Статті в журналах: "Subgroups Identification"

1

Lipponen, Jukka, Klaus Helkama, and Milla Juslin. "Subgroup Identification, Superordinate Identification and Intergroup Bias between the Subgroups." Group Processes & Intergroup Relations 6, no. 3 (July 2003): 239–50. http://dx.doi.org/10.1177/13684302030063002.

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2

Hayashi, M., A. Kawachi, and H. Kobayashi. "Quantum measurements for hidden subgroup problems with optimal sample complexity." Quantum Information and Computation 8, no. 3&4 (March 2008): 345–58. http://dx.doi.org/10.26421/qic8.3-4-8.

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One of the central issues in the hidden subgroup problem is to bound the sample complexity, i.e., the number of identical samples of coset states sufficient and necessary to solve the problem. In this paper, we present general bounds for the sample complexity of the identification and decision versions of the hidden subgroup problem. As a consequence of the bounds, we show that the sample complexity for both of the decision and identification versions is $\Theta(\log|\HH|/\log p)$ for a candidate set $\HH$ of hidden subgroups in the case \REVISE{where the candidate nontrivial subgroups} have the same prime order $p$, which implies that the decision version is at least as hard as the identification version in this case. In particular, it does so for the important \REVISE{cases} such as the dihedral and the symmetric hidden subgroup problems. Moreover, the upper bound of the identification is attained \REVISE{by a variant of the pretty good measurement}. \REVISE{This implies that the concept of the pretty good measurement is quite useful for identification of hidden subgroups over an arbitrary group with optimal sample complexity}.

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3

Seibold, Heidi, Achim Zeileis, and Torsten Hothorn. "Model-Based Recursive Partitioning for Subgroup Analyses." International Journal of Biostatistics 12, no. 1 (May 1, 2016): 45–63. http://dx.doi.org/10.1515/ijb-2015-0032.

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AbstractThe identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.

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4

Ataei, Mohammad Javad. "Identification some groups with special subgroups." International Journal of Algebra 12, no. 3 (2018): 109–14. http://dx.doi.org/10.12988/ija.2018.71050.

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5

Alagiozoglou, Lee, Freddy Sitas, and Lynn Morris. "Phylogenetic analysis of human herpesvirus-8 in South Africa and identification of a novel subgroup." Journal of General Virology 81, no. 8 (August 1, 2000): 2029–38. http://dx.doi.org/10.1099/0022-1317-81-8-2029.

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The incidence of Kaposi’s sarcoma in South Africa is increasing in parallel with the human immunodeficiency virus type 1 epidemic. An 804 bp region in the ORF75 gene of 40 human herpesvirus-8 (HHV-8) isolates from South Africa was sequenced and the phylogenetic relationships were compared to published sequences. Nineteen strains clustered with subgroup B and 11 with subgroup A; however, the bootstrap values supporting these subgroups were not significant. Three strains grouped significantly with the C subgroup, while eight sequences did not cluster with any of the previously classified subgroups and were termed novel (N). The N subgroup differed from the A, B and C subgroups by DNA distances of 4·8, 4·2 and 4·5%, respectively, although within the N subgroup there was only 0·4% variation. The inclusion of this subgroup increased the number of previously described subgroup-specific polymorphisms from 17 to 47 over an 804 bp region. There was sufficient inter-subgroup genetic diversity for a single-strand conformational polymorphism assay to be used to identify them rapidly. Thus, based on analysis of the ORF75 gene, a unique HHV-8 subgroup, termed N, is present in South Africa, which accounts for 20% of circulating strains. Further studies are required to determine the degree of genetic divergence, distribution and pathogenic potential of this novel subgroup.

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6

Lötsch, Jörn, and Alfred Ultsch. "Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data." International Journal of Molecular Sciences 21, no. 1 (December 20, 2019): 79. http://dx.doi.org/10.3390/ijms21010079.

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Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to the visualization plane require the correct representation of the structures in the data. This work shows that frequently used techniques are unreliable in this respect. One of the most important methods for data projection in this area is the t-distributed stochastic neighbor embedding (t-SNE). We analyzed its performance on artificial and real biomedical data sets. t-SNE introduced a cluster structure for homogeneously distributed data that did not contain any subgroup structure. In other data sets, t-SNE occasionally suggested the wrong number of subgroups or projected data points belonging to different subgroups, as if belonging to the same subgroup. As an alternative approach, emergent self-organizing maps (ESOM) were used in combination with U-matrix methods. This approach allowed the correct identification of homogeneous data while in sets containing distance or density-based subgroups structures; the number of subgroups and data point assignments were correctly displayed. The results highlight possible pitfalls in the use of a currently widely applied algorithmic technique for the detection of subgroups in high dimensional cytometric data and suggest a robust alternative.

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7

Martinovic, Borja, Jolanda Jetten, Anouk Smeekes, and Maykel Verkuyten. "Collective memory of a dissolved country: Group-based nostalgia and guilt assignment as predictors of interethnic relations between diaspora groups from former Yugoslavia." Journal of Social and Political Psychology 5, no. 2 (January 15, 2018): 588–607. http://dx.doi.org/10.5964/jspp.v5i2.733.

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In this study we examined intergroup relations between immigrants of different ethnic backgrounds (Croats, Serbs, and Bosniaks) originating from the same conflict area (former Yugoslavia) and living in the same host country (Australia). For these (formerly) conflicted groups we investigated whether interethnic contacts depended on superordinate Yugoslavian and subgroup ethnic identifications as well as two emotionally laden representations of history: Yugonostalgia (longing for Yugoslavia from the past) and collective guilt assignment for the past wrongdoings. Using unique survey data collected among Croats, Serbs and Bosniaks in Australia (N = 87), we found that Yugoslavian identification was related to stronger feelings of Yugonostalgia, and via Yugonostalgia, to relatively more contact with other subgroups from former Yugoslavia. Ethnic identification, in contrast, was related to a stronger assignment of guilt to out-group relative to in-group, and therefore, to relatively less contact with other subgroups in Australia. We discuss implications of transferring group identities and collective memories into the diaspora.

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8

GEBERT, JÖRG, ANDREY V. MATALIN, and FABIAN A. BOETZL. "Revision of the Palearctic Cicindela campestris species complex—Part 1: On the taxonomy, identification and ecology of Cicindela herbacea Klug, 1832 and Cicindela javetii Chaudoir, 1861 (Coleoptera, Cicindelidae)." Zootaxa 4990, no. 3 (June 22, 2021): 469–510. http://dx.doi.org/10.11646/zootaxa.4990.3.3.

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We revise the taxonomically problematic Palearctic Cicindela campestris species complex, a group of green tiger beetle species, using an integrative approach combining morphology, morphometry and biogeography. In this first part, an identification key to all subgroups of these green tiger beetles (Cicindela herbacea-subgroup, Cicindela javetii-subgroup, Cicindela desertorum-subgroup, Cicindela campestris-subgroup, Cicindela turkestanica-subgroup and Cicindela asiatica-subgroup) based on large series taken from private and museum collections as well as on literature sources is provided and diagnostic characters are illustrated by detailed photographs. The Cicindela herbacea- and Cicindela javetii-subgroups are revised and illustrated and identification keys as well as distribution maps for both are given. Four new synonyms are established: Cicindela herbacea herbacea Klug, 1832 = Cicindela herbacea aleppensis Deuve, 2012, syn. n.; Cicindela herbacea turkestanicoides W. Horn, 1938 = Cicindela herbacea perreaui Deuve, 1987, syn. n. = Cicindela herbacea colasi Deuve, 2011, syn. n.; Cicindela javetii javetii Chaudoir, 1861 = Cicindela thughurica Franzen, 2007, syn. n.

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9

Apfelbaum, April A., Olivia Waltner, Shruti S. Bhise, Sami Kanaan, Jay F. Sarthy, Scott N. Furlan, and Elizabeth R. Lawlor. "Abstract B020: Multimodal single-cell analyses reveal identification of unique transcriptional subgroups in ewing sarcoma." Clinical Cancer Research 28, no. 18_Supplement (September 15, 2022): B020. http://dx.doi.org/10.1158/1557-3265.sarcomas22-b020.

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Abstract Ewing sarcoma (EwS) tumors are driven by pathognomonic fusions between FET proteins and ETS family transcription factors (most frequently EWS::FLI1). EWS::FLI1 drives tumorigenesis through massive transcriptomic and epigenomic rewiring. Despite mutational homogeneity, EwS are highly transcriptionally heterogeneous suggesting that cell context is a key determinant of EWS::FLI1-driven gene signatures. Fusion protein activity has been revealed as a core source of EwS heterogeneity. However, deep investigation into the transcriptomic and epigenomic heterogeneity of EwS cells has yet to be described. Molecular subtyping of EwS has thus far proven to be difficult to achieve with bulk RNA-sequencing approaches. We hypothesized that single-cell characterization of EwS cells would provide the necessary resolution to allow identification and characterization of transcriptionally distinct tumor subgroups. We subjected eight established EwS cell lines, one PDX-derived EwS line, and five non-EwS cell lines to single-cell multiomic (ATAC + RNA) sequencing. A range of 1300-3800 live cells were captured per sample, with an average of 25,000 transcript and accessible reads per cell. Initial analysis of transcriptomes confirmed the EwS-specific, EWS::FLI1-dependent signature gene sets in EwS samples. Integrated analysis of the multiome data from the 9 EwS samples identified inter-tumor heterogeneity and unsupervised k-means clustering revealed three molecular subgroups that were not evident from expression data alone. Subgroup 1 comprised only the A673 cell line, while the remaining 8 EwS samples were split between subgroups 2 and 3. Gene ontology analysis defined enrichment of distinct gene sets across the 3 subgroups. Interestingly, expression of subgroup 2-defining genes was reduced following knockdown of EWSR1::FLI1, while expression of subgroup 3-defining genes increased. Thus, these data show that transcriptionally distinct subgroups of EwS exist and these are defined, in part, by whether EWS::FLI1-dependent gene activation or gene repression dominates transcriptional rewiring. Our ongoing studies suggest that these molecular subgroups are defined by cooperation between the fusion and cell context-dependent transcription factors. Future analyses will determine if these distinct multiomic programs exist in primary patients tumors and test the pathobiologic and clinical significance of these novel molecular subgroups. Citation Format: April A. Apfelbaum, Olivia Waltner, Shruti S. Bhise, Sami Kanaan, Jay F. Sarthy, Scott N. Furlan, Elizabeth R. Lawlor. Multimodal single-cell analyses reveal identification of unique transcriptional subgroups in ewing sarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B020.

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10

Pulver, A. E., S. E. Antonarakis, J. L. Blouin, D. Housman, H. H. Kazazian, V. K. Lasseter, J. G. Mulle, G. Nestadt, and P. S. Wolyniec. "221. Schizophrenia: the identification of genetic subgroups." Biological Psychiatry 47, no. 8 (April 2000): S67. http://dx.doi.org/10.1016/s0006-3223(00)00485-6.

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11

Raymond, R., R. Conley, C. Gounaris, and D. Medoff. "Identification of subgroups of poorly responsive schizophrenics." Schizophrenia Research 4, no. 3 (May 1991): 324. http://dx.doi.org/10.1016/0920-9964(91)90223-e.

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12

Zhang, Ning, Yali Guo, Cong Wu, Bohan Jiang, and Yuguang Wang. "Identification of the Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Gene Expression Profiles." Computational and Mathematical Methods in Medicine 2021 (October 4, 2021): 1–11. http://dx.doi.org/10.1155/2021/7922594.

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Background. Idiopathic Pulmonary Fibrosis (IPF) is one of the most common idiopathic interstitial pneumonia, which can occur all over the world. The median survival time of patients is about 3-5 years, and the mortality is relatively high. Objective. To reveal the potential molecular characteristics of IPF and deepen the understanding of the molecular mechanism of IPF. In order to provide some guidance for the clinical treatment, new drug development, and prognosis judgment of IPF. Although the preliminary conclusion of this study has certain guiding significance for the treatment of IPF and so on, it needs more accurate analytical approaches and large sample clinical trials to verify. Methods. 220 patients with IPF were divided into different subgroups according to the gene expression profiles, which were obtained from the Gene Expression Omnibus (GEO) database. In addition, these subgroups present different expression forms and clinical features. Therefore, weighted gene coexpression analysis (WGCNA) was used to seek the differences between subtypes. And six subgroup-specific WGCNA modules were identified. Results. Combined with the characteristics of WGCNA and KEGG enrichment modules, the autophagic pathway was only upregulated in subgroup I and enriched significantly. The differentiation pathways of Th1 and Th2 cells were only upregulated and enriched in subgroup II. At the same time, combined with clinical information, IPF patients in subgroup II were older and more serious, which may be closely related to the differentiation of Th1 and Th2 cells. In contrast, the neuroactive ligand-receptor interaction pathway and Ca+ signaling pathway were significantly upregulated and enriched in subgroup III. Although there was no significant difference in prognosis between subgroup I and subgroup III, their intrinsic biological characteristics were very different. These results suggest that the subtypes may represent risk factors of age and intrinsic biological characteristics and may also partly reflect the severity of the disease. Conclusion. In conclusion, current studies have improved our understanding of IPF-related molecular mechanisms. At the same time, because the results show that patients from different subgroups may have their own unique gene expression patterns, it reminds us that patients in each subgroup should receive more personalized treatment.

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13

Wei, Yishu, Lei Liu, Xiaogang Su, Lihui Zhao, and Hongmei Jiang. "Precision medicine: Subgroup identification in longitudinal trajectories." Statistical Methods in Medical Research 29, no. 9 (February 19, 2020): 2603–16. http://dx.doi.org/10.1177/0962280220904114.

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In clinical studies, the treatment effect may be heterogeneous among patients. It is of interest to identify subpopulations which benefit most from the treatment, regardless of the treatment’s overall performance. In this study, we are interested in subgroup identification in longitudinal studies when nonlinear trajectory patterns are present. Under such a situation, evaluation of the treatment effect entails comparing longitudinal trajectories while subgroup identification requires a further evaluation of differential treatment effects among subgroups induced by moderators. To this end, we propose a tree-structured subgroup identification method, termed “interaction tree for longitudinal trajectories”, which combines mixed effects models with regression splines to model the nonlinear progression patterns among repeated measures. Extensive simulation studies are conducted to evaluate its performance and an application to an alcohol addiction pharmacogenetic trial is presented.

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14

Shealy, Lucinda, Seth C. Kalichman, Margit C. Henderson, David Szymanowski, and Geoffrey McKee. "MMPI Profile Subtypes of Incarcerated Sex Offenders Against Children." Violence and Victims 6, no. 3 (January 1991): 201–12. http://dx.doi.org/10.1891/0886-6708.6.3.201.

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Sex offenders are heterogeneous in personality and behavioral characteristics. The present study was conducted to identify homogeneous subgroups of incarcerated sexual offenders against children on the basis of the Minnesota Multiphasic Personality Inventory (MMPI). Subjects were 90 men undergoing initial evaluation for a treatment program conducted through a state department of corrections. Hierarchical cluster analysis resulted in the identification of four profile subgroups. While two subgroups presented mean profiles within normal limits, the patterns indicated differences in personality functioning: one presenting signs of sociopathy and the other emotional disturbance. In contrast, the other two profile subgroups presented several scale elevations: One subgroup indicated anger and aggression and the other severe psychopathology. Both of the latter subgroups were similar to two rapist subtypes identified in previous research. Subgroups were further differentiated on the basis of psychosexual, affective, and psychosocial variables. Results suggest the existence of homogeneous subgroups of incarcerated sexual offenders against children as delineated by the MMPI.

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15

Jurenec, Gregory S. "Identification of Subgroups of Childhood Asthmatics: A Review." Journal of Asthma 25, no. 1 (January 1988): 15–25. http://dx.doi.org/10.3109/02770908809070976.

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16

Baker, J. C., E. G. Wilson, G. L. McKay, R. J. Stanek, W. J. Underwood, L. F. Velicer, and M. A. Mufson. "Identification of subgroups of bovine respiratory syncytial virus." Journal of Clinical Microbiology 30, no. 5 (1992): 1120–26. http://dx.doi.org/10.1128/jcm.30.5.1120-1126.1992.

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17

Lukić, Natalija. "Girl's crime in Belgrade: Study of subgroups identification." Crimen 10, no. 3 (2019): 257–77. http://dx.doi.org/10.5937/crimen1903257l.

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18

Boyd, Carol J., Sean E. McCabe, and J. Cranford. "Adolescent substance use and the identification of subgroups." Drug and Alcohol Dependence 140 (July 2014): e17-e18. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.069.

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19

Chen, Yin-Chun, Chieh-Shan Wu, Gwo-Shing Chen, Gim-Thean Khor, Chun-Hung Chen, and Poyin Huang. "Identification of Subgroups of Acquired Idiopathic Generalized Anhidrosis." Neurologist 14, no. 5 (September 2008): 318–20. http://dx.doi.org/10.1097/nrl.0b013e318173e818.

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20

Guedj, E., E. J. Barbeau, M. Didic, O. Felician, C. de Laforte, M. Ceccaldi, O. Mundler, and M. Poncet. "Identification of subgroups in amnestic mild cognitive impairment." Neurology 67, no. 2 (July 24, 2006): 356–58. http://dx.doi.org/10.1212/01.wnl.0000225076.73312.d4.

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21

Parh, Md Yasin Ali, Munni Begum, Matthew Harber, Bradley S. Fleenor, Mitchell Whaley, and W. Holmes Finch. "Subgroup identification for differential cardio-respiratory fitness effect on cardiovascular disease risk factors: A model-based recursive partitioning approach." Journal of Statistical Research 54, no. 2 (March 4, 2021): 147–65. http://dx.doi.org/10.47302/jsr.2020540204.

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The goal of this study is twofold: i) identification of features associated with three cardiovascular disease (CVD) risk factors, and (ii) identification of subgroups with differential treatment effects. Multivariate analysis is performed to identify the features associated with the CVD risk factors: hypertension, diabetes, and dyslipidemia. For subgroup identification, we applied model-based recursive partitioning approach. This method fits a local model in each subgroup of the population rather than fitting one global model for the whole population. The method starts with a model for the overall effect of treatment and checks whether this effect is equally applicable for all individuals under the study based on parameter instability of M fluctuation test over a set of partitioning variables. The procedure produces a segmented model with a differential effect of cardio-respiratory fitness (CRF) corresponding to each subgroup. The subgroups are linked to predictive factors learned by the recursive partitioning approach. This approach is applied to the data from the Ball State Adult Fitness Program Longitudinal Lifestyle Study (BALL ST), where we considered the level of CRF as a treatment variable. The overall results indicate that CRF is inversely associated with hypertension, diabetes and dyslipidemia. The partitioning factors that are selected are related to these risk factors. The subgroup-specific results indicate that for each subgroup, the chance of hypertension, diabetes and dyslipidemia increases with low CRF.

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22

Okonechnikov, Konstantin, Mari Sepp, Kevin Leiss, Lena Kutscher, Kati Ernst, David Jones, Natalie Jäger, Kristian W. Pajtler, Henrik Kaessmann, and Stefan M. Pfister. "MBRS-03. SINGLE NUCLEUS TRANSCRIPTOME PROFILES FROM HUMAN DEVELOPING CEREBELLUM REVEAL POTENTIAL CELLULAR ORIGINS OF MEDULLOBLASTOMA BRAIN TUMORS." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii399. http://dx.doi.org/10.1093/neuonc/noaa222.524.

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Abstract Medulloblastoma (MB) is a highly malignant pediatric brain tumor originating from the cerebellum and brainstem. Identification of molecular subgroups forming this heterogeneous tumor entity was initially achieved from transcriptome characterization and further strengthened using DNA methylation profiling. While subgroup classification improved clinical diagnosis and treatment options, the lack of knowledge of the cell-of-origin for some of the subgroups hinders further treatment improvements. In addition identification of the precise cells of origin for each subgroup could help to understand tumor cell biology. Single cell sequencing is the optimal way to solve this task; recently, there were attempts to uncover putative MB cell-of-origin by using such information obtained from mouse embryonic cerebellum. However, such a comparative strategy can miss important results due to the differences between mouse and human. To solve this issue, we performed global single nucleus sequencing on human cerebellum pre- and postnatal materials across several developmental time points and generated transcriptome profiles from ~200k single cells. We identified known cell types forming the human cerebellum and performed detailed comparison of normal cells to RNA-seq bulk data from MB brain tumors across all subgroups. By selecting an optimal analysis strategy, we verified granule neuron precursors as cells of origin for the SHH MB subgroup. Additionally, we also found other cell types in conjunction with the remaining MB subgroups, suggesting new potential targets for investigation. Notably, this strategy can be further applied to the examination of other brain tumors and has perspectives in medical application.

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23

Ikegaya, Hiroshi, Pekka J. Saukko, Risto Tertti, Kaj P. Metsärinne, Michael J. Carr, Brendan Crowley, Koichi Sakurada, Huai-Ying Zheng, Tadaichi Kitamura, and Yoshiaki Yogo. "Identification of a genomic subgroup of BK polyomavirus spread in European populations." Journal of General Virology 87, no. 11 (November 1, 2006): 3201–8. http://dx.doi.org/10.1099/vir.0.82266-0.

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BK polyomavirus (BKV) is highly prevalent in the human population, infecting children without obvious symptoms and persisting in the kidney in a latent state. In immunosuppressed patients, BKV is reactivated and excreted in urine. BKV isolates worldwide are classified into four serologically distinct subtypes, I–IV, with subtype I being the most frequently detected. Furthermore, subtype I is subdivided into subgroups based on genomic variations. In this study, the distribution patterns of the subtypes and subgroups of BKV were compared among four patient populations with various immunosuppressive states and of various ethnic backgrounds: (A) Finnish renal-transplant recipients; (B) Irish/English haematopoietic stem-cell transplant recipients with and without haemorrhagic cystitis; (C) Japanese renal-transplant recipients; and (D) Japanese bone-marrow transplant recipients. The typing sequences (287 bp) of BKV in population A were determined in this study; those in populations B–D have been reported previously. These sequences were subjected to phylogenetic and single nucleotide polymorphism analyses. Based on the results of these analyses, the BKV isolates in the four patient populations were classified into subtypes and subgroups. The incidence of subtype IV varied significantly among patient populations. Furthermore, the incidence of subgroup Ib-2 within subtype I was high in populations A and B, whereas that of Ic was high in populations C and D (P<0.01). These results suggest that subgroup Ib-2 is widespread among Europeans, whereas Ic is unique to north-east Asians. Furthermore, a phylogenetic analysis based on complete BKV DNA sequences supported the hypothesis that there is geographical separation of European and Asian BKV strains.

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24

Bullinger, Lars, Claudia Scholl, Eric Bair, Konstanze Dohner, Stefan Frohling, Richard F. Schlenk, Robert Tibshirani, Hartmut Dohner, and Jonathan R. Pollack. "Identification of Distinct inv(16) Subclasses in Adult Acute Myeloid Leukemia Based on Gene Expression Profiling." Blood 104, no. 11 (November 16, 2004): 2037. http://dx.doi.org/10.1182/blood.v104.11.2037.2037.

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Abstract Recurrent cytogenetic aberrations have been shown to constitute markers of diagnostic and prognostic value in acute myeloid leukemia (AML). However, even within the well-defined cytogenetic AML subgroup with an inv(16) we see substantial biological and clinical heterogeneity which is not fully reflected by the current classification system. To better characterize this cytogenetic group on the molecular level we profiled gene expression in a series of adult AML patients (n=26) with inv(16) using 42k cDNA microarrays. By unsupervised hierarchical clustering we observed that samples with inv(16) separated primarily into two different subgroups. These showed no significant differences regarding known risk factors like age, WBC, LDH, etc. However, these newly defined inv(16)-subgroups were characterized by distinct clinical behavior. There was a strong trend towards unfavorable outcome with shorter overall survival times in one group (P=0.09, log rank test). Since the primary translocation/inversion events themselves are not sufficient for leukemogenesis, distinct patterns of gene expression found within each of these cytogenetic groups may suggest alternative cooperating mutations and deregulated pathways leading to transformation. Therefore, we performed a supervised analysis to determine the characteristic gene expression patterns underlying the cluster-defined subgroups. This Significance Analysis of Microarrays (SAM) method identified 260 genes significantly differentially expressed between the two newly defined inv(16)-subgroups (false discovery rate = 0.002). High expression levels of JUN, JUNB, JUND, FOS and FOSB characterized the first inv(16) subgroup (having less favorable prognosis). FOS gene family members can dimerize with proteins of the JUN family, forming the transcription factor complex AP-1 which has been implicated in the regulation of cell proliferation, differentiation, and transformation. Among the second subgroup, the proto-oncogene ETS1,displayed elevated expression, possibly resulting from aberrant MEK/ERK pathway activation as these cases also showed an over-expression of MAP3K1 and MAP3K2. In conclusion, both supervised and unsupervised methods provide numerous insights into the pathogenesis of AML with inv(16), identifying clinically significant patterns of gene expression, as well as candidate target genes involved in leukemogenesis.

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25

Olsson, Martin L., Nidal M. Irshaid, Bahram Hosseini-Maaf, Åsa Hellberg, Marilyn K. Moulds, Hannele Sareneva, and M. Alan Chester. "Genomic analysis of clinical samples with serologic ABO blood grouping discrepancies: identification of 15 novel A and B subgroup alleles." Blood 98, no. 5 (September 1, 2001): 1585–93. http://dx.doi.org/10.1182/blood.v98.5.1585.

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Since the cloning in 1990 of complementary DNA corresponding to messenger RNA transcribed at the blood group ABO locus, polymorphisms and phenotype-genotype correlations have been reported by several investigators. Exons 6 and 7, constituting 77% of the gene, have been analyzed previously in samples with variant phenotypes but for many subgroups the molecular basis remains unknown. This study analyzed 324 blood samples involved in ABO grouping discrepancies and determined their ABO genotype. Samples from individuals found to have known subgroup alleles (n = 53), acquired ABO phenotypes associated with different medical conditions (n = 65), probable chimerism (n = 3), and common red blood cell phenotypes (n = 109) were evaluated by ABO genotype screening only. Other samples (n = 94) from apparently healthy donors with weak expression of A or B antigens were considered potential subgroup samples without known molecular background. The full coding region (exons 1-7) and 2 proposed regulatory regions of the ABO gene were sequenced in selected A (n = 22) or B (n = 12) subgroup samples. Fifteen novelABO subgroup alleles were identified, 2 of which are the first examples of mutations outside exon 7 associated with weak subgroups. Each allele was characterized by a missense or nonsense mutation for which screening by allele-specific primer polymerase chain reaction was performed. The novel mutations were encountered in 28 of the remaining 60 A and B subgroup samples but not among normal donors. As a result of this study, the number of definable alleles associated with weak ABO subgroups has increased from the 14 previously published to 29.

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26

Hsu, H. T., L. Barzuna, Y. H. Hsu, W. Bliss, and K. L. Perry. "Identification and Subgrouping of Cucumber mosaic virus with Mouse Monoclonal Antibodies." Phytopathology® 90, no. 6 (June 2000): 615–20. http://dx.doi.org/10.1094/phyto.2000.90.6.615.

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Using a mixture of isolates of Cucumber mosaic virus (CMV) from subgroups I and II as immunogens, 20 mouse hybridoma cell lines secreting monoclonal antibodies were produced. A reliable method for efficient detection and accurate subgrouping of CMV isolates has been developed. Tests with 12 well-characterized strains of CMV and other cucumoviruses demonstrated the presence of epitopes that were virus and subgroup specific. Analyses of 109 accessions of CMV isolates collected from various parts of the world revealed 70% were subgroup I, with 20% identified as subgroup II. Seven isolates (6%) did not react with group-specific antibodies but did react with antibodies that recognized all CMV isolates. Differential reactions among isolates suggested a total of 10 epi-topes were recognized. The antigenic diversity among subgroup II CMVs was greater than for the subgroup I isolates, even though fewer subgroup II isolates were tested.

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Sidorenkov, Andrey V., Ekaterina S. Salnikova, Dmitry V. Vorontsov, and Alexey A. Klimov. "Dimensions of Identification in the Workgroup and Employees’ Contributions to Collaborative Activities." SAGE Open 10, no. 4 (October 2020): 215824402097638. http://dx.doi.org/10.1177/2158244020976385.

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The aim of this research is to find out connections of identifications with the work group (group identification), informal subgroups (micro-group identification), and other members in the work group (interpersonal identification), considering cognitive, affective, and behavioral components, with an employee’s contribution to cooperative activities. The sample includes 302 fellows of 35 workgroups in Russian commercial companies and public organizations. Empirical data were collected with the Questionnaire of Interpersonal Identification, the Questionnaire of Micro-group and Group Identification, and the Collaborative Group Activity Scale from the Leadership, Contribution, and Interpersonal Style Questionnaire. All the tools were assembled into the computer-based assessment program “Group Profile” (GP) to conduct the survey individually on PC. It was found that all three dimensions of group identification and affective measures of interpersonal and micro-group identification predict individual contribution to collaborative activities in groups. Employee involvement in the informal subgroup within the work group mediates relations between identification dimensions at different levels and contribution to collaborative activities. The ties of additional characteristics (gender, age, tenure with the organization) with cooperative activities have been described. The multivariate model of identification makes it possible to extend the research scope and enhance the understanding of causes and effects of employee identification in the group.

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Huang, Xifen, Chaosong Xiong, Jinfeng Xu, Jianhua Shi, and Jinhong Huang. "Mixture Modeling of Time-to-Event Data in the Proportional Odds Model." Mathematics 10, no. 18 (September 16, 2022): 3375. http://dx.doi.org/10.3390/math10183375.

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Subgroup analysis with survival data are most essential for detailed assessment of the risks of medical products in heterogeneous population subgroups. In this paper, we developed a semiparametric mixture modeling strategy in the proportional odds model for simultaneous subgroup identification and regression analysis of survival data that flexibly allows the covariate effects to differ among several subgroups. Neither the membership or the subgroup-specific covariate effects are known a priori. The nonparametric maximum likelihood method together with a pair of MM algorithms with monotone ascent property are proposed to carry out the estimation procedures. Then, we conducted two series of simulation studies to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of German breast cancer data is further provided for illustrating the proposed methodology.

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Guo, Shangjing, Mingyi Zhu, Jianjun Du, Jinglu Wang, Xianju Lu, Yu Jin, Minggang Zhang, Xinyu Guo, and Ying Zhang. "Accurate Phenotypic Identification and Genetic Analysis of the Ear Leaf Veins in Maize (Zea mays L.)." Agronomy 13, no. 3 (March 4, 2023): 753. http://dx.doi.org/10.3390/agronomy13030753.

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The ear leaf veins are an important transport structure in the maize "source" organ; therefore, the microscopic phenotypic characteristics and genetic analysis of the leaf veins are particularly essential for promoting the breeding of ideal maize varieties with high yield and quality. In this study, the microscopic image of the complete blade cross section was realized using X-ray micro-computed tomography (micro-CT) technology with a resolution of 13.5 µm. Moreover, the veins’ phenotypic traits in the cross section of the complete maize leaf, including the number of leaf veins, midvein area, leaf width, and density of leaf veins, were automatically and accurately detected by a deep-learning-integrated phenotyping pipeline. Then, we systematically collected vein phenotypes of 300 inbred lines at the silking stage of the ear leaves. It was found that the leaf veins’ microscopic characteristics varied among the different subgroups. The number of leaf veins, the density of leaf veins, and the midvein area in the stiff-stalk (SS) subgroup were significantly higher than those of the other three subgroups, but the leaf width was the smallest. The leaf width in the tropical/subtropical (TST) subgroup was the largest, but there was no significant difference in the number of leaf veins between the TST subgroup and other subgroups. Combined with a genome-wide association study (GWAS), 61 significant single-nucleotide polymorphism markers (SNPs) and 29 candidate genes were identified. Among them, the candidate gene Zm00001d018081 regulating the number of leaf veins and Zm00001d027998 regulating the midvein area will provide new theoretical support for in-depth analysis of the genetic mechanism of maize leaf veins.

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Eugenie, Reynald, and Erick Stattner. "DISGROU: an algorithm for discontinuous subgroup discovery." PeerJ Computer Science 7 (April 27, 2021): e512. http://dx.doi.org/10.7717/peerj-cs.512.

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In this paper, we focus on the problem of the search for subgroups in numerical data. This approach aims to identify the subsets of objects, called subgroups, which exhibit interesting characteristics compared to the average, according to a quality measure calculated on a target variable. In this article, we present DISGROU, a new approach that identifies subgroups whose attribute intervals may be discontinuous. Unlike the main algorithms in the field, the originality of our proposal lies in the way it breaks down the intervals of the attributes during the subgroup research phase. The basic assumption of our approach is that the range of attributes defining the groups can be disjoint to improve the quality of the identified subgroups. Indeed the traditional methods in the field perform the subgroup search process only over continuous intervals, which results in the identification of subgroups defined over wider intervals thus containing some irrelevant objects that degrade the quality function. In this way, another advantage of our approach is that it does not require a prior discretization of the attributes, since it works directly on the numerical attributes. The efficiency of our proposal is first demonstrated by comparing the results with two algorithms that are references in the field and then by applying to a case study.

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Moreira, Edson Duarte, and Ezra Susser. "Guidelines on how to assess the validity of results presented in subgroup analysis of clinical trials." Revista do Hospital das Clínicas 57, no. 2 (2002): 83–88. http://dx.doi.org/10.1590/s0041-87812002000200007.

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In observational studies, identification of associations within particular subgroups is the usual method of investigation. As an exploratory method, it is the bread and butter of epidemiological research. Nearly everything that has been learned in epidemiology has been derived from the analysis of subgroups. In a randomized clinical trial, the entire purpose is the comparison of the test subjects and the controls, and when there is particular interest in the results of treatment in a certain section of trial participants, a subgroup analysis is performed. These subgroups are examined to see if they are liable to a greater benefit or risk from treatment. Thus, analyzing patient subsets is a natural part of the process of improving therapeutic knowledge through clinical trials. Nevertheless, the reliability of subgroup analysis can often be poor because of problems of multiplicity and limitations in the numbers of patients studied. The naive interpretation of the results of such examinations is a cause of great confusion in the therapeutic literature. We emphasize the need for readers to be aware that inferences based on comparisons between subgroups in randomized clinical trials should be approached more cautiously than those based on the main comparison. That is, subgroup analysis results derived from a sound clinical trial are not necessarily valid; one must not jump to conclusions and accept the validity of subgroup analysis results without an appropriate judgment.

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Hoefnagel, Sanne J. M., Willem J. Koemans, Hina N. Khan, Jan Koster, Sybren L. Meijer, Jolanda M. van Dieren, Liudmila L. Kodach, et al. "Identification of Novel Molecular Subgroups in Esophageal Adenocarcinoma to Predict Response to Neo-Adjuvant Therapies." Cancers 14, no. 18 (September 16, 2022): 4498. http://dx.doi.org/10.3390/cancers14184498.

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Esophageal adenocarcinoma (EAC) is a highly aggressive cancer and its response to chemo- and radiotherapy is unpredictable. EACs are highly heterogeneous at the molecular level. The aim of this study was to perform gene expression analysis of EACs to identify distinct molecular subgroups and to investigate expression signatures in relation to treatment response. In this prospective observational study, RNA sequencing was performed on pre-treatment endoscopic EAC biopsies from a discovery cohort included between 2012 and 2017 in one Dutch Academic Center. Four additional cohorts were analyzed for validation purposes. Unsupervised clustering was performed on 107 patients to identify biological EAC subgroups. Specific cell signaling profiles were identified and evaluated with respect to predicting response to neo-adjuvant chemo(radio)therapy. We identified and validated three distinct biological EAC subgroups, characterized by (1) p38 MAPK/Toll-like receptor signaling; (2) activated immune system; and (3) impaired cell adhesion. Subgroup 1 was associated with poor response to chemo-radiotherapy. Moreover, an immune signature with activated T-cell signaling, and increased number of activated CD4 T memory cells, neutrophils and dendritic cells, and decreased M1 and M2 macrophages and plasma cells, was associated with complete histopathological response. This study provides a novel molecular classification for EACs. EAC subgroup 1 proved to be more therapy-resistant, while immune signaling was increased in patients with complete response to chemo-radiotherapy. Our findings give insight into the biology of EACs and in cellular signaling mechanisms underlying response to neo-adjuvant treatment. Future implementation of this classification will improve patient stratification and enhance the development of targeted therapies.

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Wang, Mingfu, Fenglin Song, and Xiaolan Cheng. "Three new species of the Fannia serena species subgroup from China (Diptera: Fanniidae)." Entomologica Fennica 28, no. 2 (August 20, 2019): 67–74. http://dx.doi.org/10.33338/ef.84677.

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The Fannia serena species group (Diptera: Fanniidae) ismainly distributed in the Holarctic region and comprises four subgroups with a total of 32 species. Three new species of the Fannia serena-subgroup, Fannia aureomarginata Wang et Cheng, sp. n., F. suberemna Wang, sp. n. and F. wui Wang, sp. n., are described from China. An identification key to all known species of the Fannia serena-subgroup is also provided.

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34

Benson, Scott J., Brian L. Ruis, Aly M. Fadly, and Kathleen F. Conklin. "The Unique Envelope Gene of the Subgroup J Avian Leukosis Virus Derives from ev/J Proviruses, a Novel Family of Avian Endogenous Viruses." Journal of Virology 72, no. 12 (December 1, 1998): 10157–64. http://dx.doi.org/10.1128/jvi.72.12.10157-10164.1998.

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ABSTRACT A new subgroup of avian leukosis virus (ALV), designated subgroup J, was identified recently. Viruses of this subgroup do not cross-interfere with viruses of the avian A, B, C, D, and E subgroups, are not neutralized by antisera raised against the other virus subgroups, and have a broader host range than the A to E subgroups. Sequence comparisons reveal that while the subgroup J envelope gene includes some regions that are related to those found inenv genes of the A to E subgroups, the majority of the subgroup J gene is composed of sequences either that are more similar to those of a member (E51) of the ancient endogenous avian virus (EAV) family of proviruses or that appear unique to subgroup J viruses. These data led to the suggestion that the ALV-Jenv gene might have arisen by multiple recombination events between one or more endogenous and exogenous viruses. We initiated studies to investigate the origin of the subgroup J envelope gene and in particular to determine the identity of endogenous sequences that may have contributed to its generation. Here we report the identification of a novel family of avian endogenous viruses that include env coding sequences that are over 95% identical to both the gp85 and gp37 coding regions of subgroup J viruses. We call these viruses the ev/J family. We also report the isolation of ev/J-encoded cDNAs, indicating that at least some members of this family are expressed. These data support the hypothesis that the subgroup J envelope gene was acquired by recombination with expressed endogenous sequences and are consistent with acquisition of this gene by only one recombination event.

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35

Zhou, Yizhao, Ao Yuan, and Ming T. Tan. "Identification of subgroups via partial linear regression modeling approach." Biometrical Journal 64, no. 3 (December 13, 2021): 506–22. http://dx.doi.org/10.1002/bimj.202000331.

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36

Joensuu, Heikki, and Sakari Toikkanen. "Identification of Subgroups with Favorable Prognosis in Breast Cancer." Acta Oncologica 31, no. 3 (January 1992): 293–301. http://dx.doi.org/10.3109/02841869209108175.

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37

Druy, A. E., L. A. Yasko, D. M. Konovalov, A. P. Ektova, E. F. Valiakhmetova, Y. V. Olshanskaya, A. A. Maschan, G. A. Novichkova, and L. I. Papusha. "Identification of medulloblastoma molecular subgroups by gene expression profiling." Voprosy gematologii/onkologii i immunopatologii v pediatrii 16, no. 4 (2017): 85–89. http://dx.doi.org/10.24287/1726-1708-2017-16-4-85-89.

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38

Nestadt, Gerald, Anjene Addington, Jack Samuels, Kung-Yee Liang, O. Joseph Bienvenu, Mark Riddle, Marco Grados, Rudolf Hoehn-Saric, and Bernadette Cullen. "The identification of OCD-related subgroups based on comorbidity." Biological Psychiatry 53, no. 10 (May 2003): 914–20. http://dx.doi.org/10.1016/s0006-3223(02)01677-3.

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39

Magerova, Hana, Martin Vyhnalek, Alexandra Varjassyova, Jan Laczo, Martin Bojar, and Jakub Hort. "P1-196: Smell identification in mild cognitive impairment subgroups." Alzheimer's & Dementia 4 (July 2008): T266. http://dx.doi.org/10.1016/j.jalz.2008.05.784.

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Boulos, N., J. D. Dapper, Y. T. Patel, M. DeCuypere, B. Bianski, K. M. Mohankumar, M. O. Jacus, et al. "193 The identification of new therapies for ependymoma subgroups." European Journal of Cancer 50 (November 2014): 63. http://dx.doi.org/10.1016/s0959-8049(14)70319-3.

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41

Kirchmayer, U., M. DʼOvidio, P. Michelozzi, M. Stafoggia, F. Forastiere, and C. A. Perucci. "Identification of Population Subgroups Susceptible to Heat in Italy." Epidemiology 17, Suppl (November 2006): S162—S163. http://dx.doi.org/10.1097/00001648-200611001-00408.

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Freeman, Stanley, Dror Minz, Marcel Maymon, and Aida Zveibil. "Genetic Diversity Within Colletotrichum acutatum sensu Simmonds." Phytopathology® 91, no. 6 (June 2001): 586–92. http://dx.doi.org/10.1094/phyto.2001.91.6.586.

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Isolates of Colletotrichum acutatum from several hosts were characterized by various molecular methods in comparison with morphological identification. Species-specific primer analysis was reliable for grouping C. acutatum isolates to their designated species. Arbitrarily primed polymerase chain reaction and A+T-rich DNA analyses identified four subgroups within C. acutatum. Subgroup I contained U.S. isolates from almond, apple, peach, and pecan, subgroup II contained isolates from anemone, olive, and strawberry, subgroup III contained isolates from almond (Israel) and strawberry (Spain), and subgroup IV contained a single isolate from anemone (the Netherlands). Likewise, sequence analysis of the internal transcribed spacer (ITS) 2 region alone or the complete ITS (ITS 1–5.8S-ITS 2) region grouped the isolates into the same four subgroups. Percent similarity of the complete ITS region within each cluster ranged from 99.6 to 100.0, 99.8 to 100.0, and 98.6% among subgroups I, II, and III, respectively. DNA sequence analysis of the ITS 2 region alone or the entire ITS 1-2 region was more informative than that of the ITS 1 region, which could only group the isolates into two main clusters. The molecular methods employed for studying genetic variation in populations of C. acutatum determined that this species is diverse, indicating that isolates within populations of each subgroup are not host specific.

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Fei, Xiang, Lingming Kong, Chao Shi, Gang Wang, Chenhai Liu, Cheng Wang, Peng Liu, and Xiaodong Tan. "Identification of Prognosis-Related Molecular Subgroups and Construction of a Prognostic Prediction Model Using Immune-Related Genes in Pancreatic Cancer." Journal of Oncology 2022 (June 7, 2022): 1–21. http://dx.doi.org/10.1155/2022/7117014.

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Background. Pancreatic cancer patients with similar clinicopathological status exhibit substantially different therapeutic responses, which might be caused by the vast molecular heterogeneity of tumors. In this study, we attempted to identify specific molecular subgroups and construct a prognostic prediction model based on the expression level of immune-related genes in pancreatic cancer. The transcriptome profiling, single nucleotide variation, copy number variation, clinicopathological information, and follow-up data of pancreatic cancer patients were obtained from The Cancer Genome Atlas database. Thereafter, the immune-related genes with prognostic significance were identified for further consensus cluster analysis. The molecular characteristics and clinicopathological information were compared between the identified subgroups, and a weighted correlation network analysis was performed to identify the hub genes associated with the subgroups. Finally, the prognostic prediction model based on immune-related genes was established using the least absolute shrinkage and selection operator (LASSO) analysis. Results. A total of 67 immune-relevant genes with prognostic significance were selected and used for the consensus cluster analysis. The total samples were divided into two groups, C1 and C2. The subgroup C1 had a significantly worse prognosis than C2, as well as lower levels of immune cell infiltration, which indicate an immunosuppressed state. The mutational rate of the cancer-related genes including KRAS, TP53, and RNF43 was higher in the C1 subgroup. The C1 subgroup was associated with more advanced tumor grade and T stage and with higher mortality. Using LASSO regression, we developed a prognostic prediction model based on the expression levels of 19 immune-related genes, which we validated in three external data sets. In addition, we identified four potential therapeutic and prognostic biomarkers (TNNT1, KCNN4, SH2D3A, and PHLDA2). Conclusion. We identified two novel molecular subgroups of pancreatic cancer and developed a prognostic prediction model based on the expression levels of immune-related genes, which could be used in a clinical setting and could aid in unraveling the molecular processes leading to the development of pancreatic cancer.

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Ammaranond, P., J. Sriyarak, S. Saejong, P. Deesin, A. Seereemaspun, and R. Rojanathanes. "Enhanced Agglutination Reaction of ABO Subgroups by Gold Nanoparticle Solution: Implication for Identification of ABO Subgroups." Journal of Biomedical Nanotechnology 7, no. 6 (December 1, 2011): 840–45. http://dx.doi.org/10.1166/jbn.2011.1351.

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Roberts, Angela, Kendra Nightingale, Greg Jeffers, Esther Fortes, Jose Marcelino Kongo, and Martin Wiedmann. "Genetic and phenotypic characterization of Listeria monocytogenes lineage III." Microbiology 152, no. 3 (March 1, 2006): 685–93. http://dx.doi.org/10.1099/mic.0.28503-0.

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Listeria monocytogenes has been previously grouped into three evolutionary groups, termed lineages I, II and III. While lineages I and II are commonly isolated from various sources, lineage III isolates are rare and have several atypical and unique phenotypic characteristics. Relative to their prevalence in other sources, lineage III strains are overrepresented among isolates from food-production animals, and underrepresented among isolates from human clinical cases and foods. This work describes an extensive genotypic and phenotypic characterization of 46 lineage III isolates. Phylogenetic analyses of partial sigB and actA sequences showed that lineage III represents three distinct subgroups, which were termed IIIA, IIIB and IIIC. Each of these lineage III subgroups is characterized by differentiating genotypic and phenotypic characteristics. Unlike typical L. monocytogenes, all subgroup IIIB and IIIC isolates lack the ability to ferment rhamnose. While all IIIC and most IIIB isolates carry the putative virulence gene lmaA, the majority of subgroup IIIA isolates lack this gene. All three lineage III subgroups contain isolates from human clinical cases as well as isolates that are cytopathogenic in a cell culture plaque assay, indicating that lineage III isolates have the potential to cause human disease. The identification of specific genotypic and phenotypic characteristics among the three lineage III subgroups suggests that these subgroups may occupy different ecological niches and, therefore, may be transmitted by different pathways.

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Shakeel, Asif. "An improved query for the hidden subbroup problem." Quantum Information and Computation 14, no. 5&6 (May 2014): 467–92. http://dx.doi.org/10.26421/qic14.5-6-6.

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The Hidden Subgroup Problem (HSP) is at the forefront of problems in quantum algorithms. In this paper, we introduce a new query, the \textit{character} query, generalizing the well-known phase kickback trick that was first used successfully to efficiently solve Deutsch's problem. An equal superposition query with $\vert 0 \rangle$ in the response register is typically used in the ``standard method" of single-query algorithms for the HSP. The proposed character query improves over this query by maximizing the success probability of subgroup identification under a uniform prior, for the HSP in which the oracle functions take values in a finite abelian group. We apply our results to the case when the subgroups are drawn from a set of conjugate subgroups and obtain a success probability greater than that found by Moore and Russell.

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Murali, Komal, Gary Yu, John D. Merriman, and Abraham A. Brody. "4228 A latent class analysis of seriously ill adults with multiple chronic conditions receiving palliative care." Journal of Clinical and Translational Science 4, s1 (June 2020): 104. http://dx.doi.org/10.1017/cts.2020.322.

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OBJECTIVES/GOALS: The purpose of this secondary data analysis was to identify latent subgroups of seriously ill adults based on multiple chronic conditions and mortality risk using the CCI. This study was conducted by performing a secondary analysis of data from a randomized controlled trial of seriously ill patients receiving palliative care. METHODS/STUDY POPULATION: A cross-sectional analysis of baseline CCI data was conducted. 381 seriously ill adults receiving palliative care were in the original study. Latent subgroups were identified based on the CCI by conducting a latent class analysis in MPlus. The LCA was modeled on each of the 19 disease items as binary latent predictor variables, an additional binary variable representing presence of any disease not accounted for by the CCI, and a final categorical variable representing the total CCI score divided based on clinically significant cutoffs including zero, low (> = 1-<2), moderate (> = 2-<5), and high CCI (> = 5). RESULTS/ANTICIPATED RESULTS: Three distinct latent subgroups were identified based on the CCI. Latent subgroup 1 included those with a low-moderate CCI consisting of MCC and non-Metastatic Cancers (n = 178), with 45% of this group having chronic obstructive pulmonary disease. The second two subgroups included individuals with a high CCI or a score greater than or equal to 5. Latent subgroup 2 (n = 64) was comprised of individuals with MCC and non-metastatic cancer. Latent subgroup 3 (n = 139) included individuals with metastatic cancer. DISCUSSION/SIGNIFICANCE OF IMPACT: In a sample of seriously ill adults with MCC, latent subgroups were identified consisting of individuals with low, moderate, or high CCI. The low to moderate CCI group consists of individuals with chronic conditions including COPD, congestive heart failure, myocardial infarction, cardiovascular disease. There were two subgroups with high CCI scores and the differentiating factor between the two subgroups was the presence of metastatic cancer in latent subgroup 3. The identification of latent subgroups sets the groundwork for further analyses to compare differences in symptom burden, quality of life, and functional status between groups. The findings have the potential to inform future studies seeking to better characterize seriously adults with MCC based on their disease burden and mortality risk.

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Zammit, Andrea R., Charles B. Hall, Richard B. Lipton, Mindy J. Katz, and Graciela Muniz-Terrera. "Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults: A Latent Class Analysis of the Einstein Aging Study." Journal of the International Neuropsychological Society 24, no. 5 (January 10, 2018): 511–23. http://dx.doi.org/10.1017/s135561771700128x.

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AbstractObjectives: The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup’s characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. Methods: We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. Results: The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. Conclusions: LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer’s disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 24, 511–523)

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Chen, Yutong, Weiran Huang, Jian Ouyang, Jingxiang Wang, and Zhengwei Xie. "Identification of Anoikis-Related Subgroups and Prognosis Model in Liver Hepatocellular Carcinoma." International Journal of Molecular Sciences 24, no. 3 (February 2, 2023): 2862. http://dx.doi.org/10.3390/ijms24032862.

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Resistance to anoikis is a key characteristic of many cancer cells, promoting cell survival. However, the mechanism of anoikis in hepatocellular carcinoma (HCC) remains unknown. In this study, we applied differentially expressed overlapping anoikis-related genes to classify The Cancer Genome Atlas (TCGA) samples using an unsupervised cluster algorithm. Then, we employed weighted gene coexpression network analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazards regression. This model was validated using external datasets from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Finally, we used a CIBERSORT algorithm to investigate the correlation between risk score and immune infiltration. Our results showed that the TCGA cohorts could be divided into two subgroups, with subgroup A having a lower survival probability. Five genes (BAK1, SPP1, BSG, PBK and DAP3) were identified as anoikis-related prognostic genes. Moreover, the prognostic risk model effectively predicted overall survival, which was validated using ICGC and GEO datasets. In addition, there was a strong correlation between infiltrating immune cells and prognostic genes and risk score. In conclusion, we identified anoikis-related subgroups and prognostic genes in HCC, which could be significant for understanding the molecular mechanisms and treatment of HCC.

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Parker, C. W., and C. B. Wiedorn. "A 7-Local Identification of the Monster." Nagoya Mathematical Journal 178 (2005): 129–49. http://dx.doi.org/10.1017/s0027763000009144.

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