches to monitor the nucleic acid hybridization-mediated recognition events in biochemical analysis, displaying obvious advantages including simplicity, low cost, convenience, and generality. However, conventional indicators either hold strong self-fluorescence or can be lighted by both ssDNA and dsDNA, lacking absolute selectivity for a certain conformation, always with high background interference and low sensitivity in sensing; and additional processing (e.g., nanomaterial-mediated background suppression, and enzyme-catalyzed signal amplification) is generally required to improve the detection performance. In this work, a carbazole derivative, EBCB, has been synthesized and screened as a dsDNA-specific fluorescent indicator. Compared with conventional indicators under the same conditions, EBCB displayed a much higher selective coefficient for dsDNA, with little self-fluorescence and negligible effect from ssDNA. Based on its superior capability in DNA conformation-discrimination, high sensitivity with minimizing background interference was demonstrated for direct detection of nucleic acid, and monitoring nucleic acid-based circuitry with good reversibity, resulting in low detection limit and high capability for discriminating base-mismatching. Thus, we expect that this highly specific DNA conformation-discriminating indicator will hold good potential for application in biochemical sensing and molecular logic switching.

Integration of bridge damage detection concepts and components, volume I : strain-based damage detection.

DOT National Transportation Integrated Search

2013-10-01

In this work, a previously-developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, a...

Integration of bridge damage detection concepts and components, volume II : acceleration-based damage detection.

DOT National Transportation Integrated Search

2013-10-01

In this work, a previously developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, a...

Acoustic-based Technology to Detect Buried Pipes

DOT National Transportation Integrated Search

2011-07-29

The objective of this project is to build a pre-commercial device, improve its performance to detect multiple buried pipes, and evaluate the pre-commercial device at utility sites. In the past, Gas Technology Institute (GTI) and SoniVerse Inc. (SVI) ...

Explosive detection technology

NASA Astrophysics Data System (ADS)

Doremus, Steven; Crownover, Robin

2017-05-01

The continuing proliferation of improvised explosive devices is an omnipresent threat to civilians and members of military and law enforcement around the world. The ability to accurately and quickly detect explosive materials from a distance would be an extremely valuable tool for mitigating the risk posed by these devices. A variety of techniques exist that are capable of accurately identifying explosive compounds, but an effective standoff technique is still yet to be realized. Most of the methods being investigated to fill this gap in capabilities are laser based. Raman spectroscopy is one such technique that has been demonstrated to be effective at a distance. Spatially Offset Raman Spectroscopy (SORS) is a technique capable of identifying chemical compounds inside of containers, which could be used to detect hidden explosive devices. Coherent Anti-Stokes Raman Spectroscopy (CARS) utilized a coherent pair of lasers to excite a sample, greatly increasing the response of sample while decreasing the strength of the lasers being used, which significantly improves the eye safety issue that typically hinders laser-based detection methods. Time-gating techniques are also being developed to improve the data collection from Raman techniques, which are often hindered fluorescence of the test sample in addition to atmospheric, substrate, and contaminant responses. Ultraviolet based techniques have also shown significant promise by greatly improved signal strength from excitation of resonance in many explosive compounds. Raman spectroscopy, which identifies compounds based on their molecular response, can be coupled with Laser Induced Breakdown Spectroscopy (LIBS) capable of characterizing the sample's atomic composition using a single laser.

Enhancing Community Detection By Affinity-based Edge Weighting Scheme

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Andy; Sanders, Geoffrey; Henson, Van

Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is idealmore » for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.« less

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

PubMed Central

Wang, Guohua; Liu, Qiong

2015-01-01

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians’ head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians’ size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only. PMID:26703611

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching.

PubMed

Wang, Guohua; Liu, Qiong

2015-12-21

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians' head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians' size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only.

Mammographic enhancement with combining local statistical measures and sliding band filter for improved mass segmentation in mammograms

NASA Astrophysics Data System (ADS)

Kim, Dae Hoe; Choi, Jae Young; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

In this study, a novel mammogram enhancement solution is proposed, aiming to improve the quality of subsequent mass segmentation in mammograms. It has been widely accepted that characteristics of masses are usually hyper-dense or uniform density with respect to its background. Also, their core parts are likely to have high-intensity values while the values of intensity tend to be decreased as the distance to core parts increases. Based on the aforementioned observations, we develop a new and effective mammogram enhancement method by combining local statistical measurements and Sliding Band Filtering (SBF). By effectively combining local statistical measurements and SBF, we are able to improve the contrast of the bright and smooth regions (which represent potential mass regions), as well as, at the same time, the regions where their surrounding gradients are converging to the centers of regions of interest. In this study, 89 mammograms were collected from the public MAIS database (DB) to demonstrate the effectiveness of the proposed enhancement solution in terms of improving mass segmentation. As for a segmentation method, widely used contour-based segmentation approach was employed. The contour-based method in conjunction with the proposed enhancement solution achieved overall detection accuracy of 92.4% with a total of 85 correct cases. On the other hand, without using our enhancement solution, overall detection accuracy of the contour-based method was only 78.3%. In addition, experimental results demonstrated the feasibility of our enhancement solution for the purpose of improving detection accuracy on mammograms containing dense parenchymal patterns.

Adding retinal photography to screening for diabetic retinopathy: a prospective study in primary care.

PubMed

O'Hare, J P; Hopper, A; Madhaven, C; Charny, M; Purewell, T S; Harney, B; Griffiths, J

1996-03-16

To evaluate whether adding retinal photography improved community screening for diabetic retinopathy. Mobile screening unit at rural and urban general practices in south west England. 1010 diabetic patients from primary care. Prospective study; patients were examined by ophthalmoscopy by general practitioners or opticians without fundal photographs and again with photographs, and assessments were compared to those of an ophthalmologist. Whether fundal photography improved the sensitivity of detection of retinopathy and referrable diabetic retinopathy, and whether this sensitivity could be improved by including a review of the films by the specialist. Diabetic retinopathy was detected by the ophthalmologist in 205 patients (20.5%) and referrable retinopathy in 49 (4.9%). The sensitivity of the general practitioners and opticians for referrable retinopathy with ophthalmoscopy was 65%, and improved to 84% with retinal photographs. General practitioners' sensitivity in detecting background retinopathy improved with photographs from 22% to 65%; opticians' sensitivity in detecting background retinopathy improved from 43% to 71%. The sensitivity of detecting referrable retinopathy by general practitioners improved from 56% to 80% with photographs; for opticians it improved from 75% to 88%. Combining modalities of screening by providing photography with specialist review of all films in addition to direct ophthalmoscopy through dilated pupils improves assessment and referral for diabetic retinopathy by general practitioners and opticians. With further training and experience, primary care screeners should be able to achieve a sensitivity that will achieve an effective, acceptable, and economical community based screening programme for this condition.

Fusion of local and global detection systems to detect tuberculosis in chest radiographs.

PubMed

Hogeweg, Laurens; Mol, Christian; de Jong, Pim A; Dawson, Rodney; Ayles, Helen; van Ginneken, Bramin

2010-01-01

Automatic detection of tuberculosis (TB) on chest radiographs is a difficult problem because of the diverse presentation of the disease. A combination of detection systems for abnormalities and normal anatomy is used to improve detection performance. A textural abnormality detection system operating at the pixel level is combined with a clavicle detection system to suppress false positive responses. The output of a shape abnormality detection system operating at the image level is combined in a next step to further improve performance by reducing false negatives. Strategies for combining systems based on serial and parallel configurations were evaluated using the minimum, maximum, product, and mean probability combination rules. The performance of TB detection increased, as measured using the area under the ROC curve, from 0.67 for the textural abnormality detection system alone to 0.86 when the three systems were combined. The best result was achieved using the sum and product rule in a parallel combination of outputs.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

PubMed

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

PubMed Central

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-01-01

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. PMID:29186756

Double-hairpin molecular-beacon-based amplification detection for gene diagnosis linked to cancer.

PubMed

Xu, Huo; Zhang, Rongbo; Li, Feng; Zhou, Yingying; Peng, Ting; Wang, Xuedong; Shen, Zhifa

2016-09-01

A powerful double-hairpin molecular beacon (DHMB) was developed for cancer-related KRAS gene detection based on the one-to-two stoichiometry. During target DNA detection, DHMB can execute signal transduction even if no any exogenous element is involved. Unlike the conventional molecular beacon based on the one-to-one interaction, one target DNA not only hybridizes with one DHMB and opens its hairpin but also promotes the interaction between two DHMBs, causing the separation of two fluorophores from quenchers. This leads to an enhanced fluorescence signal. As a result, the target KRAS gene is able to be detected within a wide dynamic range from 0.05 to 200 nM with the detection limit of 50 pM, indicating a dramatic improvement compared with traditional molecular beacons. Moreover, the point mutations existing in target DNAs can be easily screened. The potential application for target species in real samples was indicated by the analysis of PCR amplicons of DNAs from the DNA extracted from SW620 cell. Besides becoming a promising candidate probe for molecular biology research and clinical diagnosis of genetic diseases, the DHMB is expected to provide a significant insight into the design of DNA probe-based homogenous sensing systems. Graphical Abstract A powerful double-hairpin molecular beacon (DHMB) was developed for cancer-related gene KRAS detection based on the one-to-two stoichiometry. Without the help of any exogenous probe, the point mutation is easily screened, and the target DNA can be quantified down to 50 pM, indicating a dramatic improvement compared with traditional molecular beacons.

Four-Wave-Mixing Approach to In Situ Detection of Nanoparticles

NASA Astrophysics Data System (ADS)

Gerakis, Alexandros; Yeh, Yao-Wen; Shneider, Mikhail N.; Mitrani, James M.; Stratton, Brentley C.; Raitses, Yevgeny

2018-01-01

We report on the development and experimental validation of a laser-based technique which uses coherent Rayleigh-Brillouin scattering (CRBS) to detect nanoparticles with characteristic sizes ranging from the atomic scale to tens of nanometers. This technique is aimed (nonexclusively) at the detection of nanoparticles produced by volumetric nanoparticle synthesis methods. Using CRBS, carbon nanoparticles of dimensions less than 10 nm and concentrations of 1010 cm-3 are detected in situ in a carbon arc discharge with graphite electrodes. This four-wave-mixing approach should enable advances in the understanding of nanoparticle growth that could potentially lead to improved modeling of the growth mechanisms, and thus to improve synthesis selectivity of nanoparticles and yield.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System.

PubMed

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-02-20

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequencydomain and achieves computational complexity reduction.

Face verification system for Android mobile devices using histogram based features

NASA Astrophysics Data System (ADS)

Sato, Sho; Kobayashi, Kazuhiro; Chen, Qiu

2016-07-01

This paper proposes a face verification system that runs on Android mobile devices. In this system, facial image is captured by a built-in camera on the Android device firstly, and then face detection is implemented using Haar-like features and AdaBoost learning algorithm. The proposed system verify the detected face using histogram based features, which are generated by binary Vector Quantization (VQ) histogram using DCT coefficients in low frequency domains, as well as Improved Local Binary Pattern (Improved LBP) histogram in spatial domain. Verification results with different type of histogram based features are first obtained separately and then combined by weighted averaging. We evaluate our proposed algorithm by using publicly available ORL database and facial images captured by an Android tablet.

New method for enhanced efficiency in detection of gravitational waves from supernovae using coherent network of detectors

NASA Astrophysics Data System (ADS)

Mukherjee, S.; Salazar, L.; Mittelstaedt, J.; Valdez, O.

2017-11-01

Supernovae in our universe are potential sources of gravitational waves (GW) that could be detected in a network of GW detectors like LIGO and Virgo. Core-collapse supernovae are rare, but the associated gravitational radiation is likely to carry profuse information about the underlying processes driving the supernovae. Calculations based on analytic models predict GW energies within the detection range of the Advanced LIGO detectors, out to tens of Mpc for certain types of signals e.g. coalescing binary neutron stars. For supernovae however, the corresponding distances are much less. Thus, methods that can improve the sensitivity of searches for GW signals from supernovae are desirable, especially in the advanced detector era. Several methods have been proposed based on various likelihood-based regulators that work on data from a network of detectors to detect burst-like signals (as is the case for signals from supernovae) from potential GW sources. To address this problem, we have developed an analysis pipeline based on a method of noise reduction known as the harmonic regeneration noise reduction (HRNR) algorithm. To demonstrate the method, sixteen supernova waveforms from the Murphy et al. 2009 catalog have been used in presence of LIGO science data. A comparative analysis is presented to show detection statistics for a standard network analysis as commonly used in GW pipelines and the same by implementing the new method in conjunction with the network. The result shows significant improvement in detection statistics.

Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression

NASA Astrophysics Data System (ADS)

Zhang, Jun; Cain, Elizabeth Hope; Saha, Ashirbani; Zhu, Zhe; Mazurowski, Maciej A.

2018-02-01

Breast mass detection in mammography and digital breast tomosynthesis (DBT) is an essential step in computerized breast cancer analysis. Deep learning-based methods incorporate feature extraction and model learning into a unified framework and have achieved impressive performance in various medical applications (e.g., disease diagnosis, tumor detection, and landmark detection). However, these methods require large-scale accurately annotated data. Unfortunately, it is challenging to get precise annotations of breast masses. To address this issue, we propose a fully convolutional network (FCN) based heatmap regression method for breast mass detection, using only weakly annotated mass regions in mammography images. Specifically, we first generate heat maps of masses based on human-annotated rough regions for breast masses. We then develop an FCN model for end-to-end heatmap regression with an F-score loss function, where the mammography images are regarded as the input and heatmaps for breast masses are used as the output. Finally, the probability map of mass locations can be estimated with the trained model. Experimental results on a mammography dataset with 439 subjects demonstrate the effectiveness of our method. Furthermore, we evaluate whether we can use mammography data to improve detection models for DBT, since mammography shares similar structure with tomosynthesis. We propose a transfer learning strategy by fine-tuning the learned FCN model from mammography images. We test this approach on a small tomosynthesis dataset with only 40 subjects, and we show an improvement in the detection performance as compared to training the model from scratch.

High incidence of antibodies to HTLV-I tax in blood relatives of adult T cell leukemia patients.

PubMed

Okayama, A; Chen, Y M; Tachibana, N; Shioiri, S; Lee, T H; Tsuda, K; Essex, M

1991-01-01

Adult T cell leukemia (ATL) is caused by the human T cell leukemia virus type I (HTLV-I). Although the mechanisms of the leukemogenic process are unknown, the tax gene may have a role in this process. Because clustering occurs with HTLV-I and ATL, members of ATL families were examined for antibodies to the tax protein and compared with matched HTLV-I-positive blood donors. To investigate the antibody response to this protein, a plasmid, pBHX-4, was constructed to express a recombinant tax protein (r-tax). For ATL patients and their HTLV-I antibody-positive blood relatives, the rate of seroreactivity with the r-tax protein was 67.3% (35/52), compared with 51.6% (97/188) for HTLV-I antibody-positive control blood donors (P less than .05). The difference between direct offspring of ATL patients and matched HTLV-I blood donors was even greater (84.2% [16/91] vs. 44.2% [42/95]; P less than .005). Thus, tax antibody positivity in direct offspring of ATL patients may reflect differences in time or route of HTLV-I infection. Alternatively, it might reflect genetic differences in host susceptibility or virus strain.

Comparison of males versus females with culture-confirmed early Lyme disease at presentation and at 11-20 years after diagnosis.

PubMed

Weitzner, Erica; Visintainer, Paul; Wormser, Gary P

2016-08-01

Lyme disease is the most common vector-borne infection in the United States with 300,000 estimated cases per year. The purpose of this study was to compare the presenting clinical features and long-term outcome of males versus females with culture-confirmed early Lyme disease. 174 males and 109 females with culture-confirmed erythema migrans were entered into a prospective study with follow-up visits scheduled at six months, 12 months and annually thereafter for up to 20 years. Males and females with early Lyme disease had a similar likelihood of having multiple erythema migrans skin lesions and had a similar number of additional subjective symptoms, such as fatigue, at study entry. Among the 71 males and 57 females able to be followed up for 11-20 years, there were no significant differences in baseline symptoms, rate of seroreactivity to Borrelia burgdorferi, or in frequency of post-treatment symptoms. Females, however, were significantly more likely than males to return for follow-up visits (P = 0.0003). Males and females with culture-confirmed early Lyme disease had similar clinical features, rates of seropositivity, and long-term outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

Spotted fever group--Rickettsiae in the Tyrols: evidence by seroepidemiology and PCR.

PubMed

Sonnleitner, S T; Simeoni, J; Lang, S; Dobler, G; Speck, S; Zelger, R; Schennach, H; Lass-Flörl, C; Walder, G

2013-06-01

The aim of our study was to assess the occurrence of Rickettsia in the inner-alpine valleys of the Eastern Alps and to determine the amount of seroreaction among the local human population. Ticks were investigated by PCR and the percentage of seropositives was determined among local blood donors by an in-house immunofluorescence assay. The local cut-off titre for screening of IgG was set at 1 : 128 with a well-characterised low-risk collective according to WHO-guidelines. Positive sera were confirmed by independent re-testing. Rickettsia is present in ticks north and south of the continental divide. Of 259 ticks investigated, 12.4% are positive for Rickettsia. Of over 1200 blood donors tested so far, 7.7% bear IgG at a titre of 1 : 128 or higher against R. helvetica. R. helvetica is present in the study area, causes immunoreaction among local residents and is associated with anamnestic erythema. Furthermore, screening with a second Spotted Fever Group Rickettsia indicates that significant parts of the Tyrolean population are exposed to a Rickettsia other than R. helvetica. © 2012 Blackwell Verlag GmbH.

The panorama of animal leptospirosis in Rio de Janeiro, Brazil, regarding the seroepidemiology of the infection in tropical regions.

PubMed

Martins, Gabriel; Lilenbaum, Walter

2013-12-01

Leptospirosis is an important disease caused by various serovars of Leptospira sp. It can affect humans as well as domestic and wild animals; therefore, it has importance for public health, animal production, and wild species. The aim of this paper is to discuss the epidemiology of animal leptospirosis in Rio de Janeiro, Brazil, as a possible model for other tropical regions. In several studies conducted in the last 20 years, a total of 47 rats, 120 dogs, 875 cows, 695 horses, 1,343 goats, 308 sheep and 351 pigs from all regions of the state, in addition to 107 wild mammals and 73 golden-lion tamarins were tested (MAT) for anti-Leptospira antibodies. Seroreactivity was frequent in all studied species, confirming that the infection is endemic in Rio de Janeiro. Serogroups Icterohaemorrhagiae and Sejroe were the most prevalent in urban and rural scenarios, respectively. This paper reviews the current knowledge on animal leptospirosis in Rio de Janeiro and describes important differences between urban versus rural cycles of the infection in various species. Identification of the prevailing serogroups and their reservoirs is essential for understanding agent-host-environment interactions under tropical conditions.

Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

PubMed

Goense, J B M; Ratnam, R

2003-10-01

An important problem in sensory processing is deciding whether fluctuating neural activity encodes a stimulus or is due to variability in baseline activity. Neurons that subserve detection must examine incoming spike trains continuously, and quickly and reliably differentiate signals from baseline activity. Here we demonstrate that a neural integrator can perform continuous signal detection, with performance exceeding that of trial-based procedures, where spike counts in signal- and baseline windows are compared. The procedure was applied to data from electrosensory afferents of weakly electric fish (Apteronotus leptorhynchus), where weak perturbations generated by small prey add approximately 1 spike to a baseline of approximately 300 spikes s(-1). The hypothetical postsynaptic neuron, modeling an electrosensory lateral line lobe cell, could detect an added spike within 10-15 ms, achieving near ideal detection performance (80-95%) at false alarm rates of 1-2 Hz, while trial-based testing resulted in only 30-35% correct detections at that false alarm rate. The performance improvement was due to anti-correlations in the afferent spike train, which reduced both the amplitude and duration of fluctuations in postsynaptic membrane activity, and so decreased the number of false alarms. Anti-correlations can be exploited to improve detection performance only if there is memory of prior decisions.

Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

PubMed

Du, Pan; Kibbe, Warren A; Lin, Simon M

2006-09-01

A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.

A Comprehensive Approach in Dissemination of Evidence-Based Care for PTSD

DTIC Science & Technology

2012-09-01

been published, to provide evidence-based resources to facilitate practice evaluation and identification of potential gaps in care. In order to...practice’s capacity to provide evidence-based care and identify potential gaps in care as targets for improvement. Finally, strategies to implement...existing patients, the PIP tools can inform improvement efforts at the clinician-, practice-or systems-level, facilitate detection of potential gaps in

Polymerase chain reaction-based detection of Pneumocystis jirovecii in bronchoalveolar lavage fluid for the diagnosis of pneumocystis pneumonia.

PubMed

Oren, Ilana; Hardak, Emilia; Finkelstein, Renato; Yigla, Mordechai; Sprecher, Hannah

2011-09-01

The diagnosis of pneumocystis pneumonia (PCP) in non-human immunodeficiency virus (HIV)-infected immunocompromised patients is notoriously difficult. The recent advent of polymerase chain reaction (PCR)-based detection systems, based on the identification of single fungal genes, has markedly improved diagnostic accuracy in this ominous disease. In an attempt to further improve diagnostic yield, the authors used a PCR-based detection system for Pneumocystis jirovecii, based on targeting 3 distinct genes. During the 4-year period (January 2005 to January 2009), all consecutive immunocompromised patients suspected of having PCP in the differential diagnosis underwent bronchoscopy with bronchoalveolar lavage sampling for the evaluation of the etiology of pulmonary infiltrates. Bronchoalveolar fluid was tested for the presence of a wide variety of possible etiological microorganisms. In a cohort of 214 immunocompromised patients (of which 198 were non-HIV immunocompromised patients) who underwent bronchoscopy with bronchoalveolar lavage for evaluation of pulmonary infiltrates, PCR correctly diagnosed PCP in 75% (42/56) compared with 14% (8/56) diagnosed by traditional stains, and increased diagnostic yield 5.4-fold. Given the absence of a sensitive gold standard, this study demonstrates the usefulness of a multigene PCR-based detection of Pneumocystis jirovecii DNA for supporting the clinical diagnosis of PCP, with high sensitivity and negative predictive value rates compared with direct stains, especially in non-HIV immunocompromised patients.

An aptamer nanopore-enabled microsensor for detection of theophylline.

PubMed

Feng, Silu; Chen, Changtian; Wang, Wei; Que, Long

2018-05-15

This paper reports an aptamer-based nanopore thin film sensor for detecting theophylline in the buffer solution and complex fluids including plant extracts and serum samples. Compared to antibody-based detection, aptamer-based detection offers many advantages such as low cost and high stability at elevated temperatures. Experiments found that this type of sensor can readily detect theophylline at a concentration as low as 0.05µM, which is much lower than the detection limit of current lab-based equipment such as liquid chromatography (LC). Experiments also found that the aptamer-based sensor has good specificity, selectivity, and reasonable reusability with a significantly improved dynamic detection range. By using the same nanopore thin film sensors as the reference sensors to further mitigate the non-specific binding effect, the theophylline in plant extracts and serum has been detected. Only a small amount (~1μL) of plant extracts or serum samples is required to measure theophylline. Its low cost and ease-of-operation make this type of sensor suitable for point-of-care application to monitor the theophylline level of patients in real time. Copyright © 2018 Elsevier B.V. All rights reserved.

Direct conversion semiconductor detectors in positron emission tomography

NASA Astrophysics Data System (ADS)

Cates, Joshua W.; Gu, Yi; Levin, Craig S.

2015-05-01

Semiconductor detectors are playing an increasing role in ongoing research to improve image resolution, contrast, and quantitative accuracy in preclinical applications of positron emission tomography (PET). These detectors serve as a medium for direct detection of annihilation photons. Early clinical translation of this technology has shown improvements in image quality and tumor delineation for head and neck cancers, relative to conventional scintillator-based systems. After a brief outline of the basics of PET imaging and the physical detection mechanisms for semiconductor detectors, an overview of ongoing detector development work is presented. The capabilities of semiconductor-based PET systems and the current state of these devices are discussed.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Optimization of strand displacement amplification-sensitized G-quadruplex DNAzyme-based sensing system and its application in activity detection of uracil-DNA glycosylase.

PubMed

Du, Yi-Chen; Jiang, Hong-Xin; Huo, Yan-Fang; Han, Gui-Mei; Kong, De-Ming

2016-03-15

As an isothermal nucleic acid amplification technique, strand displacement amplification (SDA) reaction has been introduced in G-quadruplex DNAzyme-based sensing system to improve the sensing performance. To further provide useful information for the design of SDA-amplified G-quadruplex DNAzyme-based sensors, the effects of nicking site number in SDA template DNA were investigated. With the increase of the nicking site number from 1 to 2, enrichment of G-quadruplex DNAzyme by SDA is changed from a linear amplification to an exponential amplification, thus greatly increasing the amplification efficiency and subsequently improving the sensing performance of corresponding sensing system. The nicking site number cannot be further increased because more nicking sites might result in high background signals. However, we demonstrated that G-quadruplex DNAzyme enrichment efficiency could be further improved by introducing a cross-triggered SDA strategy, in which two templates each has two nicking sites are used. To validate the proposed cross-triggered SDA strategy, we used it to develop a sensing platform for the detection of uracil-DNA glycosylase (UDG) activity. The sensor enables sensitive detection of UDG activity in the range of 1 × 10(-4)-1 U/mL with a detection limit of 1 × 10(-4)U/mL. Copyright © 2015 Elsevier B.V. All rights reserved.

Automated high-grade prostate cancer detection and ranking on whole slide images

NASA Astrophysics Data System (ADS)

Huang, Chao-Hui; Racoceanu, Daniel

2017-03-01

Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection ( 95:57% +/- 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

New radiological material detection technologies for nuclear forensics: Remote optical imaging and graphene-based sensors.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harrison, Richard Karl; Martin, Jeffrey B.; Wiemann, Dora K.

We developed new detector technologies to identify the presence of radioactive materials for nuclear forensics applications. First, we investigated an optical radiation detection technique based on imaging nitrogen fluorescence excited by ionizing radiation. We demonstrated optical detection in air under indoor and outdoor conditions for alpha particles and gamma radiation at distances up to 75 meters. We also contributed to the development of next generation systems and concepts that could enable remote detection at distances greater than 1 km, and originated a concept that could enable daytime operation of the technique. A second area of research was the development ofmore » room-temperature graphene-based sensors for radiation detection and measurement. In this project, we observed tunable optical and charged particle detection, and developed improved devices. With further development, the advancements described in this report could enable new capabilities for nuclear forensics applications.« less

Infrared small target detection based on Danger Theory

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Yang, Xiao

2009-11-01

To solve the problem that traditional method can't detect the small objects whose local SNR is less than 2 in IR images, a Danger Theory-based model to detect infrared small target is presented in this paper. First, on the analog with immunology, the definition is given, in this paper, to such terms as dangerous signal, antigens, APC, antibodies. Besides, matching rule between antigen and antibody is improved. Prior to training the detection model and detecting the targets, the IR images are processed utilizing adaptive smooth filter to decrease the stochastic noise. Then at the training process, deleting rule, generating rule, crossover rule and the mutation rule are established after a large number of experiments in order to realize immediate convergence and obtain good antibodies. The Danger Theory-based model is built after the training process, and this model can detect the target whose local SNR is only 1.5.

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

PubMed Central

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-01-01

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. PMID:27548179

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

PubMed

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-08-19

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

LEA Detection and Tracking Method for Color-Independent Visual-MIMO

PubMed Central

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-01-01

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement. PMID:27384563

LEA Detection and Tracking Method for Color-Independent Visual-MIMO.

PubMed

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-07-02

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement.

Ultrasensitive aptamer-based protein detection via a dual amplified biocatalytic strategy

PubMed Central

Xiang, Yun; Zhang, Yuyong; Qian, Xiaoqing; Chai, Yaqin; Wang, Joseph; Yuan, Ruo

2010-01-01

We present an ultrasensitive aptasensor for electronic monitoring of proteins through a dual amplified strategy in this paper. The target protein thrombin is sandwiched between an electrode surface confined aptamer and an aptamer-enzyme-carbon nanotube bioconjugate. The analytical signal amplification is achieved by coupling the signal amplification nature of multiple enzymes with the biocatalytic signal enhancement of redox-recycling. Our novel dramatic signal amplification strategy, with a detection limit of 8.3 fM, shows about 4 orders of magnitude improvement in sensitivity for thrombin detection compared to other universal single enzyme-based assay. This makes our approach an attractive alternative to other common PCR-based signal amplification in ultralow level of protein detection. PMID:20452761

A novel track-before-detect algorithm based on optimal nonlinear filtering for detecting and tracking infrared dim target

NASA Astrophysics Data System (ADS)

Tian, Yuexin; Gao, Kun; Liu, Ying; Han, Lu

2015-08-01

Aiming at the nonlinear and non-Gaussian features of the real infrared scenes, an optimal nonlinear filtering based algorithm for the infrared dim target tracking-before-detecting application is proposed. It uses the nonlinear theory to construct the state and observation models and uses the spectral separation scheme based Wiener chaos expansion method to resolve the stochastic differential equation of the constructed models. In order to improve computation efficiency, the most time-consuming operations independent of observation data are processed on the fore observation stage. The other observation data related rapid computations are implemented subsequently. Simulation results show that the algorithm possesses excellent detection performance and is more suitable for real-time processing.

Automobile inspection system based on wireless communication

NASA Astrophysics Data System (ADS)

Miao, Changyun; Ye, Chunqing

2010-07-01

This paper aims to research the Automobile Inspection System based on Wireless Communication, and suggests an overall design scheme which uses GPS for speed detection and Bluetooth and GPRS for communication. The communication between PDA and PC was realized by means of GPRS and TCP/IP; and the hardware circuit and software for detection terminal were devised by means of JINOU-3264 Bluetooth Module after analyzing the Bluetooth and its communication protocol. According to the results of debugging test, this system accomplished GPRS based data communication and management as well as the real-time detection on auto safety performance parameters in crash test via PC, whereby the need for mobility and reliability was met and the efficiency and level of detection was improved.

Investigating prior probabilities in a multiple hypothesis test for use in space domain awareness

NASA Astrophysics Data System (ADS)

Hardy, Tyler J.; Cain, Stephen C.

2016-05-01

The goal of this research effort is to improve Space Domain Awareness (SDA) capabilities of current telescope systems through improved detection algorithms. Ground-based optical SDA telescopes are often spatially under-sampled, or aliased. This fact negatively impacts the detection performance of traditionally proposed binary and correlation-based detection algorithms. A Multiple Hypothesis Test (MHT) algorithm has been previously developed to mitigate the effects of spatial aliasing. This is done by testing potential Resident Space Objects (RSOs) against several sub-pixel shifted Point Spread Functions (PSFs). A MHT has been shown to increase detection performance for the same false alarm rate. In this paper, the assumption of a priori probability used in a MHT algorithm is investigated. First, an analysis of the pixel decision space is completed to determine alternate hypothesis prior probabilities. These probabilities are then implemented into a MHT algorithm, and the algorithm is then tested against previous MHT algorithms using simulated RSO data. Results are reported with Receiver Operating Characteristic (ROC) curves and probability of detection, Pd, analysis.

Phase editing as a signal pre-processing step for automated bearing fault detection

NASA Astrophysics Data System (ADS)

Barbini, L.; Ompusunggu, A. P.; Hillis, A. J.; du Bois, J. L.; Bartic, A.

2017-07-01

Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre- whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.

Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection.

PubMed

Olson, Sarah H; Benedum, Corey M; Mekaru, Sumiko R; Preston, Nicholas D; Mazet, Jonna A K; Joly, Damien O; Brownstein, John S

2015-08-01

The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

A study on real-time low-quality content detection on Twitter from the users' perspective.

PubMed

Chen, Weiling; Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu Sung

2017-01-01

Detection techniques of malicious content such as spam and phishing on Online Social Networks (OSN) are common with little attention paid to other types of low-quality content which actually impacts users' content browsing experience most. The aim of our work is to detect low-quality content from the users' perspective in real time. To define low-quality content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users' opinions on different categories of low-quality content. Both direct and indirect features including newly proposed features are identified to characterize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection performance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low-quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content.

A study on real-time low-quality content detection on Twitter from the users’ perspective

PubMed Central

Yeo, Chai Kiat; Lau, Chiew Tong; Lee, Bu Sung

2017-01-01

Detection techniques of malicious content such as spam and phishing on Online Social Networks (OSN) are common with little attention paid to other types of low-quality content which actually impacts users’ content browsing experience most. The aim of our work is to detect low-quality content from the users’ perspective in real time. To define low-quality content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users’ opinions on different categories of low-quality content. Both direct and indirect features including newly proposed features are identified to characterize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection performance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low-quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content. PMID:28793347

Plasmonic Enzyme-Linked Immunosorbent Assay Using Nanospherical Brushes as a Catalase Container for Colorimetric Detection of Ultralow Concentrations of Listeria monocytogenes.

PubMed

Chen, Rui; Huang, Xiaolin; Xu, Hengyi; Xiong, Yonghua; Li, Yanbin

2015-12-30

Plasmonic enzyme-linked immunosorbent assay (pELISA) based on catalase (CAT)-mediated gold nanoparticle growth exhibits ultrahigh sensitivity for detecting disease-related biomarkers using sandwich formats. However, the limit of detection (LOD) of this strategy for Listeria monocytogenes is only around 10(3) CFU/mL, which considerably exceeds the amount of L. monocytogenes commonly present in food products (<100 CFU/g). Herein, we report an improved pELISA method for detection of L. monocytogenes at ultralow concentrations with the sandwich formats using silica nanoparticles carrying poly(acrylic acid) brushes as a "CAT container" to increase enzyme loading for enhancing the detection signal. Under optimal conditions, the proposed pELISA exhibits good specificity and excellent sensitivity for L. monocytogenes with a LOD of 8 × 10(1) CFU/mL in 0.01 M phosphate-buffered saline, via a reaction that can be discriminated by the naked eye. The LOD obtained by this method was 2 and 5 orders of magnitude lower than that of conventional CAT-based pELISA and horseradish peroxidase (HRP)-based conventional ELISA, respectively. Coupled with large-volume immunomagnetic separation, the LOD for L. monocytogenes-spiked lettuce samples reached 8 × 10(1) CFU/g. The improved pELISA also exhibited a great potential in detecting a single cell of L. monocytogenes in 100 μL of solution.

Improving non-native fish larvae detection based on temporal habitat use.

EPA Science Inventory

As part of the development of an early detection monitoring strategy for non-native fishes, larval fish surveys have been conducted since 2012 in the St. Louis River estuary. Survey data demonstrates considerable variability in fish abundance and species assemblages across habit...

Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system

NASA Astrophysics Data System (ADS)

Budzan, Sebastian

2018-04-01

In this paper, the automatic method of grain detection and classification has been presented. As input, it uses a single digital image obtained from milling process of the copper ore with an high-quality digital camera. The grinding process is an extremely energy and cost consuming process, thus granularity evaluation process should be performed with high efficiency and time consumption. The method proposed in this paper is based on the three-stage image processing. First, using Seeded Region Growing (SRG) segmentation with proposed adaptive thresholding based on the calculation of Relative Standard Deviation (RSD) all grains are detected. In the next step results of the detection are improved using information about the shape of the detected grains using distance map. Finally, each grain in the sample is classified into one of the predefined granularity class. The quality of the proposed method has been obtained by using nominal granularity samples, also with a comparison to the other methods.

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Imaging and Elastometry of Blood Clots Using Magnetomotive Optical Coherence Tomography and Labeled Platelets

PubMed Central

Oldenburg, Amy L.; Wu, Gongting; Spivak, Dmitry; Tsui, Frank; Wolberg, Alisa S.; Fischer, Thomas H.

2013-01-01

Improved methods for imaging and assessment of vascular defects are needed for directing treatment of cardiovascular pathologies. In this paper, we employ magnetomotive optical coherence tomography (MMOCT) as a platform both to detect and to measure the elasticity of blood clots. Detection is enabled through the use of rehydrated, lyophilized platelets loaded with superparamagnetic iron oxides (SPIO-RL platelets) that are functional infusion agents that adhere to sites of vascular endothelial damage. Evidence suggests that the sensitivity for detection is improved over threefold by magnetic interactions between SPIOs inside RL platelets. Using the same MMOCT system, we show how elastometry of simulated clots, using resonant acoustic spectroscopy, is correlated with the fibrin content of the clot. Both methods are based upon magnetic actuation and phase-sensitive optical monitoring of nanoscale displacements using MMOCT, underscoring its utility as a broad-based platform to detect and measure the molecular structure and composition of blood clots. PMID:23833549

DNA polymorphism sensitive impedimetric detection on gold-nanoislands modified electrodes.

PubMed

Bonanni, Alessandra; Pividori, Maria Isabel; del Valle, Manel

2015-05-01

Nanocomposite materials are being increasingly used in biosensing applications as they can significantly improve biosensor performance. Here we report the use of a novel impedimetric genosensor based on gold nanoparticles graphite-epoxy nanocomposite (nanoAu-GEC) for the detection of triple base mutation deletion in a cystic-fibrosis (CF) related human DNA sequence. The developed platform consists of chemisorbing gold nano-islands surrounded by rigid, non-chemisorbing, and conducting graphite-epoxy composite. The ratio of the gold nanoparticles in the composite was carefully optimized by electrochemical and microscopy studies. Such platform allows the very fast and stable thiol immobilization of DNA probes on the gold islands, thus minimizing the steric and electrostatic repulsion among the DNA probes and improving the detection of DNA polymorphism down to 2.25fmol by using electrochemical impedance spectroscopy. These findings are very important in order to develop new and renewable platforms to be used in point-of-care devices for the detection of biomolecules. Copyright © 2015 Elsevier B.V. All rights reserved.

A novel through-wall respiration detection algorithm using UWB radar.

PubMed

Li, Xin; Qiao, Dengyu; Li, Ye; Dai, Huhe

2013-01-01

Through-wall respiration detection using Ultra-wideband (UWB) impulse radar can be applied to the post-disaster rescue, e.g., searching living persons trapped in ruined buildings after an earthquake. Since strong interference signals always exist in the real-life scenarios, such as static clutter, noise, etc., while the respiratory signal is very weak, the signal to noise and clutter ratio (SNCR) is quite low. Therefore, through-wall respiration detection using UWB impulse radar under low SNCR is a challenging work in the research field of searching survivors after disaster. In this paper, an improved UWB respiratory signal model is built up based on an even power of cosine function for the first time. This model is used to reveal the harmonic structure of respiratory signal, based on which a novel high-performance respiration detection algorithm is proposed. This novel algorithm is assessed by experimental verification and simulation and shows about a 1.5dB improvement of SNR and SNCR.

GaAs Coupled Micro Resonators with Enhanced Sensitive Mass Detection

PubMed Central

Chopard, Tony; Lacour, Vivien; Leblois, Therese

2014-01-01

This work demonstrates the improvement of mass detection sensitivity and time response using a simple sensor structure. Indeed, complicated technological processes leading to very brittle sensing structures are often required to reach high sensitivity when we want to detect specific molecules in biological fields. These developments constitute an obstacle to the early diagnosis of diseases. An alternative is the design of coupled structures. In this study, the device is based on the piezoelectric excitation and detection of two GaAs microstructures vibrating in antisymmetric modes. GaAs is a crystal which has the advantage to be micromachined easily using typical clean room processes. Moreover, we showed its high potential in direct biofunctionalisation for use in the biological field. A specific design of the device was performed to improve the detection at low mass and an original detection method has been developed. The principle is to exploit the variation in amplitude at the initial resonance frequency which has in the vicinity of weak added mass the greatest slope. Therefore, we get a very good resolution for an infinitely weak mass: relative voltage variation of 8%/1 fg. The analysis is based on results obtained by finite element simulation. PMID:25474375

Advanced DNA- and Protein-based Methods for the Detection and Investigation of Food Allergens.

PubMed

Prado, M; Ortea, I; Vial, S; Rivas, J; Calo-Mata, P; Barros-Velázquez, J

2016-11-17

Currently, food allergies are an important health concern worldwide. The presence of undeclared allergenic ingredients or the presence of traces of allergens due to contamination during food processing poses a great health risk to sensitized individuals. Therefore, reliable analytical methods are required to detect and identify allergenic ingredients in food products. The present review addresses the recent developments regarding the application of DNA- and protein-based methods for the detection of allergenic ingredients in foods. The fitness-for-purpose of reviewed methodology will be discussed, and future trends will be highlighted. Special attention will be given to the evaluation of the potential of newly developed and promising technologies that can improve the detection and identification of allergenic ingredients in foods, such as the use of biosensors and/or nanomaterials to improve detection limits, specificity, ease of use, or to reduce the time of analysis. Such rapid food allergen test methods are required to facilitate the reliable detection of allergenic ingredients by control laboratories, to give the food industry the means to easily determine whether its product has been subjected to cross-contamination and, simultaneously, to identify how and when this cross-contamination occurred.

An incremental anomaly detection model for virtual machines.

PubMed

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

An incremental anomaly detection model for virtual machines

PubMed Central

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

Final Technical Report. Project Boeing SGS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bell, Thomas E.

Boeing and its partner, PJM Interconnection, teamed to bring advanced “defense-grade” technologies for cyber security to the US regional power grid through demonstration in PJM’s energy management environment. Under this cooperative project with the Department of Energy, Boeing and PJM have developed and demonstrated a host of technologies specifically tailored to the needs of PJM and the electric sector as a whole. The team has demonstrated to the energy industry a combination of processes, techniques and technologies that have been successfully implemented in the commercial, defense, and intelligence communities to identify, mitigate and continuously monitor the cyber security of criticalmore » systems. Guided by the results of a Cyber Security Risk-Based Assessment completed in Phase I, the Boeing-PJM team has completed multiple iterations through the Phase II Development and Phase III Deployment phases. Multiple cyber security solutions have been completed across a variety of controls including: Application Security, Enhanced Malware Detection, Security Incident and Event Management (SIEM) Optimization, Continuous Vulnerability Monitoring, SCADA Monitoring/Intrusion Detection, Operational Resiliency, Cyber Range simulations and hands on cyber security personnel training. All of the developed and demonstrated solutions are suitable for replication across the electric sector and/or the energy sector as a whole. Benefits identified include; Improved malware and intrusion detection capability on critical SCADA networks including behavioral-based alerts resulting in improved zero-day threat protection; Improved Security Incident and Event Management system resulting in better threat visibility, thus increasing the likelihood of detecting a serious event; Improved malware detection and zero-day threat response capability; Improved ability to systematically evaluate and secure in house and vendor sourced software applications; Improved ability to continuously monitor and maintain secure configuration of network devices resulting in reduced vulnerabilities for potential exploitation; Improved overall cyber security situational awareness through the integration of multiple discrete security technologies into a single cyber security reporting console; Improved ability to maintain the resiliency of critical systems in the face of a targeted cyber attack of other significant event; Improved ability to model complex networks for penetration testing and advanced training of cyber security personnel« less

Electrokinetic Stringency Control in Self-Assembled Monolayer-based Biosensors for Multiplex Urinary Tract Infection Diagnosis

PubMed Central

Liu, Tingting; Sin, Mandy L. Y.; Pyne, Jeff D.; Gau, Vincent; Liao, Joseph C.; Wong, Pak Kin

2013-01-01

Rapid detection of bacterial pathogens is critical toward judicious management of infectious diseases. Herein, we demonstrate an in situ electrokinetic stringency control approach for a self-assembled monolayer-based electrochemical biosensor toward urinary tract infection diagnosis. The in situ electrokinetic stringency control technique generates Joule heating induced temperature rise and electrothermal fluid motion directly on the sensor to improve its performance for detecting bacterial 16S rRNA, a phylogenetic biomarker. The dependence of the hybridization efficiency reveals that in situ electrokinetic stringency control is capable of discriminating single-base mismatches. With electrokinetic stringency control, the background noise due to the matrix effects of clinical urine samples can be reduced by 60%. The applicability of the system is demonstrated by multiplex detection of three uropathogenic clinical isolates with similar 16S rRNA sequences. The results demonstrate that electrokinetic stringency control can significantly improve the signal-to-noise ratio of the biosensor for multiplex urinary tract infection diagnosis. PMID:23891989

Measuring Time-of-Flight in an Ultrasonic LPS System Using Generalized Cross-Correlation

PubMed Central

Villladangos, José Manuel; Ureña, Jesús; García, Juan Jesús; Mazo, Manuel; Hernández, Álvaro; Jiménez, Ana; Ruíz, Daniel; De Marziani, Carlos

2011-01-01

In this article, a time-of-flight detection technique in the frequency domain is described for an ultrasonic Local Positioning System (LPS) based on encoded beacons. Beacon transmissions have been synchronized and become simultaneous by means of the DS-CDMA (Direct-Sequence Code Division Multiple Access) technique. Every beacon has been associated to a 255-bit Kasami code. The detection of signal arrival instant at the receiver, from which the distance to each beacon can be obtained, is based on the application of the Generalized Cross-Correlation (GCC), by using the cross-spectral density between the received signal and the sequence to be detected. Prior filtering to enhance the frequency components around the carrier frequency (40 kHz) has improved estimations when obtaining the correlation function maximum, which implies an improvement in distance measurement precision. Positioning has been achieved by using hyperbolic trilateration, based on the Time Differences of Arrival (TDOA) between a reference beacon and the others. PMID:22346645

Measuring time-of-flight in an ultrasonic LPS system using generalized cross-correlation.

PubMed

Villladangos, José Manuel; Ureña, Jesús; García, Juan Jesús; Mazo, Manuel; Hernández, Alvaro; Jiménez, Ana; Ruíz, Daniel; De Marziani, Carlos

2011-01-01

In this article, a time-of-flight detection technique in the frequency domain is described for an ultrasonic local positioning system (LPS) based on encoded beacons. Beacon transmissions have been synchronized and become simultaneous by means of the DS-CDMA (direct-sequence code Division multiple access) technique. Every beacon has been associated to a 255-bit Kasami code. The detection of signal arrival instant at the receiver, from which the distance to each beacon can be obtained, is based on the application of the generalized cross-correlation (GCC), by using the cross-spectral density between the received signal and the sequence to be detected. Prior filtering to enhance the frequency components around the carrier frequency (40 kHz) has improved estimations when obtaining the correlation function maximum, which implies an improvement in distance measurement precision. Positioning has been achieved by using hyperbolic trilateration, based on the time differences of arrival (TDOA) between a reference beacon and the others.

A versatile quantitation platform based on platinum nanoparticles incorporated volumetric bar-chart chip for highly sensitive assays.

PubMed

Wang, Yuzhen; Zhu, Guixian; Qi, Wenjin; Li, Ying; Song, Yujun

2016-11-15

Platinum nanoparticles incorporated volumetric bar-chart chip (PtNPs-V-Chip) is able to be used for point-of-care tests by providing quantitative and visualized readout without any assistance from instruments, data processing, or graphic plotting. To improve the sensitivity of PtNPs-V-Chip, hybridization chain reaction was employed in this quantitation platform for highly sensitive assays that can detect as low as 16 pM Ebola Virus DNA, 0.01ng/mL carcinoembryonic antigen (CEA), and the 10 HER2-expressing cancer cells. Based on this amplified strategy, a 100-fold decrease of detection limit was achieved for DNA by improving the number of platinum nanoparticle catalyst for the captured analyte. This quantitation platform can also distinguish single base mismatch of DNA hybridization and observe the concentration threshold of CEA. The new strategy lays the foundation for this quantitation platform to be applied in forensic analysis, biothreat detection, clinical diagnostics and drug screening. Copyright © 2016 Elsevier B.V. All rights reserved.

Practical Considerations for Optic Nerve Estimation in Telemedicine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karnowski, Thomas Paul; Aykac, Deniz; Chaum, Edward

The projected increase in diabetes in the United States and worldwide has created a need for broad-based, inexpensive screening for diabetic retinopathy (DR), an eye disease which can lead to vision impairment. A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a low-cost way of achieving broad-based screening. In this work we report on the effect of quality estimation on an optic nerve (ON) detection method with a confidence metric. We report on an improvement of the fusion technique using a data set from an ophthalmologists practice then show themore » results of the method as a function of image quality on a set of images from an on-line telemedicine network collected in Spring 2009 and another broad-based screening program. We show that the fusion method, combined with quality estimation processing, can improve detection performance and also provide a method for utilizing a physician-in-the-loop for images that may exceed the capabilities of automated processing.« less

A dynamic bead-based microarray for parallel DNA detection

NASA Astrophysics Data System (ADS)

Sochol, R. D.; Casavant, B. P.; Dueck, M. E.; Lee, L. P.; Lin, L.

2011-05-01

A microfluidic system has been designed and constructed by means of micromachining processes to integrate both microfluidic mixing of mobile microbeads and hydrodynamic microbead arraying capabilities on a single chip to simultaneously detect multiple bio-molecules. The prototype system has four parallel reaction chambers, which include microchannels of 18 × 50 µm2 cross-sectional area and a microfluidic mixing section of 22 cm length. Parallel detection of multiple DNA oligonucleotide sequences was achieved via molecular beacon probes immobilized on polystyrene microbeads of 16 µm diameter. Experimental results show quantitative detection of three distinct DNA oligonucleotide sequences from the Hepatitis C viral (HCV) genome with single base-pair mismatch specificity. Our dynamic bead-based microarray offers an effective microfluidic platform to increase parallelization of reactions and improve microbead handling for various biological applications, including bio-molecule detection, medical diagnostics and drug screening.

Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Virtanen, Jaakko; Noponen, Tommi; Kotilahti, Kalle; Virtanen, Juha; Ilmoniemi, Risto J.

2011-08-01

In medical near-infrared spectroscopy (NIRS), movements of the subject often cause large step changes in the baselines of the measured light attenuation signals. This prevents comparison of hemoglobin concentration levels before and after movement. We present an accelerometer-based motion artifact removal (ABAMAR) algorithm for correcting such baseline motion artifacts (BMAs). ABAMAR can be easily adapted to various long-term monitoring applications of NIRS. We applied ABAMAR to NIRS data collected in 23 all-night sleep measurements and containing BMAs from involuntary movements during sleep. For reference, three NIRS researchers independently identified BMAs from the data. To determine whether the use of an accelerometer improves BMA detection accuracy, we compared ABAMAR to motion detection based on peaks in the moving standard deviation (SD) of NIRS data. The number of BMAs identified by ABAMAR was similar to the number detected by the humans, and 79% of the artifacts identified by ABAMAR were confirmed by at least two humans. While the moving SD of NIRS data could also be used for motion detection, on average 2 out of the 10 largest SD peaks in NIRS data each night occurred without the presence of movement. Thus, using an accelerometer improves BMA detection accuracy in NIRS.

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

PubMed

Ge, Jing; Zhang, Guoping

2015-01-01

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

PubMed Central

Habib, Mohammad Ashfak; Mohktar, Mas S.; Kamaruzzaman, Shahrul Bahyah; Lim, Kheng Seang; Pin, Tan Maw; Ibrahim, Fatimah

2014-01-01

This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems. PMID:24759116

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eck, Brendan L.; Fahmi, Rachid; Miao, Jun

2015-10-15

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated usingmore » a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.« less

Evaluation of the morphology structure of meibomian glands based on mask dodging method

NASA Astrophysics Data System (ADS)

Yan, Huangping; Zuo, Yingbo; Chen, Yisha; Chen, Yanping

2016-10-01

Low contrast and non-uniform illumination of infrared (IR) meibography images make the detection of meibomian glands challengeable. An improved Mask dodging algorithm is proposed. To overcome the shortage of low contrast using traditional Mask dodging method, a scale factor is used to enhance the image after subtracting background image from an original one. Meibomian glands are detected and the ratio of the meibomian gland area to the measurement area is calculated. The results show that the improved Mask algorithm has ideal dodging effect, which can eliminate non-uniform illumination and improve contrast of meibography images effectively.

Immunodiagnosis of Human Fascioliasis: An Update of Concepts and Performances of the Serological Assays

PubMed Central

Khabisi, Samaneh Abdolahi

2017-01-01

Human Fascioliasis (HF) is a foodborne neglected parasitic disease caused by Fasciola hepatica and Fasciola gigantica. New epidemiological data suggest that the endemic areas of the disease are expanding and HF is being reported from areas where it was previously not observed. Diagnosis of HF is challenging. Performances of parasitological approaches, based on the detection of parasite’s egg in the stool, are not satisfactory. Currently serological methods for the diagnosis of HF are mainly based on detection of anti-Fasciola antibodies in serum. Although, there have been some improvement in the development of immunological diagnostic tests for the diagnosis of HF, yet these tests suffer from insufficiency in sensitivity or/and specificity. Detection of antigens, rather than antibodies, seems to be a suitable approach in the diagnosis of HF. Antigen can be detected in sera or stool of the fascioliasis patients. Circulating antigen in serum disappears within a short time and most of the circulating antigens are in immune complex forms which are not freely available to be detected. Therefore, antigenemia might not be an appropriate method for the diagnosis of HF. Detection of antigen in stool (coproantigens) seems to be a suitable alternative method for the diagnosis of HF. Recent data provided convincing evidence that detection of coproantigen improved and simplified the diagnosis of HF. The present review highlights the new achievements in designing and improvement of diagnostic approaches for the immunodiagnosis of HF. Moreover, current status of the available immunodiagnostic techniques for the diagnosis of HF, their strengths and weaknesses has been discussed. PMID:28764235

University of Glasgow at TREC 2008: Experiments in Blog, Enterprise, and Relevance Feedback Tracks with Terrier

DTIC Science & Technology

2008-11-01

improves our TREC 2007 dictionary -based approach by automatically building an internal opinion dictionary from the collection itself. We measure the opin...detecting opinionated documents. The first approach improves our TREC 2007 dictionary -based approach by automat- ically building an internal opinion... dictionary from the collection itself. The second approach is based on the OpinionFinder tool, which identifies subjective sentences in text. In particular

No Evidence for Xenotropic Murine Leukemia-Related Virus Infection in Sweden Using Internally Controlled Multiepitope Suspension Array Serology

PubMed Central

Blomberg, Fredrik; Sjösten, Anna; Sheikholvaezin, Ali; Bölin-Wiener, Agnes; Elfaitouri, Amal; Hessel, Sanna; Gottfries, Carl-Gerhard; Zachrisson, Olof; Öhrmalm, Christina; Jobs, Magnus; Pipkorn, Rüdiger

2012-01-01

Many syndromes have a large number of differential diagnoses, a situation which calls for multiplex diagnostic systems. Myalgic encephalomyelitis (ME), also named chronic fatigue syndrome (CFS), is a common disease of unknown etiology. A mouse retrovirus, xenotropic murine leukemia-related virus (XMRV), was found in ME/CFS patients and blood donors, but this was not corroborated. However, the paucity of serological investigations on XMRV in humans prompted us to develop a serological assay which cover many aspects of XMRV antigenicity. It is a novel suspension array method, using a multiplex IgG assay with nine recombinant proteins from the env and gag genes of XMRV and 38 peptides based on known epitopes of vertebrate gammaretroviruses. IgG antibodies were sought in 520 blood donors and 85 ME/CFS patients and in positive- and negative-control sera from animals. We found no differences in seroreactivity between blood donors and ME/CFS patients for any of the antigens. This did not support an association between ME/CFS and XMRV infection. The multiplex serological system had several advantages: (i) biotinylated protein G allowed us to run both human and animal sera, which is essential because of a lack of XMRV-positive humans; (ii) a novel quality control was a pan-peptide positive-control rabbit serum; and (iii) synthetic XMRV Gag peptides with degenerate positions covering most of the variation of murine leukemia-like viruses did not give higher background than nondegenerate analogs. The principle may be used for creation of variant tolerant peptide serologies. Thus, our system allows rational large-scale serological assays with built-in quality control. PMID:22787191

Prevalence of serological response to Borrelia burgdorferi in farmers from eastern and central Poland.

PubMed

Zając, V; Pinkas, J; Wójcik-Fatla, A; Dutkiewicz, J; Owoc, A; Bojar, I

2017-03-01

Lyme borreliosis (Lyme disease) caused by the Borrelia burgdorferi sensu lato spirochete is the most common tick-borne infection manifested by a wide spectrum of clinical symptoms. In Poland, the preventive health care does not comprise individual farmers as it is practiced in foresters. The objective of this study was to evaluate the exposure of Polish farmers to infection with B. burgdorferi, based on serological screening test and epidemiological investigation. A total of 3,597 farmers were examined for the presence of B. burgdorferi antibodies, as well as interviewed regarding exposure to ticks and prophylaxis of tick-borne diseases. The prevalence varied between 18.2 and 50.7 % suggesting a focal occurrence of borreliosis. A significant increase in the frequency of positive reactions in the oldest age ranges was observed, equaling 30.9 % in the range of 60-69 years and 53.6 % in the range of 80-91 years. The prevalence of the anti-B. burgdorferi antibodies of IgG class (14.7 %) was similar to that of IgM class (16.0 %). Seroreactivity to B. burgdorferi antigen was significantly higher in the group of farmers exposed to repeated tick bites. Significant relationships were also found between some other risk factors and occurrence of seropositive reactions to B. burgdorferi. To the best of our knowledge, this is the first study concerning seroprevalence to B. burgdorferi carried out on such a large group of farmers. Results indicate a high risk of B. burgdorferi infection among Polish farmers and associations between some risk factors and the presence of seropositive reactions.

Seroprevalence of anti-HCV antibodies among blood donors of north India

PubMed Central

Makroo, R.N.; Walia, Rimpreet Singh; Chowdhry, Mohit; Bhatia, Aakanksha; Hegde, Vikas; Rosamma, N.L.

2013-01-01

Background & objectives: Transfusion of blood and blood products although considered as a life saving treatment modality, but may lead to certain infectious and non-infectious complications in the recipients. The purpose of this analysis was to monitor the seroprevalence of anti-HCV antibody in the blood donor population in a hospital based blood bank in north India, to evaluate the trends over the years (2001-2011). Methods: Relevant information of all the blood donors who donated whole blood at the department of Transfusion Medicine, Indraprastha Apollo Hospitals, New Delhi from the January 1, 2001 to December 31, 2011 was retrieved from the departmental records. The number of donors who were found reactive for anti-HCV anatibodies was calculated. Results: Of the 2,06,022 blood donors, 1,93,661 were males and 12,361 were females. The percentage of whole blood donors found seroreactive for anti-HCV antibodies was 0.39 per cent (n=795). The seroprevalence of anti-HCV in male blood donors was 0.38 per cent (n=750) and the respective seroprevalence in female blood donors was 0.36 per cent (n=45). No significant change in the trend of HCV seroprevalence was observed over the period under consideration. Maximum seroprevalence of anti-HCV was observed in the age group of 18 to 30 yr (0.41%) and the minimum in the age group of 51 to 60 yr (0.26%). Interpretation & conclusion: HCV seroprevalence in our study was 0.39 per cent and a decreasing trend with age was observed. No significant change in the trend of anti-HCV seroprevalence was seen over a decade. Since, no vaccine is presently available for immunization against HCV infection, transfusion transmitted HCV infection remains a potential threat to the safety of the blood supply. PMID:24056566

Neuroimaging Evidence for 2 Types of Plasticity in Association with Visual Perceptual Learning.

PubMed

Shibata, Kazuhisa; Sasaki, Yuka; Kawato, Mitsuo; Watanabe, Takeo

2016-09-01

Visual perceptual learning (VPL) is long-term performance improvement as a result of perceptual experience. It is unclear whether VPL is associated with refinement in representations of the trained feature (feature-based plasticity), improvement in processing of the trained task (task-based plasticity), or both. Here, we provide empirical evidence that VPL of motion detection is associated with both types of plasticity which occur predominantly in different brain areas. Before and after training on a motion detection task, subjects' neural responses to the trained motion stimuli were measured using functional magnetic resonance imaging. In V3A, significant response changes after training were observed specifically to the trained motion stimulus but independently of whether subjects performed the trained task. This suggests that the response changes in V3A represent feature-based plasticity in VPL of motion detection. In V1 and the intraparietal sulcus, significant response changes were found only when subjects performed the trained task on the trained motion stimulus. This suggests that the response changes in these areas reflect task-based plasticity. These results collectively suggest that VPL of motion detection is associated with the 2 types of plasticity, which occur in different areas and therefore have separate mechanisms at least to some degree. © The Author 2016. Published by Oxford University Press.

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system

PubMed Central

Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

2017-01-01

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness. PMID:28296902

Active shape models incorporating isolated landmarks for medical image annotation

NASA Astrophysics Data System (ADS)

Norajitra, Tobias; Meinzer, Hans-Peter; Stieltjes, Bram; Maier-Hein, Klaus H.

2014-03-01

Apart from their robustness in anatomic surface segmentation, purely surface based 3D Active Shape Models lack the ability to automatically detect and annotate non-surface key points of interest. However, annotation of anatomic landmarks is desirable, as it yields additional anatomic and functional information. Moreover, landmark detection might help to further improve accuracy during ASM segmentation. We present an extension of surface-based 3D Active Shape Models incorporating isolated non-surface landmarks. Positions of isolated and surface landmarks are modeled conjoint within a point distribution model (PDM). Isolated landmark appearance is described by a set of haar-like features, supporting local landmark detection on the PDM estimates using a kNN-Classi er. Landmark detection was evaluated in a leave-one-out cross validation on a reference dataset comprising 45 CT volumes of the human liver after shape space projection. Depending on the anatomical landmark to be detected, our experiments have shown in about 1/4 up to more than 1/2 of all test cases a signi cant improvement in detection accuracy compared to the position estimates delivered by the PDM. Our results encourage further research with regard to the combination of shape priors and machine learning for landmark detection within the Active Shape Model Framework.

On the performance of energy detection-based CR with SC diversity over IG channel

NASA Astrophysics Data System (ADS)

Verma, Pappu Kumar; Soni, Sanjay Kumar; Jain, Priyanka

2017-12-01

Cognitive radio (CR) is a viable 5G technology to address the scarcity of the spectrum. Energy detection-based sensing is known to be the simplest method as far as hardware complexity is concerned. In this paper, the performance of spectrum sensing-based energy detection technique in CR networks over inverse Gaussian channel for selection combining diversity technique is analysed. More specifically, accurate analytical expressions for the average detection probability under different detection scenarios such as single channel (no diversity) and with diversity reception are derived and evaluated. Further, the detection threshold parameter is optimised by minimising the probability of error over several diversity branches. The results clearly show the significant improvement in the probability of detection when optimised threshold parameter is applied. The impact of shadowing parameters on the performance of energy detector is studied in terms of complimentary receiver operating characteristic curve. To verify the correctness of our analysis, the derived analytical expressions are corroborated via exact result and Monte Carlo simulations.

The ship edge feature detection based on high and low threshold for remote sensing image

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

MO-FG-CAMPUS-TeP1-03: Pre-Treatment Surface Imaging Based Collision Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiant, D; Maurer, J; Liu, H

2016-06-15

Purpose: Modern radiotherapy increasingly employs large immobilization devices, gantry attachments, and couch rotations for treatments. All of which raise the risk of collisions between the patient and the gantry / couch. Collision detection is often achieved by manually checking each couch position in the treatment room and sometimes results in extraneous imaging if collisions are detected after image based setup has begun. In the interest of improving efficiency and avoiding extra imaging, we explore the use of a surface imaging based collision detection model. Methods: Surfaces acquired from AlignRT (VisionRT, London, UK) were transferred in wavefront format to a custommore » Matlab (Mathworks, Natick, MA) software package (CCHECK). Computed tomography (CT) scans acquired at the same time were sent to CCHECK in DICOM format. In CCHECK, binary maps of the surfaces were created and overlaid on the CT images based on the fixed relationship of the AlignRT and CT coordinate systems. Isocenters were added through a graphical user interface (GUI). CCHECK then compares the inputted surfaces to a model of the linear accelerator (linac) to check for collisions at defined gantry and couch positions. Note, CCHECK may be used with or without a CT. Results: The nominal surface image field of view is 650 mm × 900 mm, with variance based on patient position and size. The accuracy of collision detections is primarily based on the linac model and the surface mapping process. The current linac model and mapping process yield detection accuracies on the order of 5 mm, assuming no change in patient posture between surface acquisition and treatment. Conclusions: CCHECK provides a non-ionizing method to check for collisions without the patient in the treatment room. Collision detection accuracy may be improved with more robust linac modeling. Additional gantry attachments (e.g. conical collimators) can be easily added to the model.« less

Adaptive Image Processing Methods for Improving Contaminant Detection Accuracy on Poultry Carcasses

USDA-ARS?s Scientific Manuscript database

Technical Abstract A real-time multispectral imaging system has demonstrated a science-based tool for fecal and ingesta contaminant detection during poultry processing. In order to implement this imaging system at commercial poultry processing industry, the false positives must be removed. For doi...

Characterizing body temperature and activity changes at the onset of estrus in replacement gilts

USDA-ARS?s Scientific Manuscript database

Accurate estrus detection can improve sow conception rates and increase swine production efficiency. Unfortunately, current estrus detection practices based on individual animal behavior may be inefficient due to large sow populations at commercial farms and the associated labor required. Therefore,...

Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm

NASA Astrophysics Data System (ADS)

Neri, P.

2017-05-01

Recent papers introduced the Non-Harmonic Fourier Analysis for bladed wheels damage detection. This technique showed its potential in estimating the frequency of sinusoidal signals even when the acquisition time is short with respect to the vibration period, provided that some hypothesis are fulfilled. Anyway, previously proposed algorithms showed severe limitations in cracks detection at their early stage. The present paper proposes an improved algorithm which allows to detect a blade vibration frequency shift due to a crack whose size is really small compared to the blade width. Such a technique could be implemented for condition-based maintenance, allowing to use non-contact methods for vibration measurements. A stator-fixed laser sensor could monitor all the blades as they pass in front of the spot, giving precious information about the wheel health. This configuration determines an acquisition time for each blade which become shorter as the machine rotational speed increases. In this situation, traditional Discrete Fourier Transform analysis results in poor frequency resolution, being not suitable for small frequency shift detection. Non-Harmonic Fourier Analysis instead showed high reliability in vibration frequency estimation even with data samples collected in a short time range. A description of the improved algorithm is provided in the paper, along with a comparison with the previous one. Finally, a validation of the method is presented, based on finite element simulations results.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

PubMed

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Improving Access to Eye Care among Persons at High-Risk of Glaucoma in Philadelphia — Design and Methodology: The Philadelphia Glaucoma Detection and Treatment Project

PubMed Central

Myers, Jonathan S.; Henderer, Jeffrey; Crews, John E.; Saaddine, Jinan B.; Molineaux, Jeanne; Johnson, Deiana; Sembhi, Harjeet; Stratford, Shayla; Suleiman, Ayman; Pizzi, Laura; Spaeth, George L.; Katz, L. Jay

2016-01-01

Purpose The Wills Eye Glaucoma Research Center initiated a 2-year demonstration project to develop and implement a community-based intervention to improve detection and management of glaucoma in Philadelphia. Methods The glaucoma detection examination consisted of: ocular, medical, and family history; visual acuity testing; corneal pachymetry; biomicroscopy of the anterior segment; intraocular pressure (IOP) measurement; gonioscopy; funduscopy; automated visual field testing; and fundus-color photography. Treatment included laser surgery and/or IOP-lowering medication. A cost analysis was conducted to understand resource requirements. Outcome measures included; prevalence of glaucoma-related pathology and other eye diseases among high-risk populations; the impact of educational workshops on level of knowledge about glaucoma (assessed by pre- and post-test evaluation); and patient satisfaction of the glaucoma detection examinations in the community (assessed by satisfaction survey). Treatment outcome measures were change in IOP at 4–6 weeks and 4–6 months following selective laser trabeculoplasty treatment, deepening of the anterior chamber angle following laser-peripheral iridotomy treatment, and rate of adherence to recommended follow-up examinations. Cost outcomes included total program costs, cost per case of glaucoma detected, and cost per case of ocular disease detected. Results This project enrolled 1649 participants (African Americans aged 50+ years, adults 60+ years and individuals with a family history of glaucoma). A total of 1074 individuals attended a glaucoma educational workshop and 1508 scheduled glaucoma detection examination appointments in the community setting. Conclusions The Philadelphia Glaucoma Detection and Treatment Project aimed to improve access and use of eye care and to provide a model for a targeted community-based glaucoma program. PMID:26950056

Detection capability of the IMS seismic network based on ambient seismic noise measurements

NASA Astrophysics Data System (ADS)

Gaebler, Peter J.; Ceranna, Lars

2016-04-01

All nuclear explosions - on the Earth's surface, underground, underwater or in the atmosphere - are banned by the Comprehensive Nuclear-Test-Ban Treaty (CTBT). As part of this treaty, a verification regime was put into place to detect, locate and characterize nuclear explosion testings at any time, by anyone and everywhere on the Earth. The International Monitoring System (IMS) plays a key role in the verification regime of the CTBT. Out of the different monitoring techniques used in the IMS, the seismic waveform approach is the most effective technology for monitoring nuclear underground testing and to identify and characterize potential nuclear events. This study introduces a method of seismic threshold monitoring to assess an upper magnitude limit of a potential seismic event in a certain given geographical region. The method is based on ambient seismic background noise measurements at the individual IMS seismic stations as well as on global distance correction terms for body wave magnitudes, which are calculated using the seismic reflectivity method. From our investigations we conclude that a global detection threshold of around mb 4.0 can be achieved using only stations from the primary seismic network, a clear latitudinal dependence for the detection threshold can be observed between northern and southern hemisphere. Including the seismic stations being part of the auxiliary seismic IMS network results in a slight improvement of global detection capability. However, including wave arrivals from distances greater than 120 degrees, mainly PKP-wave arrivals, leads to a significant improvement in average global detection capability. In special this leads to an improvement of the detection threshold on the southern hemisphere. We further investigate the dependence of the detection capability on spatial (latitude and longitude) and temporal (time) parameters, as well as on parameters such as source type and percentage of operational IMS stations.

Improving Access to Eye Care among Persons at High-Risk of Glaucoma in Philadelphia--Design and Methodology: The Philadelphia Glaucoma Detection and Treatment Project.

PubMed

Hark, Lisa; Waisbourd, Michael; Myers, Jonathan S; Henderer, Jeffrey; Crews, John E; Saaddine, Jinan B; Molineaux, Jeanne; Johnson, Deiana; Sembhi, Harjeet; Stratford, Shayla; Suleiman, Ayman; Pizzi, Laura; Spaeth, George L; Katz, L Jay

2016-01-01

The Wills Eye Glaucoma Research Center initiated a 2-year demonstration project to develop and implement a community-based intervention to improve detection and management of glaucoma in Philadelphia. The glaucoma detection examination consisted of: ocular, medical, and family history; visual acuity testing; corneal pachymetry; biomicroscopy of the anterior segment; intraocular pressure (IOP) measurement; gonioscopy; funduscopy; automated visual field testing; and fundus-color photography. Treatment included laser surgery and/or IOP-lowering medication. A cost analysis was conducted to understand resource requirements. Outcome measures included; prevalence of glaucoma-related pathology and other eye diseases among high-risk populations; the impact of educational workshops on level of knowledge about glaucoma (assessed by pre- and post-test evaluation); and patient satisfaction of the glaucoma detection examinations in the community (assessed by satisfaction survey). Treatment outcome measures were change in IOP at 4-6 weeks and 4-6 months following selective laser trabeculoplasty treatment, deepening of the anterior chamber angle following laser-peripheral iridotomy treatment, and rate of adherence to recommended follow-up examinations. Cost outcomes included total program costs, cost per case of glaucoma detected, and cost per case of ocular disease detected. This project enrolled 1649 participants (African Americans aged 50+ years, adults 60+ years and individuals with a family history of glaucoma). A total of 1074 individuals attended a glaucoma educational workshop and 1508 scheduled glaucoma detection examination appointments in the community setting. The Philadelphia Glaucoma Detection and Treatment Project aimed to improve access and use of eye care and to provide a model for a targeted community-based glaucoma program.

Four-Wave-Mixing Approach to In Situ Detection of Nanoparticles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerakis, Alexandros; Yeh, Yao -Wen; Shneider, Mikhail N.

Here, we report on the development and experimental validation of a laser-based technique which uses coherent Rayleigh-Brillouin scattering (CRBS) to detect nanoparticles with characteristic sizes ranging from the atomic scale to tens of nanometers. This technique is aimed (nonexclusively) at the detection of nanoparticles produced by volumetric nanoparticle synthesis methods. Using CRBS, carbon nanoparticles of dimensions less than 10 nm and concentrations of 10 10 cm –3 are detected in situ in a carbon arc discharge with graphite electrodes. This four-wave-mixing approach should enable advances in the understanding of nanoparticle growth that could potentially lead to improved modeling of themore » growth mechanisms, and thus to improve synthesis selectivity of nanoparticles and yield.« less

High Sensitivity Detection of CdSe/ZnS Quantum Dot-Labeled DNA Based on N-type Porous Silicon Microcavities

PubMed Central

Lv, Changwu; Jia, Zhenhong; Lv, Jie; Zhang, Hongyan; Li, Yanyu

2017-01-01

N-type macroporous silicon microcavity structures were prepared using electrochemical etching in an HF solution in the absence of light and oxidants. The CdSe/ZnS water-soluble quantum dot-labeled DNA target molecules were detected by monitoring the microcavity reflectance spectrum, which was characterized by the reflectance spectrum defect state position shift resulting from changes to the structures’ refractive index. Quantum dots with a high refractive index and DNA coupling can improve the detection sensitivity by amplifying the optical response signals of the target DNA. The experimental results show that DNA combined with a quantum dot can improve the sensitivity of DNA detection by more than five times. PMID:28045442

High Sensitivity Detection of CdSe/ZnS Quantum Dot-Labeled DNA Based on N-type Porous Silicon Microcavities.

PubMed

Lv, Changwu; Jia, Zhenhong; Lv, Jie; Zhang, Hongyan; Li, Yanyu

2017-01-01

N-type macroporous silicon microcavity structures were prepared using electrochemical etching in an HF solution in the absence of light and oxidants. The CdSe/ZnS water-soluble quantum dot-labeled DNA target molecules were detected by monitoring the microcavity reflectance spectrum, which was characterized by the reflectance spectrum defect state position shift resulting from changes to the structures' refractive index. Quantum dots with a high refractive index and DNA coupling can improve the detection sensitivity by amplifying the optical response signals of the target DNA. The experimental results show that DNA combined with a quantum dot can improve the sensitivity of DNA detection by more than five times.

Four-Wave-Mixing Approach to In Situ Detection of Nanoparticles

DOE PAGES

Gerakis, Alexandros; Yeh, Yao -Wen; Shneider, Mikhail N.; ...

2018-01-29

Here, we report on the development and experimental validation of a laser-based technique which uses coherent Rayleigh-Brillouin scattering (CRBS) to detect nanoparticles with characteristic sizes ranging from the atomic scale to tens of nanometers. This technique is aimed (nonexclusively) at the detection of nanoparticles produced by volumetric nanoparticle synthesis methods. Using CRBS, carbon nanoparticles of dimensions less than 10 nm and concentrations of 10 10 cm –3 are detected in situ in a carbon arc discharge with graphite electrodes. This four-wave-mixing approach should enable advances in the understanding of nanoparticle growth that could potentially lead to improved modeling of themore » growth mechanisms, and thus to improve synthesis selectivity of nanoparticles and yield.« less

[Detection of endpoint for segmentation between consonants and vowels in aphasia rehabilitation software based on artificial intelligence scheduling].

PubMed

Deng, Xingjuan; Chen, Ji; Shuai, Jie

2009-08-01

For the purpose of improving the efficiency of aphasia rehabilitation training, artificial intelligence-scheduling function is added in the aphasia rehabilitation software, and the software's performance is improved. With the characteristics of aphasia patient's voice as well as with the need of artificial intelligence-scheduling functions under consideration, the present authors have designed a set of endpoint detection algorithm. It determines the reference endpoints, then extracts every word and ensures the reasonable segmentation points between consonants and vowels, using the reference endpoints. The results of experiments show that the algorithm is able to attain the objects of detection at a higher accuracy rate. Therefore, it is applicable to the detection of endpoint on aphasia-patient's voice.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System

PubMed Central

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-01-01

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequency-domain and achieves computational complexity reduction. PMID:28230763

Enhancing detection of steady-state visual evoked potentials using individual training data.

PubMed

Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Jung, Tzyy-Ping

2014-01-01

Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.

A novel classification of prostate specific antigen (PSA) biosensors based on transducing elements.

PubMed

Najeeb, Mansoor Ani; Ahmad, Zubair; Shakoor, R A; Mohamed, A M A; Kahraman, Ramazan

2017-06-01

During the last few decades, there has been a tremendous rise in the number of research studies dedicated towards the development of diagnostic tools based on bio-sensing technology for the early detection of various diseases like cardiovascular diseases (CVD), many types of cancer, diabetes mellitus (DM) and many infectious diseases. Many breakthroughs have been developed in the areas of improving specificity, selectivity and repeatability of the biosensor devices. Innovations in the interdisciplinary areas like biotechnology, genetics, organic electronics and nanotechnology also had a great positive impact on the growth of bio-sensing technology. As a product of these improvements, fast and consistent sensing policies have been productively created for precise and ultrasensitive biomarker-based disease diagnostics. Prostate-specific antigen (PSA) is widely considered as an important biomarker used for diagnosing prostate cancer. There have been many publications based on various biosensors used for PSA detection, but a limited review was available for the classification of these biosensors used for the detection of PSA. This review highlights the various biosensors used for PSA detection and proposes a novel classification for PSA biosensors based on the transducer type used. We also highlight the advantages, disadvantages and limitations of each technique used for PSA biosensing which will make this article a complete reference tool for the future researches in PSA biosensing. Copyright © 2017 Elsevier B.V. All rights reserved.

Pornographic information of Internet views detection method based on the connected areas

NASA Astrophysics Data System (ADS)

Wang, Huibai; Fan, Ajie

2017-01-01

Nowadays online porn video broadcasting and downloading is very popular. In view of the widespread phenomenon of Internet pornography, this paper proposed a new method of pornographic video detection based on connected areas. Firstly, decode the video into a serious of static images and detect skin color on the extracted key frames. If the area of skin color reaches a certain threshold, use the AdaBoost algorithm to detect the human face. Judge the connectivity of the human face and the large area of skin color to determine whether detect the sensitive area finally. The experimental results show that the method can effectively remove the non-pornographic videos contain human who wear less. This method can improve the efficiency and reduce the workload of detection.

Edge detection of optical subaperture image based on improved differential box-counting method

NASA Astrophysics Data System (ADS)

Li, Yi; Hui, Mei; Liu, Ming; Dong, Liquan; Kong, Lingqin; Zhao, Yuejin

2018-01-01

Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can't be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats.

PubMed

Tejedor, Javier; Macias-Guarasa, Javier; Martins, Hugo F; Piote, Daniel; Pastor-Graells, Juan; Martin-Lopez, Sonia; Corredera, Pedro; Gonzalez-Herraez, Miguel

2017-02-12

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Regional fringe analysis for improving depth measurement in phase-shifting fringe projection profilometry

NASA Astrophysics Data System (ADS)

Chien, Kuang-Che Chang; Tu, Han-Yen; Hsieh, Ching-Huang; Cheng, Chau-Jern; Chang, Chun-Yen

2018-01-01

This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.

Factors influencing variation in physician adenoma detection rates: a theory-based approach for performance improvement.

PubMed

Atkins, Louise; Hunkeler, Enid M; Jensen, Christopher D; Michie, Susan; Lee, Jeffrey K; Doubeni, Chyke A; Zauber, Ann G; Levin, Theodore R; Quinn, Virginia P; Corley, Douglas A

2016-03-01

Interventions to improve physician adenoma detection rates for colonoscopy have generally not been successful, and there are little data on the factors contributing to variation that may be appropriate targets for intervention. We sought to identify factors that may influence variation in detection rates by using theory-based tools for understanding behavior. We separately studied gastroenterologists and endoscopy nurses at 3 Kaiser Permanente Northern California medical centers to identify potentially modifiable factors relevant to physician adenoma detection rate variability by using structured group interviews (focus groups) and theory-based tools for understanding behavior and eliciting behavior change: the Capability, Opportunity, and Motivation behavior model; the Theoretical Domains Framework; and the Behavior Change Wheel. Nine factors potentially associated with adenoma detection rate variability were identified, including 6 related to capability (uncertainty about which types of polyps to remove, style of endoscopy team leadership, compromised ability to focus during an examination due to distractions, examination technique during withdrawal, difficulty detecting certain types of adenomas, and examiner fatigue and pain), 2 related to opportunity (perceived pressure due to the number of examinations expected per shift and social pressure to finish examinations before scheduled breaks or the end of a shift), and 1 related to motivation (valuing a meticulous examination as the top priority). Examples of potential intervention strategies are provided. By using theory-based tools, this study identified several novel and potentially modifiable factors relating to capability, opportunity, and motivation that may contribute to adenoma detection rate variability and be appropriate targets for future intervention trials. Copyright © 2016 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Adaboost multi-view face detection based on YCgCr skin color model

NASA Astrophysics Data System (ADS)

Lan, Qi; Xu, Zhiyong

2016-09-01

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

Cyber situation awareness: modeling detection of cyber attacks with instance-based learning theory.

PubMed

Dutt, Varun; Ahn, Young-Suk; Gonzalez, Cleotilde

2013-06-01

To determine the effects of an adversary's behavior on the defender's accurate and timely detection of network threats. Cyber attacks cause major work disruption. It is important to understand how a defender's behavior (experience and tolerance to threats), as well as adversarial behavior (attack strategy), might impact the detection of threats. In this article, we use cognitive modeling to make predictions regarding these factors. Different model types representing a defender, based on Instance-Based Learning Theory (IBLT), faced different adversarial behaviors. A defender's model was defined by experience of threats: threat-prone (90% threats and 10% nonthreats) and nonthreat-prone (10% threats and 90% nonthreats); and different tolerance levels to threats: risk-averse (model declares a cyber attack after perceiving one threat out of eight total) and risk-seeking (model declares a cyber attack after perceiving seven threats out of eight total). Adversarial behavior is simulated by considering different attack strategies: patient (threats occur late) and impatient (threats occur early). For an impatient strategy, risk-averse models with threat-prone experiences show improved detection compared with risk-seeking models with nonthreat-prone experiences; however, the same is not true for a patient strategy. Based upon model predictions, a defender's prior threat experiences and his or her tolerance to threats are likely to predict detection accuracy; but considering the nature of adversarial behavior is also important. Decision-support tools that consider the role of a defender's experience and tolerance to threats along with the nature of adversarial behavior are likely to improve a defender's overall threat detection.

A novel material detection algorithm based on 2D GMM-based power density function and image detail addition scheme in dual energy X-ray images.

PubMed

Pourghassem, Hossein

2012-01-01

Material detection is a vital need in dual energy X-ray luggage inspection systems at security of airport and strategic places. In this paper, a novel material detection algorithm based on statistical trainable models using 2-Dimensional power density function (PDF) of three material categories in dual energy X-ray images is proposed. In this algorithm, the PDF of each material category as a statistical model is estimated from transmission measurement values of low and high energy X-ray images by Gaussian Mixture Models (GMM). Material label of each pixel of object is determined based on dependency probability of its transmission measurement values in the low and high energy to PDF of three material categories (metallic, organic and mixed materials). The performance of material detection algorithm is improved by a maximum voting scheme in a neighborhood of image as a post-processing stage. Using two background removing and denoising stages, high and low energy X-ray images are enhanced as a pre-processing procedure. For improving the discrimination capability of the proposed material detection algorithm, the details of the low and high energy X-ray images are added to constructed color image which includes three colors (orange, blue and green) for representing the organic, metallic and mixed materials. The proposed algorithm is evaluated on real images that had been captured from a commercial dual energy X-ray luggage inspection system. The obtained results show that the proposed algorithm is effective and operative in detection of the metallic, organic and mixed materials with acceptable accuracy.

Echo-Planar Imaging Based J-Resolved Spectroscopic Imaging for Improved Metabolite Detection in Prostate Cancer

DTIC Science & Technology

2013-10-01

Scope: A major outcome is expected to be on improved detection ( specificity ) in differentiating malignant from benign prostate cancer using a novel...Digital Rectal Examination, prostate specific antigen , Four Dimensional (4D) Echo-Planar J-Resolved Spectroscopic Imaging (EP-JRESI); Citrate, Choline... prostate biopsy ranged from 3 to 8, while prostate - specific antigen varied from 2.8 to 20.6 ng/mL (mean of 6.84 ng/mL). A Siemens 3T MRI Scanner with

Capacitance-based damage detection sensing for aerospace structural composites

NASA Astrophysics Data System (ADS)

Bahrami, P.; Yamamoto, N.; Chen, Y.; Manohara, H.

2014-04-01

Damage detection technology needs improvement for aerospace engineering application because detection within complex composite structures is difficult yet critical to avoid catastrophic failure. Damage detection is challenging in aerospace structures because not all the damage detection technology can cover the various defect types (delamination, fiber fracture, matrix crack etc.), or conditions (visibility, crack length size, etc.). These defect states are expected to become even more complex with future introduction of novel composites including nano-/microparticle reinforcement. Currently, non-destructive evaluation (NDE) methods with X-ray, ultrasound, or eddy current have good resolutions (< 0.1 mm), but their detection capabilities is limited by defect locations and orientations and require massive inspection devices. System health monitoring (SHM) methods are often paired with NDE technologies to signal out sensed damage, but their data collection and analysis currently requires excessive wiring and complex signal analysis. Here, we present a capacitance sensor-based, structural defect detection technology with improved sensing capability. Thin dielectric polymer layer is integrated as part of the structure; the defect in the structure directly alters the sensing layer's capacitance, allowing full-coverage sensing capability independent of defect size, orientation or location. In this work, capacitance-based sensing capability was experimentally demonstrated with a 2D sensing layer consisting of a dielectric layer sandwiched by electrodes. These sensing layers were applied on substrate surfaces. Surface indentation damage (~1mm diameter) and its location were detected through measured capacitance changes: 1 to 250 % depending on the substrates. The damage detection sensors are light weight, and they can be conformably coated and can be part of the composite structure. Therefore it is suitable for aerospace structures such as cryogenic tanks and rocket fairings for example. The sensors can also be operating in space and harsh environment such as high temperature and vacuum.

Sequential projection pursuit for optimised vibration-based damage detection in an experimental wind turbine blade

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2018-02-01

To advance the concept of smart structures in large systems, such as wind turbines (WTs), it is desirable to be able to detect structural damage early while using minimal instrumentation. Data-driven vibration-based damage detection methods can be competitive in that respect because global vibrational responses encompass the entire structure. Multivariate damage sensitive features (DSFs) extracted from acceleration responses enable to detect changes in a structure via statistical methods. However, even though such DSFs contain information about the structural state, they may not be optimised for the damage detection task. This paper addresses the shortcoming by exploring a DSF projection technique specialised for statistical structural damage detection. High dimensional initial DSFs are projected onto a low-dimensional space for improved damage detection performance and simultaneous computational burden reduction. The technique is based on sequential projection pursuit where the projection vectors are optimised one by one using an advanced evolutionary strategy. The approach is applied to laboratory experiments with a small-scale WT blade under wind-like excitations. Autocorrelation function coefficients calculated from acceleration signals are employed as DSFs. The optimal numbers of projection vectors are identified with the help of a fast forward selection procedure. To benchmark the proposed method, selections of original DSFs as well as principal component analysis scores from these features are additionally investigated. The optimised DSFs are tested for damage detection on previously unseen data from the healthy state and a wide range of damage scenarios. It is demonstrated that using selected subsets of the initial and transformed DSFs improves damage detectability compared to the full set of features. Furthermore, superior results can be achieved by projecting autocorrelation coefficients onto just a single optimised projection vector.

Determining the Requisite Components of Visual Threat Detection to Improve Operational Performance

DTIC Science & Technology

2014-04-01

cognitive processes, and may be enhanced by focusing training development on the principle components such as causal reasoning. The second report will...discuss the development and evaluation of a research-based training exemplar. Visual threat detection pervades many military contexts, but is also... developing computer-controlled exercises to study the primary components of visual threat detection. Similarly, civilian law enforcement officers were

Optimization of single photon detection model based on GM-APD

NASA Astrophysics Data System (ADS)

Chen, Yu; Yang, Yi; Hao, Peiyu

2017-11-01

One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.

Current and future molecular diagnostics for ocular infectious diseases.

PubMed

Doan, Thuy; Pinsky, Benjamin A

2016-11-01

Confirmation of ocular infections can pose great challenges to the clinician. A fundamental limitation is the small amounts of specimen that can be obtained from the eye. Molecular diagnostics can circumvent this limitation and have been shown to be more sensitive than conventional culture. The purpose of this review is to describe new molecular methods and to discuss the applications of next-generation sequencing-based approaches in the diagnosis of ocular infections. Efforts have focused on improving the sensitivity of pathogen detection using molecular methods. This review describes a new molecular target for Toxoplasma gondii-directed polymerase chain reaction assays. Molecular diagnostics for Chlamydia trachomatis and Acanthamoeba species are also discussed. Finally, we describe a hypothesis-free approach, metagenomic deep sequencing, which can detect DNA and RNA pathogens from a single specimen in one test. In some cases, this method can provide the geographic location and timing of the infection. Pathogen-directed PCRs have been powerful tools in the diagnosis of ocular infections for over 20 years. The use of next-generation sequencing-based approaches, when available, will further improve sensitivity of detection with the potential to improve patient care.

Deep neural network-based bandwidth enhancement of photoacoustic data.

PubMed

Gutta, Sreedevi; Kadimesetty, Venkata Suryanarayana; Kalva, Sandeep Kumar; Pramanik, Manojit; Ganapathy, Sriram; Yalavarthy, Phaneendra K

2017-11-01

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.

PubMed

Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

2016-12-01

In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow's. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

Microwave-Accelerated Method for Ultra-Rapid Extraction of Neisseria gonorrhoeae DNA for Downstream Detection

PubMed Central

Melendez, Johan H.; Santaus, Tonya M.; Brinsley, Gregory; Kiang, Daniel; Mali, Buddha; Hardick, Justin; Gaydos, Charlotte A.; Geddes, Chris D.

2016-01-01

Nucleic acid-based detection of gonorrhea infections typically require a two-step process involving isolation of the nucleic acid, followed by the detection of the genomic target often involving PCR-based approaches. In an effort to improve on current detection approaches, we have developed a unique two-step microwave-accelerated approach for rapid extraction and detection of Neisseria gonorrhoeae (GC) DNA. Our approach is based on the use of highly-focused microwave radiation to rapidly lyse bacterial cells, release, and subsequently fragment microbial DNA. The DNA target is then detected by a process known as microwave-accelerated metal-enhanced fluorescence (MAMEF), an ultra-sensitive direct DNA detection analytical technique. In the present study, we show that highly focused microwaves at 2.45 GHz, using 12.3 mm gold film equilateral triangles, are able to rapidly lyse both bacteria cells and fragment DNA in a time- and microwave power-dependent manner. Detection of the extracted DNA can be performed by MAMEF, without the need for DNA amplification in less than 10 minutes total time or by other PCR-based approaches. Collectively, the use of a microwave-accelerated method for the release and detection of DNA represents a significant step forward towards the development of a point-of-care (POC) platform for detection of gonorrhea infections. PMID:27325503

Real-Time Detection of Rupture Development: Earthquake Early Warning Using P Waves From Growing Ruptures

NASA Astrophysics Data System (ADS)

Kodera, Yuki

2018-01-01

Large earthquakes with long rupture durations emit P wave energy throughout the rupture period. Incorporating late-onset P waves into earthquake early warning (EEW) algorithms could contribute to robust predictions of strong ground motion. Here I describe a technique to detect in real time P waves from growing ruptures to improve the timeliness of an EEW algorithm based on seismic wavefield estimation. The proposed P wave detector, which employs a simple polarization analysis, successfully detected P waves from strong motion generation areas of the 2011 Mw 9.0 Tohoku-oki earthquake rupture. An analysis using 23 large (M ≥ 7) events from Japan confirmed that seismic intensity predictions based on the P wave detector significantly increased lead times without appreciably decreasing the prediction accuracy. P waves from growing ruptures, being one of the fastest carriers of information on ongoing rupture development, have the potential to improve the performance of EEW systems.

Improvements in Space Surveillance Processing for Wide Field of View Optical Sensors

NASA Astrophysics Data System (ADS)

Sydney, P.; Wetterer, C.

2014-09-01

For more than a decade, an autonomous satellite tracking system at the Air Force Maui Optical and Supercomputing (AMOS) observatory has been generating routine astrometric measurements of Earth-orbiting Resident Space Objects (RSOs) using small commercial telescopes and sensors. Recent work has focused on developing an improved processing system, enhancing measurement performance and response while supporting other sensor systems and missions. This paper will outline improved techniques in scheduling, detection, astrometric and photometric measurements, and catalog maintenance. The processing system now integrates with Special Perturbation (SP) based astrodynamics algorithms, allowing covariance-based scheduling and more precise orbital estimates and object identification. A merit-based scheduling algorithm provides a global optimization framework to support diverse collection tasks and missions. The detection algorithms support a range of target tracking and camera acquisition rates. New comprehensive star catalogs allow for more precise astrometric and photometric calibrations including differential photometry for monitoring environmental changes. This paper will also examine measurement performance with varying tracking rates and acquisition parameters.

Improved Resolution and Reduced Clutter in Ultra-Wideband Microwave Imaging Using Cross-Correlated Back Projection: Experimental and Numerical Results

PubMed Central

Jacobsen, S.; Birkelund, Y.

2010-01-01

Microwave breast cancer detection is based on the dielectric contrast between healthy and malignant tissue. This radar-based imaging method involves illumination of the breast with an ultra-wideband pulse. Detection of tumors within the breast is achieved by some selected focusing technique. Image formation algorithms are tailored to enhance tumor responses and reduce early-time and late-time clutter associated with skin reflections and heterogeneity of breast tissue. In this contribution, we evaluate the performance of the so-called cross-correlated back projection imaging scheme by using a scanning system in phantom experiments. Supplementary numerical modeling based on commercial software is also presented. The phantom is synthetically scanned with a broadband elliptical antenna in a mono-static configuration. The respective signals are pre-processed by a data-adaptive RLS algorithm in order to remove artifacts caused by antenna reverberations and signal clutter. Successful detection of a 7 mm diameter cylindrical tumor immersed in a low permittivity medium was achieved in all cases. Selecting the widely used delay-and-sum (DAS) beamforming algorithm as a benchmark, we show that correlation based imaging methods improve the signal-to-clutter ratio by at least 10 dB and improves spatial resolution through a reduction of the imaged peak full-width half maximum (FWHM) of about 40–50%. PMID:21331362

Improved resolution and reduced clutter in ultra-wideband microwave imaging using cross-correlated back projection: experimental and numerical results.

PubMed

Jacobsen, S; Birkelund, Y

2010-01-01

Microwave breast cancer detection is based on the dielectric contrast between healthy and malignant tissue. This radar-based imaging method involves illumination of the breast with an ultra-wideband pulse. Detection of tumors within the breast is achieved by some selected focusing technique. Image formation algorithms are tailored to enhance tumor responses and reduce early-time and late-time clutter associated with skin reflections and heterogeneity of breast tissue. In this contribution, we evaluate the performance of the so-called cross-correlated back projection imaging scheme by using a scanning system in phantom experiments. Supplementary numerical modeling based on commercial software is also presented. The phantom is synthetically scanned with a broadband elliptical antenna in a mono-static configuration. The respective signals are pre-processed by a data-adaptive RLS algorithm in order to remove artifacts caused by antenna reverberations and signal clutter. Successful detection of a 7 mm diameter cylindrical tumor immersed in a low permittivity medium was achieved in all cases. Selecting the widely used delay-and-sum (DAS) beamforming algorithm as a benchmark, we show that correlation based imaging methods improve the signal-to-clutter ratio by at least 10 dB and improves spatial resolution through a reduction of the imaged peak full-width half maximum (FWHM) of about 40-50%.

Accurate detection of blood vessels improves the detection of exudates in color fundus images.

PubMed

Youssef, Doaa; Solouma, Nahed H

2012-12-01

Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the lesions and the anatomic structures of the retina. In this paper, we provide a new method for the detection of blood vessels that improves the detection of exudates in fundus photographs. The method starts with an edge detection algorithm which results in a over segmented image. Then the new feature-based algorithm can be used to accurately detect the blood vessels. This algorithm considers the characteristics of a retinal blood vessel such as its width range, intensities and orientations for the purpose of selective segmentation. Because of its bulb shape and its color similarity with exudates, the optic disc can be detected using the common Hough transform technique. The extracted blood vessel tree and optic disc could be subtracted from the over segmented image to get an initial estimate of exudates. The final estimation of exudates can then be obtained by morphological reconstruction based on the appearance of exudates. This method is shown to be promising since it increases the sensitivity and specificity of exudates detection to 80% and 100% respectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Concentration of enteric viruses from tap water using an anion exchange resin-based method.

PubMed

Pérez-Méndez, A; Chandler, J C; Bisha, B; Goodridge, L D

2014-09-01

Detecting low concentrations of enteric viruses in water is needed for public health-related monitoring and control purposes. Thus, there is a need for sensitive, rapid and cost effective enteric viral concentration methods compatible with downstream molecular detection. Here, a virus concentration method based on adsorption of the virus to an anion exchange resin and direct isolation of nucleic acids is presented. Ten liter samples of tap water spiked with different concentrations (10-10,000 TCID50/10 L) of human adenovirus 40 (HAdV-40), hepatitis A virus (HAV) or rotavirus (RV) were concentrated and detected by real time PCR or real time RT-PCR. This method improved viral detection compared to direct testing of spiked water samples where the ΔCt was 12.1 for AdV-40 and 4.3 for HAV. Direct detection of RV in water was only possible for one of the three replicates tested (Ct of 37), but RV detection was improved using the resin method (all replicates tested positive with an average Ct of 30, n=3). The limit of detection of the method was 10 TCID50/10 L for HAdV-40 and HAV, and 100 TCID50/10 L of water for RV. These results compare favorably with detection limits reported for more expensive and laborious methods. Copyright © 2014 Elsevier B.V. All rights reserved.

Use-related risk analysis for medical devices based on improved FMEA.

PubMed

Liu, Long; Shuai, Ma; Wang, Zhu; Li, Ping

2012-01-01

In order to effectively analyze and control use-related risk of medical devices, quantitative methodologies must be applied. Failure Mode and Effects Analysis (FMEA) is a proactive technique for error detection and risk reduction. In this article, an improved FMEA based on Fuzzy Mathematics and Grey Relational Theory is developed to better carry out user-related risk analysis for medical devices. As an example, the analysis process using this improved FMEA method for a certain medical device (C-arm X-ray machine) is described.

Comparison of three PCR-based assays for SNP genotyping in sugar beet

USDA-ARS?s Scientific Manuscript database

Background: PCR allelic discrimination technologies have broad applications in the detection of single nucleotide polymorphisms (SNPs) in genetics and genomics. The use of fluorescence-tagged probes is the leading method for targeted SNP detection, but assay costs and error rates could be improved t...

Improving Target Detection in Visual Search Through the Augmenting Multi-Sensory Cues

DTIC Science & Technology

2013-01-01

target detection, visual search James Merlo, Joseph E. Mercado , Jan B.F. Van Erp, Peter A. Hancock University of Central Florida 12201 Research Parkway...were controlled by a purpose-created, LabView- based software computer program that synchronised the respective displays and recorded response times and

IMPROVED METHODS FOR HEPATITIS A VIRUS AND ROTAVIRUS CONCENTRATION AND DETECTION IN RECREATIONAL, RAW POTABLE, AND FINISHED WATERS

EPA Science Inventory

The report contains procedures for detecting rotaviruses based upon an immunofluorescence test using a monoclonal antibody and fluorescein-isothiocyanate-conjugated antibody staining method to visualize virus-infected cells. Also contained in the report are test methods for detec...

Accelerated SPECT Monte Carlo Simulation Using Multiple Projection Sampling and Convolution-Based Forced Detection

NASA Astrophysics Data System (ADS)

Liu, Shaoying; King, Michael A.; Brill, Aaron B.; Stabin, Michael G.; Farncombe, Troy H.

2008-02-01

Monte Carlo (MC) is a well-utilized tool for simulating photon transport in single photon emission computed tomography (SPECT) due to its ability to accurately model physical processes of photon transport. As a consequence of this accuracy, it suffers from a relatively low detection efficiency and long computation time. One technique used to improve the speed of MC modeling is the effective and well-established variance reduction technique (VRT) known as forced detection (FD). With this method, photons are followed as they traverse the object under study but are then forced to travel in the direction of the detector surface, whereby they are detected at a single detector location. Another method, called convolution-based forced detection (CFD), is based on the fundamental idea of FD with the exception that detected photons are detected at multiple detector locations and determined with a distance-dependent blurring kernel. In order to further increase the speed of MC, a method named multiple projection convolution-based forced detection (MP-CFD) is presented. Rather than forcing photons to hit a single detector, the MP-CFD method follows the photon transport through the object but then, at each scatter site, forces the photon to interact with a number of detectors at a variety of angles surrounding the object. This way, it is possible to simulate all the projection images of a SPECT simulation in parallel, rather than as independent projections. The result of this is vastly improved simulation time as much of the computation load of simulating photon transport through the object is done only once for all projection angles. The results of the proposed MP-CFD method agrees well with the experimental data in measurements of point spread function (PSF), producing a correlation coefficient (r2) of 0.99 compared to experimental data. The speed of MP-CFD is shown to be about 60 times faster than a regular forced detection MC program with similar results.

Stratification-Based Outlier Detection over the Deep Web.

PubMed

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

Paper-based electrochemical sensor for on-site detection of the sulphur mustard.

PubMed

Colozza, Noemi; Kehe, Kai; Popp, Tanja; Steinritz, Dirk; Moscone, Danila; Arduini, Fabiana

2018-06-22

Herein, we report a novel paper-based electrochemical sensor for on-site detection of sulphur mustards. This sensor was conceived combining office paper-based electrochemical sensor with choline oxidase enzyme to deliver a sustainable sensing tool. The mustard agent detection relies on the evaluation of inhibition degree of choline oxidase, which is reversibly inhibited by sulphur mustards, by measuring the enzymatic by-product H 2 O 2 in chronoamperometric mode. A nanocomposite constituted of Prussian Blue nanoparticles and Carbon Black was used as working electrode modifier to improve the electroanalytical performances. This bioassay was successfully applied for the measurement of a sulphur mustard, Yprite, obtaining a detection limit in the millimolar range (LOD = 0.9 mM). The developed sensor, combined with a portable and easy-to-use instrumentation, can be applied for a fast and cost-effective detection of sulphur mustards.

Stratification-Based Outlier Detection over the Deep Web

PubMed Central

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S.; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. PMID:27313603

The Iterative Research Cycle: Process-Based Model Evaluation

NASA Astrophysics Data System (ADS)

Vrugt, J. A.

2014-12-01

The ever increasing pace of computational power, along with continued advances in measurement technologies and improvements in process understanding has stimulated the development of increasingly complex physics based models that simulate a myriad of processes at different spatial and temporal scales. Reconciling these high-order system models with perpetually larger volumes of field data is becoming more and more difficult, particularly because classical likelihood-based fitting methods lack the power to detect and pinpoint deficiencies in the model structure. In this talk I will give an overview of our latest research on process-based model calibration and evaluation. This approach, rooted in Bayesian theory, uses summary metrics of the calibration data rather than the data itself to help detect which component(s) of the model is (are) malfunctioning and in need of improvement. A few case studies involving hydrologic and geophysical models will be used to demonstrate the proposed methodology.

Eye gazing direction inspection based on image processing technique

NASA Astrophysics Data System (ADS)

Hao, Qun; Song, Yong

2005-02-01

According to the research result in neural biology, human eyes can obtain high resolution only at the center of view of field. In the research of Virtual Reality helmet, we design to detect the gazing direction of human eyes in real time and feed it back to the control system to improve the resolution of the graph at the center of field of view. In the case of current display instruments, this method can both give attention to the view field of virtual scene and resolution, and improve the immersion of virtual system greatly. Therefore, detecting the gazing direction of human eyes rapidly and exactly is the basis of realizing the design scheme of this novel VR helmet. In this paper, the conventional method of gazing direction detection that based on Purklinje spot is introduced firstly. In order to overcome the disadvantage of the method based on Purklinje spot, this paper proposed a method based on image processing to realize the detection and determination of the gazing direction. The locations of pupils and shapes of eye sockets change with the gazing directions. With the aid of these changes, analyzing the images of eyes captured by the cameras, gazing direction of human eyes can be determined finally. In this paper, experiments have been done to validate the efficiency of this method by analyzing the images. The algorithm can carry out the detection of gazing direction base on normal eye image directly, and it eliminates the need of special hardware. Experiment results show that the method is easy to implement and have high precision.

Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms.

PubMed

Barta, András; Horváth, Gábor; Horváth, Ákos; Egri, Ádám; Blahó, Miklós; Barta, Pál; Bumke, Karl; Macke, Andreas

2015-02-10

Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations of sea surface insolation, and building energy transfer models of the atmosphere. Currently, the most widespread ground-based method to measure cloudiness is based on analyzing the unpolarized intensity and color distribution of the sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided by the additional use of skylight polarization measured by 180° field-of-view imaging polarimetry. In the fall of 2010, we tested such a novel polarimetric cloud detector aboard the research vessel Polarstern during expedition ANT-XXVII/1. One of our goals was to test the durability of the measurement hardware under the extreme conditions of a trans-Atlantic cruise. Here, we describe the instrument and compare the results of several different cloud detection algorithms, some conventional and some newly developed. We also discuss the weaknesses of our design and its possible improvements. The comparison with cloud detection algorithms developed for traditional nonpolarimetric full-sky imagers allowed us to evaluate the added value of polarimetric quantities. We found that (1) neural-network-based algorithms perform the best among the investigated schemes and (2) global information (the mean and variance of intensity), nonoptical information (e.g., sun-view geometry), and polarimetric information (e.g., the degree of polarization) improve the accuracy of cloud detection, albeit slightly.

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

PubMed Central

Yuan, Xin; Martínez, José-Fernán; Eckert, Martina; López-Santidrián, Lourdes

2016-01-01

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments. PMID:27455279

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation.

PubMed

Yuan, Xin; Martínez, José-Fernán; Eckert, Martina; López-Santidrián, Lourdes

2016-07-22

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments.

Modeling the Dynamics of High-Grade Serous Ovarian Cancer Progression for Transvaginal Ultrasound-Based Screening and Early Detection

PubMed Central

Lee, Jung-Min; Levy, Doron

2016-01-01

High-grade serous ovarian cancer (HGSOC) represents the majority of ovarian cancers and accounts for the largest proportion of deaths from the disease. A timely detection of low volume HGSOC should be the goal of any screening studies. However, numerous transvaginal ultrasound (TVU) detection-based population studies aimed at detecting low-volume disease have not yielded reduced mortality rates. A quantitative invalidation of TVU as an effective HGSOC screening strategy is a necessary next step. Herein, we propose a mathematical model for a quantitative explanation on the reported failure of TVU-based screening to improve HGSOC low-volume detectability and overall survival.We develop a novel in silico mathematical assessment of the efficacy of a unimodal TVU monitoring regimen as a strategy aimed at detecting low-volume HGSOC in cancer-positive cases, defined as cases for which the inception of the first malignant cell has already occurred. Our findings show that the median window of opportunity interval length for TVU monitoring and HGSOC detection is approximately 1.76 years. This does not translate into reduced mortality levels or improved detection accuracy in an in silico cohort across multiple TVU monitoring frequencies or detection sensitivities. We demonstrate that even a semiannual, unimodal TVU monitoring protocol is expected to miss detectable HGSOC. Lastly, we find that circa 50% of the simulated HGSOC growth curves never reach the baseline detectability threshold, and that on average, 5–7 infrequent, rate-limiting stochastic changes in the growth parameters are associated with reaching HGSOC detectability and mortality thresholds respectively. Focusing on a malignancy poorly studied in the mathematical oncology community, our model captures the dynamic, temporal evolution of HGSOC progression. Our mathematical model is consistent with recent case reports and prospective TVU screening population studies, and provides support to the empirical recommendation against frequent HGSOC screening. PMID:27257824

Weak wide-band signal detection method based on small-scale periodic state of Duffing oscillator

NASA Astrophysics Data System (ADS)

Hou, Jian; Yan, Xiao-peng; Li, Ping; Hao, Xin-hong

2018-03-01

The conventional Duffing oscillator weak signal detection method, which is based on a strong reference signal, has inherent deficiencies. To address these issues, the characteristics of the Duffing oscillatorʼs phase trajectory in a small-scale periodic state are analyzed by introducing the theory of stopping oscillation system. Based on this approach, a novel Duffing oscillator weak wide-band signal detection method is proposed. In this novel method, the reference signal is discarded, and the to-be-detected signal is directly used as a driving force. By calculating the cosine function of a phase space angle, a single Duffing oscillator can be used for weak wide-band signal detection instead of an array of uncoupled Duffing oscillators. Simulation results indicate that, compared with the conventional Duffing oscillator detection method, this approach performs better in frequency detection intervals, and reduces the signal-to-noise ratio detection threshold, while improving the real-time performance of the system. Project supported by the National Natural Science Foundation of China (Grant No. 61673066).

Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Ishida, Kyoko; Sawada, Akira; Hatanaka, Yuji; Yamamoto, Tetsuya; Fujita, Hiroshi

2016-03-01

Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.

Case base classification on digital mammograms: improving the performance of case base classifier

NASA Astrophysics Data System (ADS)

Raman, Valliappan; Then, H. H.; Sumari, Putra; Venkatesa Mohan, N.

2011-10-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

Locally adaptive decision in detection of clustered microcalcifications in mammograms.

PubMed

Sainz de Cea, María V; Nishikawa, Robert M; Yang, Yongyi

2018-02-15

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10 -4 ). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Locally adaptive decision in detection of clustered microcalcifications in mammograms

NASA Astrophysics Data System (ADS)

Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

2018-02-01

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10-4). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Hall effect biosensors with ultraclean graphene film for improved sensitivity of label-free DNA detection.

PubMed

Loan, Phan Thi Kim; Wu, Dongqin; Ye, Chen; Li, Xiaoqing; Tra, Vu Thanh; Wei, Qiuping; Fu, Li; Yu, Aimin; Li, Lain-Jong; Lin, Cheng-Te

2018-01-15

The quality of graphene strongly affects the performance of graphene-based biosensors which are highly demanded for the sensitive and selective detection of biomolecules, such as DNA. This work reported a novel transfer process for preparing a residue-free graphene film using a thin gold supporting layer. A Hall effect device made of this gold-transferred graphene was demonstrated to significantly enhance the sensitivity (≈ 5 times) for hybridization detection, with a linear detection range of 1pM to 100nM for DNA target. Our findings provide an efficient method to boost the sensitivity of graphene-based biosensors for DNA recognition. Copyright © 2017 Elsevier B.V. All rights reserved.

Multifunctional paper strip based on GO-veiled Ag nanoparticles with highly SERS sensitive and deliverable properties for high-performance molecular detection.

PubMed

Yang, Cheng; Xu, Yuanyuan; Wang, Minghong; Li, Tianming; Huo, Yanyan; Yang, Chuanxi; Man, Baoyuan

2018-04-16

The development of paper-based SERS substrates that can allow multi-component detection in real-word scenarios is of great value for applications in molecule detection under complex conditions. Here, a multifunctional SERS-based paper sensing substrate has been developed through the uniform patterning of high-density arrays of GO-isolated Ag nanoparticles on the hydrophilic porous cellulose paper strip (GO@AgNP@paper). Wet-chemical synthesis was used to provide the cover of SERS hot spots on any part of the paper, not just limited surface deposition. In virtue of the inherent ability of paper to deliver analytes by the capillary force, the detection ability of the GO@AgNP@paper substrate was greatly promoted, allowing as low as 10 -19 M R6G detection from microliter-volume (50 μL) samples. For the components with different polarity, the paper substrate can be used as an all-in-one machine to achieve the integration of separation and high-sensitive detection for ultralow mixture components, which improves the practical application value of SERS-based paper devices.

Multi-capillary based optical sensors for highly sensitive protein detection

NASA Astrophysics Data System (ADS)

Okuyama, Yasuhira; Katagiri, Takashi; Matsuura, Yuji

2017-04-01

A fluorescence measuring method based on glass multi-capillary for detecting trace amounts of proteins is proposed. It promises enhancement of sensitivity due to effects of the adsorption area expansion and the longitudinal excitation. The sensitivity behavior of this method was investigated by using biotin-streptavidin binding. According to experimental examinations, it was found that the sensitivity was improved by a factor of 70 from common glass wells. We also confirmed our measuring system could detect 1 pg/mL of streptavidin. These results suggest that multi-capillary has a potential as a high-sensitive biosensor.

Rubber hose surface defect detection system based on machine vision

NASA Astrophysics Data System (ADS)

Meng, Fanwu; Ren, Jingrui; Wang, Qi; Zhang, Teng

2018-01-01

As an important part of connecting engine, air filter, engine, cooling system and automobile air-conditioning system, automotive hose is widely used in automobile. Therefore, the determination of the surface quality of the hose is particularly important. This research is based on machine vision technology, using HALCON algorithm for the processing of the hose image, and identifying the surface defects of the hose. In order to improve the detection accuracy of visual system, this paper proposes a method to classify the defects to reduce misjudegment. The experimental results show that the method can detect surface defects accurately.

Detecting, Monitoring, and Reporting Possible Adverse Drug Events Using an Arden-Syntax-based Rule Engine.

PubMed

Fehre, Karsten; Plössnig, Manuela; Schuler, Jochen; Hofer-Dückelmann, Christina; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2015-01-01

The detection of adverse drug events (ADEs) is an important aspect of improving patient safety. The iMedication system employs predefined triggers associated with significant events in a patient's clinical data to automatically detect possible ADEs. We defined four clinically relevant conditions: hyperkalemia, hyponatremia, renal failure, and over-anticoagulation. These are some of the most relevant ADEs in internal medical and geriatric wards. For each patient, ADE risk scores for all four situations are calculated, compared against a threshold, and judged to be monitored, or reported. A ward-based cockpit view summarizes the results.

Current trends in mass spectrometry of peptides and proteins: Application to veterinary and sports-doping control.

PubMed

van den Broek, Irene; Blokland, Marco; Nessen, Merel A; Sterk, Saskia

2015-01-01

Detection of misuse of peptides and proteins as growth promoters is a major issue for sport and food regulatory agencies. The limitations of current analytical detection strategies for this class of compounds, in combination with their efficacy in growth-promoting effects, make peptide and protein drugs highly susceptible to abuse by either athletes or farmers who seek for products to illicitly enhance muscle growth. Mass spectrometry (MS) for qualitative analysis of peptides and proteins is well-established, particularly due to tremendous efforts in the proteomics community. Similarly, due to advancements in targeted proteomic strategies and the rapid growth of protein-based biopharmaceuticals, MS for quantitative analysis of peptides and proteins is becoming more widely accepted. These continuous advances in MS instrumentation and MS-based methodologies offer enormous opportunities for detection and confirmation of peptides and proteins. Therefore, MS seems to be the method of choice to improve the qualitative and quantitative analysis of peptide and proteins with growth-promoting properties. This review aims to address the opportunities of MS for peptide and protein analysis in veterinary control and sports-doping control with a particular focus on detection of illicit growth promotion. An overview of potential peptide and protein targets, including their amino acid sequence characteristics and current MS-based detection strategies is, therefore, provided. Furthermore, improvements of current and new detection strategies with state-of-the-art MS instrumentation are discussed for qualitative and quantitative approaches. © 2013 Wiley Periodicals, Inc.

Robust multiperson detection and tracking for mobile service and social robots.

PubMed

Li, Liyuan; Yan, Shuicheng; Yu, Xinguo; Tan, Yeow Kee; Li, Haizhou

2012-10-01

This paper proposes an efficient system which integrates multiple vision models for robust multiperson detection and tracking for mobile service and social robots in public environments. The core technique is a novel maximum likelihood (ML)-based algorithm which combines the multimodel detections in mean-shift tracking. First, a likelihood probability which integrates detections and similarity to local appearance is defined. Then, an expectation-maximization (EM)-like mean-shift algorithm is derived under the ML framework. In each iteration, the E-step estimates the associations to the detections, and the M-step locates the new position according to the ML criterion. To be robust to the complex crowded scenarios for multiperson tracking, an improved sequential strategy to perform the mean-shift tracking is proposed. Under this strategy, human objects are tracked sequentially according to their priority order. To balance the efficiency and robustness for real-time performance, at each stage, the first two objects from the list of the priority order are tested, and the one with the higher score is selected. The proposed method has been successfully implemented on real-world service and social robots. The vision system integrates stereo-based and histograms-of-oriented-gradients-based human detections, occlusion reasoning, and sequential mean-shift tracking. Various examples to show the advantages and robustness of the proposed system for multiperson tracking from mobile robots are presented. Quantitative evaluations on the performance of multiperson tracking are also performed. Experimental results indicate that significant improvements have been achieved by using the proposed method.

Microbial Fuels Cell-Based Biosensor for Toxicity Detection: A Review

PubMed Central

Zhou, Tuoyu; Han, Huawen; Liu, Pu; Xiong, Jian; Tian, Fake; Li, Xiangkai

2017-01-01

With the unprecedented deterioration of environmental quality, rapid recognition of toxic compounds is paramount for performing in situ real-time monitoring. Although several analytical techniques based on electrochemistry or biosensors have been developed for the detection of toxic compounds, most of them are time-consuming, inaccurate, or cumbersome for practical applications. More recently, microbial fuel cell (MFC)-based biosensors have drawn increasing interest due to their sustainability and cost-effectiveness, with applications ranging from the monitoring of anaerobic digestion process parameters (VFA) to water quality detection (e.g., COD, BOD). When a MFC runs under correct conditions, the voltage generated is correlated with the amount of a given substrate. Based on this linear relationship, several studies have demonstrated that MFC-based biosensors could detect heavy metals such as copper, chromium, or zinc, as well as organic compounds, including p-nitrophenol (PNP), formaldehyde and levofloxacin. Both bacterial consortia and single strains can be used to develop MFC-based biosensors. Biosensors with single strains show several advantages over systems integrating bacterial consortia, such as selectivity and stability. One of the limitations of such sensors is that the detection range usually exceeds the actual pollution level. Therefore, improving their sensitivity is the most important for widespread application. Nonetheless, MFC-based biosensors represent a promising approach towards single pollutant detection. PMID:28956857

Microbial Fuels Cell-Based Biosensor for Toxicity Detection: A Review.

PubMed

Zhou, Tuoyu; Han, Huawen; Liu, Pu; Xiong, Jian; Tian, Fake; Li, Xiangkai

2017-09-28

With the unprecedented deterioration of environmental quality, rapid recognition of toxic compounds is paramount for performing in situ real-time monitoring. Although several analytical techniques based on electrochemistry or biosensors have been developed for the detection of toxic compounds, most of them are time-consuming, inaccurate, or cumbersome for practical applications. More recently, microbial fuel cell (MFC)-based biosensors have drawn increasing interest due to their sustainability and cost-effectiveness, with applications ranging from the monitoring of anaerobic digestion process parameters (VFA) to water quality detection (e.g., COD, BOD). When a MFC runs under correct conditions, the voltage generated is correlated with the amount of a given substrate. Based on this linear relationship, several studies have demonstrated that MFC-based biosensors could detect heavy metals such as copper, chromium, or zinc, as well as organic compounds, including p -nitrophenol (PNP), formaldehyde and levofloxacin. Both bacterial consortia and single strains can be used to develop MFC-based biosensors. Biosensors with single strains show several advantages over systems integrating bacterial consortia, such as selectivity and stability. One of the limitations of such sensors is that the detection range usually exceeds the actual pollution level. Therefore, improving their sensitivity is the most important for widespread application. Nonetheless, MFC-based biosensors represent a promising approach towards single pollutant detection.

Review of automatic detection of pig behaviours by using image analysis

NASA Astrophysics Data System (ADS)

Han, Shuqing; Zhang, Jianhua; Zhu, Mengshuai; Wu, Jianzhai; Kong, Fantao

2017-06-01

Automatic detection of lying, moving, feeding, drinking, and aggressive behaviours of pigs by means of image analysis can save observation input by staff. It would help staff make early detection of diseases or injuries of pigs during breeding and improve management efficiency of swine industry. This study describes the progress of pig behaviour detection based on image analysis and advancement in image segmentation of pig body, segmentation of pig adhesion and extraction of pig behaviour characteristic parameters. Challenges for achieving automatic detection of pig behaviours were summarized.

Increased detection of precancerous cervical lesions with adjunctive dynamic spectral imaging.

PubMed

DeNardis, Sara A; Lavin, Philip T; Livingston, Jeff; Salter, William R; James-Patrick, Nanette; Papagiannakis, Emmanouil; Olson, Christopher G; Weinberg, Lori

2017-01-01

To validate, in US community-based colposcopy clinics, previous reports of increased detection of high-grade cervical intraepithelial neoplasia (CIN2+) with biopsies selected using dynamic spectral imaging (DSI) mapping after standard colposcopy. Cross-sectional observational study of 26 colposcopists across nine clinics recruiting consecutive colposcopy patients. Standard assessment with biopsy selections was completed before seeing the DSI map which was subsequently interpreted and used for additional biopsies per clinical judgment. Primary measure was the number of women with CIN2+ detected by DSI-assisted biopsies, over those detected by standard colposcopy biopsies. A total of 887 women were recruited. After exclusions, 881 women and 1,189 biopsies were analyzed. Standard biopsy detected 78 women with CIN2+ and DSI-assisted biopsies another 34, increasing the detection rate from 8.85% to 12.71% ( p =0.00016). This was achieved with 16.16% of DSI-assisted biopsies finding CIN2+ compared to 13.24% for the preceding standard biopsies. For secondary specificity analysis, 431 women had only

Increased detection of precancerous cervical lesions with adjunctive dynamic spectral imaging

PubMed Central

DeNardis, Sara A; Lavin, Philip T; Livingston, Jeff; Salter, William R; James-Patrick, Nanette; Papagiannakis, Emmanouil; Olson, Christopher G; Weinberg, Lori

2017-01-01

Objective To validate, in US community-based colposcopy clinics, previous reports of increased detection of high-grade cervical intraepithelial neoplasia (CIN2+) with biopsies selected using dynamic spectral imaging (DSI) mapping after standard colposcopy. Study design Cross-sectional observational study of 26 colposcopists across nine clinics recruiting consecutive colposcopy patients. Standard assessment with biopsy selections was completed before seeing the DSI map which was subsequently interpreted and used for additional biopsies per clinical judgment. Primary measure was the number of women with CIN2+ detected by DSI-assisted biopsies, over those detected by standard colposcopy biopsies. Results A total of 887 women were recruited. After exclusions, 881 women and 1,189 biopsies were analyzed. Standard biopsy detected 78 women with CIN2+ and DSI-assisted biopsies another 34, increasing the detection rate from 8.85% to 12.71% (p=0.00016). This was achieved with 16.16% of DSI-assisted biopsies finding CIN2+ compared to 13.24% for the preceding standard biopsies. For secondary specificity analysis, 431 women had only

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers.

PubMed

Bellesi, Luca; Wyttenbach, Rolf; Gaudino, Diego; Colleoni, Paolo; Pupillo, Francesco; Carrara, Mauro; Braghetti, Antonio; Puligheddu, Carla; Presilla, Stefano

2017-01-01

The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images. Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

Saliency detection algorithm based on LSC-RC

NASA Astrophysics Data System (ADS)

Wu, Wei; Tian, Weiye; Wang, Ding; Luo, Xin; Wu, Yingfei; Zhang, Yu

2018-02-01

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.

Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

PubMed

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

NASA Astrophysics Data System (ADS)

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).