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Self Confidence

Self Confidence: A person's belief in their own abilities, competence, and worth.
It involves a positive attittude towards oneself and one's capabilities, which can enhance motivation, resilience, and overall well-being.
Boosting self-confidence can help individuals tackle challenges, take risks, and pursue their goals with greater enthusiasm and determination.
Fostering self-confidence is an important aspect of personal growth and development.

Most cited protocols related to «Self Confidence»

1
Efficient ChIP-Seq Peak Calling with MACS
MACS is implemented in Python and freely available with an open source Artistic License at [16 ]. It runs from the command line and takes the following parameters: -t for treatment file (ChIP tags, this is the ONLY required parameter for MACS) and -c for control file containing mapped tags; --format for input file format in BED or ELAND (output) format (default BED); --name for name of the run (for example, FoxA1, default NA); --gsize for mappable genome size to calculate λBG from tag count (default 2.7G bp, approximately the mappable human genome size); --tsize for tag size (default 25); --bw for bandwidth, which is half of the estimated sonication size (default 300); --pvalue for p-value cutoff to call peaks (default 1e-5); --mfold for high-confidence fold-enrichment to find model peaks for MACS modeling (default 32); --diag for generating the table to evaluate sequence saturation (default off).
In addition, the user has the option to shift tags by an arbitrary number (--shiftsize) without the MACS model (--nomodel), to use a global lambda (--nolambda) to call peaks, and to show debugging and warning messages (--verbose). If a user has replicate files for ChIP or control, it is recommended to concatenate all replicates into one input file. The output includes one BED file containing the peak chromosome coordinates, and one xls file containing the genome coordinates, summit, p-value, fold_enrichment and FDR (if control is available) of each peak. For FoxA1 ChIP-Seq in MCF7 cells with 3.9 million and 5.2 million ChIP and control tags, respectively, it takes MACS 15 seconds to model the ChIP-DNA size distribution and less than 3 minutes to detect peaks on a 2 GHz CPU Linux computer with 2 GB of RAM. Figure S6 in Additional data file 1 illustrates the whole process with a flow chart.
Zhang Y., Liu T., Meyer C.A., Eeckhoute J., Johnson D.S., Bernstein B.E., Nusbaum C., Myers R.M., Brown M., Li W, & Liu X.S. (2008). Model-based Analysis of ChIP-Seq (MACS). Genome Biology, 9(9), R137.
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Publication 2008
Chromatin Immunoprecipitation Sequencing Chromosomes DNA Chips FOXA1 protein, human Genome Homo sapiens MCF-7 Cells Neoplasm Metastasis Python
2
16S Taxonomic Profiling with LEfSe
Except as stated otherwise, taxonomic abundances for 16S samples were generated from filtered sequence reads using the RDP classifier [101 (link)], with confidences below 80% rebinned to 'uncertain'. For all the datasets described below, the final input for LEfSe is a matrix of relative abundances obtained from the read counts with per-sample normalization to sum to one. Witten-Bell smoothing [102 ] was used to accommodate rare types, but due to LEfSe's non-parametric approach, this has minimal effect on the discovered biomarkers and on the LDA score. This also allows our biomarker discovery method to avoid most effects of sequence quality issues as long as any sequencing biases are homogeneous among different conditions, as no specific assumptions on the statistical distribution and noise model are made by the algorithm as is standard for non-parametric approaches.
Segata N., Izard J., Waldron L., Gevers D., Miropolsky L., Garrett W.S, & Huttenhower C. (2011). Metagenomic biomarker discovery and explanation. Genome Biology, 12(6), R60.
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Publication 2011
Biological Markers Self Confidence
3
Sensitive Protein Homology Detection
In the first step, an alignment of homologs is built for the query sequence by multiple iterations of PSI-BLAST searches against the non-redundant database from NCBI. The maximum number of PSI-BLAST iterations and the E-value threshold can be specified on the start page (Figure 1). Instead of a single sequence, the user may also enter a multiple alignment to jumpstart PSI-BLAST, or he can choose to skip the PSI-BLAST iterations altogether by choosing zero for the maximum number of PSI-BLAST iterations.
The user can further specify a minimum coverage of the query by the PSI-BLAST matches. With a value of 50%, at least half of the query residues must be aligned (‘covered’) with residues from the matched sequence in order for it to enter into the profile. Similarly, a minimum sequence identity of the PSI-BLAST match to the query sequence can be demanded. Our benchmarks (data not published) have shown that a value between 20 and 25% improves selectivity without compromising sensitivity. The final alignment from PSI-BLAST is annotated with the predicted secondary structure and confidence values from PSIPRED (30 (link)).
In the next step, a profile HMM is generated from the multiple alignment that includes the information about predicted secondary structure. A profile HMM is a concise statistical description of the underlying alignment. For each column in the multiple alignment that has a residue in the query sequence, an HMM column is created that contains the probabilities of each of the 20 amino acids, plus 4 probabilities that describe how often amino acids are inserted and deleted at this position (insert open/extend, delete open/extend). These insert/delete probabilites are translated into position-specific gap penalties when an HMM is aligned to a sequence or to another HMM.
The query HMM is then compared with each HMM in the selected database. The database HMMs have been precalculated and also contain secondary structure information, either predicted by PSIPRED, or assigned from 3D structure by DSSP (31 (link)). The database search is performed with the HHsearch software for HMM–HMM comparison (28 (link)). Compared to methods that rely on pairwise comparison of simple sequence profiles, HHsearch gains sensitivity by using position-specific gap penalties. If the default setting ‘Score secondary structure’ is active, a score for the secondary structure similarity is added to the total score. This increases the sensitivity for homologous proteins considerably (28 (link)). As a possible drawback, it may lead to marginally significant scores for structurally analogous, but non-homologous proteins.
The user can choose between local and global alignment mode. In global mode alignments extend in both directions up to the end of either the query or the database HMM. No penalties are charged for end gaps. In local mode, the highest-scoring local alignment is determined, which can start and end anywhere with respect to the compared HMMs. It is recommended to use the local alignment mode as a default setting since it has been shown in our benchmarks to be on average more sensitive in detecting remote relationships as well as being more robust in the estimation of statistical significance values. A global search might be appropriate when one expects the database entries to be (at least marginally) similar over their full length with the query sequence. In most cases it will be advisable to run a search in both modes to gain confidence in one's results.
Söding J., Biegert A, & Lupas A.N. (2005). The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Research, 33(Web Server issue), W244-W248.
Publication 2005
Amino Acids Genetic Selection Hypersensitivity Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Proteins
4
Microbiome Data Analysis with Hypothesis Testing
Although useful for exploring and summarizing microbiome data, many of the graphics and ordination methods discussed here are not formal tests of any particular hypothesis. The most common framework for testing in microbiome studies is the comparisons of samples from different categories (e.g. healthy and obese; control and treated; different environments). Standard test statistics include the t-test, the paired permutation t-test, and ANOVA type tests based on F or pseudo-F statistics. However, microbiome data have two particularities. First, the raw abundance counts are never normally distributed, so the preferred methods are nonparametric. Second, there is contiguous information available about the relationships between OTUs, as well as for variables measured on the samples, so testing is sometimes more elaborate than a two-sample test. The hypergeometric test, also known as Fisher's exact test, is used in cases when we have a test statistic for each of the different OTUs. The goal is to confirm that a certain property of these significant OTUs is overrepresented compared to the general population of OTUs, often called “the universe”. For instance in Holmes et al [65] (link) and Nelson et al [68] several phyla were shown to be significantly over-abundant in IBS rats as compared to healthy controls using this hypergeometric test.
An organizing principle in many nonparametric testing protocols is that the repetition of an analysis multiple times enables the user to control for multiple testing, or to evaluate the quality of estimators or the optimal values of tuning parameters. Modern confirmatory analyses currently depend on these repeated analyses under various data perturbation schemes, of which resampling, permutations, and Monte Carlo simulations are the most common. For instance the bootstrap uses many thousands of analyses of resampled data to address problems such as statistical stability or bias estimation [69] , and can even provide confidence regions [69] for nonstandard parameters, such as phylogenetic trees [70] . Repeating analyses on permuted data can allow for control of the probability of encountering 1 or more false positives (falsely rejected nulls) among your group of simultaneous hypotheses, also called the Family Wise Error Rate (FWER). For instance, Westfall and Young's permutation-based minP procedure controls the FWER [71] and is implemented within the multtest package [72] . The phyloseq package interfaces with minP in multtest through a wrapper function, called mt. In the following example code we use the mt wrapper to control the FWER while simultaneously testing whether each OTU correlates with the “Enterotypes” classification of the samples. Note that we first remove samples that were not assigned an enterotype by the original authors (Table 1).
McMurdie P.J, & Holmes S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217.
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Publication 2013
Microbiome neuro-oncological ventral antigen 2, human Obesity Rattus norvegicus
5
Ontology Term Enrichment Analysis
Statistical enrichment of ontology terms is dependent upon the genome-wide gene set used in the analysis. GREAT currently supports testing of human (Homo sapiens NCBI Build 36.1, or UCSC hg18) and mouse (Mus musculus NCBI Build 37, or UCSC mm9). To limit the gene sets to only high-confidence genes and gene predictions, we use only the subset of the UCSC Known Genes45 that are protein coding, are on assembled chromosomes and possess at least one meaningful GO annotation14 (link). GO is an ontological representation of information related to the biological processes, cellular components and molecular functions of genes. We rely on the idea that if a gene has been annotated for function it should be included in the gene set, and if no function has been ascribed to a gene its status may be unclear and thus it is best omitted from the gene set. In GREAT version 1.1.3, we use GO data downloaded on 5 March 2009 for human and 23 March 2009 for mouse, leading to gene sets of 17,217 and 17,506 genes for human and mouse, respectively.
A single gene may have multiple splice variants. As annotations are generally given at the gene level, GREAT uses a single transcription start site (TSS) to specify the location of each gene. The TSS used is that of the ‘canonical isoform’ of the gene as defined by the UCSC Known Genes track45 .
McLean C.Y., Bristor D., Hiller M., Clarke S.L., Schaar B.T., Lowe C.B., Wenger A.M, & Bejerano G. (2010). GREAT improves functional interpretation of cis-regulatory regions. Nature biotechnology, 28(5), 495-501.
Publication 2010
Biological Processes Cellular Structures Chromosomes Genes Genome Homo sapiens Mice, House Mice, Laboratory Operator, Genetic Protein Isoforms Proteins Transcription Initiation Site

Most recents protocols related to «Self Confidence»

1
Efficacy of Corn Event MON 87411 Against Rootworm
Not available on PMC !

Example 5

This example describes the superior protection of plant comprising event MON 87411 from corn rootworm damage when compared to current commercial products (MON 88017 and DAS-59122-7) and negative control plants. Efficacy field trials were conducted comparing 135 plants each of event MON 87411, MON 88017, DAS-59122-7, and negative controls. Root damage ratings (RDR) were collected, and the percentage plants with an RDR less than the economic injury level (0.25 RDR) is shown in Table 8.

Table 8 shows that only about 4% of plants containing event MON 87411 exhibited RDRs greater than the economic threshold of 0.25 RDR. In contrast, 22% of the commercially available plants containing MON 88017 exhibited RDRs greater than the economic threshold of 0.25 RDR. And, 20% of the commercially available plants containing DAS-59122-7 exhibited RDRs greater than the economic threshold of 0.25 RDR. And, 96% of the negative control plants exhibited RDRs greater than the economic threshold of 0.25 RDR. The conclusion from these data is that event MON 87411 is clearly superior at providing protection from corn rootworm damage as compared to commercial products MON 88071 and DAS-59122-7, and a negative control.

TABLE 8
Results of efficacy field trial with the approximate
percentage of plants exhibiting ≤ 0.25 RDR.
Approximate percentage
of plants exhibiting ≤
Event tested0.25 RDR
event MON 8741196
MON 8801778
DAS-59122-780
negative control plants 4

Trial included 135 plants for each event tested.

Efficacy green house trials were conducted to test the performance of event MON 87411 with extreme infestation pressure of corn root worm. In this trial the following event were evaluated: event MON 87411, an event from transformation with DNA vector #890 expressing only the dsRNA; MON 88017; DAS-59122-7; and negative control. For these high-pressure efficacy trials, the corn plants under evaluation were grown in pots in a green house. Extreme infestation pressure was achieved by sequential infestation of each potted plant with approximately 2,000 WCR eggs per pot at their V2 growth stage, and, at 4 additional times occurring at 1 to 1½ week intervals with approximately 1,000 WCR eggs per pot per infestation for a total of approximately 6,000 WCR eggs added to each pot. Plant roots were removed, washed, and rated for RDR at their VT growth stage. The roots from all thirteen (N=13) negative control plants exhibited maximum root damage, or an absolute RDR of 3 RDR. These results illustrate that event MON 87411 is more superior to other corn events available for controlling corn rootworm (Table 9).

TABLE 9
Root Damage Rating (RDR) under high
corn rootworm infestation pressure.
Lower and Upper
Average95% confidence
EventRDRlimits
Negative Control3.0Absolute
(N = 13)
only dsRNA0.360.17/0.54
(N = 11)
MON 880172.11.8/2.4
(N = 11)
DAS-59122-70.290.17/0.42
(N = 16)
MON 874110.060.03/0.08
(N = 13)
(N = the number of plants evaluated).

One measure of efficacy of corn rootworm transgenic events is by a determining the emergence of adult beetles from the potted soil of plants cultivated in a green house. To determine adult corn rootworm beetle emergence from the soil of event MON 87411 plants grown in pots, 10 to 15 plants were germinated in pots containing soil infested with WCR eggs, similar to that described above. Throughout the growth period, each corn plant was covered with mesh bag to contain any emerging adult beetles.

Counts of above ground adult beetles were made at 6, 12, and 18 weeks after plant emergence, and at the end of the trial the roots were evaluated for RDR. Plants containing event MON 87411 were compared to negative control plants, and other corn rootworm protective transgenic events. The results were that significantly fewer beetles were observed to emerge from soils in which event MON 87411 plants were potted compared to the other corn rootworm protective transgenic events, illustrating the superior properties of event MON 87411 to protect against corn rootworm damage.

US11859198B2. Corn event MON 87411 (2024-01-02). Monsanto Technology LLC [US]. Inventors: Wen C. Burns [US], Catherine A. Chay [US], Cheryl L. Cloninger [US], Mingqi Deng [US], Stanislaw Flasinski [US], Kunsheng Wu [US].
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Patent 2024
Adult Animals, Transgenic Beetles Cloning Vectors Eggs Helminthiasis Injuries Parasitic Diseases Plant Roots Plants Pressure RNA, Double-Stranded Zea mays
2
Multitope Vaccine Structure Prediction
After the design with physicochemical and immunological properties assessment of the multitope vaccine construct, we aimed to predict its 3D structure to be used for a docking study with the human immune receptor. For this purpose, the Robetta server (Kim et al., 2004 (link)) was employed. Robetta server utilizes a unique approach for protein structure prediction where if a confident match to a protein of known structure is found using BLAST, PSI-BLAST, FFAS03, or 3D-Jury, this protein is employed for the modeling process. Alternatively, if no match is found, the modeling process is performed through the de novo Rosetta fragment insertion method. Following that, we utilized the GalaxyRefine server (Heo et al., 2013 (link)) to refine the 3D protein structure estimated by Robetta and evaluated this refinement through the generated scores of Ramachandran plot analysis (Laskowski et al., 1993 (link)) and ProSA (Wiederstein and Sippl, 2007 (link)).
Gouda A.M., Soltan M.A., Abd-Elghany K., Sileem A.E., Elnahas H.M., Ateya M.A., Elbatreek M.H., Darwish K.M., Bogari H.A., Lashkar M.O., Aldurdunji M.M., Elhady S.S., Ahmad T.A, & Said A.M. (2023). Integration of immunoinformatics and cheminformatics to design and evaluate a multitope vaccine against Klebsiella pneumoniae and Pseudomonas aeruginosa coinfection. Frontiers in Molecular Biosciences, 10, 1123411.
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Publication 2023
Homo sapiens Proteins Receptors, Immunologic Self Confidence Staphylococcal Protein A Vaccines
3
Menopause and Obesity in Chinese Women
This cross-sectional study analyzed the clinical data of 2514 Chinese women between the ages of 45 and 55 years, and within ten years after menopause, that were evaluated at the Health Examination Center, Huadong Sanatorium between June 2021 and December 2021. Menopause was defined as at least one year since the last menstrual period. The study subjects were divided into lean group (n=1194) with BMI < 23 kg/m2 and obese group (n=1320) with BMI ≥ 23 kg/m2. All the participants underwent comprehensive anthropometric measurements, abdominal ultrasonography, and fasting blood tests. The exclusion criteria included incomplete medical records, surgical menopause, hormone replacement therapy, and severe medical diseases such as cancer or organ failure. This study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics and Research Committee of Huadong Sanatorium (Approval No. ECHS2023-01). The requirement for written informed consent was waived because of the retrospective nature of the study and the data analysis was anonymous and confidential.
Lu Y., Ge L., Yang H., He Y, & Wang Y. (2023). Chinese Visceral Adipose Index Shows Superior Diagnostic Performance in Predicting the Risk of Metabolic Dysfunction Associated Fatty Liver Disease in Early Postmenopausal Chinese Women. Diabetes, Metabolic Syndrome and Obesity, 16, 607-617.
Publication 2023
Abdomen Chinese Hematologic Tests Malignant Neoplasms Menopause Menstruation Obesity Operative Surgical Procedures Therapy, Hormone Replacement Ultrasonography Woman
4
Consecutive Patient Stroke Alteplase Study
We conducted this study on consecutive patients to avoid any selection bias. In order to address information bias, two aspects should be considered: the number of lost to follow-up was acceptable (Figure 1); the admission FIB-4 score was calculated only after the 3-month assessment, so the experimenter did not know the score value when assessing the 3-month mRS (primary outcome measure). Based on previous RCTs on Alteplase effectiveness (22 (link)), the minimum number of samples required to achieve a 95% confidence level with a marginal error of 0.05 was 241.
Norata D., Lattanzi S., Broggi S., Rocchi C., Bartolini M, & Silvestrini M. (2023). Liver fibrosis-4 score predicts outcome of patients with ischemic stroke undergoing intravenous thrombolysis. Frontiers in Neurology, 14, 1103063.
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Publication 2023
Alteplase Patients
5
Sensitivity Analysis for Economic Evaluation
To quantify the level of confidence in the models' results, a deterministic sensitivity analysis (DSA) and a probabilistic sensitivity analysis (PSA) were performed. In the DSA, the input parameters were varied to the maximum and minimum possible values. This range is usually defined by the confidence interval of parameters. Therefore, for the examined parameters, a range of 95% confidence interval was specified and then based on this range (maximum and minimum input values), one value in the model was varied manually each time. For the patients' adherence, the range of 50%−100% was considered. The results (new ICERs) were collected and expressed with tornado diagrams. The tornado diagrams depict the impact on the ICER whenever one single parameter changed.
For the PSA, as the main assumption, it was considered the deterministic input values in the parameter sheet as the mean values. As the standard errors of the cost items were not available, it was considered to mean value times by 0.1. Based on logical constraints, the probabilistic distribution for each of the different sources of uncertainty was defined. A gamma distribution for all cost items and a beta distribution for the utilities were defined. The PSA was conducted by drawing a random number for each of the input distributions and each time, the ICER was calculated by Excel. By running a macro, its action is repeated 1,000 times.
Jamshidi A., Daroudi R., Aas E, & Khalili D. (2023). A cost-effectiveness analysis of risk-based intervention for prevention of cardiovascular diseases in IraPEN program: A modeling study. Frontiers in Public Health, 11, 1075277.
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Publication 2023
Gamma Rays Hypersensitivity Tornadoes

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More about "Self Confidence"

Self-Assurance, Self-Belief, Self-Esteem, Self-Reliance, Confidence, Self-Trust, Assertiveness, Efficacy, Self-Assuredness, Self-Assurance, Conviction, Positive Mindset, Resilience, Motivation, Personal Growth, Development, Optimization, Reproducibility, Research Protocols, Workflows, AI-Driven Comparisons, Prism 6, Prism 8, GraphPad Prism 7, SAS 9.4, GraphPad Prism 5, SAS version 9.4, Sample Size Calculator, Prism 9.
Self-confidence is a fundamental aspect of personal growth and development.
It refers to an individual's belief in their own abilities, competence, and worth.
A person with high self-confidence typically exhibits a positive attitude towards themselves and their capabilities, which can enhance their motivation, resilience, and overall well-being.
Self-confident individuals are more likely to tackle challenges, take risks, and pursue their goals with greater enthusiasm and determination.
Boosting self-confidence can be a game-changer in various areas of life, including professional and academic pursuits.
Tools like PubCompare.ai, an AI-driven platform, can help individuals optimize their research protocols and enhance the reproducibility of their work.
By easily locating protocols from literature, pre-prints, and patents, and using AI-driven comparisons to identify the best protocols and products for their research needs, researchers can take the guesswork out of their workflows and elevate their confidence in their work.
Additionally, statistical software like Prism 6, Prism 8, GraphPad Prism 7, SAS 9.4, GraphPad Prism 5, SAS version 9.4, and Prism 9 can provide valuable insights and tools to support researchers in their quest for self-assurance and confidence.
These tools can help with tasks such as sample size calculations, data analysis, and visualization, further empowering individuals to make informed decisions and feel more confident in their research processes.
By embracing self-confidence and utilizing the right tools and resources, individuals can unlock their full potential, overcome challenges, and achieve their goals with greater ease and enthusiasm.