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Entropy

Entropy is a fundamental concept in thermodynamics and information theory that quantifies the disorder or uncertainty within a system.
It represents the level of randomness or unpredictability associated with a given state or process.
In the context of scientific research, entropy analysis can be a valuable tool for optimizing reproducibility and identifying the most reliable and effective experimental protocols.
PubCompare.ai's AI-driven entropy analysis helps researchers locate the best protocols from literature, preprints, and patents, ensuring they find the most robust and effective methods for their studies.
By intelligently comparing and analyzing entropy-related data, PubCompare.ai's advanced AI can help researchers enhance the reproducibility and effectiveness of their research findings.

Most cited protocols related to «Entropy»

1
Sequence Complexity Evaluation Using PRINSEQ
The sequence complexity is evaluated as the mean of complexity values using a window of size 64 and a step size of 32. There are two types of sequence complexity measures implemented in PRINSEQ. Both use overlapping nucleotide triplets as words and are scaled to a maximum value of 100. The first is an adaptation of the DUST algorithm (Morgulis et al., 2006 (link)) used as BLAST search preprocessing for masking low complexity regions:

where k = 43 is the alphabet size, w is the window size, ni is the number of words i in a window, l ≤ 62 is the number of possible words in a window of size 64 and s = 100/31 is the scaling factor.
The second method evaluates the block-entropies of words using the Shannon–Wiener method:

where ni is the number of words i in a window of size w, l is the number of possible words in a window and k is the alphabet size. For windows of size w < 66, k = l and otherwise k = 43.
Schmieder R, & Edwards R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27(6), 863-864.
Publication 2011
Acclimatization Entropy Nucleotides S100 Proteins Triplets
2
Evaluating Power to Detect Latent Classes
We used a Monte Carlo simulation to evaluate statistical power to detect the correct number of latent classes. We included the following factors that can affect the ability to detect the correct number of classes: 1) true number of classes, 2) sample size, 3) total number of indicators for class membership, and 4) distance or separation between classes. All data were generated in SAS 9.2 and analyzed in Mplus 5 (Muthén & Muthén, 1998–2010 ). The true number of classes in the population was either three or five. We evaluated sample sizes of 250, 500, and 1000. Class membership was identified by 6, 10, or 15 indicators. As previously described, the separation or distance between classes was calculated as the standardized distance between the mean values of the indicators (i.e., Cohen’s d). The distance between indicators across the adjacent classes was .2, .5, .8, or 1.5, representing small, medium, large, and very large distances corresponding to effect sizes (Cohen, 1988 ). In the rest of the article, we refer to the distance as inter-class distance or Cohen’s d. The four manipulated factors were varied in a fully factorial design, resulting in 3 × 2 × 3 × 4 = 72 conditions. For each condition, 500 replications were conducted.
For each replication, individual cases were assigned to classes in roughly equal numbers. For example, for a sample size of 1,000 and 3 classes, 333, 333, and 334 cases were assigned to Classes 1, 2, and 3 respectively. Observed values for each of the 6, 10, or 15 indicators were generated based on the latent class membership, inter-class distance between classes, and random normal error. All indicator variables were equally good indicators of class membership. The indicators for all classes were simulated as normal distributions with a variance of 1, with the mean of the distributions determined by the class membership and the specified inter-class distance between the classes. For example, for a 3-class model with 6 indicators and an inter-class distance between classes of .5, the individual cases have values on all 6 indicators that are drawn from a standard normal distribution with a mean of −.5 in Class 1, with a mean of 0 in Class 2, and with a mean of .5 in Class 3. Each indicator was generated separately. Figure 1 illustrates the normal distributions for the 3-class models with the four values of inter-class distance.
All data were analyzed in Mplus 5 using the Monte Carlo feature to allow us to easily save the fit indices of each replication from each selection method. Latent class analyses under mixture modeling were used. For each dataset, we conducted the analyses with the specified number of latent classes in the mixture models being either the correct number of classes from the population (i.e., K0 = 3 or 5), one class less than in the population (i.e., K −1 = 2 or 4), or one class more than in the population (i.e., K+1 = 4 or 6). To avoid local maximum solutions, all models used 100 sets of random starting values and 10 final stage optimizations. For the likelihood-ratio bootstrap tests, which were time-intensive procedures, we used 100 bootstrap samples, each with two sets of random starting values and one final stage optimization for the null model (model with one less class) as well as 50 sets of random starting values and 15 final stage optimizations for the alternative model (see Muthén & Muthén, 1998–2010 ). Seven model selection techniques were used to evaluate statistical power to detect the correct model and number of classes: AIC, BIC, sample-size adjusted BIC (SABIC), entropy, LMR, adjusted LMR, and bootstrap LRT (BLRT).
Tein J.Y., Coxe S, & Cham H. (2013). Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis. Structural equation modeling : a multidisciplinary journal, 20(4), 640-657.
Publication 2012
DNA Replication Entropy
3
Detecting Novel Splice Junctions Using GSNAP
GSNAP can align transcriptional reads that cross exon–exon junctions involving known or novel splice sites. For known splice sites, the program depends upon a user-provided set of splice sites, which belong to one of four categories: donors and acceptors on the plus genomic strand, and donors and acceptors on the minus genomic strand. Identification of novel splice sites is assisted by a probabilistic model, currently implemented as a maximum entropy model (Yeo and Burge, 2004 (link)), which uses frequencies of nucleotides neighboring a splice site to discriminate between true and false splice sites.
We use two methods for detecting splice junctions, one for short-distance and one for long-distance splicing. Short-distance splicing involves two splice sites that are on the same chromosomal strand, with the acceptor site being downstream of the donor site, within a user-specified parameter (default 200 000 nt). Short-distance splice junctions can be detected using a method similar to that for middle deletions, except that the distance allowed between candidate regions is much longer (Fig. 5B). As with middle indel detection, the positions of mismatches in the two regions determine whether a crossover area exists with the allowed number of mismatches (K − S), where S is the opening gap penalty for a splice. This crossover area is searched for donor and acceptor splice sites that are either known or supported by a splice site model at a sufficiently high probability. The probability score required is dependent on the length of short read sequence available for alignment in the exon region. When the aligned exon sequence is short, on the order of 12–20 nt, a relatively high probability score is needed. But when the aligned exon sequence is sufficiently long, more than 35 nt, only the expected dinucleotides at the intron end are needed.
For long-distance splicing, probability scores are also used to help find novel splice sites, although the required probability scores are higher for a given length of aligned sequence to compensate for the larger search space over the entire genome. To detect cases of long-distance splicing, the program identifies known or novel splice ends within single candidate regions, in the area delimited by the constraint level K of allowed mismatches (Fig. 5D). Candidate regions with donor and acceptor splice sites are then paired if they have the same breakpoint on the read, and have an acceptable number of total mismatches.
Reads that lie predominantly on one end of a splice junction may have too little sequence at the distant end to identify the other exon. Such alignments can still be reported by our program as partial splicing or ‘half intron’ alignments, if there is sufficient sequence on one end to determine a splice site, but insufficient sequence on the other end for the other site.
Wu T.D, & Nacu S. (2010). Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics, 26(7), 873-881.
Publication 2010
Chromosomes Dinucleoside Phosphates Donors Entropy Exons Gene Deletion Genome INDEL Mutation Introns Nucleotides Tissue Donors Transcription, Genetic
4
Conformational Entropy Analysis of Ligand Binding
The normal-mode analysis was performed to evaluate the conformational entropy change upon ligand binding (−TΔS) using the nmode program in AMBER9.0.46 (link) Because the normal mode analysis is computationally expensive, we only considered the residues within a 12 Å sphere centered at the ligand and these residues were retrieved from an MD snapshot for each ligand-protein complex. The open valences were saturated by adding hydrogen atoms using the tleap program of AMBER9.0.46 (link) The corresponding ligand and receptor were extracted from the reduced complex structure. Then each structure was fully minimized for 100,000 steps using a distance-dependent dielectric of 4rij (rij is the distance between two atoms) to mimic the solvent dielectric change from the solute to solvent until the root-mean-square of the elements of the gradient vector was less than 5 × 10−4 kcal·mol−1·Å−1. To reduce the computational demand, 125 snapshots were taken from 0 to 5 ns to estimate the contribution of the entropy to binding. The final conformational entropy was obtained from the average over the snapshots. It should be noted that different from the other energy terms the entropy contribution is computed in a way independent of the internal dielectric constant.
Hou T., Wang J., Li Y, & Wang W. (2010). Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of chemical information and modeling, 51(1), 69-82.
Publication 2010
Cloning Vectors Entropy Hydrogen Ligands Plant Roots Proteins Solvents
5
Effective Number of Species Estimation
The entropy effective number of species of a community, is defined as where is the entropy of the community [18] . Sample entropies were computed according to the method descried by Chao and Shen [25] , as implemented in the R ‘entropy’ package [26] . For each body-site, the effective number of species reported in the main text is the average of the effective number of species of all samples corresponding to that body-site.
Friedman J, & Alm E.J. (2012). Inferring Correlation Networks from Genomic Survey Data. PLoS Computational Biology, 8(9), e1002687.
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Publication 2012
Entropy Human Body

Most recents protocols related to «Entropy»

1
Synthesis of High-Conductivity Entropy-Stabilized Nanometal Oxide
Not available on PMC !

Example 1

Provided is a preparation method for an A-site high-entropy nanometer metal oxide (Gd0.4Er0.3La0.4Nd0.5Y0.4)(Zr0.7, Sn0.8, V0.5)O7 with high conductivity, the method including the following steps.

    • (1) Gd(NO3)3, Er(NO3)3, La(NO3)3, Nd(NO3)3, Y(NO3)3, ZrOSO4, SnC14 and NH4VO3 were taken at a molar ratio of 0.4:0.3:0.4:0.5:0.4:0.7:0.8:0.5, added to a mixed solution of deionized water/absolute ethyl alcohol/tetrahydrofuran at a mass ratio of 0.3:3:0.5, and stirred for five minutes to obtain a mixed liquid I. The ratio of the total mass of Gd(NO3)3, Er(NO3)3, La(NO3)3, Nd(NO3)3, Y(NO3)3, ZrOSO4, SnC14 and NH4VO3 to that of the mixed solution of deionized water/absolute ethyl alcohol/tetrahydrofuran (0.3:3:0.5) is 12.6%.
    • (2) Para-phenylene diamine, hydrogenated tallowamine, sorbitol and carbamyl ethyl acetate at a mass ratio of 1:0.2:7:0.01 were taken, added to propyl alcohol, and stirred for one hour to obtain a mixed liquid II. The ratio of the total mass of the para-phenylene diamine, the hydrogenated tallowamine, the sorbitol and the carbamyl ethyl acetate to that of the propyl alcohol is 7.5%;
    • (3) The mixed liquid I obtained in step (1) was heated to 50° C., and the mixed liquid II obtained in step (2) was dripped at the speed of one drop per second, into the mixed liquid I obtained in step (1) with stirring and ultrasound, and heated to the temperature of 85° C. after the dripping is completed and the temperature was maintained for three hours while stopping stirring, and the temperature was decreased to the room temperature, so as to obtain a mixed liquid III. The mass ratio of the mixed liquid I to the mixed liquid II is 10:4.
    • (4) The mixed liquid III was added to an electrolytic cell with using a platinum electrode as an electrode and applying a voltage of 3 V to two ends of the electrode, and reacting for 13 minutes, to obtain a mixed liquid IV.
    • (5) The mixed liquid IV obtained in step (4) was heated with stirring, another mixed liquid II was taken and dripped into the mixed liquid IV obtained in step (4) at the speed of one drop per second. The mass ratio of the mixed liquid II to the mixed liquid IV is 1.05:1.25; and after the dripping is completed, the temperature was decreased to the room temperature under stirring, so as to obtain a mixed liquid V.
    • (6) A high-speed shearing treatment was performed on the mixed liquid V obtained in step (5) by using a high-speed shear mulser at the speed of 20000 revolutions per minute for one hour, so as to obtain a mixed liquid VI.
    • (7) Lyophilization treatment was performed on the mixed liquid VI to obtain a mixture I;
    • (8) The mixture I obtained in step (7) and absolute ethyl alcohol were mixed at a mass ratio of 1:2 and uniformly stirred, and were sealed at a temperature of 210° C. for performing solvent thermal treatment for 18 hours. The reaction was cooled to the room temperature, the obtained powder was collected by centrifugation, washed with deionized water and absolute ethyl alcohol eight times respectively, and dried to obtain a powder I.
    • (9) The powder I obtained in step (8) and ammonium persulfate was uniformly mixed at a mass ratio of 10:1, and sealed and heated to 165° C. The temperature was maintained for 13 hours. The reaction was cooled to the room temperature, the obtained mixed powder was washed with deionized water ten times, and dried to obtain a powder II.
    • (10) The powder II obtained in step (4) was placed into a crucible, heated to a temperature of 1500° C. at a speed of 3° C. per minute. The temperature was maintained for 7 hours. The reaction was cooled to the room temperature, to obtain an A-site high-entropy nanometer metal oxide (Gd0.4Er0.3La0.4Nd0.5Y0.4)(Zr0.7, Sn0.8, V0.5)O7 with high conductivity.

As observed via an electron microscope, the obtained A-site high-entropy nanometer metal oxide with high conductivity is a powder, and has microstructure of a square namometer sheet with a side length of about 4 nm and a thickness of about 1 nm.

The product powder was taken and compressed by using a powder sheeter at a pressure of 550 MPa into a sheet. Conductivity of the sheet is measured by using the four-probe method, and the conductivity of the product is 2.1×108 S/m.

A commercially available ITO (indium tin oxide) powder is taken and compressed by using a powder sheeter at a pressure of 550 MPa into a sheet, and the conductivity of the sheet is measured by using the four-probe method.

As measured, the conductivity of the commercially available ITO (indium tin oxide) is 1.6×106 S/m.

US11866343B2. A-site high-entropy nanometer metal oxide with high conductivity, and preparation method thereof (2024-01-09). Zhejiang University [CN]. Inventors: Lingjie Zhang [CN], Weiwei Cai [CN], Ningzhong Bao [CN].
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Patent 2024
1-Propanol 4-phenylenediamine Absolute Alcohol ammonium peroxydisulfate Cells Centrifugation Electric Conductivity Electrolytes Electron Microscopy Entropy Ethanol ethyl acetate Freeze Drying indium tin oxide Metals Molar Oxides Platinum Powder Pressure propyl acetate Solvents Sorbitol tetrahydrofuran Ultrasonography
2
Programmable Nanocomposite Tectons for Ordered Assembly
Not available on PMC !

Example 1

This example describes an exemplary nanostructure (i.e. nanocomposite tecton) and formation of a material using the nanostructure.

A nanocomposite tecton consists of a nanoparticle grafted with polymer chains that terminate in functional groups capable of supramolecular binding, where supramolecular interactions between polymers grafted to different particles enable programmable bonding that drives particle assembly (FIG. 4). Importantly, these interactions can be manipulated separately from the structure of the organic or inorganic components of the nanocomposite tecton, allowing for independent control over the chemical composition and spatial organization of all phases in the nanocomposite via a single design concept. Functionalized polystyrene polymers were made from diaminopyridine or thymine modified initiators via atom transfer radical polymerization, followed by post-functionalization to install a thiol group that allowed for particle attachment (FIG. 5). The polymers synthesized had three different molecular weights (˜3.7, ˜6.0, and ˜11.0 kDa), as shown in FIG. 6, with narrow dispersity (Ð<1.10), and were grafted to nanoparticles of different diameters (10, 15, 20, and nm) via a “grafting-to” approach.

Once synthesized, nanocomposite tectons were functionalized with either diaminopyridine-polystyrene or thymine-polystyrene were readily dispersed in common organic solvents such as tetrahydrofuran, chloroform, toluene, and N,N′-dimethylformamide with a typical plasmonic resonance extinction peak at 530-540 nm (FIG. 7A) that confirmed their stability in these different solvents. Upon mixing, diaminopyridine-polystyrene and thymine-polystyrene coated particles rapidly assembled and precipitated from solution, resulting in noticeable red-shifting, diminishing, and broadening of the extinction peak within 1-2 minutes (example with 20 nm gold nanoparticles and 11.0 kDa polymers, FIG. 7B). Within 20 minutes, the dispersion appeared nearly colorless, and large, purple aggregates were visible at the bottom of the tube. After moderate heating (˜55° C. for ˜1-2 minutes for the example in FIG. 7B), the nanoparticles redispersed and the original color intensity was regained, demonstrating the dynamicity and complete reversibility of the diaminopyridine-thymine directed assembly process. Nanocomposite tectons were taken through multiple heating and cooling cycles without any alteration to assembly behavior or optical properties, signifying that they remained stable at each of these thermal conditions (FIG. 7C).

A key feature of the nanocomposite tectons is that the sizes of their particle and polymer components can be easily modified independent of the supramolecular binding group's molecular structure. However, because this assembly process is driven via the collective interaction of multiple diaminopyridine and thymine-terminated polymer chains, alterations that affect the absolute number and relative density of diaminopyridine or thymine groups on the nanocomposite tecton surface impact the net thermodynamic stability of the assemblies. In other words, while all constructs should be thermally reversible, the temperature range over which particle assembly and disassembly occurs should be affected by these variables. To better understand how differences in nanocomposite tecton composition impact the assembly process, nanostuctures were synthesized using different nanoparticle core diameters (10-40 nm) and polymer spacer molecular weights (3.7-11.0 kDa), and allowed to fully assemble at room temperature (˜22° C.) (FIG. 8). Nanocomposite tectons were then monitored using UV-Vis spectroscopy at 520 nm while slowly heating at a rate of 0.25° C./min, resulting in a curve that clearly shows a characteristic disassembly temperature (melting temperature, Tm) for each nanocomposite tecton composition.

From these data, two clear trends can be observed. First, when holding polymer molecular weight constant, Tm increases with increasing particle size (FIG. 8A). Conversely, when keeping particle diameter constant, Tm drastically decreases with increasing polymer length (FIG. 8B). To understand these trends, it is important to note that nanocomposite tecton dissociation is governed by a collective and dynamic dissociation of multiple individual diaminopyridine-thymine bonds, which reside at the periphery of the polymer-grafted nanoparticles. The enthalpic component of nanocomposite tecton bonding behavior is therefore predominantly governed by the local concentration of the supramolecular bond-forming diaminopyridine and thymine groups, while the entropic component is dictated by differences in polymer configuration in the bound versus unbound states.

All nanocomposite tectons possess similar polymer grafting densities (i.e. equivalent areal density of polymer chains at the inorganic nanoparticle surface, FIG. 9) regardless of particle size or polymer length. However, the areal density of diaminopyridine and thymine groups at the periphery of the nanocomposite tectons is not constant as a function of these two variables due to nanocomposite tecton geometry. When increasing inorganic particle diameter, the decreased surface curvature of the larger particle core forces the polymer chains into a tighter packing configuration, resulting in an increased areal density of diaminopyridine and thymine groups at the nanocomposite tecton periphery; this increased concentration of binding groups therefore results in an increased Tm, explaining the trend in FIG. 8A.

Conversely, for a fixed inorganic particle diameter (and thus constant number of polymer chains per particle), increasing polymer length decreases the areal density of diaminopyridine and thymine groups at the nanocomposite tecton periphery due to the “splaying” of polymers as they extend off of the particle surface, thereby decreasing Tm in a manner consistent with the trend in FIG. 8B. Additionally, increasing polymer length results in a greater decrease of system entropy upon nanocomposite tecton assembly, due to the greater reduction of polymer configurations once the polymer chains are linked via a diaminopyridine-thymine bond; this would also be predicted to reduce T m. Within the temperature range tested, all samples were easily assembled and disassembled via alterations in temperature. Inorganic particle diameter and polymer length are therefore both effective handles to control nanocomposite tecton assembly behavior.

Importantly, because the nanocomposite tecton assembly process is based on dynamic, reversible supramolecular binding, it should be possible to drive the system to an ordered equilibrium state where the maximum number of binding events can occur. The particle cores and polymer ligands are polydisperse (FIG. 10) and ordered arrangements represent the thermodynamically favored state for a set of assembled nanocomposite tectons. When packing nanocomposite tectons into an ordered lattice, deviations in particle diameter would be expected to generate inconsistent particle spacings that would decrease the overall stability of the assembled structure. However, the inherent flexibility of the polymer chains should allow the nanocomposite tectons to adopt a conformation that compensates for these structural defects. As a result, an ordered nanocomposite tecton arrangement would still be predicted to be stable if it produced a larger number of diaminopyridine-thymine binding events than a disordered structure and this increase in binding events outweighed the entropic penalty of reduction in polymer chain configurations.

To test this hypothesis, multiple sets of assembled nanocomposite tectons were thermally annealed at a temperature just below their Tm, allowing particles to reorganize via a series of binding and unbinding events until they reached the thermodynamically most stable conformation. The resulting structures were analyzed with small angle X-ray scattering, revealing the formation of highly ordered mesoscale structures where the nanoparticles were arranged in body-centered cubic superlattices (FIG. 11). The body-centered cubic structure was observed for multiple combinations of particle size and polymer length, indicating that the nanoscopic structure of the composites can be controlled as a function of either the organic component (via polymer length), the inorganic component (via particle size), or both, making this nanocomposite tecton scheme a highly tailorable method for the design of future nanocomposites.

US11873396B2. Nanostructures for the assembly of materials (2024-01-16). Massachusetts Institute of Technology [US]. Inventors: Robert J. Macfarlane [US], Jianyuan Zhang [US], Peter Jeffries Santos [US], Paul Anthony Gabrys [US].
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Patent 2024
chemical composition Chloroform Cuboid Bone Dimethylformamide Entropy Extinction, Psychological Gold Human Body Ligands Molecular Structure Polymerization Polymers Polystyrenes Radiography Solvents Spectrum Analysis Sulfhydryl Compounds tetrahydrofuran Thymine Toluene Vibration Vision
3
Energy Distribution Analysis of Current
Not available on PMC !

Example 9

Method according to either of Claims 7 or 8, wherein the set of first values, the set of second values and/or the set of third values characterizes a distribution of the energy of the current over frequency.

Example 10

Method according to example 9, wherein the set of first values, the set of second values and/or the set of third values characterizes a signal entropy of the current.

US11867771B2. Methods and devices for detecting an electrical arc (2024-01-09). Infineon Technologies AG [DE]. Inventors: Felix Kaiser [DE], Christoph Grund [DE], Goran Keser [DE], Christopher Roemmelmayer [DE].
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Patent 2024
Electricity Entropy Medical Devices
4
Quantifying Cell Variability Patterns
Not available on PMC !

Example 2

A dataset of variability patterns at the cellular levels is generated using several single cell-based techniques. Assessments of single cell variabilities in gene expression are performed using single cell RNA sequencing; single cell proteomics; single cell metabolomics; single cell epitopes expression and cytokine secretion. Cell are harvested from patients before and after chronic disease therapy. The results are incorporated into a database. A method for quantifying the variability patterns is selected, based for example on methods for quantifying nonlinear or chaotic systems; methods for quantifying entropy; use of ratios between two consecutive measurements; mean of ratios of variabilities between two or more consecutive measurements; sample entropy algorithm; complexity index; multiscale entropy measurements; and any type of combinations of methods, which are used for signifying variability pattern(s). The resulting number(s)/factor(s), which are generated by using one or more of these methods are implemented into operating systems for improving their function and for reaching a pre-determined goal.

US11881296B2. Identifying and quantifying individualized variability-patterns and implementing them for improved efficacy of systems (2024-01-23). OBERON SCIENCES ILAN LTD. [IL]. Inventors: Yaron Ilan [IL].
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Patent 2024
Biotin Cells Cytokine Disease, Chronic Entropy Epitopes Gene Expression Patients secretion Therapeutics
5
Symptom Patterns and Contributing Factors by Age
Statistical analyses were performed by using SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA), R version 4.1.0 and Mplus version 8.0. Descriptive analysis was used for the distribution of sociodemographic, clinical, symptoms, and function characteristics. Categorical variables were presented as frequencies and percentages, and continuous variables as means and SDs. A symptom network analysis was used to identify the most central symptom in the entire sample and in each age group. In the symptom networks, a node indicates an independent symptom, an edge indicates the conditional relationships between two symptoms, and the edge thickness shows the strength of the relationship between them [16 (link)]. Thus, two centrality indices (strength and closeness) were output to quantify the relationship. The strength value represents the probability of one symptom and other symptoms occurring together, and the closeness value represents the path from one symptom to all other symptoms [16 (link)].
The questionnaires were scored according to the PROMIS Scoring Manual, and were dichotomized as 0 or 1 according to the cutoff scores for clinical differences (https://www.healthmeasures.net/). After data processing, LCA was performed to identify clusters of individuals displaying similar patterns of symptoms by age groups (15–39, 40–59, and over 60 years). Models with an increasing number of latent classes were assessed until the best fitting model was determined. To select the optimal LCA model, the following indices were included: the Akaike information criterion (AIC), Bayesian information criterion (BIC), and adjusted BIC (aBIC) were used to assess information criteria; and the Lo-Mendell-Rubin (LMR) test and bootstrapped likelihood ratio test (BLRT) were used to improve the model fit, with significant values indicating a better fit for the k-class model than the k-1-class model. Entropy values that exceed 0.80 indicate a satisfactory classification accuracy [17 (link)]. Among the LCA models with different numbers of latent classes, a lower AIC, BIC, aBIC, larger entropy, and significant LMR-LRT and BLRT p values were indicative of good model fit [18 (link)]. Clinical interpretability was also considered to decide the best option. After the optimal model was determined, between-group difference was examined using Chi-square tests, Fisher’s exact tests or analysis of variance (ANOVA) where appropriate. Only statistically significant variables were entered into the stepwise logistic regression model. The regression was conducted separately by age groups to determine the contributing factors of symptoms for each group. P < 0.05 was considered statistically significant.
Cai T., Zhou T., Chen J., Huang Q., Yuan C, & Wu F. (2023). Identification of age differences in cancer-related symptoms in women undergoing chemotherapy for breast cancer in China. BMC Women's Health, 23, 100.
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Publication 2023
Age Groups Entropy

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More about "Entropy"

Entropy is a fundamental concept in thermodynamics and information theory that quantifies the disorder, randomness, or uncertainty within a system.
It represents the level of unpredictability associated with a given state or process.
In scientific research, entropy analysis can be a valuable tool for optimizing reproducibility and identifying the most reliable and effective experimental protocols.
PubCompare.ai's AI-driven entropy analysis helps researchers locate the best protocols from literature, preprints, and patents, ensuring they find the most robust and effective methods for their studies.
By intelligently comparing and analyzing entropy-related data, PubCompare.ai's advanced AI can help researchers enhance the reproducibility and effectiveness of their research findings.
The concept of entropy is closely tied to the second law of thermodynamics, which states that the total entropy of an isolated system not in equilibrium will tend to increase over time, approaching a maximum value at equilibrium.
This principle is crucial in understanding the behavior of systems and processes in fields such as physics, chemistry, biology, and engineering.
Techniques like MATLAB, Origin 7.0, ITC200, VP-ITC microcalorimeter, MicroCal iTC200, ITC200 microcalorimeter, and MicroCal PEAQ-ITC can be used to measure and analyze entropy-related data.
These tools, combined with the power of PubCompare.ai's AI, can help researchers optimize their experimental protocols and improve the overall quality and reproducibility of their research.
Additionally, the use of high-performance computing resources, such as the Tesla V100 GPU, can enhance the speed and accuracy of entropy analysis, further aiding researchers in their quest for reliable and effective experimental methods.