Human ESC (H9, H1) and iPSC lines (2C6 and SeV6) were subjected to a modified dual SMAD-inhibition13 (link) based FP induction12 (link) protocol. Exposure to SHH C25II, Purmorphamine, FGF8 and CHIR99021 were optimized for midbrain FP and DA neuron yield (see Figure 1d ). Following FP induction, further maturation was carried out in Neurobasal/B27 medium supplemented with AA, BDNF, GDNF, TGFβ3 and dbcAMP (see full methods for details). The resulting DA neurons were subjected to extensive phenotypic characterization via immunocytochemistry, qRT-PCR, gene expression profiling, HPLC analysis for DA and in vitro electrophysiological recordings. In vivo studies were performed in 6-hydroxydopamine lesioned, hemiparkinsonian rodents (adult NOD-SCID IL2Rgc mice and Sprague Dawley rats) as well as in two adult rhesus monkeys treated with carotid injections of MPTP. DA neurons were injected stereotactically in the striata of the animals (150 × 103 cells in mice, 250 × 103 cells in rats) and a total of 7.5 × 106 cells (distributed in 6 tracts; 3 on each side of brain) in monkeys. Behavioral assays were performed at monthly intervals post grafting, including amphetamine mediated rotational analysis as well as a test for focal akinesia (“stepping test”) and forelimb use (cylinder test). Rats and mice were sacrificed at 18–20 weeks and the primates at 1 month post grafting. Characterization of the grafts was performed via stereological analyses of cell numbers and graft volumes and comprehensive immunohistochemistry.
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Step Test
Step Test
The Step Test is a physiological assessment that evaluates an individual's cardiovascular fitness and exercise capacity.
It involves stepping up and down on a raised platform at a standardized pace, with measurements of heart rate, blood pressure, and other parameters monitored before, during, and after the test.
This simple, noninvasive procedure provides valuable insights into an individual's aerobic fitness, allowing for the identification of potential health concerns and the development of targeted exercise prescriptions.
The Step Test is widely used in clinical settings, research studies, and fitness assessments to prromote optimal cardiovascular health and physical performance.
It involves stepping up and down on a raised platform at a standardized pace, with measurements of heart rate, blood pressure, and other parameters monitored before, during, and after the test.
This simple, noninvasive procedure provides valuable insights into an individual's aerobic fitness, allowing for the identification of potential health concerns and the development of targeted exercise prescriptions.
The Step Test is widely used in clinical settings, research studies, and fitness assessments to prromote optimal cardiovascular health and physical performance.
Most cited protocols related to «Step Test»
1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine
Adult
Amphetamine
Animals
Biological Assay
Brain
Bucladesine
Carotid Arteries
Cells
Chir 99021
FGF8 protein, human
Forelimb
Glial Cell Line-Derived Neurotrophic Factor
Grafts
High-Performance Liquid Chromatographies
Homo sapiens
Hydroxydopamine
Immunocytochemistry
Immunohistochemistry
Induced Pluripotent Stem Cells
Macaca mulatta
Mesencephalon
Mice, Inbred NOD
Monkeys
Mus
Neurons
Phenotype
Primates
purmorphamine
Rats, Sprague-Dawley
Rattus
Rodent
SCID Mice
Step Test
Striatum, Corpus
optiCall uses deviation from Hardy–Weinberg equilibrium (HWE) as an indicator of clustering quality. A χ2 test is used to test HWE unless sample size is small (<50 expected counts of any genotype, assuming HWE or allele counts of <100 for either allele), in which case an exact test is used (Wigginton et al., 2005 (link)). SNPs with a HWE P-value less than a given threshold (P<5×10−15 by default) are deemed to be poorly called. optiCall attempts to improve the genotype calls at these SNPs by again running a Student's t-based mixture model, but this time omitting the SNP and sample-wise prior. This rescue step is primarily implemented to give better genotype calls at SNPs where the genotype intensity clouds lie outside of the expected regions defined by the within and across sample prior. The statistical model is as described in (1) and (2), with the intensity values first transformed according to (8), to improve calling of SNPs with shifted intensities (Teo et al., 2007 (link)).
Inference is as in (2.2), by the EM algorithm. The νi are fixed at 1 for all classes except the heterozygous class, which is fixed at 1.3. The values of μi, Σi for the unknown class are fixed with identical values to (2.2).
All four classes have initial class probabilities set to 0.25, and for the three genotype classes initial covariance matrices are set to (2c/N)×I2 with c the cost (Arthur and Vassilvitskii, 2007 ) of a k means ++ clustering on the data, and N the number of intensity points. The transformation of intensities has accounted for shifts, and so location parameters of the two homozygous classes can be initialized to the extremes of y(1), and the heterozygous class will then fall somewhere in between, thus the μi are initialized to
where the min/max are taken over a filtered version of the intensity data, with the lowest 1% of untransformed intensity values in the x(1) direction and lowest one percent in the x(2) direction removed. is the mean of the yj over the second axis, and k is a shift parameter for the location of the heterozygous class, that takes one of three values, 0.45, 0.5 or 0.55, resulting in three sets of initial values dependent on the value of k. For each set of starting values, the EM algorithm is run until genotype calls are concordant for two consecutive iterations, and the optimal parameters are chosen to be the final values with the highest likelihood.
Genotype calls are made using genotype posterior probabilities [using the πi inferred from this step unlike (2.3)] with a 0.7 call threshold. By default, SNPs that fail the HWE test subsequent to this step have all genotypes called unknown.
In our experiments, we have found the occurrence of the rescue step, and the subsequent chances of a successful rescue, to vary with the quality of the dataset. On a number of Immunochip datasets, rescue steps tended to occur on between 3 and 10% of SNPs, with 30–50% being successful.
Inference is as in (2.2), by the EM algorithm. The νi are fixed at 1 for all classes except the heterozygous class, which is fixed at 1.3. The values of μi, Σi for the unknown class are fixed with identical values to (2.2).
All four classes have initial class probabilities set to 0.25, and for the three genotype classes initial covariance matrices are set to (2c/N)×I2 with c the cost (Arthur and Vassilvitskii, 2007 ) of a k means ++ clustering on the data, and N the number of intensity points. The transformation of intensities has accounted for shifts, and so location parameters of the two homozygous classes can be initialized to the extremes of y(1), and the heterozygous class will then fall somewhere in between, thus the μi are initialized to
where the min/max are taken over a filtered version of the intensity data, with the lowest 1% of untransformed intensity values in the x(1) direction and lowest one percent in the x(2) direction removed. is the mean of the yj over the second axis, and k is a shift parameter for the location of the heterozygous class, that takes one of three values, 0.45, 0.5 or 0.55, resulting in three sets of initial values dependent on the value of k. For each set of starting values, the EM algorithm is run until genotype calls are concordant for two consecutive iterations, and the optimal parameters are chosen to be the final values with the highest likelihood.
Genotype calls are made using genotype posterior probabilities [using the πi inferred from this step unlike (2.3)] with a 0.7 call threshold. By default, SNPs that fail the HWE test subsequent to this step have all genotypes called unknown.
In our experiments, we have found the occurrence of the rescue step, and the subsequent chances of a successful rescue, to vary with the quality of the dataset. On a number of Immunochip datasets, rescue steps tended to occur on between 3 and 10% of SNPs, with 30–50% being successful.
Alleles
Epistropheus
Genotype
Heterozygote
Homozygote
Single Nucleotide Polymorphism
Step Test
Strains
Association of expression levels with probabilities of imputed genotypes were tested in samples of related individuals using a two-step mixed model-based score test developed in the works of Aulchenko et al. and Chen and Abecasis 33 (link),34 (link) and implemented in the GenABEL/ProbABEL packages 35 (link),36 (link). Briefly, the approach is an approximation of a full linear mixed model where the first step includes a mixed model containing all terms but those involving SNPs fitted by maximum-likelihood i.e. fixed effects as well as kinship matrix based on genomic data. Fixed effects included age and experimental batch in the adipose and LCL analysis, while age, batch and sample processing were used in the skin analysis. This step is performed using the GenABEL software 35 (link)using the polygenic() function. The resulting object contains the inverse variance-covariance matrix of the estimates and expression trait residuals which are used in the second step together with posterior genotypic probabilities performing the score test in ProbABEL 36 (link), using the “—mmscore” option. In total, 776 adipose, 777 LCL and 667 skin samples had both expression profiles and imputed genotypes and were included in the analysis. Cis analysis was limited to SNPs located within 1MB either side of the transcription start or end site or within the gene body. False discovery rate (FDR) for the cis analysis was calculated from the complete list of p values using the qvalue package20 (link) implemented in R2.11 37 . In order to characterize likely independent regulatory effects, the identified cis-eQTLs were mapped to recombination hotspot intervals 38 (link). For each gene, the most significant SNP per hotspot interval were selected followed by additional LD filtering (for each remaining SNP pair with D’ > 0.5 and r2>0.1 the least significant SNP was ignored).
Trans analysis was limited to SNPs located on a different chromosome than the tested transcript. Post-QC analysis of the trans-eQTLs revealed 52 probes with extreme outlier effects which were filtered from further trans analysis. Transcripts associated with a trans-SNP at P<5×10−8 were used for calculations of transcript-wise FDR from the complete list of p values using the qvalue package20 (link) implemented in R2.11 37 .
The score test is known to slightly underestimate additive effect sizes 34 (link), so the top association per probe was validated with a linear mixed-effects model in R, using the lmer() function in the lme4 package 39 , fitted by maximum-likelihood(Supplementary Fig. 2 ). The linear mixed-effects model were adjusted for both fixed (age, experimental batch effect and sample processing effect (skin tissue only)) and random effects (family relationship and zygosity). A likelihood ratio test was applied to assess the significance of the SNP effect. The p-value of the SNP effect in each model was calculated from the Chi-square distribution with 1 degree of freedom using -2log(likelihood ratio) as the test statistic.
Trans analysis was limited to SNPs located on a different chromosome than the tested transcript. Post-QC analysis of the trans-eQTLs revealed 52 probes with extreme outlier effects which were filtered from further trans analysis. Transcripts associated with a trans-SNP at P<5×10−8 were used for calculations of transcript-wise FDR from the complete list of p values using the qvalue package20 (link) implemented in R2.11 37 .
The score test is known to slightly underestimate additive effect sizes 34 (link), so the top association per probe was validated with a linear mixed-effects model in R, using the lmer() function in the lme4 package 39 , fitted by maximum-likelihood(
Chromosomes
Genes
Genome
Genotype
Human Body
Obesity
Recombination, Genetic
Skin
Step Test
Strains
Transcription, Genetic
For functional tests, we adopted the Two-Step test and Stand-Up test. The Two-Step test, shown in Fig. 1 a, which was previously examined by Muranaga et al. [9 ] has been developed as a screening tool for walking ability. The subject starts from the standing posture and moves two steps forward with maximum stride with the caution not to lose balance. If the subject succeeds in holding the final standing position longer than 3 s without any additional steps, the trial is judged as completed. The distance is then standardized by dividing it by the subject’s height. The test is performed twice, and the best result is recorded. Muranaga et al. [9 ]. reported that the value of the Two-Step test has a strong correlation with maximum walking speed. The Stand-Up test, shown in Fig. 1 b, was also developed by Muranaga et al. [10 ] and is performed with stools of 10, 20, 30, and 40 cm in height. Subjects are requested to stand from each stool with one leg or two legs. If the subject succeeds in holding the final standing position longer than 3 s without any additional steps, the trial is judged as completed. A 0–8 score is allocated to the performance as shown in Table 1 . Muranaga et al. [10 ] reported a significant correlation between the Stand-Up test score and the weight bearing index which is calculated as knee extension torque divided by body weight. To evaluate the reliability of these functional tests, we examined test–retest reproducibility. For that purpose, another 88 subjects were recruited and performed the Two-Step test and Stand-Up test two times each with 5–9 day intervals.![]()
The schematic procedure of the Two-Step test (
Scoring system of Stand-Up test
| Two-leg stand | One-leg stand | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Height | Fail at | 40 cm | 30 cm | 20 cm | 10 cm | 40 cm | 30 cm | 20 cm | 10 cm |
| Score | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
One-leg stand requires subjects to succeed at indicated height in both right and left leg
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Body Weight
Feces
Knee Joint
Step Test
Torque
Most recents protocols related to «Step Test»
To determine the stabilities of the ion sensors, we tested the same sensors with five sets of signals at three fixed concentration steps. We investigated the temporal stabilities of the ion sensors by performing sensitivity tests three times a day for one week after their preparation, and the daily results were averaged. The ion sensors were stored at room temperature in a light-proof environment. To obtain more reliable results, all the electrochemical sensors were subjected to concentration changes during the measurement period in the concentration range of the step response test.
The reproducibility of each ion was assessed by testing the slopes and intercepts of the five microneedle ion electrodes. As shown in Fig.3g , the average slope and intercept values for the Na+ channel were 50.7 mV/decade and 221.6 mV, respectively, with relative standard deviations (RSDs) of 2.3 and 2.4%, respectively. The measured slope and intercept values for the K+ channel were 53.6 mV/decade with an RSD of 2.5% and 236.8 mV with an RSD of 2.2%, respectively; these results indicated satisfactory reproducibility of the microneedle ion electrode between batches to detect individual ions. Thus, the prepared ion-sensing microneedle electrode exhibited high stability and met the requirements for home smart sensing.
For the selective detection of different ions, we constructed a two-electrode system to test each microneedle ion sensor. The system was evaluated by sequentially adding markers and multiple interfering substances at a particular concentration for the corresponding markers. Specifically, for the Na+ detection electrode, K+, Mg2+, Ca2+, and Na+ were added sequentially to the 10 mmol/L NaCl solution (the concentration of each ion corresponds to the proportion of the actual concentration of the test marker), and the change in the voltage signal was obtained. For the Ca2+ detection electrode, Na+, Mg2+, K+, and Ca2+ were sequentially added to a 0.5 mmol/L CaCl2 solution (the concentration of each ion corresponds to the proportion of the actual concentration of the test specimen), and the voltage signal was obtained. Similarly, for the K+ detection electrode, Na+, Mg2+, Ca2+, and K+ were sequentially added to a 2 mmol/L K+ solution (the concentration of each ion corresponds to the proportion of the actual concentration of the test specimen), and the changes in the voltage signal were obtained. The relative signal shift was evaluated using the change in voltage signal before and after marker addition and by setting the change in the absolute signal generated by each electrochemical sensor before and after the marker concentration increased to 100%. Thus, the signal difference between each interfering substance addition and the initial concentration was normalized.
The reproducibility of each ion was assessed by testing the slopes and intercepts of the five microneedle ion electrodes. As shown in Fig.
For the selective detection of different ions, we constructed a two-electrode system to test each microneedle ion sensor. The system was evaluated by sequentially adding markers and multiple interfering substances at a particular concentration for the corresponding markers. Specifically, for the Na+ detection electrode, K+, Mg2+, Ca2+, and Na+ were added sequentially to the 10 mmol/L NaCl solution (the concentration of each ion corresponds to the proportion of the actual concentration of the test marker), and the change in the voltage signal was obtained. For the Ca2+ detection electrode, Na+, Mg2+, K+, and Ca2+ were sequentially added to a 0.5 mmol/L CaCl2 solution (the concentration of each ion corresponds to the proportion of the actual concentration of the test specimen), and the voltage signal was obtained. Similarly, for the K+ detection electrode, Na+, Mg2+, Ca2+, and K+ were sequentially added to a 2 mmol/L K+ solution (the concentration of each ion corresponds to the proportion of the actual concentration of the test specimen), and the changes in the voltage signal were obtained. The relative signal shift was evaluated using the change in voltage signal before and after marker addition and by setting the change in the absolute signal generated by each electrochemical sensor before and after the marker concentration increased to 100%. Thus, the signal difference between each interfering substance addition and the initial concentration was normalized.
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Hypersensitivity
Light
Sodium Chloride
Step Test
A 25 mL beaker containing the solution was utilized, and a twice-folded cling film that was used to simulate the skin was fixed onto the surface of the beaker. An ion-sensing microneedle array was used to puncture the cling film to simulate skin penetration, and it was fully exposed to the solution to detect ions in the solution. A series of characterizations of the electrochemical sensors was performed using a CHI 760E workstation (CH Instruments Inc., USA) and commercial Ag/AgCl electrodes to evaluate the performance levels of the working electrodes. A two-electrode system was constructed to test the performance of the ion sensor in deionized water.
Step-response tests were performed on all ion sensors using an ion microneedle electrode as the working electrode and a commercial Ag/AgCl electrode as the counter electrode to evaluate their linear response capability for each subject over a specific range. During the test, each change in the solution required a test pause of 60 s to allow the complete diffusion of the solute in the solution. For the detection of Ca2+, the concentration of Ca2+ in the solution increased gradually from 0.01 to 0.1, 1, 10, and 100 mmol/L, and the response voltage of the electrode gradually increased with the change in the concentration of Ca2+. Similarly, to detect K+, the concentration of K+ in the solution gradually increased from 1 to 2, 4, 8, 16, and 32 mmol/L; the response signal of the electrode to the ions gradually increased with an increase in the K+ concentration. Similarly, to detect Na+, the concentration of Na+ in the solution gradually changed from 5 to 10, 20, 40, 80, and 160 mmol/L. The response signal of the electrode gradually increased as the concentration of Na+ increased.
Step-response tests were performed on all ion sensors using an ion microneedle electrode as the working electrode and a commercial Ag/AgCl electrode as the counter electrode to evaluate their linear response capability for each subject over a specific range. During the test, each change in the solution required a test pause of 60 s to allow the complete diffusion of the solute in the solution. For the detection of Ca2+, the concentration of Ca2+ in the solution increased gradually from 0.01 to 0.1, 1, 10, and 100 mmol/L, and the response voltage of the electrode gradually increased with the change in the concentration of Ca2+. Similarly, to detect K+, the concentration of K+ in the solution gradually increased from 1 to 2, 4, 8, 16, and 32 mmol/L; the response signal of the electrode to the ions gradually increased with an increase in the K+ concentration. Similarly, to detect Na+, the concentration of Na+ in the solution gradually changed from 5 to 10, 20, 40, 80, and 160 mmol/L. The response signal of the electrode gradually increased as the concentration of Na+ increased.
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Diffusion
Ions
Punctures
Skin
Step Test
Protocol full text hidden due to copyright restrictions
Open the protocol to access the free full text link
Cognitive Impairments, Mild
Hypersensitivity
Mini Mental State Examination
Neurobehavioral Manifestations
Step Test
To explore the relationship between the articles’ impact and the author's gender, we estimate the following baseline model: where i represents the article, and t represents the year. represents the research of article (i) in the year (t). is a dummy variable coded 1 for the female author and 0 for the male author. Our control variables are based on the variables we analyzed above. As the dependent variable in our data is compressed at 0 for some observations, we employ the Tobit model (Zhu et al., 2022 (link)).
To examine the mechanism for the articles’ impact, we use a modified version of Baron & Kenny’s (1986 ) three-step mediation test proposed by Zhao et al. (2010 ), in which the Sobel test is replaced by bootstrap (Zhu et al., 2022 (link)). To enhance the diversity of analytical methods, we also use the Monte Carlo method (Li et al., 2021 (link); Selig & Preacher, 2008 ) with 50,000 bootstrapping samples. The mediation effect model consists of the following components: where is the writing style of the article of article (i) in the year (t).
To examine the mechanism for the articles’ impact, we use a modified version of Baron & Kenny’s (1986 ) three-step mediation test proposed by Zhao et al. (2010 ), in which the Sobel test is replaced by bootstrap (Zhu et al., 2022 (link)). To enhance the diversity of analytical methods, we also use the Monte Carlo method (Li et al., 2021 (link); Selig & Preacher, 2008 ) with 50,000 bootstrapping samples. The mediation effect model consists of the following components: where is the writing style of the article of article (i) in the year (t).
Males
Step Test
Woman
At the recruitment stage, the participants completed the questionnaires on their personal background and their score of CET-41 as well as their score of translation in CET-4. In order to ensure the participants with similar English proficiency level, a pre-test was conducted with suitable translation tasks and the participants with total scores above 18 points were enrolled. In the formal experiment, after each participant was informed of the test steps, the subjects were positioned at their stations in accordance with the necessary guidelines to ensure that the experiment was not adversely impacted by the physical movement of the participants. During the experiment, the participants did the translation task and output the translation in the right window of Translog II User. And the monocular mode of the SR Research Eyelink 1,000 plus system was used in this study, so only the right eye of each participants was tracked. After completing the translation task, the participants immediately filled out a BAI scale based on their personal physiological symptoms and evaluations. During the experiment, the participants were not aware that anxiety evaluation would be provided after the translation tasks, otherwise, they would not totally focus their attention on the translation task. Moreover, if they had known that they would evaluate their anxiety after the translation task, they might think of some strategy to show their confidence. If that happened, we would never collect the true data for their anxiety.
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Anxiety
Attention
Movement
Physical Examination
physiology
Step Test
System 1 plus
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More about "Step Test"
The Cardiovascular Fitness Evaluation, or Step Test, is a valuable tool for assessing an individual's aerobic capacity and overall cardiovascular health.
This non-invasive physiological assessment involves stepping up and down on a raised platform at a standardized pace, with measurements of heart rate, blood pressure, and other key parameters monitored before, during, and after the test.
The Step Test provides crucial insights into an individual's exercise tolerance and fitness level, allowing healthcare professionals and researchers to identify potential health concerns and develop targeted exercise prescriptions.
This simple yet effective procedure is widely used in clinical settings, research studies, and fitness assessments, including those leveraging advanced data analysis tools like GraphPad Prism 7, Prism 9, and Prism 6, as well as statistical software such as SAS version 9.4 and MCR 302.
In addition to its applications in cardiovascular health, the Step Test may also be used in conjunction with other specialized equipment, such as Stereotaxic frames and Signa Explorer 1.5T MRI scanners, to provide a comprehensive evaluation of an individual's overall physical fitness and performance.
By incorporating the insights gained from the Step Test, researchers and healthcare providers can optimize exercise protocols and promote improved cardiovascular and physical well-being for their patients and study participants.
This non-invasive physiological assessment involves stepping up and down on a raised platform at a standardized pace, with measurements of heart rate, blood pressure, and other key parameters monitored before, during, and after the test.
The Step Test provides crucial insights into an individual's exercise tolerance and fitness level, allowing healthcare professionals and researchers to identify potential health concerns and develop targeted exercise prescriptions.
This simple yet effective procedure is widely used in clinical settings, research studies, and fitness assessments, including those leveraging advanced data analysis tools like GraphPad Prism 7, Prism 9, and Prism 6, as well as statistical software such as SAS version 9.4 and MCR 302.
In addition to its applications in cardiovascular health, the Step Test may also be used in conjunction with other specialized equipment, such as Stereotaxic frames and Signa Explorer 1.5T MRI scanners, to provide a comprehensive evaluation of an individual's overall physical fitness and performance.
By incorporating the insights gained from the Step Test, researchers and healthcare providers can optimize exercise protocols and promote improved cardiovascular and physical well-being for their patients and study participants.