Age is just a number, the wrong number

Cancer ◽  
2020 ◽  
Vol 126 (23) ◽  
pp. 5014-5016
Author(s):  
Susan Bal ◽  
Luciano J. Costa
Keyword(s):  
Wrong Number

A Monte Carlo evaluation of growth mixture modeling

2021 ◽  
pp. 1-14
Author(s):  
Tiffany M. Shader ◽  
Theodore P. Beauchaine
Keyword(s):  
Monte Carlo ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Fit Indices ◽  
Linear Quadratic ◽  
Growth Mixture ◽  
True Number ◽  
Monte Carlo Evaluation ◽  
Wrong Number ◽  
State Of Affairs

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

split ends encodes large nuclear proteins that regulate neuronal cell fate and axon extension in the Drosophila embryo

Development ◽  
2000 ◽  
Vol 127 (7) ◽  
pp. 1517-1529 ◽  
Author(s):  
B. Kuang ◽  
S.C. Wu ◽  
Y. Shin ◽  
L. Luo ◽  
P. Kolodziej
Keyword(s):  
Cell Fate ◽  
Egf Receptor ◽  
Neuronal Cell ◽  
Drosophila Embryo ◽  
Neuronal Precursors ◽  
Split Ends ◽  
Recognition Motifs ◽  
Midline Cells ◽  
Wrong Number ◽  
Nuclear Levels

split ends (spen) encodes nuclear 600 kDa proteins that contain RNA recognition motifs and a conserved C-terminal sequence. These features define a new protein family, Spen, which includes the vertebrate MINT transcriptional regulator. Zygotic spen mutants affect the growth and guidance of a subset of axons in the Drosophila embryo. Removing maternal and zygotic protein elicits cell-fate and more general axon-guidance defects that are not seen in zygotic mutants. The wrong number of chordotonal neurons and midline cells are generated, and we identify defects in precursor formation and EGF receptor-dependent inductive processes required for cell-fate specification. The number of neuronal precursors is variable in embryos that lack Spen. The levels of Suppressor of Hairless, a key transcriptional effector of Notch required for precursor formation, are reduced, as are the nuclear levels of Yan, a transcriptional repressor that regulates cell fate and proliferation downstream of the EGF receptor. We propose that Spen proteins regulate the expression of key effectors of signaling pathways required to specify neuronal cell fate and morphology.

scholarly journals STRATEGIES IN TEACHING LITERATURE IN ENGLISH CLASSROOM

2014 ◽  
Vol 5 (1) ◽  
pp. 113-126
Author(s):  
Maulizan ZA
Keyword(s):  
Seventh Grade ◽  
Teaching Literature ◽  
Sense And Sensibility ◽  
English Classroom ◽  
Wrong Number

This essay discusses about some strategies in teaching literature in English classroom, focusing on teaching poems, prose, and plays. There are some strategies can be applied by the lecturer in teaching literature in English classroom. First of all, the lecturer should prepare the material presented to the students in the class. Secondly, lecturer can set some questions. Thirdly, always ask "why". Fourth, add fuel to the fire. Fifth, add history to the material. Sixth, involve every single student. Seventh, grade the thought, not the content. Eighth, give appropriate homework. Ninth, emphasize references. Tenth, read out the work of other scholars and finally, enjoy the experience. Then some possible way and solution are suggested in teaching literature as the example as follows; first, strategies and procedures in teaching poem entitled “Richard Cory” by Edwin Arlington Robinson , second, strategies and procedures in teaching prose entitled “Sorry, wrong number”, third, strategies and procedures in teaching play entitled “Sense and Sensibility.”

Sorry, Wrong Number!

2015 ◽  
pp. 143-148
Author(s):  
Ashley Vaughan
Keyword(s):  
Wrong Number

How to Protect Yourself from Hoaxes, Frauds, and Identity Theft

2004 ◽  
Author(s):  
Jerry Pournelle
Keyword(s):  
Cell Phones ◽  
Identity Theft ◽  
The Internet ◽  
False Information ◽  
Primary Object ◽  
Common Letter ◽  
Bill Gates ◽  
Wrong Number ◽  
E Mail ◽  
Endless Variety

There are two basic categories of Internet lies: frauds, where the object is to get your money, and hoaxes, where the primary object is just to pull your chain, but there can be far-reaching consequences as well. Hoaxes give the perpetrators ego gratification as they watch their creation spread throughout the Internet. Frauds may give the perpetrators your life savings, and give you months—maybe years—of hassle as you try to repair your credit record and retrieve your very identity. Hoaxes—Hoaxes are spread by e-mail and come in an endless variety of guises. There are, for example, fake virus warnings, chain letters promising riches if you follow their instructions (or threatening dire consequences if you don’t); urban myths about women in peril, dogs in microwaves, and hypodermic needles on theater seats; letters that tug at your heart strings or appeal to your greedy side; Internet petitions (often based on false information); and letters claiming that Bill Gates wants to give you money. Yeah, right. Even the most “innocent” hoaxes are harmful. At the very least, they take up your time, and they try to get you to forward them to other people as well. If you forward a letter to just 40 people, and each of them does the same, and so on, then after just four steps, more than two and a half million copies will have been sent out. That’s a lot of wasted time and wasted bandwidth. These letters can also contain dangerous misinformation and bad advice. One example is a common letter advising women not to stop when pulled over by the highway patrol, but instead to dial #77 on their cell phones to talk to the police—a wrong number in 48 of the 50 states! Perhaps the most common example is the virus hoax—typically a letter forwarded by someone you know warning you that if you find a certain file on your computer it means you are infected with a virus.

scholarly journals Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data

2019 ◽  
Vol 21 (1) ◽  
pp. 79 ◽  
Author(s):  
Jörn Lötsch ◽  
Alfred Ultsch
Keyword(s):  
Dimensional Space ◽  
Cluster Structure ◽  
Projection Methods ◽  
High Dimensional ◽  
Computational Techniques ◽  
Correct Identification ◽  
Data Sets ◽  
Biomedical Data ◽  
Self Organizing Maps ◽  
Wrong Number

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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