DNA templates (IDT) were synthesized for tRNAPhe, TPP riboswitch, E. coli 5S, hepatitis C virus IRES domain, T. thermophila group I intron, or O. iheyensis group II intron RNAs in the context of flanking 5' and 3' structure cassettes. Templates were amplified by PCR and transcribed into RNA using T7 RNA polymerase43 . RNAs were purified by denaturing polyacrylamide gel electrophoresis, appropriate regions excised, and RNAs passively eluted from the gel overnight at 4 °C. 16S and 23S rRNAs were isolated from DH5α cells during mid-log phase using non-denaturing conditions38 (link). For each sample, 5 pM of RNA was refolded in 100 mM HEPES, pH 8.0, 100 mM NaCl, and 10 mM MgCl2 in a final volume of 10 µL. After folding, RNAs were modified in the presence of 10 mM SHAPE reagent and incubated at 37 °C for 3 min (1M6 and 1M7) or 22 min (NMIA). No-reagent controls, containing neat DMSO rather than SHAPE reagent, were performed in parallel. To account for sequence-specific biases in adduct detection, RNAs were modified using NMIA, 1M7, or 1M6 under strongly denaturing conditions in 50 mM HEPES (pH 8.0), 4 mM EDTA, and 50% formamide at 95 °C. Following modification, RNAs were isolated using either RNA affinity columns (RNeasy MinElute; Qiagen) or G-50 spin columns (GE Healthcare).
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Sensitivity Training Groups
Sensitivity Training Groups
Sensitivity Training Groups are a valuable tool for enhancing research reproducibility.
These groups leverage AI-powered comparisons to locate the most effective protocols from a vast pool of literature, pre-prints, and patents.
By engaging in sensitivity training, researchers can improve the quality and reliability of their work, unleashing the power of data-driven decision making.
PubCompare.ai's platform simplifies this process, offering a streamlined solution to optimize research protocols and drive scientific discovery.
Discover how Sensitivity Training Groups can elevate your research today.
These groups leverage AI-powered comparisons to locate the most effective protocols from a vast pool of literature, pre-prints, and patents.
By engaging in sensitivity training, researchers can improve the quality and reliability of their work, unleashing the power of data-driven decision making.
PubCompare.ai's platform simplifies this process, offering a streamlined solution to optimize research protocols and drive scientific discovery.
Discover how Sensitivity Training Groups can elevate your research today.
Most cited protocols related to «Sensitivity Training Groups»
Cells
Edetic Acid
Escherichia coli
formamide
Hepatitis C virus
HEPES
Internal Ribosome Entry Sites
Introns
Magnesium Chloride
methylisoamylnitrosamine
Phenylalanine-Specific tRNA
Polyacrylamide Gel Electrophoresis
Riboswitch
RNA, Ribosomal, 23S
RNA II
Sensitivity Training Groups
Sodium Chloride
Sulfoxide, Dimethyl
Our program, DomClust, was implemented in the C programming language. The program was validated using the COG database (
To evaluate the agreement between the two grouping systems, we first identified a set of corresponding group pairs; for each reference (COG) group, the best compatible group was selected from the target groups. The compatibility between the reference (R) and the target (T) groups was evaluated using Jaccard's coefficient |R ∧ T| / (|R| + |T| − |R ∧ T|), where R ∧ T denotes a set of segment pairs overlapping between R and T. Here, we considered that the segments a and b are overlapped if |a ∩ b|/max(|a|,|b|) > 0.5. After the set of corresponding group pairs was identified, the total agreement was evaluated by the number of matching group pairs (MGPs). For this, we introduced the following MGPs: an exact MGP is a group pair satisfying |R ∧ T| = |R| = |T|, a phylo-MGP is a group pair satisfying |Ph(R ∧ T)| = |Ph(R)| = |Ph(T)| and | R| ≥ |T|, and a m%-MGP is a group pair satisfying |R ∧ T|/|R| ≥ m/100 and |R ∧ T|/|T| ≥ m/100. Let MEx, MPh, M80 and M60 be the number of exact, phylo-, 80%-, 60%-MGPs, respectively. The total agreement was evaluated by MTot = MEx + MPh + M80 + M60. Note that there are overlaps between the various types of MGP, e.g. an exact MGP is also any other type of MGP, and therefore is weighted 4 times as much as a proper 60%-MGP.
For the purpose of comparison, we also tested the following clustering methods: (i) single linkage clustering (SLink), (ii) ‘triangular linkage clustering’ (TriLink), which is our implementation of the original method described in the COG paper (22 (link)) and consists in the merging of triangles in the graph of the best hits when they share the same side and (iii) the TribeMCL algorithm (9 (link)) (the program available at
For these methods, the BBH relationships were used as input. Here, we used a relaxed criterion: a gene pair (a, b) of the genomes A and B is considered to be in a BBH relationship when the genes satisfy score(a,b) ≥ rBH max {maxy∈B[score(a,y)],maxx∈A[score(b,x)]}, with one parameter, 0 ≤rBH ≤ 1 (rBH = 1 corresponds to the rigorous BBH). Intra-species homologs were also included when a gene pair (a,b) of the genome A satisfied score(a,b) ≥ rBH max {maxx∈G−A[score(a,x)],maxx∈G−A[score(b,x)]}, where G–A denotes all genomes except A. In addition, we also prepared BBH relationships from which alignments with low coverage, defined as max(|ali12|/|s1|,|ali21|/|s2|) < rcov, were filtered out. During the evaluation, we systematically changed the parameters rBH and rcov together with the score cutoff c (for SLink and TriLink) or the inflation parameter I (for TribeMCL) to find the parameter set that gives the best MTot. On the other hand, all similarity relationships were used in DomClust, since the algorithm already includes such best-hit-first criteria.
To evaluate the agreement between the two grouping systems, we first identified a set of corresponding group pairs; for each reference (COG) group, the best compatible group was selected from the target groups. The compatibility between the reference (R) and the target (T) groups was evaluated using Jaccard's coefficient |R ∧ T| / (|R| + |T| − |R ∧ T|), where R ∧ T denotes a set of segment pairs overlapping between R and T. Here, we considered that the segments a and b are overlapped if |a ∩ b|/max(|a|,|b|) > 0.5. After the set of corresponding group pairs was identified, the total agreement was evaluated by the number of matching group pairs (MGPs). For this, we introduced the following MGPs: an exact MGP is a group pair satisfying |R ∧ T| = |R| = |T|, a phylo-MGP is a group pair satisfying |Ph(R ∧ T)| = |Ph(R)| = |Ph(T)| and | R| ≥ |T|, and a m%-MGP is a group pair satisfying |R ∧ T|/|R| ≥ m/100 and |R ∧ T|/|T| ≥ m/100. Let MEx, MPh, M80 and M60 be the number of exact, phylo-, 80%-, 60%-MGPs, respectively. The total agreement was evaluated by MTot = MEx + MPh + M80 + M60. Note that there are overlaps between the various types of MGP, e.g. an exact MGP is also any other type of MGP, and therefore is weighted 4 times as much as a proper 60%-MGP.
For the purpose of comparison, we also tested the following clustering methods: (i) single linkage clustering (SLink), (ii) ‘triangular linkage clustering’ (TriLink), which is our implementation of the original method described in the COG paper (22 (link)) and consists in the merging of triangles in the graph of the best hits when they share the same side and (iii) the TribeMCL algorithm (9 (link)) (the program available at
For these methods, the BBH relationships were used as input. Here, we used a relaxed criterion: a gene pair (a, b) of the genomes A and B is considered to be in a BBH relationship when the genes satisfy score(a,b) ≥ rBH max {maxy∈B[score(a,y)],maxx∈A[score(b,x)]}, with one parameter, 0 ≤rBH ≤ 1 (rBH = 1 corresponds to the rigorous BBH). Intra-species homologs were also included when a gene pair (a,b) of the genome A satisfied score(a,b) ≥ rBH max {maxx∈G−A[score(a,x)],maxx∈G−A[score(b,x)]}, where G–A denotes all genomes except A. In addition, we also prepared BBH relationships from which alignments with low coverage, defined as max(|ali12|/|s1|,|ali21|/|s2|) < rcov, were filtered out. During the evaluation, we systematically changed the parameters rBH and rcov together with the score cutoff c (for SLink and TriLink) or the inflation parameter I (for TribeMCL) to find the parameter set that gives the best MTot. On the other hand, all similarity relationships were used in DomClust, since the algorithm already includes such best-hit-first criteria.
Genes
Genome
Muscle Rigidity
Sensitivity Training Groups
The recorded data (1 min bins) and derived parameters, Vt /Ti and Response Area (cumulative percent changes from pre-values) were taken for statistical analyses. The pre-drug 1 min bins excluded occasional marked deviations from resting due to movements or scratching by the rats. These exclusions ensured accurate determinations of baseline parameters. The data are presented as mean ± SEM. All data unless otherwise stated (see immediately below) were analyzed by one-way or two-way analysis of variance followed by Student's modified t test with Bonferroni corrections for multiple comparisons between means using the error mean square terms from each ANOVA40 –43 (link). A value of P < 0.05 denoted the initial level of statistical significance that was modified according to the number of comparisons between means as detailed by Wallenstein et al. (1980)41 (link). The modified t-statistic is t = (mean group 1—mean group 2)/[s × (1/n1 + 1/n2)1/2] where s2 = the mean square within groups term from the ANOVA (the square root of this value is used in the modified t-statistic formula) and n1 and n2 are the number of rats in each group under comparison. Based on an elementary inequality called Bonferroni's inequality, a conservative critical value for the modified t-statistics taken from tables of t-distribution using a significance level of P/m, where m is the number of comparisons between groups to be performed. The degrees of freedom are those for the mean square for within group variation from the ANOVA table. In most cases, the critical Bonferroni value cannot be obtained from conventional tables of the t- distribution but may be approximated from widely available tables of the normal curve by t* = z + (z + z3)/4n, with n being the degrees of freedom and z being the critical normal curve value for P/m40 –43 (link). Wallenstein et al.41 (link) first demonstrated that the Bonferroni procedure is preferable for general use since it is easiest to apply, has the widest range of applications, and gives critical values that will be lower than those of other procedures if the investigator is able to limit the number of comparisons, and that will be only slightly larger than those of other procedures if many comparisons are made. The practical application of the Bonferroni procedure first demonstrated by Wallenstein et al.41 (link) has been supported and expanded upon by Ludbrook42 (link) and by McHugh43 (link). A value of P < 0.05 was taken as the initial level of statistical significance40 ,41 (link). With respect to Supplemental Figures S4 –S7 , the data were analyzed by one-way ANOVA and Tukey’s least significance difference (LSD) test, with statistical differences taken as P < 0.0540 ,41 (link).
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Movement
neuro-oncological ventral antigen 2, human
Pharmaceutical Preparations
Plant Roots
Rattus norvegicus
Sensitivity Training Groups
We included seven crested newts (see Additional file 2 ), representing all recognized species, as well as three distinct mitochondrial DNA clades that constitute the T. karelinii group (cf. [16 (link)]). We follow [16 (link)] in awaiting a taxonomic revision of the T. karelinii group before applying specific names to the three constituent mitochondrial DNA clades (the name T. karelinii sensu stricto would apply to the 'eastern clade' and T. arntzeni has been applied to the 'western clade'; no name has as yet been proposed for the 'central clade'). We also sequenced the two marbled newts (T. marmoratus and T. pygmaeus), the remaining members of the genus Triturus, to function as outgroup taxa. Additionally, we added a sequence of Calotriton asper, sister to the genus Triturus, available from [31 (link)] (GenBank accession number EU880307 ).
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DNA, Mitochondrial
Newts
Sensitivity Training Groups
Triturus
angiogen
Animals
Apoptosis
Biotin
Body Weight
Buffers
celastrol
Cells
cremophor
deoxyuridine triphosphate
DNA Nucleotidylexotransferase
Eosin
Ethanol
Hematoxylin
Mice, Nude
Mus
Neoplasms
Paraffin Embedding
paraform
PC 3 Cell Line
Proteins
Psychological Inhibition
Radiation
Radiotherapy
Sensitivity Training Groups
Sulfoxide, Dimethyl
Tail
Transferase
Woman
X-Rays, Diagnostic
Most recents protocols related to «Sensitivity Training Groups»
AlphaFold 2 (61 (link), 62 (link)) was used via collabfold (78 (link)) to predict the three-dimensional structure of AAK1 (961 amino acids) using the sequence NM_Q2M218 and an eight alanine (S447, T507, S519, T359, T360, T448, T445, S650) mutant sequence (mutant AAK1). Structures were visualized using ChimeraX (79 (link), 80 (link)), and O-GlcNac residues were added by overlaying sugar onto S/T hydroxyl groups. Structural predictions of the low complexity regions were independently identified using Protein Disorder Prediction System Server (PrDOS) (81 (link)). A scatter plot was generated using the disorder predictions of both the WT and the mutant forms of AAK1. Experimentally determined crystal structures available to date of the kinase domain from human AAK1 were modeled to the predicted structure of AAK1 using ChimeraX (79 (link), 80 (link)).
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Alanine
Amino Acids
Carbohydrates
Homo sapiens
Hydroxyl Radical
Phosphotransferases
Proteins
Sensitivity Training Groups
We purchased 36 KM mice from Henan Scribes Biotechnology Co., Ltd. The mice were fed for 7 days before the experiment to acclimate to the environment. The mice were randomly divided into six groups, namely normal (N), model (M), treatment (T), RGO low dose (RL, 0.25 g/kg), RGO medium dose (RM, 0.5 g/kg), and RGO high dose (RH, 1 g/kg) groups. Six mice were in each group, and the test period was 21 days. Except for the normal group, on the 9th, 13th, 17th, and 21st days, 0.2 mL of normal saline was injected intraperitoneally. 0.2 mL of 1 mg/kg LPS was injected intraperitoneally in all other groups to develop mice models of intestinal inflammation and barrier injury (14 (link), 15 (link)). From the first day of the test, mice in RL, RM, and RH groups were provided 0.2 mL of RGO solution by gavage once a day for 21 days. The mice in the N, M, and T groups were gavaged with an equal volume of normal saline daily. After each intraperitoneal LPS injection, the mice in the T group were gavaged with 0.2 mL dexamethasone solution at 0.5 mg/kg dose 30 min. All the groups were fed adequate food and free to drink water. The weight of mice was determined on the 1st, 7th, 14th, and 21st days of the test. Additionally, the food intake, fecal properties, and health status of mice were observed and recorded daily.
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Dexamethasone
Eating
Feces
Food
Inflammation
Injections, Intraperitoneal
Injuries
Intestines
Kunming mice
Mice, House
Normal Saline
Sensitivity Training Groups
Statistical analyses were conducted using the software programs R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria) and a graphical interface EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan)27 (link) or GraphPad Prism version 9 (GraphPad Software, San Diego, CA). Missing data were handled using pairwise deletion approach. Categorical variables are presented as percentages, and differences between groups were evaluated using the Fisher exact test. Post hoc analyses for multiple comparison were performed using Bonferroni correction. Continuous variables are presented as mean ± SD. Differences between groups were evaluated using the Welch t test or one-way analysis of variance followed by post hoc analyses using the Tukey test. Analysis of covariance (ANCOVA) was performed to evaluate the difference of CSF p-tau181 between NIID and disease controls using CSF t-tau as a covariate, after confirming the correlation between CSF p-tau181 and t-tau in each group. Pearson correlation analysis was conducted between CSF p-tau181 and other continuous variables in patients with NIID. Multivariate analysis was not conducted due to a limited number of patients with NIID. p Values of <0.05 were considered statistically significant for all analyses.
Deletion Mutation
Neuronal intranuclear inclusion disease
Patients
prisma
Sensitivity Training Groups
Seventy-two 4-week-old male and female BALB/C mice (20–22 g) were provided by Liao Ning Chang Sheng Biotechnology Co., Ltd., (China), and randomly divided into 6 groups (n = 12): control (PBS), S.T (S.T + PBS), S.T + LD (S.T + 0.5 mg/mL EPSs), S.T + MD (S.T + 1.0 mg/mL EPSs), S.T + HD (S.T + 2.0 mg/mL EPSs) and S.T + P (S.T + 2.0 mg/mL penicillin) (Fig. 1 ). The mice were housed in an air-conditioned animal room with an indoor temperature of 23 ± 1 ℃, 40%–60% relative humidity, and 12 h of light daily. The mice received adequate food and drinking water for one week before the start of the experiment. Mouse body weight data were recorded daily throughout the duration of our experiment. At the time of sacrifice, the mice were anesthetized by diethyl ether inhalation, and blood samples were collected retro orbitally. The mice were sacrificed by the cervical dislocation method, and organs were weighed at sacrifice to calculate organ indices as follows: organ index = fresh weight of organs (g)/body live weight (g) × 100%. The collected blood was allowed to stand at room temperature for 30 min and then centrifuged at 1000 × g for 20 min, and the serum was collected and stored at −80 ℃ for subsequent experiments. Ileal tissues were then rapidly transferred into 10% (v/v) formaldehyde solution to observe changes in the intestinal morphology, and other parts of the tissues were frozen in liquid nitrogen and stored at −80 ℃ for subsequent experiments.![]()
Experimental design. On the first day of the official experiment, 2×108 CFU/mL S.T solution and an equivalent volume of PBS (control group) were administered orally for 1 d. On the fourth day, the mice in the S.T + LD, S.T + MD, S.T + HD and S.T + P groups were intragastrically administered 0.5 mg/mL EPSs, 1.0 mg/mL EPSs, 2.0 mg/mL EPSs, or 2.0 mg/mL penicillin for 7 d. The mice in the control and S.T groups were administered the same amount of PBS
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Animals
BLOOD
Body Weight
Ethyl Ether
Females
Food
Formalin
Freezing
Humidity
Ileum
Inhalation
Intestines
Joint Dislocations
Light
Males
Mice, House
Mice, Inbred BALB C
Neck
Nitrogen
Penicillins
Sensitivity Training Groups
Serum
Tissues
Fifty-four male Sprague-Dawley rats weighing between 160–180 g were randomly assigned into two experimental groups, namely, T. zimbabwensis infected group (Tz) (n = 36) and the non-infected control group (n = 18). Rats were euthanized using isofor inhalation in a gas chamber at day 0, 7, 14, 21, 28, and 35 post-infections (pi). At each day of sacrifice, six rats were euthanized from the infected group while three rats were euthanized from the control group. On each day of sacrifice, blood samples were collected from rats via cardiac puncture to obtain serum. Serum samples were analyzed using an untargeted metabolomics approach with a two-dimensional Gas Chromatographic Time of Flight Mass Spectrometry (GCxGC-TOF/MS) (Figure 1 ). To ensure precision and accuracy of the data obtained, Quality Control (QC) samples were used to check the stability of the instrument and the reliability of the method. For the QC, a pooled QC was compiled by combining 100 μL sample from each sample group. The schematic diagram of procedures followed in the identification of compounds and pathway analysis is depicted in Supplementary Figure S1 .
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BLOOD
Gas Chromatography-Mass Spectrometry
Heart
Infection
Inhalation
Males
Punctures
Rats, Sprague-Dawley
Rattus norvegicus
Sensitivity Training Groups
Serum
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More about "Sensitivity Training Groups"
Sensitivity Training Groups (STGs) are a valuable tool for enhancing research reproducibility.
These groups leverage AI-powered comparisons to locate the most effective protocols from a vast pool of literature, pre-prints, and patents.
By engaging in sensitivity training, researchers can improve the quality and reliability of their work, unleashing the power of data-driven decision making.
PubCompare.ai's platform simplifies this process, offering a streamlined solution to optimize research protocols and drive scientific discovery.
Sensitivity Training Groups can help researchers identify the best experimental methods, using insights from SAS 9.4, Elutrap Electroelution System, Triethylamine trihydrofluoride, 394DNA/RNA synthesizer, 5-bromocytidine, GraphPad Prism 7, RNeasy MinElute, Triethylamine, Testosterone propionate, and Rompun.
Thise AI-driven approach allows researchers to compare and contrast protocols, select the most robust and reliable methods, and ultimately enhance the reproducibility of their studies.
By embracing Sensitivity Training Groups, scientists can elevate their research, make more informed decisions, and drive scientific discovery forward.
Don't miss out on the power of data-driven optimization - discover how Sensitivity Training Groups can take your research to new heights today.
These groups leverage AI-powered comparisons to locate the most effective protocols from a vast pool of literature, pre-prints, and patents.
By engaging in sensitivity training, researchers can improve the quality and reliability of their work, unleashing the power of data-driven decision making.
PubCompare.ai's platform simplifies this process, offering a streamlined solution to optimize research protocols and drive scientific discovery.
Sensitivity Training Groups can help researchers identify the best experimental methods, using insights from SAS 9.4, Elutrap Electroelution System, Triethylamine trihydrofluoride, 394DNA/RNA synthesizer, 5-bromocytidine, GraphPad Prism 7, RNeasy MinElute, Triethylamine, Testosterone propionate, and Rompun.
Thise AI-driven approach allows researchers to compare and contrast protocols, select the most robust and reliable methods, and ultimately enhance the reproducibility of their studies.
By embracing Sensitivity Training Groups, scientists can elevate their research, make more informed decisions, and drive scientific discovery forward.
Don't miss out on the power of data-driven optimization - discover how Sensitivity Training Groups can take your research to new heights today.