Alternatives to the Neutral Theory

Acclimatization is a process that occurs in individual cells to a drastic change in micro and macro environments. When an organism is subjected to a new environment or a change in its normal growing conditions, the cellular mechanisms initiate a warning sign and over a period of time or over generations the acquired, modified traits are being communicated and fixed as a new trait. If there is lack of equilibrium within the cell due to over expression of a single gene or network of associated genes either manmade or due to mutations, the organism or plant tries to fix it by initiating gene regulatory mechanisms. According to our neutral theory of gene expression, always a cell tries to maintain its pH by modifying its cytosol through altered gene expression. In the present investigation, 198 AtMYB genes were analyzed and found to play an intrinsic photosystem linked network of 38 nodes where MYB being regulated by a set of 48 miRNAs. Members of the network have evidence-based link to energy related mechanisms. Altering gene expression to an extent where, the cell may not be able to fix it or a trait, which requires excessive energy loss escorts the organism’s gene regulation by breakdown of the introduced sequence over few generations. Events with constitutive overexpression may suffer poor performance over the years based on gene network prevailing in the crop of interest. Hence, network rewiring with minimal energy expenses is concerned.

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Neutral theory: applicability and neutrality of using generic health-related quality of life tools in diseases or conditions where specific tools are available

BMC Medical Research Methodology ◽

10.1186/s12874-021-01279-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ravi Jandhyala

Keyword(s):

Rare Disease ◽

Neutral Theory ◽

Health Related Quality ◽

Poor Hrqol ◽

Sf 36 ◽

Disease Condition ◽

Related Quality ◽

Health Related ◽

Sensitivity Specificity

Abstract Background Health-related quality of life (HRQoL) tools are limited by the indicators included in the construct and variation in interpretation by different researchers. Neutral Theory describes the ideal construct that includes all relevant indicators and, therefore, complete accuracy, or neutrality. Neutral Theory can thereby provide the framework to develop or test constructs. To assess the application of Neutral Theory, the neutrality of generic tools (SF-36 and EQ-5D) at measuring HRQoL was compared to disease/condition-specific tools, with the latter considered surrogates for the Neutral construct. Methods Full descriptions of all disease/condition-specific HRQoL tools published on PubMed (to 01-Jul-19) were sourced. For each tool, the number of items with and without a direct match within the SF-36 and EQ-5D was recorded and the sensitivity/specificity calculated. Results The SF-36 and EQ-5D did not achieve a sensitivity/specificity both > 50% against any of the 163 disease/condition-specific tools identified. At 20% prevalence of poor HRQoL, the false positive rate (FPR) was > 75% for all but two tools against the SF-36 and six tools against the EQ-5D. Increasing poor HRQoL to 80%, 47 tools for the SF-36 and 48 tools for the EQ-5D had a FPR < 50%. For rare disease tools (< 1/2000 population; n = 17), sensitivity/specificity ranged from 0 to 40%/5–31% for the SF-36 and 0–22%/29–100% for the EQ-5D. For non-rare (n = 75) and symptom-specific tools (n = 71) sensitivity/specificity was: 0–100%/0–100% (SF-36) and 0–50%/0–100% (EQ-5D); and 0–60%/0–19% (SF-36) and 0–25%/0–100% (EQ-5D), respectively. No concordance was recorded for 18% (2/11) of results from studies of rare disease tools versus the SF-36 (no data vs EQ-5D). For non-rare, disease-specific tools, results were discordant for 30% (25/84) and 35% (23/65) of studies against the SF-36 and EQ-5D, respectively. For symptom-specific tools, corresponding results were 36% (24/66) and 16% (5/31). Conclusions Generic HRQoL tools appear poorly correlated with disease/condition-specific tools, which indicates that adoption of Neutral Theory in the development and assessment of HRQoL tools could improve their relevance, accuracy, and utility in economic evaluations of health interventions.

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Nucleotide Variation at theCHALCONE ISOMERASELocus inArabidopsis thaliana

Genetics ◽

10.1093/genetics/155.2.863 ◽

2000 ◽

Vol 155 (2) ◽

pp. 863-872 ◽

Cited By ~ 14

Author(s):

Helmi Kuittinen ◽

Montserrat Aguadé

Keyword(s):

Nucleotide Diversity ◽

Direct Relationship ◽

Selective Sweep ◽

Balancing Selection ◽

Neutral Theory ◽

Low Frequency ◽

The Other ◽

Nucleotide Variation ◽

Rapid Population Growth ◽

History Of

AbstractAn ~1.9-kb region encompassing the CHI gene, which encodes chalcone isomerase, was sequenced in 24 worldwide ecotypes of Arabidopsis thaliana (L.) Heynh. and in 1 ecotype of A. lyrata ssp. petraea. There was no evidence for dimorphism at the CHI region. A minimum of three recombination events was inferred in the history of the sampled ecotypes of the highly selfing A. thaliana. The estimated nucleotide diversity (θTOTAL = 0.004, θSIL = 0.005) was on the lower part of the range of the corresponding estimates for other gene regions. The skewness of the frequency spectrum toward an excess of low-frequency polymorphisms, together with the bell-shaped distribution of pairwise nucleotide differences at CHI, suggests that A. thaliana has recently experienced a rapid population growth. Although this pattern could also be explained by a recent selective sweep at the studied region, results from the other studied loci and from an AFLP survey seem to support the expansion hypothesis. Comparison of silent polymorphism and divergence at the CHI region and at the Adh1 and ChiA revealed in some cases a significant deviation of the direct relationship predicted by the neutral theory, which would be compatible with balancing selection acting at the latter regions.

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On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn’t

Genes ◽

10.3390/genes12040527 ◽

2021 ◽

Vol 12 (4) ◽

pp. 527

Author(s):

Eran Elhaik ◽

Dan Graur

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

A Priori ◽

Neutral Theory ◽

Dominant Mode ◽

Supervised Machine Learning ◽

Training Dataset ◽

Selective Sweeps ◽

Two Factors ◽

Negative Controls

In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transformation was aided by a series of articles by Daniel Schrider and Andrew Kern. Within this series, a paper entitled “Soft sweeps are the dominant mode of adaptation in the human genome” (Schrider and Kern, Mol. Biol. Evolut. 2017, 34(8), 1863–1877) attracted a great deal of attention, in particular in conjunction with another paper (Kern and Hahn, Mol. Biol. Evolut. 2018, 35(6), 1366–1371), for purporting to discredit the Neutral Theory of Molecular Evolution (Kimura 1968). Here, we address an alleged novelty in Schrider and Kern’s paper, i.e., the claim that their study involved an artificial intelligence technique called supervised machine learning (SML). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known empirically to be true. Curiously, Schrider and Kern did not possess a training dataset of genomic segments known a priori to have evolved either neutrally or through soft or hard selective sweeps. Thus, their claim of using SML is thoroughly and utterly misleading. In the absence of legitimate training datasets, Schrider and Kern used: (1) simulations that employ many manipulatable variables and (2) a system of data cherry-picking rivaling the worst excesses in the literature. These two factors, in addition to the lack of negative controls and the irreproducibility of their results due to incomplete methodological detail, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S/HIC) should be taken with a huge shovel of salt.

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