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Journal articles on the topic 'Latent'

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Published: 4 June 2021
Last updated: 10 March 2023

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1

Keeling, R. "Latest developments in latent defects insurance." Property Management 11, no. 3 (March 1993): 220–25. http://dx.doi.org/10.1108/eum0000000003400.

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2

Wolfson, Susan J. "Yeats's Latent Keats / Keats's Latent Yeats." PMLA/Publications of the Modern Language Association of America 131, no. 3 (May 2016): 603–21. http://dx.doi.org/10.1632/pmla.2016.131.3.603.

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Keats's tracks into the nineteenth century angle toward a “modernism” often defined at his expense—yet with latent identifications. In relations of past and present, figurai identifications may register in nuances different from conscious allusion or the psychodramas of influence, ravages and resistance, hauntings and felt belatedness that issue in self-interested misreadings. “Latent Keats” and “Latent Yeats” play into an important, underreported current in both Keats studies and Yeats studies: a “Long Romanticism” in intimate verbal figures that trouble any “Modernism” of definitional difference from “Romantic.” Keats's writing harbors figures to which Yeats could respond, even correspond, vexed as he was by “Keats” as the name for the puerile outsider's dreamy sensuousness that a proper “modernist” needed to spurn. Such complication is one of the variable formations by which a “modernist” program manages to conjure the “Romantic” precedence it would supersede.
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3

McCutcheon, Allen L., Rolf Langeheine, and Jurgen Rost. "Latent Trait and Latent Class Models." Contemporary Sociology 18, no. 5 (September 1989): 836. http://dx.doi.org/10.2307/2073408.

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4

Guo, J. "Latent class regression on latent factors." Biostatistics 7, no. 1 (May 25, 2005): 145–63. http://dx.doi.org/10.1093/biostatistics/kxi046.

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5

FLORES, RICARDO, SONIA DELGADO, MARÍA-ELENA RODIO, SILVIA AMBRÓS, CARMEN HERNÁNDEZ, and FRANCESCO DI SERIO. "Peach latent mosaic viroid: not so latent." Molecular Plant Pathology 7, no. 4 (July 2006): 209–21. http://dx.doi.org/10.1111/j.1364-3703.2006.00332.x.

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6

Croon, Marcel. "Latent class analysis with ordered latent classe." British Journal of Mathematical and Statistical Psychology 43, no. 2 (November 1990): 171–92. http://dx.doi.org/10.1111/j.2044-8317.1990.tb00934.x.

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7

Chaudhary, Neha, and Priti Dimri. "LATENT FINGERPRINT IMAGE ENHANCEMENT BASED ON OPTIMIZED BENT IDENTITY BASED CONVOLUTIONAL NEURAL NETWORK." Indian Journal of Computer Science and Engineering 12, no. 5 (October 20, 2021): 1477–93. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205124.

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Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info when observed as an image. Image Enhancement is necessary in latents, to transform the latent (noisy) image into fine-quality (enhanced) image. In this work, a new image enhancement approach named BI-CNN (Bent Identity-Convolution Neural Network) with Spatial Pyramid Max Pooling (SPMP) model optimized using TSOA (Tunicate Swarm Optimization Algorithm) is presented to produce an enhanced latent at the output. This procedure involves the integration of ROI (Region Of Interest) Estimation, Anisotropic Gaussian Filter (AGF) based Pre-filtering, Fingerprint alignment using Sobel Filter, Intrinsic Feature patch extraction using Optimized BI-CNN, GAT (Graph Attention) network based Similarity Estimation followed by image reconstruction and feedback module. The implementation tool used in this work is PYTHON platform. The proposed optimized BI-CNN framework tested on dual public datasets namely IIITD-latent finger print and IIITD-MOLF have shown enhanced outcomes. Thus, the IIITD -latent fingerprint database obtained 83.33% on Rank-10 accuracy and 39.33% on Rank-25 accuracy.
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8

Zellweger, Jean-Pierre. "Latent Tuberculosis Infection." European Respiratory & Pulmonary Diseases 4, no. 1 (2018): 21. http://dx.doi.org/10.17925/erpd.2018.4.1.21.

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Tuberculosis (TB) is a major global public health problem and is the leading cause of death linked to a single pathogen, ranking above human immunodeficiency virus (HIV).1 Clinically, TB has been categorised as active disease (patients who are generally symptomatic and may be infectious if pulmonary involvement is present) and latent infection (asymptomatic and not infectious, but at variable risk for progression to active TB disease). It is increasingly being recognised that latent TB infection (LTBI) reflects diverse responses to infection with Mycobacterium tuberculosis and may lead to heterogeneous clinical outcomes. In an expert interview, Jean-Pierre Zellweger discusses the latest World Health Organisation (WHO) guidelines on the management of LTBI.
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9

IQBAL, JAVED, ALTAF HUSSAIN MALIK, and Aftab JAMIL. "LATENT TUBERCULOSIS;." Professional Medical Journal 19, no. 01 (January 3, 2012): 059–62. http://dx.doi.org/10.29309/tpmj/2012.19.01.1949.

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Objectives: To assess the degree to which Latent Tuberculosis exist among long–term inmates in jail. Study Design:Prospective Cohort Analytic Experimental Quantitative Data. Setting and Period: Jails at Bahawalpur between 2009-2010. Methods: Wemonitored the Mountex Test of prisoners within 48 to 72 h and those who were 10 mm or more were considered positive and for HIV positive 5mm criteria were set to declare positive. Group 1-who were in jail for more than a year and those, Group- 2-who were in jail for less than onemonth. Data was collected on a proforma. Each prisoner had thorough clinical examination with detailed clinical history and Chest X-ray.Inclusion Criteria: 1. All those prisoners who never had tuberculosis in past. 2. All those prisoners who were not on Anti Tuberculous Therapy.3. All those prisoner whose chest x ray was normal and had no symptoms of tuberculosis. Exclusion Criteria: 1. All those who had tuberculosisin past or were on antituberculous treatment currently. 2. All those who were having chronic cough. Results: Total number of prisoners in group1 were 298 and number of prisoners in group 2 were 128. Latent tuberculosis was found in total of 31(10.40%) of prisoners in group 1 and noneof prisoners in group 2 were having latent tuberculosis. Conclusions: Jail inmates for more than 1 year did show more numbers of latenttuberculosis patients than the new inmates. These results suggest that the close contacts harbor the live tubercle bacilli and in future they mayconvert into active cases.
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10

Toister, Yanai. "Latent digital." Journal of Visual Art Practice 19, no. 2 (January 14, 2020): 125–36. http://dx.doi.org/10.1080/14702029.2019.1701915.

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11

Pino-Ojeda, Walescka. "Latent Image." Latin American Perspectives 36, no. 5 (September 2009): 133–46. http://dx.doi.org/10.1177/0094582x09341980.

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12

Brodsky, M. C. "Latent heliotropism." British Journal of Ophthalmology 86, no. 12 (December 1, 2002): 1327–28. http://dx.doi.org/10.1136/bjo.86.12.1327.

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13

BRENNAN, MICHAEL J. "Latent Assets." Journal of Finance 45, no. 3 (July 1990): 709–30. http://dx.doi.org/10.1111/j.1540-6261.1990.tb05102.x.

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14

Walton, Gregory M., and Steven J. Spencer. "Latent Ability." Psychological Science 20, no. 9 (September 2009): 1132–39. http://dx.doi.org/10.1111/j.1467-9280.2009.02417.x.

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Past research has assumed that group differences in academic performance entirely reflect genuine differences in ability. In contrast, extending research on stereotype threat, we suggest that standard measures of academic performance are biased against non-Asian ethnic minorities and against women in quantitative fields. This bias results not from the content of performance measures, but from the context in which they are assessed—from psychological threats in common academic environments, which depress the performances of people targeted by negative intellectual stereotypes. Like the time of a track star running into a stiff headwind, such performances underestimate the true ability of stereotyped students. Two meta-analyses, combining data from 18,976 students in five countries, tested this latent-ability hypothesis. Both meta-analyses found that, under conditions that reduce psychological threat, stereotyped students performed better than nonstereotyped students at the same level of past performance. We discuss implications for the interpretation of and remedies for achievement gaps.
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15

Lathouri, Marina. "Latent Modernities." Architectural Theory Review 24, no. 1 (January 2, 2020): 116–19. http://dx.doi.org/10.1080/13264826.2020.1795975.

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16

Sweet, John. "Latent Meanings." Theology 112, no. 870 (November 2009): 403–9. http://dx.doi.org/10.1177/0040571x0911200602.

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17

Koul, AjazNabi. "Latent tuberculosis." Lung India 33, no. 3 (2016): 352. http://dx.doi.org/10.4103/0970-2113.180963.

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18

Persing, David H. "Latent Tuberculosis." Journal of Molecular Diagnostics 17, no. 1 (January 2015): 2–3. http://dx.doi.org/10.1016/j.jmoldx.2014.08.004.

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19

Ettema, Dick. "Latent Activities." Transportation Research Record: Journal of the Transportation Research Board 1926, no. 1 (January 2005): 171–80. http://dx.doi.org/10.1177/0361198105192600120.

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This paper describes a discrete continuous Tobit model of activity participation and duration. The model extends existing models by accounting for multiple activities and the effect of travel time needed for activity participation on time allocation. The effect of travel time was operationalized by incorporating the travel time ratio into the time allocation procedure. The model was tested on a Dutch data set and appears to outperform less advanced models. The model provides a useful tool for assessing the effect of spatial and transportation policies on individuals’ time allocation and their ability to realize so-called latent activities.
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20

Reichman, Lee B., Anita Khilall, and Alfred A. Lardizabal. "Whither “latent”?" Lancet Infectious Diseases 17, no. 8 (August 2017): 802. http://dx.doi.org/10.1016/s1473-3099(17)30401-2.

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21

Chandra, Divay, Babith J. Manikdiyil, Elizabeth Guy, and Richard J. Hamill. "Latent Enigma." American Journal of Medicine 119, no. 7 (July 2006): 581–83. http://dx.doi.org/10.1016/j.amjmed.2006.05.020.

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22

Lucero, Jorge. "Latent Article." Teaching Artist Journal 1, no. 2 (June 2003): 100–101. http://dx.doi.org/10.1207/s1541180xtaj0102-06.

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23

Fehervari, Zoltan. "Latent enhancement." Nature Immunology 14, no. 3 (February 15, 2013): 204. http://dx.doi.org/10.1038/ni.2558.

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24

Fieldhouse, Ed, and Andrew Russell. "Latent Liberalism?" Party Politics 7, no. 6 (November 2001): 711–38. http://dx.doi.org/10.1177/1354068801007006003.

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25

Ahlskog, Gary. "Latent Theology." Psychotherapy Patient 1, no. 3 (February 8, 1985): 63–70. http://dx.doi.org/10.1300/j358v01n03_07.

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26

Pearson, Eric F. "Latent evidence." Forensic Science International 46, no. 1-2 (May 1990): 41–45. http://dx.doi.org/10.1016/0379-0738(90)90132-i.

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27

Brodsky, Michael C. "Latent Nystagmus." Archives of Ophthalmology 122, no. 2 (February 1, 2004): 202. http://dx.doi.org/10.1001/archopht.122.2.202.

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28

Gradstein, Libe, Herschel P. Goldstein, Sheryl S. Wizov, and Robert D. Reinecke. "Extended Slow Phase in Latent/Manifest Latent Nystagmus." Investigative Opthalmology & Visual Science 45, no. 4 (April 1, 2004): 1139. http://dx.doi.org/10.1167/iovs.03-0324.

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29

Lee, Jung Wun, and Hwan Chung. "Latent class analysis with multiple latent group variables." Communications for Statistical Applications and Methods 24, no. 2 (March 31, 2017): 173–91. http://dx.doi.org/10.5351/csam.2017.24.2.173.

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30

Xu, Gongjun, and Zhuoran Shang. "Identifying Latent Structures in Restricted Latent Class Models." Journal of the American Statistical Association 113, no. 523 (June 8, 2018): 1284–95. http://dx.doi.org/10.1080/01621459.2017.1340889.

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31

Hiraoka, Tatsuya, Sho Takase, Kei Uchiumi, Atsushi Keyaki, and Naoaki Okazaki. "Recurrent Neural Hidden Markov Model for High-order Transition." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (March 31, 2022): 1–15. http://dx.doi.org/10.1145/3476511.

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We propose a method to pay attention to high-order relations among latent states to improve the conventional HMMs that focus only on the latest latent state, since they assume Markov property. To address the high-order relations, we apply an RNN to each sequence of latent states, because the RNN can represent the information of an arbitrary-length sequence with their cell: a fixed-size vector. However, the simplest way, which provides all latent sequences explicitly for the RNN, is intractable due to the combinatorial explosion of the search space of latent states. Thus, we modify the RNN to represent the history of latent states from the beginning of the sequence to the current state with a fixed number of RNN cells whose number is equal to the number of possible states. We conduct experiments on unsupervised POS tagging and synthetic datasets. Experimental results show that the proposed method achieves better performance than previous methods. In addition, the results on the synthetic dataset indicate that the proposed method can capture the high-order relations.
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32

Collins, Linda M., and Stuart E. Wugalter. "Latent Class Models for Stage-Sequential Dynamic Latent Variables." Multivariate Behavioral Research 27, no. 1 (January 1992): 131–57. http://dx.doi.org/10.1207/s15327906mbr2701_8.

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33

Stout, Jason E., Yanjue Wu, Christine S. Ho, April C. Pettit, Pei-Jean Feng, Dolly J. Katz, Smita Ghosh, Thara Venkatappa, and Ruiyan Luo. "Evaluating latent tuberculosis infection diagnostics using latent class analysis." Thorax 73, no. 11 (July 7, 2018): 1062–70. http://dx.doi.org/10.1136/thoraxjnl-2018-211715.

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BackgroundLack of a gold standard for latent TB infection has precluded direct measurement of test characteristics of the tuberculin skin test and interferon-γ release assays (QuantiFERON Gold In-Tube and T-SPOT.TB).ObjectiveWe estimated test sensitivity/specificity and latent TB infection prevalence in a prospective, US-based cohort of 10 740 participants at high risk for latent infection.MethodsBayesian latent class analysis was used to estimate test sensitivity/specificity and latent TB infection prevalence among subgroups based on age, foreign birth outside the USA and HIV infection.ResultsLatent TB infection prevalence varied from 4.0% among foreign-born, HIV-seronegative persons aged <5 years to 34.0% among foreign-born, HIV-seronegative persons aged ≥5 years. Test sensitivity ranged from 45.8% for the T-SPOT.TB among foreign-born, HIV-seropositive persons aged ≥5 years to 80.7% for the tuberculin skin test among foreign-born, HIV-seronegative persons aged ≥5 years. The skin test was less specific than either interferon-γ release assay, particularly among foreign-born populations (eg, the skin test had 70.0% specificity among foreign-born, HIV-seronegative persons aged ≥5 years vs 98.5% and 99.3% specificity for the QuantiFERON and T-SPOT.TB, respectively). The tuberculin skin test’s positive predictive value ranged from 10.0% among foreign-born children aged <5 years to 69.2% among foreign-born, HIV-seropositive persons aged ≥5 years; the positive predictive values of the QuantiFERON (41.4%) and T-SPOT.TB (77.5%) were also low among US-born, HIV-seropositive persons aged ≥5 years.ConclusionsThese data reinforce guidelines preferring interferon-γ release assays for foreign-born populations and recommending against screening populations at low risk for latent TB infection.Trial registration numberNCT01622140.
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34

Asparouhov, Tihomir, and Bengt Muthén. "Residual Associations in Latent Class and Latent Transition Analysis." Structural Equation Modeling: A Multidisciplinary Journal 22, no. 2 (October 8, 2014): 169–77. http://dx.doi.org/10.1080/10705511.2014.935844.

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35

Deng, Jiahuan, Zhuoyun Wu, Jiaqi Liu, Qiuyun Ji, and Chunmei Ju. "The Role of Latency-Associated Transcripts in the Latent Infection of Pseudorabies Virus." Viruses 14, no. 7 (June 24, 2022): 1379. http://dx.doi.org/10.3390/v14071379.

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Pseudorabies virus (PRV) can cause neurological, respiratory, and reproductive diseases in pigs and establish lifelong latent infection in the peripheral nervous system (PNS). Latent infection is a typical feature of PRV, which brings great difficulties to the prevention, control, and eradication of pseudorabies. The integral mechanism of latent infection is still unclear. Latency-associated transcripts (LAT) gene is the only transcriptional region during latent infection of PRV which plays the key role in regulating viral latent infection and inhibiting apoptosis. Here, we review the characteristics of PRV latent infection and the transcriptional characteristics of the LAT gene. We also analyzed the function of non-coding RNA (ncRNA) produced by the LAT gene and its importance in latent infection. Furthermore, we provided possible strategies to solve the problem of latent infection of virulent PRV strains in the host. In short, the detailed mechanism of PRV latent infection needs to be further studied and elucidated.
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36

Hickendorff, Marian, Peter A. Edelsbrunner, Jake McMullen, Michael Schneider, and Kelly Trezise. "Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis." Learning and Individual Differences 66 (August 2018): 4–15. http://dx.doi.org/10.1016/j.lindif.2017.11.001.

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37

Bray, Bethany C., and John J. Dziak. "Commentary on latent class, latent profile, and latent transition analysis for characterizing individual differences in learning." Learning and Individual Differences 66 (August 2018): 105–10. http://dx.doi.org/10.1016/j.lindif.2018.06.001.

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38

Borkowska, Dagmara I., Agnieszka M. Napiórkowska, Sylwia A. Brzezińska, Monika Kozińska, Anna T. Zabost, and Ewa M. Augustynowicz-Kopeć. "From Latent Tuberculosis Infection to Tuberculosis. News in Diagnostics (QuantiFERON-Plus)." Polish Journal of Microbiology 66, no. 1 (March 30, 2017): 5–8. http://dx.doi.org/10.5604/17331331.1234987.

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It is estimated that one third of the world’s population have latent tuberculosis infection and that this is a significant reservoir for future tuberculosis cases. Most cases occur within two years following initial infection. The identification of individuals with latent tuberculosis infection is difficult due to the lack of an ideal diagnostic assay and incomplete understanding of latent infection. Currently, there are three tests: the oldest tuberculin skin test, T-SPOT.TB and the latest QuantiFERON-Plus for the detection of Mycobacterium tuberculosis infection. The interpretation of the test results must be used in the conjunction with a patient’s epidemiological history, risk assessment, current clinical status, radiography and microbiological methods to ensure accurate diagnosis.
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39

Usherwood, Edward J., Douglas J. Roy, Kim Ward, Sherri L. Surman, Bernadette M. Dutia, Marcia A. Blackman, James P. Stewart, and David L. Woodland. "Control of Gammaherpesvirus Latency by Latent Antigen-Specific Cd8+ T Cells." Journal of Experimental Medicine 192, no. 7 (September 25, 2000): 943–52. http://dx.doi.org/10.1084/jem.192.7.943.

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The contribution of the latent antigen-specific CD8+ T cell response to the control of gammaherpesvirus latency is currently obscure. Some latent antigens induce potent T cell responses, but little is known about their induction or the role they play during the establishment of latency. Here we used the murine gammaherpesvirus system to examine the expression of the latency-associated M2 gene during latency and the induction of the CD8+ T cell response to this protein. M2, in contrast to the M3 latency-associated antigen, was expressed at day 14 after infection but was undetectable during long-term latency. The induction of the M291–99/Kd CD8+ T cell response was B cell dependent, transient, and apparently induced by the rapid increase in latently infected cells around day 14 after intranasal infection. These kinetics were consistent with a role in controlling the initial “burst” of latently infected cells. In support of this hypothesis, adoptive transfer of an M2-specific CD8+ T cell line reduced the initial load of latently infected cells, although not the long-term load. These data represent the first description of a latent antigen-specific immune response in this model, and suggest that vaccination with latent antigens such as M2 may be capable of modulating latent gammaherpesvirus infection.
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40

Mindrila, Diana. "Latent Class Analysis." International Journal for Cross-Disciplinary Subjects in Education 11, no. 2 (December 31, 2020): 4323–28. http://dx.doi.org/10.20533/ijcdse.2042.6364.2020.0529.

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41

Reed, Phil. "Blocking latent inhibition." Bulletin of the Psychonomic Society 29, no. 4 (April 1991): 292–94. http://dx.doi.org/10.3758/bf03333922.

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42

Cubitt, Sean. "The Latent Image." International Journal of the Image 1, no. 2 (2011): 27–38. http://dx.doi.org/10.18848/2154-8560/cgp/v01i02/44189.

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43

Xu, Xiaoxi. "Discovering Latent Strategies." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1834–35. http://dx.doi.org/10.1609/aaai.v25i1.8058.

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Strategy mining is a new area of research about discovering strategies in decision-making. In this paper, we formulate the strategy-mining problem as a clustering problem, called the latent-strategy problem. In a latent-strategy problem, a corpus of data instances is given, each of which is represented by a set of features and a decision label. The inherent dependency of the decision label on the features is governed by a latent strategy. The objective is to find clusters, each of which contains data instances governed by the same strategy. Existing clustering algorithms are inappropriate to cluster dependency because they either assume feature independency (e.g., K-means) or only consider the co-occurrence of features without explicitly modeling the special dependency of the decision label on other features (e.g., Latent Dirichlet Allocation (LDA)). In this paper, we present a baseline unsupervised learning algorithm for dependency clustering. Our model-based clustering algorithm iterates between an assignment step and a minimization step to learn a mixture of decision tree models that represent latent strategies. Similar to the Expectation Maximization algorithm, our algorithm is grounded in the statistical learning theory. Different from other clustering algorithms, our algorithm is irrelevant-feature resistant and its learned clusters (modeled by decision trees) are strongly interpretable and predictive. We systematically evaluate our algorithm using a common law dataset comprised of actual cases. Experimental results show that our algorithm significantly outperforms K-means and LDA on clustering dependency.
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44

Chee, Cynthia Bin Eng. "Latent TB Infection." Singapore Family Physician 43, no. 4 (October 1, 2017): 11–14. http://dx.doi.org/10.33591/sfp.43.4.u2.

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45

Marchenko, Anatoliy, Georgyi Koidan, Anastasiya Hurieva, Kostiantyn Shvydenko, Alexander B. Rozhenko, Eduard B. Rusanov, Andrii A. Kyrylchuk, and Aleksandr Kostyuk. "Latent Nucleophilic Carbenes." Journal of Organic Chemistry 87, no. 1 (December 13, 2021): 373–85. http://dx.doi.org/10.1021/acs.joc.1c02397.

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46

Ziegel, Eric R., and J. Loehlin. "Latent Variable Models." Technometrics 35, no. 4 (November 1993): 465. http://dx.doi.org/10.2307/1270304.

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47

SHIRAZ, MOHAMMAD ATIF, and ABDULLAH KHAN. "LATENT PULMONARY TUBERCULOSIS." Professional Medical Journal 14, no. 01 (March 10, 2007): 49–55. http://dx.doi.org/10.29309/tpmj/2007.14.01.3623.

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Objective: To assess the prevalence of latent pulmonary tuberculosis (TB) in young adults males on MMR screening. Design: Descriptive study Place and duration of study: The study was carried out at Combined Military Hospital Kohat from January 2004 to August 2005. Material and Methods: A total of 4000 freshly inducted recruits aged between 18-23 years were subjected to MMR using an Odalka camera. Individuals with suspicious findings had a standard chest radiograph taken and on confirmation of findings they were inoculated with tuberculin and readings of Mantoux test were recorded. Results: Out of 4000 MMR films, 2.15% were judged suspicious and standard chest radiographs were taken. 1.2% showedfeatures suggestive of pulmonary TB. 0.175% showed non-tuberculous pulmonary findings and 0.05% showed cardiac lesions. 0.725 % chest radiographs were normal. 1.2 % suspected cases of pulmonary TB were investigated further with Mantoux test. 0.745% had strongly positive Mantoux test and were labeled as latent pulmonary TB. Conclusions: Pulmonary TB is a major health issue in our country. There is a highprevalence of latent pulmonary TB in our asymptomatic adult population. MMR is an effective and cheap method of early detection and should be considered for mass screening of our younger population on a larger scale.
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48

Shah, Maunank, and Susan E. Dorman. "Latent Tuberculosis Infection." New England Journal of Medicine 385, no. 24 (December 9, 2021): 2271–80. http://dx.doi.org/10.1056/nejmcp2108501.

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49

GAMBIOLI, Andrea. "Latent Quaternionic Geometry." Tokyo Journal of Mathematics 31, no. 1 (June 2008): 203–23. http://dx.doi.org/10.3836/tjm/1219844833.

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50

Eshima, Nobuoki. "Latent Scalogram Analysis." Behaviormetrika 18, no. 30 (July 1991): 1–21. http://dx.doi.org/10.2333/bhmk.18.30_1.

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